Water Quality and Landscape Processes of Four Watersheds 

in Eastern Puerto Rico

Professional Paper 1789

U.S. Department of the Interior

U.S. Geological Survey
Cover: Main channels of four study rivers in eastern Puerto Rico. The top row shows the rivers with naturally forested watersheds (left, 

Río Mameyes; right, Río Icacos), and the bottom row shows the rivers with agriculturally developed watersheds (left, Río Canóvanas; 

right, Río Cayaguás). Rivers that drain volcanic and volcaniclastic bedrock with little to no quartz are on the left, and rivers that drain 

quartz-rich granitic bedrock are on the right. Whether natural or developed, channels of rivers draining bedrock with little to no quartz 

contain mostly cobbles and boulders, and channels of rivers draining quartz-rich bedrock contain quartz-rich sand.
Water Quality and Landscape Processes of 

Four Watersheds in Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Contributions by Heather L. Buss, William A. Gould,1

 Matthew C. Larsen, 

Zhigang Liu,2

 Sebastián Martinuzzi,3 Sheila F. Murphy, Robert F. Stallard, 

Isabel K. Parés-Ramos,4

 Arthur F. White, and Xiaoming Zou2,5

1 U.S. Department of Agriculture Forest Service, International Institute of Tropical Forestry

2

 Institute for Tropical Ecosystem Studies, University of Puerto Rico

3

 Department of Forest and Wildlife Ecology, University of Wisconsin

4

 Department of Biology, University of Puerto Rico

5

 XishuangbannaTropical Botanical Garden, Chinese Academy of Sciences

Volume comprises Chapters A, B, C, D, E, F, G, H, and I

Professional Paper 1789

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Murphy, S.F., and Stallard, R.F., eds., 2012, Water quality and landscape processes of four watersheds in eastern 

Puerto Rico: U.S. Geological Survey Professional Paper 1789, 292 p.

Library of Congress Cataloging-in-Publication Data

Water quality and landscape processes of four watersheds in eastern Puerto Rico / edited by Sheila F. Murphy and 

Robert F. Stallard ; contributions by Heather L. Buss ... [et al.].

 p. cm. -- (Professional paper ; 1789)

 Includes bibliographical references.

 ISBN 978-1-4113-3432-8 (pbk.)

 1. Watersheds--Puerto Rico. 2. Hydrology--Puerto Rico. 3. Water quality--Puerto Rico. I. Murphy, 

 Sheila F. II. Stallard, Robert F. (Robert Forster), 1952- III. Buss, Heather L.

 GB717.W38 2012

 553.7’8097295--dc23

 2012027552
iii

Volume Contents

Foreword ...............................................................................................................................................iv

By Matthew C. Larsen

Executive Summary..............................................................................................................................v

By Sheila F. Murphy and Robert F. Stallard

A. Physiography, Geology, and Land Cover of Four Watersheds in Eastern 

Puerto Rico...............................................................................................................................1

By Sheila F. Murphy, Robert F. Stallard, Matthew C. Larsen, and William A. Gould

B. Land Use, Population Dynamics, and Land-Cover Change in Eastern 

Puerto Rico.............................................................................................................................25

By William A. Gould, Sebastián Martinuzzi, and Isabel K. Parés-Ramos

C. Hydrology and Climate of Four Watersheds in Eastern Puerto Rico..................................43

By Sheila F. Murphy and Robert F. Stallard

D. Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern 

Puerto Rico.............................................................................................................................85

By Robert F. Stallard

E. Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico...........113

By Robert F. Stallard and Sheila F. Murphy

F. Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico ............153

By Matthew C. Larsen

G. Effects of Earthworms on Slopewash, Surface Runoff, and Fine-Litter Transport 

on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico..................................179

By Matthew C. Larsen, Zhigang Liu, and Xiaoming Zou

H. Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in 

Eastern Puerto Rico ............................................................................................................199

By Robert F. Stallard

I. Weathering Processes in the Icacos and Mameyes Watersheds in Eastern 

Puerto Rico...........................................................................................................................249

By Heather L. Buss and Arthur F. White

Appendix 1. Data Processing and Computation to Characterize Hydrology and 

Compare Water Quality of Four Watersheds in Eastern Puerto Rico.........................263

Appendix 2. Methods Used to Analyze Water Quality of Four Watersheds in Eastern 

Puerto Rico...........................................................................................................................289
iv

Foreword

By Matthew C. Larsen

Humans face a future limited by the increasing effects of environmental problems and resource 

scarcity. The world’s population is now consuming water and energy, and using agricultural 

and other land resources, at per capita rates that will be challenging to sustain. Environmental 

degradation is intensifying conflict over resources. Local consequences are exacerbated by 

climate change, manifested as changes in the timing and distribution of rainfall and runoff. As 

our influence on Earth’s lands and waters grows more substantial, we need to understand and 

quantify these effects to preserve our well-being. Earth sciences are central to meeting this 

need because they guide the monitoring, assessment, and modeling of natural processes that 

help us to understand our interaction with the environment. The U.S. Geological Survey has a 

long history of providing authoritative earth science to inform local and national decision makers 

as they confront difficult choices in a changing world.

The low-latitude regions of Earth, in particular, are undergoing profound, rapid landscape 

change. Humid-tropical watersheds of eastern Puerto Rico, at 18° North latitude, provide a natural laboratory that allows researchers to quantify and better understand this change and how it 

impinges on the human world. In recognition of this opportunity, the National Science Foundation’s Long-Term Ecological Research program and the U.S. Geological Survey’s Water, Energy, 

and Biogeochemical Budgets (WEBB) program have operated in eastern Puerto Rico for several 

decades. WEBB program scientists have studied hydrologic, geologic, geomorphic, and anthropogenic processes in eastern Puerto Rico in an effort to generalize their results to the broader 

tropical world. Long time-series of streamflow, water-quality, suspended-sediment, meteorological, and land-use data, collected by Federal and by Commonwealth of Puerto Rico agencies, 

record the effects of landscape changes that are characteristic of environmental challenges that 

we all face. This publication summarizes research based on these data, and it provides insights 

into how landscape change may increase soil erosion, reservoir sedimentation, and landslide 

hazards while decreasing soil-nutrient content and surface-water quality. 

In the 1990s the Puerto Rico Tourism Company invited travelers to “discover the continent of 

Puerto Rico.” In a very different sense, Puerto Rico’s landscape and the results of our studies 

there provide a microcosm of the challenges faced by societies on islands and continental landmasses. What choices will we make as we confront future limitations of land and resources? 

What scientific information will we need to guide these choices? The science presented in this 

volume improves our understanding of the ramifications of landscape changes by using analysis of watershed-scale geologic, hydrologic, and geomorphic processes, and it can help guide 

policymakers and resource managers as they confront difficult choices.
Executive Summary

By Sheila F. Murphy and Robert F. Stallard

Puerto Rico is in a state of rapid, ongoing change. Locally, agricultural lands are undergoing 

reforestation, while coastal areas are becoming heavily urbanized. The area is also changing 

because of the introduction of nonnative species, water supply projects, and the construction of 

roads and other infrastructure. Superimposed on these local phenomena are slower, larger scale 

changes, such as the deposition of airborne pollutants and natural and human-induced climate 

change. Owing to the island’s steep topography, low water-storage capacity, and dependence on 

trade-wind precipitation, Puerto Rico’s people, ecosystems, and water supply are vulnerable to 

extreme weather such as hurricanes, floods, and droughts. Eastern Puerto Rico offers a natural 

laboratory for separating geologic and land-cover influences from regional- and global-scale 

influences because of its various bedrock types and the changing land cover surrounding intact, 

mature forest of the Luquillo Experimental Forest (which is contiguous with El Yunque National 

Forest). Accordingly, a multiyear assessment of hydrological and biogeochemical processes 

was designed to develop an understanding of the effects of these differences on local climate, 

streamflow, water quality, and ecosystems, and to form the basis for a long-term and eventbased program of climate and hydrologic monitoring. 

Focusing on small watersheds allows for integrated studies of hydrologic and chemical processes, owing to minimal spatial variability of geology, land use, and climate. For two decades, 

the U.S. Geological Survey has been evaluating the processes controlling fluxes of water, 

energy, and elements throughout a range of temporal and spatial scales in small watersheds at 

five sites in different parts of the nation. The Water, Energy, and Biogeochemical Budgets Project in eastern Puerto Rico represents a montane, humid-tropical environment, in which lie four 

watersheds of differing geology and land use. Two watersheds are located on coarse-grained 

granitic rocks (Icacos and Cayaguás), and two are located on fine-grained volcanic rocks and volcaniclastic sedimentary rocks (Mameyes and Canóvanas). For each bedrock type, one watershed 

is covered with mature rainforest (Icacos and Mameyes); the other is undergoing reforestation 

after being used as agricultural land (Cayaguás and Canóvanas). These watersheds, like most 

of the rest of Puerto Rico, were subjected to intensive agriculture in the 19th and early 20th 

century, but they have been undergoing reforestation as a result of a shift from an agricultural 

economy to an industrial one and subsequent human migration to urban areas (discussed in 

chapters A and B in this volume). 

Puerto Rico lies directly in the path of the easterly trade winds, which deliver steady rainfall to 

the mountains and steer weather systems called tropical waves toward the island. Hurricanes 

and tropical storms derived from these systems typically deliver the majority of yearly rainfall 

and occur from May to December (chapter C). Northern cold fronts can also deliver heavy rainfall 

for several days at a time, typically from December through February. These storms vary greatly 

in frequency and intensity, contributing to substantial interannual variation in precipitation and 

stream discharge. The largest storms can have profound geomorphic consequences, such as 

landslides, debris flows, deep gullying on deforested lands, excavation and suspension of sediment in stream channels, and delivery of a substantial fraction of annual stream sediment load 

(chapters F and G). Past deforestation and agricultural activities in the Cayaguás and Canóvanas 

watersheds led to profoundly accelerated erosion and soil loss, and this material continues to 

be remobilized during large storms. 

v
vi

Rainfall varies greatly over small distances in eastern Puerto Rico owing to differences in 

elevation, topographic position, aspect, and proximity to the ocean. The Icacos and Mameyes 

watersheds, located on the eastern side of the Luquillo Mountains, are the wettest of the four 

watersheds studied, and their highest elevations receive more than 4,000 millimeters of rain 

annually (chapter C). Precipitation increases with elevation in these watersheds. The Canóvanas 

and Cayaguás watersheds, located on the western side of the Luquillo Mountains, are considerably drier, and precipitation and elevation are not correlated. Precipitation and runoff in all 

watersheds show large interannual variation and are highest in years when major storms—such 

as Hurricanes Hortense (1996) and Georges (1998)—strike eastern Puerto Rico. These large 

storms typically produce similar runoff in all of the study watersheds, suggesting that higher 

annual runoff in the eastern, windward side of the Luquillo Mountains (which includes the 

Mameyes and Icacos watersheds) is caused by smaller, more-routine rain events. When one 

considers watershed-wide water budgets, the windward or leeward aspect of a watershed is 

more important than differing geology and land cover. 

Regional weather patterns and consequent sources of air masses influence the type and 

timing of atmospheric contributions to eastern Puerto Rico. Nitrogen loads in precipitation at 

a National Atmospheric Deposition Project site in eastern Puerto Rico have roughly doubled 

since measurements began in 1985 (chapter D). Eastern Puerto Rico also receives marine 

salts and Saharan Desert dust in rainfall. The proportion of material delivered by these 

sources varies seasonally; deposition of marine salts is greatest in January, whereas material 

from North America is deposited primarily in January, April, and May, and Saharan Desert 

dust peaks in June and July. Saharan dust typically contributes enough alkalinity in June and 

July to neutralize acidity in precipitation. During large storms, entrainment of ocean spray and 

foam can lead to highly elevated concentrations of chloride in stream waters (chapter E). 

Because infrequent, large storms play a major role in erosion of landscapes and can lead to 

a change in hydrologic flow paths, the Water, Energy and Biogeochemical Budgets Project 

focused on high-runoff events; we sampled 263 storms, including all major hurricanes that 

occurred between 1991 and 2005 (chapter C, appendix 1). Nearly 5,000 routine and event 

samples were analyzed for parameters that allow determination of denudation rates based on 

suspended and dissolved loads; 860 of these samples were analyzed for a comprehensive suite 

of chemical constituents. Of samples analyzed for comprehensive chemistry and for sediment, 

543 were collected at runoff rates greater than 1 millimeter per hour, 256 at rates exceeding 10 

millimeters per hour, and 3 at rates exceeding 90 millimeters per hour. Streams have rarely been 

sampled during events with such high runoff rates.

The rivers studied are generally similar in water-quality characteristics. Most chemical constituents show similar trends in the four watersheds, which imply considerable similarity in 

runoff generation and flow-path structuring despite differences in geology, soils, land cover, 

and weathering styles (chapter E). The rivers with lowest mean-annual runoff rates and highest 

ratios of evapotranspiration to runoff (Cayaguás and Canóvanas) tend to have higher concentrations of nonbioactive constituents. These developed watersheds typically have higher concentrations of nutrients (potassium, nitrate, ammonium ion, phosphate), perhaps indicating additional 

agricultural or wastewater sources. Projecting watershed yields to a common, intermediate 

mean-annual runoff (1,860 millimeters per year) generally decreased or did not change the range 

of yields of constituents that are the primary indicators of chemical weathering, biological activity on the landscape, or atmospheric contributions (dissolved bedrock, sodium, silica, chloride, 

dissolved organic carbon, and calcium), further indicating no dominant influence of either 
vii

geology or land cover (chapter H). Magnesium and inorganic carbon showed a dependence on 

geology, possibly due to the presence of carbonates or mafic rocks. Projected yields of nutrients 

and particulate constituents (suspended solids and particulate organic carbon), however, were 

far in excess of equilibrium yields, and they were much greater for developed landscapes as 

compared with forested watersheds, consistent with the known effects of land clearing, agricultural activities, and domestic wastewater inputs.

Physical and chemical weathering rates of the four watersheds studied are high. Bedrock in the 

Icacos (granitic rock) and Mameyes (volcaniclastic rock) watersheds have some of the highest 

documented rates of chemical weathering of silicate rocks in the world (chapter I). Physical 

denudation rates based on mass balances are higher than expected for a steady-state system; 

this excess is substantial in all watersheds except the Mameyes (forested watershed on volcaniclastic bedrock; chapter H). Deforestation and agriculture can explain the accelerated physical 

erosion in the two developed watersheds (Canóvanas and Cayaguás). Physical erosion rates 

of the granitic watersheds are seven-fold as great as those for the volcaniclastic watersheds, 

owing to greater permeability and thus higher rates of water filtration and greater susceptibility 

to landsliding (chapters C and F). The reason for such high rates of physical erosion in the Icacos 

watershed (forested watershed on granitic bedrock) is unclear but may be related to changes 

in forest quality or to the history of road construction. The elevated physical erosion drives an 

increased particulate organic carbon flux, one that is large and is important to the carbon cycle. 

This increased flux is also sustainable because soil-carbon replacement is rapid. 

It is crucial to understand long-term geomorphical, hydrological, and biogeochemical processes 

in tropical regions, because these regions occupy about a quarter of Earth’s land surface, yet 

they contribute a substantially higher fraction of the water, solutes, and sediment discharged 

to the world’s oceans. Nearly half of Earth’s population lives in the tropics, and therefore 

development stresses are intense and can potentially harm soil resources, water quality, and 

water supply and in addition increase landslide and flood hazards. Small watersheds in eastern 

Puerto Rico provide an excellent opportunity to examine these processes and their connection 

to climate, geology, and land cover. The 15-year Water, Energy, and Biogeochemical Budget 

dataset, which includes discharge, field parameters, suspended sediment, major cations and 

anions, and nutrients, is available from the U.S. Geological Survey’s National Water Information 

System (http://waterdata.usgs.gov/nwis). The dataset provides a baseline for characterizing 

future environmental change and will improve our understanding of the interdependencies of 

land, water, and biological resources and their responses to changes in climate and land use. 

Because eastern Puerto Rico resembles many tropical regions in terms of geology and patterns 

of development, implications from this study are transferable to other tropical regions facing 

deforestation, rapid land-use change, and climate change.

Acknowledgments

The work in Puerto Rico could not have been done without the assistance of numerous people. 

In alphabetical order, an incomplete list includes the following: George Aiken, Ellen Axtmann, 

Sean Baran, Susan Brantley, Mary-Margaret Coates, Paul Collar, Iris Concepción, Russ Curtis, 

Pedro Díaz, Marcelle Fabregas, Robert Hirsch, Laura Hubbard, Mari Kauffmann, Ariel Lugo, 

Deborah Martin, Joel Martinez, Erik Oerter, Jennifer Riggs, Manuel Rosario, Cathy Rubin, 

Abdul Wahab Sadeqi, F.N. Scatena, Jamie Shanley, Angel Torres-Sánchez, Heriberto Torres, 
viii

Joe Troester, Ank Webbers, and Allen Zack. Helpful reviews of individual chapters and appendices were provided by Brent Aulenbach, Alex Blum, W. Troy Baisden, Brian Ebel, Allen Gellis, 

Jonathan Godt, G. González, Cliff R. Hupp, Andy Kurtz, Ariel Lugo, William McDowell, John 

Moody, Jake Peters, Fred Quiñones, Deborah Repert, Susan Riggins, F.N. Scatena, Martha 

Scholl, Jamie Shanley, Jonathan Stock, Gordon Tribble, Frank Wadsworth, John Walker, and 

Richard Webb. Two individuals deserve special acknowledgment: Ellen Axtmann helped implement and ran chemical analyses for the first half of the program, and Angel Torres-Sánchez, 

who was the first technician, was crucial in field-testing procedures and in helping adapt to 

the complexities of event sampling in a challenging environment.
Physiography, Geology, and Land Cover of 

Four Watersheds in Eastern Puerto Rico

By Sheila F. Murphy, Robert F. Stallard, Matthew C. Larsen, and William A. Gould

Chapter A of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–A

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract...........................................................................................................................................................5

Introduction.....................................................................................................................................................5

Site Description..............................................................................................................................................5

Physiography.........................................................................................................................................7

Geology.................................................................................................................................................10

Geology in Watersheds Dominated by Volcaniclastic Rock ..............................................10

Geology in Watersheds Dominated by Granitic Rock..........................................................12

Soil and Saprolite................................................................................................................................13

Soils in Watersheds Dominated by Volcaniclastic Rock ....................................................13

Soils in Watersheds Dominated by Granitic Rock................................................................14

Vegetation and Land Use...................................................................................................................15

Land Cover of Forested Watersheds ......................................................................................16

Land Cover of Developed Watersheds...................................................................................18

Summary........................................................................................................................................................18

Acknowledgments.......................................................................................................................................20

References....................................................................................................................................................20

Figures

1. Maps of Puerto Rico and study watersheds............................................................................6

2. Digital elevation model of elevation and relief of Puerto Rico, showing major 

mountain ranges and study watersheds ..................................................................................7

3. Digital elevation model of relief of eastern Puerto Rico showing study watersheds, 

and elevation profiles along three lines of cross section......................................................8

4. Diagrams of percentage of watershed area with each aspect, for the Canóvanas, 

Cayaguás, Icacos, and Mameyes watersheds........................................................................9

5–9. Maps of the following:

 5. Generalized geology of Puerto Rico.................................................................................10

 6. Detailed geology of study watersheds, Puerto Rico .....................................................11

 7. Ecological life zones of Puerto Rico.................................................................................15

 8. Generalized land cover of Puerto Rico in 2003...............................................................16

 9. Detailed land cover of study watersheds in 2003 ..........................................................17

10. Diagram of population density in a barrio in Canóvanas watershed and a barrio in 

Cayaguás watershed .................................................................................................................20

Tables

1. Geomorphic and geographic characteristics of study watersheds ....................................9

2. Geology of study watersheds, eastern Puerto Rico .............................................................12

3. Mineral content of granitic intrusions, eastern Puerto Rico...............................................13

4. Land cover and land-cover dynamics in study watersheds, eastern Puerto Rico..........19
Abbreviations Used in This Report

cm centimeter

km kilometer

km2 square kilometer

m meter

mm millimeter

Ma mega-annum, 1 million years old

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

WEBB Water, Energy, and Biogeochemical Budgets

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2

) 0.3861 square mile (mi2)

Abstract

Four watersheds with differing geology and land cover 

in eastern Puerto Rico have been studied on a long-term 

basis by the U.S. Geological Survey to evaluate water, 

energy, and biogeochemical budgets. These watersheds are 

typical of tropical, island-arc settings found in many parts 

of the world. Two watersheds are located on coarse-grained 

granitic rocks that weather to quartz- and clay-rich, sandy 

soils, and two are located on fine-grained volcanic rocks and 

volcaniclastic sedimentary rocks that weather to quartz-poor, 

fine-grained soils. For each bedrock type, one watershed is 

covered with mature forest, and the other watershed, like 

most of Puerto Rico, has transformed from relatively undisturbed pre-European forest to intensive agriculture in the 

19th and early 20th centuries, and further to ongoing reforestation that began in the middle of the 20th century. The 

comparison of water chemistry and hydrology in these watersheds allows an evaluation of the effects of land-use history 

and geology on hydrologic regimes and erosion rates. This 

chapter describes the physiography, geology, and land cover 

of the four watersheds and provides background information 

for the remaining chapters in this volume.

Introduction

Five field sites across the nation are being monitored 

long-term as part of the U.S. Geological Survey’s (USGS) 

Water, Energy, and Biogeochemical Budgets (WEBB) 

program (Baedecker and Friedman, 2000). The Puerto Rico 

WEBB site, located in eastern Puerto Rico, represents a montane, humid-tropical environment. Its geology and land cover 

are typical of large regions of the humid tropics, particularly 

montane regions that are affected by hurricanes (Larsen and 

Stallard, 2000). The Puerto Rico WEBB project has been 

evaluating hydrological, chemical, and sediment budgets and 

processes in four watersheds of differing geology and land use 

in eastern Puerto Rico since 1991 (Larsen and others, 1993). 

Two watersheds are located on coarse-grained granitic rocks, 

and two are located primarily on a combination of fine-grained 

volcanic and volcaniclastic sedimentary rocks. For each bedrock type, one watershed is covered with mature forest, and 

the other watershed has been affected by agricultural land use 

typical of eastern Puerto Rico. The comparison of water chemistry and hydrology in these watersheds allows an evaluation 

of the effects of land-use history and geology on hydrologic 

regimes and erosion rates.

This chapter describes and compares the physiography, 

geology, soils, vegetation, and land cover of the watersheds. 

Climate and hydrology are described in Murphy and Stallard 

(2012), land-cover change is discussed in Gould and others 

(2012), and erosional processes are described in Larsen (2012). 

These comparisons provide context for data presented in the 

remaining chapters.

Site Description

Puerto Rico is a 9,000-square kilometer (km2) island 

within the Greater Antilles, located about 1,700 km southeast of Miami, Fla., at about latitude 18°N., longitude 66°W. 

(fig. 1). The WEBB study watersheds discussed here and in 

other chapters of this professional paper are gaged subwatersheds of larger watersheds having the same name. The Mameyes (17.8 km2

) and Icacos (3.26 km2) watersheds are located 

inside El Yunque National Forest, a 113-km2 forest preserve 

administered by the U.S. Department of Agriculture (USDA) 

Forest Service. El Yunque National Forest is conterminous 

with the Luquillo Experimental Forest (fig. 1), which has 

been the site of a National Science Foundation Long-Term 

Ecological Research project since 1985, and it is designated 

an International Biosphere Reserve by the United Nations 

Educational, Scientific, and Cultural Organization. The Cayaguás (26.4 km2

) and Canóvanas (25.5 km2) watersheds are 

located within the partially urbanized, agriculturally developed yet reforesting, Río Grande de Loíza watershed (fig. 1). 

Physiography, Geology, and Land Cover of Four 

Watersheds in Eastern Puerto Rico

By Sheila F. Murphy,1 Robert F. Stallard,1 Matthew C. Larsen,2 and William A. Gould3

1

U.S. Geological Survey, 3215 Marine St. Suite E127, Boulder, Colo. 80303, 

USA

2

U.S. Geological Survey, 436 National Center, Reston, Va. 20192, USA

3

U.S. Department of Agriculture Forest Service, International Institute of 

Tropical Forestry, Jardín Botánico Sur, 1201 Calle Ceiba, Río Piedras, Puerto 

Rico 00926
6 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Area shown in B

A

B

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

L e s s e r

A n

t i l l e s

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5 

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_A_figures\BW_figures\BWfigure_01A.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

Mameyes

Icacos

LUQUILLO

MOUNTAINS Río Grande de Loíza Watershed

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Figure 1. Location of Puerto Rico and study watersheds, eastern Puerto Rico. 
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 7

Physiography

Puerto Rico is mountainous with central highlands 

surrounded by flat-lying coastal plains and alluvial valleys 

(fig. 2). Mountain ranges generally are oriented east-west 

and dominate the southern two-thirds of the island. Stream 

channels are steep, and most river valleys are deeply incised. 

Accelerated weathering in the north-facing watersheds, compared with the slower weathering on the drier southern slopes, 

has shifted the divide south such that streams are longer and 

their slopes are gentler on the north side of the island than on 

the south (Ramos-Ginés, 1999).

The Mameyes, Icacos, and Canóvanas watersheds drain 

the rugged Luquillo Mountains (fig. 2). The Mameyes watershed is located on the eastern side of the Luquillo Mountains 

(fig. 3). Slopes in this watershed mostly face north, northeast, 

and east (fig. 4), directly into the dominant east and northeast 

wind directions (Calvesbert, 1970). The Canóvanas watershed 

is on the western side of the Luquillo Mountains, with southwest to northwest aspects, facing away from prevailing winds. 

Relief in the Mameyes and Canóvanas watersheds is almost 

1,000 meters (m) (table 1). The smaller Icacos watershed 

is located at elevations between about 620 and 832 m, and 

slopes typically face east, southeast, and southwest (fig. 4). A 

ridge approaching 800 m elevation separates the Icacos and 

Mameyes watersheds. Five peaks are higher than 1,000 m 

in the Luquillo Mountains (U.S. Geological Survey, 1967); 

the highest, El Toro (1,075 m), is located between the Icacos 

and Canóvanas watersheds (fig. 3). The Cayaguás watershed, 

located southwest of the Luquillo Mountains, consists of low 

hills and alluvial valleys; the Cayey Mountains are located 

to the west (fig. 2). Slopes in the Cayaguás watershed show 

no predominant orientation (fig. 4). Hillslopes and channels 

are steep in all of the WEBB watersheds, and mean hillslopes 

range from 0.189 (10.7°) in the Cayaguás watershed to 0.365 

(20.0°) in the Mameyes watershed (table 1). 

Figure 2. Elevation and relief of Puerto Rico, showing major mountain ranges and study watersheds (outlined).

EXPLANATION

Elevation—In meters

−29–0

1–200

201–400

401–600

601–800

801–1,000

Base from U.S. Geological Survey National Elevation

Dataset 30-meter digital elevation model 0

0

10 20 KILOMETERS

10 20 MILES

Study watershed boundary

Canóvanas

Cayaguás C O R D I L L E R A C E N T R A L

CAYEY

MOUNTAINS

LUQUILLO

MOUNTAINS

Icacos

Mameyes

67°00’ 66°30’

18°30’

18°00’

66°00’

C A R I B B E A N S E A

A T L A N T I C O C E A N

PUERTO RICO
8 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A A'

B B'

C

C'

El Cacique

El Toro

Pico del Oeste

Pico del Este

El Yunque

66°05’ 66°00’ 65°55’ 65°50’ 65°45’ 65°40’ 65°35’

18°20’

18°15’

18°10’

18°05’

Base from U.S. Geological Survey National Elevation 

Dataset 30-meter digital elevation model

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_A_figures\BW_figures\BWfigure_03A.ai

Cayaguás

Mameyes

Icacos

Canóvanas

0

0

4 8 KILOMETERS

4 8 MILES

A A'

EXPLANATION

Elevation—In meters

–29–0

1–200

201–400

401–600

601–800

801–1,000

Study watershed boundary

Line of section

Peaks higher than 1,000 meters

A

B

C A R I B B E A N S E A

PUERTO RICO

WEST

B

EAST

B'

METERS

WEST

1,000

800

600

400

400

0

METERS

1,000

800

600

400

400

0

A

METERS

SOUTHWEST

1,000

800

600

400

400

0

C

NORTHEAST

C'

EAST

A'

VERTICAL EXAGGERATION X10

0

0

2 4 KILOMETERS

2 4 MILES

Canóvanas

Cayaguás

Cayaguás

Icacos

Icacos

Mameyes

Mameyes

Figure 3. A, Elevation and relief of eastern Puerto Rico, showing study watersheds (outlined), and three lines of cross section; 

B, elevation profiles along lines of cross section.
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 9

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_A_figures\BW_figures\BWfigure_04A.ai

0

45°

270° 90°

135°

315°

225°

180°

0

45°

270° 90°

135°

315°

225°

180°

0

45°

270° 90°

135°

315°

225°

180°

0

45°

270° 90°

135°

315°

225°

180°

North

North

North

A. Canóvanas North B. Cayaguás

C. Icacos D. Mameyes

12

12

8

8

4

4

0

Percentage of watershed area 

12

12

8

8

4

4

0

Percentage of watershed area 

Figure 4. Percentage of watershed area with each aspect, Canóvanas, Cayaguás, Icacos, and 

Mameyes watersheds (determined from elevation data from U.S. Geological Survey).

Table 1. Geomorphic and geographic characteristics of study watersheds.

[Values calculated from geographic information system data unless otherwise noted. Slope values are dimensionless]

Characteristic Icacos Mameyes Canóvanas Cayaguás

Area, square kilometers1 3.26 17.8 25.5 26.4

Minimum elevation, meters 620 83 70 156

Maximum elevation, meters 832 1,050 956 445

Mean elevation, meters 686 508 464 287

Mean hillslope of watershed2 0.222 0.365 0.255 0.189

Mean channel slope2 0.073 0.21 0.151 0.12

Main channel length, kilometers2 2.01 13.6 21.33 23.5

Total channel length, kilometers2 2.91 24.02 34.37 49.46

Dominant bedrock type Granitic Volcaniclastic Volcaniclastic Granitic

Dominant land use history Forest Forest Agricultural Agricultural 1

Díaz and others (2004).

2

Larsen (1997).
10 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Geology

Puerto Rico is a volcanic island-arc terrain with a geologic 

record spanning about 150 million years (Bawiec, 1998). It 

was formed by volcanism and sedimentation characteristic of 

tectonically active plate boundaries, and it consists of a core 

of volcanic and plutonic rocks surrounded by younger sedimentary rocks. The WEBB watersheds overlie igneous rocks 

typical of island-arc terrains. The Luquillo Mountains and the 

Loíza watershed are underlain largely by marine-deposited 

volcaniclastic rock with intrusions of granitic rock (figs. 5 

and 6; Broedel, 1961; Seiders, 1971a,b,c; M’Gonigle, 1978, 

1979; Rogers and others, 1979; Bawiec, 1998). Folding and 

large-scale strike-slip faulting have deformed strata, resulting 

in westerly dips such that strata grow progressively younger 

toward the northwest (Jolly and others, 1998).

Geology in Watersheds Dominated by 

Volcaniclastic Rock

The Mameyes and Canóvanas watersheds are underlain 

primarily by Cretaceous, marine-deposited, quartz-poor volcaniclastic rocks. The bedrock of the Mameyes watershed is primarily Fajardo, Tabonuco, and Hato Puerco Formations (41, 38, 

and 3 percent of the watershed, respectively; another 18 percent 

of the watershed is underlain by the Río Blanco quartz diorite 

intrusion, discussed in the next section). The Canóvanas watershed is almost entirely underlain by the Hato Puerco and Lomas 

Formations (87 and 12 percent, respectively; fig. 6, table 2). 

The oldest of these formations, the Fajardo Formation, is about 

105 to 100 million years old (Ma) (Jolly and others, 1998). Its 

lithology varies substantially and it has been divided into several members (Briggs, 1973; Briggs and Aguilar-Cortés, 1980). 

0

0

10 20 KILOMETERS

10 20 MILES

67°00’ 66°30’

18°30’

18°00’

66°00’

Canóvanas

Cayaguás

Icacos

Mameyes

EXPLANATION

Geology

Quaternary alluvium

Tertiary through Cretaceous limey sedimentary rock

Tertiary through Cretaceous nonlimey sedimentary rock

Tertiary through Cretaceous granitoid rock

Tertiary through Cretaceous gabbroic rock

Cretaceous Subaerial volcaniclastic rock

Tertiary marine volcaniclastic rock

Tertiary and Cretaceous marine volcaniclastic rock

Cretaceous marine volcaniclastic rock

Alteration terrane

age

unknown Submarine basalt and chert terrane

Ultramafic and amphibolite terrane

 Water

Geology modified from U.S. Geological Survey

Open-File Report 98–38, Bawiec, 1998

Study watershed boundary

PUERTO RICO

Area shown in 6B

Area shown in 6A

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from U.S. Geological Survey digital data, 1998

1:100,000, polyconic projection

Central Meridian 67°W, Latitude of projection origin 18°N

North American Datum of 1927 (NAD 1927)

Figure 5. Generalized geology of Puerto Rico (from Bawiec, 1998); study watersheds outlined.
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 11

0

0

1 2 KILOMETERS

1 2 MILES

0 0.5

0 0.5

1 KILOMETER

1 MILE

A

B

Base from U.S. Geological Survey digital data, 1998

1:100,000, polyconic projection

Central Meridian 67°W, Latitude of projection origin 18°N

North American Datum of 1927 (NAD 1927)

Base from U.S. Geological Survey digital data, 1998

1:100,000, polyconic projection

Central Meridian 67°W, Latitude of projection origin 18°N

North American Datum of 1927 (NAD 1927)

Kh

Kh

Kh

TKh

TKh

TKh

TKh

TKh

Kfa

Kfa

Kta

Kta

Kta

Klo

Klo

TKap

TKha

Canóvanas

Icacos

Mameyes

Qa

Kf

Kf

TKap

Qa TKap

Qa

Qa

Qa

Qa

Qa

Qa

Ksl

Ksl

Ksl

Kpgg

Cayaguás

Qa

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_A_figures\BW_figures\BWfigure_06A.ai

Kpgqm

18°06’

18°09’

18°07’

18°08’

65°58’ 65°57’ 65°56’ 65°55’

18°19’

18°18’

18°17’

18°16’

18°20’ 65°53’ 65°52’ 65°51’ 65°50’ 65°49’ 65°48’ 65°47’ 65°46’ 65°45’

Study watershed boundary

Note: Geology modified from U.S. Geological Survey

 Open-File Report 98–38, Bawiec, 1998

EXPLANATION

Alluvium

Figuera Lava

Lomas Formation

Hato Puerco Formation

Tabonuco Formation

Fajardo Formation

Hydrothermally altered

 rock

Río Blanco quartz diorite

Augite andesite porphyry

Punta Guayanes quartz monzonite

Punta Guayanes granodiorite

San Lorenzo granodiorite-quartz

 diorite

Fault

Qa

Kf

Klo

TKha

Kpgqm

TKh

TKap

Kh

Kta

Kpgg

Ksl

Kfa

Geology

Qa

Figure 6. Detailed geology of study watersheds, eastern Puerto Rico (from Bawiec, 1998).
12 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Thick-bedded tuff breccias, with large clasts of andesite, dominate. They are interbedded with coarse-grained tuff, tuffaceous 

sandstone, and black cherty siltstone. The Fajardo Formation is 

largely noncalcareous, except for sporadic calcareous sandstone 

and siltstone beds and calcareous tuffs (Briggs, 1973).

The Fajardo Formation grades into the overlying Tabonuco Formation, which is about 100 Ma (Jolly and others, 

1998). It contains more mudstone and less very thick bedded 

sandstone and breccia than the Fajardo Formation (Seiders, 

1971b). About 60 percent of the Tabonuco Formation consists 

of volcanic sandstones, which are usually light gray, calcareous, moderately sorted, and fine to very coarse grained, and 

which are composed primarily of angular volcanic rock fragments and subordinate plagioclase, clinopyroxene, and rare 

quartz grains (Seiders, 1971b). About 30 percent of the Tabonuco Formation consists of dark gray, thin-bedded to rarely 

thick-bedded, laminated mudstones, which can be slightly to 

highly calcareous. About 10 percent of the Tabonuco Formation consists of volcanic breccia and conglomerate. 

The Hato Puerco Formation overlies the Tabonuco 

Formation and is about 97 to 90 Ma (fig. 6; Jolly and others, 

1998). More than half of the formation is composed of andesitic to basaltic volcanic sandstone, which contains volcanic 

lithic clasts, grains of plagioclase, clinopyroxene, and hornblende, and sparse grains and boulders of limestone (Seiders, 

1971a). About 30 to 40 percent of the formation consists of 

volcanic breccias that contain angular to subrounded pieces 

of andesite or basalt, with fragments of limestone that can 

make up as much as 30 percent of the breccia. Calcareous 

mudstone and andesitic to basaltic porphyritic lava are present 

in minor amounts. The Hato Puerco contains more carbonate 

material than the Fajardo or Tabonuco Formations, particularly 

in the vicinity of the Río Canóvanas stream channel, where 

outcrops of calcareous shales and tuffaceous limestone are 

common (Meyerhoff, 1931; Seiders, 1971a). Calcareous rocks 

require relatively small outcrop areas to have a marked effect 

on stream chemistry (Stallard, 1995), and the larger amount 

of carbonate rocks in the Canóvanas watershed compared 

with the Mameyes watershed is reflected in stream chemistry 

(Stallard and Murphy, 2012).

The Lomas Formation, found in the southern part of the 

Canóvanas watershed, consists of gray-green, thick-bedded, 

poorly sorted andesitic to basaltic volcanic breccia and 

sandstone, with minor pillow lava locally (Seiders, 1971c). 

Strata commonly contain pumice, vesicular lava fragments, 

and grains of plagioclase and clinopyroxene, and calcite 

is locally abundant in the matrix (Seiders, 1971c). This 

formation is seen at the surface only in fault blocks and is 

not observed in depositional contact with other formations; 

on the basis of its similarities with other formations in the 

region, it has been estimated to be between 90 and 85 Ma 

(Jolly and others, 1998).

The intrusion of the Río Blanco quartz diorite (discussed in the next section) metamorphosed the Fajardo, 

Tabonuco, and Hato Puerco Formations within a 2–4 km 

contact zone (Bawiec, 1998), resulting in rocks that are typically harder; the divide between the Mameyes and Icacos 

watersheds was formed on the slightly hardened rocks of 

the metamorphic aureole. In the metamorphosed rocks, 

hornblende, garnet, epidote, chlorite, iron sulfide minerals, 

gold, copper, and quartz, and aplitic veins have been identified (Seiders, 1971a,c; Bawiec, 1998). Gold-, silver-, and 

copper-bearing deposits were first worked by the Spanish in 

the early 1600s; production was limited to several small adits 

within the contact zone (Wadsworth, 1970; Cardona, 1984; 

Bawiec, 1998). Placer gold from weathered metamorphosed 

rocks was mined intermittently in the Mameyes and Loíza 

watersheds beginning in the 1500s.

Geology in Watersheds Dominated by Granitic 

Rock

The Icacos and Cayaguás watersheds are primarily 

underlain by Upper Cretaceous and Tertiary granitic rocks. 

Almost all (99 percent) of the Icacos watershed is underlain by 

the Río Blanco quartz diorite (fig. 6, table 2), which intruded 

the surrounding volcaniclastic rocks about 49 to 42 Ma (Smith 

and others, 1998). The Río Blanco quartz diorite is light-gray, 

medium- to coarse-grained, and commonly contains plagioclase, quartz, amphibole, and minor biotite in a fine- to mediumcrystalline groundmass of quartz, amphibole, orthoclase, and 

plagioclase, and with accessory zircon, tourmaline, sphene, and 

epidote (Seiders, 1971a,b; table 3). Plagioclase ranges from 

fresh to sericitized. Hornblende is commonly altered to chlorite, 

calcite, and epidote (Seiders, 1971a). Biotite is commonly fresh, 

but it is also altered to chlorite (Murphy and others, 1998). In 

some areas the Río Blanco quartz diorite contains pyrite, molybdenite, or disseminated chalcopyrite (Bawiec, 1998). 

Table 2. Geology of study watersheds, eastern Puerto Rico.1

Watershed and formation Symbol Rock type Percent

Canóvanas

Hato Puerco Formation Kh Volcaniclastic 87.2

Lomas Formation Klo Volcaniclastic 11.5

Río Blanco quartz diorite TKh Intrusive 0.5

Alluvium Qa Alluvium 0.4

Augite andesite porphyry Tkap Intrusive 0.3

Cayaguás

San Lorenzo granodioritequartz dioriteKsl Intrusive 88.0

Punta Guayanes quartz monzonite Kpgqm Intrusive 11.3

Alluvium Qa Alluvium 0.6

Punta Guayanes granodiorite Kpgg Intrusive 0.02

Icacos

Río Blanco quartz diorite TKh Intrusive 98.5

Fajardo Formation Kfa Volcaniclastic 1.2

Tabonuco Formation Kta Volcaniclastic 0.4

Mameyes

Fajardo Formation Kfa Volcaniclastic 40.9

Tabonuco Formation Kta Volcaniclastic 37.9

Río Blanco quartz diorite TKh Intrusive 17.8

Hato Puerco Formation Kh Volcaniclastic 3.3

Alluvium Qa Alluvium 0.04 1

Adapted from Bawiec (1998).
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 13

About 88 percent of the Cayaguás watershed is underlain by the San Lorenzo granodiorite–quartz diorite intrusion 

(fig. 6, table 2), which has been dated to be about 73 Ma 

(Rogers and others, 1979; Bawiec, 1998). This intrusion 

occupies a 500-km2 area in southeastern Puerto Rico. It is a 

medium- to dark-gray, granitic-textured, medium-crystalline, 

generally massive granodiorite which grades into quartz diorite and locally into quartz monzonite. It is composed generally of plagioclase, potassium feldspar, quartz, hornblende, 

and minor biotite and accessory magnetite, sphene, apatite, 

and zircon (Rogers and others, 1979) (table 3). Many plagioclase grains are partly sericitized and argillized. Hornblende 

is commonly altered to chlorite and epidote, and biotite is 

commonly altered to chlorite. 

About 11 percent of the Cayaguás watershed has been 

mapped as Punta Guayanes quartz monzonite, which has 

been dated at about 66 Ma (Rogers and others, 1979). This 

rock type ranges in composition from quartz monzonite to 

granodiorite, but in general it is granitic-textured, medium 

crystalline, and composed of abundant plagioclase, potassium feldspar, and quartz, with subordinate hornblende, minor 

biotite, and accessory magnetite, sphene, apatite, and zircon 

(table 3). Most plagioclase grains are sericitized to various 

degrees, and hornblende is commonly altered to chlorite and 

epidote (Rogers and others, 1979). The southern part of the 

watershed was mapped to greater detail by Rogers and others 

(1979) than the northern part (Broedel, 1961); Broedel classified all rocks simply as granodiorite. The greater amount of 

quartz and lesser amount of plagioclase in the quartz monzonite may make the monzonite more resistant to weathering 

than the granodiorite, and they may explain why hillslopes 

underlain by monzonite in the watershed have higher elevation and greater relief that elsewhere in the basin (Larsen, 

1997). The quartz monzonite may actually underlie a larger 

portion of the Cayaguás basin than the reported 11 percent, 

because this accentuated topography is expressed elsewhere 

in areas mapped as granodiorite.

The effect of geology on channel morphology in the 

WEBB watersheds is evident in the bed material and particle size (see cover photograph). Stream channels in the 

Icacos and Cayaguás watersheds typically have sand-bed 

channels and floodplains that are dominantly sandy alluvium with occasional boulders. Median grain size (D50) is 

0.6 millimeters (mm) and 0.5 mm, respectively, in the two 

channels (Larsen, 1997). Channels in the Mameyes and 

Canóvanas watersheds are generally characterized by little 

to no floodplain and channels lined with bedrock, boulders, 

or cobbles, with little bed sediment. Median grain size in 

these two channels is 70 mm and 110 mm, respectively 

(Larsen, 1997). 

Soil and Saprolite

Intense, continuous weathering in the warm, humidtropical climate of the Luquillo Mountains and the Loíza 

watershed has produced a mantle of soil overlying both granitic and volcaniclastic bedrock that is as much as 24 m thick 

(Simon and others, 1990; White and others, 1998). Below the 

surface-soil horizons, which show strong bioturbation (Brown 

and others, 1983; Brown and others, 1995; González and 

others, 1996) and subsoil movement (Lewis, 1974; Larsen 

and others, 1999), is commonly a layer of saprolite. Saprolite 

is soil that is derived from bedrock that has been thoroughly 

decomposed through isovolumetric chemical weathering; it 

remains in place and retains the appearance of the bedrock 

structure (American Geological Institute, 1976). The composition of the underlying bedrock, particularly the quartz 

content, determines the chemical and physical properties of 

the saprolite (Meyerhoff, 1931; Schellekens and others, 2004; 

Buss and White, 2012; Stallard, 2012).

Soils in Watersheds Dominated by Volcaniclastic 

Rock

The volcaniclastic rocks underlying the Mameyes and 

Canóvanas watersheds are generally quartz poor, and they 

are composed predominantly of basic feldspars and ferromagnesian minerals (Seiders, 1971a,b,c; Briggs, 1973). In 

the saprolite, these minerals weather to kaolinite and other 

clay minerals of the 1:1 lattice type, iron and aluminum 

oxides, and small amounts of quartz (Huffaker, 2002). Relic 

grains of plagioclase and mafic minerals are recognizable in 

some hand specimens of the saprolite (Scatena, 1989). Geoelectric soundings indicate a gradual change from unweathered bedrock to less-weathered saprolite to highly weathered 

saprolite; active weathering occurs in the less-weathered 

saprolite (Schellekens and others, 2004). Clay-lined faults 

and joints, which are common in the saprolite, make primary 

sedimentary structures difficult to distinguish. 

Table 3. Mineral content of granitic intrusions, eastern Puerto Rico.

[--, not available]

Mineral

Average mineral content of bedrock

(weight percent)

Río Blanco1 San Lorenzo2 Quartz monzonite 

of Punta Guayanes2

Plagioclase 55.8 51 35

Quartz 26.0 20.5 33

Amphibole 7.3 11 1.5

K-feldspar 5.0 11 28

Biotite 3.6 4.5 2

Accessory3 1.4 -- --

Apatite 0.2 -- --

Iron oxide 0.7 1.5 0.5

Total 100 99.5 100 1

Seiders (1971b).

2

Rogers and others (1979).

3

Accessory minerals: Río Blanco—epidote, sphene, tourmaline, zircon; 

San Lorenzo—apatite, sphene, zircon, and minor augite, myrmekite; 

Punta Guayanes—apatite, sphene, zircon.
The volcaniclastic saprolite is overlain by clay-rich, 

quartz-poor soils that are typically 0.8 to 1.0 m deep (Huffaker, 2002). Soils developed on the volcaniclastic rocks 

are finer grained than those developed on the granitic rocks, 

and they have lower permeability and infiltration rates than 

granitic soils (Simon and others, 1990; Murphy and Stallard, 

2012). Dense clays in the volcaniclastic soils retard deep 

infiltration, causing water to follow a shallow trajectory and 

reach streams quickly (McDowell and others, 1992). Because 

of the generally high soil moisture content in the Luquillo 

Mountains, soils are quickly saturated during storms, leading 

to saturation overflow (Larsen and others, 1999). As such, 

during periods of intense rainfall, water is commonly observed 

flowing over the surface in rills and ephemeral gullies. 

At the highest elevations in the Mameyes watershed, corresponding with palo colorado forest (see Vegetation and Land 

Use section), soils overlying the volcaniclastic rock consist 

primarily of the Yunque clay loam and Moteado clay soils, 

which are Oxisols, and the Guayabota clay loam and Palm clay 

soils, which are Inceptisols (Huffaker, 2002). These soils range 

from poorly drained (on concave slopes) to moderately well 

drained (on convex slopes). The Inceptisols presumably reflect 

the greater rainfall in the highest elevations and wetter areas 

near rivers. At lower elevations, corresponding with tabonuco 

forest, soils overlying the volcaniclastic rocks consist primarily 

of Zarzal clay and Cristal clay loam soils, which are Oxisols, 

and range from somewhat poorly drained (concave slopes) to 

well drained (convex slopes) (Huffaker, 2002). 

The Canóvanas watershed contains a variety of soil types, 

including Oxisols (Yunque, Moteado, Zarzal, and Cristal clay 

or clay loams, discussed in the previous paragraph, and the 

deep, moderately well drained Los Guineos clay), Inceptisols (Mucara silty clay loam and Caguabo clay loam, which 

are shallow to moderately deep and well drained), Ultisols 

(Humatas clay and Naranjito silty clay loam, which are deep 

to moderately deep and well drained), and minor Mollisols 

(Reilly silty clay loam, found in the floodplain) (Boccheciamp, 

1977; Huffaker, 2002). As a result of past agricultural cultivation, much of the original surface layers of several of the soils 

in the lower part of the Canóvanas watershed, including the 

Los Guineos, Humatas, Mucara, and Naranjito soils, have 

been removed by erosion (Boccheciamp, 1977).

Soils in Watersheds Dominated by Granitic Rock

The quartz-rich granitic rocks underlying the Icacos and 

Cayaguás watersheds weather to a saprolite composed of 

quartz, weathered biotite, and kaolinite, and other clays and 

sesquioxides (Boccheciamp, 1978; Murphy and others, 1998; 

White and others, 1998). Huffaker (2002) contends that the 

granitic rocks weather more rapidly than the volcanic rocks, 

and that if other conditions are equal, the regolith overlying the 

granitic rocks is thicker than that in volcanic areas. The Río 

Blanco quartz diorite, which underlies the Icacos watershed, 

has been the subject of extensive studies on chemical weathering rates (including White and Blum, 1995; Dong and others, 

1998; Murphy and others, 1998; White and others, 1998; 

Schulz and White, 1999; Turner and others, 2003; Buss and 

others, 2005, 2008, 2009; Fletcher and others, 2006; Pett-Ridge 

and others, 2009; Minyard and others, 2011; Buss and White, 

2012). The relatively young age of the Río Blanco quartz 

diorite, combined with high rainfall and temperature, results 

in the Icacos watershed having one of the highest documented 

chemical weathering rates of granitic rocks in the world (White 

and Blum, 1995; White and others, 1998). The weathering 

front has been estimated to proceed about 1 centimeter (cm) 

per 100 years on the basis of cosmogenic evidence (Brown 

and others, 1995). Stallard (1995) argues that minerals such as 

intergranular calcite and volatiles in fluid inclusions derived 

from the late-stage cooling of magma greatly enhance the susceptibility of younger plutons, such as the Río Blanco quartz 

diorite, to weathering, compared with identical rock types 

of Precambrian age, where volatiles have migrated out and 

late-stage minerals have recrystallized. The transition between 

the quartz-diorite bedrock and saprolite consists of a 20- to 

60-cm-thick zone characterized by fracture-bound concentric 

shells, termed rindlets (Turner and others, 2003; Fletcher and 

others, 2006; Buss and others, 2008; Buss and White, 2012). 

The majority of weathering in the watershed occurs in this transition zone, as individual rindlets become progressively more 

altered away from joint planes. The San Lorenzo granodiorite–

quartz diorite, which underlies the Cayaguás watershed, has not 

been well studied, but because of its similar composition and 

age it presumably weathers in a comparable manner.

On hillslopes, saprolite is overlain by 0.5–2 m of soil, 

which is typically sandy and loamy, with a thin, organic-rich A 

horizon and a layer of translocated clays concentrated in the B 

horizon (Simon and others, 1990; Huffaker, 2002). Soil thickness on ridges in the Icacos watershed has been observed to 

be more than ten-fold greater (Simon and others, 1990). Soils 

on the granitic rocks have higher permeability and infiltration 

rates than those on the volcaniclastic rocks, allowing greater 

water infiltration (Simon and others, 1990). The coarse soil 

is easily eroded and is highly susceptible to landslides when 

subjected to intensive agricultural activity or other disturbance 

(Larsen, 2012).

Soils overlying the Río Blanco quartz diorite in the Icacos watershed primarily consist of Picacho and Utuado loamy 

soils. These Inceptisols are very deep (greater than 150 cm) 

and somewhat poorly drained (Huffaker, 2002). The Icacos 

flood plain and low terraces at middle elevations are overlain 

by the Icacos loam, an Inceptisol which is very deep (greater 

than 150 cm) and somewhat poorly drained. Quartz persists 

through the entire profile, becoming more fine grained nearer 

the surface (Brown and others, 1995). 

Soils at the highest elevations in the Cayaguás watershed consist primarily of the Lirios silty clay loam, which is 

a well-drained Ultisol (Boccheciamp, 1978). Soils at lower 

elevations primarily consist of the Pandura sandy loam, 

an Inceptisol. Owing to past agricultural cultivation, both 

of these soils have lost much of their original surface layers through erosion. By using geomorphic evidence, Larsen 

14 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 15

and Santiago-Román (2001) estimate that the surface of the 

Cayaguás basin has been lowered an average of 660 mm since 

about 1820, comparable to the 500 mm of surface-lowering 

estimated by using cosmogenic Be10 (Brown and others, 1998).

Vegetation and Land Use

All of the natural ecological life zones of Puerto Rico (as 

developed by Ewel and Whitmore, 1973) are forest (fig. 7). 

Three life zones dominate the island: subtropical moist forest (58 percent of the main island), subtropical wet forest 

(23 percent), and subtropical dry forest (18 percent) (Birdsey 

and Weaver, 1982). Subtropical rain forest, lower montane 

wet forest, and lower montane rain forest occupy a little more 

than 1 percent of the island at high elevations, including most 

of the Mameyes and Icacos watersheds. Historical accounts 

suggest that, like many other Caribbean islands, Puerto Rico 

was heavily forested (about 95 percent) prior to European 

settlement (Wadsworth, 1950; Birdsey and Weaver, 1982). The 

island’s diverse physiography and tropical location result in 

diverse flora and fauna and a high level of endemism (Gould 

and others, 2008); however, plant diversity is lower than that 

at mainland tropical sites (Ashton and others, 2004).

In the 16th century, European settlers began clearing 

forests for pasture, cropland, timber, and fuel. By 1828, forest 

covered about 66 percent of Puerto Rico (Wadsworth, 1950). 

Pressure on land resources increased in the 19th and 20th 

centuries owing to population growth, expanding production 

of sugar cane and coffee, economic depression, and the effects 

of several severe hurricanes. By the late 1940s, forest cover of 

Puerto Rico had declined to 6 percent; cropland and pasture 

each accounted for about 42 percent of land cover; the remaining 10 percent contained urban areas and wasteland (Koenig, 

1953; Birdsey and Weaver, 1982). During this period, Puerto 

Rico was one of the most severe1y deforested regions in the 

world. Soil erosion related to this deforestation released high 

loads of sediment from hillslopes to footslopes, valley floors, 

Suptropical moist forest

Subtropical rain forest

Lower montane rain forest

EXPLANATION

Lower montane wet forest

Subtropical dry forest

Subtropical wet forest

Holdridge Life Zone

Map data from Ewel and Whitmore, 1973, as digitized by

the Luquillo Long-Term Ecological Research program

Canóvanas

Cayaguás

Icacos

Mameyes

18°30’

18°00’

67°00’ 66°30’ 66°00’

0

0

10 20 KILOMETERS

10 20 MILES

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from USDA Forest Service digital data, 1973, 1:240,000

Puerto Rico State Plane Coordinate System of 1927

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

Puerto Rico Datum 

PUERTO RICO

Figure 7. Ecological life zones of Puerto Rico (from Luquillo Long-Term Ecological Research program; digitized from Ewel and 

Whitmore, 1973).
16 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

and streams channels (Larsen, 2012). Much of the remaining 

forest was in the Luquillo Mountains, because access to this 

area was limited by steep slopes, high annual rainfall, and its 

designation as a reserve by the Spanish crown in 1876 and 

later by the U.S. Government (Zimmerman and others, 1995). 

A shift in Puerto Rico’s economic base that began in the 

late 1940s, partly as a result of the U.S. Government’s “Operation Bootstrap,” led to reforestation (Rudel and others, 2000). 

In 1934, about 43 percent of the island’s gross national product came from agriculture but only 7 percent from industry; 

by 1996, agriculture and industry contributed 1 percent and 

41 percent respectively to the gross national product (López 

and others, 2001). As Puerto Ricans moved from rural areas to 

cities on the island or migrated to the United States, agricultural 

land cover in Puerto Rico declined by 95 percent from 1951 to 

2000 (Kennaway and Helmer, 2007). Much of this abandoned 

land reverted to forest. By 1980, forest cover of Puerto Rico 

had increased to 31 percent (Franco and others, 1997). In 2003, 

forest, woodland, and shrubland represented about 44 percent of 

land area (Gould and others, 2012) (figs. 8 and 9).

Meanwhile, population density of Puerto Rico has 

increased more than threefold during the last century, 

resulting in one of the highest densities in the world: more 

than 400 people/km2

 (López and others, 2001). Puerto Rico 

now has the highest road density of any Caribbean island, 

2.5 km of paved roads per square kilometer (Lugo, 1996). 

On the basis of population density and remote sensing of 

developed areas, about 16 percent of the island is considered 

urban land use, 50 percent is suburban, and 34 percent is 

rural (Martinuzzi and others, 2007). Eleven percent of the 

island has developed surface, and about 60 percent of this 

surface corresponds with high-density development (Martinuzzi and others, 2007). In the San Juan metropolitan area, 

filling of the mangroves and mudflats has restricted circulation and impaired water quality (Webb and Gómez-Gómez, 

1998). Land has been converted to urban uses mostly in 

lowland regions and on lesser slopes.

Land Cover of Forested Watersheds

The four study watersheds were selected, in part, for 

differing land cover. The Icacos watershed is almost entirely 

covered by mature montane wet evergreen forest, which 

EXPLANATION

Land Cover

Barren

Forest

Nonforest vegetation

Urban

Woodland and shrubland

Water

0

0

10 20 KILOMETERS

10 20 MILES

Study watershed boundary

67°00’

18°30’

18°00’

66°30’ 66°00’

C A R I B B E A N S E A

A T L A N T I C O C E A N

Cayaguás

Canóvanas

Icacos

Mameyes

m 01 KILOMETERS

Land cover data from USDA Forest Service,

International Institute of Tropical Forestry, 

Puerto Rico GAP Analysis Project (2003)

Base from USDA Puerto Rico GAP Analysis Project digital data, 2006

Lambert Conformal Conic projection

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

PUERTO RICO

Area shown

in figure 9

Figure 8. Generalized land cover of Puerto Rico in 2003 (from Gould and others, 2008).
Physiography, Geology, and Land Cover of Four Watersheds in Eastern Puerto Rico 17

Map data modified from U.S. Department of Agriculture

 Forest Service, International Institute of Tropical Forestry,

Puerto Rico GAP Analysis Project (2003)

Cayaguás

18°20’

18°15’

18°10’

18°05’

66°00’ 66°55’ 66°50’ 66°45’

Canóvanas

Icacos

Mameyes

EXPLANATION

Land Cover

Elfin woodland forest

Sierra Palm forest

Palo colorado forest

Tabonuco forest

Other wet forest

Moist forest

Dry forest

Grassland and pasture

Agriculture

Urban

Barren

Water

Study watershed boundary

0

0

3 6 KILOMETERS

3 6 MILES

C A R I B B E A N

S E A

Base from USDA Puerto Rico GAP Analysis Project digital data, 2006

Lambert Conformal Conic projection

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

Figure 9. Detailed land cover of study watersheds in 2003 (from Gould and others, 2008).
18 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

includes about 85 percent palo colorado forest, 13 percent 

sierra palm forest, and 2 percent other montane wet forest 

(Gould and others, 2008) (fig. 9; table 4). Prominent trees are 

palo colorado (Cyrilla racemiflora), caimitillo (Micropholis garciniaefolia, M. chrysophylloides), and laurel sabino 

(Magnolia splendens) (Foster and others, 1999). The Mameyes watershed also consists primarily of mature montane 

wet evergreen forest, but it is larger and has a much greater 

variation in elevation than the Icacos watershed, and thus 

it has more varied forest. Land cover is about 57 percent 

tabonuco forest, 25 percent sierra palm forest, 14 percent palo 

colorado forest, 1 percent elfin woodland, and 2 percent other 

montane wet forest (Gould and others, 2008, 2012) (table 4). 

Above 600 m, vegetation in the Mameyes watershed is primarily palo colorado and sierra palm forest. Elfin woodland, 

characterized by short, slow-growing vegetation, develops on 

ridges above 900 m elevation (Weaver, 1986, 1989, 1990). 

Elevations below 600 m are primarily tabonuco forest. This 

forest is dominated by the tabonuco tree (Dacryodes excelsa), 

but 170 tree species have been described for this forest type 

(Foster and others, 1999).

Forest cover in much of the Icacos and Mameyes watersheds persisted during the nadir of forest cover in Puerto Rico 

in the 1940s, and those watersheds have maintained a continuous forest canopy since that time (table 4). As discussed 

above, steep topography, harsh conditions, and governmental 

protection limited access for development or forest conversion. Although the Luquillo Mountains were less affected by 

development than other areas of Puerto Rico, they are not 

pristine; cattle production, shade coffee, timber extraction, 

subsistence farming, and charcoal production were practiced 

in the late 1800s and in the 1900s, primarily below 600 m 

elevation. The USDA Forest Service purchased much of the 

lower-elevation land in the Luquillo Mountains in the 1930s 

(Zimmerman and others, 1995). Nonnative bamboo was 

introduced from Asia in the 1930s and 1940s along roads to 

stabilize slopes; however, during major disturbances such as 

hurricanes, extensive bamboo root systems can actually help 

cause catastrophic slope failures (O’Connor and others, 2000). 

Bamboo is expanding downstream in streams within the 

Mameyes watershed at a rate of 8 meters per year (O’Connor 

and others, 2000). Large stands of bamboo are observed in the 

Mameyes and Icacos watersheds, especially near roads.

Land Cover of Developed Watersheds

In the Canóvanas watershed, land cover is currently 

about 71 percent forest, 26 percent grassland or pasture, and 

3 percent urban (Gould and others, 2008) (table 4). Above 

600 m elevation, land cover is primarily palo colorado forest, similar to the Icacos and Mameyes watersheds (fig. 9, 

table 4). Between 400 and 600 m, land cover is primarily 

tabonuco forest and grassland or pasture. Below 400 m, 

land cover is primarily secondary lowland moist forest and 

grassland or pasture. The Canóvanas watershed has a mixed 

land-use history. Elevations above 600 m were forested in 

the middle 20th century, but most of the lower watershed 

was used for pasture, coffee, and fruit crops, and to a lesser 

degree, bananas and sugar cane (Kennaway and Helmer, 

2007). Between the 1950s and the 1970s, the predominant 

land cover shifted from agriculture and pasture to forest, 

and forest cover in the watershed increased by 44 percent 

(table 4). These decades correspond with a low point in 

population density of the Cubuy barrio, which is entirely 

within the Canóvanas watershed and represents the majority 

of the land area in the watershed (fig. 10). Current population 

density has returned to that of the 1950s, but land use is predominantly residential as opposed to agricultural (Martinuzzi 

and others, 2007; Parés-Ramos and others, 2008).

Current land cover in the Cayaguás watershed is 

57 percent pasture, 41 percent forest or shrubland (primarily 

lowland moist forest and montane wet forest) and 2 percent 

urban (fig. 9, table 4). Fifty years ago, the Cayaguás watershed 

was almost entirely devoted to agricultural activities, such 

as pasture, fruit crops, and tobacco (Kennaway and Helmer, 

2007). Forest cover has increased about 20 percent since the 

late 1970s, primarily at the expense of pasture and grasslands 

(table 4). Population densities in the Quebrada Arenas barrio, 

which is entirely within the Cayaguás watershed and represents most of the land area in the watershed, reached a low 

point in the 1980 census (fig. 10). Population has since been 

rising owing to increased residential land use (Martinuzzi and 

others, 2007; Parés-Ramos and others, 2008). 

Summary

Four watersheds in eastern Puerto Rico have been 

studied since 1991 as part of the U.S. Geological Survey’s 

Water, Energy, and Biogeochemical Budget program. 

These watersheds differ in geology, soils, and land cover. 

The Icacos watershed is underlain by granitic rock, which 

weathers to quartz- and clay-rich, sandy soils. Land cover is 

primarily old-growth wet forest, and human disturbance has 

been minimal. The Mameyes watershed is primarily underlain by volcaniclastic rock, which weathers to quartz-poor, 

fine-grained soils. This watershed also contains largely oldgrowth wet forest. The Canóvanas watershed is underlain 

by volcaniclastic rocks and soils that are similar to those in 

the Mameyes, but historical agricultural practices at lower 

elevations have led to substantial erosion of soils. This 

watershed has largely reforested since the 1950s. The Cayaguás watershed is underlain by granitic rock; this watershed was also historically used for agriculture and has been 

deeply eroded. It has undergone some reforestation since 

the 1970s. Past soil erosion in the Canóvanas and Cayaguás 

watersheds released high loads of sediment from hillslopes 

to footslopes, valley floors, and streams channels. The reforestation of these watersheds, and of Puerto Rico, may serve 

as a prototype for the hydrological and chemical response of 

other tropical areas that are shifting from an agricultural to 

an industrial economy.
Table 4. Land cover and land-cover dynamics in study watersheds, eastern Puerto Rico.[Land-cover dynamics are indicated as percentage increase or decrease from 1936, 1950 (Lugo and others, 2004), and 1977 (Ramos and Lugo, 1994) for woody (forest, shrublan(grassland, pastures, agriculture), and developed areas. In 1936 and 1950, the Cayaguás watershed was primarily agricultural and nonforested. --, not analyzed]

Land cover type

Icacos Mameyes Canóvanas 

2003

land cover1

percent

Percent

change since

2003

land cover

percent

Percent

change since

2003

land cover

percent

Percent

change since

lan

p

1936 1950 1977 1936 1950 1977 1936 1950 1977 

Woody 99.9 0 0 0 99.6 1 0 0 71.3 47 44 2 

Palo colorado forest 85.4 -- -- -- 13.9 -- -- -- 14.2 -- -- -- 

Sierra palm forest 12.7 -- -- -- 25.3 -- -- -- 1.60 -- -- -- 

Elfin woodland 0.00 -- -- -- 1.07 -- -- -- 0.38 -- -- -- 

Tabonuco forest 0.00 -- -- -- 57.0 -- -- -- 24.5 -- -- -- 

Other montane wet forest, shrubland, woodland 1.84 -- -- -- 2.36 -- -- -- 10.4 -- -- -- 

Lowland moist forest, shrubland, woodland 0.00 -- -- -- 0.00 -- -- -- 20.2 -- -- -- 

Abandoned and active coffee plantations 0.00 -- -- -- 0.00 -- -- -- 0.00 -- -- -- 

Herbaceous 0.15 0 0 0 0.32 –1 0 0 25.68 –41 –48 3 

Moist grasslands and pastures 0.15 -- -- -- 0.32 -- -- -- 25.68 -- -- -- 

Developed 0.00 0 0 0 0.02 0 0 0 2.79 3 3 –4 

Low-density urban development 0.00 -- -- -- 0.02 -- -- -- 2.79 -- -- -- 

Other 0.00 0 0 0 0.05 0 0 0 0.25 0 0 0 

Barren 0.00 -- -- -- 0.04 -- -- -- 0.25 -- -- -- 

Freshwater 0.00 -- -- -- 0.004 -- -- -- 0.00 -- -- -- 

Total 100 100 100 

1From Gould and others, 2008.
20 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Acknowledgments

This chapter was greatly improved by reviews by John F. 

Walker and Richard M.T. Webb of the U.S. Geological Survey 

and F.N. Scatena of the University of Pennsylvania.

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Land Use, Population Dynamics, and 

Land-Cover Change in Eastern Puerto Rico

By William A. Gould, Sebastián Martinuzzi, and Isabel K. Parés-Ramos

Chapter B of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–B

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.........................................................................................................................................................29

Introduction...................................................................................................................................................29

Land Cover....................................................................................................................................................32

Land Use........................................................................................................................................................32

Population Dynamics...................................................................................................................................38

Land-Cover Change.....................................................................................................................................38

Conclusion.....................................................................................................................................................40

Acknowledgments.......................................................................................................................................41

References....................................................................................................................................................41

Figures

1–4. Maps showing the following:

 1. Location of Puerto Rico and study area ..........................................................................30

 2. Simplified land cover of eastern Puerto Rico .................................................................35

 3. Urban, suburban, and rural land-use classes and protected conservation 

areas in eastern Puerto Rico.............................................................................................36

 4. Neighborhood population dynamics in eastern Puerto Rico, 1990 to 2000................37

5. Diagram of population trends in land-use categories, 1950 to 2000 ..................................35

6. Map of trends in forest, pasture and agriculture, urban, and mangrove extent, 

1936–2003, eastern Puerto Rico ...............................................................................................39

7. Diagram of trends in forest, pasture and agriculture, urban, and mangrove extent, 

1936–2003, eastern Puerto Rico ...............................................................................................40

Tables

1. Percentage of land area occupied by watershed, municipality, and national forest 

areas in eastern Puerto Rico ....................................................................................................31

2. Area of land-cover types in eastern Puerto Rico..................................................................33

3. Area of simplified land cover in eastern Puerto Rico...........................................................34

4. Urban, suburban (densely populated rural), and rural (sparsely populated rural) 

land-use classes and protected conservation areas for eastern Puerto Rico................34

5. Land-cover transition matrix of eastern Puerto Rico between 1991 and 2003.................40
Abbreviations Used in This Report

km kilometer

km2 square kilometer

m2 square meter

mm millimeter

WEBB Water, Energy, and Biogeochemical Budgets

Conversion Factors

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 10.76 square foot (ft2

) 

square kilometer (km2

) 0.3861 square mile (mi2

)

Abstract

We assessed current and historic land use and land 

cover in the Luquillo Mountains and surrounding area in 

eastern Puerto Rico, including four small subwatersheds that 

are study watersheds of the U.S. Geological Survey’s Water, 

Energy, and Biogeochemical Budgets (WEBB) program. 

This region occupies an area of 1,616 square kilometers, 

about 18 percent of the total land in Puerto Rico. Closed 

forests occupy about 37 percent of the area, woodlands and 

shrublands 7 percent, nonforest vegetation 43 percent, urban 

development 10 percent, and water and natural barrens 

total less than 2 percent. The area has been classified into 

three main land-use categories by integrating recent census 

information (population density per barrio in the year 2000) 

with satellite image analyses (degree of developed area 

versus natural land cover). Urban land use (in this analysis, 

land with more than 20 percent developed cover within a 

1-square-kilometer area and population density greater than 

500 people per square kilometer) covered 16 percent of eastern Puerto Rico. Suburban land use (more than 80 percent 

natural land cover, more than 500 people per square kilometer, and primarily residential) covers 50 percent of the area. 

Rural land use (more than 80 percent natural land cover, less 

than 500 people per square kilometer, and primarily active 

or abandoned agricultural, wetland, steep slope, or protected 

conservation areas) covered 34 percent of the area. Our analysis of land-cover change indicates that in the 1990s, forest 

cover increased at the expense of woodlands and grasslands. 

Urban development increased by 16 percent during that time. 

The most pronounced change in the last seven decades has 

been the shift from a nonforested to a forested landscape and 

the intensification of the ring of urbanization that surrounds 

the long-protected Luquillo Experimental Forest. 

Introduction

The island of Puerto Rico is at a geographic and cultural 

crossroads where human activities are influencing changes in 

the environment, climate, and land use and land cover. Puerto 

Rico, which is bordered by the Atlantic Ocean on the north 

and the Caribbean Sea on the south, lies at the juncture of the 

Greater and Lesser Antilles (fig. 1). It is in the path of hurricanes and tropical storms approaching the western hemisphere 

(Salivia, 1972; Weaver, 1986; Scatena and Larsen, 1991, 

Murphy and Stallard, 2012), atmospheric dust from the African 

continent (Reid and others, 2003, Stallard, 2012), and distinct 

air masses arriving from across the Atlantic or from the eastern 

seaboard of the United States (Malmgren and others, 1998). 

At a broad scale, these climatic events are controlled by cyclic 

global climate patterns as well as by human-induced carbon 

emissions that are changing climate (Huber and Knutti, 2012). 

At a more local scale, ecosystems respond to a range of environmental conditions and are influenced by historical patterns 

of changing land use in eastern Puerto Rico.

Eastern Puerto Rico is a good region in which to 

assess changes in land use and land cover for a number of 

ecological and social reasons. All six of the Holdridge life 

zones in Puerto Rico can be found along a transect of less 

than 15 kilometers (km) between the coast and the upper 

Luquillo Mountains (Holdridge, 1967; Ewel and Whitmore, 

1973). Ecological diversity is affected by natural and human 

factors and by the strong climatic gradients caused by 

moisture-laden trade winds rising to pass over the Luquillo 

Mountains (García-Martinó and others, 1996; Murphy and 

Stallard, 2012). Mean annual rainfall increases with elevation from about 1,400 millimeters (mm) to about 5,000 mm 

per year, and mean annual temperature decreases with elevation from about 27.5°C to about 19.5°C along this gradient 

(Gould and others, 2006). The coastal hills and plains, along 

with the Luquillo Mountains, form a matrix of ridge, slope, 

valley, and depression that influences soil moisture, soil 

development, and vegetation (Weaver, 1991; Basnet, 1992). 

Long-term climatic trends, aeolian inputs (Reid and others, 

2003, Stallard, 2012), storm events (Salivia, 1972; Weaver, 

1986; Scatena and Larsen, 1991), climatic gradients, and 

topographic patterns all help to shape the landscape and 

control potential vegetation and land cover. 

Land Use, Population Dynamics, and Land-Cover Change 

in Eastern Puerto Rico

By William A. Gould,1 Sebastián Martinuzzi,2 and Isabel K. Parés-Ramos3

1

U.S. Department of Agriculture Forest Service, International Institute of 

Tropical Forestry, 1201 Calle Ceiba, Río Piedras, Puerto Rico

2

Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 

Linden Drive, Madison, Wisconsin 53706

3

Department of Biology, University of Puerto Rico, Río Piedras, Puerto 

Rico 00931–3360.
30 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

65°40’

RÍO GRANDE

LUQUILLO

FAJARDO

CEIBA

NAGUABO

LAS PIEDRAS

JUNCOS

Río Grande de Loíza Watershed

Río Maunabo to Río Humacao Watersheds

Río Anton Ruiz to Río Fajardo Watersheds

Río Herrera to Las Cabezas de San Juan Coastal Watersheds

18°30’

18°20’

18°10’

66°00’ 65°50’

San Juan

A T L A N T I C O C E A N

C A R I B B E A N S E A

Mameyes

Cayaguás

Canóvanas

CANÓVANAS

B

LUQUILLO

MOUNTAINS Icacos

EXPLANATION

0 4

0

8 KILOMETERS

4 8 MILES

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_01B.ai

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

Luquillo Experimental

 Forest

Study watershed

 boundary

Principal watershed or

 watershed group

 boundary

Municipality boundary

El Yunque National

 Forest proclamation

 area boundary

Area shown in B

A

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

L e s s e r

A n

t i l l e s

Figure 1. Location of Puerto Rico and study area, eastern Puerto Rico (see also table 1).
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 31

In addition to these complex environmental gradients and 

dynamics, eastern Puerto Rico has a long history of human 

activity, and the intensity of land use has differed with time. 

Pre-Columbian, low-intensity subsistence fishing, farming, 

hunting, and gathering lasted several thousand years. European 

colonization and exploitation of forest resources for timber 

and subsistence agriculture began about 500 years ago and 

increased slowly for the next three centuries. Intensive farming of sugarcane, tobacco, and coffee for export began more 

than 200 years ago (Dietz, 1986). The decline in agricultural 

production and growth of industry during the last six decades 

shifted much former agricultural land to cattle pasture or to 

completely abandoned land. Forest is now regenerating on 

those abandoned lands (Thomlinson and others, 1996; Aide 

and others, 2000; Chinea, 2002). Recent improvements in 

infrastructure and transportation systems, increasing population, and the virtual absence of land-use planning strategies 

have led to the conversion of many former agricultural lands to 

residential and commercial uses, suburban sprawl (Thomlinson 

and Rivera, 2000; López and others, 2001; Grau and others, 

2003; Martinuzzi and others, 2007), depopulation of urban 

centers, and urban to suburban migration (Parés-Ramos and 

others, 2008). Finally, the region has a complex matrix of protected and unprotected areas (Helmer, 2004; Gould and others, 

2008) and diverse zoning regulations (Lugo and others, 2000, 

2004), which have led to differences in human use and in the 

responses of the landscape to the history of human activities 

(Zimmerman and others, 1995; Aide and others, 2000; Thompson, 2002). All of these human activities have had considerable 

effect on the land-cover and land-use history of eastern Puerto 

Rico. The resulting land-use dynamics span a gradient from 

stable (areas that have maintained relatively consistent land-use 

intensities (older urban centers, current agricultural areas, mangrove forests, and forested peaks) to dynamic (areas that have 

fluctuated substantially during the previous 100 years between 

high- and low-intensity agriculture and agricultural abandonment and, more recently, shifted to urbanization).

Analyses of changes in land use and land cover in 

eastern Puerto Rico have focused mainly on land cover and 

change in four areas: (1) within the boundaries of the Luquillo 

Experimental Forest (whose boundaries match those of El 

Yunque National Forest) (Crow, 1980; Brown and others, 

1983; Scatena, 1989; Weaver, 1990; Foster and others, 1999; 

Thompson and others, 2002), (2) within the proclamation area 

(a zone outside the national forest boundary in which the U.S. 

Congress authorized the U.S. Forest Service to purchase lands 

for conservation purposes) (Lugo and others, 2000), (3) within 

the eight municipalities that have jurisdiction of land outside 

the periphery of the Luquillo Experimental Forest (Thomlinson 

and others, 1996; Ramos González, 2001; Lugo and others, 

2004), and (4) within the four principal watersheds or watershed groups that have all or part of their source in the Luquillo 

mountains (Ortíz-Zayas and Scatena, 2004) (fig. 1).

In this chapter we assess land use and land cover of the 

area encompassed by four principal watersheds or watershed 

groups in eastern Puerto Rico (table 1): Río Grande de Loíza, 

Río Anton Ruiz to Río Fajardo, Río Herrera to Las Cabezas 

de San Juan, and Río Maunabo to Río Humacao. These 

watersheds include four small subwatersheds that are study 

Table 1. Watershed, municipality, and national forest areas of eastern Puerto Rico and their percentage of 

land area in Puerto Rico.

[<, less than]

Region Area

(hectares) (percent)

Principal watershed or watershed group 161,650 18.1

Río Grande de Loiza watershed 75,098

Río Anton Ruiz to Río Fajardo watersheds 31,336

Río Herrera to Las Cabezas de San Juan Coastal watersheds 26,865

Río Maunabo to Río Humacao watersheds 28,351

Municipalities surrounding Luquillo Experimental Forest 75,234 8.4

Río Grande 15,747

Canóvanas 8,552

Luquillo 6,710

Fajardo 7,709

Ceiba 7,429

Naguabo 13,404

Las Piedras 8,790

Juncos 6,893

Study watersheds 7,346 <1

Cayaguás 2,605

Canóvanas 2,655

Icacos 325

Mameyes 1,761

El Yunque National Forest proclamation area 22,417 2.5

El Yunque National Forest/Luquillo Experimental Forest 11,429 1.3
32 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

watersheds of the U.S. Geological Survey’s Water, Energy, and 

Biogeochemical Budgets (WEBB) program (fig. 1, table 1): 

Cayaguás, Canóvanas, Icacos, and Mameyes (Murphy and 

others, 2012). This region occupies an area of 1,616 square 

kilometers (km2), about 18 percent of the total land in Puerto 

Rico (table 1). In addition, we describe population dynamics 

and analyze land-cover changes within the eight municipalities 

that surround the Luquillo Experimental Forest. This region 

has been an important area for research and land management 

because of the regulatory control that the surrounding municipal governments have on development in its periphery. However, a historic lack of enforcement of these regulations has 

resulted in subsequent building violations (Lugo and others, 

2004). Nevertheless, the residents of these municipalities retain 

a strong interest in and reliance on the ecosystem services of 

the Luquillo Mountains, particularly as a source of fresh water 

and recreational opportunities.

Land Cover

A recent land-cover assessment developed by the Puerto 

Rico Gap Analysis Project identified 70 land-cover types 

on the island (Gould and others, 2008). Fifty-one of these 

land-cover types occur within the four principal watersheds of 

eastern Puerto Rico (table 2). Forest, woodland, and shrubland 

were the most common land-use types, covering 44 percent 

of the land area. Thirty-three forest, woodland, or shrubland 

classes were identified; these classes are based on forest age, 

degree of canopy cover, underlying substrate, and whether 

the vegetation is dry, moist, wet, or flooded. Eight dry forest or woodland classes cover about 1 percent of the total 

area, and they include important eastern dry forests near the 

coast. Twelve moist-forest types, which cover 25 percent of 

the area, are primarily composed of the lowland moist forests 

and woodlands on the lower slopes of the Luquillo Mountains and in the southeastern part of the Río Grande de Loíza 

watershed. Eleven wet-forest classes, which cover 16 percent 

of the region, are found on the upper slopes of the Luquillo 

Mountains (including most of the protected forests within 

the Luquillo Experimental Forest) and in mountains to the 

southeast of the Forest. Flooded coastal forests, which cover 

1.4 percent of the total land area, include saline mangrove 

forests and freshwater Pterocarpus swamps. Structurally 

developed forests (in this classification, those forests more 

than 25 years old) were identified on 1977 imagery (Ramos 

and Lugo, 1994) in 20 percent of the area. 

Six grassland or herbaceous classes, covering 41 percent 

of the land area of eastern Puerto Rico, include dry and moist 

pasture and grassland, and seasonally flooded and emergent 

saline and nonsaline wetlands (table 2). Three developed 

classes, covering 12 percent of the area, are high-density urban 

development (more than 50 percent impervious surface within 

a 9-hectare area), low-density urban development (more than 

50 percent impervious, primarily vegetated, surface within 

a 9-hectare area), and artificial barrens, which are typically 

bulldozed areas (table 2; Martinuzzi and others, 2007). The 

remaining 3 percent of land area consists of natural barrens, 

agricultural lands other than pasture, and open water. 

This detailed classification can be simplified into a set 

of six classes: forests (closed forest, greater than 60 percent 

tree cover), woodlands (open forests, 25 to 60 percent tree 

cover) and shrublands, nonforest vegetation (grasslands 

and wetlands), natural barrens, urban and artificial barrens 

(bulldozed areas), and water (fig. 2). Forests occupy about 

37 percent of the total land area, woodlands and shrublands 

7 percent, nonforest vegetation 43 percent, urban and artificial barrens 12 percent, and water and natural barrens total 

less than 2 percent (table 3).

Land Use

Eastern Puerto Rico consists of 175 barrios, or neighborhoods, a geographic unit used in the census of population in the U.S. Census every 10 years (U.S. Census Bureau, 

2001). This area has been classified into three main land-use 

categories by integrating recent census information (population density per barrio in the year 2000) with satellite image 

analyses (relative amount of developed area and natural land 

cover (Martinuzzi and others, 2007, 2008; Gould and others, 

2008). In this analysis, the urban land-use class has greater than 

20 percent developed cover within a 1-km2 area and population 

density greater than 500 people per square kilometer. Two rural 

classes, both with more than 80 percent natural land cover, 

were identified: a densely populated rural (“suburban”) class, 

which is primarily residential and has more than 500 people 

per square kilometer, and a sparsely populated rural (“rural”) 

class, which has less than 500 people per square kilometer and 

is primarily active or abandoned agricultural, wetland, steep 

slope, or protected conservation areas (Martinuzzi and others, 

2007). The urban, suburban, and rural classes cover 16 percent, 

50 percent, and 34 percent, respectively, of eastern Puerto Rico 

(fig. 3, table 4). This is a higher suburban-to-rural ratio when 

compared with the island as a whole (Martinuzzi and others, 

2007). These patterns of population growth and distribution 

have been controlled by the topography of the island (flat areas 

are more easily developed), as well as by socioeconomic factors such as sugarcane farming, housing, road networks, and 

conservation efforts (Grau and others, 2003; Helmer, 2004; 

Parés-Ramos and others, 2008). Ten percent of eastern Puerto 

Rico is protected for conservation, compared with an 8 percent 

average of protected area in the island as a whole (Gould and 

others, 2008) (fig. 3, table 4).
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 33

Table 2. Area of land-cover types in eastern Puerto Rico, from derived analysis of 1999–2003 Landsat 

ETM+ imagery.—Continued

[Gould and others (2008). Land-cover types organized by dominant physiography and climate. <, less than; *, most abundant landcover types] 

Land-cover type Area

(hectares) (percent)

Total area 161,700 100

Forest, woodland, and shrubland 71,112 44.0

Dry 1,775 1.1

Mature secondary lowland dry alluvial semideciduous forest 135 0.1

Young secondary lowland dry alluvial semideciduous forest 277 0.2

Lowland dry alluvial shrubland and woodland 104 0.1

Mature secondary lowland dry noncalcareous semideciduous forest 329 0.2

Young secondary lowland dry noncalcareous semideciduous forest 666 0.4

Lowland dry noncalcareous shrubland and woodland 246 0.2

Lowland dry riparian forest 14 <0.1

Lowland dry riparian shrubland and woodland 4 <0.1

Moist 40,908 25.3

Mature secondary lowland moist alluvial evergreen forest 362 0.2

Young secondary lowland moist alluvial evergreen forest 1,804 1.12

Lowland moist alluvial shrubland and woodland 1,235 0.8

Mature secondary moist limestone evergreen and semideciduous forest 119 0.1

Young secondary moist limestone evergreen and semideciduous forest 28 <0.1

Moist limestone shrubland and woodland 27 <0.1

*Mature secondary lowland moist noncalcareous evergreen forest 10,352 6.4

*Young secondary lowland moist noncalcareous evergreen forest 18,881 11.7

Lowland moist noncalcareous shrubland and woodland 7,625 4.7

Lowland moist abandoned and active coffee plantations 41 <0.1

Lowland moist riparian forest 241 0.2

Lowland moist riparian shrubland and woodland 193 0.1

Wet 26,126 16.2

Mature secondary montane wet alluvial evergreen forest 180 0.1

Young secondary montane wet alluvial evergreen forest 164 0.1

Montane wet alluvial shrubland and woodland 60 <0.1

Mature secondary montane wet noncalcareous evergreen forest 3,315 2.1

Mature primary tabonuco and secondary montane wet

noncalcareous evergreen forest 8,715 5.4

Mature primary palo colorado and secondary montane wet

noncalcareous evergreen forest 3,712 2.3

Mature primary sierra palm and secondary montane wet

noncalcareous evergreen forest 2,633 1.6

Mature primary elfin woodland and secondary montane wet

noncalcareous evergreen cloud forest 257 0.2

Young secondary montane wet noncalcareous evergreen forest 5,315 3.3

Montane wet noncalcareous evergreen shrubland and woodland 1,698 1.1

Montane wet evergreen abandoned and active coffee plantation 77 0.1

Flooded 2,304 1.4

Mangrove forest and shrubland 2,129 1.3

Freshwater Pterocarpus swamp 175 0.1

Grasslands and herbaceous vegetation 66,063 40.9

Dry grasslands and pastures 487 0.3

*Moist grasslands and pastures 58,444 36.1

Seasonally flooded herbaceous nonsaline wetlands 5,620 3.5

Seasonally flooded herbaceous saline wetlands 1,210 0.7

Emergent herbaceous nonsaline wetlands 248 0.2

Emergent herbaceous saline wetlands 53 <0.1

Agriculture 3,223 2.0

Hay and row crops 2,895 1.8

Woody agriculture and plantations 328 0.2
34 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 2. Area of land-cover types in eastern Puerto Rico, from derived analysis of 1999–2003 Landsat 

ETM+ imagery.—Continued

[Gould and others (2008). Land-cover types organized by dominant physiography and climate. <, less than; *, most abundant landcover types] 

Land-cover type Area

(hectares) (percent)

Natural barrens 444 0.3

Rocky cliffs and shelves 29 <0.1

Gravel beaches and stony shoreline 14 <0.1

Fine to coarse sandy beaches, mixed sand and gravel beaches 214 0.1

Riparian and other natural barrens 45 <0.1

Salt and mudflats 142 0.1

Developed areas 19,542 12.1

*High-density urban development 9,224 5.7

Low-density urban development 7,474 4.6

Artificial barrens 2,844 1.8

Water 1,317 0.8

Freshwater 823 0.5

Salt water 494 0.3

Table 3. Area of simplified land cover in eastern Puerto Rico from

derived analysis of recent (1999–2003) Landsat ETM+ imagery.

[From Gould and others (2008); <, less than]

Land-cover type Area

(hectares) (percent)

Forest 60,157 37

Woodland and shrubland 11,183 7

Nonforest vegetation 68,883 43

Natural barrens 324 <1

Urban and artificial barrens 19,494 12

Water 1,309 1

Total 161,351 100

Table 4. Urban, suburban (densely populated rural), and rural (sparsely populated rural) landuse classes and protected conservation areas in eastern Puerto Rico.

[From Martinuzzi and others (2007); <, less than]

Land-use 

class

Protected area

(hectares)

Unprotected area

(hectares)

Total area

(hectares)

Percent

protected

Percent of

161,401 hectares

Urban 155 25,644 25,809 <1 16

Suburban 2,884 77,283 80,167 4 50

Rural 13,125 42,300 55,425 24 34

Total 16,173 145,228 161,401 10 100
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 35

18°25’

18°20’

18°15’

18°10’

18°05’

18°00’

66°05’ 66°00’ 65°55’ 65°50’ 65°45’ 65°40’ 65°35’

A T L A N T I C O C E A N

C A R I B B E A N S E A

EXPLANATION

Forest

Woodland and shrubland

Nonforest vegetation

Natural barren

Urban and artificial barren

Water

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_02B.ai

Icacos

Mameyes

Cayaguás

Canóvanas

0 4

0

8 KILOMETERS

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000 4 8 MILES

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

Land-use classes

Study watershed boundary

PUERTO RICO

MAP LOCATION

Figure 2. Simplified land cover of eastern Puerto Rico (modified from Gould and others, 2008).
36 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

18°25’

18°20’

18°15’

18°10’

18°05’

18°00’

66°05’ 66°00’ 65°55’ 65°50’ 65°45’ 65°40’ 65°35’

A T L A N T I C O C E A N

C A R I B B E A N S E A

EXPLANATION

Urban

Land-use classes

Suburban

Rural

Conservation area

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_03B.ai

Icacos

Mameyes

Cayaguás

Canóvanas

0 4

0

8 KILOMETERS

4 8 MILES

Study watershed boundary

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

PUERTO RICO

MAP LOCATION

Figure 3. Urban, suburban, and rural land-use classes and protected conservation areas in eastern Puerto Rico (following Martinuzzi 

and others, 2007).
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 37

18°25’

18°20’

18°15’

18°10’

18°05’

18°00’

66°05’ 66°00’ 65°55’ 65°50’ 65°45’ 65°40’ 65°35’

A T L A N T I C O C E A N

C A R I B B E A N S E A

EXPLANATION

Increase > 300 people 

Barrio population change

Change ± 300 people

Decrease > 300 people 

Study watershed boundary

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_04B.ai

Icacos

Mameyes

Cayaguás

Canóvanas

0 4

0

8 KILOMETERS

4 8 MILES

PUERTO RICO

MAP LOCATION

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

Figure 4. Barrio (neighborhood) population dynamics in eastern Puerto Rico, 1990–2000 (from Parés-Ramos and others, 2008).
38 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

1950 1960 1970 1980 1990 2000

Population , in thousands of people

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0

Census year

EXPLANATION

Urban population

Decade of rapid increase

 in suburban population

Suburban population

Rural population

Figure 5. Population trends in land-use categories, 1950–2000, 

in eastern Puerto Rico. 

Population Dynamics

From 1990 to 2000, overall population trends in Puerto 

Rico were characterized by an increase in suburban population and housing density, by suburbanization of the rural 

landscape, and by population decline in several urban centers 

(Parés-Ramos and others, 2008). In eastern Puerto Rico, only 

10 of 175 barrios (6 percent) lost population from 1990 to 

2000 (fig. 4), and nine of these barrios were urban. Seventythree barrios (42 percent) showed little population change 

(±300 people) and of these, 42 percent were classified as 

rural, 40 percent as suburban, and 12 percent as urban barrios. 

In 92 barrios, population increased markedly (300–6,800 new 

inhabitants), and of these barrios 9 percent were classified as 

urban, 77 percent as suburban, and 14 percent as rural. This 

trend follows the island-wide trend of population increasing 

in the suburban areas and decreasing in older urban centers 

outside of the island’s main metropolitan area (San Juan), 

which has occurred mostly at the expense of grasslands 

(Parés-Ramos and others, 2008). In the past, suburban and 

urban areas in eastern Puerto Rico were characterized by 

relatively similar rates of population growth, but recently 

(1990–2000) there has been a more rapid increase in suburban population density and housing units (fig. 5), influencing 

land-use patterns in this area.

Land-Cover Change

We compared two recent classifications of land cover to 

assess land-cover change in eastern Puerto Rico between 1991 

(Helmer and others, 2002) and 2003 (Gould and others, 2008). 

Both assessments are based on the classification of Landsat satellite imagery but differ in methods and classification criteria. 

The 1991 assessment used a 30-square meter (m2) pixel resolution, whereas the 2003 assessment used a 15-m2

 pixel resolution. The classification of 1991 imagery has 28 classes for 

eastern Puerto Rico, whereas the classification of 2003 imagery 

has 51 classes. The finer resolution of the 2003 classification 

resulted in areas of residential development (such as houses 

surrounded by vegetation) to be more precisely delineated, 

reducing the mapped amount of developed area. Therefore, the 

1991 classification overestimates urban areas relative to the 

2003 classification. Additionally, geographic representation of 

mountainous areas in two dimensions always results in distortion of the spatial relationship between land-surface features, 

and lack of coregistration of pixels between the two classifications may lead to errors. In order to minimize differences in 

classification schemes, both classifications were reduced to 

six general classes (see fig. 2) and resampled to 15-m2

-pixel 

resolution. Although these differences in methods affect the 

accuracy of the land-cover change assessment, some inferences 

about the direction of change in land cover can be drawn from 

an analysis of a transition matrix of the two classifications. 

Our analysis showed that forest cover increased at the 

expense of woodlands and grasslands, as the trend toward 

forest regeneration on abandoned land continued (table 5). 

Urban development (urban and artificial barrens) increased 

by 16 percent during the period 1991 to 2003. We also 

assessed long-term land cover by comparing the degree of 

forest, pasture and agriculture, urban, and mangrove cover 

in the eight municipalities surrounding the Luquillo Experimental Forest in the years 1936, 1950, 1977, 1995, and 2003 

(fig. 6) (Lugo and others, 2004, Gould and others, 2008). We 

modified the original estimates of urban cover of Lugo and 

others (2004) for the 1936–1995 classification because of 

the finer resolution of the 2003 estimate of urban cover. We 

made the assumption (generally true, but with some exceptions) that once areas were urbanized, they would not revert 

to natural cover. Therefore, any areas classified as urban in 

early years, but as natural vegetation in 2003, were changed 

to that natural cover for all years. The most pronounced 

change in the last 7 decades has been the shift from pasture 

and agriculture to a forested landscape and the intensification 

of the ring of urbanization that surrounds the long-protected 

Luquillo Experimental Forest (figs. 6, 7). 
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 39

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_06B.ai

0 4

0

8 KILOMETERS

4 8 MILES

EXPLANATION

Forest

Land-use classes

Pasture and agriculture

Urban

Mangrove

Luquillo Experimental Forest

 boundary

ATLANTIC OCEAN

CARIBBEAN SEA

ATLANTIC OCEAN

CARIBBEAN SEA

ATLANTIC OCEAN

CARIBBEAN SEA

ATLANTIC OCEAN

CARIBBEAN SEA

ATLANTIC OCEAN

CARIBBEAN SEA

Study watershed boundary

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

18°25’

18°20’

18°15’

18°10’

A. 1936

65°55’ 65°50’ 65°54’ 65°40’

C. 1977

E. 2003

B. 1950

D. 1995

18°25’

18°20’

18°15’

18°10’

65°55’ 65°50’ 65°54’ 65°40’

18°25’

18°20’

18°15’

18°10’

65°55’ 65°50’ 65°54’ 65°40’

18°25’

18°20’

18°15’

18°10’

65°55’ 65°50’ 65°54’ 65°40’

18°25’

18°20’

18°15’

18°10’

65°55’ 65°50’ 65°54’ 65°40’

Mameyes

Icacos

Cayaguás

Canóvanas Mameyes

Icacos

Cayaguás

Canóvanas

Mameyes

Icacos

Cayaguás

Canóvanas

Mameyes

Icacos

Cayaguás

Canóvanas

Mameyes

Icacos

Cayaguás

Canóvanas

PUERTO RICO

MAP LOCATION

Figure 6. Trends in forest, pasture and agriculture, urban, and mangrove extent, 1936–2003, in eastern Puerto Rico. (Information for 

years 1936–1995 modified from Lugo and others, 2004.)
40 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Figure 7. Diagram of trends in forest, pasture and agriculture, urban, and mangrove extent, 

1936–2003, eastern Puerto Rico.

1936 1950 1977 1995 2003

Percent 

Area, in hectares

10

0

20

30

40

50

60

70

80

90

10,000

0

20,000

30,000

40,000

50,000

60,000

70,000

Year

EXPLANATION

Pasture and agriculture

Forest

Urban

Mangrove

Table 5. Land-cover transition matrix of eastern Puerto Rico, 1991 and 2003, based on Landsat satellite imagery analyses for those two 

years.

[The 1991 classification modified from Helmer and others (2002); 2003 classification modified from Gould and others (2008). Example of how to read matrix: 

cell in row 1, column 2 states that 1,902 hectares were classified as Forest in 1991 and as Woodland and shrubland in 2003; <, less than]

Land-cover categories,

1991 imagery

(hectares)

Land-cover categories, 2003 imagery (hectares)

Total 1991

(hectares)

1991

Percent

Change

Forest 1991 to 2003

Woodland 

and

shrubland

Pasture

and

agriculture

Natural

barrens

Urban and

artificial

barrens

Water

(hectares) (percent)

Forest 29,011 1,902 5,063 103 1,045 237 37,361 23 22,647 61

Woodland and shrubland 15,086 3,052 8,800 9 1,469 42 28,458 18 –17,335 –60

Pasture and agriculture 14,342 5,407 48,675 80 7,939 458 76,901 48 –8,282 –11

Natural barrens 106 15 165 166 40 20 512 <1 –103 –20

Urban and artificial

barrens (developed areas) 1,367 721 5,703 43 8,752 73 16,659 10 2,620 16

Water 96 26 213 7 34 451 827 1 453 55

Total 2003 60,008 11,123 68,619 408 19,279 1,281 160,716 100

Percent 2003 37 7 43 <1 12 1 100

Conclusion

This analysis provides a comprehensive view of land 

cover, land use, population dynamics, and land-cover change 

in eastern Puerto Rico. Principal trends in land use and landcover patterns during the last several decades include a sharp 

decline in agriculture; the conversion of grassland, woodland, 

and shrubland to closed forest; and urbanization of the landscape. Land-cover changes differ in their degree of permanence 

and in their effects on ecosystem services. However, the shift 

from vegetated natural surfaces to impermeable developed 

surfaces has stronger and more long-term effects. Not only 

are impermeable surfaces difficult to restore from an ecological standpoint, they are a result of human investments of time 

and capital that represent an inertia difficult to counteract in 

restoring natural systems. Therefore, the trend towards increasing urbanization in the limited land area of eastern Puerto Rico 

needs to be assessed in the context of those limitations—that is, 

which portions of this diverse landscape are being urbanized, 

what degree of urbanization is desirable, and what ecosystem 

services are now being lost or may be lost owing to current 

and future levels of urbanization. An important observation 

from this analysis is the loss of natural land cover in the coastal 

areas, highlighting the need to protect the coastal hills and 

plains and the matrix of habitats that include the mangrove 

forests and river systems of the coastal area. Additionally, 

although areas of new forest cover are increasing, little research 

describes their ecosystem structure or the function of these 

secondary forests in eastern Puerto Rico.
Land Use, Population Dynamics, and Land-Cover Change in Eastern Puerto Rico 41

Acknowledgments

Thanks to reviewers G. González, F. Wadsworth, and 

A.E. Lugo. This research was supported by the U.S. Geological Survey Biological Resources Division Gap Analysis Project: Puerto Rico Gap Analysis Project (agreement 

01HQPG0031) and the International Institute of Tropical 

Forestry (IITF) GIS and Remote Sensing Laboratory. All 

research at IITF is done in collaboration with the University 

of Puerto Rico.

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Hydrology and Climate of Four Watersheds 

in Eastern Puerto Rico

By Sheila F. Murphy and Robert F. Stallard

Chapter C of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–C

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.........................................................................................................................................................47

Introduction...................................................................................................................................................47

Climate and Hydrology of Eastern Puerto Rico..............................................................................49

Climate Variability and Change.........................................................................................................53

Relation of Land Cover to Climate and Hydrology ........................................................................54

Cloud Forest.........................................................................................................................................54

Interpretive Approach.................................................................................................................................56

Precipitation.........................................................................................................................................56

Regression Model of Precipitation Based on Elevation......................................................57

Spatially Based Precipitation Model......................................................................................57

Precipitation Based on Land Cover........................................................................................57

Cloud Drip.............................................................................................................................................57

Runoff....................................................................................................................................................59

Groundwater and Storage.................................................................................................................60

Water-Supply Inputs and Outputs....................................................................................................60

Evapotranspiration..............................................................................................................................60

Error Estimation for Water Budgets.................................................................................................61

Precipitation and Runoff in Eastern Puerto Rico....................................................................................62

Water Budgets for Study Watersheds .....................................................................................................75

Summary........................................................................................................................................................77

Acknowledgments.......................................................................................................................................78

References....................................................................................................................................................78

Figures

1. Maps of Caribbean showing Puerto Rico and study watersheds ......................................48

2. Map of hurricane tracks over or near Puerto Rico since 1850 ...........................................50

3. Digital elevation model of elevation and relief of Puerto Rico............................................51

4. Map of mean annual precipitation of Puerto Rico................................................................51

5. Map of eastern Puerto Rico showing location of sites, Luquillo Experimental Forest, 

and El Toro Wilderness ..............................................................................................................52

6–15. Diagrams of the following:

 6. Indices of oscillations that affected Puerto Rico, 1950–2010.......................................55

 7. Elevation versus mean annual precipitation (1991–2005) at stations in or near 

study watersheds, eastern Puerto Rico ..........................................................................62

 8. Mean daily precipitation by year at stations in or near study watersheds, 

eastern Puerto Rico ............................................................................................................64

 9. Mean daily runoff by year at U.S. Geological Survey stream-gaging stations, 

eastern Puerto Rico ............................................................................................................66

 10. Distribution of log of mean annual runoff from study watersheds and Río

Fajardo for periods of record.............................................................................................69

 11. Mean daily precipitation by month at stations in or near study watersheds............70
 12. Mean daily runoff per month at U.S. Geological Survey stream-gaging stations,

eastern Puerto Rico ............................................................................................................71

 13. Percent occurrence of hourly precipitation intensity at U.S. Geological 

Survey stream-gaging stations (1991–2005), eastern Puerto Rico..............................72

 14. Percent occurrence of hourly runoff intensity at U.S. Geological Survey 

stream-gaging stations (1991–2005), eastern Puerto Rico ...........................................73

 15. Runoff and precipitation at U.S. Geological Survey stream-gaging stations 

during and after Tropical Storm Dean, August 2001......................................................74

Tables

1. Temperature statistics for 1991–2005 at meteorological stations in or near study 

watersheds, eastern Puerto Rico ............................................................................................50

2. Stream-gaging and precipitation stations, eastern Puerto Rico........................................53

3. Precipitation and evapotranspiration rates reported in literature, eastern

Puerto Rico ..................................................................................................................................58

4. Total annual precipitation at stations in or near study watersheds, 1991–2005 ..............63

5. Annual runoff, stream statistics, and stream characteristics study of 

watersheds ..................................................................................................................................65

6. Statistical properties of annual runoff of study watersheds and Río Fajardo,

1991–2005 .....................................................................................................................................67

7. Summaries of successive regressions of annual runoff and precipitation in eastern 

Puerto Rico ..................................................................................................................................68

8. Rainfall intensity at U.S. Geological Survey stations during major storms in eastern 

Puerto Rico, 1991–2005 ..............................................................................................................72

9. Runoff rates from study watersheds and percentage of time that hourly runoff is 

below those rates, at percentiles of annual runoff volume.................................................73

10. Modeled recessions of study watersheds after storm events ...........................................76

11. Annual precipitation estimated by various methods ............................................................77

12. Annual water budgets of study watersheds ..........................................................................77

Abbreviations Used in This Report

km kilometer

km2 square kilometer

m meter

mm millimeter

mm day−1 millimeters per day

mm h−1 millimeters per hour

mm yr−1 millimeters per year

m s−1 meters per second

m3 s–1 cubic meters per second

yr year
AR autoregressive 

BP before present

ENSO El Niño–Southern Oscillation 

ET evapotranspiration

IMA integrated-moving-average

LTER Long-Term Ecological Research

NOAA National Oceanographic and Atmospheric Administration

PRISM parameter-elevation regressions independent slopes model

USGS U.S. Geological Survey

WEBB Water, Energy, and Biogeochemical Budgets

Conversion Factors

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2

) 0.3861 square mile (mi2)

Flow rate

meter per second (m s−1) 3.281 feet per second (ft s−1) 

cubic meters per second (m3

 s−1) 35.31 cubic feet per second (ft3 s−1)

millimeters per hour (mm h−1) 0.03937 inches per hour (in. h−1) 

millimeters per day (mm h−1) 0.03937 inches per day (in. h−1) 

millimeters per year (mm yr−1) 0.03937 inches per year (in. yr−1) 
Abstract

Puerto Rico lies directly in the path of the easterly trade 

winds, which deliver steady rainfall to the mountains and 

steer tropical wave systems toward the island. Hurricanes and 

tropical storms derived from these tropical waves differ in 

frequency and intensity, contributing to substantial interannual 

variation in precipitation and stream discharge. Puerto Rico’s 

steep topography and small water-storage capacity leave the 

island’s water supply and developed flood plains vulnerable 

to extreme weather events, such as hurricanes, floods, and 

droughts. This vulnerability may increase in the future owing 

to ongoing change, both local (such as land-cover shifts, 

water-supply projects, and construction of roads and other 

infrastructure) and regional (climate variability and change). 

Climate change, which could lead to more intense and prolonged droughts as well as an increase in the magnitude and 

frequency of destructive storms in the Caribbean, may alter 

temperature and affect the availability of water for human and 

ecosystem needs. Accurate assessment of hydrologic regimes 

and water budgets is therefore crucial for effective management of water resources. 

As part of the U.S. Geological Surveyʼs Water, Energy, 

and Biogeochemical Budgets program, hydrologic and geomorphologic processes and stream chemistry of four small 

watersheds in eastern Puerto Rico, which differ in geology and 

land cover, have been studied since 1991. Spatial and temporal 

characteristics of precipitation and stream discharge, along 

with water budgets, were determined for the watersheds for 

the period 1991 to 2005. The locations of the watersheds relative to the Luquillo Mountains and the range’s associated rain 

shadow dominate hydrological processes, dwarfing influences 

of land cover. The influence of geology is reflected in recession characteristics of the rivers (recession is faster in soils 

overlying volcaniclastic bedrock) and in hillslope geomorphic 

processes (sediment is delivered at higher rates from soils 

overlying granitic bedrock).

Introduction

Five field sites throughout the United States have been 

monitored since 1991 as part of the U.S. Geological Surveyʼs 

(USGS) Water, Energy, and Biogeochemical Budgets (WEBB) 

program (Baedecker and Friedman, 2000). The WEBB site 

located in eastern Puerto Rico represents a montane, humidtropical environment. The site consists of four small watersheds that are part of two important water-supply sources for 

the island: the Luquillo Mountains and the Río Grande de 

Loíza watershed (fig. 1). River segments within the Icacos and 

Mameyes watersheds are designated as wild and scenic rivers, 

which requires that they remain in an essentially primitive 

condition and free of impoundments (Ortiz-Zayas and Scatena, 

2004; Interagency Wild and Scenic Rivers Council, 2008). 

The four watersheds have different geology and land cover. 

Two watersheds (Icacos and Cayaguás) are located on coarsegrained granitic rocks, and two (Mameyes and Canóvanas) 

are located on fine-grained volcanic and volcaniclastic rocks. 

For each bedrock type, one watershed is dominated by mature 

wet tropical forest (Icacos and Mameyes), and one watershed 

has been affected by agricultural land use (Canóvanas and 

Cayaguás). The four watersheds have been closely monitored 

since 1991 to evaluate the effects of geology, land cover, 

geomorphic processes, atmospheric deposition, and other factors on stream water quality and quantity. Geology and land 

cover are discussed in Murphy and others (2012), geomorphic processes in Larsen (2012), and water quality in Stallard 

and Murphy (2012). This chapter describes the climate and 

hydrology of these watersheds, assesses spatial and temporal 

characteristics of precipitation inputs and stream-discharge 

outputs, and assesses water budgets by using several methods. 

This analysis considers stream-discharge, precipitation, and 

evapotranspiration data, mainly from the USGS, the National 

Atmospheric and Oceanic Administration (NOAA), and the 

National Science Foundation-funded Long-Term Ecological 

Research Program (LTER) Program. 

Hydrology and Climate of Four Watersheds in Eastern 

Puerto Rico

By Sheila F. Murphy and Robert F. Stallard
48 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Area shown in B

A

B

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

L e s s e r

A n

t i l l e s

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_01C.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

LUQUILLO

MOUNTAINS

Mameyes

Icacos

Guabá

Río Grande de Loíza Watershed

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Figure 1. Location of Puerto Rico and study watersheds, eastern Puerto Rico.
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 49

Climate and Hydrology of Eastern Puerto Rico

The island of Puerto Rico has a humid tropical climate 

with a narrow range of daily temperature because of its location in the tropics and the buffering effect of the ocean (table 1). 

Weather is dominated by the easterly trade winds, which are 

predominantly from the east-northeast (Calvesbert, 1970). 

Hurricanes and other large storms embedded in the trade winds 

deliver about 70 percent of yearly rainfall; such storms affect 

the Caribbean about nine times a year on average (Calvesbert, 

1970; Musk, 1988). Since 1851, 10 hurricanes have passed 

within 50 kilometers (km) of the Luquillo Mountains (fig. 2). 

During this study (in 1998), Hurricane Georges, the most 

destructive hurricane to strike Puerto Rico since San Ciprian 

in 1932, delivered more than 630 mm of rainfall in the central 

mountains and triggered extensive flooding and debris flows 

(Bennett and Mojica, 1998; Smith and others, 2005; Larsen and 

Webb, 2009). Cold fronts moving from North America into the 

Caribbean during winter months (December through April) can 

also cause major rainstorms that last for days; the amount of 

precipitation caused by these storms depends on the intensity 

and rate of progression of the cold front (Calvesbert, 1970). 

Such massive storms can mobilize sediment and debris into 

stream channels, cause sedimentation of reservoirs, and degrade 

coral reefs (Zack and Larsen, 1993; Webb and Soler-López, 

1997; Warne and others, 2005; Larsen and Webb, 2009). 

The path of trade winds over Puerto Ricoʼs substantial 

topography (fig. 3) causes rainfall to vary greatly throughout 

the island (fig. 4). Pico del Este, on the southeastern side of the 

Luquillo Mountains (fig. 5) is the first summit crossed by the 

trade winds; orographic lifting produces frequent rain showers and the highest annual precipitation recorded in Puerto 

Rico. About 29 percent of precipitation at higher altitude in the 

mountains is from trade-wind–associated rainfall (Scholl and 

others, 2009). Precipitation on the eastern side of the Luquillo 

Mountains (and thus in the Mameyes and Icacos watersheds) 

is closely related to elevation: it ranges from 1,500 mm annually on the north coastal plain to almost 5,000 mm at high 

elevations (Brown and others, 1983). Precipitation on the 

western side of the Luquillo Mountains, including the Canóvanas watershed, is less well characterized; few data have been 

collected (table 2) and little work has been done there, except 

to note the existence of a rain shadow and that forests characteristic of drier conditions are found there (Ewel and Whitmore, 1973; Brown and others, 1983). 

Stream valleys in Puerto Rico are narrow, short, and 

steep owing to the island’s east-west-trending mountain chain 

(Cordillera Central) that divides a relatively narrow northsouth dimension (fig. 3). Streams therefore respond rapidly to 

precipitation; Puerto Rico has the greatest threat of flash flooding of any state or territory under the jurisdiction of the U.S. 

National Weather Service (Carter, 1997). In order to mitigate 

flood peaks and to store this water for year-round use, several 

reservoirs have been constructed. The Loíza Reservoir, which 

receives water from the Cayaguás and Canóvanas watersheds 

(fig. 1), supplies about one third of the 11.9 cubic meters 

per second (m3

 s−1) of water delivered to San Juan (population 421,958 in 2000) (U.S. Census Bureau, 2000; Quiñones, 

2010). Water withdrawals in and around the Luquillo Experimental Forest provide about 2.2 m3

 s−1 of potable water to 

Puerto Rico (Scatena and Johnson, 2001), which is about 

7 percent of the 2010 island consumption of 31.8 m3 s−1

(Quiñones, 2010). In 1999, water extracted from the streams 

that drain the Luquillo Mountains was estimated to be worth 

about US$25 million per year in terms of the cost paid by 

consumers (Larsen and Stallard, 2000). Water shortages are 

a chronic problem in Puerto Rico. Reservoir storage is lost 

because of high sedimentation associated with storms (Webb 

and Soler-López, 1997). In the 20th century, major droughts 

affected the island in 1966–1968, 1971–1974, 1976–1977, and 

1993–1995 (Larsen, 2000). During this study, the 1993–1995 

drought led to severe water rationing for the city of San Juan; 

in response, residents collected water in open containers, 

which lead to outbreaks of dengue fever (Rigau-Pérez and 

others, 2001). Droughts may also contribute (through dryness 

or warming), along with atmospheric chemical changes, to 

Puerto Rico’s ongoing amphibian die-off (Stallard, 2001; 

Burrowes and others, 2004; Longo and others, 2010). Because 

of strong orographic controls on the distribution of rainfall 

and the characteristic patchiness of convective rainfall in the 

tropics, the intensity of droughts in Puerto Rico can differ 

markedly in short distances (Larsen, 2000).
50 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Figure 2. Hurricanes that have passed over or near Puerto Rico since 1850 (from National Oceanic and Atmospheric Administration 

Coastal Service Center, 2007).

EXPLANATION

Hurricane category—Based on Saffir-Simpson Hurricane Scale, 1971.

 Triangles indicate direction of hurricane path.

18°30’

18°00’

67°00’ 66°30’ 66°00’

Category 1 (least intense)

Category 2

Category 3

Category 4

Category 5 (most intense)

Study watershed boundary

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

HUGO, 1989

GEORGES, 1998

GEORGES, 1998

SAN CIPRIAN, 1932

HORTENSE, 1996

SAN FELIPE II, 1928

BETSY (SANTA CLARA), 1956

SAN NICOLAS, 1931

UNNAMED, 1916

UNNAMED, 1926

SAN NARCISO, 1867

SAN ROQUE, 1893

SAN CIRIACO, 1899

SAN FELIPE I, 1876

UNNAMED, 1852

UNNAMED, 1896

Canóvanas

Cayaguás

Icacos

Mameyes

C A R I B B E A N S E A

A T L A N T I C O C E A N

Map data modified from National Oceanic and Atmospheric

Administration Coastal Services Center, 2007

Luquillo Experimental Forest

0

0

10 20 KILOMETERS

10 20 MILES

PUERTO RICO

Table 1. Temperature statistics for 1991–2005 at meteorological stations in or near study watersheds, eastern Puerto Rico.

[From National Oceanic and Atmospheric Administration (2008), and U.S. Geological Survey Automated Data Processing System internal data, written commun. 

(2008). ID, identification number; Met, meteorological station; USGS, U.S. Geological Survey; NOAA, National Oceanic and Atmospheric Administration]

Station Operator Operator

Station ID

Elevation 

(meters)

Mean daily

temperature

(degrees Celsius)

Mean minimum

daily temperature

(degrees Celsius)

Mean maximum

daily temperature

(degrees Celsius)

Dates excluded1

Bisley Met USGS 50065549 482 22.8 21.2 25.5 1/1991–5/1991,

5/1998–2/2000

Icacos Met USGS 50075001 600 21.4 19.1 25.3 1/1991–3/1992, 

2/1997–2/1999, 

3/2002–12/2005

Juncos 1SE NOAA 665064 65 25.7 20.6 30.6 None

Pico Del Este NOAA 666992 1,051 18.6 16.4 20.4 7/2005–12/2005 1

Some dates excluded due to missing data or obvious errors; shorter time periods may also be excluded.
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 51

Figure 3. Elevation and relief of Puerto Rico.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_03C.ai

EXPLANATION

Elevation—In meters

–29–0

1–200

201–400

401–600

601–800

801–1,000

Base from U.S. Geological Survey National Elevation

Dataset 30-meter digital elevation model 0

0

10 20 KILOMETERS

10 20 MILES

Study watershed boundary

Canóvanas

Cayaguás C O R D I L L E R A C E N T R A L

CAYEY

MOUNTAINS

LUQUILLO

MOUNTAINS

Icacos

Mameyes

67°00’ 66°30’

18°30’

18°00’

66°00’

C A R I B B E A N S E A

A T L A N T I C O C E A N

PUERTO RICO

18°30’

18°00’

67°00’ 66°30’ 66°00’

0

0

10 20 KILOMETERS

10 20 MILES

C A R I B B E A N S E A

A T L A N T I C O C E A N

Map data modified from Daly and others, 2003

Canóvanas

Cayaguás

Icacos

Mameyes

EXPLANATION

4,001–4,607

3,501–4,000

2,001–2,500 3,001–3,500

1,501–2,000

1,001–1,500

697–1,000 2,501–3,000

Study watershed boundary

Mean annual precipitation—In millimeters

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

PUERTO RICO

Figure 4. Mean annual precipitation of Puerto Rico, based on PRISM model for 1963 to 1995 (from Daly and others, 2003).
52 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_05C.ai

66°00' 65°55' 65°50' 65°45' 65°40' 65°35'

18°15'

18°10'

18°05'

18°20'

18°25'

EXPLANATION

Luquillo Experimental Forest

El Toro Wilderness

Study watershed boundary

Peak higher than 1,000 meters

Stream-gaging station and identifier

Weather station and identifier—

 Precipitation measurement

Weather station and identifier—

 Temperature measurement

Weather station and identifier—

 Temperature and precipitation

 measurement

A

C A R I B B E A N S E A

A T L A N T I C O C E A N

0

0

2 4 KILOMETERS

2 4 MILES

Icacos

ARE

SLZ CAY

QSS

RGL

JSE

FAJ

GBA

CNV

SJM

65°50'

18°20'

65°52'30" 65°45'

18°15'

18°17'30"

B 65°57'30" 65°42'30"

0

0 1

1 2 KILOMETERS

2 MILES

SAB

BIM

PDE

PDE Icacos

RES

El Toro

El Cacique

El Yunque

Mameyes

MAM SAB

BIM BIS

PDE

GUA

ICA

ICM

Pico del Oeste

Pico del Este

Canóvanas

RGE

EVR

CUB

PDR

SON

Cayaguás

Mameyes

Canóvanas

PUERTO RICO

MAP LOCATION

CAN

Area shown in B

Base from U.S. Geological Survey digital data, 2011, 1:2,000,000

Puerto Rico Virgin Island State Plane Coordinate System

Standard parallels 18°26’N and 18°02’N, central meridian 66°26’W

North American Datum of 1983, High Accuracy Reference Network

(NAD 1983 HARN)

Figure 5. Location of stations, Luquillo Experimental Forest, and El Toro Wilderness (refer to table 2 for 

names of stations represented here by three initials; “CUB” was located at the southern site from 1986 to 

1996 and at the northern site from 1996 to present).
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 53

Climate Variability and Change

The climate of Puerto Rico is influenced by several 

global-scale climate patterns. The El Niño–Southern Oscillation (ENSO), which involves both ocean and atmospheric 

interactions, has an average recurrence interval of 3.8 years 

for El Niño episodes of moderate and greater intensity 

(Quinn and others, 1987). A negative Southern Oscillation 

Index, which corresponds with an El Niño episode, tends to 

be associated with warmer years and fewer tropical storms 

in Puerto Rico (Gray, 1984; Malmgren and others, 1998). 

The North Atlantic Oscillation, which is strictly atmospheric, is a slower and more irregular (8–10 years) climate 

fluctuation (Hurrell and others, 2003). A low North Atlantic 

Oscillation appears to force storms south towards the Caribbean (Elsner and others, 2000) and results in higher mean 

annual precipitation in Puerto Rico (Malmgren and others, 

1998). The Atlantic Multidecadal Oscillation involves both 

the ocean and the atmosphere and is manifested as a 0.4°C 

fluctuation of North Atlantic sea-surface temperatures that 

lasts several decades (Kerr, 2000, 2005; Knight and others, 

2006). Long-term records of tree rings (Gray and others, 

2004) and African lake sediments (Shanahan and others, 

2009) indicate that this oscillation has persisted for at least 

3,000 years as strong, well-coupled oscillations in 30- to 

50-year cycles. A high Atlantic Multidecadal Oscillation 

corresponds to a wet Sahel and to more frequent and intense 

hurricanes in the Atlantic Ocean (Landsea and Gray, 1992; 

Kerr, 2005; Knight and others, 2006). The effects of warming on hurricanes may be strictly physical (Knight and others, 2006); however, Sahel rainfall is best modeled with vegetative feedbacks acting on oscillation-induced temperature 

fluctuations, which produce both lags and amplifications 

(Zeng and others, 1999). Saharan dust appears to suppress 

hurricane formation (Dunion and Veldon, 2004), offering an 

alternative physical explanation of why wetter times in the 

Sahel (which generate less dust) might increase the formation or intensity of hurricanes. During the 15 years of this 

study, the Southern Oscillation Index, North Atlantic Oscillation, and Atlantic Multidecadal Oscillation indices varied 

Table 2. Stream-gaging and precipitation stations, eastern Puerto Rico.

[--, not available; m, meters; ID, identification code; USGS, U.S. Geological Survey; LTER, Long-Term Ecological Research; NOAA, National Oceanic and 

Atmospheric Administration; P, precipitation station; T, air temperature station; S, stream-gaging station]

ID Station Operator Operator

station ID

Elevation

(m) Type Land cover1

ARE Quebrada Arenas USGS 50999960 270 P Pasture, lowland wet forest

BIM Bisley Meteorological Station USGS 50065549 482 T Tabonuco forest

BIS Bisley Tower LTER -- 360 P Tabonuco forest

CAN Río Canóvanas near Campo Rico USGS 50061800 68 P, S Pasture

CAY Río Cayaguás at Cerro Gordo USGS 50051310 150 P, S Pasture, urban, moist forest

CNV Canóvanas (NOAA) NOAA 661590 9 P Urban, wetlands

CUB Cubuy NOAA 663113 6002 P Pasture, montane wet forest

EVR El Verde LTER -- 350 P Tabonuco forest

FAJ Río Fajardo near Fajardo USGS 50071000 42 S Lowland moist forest, pasture

GBA Gurabo Abajo USGS 50999959 285 P Pasture, moist forest

GUA Quebrada Guabá near Naguabo USGS 50074950 6403 S Colorado forest, palm forest

ICA Río Icacos near Naguabo USGS 50075000 6164 P, S Colorado forest, palm forest

ICM Icacos Meteorological Station USGS 50075001 600 T Tabonuco forest

JSE Juncos 1SE NOAA 665064 65 P, T Urban, pasture

MAM Río Mameyes near Sabana USGS 50065500 84 P, S Tabonuco forest

PDE Pico del Este NOAA 666992 1,051 P, T Palm forest, colorado forest

PDR Pueblito del Río USGS 50999958 341 P Pasture, moist forest

QSS Quebrada Salvatierra near San Lorenzo USGS 50051180 100 S Pasture, moist forest

RES Río Espíritu Santo near Río Grande USGS 50063800 12 S Tabonuco forest

RGE Río Grande near El Verde USGS 50064200 40 S Moist forest, pasture

RGL Río Grande de Loíza at Highway 183 near San Lorenzo USGS 50051800 80 S Pasture, urban, moist forest

RSS Río Sabana at Sabana USGS 50067000 80 S Montane wet forest

SJM San Juan LM Marin Airport NOAA 668812 3 P Urban

SLZ San Lorenzo 3S NOAA 668815 155 P Pasture, urban

SON Quebrada Sonadora near El Verde5 USGS 50063435 375 S Tabonuco forest 1

Gould and others, 2008.

2

Located at 600 m from 1996 to 2005 (northern site, fig. 5), and at 500 m from 1991 to 1996 (southern site, fig. 5).

3

Rain gage located at 648 m, but not included owing to potential underestimates (see Interpretive Approach section).

4

Rain gage located at 643 m.

5

Owing to large number of missing years during this study, precipitation data not used.
54 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

widely (fig. 6). The Atlantic Multidecadal Oscillation, after 

several decades of predominantly negative values, reversed 

to positive values in 1995 and remained so for most of this 

study. The close relations between these indices and precipitation complicate the interpretation of long-term hydrologic 

trends, and they suggest that rigorous characterizations of 

precipitation or streamflow in the region will require many 

decades, particularly if the effects of climate change are 

being evaluated.

Assessing long-term climate change in Puerto Rico is 

difficult owing to the lack of robust climate proxies, such 

as tree rings or natural lakes, which would provide pollen 

records to assess past vegetation and hence climate. However, pollen from lake sediments in nearby Haiti provides 

clues to the climate of the region (Hodell and others, 1991; 

Higuera-Gundy and others, 1999). Pollen at the base of a 

lake core (estimated to be 10,000 years old) indicates cool, 

dry conditions. Xeric vegetation persisted until a forest 

expansion at about 7,000 years before present (BP). During the mid-Holocene (7,000 years to 3,500 years BP), 

lake levels were high and moist-forest vegetation was at its 

most abundant. Since 3,500 years BP, vegetation reflected 

ongoing drying conditions; many of the land mammals went 

extinct in this interval. Other circum-Caribbean sites show 

parallel trends (Higuera-Gundy and others, 1999). Historical 

data in Puerto Rico, such as the loss of water from springs 

and wells that had historically been used for water supply, 

corroborate ongoing drying for the last 500 years (Zack and 

Larsen, 1993; Scatena, 1998; Larsen, 2000); however, for 

these indicators of drying it would be difficult to entirely 

separate climate shifts from locally driven land-use changes, 

such as deforestation and soil compaction by grazing and 

agriculture.

Climate models produced by the Intergovernmental 

Panel on Climate Change (Christensen and others, 2007) have 

forecast that the trend of increasing dryness in Puerto Rico 

will continue. An increase in the frequency and severity of 

droughts, and changes in the temperature and humidity of the 

lower atmosphere, could lead to an increase in the altitude 

of the typical cloud base and the concomitant upward shift 

of ecological zones on mountains (Still and others, 1999; 

Lawton and others, 2001; van der Molen and others, 2006). 

This higher elevation of cloud base could help to decrease 

orographic precipitation, which provides about 30 percent of 

annual precipitation in the Luquillo Mountains (Scholl and 

others, 2009), and could reduce part of Puerto Rico’s water 

supply. The intensity and number of large hurricanes in the 

Atlantic are predicted to increase this century (Emanuel, 

2005), which could lead to greater loss of human life, infrastructure, and habitat as a result of mass wasting, flooding, 

and defoliation. Hurricane Hugo nearly decimated the population of the endangered Puerto Rican parrot (Amazona vittata), 

which had already been made vulnerable through decades of 

deforestation and the encroachment of nest-site competitors 

(Beissinger and others, 2008). 

Relation of Land Cover to Climate and Hydrology

In less than 500 years, Puerto Rico was transformed from 

an island covered by about 95 percent forest (before European 

settlement) to about 6 percent forest in the 1950s (Wadsworth, 

1950; Birdsey and Weaver, 1982; Kennaway and Helmer, 

2007). A shift from an agricultural to an industrial economy, 

and a subsequent population migration from rural to urban 

areas, has led to a regrowth of forests; today, about half of the 

island is mature or secondary forest (Kennaway and Helmer, 

2007; Gould and others, 2012). The reforestation of Puerto 

Rico, including two of the watersheds in this study (Canóvanas and Cayaguás), may affect local climate and hydrologic 

processes. Nonforested landscapes tend to have lower rates of 

evapotranspiration owing to the reduction of canopy interception and transpiration compared to forests (Lawton and others, 

2001, and references therein), leading to an increase in annual 

runoff of typically 300 to 500 mm (Bruijnzeel, 2004; Jackson 

and others, 2005). Deforestation may also modify flood peaks 

(Bruijnzeel, 2004; Center for International Forestry Research, 

2005; Bradshaw and others, 2007; Alila and others, 2009; 

van Dijk and others, 2009). Where soil and drainage patterns 

are not highly disturbed, increased runoff from deforestation 

infiltrates soil and may be manifested as increased base flow. 

When soil is compacted or additional drainage networks such 

as trails and roads are created, much of the increase is in nearsurface or surface flow; storm peaks increase and streams rise 

and fall more quickly in response to storms. These changes 

may have a pronounced downstream effect only in watersheds 

less than 100 km2

 in area (Center for International Forestry 

Research, 2005).

Cloud Forest

One of the primary factors controlling vegetation type 

in the Luquillo Mountains is cloud-base elevation (Wadsworth, 1951; Ewel and Whitmore, 1973; Brown and others, 

1983). The highest peaks in the Luquillo Mountains are often 

shrouded in clouds, which typically form when trade winds 

force air upslope (Roe, 2005). The elevation of the cloud base 

is related to the trade-wind boundary layer, a zone of turbulently mixed air below the base of trade-wind cumulus clouds. 

Over the oceans, complex feedbacks associated with moisture 

transport (cloud growth and rain) driven by shallow-cumulus 

convection, control the elevation of the trade-wind boundary layer and the rate of sea-surface evaporation (Rauber and 

others, 2007). Accordingly, the elevation of the cloud base is 

largely controlled by sea-surface conditions, but its altitude is 

susceptible to changes in regional climate, sea-surface temperature, and land cover. The cloud base over the ocean region 

to the east of Puerto Rico is typically around 600 meters (m), 

but it can be as low as 400 m (Snodgrass and others, 2009, and 

references therein). In the Luquillo Mountains, the cloud condensation level is typically between 600 and 800 m (Eugster 

and others, 2006), and the cloud base moves up during the day 

and down at night. We do not have a precise description of this 
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 55

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_06C.ai

–0.6

–0.4

–0.2

0.0

0.2

0.4

0.6

 Year

Atlantic Multidecadal Oscillation Index North Atlantic Oscillation Index

1950 1960 1970 1980 1990 2000 2010

–5

–4

–3

–2

–1

0

1

2

3

4

5

–5

–4

–3

–2

–1

0

1

2

3

4

5

Southern Oscillation Index

Period of this study

A. Southern Oscillation Index

B. North Atlantic Oscillation Index

C. Atlantic Multidecadal Oscillation Index

Figure 6. Indices of oscillations that affected Puerto Rico, 1950–2010 (National Oceanic and 

Atmospheric Administration, 2010).
56 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

cycle, but we have observed that as the day progresses, cloud 

base rises tens of meters above the low divide between the 

Mameyes and Icacos watersheds (at about 750-m elevation). 

Cloud forest vegetation (colorado forest, mixed palm forest, 

elfin cloud forest, and montane shrublands) grows above about 

600 m and is adapted to high humidity, droplet deposition, 

reduced transpiration, and low-light conditions (Murphy and 

others, 2012). 

Conversely, land cover can influence the altitude of the 

cloud base, and even the presence or absence of trade-wind 

cumuli, in eastern Puerto Rico. Trade wind velocities in the 

region are typically 1 to 5 meters per second (m s–1) (Lawton 

and others, 2001), and the Luquillo Mountains are less than 

20 km from the coast, so air from the ocean has about 1 to 

5 hours to interact with land before reaching the mountain 

summits. At these time scales, terrestrial heating and evapotranspiration may decrease or increase precipitation, depending on land cover. For example, the defoliation of much of 

the east flank of the Luquillo Mountains by Hurricane Hugo 

in 1989 was associated with an observable increase in the 

altitude of the cloud base (Scatena and Larsen, 1991).

Interpretive Approach

Hydrology in the Luquillo Experimental Forest, which 

contains the Mameyes and Icacos watersheds, has been studied by many researchers (including Lugo, 1986; McDowell 

and others, 1992; Scatena, 1995, 2001; García-Martinó and 

others, 1996; Larsen and Concepción, 1998; Pringle, 2000; 

Schellekens and others, 2000, 2004; Scatena and Johnson, 

2001; Rivera-Ramírez and others, 2002; Ortiz-Zayas and 

Scatena, 2004; Peters and others, 2006; Crook and others, 

2007; and many others). Much less work has been done on 

the hydrology of the Cayaguás and Canóvanas watersheds 

(Larsen and Concepción, 1998; Larsen and Simon, 1993).

Our analysis of precipitation, discharge, and evapotranspiration data in eastern Puerto Rico for the period 1991 to 2005 

had three objectives: (1) comparison of the spatial and temporal characteristics of precipitation inputs and stream-discharge 

outputs for the four WEBB watersheds, (2) determination 

of water budgets, and (3) hydrologic interpretation to allow 

accurate assessments of weathering rates and fluxes of dissolved constituents and sediment (Larsen, 2012; Stallard, 

2012; Stallard and Murphy, 2012).

Precipitation

Precipitation in eastern Puerto Rico shows wide spatial 

variation related to elevation, topographic position, and proximity to the ocean (fig. 4). Therefore, the density of precipitation stations in the region (fig. 5) is not ideal for estimating 

precipitation at the local scale. The Canóvanas watershed is 

particularly challenging, owing to its wide range in elevation 

(70 to 960 m) and the sparcity of precipitation stations in and 

near the watershed. Three methods were used to estimate 

mean annual precipitation in the study watersheds: (1) the 

relation between elevation and precipitation; (2) a spatially 

based model of precipitation in Puerto Rico (Daly and others, 

2003; and (3) reported evapotranspiration and precipitation 

data for land-cover types within each watershed. Land-coverbased estimates of water budgets have numerous methodological difficulties, but because of earlier deforestation 

and recent afforestation, they are used here as a guide to the 

potential effects of the ongoing afforestation in the Canóvanas 

and Cayaguás watersheds.

Daily precipitation data from NOAA (National Oceanic 

and Atmospheric Administration, 2008), the USGS (Automated Data Processing System internal data, written commun., 2008), and the Luquillo LTER program (Long Term 

Ecological Research Network, 2008) for stations in and near 

the watersheds were summed to obtain annual precipitation during the study period (1991–2005). Hourly data from 

the NOAA cooperator station Cubuy, within the Canóvanas 

watershed (CUB, fig. 5), were summed to obtain daily and 

annual precipitation in order to improve spatial coverage 

(data for this station are reported only on days when precipitation is recorded). Annual precipitation data for the 

LTER Bisley Tower station (BIS, fig. 5) were obtained from 

Heartsill-Scaley and others (2007) and from F.N. Scatena, 

University of Pennsylvania (written commun., 2009). Mean 

annual precipitation during the study period was then calculated for each station.

In or near the Icacos and Mameyes watersheds, precipitation stations included in this analysis are located at the streamgaging stations at the mouth of each watershed (ICA and 

MAM), within the Mameyes watershed at the LTER Bisley 

Tower (BIS), and at two stations located within 4 km of the 

watershed boundaries (EVR and PDE) (table 2, fig. 5). Precipitation data from the USGS Guabá stream-gaging station 

and the USGS Bisley and Icacos meteorological stations were 

not included because we believe that these stations underreport precipitation. Mean annual precipitation values were 

anomalously low for their elevations, and precipitation at the 

USGS Bisley station was much lower than at the nearby LTER 

Bisley Tower station. The USGS Bisley station is located at 

the top of a ridge that is exposed to high winds; precipitation 

has been observed to fall at 45 degree angles and not enter the 

gage (Angel Torres-Sánchez, USGS, oral commun., 2008). 

The Guabá station is located along a narrow road and is overshadowed by forest canopy, which probably prevents some 

precipitation from reaching the station. 

In or near the Canóvanas watershed, precipitation stations included in this analysis are located at the stream-gaging 

station at the mouth of the watershed (CAN), within the 

watershed (CUB), and at three stations located between 2 and 

5 km from the watershed boundary (GBA, JSE, and PDR) 

(table 2, fig. 5). Precipitation stations located within or near 

the Cayaguás watershed included in this analysis are located 

within the watershed (ARE), and at or near the mouth of the 

watershed (CAY and SLZ). 
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 57

We also evaluated variable-time-step precipitation data 

from USGS stations at the mouth of each of the four studied 

watersheds to evaluate storm duration and maximum precipitation events. These data were obtained internally from 

the USGS (on file at the Caribbean Water Science Center, 

Guaynabo, Puerto Rico) and converted to 15-minute and 

1-hour intervals. Missing data were interpolated. 

In order to evaluate the effect of climate variability on 

precipitation and runoff in eastern Puerto Rico, the relations of three climate indices (El Niño–Southern Oscillation, 

North Atlantic Oscillation, and Atlantic Multidecadal Oscillation) with precipitation and runoff were evaluated with 

a least-squares linear regression. The NOAA precipitation 

stations Pico del Este (PDE) and Canóvanas (CNV, which is 

in a different location than the USGS stream-gaging station 

of the same name; table 2, fig. 5) have the longest records in 

eastern Puerto Rico and were thus used in this assessment. 

Only one stream-gaging station in eastern Puerto Rico, on 

the Río Fajardo (station 50071000, Río Fajardo near Fajardo; 

FAJ on fig. 5), has a discharge record adequately long to 

compare with multidecade climate indices. In order to assess 

how much variance in annual precipitation in eastern Puerto 

Rico can be accounted for by regional-scale climate events, 

a simple linear regression was performed to determine how 

well precipitation at each station can be predicted by precipitation at nearby stations.

Regression Model of Precipitation Based on 

Elevation

Elevation and precipitation have been found to be 

closely related in the Luquillo Experimental Forest, on the 

basis of stations located on the northern and eastern side of 

the Luquillo Mountains (Brown and others, 1983; GarcíaMartino and others, 1996). Following García-Martino and 

others’ (1996) evaluation of 18 stations, a simple polynomial 

regression of elevation (in meters) and mean annual precipitation (in millimeters) was developed by using the Pico del 

Este, Icacos, Mameyes, LTER Bisley Tower, and El Verde 

stations for the study period. The equation

Mean annual precipitation=2,177.7+4.498•elevation

−0.0023•(elevation)2 (1)

is very similar to that of García-Martino and others (1996) 

(2,301.21+3.8•elevation−0.0016•(elevation2). For the 

WEBB watersheds on the eastern side of the Luquillo 

Mountains (Icacos and Mameyes), we used equation 1 to 

estimate precipitation at the midpoint elevation of 50-m 

contour intervals (determined for each watershed by using 

a geographic information system, http://www.esri.com/), 

multiplied precipitation at each interval by the fraction of 

watershed within that interval, and summed to obtain total 

precipitation. Owing to the lack of precipitation data on the 

western side of the Luquillo Mountains, the extent of the 

rain shadow they cause is not well known; stations west of 

the Luquillo Experimental Forest (fig. 5) do not have the 

close relation of elevation and precipitation. To accommodate for rain-shadow effects in the Canóvanas and Cayaguás 

watersheds, we corrected equation 1 as follows. For each 

watershed (the two watersheds were handled separately, 

because we assumed different rain-shadow effects), we calculated the rainfall that would be expected for each nearby 

precipitation station on the basis of equation 1. Observed 

and predicted precipitation at nearby stations were each 

averaged, and the ratio of observed to predicted was used to 

calculate a “rain shadow correction” —one for the Canóvanas watershed (0.560), and one for the Cayaguás (0.721). 

We multiplied the precipitation estimated by equation 1 at 

50-m contour elevation midpoints by the correction factor 

for each watershed, and then we estimated total precipitation 

in each watershed by weighting for elevation.

Spatially Based Precipitation Model

A spatially based model of precipitation was also used 

to estimate precipitation in the watersheds. The PRISM 

(parameter-elevation regressions independent slopes model) 

mean annual precipitation dataset (Daly and others, 2003) 

incorporates point data, a digital elevation model, and other geographic data sets to generate gridded estimates of precipitation 

(fig. 4) and other climatic parameters. These data were overlain 

with the four watershed boundaries using geographic information systems to estimate mean precipitation in the watersheds.

Precipitation Based on Land Cover

Published mean annual precipitation values for landcover types present in the four studied watersheds were averaged to obtain mean annual precipitation for each land-cover 

type (table 3). The mean annual precipitation for each land 

cover was then multiplied by the percentage of land cover in 

each watershed (see Murphy and others, 2012, their table 4), 

and these values were summed to obtain the mean annual 

precipitation for each watershed. Precipitation data have not 

been reported for secondary montane wet forest and montane 

wet shrubland or woodland, so these land-cover types were 

assigned mean annual precipitation of the most similar landcover type, tabonuco forest. Because urban land, barren land, 

and water totaled less than 7 percent of the area in each watershed and they were typically located next to pasture (Murphy 

and others, 2012, their fig. 9), mean annual precipitation for 

pasture was used for these land-cover types.

Cloud Drip

Direct deposition of water from clouds onto vegetation is 

an important, but difficult to quantify, ecological and hydrological phenomenon in higher tropical mountains. Researchers have estimated that cloud drip contributes an additional 
58 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 3. Precipitation and evapotranspiration rates reported in literature, eastern Puerto Rico.

[Mean values rounded to three significant figures. km, kilometers; m, meters; mm yr–1, millimeters per year; P, precipitation; ET, evapotranspiration; --, used 

value from previous study (see P method); LEF, Luquillo Experimental Forest]

Study Region Land cover Elevation

(m)

Annual P

(mm yr–1)

Annual ET

(mm yr–1) P method ET method

Pasture

Van der Molen, 2002 15 km west of San Juan Pasture (Paspalum) 2 1,700 1,383 Climate station data Penman-Monteith method

Van der Molen, 2002 Fajardo airport Grassland 30 1,625 1,011 Climate station data Penman-Monteith method

Mean 1,660 1,200

Lowland moist forest

Ewel and Whitmore, 

1973

San Juan airport Subtropical moist forest 4 1,632 1,341 Climate station data Tosi method (Ewel and 

others, 1968)

Van der Molen, 2002 15 km west of San Juan Lowland wetland forest1 2 1,700 1,219 Climate station data Penman-Monteith method

Mean 1,670 1,280

Tabonuco

Odum, Moore, and 

Burns, 1970

Espíritu Santo watershed 

(within LEF)

Tabonuco 424 3,869 1,5582 Measured Field experiments 

(throughfall, stemflow, 

transpiration)

García-Martino and 

others, 1996

LEF Tabonuco 4023 3,537 1,707 Precipitation-elevation 

regression

Water budget

Schellekens and others, 2000Bisley (within Mameyes) Tabonuco 265–456 3,5842 1,0932 Measured Penman-Monteith and 

temperature fluctuation 

methods

Van der Molen, 2002 LEF Tabonuco 356 -- 1,066 García-Martino and 

others (1996)

Penman-Monteith method

Wu and others, 2006 LEF Tabonuco <600 -- 1,252 Garcia-Martino and 

others (1996)

Satellite imagery and model4

Mean 3,660 1,340

Colorado

García-Martino and 

others, 1996

LEF Colorado 7203 4,191 994 Precipitation-elevation 

regression

Water budget

Van der Molen, 2002 LEF Colorado 780 -- 1,044 García-Martino and 

others (1996)

Penman-Monteith method

Wu and others, 2006 LEF Colorado 600–900 -- 1,069 García-Martino and 

others (1996)

Satellite imagery and model4

Mean 4,190 1,040

Palm

Frangi and Lugo, 1985 Espíritu Santo watershed 

(within LEF)

Palm 750 3,725 831 Measured Field experiment 

(throughfall, stemflow, 

transpiration)

García-Martino and 

others, 1996

LEF Palm 7113 4,167 1,009 Precipitation-elevation 

regression

Water budget

Van der Molen, 2002 LEF Palm 900 -- 694 García-Martino and 

others (1996)

Penman-Monteith method

Wu and others, 2006 LEF Palm 600–900 -- 1,051 García-Martino and 

others (1996)

Satellite imagery and model4

Mean 4,020 900

Elfin

García-Martino and 

others, 1996

LEF Elfin 8973 4,4085 1,144 Precipitation-elevation 

regression

Water budget

Van der Molen, 2002 LEF Short cloud forest >900 -- 704 García-Martino and 

others (1996)

Penman-Monteith method

Wu and others, 2006 LEF Elfin >900 -- 591 García-Martino and 

others (1996)

Satellite imagery and model4

Mean 4,410 810

1

Pterocarpus officinalis.

2

Average of two values.

3

Weighted average elevation of forest type in Luquillo Experimental Forest.

4

Granger and Gray (1989).

5

Does not include 10 percent addition for cloud moisture reported in García-Martino and others (1996). 
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 59

6.6 to 18 percent to measured rainfall in the eastern Luquillo 

Mountains (Baynton, 1969; Weaver, 1972; Schellekens and 

others, 1998; Holwerda and others, 2006). Holwerda and others (2006) estimated annual cloud drip at 1,010-m elevation on 

Pico del Este to be 770 millimeters per year (mm yr-1) by using 

a water budget method and 785 mm yr–1 by using the eddycovariance method (which was corrected by using the water 

budget method). We estimated cloud drip at different elevations in the Icacos and Mameyes watersheds by interpolation, 

assigning a rate of zero mm yr–1 at 600 m, and Holwerda’s 

water budget rate of 770 mm yr–1 at 1,000 m. To simplify the 

calculation we assigned interpolated values to 50-m elevation 

zones: 600 to 650 m, 650 to 700 m, and so on. We then calculated the percentage of each watershed in the 50-m elevation 

zones, estimated the total cloud drip from each elevation zone, 

and summed to estimate total cloud drip contribution for the 

Icacos and Mameyes watersheds.

It is uncertain to what degree cloud drip in a watershed is affected by the rain shadow west of the Luquillo 

Mountains. Leeward sites on Pico del Este receive about 

60 percent less cloud drip than windward sites (Weaver, 

1972), similar to the ratio of observed to predicted rainfall 

(0.560) that was used to calculate a correction for rainfall 

in the Canóvanas watershed. We therefore calculated the 

amount of cloud drip in the Canóvanas watershed by the 

same method as we used for the Icacos and Mameyes watersheds, then multiplied that amount by 0.560 to correct for 

this watershed’s leeward location. The maximum elevation of 

the Cayaguás watershed is 445 m (Murphy and others, 2012, 

their table 1), below the cloud condensation level in eastern 

Puerto Rico, and therefore this watershed is assumed to not 

receive any cloud drip.

Runoff

Instantaneous discharge data were obtained internally for 

the five USGS WEBB stream-gaging stations: four located at 

the mouths of the studied watersheds (CAN, CAY, ICA, and 

MAM), plus one station within the Icacos watershed that is 

near the mouth of the Quebrada Guabá watershed (GUA) (table 

2, fig. 5) (U.S. Geological Survey, 2010). The Icacos, Guabá, 

Cayaguás, and Canóvanas stations typically record discharge 

every 15 minutes; discharge at the Mameyes station is typically recorded every 5 minutes. All stream gages had gaps in 

discharge data during the study period, ranging from 30 minutes to more than a year. The longest data gaps were generally 

associated with the time needed to rebuild a gage lost in a large 

storm. For data gaps of less than 1 day, real-time discharge 

values were linearly interpolated from adjacent data. For all 

longer data gaps, discharge data were interpolated from dailymean-discharge data using cubic splines on cumulative daily 

discharge (appendix 1). The Canóvanas and Guabá streamgaging stations had especially long data gaps in 1991 and 1992. 

Discharge data from the Guabá stream-gaging station (activated in June 1992) are considered dubious after April 2003, 

when a large storm severely altered the Guabá stream channel. 

Available daily mean-discharge values for the Canóvanas and 

Guabá stream-gaging stations correlate well with discharge at 

the Cayaguás and Icacos stream-gaging stations, respectively, 

so linear regressions (Helsel and Hirsch, 2002) were used to 

estimate discharge for the missing data. Corrections to discharge data for the Icacos stream-gaging station caused by a 

gradual datum shift from 1973 to 1993 (recognized in 2003) 

were also included in this analysis (appendix 1). 

Discharge from each watershed was converted to runoff 

by dividing by drainage area. The drainage area of the Guabá 

stream-gaging station has been previously reported as 0.13 

or 0.31 km2

, and reported runoff was based on those drainage areas (U.S. Geological Survey, 1994–2006). We estimate 

a drainage area of 0.115 km2

, on the basis of walking the 

drainage divide, recording the route with a global positioning 

system, and confirming the route by using satellite imagery. 

Calendar-year annual runoff values are reported for the study 

period (1991−2005) and were used to calculate mean annual 

runoff for that period.

Annual runoff values for the periods of record for the 

WEBB stream-gaging stations, along with those from the 

nearby Río Fajardo stream-gaging station (which has the 

longest runoff record in eastern Puerto Rico; fig. 5) were 

evaluated for statistical properties and variability. Annual 

runoff for every year was normalized to the geometric mean 

of annual runoff (normalized runoff=raw runoff/geometric 

mean) for the period of record for each river (based on the 

assumption that the processes that drive the variance around 

the geometric means of annual runoff behave similarly in the 

watersheds). The resulting normalized mean for each river 

was thus zero; this mean, along with the standard deviation of 

the log-runoff data, was used to calculate the lowest 1 percent 

of annual runoff (a one-in-one-hundred-years dry year) and 

the highest 1 percent (a one-in-one-hundred-years wet year). 

Annual runoff for all watersheds was aggregated into one 

dataset, and the variance of the aggregated data was evaluated. Also, a simple linear regression was used to test how 

well annual runoff for each WEBB river can be predicted by 

annual runoff in nearby rivers (Helsel and Hirsch, 2002). 

Flow duration, maximum runoff events, and recession 

characteristics were evaluated from corrected 15-minute and 

hourly runoff data at the five USGS WEBB stream-gaging 

stations (CAN, CAY, GUA, ICA, and MAM, table 2). Runoffrate percentile classes, which are based on the amount of time 

a river is within a given runoff range, were developed using 

an approach similar to that used by USGS WaterWatch (U.S. 

Geological Survey, 2009). The runoff rates in each percentile 

class for each river were calculated using all real-time runoff 

measurements during the 15-year study period.

Base-flow recession curves were assessed for the five 

USGS WEBB stream-gaging stations. Periods with monotonically decreasing runoff were identified from smoothed 

5- or 15-minute measurements recorded at stream-gaging 

stations and from daily average data (appendix 1) and confirmed by a lack of precipitation at nearby rain gages. A rise 

in discharge indicated a new storm and signaled the end of 
60 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

each recession. Following standard practice, the first 2 days 

of each recession were excluded to eliminate the influence 

of shallow-soil flow paths. We used observed discharge 

at 24-hour intervals rather than mean daily discharge and 

started data tabulation from the end of the recession and 

worked backward to minimize the lag period.

Mathematical formulations of base-flow recession 

curves can be empirical or based on reservoir models. The 

simplest recession model, and the only valid model for 

recessions of less than 10 days, is a linear (short for loglinear) recession (Chapman, 1999):

Qt=Q0•e−t/τ=Q0•kt, (2)

where Q is discharge (in our calculations, we used runoff, 

R=Q/area, to compare watersheds of different drainage 

areas), Q0

 is discharge at the beginning of the recession, t is 

time (t=0 at the start of recession), τ is the turnover time, 

and k is the recession constant. A large k value indicates slow 

drainage (and greater storage), whereas a smaller k value 

indicates rapid drainage (and little storage). If many distinct 

groundwater reservoirs exist, a nonlinear model may be 

required (Chapman, 1999).

Assuming a simple aquifer with linear response, storage, 

S, is given by

St=Qt •τ=Qt / ln(k). (3)

Rivera-Ramírez and others (2002) examined recessions in 

the Río Fajardo and Río Espíritu Santo, in eastern Puerto Rico, 

using a variety of graphical and statistical approaches, and concluded that two regression approaches developed by Vogel and 

Kroll (1996) for interpreting the linear recession were the most 

robust. These approaches treat consecutive pairs of discharge 

measurements as either an integrated-moving-average (IMA) 

process or as a simple autoregressive (AR(1)) process, both of 

which have straightforward least-squares solutions for k. We 

evaluated recessions using these two approaches. We divided 

the predicted runoff rate at the end of recessions by the observed 

rate and calculated a margin of error at 95 percent confidence 

for this ratio for all modeled recessions. We also calculated the 

length of time needed to recede from the 10-percent runoff-rate 

percentile to the 1-percent runoff-rate percentile, and from the 

1-percent runoff-rate percentile to the minimum runoff recorded 

at each river during the study period.

Groundwater and Storage

The four studied watersheds are underlain primarily 

by granitic, volcanic, and densely cemented volcaniclastic rocks (Murphy and others, 2012) that are typically not 

noted for being highly permeable. Springs are not common 

in the watersheds. Limestones are rare, and karst topography is nonexistent. Eastern Puerto Rico streams respond 

quickly to precipitation; recession curves suggest that 

little water remains in storage (Rivera-Ramírez and others, 

2002). Groundwater in storage in these upland watersheds 

is not well characterized but is sufficient to maintain flow 

in rivers that drain the studied watersheds, even during the 

driest periods. In general, base flow represents groundwater drainage, and we posit our assessment of groundwater 

storage on this assumption. Hall (1968) presents numerous 

mathematical formulations of base-flow recession curves; 

some are based on reservoir models and others are empirical. 

The recession curves discussed in the previous section were 

used to estimate base flow, and hence groundwater storage 

(as an average depth throughout the area of the watershed, 

or linear storage) for the 1-percent runoff-rate percentile, SI 

(subscript I refers to the IMA recession model), and for the 

minimum observed runoff rate, SA (subscript A refers to the 

AR(1) recession model). We used the effective water column 

needed to support the 1-percent runoff-rate to estimate and 

compare groundwater storage in the watersheds and for estimating errors in this intercomparison.

Water-Supply Inputs and Outputs

Comprehensive reviews of water intakes in the Luquillo 

Experimental Forest are available from Larsen and Concepción (1998) and Crook and others (2007, 2009). Thirty-one 

water intakes withdraw water from rivers draining this forest, 

including six that were added between 1994 and 2004. Few 

of these intakes are upstream of the USGS stream-gaging 

station evaluated here. There are no intakes in the Icacos 

watershed. U.S. Department of Agriculture Forest Service facilities withdraw less than 4 mm annually from the 

Mameyes watershed, and much of this water remains within 

the watershed (Larsen and Concepción, 1998). Documented 

public supply withdrawals are few in the Canóvanas and 

Cayaguás watersheds and total about 46 and 3 mm annually, 

respectively (Larsen and Concepción, 1998; Crook and others, 2007). Not only are these withdrawals small, but in order 

to be used to correct a water budget, the water must cross a 

drainage divide and bypass the watershed outlet, which is 

unlikely for these watersheds. Larsen and Concepción (1998) 

estimate that 78 percent and 89 percent of households in the 

Canóvanas and Cayaguás watersheds, respectively, have 

septic systems; using U.S. Census data and mean water consumption rates, they estimated that septic systems contribute 

about 10 and 5 mm to these watersheds. 

Evapotranspiration

Evapotranspiration (ET) consists of several distinct 

sources: evaporation from the soil surface (ES

), evaporation 

from wet canopy (EC), and transpiration (T), which is the 

transfer of water through plant stomata during photosynthesis:

ET=ES+EC+T. (4)
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 61

All of these sources can be difficult to quantify. 

Some workers have calculated potential ET, or maximum 

ET under conditions of unlimited moisture supply, in the 

Luquillo Experimental Forest (Schellekens and others, 

2000; Van der Molen, 2002; Wang and others, 2003) 

(table 3). This equation can be used to estimate wind-caused 

evaporation (which is essentially zero with either no wind or 

100 percent humidity) and solar-radiation-driven evaporation (which is zero in darkness and reduced under low light) 

(Monteith, 1965; Howell and Evett, 2004). Other workers 

have also estimated ET in this forest with field experiments 

(Odum, Moore, and Burns, 1970; Frangi and Lugo, 1985), 

water budgets (García-Martino and others, 1996; Larsen and 

Concepción, 1998; Crook and others, 2007), and hydrologic models combined with satellite imagery (Wu and 

others, 2006) (table 3). These studies describe ET rates that 

generally decrease with increasing elevation in the forest. 

Reported ET rates are highest in the tabonuco forest (elevations below 600 m; table 3); ET in lowland rainforest, such 

as the tabonuco forest, is driven predominantly by solar 

radiation (Dietrich and others, 1982). ET is lowest in the 

elfin forest (elevations over 900 m), where frequent clouds 

and humidity near 100 percent for much of the time reduce 

ET substantially.

Few studies of ET have been undertaken in eastern 

Puerto Rico in the two major land covers in the Cayaguás 

and Canóvanas watersheds, pasture and lowland moist forest 

(Lugo, 1986; van der Molen, 2002). These studies typically 

suggest higher rates of ET in pasture and lowland moist 

forest than in the other forest types, with the exception of 

tabonuco forest. Nonforested landscapes tend to have lower 

rates of ET than forested watersheds, owing to the reduction of canopy interception (Lawton and others, 2001, and 

references therein). One study in Puerto Rico, however, had 

opposite results: van der Molen and others (2006) found 

grasslands to be cooler and more humid than nearby forest. 

However, this study compared an unusually wet grassland to 

a coastal monodominant forest type, a Pterocarpus swamp 

forest (Ewel and Whitmore, 1973). Water in the Pterocarpus

swamp is brackish and soils are presumably anoxic; both 

characteristics would reduce ET, leading to both less cooling 

and drier conditions.

Evapotranspiration estimates for different land-cover 

types (table 3) were averaged to obtain mean ET values for 

each land-cover type in each watershed. The mean ET for each 

land cover was multiplied by the percentage of land cover in 

each watershed; these values were summed to obtain the mean 

ET for each watershed.

Evapotranspiration was also estimated from water budgets by subtracting watershed outputs from inputs: 

ET=P+D+S–R–W–G, (5)

where P is precipitation, D is cloud drip, S is septic 

sources, R is runoff, W is withdrawals, and G is groundwater recharge. If we assume that errors and uncertainty are 

small, the differences between outputs and inputs for each 

watershed should approximate ET estimated by use of land 

cover estimations.

Error Estimation for Water Budgets

Many potential errors exist in measuring rainfall, both 

systematic (related to gage height and design, evaporation 

losses, splash in and out, and wind speed over the gage) and 

nonsystematic (related to site-specific factors, instrument 

malfunction, and observer error) (Barry, 2008). The overall 

systematic error for rain gages has been estimated to be about 

5 to 15 percent for rainfall (Barry, 2008). The high level of 

spatial variability in precipitation in eastern Puerto Rico adds 

potential error when one estimates watershed-wide precipitation. Daly and others (2003) estimated error in the PRISM 

model for Puerto Rico with cross-validation and found that 

errors were greatest where spatial precipitation gradients 

are greatest and there is the most station-to-station variability in precipitation; in eastern Puerto Rico, error ranged 

from 0 to 35 percent (Daly and others, 2003, their fig. 12). 

A simple linear regression between one precipitation station and another in eastern Puerto Rico accounts for between 

61 and 84 percent of variance; estimating precipitation at one 

station from three other stations accounts for between 75 and 

94 percent of variance. It is unlikely that measurement error 

would exceed variance; therefore we estimate a maximum 

error in precipitation for the 15-year period of this study to be 

15 percent, on the basis of the higher end of the error range of 

Barry (2008). 

For error in cloud drip, we estimated an error of 

45 percent, based on the error reported by Holwerda and others (2006) for hourly cloud-drip measurements.

The accuracy of discharge measurements can be affected 

by several factors, such as cross-section uniformity, velocity uniformity, and streambed conditions (Sauer and Meyer, 

1992). The accuracy of the USGS stream-gaging stations 

is described in water-data reports as “excellent” (about 

95 percent of the daily discharges is within 5 percent); “good” 

(within 10 percent); “fair” (within 15 percent) and “poor” 

(less than “fair” accuracy). Records for WEBB stream-gaging 

stations were considered fair during the study period with the 

exception of the Cayaguás, which was poor owing to shifting 

waves of sand (Díaz and others, 2004). A simple linear regression between runoff for any WEBB river and another eastern 

Puerto Rico river accounts for 78 to 86 percent of variance; 

estimating runoff for a WEBB river using runoff for three 

nearby rivers accounted for between 94 and 99 percent of variance. It is unlikely that measurement error would exceed variance; therefore we estimate a maximum error in discharge for 

the 15-year period of this study to be 15 percent for the Icacos, 

Mameyes, and Canóvanas rivers, and 20 percent for the Cayaguás rivers, on the basis of USGS estimates of accuracy for 

these stream-gaging stations.
62 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

The net water input or output for a watershed is zero 

if water is withdrawn from within a watershed and then 

recycled within the watershed. It is impractical to determine 

the cycle for all withdrawals and septic systems in the watersheds. In addition, the accuracy of reported values is probably low; 63 percent of the total water deliveries in Puerto 

Rico are unaccounted for, owing to leaks in the distribution 

system (half of the total deliveries), illegal connections, or 

accounting errors (Quiñones, 2010). Therefore, we have 

estimated an error of 100 percent for water withdrawals and 

septic system inputs.

Groundwater storage was estimated from recession 

curves and converted to an average depth throughout the area 

of each watershed. The 95-percent confidence interval of the 

logarithms (Helsel and Hirsch, 2002) of predicted base flow to 

the observed base flow was used to estimate error in effective 

groundwater storage.

Errors in estimating evapotranspiration from water budgets 

were calculated as the square root of the sum of squares for 

errors (Helsel and Hirsch, 2002) of each budget component.

Precipitation and Runoff in Eastern 

Puerto Rico

Mean annual precipitation at stations on the eastern side 

of the Luquillo Mountains (and hence the Mameyes and Icacos watersheds) ranged from about 2,500 mm to more than 

4,000 mm (about 6.8 to 11 mm per day (mm day−1)) during 

the study period, and it was closely correlated with elevation (table 4; figs. 7, 8). Precipitation at stations in and near 

the Canóvanas and Cayaguás watersheds, on the western 

and southwestern side of the mountains, was substantially 

lower and not well correlated with elevation. Differences in 

precipitation at stations of similar elevations there may be 

due to differing rain-shadow effects, local topography, or 

recording errors.

As with precipitation, runoff was higher from the Icacos 

and Mameyes watersheds than from the Canóvanas and 

Cayaguás watersheds (table 5, fig. 9). While all four watersheds had substantial interannual variation in precipitation 

and runoff, mean annual runoff during the 1991–2005 study 

period was similar to the mean annual runoff for the periods 

of record (tables 5 and 6). A simple linear regression between 

runoff from any of the four watersheds and another eastern 

Puerto Rico river accounts for between 78 and 86 percent of 

variance (table 7), indicating that regional-scale weather patterns account for most of the interannual variability of runoff. 

Some of these regional weather patterns are related to climate 

oscillations (fig. 6); three climate indices (El Nino–Southern 

Oscillation, North Atlantic Oscillation, and Atlantic Multidecadal Oscillation) accounted for 46 percent of annual variance in precipitation at Pico del Este. The period of the longest 

climate cycle that affects the region, the Atlantic Multidecadal 

Oscillation (60 to 80 years; Kerr, 2000, 2005), is substantially 

longer than the longest period of record for any river in the 

region (47 years; table 6). Therefore, continued long-term 

streamflow monitoring will be needed to determine the effects 

of secular climate change, as compared to natural cycles, in 

these watersheds.

For WEBB rivers with the longest periods of record 

(Canóvanas, Cayaguás, and Mameyes), and the nearby Río 

Fajardo (FAJ in fig. 5), distributions of mean annual runoff 

are nearly log normal (fig. 10). When all the data are normalized to their geometric means and aggregated into a single 

data set, the distribution is even closer to normal (table 6). 

Because most of the interannual variability of the rivers 

appears to be caused by regional weather patterns (table 7), it 

is probable that rivers with shorter periods of records (Icacos 

and Guabá) are also log normal. Therefore we can calculate 

the lowest and highest 1 percent of annual runoff (that is, the 

one-in-one-hundred-years driest year, and the one-in-onehundred-years wettest year, respectively) for all rivers; these 

percentiles are about a factor of 1.5 to 2.6 greater or less 

than the geometric mean (table 6). The rivers with the lowest 

mean-annual runoff (Canóvanas, Cayaguás, and Fajardo; 

table 6) have the highest fractions, indicating deeper droughts 

Figure 7. Elevation compared with mean annual precipitation 

(1991–2005) at stations in or near the study watersheds (Refer to 

table 2 for names of stations represented here by three initials). 

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_07C

5,000

4,500

4,000

3,500

3,000

2,000

1,500

1,000 0 200 400 600 800 1,000 1,200

2,500

Mean annual precipitation, in millimeters

Elevation, in meters

EXPLANATION

Regression—Based on precipitation measurements at

 five stations

95th percentile confidence interval

Regression—Based on García-Martino and others (1996)

Station included in regression and identifier—Represented

 by solid black line

Other station and identifier

PDE

ICA

BIS

MAM

SLZ

CAY

CAN

JSE GBA

GBA

CUB

AREPDR

MAM

EVR
Table 4. Total annual precipitation at stations in or near study watersheds, 1991–2005.[For information on sources and locations, see table 2. --, not available; mm d–1, millimeters per day; mm yr–1, millimeters per year; P, precipitation]

Year

Quebrada

Arenas

Bisley

Tower

Río

Canóvanas

Río

Cayaguás Cubuy El Verde Gurabo Abajo Río Icacos Juncos 1SE Río Mameyes P

de

Annual precipitation (mm yr–1)

1991 1,890 2,880 -- 1,490 1,550 2,650 990 -- 1,140 -- 3

1992 2,870 3,860 -- 1,810 1,950 1,880 1,620 -- 1,600 2,220 4

1993 2,210 3,340 -- 1,670 1,770 2,610 1,130 2,890 1,630 -- 4

1994 1,860 2,680 1,000 1,280 1,420 1,320 800 3,240 1,180 1,980 

1995 2,050 3,290 1,840 1,360 1,730 3,530 1,490 3,310 1,490 2,550 

1996 2,050 3,750 2,200 2,280 1,950 4,580 -- 4,880 1,950 3,040 

1997 2,050 3,490 1,230 1,770 1,490 3,320 970 3,680 1,440 2,080 4

1998 2,590 4,010 1,630 2,710 2,220 5,290 2,110 4,970 2,430 2,890 5

1999 2,290 3,840 2,140 2,020 1,880 3,990 1,490 4,290 1,670 1,990 4

2000 1,640 2,690 1,270 1,770 1,380 2,830 1,280 3,600 1,310 1,760 

2001 2,040 3,240 1,720 1,830 1,810 3,400 1,390 4,220 1,620 2,270 4

2002 1,780 2,650 1,310 1,500 1,640 3,130 1,240 3,670 1,100 1,970 

2003 2,740 5,060 1,670 2,600 1,720 4,150 2,040 4,740 2,520 3,660 4

2004 2,300 4,500 2,080 2,110 1,690 5,210 1,400 5,000 1,440 3,410 4

2005 2,570 4,300 2,150 2,070 -- 3,810 2,100 4,120 1,980 2,900 4

Mean2 2,200 3,5703 1,690 1,880 1,7304 3,450 1,430 4,050 1,630 2,520 4

Years of missing data None None 1991–1993 None 2005 None 1996 1991–1992 None 1990–1991, 1993 N

Maximum daily P, mm d–1 240 -- 310 504 246 243 427 458 313 343 

Date of maximum daily P, mm d–1 8/22/2001 -- 9/10/1996 9/10/1996 9/21/1998 11/8/2001 9/22/1998 9/10/1996 9/10/1996 4/17/2003 11/

1Several dates were missing for this station; a regression based on the relation between precipitation at the Pico del Este station and the Icacos and El Verde stations was used 

on missing dates (Precipitation at Pico del Este = 0.649 • Precipitation at El Verde + 0.599 • Precipitation at Icacos).

2Calculated from daily data, unless noted; if more than 10 percent of daily data were missing in a year, that year was excluded from calculating the annual mean.3Calculated from annual totals from Heartsill-Scaley and others (2007) and F.N. Scatena, University of Pennsylvania.4Obtained from daily totals of hourly precipitation.5A higher value of 550 mm was recorded on 9/11/96, but because no value was recorded on 9/10/96, the value was divided over the 2-day period.
64 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_08C

14

12

13

15

16

11

10

9

8

6

5

4

3

2

1

0

A

7

14

12

13

15

16

11

10

9

8

6

5

4

2

1

0

Precipitation, in millimeters

7

3

14

12

13

15

16

11

10

9

8

6

5

4

2

1

0

7

3

 Year

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

C

EXPLANATION

El Verde

Río Icacos

Río Mameyes

Pico del Este

EXPLANATION

Quebrada Arenas

Río Cayaguás

San Lorenzo 3S

EXPLANATION

Río Canóvanas

Gurabo Abajo

Juncos 1SE

Pueblito del Río

B

Figure 8. Mean daily precipitation by year at stations in or near study watersheds, eastern Puerto Rico. A, 

Mameyes and Icacos watersheds, B, Canóvanas watershed, and C, Cayaguás watershed (from Long Term 

Ecological Research Network, 2008; National Oceanic and Atmospheric Administration, 2008; U.S. Geological 

Survey Automated Data Processing System internal data, written commun., 2008). Missing data for Pico del Este 

estimated by regression with El Verde and Icacos stations.
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 65

Table 5. Annual runoff, stream statistics, and stream characteristics of study watersheds.

[km, kilometers; km2, square kilometers; m, meters; m3 s–1, cubic meters per second; mm d–1, millimeters per day; mm yr–1, millimeters per year; -- not determined]

Parameter Canóvanas1 Cayaguás Guabá2 Icacos Mameyes

Annual runoff3 (mm yr–1)

1991 470 650 2,700 3,090 2,330

1992 910 1,550 4,210 3,550 2,900

1993 670 1,600 2,960 2,940 2,690

1994 370 850 2,510 2,380 1,680

1995 810 940 2,760 3,510 2,320

1996 1,450 2,240 4,110 4,990 2,930

1997 700 1,760 3,480 3,930 2,430

1998 1,550 3,300 4,250 4,960 3,650

1999 1,310 2,120 4,120 4,190 3,250

2000 600 1,340 3,490 3,330 2,220

2001 710 1,060 3,520 3,400 2,630

2002 680 900 2,960 3,100 2,400

2003 1,420 1,910 4,450 4,170 3,400

2004 1,540 1,770 4,590 4,610 3,550

2005 1,450 2,240 4,260 4,290 2,930

Mean, 1991–2005 970 1,620 3,630 3,760 2,750

Mean daily runoff, mm d–1 2.7 4.4 9.9 10 7.5

Highest daily runoff, mm d–1 410 480 4904 430 257

Date of maximum daily runoff, mm d–1 9/10/1996 9/10/1996 9/10/1996 9/10/1996 9/10/1996

Period of record statistics

Period of record5 1967–2006 1977–2006 1992–2002 1945–1953, 1979–2006 1967–1973, 1983–2006

Mean annual discharge6

, m3 s–1 0.80 1.3 0.012 0.41 1.6

Mean annual runoff6

, mm yr–1 990 1,580 3,4504 3,940 2,860

Highest daily mean discharge, m3

 s–1 120.0 140.0 0.65 20.0 80.0

Highest daily mean runoff, mm d–1 410 480 4904 430 380

Date of highest daily mean runoff 9/10/1996 9/10/1996 9/10/1996 9/10/1996 9/18/1989

Lowest daily mean discharge, m3

 s–1 0.023 0.20 0.002 0.042 0.20

Lowest daily mean runoff, mm d–1 0.077 0.66 1.3 1.1 0.95

Date of lowest daily mean runoff 7/24/1977 2/4/1981 5/31/1999 3/22/1946 4/20/1970

Maximum peak flow, m3

 s–1 489.9 402.1 3.3 -- 580.6

Date of maximum peak flow 9/21/1998 8/22/2001 5/30/2002 4/17/2003 9/18/1989

Instantaneous low flow, m3

 s–1 0.023 0.20 0.001 -- 0.14

Date of instantaneous low flow 7/24/1977 2/4/1981 5/1/1997 -- 4/8/1970

Stream characteristics

Elevation of gage, m 68 150 640 616 84

Mean elevation of watershed, m 464 287 702 686 508

Maximum elevation of watershed, m 956 445 767 832 1,050

Drainage area, km2 25.5 26.4 0.1144 3.26 17.8

Stream order7 4 4 -- 2 3

Mean channel slope7 0.15 0.12 -- 0.073 0.21

Main channel length7

, km 21.3 23.5 -- 2.0 13.6

Total channel length7

, km 34.4 49.5 -- 2.9 24.0 1

Runoff in parts of 1991 and 1992 estimated from regression with Cayaguás stream-gaging station.

2

Runoff in 1991, 2004, 2005 and parts of 1992 and 2003 estimated from regression with Icacos stream-gaging station.

3

Based on corrected daily values for calendar year, as described in text.

4

Based on area determined as described in text (which is different from that reported in U.S. Geological Survey, 2006).

5

From U.S. Geological Survey (2006), except for Guabá (Díaz and others, 2004). A large storm on April 17, 2003, severely damaged the Guabá gage; data 

reported after that date are not used in this report.

6

Water year (October 1–September 30).

7

From Larsen (1997).
66 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Figure 9. Mean daily runoff by year at U.S. Geological Survey stream-gaging stations (estimated from 5- or 15-minute discharge data 

from U.S. Geological Survey, 2010), eastern Puerto Rico. A–E, Standard box plots (Helsel and Hirsch, 2008) showing interquartile range 

in five watersheds; F, mean annual precipitation of same five watersheds. Runoff for Guabá in years 1991, 2004, and 2005 and portions 

of 1992 and 2003 estimated by regression with Icacos station; runoff for Canóvanas for portions of years 1991 and 1992 estimated by 

regression with Cayaguás station.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_09C

100

10

1

0

1,000 A. Cayaguás

 Year Year

199119921993199419951996199719981999200020012002200320042005199119921993199419951996199719981999200020012002200320042005

 Year

199119921993199419951996199719981999200020012002200320042005

B. Canóvanas

D. Mameyes

100

10

1

0

1,000

Runoff, in millimeters

C. Icacos

100

10

1

0

1,000 E. Guabá F. All watersheds

14

12

10

8

6

4

2

0

16

20

18

Mean runoff, in millimeters

EXPLANATION

Canóvanas

Cayaguás

Guabá

Icacos

Mameyes

EXPLANATION

Outlier

90th percentile

10th percentile

Mean 75th percentile

50th percentile, median

25th percentile
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 67

Table 6. Statistical properties of annual runoff of study watersheds and Río Fajardo (stream-gaging station 50071000, Río Fajardo near 

Fajardo) for years of record.

[mm yr–1, millimeters per year; --, not applicable] 

Property

Watershed All rivers,

combined

and normalized Fajardo Canóvanas Cayaguás Mameyes Icacos Guabá

Years of record 47 41 31 31 14 10 174

Annual runoff (mm yr–1)

Mean 1,540 990 1,550 2,830 3,810 3,420 --

Geometric mean 1,460 910 1,440 2,760 3,730 3,370 --

Median 1,460 840 1,550 2,750 3,770 3,490 --

Standard deviation 520 420 590 620 740 580 --

Skewness 1.08 1.08 0.73 1.21 –0.08 0.03 --

Kurtosis 2.15 1.14 0.66 3.23 –0.57 –1.35 --

Log of runoff

Mean 3.16 2.96 3.16 3.44 3.57 3.53 0.00

Median 3.16 2.93 3.19 3.44 3.58 3.54 –0.01

Standard deviation 0.14 0.18 0.17 0.09 0.09 0.07 0.14

Skewness 0.08 0.10 –0.22 0.25 –0.53 –0.19 0.00

Kurtosis –0.19 –0.23 –0.41 1.19 0.06 –1.20 0.26

Highest and lowest 1 percent of mean annual runoff

Lowest 1 percent (fraction of geometric mean) 0.47 0.39 0.41 0.61 0.62 0.67 0.47

Highest 1 percent (fraction of geometric mean) 2.1 2.6 2.5 1.6 1.6 1.5 2.1

1-in-100-year dry year, mm yr–1 680 350 580 1,690 2,330 2,260 --

1-in-100-year wet year, mm yr–1 3,130 2,340 3,540 4,510 5,970 5,030 --

and relatively greater wet conditions for the wettest years. 

During the periods of records, annual runoff values greater 

than the one-in-one-hundred-years wet year occurred in the 

Mameyes in 1970 and in the Fajardo in 1979 (U.S. Geological Survey, 2010). Annual runoff values less than the onein-one hundred-years dry year occurred in the Mameyes in 

1994; runoff from the Canóvanas that year was very close to 

the driest year (U.S. Geological Survey, 2010). Runoff from 

the Canóvanas watershed (2,340 mm yr−1) during the wettest 

1 percent of years is only slightly greater than the driest 1 percent of years for the Icacos watershed (2,330 mm yr−1).

The driest time of the year in eastern Puerto Rico is 

typically January through April, and March is the driest 

month (fig. 11). According to the criterion used by tropical 

forest biologists to indicate a markedly dry season—at least 

3 consecutive months of precipitation averaging less than 

100 mm per month (Ashton and others, 2004)—none of the 

studied watersheds have a markedly dry season. Precipitation 

is typically greatest September through November; stations on 

the north and east sides of the Luquillo Mountains commonly 

also record high precipitation in May. Monthly runoff generally follows the same trend as monthly rainfall, and runoff 

typically is least during February through April and greatest 

during September through December (fig. 12). 

On average, rain fell between 11 percent (Canóvanas and 

Cayaguás) and 21 percent (Icacos) of the time (on an hourly 

basis) in the study watersheds during the study period (fig. 13). 

Rainfall intensities greater than 10 mm h–1 occurred 0.8 percent 

of the time that rain fell; when rainless time is included, this 

percentage is about 0.008 percent (about 42 minutes) of a year. 

Peak hourly rainfall ranged from 64 mm (at the Canóvanas 

stream-gaging station) to 93 mm (at the Icacos stream-gaging 

station), and peak 15-minute rainfall ranged from 27 mm (at 

the Canóvanas stream-gaging station) to 34 mm (at the Mameyes stream-gaging station) (table 8). 

For 84 percent of the time or more, on an hourly 

basis, runoff from the studied watersheds was in the lowest 50th percentile (table 9). At these lower runoff rates, the 

hourly runoff from the WEBB rivers were arrayed in the same 

sequence as annual runoff; at the 50th percentile, runoff from 

the Canóvanas was 0.25 mm h−1, and runoff from the Icacos 

was 0.57 mm h−1. Runoff from the WEBB watersheds was in 

the top 10th percentile only between 0.06 and 0.33 percent of 

the time; at this runoff percentile, runoff rates for the watersheds ranged from 5 to 11 mm h−1 and were not proportional to 

annual runoff (fig. 14). Maximum runoff rates for the largest 

storms were commonly similar among all four study watersheds (table 9), suggesting that higher annual runoff in the 

Icacos and Mameyes watersheds is probably controlled by frequent, smaller rain events related to orographic precipitation.

The flow duration curves for all studied watersheds are 

generally steep (fig. 14), consistent with streams that have 

highly variable flows and are dominated by direct runoff 

(Searcy, 1959). The steep slope at the ends of the curves suggests a negligible amount of storage.

Greatest precipitation and runoff (both totals and rates) 

during the study period are associated with major storms that 

struck eastern Puerto Rico, such as Hurricane Hortense in 

1996, Hurricane Georges in 1998, an upper level trough and 

a tropical wave in 2003, and Tropical Storm Jeanne in 2004 
Table 7. Summaries of successive regressions of annual runoff and precipitation in eastern Puerto Rico.[For river and stream-gage abbreviations, see table 2. mm yr–1, millimeters per year; r, correlation coefficient]

Stream gage Constant (mm yr–1) River A River B River C 

Slope 

Runoff (mm yr–1) Slope Runoff (mm yr–1) Slope Runoff (mm yr–1)

Canóvanas

CAN = –105 + 0.723 × FAJ -- -- -- -- 

CAN = –337 + 0.546 × FAJ + 0.212 × SON -- -- 

CAN = –425 + 0.362 × FAJ + 0.176 × SON + 0.483 × RGL 

Cayaguás

CAY = –193 + 0.775 × RES -- -- -- -- 

CAY = –387 + 0.432 × RES + 1.075 × RGL -- -- 

CAY = –87 + 0.570 × RES + 1.198 × RGL + –0.314 × SON 

Icacos

ICA = 1,684 + 0.891 × RES -- -- -- -- 

ICA = 1,387 + 0.654 × RES + 0.480 × RSS -- -- 

ICA = 1,533 + 0.475 × RES + 0.337 × RSS + 0.538 × CAN 

Mameyes

MAM = 1,614 + 1.169 × CAN -- -- -- -- 

MAM = 1,465 + 0.668 × CAN + 0.351 × RGE -- -- 

MAM = 1,390 + 0.313 × CAN + 0.425 × RGE + 0.473 × QSS 

Rain gage Constant (mm yr–1)

River A River B River C

Slope 

Rain gage A (mm yr–1) Slope Rain gage B (mm yr–1) Slope Rain gage C (mm yr–1)

Canóvanas

CAN = 179 + 1.033 × SJM -- -- -- -- 

CAN = 180 + 1.380 × SJM + –0.293 × JSE -- -- 

CAN = –11 + 1.167 × SJM + –0.511 × JSE + 0.235 × BIS 

Cayaguás

CAY = –191 + 0.940 × SLZ -- -- -- -- 

CAY = –802 + 0.655 × SJM + 0.295 × PDE -- -- 

CAY –649 + 0.837 × SJM + 0.222 × JSE + –0.102 × MAM 

Cubuy

CUB = 629 + 0.784 × SJM -- -- -- -- 

CUB = 674 + 1.001 × SJM + –0.139 × MAM -- -- 

CUB = 719 + 1.254 × SJM + –0.149 × MAM + –0.230 × CNR 

Gurabo Abajo

GBA = –173 + 1.168 × SJM -- -- -- -- 

GBA = –174 + 0.522 × SJM + 0.546 × JSE -- -- 

GBA = –164 + 1.024 × SJM + 0.390 × JSE + –0.207 × PDR 

Icacos

ICA = 1,414 + 1.494 × CNR -- -- -- -- 

ICA = 2,546 + 2.271 × CNR + –1.481 × CUB -- -- 

ICA = 1,993 + 2.067 × CNR + –1.731 × CUB + 0.318 × PDE 

Mameyes

MAM = –109 + 0.724 × BIS -- -- -- -- 

MAM = 930 + 0.942 × BIS + –0.430 × PDE -- -- 

MAM = 1,536 + 0.827 × BIS + –0.734 × PDE + 0.765 × GBA 
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 69

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_10C

A. Cayaguás (N=31)

10

5

0

15

2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

B. Canóvanas (N=41)

F. Fajardo (N=47)

D. Mameyes (N=31)

Number of years

Log of mean annual runoff, in millimeters per year

2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

G. All six rivers (N=174)

Log of mean annual runoff, in millimeters per year

C. Icacos (N=14)

10

5

0

15

E. Guabá (N=10)

10

5

0

30

25

20

15

10

5

0 

15

Figure 10. Distribution of log of mean annual runoff from study watersheds and Río Fajardo 

(stream-gaging station 50071000, Río Fajardo near Fajardo) for periods of record.
70 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_11C

16

13

15

17

18

12

11

10

9

7

6

5

4

2

3

1

0

14

A

8

Precipitation, in millimeters

 Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

C

B

16

13

15

17

18

12

11

10

9

7

6

5

4

2

3

1

0

14

8

16

13

15

17

18

12

11

10

9

7

6

5

4

2

3

1

0

14

8

EXPLANATION

El Verde

Río Icacos

Río Mameyes

Pico del Este

EXPLANATION

Río Canóvanas

Gurabo Abajo

Juncos 1SE

Pueblito del Río

EXPLANATION

Quebrada Arenas

Río Cayaguás

San Lorenzo 3S

Figure 11. Mean daily precipitation by month at stations in or near study watersheds. A, Mameyes and Icacos 

watersheds, B, Canóvanas watershed, and C, Cayaguás watershed (from Long Term Ecological Research 

Network, 2008; National Oceanic and Atmospheric Administration, 2008; U.S. Geological Survey Automated 

Data Processing System internal data, written commun., 2008). Missing data for Pico del Este estimated by 

regression with El Verde and Icacos stations.
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 71

Figure 12. Mean daily runoff by month at U.S. Geological Survey stream-gaging stations (estimated from 5- or 15-minute discharge 

data from U.S. Geological Survey, 2010). A–E, Standard box plots (Helsel and Hirsch, 2008) showing interquartile range in five 

watersheds; F, mean annual precipitation of same five watersheds. Runoff for Guabá in years 1991, 2004, and 2005 and portions of 

1992 and 2003 estimated by regression with Icacos station; runoff for Canóvanas for portions of years 1991 and 1992 estimated by 

regression with Cayaguás station.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_12C.ai

100

10

1

0

1,000 A. Cayaguás

 Month

B. Canóvanas

D. Mameyes

100

10

1

0

1,000

Runoff, in millimeters

C. Icacos

100

10

1

0

1,000 E. Guabá F. All watersheds

14

12

10

8

6

4

2

0

16

20

18

Mean runoff, in millimeters

EXPLANATION

Outlier

90th percentile

10th percentile

75th percentile Mean

50th percentile, median

25th percentile

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

 Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

 Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

EXPLANATION

Canóvanas

Cayaguás

Guabá

Icacos

Mameyes
72 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Figure 13. Percent occurrence of hourly precipitation intensity

at U.S. Geological Survey stream-gaging stations (1991–2005).

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_13C

EXPLANATION

Canóvanas

Cayaguás

Icacos

Mameyes

0.0001 0.001 0.01 0.1 1 10 100

Percent occurrence

100

10

1

0.1

0.01

Precipitation, in millimeters per hour

Table 8. Rainfall intensity at U.S. Geological Survey stations during major storms in eastern Puerto Rico, 1991–2005.

[From U.S. Geological Survey Automated Data Processing System internal data, written commun., 2008. --, not available; h, hour; mm h–1, millimeters per hour]

Date Event

Canóvanas Cayaguás Guabá Icacos Mameyes

Time period (h) Time period (h) Time period (h) Time period (h) Time period (h)

0.25 1 24 0.25 1 24 0.25 1 24 0.25 1 24 0.25 1 24

Rainfall intensity (mm h–1)

25-Feb-1995 Unknown 28 17 2 12 4 0 64 29 3 125 83 6 44 18 3

13-May-1996 Unknown 12 3 0 4 1 0 52 28 3 60 28 4 136 64 9

10-Sep-1996 Hurricane Hortense 76 37 131 76 72 21 96 48 17 108 47 19 36 29 12

21-Sep-1998 Hurricane Georges 96 64 5 84 67 9 72 51 9 96 72 12 36 34 5

22-Oct-1998 Unknown 84 28 4 88 45 8 40 32 5 72 38 6 44 20 3

22-Aug-2001 Tropical Storm Dean 108 46 4 132 76 9 88 58 10 76 57 11 109 71 7

17-Apr-2003 Upper-level trough 40 19 5 56 40 5 120 92 15 124 93 18 88 71 14

12-Nov-2003 Tropical wave -- -- -- 44 18 6 64 53 12 68 55 14 72 58 11

15-Sep-2004 Tropical Storm Jeanne 88 55 9 52 27 6 80 57 9 96 60 14 76 57 13

11-Apr-2005 Unknown 4 1 0 125 40 4 52 29 2 48 32 2 36 30 2

Maximum 108 64 13 132 76 21 120 92 17 125 93 19 136 71 14 1

The hurricane destroyed the gage during storm, so precipitation may actually have been higher.
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 73

(table 8). Discharge caused by such storms can be several 

hundred times greater than average discharge and can deliver a 

substantial fraction of annual discharge. For example, Hurricane Hortense delivered more than 500 mm of rain in eastern 

Puerto Rico in less than 3 days, which produced the highest 

daily runoff (about 260–490 mm day–1) of all studied watersheds during the study period (table 5); this 1 day of runoff 

accounted for about 28 percent of total annual discharge from 

the Canóvanas watershed in 1996. An upper-level trough in 

April 2003 produced more than 400 mm rain in 3 days. This 

storm was particularly intense in the Icacos watershed, where 

it delivered more than 90 mm in 1 hour (table 8) and altered 

the Guabá’s stream channel so severely that discharge data at 

the Guabá stream-gaging station after that date are not useable. 

At the high runoff rates that result from these large 

storms, hydrologic flow paths shift from moving into groundwater to shallower, lateral flow paths or overland flow 

(Schellekens and others, 2004), and water is rapidly transported to streams. Runoff can increase from base flow to peak 

discharge in minutes; high flows typically last only a few 

hours, and rivers typically return to base-flow conditions a few 

hours later. For example, when Tropical Storm Dean passed 

through eastern Puerto Rico in August 2001, the rise to peak 

discharge in the studied watersheds occurred in minutes to a 

few hours, and the recession typically lasted less than a day 

(fig. 15). This storm was selected because it was large enough 

to affect all of the studied watersheds but did not destroy any 

stream or rain gages, and it was preceded and followed by 

relatively rainless periods.

Table 9. Runoff rates (1991–2005) from study watersheds and percentage of time that hourly runoff was below those rates, at 

percentiles of annual runoff volume.

[mm h–1, millimeters per hour]

Watershed Minimum

runoff

Percentile of annual runoff volume Maximum

1 5 10 25 50 75 90 95 99 runoff

Runoff rate (mm h–1)

Canóvanas 0.009 0.019 0.030 0.041 0.083 0.25 1.4 8.1 20 43 66

Cayaguás 0.020 0.042 0.057 0.073 0.11 0.27 1.8 7.8 18 41 54

Guabá 0.036 0.11 0.16 0.20 0.29 0.48 1.8 11 25 64 98

Icacos 0.059 0.13 0.17 0.20 0.29 0.57 2.1 7.5 14 36 85

Mameyes 0.024 0.084 0.12 0.14 0.22 0.46 1.5 4.9 11 43 86

Time spent below given runoff (percent)

Canóvanas 0 7 26 42 72 93 99.3 99.94 99.981 99.998 100

Cayaguás 0 5 19 33 63 91 99.0 99.89 99.967 99.996 100

Guabá 0 5 17 29 55 84 98 99.82 99.951 99.995 100

Icacos 0 4 16 28 57 86 98 99.67 99.901 99.991 100

Mameyes 0 5 18 31 59 86 98 99.68 99.921 99.994 100

Figure 14. Percent occurrence of hourly runoff intensity at 

U.S. Geological Survey stream-gaging stations (1991–2005).

0.0001 0.001 0.01 0.1 1 10 100

Percent occurrence

100

10

1

0.1

0.01

Runoff, in millimeters per hour

EXPLANATION

Canóvanas

Cayaguás

Icacos

Mameyes
74 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_C_figures\BW_figures\BWfigure_15C

A. Canóvanas

10

1

0.1

0.01

100 0.01

0.1

1

1

1

1

10

100

0.01

0.1

10

100

0.01

0.1

10

100

0.01

0.1

10

100

Runoff, in millimeters per hour

Precipitation, in millimeters per hour

0 1 2 3 4

D. Mameyes

Days since August 22, 2001, at 00:00

B. Cayaguás

C. Icacos

10

1

0.1

0.01

100

10

1

0.1

0.01

100

10

1

0.1

0.01

100

EXPLANATION

Precipitation

Runoff

Figure 15. Runoff and precipitation at U.S. Geological Survey stream-gaging stations during and after Tropical Storm 

Dean, August 2001 (runoff and precipitation shown in 15-minute increments, adjusted to units of millimeters per hour; 

from U.S. Geological Survey Automated Data Processing System internal data, written commun., 2008; U.S. Geological 

Survey, 2010).
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 75

During the 1991–2005 study period, 130 recessions that 

lasted 5 days or more, including 7 recessions that lasted longer 

than 10 days, were identified in the studied watersheds (table 

10). Recession models using integrated moving average and 

simple autoregressive regressions yielded tight 95-percent 

confidence intervals for recession constants (k) and similar average model recession end runoff rates for each river. 

Watersheds underlain by granitic rocks (Icacos, Guabá, and 

Cayaguás) had both higher kI

 (subscript I refers to the IMA 

recession model) and kA (subscript A refers to the AR(1) recession model) values, suggesting slower drainage and greater 

storage (table 10). Watersheds underlain by volcaniclastic 

rocks (Mameyes and Canóvanas) had lower kI

 and kA values, 

which were 95 percent likely to be different from those of the 

granitic watersheds; these values were spanned by kI

 and kA

values reported for the geologically similar Espíritu Santo and 

Fajardo Rivers (Rivera-Ramírez and others, 2002), further 

suggesting that bedrock controls recession properties. Effective groundwater reservoir turnover times, τI

, and τA, were 

also similar among rock types: shorter in the volcaniclastic 

watersheds (12 to 19 days, depending on the regression) than 

in the granitic watersheds (23 to 32 days). These results are 

consistent with water in the deep-soil environment having 

about twice the contact time in the granitic watersheds as in 

the volcaniclastic watersheds.

Field studies have found that surface infiltration was 

faster and hydraulic conductivities were greater in the Icacos watershed than in the Mameyes watershed (McDowell 

and others, 1992; Larsen, 1995). In both kinds of soil, macropore flow is likely important; soil pipes are visible along 

cut banks and steep slopes in hillslope and alluvial soils. 

Soil pore pressure response measurements by Simon and 

others (1990) found that granitic soils responded about three 

times as fast as volcaniclastic soils, suggesting that granitic 

soils are more permeable to a greater depth than volcaniclastic soils. Dense clays in the volcaniclastic soils likely 

retard deep infiltration and cause water to follow a shallower 

trajectory and thus reach streams more quickly (McDowell 

and others, 1992). 

The recession analysis suggested that it would take 

between 6 and 19 days for runoff in the studied watersheds 

to drop from a 10-percent runoff rate (which occurs about 

28 to 42 percent of the time; table 9) to a 1-percent runoff 

rate (which occurs about 4 to 7 percent of the time), and an 

additional 10 to 29 days to drop to minimum observed runoff 

rates (table 10). The length of time for such decreases was 

not strongly related to rock type, nor was the effective water 

column remaining in storage at the lowest flows. This stored 

water was greatest in the wettest watersheds (Icacos and 

Guabá) and least in the driest (Canóvanas). The effective water 

storage at 1-percent runoff rates, which were used to estimate 

groundwater storage, represents less than 3 percent of mean 

annual runoff from any watershed (table 10). It is therefore 

unlikely that long-term storage is a major factor in the interannual water budget. However, there was sufficient storage 

that none of the rivers completely dried up during the study, 

even during the 1993–1995 drought (table 10, fig. 9), when 

minimum hourly runoff ranged from 0.009 mm h−1 (Canóvanas) to 0.059 mm h−1 (Icacos). During the periods of record 

for the gages, lowest daily mean runoff was 0.077 mm day–1

(0.003 mm h–1) or greater (table 5). 

Water Budgets for Study Watersheds

Three methods of estimating precipitation in the Icacos 

and Mameyes watersheds resulted in values within about 

5 percent of each other (between 3,980 and 4,160 mm in 

the Icacos watershed and between 3,660 and 3,830 mm in 

the Mameyes watershed (table 11)). In the Cayaguás watershed, the elevation-weighted method and PRISM resulted 

in similar estimates of mean annual precipitation (about 

2,350 mm), whereas calculations based on land cover were 

about 11 percent lower (2,100 mm). Estimates of mean annual 

precipitation in the Canóvanas watershed ranged widely, from 

2,070 mm (elevation-weighted method) to about 3,480 mm 

(PRISM). PRISM therefore appears to model precipitation in 

the Cayaguás, Icacos, and Mameyes watersheds effectively, 

but it does not capture rain-shadow effects in the Canóvanas 

watershed accurately. Owing to the lack of precipitation data 

for pasture and lowland forest, the dominant land covers in 

the Canóvanas and Cayaguás watersheds (Murphy and others, 

2012), estimating precipitation from land cover is not likely to 

be highly accurate in these watersheds. Therefore precipitation 

based on elevation (adjusted for rain-shadow when necessary) 

was used in water budgets (table 12).

Rainfall is the dominant input to all watersheds; runoff 

and ET are the dominant outputs, with differing importance 

in the watersheds (table 12). Elevation-weighted estimates 

indicate that cloud drip adds about 1, 2, and 4 percent to the 

annual precipitation budget in the Canóvanas, Mameyes, and 

Icacos watersheds, respectively (table 12). Inputs from septic 

systems, and outputs from withdrawals and groundwater storage, are minor. Runoff is about 47 percent of precipitation plus 

cloud drip in the Canóvanas watershed, about 70 percent in 

the Mameyes and Cayaguás watersheds, and about 87 percent 

in the Icacos watershed. Evapotranspiration values estimated 

from water budgets ranged from 450 mm yr−1 in the Icacos 

watershed to about 1,070 mm yr−1 in the Canóvanas watershed. Evapotranspiration values estimated from land cover 

were slightly higher (12 and 15 percent, respectively, of ET 

estimated from water budgets) in the Mameyes and Canóvanas 

watersheds, but much higher (77 and 127 percent, respectively) in the Cayaguás and Icacos watersheds (table 12). The 

high runoff-to-precipitation ratio and substantial discrepancy 

in ET estimated from water budgets and from land cover in 

the undeveloped Icacos watershed suggest that evapotranspiration is lower than reported in vegetation-based estimates 

(table 3), or precipitation is greater than recorded, or both. 

Most ET studies in the Luquillo Experimental Forest were 

done in the Espíritu Santo (RES, table 2, fig. 5) and Mameyes 

watersheds; ET may be lower in the Icacos watershed because 
Watershed k (fraction/ day) 95% error in k (fraction/ day) k (% / day) Modeled average runoff at recession end (mm h–1) 95% error in modeled average runoff at recession end (%) Turnover time4 (days) Time from 10% to 1% of average runoff (days) Time from 1% to minimum runoff (days) Linear storage5 at 1% runoff (mm) Linear storage at minimum runoff (mm) 

6IMA regression to determine kI Canóvanas 0.95 0.007 –5.4 0.067 –7.8 18 14 15 8 4 

Cayaguás 0.97 0.005 –3.2 0.063 –5.3 31 18 23 32 15 

Guabá 0.97 0.014 –3.3 0.21 –2.9 30 19 29 76 29 

Icacos 0.97 0.008 –3.1 0.23 –4.5 32 14 25 100 45 

Mameyes 0.95 0.005 –5.2 0.13 –8.4 19 10 23 37 11 

7AR(1) regression to determine kA Canóvanas 0.92 0.007 –7.9 0.058 10 12 9 10 6 2 

Cayaguás 0.96 0.006 –3.8 0.061 5.5 26 15 20 27 13 

Guabá 0.96 0.015 –3.5 0.21 3.1 28 17 27 70 26 

Icacos 0.96 0.008 –4.3 0.21 4.9 23 10 18 72 33 

Mameyes 0.92 0.007 –8.1 0.11 12 12 6 14 23 7 

11991–2005; from table 5.2Excluding first 2 days after storm event.3During 1991–2005 period.4Turnover time (τ in Precipitation and Runoff in Eastern Puerto Rico section) = 1/ln(k).5Water in excess of the field capacity in the soil available for drainage, or aquifer water.6The error for the recession constant, kI in the IMA regression, treats the estimate for ln(k) as a population average.7The error for the recession constant, kA in the AR(1) regression, which determines ln(k) from the slope of a regression forced through 0,0, is based on the error of that slope.

Table 10. Modeled recessions for study watersheds after storm events, eastern Puerto Rico.[Following Vogel and Kroll (1996) for instantaneous runoff. Includes recessions that lasted 5 days or more; first 2 days excluded from recession. AR(1), autoregressive; h, hour; average; k, recession constant; km2, square kilometers; mm, millimeters; mm h–1, millimeters per hour; mm yr–1, millimeters per year; %, percent]

Watershed

Annual

runoff1

(mm yr–1)

Interval

(minutes)

Number of

recessions

Recession

points

Maximum runoff

during recessions2

(mm h–1)

Average runoff

at recession end

(mm h–1)

Minimum runoff

during recessions

(mm h–1)

Runoff at 10%

(mm h–1)

Run

(m

Canóvanas 970 15 34 190 0.24 0.062 0.012 0.041 

Cayaguás 1,620 15 29 157 0.20 0.061 0.030 0.073 

Guabá 3,630 15 8 19 0.48 0.21 0.13 0.20 

Icacos 3,760 15 16 86 0.71 0.22 0.13 0.20 

Mameyes 2,750 5 43 228 0.57 0.12 0.039 0.14 
Hydrology and Climate of Four Watersheds in Eastern Puerto Rico 77

it less exposed to winds from the east and southeast (Murphy and others, 2012). The high humidity, often completely 

soaked conditions, and the cloudiness of the cloud-forest zone 

all contribute to much-reduced rates of evapotranspiration. 

Studies on nearby Pico del Este (elevation 1,051 m) and El 

Yunque (elevation 1,059 m; U.S. Geological Survey, 1967) 

have shown that relative humidity was usually at or near 

100 percent, which would cause ET to be very low (Briscoe, 

1966; Weaver, 1972). It is possible that precipitation is higher 

than recorded in this watershed; cloud drip contribution is 

poorly understood, and the elevation of the watershed is higher 

than average cloud base. Also, horizontal precipitation during 

rainstorms is often undermeasured by rain gages; as such, 

precipitation recorded on windy ridgetops, such as at the Pico 

del Este station, may underrepresent rainfall accumulation 

(García-Martino and others, 1996). 

In the Cayaguás watershed, the water budget estimate 

for ET was 700 mm, whereas land cover suggested 1,240 mm 

(table 12). Although there are few ET data for the dominant 

land-cover types in this watershed (about 57 percent pasture 

and 20 percent lowland forest; Murphy and others, 2012), 

annual ET in lowland tropical forests is typically about 

1,400 mm (Leigh, 1999), and nonforested landscapes tend to 

have lower rates of ET (Lawton and others, 2001). Therefore, 

the land-cover estimate of ET for this watershed seems more 

plausible than the lower water budget estimate. The low ET 

estimated by water budget could be due to greater actual precipitation than is recorded, erroneously high runoff measurements, or under-reported septic system inputs. Substantial 

differences in two precipitation stations near the mouth of 

the Cayaguás watershed (Río Cayaguás and San Lorenzo 

3S, table 4; CAY and SLZ, fig. 5) indicate considerable 

short-range variation in precipitation or that some precipitation 

measurements are erroneous. Runoff data for the Cayaguás 

stream-gaging station have high uncertainty because of episodic scour and deposition of approximately 1 m in the sandy 

streambed (Larsen and Santiago-Román, 2001; Díaz and 

others, 2004). Finally, the Cayaguás watershed has a growing 

population, so it is possible that septic input is greater than 

reported, particularly if some households import water from 

another basin, although this possibility is unlikely.

Summary

Hydrologic regimes in the four U.S. Geological Survey 

Water, Energy, and Biogeochemical Budgets watersheds in 

eastern Puerto Rico are dominated by their location relative to the Luquillo Mountains. The Icacos and Mameyes 

watersheds, located in the eastern half of the mountains, are 

the wettest of the four watersheds, annually receiving more 

than 4,300 millimeters and 3,800 millimeters, respectively, 

of combined rainfall and cloud drip. Precipitation is closely 

correlated with elevation in these watersheds. The Canóvanas 

and Cayaguás watersheds, located in the rain shadow to the 

east and southeast of the mountains, receive roughly half the 

Table 11. Annual precipitation estimated by three methods.

[PRISM, parameter-elevation regressions independent slopes model (Daly and 

others, 2003). mm yr–1, millimeters per year]

Watershed

Annual precipitation

(mm yr–1)

Elevationweighted 

regression1

PRISM Calculated from 

land cover2

Canóvanas 2,070 3,480 2,770

Cayaguás 2,350 2,350 2,100

Icacos 4,150 3,980 4,160

Mameyes 3,760 3,660 3,830 1

 Rain-shadow-adjusted for Canóvanas and Cayaguás (see Interpretive 

Approach section).

2

 Based on 2003 land cover from Gould and others (2008) and land-cover 

types from literature (table 3).

Table 12. Annual water budgets of study watersheds.

[All values are millimeters per year unless otherwise noted]

Watershed

Inputs

Precipitation1 Cloud drip2 Septic3

Estimated Error Estimated Error Estimated Error

Canóvanas 2,070 310 22 10 10 10

Cayaguás 2,350 350 0 0 5 5

Icacos 4,150 620 160 72 0 1

Mameyes 3,760 560 84 38 0 1

Watershed

Outputs

Runoff4 Withdrawals3 Groundwater5

Estimated Error Estimated Error Estimated Error 

Canóvanas 980 150 46 46 8 1

Cayaguás 1,620 320 3 3 32 2

Icacos 3,760 560 0 0 100 5

Mameyes 2,750 410 4 4 37 3

Watershed

Outputs/

inputs

(percent)

Runoff 

(as percent of

precipitation +

cloud drip)

Evapotranspiration

From

water budget

From

land 

cover6 Estimated Error

Canóvanas 49 47 1,070 350 1,230

Cayaguás 70 69 700 470 1,240

Icacos 90 87 450 840 1,020

Mameyes 73 72 1,050 700 1,180 1

Based on elevation-weighted regression; rain-shadow-adjusted for 

Canóvanas and Cayaguás.

2

Based on Schellekens and others (2000), adjusted for elevation.

3

From Larsen and Concepción (1998).

4

U.S. Geological Survey stream-gaging stations, mean of 1991–2005.

5

From recession calculations (table 10).

6

Based on 2003 land cover from Gould and others (2008) and land-cover 

types from literature (table 3).
78 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

precipitation recorded for the Icacos and Mamyes watersheds. 

Precipitation and watershed elevation are not well correlated 

in these leeward watersheds, and differences in precipitation 

are probably related to local topography. Therefore, estimates 

of watershed-wide precipitation are likely to be much more 

accurate in windward watersheds (such as the Icacos and 

Mameyes) than in leeward watersheds (such as the Canóvanas 

and Cayaguás) in eastern Puerto Rico.

Precipitation and runoff in all watersheds show large 

interannual variation, which is partly explained by several 

large-scale climate oscillations. Precipitation and runoff are 

substantially higher in years when major storms strike Puerto 

Rico. These large storms typically result in similar runoff in all 

of the study watersheds, suggesting that higher annual runoff 

in the Icacos and Mameyes watersheds results from frequent, 

smaller storms related to orographic precipitation.

Precipitation leaves all of the studied watersheds primarily through runoff and evapotranspiration. Evapotranspiration 

estimates based on land cover and on water budgets were 

similar in the Mameyes and Canóvanas watersheds, but they 

differed substantially in the Icacos and Cayaguás watersheds. 

In the Icacos watershed, this discrepancy is probably due to 

reduced actual evapotranspiration caused by high humidity, 

extensive cloud cover, and reduced wind from eastern and 

southeastern directions, or greater actual water input than 

recorded by precipitation stations (owing to cloud drip or 

horizontal precipitation), or both. In the Cayaguás watershed, 

the discrepancy is likely due to measurement deficiencies in 

precipitation, runoff, or septic inputs, leading to low estimates 

of evapotranspiration by water budgets. 

Because of the influence of windward-leeward effects, 

differences in hydrologic regimes in the four watersheds 

associated with bedrock geology or land cover are difficult to 

detect. Groundwater storage is small compared with annual 

runoff in all watersheds. Geology appears to control the recession characteristics of the rivers; recession is faster in volcaniclastic soils, probably because of impermeable clay layers, 

than in granitic rocks. 

The effects of reforestation or climate change with time 

are difficult to distinguish from the large interannual variations 

in weather and the occasional large storm. Similarly, potentially increased evapotranspiration and subsequent decrease 

in runoff due to ongoing afforestation in the Canóvanas and 

Cayaguás watersheds would be a small factor compared to the 

windward-leeward differences. However, climate change may 

increase Puerto Rico’s vulnerability to hurricanes, flooding, 

and drought, so afforestation remains an important topic for 

future study. If the effects of climate and land-use change are 

to be addressed in eastern Puerto Rico, better spatial density 

of precipitation stations, longer periods of both precipitation and stream-gaging station data, and additional studies of 

evapotranspiration in different land covers are needed.

Acknowledgments

This chapter would not have been possible without the 

assistance of Pedro L. Díaz, Angel J. Torres-Sánchez, and 

others at the U.S. Geological Survey Caribbean Water Science Center. This chapter was greatly improved by suggestions made by Matthew C. Larsen, John A. Moody, Norman 

E. Peters, and Martha A. Scholl of the U.S. Geological 

Survey and F.N. Scatena of the University of Pennsylvania.

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in Puerto Rico, and their potential influence on coral reefs: 

U.S. Geological Survey Scientific Investigations Report 

2005–5206, 58 p.

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Luquillo Mountains of Puerto Rico: Caribbean Journal of 

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history of Lago Loíza, Puerto Rico, 1953–94: U.S. Geological Survey Water-Resources Investigations Report 97–4108, 

18 p. and 9 plates.

Wu, Wei, Hall, C.A.S., Scatena, F.N., and Quackenbush, 

L.J., 2006, Spatial modeling of evapotranspiration in 

the Luquillo Experimental Forest of Puerto Rico using 

remotely sensed data: Journal of Hydrology, v. 328, 

p. 733–752.

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Puerto Rico and the U.S. Virgin Islands: National 

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1999, Enhancement of interdecadal climate variability 

in the Sahel by vegetation interaction: Science, v. 286, 

p. 1537–1540.

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mass transport in four watersheds in eastern Puerto Rico, 

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1789, p. 113–152.

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accessed on October 27, 2011, at http://www.census.gov/

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use change in the maritime tropics: Amsterdam, Netherlands, Vrije Universiteit, Ph.D. dissertation, 262 p. 

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Forest-flood relation still tenuous—Comment on “Global 

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v. 15, p. 110–115.

Atmospheric Inputs to Watersheds of the 

Luquillo Mountains in Eastern Puerto Rico

By Robert F. Stallard

Chapter D of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–D

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.........................................................................................................................................................89

Introduction...................................................................................................................................................89

Background—Solute Sources in Tropical Rain .....................................................................................91

Methods Used to Quantify Inputs and Sources of Solutes in Precipitation ......................................92

Grouping Data......................................................................................................................................95

Missing Samples and Big Storms ....................................................................................................95

Estimating Source Strengths ............................................................................................................99

Seasonality and Trends in Inputs and Sources ....................................................................................107

Conclusions.................................................................................................................................................109

Acknowledgments.....................................................................................................................................109

References..................................................................................................................................................109

Figures

1. Map of Puerto Rico and study watersheds............................................................................90

2–12. Diagrams showing the following:

 2. Aggregated monthly rainfall, monthly mean concentrations, and monthly 

mean loading for chemical constituents in rain sampled at the El Verde 

National Atmospheric Deposition Program site, eastern Puerto Rico.......................97

 3. Three samples with the highest chloride concentrations measured for the 

Río Mameyes, eastern Puerto Rico, during Hurricanes Georges compared 

with seawater and the first base-flow sample following the hurricane ....................99

 4. Time series of runoff rate and chloride concentrations measured for the 

Río Mameyes, eastern Puerto Rico, during Hurricanes Hortense and Georges....100

 5. Time series of concentration estimates for the three primary components 

(marine aerosols, temperate contamination, and desert dust) that 

contribute to rain chemistry in El Verde, Puerto Rico, and inorganic 

nitrogen and acidity values..............................................................................................101

 6. Monthly and annual time series of rainfall, chloride concentrations, and 

chloride loadings from the National Atmospheric Deposition Program data 

set for El Verde, Puerto Rico............................................................................................102

 7. Time series of monthly and annual loading estimates for the three primary 

components (marine aerosols, temperate contamination, and desert dust) that 

contribute to rain chemistry in El Verde, Puerto Rico, and inorganic 

nitrogen and acidity values..............................................................................................106

 8. Aggregated monthly concentration estimates for the three primary 

components (marine aerosols, temperate contamination, and desert dust) 

that contribute to rain chemistry in El Verde, Puerto Rico .........................................107

 9. Aggregated monthly loading estimates for the three primary components 

(marine aerosols, temperate contamination, and desert dust) that contribute 

to rain chemistry in El Verde, Puerto Rico.....................................................................107

 10. Fractional contribution of the three primary components (marine aerosols,

temperate contamination, and desert dust) that contribute to rain chemistry

in El Verde, Puerto Rico....................................................................................................108
 11. Monthly total acidity concentrations compared to acidity concentration 

estimates for the three primary components (marine aerosols, temperate 

contamination, and desert dust) that contribute to rain chemistry 

in El Verde, Puerto Rico....................................................................................................108

 12. Monthly total acidity loadings compared to acidity loading estimates 

for the three primary components (marine aerosols, temperate contamination, 

and desert dust) that contribute to rain chemistry in El Verde, Puerto Rico...........108

Tables

1. Rainfall-weighted average monthly concentration of various constituents at 

El Verde, Puerto Rico, 1983 to 2005 ..........................................................................................93

2. Rainfall-weighted average annual concentration of various constituents at 

El Verde, Puerto Rico, 1983 to 2005. .........................................................................................94

3. Rainfall-weighted monthly loadings of various constituents at El Verde, 

Puerto Rico, 1983 to 2005...........................................................................................................94

4. Rainfall-weighted annual loadings of various constituents at El Verde, 

Puerto Rico, 1983 to 2005...........................................................................................................95

5. Constituent correlations for all weekly concentration data (n=818), ranked 

by correlation with Cl−, 1983 to 2005 ........................................................................................96

6. Constituent correlations for aggregated monthly averaged concentration

data (n=12), ranked by correlation with Cl−, 1983 to 2005.....................................................96

7. Storms that substantially affected WEBB rivers during the period of study, 

1991–2005 .....................................................................................................................................98

8. Averaged monthly rain chemistry at El Verde, Puerto Rico, with contributions 

from different sources identified............................................................................................103

9. Derived properties from monthly data at El Verde, Puerto Rico, with 

contributions from different sources identified...................................................................103

10. Rainfall-weighted average monthly concentration of total ionic charge and 

acidity at El Verde, Puerto Rico, 1983 to 2005.......................................................................104

11. Rainfall-weighted average annual concentration of total ionic charge and

acidity at El Verde, Puerto Rico, 1983 to 2005.......................................................................104

12. Rainfall-weighted monthly loadings of total ionic charge and acidity at

El Verde, Puerto Rico, 1983 to 2005 ........................................................................................105

13. Rainfall-weighted average loadings of total ionic charge and acidity at

El Verde, Puerto Rico, 1983 to 2005 ........................................................................................105
Abbreviations Used in This Report

μeq L−1 microequivalents per liter

μm micrometer

μmol L−1 micromoles per liter

km kilometer

km h–1 kilometers per hour

m meter

mm millimeter

mm h−1 millimeters per hour

ppmv parts per million volume

r

2

 correlation coefficient, squared

t km−2 yr−1 metric tons per square kilometer per year

Ca*, Cl*, or Mg* calculated nonseasalt concentration of Ca, Cl, or Mg

NADP National Atmospheric Deposition Program

WEBB Water, Energy, and Biogeochemical Budgets

Conversion Factors

Multiply By To obtain

Length

micrometer (μm) 0.00003937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Flow rate

millimeters per hour (mm h−1) 0.03937 inches per hour (in h−1) 

Other

metric tons per square kilometer per 

year (t km−2 yr−1) 

2.855 short tons per square mile per 

year (tons mi−2 yr−1)
Atmospheric Inputs to Watersheds of the Luquillo 

Mountains in Eastern Puerto Rico

By Robert F. Stallard

Abstract

Twenty years of precipitation-chemistry data from the 

National Atmospheric Deposition Program site at El Verde, 

Puerto Rico, demonstrate that three major sources control 

the composition of solutes in rain in eastern Puerto Rico. In 

order of importance, these sources are marine salts, temperate contamination from the Northern Hemisphere, and Sahara 

Desert dust. Marine salts are a source of roughly 82 percent 

of the ionic charge in precipitation; marine salt inputs are 

greatest in January. Evaluation of 15 years of U.S. Geological Survey data for four watersheds in eastern Puerto Rico 

suggests that large storms, including hurricanes, are associated with exceptionally high chloride concentrations in stream 

waters. Some of these storms were missed in sampling by the 

National Atmospheric Deposition Program, and therefore its 

data on the marine contribution likely underestimate chloride. 

The marine contribution is a weak source of acidity. Temperate 

contamination contributes about 10 percent of the ionic charge 

in precipitation; contaminants are primarily nitrate, ammonia, 

and sulfate derived from various manmade and natural sources. 

Peak deposition of temperate contaminants is during January, 

April, and May, months in which strong weather fronts arrive 

from the north. Temperate contamination, a strong source of 

acidity, is the only component that is increasing through time. 

Sahara Desert dust provides 5 percent of the ionic charge in 

precipitation; it is strongly seasonal, peaking in June and July 

during times of maximum dust transport from the Sahara and 

sub-Saharan regions. This dust contributes, on average, enough 

alkalinity to neutralize the acidity in June and July rains.

Introduction

A large portion of the dissolved load in near-coastal 

tropical rivers comes from the atmosphere (Stallard and 

Edmond, 1981). This load is obtained by several processes 

such as precipitation, dry deposition (fallout, impaction on 

surfaces), and chemical fixation by biological processes or 

chemical-weathering reactions. Precipitation removes aerosols and some gases directly from the atmosphere, while dry 

deposition transfers aerosols to plant (and other) surfaces to 

be washed off by precipitation or condensation. Precipitation and dry deposition are commonly difficult to distinguish 

chemically, and only precipitation is readily sampled. Dry 

deposition loadings can be similar to precipitation loadings 

(Peters and others, 2006). Chemical fixation (the conversion 

of atmospheric gases into dissolved and solid compounds by 

chemical or biological means) is the source of much of the 

dissolved organic and inorganic carbon in surface waters and 

of considerable dissolved nitrogen. Accordingly, atmospheric 

inputs to rivers are commonly underestimated. In this report, 

atmospheric chemical inputs to eastern Puerto Rico were 

characterized by using relations among chemical constituents 

to identify constituent sources, and then using constituent 

deposition rates to estimate source loadings. The rainfall and 

runoff chemical datasets are from the National Atmospheric 

Deposition Program (NADP) and the U.S. Geological Survey 

Water, Energy, and Biogeochemical Budgets (WEBB) program. The NADP record is long (1985 to the present (2010)), 

but it has several large gaps. The WEBB precipitation data set 

(1991–1995) is more limited but helps in generalizing the data 

throughout the region. Runoff data are available for five rivers 

(Canóvanas, Cayaguás, Guabá, Icacos, and Mameyes) in four 

study watersheds (fig. 1).

Many of the sources of aerosols and gases that dissolve 

in precipitation have distinct chemical signatures. In undeveloped tropical regions within about 300 kilometers (km) of the 

ocean, the ocean is the largest source of solutes in rain (Stallard and Edmond, 1981). Within the ocean, solutes arise in two 

ways: seasalt from breaking waves and bursting bubbles, and 

gases emitted, generally from biological activity. The many 

solutes in seasalt are in characteristic proportions referred to 

as the “seasalt ratio.” The nutrients nitrate, ammonia, silica, 

and phosphate are important exceptions and are typically 

stripped from surface seawater by biological activity. Of the 

major seawater ions, only sulfate has gaseous sources. Algae 

release to the atmosphere organic sulfur gases such as methyl 

sulfide, dimethyl sulfide, and carbon disulfide. Ultraviolet light 

quickly oxidizes these gases into precursors of sulfuric acid. 

Thus, the ocean can be a natural source of acid precipitation in 

the coastal tropics (Stallard and Edmond, 1981).

A variety of nitrogen and sulfur compounds are emitted 

by natural and human activities. Natural sources of sulfate 

include emissions from vegetation, anaerobic soils, and 
90 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

B

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Icacos

Guabá

Bisley

Mameyes

LUQUILLO

MOUNTAINS

El Verde

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_01D.ai

Weather station and 

 identifier—Precipitation

 measurement

Bisley

Area shown in B

A

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

L e s s e r

A n

t i l l e s

Figure 1. Location of Puerto Rico and study watersheds, eastern Puerto Rico.
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 91

volcanoes. Some ammonia and nitrate come from vegetation, 

biogenic emissions from soils, and from lightning (Junge, 

1963). Eastern Puerto Rico is not close to any major terrestrial 

sources of sulfur or nitrogen, although Soufrière Hills Volcano 

on Montserrat (an island in the Lesser Antilles about 300 miles 

east-southeast of Puerto Rico), active 1995 to present, is arguably close enough to have some influence (Heartsill-Scalley 

and others, 2007). Human activities, especially on lands of the 

temperate northern hemisphere, are a major and often dominant source of nitrate, ammonia, and sulfate ions in the atmosphere, cloud waters, and precipitation (Junge, 1963; Weathers 

and others, 1988; Duce and others, 1991; Galloway and others, 

1996; Galloway, 1998; Holland and others, 1999; Rasch and 

others, 2000; Rodhe and others, 2002). The sulfur and nitrogen 

compounds typically form aerosols that are strong acids, 

which are the main source of acid rain in polluted regions. Sulfur compounds, which are typically oxidized to form sulfuric 

acid, come from the burning of fossil fuels. Nitrogen compounds, mainly ammonium and nitrate ions, have a complex 

mix of sources including burning of fossil fuels and vegetation and biogenic emissions from soils enriched in nitrogen 

through the use of artificial fertilizers (Junge, 1963). There 

are no major local sources of such contamination immediately 

upwind (north and east) of eastern Puerto Rico (Brown and 

others, 1983, Ortiz-Zayas and others, 2006).

A final major source of solutes in tropical Atlantic coastal 

regions and islands of the Americas is dust from the Sahara 

Desert in Africa (Prospero and Carlson, 1972; Stallard, 2001; 

Reid and others, 2003a,b; Garrison and others, 2003). Fallout 

of dust from northern Africa blankets a zone from southern 

North America through Central America and northern South 

America (Prospero and Carlson, 1972). Seasonally averaged data from a coastal zone color-scanner satellite shows 

that the biggest desert-dust plume is derived from the Sahara 

and is carried westward by the trade winds (Stegmann and 

Tindale, 1999). The structure of this plume is complex and 

hard to characterize in detail (Reid and others, 2003a). The 

dust plume typically bathes Puerto Rico from June through 

August. Farther south, in Costa Rica and Panama, the plume is 

strong starting in April. The fallout of fine clay and quartz that 

forms the bulk of the dust is so great that it is the source of 

soil minerals on carbonate islands such as Bermuda (Herwitz 

and others, 1996). Although this dustfall has been going on for 

millions of years (Parkin and Shackleton, 1973), the clearing 

of land south of the Sahara may provide a new contribution 

(Lundholm, 1977; Shinn and others, 2000). Recent studies 

(Dunion and Velden, 2004; Evan and others, 2006) indicate a 

strong interaction between dry dusty air coming off the Sahara 

and tropical cyclones, with an inverse relation between dustiness, as measured by satellites, and cyclone activity. Sahara 

dust may carry pathogens that affect coral (Shinn and others, 

2000), amphibians (Stallard, 2001), and people (Kuehn, 2006). 

Enough dust comes from the Sahara to affect the strontium isotope composition of water and plants in the Luquillo 

Mountains (Pett-Ridge and others, 2009a,b) and to be a significant source of phosphate (Pett-Ridge, 2009). (The isotopic 

ratios of strontium from the Sahara differ from those ratios 

in Luquillo rocks and soils.) Pett-Ridge and others (2009b) 

use the Sr isotopic budget to estimate the long-term average 

deposition rate for Sahara dust at 21±7 metric tons per square 

kilometer per year (t km−2 yr−1).

Reid and others (2003b) used x-ray fluorescence to 

analyze 60,500 individual particle aggregates of air-borne 

Sahara dust collected on a Davis rotating drum collector, 

which separates the dust into eight particle-size stages from 

0.1 to 12 micrometers (μm). Intercorrelations among various 

elements provide indications of dominant particle sources. The 

bulk of particles sampled from air-borne Sahara dust are silicate minerals and insoluble oxides; a few percent of the total 

consists of sodium-chloride, calcium-rich, and calcium-sulfur 

particles (Reid and others, 2003b). Seasalt is the dominant 

source of sodium and chloride in the dust. Some salts of desert 

soils, such as halite (NaCl), are not readily distinguished 

from seasalt, especially because photochemical processes can 

slightly shift the original elemental proportions in air-borne 

seasalt (Stallard and Edmond, 1981, Reid and others, 2003b). 

Soluble calcium in this dust presumably comes from the 

calcium carbonate and calcium sulfate minerals that typically 

accumulate in desert soils (Birkeland, 1999, Reid and others, 

2003b). Calcium carbonate reacts with and neutralizes nitric 

and sulfuric acids, thus generating carbon dioxide.

Elements with especially strong correlations with 

silicon (correlation coefficient (r2

) >0.8) were aluminum 

(Al/Si= 0.49, r2

=0.90), potassium (K/Si=0.059, r2=0.92), titanium (Ti/Si=0.012, r2

=0.81), and iron (Fe/Si=0.070, r2=0.92). 

Excluding sodium and chlorine, which originate largely from 

seasalt, the most abundant elements identified by analysis 

were silicon, aluminum (Al/Si=0.49), calcium (Ca/Si=0.13), 

sulfur (S/Si=0.1), iron (Fe/Si=0.070), magnesium 

(Mg/Si=0.066), and potassium (K/Si=0.059). Reid and others 

(2003b) estimate that more than 70 percent of dust-particle 

mass can be attributed to aluminosilicate clay minerals such 

as illite, kaolinite, and montmorillonite. Of these minerals, 

illite, K0.6(H3

O)0.4Al1.3Mg0.3Fe2+

0.1Si3.5O10(OH)2

∙(H2O), is most 

important. Amorphous silicon and quartz make up the next 

largest group, composing another 10 to 15 percent. Although 

these clay minerals and oxides come from desert soils, presumably they were derived from weathering during humid 

periods, and because these clays are solid weathering products, 

it is most likely that they contribute to the solid load in the rivers rather than the dissolved load (Herwitz and others, 1996).

Background—Solute Sources in 

Tropical Rain

The lack of local contamination sources makes the 

Luquillo Experimental Forest in easternmost Puerto Rico an 

ideal setting for studying precipitation chemistry (fig. 1). Like 

the prow of a ship, the Luquillo Mountains meet the east-towest trade-wind air flow after it transits largely open ocean. 
92 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Rainfall for the region is derived from a mix of locally generated showers, tropical systems moving from the east, and frontal systems from the north (McDowell and others, 1990; Lugo 

and Scatena, 1992; Malmgren and others, 1998). The frontal 

systems are most important from December to May. Trade 

winds blow throughout the year but are strongest from June 

to November (García-Martino and others, 1996). Trade-wind 

meteorology has been extensively studied in the region east 

of Puerto Rico (Snodgrass and others, 2009, and references 

therein). The elevation of the trade-wind cloud base is largely 

controlled by sea-surface conditions, and it may be as low as 

400 meters (m) but is typically around 600 m. Orographic control of rainfall is important (García-Martino and others, 1996; 

Murphy and Stallard, 2012); about 1,500 millimeters (mm) 

falls on the coast and more than 4,000 mm falls at highest 

elevations. More falls on the north and east sides of mountains 

and less on the south and west. Water from cloud interception 

provides 10 to 14 percent of the water budget in elfin forests 

(Baynton, 1968, 1969; Weaver, 1972; Brown and others, 1983; 

Larsen and Concepción, 1998; Schellekens and others, 1998; 

Eugster and others, 2006; Holwerda and others, 2006), and it 

may provide water to the upper portion of palo colorado forest 

as well.

Several studies have analyzed rain chemistry in the 

Luquillo Mountains (fig. 1). The best record of rainfall 

chemistry is from El Verde, which, with few exceptions, has 

been sampled by the NADP on a weekly basis since early 

1985 (National Atmospheric Deposition Program (NRSP-3)/

National Trends Network, 2007c, Station PR20). A 2-year gap 

was sustained from mid-1989 to mid-1991, starting the week 

that Hurricane Hugo hit, and occasional short gaps have commonly coincided with huge storms.

McDowell and others (1990) used bulk and wet precipitation from the NADP site at El Verde for 1983–1987 to identify 

three dominant sources of solutes in the rain: (1) marine aerosols (Na+

, K+, Mg2+, Cl −), (2) temperate contamination (NH4

+

, 

SO4

2–, NO3−

), and (3) Sahara dust (K+, Ca2+, Mg2+). Contaminants arrive in frontal systems from North America, and the 

trade winds carry contaminants from North America, Europe, 

and Africa. Stallard (2001) extended the analysis by using 

the NADP data set from 1985 through 1998 and provides a 

detailed analysis of trends and of the seasonality of these three 

sources. Stallard (2001) noted that nitrogen loading in precipitation was increasing with time, an observation also later 

reported by Ortiz-Zayas and others (2006). Stallard (2001) did 

not observe a significant contribution of K+

 and Mg2+ in the 

desert-dust component.

 Heartsill-Scalley and others (2007) examined a 15-year 

time series, 1988–2002, of chemistry data in precipitation 

and throughfall sampled at the ridge above the Bisley site in 

the most northeast part of the Luquillo Experimental Forest (fig. 1). In addition to the constituents determined by the 

NADP, that study also determined total dissolved nitrogen 

and PO4

3−. These workers found that for constituents that 

are common in seasalt and dust (Na+

, Ca2+, Cl–, SiO2, and 

SO4

2−), annual rainfall deposition is large compared with 

their deposition at other tropical sites, and the enrichment of 

throughfall by the canopy is small. Plant-active constituents 

(K+

, NH4

+

, NO3

−

, total dissolved nitrogen, and PO4

3−) had 

lesser deposition in rain (rainout) than was true at many other 

tropical sites, and enrichment of throughfall by the canopy 

is large and quite variable. After hurricanes, which destroy 

canopy, throughfall is a greater fraction of total rainfall but 

it is less enriched in plant-active constituents because of less 

interaction with canopy. The enrichment of throughfall by 

plant-active solutes increased during droughts. HeartsillScalley and others (2007) show a weak increase in precipitation fluxes (cations, N, P) that might be associated with the 

Soufrière Hills Volcano.

The present paper modifies the analytical approach of 

Stallard (2001) to extend the analysis of the NADP data set 

though the time period of the WEBB research program. In 

addition, the effects of data gaps are examined, because the 

NADP data set used here, from February 12, 1985, through 

May 15, 2007, contains 123 data gaps ranging from 1 to 

89 weeks for a total of 336 weeks out of 1,155 weeks, or 

29 percent of the time. The average gap length is 2 weeks, if 

the four longest gaps of 11, 12, 29, and 89 weeks are excluded. 

Reasons for the gaps are not given except for the longest, 

which was caused by a lack of funding. Several gaps coincide with large storms. Large storms, especially hurricanes, 

defoliate the canopy, carpet the entire landscape with leaves 

and branches, and topple many trees (Scatena and Larsen, 

1991; Schaefer and others, 2000). Collection equipment is 

commonly compromised, and rain samplers are typically 

filled with debris. Finally, access roads are sometimes blocked 

for days to weeks after a storm, and it is often a challenge to 

recover samples in a timely manner.

Methods Used to Quantify Inputs and 

Sources of Solutes in Precipitation

Samples at the El Verde NADP Station PR20 are collected weekly from a wet collector and analyzed using a 

standard protocol (National Atmospheric Deposition Program 

(NRSP–3)/National Trends Network, 2007a,b). The objective 

of this standardization is to provide a basis for comparison of 

major ionic constituents (H+, Na+, K+, Ca2+, Mg2+, NH4

+

, Cl−, 

SO4

2−, NO3−

) among sites and to identify trends through time. 

On 11 January 1994, procedures changed slightly, resulting in a slight apparent decrease in concentration of some 

constituents (National Atmospheric Deposition Program 

(NRSP–3)/National Trends Network, 2007d); however, this 

decrease is small compared with concentrations observed for 

El Verde samples. The NADP data for El Verde are available 

on the internet (National Atmospheric Deposition Program 

(NRSP–3)/National Trends Network, 2007a). The largest 

data gap was almost 2 years (mid-1989 to mid-1991), when 

samples were not collected. The NADP website provides the 

raw weekly data as well as monthly and annual digests of the 
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 93

data. These digests were used and modified as needed for this 

discussion. Stallard and Edmond (1981), who collected rain 

samples from a ship on the Amazon River, noted that samples 

collected close to forest are enriched in nutrients (plant-active 

constituents) compared with those collected on the river hundreds of meters to kilometers from the forest edge. Accordingly, one would expect some local contamination, by bits of 

plant debris or insects, of these constituents in the El Verde 

samples, which are collected close to forest.

The chemical constituents identified by the NADP do not 

complete a charge balance. The concentration of hydrogen 

ions in rainwater is controlled by the imbalance of charge 

between nonreactive anions and cations and equilibration 

with atmospheric carbon dioxide. Accordingly, two additional 

constituent concentrations, bicarbonate ion (HCO3

−

) and acidity. were calculated from the hydrogen ion concentration and 

other constituents. Acidity is the charge difference between 

nonreactive anions and cations. Acidity is conservative and 

is the same as the negative of alkalinity (also called acidneutralization capacity). Organic anions in tropical rainwater 

are quite unstable (half-lives of only a few hours (Andreae and 

others, 1990)), and they should not contribute to the acidity of 

samples that were collected during a week. For the pH range 

and ionic strength of the NADP samples, at equilibrium with 

an atmospheric partial pressure of carbon dioxide (PCO2) of 370 

parts per million volume (ppmv), the concentrations of these 

calculated constituents are given by several equations (Stumm 

and Morgan, 1981; Stallard, 2001):

Hydrogen ion: H+ in microequivalents per liter

(μeq L−1)=10(6-pH) (1)

Bicarbonate ion: HCO3

−

=6.36/(H+) (2)

Acidity: acidity=(total anion charge of strong acids)–

(total cation charge of strong bases plus NH4

+

) (3)

Acidity: acidity » H+–HCO3

−

. (4)

These two independent calculations of acidity, equations 3 and 4, allow for a quality-control check of the overall 

analyses. Acidity calculated from laboratory pH is in better 

agreement with acidity calculated from charge balance than is 

acidity calculated from field pH, indicating that laboratory pH 

is of better data quality. When the laboratory pH is used to calculate H+

 and HCO3

–

 to complete charge balances, the resultant charge balances are generally within 10 percent. Many of 

the samples that have excess positive charge also have excess 

Na+ relative to that expected from seasalt. Likewise, many of 

the samples with an excess negative charge also have a sodium 

deficit. If the pH values are valid, this result points to analytical problems for sodium.

The NADP uses standard procedures to calculate monthly 

and annual digests of data (National Atmospheric Deposition 

Program (NRSP–3)/National Trends Network, 2007b). The 

monthly and annual digests prepared by NADP were used 

to prepare summary tables of constituent concentrations and 

loadings (inputs) (tables 1–4). Acidity was calculated from the 

analyses by using equation 3, and H+ and HCO3

−

 were, in turn, 

calculated from acidity using equations 2 and 4. This calculation forces a perfect charge balance and leaves problem data, 

such as the excess Na+, just described, intact. The rainfallvolume-weighted averaging (calculated as the sum of weekly 

volume times concentration divided by total volume) of 4 to 

5 weeks for each month and 52 weeks for each year reduces 

the influence of problem data.

Table 1. Rainfall-weighted monthly average concentration of various constituents at El Verde, Puerto Rico, derived from averaged 

monthly data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; μmol L−1, micromoles per liter]

Month Rainfall

(mm)

H+

(μmol L–1)

Na+

(μmol L–1)

K+

(μmol L–1)

Mg2+

(μmol L–1)

Ca2+

(μmol L–1)

NH4

+

(μmol L–1)

Cl–

(μmol L–1)

SO4

2–

(μmol L–1)

NO3

–

(μmol L–1)

January 278 9.0 134.7 3.0 15.0 3.7 2.0 153.6 13.3 5.2

February 165 9.2 108.6 2.4 11.5 3.4 2.2 122.5 11.6 5.6

March 193 9.1 82.7 2.0 9.2 3.3 1.8 97.0 9.2 4.4

April 223 13.9 57.4 1.5 6.4 2.8 3.3 67.4 10.0 6.4

May 280 10.6 51.3 1.8 5.9 2.9 2.5 61.0 8.4 5.4

June 226 2.7 64.1 1.9 7.3 6.5 2.1 74.0 8.5 5.0

July 306 2.3 66.1 1.8 7.4 6.6 1.5 75.8 8.7 3.8

August 325 4.9 46.1 1.3 5.2 4.8 1.3 54.7 7.1 3.7

September 287 6.2 70.2 1.8 8.2 4.3 1.6 83.2 8.3 3.9

October 240 7.8 38.0 0.9 4.0 2.1 1.0 44.1 5.7 3.6

November 343 7.4 62.4 1.5 6.7 1.9 0.9 72.3 6.6 2.8

December 246 10.0 90.1 2.0 9.9 2.4 1.1 105.8 9.1 3.3

Total annual 3,113

Average annual 7.3 71.0 1.8 7.9 3.8 1.7 82.5 8.7 4.3
94 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 2. Rainfall-weighted average annual concentration of various constituents at El Verde, Puerto Rico, derived from averaged 

annual data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; μmol L−1, micromoles per liter]

Year Rainfall

(mm)

H+

(μmol L–1)

Na+

(μmol L–1)

K+

(μmol L–1)

Mg2+

(μmol L–1)

Ca2+

(μmol L–1)

NH4

+

(μmol L–1)

Cl–

(μmol L–1)

SO4

2–

(μmol L–1)

NO3

–

(μmol L–1)

1985 3,291 4.2 61.8 1.6 7.4 4.3 0.7 74.4 7.2 1.3

1986 3,345 8.2 53.8 1.5 6.2 3.7 0.6 66.8 7.2 1.9

1987 3,892 6.7 46.5 1.2 5.6 3.7 1.1 56.0 6.9 3.2

1988 3,833 5.3 57.7 1.4 6.7 4.0 0.7 68.2 7.6 2.0

19891 2,675 8.2 68.1 1.7 7.7 3.7 2.7 81.1 8.6 4.4

1991 1,178 6.9 67.5 1.8 8.1 5.3 1.1 81.2 8.5 4.8

1992 2,930 5.4 69.3 1.8 8.1 3.7 1.7 77.7 8.8 5.2

1993 2,888 7.6 61.9 2.6 7.3 3.0 2.1 73.9 7.6 4.9

1994 2,314 3.9 70.2 1.7 7.9 4.0 2.1 80.0 7.9 4.3

1995 2,624 4.2 74.6 1.7 8.0 3.8 2.9 83.4 8.4 5.2

1996 3,568 4.0 77.6 1.8 7.9 3.1 1.9 84.7 8.3 4.5

1997 2,734 11.6 55.7 1.8 6.1 3.8 1.2 68.8 8.3 4.2

1998 4,400 5.1 83.4 2.1 9.4 3.5 1.1 95.7 8.6 3.4

1999 3,153 2.0 67.2 1.6 7.0 3.8 1.7 70.7 7.7 4.8

2000 2,466 6.9 65.8 1.6 6.9 4.4 1.3 76.4 8.0 4.8

2001 2,825 9.2 67.3 1.7 6.8 4.0 1.9 79.0 8.4 5.3

2002 2,772 12.4 48.4 1.2 5.3 2.9 1.7 61.4 7.0 4.2

2003 3,266 13.6 55.4 1.4 6.1 3.5 2.3 68.6 9.0 5.0

2004 3,794 8.7 77.3 1.9 8.6 4.0 1.8 91.4 9.0 4.8

2005 3,155 9.4 99.9 2.4 11.5 4.0 2.3 113.5 12.4 6.0

2006 3,104 13.9 71.0 1.8 8.0 4.0 2.2 87.2 10.3 4.9 1

Samples were not collected between 12 September 1989 (the day that Hurricane Hugo hit) and 4 June 1991. Rainfall totals in 1989 and 1991 are reduced 

accordingly.

Table 3. Rainfall-weighted monthly loadings of various constituents at El Verde, Puerto Rico, derived from averaged monthly data of 

the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; mmol m−2, millimoles per meter squared]

Month Rainfall

(mm)

H+

(mmol m–2)

Na+

(mmol m–2)

K+

(mmol m–2)

Mg2+

(mmol m–2)

Ca2+

(mmol m–2)

NH4

+

(mmol m–2)

Cl–

(mmol m–2)

SO4

2–

(mmol m–2)

NO3

–

(mmol m–2)

January 278 2.51 37.50 0.84 4.18 1.03 0.55 42.78 3.70 1.45

February 165 1.51 17.92 0.40 1.89 0.55 0.37 20.21 1.92 0.92

March 193 1.75 15.92 0.39 1.78 0.64 0.34 18.68 1.78 0.86

April 223 3.10 12.81 0.33 1.42 0.61 0.74 15.05 2.23 1.44

May 280 2.96 14.37 0.51 1.66 0.80 0.69 17.10 2.34 1.51

June 226 0.62 14.51 0.43 1.64 1.48 0.47 16.75 1.93 1.13

July 306 0.70 20.20 0.55 2.27 2.00 0.47 23.15 2.66 1.15

August 325 1.59 14.98 0.41 1.70 1.57 0.42 17.76 2.29 1.21

September 287 1.78 20.14 0.51 2.35 1.25 0.47 23.88 2.39 1.12

October 240 1.88 9.13 0.22 0.96 0.51 0.25 10.60 1.37 0.87

November 343 2.54 21.39 0.51 2.29 0.64 0.29 24.78 2.27 0.96

December 246 2.47 22.20 0.50 2.43 0.60 0.26 26.06 2.23 0.81

Total annual 3,113 23.4 221.1 5.6 24.6 11.7 5.3 256.8 27.1 13.4
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 95

Grouping Data

Procedures presented by Stallard and Edmond (1981) and 

Stallard (2001) were used to quantify the different sources of 

ions in NADP samples: marine aerosols, temperate contamination, and desert dust. Each source has at least one characteristic marker ion. In marine aerosol, Cl−

, Na+, and Mg2+ ions are 

sufficiently abundant to be marker ions; Cl− is most abundant 

and was used here. In temperate contamination, NO3

−

 is the 

most abundant ion. NH4

+

 is also attributable to this source. 

In desert dust, Ca2+, after correction for seasalt, is the only 

marker. A possible fourth source, local vegetation, is considered by using K+

 after a correction for seasalt.

Constituent groupings can be seen by using crosscorrelation tables of constituent concentrations for the entire 

weekly data set (table 5) and for aggregated monthly mean 

concentrations (table 6). Rows and columns in these tables 

are ranked by correlation with Cl−. For the entire data set, Cl−

correlates well with Na+, Mg2+, and SO4

2− (r2

>0.5, indicating 

that more than half of the variance between Cl− and the other 

constituent is shared) (Bevington and Robinson, 2003). For 

monthly mean data, K+ joins this group with a strong correlation (r2

=0.89). The lack of such a strong correlation for 

weekly K+ likely reflects major, but temporally random, local 

inputs. The intercorrelation among NO3

−

, NH4

+

, and SO4

2− is 

moderate (r2>0.25, more than one quarter variance shared) 

to strong (r2>0.9, most variance is shared) in the weekly data 

(0.23<r2

<0.49) and the monthly mean data (0.29<r2<0.92). 

Finally, Ca2+ does not correlate strongly with any parameter 

in the weekly data but anticorrelates strongly with acidity 

(r2=0.67; r=−0.82) in the monthly mean data set. The groupings are illustrated as monthly mean concentrations in figure 2 

and table 1 and monthly loadings in figure 2 and table 3.

Missing Samples and Big Storms

A common practice, and one used by the NADP, is to 

fill gaps in a long time series of data by assuming that missing data resemble data that were collected. During the WEBB 

Project from 1991 to 2005, in the four study watersheds and 

one subwatershed described here, 507 hydrologic events 

were sampled from 263 storms using automated samplers 

(appendix 1). A total of 4,894 stream-water event samples 

were analyzed for conductivity, most for chloride, and many 

for a broad suite of other constituents. Several of the largest 

storms during which rivers were sampled (table 7) coincide 

with data gaps in the NADP record. Seven storms produced 

stream-water chloride concentrations that were substantially 

greater than typical values in 117 samples. Many of these 

117 samples were analyzed for a full suite of constituents, and 

concentrations of nearly seasalt proportions were observed 

for all ions that are contained abundantly in seasalt (fig. 3). 

Heartsill-Scalley and others (2007) note that throughfall is 

Table 4. Rainfall-weighted annual loadings of various constituents at El Verde, Puerto Rico, derived from averaged annual data of the 

National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; mmol m−2, millimoles per meter squared]

Year Rainfall

(mm)

H+

(mmol m–2)

Na+

(mmol m–2)

K+

(mmol m–2)

Mg2+

(mmol m–2)

Ca2+

(mmol m–2)

NH4

+

(mmol m–2)

Cl–

(mmol m–2)

SO4

2–

(mmol m–2)

NO3

–

(mmol m–2)

1985 3,291 13.7 203.4 5.4 24.2 14.0 2.4 244.9 23.6 4.4

1986 3,345 27.5 179.8 5.0 20.8 12.4 1.9 223.4 24.1 6.2

1987 3,892 26.0 180.8 4.8 21.6 14.6 4.1 217.8 27.0 12.5

1988 3,833 20.4 221.1 5.3 25.7 15.4 2.8 261.5 29.0 7.6

19891 2,675 22.0 182.2 4.7 20.6 9.8 7.3 217.0 23.0 11.9

1991 1,178 8.1 79.5 2.1 9.5 6.2 1.3 95.6 10.0 5.7

1992 2,930 15.7 202.9 5.2 23.7 10.7 5.0 227.5 25.8 15.3

1993 2,888 22.0 178.8 7.4 21.1 8.6 5.9 213.4 21.8 14.2

1994 2,314 9.1 162.4 4.0 18.3 9.2 4.9 185.2 18.2 9.9

1995 2,624 11.1 195.8 4.5 20.9 10.0 7.6 218.7 22.1 13.8

1996 3,568 14.2 276.9 6.4 28.3 11.2 6.7 302.2 29.6 16.2

1997 2,734 31.8 152.3 5.0 16.6 10.4 3.2 188.2 22.7 11.4

1998 4,400 22.4 367.1 9.0 41.5 15.5 5.0 421.2 37.8 15.1

1999 3,153 6.4 211.8 5.0 21.9 12.0 5.2 222.8 24.2 15.3

2000 2,466 17.0 162.3 4.0 16.9 10.8 3.3 188.5 19.8 11.8

2001 2,825 26.0 190.1 4.8 19.3 11.2 5.5 223.1 23.7 14.9

2002 2,772 34.4 134.2 3.3 14.7 8.2 4.6 170.2 19.5 11.8

2003 3,266 44.6 181.0 4.6 19.9 11.6 7.4 224.2 29.3 16.2

2004 3,794 33.1 293.4 7.1 32.8 15.1 6.7 346.8 34.2 18.2

2005 3,155 29.8 315.2 7.5 36.2 12.7 7.2 358.1 39.1 19.0

2006 3,104 43.3 220.5 5.6 24.8 12.5 6.9 270.7 31.8 15.1 1

Samples were not collected between 12 September 1989 (the day that Hurricane Hugo hit) and 4 June 1991. Rainfall totals in 1989 and 1991 are reduced 

accordingly.
96 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

only slightly enriched in Cl− and other seasalt ions. Because 

throughfall was collected continuously from 1988 to 2002, 

it cannot be the source. Similarly, soils and bedrock do not 

retain substantial Cl− (Stallard, 2012) and base flow is strongly 

enriched in alkaline ions and calcium, which are both low 

in concentration in the event samples (Stallard and Murphy, 

2012). Groundwater cannot be a source of the high-chloride 

event samples. The only reasonable source of elevated Cl−

and other seasalt ions in these storms is seasalt carried inland 

by wind and rain. The traditional approach of assuming that 

missing samples resemble successfully collected samples is 

therefore invalid for this NADP data set, because several of 

the data gaps that corresponded to storms were among the nine 

that had exceptionally high Cl− concentrations. One of these 

gaps occurred during Hurricane Georges, the largest event 

during this 15-year study. The second-largest storm, Hurricane 

Hortense, lacked both a data gap and high chloride.

By assuming that Cl− in the stream water sampled during the storm was derived from the storm, one can integrate 

water and chloride discharge through the storm hydrograph 

to calculate total rainfall and the Cl− concentration and 

loading of that rain. Two contrasting hurricanes, Hortense 

(September 9–10, 1996) and Georges (September 21, 1998), 

had comparable rainfall but show markedly contrasting Cl− 

behavior (fig. 4). Georges (category 2 on the Saffir-Simpson 

Hurricane Wind Scale (National Oceanic and Atmospheric 

Administration, 2010b), winds of 154–177 kilometers per 

hour (km h−1)) was a shorter and more intense hurricane than 

Hortense (category 1, winds of 119–153 km h−1 hitting the 

west of Puerto Rico) (Murphy and others, 2012). Hortense, 

which was sampled by NADP, was a typical low-Cl− storm, 

whereas Georges, which was not sampled by NADP, was 

a high Cl− event. In the Mameyes watershed, the WEBB 

watershed closest to El Verde, Hurricane Hortense produced 

333 mm total runoff with an average stream-water Cl− concentration of 111 micromoles per liter (μmol L−1) and a 

maximum Cl− concentration of 253 μmol L−1 in the first few 

hours of the storm. In contrast, Hurricane Georges produced 

317 mm total runoff with an average stream-water Cl− concentration of 455 μmol L−1 and a maximum Cl−

 concentration of 1,567 μmol L−1 in the first few hours of the storm. 

Most of the samples from Hurricane Georges were analyzed 

for all major constituents. The constituents in these samples 

are in nearly seasalt proportions, dominated by Na+ and Cl−, 

whereas the first base-flow sample collected after the storm is 

dominated by alkalinity and Ca2+ (fig. 3).

Table 5. Constituent correlations for all weekly concentration data (number of samples=818), ranked by correlation with Cl–, derived 

from weekly data of the National Atmospheric Deposition Program for 1983 to 2005.

[All values are squared correlation coefficients, r2; ( ), r2<0; --, not relevant]

Constituent Cl– Na+ Mg2+ SO4

2– K+ Ca2+ NO3– NH4+ Acidity

Cl– 1 -- -- -- -- -- -- -- --

Na+ 0.97 1 -- -- -- -- -- -- --

Mg2+ 0.96 0.97 1 -- -- -- -- -- --

SO4

2– 0.51 0.47 0.50 1 -- -- -- -- --

K+ 0.32 0.28 0.32 0.20 1 -- -- -- --

Ca2+ 0.17 0.16 0.19 0.22 0.11 1 -- -- --

NO3

– 0.07 0.07 0.07 0.45 0.05 0.07 1 -- --

NH4

+ 0.02 0.02 0.02 0.23 0.02 0.01 0.49 1 --

Acidity 0.02 0.01 0.01 0.29 0.00 (0.04) 0.23 0.10 1

Table 6. Constituent correlations for aggregated monthly averaged concentration data (number of samples=12), ranked by correlation 

with Cl−, derived from averaged monthly data of the National Atmospheric Deposition Program for 1983 to 2005.

[All values are squared correlation coefficients, r2; ( ), r2<0; --, not relevant]

Constituent Cl– Na+ Mg2+ K+ SO4

2– NO3– NH4+ Acidity Ca2+

Cl– 1 -- -- -- -- -- -- -- --

Na+ 1.00 1 -- -- -- -- -- -- --

Mg2+ 1.00 0.99 1 -- -- -- -- -- --

K+ 0.89 0.89 0.92 1 -- -- -- -- --

SO4

2– 0.78 0.79 0.80 0.83 1 -- -- -- --

NO3

– 0.06 0.06 0.07 0.15 0.41 1 -- -- --

NH4

+ 0.01 0.01 0.02 0.07 0.29 0.93 1 -- --

Acidity 0.04 0.04 0.03 0.01 0.10 0.19 0.19 1 --

Ca2+ (0.00) (0.00) 0.00 0.02 0.00 0.01 0.01 (0.68) 1
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 97

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_02D.ai

400

300

200

100

0

Monthly average rainfall,

in millimeters

200

150

100

50

0

Monthly average concentration,

in micromoles per liter

Monthly loading, in millimoles

per square meter

A. Monthly average rainfall

B. Concentration of chloride and sodium

D. Monthly loading of chloride and sodium

C. Concentration of more-dilute constituents

E. Monthly loading of more-dilute constituents

25

20

15

10

0

5

25

15

10

0

25

15

10

0

 Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

EXPLANATION

Chloride (Cl−)

Sodium (Na+

)

More-dilute constituents

Chloride and sodium

Hydrogen (H+

)

Ammonium (NH4

+

)

Potassium (K+

)

Bicarbonate (HCO3

−

)

Magnesium (Mg2+)

Sulfate (SO4

2−)

Calcium (Ca2+)

Nitrate (NO3

−

)

Rainfall

Figure 2. Aggregated monthly rainfall, monthly mean concentrations, and monthly mean loading for chemical constituents in rain 

sampled at the El Verde National Atmospheric Deposition Program site, eastern Puerto Rico. Light gray lines, constituents with 

dominantly seawater sources; dark grey lines, constituents strongly influenced by desert-dust inputs; black lines, constituents

dominantly influenced by temperate-contamination sources.
98 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 7. Storms that substantially affected the Water, Energy, and Biogeochemical Budgets program rivers during the period of study, 

1991–2005.

[NADP, National Atmospheric Deposition Program; Can, Canóvanas; Cay, Cayaguás; Gua, Guabá; Ica, Icacos; Mam, Mameyes; n/c, normal chloride; 

n/g, no gap; n/s, not studied]

Date Storm1 Rivers affected 

by the storm

Rivers with 

high-chloride events2 NADP gaps

1991–11–10 Event Ica, Mam n/s Gap

1992–05–02 Event Mam n/c n/g

1992–07–17 Event Gua n/s n/g

1993–05–01 Event Gua n/c n/g

1994–02–19

1994–06–03

Event

Minor event

Gua, Ica

Can

n/c

Can

n/g

n/g

1995–02–26 Event Ica n/c n/g

1995–09–06 Hurricane Luis Can Can Gap

1996–01–14 Event Gua, Ica n/c n/g

1996–07–09 Hurricane Bertha Can, Gua, Ica, Mam Can, Gua, Ica, Mam Gap

1996–09–10 Hurricane Hortense Can, Cay, Gua, Ica, Mam n/c n/g

1997–09–25 Event Mam n/c n/g

1997–10–14 Event Gua, Ica, Cay n/c n/g

1998–08–25 Event Cay n/c n/g

1998–09–22 Hurricane Georges Can, Cay, Gua, Ica, Mam Cay, Gua, Ica, Mam Gap

1998–10–14 Event Gua, Ica Gua, Ica n/g

1998–10–22 Event Gua, Cay Can n/g

1998–12–02 Event Gua, Ica n/c Gap

1999–11–17 Hurricane Lenny Gua, Ica Can Gap

1999–12–05 Event Gua, Ica, Mam n/c n/g

2000–08–22 Hurricane Debby Gua Can n/g

2001–08–23 Tropical Storm Dean Cay, Gua, Ica n/c n/g

2001–11–08 Cold front Cay, Gua, Ica, Mam n/c n/g

2001–12–23 Event Gua, Ica, Mam Can n/g

2002–05–30 Event Gua, Ica n/c n/g

2003–04–18 Upper-level trough Gua, Ica, Mam n/c n/g

2003–11–13 Tropical wave Can, Cay, Gua, Ica, Mam n/c n/g

2004–09–16 Tropical Storm Jeanne Can, Cay, Gua, Ica, Mam n/s Gap

2004–11–13 Upper-level trough Gua, Ica, Mam n/c n/g

2005–10–03 Event Cay n/s n/g

2005–10–11 Upper-level low Cay, Gua, Ica n/s Gap

2005–10–23 Event Cay n/s n/g 1

Storms that, on the basis of runoff, could have generated landslides in at least one watershed (appendix 1, this volume), except for a minor event on the 

Río Canóvanas, which was the only high-chloride event sampled that did not also have substantial runoff.

2

As identified in U.S. Geological Survey Water, Energy, and Biogeochemical Budgets program data (appendix 1, this volume).

The source of the seasalt is probably wind-generated 

spray. In a hurricane, mechanical energy, generated by extracting heat from the ocean, is balanced by frictional dissipation of 

wind energy, mostly at the sea-air interface (Emanuel, 1991). 

The Beaufort Scale, developed in 1805, represents a wellestablished description of sea-surface conditions for a wide 

range of wind speeds (National Oceanic and Atmospheric 

Administration, 2010a). For violent storms (winds of 56 to 

63 knots or 104 to 117 km h−1), the sea surface is described as 

“completely covered with long white patches of foam lying 

along the direction of the wind. Everywhere the edges of the 

wave crests are blown into froth. Visibility affected.” For 

hurricane-force winds (at least 64 knots or 117 km h−1) “the 

air is filled with foam and spray. Sea completely white with 

driving spray; visibility very seriously affected.” Some of this 

airborne spray and foam must be incorporated into the storm. 

The amount of Cl− deposited by Hurricane Georges over the 

entire Mameyes Basin is equivalent to 0.3 mm of seawater.

A primary reason that this phenomenon has not been 

described before is the unique approached used in this 

research that focused on event sampling (appendix 1). In 

this study, 332 samples that were collected at runoff rates 

of greater than 20 millimeters per hour (mm h−1) were later 

analyzed for suspended sediment and a comprehensive 

suite of solutes. Globally, no streams have been sampled for 

later chemical analysis during hurricanes at runoff rates of 
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 99

greater than 20 mm h−1; two samples were collected at about 

16 mm h–1 by the U.S. Geological Survey in Puerto Rico in 

1970 during a tropical depression (Haire, 1972). Goldsmith 

and others (2008) collected samples during a Typhoon Mindulle at runoff rates of as much as 7.3 mm h−1, and Hicks and 

others collected samples during Cyclone Bola at runoff rates 

of as much as 4.8 mm h−1.

To show the consequences of missing samples, the NADP 

monthly digests (fig. 5) were adjusted to include Hurricane 

Georges in the monthly NADP record (fig. 6). The NADP 

monthly data set constructs September 1998 by using only 

the first 2 weeks of that month. NADP also reports a total 

of 467 mm rain for this month; 353 mm was measured in 

the first 2 weeks. The National Oceanic and Atmospheric 

Administration rain gage at El Verde reported 730 mm for the 

same month (National Oceanic and Atmospheric Administration, 2008). It is not obvious why the NADP total is missing 263 mm. The difference between National Oceanic and 

Atmospheric Administration monthly and NADP 2-week 

totals is 377 mm. This 377 mm was assigned a Cl− concentration of 455 μmol L−1, the average concentration of Cl−

 in the 

Mameyes River during Georges, along with other common 

seasalt constituents (Na+

, K+, Ca2+, Mg2+, SO4

2−) in seasalt 

ratios relative to Cl−. A revised monthly average for September 

1998 was then reconstructed for the NADP data set (fig. 6). 

The reconstructed NADP Cl− time series (fig. 6) demonstrates a prominent spike in September 1998. The only other 

comparable large spike recorded by the NADP occurred in 

January 2005. The January 2005 spike appears to record a 

series of smaller storms associated with cold fronts. Stream 

water was not sampled by the WEBB Project during these 

storms. This examination of Hurricanes Hortense and Georges 

demonstrates that the NADP may undersample Cl− and other 

seasalt-related ions. This undersampling would be hard to 

reconstruct. Not only are there other high-Cl− events that were 

not sampled by NADP (table 7), but two big storms were 

not sampled by either NADP or WEBB Project, and several 

storms missed by NADP had high Cl− in some WEBB watersheds and not others (table 7). In the remaining discussion, the 

above correction for Hurricane Georges, which is the largest, 

is retained.

Estimating Source Strengths

Partitioning constituents among marine, temperatecontamination, and desert sources requires several steps. 

First, the marine component of all relevant constituents is 

calculated. NO3

−

 and NH4

+

 are assumed to have zero marine 

contributions because of their low concentration in surface sea 

water. Second, nonmarine Ca2+ is equated with the desert-dust 

contribution. Finally, all NO3

−

 and NH4

+

 were assumed to 

come from the temperate (contamination) source, because of 

the lack of plausible contributions from any other source.

All chloride is initially assumed to be derived from 

seasalt, and the chloride concentration is used to predict the 

marine contribution to all other ions. The seasalt contribution was then subtracted from all ions, and seasalt-corrected 

concentrations were obtained. Following Stallard and 

Edmond (1981), the concentrations that result from this 

calculation are designated with a superscript (*): for example, 

Ca*=Ca2+−Cl− • (seasalt Ca:Cl). Normally, predicted measured 

sodium exceeded seasalt sodium, but when the reverse was 

true (which seldom happens and is most likely due to statistical noise or analytical error), sodium was used to calculate the 

seasalt contribution and a nonseasalt chloride component was 

calculated, to avoid negative concentrations. Elevated concentrations of nonseasalt Cl* could indicate volcanic input, as is 

seen in the rain of La Selva, Costa Rica (Eklund and others, 

1997), or long-distance transport and photochemical processing (Stallard and Edmond, 1981). The only recent eruption 

near Puerto Rico was Soufrière Hills Volcano from 1996 to 

1997 (Heartsill-Scalley and others, 2007), and no Cl* anomalies are noted during those times. For the NADP data set, magnesium concentrations seem slightly low and are consistently 

present in less-than-seasalt proportions, an unlikely situation. 

When a calculated Mg concentration (Mg*) is negative, again 

probably from analytical error, it is set to zero. 

hapter_D_figures\BW_figures\BWfigure_03D.ai

Chloride

Sodium

Magnesium

Sulfate

Calcium

Potassium

Alkalinity

Silica

Nitrate

Phosphate

Constituent concentration, as

mole ratio to chloride

0

1

2

3

4

Constituent

EXPLANATION

Seawater

Hurricane

Base flow

Figure 3. The three samples with the highest chloride

concentrations measured for the Río Mameyes, eastern Puerto 

Rico, during Hurricanes Georges compared with seawater and 

the first base-flow sample following the hurricane, collected 

October 7, 1998. Chemical constituent concentrations are 

normalized to total chloride and are ordered by decreasing 

seawater concentration. The stormwater constituent proportions 

are close to those in seawater and are only slightly elevated in 

calcium, the cation most abundant in base flow.
100 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_04D

A. Runoff rate during Hurricane Hortense, September 10, 1996

40

20

0

60

Runoff rate, in millimeters per hour Runoff rate, in millimeters per hour Chloride, in millimoles per liter Chloride, in millimoles per liter

0 2 4 6 8 10 12 14 16 18 20 22 24

Hours into the hurricane

B. Chloride during Hurricane Hortense, September 10, 1996

C. Runoff rate during Hurricane Georges, September 21, 1998

D. Chloride during Hurricane Georges, September 21, 1998

1,500

1,000

500

0

2,000

0

60

1,500

1,000

500

0

2,000

EXPLANATION

Chloride trend—Interpolated from measured

 values, including routine samples collected

 several days before and after storm 

Chloride—Measured

Runoff

40

20

Figure 4. Time series of runoff rate and chloride concentrations measured for the Río Mameyes during Hurricanes 

Hortense and Georges. (Stallard and Murphy, 2012).
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 101

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_05D.ai

400

200

0

75

50

25

0

Component concentrations, in microequivalents per liter

A. Marine aerosols anion charge

B. Temperate pollution acidity

D. Total nitrogen

C. Desert dust alkalinity

E. Acidity

100

300

20

15

10

5

0

50

30

20

0

10

40

60

40

0

–40

–20

20

 Year

1984 1988 1992 1996 2000 2004 2008

EXPLANATION

Monthly average

Annual average

Zero line

Figure 5. Time series of concentration estimates for the three primary components that contribute to rain chemistry in El Verde, 

Puerto Rico: A, marine aerosols, B, temperate contamination, and C, desert dust. D, total inorganic nitrogen; E, acidity. Negative 

acidity values indicate alkaline rain.
102 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Geochemically, sulfate ion is associated with seasalt, 

marine-gas, temperate-contamination, and desert sources. The 

strong intercorrelations of SO4

2− with both the seasalt (Na+

, K+, 

Mg2+, Cl−) and temperate-contamination (NH4

+

, NO3

−

) groups 

in tables 5 and 6 support this association. Accordingly, distinguishing among the sources of SO4

2− requires several steps. The 

marine source for SO4

2− can be separated into seasalt sulfate 

(SO4

2−

seasalt), for which a seasalt SO4

2−:Cl−

 ratio can be assumed, 

and gas emissions (SO4

2−

marine gas). The SO4

2− signatures of the 

four sources were determined for the weekly data using multiple 

linear regressions (Bevington and Robinson, 2003). Three 

properties were used as dependent variables: seasalt chloride 

(Clseasalt), nitrate (NO3

−

), and Ca*. In addition, the use of an additive constant, equivalent to assuming a “global” background 

SO4

2− source, was tested. Most of the variance in SO42− is 

accounted for by Clseasalt and NO3

−

, without an additive constant. 

Three additional regressions were compared to evaluate whether 

desert or background sources of SO4

2− might be important. 

Inclusion of the desert component, Ca*, was significant at the 

76-percent level (f–test comparing a regression using Clseasalt and 

NO3

−

, with one using Clseasalt, NO3

−

, and Ca*). The inclusion of a 

background constant was not significant. Thus, the best model is

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_06D.ai Chloride loading, in millimoles per square meter Average chloride, in micromoles per liter Monthly rainfall, in millimeters

B. Monthly and annual average chloride concentration

A. Monthly rainfall showing correction for Hurricane Georges

C. Monthly and annual chloride loading

800

600

400

200

0

400

200

100

0

300

300

200

100

0

 Year

1984 1988 1992 1996 2000 2004 2008

EXPLANATION

Monthly average

Annual average

Monthly average including

 Hurricane Georges

Annual average including

 Hurricane Georges

Figure 6. A, Monthly and annual time series of rainfall; B, chloride concentration; and C, chloride loading from the National 

Atmospheric Deposition Program data set for El Verde, Puerto Rico, showing effects of including data from Hurricane Georges. 

(Because of sample loss, rainfall amounts and rainfall chemistry were missing from the El Verde dataset for Hurricane Georges. 

Runoff rates and chemical samples for the Río Mameyes were used to estimate the missing data, as indicated in this graph.)
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 103

SO4

2−

=0.060•Clseasalt+0.742•NO3

−

+0.229•Ca* 

(r2=0.778; n=818). (5)

Rather than predicting SO4

2− from each source using each 

term in equation 5, SO4

2−

seasalt was assumed to always be present and SO4

* was calculated as

SO4

2−

seasalt=0.052•Clseasalt (6)

SO4*=SO4

2−–SO42−

seasalt=0.008•Clseasalt+0.742•NO3

−

+

0.229•Ca*. (7)

Then SO4* was partitioned among the three sources 

according the proportions below:

SO4

2−

marine gas:SO4

2−

temperate contamination:SO4

2−

desert dust= 

(0.008•Clseasalt):(0.742•NO3

−

):(0.229•Ca*). (8)

The WEBB Project also collected precipitation between 

1991 and 1995 (36 samples from a wet-dry collector in the 

Bisley area, 42 samples from a wet-dry collector near the 

Icacos gage, and 29 samples from a continuous collector in the 

central Icacos watershed). When WEBB precipitation samples 

are added to the regression, the best model was

SO4

2−

=0.062•Clseasalt+0.677•NO3

−

+0.247•Ca* 

(r2=0.766; n=925). (9)

In this regression, somewhat more of the SO4

2− is attributed 

to a marine gas source, and somewhat less to contamination.

For the entire El Verde NADP record, weekly concentrations of marine, temperate-pollution, and desert-dust components were calculated, as were volume-weighted monthly 

and annual averages of all rain samples (tables 8–13). Acidity 

was used to calculate H+ and HCO3

−

, by using equations 2–4, 

and pH was calculated from H+. Because alkaline desert dust 

reacts with acid components, thereby neutralizing some H+, 

Table 8. Averaged monthly rain chemistry from the National Atmospheric Deposition Program site at El Verde, Puerto Rico, with 

contributions of various sources.

[μmol L−1, micromoles per liter; μeq L−1, microequivalents per liter; --, not applicable]

Component H+

(μmol L–1)

Na+

(μmol L–1)

K+

(μmol L–1)

Mg2+

(μmol L–1)

Ca2+

(μmol L–1)

NH4

+

(μmol L–1)

Acidity

(μeq L–1)

Cl–

(μeq L–1)

SO4

2–

(μmol L–1)

NO3

–

(μmol L–1)

Average 7.3 71.0 1.8 7.9 3.8 1.7 6.4 82.5 8.7 4.3

Marine

Seasalt 2.3 68.7 1.4 8.2 1.5 0 –0.5 80.6 4.2 0

Sulfur (sea) gases 3.4 0 0 0 0 0 1.5 0 0.8 0

Temperate 9.7 0 0 0 0 1.7 9.0 0 3.2 4.3

Desert 1.4 0 0 0 2.2 0 –3.3 0 0.6 0

Residual1 -- 2.3 0.4 –0.3 0.0 0 –0.3 1.9 0 0 1

All constituents not accounted for by other components; H+ is not included because it is not a conservative constituent.

Table 9. Derived properties from monthly data of the National Atmospheric Deposition Program site at El Verde, Puerto 

Rico, with contributions of various sources.

[H+ is reactive and is calculated separately for each source, the average, and the residual. Tz±, total ionic charge; μeq L−1, microequivalents per 

liter; μeq L−1 yr−1, microequivalents per liter per year; -- not applicable] 

Component pH Tz±

(μeq L–1) Percent Slope1(μeq L–1 yr–1)

Slope,

95 percent error

Probability of trend2

(percent)

Average 5.1 105.1 90 1.22 0.95 76

Marine 5.6 95.2 83 0.94 0.92 68

Seasalt 5.6 91.8 79 -- -- --

Sulfur (sea) gases 5.5 3.4 3 -- -- --

Temperate 5.0 11.4 10 0.33 0.14 97

Desert 5.9 5.8 5 –0.05 0.07 60

Residual3 5.6 4.5 4 –0.00 -- 50 1

Slope of the regression concentration=slope•year+intercept.

2

Probability that the regression slope is non-zero.

3

All ionic charge not accounted for by other components.
104 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 10. Rainfall-weighted monthly average concentration of total ionic charge and acidity at El Verde, Puerto Rico, derived from 

averaged monthly data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; μeq L−1, microequivalents per liter; Tz±, total ionic charge]

Month Rainfall

(mm)

Acidity (μeq L–1) Tz±

 (μeq L–1)

Total Marine Temperate Desert Total Marine Temperate Desert

January 278 8.3 1.3 8.3 –1.3 186.3 172.0 12.6 1.7

February 165 8.5 1.0 9.1 –1.6 154.4 136.4 13.3 2.1

March 193 8.4 0.9 9.9 –2.3 121.4 108.0 10.0 3.0

April 223 13.4 0.7 14.8 –2.1 94.5 75.0 16.5 3.0

May 280 9.9 0.6 11.8 –2.5 84.2 67.9 12.9 3.5

June 226 0.4 0.6 7.9 –8.0 99.3 79.6 9.1 10.3

July 306 –0.5 0.8 6.1 –7.5 101.1 82.7 6.9 10.3

August 325 3.6 0.6 8.8 –5.7 74.8 59.4 7.5 7.7

September 287 5.2 0.8 8.7 –4.3 105.3 91.7 8.0 5.6

October 240 7.0 0.4 8.5 –1.9 60.9 49.0 8.4 2.6

November 343 6.5 0.8 6.5 –0.8 89.5 81.5 6.8 1.0

December 246 9.4 1.1 9.0 –0.7 128.2 118.8 8.2 0.9

Annual 3,113 6.4 0.8 8.9 –3.3 106.1 91.5 9.7 4.4

Table 11. Rainfall-weighted average annual concentration of total ionic charge and acidity at El Verde, Puerto Rico, derived from 

averaged annual data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; μeq L–1, microequivalents per liter; Tz±, total ionic charge]

Year Rainfall

(mm)

Acidity (μeq L–1) Tz±

 (μeq L–1)

Total Marine Temperate Desert Total Marine Temperate Desert

1985 3,291 2.6 1.2 5.2 –3.8 91.6 83.6 2.3 5.7

1986 3,345 7.4 1.1 9.5 –3.2 83.9 74.6 4.3 4.9

1987 3,892 5.7 0.6 9.0 –3.9 74.0 61.3 7.4 5.4

1988 3,833 4.1 1.1 6.5 –3.6 86.5 76.5 4.6 5.5

19891 2,675 7.5 0.7 10.1 –3.4 103.5 89.2 10.0 4.3

1991 1,178 5.9 0.5 11.5 –6.0 103.9 87.0 9.5 7.5

1992 2,930 4.2 0.6 7.0 –3.5 101.7 85.6 11.7 4.4

1993 2,888 6.8 0.4 9.0 –2.7 94.8 81.3 10.2 3.3

1994 2,314 2.3 0.5 5.8 –4.0 101.7 88.0 8.7 5.0

1995 2,624 2.7 0.5 5.9 –3.6 107.0 91.8 10.7 4.5

1996 3,568 2.4 0.6 4.3 –2.5 107.4 94.4 9.9 3.1

1997 2,734 11.1 0.7 14.1 –3.8 90.1 75.3 9.8 5.1

1998 4,400 3.8 0.8 5.7 –2.7 117.6 106.5 7.7 3.5

1999 3,153 –1.1 0.4 2.5 –4.0 94.0 79.0 10.0 5.0

2000 2,466 6.0 0.5 10.3 –4.8 98.2 82.7 9.6 5.9

2001 2,825 8.5 0.5 12.0 –4.0 101.7 85.8 10.9 5.0

2002 2,772 11.9 0.5 14.2 –2.8 80.2 67.1 9.4 3.6

2003 3,266 13.2 0.7 15.8 –3.3 92.0 75.3 12.2 4.5

2004 3,794 8.0 0.6 11.0 –3.6 115.0 100.2 10.2 4.6

2005 3,155 8.8 1.2 10.4 –2.8 145.0 126.1 15.1 3.8

2006 3,104 13.5 1.0 16.0 –3.5 113.0 96.1 12.1 4.8 1

Samples were not collected between 12 September 1989 (the day that Hurricane Hugo hit) and 4 June 1991. Rainfall totals in 1989 and 1991 are reduced 

accordingly.
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 105

Table 12. Rainfall-weighted monthly loadings of total ionic charge and acidity at El Verde, Puerto Rico, derived from averaged monthly 

data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; meq m−2, milliequivalents per meter squared; Tz±, total ionic charge]

Month Rainfall

(mm)

Acidity (meq m–2) Tz±

 (meq m–2)

Total Marine Temperate Desert Total Marine Temperate Desert

January 278 2.31 0.36 2.30 –0.35 51.87 47.89 3.51 0.47

February 165 1.40 0.17 1.50 –0.27 25.48 22.52 2.20 0.35

March 193 1.62 0.17 1.90 –0.45 23.37 20.79 1.93 0.57

April 223 3.00 0.17 3.30 –0.46 21.09 16.74 3.68 0.66

May 280 2.79 0.18 3.31 –0.71 23.57 19.00 3.60 0.97

June 226 0.10 0.14 1.78 –1.82 22.48 18.03 2.06 2.32

July 306 –0.16 0.26 1.88 –2.29 30.88 25.27 2.10 3.14

August 325 1.17 0.18 2.85 –1.86 24.27 19.29 2.45 2.48

September 287 1.48 0.22 2.49 –1.22 30.21 26.32 2.29 1.60

October 240 1.69 0.10 2.04 –0.46 14.65 11.77 2.02 0.61

November 343 2.25 0.27 2.24 –0.26 30.68 27.94 2.32 0.35

December 246 2.31 0.28 2.21 –0.17 31.58 29.27 2.01 0.23

Annual 3,113 19.9 2.5 27.8 –10.3 330.1 284.8 30.2 13.8

Table 13. Rainfall-weighted average loadings of total ionic charge and acidity at El Verde, Puerto Rico, derived from averaged annual 

data of the National Atmospheric Deposition Program for 1983 to 2005.

[mm, millimeter; meq m−2, milliequivalents per meter squared; Tz±, total ionic charge]

Year Rainfall

(mm)

Acidity (meq m–2) Tz±

 (meq m–2)

Total Marine Temperate Desert Total Marine Temperate Desert

1985 3,291 8.7 3.9 17.2 –12.5 301.4 275.0 7.5 18.9

1986 3,345 24.9 3.7 31.9 –10.7 280.5 249.5 14.5 16.5

1987 3,892 22.3 2.4 35.2 –15.3 288.0 238.4 28.6 21.0

1988 3,833 15.8 4.4 25.1 –13.7 331.7 293.1 17.5 21.1

19891 2,675 19.9 1.8 27.1 –9.0 276.9 238.6 26.7 11.6

1991 1,178 7.0 0.6 13.5 –7.1 122.4 102.5 11.1 8.8

1992 2,930 12.3 1.8 20.6 –10.1 297.9 250.7 34.2 13.0

1993 2,888 19.6 1.2 26.1 –7.7 273.7 234.7 29.5 9.4

1994 2,314 5.3 1.2 13.4 –9.3 235.3 203.6 20.2 11.5

1995 2,624 7.2 1.2 15.4 –9.5 280.7 240.8 28.2 11.7

1996 3,568 8.5 2.1 15.3 –8.8 383.3 336.8 35.4 11.1

1997 2,734 30.3 2.0 38.6 –10.3 246.4 205.7 26.9 13.8

1998 4,400 16.9 3.5 25.2 –11.8 517.5 468.5 33.7 15.3

1999 3,153 –3.4 1.4 7.9 –12.7 296.4 249.1 31.5 15.7

2000 2,466 14.7 1.2 25.3 –11.8 242.1 203.8 23.7 14.6

2001 2,825 24.0 1.4 34.0 –11.4 287.3 242.4 30.9 14.0

2002 2,772 33.0 1.3 39.5 –7.8 222.3 186.1 26.2 9.9

2003 3,266 43.1 2.4 51.5 –10.9 300.5 245.9 39.8 14.7

2004 3,794 30.3 2.4 41.7 –13.8 436.2 380.1 38.7 17.4

2005 3,155 27.6 3.7 32.8 –8.9 457.4 397.9 47.6 11.9

2006 3,104 41.9 3.1 49.6 –10.9 350.8 298.3 37.6 14.9 1

Samples were not collected from 12 September 1989, the week that Hurricane Hugo hit, until 4 June 1991. Rainfall totals in 1989 and 1991 are reduced 

accordingly.
106 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_07D.ai

300

100

0

25

20

5

0

Component loading, in milliequivalents per square meter

A. Marine aerosols anion charge

B. Temperate contamination acidity

D. Total nitrogen

C. Desert dust alkalinity

E. Acidity

200

10

15

20

15

10

5

0

50

30

20

0

60

40

0

−40

−20

20

 Year

1984 1988 1992 1996 2000 2004 2008

EXPLANATION

Monthly average

Annual average

Zero line

Figure 7. Time series of monthly and annual loading estimates for the three primary components that contribute to rain chemistry 

in El Verde, Puerto Rico: A, marine aerosols, B, temperate contamination, and C, desert dust. D, total inorganic nitrogen; E, acidity. 

Negative acidity values indicate alkaline rain.
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 107

total ion charge in rainwater is less than the charge contributed 

by each component (table 9). Given this caveat, the fractions 

of total charge attributable to these sources were as follows: 

marine=83 percent; temperate=10 percent; desert=5 percent; 

not attributed in this calculation=4 percent.

Of the nonreactive ions, K+ has the greatest residual concentration when expressed as a fraction of total input, about 

20 percent (table 9). This concentration is followed by acidity 

at 4 percent. This large residual indicates an additional source 

of K+ beyond the three examined here. Of the constituents analyzed by both NADP and by the study of throughfall chemistry 

of Heartsill-Scalley and others (2007), throughfall was by far 

most strongly enriched in K+, about 10 times the contribution of rainfall. Potassium was followed by Ca2+ and SO4

2−, 

which are each about 1.5 times more abundant in throughfall. 

Accordingly, the most likely source of the residual K+ is windblown throughfall or traces of plant matter contaminating the 

collector, both of which are locally derived. 

Seasonality and Trends in Inputs and 

Sources

The variability in the time series of weekly component 

concentrations (fig. 5) reflects various types of rainstorms and 

different air trajectories bringing air from afar into the storm. 

The 2-year, midrecord gap (1989–1991) was followed by 

more weeks with especially high concentrations of temperate 

contaminants, per year, than before. This abundance of especially high concentrations is reflected in the low total cation 

charge for 1985 to 1988 compared with later years (table 11). 

Although each component contains high-concentration spikes, 

there are too few to establish whether they are real or an 

artifact. When samples are averaged on a monthly basis across 

all years, all components show seasonality (figs. 5, 7–10). The 

desert-dust input, which is profoundly seasonal, appears as a 

major peak in concentration (figs. 5, 8) and loading (figs. 7, 9), 

centered on July when trade winds are strongest. The peak seasalt (marine-source) concentration (fig. 8) and loading (input) 

(fig. 9) is in January. Seasalt makes the smallest fractional 

contribution from April to July (fig. 10). The peak of hurricane season in Puerto Rico is September (table 7), but it is not 

reflected in a seasalt maximum. Low-chloride storms, such as 

Hurricane Hortense (fig. 4), presumably dominate, whereas 

high-chloride storms, such as Georges (fig. 4), are probably 

rarer and perhaps substantially undersampled. Temperate 

contaminants peak in January and in April and May (figs. 8, 9) 

under the influence of cold fronts from North America. During 

June to August, when the trade winds are strongest and most 

sustained, a large volume of contaminants may also be contributed by Europe and Africa.

The only component that shows a well-established longterm trend is temperate contamination. Stallard (2001) and 

Ortiz-Zayas and others (2006) note an increase in annual totalnitrogen concentrations in the NADP data set. To determine 

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_08D.ai

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Average source-ion concentration, in

microequivalents per liter 150

100

50

0

200

250

Month

EXPLANATION

Temperate contamination

Marine aerosols

Desert dust

Figure 8. Aggregated monthly concentration estimates for the

three primary components that contribute to rain chemistry in El 

Verde, Puerto Rico—marine aerosols, temperate contamination, 

and desert dust.

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Source-ion loading, in milliequivalents

per square meter

25

0

50

75

Month

EXPLANATION

Temperate contamination

Marine aerosols

Desert dust

Figure 9. Aggregated monthly loading estimates for the three 

primary components that contribute to rain chemistry in El Verde, 

Puerto Rico—marine aerosols, temperate contamination, and 

desert dust.
108 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

significance of trends for the data in this report, the slopes of 

linear regressions of mean annual source concentrations compared to time were tested against a zero slope using a Student’s 

t–test at a 95 percent confidence level (Stallard, 2001; Bevington and Robinson, 2003). Since sample collection started 

in 1985, there has been a significant and systematic increase 

in the mean-annual concentrations of ions presumably coming 

from the temperate contamination source (figs. 5, 7, table 9). 

Through time, the ratios of nitrate, ammonia, and nonmarine 

sulfate in rain from El Verde appear to have fairly characteristic proportions (in moles, NO3

−

:NH4

+

:SO4

2−=1: 0.40: 0.74). 

Neither marine nor desert sources show significant trends.

Despite the increase in acid-rain components from temperate contamination, on the basis of a t–test, acidity shows no 

significant trend in for the period of record. Trends in acidity 

that are small and variable could be obscured by interactions 

among the three components that effectively introduce noise 

into the estimates of acidity (figs. 11, 12); the desert-dust input 

neutralizes some of the acid, and the contributions of marinesource sulfuric acid derived from algae are small and variable. Because acidity is a calculated parameter, the lack of a 

trend may also be confounded by the anomalous Na+

 analyses 

mentioned earlier. The greatest acidity, both as a concentration 

and a loading, develops during April and May, reflecting substantial temperate contamination. The least acidity is in June 

and July, reflecting reduced contamination inputs and strong 

loadings of desert dust.

er_D_figures\BW_figures\BWfigure_10D.ai

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Fractional contribution of

three sources

0.4

0

Month

EXPLANATION

Temperate contamination

Marine aerosols

Desert dust

1.0

0.8

0.6

0.2

Figure 10. Fractional contribution for the three primary 

components that contribute to rain chemistry in El Verde, Puerto 

Rico—marine aerosols, temperate contamination, and desert dust.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_D_figures\BW_figures\BWfigure_11D.ai

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Acidity source concentrations, in

microequivalents per liter 10

5

–5

0

–10

15

20

Month

EXPLANATION

Marine aerosols

Temperate contamination

Desert dust

Total

Acidity source loading, in milliequivalents

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

per square meter

4

0

1

2

3

–3

–2

–1

Month

EXPLANATION

Marine aerosols

Temperate contamination

Desert dust

Total

Figure 11. Monthly total acidity concentrations compared to 

acidity concentration estimates for the three primary components 

that contribute to rain chemistry in El Verde, Puerto Rico—marine, 

aerosols, temperate contamination, and desert dust.

Figure 12. Monthly total acidity loadings compared to acidity 

loading estimates for the three primary components that 

contribute to rain chemistry in El Verde, Puerto Rico—marine, 

aerosols temperate contamination, and desert dust.
Atmospheric Inputs to Watersheds of the Luquillo Mountains in Eastern Puerto Rico 109

Conclusions

Three major sources control the composition of rain in 

eastern Puerto Rico. In order of importance, these sources are 

marine salts, temperate contamination, and desert dust.

Marine salts, which produce roughly 83 percent of the 

ionic charge, are a source of weak acidity. Peak inputs are 

in January, when weather in Puerto Rico is influenced by 

fronts and storms of Northern Hemisphere winter. Nine large 

storms have been associated with exceptionally high chloride 

concentrations in eastern Puerto Rico stream waters. Five of 

these storms, including the two largest, were missed in the 

weekly NADP sampling but not the WEBB Project sampling. Of eight hurricanes and tropical storms that affected 

the region, all were sampled by WEBB, but only three by 

NADP; three storms were sampled by neither project, and 

one high-chloride event, a front, was sampled by NADP but 

not WEBB. The NADP also did not sample Hurricane Hugo 

(category 3, winds of 178–209 km h−1) in 1989, which was 

larger than Hurricane Georges. Accordingly, the marine input 

based exclusively on NADP data is underestimated, perhaps 

substantially, as is indicated by the example of Hurricane 

Georges explored in this report.

The temperate-contamination source includes nitrate, 

ammonia, and sulfate derived from anthropogenic and 

natural sources. Peak deposition is during January, April, 

and May, months in which storms and cold fronts from the 

north strongly influence local weather. Trade winds presumably bring contamination from Europe and Africa during 

the summer months. This source is the only component 

that is obviously increasing through time, consistent with 

it being a contamination source. The lesser loadings of the 

contamination component in the first 4 years of the record 

correspond with fewer samples with extreme loadings or 

concentrations for the same period. This trend could reflect 

greater incursion of contaminated air masses in subsequent 

years as opposed to an increase in background contamination. Temperate contamination is a strong source of acidity 

in rain of eastern Puerto Rico and is the chief source of 

acidity in the rain; by itself it would produce rain with an 

average pH of 5.0. Combined with other components, rain 

has an average pH of 5.1 (table 9), and based on application of equations 1–4, without the temperate-contamination 

component, rain would have an average pH of 5.6. The 

trend of increasingly acid rain, reflected by the increasing number of especially acid samples, may prove to be 

deleterious (figs. 6, 7) and by itself would justify continued 

long-term monitoring at this site.

The desert-dust source is strongly seasonal, peaking 

in June and July, during times of maximum dust transport 

from the Sahara and sub-Saharan regions. This dust usually 

contributes enough alkalinity to neutralize the acidity in June 

and July rains.

Acknowledgments

This chapter was greatly improved by suggestions 

made by Jamie B. Shanley and Martha A. Scholl of the U.S. 

Geological Survey.

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Water Quality and Mass Transport in Four 

Watersheds in Eastern Puerto Rico

By Robert F. Stallard and Sheila F. Murphy

Chapter E of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–E

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.......................................................................................................................................................117

Introduction.................................................................................................................................................117

Factors Affecting Water Chemistry of Tropical Rivers ...............................................................119

Study Design and Objectives..........................................................................................................121

Methods Used to Collect and Interpret Data ........................................................................................122

Sample Collection and Field Measurements ...............................................................................122

Stream Discharge.............................................................................................................................122

Analytical Procedures......................................................................................................................123

Discharge, Load, Runoff, and Yield................................................................................................123

Water Quality of Eastern Puerto Rico WEBB Project Watersheds...................................................125

Runoff Percentile Classes, Concentration, and Yield .................................................................125

Water-Quality Constituents.............................................................................................................130

Field Measurements................................................................................................................130

Silica...........................................................................................................................................130

Chloride......................................................................................................................................133

Calcium, Magnesium, and Sodium .......................................................................................135

Dissolved Organic Carbon......................................................................................................135

The Nutrients: Nitrate, Ammonia, Phosphate, and Potassium.........................................137

Sulfate........................................................................................................................................139

Alkalinity, Carbon Dioxide, and Calcite Saturation ............................................................139

Suspended Sediment ..............................................................................................................141

Conclusions.................................................................................................................................................143

Acknowledgements...................................................................................................................................147

References..................................................................................................................................................147

Figures

1. Map of Puerto Rico and study watersheds..........................................................................118

2–12. Diagrams showing the following:

 2. Functional classification of tropical forest watersheds according to 

prevailing hydrological flow paths, based on measured saturated hydraulic 

conductivities (Ksat)............................................................................................................121

 3. Temperature, pH, conductivity, and dissolved oxygen in the eastern 

Puerto Rico WEBB rivers, 1991–2005.............................................................................131

 4. Runoff rate–concentration graphs for dissolved silica...............................................133

 5. Runoff rate–concentration graphs for chloride ...........................................................135

 6. Runoff rate–concentration graphs for sodium, magnesium, and calcium ions......137

 7. Runoff rate–concentration graphs for dissolved organic carbon.............................139

 8. Runoff rate–concentration graphs for potassium ion .................................................141

 9. Runoff rate–concentration graphs for nitrate, ammonium, and phosphate ions ...143

 10. Runoff rate–concentration graphs for sulfate, and sulfate:chloride ratio...............144
 11. Alkalinity, carbon dioxide saturation, and calcite saturation in the eastern 

Puerto Rico WEBB rivers, 1991–2005.............................................................................144

 12. Runoff rate–concentration graphs for suspended sediment.....................................146

Tables

1. Average constituent concentrations and yields for the study period (1991–2005)........124

2. Runoff rates for WEBB rivers during the study period (1991–2005) at certain 

thresholds ..................................................................................................................................125

3. Discharge-weighted average concentrations of each percentile class 

estimated by using LOADEST and hourly discharge ..........................................................127

4. Percentage of constituent discharged compared with percentage of water 

discharged (calculated using LOADEST) and comparisons with regressions 

of log (concentration) to log (runoff rate).............................................................................128

Abbreviations Used in This Report

> greater than

— preceding a compound, such as −PO4

3−, indicates likelihood of linkage to 

 a cation

μm micrometer

μS cm−1 microsiemens per centimeter

km kilometer

km2 square kilometer

mm millimeter

mm h−1 millimeters per hour

mm yr−1 millimeters per year

t km−2 yr−1 metric tons per square kilometer per year

DOC dissolved organic carbon

SI saturation index

WEBB Water, Energy, and Biogeochemical Budgets
Conversion Factors

Multiply By To obtain

Length

micrometer (μm) 0.00003937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2

) 0.3861 square mile (mi2)

Flow rate

millimeters per hour (mm h−1) 0.03937 inches per year (in. h−1) 

millimeters per year (mm yr−1) 0.03937 inches per year (in. yr−1) 

Specific Conductance

microsiemens per centimeter (μS cm−1) 1.000 micromho per centimeter (μmho cm−1)

Other

metric tons per square kilometer per 

year (t km−2 yr−1) 2.855 short tons per square mile per year 

(tons mi−2 yr−1)
Water Quality and Mass Transport in Four Watersheds in 

Eastern Puerto Rico

By Robert F. Stallard and Sheila F. Murphy

Abstract

Water quality of four small watersheds in eastern Puerto 

Rico has been monitored since 1991 as part of the U.S. 

Geological Survey’s Water, Energy, and Biogeochemical 

Budgets program. These watersheds represent a montane, 

humid-tropical environment and differ in geology and land 

cover. Two watersheds are located on granitic rocks, and two 

are located on volcaniclastic rock. For each bedrock type, one 

watershed is covered with mature rainforest in the Luquillo 

Mountains, and the other watershed is undergoing reforestation after being affected by agricultural practices typical of 

eastern Puerto Rico. A subwatershed of the Icacos watershed, 

the Guabá, was also monitored to examine scaling effects. The 

water quality of the rivers draining forest, in the Icacos and 

Guabá (granitic watersheds) and Mameyes (a volcaniclastic 

watershed), show little contamination by human activities. 

The water is well oxygenated and has a nearly neutral pH, and 

nutrient concentrations are low. Concentrations of nutrients 

in the disturbed watersheds, the Cayaguás (granitic rock) and 

Canóvanas (volcaniclastic rock), are greater than in the forested watersheds, indicating some inputs from human activities. High in-stream productivity in the Canóvanas watershed 

leads to occasional oxygen and calcite supersaturation and 

carbon dioxide undersaturation. Suspended sediment concentrations in all watersheds are low, except during major storms. 

Most dissolved constituents derived from bedrock weathering 

or atmospheric deposition (including sodium, magnesium, 

calcium, silica, alkalinity, and chloride) decrease in concentration with increasing runoff, reflecting dilution from increased 

proportions of overland or near-surface flow. Strongly bioactive constituents (dissolved organic carbon, potassium, nitrate, 

ammonium ion, and phosphate) commonly display increasing concentration with increasing runoff, regardless of their 

ultimate origin (bedrock or atmosphere). The concentrations 

of many of the bioactive constituents eventually decrease at 

runoff rates greater than 3 to 10 millimeters per hour, presumably reflecting an increased relative contribution from overland flow. Sulfate behaves like the nonbioactive constituents in 

the Canóvanas, Cayaguás, and Mameyes watersheds but like 

a bioactive constituent in the Icacos and Guabá watersheds. 

Storms resulted in several anomalous sample compositions. 

Runoff waters from a number of storms—mostly hurricanes, 

but also other storms—have exceptionally high chloride 

concentrations, presumably resulting from windborne seasalt 

from the ocean, and low nitrate concentrations, reflecting a 

dominance of maritime air masses contributing moisture to the 

storms. High-potassium samples, without high chloride, are 

also associated with some smaller storms that followed Hurricane Georges in 1998; they are likely related to the breakdown 

of fallen vegetation. Finally, occasional low-silica events are 

observed in the Icacos and Guabá watersheds in the years prior 

to Hurricane Georges, but not after; this difference may be 

related to a change in hydrologic flow paths. 

Introduction

The U.S. Geological Survey initiated the Water, Energy, 

and Biogeochemical Budgets (WEBB) program in 1990 to 

study water, energy, and biogeochemical fluxes and interactions throughout a range of temporal and spatial scales (Lins, 

1994). The WEBB site in eastern Puerto Rico represents 

a montane, humid-tropical environment with a bedrock 

geology typical of many parts of the world, and it therefore 

provides an excellent platform for obtaining a better understanding of processes that control the composition of water 

in other tropical environments (Larsen and others, 1993). At 

the Puerto Rico site, four watersheds with different geology 

and land cover can be compared (Murphy and others, 2012). 

Two watersheds are located on coarse-grained granitic rocks 

(Icacos and Cayaguás), and two are located on fine-grained 

volcanic and volcaniclastic rocks (Mameyes and Canóvanas). 

For each bedrock type, one watershed is covered with mature 

forest (Icacos and Mameyes), and the other watershed has 

been affected by agricultural land use typical of eastern 

Puerto Rico (Cayaguás and Canóvanas). In addition, a subwatershed of the Icacos watershed, the Guabá, was studied to 

examine scaling effects.

The warm, wet Luquillo Mountains of eastern Puerto 

Rico are the headwaters of numerous streams and rivers, 

including the Río Icacos and Río Mameyes (fig. 1). Visitors to 

the El Yunque National Forest (which is contiguous with the 

Luquillo Experimental Forest) typically visit the forest during 

calm weather, when rivers are clear and gently flowing over 

their beds. This water has a neutral pH and is well oxygenated, 
118 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Area shown in B

A

B

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_01E.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

LUQUILLO

MOUNTAINS

Mameyes

Icacos

Guabá

L e s s e r

A n

t i l l e s

Río Grande de Loíza Watershed

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Figure 1. Location of Puerto Rico and study watersheds, eastern Puerto Rico.
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 119

and if it is treated to make it potable, it is quite delicious—

presumably being flavored by the chemical breakdown of 

bedrock, a little seasalt, and bit of dissolved organic matter. 

Most visitors avoid the forest during intense storms, such as 

hurricanes and major cold fronts, which turn the rivers into 

muddy, raging torrents that threaten life and infrastructure. 

Sometimes so much rain falls that entire hillslopes of soil 

crash down in landslides. Occasionally, winds are so strong 

that forest vegetation is shredded and large quantities of salt 

spray from the ocean are blown over the forest. Less visible 

contributions to the forest are made by atmospheric deposition; nitrogen in rainfall has almost doubled since it was first 

monitored in 1985 (Stallard, 2012a). A fungus has led to a 

die-off of some endemic frogs, and droughts and pollution 

from North America, Europe, and Africa may have increased 

the virulence of the infection (Stallard, 2001; Burrowes and 

others, 2004).

The Río Grande de Loíza watershed drains the western 

flank of the Luquillo Mountains and the flatter area to the 

west (fig. 1). Deforestation for hundreds of years (Gould and 

others, 2012; Murphy and others, 2012) and the construction 

of roads in steep terrain have increased surface runoff and 

sediment delivery to streams (Larsen and Parks, 1997; Clark 

and Wilcock, 2000). As Puerto Rico has urbanized during the 

past half century, much of the island’s previously denuded 

lands, including the Cayaguás and Canóvanas watersheds, are 

undergoing reforestation; however, stored sediment continues 

to be released to the reservoirs and the ocean during floods 

(Webb and Soler-López, 1997; Larsen and Santiago-Román, 

2001). The water quality of rivers in the recovering landscape 

is affected by runoff from remaining agricultural lands and 

waste from domestic and industrial sources.

Factors Affecting Water Chemistry of Tropical 

Rivers

The processes by which river waters acquire their composition, as related to the movement of water through soil 

and its interaction with the overlying vegetation and underlying bedrock, are an area of active research. Previous studies of large tropical rivers (Gibbs, 1967, 1972; Stallard and 

Edmond, 1981, 1983, 1987; Stallard, 1985, 1988; Lewis and 

others, 1987; Saunders and Lewis, 1988, 1989; 1990; Fox, 

1989, 1993; Lewis and Saunders, 1989; Stallard and others, 

1991; Devol and others, 1995; Edmond and others, 1995, 

1996; Tardy and others, 2005, 2009) and of the Luquillo 

Mountains (McDowell and others, 1990, 1996; McDowell, 

1991, 1998; Lodge and others, 1994; McDowell and Asbury, 

1994; Chestnut and others, 1999; Aitkenhead and McDowell, 

2000; Chestnut and McDowell, 2000; Zeigler and others, 

2005; Derry and others, 2006; Peters and others, 2006; Bhatt 

and McDowell, 2007; Heartsill-Scalley and others, 2007; 

Pett-Ridge, 2009; Pett-Ridge and others, 2009a,b; Shanley 

and others, 2011) demonstrate that it is possible to classify 

and organize river-borne chemical constituents on the basis 

of their primary sources and bioactivity. Constituents that are 

largely bedrock derived include calcium (Ca2+), alkalinity, silica (Si(OH)4

), and phosphate (−PO4

3−). Phosphate is susceptible to attaching to aluminum and iron oxides and hydroxides 

and being lost from solution, and the preceding “−” indicates 

linkages with other constituents such as iron, aluminum, and 

organic matter. Chloride (Cl−

) and sulfate (SO4

2−) are largely 

of seasalt origin; additional SO4

2− comes from the weathering of sulfide minerals, sometimes exposed through mining, 

and from atmospheric sulfuric acid, which has both natural 

and combustion sources. The cations sodium (Na+

), potassium (K+

), and magnesium (Mg2+) are predominantly bedrock 

derived, with lesser seasalt contributions. Siliceous bedrock is 

a source of all of the bedrock-derived constituents, and Na+

-

bearing and Ca2+-bearing bedrock-forming minerals weather 

the most rapidly and completely. Where present, carbonate 

minerals are strong sources of Ca2+, alkalinity, and sometimes 

Mg2+. Several constituents, such as nitrate (NO3

−

), ammonium 

ion (NH4

+

), and dissolved organic carbon (DOC), are ultimately derived from atmospheric gases that have chemical, 

biological, and sometimes anthropogenic origins.

Biological processes associated with the growth and 

maintenance of a forest can have a considerable effect on 

the ultimate chemical composition of that water. The classic 

model of nutrient cycling in tropical rainforests (Herrera and 

others, 1978a,b; Stark and Jordan, 1978; invoked for Luquillo 

by McDowell, 1998, 2001) is a tight, vertical, almost-closed 

cycle where nutrients released from vegetation by throughfall 

or decay are efficiently recovered by plant roots and reincorporated into vegetation as water moves through the upper 

soil profile. Of the constituents studied here, K+

, SO4

2−, DOC, 

NO3

−

, NH4

+

, and −PO4

3−, are strongly bioactive, and biological 

processes, including this internal cycling, can have a profound 

effect on their distribution in surface waters. The bioactivity 

of these constituents is manifested by a strong enrichment 

in throughfall (water that has interacted with leaves and 

branches) compared with rainfall (Heartsill-Scalley and others, 

2007) and in streams following hurricanes (McDowell and 

others, 1996; Schaefer and others, 2000; Shanley and others, 

2011). Sulfate, DOC, NO3

−

, and NH4

+

 can be also be lost to the 

atmosphere through transformation back into various gases. 

All of the biologically active constituents have domestic and 

agricultural sources.

The chemistry of underlying bedrock is a controlling 

factor of the water chemistry of any river. In Puerto Rico’s 

WEBB program watersheds, the two rock types primarily 

differ in their quartz content: the granitic rocks typically 

contain between 20 and 33 percent quartz, whereas the 

volcaniclastic rocks have little or no quartz (Murphy and 

others, 2012). The presence of quartz in bedrock leads to a 

sandy texture in overlying soils and tends to increase their 

ability to transmit water (Murphy and Stallard, 2012). Where 

quartz is absent, as in volcanic bedrock, sand-sized grains in 

associated soils and river sediments are rare (Johnsson and 

Stallard, 1989; Johnsson, 1990), and soils are typically finer 

grained and have lower permeability and infiltration rates 

(Simon and others, 1990). 
120 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

The presence or absence of quartz in bedrock is clearly 

evident in the channel forms of eastern Puerto Rico rivers 

(see cover photograph). The two WEBB study-area rivers 

that drain quartz-poor, volcaniclastic rocks (Mameyes and 

Canóvanas) have abundant exposed bedrock, bouldery beds, 

and poorly developed flood plains and riparian areas. Many 

nooks and crannies shelter aquatic creatures. In contrast, the 

rivers that drain granitic rocks (Icacos, Cayaguás, and Guabá) 

have beds of quartz sand, well-developed flood plains, and 

extensive riparian areas, especially where their stream gradients are gentler. Their channels support hyporheic flow, and 

the flood plains and riparian areas can modify water quality.

When quartz chemically weathers, it produces only 

dissolved silica (Si(OH)4

), which is a common weathering 

product of many silicate minerals. Therefore, quartz dissolution is difficult to differentiate from other reactions when one 

examines solute composition alone (pitted quartz surfaces, 

which have been observed on grains from the Icacos watershed, have been used to identify dissolution; Brantley and others, 1986; Schulz and White, 1999). If quartz is ignored, the 

remaining minerals in the granitic and volcaniclastic bedrock 

types are similar, with a slight bias to more Mg- and Ca-rich 

minerals in the volcaniclastic rocks (Smith and others, 1998, 

their fig. 3, table 2). Therefore, solute signatures from weathering of the granitic or volcaniclastic rocks should be similar. 

Differences in solute composition, however, can be caused 

by other rock types, such as carbonate rocks, sulfide ores, and 

ultramafic rocks, or by indirect effects, such as different rates 

and pathways for water movement through soils and the presence or absence of riparian areas.

The interaction between water, soil, and bedrock, as 

manifested by the relation between concentration (C) and 

runoff rate (R), appears to be similar in a wide range of rivers, indicating a commonality in the physical and chemical 

mechanisms that mobilize and export solutes from watersheds. Godsey and others (2009) compare data from the U.S. 

Geological Survey Hydrologic Benchmark Network for 59 

small, geochemically diverse watersheds in the United States 

in landscapes that are not heavily affected by human activities. 

They focused on chemical constituents that are derived from 

bedrock weathering and are not biologically active—Na+

, 

Ca2+, Mg2+, and Si(OH)4

—and looked for crosscutting patterns 

that would provide insight as to how these constituents were 

acquired by the streams.

Godsey and others (2009) note that most streams demonstrated a linear relation between log(C) and log(R),

log(C) = a + b•log(R),

where a is a constant and b is a slope. A zero slope implies a 

fixed concentration and is termed a “chemostat,” whereas a 

slope of −1 implies a constant input rate of the constituent, 

independent of runoff. A positive slope implies that increasing rainfall and runoff serve to mobilize the constituent. For 

Na+

, Ca2+, Mg2+, and Si(OH)4, b is typically negative, mostly 

between −0.05 and −0.15 and is statistically different from 

zero. The slightly negative slope is close to being a chemostat, implying that subsoil processes substantially increase 

the supply of these constituents in response to more water 

flowing through the soil and that many watersheds respond in 

a similar way, despite marked differences in geology, vegetation, and climate. 

Godsey and others (2009) compared three simple models 

that each successfully reproduce nearly log-linear relations 

between runoff and constituent concentration. For two of these 

models, the Hubbard Brook “working model” of Johnson and 

others (1969) and the rapid, flow-through-reaction model of 

Langbein and Dawdy (1964), the model relation has a sigmoidal shape, leveling off at low and high runoff rates. Successful 

models require that the concentrations of constituents in the 

input to the soil reservoir greatly exceed those of rainwater 

and could be thought of as soil-water concentrations. The 

authors also develop their own strictly log-linear model, the 

“permeability-porosity-aperture” model. This model assumes 

that permeability, porosity, and average pore aperture decrease 

exponentially with depth; precipitation rates are approximately 

the same across a hillslope; flows through the watershed 

behave according to Darcy’s Law; minimal flows originate 

near the divide and the stream; and solute flux is proportional 

to reactive surface area such that secondary and back-reactions 

do not control solute fluxes (Godsey and others, 2009). The 

model handles stored water.

The slope, b, of the permeability-porosity-aperture model 

is derived from the depth-dependent e-folding lengths of 

four different structural and chemical properties. The model 

predicts that b should be the same for all bedrock-derived constituents, although the authors allow that differences for slopes 

among various constituents could be attributable to differences 

in the depth distribution of reactive minerals throughout the 

soil profile. The authors also caution against trying to derive b

from field measurements, stating “Any such attempt at direct 

measurement [of the properties that go into determining the 

slope] would be complicated by the spatial heterogeneity in 

subsurface properties, as well as the large differences between 

field and laboratory weathering rates.”

During big storms, water does not necessarily flow 

through the soil profile. Storm events play a major, often dominant, role in the mobilization of river-borne materials from 

hillslopes and stream channels and in the transport of these 

materials downriver (Wolman and Miller, 1960). In tropical, 

high-runoff settings, near-surface and overland flow can be 

more important than soil infiltration during storms (Elsenbeer, 

2001, Godsey and others, 2004). Elsenbeer (2001) developed 

a simplified classification scheme based on infiltration styles 

(fig. 2). When rainfall intensities for a given locale exceed the 

hydrologic conductivity (Ksat) in the shallow parts of the soil 

profile, water is shunted into shallower, lateral flow paths or 

into overland flow. In hilly watersheds with shallow bedrock 

or impermeable soils (Acrisols), surface lateral flow of water 

will predominate (fig. 2), because Ksat decreases rapidly in the 

soil profile (a change of 2 or 3 orders of magnitude in the first 

meter of soil). In contrast, in watersheds with deep, porous 
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 121

soils (Ferralsols), Ksat remains high through the soil profile 

(a change of 1 order of magnitude or less in the first meter 

of soil), and deep infiltration is likely. In Acrisols, water and 

nutrients can both leave the watershed during storms before 

ever interacting with soil and roots.

For all soils, pipes and macropores (large-diameter connected flow paths characteristically produced by soil cracks, 

decayed roots, and burrows) can be sufficiently abundant 

that they dominate infiltration and lateral shallow-soil flow. 

Shallow-soil, overland, macropore, and pipe flow all provide pathways for water and solutes that modify interaction 

with plant roots, especially during large storms (Beven and 

Germann, 1982; Elsenbeer and Cassel, 1990; Elsenbeer and 

Lack, 1996; Elsenbeer and others, 1996; Godsey and others, 

2004, Kinner and Stallard, 2004; Chappell and others, 2005; 

Larsen and others, 2012). In fact, macropore effects can dominate soil-matrix flow at hillslope scale, considerably changing 

how a soil might be classified according to figure 2 (Chappell 

and others 1998, 2005). 

The most intense rainfall events in eastern Puerto Rico 

occur during hurricanes and large frontal storms. These large 

storms shred and topple vegetation, generate landslides that 

strip soils and vegetation off slopes, and substantially alter 

stream-water chemistry (McDowell and others, 1996; Schaefer 

and others, 2000; Heartsill-Scalley and others, 2007; Larsen, 

2012). Although granitic soils have greater groundwater storage and somewhat faster infiltration (Murphy and Stallard, 

2012), in both the Mameyes and Icacos watersheds, high 

runoff rates produce substantial overland flow on forested soils 

on both granitic and volcaniclastic bedrock as well as on fresh 

and recovering landslides (Larsen, 2012), indicating Acrisoltype soil hydrology in both types of bedrock.

Study Design and Objectives

This chapter presents water chemistry for thousands of 

samples collected during the first 15 years (1991–2005) of the 

Puerto Rico WEBB program. The data, which are available 

from the U.S. Geological Survey’s National Water Information System website (http://nwis.waterdata.usgs.gov/usa/nwis/

qwdata), provide a unique opportunity to examine the composition of stream water during storms. We are not aware of 

any published data anywhere in the world for water samples 

collected at runoff rates greater than 20 millimeters per hour 

(mm h−1) that have been analyzed for chemistry and suspended 

sediment (appendix 1). The Hydrologic Benchmark Network 

dataset examined by Godsey and others (2009) contains comprehensive chemistry for only 30 samples, from 12 rivers, that 

were sampled at runoff rates greater than 1 mm h−1; of these, 

the maximum runoff rate was 5.9 mm h−1. The eastern Puerto 

Rico WEBB study analyzed comprehensive chemistry in 543 

samples collected at runoff rates greater than 1 mm h−1, 256 

samples at greater than 10 mm h−1, and 3 samples at greater 

than 90 mm h−1. Because the soils of the study watersheds 

generally behave like Acrisols (Murphy and Stallard, 2012), 

strong, downslope, near-surface water flow and concomitant 

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_02E.ai

Light rain

“Acrisol-like”

end member

“Ferrasol-like”

end member

Moderate rain

Intense rain

Local weak

lateral flow

Local vertical

flow

Local vertical

flow

Near-surface

lateral flow

Local weak

lateral flow

Vertical flow

Surface lateral

flow

Near-surface

lateral flow

Deep lateral

flow

Figure 2. Adaptation of Elsenbeer’s (2001) functional 

classification of tropical forest watersheds according to 

prevailing hydrological flow paths, based on measured 

saturated hydraulic conductivities (Ksat). Each row represents 

soil response under different rainfall intensities. Arrows 

indicate movement of water overland and through soil; 

thickness of arrows indicates approximate relative proportions. 

For light rain, much moisture does not get to the stream and 

is probably lost through transpiration. Surface lateral flow 

is encountered in hilly watersheds with shallow bedrock or 

impermeable soils (left diagram), where Ksat typically decrease 

rapidly in the profile and rainfall is intense. Deep lateral flow 

(right diagram) occurs where Ksat do not decrease rapidly in 

the profile. Note that Acrisols are roughly equivalent to Ultisols 

in the United States soil classification and Ferralsols are 

roughly equivalent to Oxisols (Soil Survey Staff, 1995), and that 

macropores normally complicate the role of matrix porosity 

(Chappell and others, 1998; Chappell and Sherlock, 2005).
122 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

nutrient export are expected during storms. The models 

presented by Godsey and others (2009) are for water moving 

through soil; thus, at the high-flow regimes sampled during the 

WEBB study, deviations from linear trends of log(C) compared with log(R) might be expected because different flow 

paths are active.

In this report, we use the relation between constituent concentration and discharge as a basis for examining 

processes operating at different flow regimes. Similar to the 

approach used by Godsey and others (2009), we examined 

the structure of the primary data graphically and through 

fitting simple regressions to the data, and we compared these 

results among study rivers and with the results of other studies. We also estimated continuous rates of watershed export 

(loads) of each constituent and averaged the loads for ranges 

of discharge in order to construct averaged constituentdischarge relations. We asked: (1) Do we see nonlinear 

features in the high-runoff samples in this dataset that are 

not seen elsewhere, such as in the Hydrologic Benchmark 

Network’s summary of Godsey and others (2009)? (2) How 

do atmospherically derived and biologically active constituents differ from largely bedrock-derived constituents? And, 

(3) Do we see features in these data comparisons that can be 

used to inform the selection of models for describing the 

biogeochemistry of tropical and other watersheds? To ask 

these questions, we performed a set of tests:

• Does each constituent show similar behavior among all 

sites?

• Do all bedrock-derived constituents have nearly linear 

relations between log(C) and log(R)?

• For a given river or for rivers with similar watershed 

geology, are the slopes similar for all bedrock-derived 

constituents?

• If slopes are not similar, are they arrayed according to 

the reactivity of the parent minerals in the soil profile, 

or in some other biogeochemically consistent manner?

• Do differences of slopes in the linear relations between 

log(C) and log(R) among rivers relate to geology, 

supporting an assumption that soil porosity and pore 

openings, and their vertical distribution, are controlled 

by geology?

• Is there a breakdown in the log-linear relation (curvature) at high flow, indicating that other flow paths are 

active?

• Do bioactive constituents show concentration-runoff 

relations that reflect their increased abundance in the 

near-surface environment due to biological activity?

Methods Used to Collect and Interpret 

Data

Sample Collection and Field Measurements

During the eastern Puerto Rico WEBB program, two 

principal types of samples were collected: grab samples, 

which were manually collected on a regular basis from riverbanks at well-mixed cross sections near each gage site, and 

event samples, which were collected by an automated sampler 

during storm events and retrieved when weather conditions 

permitted. Conductivity, temperature, pH, and dissolved 

oxygen (O2) of grab samples were measured onsite; only 

conductivity was measured onsite for event samples, because 

the other field measurements are unstable and thus incorrect 

if not measured immediately. Depth-integrated samples for 

suspended sediment were occasionally collected at the same 

time as a grab sample. All water samples were filtered through 

0.2-micrometer (μm) filters as soon as practicable in the 

laboratory. Sample collection and processing are described in 

detail in appendix 2.

The automated sampler was programmed to begin sampling when the river rose above a preset stage (appendix 1). 

Once triggered, all 24 bottles were filled at preset times determined beforehand using representative storm hydrographs. 

The sampler had to be emptied and restarted manually. Thus, 

a small storm could prevent the sampling of a subsequent, 

larger storm and, consequently, trigger levels were gradually 

raised to ensure sampling of the largest storms. Event samples 

were a crucial part of the study, because only 20 grab samples 

were collected in the top 50 percent of runoff, and 2 in the top 

10 percent, compared with thousands of event samples. This 

protocol allowed us to sample several massive storms during 

the study period, including Hurricane Hortense in September 

1996, Hurricane Georges in September 1998, and a powerful 

upper-level trough in May 2003. Samples from these storms 

enabled accurate estimation of mass discharge of constituents at high runoff. Owing to the enormous volume of event 

samples (even after threshold stages were increased), analyses 

performed on event samples were eventually reduced to stable 

constituents that are sufficient to assess primary processes 

(conductivity, Si(OH)4

, Cl−, suspended sediment, and usually 

DOC and K+).

Stream Discharge

The characterization of mass balances requires highresolution discharge data to estimate stream discharge at the 

moment of sampling. The collection frequency was every 

5 minutes for the Río Mameyes and every 15 minutes for 

the remaining rivers, and time stamps were used to match 

chemistry samples to discharge (appendix 1). All discharge 

datasets had gaps, ranging from 30 minutes to several months. 

Commonly the longest gaps were associated with the loss of a 

streamgage caused by a huge storm, and the length of the gap 
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 123

reflects the time needed to rebuild the gage. The procedures 

for determining sample discharge and estimating missing data 

are described in appendix 1. Estimated data were not used in 

determining the relation between concentration and discharge 

discussed below.

Analytical Procedures

The water-quality analytical program was built around 

a core suite of chemical measurements: field measurements 

(temperature, conductivity, pH, and dissolved oxygen), major 

cations (Na+, K+, Mg2+, and Ca2+), nutrients (nitrite (NO2

−

), 

nitrate (NO3

−

), ammonium ion (NH4

+

), and phosphate (−PO4

3−)), 

major anions (Cl− and SO4

2−), alkalinity, Si(OH)4

, and DOC. 

Suspended solids were measured from the weight of filtered 

material per unit volume following drying to 105°Celsius (C). 

Sample preservation and analysis are described in appendix 2. 

Alkalinity, temperature, and pH were used to calculate the 

partial pressure of carbon dioxide, PCO2, CO2 saturation, O2

saturation, and the saturation index for calcite, SIcalcite, following 

Stumm and Morgan (1981), in grab samples (appendix 1). 

Discharge, Load, Runoff, and Yield

Hydrologic and water-quality data, including several 

million discharge measurements, thousands of precipitation 

measurements (from automated rain gages), and almost 10,000 

water quality samples were reprocessed for rate calculations 

as described in appendix 1. Constituent loads were calculated 

from concentrations by using the computer program LOAD 

ESTimator (LOADEST; Runkel and others, 2004), which 

estimates constituent loads in streams and rivers given a time 

series of stream discharge, constituent concentration, and additional explanatory variables (typically, seasonal cycles (sine 

and cosine of time in a one-year cycle), and time). LOADEST 

assists the user in developing a regression model of constituent load (calibration); the model then is used to estimate loads 

over a specific time interval (estimation).

The construction of the input data files used by LOADEST (Runkel and others, 2004) from our datasets was automated and required some minor adaptations (appendix 1). 

First, the shortest time step allowed by LOADEST for the time 

series of explanatory variables is 1 hour, so discharge data 

were resampled from 5-minute and 15-minute intervals into 

1-hour intervals. Estimated Na+

, K+, Mg2+, Ca2+, alkalinity, 

Cl−, SO4

2−, Si(OH)4

, DOC, NO3

−

, NH4

+

, −PO4

3−, and suspended 

solid loads are discussed here; several more parameters 

(including bedrock-derived Na+, K+, Mg2+, and Ca2+, total dissolved silicate bedrock, total dissolved bedrock cations, suspended bedrock, and particulate organic carbon) are discussed 

in Stallard (2012b). Complete chemistry was more intensely 

sampled from 1991 through 2000, whereas Cl−, Si(OH)4, K+, 

and suspended solids were sampled intensively through 2004 

(table 5 in appendix 1). These changes in sampling intensity 

means that for most properties LOADEST is primarily being 

modeled on the first 10 years of data.

LOADEST is fundamentally a regression, and biases 

produced by the logarithmic transformation of data and nonnormality of the fits to the log-transformed data can produce 

problems (Runkel and others, 2004). For example, alkalinity 

loads for the Icacos and Guabá watersheds cannot be estimated directly using LOADEST, because at highest discharge 

values, alkalinity is sometimes negative (acidity). Discharge, 

seasonal cycles, and time trends are the core explanatory 

variables in LOADEST. Some samples have real, but aberrant, 

concentrations caused by phenomena not strictly related to discharge, time, or seasonality. Some of these measurements, as 

discussed later in this report, were not included in load estimation; one example is high-chloride storms. These samples are 

summarized in appendix 1, and the effect of the excluded data 

on yields is discussed below.

The areas of the study watersheds range from 0.114 

square kilometers (km2

) to 26.42 km2, so for interwatershed 

comparisons we converted discharge and loads to runoff 

rates (discharge/area) and yields (constituent load/area). 

Average yields were totaled, and average concentrations 

were calculated from average yield divided by average runoff 

(table 1). Data are presented both in mass units and molecular units for comparison with other datasets. Concentration 

data (top two sections, table 1) allow for comparison of 

water quality, whereas yields (bottom two sections, table 1) 

allow comparison of rates at which constituents are being 

released into the rivers.

The forested rivers are wetter (mean annual runoff 2,750 

to 3,760 millimeters per year (mm yr−1) than the developed 

watersheds (970 to 1,620 mm yr−1; table 1), so to compare 

samples from equivalent parts of hydrographs, runoff rates 

were ranked by their overall contribution to annual runoff. 

All water-quality samples were assigned into runoff-rate 

percentile classes, similar to the approach used by the U.S. 

Geological Survey WaterWatch website (http://water.usgs.

gov/waterwatch/) in monitoring flood and drought conditions 

throughout the United States. WaterWatch ranks discharge into 

percentile classes based on the amount of time a river flows 

in a given discharge range. For this study, however, percentile classes (0–10, greater than (>)10–25, >25–50, >50–75, 

>75–90, >90–95, >95–99, and >99 percent) are based on 

runoff (for example, the 10th percentile corresponds to the 

runoff rate below which 10 percent of the total runoff has been 

discharged from the watershed; table 2). Rising and falling 

stage are also distinguished for each sample. The runoff rates 

and predicted constituent-yield rates for each percentile class 

of each river were calculated by using all runoff measurements for the 15-year period. Predicted constituent yields and 

instantaneous runoff were totaled for each percentile class, and 

a discharge-weighted average concentration for each percentile class was then calculated by dividing total yield for each 

constituent by total runoff within that class (table 3). When 

base flow is referred to in this text, it represents the samples in 

the 0-to-10 percent range. The cumulative fraction of constituent yield that corresponds to each percentile class was also 

calculated (table 4).
Table 1. Average constituent concentrations and yields of four watersheds during the study period (1991–2005), eastern Puerto Rico. [DOC, dissolved organic carbon; SSOL, suspended solids; mm yr−1, millimeters per year; –N, as nitrogen; –P, as phosphorus; -- not applicable] Average reporting units and watershed Years of data Average runoff (mm yr–1) Na+ K+ Mg2+ Ca2+ SiO2 Alkalinity1 Cl– SO42– DOC NO3––N NH

Mass concentration (milligrams per liter) Canóvanas 15 970 9.82 1.28 4.81 11.49 10.29 10.90 11.76 4.32 3.86 0.625 0.

Cayaguás 15 1,620 9.55 2.13 2.05 6.17 11.48 6.95 8.99 4.63 2.88 0.705 0.

Mameyes 15 2,750 5.30 0.72 1.54 6.39 7.54 4.67 6.97 2.95 2.50 0.133 0.

Icacos 15 3,760 4.40 0.59 0.95 2.76 6.26 2.73 5.42 1.37 2.63 0.163 0.

Guabá 10 3,630 4.42 0.62 0.78 2.02 6.40 1.98 6.47 1.20 2.02 0.159 0.

Mole concentration (micromoles per liter) Canóvanas 15 970 427 33 198 287 366 908 332 45 321 45 3.

Cayaguás 15 1,620 415 54 84 154 409 579 254 48 240 50 2.

Mameyes 15 2,750 230 18 63 159 268 389 197 31 208 9 0.

Icacos 15 3,760 192 15 39 69 223 227 153 14 219 12 1.

Guabá 10 3,630 192 16 32 51 228 165 182 12 169 11 0.

Mass yield (metric tons per square kilometer per year) Canóvanas 15 970 9.6 1.2 4.7 11.2 10.0 10.6 11.5 4.2 3.8 0.61 0.

Cayaguás 15 1,620 15.5 3.4 3.3 10.0 18.6 11.3 14.6 7.5 4.7 1.14 0.

Mameyes 15 2,750 14.6 2.0 4.2 17.6 20.7 12.9 19.2 8.1 6.9 0.37 0

Icacos 15 3,760 16.9 2.3 3.6 10.6 24.0 10.4 20.7 5.2 10.0 0.62 0.

Guabá 10 3,630 15.1 2.1 2.7 6.9 21.9 6.8 22.1 4.1 6.9 0.54 0.

Mole yield (kilomoles per square kilometer per year) Canóvanas 15 970 417 32 193 280 357 886 324 44 313 44 3.

Cayaguás 15 1,620 672 88 136 249 662 937 411 78 389 82 4.

Mameyes 15 2,750 634 51 174 439 739 1,071 541 85 573 26 2.

Icacos 15 3,760 733 58 150 264 853 869 585 54 837 45 3.

Guabá 10 3,630 657 54 109 173 779 562 623 43 576 39 2.

1Alkalinity mass concentration units are milligrams carbon per liter; alkalinity mole concentration units are microequivalents per liter; alkalinity mass-yield units are metric t

per year; alkalinity mole-yield units are kiloequivalents per square kilometer per year.
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 125

For each constituent for each river the relation between 

log(concentration) and log(runoff rate) was determined using 

regressions on the primary data following Godsey and others 

(2009). In each case, a linear regression,

log(C) = C0 + C1•log(R)

was compared to a quadratic regression,

log(C) = C0 + C1•log(R) + C2•log(R)

2

,

where C0, C1, and C2 are regression constants. A t-test (Bevington and Robinson, 2003) was used to determine whether 

the slope of the linear regression, C1, was statistically different, with 95 percent confidence, from a zero slope (the 

chemostat condition). An F-test (Bevington and Robinson, 

2003) was used to determine whether the quadratic regression 

significantly reduced the residual error, with 95 percent confidence, compared to the linear regression or to no regression. 

The most statistically significant regressions are indicated in 

table 4. Also, for reference, the constant-input and chemostat 

conditions of Godsey and others (2009) are provided.

The amount of variance explained by the simple regressions, as compared to LOADEST, was also compared 

(table 4). In all cases, LOADEST explains more of the data 

variance than do the simple regressions. An F-test was used to 

evaluate the likelihood that the LOADEST model results offer 

a statistically more significant explanation of the dataset.

Water Quality of Eastern Puerto Rico 

WEBB Project Watersheds

The water quality data produced during the first 15 years 

of the eastern Puerto Rico WEBB project are available from the 

U.S. Geological Survey’s National Water Information System 

website (http://waterdata.usgs.gov/nwis). The results are summarized and interpreted here. Suspended-sediment yield differed 

greatly among the study watersheds (table 1). Yields were larger 

than 1,900 metric tons per square kilometer per year (t km−2 yr−1) 

in granitic watersheds (Icacos, Guabá, and Cayaguás), and about 

one-sixth this value from volcaniclastic watersheds (Mameyes 

and Canóvanas), independent of land cover and climate (the 

developed watersheds, Canóvanas and Cayaguás, are drier, having both less rainfall and runoff (Murphy and Stallard, 2012)). 

However, dissolved constituent yields were not very different 

among watersheds of different geology. Yields of nonbioactive 

constituents derived largely from bedrock weathering (Ca2+, 

alkalinity, Si(OH)4), from seasalt (Cl−), or from a mix of the two 

sources (Na+, Mg2+ and SO4

2−) differed by a factor of less than 

2 among all watersheds, showing little relation to land cover, 

annual runoff, or bedrock type. Nutrient yields were consistently greater in the developed watersheds, despite their lesser 

runoff. Most dissolved constituents have clearly defined runoffconcentration relations; however, the field measurements and 

several nutrients (nitrate, ammonium ion, and phosphate) do not.

Runoff Percentile Classes, Concentration, and 

Yield

Percentile classes correspond to equivalent parts of the 

hydrograph for each river, so comparing constituent yield per 

percentile class of runoff provides a means of comparing the 

relation between runoff and constituent concentration among 

all watersheds. The percentage of time that runoff is below 

each threshold in each watershed and the percentage of constituent yield discharged below that threshold are shown in table 4. 

Constituents are sorted by decreasing yields at 50 percent of 

runoff. The constituents are therefore ordered from those that 

are most diluted at higher discharge to those that become most 

concentrated. For reference, the constant-rate input (equivalent 

to time) and the “chemostat” input (equivalent to runoff) of 

Godsey and others (2009) are indicated.

Eighty-five percent of the time, the runoff in the WEBB 

rivers was less than half of the maximum runoff rate (the 

constant-rate line in table 4). For example, in the Canóvanas 

watershed, the lowest 50 percent of runoff rates were recorded, 

on average, 93.8 percent of the time, or about 342 days’ worth 

of time in a year. The lowest 75 percent of runoff rates were 

recorded 99.4 percent of the time, or about 363 days’ worth 

of time in a year. In other words, the highest 25th percentile 

of runoff rates was reached only for 2 days’ worth of time in 

a year, during large storms. The lowest 50 percent of runoff 

rates corresponds to the export of 70.8 percent of Ca2+ from 

the Canóvanas watershed, but only 38.8 percent of DOC and 

4.5 percent of suspended sediment. The lowest 90 percent of 

runoff rates exports only 53.7 percent of suspended sediment; 

that is, almost half of suspended sediment is exported when 

runoff exceeds the 90th percentile, during storm events that 

total less than 1 day per year.

Table 2. Runoff rates for Water, Energy, and Biogeochemical Budgets program rivers during the study period (1991–2005) at certain 

thresholds.

River

Minimum

runoff

Percentile of annual runoff volume Maximum 

10 25 50 75 90 95 99 runoff

Runoff rate (millimeters per hour)

Canóvanas 0.009 0.041 0.083 0.246 1.43 8.10 20.3 43.1 69.0

Cayaguás 0.020 0.073 0.114 0.270 1.79 7.76 17.9 40.6 54.8

Mameyes 0.024 0.141 0.222 0.457 1.49 4.95 11.0 43.3 89.8

Icacos 0.059 0.203 0.290 0.566 2.14 7.51 14.0 36.4 86.0

Guabá 0.036 0.196 0.287 0.477 1.84 10.92 25.5 63.7 118.8
Table 3. Discharge-weighted average concentrations of each percentile class estimated by using LOADEST and hourly discharge.—Continued [DOC, dissolved organic carbon; mm h−1, millimeters per hour; m3 s−1, cubic meters per second; mg L−1, milligrams per liter; μS cm−1, microsiemens per centimeter; μmol L−1, m

μeq L−1, microequivalents per liter; ppmv, parts per million by volume] Sample percentile class (percent) Runoff rate (mm h–1) Discharge (m3 s–1) Suspended sediment (mg L–1) Conduc- tivity (μS cm–1) pH1 Na+ (μmol L–1) K+ (μmol L–1) Mg2+ (μmol L–1) Ca2+ (μmol L–1) Si(OH)4 (μmol L–1) Alkalinity (μEq L–1) Cl– (μmol L–1) SO42– (μmol L–1) DOC (μmol L–1) NO3– (μmol L–1) NH4+ (μmol L

Canóvanas 0–10 0.021 0.149 9.8 223 8.34 713 20.6 386 564 580 2,044 459 53.8 164 11.1 2.4 

>10–25 0.061 0.430 29.6 173 8.20 573 25.7 284 422 488 1,450 416 51.3 232 24.3 2.3 

>25–50 0.158 1.12 87.7 138 8.06 473 30.5 214 318 410 1,035 368 47.8 299 45.3 2.4 

>50–75 0.765 5.42 448 96 7.82 351 38.5 139 197 292 586 291 42.9 414 66.3 2.9 

>75–90 4.49 31.8 1,242 66.9 7.69 258 41.3 91.3 118 206 349 208 35.0 420 62.1 5.6 

>90–95 15.3 108 1,792 53.5 7.67 214 39.6 71.4 83.9 159 282 165 29.9 374 37.2 10.8 

>95–99 32.0 227 2,042 47.3 7.67 193 37.4 62.2 68.3 138 265 138 26.3 323 25.2 16.1 

>99–100 59.2 419 2,152 42.9 7.65 179 33.8 55.4 57.0 119 254 122 24.1 266 13.8 21.5 

Cayaguás 0–10 0.041 0.301 13.2 169 7.65 883 36.6 167 312 680 1,395 347 48.7 123 28.9 1.0 

>10–25 0.095 0.698 41.2 135 7.51 651 45.6 137 253 592 997 321 51.4 168 43.4 1.7 

>25–50 0.195 1.43 139.2 106 7.34 499 54.0 103 189 493 669 296 52.4 221 52.4 2.4 

>50–75 0.860 6.31 1,063 65.9 6.94 307 65.3 51.7 91.8 315 250 233 49.8 312 61.0 3.7 

>75–90 4.35 32.0 3,441 41.8 6.66 193 63.3 25.7 44.0 186 80 158 42.2 324 58.1 4.0 

>90–95 12.1 89.0 5,697 31.6 6.70 148 54.7 17.3 29.0 127 48 119 36.0 278 42.8 3.4 

>95–99 26.2 192 7,184 24.6 6.82 119 46.2 12.1 20.0 91.5 38 93.4 30.5 231 25.9 2.6 

>99–100 41.0 301 6,984 21.4 6.77 106 42.2 9.9 16.2 78.9 33 80.9 27.7 212 19.7 2.2 

Mameyes 0–10 0.068 0.339 1.6 110 7.97 379 19.1 114 320 571 914 252 43.5 80 7.0 1.0 

>10–25 0.179 0.884 4.8 82 7.80 295 18.7 85.1 224 372 615 227 36.7 133 7.3 0.7 

>25–50 0.324 1.61 12.8 68.4 7.68 254 18.4 69.7 176 296 469 209 32.9 178 8.0 0.6 

>50–75 0.887 4.39 56.4 51.6 7.49 202 18.1 51.0 120 205 297 184 27.8 245 9.7 0.6 

>75–90 2.81 13.9 217 38.5 7.32 164 18.0 35.6 77.7 139 173 157 23.0 308 12.4 0.7 

>90–95 7.26 36.0 567 30.9 7.18 142 18.5 26.0 53.3 98.4 106 135 19.3 358 15.2 1.1 

>95–99 21.2 105 1,275 25.0 7.02 123 19.1 18.6 35.6 69.8 61 114 16.1 347 20.1 1.8 

>99–100 45.6 226 2,054 22.3 6.87 112 19.5 15.1 27.5 53.6 42 104 14.4 320 22.8 2.6 
Table 3. Discharge-weighted average concentrations of each percentile class estimated by using LOADEST and hourly discharge.—Continued[DOC, dissolved organic carbon; mm h−1, millimeters per hour; m3 s−1, cubic meters per second; mg L−1, milligrams per liter; μS cm−1, microsiemens per centimeter; μmol L−1, m

μeq L−1, microequivalents per liter; ppmv, parts per million by volume]

Sample

percentile

class

(percent)

Runoff

rate

(mm h–1)

Discharge

(m3 s–1)

Suspended

sediment

(mg L–1)

Conductivity

(μS cm–1)

pH1 Na+ (μmol L–1) K+ (μmol L–1) Mg2+ (μmol L–1) Ca2+ (μmol L–1) Si(OH)4 (μmol L–1) Alkalinity (μEq L–1) Cl– (μmol L–1) SO42– (μmol L–1) DOC (μmol L–1) NO3– (μmol L–1) NH4+ (μmol L

Icacos

0–10 0.115 0.104 3.7 61.5 6.83 283 16.4 61.7 118 475 426 191 12.2 75 14.6 1.0 

>10–25 0.252 0.228 10.9 52.9 6.72 243 16.4 53.0 97.1 331 332 182 13.6 125 13.5 1.0 >25–50 0.429 0.389 27.7 45.7 6.62 213 16.2 45.3 80.2 256 265 167 14.3 186 12.3 1.0 >50–75 1.197 1.08 168.7 35.8 6.46 176 15.5 33.3 55.0 154 165 147 15.5 299 11.1 1.0 >75–90 3.96 3.59 832 25.6 6.35 135 13.9 21.3 32.5 86.5 79 118 15.7 368 10.0 0.9 >90–95 9.04 8.18 2,105 20.1 6.31 110 12.6 15.1 21.7 59.1 41 98.7 15.3 349 9.6 0.9 >95–99 20.8 18.8 5,637 15.6 6.38 89.9 11.1 10.2 13.9 39.8 17 80.6 14.4 285 9.1 0.9 

>99–100 51.6 46.7 21,372 12.8 5.46 67.6 8.8 6.3 8.0 25.8 –2 61.9 12.8 149 11.3 0.9 

Guabá

0–10 0.104 0.003 5.6 57.0 6.87 295 12.8 51.3 87.4 443 321 224 11.8 79 12.7 0.5 

>10–25 0.250 0.008 16.8 48.7 6.74 257 15.7 40.6 67.1 313 235 212 11.8 120 12.1 0.6 

>25–50 0.383 0.012 37.2 44.5 6.66 237 16.9 35.6 57.0 264 194 203 11.9 142 12.2 0.6 

>50–75 0.950 0.030 198 37.0 6.50 202 18.3 27.2 41.4 171 130 181 12.5 205 11.4 0.8 

>75–90 4.48 0.142 1,460 25.2 6.41 142 15.7 17.2 23.5 83.1 59 130 14.1 265 10.1 1.1 

>90–95 14.48 0.459 3,307 18.9 6.51 110 12.1 12.8 15.9 50.4 30 96.9 15.6 264 9.0 1.3 >95–99 33.1 1.05 4,943 14.3 6.46 86.4 9.2 9.5 11.3 33.7 16 69.6 16.1 259 7.8 1.5 >99–100 68.6 2.17 6,866 11.1 6.18 69.5 6.9 7.0 7.9 25.0 8 48.6 16.3 223 7.3 1.6 

1pH is calculated from CO2 vapor pressure. Grab samples collected at high runoff rates on the Icacos also indicate equilibration with the atmosphere during high-flow conditi

change from their low-runoff-rate averages to atmospheric equilibrium at high runoff rates. The transition from low runoff rate to high runoff rate values of PCO2 was assigned to 

which the slope of the DOC-runoff relation goes from positive to negative, indicating shallow flowpaths.
Table 4. Percentage of constituent discharged compared with percentage of water discharged (estimated by using LOADEST) and comparisons witlog(concentration) to log(runoff rate).—Continued[Constant rate, a hypothetical constituent introduced at a constant rate, independent of runoff rate; chemostat, a hypothetical constituent introduced at a rate that is proportional t50 percent runoff; bold rows, constant-rate and chemostat models; C1, linear regression coefficient relating log(concentration) to log(runoff); C2, quadratic coefficient, if quadrat

more significant; [], values are not statistically different from zero; F-test, likelihood that the best LOADEST model offers statistically more significant explanation of data set; D-- not applicable]

Constituent

Percentile of annual runoff volume, in percent estimated using LOADEST Regression on all 

measurements

Data

count

Regression

order

Percent

variance

explained

Best

LOADES

model

10 25 50 75 90 95 99 Yield of each constituent (percent) C1 C2

Canóvanas

Constant rate 42.8 73.2 93.8 99.4 99.9 100.0 100.0 –1 -- -- -- -- -- 

Calcium 18.8 41.6 70.8 90.4 97.3 98.9 99.8 –0.29 -- 140 1 82 1 

Magnesium 18.5 40.8 69.3 89.1 96.6 98.5 99.8 –0.26 -- 140 1 82 2 

Silica 15.4 35.8 64.9 86.8 95.9 98.2 99.7 –0.20 -- 767 1 61 9 

Sodium 15.6 35.9 63.9 85.9 95.5 98.0 99.7 –0.18 -- 140 1 80 4 

Chloride 13.9 33.1 61.5 85.2 95.5 98.0 99.7 –0.16 -- 739 1 51 9 

Sulfate 12.1 29.5 56.5 81.4 93.8 97.2 99.6 –0.09 -- 145 1 61 9 

Phosphate 9.1 25.7 55.9 84.6 96.3 98.7 99.8 [–0.03] -- 133 1 0 8 

Chemostat 10.0 25.0 50.0 75.0 90.0 95.0 99.0 0 -- -- -- -- -- 

Potassium 6.6 18.2 41.3 69.4 88.4 94.6 99.2 0.12 -- 451 1 39 9 

DOC 5.4 16.0 38.8 69.1 89.2 95.2 99.3 0.10 –0.12 315 2 56 8 

Nitrate 2.8 10.6 34.7 70.2 92.7 97.3 99.7 0.05 –0.27 145 2 67 9 

Ammonium ion 6.0 14.9 30.4 48.0 66.6 79.7 96.0 0.26 -- 85 1 21 6 

Sediment 0.2 1.0 4.5 20.4 53.7 75.0 95.8 0.74 –0.24 1,337 2 73 8 

Cayaguás

Constant rate 33.9 63.7 91.1 99.0 99.9 100.0 100.0 –1 -- -- -- -- -- 

Calcium 18.8 43.3 75.7 93.3 98.3 99.3 99.9 –0.42 -- 105 1 87 4 

Magnesium 18.5 42.7 74.9 92.9 98.2 99.2 99.9 –0.40 -- 105 1 88 4 

Phosphate 17.8 41.5 72.3 89.9 96.5 98.4 99.7 –0.27 -- 91 1 38 6 

Sodium 17.3 39.7 70.7 90.3 97.0 98.7 99.8 –0.31 -- 105 1 93 9 

Silica 15.6 37.2 68.2 89.6 97.2 98.8 99.8 –0.31 -- 774 1 87 9 

Chloride 13.1 32.0 61.4 85.7 95.7 98.1 99.7 –0.20 -- 785 1 78 9 

Sulfate 10.2 26.1 53.0 79.3 93.0 96.8 99.4 –0.08 -- 109 1 43 2 

Chemostat 10.0 25.0 50.0 75.0 90.0 95.0 99.0 0 -- -- -- -- -- 

Nitrate 6.5 19.3 44.1 73.7 92.5 97.1 99.6 [–0.02] -- 105 1 1 9 

Potassium 7.1 19.5 43.5 72.6 90.5 95.7 99.2 0.04 –0.10 395 2 42 8 

DOC 5.8 16.3 38.1 68.7 89.0 94.9 99.1 0.12 –0.14 98 2 68 8 

Ammonium ion 4.4 13.5 34.2 66.1 88.5 94.9 99.2 0.13 -- 62 1 9 2 

Sediment 0.1 0.5 2.5 16.8 50.5 72.4 94.1 1.03 –0.34 991 2 89 8 

Mameyes

Constant rate 30.8 58.7 86.0 97.5 99.7 99.9 100.0 –1 -- -- -- -- -- 

Phosphate 19.3 41.2 69.1 88.2 96.0 98.1 99.6 –0.35 -- 141 1 42 2 

Calcium 17.6 39.1 68.1 88.9 97.0 98.8 99.8 –0.37 -- 177 1 89 4 

Silica 17.2 38.4 67.3 88.2 96.7 98.7 99.8 –0.34 -- 1,165 1 59 9 

Magnesium 16.1 36.7 65.3 87.1 96.3 98.5 99.8 –0.29 -- 177 1 85 4 

Sodium 13.6 32.2 60.1 83.8 94.9 97.8 99.6 –0.19 -- 177 1 86 8 

Sulfate 13.2 31.3 58.7 82.3 94.1 97.3 99.5 –0.17 -- 179 1 86 7 
Table 4. Percentage of constituent discharged compared with percentage of water discharged (estimated by using LOADEST) and comparisons witlog(concentration) to log(runoff rate).—Continued[Constant rate, a hypothetical constituent introduced at a constant rate, independent of runoff rate; chemostat, a hypothetical constituent introduced at a rate that is proportional t50 percent runoff; bold rows, constant-rate and chemostat models; C1, linear regression coefficient relating log(concentration) to log(runoff); C2, quadratic coefficient, if quadrat

more significant; [], values are not statistically different from zero; F-test, likelihood that the best LOADEST model offers statistically more significant explanation of data set; D-- not applicable]

Constituent

Percentile of annual runoff volume, in percent estimated using LOADEST Regression on all 

measurements

Data

count

Regression

order

Percent

variance

explained

Best

LOADES

model

10 25 50 75 90 95 99 Yield of each constituent (percent) C1 C2

Mameyes—Continued

Chloride 12.4 29.8 56.9 81.1 93.5 97.1 99.5 –0.14 -- 1,134 1 33 9 

Potassium 10.3 25.5 50.5 75.1 89.8 94.8 98.9 [–0.00] -- 581 1 0 8 

Chemostat 10.0 25.0 50.0 75.0 90.0 95.0 99.0 0 -- -- -- -- -- 

Ammonium ion 10.1 23.0 42.0 60.4 74.4 82.2 94.0 [–0.07] -- 57 1 1 2 

Nitrate 7.2 18.7 39.3 63.5 82.1 89.6 97.5 0.24 -- 178 1 44 9 

DOC 4.8 14.4 34.9 62.4 83.3 91.6 98.4 0.32 –0.18 164 2 82 9 

Sediment 0.1 0.6 2.6 10.5 30.9 50.1 84.0 1.18 -- 1,248 1 77 8 

Icacos

Constant rate 28.0 56.3 85.6 97.6 99.6 99.9 100.0 –1 -- -- -- -- -- Silica 18.9 42.2 72.4 91.5 97.7 99.1 99.9 –0.54 -- 1,074 1 81 4 

Calcium 16.1 37.8 67.6 89.2 97.1 98.9 99.8 –0.42 -- 238 1 86 6 

Magnesium 15.0 35.7 65.1 87.6 96.5 98.6 99.8 –0.35 -- 237 1 85 6 

Sodium 12.8 31.1 58.9 83.0 94.5 97.6 99.6 –0.19 -- 237 1 71 8 

Chloride 12.3 30.0 57.6 82.3 94.3 97.5 99.6 –0.16 -- 1,104 1 38 9 

Nitrate 11.2 28.3 54.8 79.1 92.0 96.1 99.1 –0.13 -- 235 1 26 9 

Ammonium ion 12.0 28.5 53.5 75.9 89.3 94.1 98.6 [–0.02] -- 136 1 0 1 

Potassium 10.7 26.7 53.1 78.5 92.3 96.5 99.4 –0.07 -- 607 1 17 9 

Chemostat 10.0 25.0 50.0 75.0 90.0 95.0 99.0 0 -- -- -- -- -- Phosphate 9.8 24.6 49.4 74.4 89.7 94.8 99.0 [0.01] -- 196 1 0 1 

Sulfate 8.2 21.7 46.2 73.6 90.5 95.6 99.3 0.03 -- 238 1 6 6 

DOC 4.0 11.8 30.8 61.3 85.8 93.7 99.2 0.44 –0.32 279 2 80 9 

Sediment 0.1 0.2 1.0 5.0 19.9 35.2 67.1 1.41 -- 1,408 1 89 9 

Guabá

Constant rate 28.5 54.9 83.5 97.9 99.8 99.9 100.0 -1 -- -- -- -- -- 

Silica 19.0 41.2 71.1 92.0 98.0 99.2 99.9 –0.52 -- 604 1 93 9 

Calcium 16.8 37.8 67.1 89.5 97.2 98.9 99.8 –0.34 -- 160 1 88 9 

Magnesium 15.4 35.4 64.3 87.5 96.5 98.6 99.8 –0.28 -- 158 1 84 9 

Sodium 14.0 33.0 61.2 85.3 95.5 98.0 99.7 –0.21 -- 158 1 81 9 

Chloride 13.0 31.1 59.1 84.1 95.2 98.0 99.7 –0.20 -- 640 1 60 9 

Nitrate 11.0 26.9 53.5 79.0 92.6 96.6 99.4 –0.07 -- 156 1 9 7 

Potassium 10.4 26.1 52.4 78.3 92.2 96.3 99.3 0.03 –0.16 324 2 39 9 

Chemostat 10.0 25.0 50.0 75.0 90.0 95.0 99.0 0 -- -- -- -- -- 

Phosphate 8.9 23.4 49.1 76.3 92.0 96.5 99.4 [–0.02] -- 112 1 2 2 

Sulfate 9.4 23.5 47.0 71.3 87.6 93.7 98.8 0.06 -- 155 1 35 7 

Ammonium ion 7.0 18.4 39.1 62.8 82.3 90.4 98.0 0.17 -- 60 1 18 1 

DOC 5.1 15.0 35.0 62.0 84.8 92.6 98.7 0.35 –0.24 120 2 65 9 

Sediment 0.1 0.4 1.6 6.8 36.1 62.2 91.8 1.25 -- 802 1 86 8 

1For explanation of model number, see Runkel and others, 2004, their table 7.
130 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

The increase in percentage yield of constituents listed in 

table 4 parallels the increase in the value of the linear regression coefficient (or slope), C1

. The constituents with negative 

slopes are bedrock-weathering derived, seasalt derived, or a 

mix of the two (Si(OH)4, Ca2+, Mg2+, Na2+, and Cl−). Although 

the order varies slightly among rivers, the constituents that are 

most dominantly of bedrock origin, Si(OH)4, Ca2+, and Mg2+, 

produce more negative slopes than Na2+, which has a large 

seasalt contribution, and Cl−, nearly all of which is derived 

from seasalt. Dissolved organic carbon and suspended sediment always have a positive slope, consistent with surface 

flow-path inputs. Phosphate has slopes that are either negative 

or indistinguishable from zero, differing considerably among 

rivers. All remaining constituents have a mix of slopes that are 

zero or near zero, close to the slope of the chemostat.

Both LOADEST and simple regression models explained 

most of the data variance, typically more than 80 percent, 

in the nonbioactive bedrock-derived constituents (Si(OH)4, 

Ca2+, Mg2+, Na2+, and Cl−; table 4). The models generally 

explained less than half the variance of bioactive constituents 

(K+, NO3

−

, NH4

+

, and −PO4

3−). The LOADEST model, as 

compared to simple regressions, significantly improved the 

explanation of variance for the bioactive constituents, presumably by introducing the influence of seasonality and temporal 

trends (LOADEST models 3–9). Ammonium ion was modeled 

poorly in most watersheds. Phosphate was not well modeled 

in the Icacos and Guabá watersheds, whereas K+ was not well 

modeled in the Mameyes watershed. These results parallel 

similar models by Peters and others (2006) in a comparison 

of the Río Icacos with the four other Puerto Rico WEBB sites 

using the first years (1991–1997) of the present dataset. They 

noted that discharge variations and seasonality explained most 

of the variance in log(concentration) of alkalinity, Ca2+, Mg2+, 

Na+, Cl−, and Si(OH)4, but less than half of the variance of K+, 

NO3

−

, and SO4

2−; SO42− concentration increased with discharge. 

Water-Quality Constituents

The concentration-runoff relations for various constituents (table 3) are consistent with major processes that 

deliver the constituents to the river—bedrock weathering, 

seasalt deposition, and biological activity. These relations 

are reviewed here. Field measurements are presented first, 

followed by chemical constituents, which are grouped by the 

major processes that regulate them. 

Field Measurements

Water temperature ranges of the rivers were quite narrow 

(fig. 3), as air temperatures in Puerto Rico do not vary substantially year round. Generally, as runoff increases, temperature decreases. The forested watersheds (Icacos, Guabá, and 

Mameyes) typically had water temperature values between 18 

and 25°C. The developed watersheds (Canóvanas and Cayaguás) generally had higher water temperatures—between 22 

and 30°C—because they are located at lower elevations (and 

hence air temperatures are higher), have less water in streams, 

and have less riparian vegetation to shade stream channels (see 

cover photograph).

Dissolved oxygen concentrations of the study rivers were 

usually close to saturation or slightly undersaturated (fig. 3). 

Some samples from the Canóvanas watershed were supersaturated; this state is probably due to photosynthesis by aquatic 

vegetation, which produces oxygen and consumes carbon 

dioxide during daylight hours (when grab samples were 

collected). Respiration and decomposition, which continue 

24 hours a day, consume oxygen and produce carbon dioxide; 

therefore, O2 levels are likely lower at night. The effects of 

photosynthesis and its relation to carbonate dynamics in the 

streams are further discussed later in section Alkalinity, Carbon Dioxide, and Calcite Saturation of this chapter. 

The pH values of study rivers were typically circumneutral, between 6 and 8. Generally, pH of the study rivers 

decreases with increasing runoff (fig. 3). The granitic watersheds had the lowest pH values; the forested watersheds 

(Icacos and Guabá) were of lower pH than the developed 

watershed (Cayaguás). Highest pH values, as much as 9, were 

observed in the developed, volcaniclastic Canóvanas. These 

high pH values are likely related to both the higher alkalinity 

of the Río Canóvanas (discussed in section Alkalinity, Carbon Dioxide, and Calcite Saturation of this chapter) and to 

sampling times. Grab samples were always collected during 

the day, when photosynthesis produces oxygen and consumes 

carbon dioxide, leading to an increase in pH. Therefore, water 

likely was sampled when pH values were at a maximum for 

the day. 

Conductivity values, which are affected by all dissolved ions regardless of source, ranged from about 10 to 

300 microsiemens per centimeter (μS cm−1). Values were 

generally lowest in the forested watersheds, and values in the 

granitic watersheds (Icacos and Guabá) were lower than in the 

volcaniclastic watershed (Mameyes) (fig. 3). Highest conductivity values were observed in the volcaniclastic, developed 

Canóvanas watershed. This general pattern was repeated for 

almost all constituents: at a given runoff rate, forested streams 

were more dilute. In the pH range of the watersheds, conductivity was a conservative property that generally decreased 

with increasing runoff, as shallower flow paths during storms 

decrease the concentrations of constituents that dominate conductivity measurements (such as Cl−

 and Na+). High-chloride 

samples were a notable exception to this trend, as discussed in 

a later section entitled “Chloride.” 

Silica

Dissolved Si(OH)4 is derived primarily from the weathering of silicate minerals in bedrock and soil. Dissolved Si(OH)4

concentrations decreased markedly with increasing runoff in 

all eastern Puerto Rico WEBB rivers (fig. 4). This decrease is 

consistent with the concept that an ever-larger portion of water 

moves through shallower flow paths, and they hence have less 
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 131

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_03E.ai

A. Temperature, all data B. Field pH, all data

C. Conductivity, all data D. Oxygen, all data

0.01

0.0

0.5

Runoff rate, in millimeters per hour Runoff rate, in millimeters per hour

Temperature, in degrees Celsius Conductivity, in microsiemens per centimeter

Oxygen saturation ratio pH

0.01 0.1 1 0.1 1 10 100

5

10

100

15

20

500

35

10 100

6

5

7

8

9

10

1.0

1.5

EXPLANATION

Cayaguás High chloride 

Mameyes

Icacos Low silica

Guabá High potassium

Canóvanas Calcite

Saturation

25

30

Figure 3. Temperature, pH, conductivity, and dissolved oxygen in the eastern Puerto Rico Water, Energy, and Biogeochemical Budget 

program rivers, 1991–2005. Additional sample characteristics are indicated with black symbols: calcite, samples supersaturated with 

respect to calcite; high chloride, samples with exceptionally high chloride concentrations collected during huge storms; high potassium, 

samples with high potassium but not high chloride; low silica, Icacos and Guabá samples with unusually low silica concentrations for 

the runoff rate.
132 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_04E.ai

A. Cayaguás B. Canóvanas

C. Icacos D. Mameyes

E. Guabá F. Watershed averages

0.01

Runoff rate, in millimeters per hour

Dissolved silica, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

1

10

100

1

10

100

1,000

2,000

1,000

2,000

1

10

100

1,000

2,000

10 100

EXPLANATION

Constant input

Runoff recurrence 90

25 50 75 90 95

25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

25 50 75 90 95

Mameyes

High chloride Icacos

Calcite

Low silica

Falling stage

Guabá

Cayaguás

High potassium

Rising stage Canóvanas

Canóvanas

Cayaguás

Mameyes

LOADEST

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 133

contact with deep soil and bedrock, with increasing rainfall 

and runoff (see Elsenbeer, 2001). Although the Luquillo 

Mountains receive, on a long-term annual average, about 

21±7 t km−2 yr−1 of Saharan desert dust (Pett-Ridge and others, 

2009b), the dust likely does not contribute substantially to dissolved Si(OH)4

. More than 85 percent of the dust is composed 

of aluminosilicate clay minerals (such as illite, kaolinite, and 

montmorillonite), quartz, and amorphous silicon (Reid and 

others, 2003), which rather than contributing to dissolved load 

in rivers would contribute to the solid phases in soils, as has 

been observed on carbonate islands such as Bermuda (Herwitz 

and others, 1996). The dust contribution is also small compared to suspended-solid yields, which range from about 320 

to 2,100 t km−2 yr−1 (table 1).

The slope, C1, of the regression of the relation of 

log(Si(OH)4) to log(R) is least negative in the Canóvanas 

watershed and most negative in the Icacos and Guabá (table 4) 

watersheds. These slopes are more negative than the slope (C1) 

of most rivers studied by Godsey and others (2009), indicating 

less of a chemostat-like response (more dilution). Regressions 

on the primary data demonstrate no curvature.

Unusually low Si(OH)4

 concentrations were observed in 

the granitic, forested Icacos watershed (and its subwatershed, 

the Guabá) on the falling stage of some high-runoff hurricanes 

and frontal storms (fig. 4 and appendix 1, figs. 4, 5). Some 

samples (which plot below the constant-input line anchored 

to average base flow; see fig. 4) had Si(OH)4 yields that were 

even less than base flow yields, indicating that water that would 

normally drain from deep in the soil was not doing so during 

these events. Such low-Si(OH)4 events were not observed, 

even during the same storms, in the volcaniclastic watersheds 

(Mameyes and Canóvanas) or in the granitic watershed with an 

agricultural land-use history (Cayaguás). Soils on the volcaniclastic rocks are more dense, clay-rich, and less permeable 

(Murphy and Stallard, 2012), whereas the Cayaguás watershed 

has been deeply eroded and trampled by cattle (Brown and 

others, 1998; Larsen and Santiago-Román, 2001); it is possible that the mechanism for low-Si(OH)4

 waters involves 

soil porosity. Yield reductions indicate that pathways for 

deeper groundwater flow in the forested, granitic watersheds 

were being restricted or even reversed during such storms. 

It is possible that sediment or litterfall is mobilized in such a 

way that shallow soil pore spaces in lower slopes and along 

channels, including macropores, become clogged. This clogging would lead to decreased groundwater seepage and hence 

lower Si(OH)4

 concentrations. Most of the low-Si(OH)4 events 

occurred prior to the 1998 Hurricane Georges (which was not 

a low-Si(OH)4 event). Georges was a category 2 hurricane that 

struck eastern Puerto Rico directly and caused substantial forest damage (Lugo and Frangi, 2003). This damage may have 

led to changes in soil properties and flow paths. It is also possible that earthworms, which are quite active in these forests 

(Larsen and others, 2012) and increase soil porosity, may have 

had their activity reduced by Georges. Finally, it is possible that 

low-Si(OH)4 events may have occurred, but were not sampled, 

following Georges. Regardless of the reason, because of their 

occasional and enigmatic nature, the low-Si(OH)4 events were 

excluded from the LOADEST calculations.

Chloride

Chloride is derived almost exclusively from seasalt 

entrained in the atmosphere, and it is primarily deposited as 

rain (rather than as dry deposition) in the Luquillo Mountains 

(Heartsill-Scalley and others, 2007; Stallard, 2012a). As with 

Si(OH)4, Cl− concentrations typically decrease with increasing 

runoff in all eastern Puerto Rico WEBB rivers (fig. 5), because 

a greater proportion of stream water is derived from shallow 

and overland flow paths, rather than from base flow, during 

large storms. Base flow contains higher Cl− concentrations 

than average rainwater (Stallard, 2012a) because evaporation and transpiration concentrate Cl−

 in waters moving 

downward through the canopy, surface soil, and rooting zone. 

During high-runoff events, water passing quickly overland 

and through shallow soils is subjected to proportionately 

less evaporation and transpiration than during small events 

(fig. 2). Average Cl− concentrations are typically greatest in the 

developed Canóvanas and Cayaguás watersheds (fig. 5). These 

watershed are drier and have a greater ratio of evapotranspiration to runoff (Murphy and Stallard, 2012), which may 

concentrate the seasalt contribution.

Because Cl− does not have a bedrock source, the negative slopes (C1

) of the regression of log (Cl−) and log(R) 

(table 4) must reflect dilution of the aged, evaporated water 

by active storms. The range of slopes is quite narrow, −0.20 

to −0.14, presumably reflecting a similar style of mixing 

among all the watersheds.

Exceptionally high Cl− concentrations, as much as an 

order of magnitude greater than average, were recorded in 

the Mameyes, Icacos, and Guabá watersheds at high runoff 

rates, primarily during some hurricanes and also during some 

frontal storms (fig. 5). During Hurricane Georges, a quantity 

of Cl− roughly equivalent to 0.3 millimeters (mm) of seawater 

was deposited over the entire Mameyes watershed (Stallard, 

2012a). The Cayaguás watershed, which is leeward of the 

Figure 4 (facing page). Runoff rate–concentration graphs for 

dissolved silica. Samples in individual rivers are keyed according 

to runoff percentile class. Averages for runoff percentile class, 

estimated using LOADEST (see table 3) are indicated in the lower 

left diagram. Additional sample characteristics are indicated 

with black symbols: calcite, samples supersaturated with 

respect to calcite; high chloride, samples with exceptionally 

high chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the watershed-average curves (F) represent the averages of 

the classes of sample indicated by the superimposed character. 

These samples are not included in LOADEST models.
134 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_05E.ai

A. Cayaguás B. Canóvanas

C. Icacos D. Mameyes

E. Guabá F. Watershed averages

0.01

Runoff rate, in millimeters per hour

Dissolved chloride, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

10

100

10

1,000

100

1,000

100

1,000

2,000

2,000

10

2,000

10 100

25 50 75 90 95

25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

25 50 75 90 95

EXPLANATION

Runoff recurrence 90

Mameyes

High chloride Icacos

Calcite

Low silica

Falling stage

Guabá

Cayaguás

High potassium

Rising stage Canóvanas

Canóvanas

Cayaguás

Mameyes

LOADEST

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 135

Luquillo Mountains and is the farthest inland of the Puerto 

Rico WEBB watersheds, did not demonstrate any high-Cl−

events; the Canóvanas, on the western flank of the mountains, 

demonstrated subdued events. This pattern suggests that most 

of the high-Cl−

 rainwater is deposited on the windward side of 

the Luquillo Mountains. Substantial atmospheric loadings of 

Cl− (along with Na+, NO3

−

 and SO4

2−) to the Icacos watershed 

were also noted by Peters and others (2006). Presumably, 

the high concentrations are related to high winds and breaking waves in the ocean upwind of the watersheds (Stallard, 

2012a). Because the high-Cl− events have no relation to 

the explanatory variables (discharge, time, and seasonality) 

used in LOADEST, they were excluded from the LOADEST 

regressions. Our estimates of yields of Cl− and all constituents associated with seasalt are therefore slightly lower than 

if high-Cl− samples were included. We do not know how 

much lower, because the National Atmospheric Deposition 

Program dataset (which was used to estimate Cl− inputs) has 

major data gaps, many during storms (including Hurricane 

Georges); however, the overall error appears to be small 

(Stallard, 2012a). 

Calcium, Magnesium, and Sodium

Dissolved Ca2+, Mg2+ and Na+ are dominated by a 

bedrock weathering source, but they can have appreciable 

atmospheric inputs from seasalt (Mg2+ and Na+) or minor 

soluble desert dust (Ca2+) (Stallard, 2012a). The concentrations of Ca2+, Mg2+, and Na+

 in all of the study watersheds 

usually decreased with increasing runoff (fig. 6, table 3), and 

falling stages tended to have lower concentrations than rising 

stages. Stream samples from the Mameyes and Canóvanas 

watersheds contained distinctly elevated Ca2+ concentrations 

compared with the other study watersheds, and the Canóvanas 

also contains contained elevated Mg2+ (fig. 6). The bedrock of 

these volcaniclastic watersheds is somewhat richer in Ca2+-

bearing and Mg2+-bearing silicate minerals than the granitic 

watersheds (Jolly and others, 1998; Smith and others, 1998; 

Stallard, 2012b). Minor bedrock carbonate minerals, which 

can be a source of abundant Ca2+ and sometimes Mg2+ (Stallard, 1995a,b), have been documented in the bedrock units 

underlying the Canóvanas and Mameyes watersheds (Murphy 

and others, 2012). Rivers draining carbonate-rich watersheds in western Puerto Rico demonstrated only minor drops 

in conductivity through major storm events (Haire, 1972) 

because carbonate rocks dissolve rapidly. All eastern Puerto 

Rico WEBB watersheds, in contrast, showed decreases in 

conductivity with increasing runoff (fig. 3), suggesting that 

the elevated Ca2+ and Mg2+ were not caused by the carbonate 

minerals but rather by the silicate minerals. 

Sodium concentrations, similar to concentrations of Cl−, 

were typically highest in the developed Canóvanas and Cayaguás watersheds. This pattern may be due to a greater ratio of 

evapotranspiration to runoff in these drier watersheds (Murphy and Stallard, 2012), which would concentrate the seasalt 

contribution, or due to domestic inputs (or both). Low-Si(OH)4

samples tended to also have slightly lower Ca2+ (fig. 6), 

although the effect was not pronounced, indicating that the 

mechanism causing low Si(OH)4 may affect other bedrockderived constituents. 

All three cations demonstrate high concentrations during 

the high-runoff, high-Cl− events discussed earlier in the Chloride section. Accordingly, the cation concentration data for 

these samples were excluded from the LOADEST calculations 

and regressions. 

For all study watersheds, comparison of the slopes (C1) 

of Ca2+, Mg2+, Na+, and Cl− for the regression of log(C) and 

log(R) demonstrates that the slope of Ca2+ is the most negative, followed by Mg2+, then Na+

, and finally Cl− (table 4). 

This order represents a continuous variation from constituents 

derived primarily from bedrock weathering to those derived 

from seasalt, and it is consistent with a mix of the two sources: 

bedrock weathering deep in the profile, and evaporated rainfall, distributed more widely through the soil profile. 

Dissolved Organic Carbon

Dissolved organic carbon is uniquely derived from 

plant material, either as the product of breakdown or as 

exudates (Stumm and Morgan, 1981). The ultimate source 

of DOC is atmospheric carbon dioxide fixed through 

photosynthesis. DOC was slightly greater in the developed 

watersheds, perhaps reflecting the differences in land cover 

or the effects of agricultural on soils. For all watersheds, 

DOC concentrations increased with increasing runoff rates 

and reached a rough peak in the 75 to 90 percent or 90 to 

95 percent runoff percentiles (table 3). At the very highest 

runoff rates, DOC concentrations tended to decrease, and 

differences among the watersheds lessened (fig. 7). This 

trend was also observed by Shanley and others (2011), who 

used other sources to extend the present dataset for the Icacos watershed from 1981 to 2008. 

The decrease in concentrations at high runoff could imply 

a supply limitation, whereby the processes that generate and 

release DOC cannot supply it at a rate that compensates for 

the increased throughput of water. However, DOC concentrations are highest on falling stages (fig. 7), opposite to what is 

expected from a supply limitation.

Figure 5 (facing page). Runoff rate–concentration graphs for

chloride. Additional sample characteristics are indicated with 

black symbols: calcite, samples supersaturated with respect 

to calcite; high chloride, samples with exceptionally high 

chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the watershed-average curves (F) represent the averages of 

the classes of sample indicated by the superimposed character. 

These samples are not included in LOADEST models.
136 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A. Calcium, all data B. Calcium averages

C. Magnesium, all data D. Magnesium averages

E. Sodium, all data F. Sodium averages

0.01

Runoff rate, in millimeters per hour

Constituent concentration, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

10

100

1

10

100

1,000

2,000

1,000

1

10

100

1,000

2,000

10 100

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 137

Alternatively, a DOC decrease with increasing runoff 

rate may reflect a change to flow paths that involves less 

interaction and contact time with soil organic matter. At lowest runoff rates, stream water has moved through deep soils, 

deeper riparian zones, and hyporheic zones (fig. 2). With the 

exception of the hyporheic zone, these zones contain relatively little organic matter. At intermediate runoff rates, more 

water is presumably moving through shallow-soil pathways 

and riparian zones which, if present, are likely to be flooding; this flooding would represent optimum conditions for 

interacting with shallow-soil organic matter. Finally, at the 

highest runoff rates, overland flow is important because it 

reduces the overall interaction of water with organic matter. 

At runoff rates of roughly 3 to 10 mm h−1 DOC concentrations begin their substantial decline, reflected by the downward curvature of diagrams shown in fig. 7. This decline 

suggests that this range of runoff rates may mark a transition 

to a regime where overland flow and associated reduced soil 

interaction become important. Regardless of the mechanism, 

these data show that the highest discharge classes (greater 

than 90 percent) must be included when one calculates DOC 

loads and yields from watersheds. Without the high-runoff 

data, yields will be overestimated.

The curves of averaged concentrations based on LOADEST calculations do not always pass through the middle of the 

concentration-data field at middle-range runoff rates (fig. 7). 

A likely factor is the lower collection density of middle-range 

runoff samples (appendix 1), which will not alias the regression residual as strongly. 

For every study river, the regression of log(DOC) on 

log(R), using the primary data, has statistically significant 

downward curvature. The position of the peak is calculated for 

the zero of the first derivative

Peak = 10(−C1/(2•C2))

and peaks range from 2.5 mm h−1 to 8.6 mm h−1. 

The Nutrients: Nitrate, Ammonium Ion, 

Phosphate, and Potassium

Nitrate, NH4

+

, −PO4

3− and K+

 are considered to be the 

principal plant nutrients. Of these, throughfall is especially 

enriched in NH4

+

, −PO4

3− and K+

 when compared with 

rainwater (Heartsill-Scalley, 2007). Nitrate and NH4

+

 have 

atmospheric sources, either dissolved in precipitation or as 

a result of plant fixation. Both can be lost from a watershed 

by conversion back to gaseous forms. Phosphate is primarily 

derived from bedrock, but it can also be deposited in eastern 

Puerto Rico as Saharan dust (Pett-Ridge and others, 2009b). 

Phosphate is an important plant nutrient that is also easily 

sequestered by chemical reactions with Al3+ or Fe3+ ions; 

distinguishing these two mechanisms of −PO4

3− removal is 

difficult. Potassium is mostly bedrock derived but can also 

be atmospherically deposited in seasalt (some K+ arrives in 

Saharan dust, but in the form of insoluble clay minerals; Reid 

and others, 2003). Potassium and NH4

+

 can be weakly held on 

cation-exchange sites in soil. In the developed watersheds, all 

of these nutrients can be added by manmade products such as 

fertilizers or by domestic or industrial wastes.

The runoff-concentration relations for K+, NO3

−

, NH4

+

, 

and −PO4

3− sometimes demonstrate considerable scatter 

(figs. 8, 9), and diagrams of these relations do not demonstrate 

the steeply decreasing curves observed for Si(OH)4, Cl−, Ca2+, 

Mg2+, and Na+. For each nutrient at almost every runoff rate, 

the concentration-runoff curves representing the developed 

watersheds (Canóvanas and Cayaguás) illustrate substantially 

higher concentrations than those representing the forested 

watersheds (figs. 8, 9); it is likely that this difference reflects 

human inputs, such as fertilizers or agricultural and domestic 

wastes. Precise identification of sources would require detailed 

surveys that were not part of this study.

A number of high-runoff samples from the Mameyes, 

Icacos, and Guabá watersheds demonstrated elevated K+ concentrations (fig. 8). These samples, however, did not have high 

concentrations of Cl−, so elevated K+ probably was not caused 

by windblown seasalt. These high-K+

 events were recorded 

in the Icacos and Guabá watersheds only during the year after 

Hurricane Georges (1998–1999) and in the Mameyes watershed mostly after Georges (appendix 1, figs. 1–5). After Hurricane Hugo in 1989, stream-water concentrations of K+

, NO3

−

, 

and NH4

+

 doubled and remained elevated for almost 2 years; 

K+ and NO3

−

 returned to average concentrations at the same 

time that the canopy recovered, suggesting biological control on the cycling of these ions (Schaefer and others, 2000; 

concentrations of Na+, Mg2+, Ca2+, Cl−, and SO4

2− showed little 

effect). The elevated K+ concentrations are consistent with 

other post-Hurricane Georges observations that indicate the 

release of K+ from litter (Ostertag and others, 2003). The highK+

 events in this study, however, did not appear to be associated with higher concentrations of either −PO4

3− or NH4+

; also, 

not all the storms in the year following the hurricane resulted 

in high K+ concentrations. It is possible that the absence of 

high concentrations of either −PO4

3− or NH4+

 was a sampling 

Figure 6 (facing page). Runoff rate–concentration graphs 

for calcium, magnesium, and sodium ions. The left diagrams 

represent the Water, Energy, and Biogeochemical Budget program 

dataset. The right diagrams represent averages calculated from 

hourly estimates determined using LOADEST. Additional sample 

characteristics are indicated with black symbols; calcite, samples 

supersaturated with respect to calcite; high chloride, samples 

with exceptionally high chloride concentrations collected during 

huge storms; high potassium, samples with high potassium but 

not high chloride; low silica, Icacos and Guabá samples with 

unusually low silica concentrations for the runoff rate. The 

points separated from the watershed-average curves (B, D, F) 

represent the averages of the classes of sample indicated by 

the superimposed character. These samples are not included in 

LOADEST models.
138 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_07E.ai

A. Cayaguás B. Canóvanas

C. Icacos D. Mameyes

E. Guabá F. Watershed averages

0.01

Runoff rate, in millimeters per hour

Dissolved organic carbon, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

10

100

10

1,000

100

1,000

100

1,000

2,000

2,000

10

2,000

10 100

25 50 75 90 95

25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

25 50 75 90 95

EXPLANATION

Runoff recurrence 90

Mameyes

High chloride Icacos

Calcite

Low silica

Falling stage

Guabá

Cayaguás

High potassium

Rising stage Canóvanas

Canóvanas

Cayaguás

Mameyes

LOADEST

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 139

artifact, because K+ was analyzed in considerably more 

samples than −PO4

3− or NH4+

, and analytical errors for K+ are 

relatively smaller (appendix 2).

Some samples from the Icacos, Guabá, and Mameyes 

watersheds demonstrated especially low NO3

−

 concentrations 

(fig. 9). These samples, which were collected during hurricanes, also always have high Cl−

 concentrations. Hurricanes 

derive their energy and moisture from maritime air masses that 

presumably lack a strong contribution of pollutants, such as 

NO3

−

, from continents.

Regardless of the reason, because of their occasional 

and enigmatic nature, the high-K+ samples and low-NO3

−

samples collected during high-Cl− events have no obvious 

relation to the explanatory variables used in LOADEST, 

so these solutes for these samples were excluded from the 

LOADEST calculations.

Sulfate

Sulfur derives from a complex array of geologic and 

atmospheric sources. Geologic sources include reduced sulfur 

from sulfides, pyrite, and (rarely) elemental sulfur, and from 

sulfate salts from minerals such as anhydrite and gypsum. 

Minor sulfides and pyrite are found in small mineralized zones 

surrounding the Río Blanco intrusion in the Luquillo Mountains (Murphy and others, 2012). Atmospheric sources of 

sulfur include seasalt, Saharan dust (in the form of anhydrite 

and gypsum; Reid and others, 2003), volcanic eruptions, burning of fossil fuels, and reduced sulfur gases from marine and 

terrestrial sources (Stallard and Edmond, 1981; Andreae and 

Andreae, 1988; Stallard, 2012a). Atmospheric sulfur is deposited as SO4

2−. Sulfur, like organic carbon, NO3−

, and NH4

+

, can 

be lost from a watershed as a gas (Stallard, 2012a).

The runoff-concentration relations for SO4

2− in the Puerto 

Rico WEBB watersheds differed substantially (fig. 10). Sulfate 

concentrations were generally lowest in the forested, granitic 

watersheds (Icacos and Guabá) and highest in the developed 

watersheds (Canóvanas and Cayaguás). Sulfur concentrations in the forested, volcaniclastic Mameyes watershed were 

similar to those in the developed watersheds at low runoff 

rates, but at high runoff rates, concentrations were similar to 

those in the Icacos and Guabá watersheds (fig. 10). The Icacos 

and Guabá watersheds have runoff-concentration curves that 

are relatively flat (similar to the curves of DOC and nutrients), 

whereas SO4

2− concentrations in the remaining watersheds 

decrease with increasing runoff (similar to the constituents 

that are not particularly bioactive and have substantial bedrock 

and atmospheric sources: Na+, Mg2+, and Ca2+). Owing to the 

low level of human activity in the Mameyes watershed, the 

high concentrations at low runoff probably reflected a geologic 

source. It is hard to say whether the high concentrations at low 

discharge in the Canóvanas and Cayaguás are entirely natural 

or are influenced by minor contamination, because without 

detailed investigation, it would be difficult to distinguish 

an exclusively geologic source from one with added human 

inputs, such as fertilizers.

Sulfate-to-Cl− ratios provide information about sources 

of sulfur (Stallard and Edmond, 1981). The SO4

2−-to-Cl−

 mole 

ratio of seasalt, 0.051, is the lowest of all SO4

2− sources; 

precipitation in eastern Puerto Rico has a higher average 

SO4

2−-to- Cl−

 ratio (about 0.106), because it contains not only 

seasalt but also sulfur from gas phase and dust sources (Stallard, 2012a). Sulfate-to-Cl−

 ratios in stream samples from the 

developed, farther-inland watersheds (Canóvanas and Cayaguás) are always greater than the rainfall ratio and increase 

with runoff, perhaps reflecting an increased contribution of 

SO4

2− from vegetation or agricultural fertilizers (fig. 10). The 

forested, volcaniclastic Mameyes watershed also has SO4

2−-

to-Cl− ratios greater than the rainfall ratio (except for high 

Cl− events), but these ratios decrease slightly with increasing 

runoff. The SO4

2−-to-Cl−

 ratios in samples from the forested, 

granitic watersheds (Icacos and Guabá) are exceptionally low 

(equal to or below the seasalt ratio) at low runoff rates, and 

they increase to ratios above rainwater at high runoff rates 

(except for high-Cl− events). The exceptionally low ratios at 

low runoff may be due to the transformation of SO4

2− into 

reduced sulfur gases (such as dimethyl sulfide) in the soil, 

whereupon the sulfur is emitted to the atmosphere (Stallard 

and Edmond, 1981). Alternatively, these watersheds may have 

no substantial geologic source of sulfur, and because sulfur is 

a nutrient (a part of some amino acids), it should be retained 

in biomass and organic matter in shallow soils (such as N, P, 

and K) during lower-runoff conditions only to be subsequently 

released to the stream during high-runoff events. Soil chemical 

analyses (Stallard, 2012b), however, show no sulfur in mineral 

soil or stream sediment.

Alkalinity, Carbon Dioxide, and Calcite Saturation

 Alkalinity is the amount of anion charge per unit volume 

that can be titrated with a strong acid to reach the first equilibrium endpoint for the CO2

 system (Stumm and Morgan, 1981). 

Alkalinity is a conservative property. The primary source of 

alkalinity is the weathering of silicate and carbonate minerals; 

it can be modified by addition or loss of NO3

−

, NH4

+

, organic 

acids, and sulfuric acid from sulfide weathering and acid precipitation. Higher alkalinities tend to be associated with higher 

pH values, but for a given alkalinity, the vapor pressure of CO2

controls pH. 

Figure 7 (facing page). Runoff rate–concentration graphs for 

dissolved organic carbon. Additional sample characteristics are 

indicated with black symbols: calcite, samples supersaturated 

with respect to calcite; high chloride, samples with exceptionally 

high chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the watershed-average (F) curves represent the averages of the 

classes of sample indicated by the superimposed character.
140 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_08E.ai

A. Cayaguás B. Canóvanas

C. Icacos D. Mameyes

E. Guabá F. Watershed averages

0.01

Runoff rate, in millimeters per hour

Dissolved potassium, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

1

10

10

100

300

300

10

300

10 100

25 50 75 90 95

25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

25 50 75 90 95

10

100

10

100

EXPLANATION

Runoff recurrence 90

Mameyes

High chloride Icacos

Calcite

Low silica

Falling stage

Guabá

Cayaguás

High potassium

Rising stage Canóvanas

Canóvanas

Cayaguás

Mameyes

LOADEST

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 141

The study-area rivers draining volcaniclastic rocks tend 

to have higher alkalinities than rivers draining granitic rocks 

(fig. 11). Two geologic factors may contribute to this situation: the volcanic rocks have higher concentrations of Ca2+ 

and Mg2+ compared with the granitic rocks (Jolly and others, 

1998; Smith and others, 1998), and minor calcite is present in 

some of the volcaniclastic rocks (Murphy and others, 2012). 

Negative alkalinity (that is, acidity) is observed in event 

samples collected at the highest runoff rates in the Icacos and 

Guabá watersheds, reflecting the importance of the various 

acid inputs, such as acid rain, nitrate formation, and DOC, to 

these watersheds. 

The partial pressure of carbon dioxide (PCO2), CO2 saturation, and the saturation index for calcite, SIcalcite, were calculated from alkalinity, temperature, and pH following Stumm 

and Morgan (1981; appendix 1). These parameters are useful 

in assessing the state of carbonate-system thermodynamics 

and, when combined with O2, they can be used to identify 

in-stream respiration. Oxygen is a bioactive gas that is readily 

exchanged between water and the atmosphere and is linked to 

CO2 through photosynthesis, respiration, decay, and combustion. Using CH2

O as chemical shorthand for organic matter, 

the photosynthesis reaction is simple:

←decay, respiration

CO2

 + H2O ↔ CH2O + O2.

photosynthesis→

According to this biologically mediated reaction, when 

the concentration (and hence degree of saturation) of CO2

rises, O2 should fall (the reverse is true for combustion, decay, 

and respiration). The degree of saturation of both gases should 

rise with an increase in temperature.

The rivers that drain granitic bedrock (Icacos, Guabá, and 

Cayaguás) have higher degrees of CO2 saturation, whereas 

the rivers that drain volcaniclastic bedrock (Mameyes and 

Canóvanas) are only slightly oversaturated or are even undersaturated (fig. 11). The rivers that drain granitic rocks are less 

steep and turbulent than those that drain volcaniclastic rocks, 

and therefore they probably have greater exchange with flood 

plains and sandy beds. Turbulent flow and rapid gas exchange 

as rivers cascade over steep rocky beds in the volcaniclastic 

watersheds would drive the water towards equilibrium with 

the atmosphere. The few grab samples collected from the Icacos watershed at high runoff rates are close to saturation, indicating that this river equilibrates with the atmosphere when the 

river is quite turbulent. The undersaturation of CO2

 (fig. 11), 

primarily in the Canóvanas watershed, is probably due to 

in-channel photosynthesis by aquatic plants, which raises pH 

and O2 (fig. 3) and reduces CO2. In all the other rivers, CO2 

is usually supersaturated and O2 undersaturated, indicating a 

dominance of respiration and decay. 

The undersaturation of CO2 caused by in-stream photosynthesis appears to have driven the high-alkalinity Canóvanas 

water into supersaturation with respect to calcite at low-runoff 

conditions (fig. 11). Evidence of calcite precipitation, such as 

tufa deposits, has not been investigated. The lack of abundant limestone or calcite-bearing bedrock in the Canóvanas 

watershed suggests that the source of the supersaturated waters 

is silicate weathering, perhaps accompanied by minor calcite weathering. Soil solutions equilibrate with the high-CO2

environment of soils and outgas once groundwater enters the 

channel. The outgassing could lead to calcite supersaturation. 

Suspended Sediment

Three sources contribute suspended sediment to the 

eastern Puerto Rico WEBB watersheds: surficial erosion, 

landslides, and bed and bank erosion (Larsen, 2012). This 

sediment can either be transported out of the watershed by 

rivers or stored in the watershed as colluvium and alluvium. 

Suspended sediment concentrations in all rivers are low except 

during major storms when they increase rapidly with runoff rate (fig. 12). All of the LOADEST curves have a slight 

downward curvature, except for the Icacos watershed. For 

the Canóvanas and Cayaguás watersheds, runoff must exceed 

the 50th percentile of runoff rate before concentrations of 

100 milligrams per liter are reached; for the Icacos and Guabá, 

the 75th percentile must be exceeded, and for the Mameyes, 

the 90th percentile must be exceeded (table 4). A few samples 

from highest runoff rates in the Cayaguás, Canóvanas, and the 

Icacos watersheds had exceptionally high concentrations of 

suspended sediment; one Icacos sample contained more than 

100 grams per liter. The cause of these high concentrations is 

unknown. The Icacos and Cayaguás river beds are very sandy; 

in fact, sand is extracted commercially in the Río Cayaguás. 

It is possible that the bed was substantially mobilized and that 

the water intake nozzle of the sampler, which is not far above 

the river bed, may have been in the boundary layer. Alternatively, several major events have caused landslides to fall 

directly into the Icacos channel, so these high concentrations 

may reflect diluted debris flows. 

The suspended-sediment yields reported here are considerably greater than yields reported in all previous estimates 

(McDowell and Asbury, 1994; Brown and others, 1995; 

Larsen, 1997; Larsen and Stallard, 2000). The yields reported 

here reflect the large events captured during the Puerto 

Rico WEBB program, and they allow the relation between 

Figure 8 (facing page). Runoff rate–concentration graphs for 

potassium ion. Additional sample characteristics are indicated 

with black symbols; calcite, samples supersaturated with 

respect to calcite; high chloride, samples with exceptionally 

high chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the watershed-average curves (F) represent the averages of 

the classes of sample indicated by the superimposed character. 

These samples are not included in LOADEST models.
142 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A. Nitrate, all data B. Nitrate averages

C. Ammonium ion, all data D. Ammonium ion averages

E. Phosphate, all data F. Phosphate averages

0.01

Runoff rate, in millimeters per hour

Constituent concentration, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

0.01

0.1

0.1

1

100

10

1

10

300

0.1

1

10

100

300

10 100

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 143

Figure 9 (facing page). Runoff rate–concentration graphs for

nitrate, ammonium ion, and phosphate ions. Additional sample 

characteristics are indicated with black symbols; calcite, samples 

supersaturated with respect to calcite; high chloride, samples with 

exceptionally high chloride concentrations collected during huge 

storms; high potassium, samples with high potassium but not high 

chloride; low silica, Icacos and Guabá samples with unusually low 

silica concentrations for the runoff rate. The points separated from 

the watershed-average curves (B, D, F) represent the averages of 

the classes of sample indicated by the superimposed character. 

These samples are not included in LOADEST models.

suspended sediment and runoff rate to be characterized at 

much higher runoff rates than ever before. In addition, the 

period 1996 to 2005 was a much wetter period than during 

previous studies.

Sediment yields from all watersheds were considerably 

greater than Saharan dust loadings of about 21±7 t km−2 yr−1

(Pett-Ridge and others, 2009b). This relation indicates that 

Saharan dust is probably not a major component of the mass 

budget in this landscape.

Conclusions

The water quality of the eastern Puerto Rico WEBB 

rivers, in terms of pH, dissolved oxygen, dissolved constituents, and suspended solids, is quite good, despite evidence of 

amphibian die-offs and increasing atmospheric deposition of 

nitrate. Despite substantial differences in land cover, annual 

runoff, or bedrock type, the water quality of these WEBB rivers is not marked by extreme contrasts. The yields of constituents derived largely from bedrock weathering (Ca2+, alkalinity, 

and Si(OH)4), from seasalt (Cl−), or from a mix of the two 

sources (Na+, Mg2+, K+, and SO4

2−) differed by a factor of less 

than 2 among all watersheds, showing little relation to land 

cover, annual runoff, or bedrock type. Average concentrations 

of these constituents tended to be higher in the developed, 

leeward watersheds (Cayaguás and Canóvanas) compared 

with the forested, windward watersheds (Icacos, Guabá, 

and Mameyes). The developed watersheds had substantially 

higher yields of nutrients (NO3

−

, NH4

+

, and −PO4

3−) than the 

forested watersheds. In-stream productivity in the Canóvanas 

watershed is sufficiently great to cause oxygen supersaturation. It is possible that concentrations of some constituents are 

higher in the rivers draining the developed, leeward watersheds because of lower mean-annual runoff and higher ratios 

of evapotranspiration to runoff than is true in the forested, 

windward watersheds. However, the developed watersheds 

have higher concentrations of Na+

, Cl−, SO4

2-, K+

, NO3

−

, NH4

+

, 

and −PO4

3− at a given discharge; this constituent profile, 

along with the higher nutrients yields, suggests that additional 

sources, most likely human activities related to agriculture and 

domestic waste rather than to evapotranspiration, are causing 

the difference. Suspended-sediment yields were generally low 

during major storms, when a substantial amount of annual 

sediment export occurred. Suspended sediment yields from 

the granitic watersheds were about six times as great as from 

the volcaniclastic watersheds, independent of land cover and 

mean-annual runoff. The granitic watersheds generated about 

2,000 t km−2 yr−1; this value is greater than previous estimates 

of sediment yield from these watersheds and may be due to 

a wetter sampling period or to the sampling of greater runoff 

events (or both).

Several anomalous types of water-quality samples were 

identified. Samples with very high Cl−

 concentrations were 

associated with some hurricanes and other large storms. 

These samples were associated with elevated concentrations 

of other ions that are abundant in seasalt (Na+

, K+, Mg2+, 

Ca2+, SO4

2−), and often anomalously low NO3

−

 concentrations. This composition reflects windborne seasalt from the 

ocean and a dominance of maritime, rather than continental, 

air masses contributing to the storms. Hurricane Georges, 

the largest storm of our study period, led to short-term and 

long-term changes in water quality in the forested, windward 

watersheds. The storm itself, like those described above, 

produced anomalously high concentrations of seasalt-related 

constituents and low NO3

−

 concentrations. For about 1 to 2 

years after the hurricane, occasional high-K+

 event samples 

were recovered that lacked correspondingly high concentrations of Cl− and other seasalt-related ions; therefore the K+

cannot be attributed to seasalt inputs. These samples may 

indicate the release of potassium from litter deposited by the 

storm, consistent with post-Hurricane Georges observations 

(Ostertag and others, 2003). Prior to Hurricane Georges, but 

not after, samples with anomalously low concentrations of 

silica were recorded in the forested watersheds. The lowsilica events may have been caused by a change in hydrologic 

pathway during some storms that resulted in shallow flow 

paths; Georges may have led to the clogging or collapse of 

such macropores and pipes. These anomalous samples were 

not included in load and yield regressions.

For most constituents, the rivers show similar trends in 

runoff–dissolved constituent concentration graphs, suggesting considerable similarity in runoff generation and flow path 

structuring despite differences in geology, soils, and land 

cover. The nonbioactive, bedrock-derived and atmospherically derived constituents (Si(OH)4

, Ca2+, Mg2+, Na+, and 

Cl−

) decrease in concentration with increasing discharge. The 

trends of the primary data are linear, while some curvature is 

seen for smoothed data. No trends are sigmoidal. These observations are consistent with the permeability-porosity-aperture 

model. All the slopes, C1

, for all the bedrock- and seasaltderived constituents tend to be lower than for most of the rivers in the Hydrologic Benchmark Network dataset (which are 

mostly in the −0.15 to −0.05 range). For a given river, the various slopes of bedrock-derived constituents are not statistically 

identical. The slope of Si(OH)4

, which is exclusively derived 

from bedrock weathering, differs considerably from that of the 
144 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_10E.ai

A. Sulfate, all data B. Sulfate averages

C. Sulfate to chloride ratio, all data D. Sulfate to chloride ratio, averages

0.01

Runoff rate, in millimeters per hour

Sulfate to chloride, as mole ratio Sulfate, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

0

0.1

0.2

0.3

0.4

4

10

0.5

0.6

0.7

100

10 100

Rainwater

Seasalt

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 10. Runoff rate–concentration graphs for sulfate and sulfate:chloride ratio. Additional sample characteristics are indicated 

with black symbols; calcite, samples supersaturated with respect to calcite; high chloride, samples with exceptionally high chloride 

concentrations collected during huge storms; high potassium, samples with high potassium but not high chloride; low silica, Icacos 

and Guabá samples with unusually low silica concentrations for the runoff rate. The points separated from the watershed-average 

curves (B, D) represent the averages of the classes of sample indicated by the superimposed character. These samples are not 

included in LOADEST models.
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 145

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_11E.ai

A. Alkalinity, all data B. Alkalinity averages

C. Carbon dioxide, all data D. Calcite, all data

0.01

Runoff rate, in millimeters per hour

Carbon dioxide saturation ratio Alkalinity, in microequivalents per liter

0.01 0.1 1 0.1 1 10 100

0.1

1

10

Calcite saturation ratio

1

0.1

0.01

0.001

0.0001

10

1

10

100

1,000

100

10,000

10 100

Saturation

Saturation

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 11. Alkalinity, carbon dioxide saturation, and calcite saturation in the eastern Puerto Rico WEBB rivers, 1991–2005. 

Additional sample characteristics are indicated with black symbols; calcite, samples supersaturated with respect to calcite; 

high chloride, samples with exceptionally high chloride concentrations collected during huge storms; high potassium, samples 

with high potassium but not high chloride; low silica, Icacos and Guabá samples with unusually low silica concentrations for the 

runoff rate. The points separated from the sample-average curves (B) represent the averages of the classes of sample indicated 

by the superimposed character. These samples are not included in LOADEST models.
146 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\figure_12E.ai

A. Cayaguás B. Canóvanas

C. Icacos D. Mameyes

E. Guabá F. Watershed averages

0.01

Runoff rate, in millimeters per hour

Suspended solids, in milligrams per liter

0.01 0.1 1 0.1 1 10 100

0.1

1

10

100

1,000

10,000

100,000

0.1

1

10

100

1,000

10,000

100,000

0.1

1

10

100

1,000

10,000

100,000

10 100

25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

25 50 75 90 95 25 50 75 90 95

EXPLANATION

Runoff recurrence 90

Mameyes

High chloride Icacos

Calcite

Low silica

Falling stage

Guabá

Cayaguás

High potassium

Rising stage Canóvanas

Canóvanas

Cayaguás

Mameyes

LOADEST

Guabá

Icacos
Water Quality and Mass Transport in Four Watersheds in Eastern Puerto Rico 147

Figure 12 (facing page). Runoff rate–concentration graphs 

for suspended sediment. Additional sample characteristics are 

indicated with black symbols; calcite, samples supersaturated 

with respect to calcite; high chloride, samples with exceptionally 

high chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the watershed-average curves (F) represent the averages of the 

classes of sample indicated by the superimposed character.

most abundant bedrock-derived ion, Ca2+. This finding is not 

consistent with a single common slope for all bedrock-derived 

constituents. For each river, however, the slopes of the ionic 

constituents are ranked according to the relative contribution 

of bedrock and seasalt, from most negative to least negative, 

Ca2+, Mg2+, Na+, and Cl−. After excluding the high-Cl− storms, 

chloride has a slope of −0.20 to −0.14. Chloride is atmospherically derived, so this trend must be caused by the mixing of 

old, evaporated soil waters with younger, typically more dilute 

rainwater. Focusing on Si(OH)4, Ca2+, and Mg2+ to limit seasalt 

effects, the slopes of the linear relations for granitic rivers are 

consistently less than for the volcaniclastic rivers, suggesting 

that the physics of water flow through the different soils is 

slightly different and grouped by geology. A precise physical explanation is limited by the inability to measure, in the 

field, the parameters that contribute to C1 in the permeabilityporosity-aperture model.

The strongly bioactive constituents typically demonstrate two types of concentration-runoff relations. Some of 

the bioactive constituents have nearly constant runoff-rate–

to–concentration relations (a chemostat), ordinarily with 

considerable scatter. For DOC, and for some of the remaining bioactive constituents in some rivers, concentrations 

increase as runoff increases, until runoff rates reach between 

3 and 10 mm h−1. At still higher runoff rates, concentrations decrease. This sequence can be interpreted in terms of 

a simplified soil-hydrology model. At low discharge, most 

water is presumably arriving through deeper flow paths with 

less-available bioactive constituents. With increasing runoff, 

flow paths are more shallow, more-available constituents are 

removed, and concentrations rise. At the highest runoff, overland flow and macropore flow become important. These paths 

have less contact with the soil matrix and do not generate 

waters high in bioactive constituent concentrations; accordingly, stream concentrations drop. An alternate explanation, 

solute depletion during storms, should cause recessions to 

have lower concentrations than rises. None of the bioactive 

constituents show this behavior.

Acknowledgements

This work was supported through the United States Geological Survey Water, Energy, and Biogeochemical Budgets 

program (Larsen and others, 1993; Lins, 1994). This chapter 

benefited from reviews by Brent Aulenbach, Alisa Mast, and 

Jamie Shanley. Without the initial groundwork by Angel 

Torres-Sánchez in the field and Ellen Axtmann in the lab this 

report would not have been possible.

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Landslides and Sediment Budgets in Four 

Watersheds in Eastern Puerto Rico

By Matthew C. Larsen

Chapter F of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–F

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.......................................................................................................................................................157

Introduction.................................................................................................................................................157

Landslides ..........................................................................................................................................159

Slopewash..........................................................................................................................................159

Soil Creep...........................................................................................................................................159

Treethrow...........................................................................................................................................159

Suspended Sediment .......................................................................................................................159

Watershed Sediment Budgets ................................................................................................................161

Analysis of Geomorphic Processes........................................................................................................161

Landslides ..........................................................................................................................................161

Slopewash..........................................................................................................................................162

Soil Creep...........................................................................................................................................162

Treethrow...........................................................................................................................................164

Fluvial Sediment................................................................................................................................165

Sediment Storage .............................................................................................................................165

Amounts, Rates and Characteristics of Hillslope Erosion, Sediment Storage, and Transport, 

and Relation to Landform Development ...................................................................................165

Landslides ..........................................................................................................................................165

Landslide Frequency ...............................................................................................................166

Landslide Erosion Rate ...........................................................................................................167

Slopewash..........................................................................................................................................167

Soil Creep...........................................................................................................................................168

Treethrow...........................................................................................................................................168

Fluvial Sediment................................................................................................................................169

Sediment Storage and Sediment Budgets ...................................................................................170

Sediment Storage in Channel Beds, Bars, and Floodplains .............................................170

Colluvium, Hillslope Sediment, and Core Stones................................................................171

Time Scales and Anthropogenic Effects .............................................................................171

Erosion and Sediment Export: Are They in Balance? ........................................................172

Landform Development...........................................................................................................172

Summary and Conclusions.......................................................................................................................173

Acknowledgments.....................................................................................................................................174

References..................................................................................................................................................174
Figures

1. Map showing location of Puerto Rico and study watersheds, eastern 

Puerto Rico ................................................................................................................................158

2. Photographs of landslides in study watersheds, eastern Puerto Rico............................160

3. Shaded relief maps of study watersheds showing locations of landslide 

scars, eastern Puerto Rico......................................................................................................163

4. Graph showing fluvial suspended sediment yield versus runoff in study 

watersheds, eastern Puerto Rico ..........................................................................................170

Tables

1. Geologic, geographic, topographic, and hydrologic characteristics of study

watersheds, eastern Puerto Rico ..........................................................................................164

2. Dimensions of 2,882 landslides mapped from aerial photographs (1937, 1951, 

1962, 1972, 1974, 1979, 1990, and 1995) in study watersheds, eastern 

Puerto Rico ................................................................................................................................166

3. Types and estimated rates of hillslope erosion in study watersheds, eastern 

Puerto Rico ................................................................................................................................168

4. Estimated annual soil creep in study watersheds, eastern Puerto Rico.........................168

5. Annual fluvial suspended sediment, 1991–2005, in study watersheds, 

eastern Puerto Rico..................................................................................................................169

6. Estimated sediment in storage in study watersheds, eastern Puerto Rico, in channel 

beds, bars, floodplains, and Quaternary terraces...............................................................171

Abbreviations Used in This Report

> greater than

cm centimeter

ha hectare

ha yr−1 hectares per year

kg km−2 h−1 kilograms per square kilometer per hour

kg km−2 mm−1 kilograms per square kilometer per millimeter

km kilometer

km2 square kilometer

km–2 century–1 per square kilometer per century

m meter

m yr−1 meters per year

m2 square meter

m3 cubic meter

mg L−1 milligrams per liter
mm millimeter

mm h−1 millimeters per hour

mm yr−1 millimeters per year

t metric ton

t km−2 metric tons per square kilometer

t km−2 yr−1 metric tons per square kilometer per year

t m−3 metric tons per cubic meter

GIS geographic information system

Conversion Factors

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)

Volume

cubic meter (m3

) 35.31 cubic foot (ft3)

Flow rate

millimeters per hour (mm h−1) 0.03937 inches per hour (in. h−1)

millimeters per year (mm yr−1) 0.03937 inches per year (in. yr−1) 

meters per year (m yr−1) 3.281 feet per year (ft yr−1)

Mass

milligrams per liter (mg L−1) 0.058 41 grains per gallon (gr gal−1)

Other

metric tons per cubic meter (t m−3) 0.8428 short tons per cubic yard (tons yd−3)

metric tons per square kilometer (t km−2) 2.855 short tons per square mile (tons mi−2)

metric tons per square kilometer per year 

(t km−2 yr−1) 2.855 short tons per square mile per year 

(tons mi−2 yr−1)

kilograms per square kilometer per hour 

(kg km−2 h−1) 5.710 pounds per square mile per hour 

(lb mi−2 h−1)
Abstract

The low-latitude regions of the Earth are undergoing 

profound, rapid landscape change as forests are converted 

to agriculture to support growing population. Understanding 

the effects of these land-use changes requires analysis of 

watershed-scale geomorphic processes to better inform and 

manage this usually disorganized process. The investigation 

of hillslope erosion and the development of sediment budgets 

provides essential information for resource managers.

Four small, montane, humid-tropical watersheds in the 

Luquillo Experimental Forest and nearby Río Grande de 

Loíza watershed, Puerto Rico (18° 20′ N., 65° 45′ W.), were 

selected to compare and contrast the geomorphic effects of 

land use and bedrock geology. Two of the watersheds are 

underlain largely by resistant Cretaceous volcaniclastic rocks 

but differ in land use and mean annual runoff: the Mameyes 

watershed, with predominantly primary forest cover and runoff of 2,750 millimeters per year, and the Canóvanas watershed, with mixed secondary forest and pasture and runoff of 

970 millimeters per year. The additional two watersheds are 

underlain by relatively erodible granitic bedrock: the forested 

Icacos watershed, with runoff of 3,760 millimeters per year 

and the agriculturally developed Cayaguás watershed, with a 

mean annual runoff of 1,620 millimeters per year.

Annual sediment budgets were estimated for each 

watershed using landslide, slopewash, soil creep, treethrow, 

suspended sediment, and streamflow data. The budgets also 

included estimates of sediment storage in channel beds, bars, 

floodplains, and in colluvial deposits.

In the two watersheds underlain by volcaniclastic rocks, 

the forested Mameyes and the developed Canóvanas watersheds, landslide frequency (0.21 and 0.04 landslides per square 

kilometer per year, respectively), slopewash (5 and 30 metric 

tons per square kilometer per year), and suspended sediment 

yield (325 and 424 metric tons per square kilometer per year), 

were lower than in the two watersheds underlain by granitic 

bedrock. In these granitic watersheds, landslide frequency, 

slopewash, and suspended sediment yield were 0.43 landslides 

per square kilometer per year, 20 metric tons per square 

kilometer per year, and 2,140 metric tons per square kilometer 

per year, respectively, in the forested Icacos watershed and 

0.8 landslides per square kilometer per year, 105 metric tons 

per square kilometer per year, and 2,110 metric tons per square 

kilometer per year, respectively, in the agriculturally developed 

Cayaguás watershed. Comparison of sediment budgets from 

the forested and developed watersheds indicates that human 

activities increase landslide frequency by as much as factor of 

5 and slopewash by as much as a factor of 6. When the difference in annual runoff is considered, the effect of land use on 

suspended sediment yields is also notable. Sediment concentration, calculated as sediment yield normalized by runoff, was 

about 2.3 to 3.7 times as great in the two watersheds in secondary forest and pasture compared with sediment concentration 

in the watersheds in primary forest. Even in the two watersheds 

with primary forest cover, the Mameyes and Icacos, located in 

the Luquillo Experimental Forest, the effects of anthropogenic 

disturbance were marked: 43 to 63 percent of landslide-related 

erosion was associated with road construction and maintenance.

Introduction

Erosion and tectonics are continuous processes that shape 

the surface of the Earth, but our description of the working of 

these processes is inadequate (Stallard, 1995). The geomorphic activity of humans further complicates our understanding of erosion processes. At present humans may be the most 

important agent of geomorphic work on the planet (Hooke, 

2000). The volume of sediment moved by human activities 

such as farming, mining, construction, and quarrying may 

exceed by a factor of 10 the total quantity carried to the sea 

by the world’s rivers (Wolman, 1990) and exceed by a similar 

amount that moved by all other natural processes operating on 

the earth’s surface.

The humid tropics occupy 25 percent of the Earth’s 

land surface and support approximately 45 percent of the 

Earth’s population (CIA World Factbook, 2005). Development 

stresses in this region reduce soil resources, water quality, and 

reservoir storage capacity, and they increase landslide and 

Landslides and Sediment Budgets in Four Watersheds 

in Eastern Puerto Rico

By Matthew C. Larsen
158 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Area shown in B

A

B

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

L e s s e r

A n

t i l l e s

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_F_figures\BW_figures\BWfigure_01F.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

LUQUILLO

MOUNTAINS

Mameyes

Icacos

Río Grande de Loíza Watershed

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Figure 1. Location of Puerto Rico and study watersheds, eastern Puerto Rico.
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 159

flood hazards (Kates and others, 1990). The effect of development pressure is compounded in tropical nations, which tend 

to have limited resources for planning and carrying out careful 

economic development. Sustainable development of the tropics, as is true elsewhere, requires improved understanding of 

the long-term geomorphic effects on soil and water resources 

of economic development and human activities. 

Small watersheds provide an ideal scale for the integrated 

study of hillslope and fluvial processes because their small size 

minimizes the spatial variability of lithology, land use, and climate. Paired study of these small watersheds provides a means 

to isolate and then compare or contrast a variable of interest. Landslide, slopewash, soil creep, treethrow, suspendedsediment, streamflow, and sediment-storage data were used to 

quantify hillslope and fluvial erosion, transport, and storage 

processes, and to develop sediment budgets in two relatively 

undisturbed forested watersheds (Icacos and Mameyes) in the 

Luquillo Experimental Forest and two agriculturally developed 

watersheds in the adjacent Río Grande de Loíza watershed 

(Canóvanas and Cayaguás), Puerto Rico (fig. 1). Geology, 

climate, hydrology, and land cover of these watersheds are 

described in Murphy and others (2012), Murphy and Stallard 

(2012), and Gould and others (2012). The objectives of this 

work were to quantify and contrast geomorphic processes in 

relatively undisturbed and developed watersheds in an environment—the montane humid tropics—where relatively little geomorphic research has been done. This study provides sediment 

budgets that can be compared with budgets in other settings 

and highlights the importance of landsliding as the dominant 

mass-wasting process in the montane humid tropics.

Landslides

Landsliding triggered by earthquakes or rainfall is the 

dominant process of hillslope erosion in many montane humidtropical environments (Simonett, 1967; Garwood and others, 

1979; DeGraff and others, 1989; Maharaj, 1993; Reading and 

others, 1995; Larsen and Santiago-Román, 2001; Glade and 

others, 2005; Restrepo and Alvarez, 2006). Rainfall during 

intense or prolonged storms has been the principal cause of 

landslides in Puerto Rico during the 20th century (Jibson, 

1989; Larsen and Simon, 1993; Larsen and Torres-Sánchez, 

1998). Because of the relatively short distances from hillslopes 

to channels in upland watersheds on the island (fig. 2), hillslope 

material eroded by landslides is commonly delivered directly to 

stream channels, resulting in large ratios of sediment delivered to channels compared with that stored on footslopes or 

floodplains (Gupta, 1988; Ahmad and others, 1993; Larsen and 

Parks, 1997). Where hillslope angles are low or footslopes and 

floodplains are wide enough, some of the landslide debris or 

colluvium resides in temporary storage before being entrained 

by fluvial processes (see example of landslide-debris deposit 

in fig. 2B). The amount of sediment stored in this buffer area 

varies both spatially and temporally, complicating quantitative 

estimates of its volume, residence time, and fluvial transport 

rate (Trimble, 1977; Larsen and Santiago-Román, 2001). 

Slopewash

Slopewash is the movement of hillslope materials by 

gravity and water not concentrated in channels. Erosion of 

soil by slopewash is important in humid-temperate and arid 

areas (Young, 1960; Cerdá and García-Fayos, 1997), but 

relatively little work on this topic has been published for the 

forested humid tropics (Ruxton, 1967; Williams, 1973; Chatterjea, 1994; Goudie, 1995; Reading and others, 1995; Larsen 

and others, 1999; Gellis and others, 2006). Ruxton (1967) 

observed that slopewash under mature primary rain forest in 

northern Papua New Guinea was pronounced on steep hillslopes where leaf litter and duff were thin. Rapid rates of litter 

decomposition and gaps in forest canopy, typical of humidtropical forest, are important factors that increase slopewash, 

according to Birot (1966). Slopewash in Puerto Rico can be 

extremely high in the months immediately following disturbance by landsliding (Shiels and others, 2008).

Soil Creep

Soil creep may be the most common but least studied 

or quantified process of hillslope soil erosion (Heimsath and 

others, 2002). Much of the soil-creep research is decades old, 

so studies using precise measurement techniques are few. 

Because it is a relatively slow process compared with either 

landsliding or slopewash, direct quantification of transport 

rates from soil creep require lengthy field studies. Furthermore, because of complicating processes such as bioturbation 

and treefall, soil creep is not consistently net downslope parallel to the ground surface (Clarke and others, 1999). As such, 

estimates of sediment mass transported by soil creep discussed 

herein must be considered as general at best. 

Treethrow

Trees are uprooted by heavy winds during storms. Soil is 

lifted by roots each time a tree is uprooted and, on hillslopes, 

some of this soil falls back to the ground surface downslope 

of its source. Trees that grow on thin soils or on soils with 

low cohesive strength are generally more susceptible to being 

uprooted (Lenart, 2003; Osterkamp and others, 2006). Detailed 

explanations of the process of soil transport by treethrow are 

provided by Small and others (1990), Gabet and others (2003), 

Osterkamp and others (2006), and Phillips and Marion (2006). 

Suspended Sediment

Mean annual runoff from the entire island of Puerto Rico 

is estimated at 911 millimeters (mm) (Larsen and Webb, 2009). 

This runoff results in a mean annual suspended-sediment discharge to surrounding coastal waters estimated to range from 

2.7 to 9.0 million metric tons for the water years 1990 to 2000 

(Larsen and Webb, 2009). The relation between mean annual 

sediment yield and mean annual sediment load versus drainage 
160 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_F_figures\BW_figures\BWfigure_02F.ai

A

C

D

B

Figure 2. Landslides in the study watersheds, eastern Puerto Rico. A, Roadside slump in the upper Canóvanas watershed. Roadside 

slumps are an important source of sediment in ephemeral and perennial streams. B, Coalesced debris-flow scars estimated to be 

10–20 years in age in the Cayaguás watershed, showing recovery of vegetation on scar surface and landslide debris deposited at 

footslope. C, Slump along Río Icacos channel, showing transport of landslide debris directly to river channel. D, Aerial view of two 

large debris-avalanche scars, Icacos watershed, 1989. Debris avalanche to right, triggered by Hurricane Hugo in 1989, measured 

33 m wide at the headscarp and had a volume of approximately 30,000 m3

. Debris-avalanche scar to left delivered several hundred 

thousand cubic meters of debris to the Río Icacos channel. It dates to a 1970 tropical depression (rainfall total of 976 mm).
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 161

area for Puerto Rico rivers is comparable to that of other rivers 

of the world draining similar terrain (Larsen and Webb, 2009). 

However, comparison of mean annual sediment yield to runoff 

indicates that Puerto Rico rivers are sensitive to the frequent 

high rainfall associated with tropical storms characteristic 

of this region (Larsen and Simon, 1993). Furthermore, sediment discharge from the island is highly episodic and spatially 

uneven because of the variability of tropical storm frequency 

and topography throughout the island. 

Watershed Sediment Budgets

Watershed sediment budgets have been developed 

for a variety of geographic settings and spatial scales (for 

example, Trimble, 1977; Dietrich and Dunne, 1978; Meade, 

1982; Milliman and Meade, 1983; Caine and Swanson, 1989; 

Nelson and Booth, 2002; Ramos-Scharron and MacDonald, 

2007). These authors discuss the uncertainties of watershed- to regional-scale sediment-budget calculations that are 

based on relatively short records of sediment flux, and they 

stress the importance of differentiating between human and 

“natural” rates of sediment yield. A particularly confounding uncertainty in areas with a history of human disturbance 

stems from sediment recently eroded (tens to hundreds of 

years ago) now in temporary storage on lower hillslopes, 

flood plains, and in aggraded channels, documented by 

Trimble (1977), Meade (1982), and Larsen and SantiagoRomán (2001). These authors emphasize the uncertainties 

of attempting to use present-day sediment transport rates to 

estimate long-term watershed denudation rates. In the subtropical Serra do Mar, Brazil, for example, sediment recently 

eroded (in the 19th and 20th centuries) lies as a colluvial 

apron at the foot of slopes and, as stated by Haggett (1961), 

slope conservation efforts may be a century too late. 

The work described herein follows the approach outlined 

by Dietrich and Dunne (1978) that has been widely applied 

for the development of sediment budgets for small watersheds. Their work describes the quantification of fluvial and 

hillslope sediment erosion and transport and accounts for 

debris flows, soil creep, treethrow effects, and the residence 

time of particles on the valley floor, channel, and hillslope. 

Because much of the data they required for their budget were 

unavailable, they used data from nearby or similar watersheds 

to supplement their work.

Analysis of Geomorphic Processes

For each of the watersheds studied, geomorphic processes were described and sediment budgets were estimated 

for landslides at the watershed scale. Slopewash, soil creep, 

and treethrow sediment budgets were estimated at hillslope 

and plot scale and extrapolated to watersheds. Erosion 

of channel banks during storms is also a source of sediment and, although such erosion was observed in the four 

watersheds, it was not quantified for this study. Generalized 

land use and the location and extent of landslide scars were 

mapped using black and white stereo aerial photographs 

ranging in scale from 1:17,000 to 20,000 for several years 

between 1937 and 1995 in each of the four study watersheds. 

Additionally, for the Icacos and Mameyes watersheds, photographs from 1951, 1962–67, 1971–77, and 1990 were used 

(Larsen and Torres-Sánchez, 1998).

Previously published slopewash data were used to 

characterize hillslope erosion process, rates, and budgets in 

the Icacos, Mameyes, and Río Grande de Loíza watersheds 

(Larsen and others, 1999; Gellis and others, 2006). Soil creep 

rates were based on work by Lewis (1974) in the Mameyes 

watershed. Data from Scatena and Lugo (1995) and Larsen 

(1997) were used to estimate soil eroded by treethrow in the 

Mameyes watershed. Fluvial sediment data were obtained 

from U.S. Geological Survey publications (Figueroa-Alamo 

and others, 2006) and summarized from Larsen (1997), Larsen 

and Webb (2009) and Stallard and Murphy (2012).

Landslides

Landslides mapped in each of the watersheds were classified by type or movement style (debris flow, slump, shallow soil 

slip, debris avalanche, earthflow, complex slump–earthflow, and 

indeterminate) according to scar morphology using the terminology of Campbell (1975) and Cruden and Varnes (1996). 

The mapped landslide scars were also classified by 

apparent age as either “historical” (defined herein as since 

1820) or “prehistoric” (defined herein as during the last 

1,000 years before 1820). These age classifications are 

approximate and, in the case of prehistoric scars, are based 

on size and the presence of mature forest cover, because 

no precise evidence of landslide scar age exists prior to 

1937 aerial photographs. Using their morphology and large 

dimensions as the key identifying characteristics, prehistoric 

landslides were identified on aerial photographs. The largest 

historic scars were assumed to serve as a modern equivalent 

of landslide scars estimated to have formed before widespread deforestation, which began in the studied watersheds 

about 1820 (Morales Muñoz, 1943). Debris avalanches 

triggered by storms caused by a tropical depression in 1970 

eroded hillslopes from ridgetops to stream channels and left 

scars that are comparable in dimension and morphology to 

numerous hillslope forms in the Luquillo Mountains (Larsen 

and Torres-Sánchez, 1998). Landslides classified as historical (those that occurred after about 1820) are characterized 

by bare soil, some vegetation regrowth, head- and side-scarp 

boundaries that were clear to somewhat subdued at the head, 

and partially eroded or absent debris deposits. Landslides 

classified as prehistoric had rounded, gently sloping, eroded 

head- and side-scarp boundaries, and highly eroded or absent 

debris deposits. Identification of older scars was based mainly 

on hillslope morphology that usually showed amphitheatershaped headscarps upslope of elongated rectilinear depressions that extended from ridgetop to stream channel. 
162 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

The identification of prehistoric landslides is biased 

towards the largest scars, as evidence of small landslides is 

unlikely to persist for many decades or centuries. The prehistoric age estimate is an approximation and was based on field 

and photograph observations using two factors: the rate of 

change of hillslope morphologic features (slopewash processes 

in this humid-tropical setting, where annual rainfall is measured 

in meters, subdue most landslide head and side scarps beyond 

recognition in decades to centuries), and the dimensions of the 

largest landslides scars (fig. 2D). Debris avalanches (fig. 2D) 

triggered by a 1970 tropical depression, which substantially 

exceeded the 100-year-rainfall recurrence interval (Haire, 1972), 

had a landslide frequency on the order of 0.01 to 0.1 per square 

kilometer per century (km−2 century−1) in the Luquillo Mountains (Larsen and Torres-Sánchez, 1998). It is possible that the 

prehistoric landslides were seismically triggered, but the few 

data available to evaluate this hypothesis suggest that massive 

debris avalanches were not associated with seismicity. Rockfalls 

and river-bank slumps in western Puerto Rico were attributed 

by Reid and Taber (1919) to a strong 1918 earthquake (modified Mercalli intensity exceeded VII) with an epicenter located 

approximately 25 km offshore of the northwest coast. However, 

according to the authors, “in spite of the steep slopes found over 

most of the island, no important earth-slides were observed.” 

All landslides in the Cayaguás watershed were classified 

as historical—no evidence of prehistoric landslides was visible because of an extremely high rate of historical landslide 

erosion in this watershed (fig. 2B) (Larsen and SantiagoRomán, 2001).

The length, width, and depth of landslide scars were 

estimated from aerial photographs by using stereo magnifiers 

(which provide a three-dimensional view) and a 1:20,000-scale 

calibrated ruler and field measurements (Larsen and SantiagoRomán, 2001). Landslide depth was estimated in the Canóvanas and Cayaguás watersheds (fig. 2A, B) from the photographs 

by comparison with the typical rural Puerto Rico house, which 

had a height of about 3 meters (m) in 1937, and 6 m in 1995 

(see Madej and Ozaki, 1996, for an example of this approach). 

Landslide depth in the Icacos and Mameyes watersheds 

(fig. 2C, D), was estimated by comparison with trees, assuming an average tree height of 10 m (Brown and others, 1983). 

Using the product of the average soil density and individual 

landslide volume, an eroded mass was estimated for each 

landslide scar. This value is a minimum estimate because of 

continued erosion of head and side scarps noted on some scars 

after the initial failure. These values were summed and divided 

by the watershed area to normalize the erosion rate. 

All visible scars in the Canóvanas, Icacos, and Mameyes 

watersheds were cataloged (fig. 3). The extreme abundance 

of landslide scars in the Cayaguás watershed precluded 

assessment of the entire watershed; scars were cataloged 

for selected areas which totaled 63 percent of the 26.4-km2 

watershed (fig. 2B) (Larsen and Santiago-Román, 2001). 

The mapped area represents land use and relief typical of 

that watershed. General land-use categories (forest, pasture, 

crops, and anthropogenic modification—highways or other 

structures) were noted for the hillslope on which the scar was 

mapped. Analysis of aerial photographs allowed for examination of extensive areas—the total watershed area for the 

four watersheds is about 70 km2

; however, the technique is 

limited by the poor quality of some black and white photographs taken between the 1930s and 1950s. Additionally, 

forest canopy and shadows on steep hillslopes mask landslide 

features, reducing the total number of identified landslides in 

such areas. Field reconnaissance in all four watersheds indicated abundant slump, debris-flow, and shallow soil-slip scars 

along stream channels (fig. 2C). Where concealed by riparian forest cover, these features were not observable on aerial 

photographs. Landslide erosion rates were expressed as mass 

per square kilometer per year to compare to fluvial sediment 

yield, and as mass per square kilometer per century so that 

whole numbers could be used.

Slopewash

Larsen and others (1999) and Gellis and others (2006) 

summarized slopewash and surface runoff from unbounded 

plots on hillslopes instrumented with Gerlach troughs in forest (Icacos and Mameyes watersheds), pasture, crop, and at 

construction sites (Río Grande de Loíza watershed) in Puerto 

Rico. A Gerlach trough is a sediment trap designed to retain 

soil sediment and surface runoff transported downslope by 

gravity (Gerlach, 1967; Sirvent and others, 1997; Larsen and 

others, 2012). Uncertainties in slopewash data can be large, 

resulting from factors such as the varying linear dimensions 

of the upslope contributing area and the degree to which those 

dimensions may change in association with variable rainfall 

and runoff intensity; the effectiveness of runoff and sediment 

capture by the Gerlach trough; the degree of upslope soil 

disturbance and time since it occurred; the growth rates, type, 

and abundance of vegetation or other organic material on the 

soil surface; and the presence and type of burrowing organisms (Larsen and others 1999; Shiels and others, 2008; Larsen 

and others, 2012). Additional details of the methods used and 

results are summarized in Larsen and others (1999), Gellis and 

others (2006), and Larsen and others (2012). 

Soil Creep

Soil creep was measured by Lewis (1974) at several 

locations in and near the Luquillo Mountains by using Young 

pits (Young, 1960), in which metal strips are buried vertically in soil and later excavated to measure their downslope 

deformity. The data were limited to a single pit at each of five 

sites. Soil creep data from some studies have been shown 

to correlate poorly with slope angle (Lewis, 1974; Goudie, 

1995). However, work using cosmogenic 10Be has shown that 

creep at a site in California was directly proportional to slope 

steepness (McKean and others, 1993) as was first suggested 

by Gilbert (1877). Because of the limited creep estimates 

published for sites in Puerto Rico, slope angle was not 
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 163

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_B_figures\BW_figures\BWfigure_03F

EXPLANATION

Landslide scar

Road

Topographic contour—Shows altitude

 of topography. Contour interval 100 

 meters for parts A and D, 50 meters

 for part C

USGS streamflow and rainfall

 gaging station

900

Base from U.S. Geological Survey National Elevation Dataset,

30-meter digital elevation model, decimal degrees 

Horizontal coordinate North American Datum of 1983 (NAD83)

Vertical coordinate North American Vertical Datum of 1988 (NAVD88)

A. Canóvanas

18°19’ 65°53’

65°52’

65°51’

18°16’

18°17’

18°18’

200

200

100

300

300

300

400

500

500

500

500

500

400

400

400

500

500

400

300

500

500

600

600

700

800

900

Río Canóvanas

R í o Canóvanas

500

Río Cubuy

Río Cubuy

Quebrada Los Santos

0 0.5

0

1 KILOMETER

0.5 1 MILE

18°07’

18°08’

18°09’

65°58’

65°57’

65°56’

18°06’

B. Cayaguás

0 0.5

0

1 KILOMETER

0.5 1 MILE

Río Cayaguás

Río de las Vegas

Río Cayaguás

C. Icacos

18°18’

18°17’

18°16’

18°17’30”

65°47’30”

65°47’

65°46’30”

18°16’30”

800

750

750

750

700

700

650

650

650

650

650

700

750

700

Río Icacos

Quebrada Guabá

0 0.25

0

0.5 KILOMETER

0.25 0.5 MILE

18°18’

18°19’

18°20’

18°17’

65°45’

65°46’

65°47’

D. Mameyes

1,000

900

900

900

800

800

700

600

600

500

400

600

600

500

400

300

300

300

200

200

200

500

500

700

400

700

800

800

Río Mameyes

Río de la Mina

0 0.5

0

1 KILOMETER

0.5 1 MILE

Shading indicates

areas mapped

for landslide

locations

Figure 3. Landslide scars outlined on maps of four study watersheds, eastern Puerto Rico (area examined includes a 100-meter buffer 

around each watershed). A, Canóvanas, B, Cayaguás, C, Icacos, D, Mameyes.
164 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

included in the estimates herein. The creep rates ranged from 

about 0.5 millimeters per year (mm yr−1) to 4 mm yr−1 and 

averaged about 2 mm yr−1 in the upper 45 centimeters (cm) 

of soil (Lewis, 1974). At a Mameyes watershed site in pasture 

with lower mean annual rainfall than that of the Luquillo 

Experimental Forest (table 1), the creep rate averaged 3 mm 

yr−1 (Lewis, 1974). As the Cayaguás watershed has annual 

rainfall that is intermediate between the two sites, a value of 

2.5 (mm yr−1) was chosen. In light of published creep rates 

from other humid-montane settings (1 mm yr−1 at Andrews 

Experimental Forest, Oregon; 3 mm in 7 months at site in the 

Vosges Mountains, France) the Luquillo Experimental Forest 

values seem reasonable (Caine and Swanson, 1989; Auzet and 

Ambroise, 1996). Total soil creep was estimated from the soil 

creep values discussed above with a simple model described 

by Dietrich and Dunne (1978):

Soil creep = ST •Dsoil •Fsc •Lh/A,

where ST is soil thickness (m), Dsoil is soil density in metric 

tons per cubic meter (t/m3), Fsc is creep rate in meters per 

year (m yr−1), Lh is hillslope length (m), and Ais watershed area in square kilometers (km2

). The resulting units 

are metric tons per square kilometer per year (t km−2 yr−1). 

To estimate the contribution of soil creep to the total sediment budget, hillslope length in the studied watersheds was 

estimated to be twice the total channel length (Dietrich and 

Dunne, 1978). 

Treethrow

Treethrow erosion rates were estimated by normalizing 

the soil mass produced from each uprooted tree to the annual 

uproot rate for the forested area in each watershed as

Soil mass (m3) • uproots (ha−1 yr−1) = soil mass uproots

(m3 ha−1 yr−1).

Treefall gap areas in a 13-hectare (ha) section of the 

Mameyes watershed were measured (before Hurricane Hugo 

in 1989) by ground surveys during a 2-year period by Scatena 

and Lugo (1995). Published data included the depth, area, and 

location of holes excavated by the root mass as a tree fell to the 

ground. The mean root area and hole depth were 3.74 m2 and 

1.06 m, respectively. Because of the irregular shape of depressions left by treethrow, the average depression volume (5.32 m3

) 

was greater than the product of the depth and area. On average, 

0.8 gaps ha−1 yr−1 were produced, and 47 percent of gaps were 

caused by uprooted trees. The gap rate of 0.8 gaps ha−1 yr−1

was therefore multiplied by 0.47 to obtain 0.38 uprooted trees 

ha−1 yr−1. The mean annual treefall-induced soil bioturbation 

rates for all watersheds in this study were thus estimated from 

the average volume of excavated holes (5.32 m3) and an average 

soil density of 1 t m−3 with the equation:

5.32 t of soil mass/uproot • 0.38 uproots ha−1 yr−1 • 100 ha 

km−2 = 202 t km−2 yr−1.

As time passes, most soil lifted by the uprooted tree falls 

back into the excavated hole, particularly on low-gradient 

hillslopes (Norman and others, 1995). Lenart (2003) estimated 

that as slope increases, only about one-third of the soil mass 

falls downslope of the hole. This reduction is caused by root 

wads of trees that fall downhill and block a portion of the soil 

available for downslope transport (Scatena and Lugo, 1995). It 

was therefore assumed that about two thirds of the total quantity of soil available for downslope movement is held back by 

each root wad, yielding an estimated rate of total soil transport 

due to treethrow of 67 t km−2 yr−1.

Table 1. Geologic, geographic, topographic, and hydrologic characteristics of study watersheds, eastern Puerto Rico.

[km2, square kilometers; m, meters; mm, millimeters; D50, median particle size]

Characteristic Watershed

Canóvanas Cayaguás Icacos Mameyes 1

Watershed area, km2 25.5 26.4 3.26 17.8

2

Relief, m 891 293 228 969

2

Mean slope of watershed 0.255 0.189 0.222 0.365

1

Dominant rock type Volcaniclastic rock Granitic rock Granitic rock Volcaniclastic rock

1

Principal land use Pasture,

secondary forest

Pasture,

secondary forest

Undisturbed forest,

secondary forest

Undisturbed forest,

secondary forest

3

Annual rainfall, mm 2,070 2,350 4,150 3,760

3

Annual runoff, mm 970 1,620 3,760 2,750

2

D50, Channel bed sediment, mm 70 0.5 0.6 110

1

Murphy and others (2012).

2

Larsen (1997).

3

Murphy and Stallard (2012).
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 165

To estimate the net downslope transport of soil due to 

treethrow for each watershed, the approach of Schaetzl and 

others (1990) was used to calculate the total treethrow soil 

transport rate: the product of average watershed slope (calculated from a 10-m digital elevation model and expressed 

as the tangent of the topographic slope in degrees). This net 

downslope rate was then applied to the forested areas of the 

Icacos and Mameyes watersheds to arrive at total treefallinduced transport rate of soil. The same method was used for 

forested areas of the Cayaguás and Canóvanas watersheds 

using the lowest rate (42 t km−2 yr−1) determined by Scatena 

and Lugo (1995). This lower rate is believed to be representative of the secondary forests common in areas of Puerto 

Rico undergoing afforestation, such as those in the Cayaguás 

and Canóvanas watersheds (F.N. Scatena, U.S. Department 

of Agriculture Forest Service, oral communication, 1996).

Fluvial Sediment

The U.S. Geological Survey operates surface-water gaging and sediment stations that use continuous stage recorders 

and automatic pump samplers at the outlet of each of the 

four study watersheds (Murphy and Stallard, 2012). Suspended sediment and runoff data from these locations were 

compiled from Figueroa-Alamo and others (2006), Larsen 

(1997) and Stallard and Murphy (2012) to summarize annual 

yield and runoff for the study watersheds for the period 1991 

to 2005. Bedload sediment was not analyzed for this study, 

but Larsen (1997) estimated that the suspended-sediment 

for the streams draining the watersheds underlain by granitic bedrock (Cayaguás and Icacos watersheds) includes a 

substantial bedload component, ranging from 6 to 51 percent 

of suspended sediment load. This bedload component results 

because these two streams have a median bed material grain 

size of 0.5 to 0.6 mm (table 1). Sediment with this grain size 

is entrained from the channel bed into turbulent suspension 

at even moderate discharge (Dietrich, 1982). In contrast, the 

much larger median bed material grain size (70 and 110 mm) 

in the Canóvanas and Mameyes watersheds underlain by 

volcaniclastic bedrock (volcanic sandstone, mudstone, and 

breccia) are relatively immobile on an annual basis. As such, 

suspended-sediment transport underestimates total fluvial 

sediment transport in time periods greater than 1 or 2 years 

(Larsen, 1997).

Sediment Storage

Sediment resides in various spatial and temporal storage 

compartments in a watershed before it is either entrained by 

fluvial and mass-wasting processes or sequestered for long 

time periods. These compartments are commonly described 

as active, semiactive, or inactive depending on how potentially mobile the deposits may be at time scales that range 

from days to millennia (Madej and Ozaki, 1996). In the study 

watersheds, channel bars and the channel bed were defined as 

active, flood plains and colluvium were defined as semiactive, 

and Quaternary alluvium was defined as inactive. Watershed 

sediment storage was estimated by using field surveys and 

Geographic Information System (GIS) analysis of channel 

bars, channel width, and flood-plain surface area (Larsen and 

Santiago-Román, 2001). 

Amounts, Rates and Characteristics 

of Hillslope Erosion, Sediment 

Storage, and Transport, and Relation to 

Landform Development

Landslides

Accelerated rates of rainfall-triggered landsliding in 

eastern Puerto Rico probably began in the early 19th century 

with the onset of forest clearing for agriculture, particularly in 

the Canóvanas and Cayaguás watersheds where agricultural 

development was intense (Larsen and Santiago-Román, 2001; 

Gellis and others, 2006). Because no historical records of landslides exist, the storms that triggered landslide scars cannot be 

precisely identified. However, the greatest rainfall intensities 

and widest distribution of storm rainfall in Puerto Rico are 

mainly associated with tropical disturbances, which composed 

61 percent of the landslide-triggering storms recorded in the 

central mountains of Puerto Rico between 1960 and 1990 

(Larsen and Simon, 1993). Major tropical disturbances struck 

the island in 1867, 1899, 1928, 1932, 1960, 1970, 1979, and 

1989. These storms delivered 24- to 72-hour rainfall totals 

of 300 to 800 mm, and they are the likely trigger for most of 

the landslide scars visible in aerial photographs (Scatena and 

Larsen, 1991; Larsen and Torres-Sánchez, 1998). Field and 

aerial photographic analyses plus interviews of watershed residents confirm that rainfall associated with hurricanes and other 

tropical disturbances are commonly the trigger for landslides 

(Larsen and Torres-Sánchez, 1992).

Historical landslide scars generally affected a small 

surface area, reflective of the high-intensity but relatively 

short-duration rainstorms that triggered this style of landslide (Larsen and Simon, 1993) (fig. 2; table 2). In the 

forested watersheds, historical scars were particularly small 

(400–600 square meters (m2

)). The larger mean surface area 

of historical landslide scars in the agriculturally developed 

watersheds (980–5,000 m2) indicates that landscape disturbance (forest removal, cropping) by humans can have 

a profound effect on mass wasting processes. Prehistoric 

debris avalanche scars are visible on aerial photographs of 

the Canóvanas, Icacos, and Mameyes watersheds and can be 

attributed to long-duration storms (Larsen and Simon, 1993). 

This type of storm occurred in 1970, when a stationary tropical depression released 976 mm of rainfall during a 5-day 

period, exceeding the 100-year, 7-day storm accumulation 

and triggering some of the largest debris avalanches observed 
166 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

on the island (fig. 2D; Haire, 1972). In the Cayaguás watershed, the historical landslide frequency is so great that no recognizable evidence of prehistoric landslides persists (fig. 2B). 

The large size of prehistoric landslide scars (6,300–49,000 

m2) mapped in this overall study is mainly a reflection of the 

operational definition of these features.

Most landslide scars were classified as debris flows; soil 

slips were the second most common type of scar (Larsen, 

1997). Both of these landslide types are characteristic of highintensity, short-duration storms that result in 24-hour rainfall 

totals on the order of 200 mm (Larsen and Simon, 1993) and 

have about a 5-year recurrence interval (U.S. Department of 

Commerce, 1961). The remaining landslide scars were classified mainly as slumps, debris avalanches, and complex. About 

1 percent of landslide scars could not be categorized because 

of poorly defined features.

Landslide Frequency

Watershed area disturbed by historical landsliding is 

1 percent or less in the forested Icacos and Mameyes watersheds. About 3 percent of the Canóvanas watershed surface 

area was disturbed by historical landsliding, compared with 

an impressive 19 percent of the Cayaguás watershed (table 2). 

Historical landslide frequency in the developed Canóvanas 

and Cayaguás watersheds was estimated to be four landslides 

km−2 century−1 and 80 landslides km−2 century−1, respectively 

(table 2). The 20-fold difference in the landslide frequency 

between the two watersheds can be attributed to both the more 

intensive land-use practices and more erodible bedrock in the 

Cayaguás watershed (Gould and others, 2012; Murphy and 

others, 2012). In the well-forested Icacos and Mameyes watersheds, historical landslide frequency was 43 and 21 landslides 

Table 2. Dimensions of 2,882 landslides mapped from aerial photographs (1937, 1951, 1962, 1972, 1974, 1979, 1990, and 1995) in four 

watersheds, eastern Puerto Rico.1

[m, meter; m2, square meter; m2 km–2, square meter per square kilometer; m3, cubic meter; m3 km–2, cubic meter per square kilometer; t, metric ton; km2, square 

kilometer; t km−2, metric tons per square kilometer; t km−2 yr−1, metric tons per square kilometer per year; km2 century–1, square kilometers per century]

Characteristic

Watershed and landslide age

Canóvanas2 Cayaguás2 Icacos3 Mameyes3

Historical Prehistoric Historical Historical Prehistoric Historical Prehistoric

Landslides mapped 172 44 2,321 70 24 189 62

Mean length, m 90 330 53 30 130 30 310

Mean width, m 30 100 16 20 30 15 53

Mean depth4

, m 4 20 3.5 2 9 2 12

Mean surface area, m2 5,000 49,000 980 600 6,300 400 17,000

Mean volume, m3 72,000 935,000 4,700 1,200 71,000 1,200 310,000

Mean mass, t 94,000 1,234,000 2,300 2,000 93,000 2,300 605,000

Total surface area, m2 874,000 2,168,000 2,270,000 42,000 152,000 70,600 1,031,000

Total volume, m3 12,314,000 41,159,000 10,900,000 84,000 1,696,000 218,700 19,231,000

Total mass, t 16,254,000 54,329,000 14,400,000 111,000 2,239,000 426,500 37,501,000

Landslide scars per km2 7 2 140 22 7 11 4

Landslide surface area per 

watershed area, m2

 km–2

34,000 85,000 140,000 13,000 47,000 4,000 58,000

Percent of basin surface area 3 9 14 1 5 0.4 5.8

Landslide volume per 

watershed area, m3

 km–2

483,000 1,615,000 660,000 26,000 520,000 12,000 1,079,000

Total surface lowering averaged

for study area, m

0.5 1.6 0.66 0.03 0.52 0.01 1.08

Landslide mass, t km–

2 638,000 2,132,000 870,000 34,000 687,000 24,000 2,104,000

Landslide mass, t km−2 yr−1 4,000 2,000 5,000 700 700 480 2,100

Landslides per km2

 per 100 yr 4 0.2 80 43 0.7 21 0.3 1

Prehistoric landslide scars were estimated to represent as much as 1,000 years of landslide activity. Volume was converted to mass by using an average landslide debris density of 1.3 metric tons per cubic meter. Data for Canóvanas, Icacos, and Mameyes watersheds from Larsen (1997); data for Cayaguás watershed 

from Larsen and Santiago-Román (2001). Mapped area of Cayaguás watershed was 16.6 square kilometers (63 percent of the total area of the watershed). Other 

watersheds were fully mapped.

2

In Canóvanas and Cayaguás watersheds, historical landslide scars estimated to represent landslide activity since forest clearing for settlement began in the 

1820s.

3

In forested Icacos and Mameyes watersheds, historical landslide scars estimated to represent landslide activity for a 50 year period (1945–1995).

4

Calculated as an unweighted average of all landslides.
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 167

km−2 century−1, respectively. This twofold difference is attributed to the more landslide-prone soil and saprolite weathered 

from the granitic bedrock of the Icacos watershed compared 

with the volcaniclastic Mameyes watershed.

Mean annual rainfall in the Icacos watershed is almost 

double that of the lithologically similar Cayaguás watershed 

(table 1); therefore, if other factors were equal, the landslide 

frequency in the Icacos watershed would be expected to be 

greater than in the Cayaguás watershed. However, historical 

landslide frequency in the developed Cayaguás watershed was 

about double that in the forested Icacos watershed (table 2). 

Note that the dense forest cover of the Icacos watershed 

reduces the number of visible landslide scars, so the twofold 

difference may not be as great as estimated here. Evidence of 

an anthropogenic effect on landslide frequency and location 

is apparent in the forested watersheds. Forty-three percent of 

the 189 historical landslides mapped in the Mameyes watershed were associated with road construction and maintenance 

(fig. 2A; Larsen and Parks, 1997). Most of these landslides 

were small slumps and shallow soil slips that eroded about 

80 t km−2 yr−1, or approximately one-half of all landslidederived sediment. In the Icacos watershed, the majority 

(63 percent) of the 70 historical scars were associated with 

road construction and maintenance, resulting in 500 t km−2 yr−1

of hillslope erosion.

Landslide Erosion Rate

The estimated mass wasting rate by historical landsliding 

in the Canóvanas watershed is about 4,000 t km−2 yr−1, twice 

that estimated for the prehistoric period (table 2), suggesting that forest clearing and other agricultural practices have 

significantly increased hillslope erosion in this watershed. 

Examination of aerial photography indicates that mass-wasting 

has been even greater in the Cayaguás watershed, where landslides have eroded approximately 5,000 t km−2 yr−1 since the 

watershed was first occupied for subsistence farming (fig. 3; 

table 2). This mass of material is equivalent to an average 

watershed surface lowering of 0.66 m in 175 years, comparable to the 0.5-m lowering estimate since European occupation 

of the region, determined from analysis of cosmogenic 10Be 

concentration in fluvial sediment in this watershed (Brown and 

others, 1998).

It is unlikely that the extremely high frequency of historical mass wasting in the Cayaguás watershed existed prior 

to the period of major land clearing. At present, extensive 

deposits of the material, apparently eroded during the 19th and 

20th centuries, remain in storage throughout the watershed as 

colluvium as much as 3 m deep on footslopes and as alluvium 

1 to 3 m deep along channels and flood plains (Clark and 

Wilcock, 2000; Larsen and Santiago Román, 2001). Colluvial 

deposits from groups of small landslide scars coalesce to fill 

the floors of small zero-order hollows (fig. 2B). Extensive colluvial deposits also mantle the floors of first- and second-order 

valleys and create poorly drained bog-like soils (Larsen and 

Santiago Román, 2001). 

In the granitic-bedrock Icacos watershed, the rate of 

historical landslide-induced mass wasting was estimated to 

be about 700 t km−2 yr−1. This rate is substantially less than 

that estimated for the lithologically similar but developed 

Cayaguás watershed, but about 1.5 times as great as that of 

the volcaniclastic, forested Mameyes watershed (table 2). 

Prehistoric landslide scars affected approximately 5 percent 

of the Icacos watershed surface area, or about 0.5 percent per 

100 years, essentially identical to the rate per 100 years determined for the Mameyes watershed (table 2).

A total of 26 of the 70 historical landslides in the Icacos 

watershed were not associated with roadways because they 

were distal to road corridors. These landslides had a mean 

mass of 1,200 t and eroded a total mass of 200 −2 yr−1. This 

landslide erosion rate probably represents the “natural” rate, 

or the rate that might be expected for this watershed in the 

absence of human activity. This rate is less than one-third 

that estimated for prehistoric landsliding (annual rate of 

erosion is 700 t km−2), indicating that watershed conditions 

may have been wetter or storms more frequent during the last 

1,000 years. This possibility is supported by work by Donnelly and Woodruff (2007) in which they describe increased 

tropical storm intensity in the late Holocene near Puerto Rico. 

Mayewski and others (2004) describe both wetter and drier 

periods in the Caribbean, but these periods are in the early to 

middle Holocene. Alternatively, shifting agriculture (known 

also as “slash and burn”) of pre-European populations may 

have been actively practiced on hillslopes, resulting in accelerated rates of landsliding (Denevan, 1992). Accounts of Taino 

Indian population in the Greater Antilles in the 16th century 

indicate that large numbers of inhabitants resided in these 

islands; in Puerto Rico, the estimate is as much as 600,000 

(Rouse, 1993). A population of this size would likely have 

occupied at least some hillslope settings and could have 

altered vegetation, soil stability, and hillslope erosion.

Slopewash

Slopewash erosion rates were estimated to range between 

30 and 105 t km−2 yr−1 in the developed watersheds (Canóvanas and Cayaguás, respectively) and between 5 and 20 

t km−2 yr−1 in the forested Mameyes and Icacos watersheds, 

respectively (table 3) (Larsen and others 1999; Gellis and 

others, 2006). Larsen and others (2012) reported a comparable 

slopewash rate, 12 t km−2 yr−1, on a low-gradient hillslope 

under undisturbed forest canopy in the El Verde section of 

the Luquillo Experimental Forest. Slopewash was observed 

as deposits in swales on hillslopes and on footslopes along 

ephemeral and perennial channels, where it is available for 

transport into channels. Additionally, other mass wasting 

processes, including landslides, soil creep, and slumping or 

erosion of channel banks transport slopewash into channels 

where it is entrained as fluvial sediment.

Bruenig (1975) measured average slopewash rates of 

20 t km−2 y−1 under virgin forest; slopewash may be as high 

as 100 t km−2 y−1 for the humid tropics in general (UNESCO/
168 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

UNEP/FAO, 1978). Gellis and others (2006) reported slopewash rates in the Río Grande de Loíza watershed, Puerto Rico, 

ranging from 10 t km−2 in secondary forest and pasture, to 31 

to 86 t km−2 on hillslopes in crops, and 288 to 910 t km−2 at 

construction sites. Shiels and others (2008) reported remarkably high slopewash rates of 25 to 81grams per square meter 

in 1-m2 bounded plots (from December 2003 to May 2004) at 

the center of freshly exposed landslide scars on volcaniclastic 

and quartz diorite bedrock in the Luquillo Mountains.

Soil Creep

Annual rates of soil creep for each watershed were estimated to be 9–12 t km−2 yr−1 (table 4). These rates are approximations because of the large uncertainties and variability in 

creep rate, soil thickness and density, and hillslope lengths 

within each watershed. Soil creep mobilizes an annual mass 

of sediment comparable to the contributions of treethrow, but 

generally less than the slopewash contribution in each watershed. It is assumed that this mass of soil is supplied to channel 

margins of ephemeral and perennial streams and is entrained 

by slumping, abrasion, and avulsion of stream banks during 

high discharge.

In humid-temperate environments, sediment delivery 

to river channels by soil creep in forested watersheds was 

estimated at 29 t km−2 yr−1 from Clearwater River watershed, 

Washington (Reid, 1981), and at 11 t km−2 yr−1 from watershed 10, Cascade Range, Oregon (Swanson and others, 1982). 

In New South Wales, Australia, Heimsath and others (2002) 

estimated soil creep at 6 mm yr−1 on a hillslope in intermediate to wet schlerophyll forest. Soil creep rates in the Luquillo 

Experimental Forest averaged about 2 mm yr−1 and ranged 

from about 0.5 mm yr−1 to 4 mm yr−1 (Lewis, 1974). These 

rates correlate poorly with slope gradient, indicating that 

slope angle may not be a limiting factor on most hillslopes. 

However, the paucity of data makes this difficult to confirm. 

No other soil creep data are available for the study watershed. 

Using the annual creep rate of 2 mm and mean slope length 

of 51 m in the northeastern Luquillo Experimental Forest, 

Scatena (1995) estimated that creep could remove all hillslope 

topsoil, absent any soil production from the weathering of 

bedrock, in a period of 27,000 years.

Treethrow

Estimates of treethrow contribution to hillslope soil 

movement range from 4 to 29 t km−2 yr−1 in the study watersheds (table 3). Rates are lower in the developed watersheds 

owing to less forest cover and because secondary-forest 

treethrow rates are lower, possibly as a result of lesser tree 

height. Because the treethrow model is most sensitive to slope, 

the four watersheds show additional variation as a function of 

their relative steepness. Bedrock lithology controls treethrow 

erosion because of relatively greater steeply sloping watershed 

area. The mean annual treethrow erosion rate for the Canóvanas watershed is reduced because only about 71 percent of the 

watershed is currently forested (Murphy and others, 2012). 

Because the Cayaguás watershed has less steep slopes and is 

only about 42 percent forested, the contribution of treethrow 

Table 3. Types and estimated rates of hillslope erosion in study watersheds, eastern Puerto Rico.

[--, not available]

Hillslope erosion process

Average rate

(metric tons per square kilometer per year)

Canóvanas Cayaguás Icacos Mameyes

Treethrow 11 4 19 29

Soil creep 12 11 11 9

Slopewash 30 105 20 5

Landslide: Historical 4,000 5,000 700 480

Prehistoric 2,000 -- 700 2,100

Total hillslope erosion (range varies with landslide rate used) 2,053 to 4,053 5,120 750 523 to 2,143

Suspended-sediment yield1 424 2,110 2,140 325

Bedload yield2 83 -- -- 110

Sediment delivery, percent (calculated as suspended sediment yield/

total hillslope erosion; range reflects landslide rates above)

10 to 21 41 285 15 to 62

1

From Stallard and Murphy (2012).

2

From Larsen (1997).

Table 4. Estimated annual soil creep in study watersheds, 

eastern Puerto Rico.

[m yr−1, meters per year; m, meter; t m−3, metric tons per cubic meter; km, 

kilometer; t km−2 yr−1, metric tons per square kilometer per year]

Watershed

Creep

rate

(m yr−1)

Soil

thickness

(m)

Soil

density

(t m–3)

Hillslope

length

(km)

Mean

annual soil

movement

(t km−2 yr−1)

Canóvanas 0.002 0.45 1 332 12

Cayaguás 0.0025 0.25 1.3 342 11

Icacos 0.002 0.45 1 40 11

Mameyes 0.002 0.45 0.8 219 9
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 169

soil erosion is only 4 t km−2 yr−1. Comparable values were estimated in North American settings, where sediment delivery to 

river channels by treethrow was estimated at 9 t km−2 yr−1 from 

Clearwater River watershed, Washington (Reid, 1981), and 

at 1 t km−2 yr−1 from watershed 10, Cascade Range, Oregon 

(Swanson and others, 1982).

Fluvial Sediment

Mean suspended sediment yield (or export) ranged from 

325 to 2,140 t km−2 yr−1 in the study watersheds for the period 

1991–2005, varying with land use and bedrock lithology 

(table 5). These values are comparable to island-wide estimates of 570 to 1,900 t km−2 yr−1 for the period 1990–2000 

(Larsen and Webb, 2009). Using a bedload transport equation from Parker and others (1982), Larsen (1997) estimated 

bedload transport in the Canóvanas and Mameyes rivers 

for the period 1991–1995 at 83 t km−2 yr−1 and 110 t km−2

yr−1, respectively. It should be noted that bedload-sediment 

transport models are potentially inaccurate by as much as an 

order of magnitude or more in predicting annual loads, and 

for the most part they predict maximum potential loads if the 

various frictional effects of channel obstructions that reduce 

transport are not considered (Gomez and Church, 1989). With 

this uncertainty in mind, Larsen (1997) reported that bedload 

transport was estimated to have occurred for a total of about 

7 days during the 5-year (1991–1995) study period in the 

Canóvanas and Mameyes rivers. The mass of bedload trapped 

behind wire screens (10-cm mesh) at gaging stations monitored by the U.S. Department of Agriculture Forest Service 

on two Mameyes tributaries averaged 52 t km−2 yr−1 (F.N. 

Scatena, written commun., 1996). This rate is 47 percent of the 

median rate of 110 t km−2 yr−1 estimated for the Río Mameyes. 

The factor-of-2 difference between the two values suggests 

that the modeled bedload estimates are reasonably accurate, 

if one considers the coarse screen size used on the Mameyes 

tributaries and the inherent uncertainties noted above associated with bedload transport models.

Sediment concentration, calculated here as sediment yield 

normalized by runoff, was about 2.3 to 3.7 times as great in 

the two watersheds in secondary forest and pasture compared 

with sediment concentration in the watersheds in primary 

forest (fig. 4). The influence of lithology was comparable: 

sediment concentration in granitic-bedrock (quartz diorite 

and granodiorite) watersheds was 3.0 to 4.7 times as high as 

sediment concentration in volcaniclastic-bedrock watersheds. 

These contrasts highlight the well-known effect of land use on 

sediment yield, but they also make apparent the strong control 

of bedrock geology as well. 

The relation between suspended-sediment yield and 

runoff among the four study watersheds is relatively consistent for three orders of runoff, from about 0.1 millimeters per 

hour (mm h−1) to 30 mm h−1 (fig. 4). Yield from the forested 

Mameyes watershed is the least per unit of runoff, in spite of 

the great relief (approximately 1 km) of the watershed. The 

forested Icacos watershed is comparable to the Canóvanas 

watershed with respect to suspended-sediment yield per unit of 

runoff. Although relief in the Icacos watershed is much lower 

than that of the Mameyes watershed, the more easily mobilized 

sediment in this sandy-bed river makes the Icacos productive. 

Suspended-sediment yield from the Canóvanas watershed is 

large, because relief is high and the landscape has been disturbed for agriculture and pasture. Finally, suspended-sediment 

yield from the Cayaguás watershed is the greatest of the four 

watersheds, because of the combination of easily mobilized 

sediment in the sandy-bed river and the great anthropogenic 

disturbance. Most of the landscape was deforested during the 

past two centuries for agricultural and pastoral use (Larsen and 

Santiago-Román, 2001). Mean annual suspended-sediment 

discharge from Puerto Rico into surrounding coastal waters is 

estimated to range from 2.7 to 9.0 million metric tons (Larsen 

and Webb, 2009). Storm runoff transports a substantial part 

of fluvial suspended sediment from uplands to the coast; for 

example, the highest recorded daily sediment discharge is 1 to 

3.6 times the annual suspended-sediment discharge, and runoff 

from major storms induces sediment transport 1 to 32 times the 

median annual sediment load. During this study, suspendedsediment yield (measured as instantaneous concentration) 

from the four study watersheds extended about nine orders of 

magnitude, from a low of 0.04 kg km−2 h−1 in the Canóvanas 

watershed to a remarkable 2 million kg km−2 h−1 in the Icacos 

watershed (fig. 4).

Gellis (1993) described fluvial sediment transported by 

the discharge related to Hurricane Hugo in the Loíza watershed. He noted that observed suspended-sediment loads were 

less than expected because high winds caused vegetation and 

debris to accumulate on hillslopes where it blanketed the soil 

surface, and in channels where it resulted in local backwater 

effects that reduced stream velocity. The storm-average 

suspended sediment concentration in the Río Grande de Loíza 

was 2,290 mg L−1, the second lowest of 12 storms monitored 

at the site. In another study, Gellis (1991), again in the Loíza 

watershed, evaluated the effects of construction on sediment 

Table 5. Annual fluvial suspended sediment for the period 1991–2005 in study watersheds, eastern Puerto Rico.1

[t km−2 yr−1, metric tons per square kilometer per year; kg km−2 mm−1, kilograms per square kilometer per millimeter, which is equivalent to milligrams per liter]

Measure of sediment load Canóvanas Cayaguás Icacos Mameyes

Mean suspended sediment export, t km−2 yr−1 424 2,110 2,140 325

Mean suspended sediment concentration, kg km−2 mm−1 of runoff 435 1,300 560 118 1

U.S. Geological Survey data summarized from Stallard and Murphy (2012).
170 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

production. Suspended-sediment concentrations near construction areas were as high as 80,000 mg L−1 with yields ranging 

from 7,600 to 11,600 t km−2 yr−1.

Simon and Guzmán-Ríos (1990) described upland 

sediment erosion in the Loíza watershed. Using the Yang 

transport equation (Stevens and Yang, 1989), they estimated 

bed-material yield from the two principal Río Grande de 

Loíza tributaries to be 2,900 t km−2 yr−1. They reported that 

bed material composed between 34 and 92 percent of the total 

sediment yield and that the main source of coarse material 

may have been from the introduction of mass-wasting debris 

in headwater streams.

Sediment Storage and Sediment Budgets

Hillslope soil and regolith is eroded by continuous lowmagnitude processes such as soil creep and slopewash and 

by episodic high-magnitude processes such as landsliding 

and (to a lesser extent) treethrow. Once entrained, sediment 

is exported from the watersheds by fluvial transport (that is, 

sediment yield). However, material eroded from hillslopes 

may reside in several types of storage compartments before 

leaving the watershed. This element of the sediment budgets 

was approximated in very general terms and is discussed first.

Sediment Storage in Channel Beds, Bars, and 

Floodplains

Channel bar mass ranged from a low of 350 t km−2

in the Icacos watershed to a maximum of 2,600 t km−2 in 

the Mameyes watershed (table 6). The estimated mass of 

sediment composing the beds of rivers ranged from 7,600 

to 12,100 t km−2 of watershed drainage area. Flood-plain 

and Quaternary alluvium mass ranged from 800 t km−2 in 

the Mameyes watershed to 28,700 t km−2 in the Cayaguás 

watershed. These values are approximate, as discussed in 

section Sediment Storage, and probably represent a minimum estimate because many flood-plain deposits in these 

small watersheds are not easily or accurately identified on 

1:20,000-scale maps and aerial photographs.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_F_figures\BW_figures\BWfigure_04F.ai

Canóvanas

Cayaguás

Icacos

Mameyes

EXPLANATION

0.01 0.1 1 10 100

Suspended-sediment yield, in kilograms

per square kilometer per hour

10,000,000

1,000,000

100,000

10,000

1,000

100

10

1

0.1

0.01

Runoff rate, in millimeters per hour

Figure 4. Fluvial transport of suspended-sediment yield compared with runoff rate in the four study 

watersheds, eastern Puerto Rico.
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 171

The channel bed, exposed channel bar, and flood-plain 

deposits in each watershed were assigned a degree of relative 

activity as a function of how often particles in each compartment are mobilized (Madej and Ozaki, 1996; Florsheim and 

others, 2000). The channel bed was defined as active to intermittent, because it is mobilized almost daily in the sand-bed 

rivers, but only several times per year or less in the boulderbed rivers. Channel bars were considered to be intermittently 

mobilized, approximately monthly to annually, in the four 

watersheds. Flood plains are relatively inactive on an annual 

basis, although streambank failures were observed following 

high discharge.

Colluvium, Hillslope Sediment, and Core Stones

A maximum estimate of hillslope colluvial storage can 

be derived from the volume of historical and prehistoric 

landslide scars in each watershed (table 2). These values are 

on the order of 500,000 m3 km−2 or more in each watershed. 

The magnitude of total landslide scar volume indicates that 

colluvial storage of sediment may be the greatest component 

of watershed-scale sediment storage. In the Icacos watershed, 

field observation indicates that most of the material eroded 

by landsliding is not present on footslopes, but rather it has 

been transported out of the watershed. However, even if only 

one tenth of landslide-derived colluvial material remains in 

the watersheds, it still contains more material than all other 

estimated storage compartments combined (table 6). Colluvium, residing on low-angle footslopes and adjacent flood 

plains, also represents the least active storage compartment in 

the watersheds.

Corestones are large bedrock remnants which are exposed 

gradually as surrounding saprolite is eroded away or episodically by mass wasting (Ruxton, 1967). Corestone boulders 

are commonly several meters or more in diameter and are 

abundant in stream channels, where they may remain for 

thousands of years while being subjected to chemical weathering and to abrasion (Brown and others, 1995). These large 

boulders represent one end member of the colluvial particlesize distribution.

Abundant corestones are observed in tributaries and 

in the Icacos main channel. Corestones are observed in the 

Cayaguás watershed as well, but they are buried by large 

amounts of modern sandy colluvium and alluvium in the 

upland zero- and first-order channel reaches. Excavation of 

Cayaguás tributary channels reveals their presence within 

and beneath a matrix of sandy alluvium. In contrast, massive 

boulders with diameters as large as several meters are commonly exposed in, on, or near the beds of the Canóvanas and 

Mameyes rivers. The great size and abundance of the boulders 

present in the Canóvanas and Mameyes channel beds appears 

to be largely controlled by the massively bedded volcaniclastic 

sandstone and breccia from which the boulders are derived 

(Seiders, 1971; Clark and Wilcock, 2000). The large boulders 

may remain for considerable time in local topographic lows 

on footslopes, channels, or near-channel areas because of the 

lack of a transport mechanism once the boulders have reached 

these locations (Ahmad and others, 1993; Scatena, 1995).

Time Scales and Anthropogenic Effects

The total annual hillslope sediment contribution in 

the watersheds is dominated by the material derived from 

landsliding, which highlights a timescale consideration for 

the budget estimates (table 3). As previously noted in section Analysis of Geomorphic Processes, landslide ages were 

estimated as historical and prehistoric. The rates of prehistoric 

landsliding estimated for the Icacos and Mameyes watersheds may be most appropriate for long-term hillslope inputs, 

because these watersheds have been relatively undisturbed 

by human influence during the last two centuries. However, 

this estimation contains uncertainties dependent on climate 

and land use during the past 1,000 years. In contrast, historical rates should be more appropriate in the Canóvanas and 

Cayaguás watersheds because they reflect changes in hillslope 

processes that have resulted from human influence. 

Ultimately, the episodic nature of mass wasting by 

landslides dictates that sediment budgets (in an environment 

where landslides dominate hillslope erosive processes) are 

best estimated at decadal- to century-scale time steps, rather 

than annually as described here. This longer time step serves 

to reduce the great difference in magnitude between geomorphically continuous and episodic processes. The landslidedominated hillslope sediment inputs listed in table 3 are not 

necessarily expected to compare closely with fluvial export, 

because much of the colluvium may be long retained in sediment storage sites before transport from the watershed. The 

storage sites act as an enormous buffer, periodically receiving 

Table 6. Estimated sediment in storage in study watersheds, eastern Puerto Rico, in channel beds, bars, flood plains, and Quaternary 

alluvium.

[t km−2, metric tons per square kilometer]

Storage location Canóvanas

(t km−2)

Cayaguás

(t km−2)

Icacos

(t km−2)

Mameyes

(t km−2)

Channel bed 10,100 11,200 7,600 12,100

Watershed channel bar 720 400 350 2,600

Flood plain and Quaternary alluvium 9,800 28,700 16,300 800

Total 20,620 40,300 24,250 15,500
172 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

large pulses of hillslope-derived sediment and losing it at a 

more continuous rate. Nonetheless, the fluvial sediment export 

rates are multiyear averages from a period that included a 

drought and several large hurricanes and should, therefore, 

represent long-term conditions. These variations in geomorphic process considerations, timescales, and land use notwithstanding, the approximate sediment budgets provide insights 

into the geomorphology of each watershed.

Erosion and Sediment Export: Are They in 

Balance?

Hillslope erosion in the Canóvanas watershed exceeds 

fluvial suspended-sediment export (yield) (table 3). If all 

hillslope inputs are included, this watershed has a sediment 

delivery ratio of 10 to 21 percent (13 to 25 percent if bedload 

yield estimates are included). However, because little landslide activity has been noted in the watershed during recent 

decades, most of the current annual sediment export was 

probably derived from nonlandslide inputs (slopewash, soil 

creep, and treethrow). The annual landslide input of 2,000 to 

4,000 t km−2 can therefore be left out of the budget for recent 

decades. Accordingly, at least for the period 1991 to 2005, the 

difference between the sum of the annual nonlandslide inputs 

and annual fluvial export could have been derived from the 

extensive active and intermittent storage compartments (tables 

3, 5, and 6).

In the Cayaguás watershed, hillslope erosion is more than 

double the estimate of fluvial sediment export. As discussed 

in the Landslide Frequency section, this highly modified 

landscape can yield sediment in large quantities. Much of the 

sediment in this low-gradient watershed resides in temporary 

storage in and near channels where it is available for export 

during storms. 

The estimate of hillslope erosion in the Icacos watershed 

is less than half the fluvial export rate; however, if one considers the inherent uncertainties of each element, this difference 

may not be important (table 3). Hillslope-derived inputs were 

calculated to be 750 t km−2 yr−1, while the mean annual fluvial 

export was 2,140 t km−2. The high annual rainfall, minimal 

anthropogenic disturbance, and sand-dominated particle size 

lead to a high transport-rate, high-efficiency geomorphic 

system. The large amount of material available for transport 

from active storage in the channel bars and the channel bed, 

7,950 t km−2 (table 6), means that most storms should be able 

to transport substantial sediment loads. Field observations and 

GIS analysis of the watershed indicates that extensive flat areas 

(flood plains and Quaternary alluvium) total approximately 

36,000 m2 in the Icacos watershed (Larsen, 1997). Sediment 

storage in these areas is 16,300 t km−2 (table 6). This area 

provides an additional source of sediment, although deposits 

in this area are probably rarely entrained because of the low 

channel gradient and small contributing area of the watershed 

(0.88 km2) upstream of most of the deposits. Finally, historical 

landslides not associated with roads in the Icacos watershed 

were estimated to have eroded 200 t km−2 yr−1. This volume 

indicates that two thirds of all hillslope erosion, 500 t km−2 yr−1, 

is attributable to road construction and maintenance. Anthropogenic disturbance in this watershed may be responsible for the 

majority of fluvial sediment yield.

Hillslope erosion in the Mameyes watershed exceeds 

fluvial sediment export. If all hillslope inputs are included, the 

Mameyes has a sediment delivery ratio of 15 to 62 percent (20 

to 83 percent if bedload yield estimates are included). This 

ratio indicates a more efficient linkage between hillslopes and 

river channels than in the anthropogenically disturbed Canóvanas watershed. A total of 43 percent of the 189 historical landslides mapped in the Mameyes watershed were associated with 

road construction and maintenance. Most of these landslides 

were small slumps and shallow soil slips that eroded about 

80 t km−2 yr−1, or approximately half of all landslide-derived 

sediment. Most of the total fluvial sediment yield may therefore be derived from landslide activity associated with roads. 

Landform Development

A conceptual geomorphic model of landform development for the Greater Antilles was proposed by Ahmad and 

others (1993). This model postulates that landsliding is the 

major contributor of sediment to stream channels. A similar landslide-dominated model was proposed earlier for the 

island of Dominica by Reading (1986). These models are 

supported by the work described in the four Puerto Rican 

study watersheds. 

A total of 41 landslide-triggering storms occurred 

between 1960 and 1991 in the central mountains of Puerto 

Rico (Larsen and Simon, 1993), an average of 1.3 landslidetriggering storms per year. This rate demonstrates that geomorphic work by mass wasting is a regular and important 

process in much of the island. With respect to mass wasting, landslides, for the most part, move orders of magnitude 

more material than other modes of hillslope erosion. The 

annual mass of combined slopewash, treefall, and soil creep 

is dwarfed by the amount of material eroded by landsliding. 

At decadal or longer time scales, landslides deliver orders 

of magnitude more sediment to footslopes, flood plains, and 

stream channels than all other hillslope-erosion processes 

combined, although the frequency and magnitude of landsliding differs strongly with lithology and land use.

Once the sediment is delivered to riparian areas and 

stream channels, the rate and manner of its evacuation are 

also variable, and both depend on, and contribute to, channel 

morphology. In the watersheds underlain by volcaniclastic 

bedrock (Canóvanas and Mameyes) this evacuation of sediment can be seen as an autocontrolled feedback loop in which 

a period of accelerated mass wasting (caused in the short term 

by anthropogenic disturbance, climate perturbation (a period 

of increased tropical disturbances), winter storms, or other 

heavy rainfall) introduces large amounts of sediment, including massive boulders, into the river channels. The increased 

number of large particles armors the channel bed, temporarily reduces sediment transport, and reduces channel incision. 
Landslides and Sediment Budgets in Four Watersheds in Eastern Puerto Rico 173

As incision is slowed, the rate of mass wasting of hillslopes 

may be reduced and theoretically remain so until chemical 

weathering, abrasion, or sufficient high-magnitude flows are 

able to reduce the mass of large particles and transport enough 

channel sediment to allow further incision. Thus starts another 

cycle of potentially increased mass wasting.

In the watersheds underlain by granitic bedrock (Cayaguás and Icacos), the much higher rate of chemical weathering of the coarsely crystalline rocks limits channel armoring 

with boulders, compared with the volcaniclastic bedrock of 

other watersheds. The explanation lies in the highly erodible 

nature of granitic rock in the humid tropics (White and others, 

1998). The granitic bedrock weathers to saprolite to depths of 

as much as tens of meters (Douglas, 1967; Simon and others, 

1990). As feldspars and other minerals are weathered in the 

year-round wet (>2,000 mm annual rainfall) and warm (>19°C 

mean annual temperature) environment, the rock matrix disaggregates, and easily transported grus develops at the ground 

surface (Gerrard, 1994; Buss and White, 2012). Therefore, 

landsliding and slopewash are more effective agents of erosion 

in granitic bedrock terrain than in the volcaniclastic terrain. 

Although large corestones are abundant in some channel 

reaches in the granitic bedrock, the resulting material delivered to drainage channels is mainly sand sized. Consequently, 

fluvial erosion is highly effective and promotes channel extension. This extension is manifested, in part, as active slumping 

at the heads of tributaries in zero-order valleys, leading to 

higher watershed drainage density in the watersheds underlain 

by granitic bedrock (1.87 km km−2 in the Cayaguás watershed 

and 2.54 km km−2 in the Icacos watershed), compared with 

those in volcaniclastic bedrock (about 1.35 km km−2 in both 

the Canóvanas and Mameyes watersheds). Moreover, channel 

heads that are extended by erosion during large storms tend 

to pirate neighboring drainages observable in the field or on 

topographic maps as unusual confluences in southern portion of 

Cayaguás watershed. Outward expansion resulting from mass 

wasting on valley sides also leads to widening of main channel 

valleys. This rapid erosion has lowered channel gradients in 

the granitic watersheds to the point where the Icacos watershed forms, on average, a relatively low-relief landscape in the 

central part of the watershed (fig. 3). The contact between the 

erodible granitic bedrock and resistant volcaniclastic bedrock 

downstream of the Icacos gaged outlet is a prominent nickpoint, downstream of which the stream descends from 610 m 

to 50 m elevation above mean sea level in a distance of only 

3 km. This nickpoint limits erosion and downcutting of the Río 

Icacos upstream of the gaging station. The Río Cayaguás has 

eroded its watershed similarly, such that low-gradient tributaries predominate and their headward extension has enabled the 

stream system to approach within 12 km of the south (Caribbean) coast of the island, in spite of being part of a north-coastdraining river that crosses a straight-line distance of more 

than 50 km to reach the Atlantic Ocean. However, substantial 

erosion and downcutting of the Río Cayaguás upstream of the 

gaging station is limited by its relatively low elevation, only 

150 m above sea level (Figueroa-Alamo and others, 2006).

The rates of mass-wasting erosion and fluvial sediment 

transport estimated for the Cayaguás and Icacos watersheds are 

high for Puerto Rico. Even on geologic time scales, landscape 

denudation in the Cayaguás and Icacos watersheds appears to 

have been extreme. Evidence of this denudation can be seen 

on topographic maps, where watersheds underlain by granitic 

bedrock appear anomalously low compared with surrounding nongranitic bedrock terrain. The granitic rocks are easily 

identified on topographic maps as eroded areas of the otherwise 

high-elevation Luquillo Mountains and Cayey Mountains.

Summary and Conclusions

The montane humid tropics are a geomorphologically 

dynamic environment that hosts a range of continuous to 

episodic hillslope erosion processes. The warm, moist setting 

promotes rapid development of soil and subsequent growth of 

vegetation. Steep hillslopes and heavy, intense rainfall result in 

active hillslope erosion. 

The four forested and agriculturally developed watersheds located in eastern Puerto Rico that were investigated in 

this study hold numerous possibilities for geomorphological 

comparison, only a few of which were examined here. Hillslope erosional processes— landslides, slopewash, soil creep, 

and treethrow—are important mass wasting mechanisms in 

all four study watersheds. The intensity of these processes is 

closely related to land use, bedrock geology, and rainfall. In all 

watersheds, episodic landslide activity, calculated at annual, 

decadal, or greater time scales, is far more important than any 

other form of mass wasting with respect to the mass of earth 

materials moved downslope. Landslide activity is estimated to 

have eroded about 500 to 5,000 t km−2 yr−1 in the four watersheds, whereas the combined totals of estimated slopewash, 

soil creep, and treethrow ranged from 43 to 120 t km−2 yr−1. 

Nonetheless, the low-intensity, more continuous processes of 

erosion (slopewash, soil creep, treethrow) provide a steady 

stream of sediment to fluvial systems that is sufficient to make 

up the bulk of fluvial sediment export during some years.

In contrast, anthropogenic disturbance plays a conspicuous role in mass wasting that is most apparent in the developed Cayaguás watershed. Much of the landslide-caused 

erosion in the forested watersheds was associated with highway corridors: road-related landslides composed 63 percent 

and 43 percent of the total contribution of historical landslides in the Icacos and Mameyes watersheds, respectively. 

The differing timescales upon which the landslide data were 

based require cautious comparison of annual mass wasting 

rates. However, the order of magnitude difference in rates 

between the forested and developed watersheds implies that 

human modification of hillslopes has increased landslide 

frequency and consequent availability of sediment for fluvial 

transport. The large proportion of rapid mass movements 

associated with road construction and maintenance in the forested watersheds further demonstrates the great sensitivity of 

these hillslopes to human disturbance.
174 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Suspended sediment yield from the four watersheds 

ranged from 325 to 2,140 t km−2 yr−1. Expressed as metric tons 

per square kilometer of sediment per millimeter of runoff per 

year, yield was 2.3 to 3.7 times as high in the anthropogenically disturbed watersheds (Canóvanas and Cayaguás) as in 

the forested watersheds (Icacos and Mameyes) and, similarly, 

3.0 to 4.7 times as high in the watersheds underlain by granitic 

bedrock (Cayaguás and Icacos) as in the watersheds underlain 

by volcaniclastic bedrock (Canóvanas and Mameyes). These 

comparisons highlight the importance of both land use and 

bedrock geology on fluvial sediment transport.

The quantification of the principal hillslope and fluvial 

processes and sediment budgets provides a means for evaluating small montane humid-tropical watersheds from a geomorphological perspective and for comparing them with other 

small watersheds. More important, the contrasting of watersheds with differing land use history provides insights into 

what present-day development strategies may bring in future 

decades or even centuries. Based on storage of colluvium and 

annual fluvial sediment yields, hillslope processes that were 

triggered as long ago as the early 19th century by the onset of 

forest clearing will likely affect fluvial sediment transport for 

more decades to come and possibly into the late 21st century.

Acknowledgments

This work was supported through the United States Geological Survey Water, Energy, and Biogeochemical Budgets 

program (Larsen and others, 1993; Lins, 1994). Many of the 

data used in this study were collected and compiled by U.S. 

Geological Survey Caribbean Water Science Center staff 

Angel Torres-Sánchez and Iris Margarita Concepción. The 

manuscript was greatly improved by thoughtful review comments received from Allen Gellis, and Jonathan Godt, both of 

the U.S. Geological Survey. 

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Effects of Earthworms on Slopewash, 

Surface Runoff, and Fine-Litter Transport 

on a Humid-Tropical Forested Hillslope in 

Eastern Puerto Rico

By Matthew C. Larsen, Zhigang Liu, and Xiaoming Zou

Chapter G of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–G

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.......................................................................................................................................................183

Introduction.................................................................................................................................................183

Purpose and Scope ..........................................................................................................................184

Site Description.................................................................................................................................184

Preparation and Observation of Study Plots.........................................................................................187

Data Characteristics and Limitations .....................................................................................................187

Effects of Earthworms on Slopewash, Surface Runoff, and Fine-Litter Transport ........................188

Summary and Conclusions.......................................................................................................................193

Acknowledgments.....................................................................................................................................195

References..................................................................................................................................................195

Figures

1. Map showing location of Puerto Rico and study area.......................................................185

2. Photographs of study site, El Verde, eastern Puerto Rico, 1998.......................................186

3. Annotated sketch of idealized soil cross section and a Gerlach trough.........................188

4. Diagrams of rainfall and runoff per sampling period at control and experimental 

plots, El Verde, eastern Puerto Rico......................................................................................190

5. Diagrams of daily rainfall and transported soil and organic matter at control and 

experimental plots, El Verde, eastern Puerto Rico..............................................................191

6. Diagrams of daily rainfall and transported soil and transported mineral soil at 

control and experimental plots, El Verde, eastern Puerto Rico........................................192

7. Diagrams of daily rainfall and transported soil and transported organic material 

at control and experimental plots, El Verde, eastern Puerto Rico ...................................194

Table

1. Rainfall, runoff, and transported material by sampling period, El Verde, eastern 

Puerto Rico ................................................................................................................................189
Abbreviations Used in This Report

cm centimeter

cm s−1 centimeters per second

g m−2 grams per square meter

h hour

kg ha−1 kilograms per hectare

kg m−2 yr−1 kilograms per square meter per year

m meter

m2 square meter

mm millimeter

mm 1,000 yr−1 millimeters per thousand years

r

2

 correlation coefficient 

t ha yr−1 metric tons per hectare per year

Conversion Factors

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

Area

square meter (m2

) 10.76 square foot (ft2) 

Flow rate

centimeters per second (cm s−1) 0.3937 inches per second (in. s−1)

millimeters per 1,000 years 

(mm 1,000 yr−1) 0.03937 inches per year (in. 1,000 yr−1) 

Other

grams per square meter (g m−2) 8.922 pounds per acre (lb acre−1)

kilograms per hectare (g ha−1) 0.8922 pounds per acre (lb acre−1)

kilograms per square meter per year 

(kg m−2 yr−1)

1.843 pounds per square yard per year 

(lb yd−2 yr−1)

Effects of Earthworms on Slopewash, Surface Runoff, 

and Fine-Litter Transport on a Humid-Tropical Forested 

Hillslope in Eastern Puerto Rico

By Matthew C. Larsen,1 Zhigang Liu,2 and Xiaoming Zou2, 3

1

U.S. Geological Survey, 436 National Center, Reston, Va., 20192, USA

2

Institute for Tropical Ecosystem Studies, University of Puerto Rico, P.O. 

Box 21910, Río Piedras, Puerto Rico, 00931–1910, USA

3

Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, 

88 Xuefu Road, Kunming, Yunnan, China

Abstract

Rainfall, slopewash (the erosion of soil particles), surface 

runoff, and fine-litter transport were measured in tropical 

wet forest on a hillslope in the Luquillo Experimental Forest, 

Puerto Rico, from February 1998 until April 2000. Slopewash data were collected using Gerlach troughs at eight plots, 

each 2 square meters in area. Earthworms were excluded by 

electroshocking from four randomly selected plots. The other 

four (control) plots were undisturbed. During the experiment, 

earthworm population in the electroshocked plots was reduced 

by 91 percent. At the end of the experiment, the electroshocked plots had 13 percent of earthworms by count and 

6 percent by biomass as compared with the control plots.

Rainfall during the sampling period (793 days) was 

9,143 millimeters. Mean and maximum rainfall by sampling 

period (mean of 16 days) were 189 and 563 millimeters, 

respectively. Surface runoff averaged 0.6 millimeters and 

1.2 millimeters by sampling period for the control and 

experimental plots, equal to 0.25 and 0.48 percent of mean 

rainfall, respectively. Disturbance of the soil environment 

by removal of earthworms doubled runoff and increased the 

transport (erosion) of soil and organic material by a factor of 

4.4. When earthworms were removed, the erosion of mineral 

soil (soil mass left after ashing) and the transport of fine litter 

were increased by a factor of 5.3 and 3.4, respectively. It is 

assumed that increased runoff is a function of reduced soil 

porosity, resulting from decreased burrowing and reworking 

of the soil in the absence of earthworms. The background, 

or undisturbed, downslope transport of soil, as determined 

from the control plots, was 51 kilograms per hectare and the 

“disturbance” rate, determined from the experimental plots, 

was 261 kilograms per hectare. The background rate for 

downslope transport of fine litter was 71 kilograms per hectare 

and the disturbance rate was 246 kilograms per hectare. Data 

from this study indicate that the reduction in soil macrofauna 

population, in this case, earthworms, plays a key role in 

increasing runoff and soil erosion and, therefore, has important 

implications for forest and water management.

Introduction

The hydrological role of tropical forests as compared 

with deforested landscapes has been a source of considerable 

controversy during the past century because forests, although 

they are recognized to have many benefits, such as carbon 

storage, biodiversity, and landscape aesthetics, almost universally consume more water than deforested landscapes (Bruijnzeel, 2004; Jackson and others, 2005). In seasonal-climate 

regions, this excess consumption of water can be mitigated if 

pedogenic processes characteristic of forests promote sufficient infiltration such that groundwater reserves under forests 

exceed those under deforested landscapes—the “sponge 

effect” of Bruijnzeel (2004).

One characteristic of forested environments is macroporosity and soil structure created by burrowing animals. We 

examined the generation of macroporosity by earthworms in 

forests of eastern Puerto Rico. Eastern Puerto Rico represents 

an excellent setting for such an experiment because it hosts 

fewer types of burrowing organisms than mainland tropical forests, where burrowing mammals, leaf-cutter ants, and 

cicadas are common. Earthworms at the Puerto Rico site can 

easily be excluded using electroshock techniques.

Soil erosion is a widespread problem in the humid tropics (Douglas, 1967; Haantjens, 1969; Lal, 1990; Nooren and 

others, 1995). Rates of soil erosion are high in part because 

of abundant, deep, highly weathered regolith and soils typical 

of the warm, moist environment and high rainfall intensity 

associated with convective storms and tropical disturbances 

such as hurricanes (Ruxton, 1967; Hastenrath, 1991; Reading 

and others, 1995). The rate of soil erosion is influenced by 

the character, abundance, and decomposition of plant litter, 

which is, in turn, strongly affected by soil fauna, including 
184 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

earthworms (Hazelhoff and others, 1981; Haria, 1995; 

Chauvel and others, 1999; Heneghan and others, 1999; Lavelle 

and others, 1999; Liu and Zou, 2002; Six and others, 2004). 

Land degradation has been defined as a substantial 

decrease in either or both of an area’s biological productivity 

or usefulness due to human interference (Johnson and Lewis, 

1995). One aspect of landscape degradation is increased runoff, which in disturbed environments has been thought to be 

largely a function of such factors as soil compaction, loss of 

the soil A horizon, loss of vegetative cover, decreased evapotranspiration, and decreased leaf litter (Johnson and Lewis, 

1995). In contrast, the burrowing of earthworms increases 

soil porosity, resulting in greater rainfall infiltration. As they 

burrow, earthworms exert pressure on surrounding soil and 

deposit mucus on the burrow walls; additionally, the burrow 

walls may be lined with oriented clays (Six and others, 2004). 

These two features of earthworm burrows can provide stable, 

efficient conduits of near-surface throughflow. Earthworms 

ingest organic and inorganic materials that are mixed in the 

gut and excreted as a cast (Six and others, 2004). Deposition 

of surface casts on the ground surface creates surface roughness, which can itself reduce rainfall runoff and soil erosion 

(Le Bayon and Binet, 2001). Additionally, field observations by the authors and others (Le Bayon and Binet, 2001) 

indicate that earthworm casts, if abundant, can increase soil 

surface area at the microtopographic scale. The comminuting, feeding, burrowing, and casting activities of earthworms 

regulate soil processes and are well recognized for their 

influence on the decomposition of plant materials (Darwin, 

1892; Lee, 1985). Darwin (1892) was the first to quantify the 

geomorphic work accomplished by earthworms, calculating that in Staffordshire, England, they brought to the soil 

surface 1.7 kilograms per square meter per year (kg m−2 yr−1) 

(17 metric tons per hectare per year (t ha−1 yr−1)) of dry earth. 

Chauvel and others (1999) estimated that earthworms in a 

humid-tropical area of Brazil produced 100 metric tons per 

hectare per year of casts. 

Earthworms generally represent the greatest portion of 

macrofauna biomass in moist and wet tropical forests (Camilo 

and Zou, 2001). In tropical wet forest in the Luquillo Experimental Forest, eastern Puerto Rico, earthworms compose as 

much as one third of the fauna biomass (Odum and Pigeon, 

1970). In agroecosystems, Parmelee and others (1990) showed 

that earthworms increase plant litter decomposition rates by 

20 to 50 percent. This substantial effect is reduced in disturbed landscapes or when forests are converted to other uses 

such as pasture (Liu and Zou, 2002). Where natural forest in 

Puerto Rico was replaced by Caribbean pine and mahogany 

plantations, earthworm richness and density were shown to 

be reduced by half, possibly as a result of differences in soil 

water content, soil pH, organic matter content, and nutrient 

availability associated with the quantity and quality of litter 

inputs (González and others, 1996; Liu and Zou, 2002). Exclusion of tropical earthworms P. corethrurus in the study forest 

was associated with a reduction in soil respiration rates (Liu 

and Zou, 2002)

Purpose and Scope

Soil fauna populations are known to be affected by land 

use change, but the ecological consequences of changing soil 

fauna have not been extensively evaluated in tropical ecosystems (Blanchart and others, 1997). Nonetheless, many studies 

demonstrate that tropical soil macrofauna speed the decomposition of plant litter (Bouché, 1977; Chauvel and others, 

1999; Heneghan and others, 1999; González and Seastedt, 

2001). The published literature describing the role of soil 

macrofauna in soil erosion and runoff processes in the humid 

tropics is limited (Baidgett and others, 2001). Because human 

population pressure on the natural environment has increased 

substantially in the humid tropics in recent decades, baseline 

studies are needed to better understand these process and to 

improve land-management practices (Johnson and Lewis, 

1995; Grau and others, 2003).

In an effort to quantify the association of earthworms 

with several basic physical soil and hydrological processes, in 

the present study we asked the following questions: do earthworms affect slopewash, the amount of surface rainfall runoff, 

and the transport of fine litter? A simple field experiment was 

designed to measure these quantities.

Site Description

A gentle hillslope 350 meters (m) above sea level, 

sloping 10° to the northwest (azimuth 310°) in the El Verde 

section of the Luquillo Experimental Forest (18° 20′ N., 

66° 49′ W.) was selected for the experiment (fig. 1). The 

site is classified as tropical wet forest (Ewel and Whitmore, 

1973) and is dominated by tabonuco trees (Dacryodes 

excelsa), a broad-leafed evergreen (fig. 2). Mean monthly 

temperature ranges from 20.8 to 24.4°C (Brown and others, 

1983). Mean annual rainfall is 3,536 millimeters (mm), and it 

was 4,035 mm during the study period (Schaefer and Melendez, 2001). Mean daily rainfall was 12 mm during the study 

period; maximum daily rainfall was 160 mm, recorded on 

April 16, 1998, in association with a local convective storm. 

The soil is classified as a deep, moderately well-drained Oxisol with a high clay content (89 percent; Soil Survey Staff, 

1995). Surficial soil is highly permeable, and little rainfall 

becomes surface runoff; Larsen and others (1999) reported 

runoff values of 0.2 to 0.5 percent of monthly rainfall and 

soil infiltration rates of 0.001 centimeters per second (cm s−1) 

to 0.46 cm s−1 in the Luquillo Experimental Forest. Vegetation covers 80 percent of the forest, and a layer of leaf litter 

several centimeters thick covers the soil surface (fig. 2). The 

mean fresh weight of earthworms in the forest is 25 grams 

per square meter (g m−2), and earthworm density ranges from 

32 to 137 m−2; the mean is 89 worms per square meter (Liu 

and Zou, 2002). Earthworms are dominated by nonendemic 

species P. corethrurus and Amynthas rodericensis, but the 

native species Estherella gates also is present (Zou and 

González, 1997; González and others, 1999). In mature forest, P. corethrurus is the most abundant species, but biomass 
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 185

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_01G.ai

C A R I B B E A N S E A

El Verde study site

B

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Area shown in B

A

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

LUQUILLO

MOUNTAINS

L e s s e r

A n

t i l l e s

Figure 1. Location of Puerto Rico and study area, eastern Puerto Rico.
186 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A

C

B

a

w

a

f

D

Figure 2. Study site, El Verde, eastern Puerto Rico, 1998. A, tabonuco forest; B, one of eight 2-square-meter measurement plots prior 

to installation of Gerlach trough at downslope end; C, understory; and D, closeup of forest floor and litter layer. In D, note worm castings 

(w), fine rootlets (f), and small anthills (a); scale in inches (top) and centimeters.
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 187

of native earthworms is higher (Zou and González 1997; 

Sanchez and others, 2003). Earthworms are not highly active 

during the relative dry season between January and April.

In general, three morphoecological groups of earthworms 

are described by Bouché (1977): epigeic species, anecic species, and endogeic species. Epigeic species inhabit the litter 

layer of forest soils above the mineral soil surface and have 

little effect on soil structure. Anecic species form a network 

of burrows that penetrate deep into the mineral soil, feed on 

decayed surface litter, and transport organic material from the 

surface into their burrows. Endogeic species live in mineral 

soil horizons and feed on soil enriched with organic matter. P. 

corethrurus and E. gates are endogeic species and A. rodericensis is an anecic species. 

Preparation and Observation of Study 

Plots

A forested hillslope at the El Verde study site was 

sampled from February 26, 1998, to April 28, 2000 (fig. 1). 

Eight rectilinear plots each 2 square meters (m2) in area 

(1 m wide, 2 m long) were bounded along their margins 

with plastic sheeting attached to steel rods for 26 months. 

The plastic sheets were buried to a depth of 0.5 m; 0.15 m 

was exposed above the surface to prevent runoff and earthworms from crossing the plot boundaries. Earthworms were 

removed from four of these plots by electroshocking, by 

using a 240 volt alternating electrical current (supplied by a 

portable generator) to bring earthworms to the surface. Once 

at the surface, earthworms were removed by hand. Each of 

the four plots from which earthworms were removed was 

treated with the electrical current for 1.5 hours (h) every 

3 months. Electroshocking is an effective technique for 

removing earthworms; it has been shown to result in minimal soil disturbance and no appreciable effects on microarthropods and nematodes, and it does not persist in the soil, 

unlike chemicals that can affect soil’s biological properties 

(Blair and others, 1995). At the end of the 26-month experiment, a 0.25- by 0.25-m quadrant in the center of each of the 

eight plots was excavated to a depth of 0.5 m, and all earthworms were removed and sorted for counting and weighing 

in a laboratory. 

At the start and end of the experiment, Liu and Zou 

(2002) collected soil samples from six cores (19 mm in diameter) in each plot. They determined soil bulk density by ovendrying at 105°C for 48 h and soil pH by using a 1:1 mixture of 

soil and deionized water (Liu and Zou, 2002). 

Surface-water runoff and slopewash were trapped at the 

downslope end of each plot by using Gerlach troughs (Gellis 

and others, 1999; Larsen and others, 1999). A Gerlach trough 

is a sediment trap designed to retain soil particles, organic 

material, and surface runoff transported downslope by gravity (Young, 1960; Gerlach, 1967). The Gerlach troughs were 

fabricated with materials available from a standard hardware 

store, using a plastic section of household rain gutter with 

a hinged metal lid to prevent direct interception of rainfall, 

rainsplash-transported sediment, and direct litterfall (fig. 3). 

A rectangular metal strip was attached to the upslope side 

of the trough and imbedded in the soil to allow slopewash, 

surface runoff, and fine litter to enter the trough. A 13-mmdiameter plastic tube connected the side of the trough to a 

20-liter closed bucket anchored downslope to allow excess 

runoff and fine sediment to be trapped. Regular sampling 

of each trap included scooping out sediment and fine litter 

caught in the trough and measuring the volume of the watersediment mixture in the buckets. Runoff for each of the eight 

plots was defined as the total volume of water in the Gerlach 

trough and 20-liter bucket, divided by the 2-m2 drainage area. 

Slopewash samples were collected from each Gerlach trough 

on average every 16 days (range 8 to 34 days), oven dried at 

100°C for 24 h, weighed, then ashed at 550°C for 1 h, and 

reweighed to determine loss on ignition. Average daily flux 

of material was calculated for the total mass of oven-dried 

soil and organic material; for the total mass of mineral soil 

(soil mass left after ashing); and for the total mass of organic 

material, defined as the portion of oven-dried soil and 

organic material that was lost on ignition. Daily rainfall data 

were collected at the El Verde field station, located approximately 0.4 kilometers south of the study site (Schaefer and 

Melendez, 2001). Rainfall data were expressed as average daily amounts for each sampling period. All data were 

expressed as totals for the study period; as mean, median, 

and maximum by sampling period; and as time series and 

scatter plots. Least-squares linear regression models (Helsel 

and Hirsch, 2002) were developed for each data set with 

model confidence limits set at 95 percent and number of 

samples (n)=50. 

Data Characteristics and Limitations

Although Gerlach troughs provide an inexpensive means 

for quantifying surface runoff and slopewash, their limitations should be noted. Occasional decoupling of the Gerlach 

troughs from the soil resulted in the diversion of slopewash 

and runoff and undermeasurement of the sample. Reseating 

the trough sometimes resulted in a temporary increase in the 

mass of soil particles and organic debris transported into the 

trough. The use of bounded plots for Gerlach troughs defines 

the contributing area, in this study, 2 m2

 each, but ultimately 

the contributing area for each trough is controlled by such 

factors as the soil heterogeneity, intensity of a rainstorm, 

antecedent soil moisture, and the subsequent surface runoff. 

The uncertainty of these factors increases scatter in the data. 

Odum and others (1970) measured an average of five rainfall events per day in their study of climate in the Luquillo 

Experimental Forest. The difficulty of sampling at the end of 

every rainfall event prevented the precise determination of 

the relation between rainfall, runoff, and transport of soil and 

organic material.
188 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Effects of Earthworms on Slopewash, 

Surface Runoff, and Fine-Litter 

Transport

Earthworm population in the experimental (electroshocked) plots was reduced by 91 percent by the end of the 

26-month experiment, compared with the population prior to 

shocking. When the experiment was terminated, the electroshocked plots had 13 percent of earthworms by count and 

6 percent by biomass as compared with the control plots (Liu 

and Zou, 2002). Electroshocking had no effect on soil bulk 

density or pH (Liu and Zou, 2002). 

A total of 9,423 mm of rainfall was recorded during 

the 793-day study period (table 1). Mean and maximum 

rainfall by sampling period (mean of 16 days) were 189 and 

563 mm, respectively. Runoff averaged 0.6 mm and 1.2 mm 

by sampling period for the control and experimental plots, 

respectively. The mean runoff (calculated using the runoff 

percent for each of the 50 sample periods) was 0.25 percent 

and 0.48 percent of rainfall for the control and experimental 

plots, respectively (table 1). Runoff from the control plots 

was nearly identical to earlier results using Gerlach troughs 

in other parts of the study area (Larsen and others, 1999), 

where runoff was 0.2 percent of rainfall on the same soil type 

in an undisturbed setting but with steeper slopes. Comparable runoff data were obtained by Chandler and Walter (1988) 

at tropical upland watersheds in Leyte, Philippines, where 

surface hydrologic response of forest, tilled land, slash and 

mulch, and pasture were compared; the forested site had the 

lowest annual runoff—less than 3 percent of rainfall.

At the El Verde site, time series of rainfall and runoff 

show generally increased runoff from the experimental plots 

compared with runoff from the control plots throughout the 

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_03G.ai

Sheet metal lid, hinged to trough

for opening during sample extraction,

and closed during operation to prevent

direct collection of rainfall. (Lid is

shown transparent in this diagram.)

Standard plastic rain gutter capped at

ends to retain sediment and runoff. Fitted

with tubing to drain water and fine

sediment to 20-liter bucket

Earthworm

burrow and

casts at soil

surface

Sheet metal lip, riveted to

trough and embedded in

soil upslope of trough, at

base of 2-square-meter

sample plot

20-liter sealed bucket,

downslope of trough, for

retaining excess water and

sediment from trough

13-millimeter-diameter plastic tubing

Figure 3. Idealized soil cross section and Gerlach trough showing hinged lid, which covers 

trough to prevent direct interception of rainfall and permits removal of sediment and water during 

sampling. Sheet metal lip embedded in soil upslope of trough allows soil particles, debris, and 

runoff to enter trough. Plastic tubing and bucket are attached downslope to collect overflow water 

and sediment from trough.
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 189

experiment (fig. 4A). This tendency is also indicated by the 

greater slope, 0.0099 as compared with 0.0065, of the curves 

representing the dependency of runoff on rainfall (fig. 4B). 

The approximately two-fold difference in runoff between 

the experimental and control plots increased slightly as daily 

rainfall increased (fig. 4B). As expected, daily rainfall was a 

reasonably good predictor of runoff; the least-squares linear 

regression models had correlation coefficient (r2

) values of 

0.67 and 0.60, respectively, for the experimental and control plots. In this and each of the other data sets that were 

analyzed, r2 values were greater for the experimental plots, 

suggesting that the absence of earthworms reduces some of the 

natural heterogeneity in the hydrological response to rainfall. 

Soil fauna such as earthworms and termites can substantially increase rates of infiltration through soil by creating 

macropores and channels during their feeding and burrowing 

activities (Baidgett and others, 2001). Removal of earthworms 

reduces porosity and eliminates earthworm casting activity, 

resulting in more microtopographic uniformity and a reduction 

of total surface area of the soil surface.

Daily rainfall and daily transported soil and organic 

material expressed as a time series showed a clear difference 

between the experimental and control plots (fig. 5A). Transport 

of total soil and organic material from the experimental plots 

was 4.4 times as great as from the control plots, totaling 231 and 

53 g, respectively, for the duration of the experiment (table 1). 

Shuster and others (2002) also reported increased transport of 

soil in an experiment in which earthworm populations were 

reduced in a tilled corn agroecosystem; they attributed the 

increase in sediment flux to incorporated residue levels, reduction of the earthworm population, and reduced macropore and 

midden density. In our experiment, mean transport of total soil 

and organic material was 4.6 and 1.1 g per sampling period, 

respectively, from the experimental and control plots. The transport of soil and organic material from the experimental plots 

was variable, but it showed a general correlation with rainfall 

(slope, 0.018 and r2 value, 0.35 (fig. 5B). Control plots showed 

no correlation with rainfall, presumably because of their greater 

heterogeneity in hydrologic response as noted above.

Chauvel and others (1999) also reported increased soil 

erosion in tropical pastures by P. corethrurus owing to surface 

casting associated with soil compaction due to cattle grazing. 

At El Verde, the background, undisturbed downslope transport 

of soil, as determined from the control plots, was 51 kilograms 

per hectare (kg ha−1) and the “disturbance” rate, determined 

from the experimental plots, was 261 kg ha−1. The background 

rate for downslope transport of fine litter was 71 kg ha−1 and 

the disturbance rate was 246 kg ha−1.

On steep hillslopes, earthworm casts may become 

detached and roll downslope, possibly contributing to net soil 

erosion (Larsen and others, 1999). However, when earthworm 

casts are dried or aged, they become quite stable (Shipitalo 

and Protz, 1988), and on a gentle slope such as the one studied 

here, casting activity in and of itself was not observed to contribute directly to net downslope movement of soil, at least by 

Table 1.

movement of detached casts. 

Rainfall, runoff, and transported material by sampling period, El Verde, eastern Puerto Rico.

[transported material, grams transported from each 2×2-meter plot; 50 sampling periods with a mean of 16 days each; g, gram; mm, millimeter; SD, standard deviation]

Sampling

period

(days)

Rainfall

(mm)

Mean runoff

(mm)

Runoff

(percent of rainfall)

Mean soil and

organic material

(g)

Mean mineral soil

(g)

Mean organic material

portion

(g)

Control Experimental Control Experimental Control Experimental Control Experimental Control Experimental

Mean 16 189 0.6 1.2 0.25 0.48 1.1 4.6 0.40 2.30 0.60 2.20

Median 15 167 0.2 0.5 0.03 0.16 0.7 3.5 0.30 1.00 0.40 1.70

Maximum 34 563 4.8 6.6 1.22 2.07 5.9 36.1 3.10 27.00 2.80 9.10

Minimum 8 1.3 0.0 0.0 0.0 0.0 0.2 0.7 0.05 0.25 0.03 0.35

SD 4.0 143 1.0 1.6 0.20 0.40 1.1 5.7 0.50 4.40 0.70 1.70

Total 793 9,423 31 59 0.33 0.62 53 231 221 1141 311 1071

1Totals for mean mineral soil and mean organic portion do not sum to soil and organic material because data are missing in one sample set.
190 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_04G.ai Runoff, in millimeters Daily runoff, in millimeters 

Rainfall, in millimeters 

 Daily rainfall, in millimeters 

0

0

0.1 1 10 100

0.1

0.2

0.3

0.4

0.5

2 100

0

200

300

400

500

600

4

6

8

10

12

A

B

Mar.

1998

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2000

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1998

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1999

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July

1999

Sept.

1998

Sept.

1999

Nov.

1998

Nov.

1999

Jan.

1999

Jan.

2000

Month

EXPLANATION

Rainfall

Runoff, experimental plots

Runoff, control plots

EXPLANATION

Experimental plots—Average

Control plots—Average

Experimental plots—Data

Control plots—Data

Figure 4. Rainfall and runoff per sampling period (mean of 16 days) at control and experimental plots, El Verde, eastern Puerto Rico. 

A, Time series; B, Scatterplot (control: daily runoff = 0.0065 • daily rainfall − 0.0353, correlation coefficient (r2

) = 0.60; experimental: 

daily runoff = 0.0099 • daily rainfall – 0.0407, r2

 = 0.67).
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 191

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_05G.ai Soil and organic material, in grams per day

 Daily rainfall, in millimeters 

0.001

0.1 1 10 100

0.01

0.1

1

10

B

Soil and organic material, in grams per day

Daily rainfall, in millimeters 

0.0

0.2 5

0

15

20

30

35

40

0.6

1.0

1.2

1.6

1.8

A

Mar.

1998

Mar.

1999

Mar.

2000

May

1998

May

1999

July

1998

July

1999

Sept.

1998

Sept.

1999

Nov.

1998

Nov.

1999

Jan.

1999

Jan.

2000

Month

0.4

10

25

0.8

1.4

EXPLANATION

EXPLANATION

Rainfall

Soil and organic material, experimental plots

Soil and organic material, control plots

Experimental plots—Average

Control plots—Average

Experimental plots—Data

Control plots—Data

Figure 5. Daily rainfall and transported soil and organic matter at control and experimental plots, El Verde, eastern Puerto Rico. 

A, Time series; B, Scatterplot (control: soil and organic material = 0.0008 • daily rainfall + 0.0391, correlation coefficient (r2

) = 0.02; 

experimental: soil and organic material = 0.018 • daily rainfall + 0.0012, r2

 = 0.35).
192 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_06G.ai Mineral soil, in grams per day

0.001

0.1 1 10 100

0.01

0.1

1

10

B

Mineral soil, in grams per day

Daily rainfall, in millimeters 

0

0.2 5

0

15

20

30

35

40

0.6

1.0

1.2

1.6

1.8

A

Mar.

1998

Mar.

1999

Mar.

2000

May

1998

May

1999

July

1998

July

1999

Sept.

1998

Sept.

1999

Nov.

1998

Nov.

1999

Jan.

1999

Jan.

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Month

0.4

10

25

0.8

1.4

EXPLANATION

EXPLANATION

Rainfall

Mineral soil, experimental plots

Mineral soil, control plots

Experimental plots—Average

Control plots—Average

Experimental plots—Data

Control plots—Data

 Daily rainfall, in millimeters 

Figure 6. Daily rainfall and transported soil and transported mineral soil at control and experimental plots, El Verde, eastern 

Puerto Rico. A, Time series; B, Scatterplot (control: mineral soil = 7 x 10–5 • daily rainfall + 0.0077, correlation coefficient (r2

) = 0.17; 

experimental: mineral soil = 0.0007 • daily rainfall – 0.0335, r2

 = 0.28).
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 193

Earthworm casts on the soil surface may mitigate soil 

erosion by increasing surface roughness (Le Bayon and Binet, 

2001). Additionally, field observations by the authors indicate 

that earthworm casts, if abundant, increase soil surface area at 

the microtopographic scale. The increased surface area can be 

estimated by calculating the area of the one half of a spherical 

cast that is exposed to direct rainfall and summing the surface 

area of all casts per square meter. The worm castings shown in 

figure 2D are roughly spherical and on the order of 1 centimeter (cm) in diameter. Using an estimate of a single 1-cm cast 

produced by each of the average of 89 earthworms per square 

meter, the increase in surface area would be 280 cm2

, approximately 3 percent of a 1-m2

 area. At another humid-tropical 

site, Henrot and Brussaard (1997) describe P. corethrurus

surface casts up to 3 cm thick. The increase in surface area at 

this site would be approximately 840 cm2

, or approximately 

8 percent of a square meter. These effects of casting would be 

expected to reduce runoff and enhance infiltration during lowto moderate-intensity rainfall.

The pattern of daily rainfall and daily transport of 

mineral soil was comparable to that of total soil and organic 

material (figs. 5A, 6A). The transport of mineral soil was 

5.2 times as great from the experimental plots as from the 

control plots, totaling 114 and 22 g, respectively, for the 

duration of the experiment (table 1). Mean transport of mineral soil was 2.3 and 0.4 g per sampling period, respectively, 

from the experimental and control plots. The least-squares 

linear regression models developed for daily transport of 

mineral soil as compared with daily rainfall had r2 values 

that were similar to those developed for the total soil and 

organic material (figs. 5B, 6B). Transport of organic material was calculated using a residual value (loss on ignition), 

so the magnitude and frequency of mineral-soil transport 

was similar to that discussed above (see fig. 5) for totalsoil and organic-material transport (fig. 7). On average, 

organic-material transport from the experimental plots was 

46 percent of the total material collected, compared with 

59 percent in the case of the control plots (table 1). 

The three-fold increase in fine litter trapped in Gerlach 

troughs at the experimental plots likely reflects the greater 

availability of fine litter—the result of a decrease in finelitter decomposition associated with the lowered earthworm 

population (Liu and Zou, 2002). The relatively lower rate of 

fine-litter export in the control plots may also be due to the 

gluing effect of earthworm casts (Chauvel and others, 1999). 

Shipitalo and Protz (1988) report that clay minerals and 

organic material become encrusted with mucus when they 

are mixed in the earthworm gut, contributing to microaggregate (cast) formation. It is possible that the bonding of cast 

material may also inhibit litter transport downslope. 

The results of our study at El Verde indicate that the 

reduction of earthworm populations in a managed tropical 

forest with moderate-gradient hillslopes can substantially 

increase soil erosion, from a background rate (defined by the 

control plots) of 51 kg ha−1 to the disturbance rate (defined 

by the experimental plots) of 261 kg ha−1. These rates of 

soil erosion are equal to a background surface-lowering rate 

of 9 millimeters per thousand years (mm 1,000 yr−1) and a 

disturbance surface-lowering rate of 48 mm 1,000 yr−1. The 

background rate for downslope transport of fine litter was 

71 kg ha−1 and the disturbance rate was 246 kg ha−1. The 

increased mass of soil and fine litter moving downslope may 

be temporarily sequestered in local swales or depressions on 

the hillslope as well as at the footslope; however, no measurements were made to test this assumption. In the highly 

dissected Luquillo Experimental Forest, however, upland 

hillslopes have limited flood plain or other low-gradient surfaces. As a result, most of the soil and fine litter is presumed 

to be delivered to ephemeral and perennial channels, where 

it is easily entrained and incorporated by streamflow into the 

suspended-sediment load.

The effect of reduced earthworm population on hillslope 

hydrology is evident in the runoff rate, which more than 

doubled in the plots within which earthworm populations 

were reduced (table 1). If extended across a landscape, this 

association indicates that in areas where widespread changes 

in land use also markedly reduce earthworm population, 

streamflow peaks may be greater and may rise and fall more 

quickly in response to storms. Such earthworm reduction 

may increase downstream channel scour and flash-flood risk. 

Additionally, lower rates of rainfall infiltration (more than 

a 50 percent decline) in areas where earthworm populations 

have been greatly reduced would be expected to diminish 

soil moisture and groundwater recharge. Although we were 

not able to measure rates of infiltration below the rooting 

depth of 15 to 30 cm (Brown and others, 1983), earthworm 

burrows in the mineral horizon would serve to divert nearsurface flow (in the meaning used by Elsenbeer, 2001; see 

fig. 2 in chapter E of this report) into deeper groundwater 

where it would contribute to higher dry-season base flow. 

This possibility is a potential area for future research. It is 

clear that the effects of reducing earthworm population on 

hydrologic and soil properties could be substantial in the 

montane humid tropics, where effective management of these 

critical natural resources is often a challenging task (Larsen, 

2000; Bruijnzeel, 2004).

Summary and Conclusions

Disturbance of the soil environment by removal of 

earthworms doubled runoff and increased the erosion of soil 

and downslope transport of fine litter. This doubling can be 

attributed to two factors: reduced soil porosity resulting from 

decreased burrowing and reworking of the soil, and decreased 

soil surface area and surface roughness from a reduction in 

earthworm casting activity. Earthworm casts increase soil surface area and roughness at the microtopographic scale, thereby 

reducing runoff, at least during light- to moderate-intensity 

rainfall. Increased runoff in disturbed environments has been 

thought to be largely a function of such factors as soil compaction, loss of the soil A horizon, loss of vegetative cover, 

decreased evapotranspiration, and decreased leaf litter. In 
194 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_G_figures\BW_figures\BWfigure_07G.ai Organic material, in grams per day

 Daily rainfall, in millimeters 

0.001

0.1 1 10 100

0.01

0.1

1

B

Organic material, in grams per day

Daily rainfall, in millimeters 

0

0.2 5

0

15

20

30

35

40

0.6

1.0

1.2

1.6

1.8

A

Mar.

1998

Mar.

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2000

May

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May

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1.4

EXPLANATION

EXPLANATION

Rainfall

Organic material, experimental plots

Organic material, control plots

Experimental plots—Average

Control plots—Average

Experimental plots—Data

Control plots—Data

Figure 7. Daily rainfall and transported soil and transported organic material at control and experimental plots, El Verde, eastern 

Puerto Rico. A, Time series; B, Scatterplot (control: organic material = 0.0002 • daily rainfall + 0.0269, correlation coefficient (r2

) = 0.003; 

experimental: organic material = 0.0045 • daily rainfall + 0.0471, r2

 = 0.25).
Effects of Earthworms on a Humid-Tropical Forested Hillslope in Eastern Puerto Rico 195

the Luquillo Experimental Forest, and presumably in similar 

humid tropical forests, a decrease in burrowing soil macrofauna should be added to this list.

Land degradation has been defined as a substantial 

decrease in either or both of an area’s biological productivity 

or usefulness due to human interference. Anthropogenic reduction in earthworm population, whether intentional (as in this 

experiment) or unplanned (as the result of the disturbance of 

soil or vegetative cover) is a form of land degradation that has 

unanticipated consequences. The data from this study indicate 

that the reduction in soil macrofauna population, in this case 

earthworms, can substantially alter soil physical properties, 

resulting in the degradational processes of markedly reduced 

rainfall infiltration, increased runoff, and increased soil erosion. As land and ecosystem management strategies become 

increasingly complex, improved understanding of the role of 

macrofauna on soil and water resources is needed.

Acknowledgments

This work was supported by National Science Foundation 

grant DEB–9705814, National Aeronautics and Space Administration grant NAGW 4059, and the U.S. Geological Survey 

Water, Energy, and Biogeochemical Budgets program (Larsen 

and others, 1993; Lins, 1994). Special thanks to Angel J. Torres Sánchez and Iris M. Concepción, U.S. Geological Survey, 

and to Joel Martinez and Marcelle Fabregas, University of 

Puerto Rico, for laboratory processing of sample material. 

Helpful manuscript review suggestions were offered by Robert 

F. Stallard, Cliff R. Hupp, and Allen C. Gellis, U.S. Geological 

Survey, and by two anonymous reviewers.

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Weathering, Landscape Equilibrium, and 

Carbon in Four Watersheds in Eastern 

Puerto Rico

By Robert F. Stallard

Chapter H of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–H

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.......................................................................................................................................................205

Introduction.................................................................................................................................................205

Research Setting...............................................................................................................................207

Purpose and Scope ..........................................................................................................................207

Weathering, Erosion, and the Carbon Cycle .........................................................................................209

Solute Loads and Chemical Erosion in Eastern Puerto Rico .....................................................209

Solid Loads and Physical Erosion in Eastern Puerto Rico .........................................................210

Steady-State Erosion .......................................................................................................................211

Carbon, Weathering, and Erosion ..................................................................................................211

Landslides, Erosion, and Carbon....................................................................................................212

Methods Used to Assess the Effects of Weathering and Erosion in the Carbon Cycle.................213

Chemical Analyses of Dissolved and Solid Phases ....................................................................213

Estimation of Constituent Yields .....................................................................................................215

Data Manipulation and Processing of Dissolved Constituents.................................................217

Correction for Atmospherically Derived Contributions to the Dissolved 

and Solid Load ............................................................................................................217

Total Dissolved Bedrock .........................................................................................................219

Equilibrium Model: Calculating Steady-State Erosion from Dissolved Bedrock...........219

Suspended Bedrock, Loss on Ignition, and Particulate Organic Carbon.......................224

Projection to a Common Intermediate Yield.................................................................................224

Suspended Bedrock and Runoff ....................................................................................................225

Solid Loads for Individual Years ............................................................................................225

Calculating the Long-Term Average for Nonlinear Sediment-Yield Models..................233

Interpretation of Projections to a Common Yield.........................................................................233

Results, Comparison of Constituent Yields, and Assessment of Landscape Equilibrium..............235

Constituent-Yield Comparisons ......................................................................................................235

Landscape Equilibrium.....................................................................................................................237

Summary and Conclusion: General Landscape Disequilibrium and the Carbon Cycle .................239

Acknowledgments.....................................................................................................................................241

References..................................................................................................................................................241
Figures

1. Map showing location of Puerto Rico and study watersheds ..........................................208

2–13. Diagrams showing the following:

 2. Carbon accumulation on a chronosequence of landslides from the 

Luquillo Mountains, eastern Puerto Rico ......................................................................213

 3. Concentration of dissolved silicate bedrock and two dissolved constituents 

used in calculating bedrock denudation rates and in estimating equilibrium 

physical denudation rates, eastern Puerto Rico..........................................................219

 4. Concentration of the three major components of carbon transport in tropical

rivers, eastern Puerto Rico ..............................................................................................221

 5. Concentration of an operationally derived constituent, suspended bedrock,

used in calculating physical denudation rates, eastern Puerto Rico.......................221

 6. Percent loss on ignition of sediment collected in eastern Puerto Rico...................222

 7. Time series of annual runoff and annual yields of chloride, sodium, calcium, 

and of bedrock-derived sodium and calcium, eastern Puerto Rico .........................226

 8. Time series of annual yields of magnesium, potassium, sulfate, nitrate, 

ammonia, and phosphate, eastern Puerto Rico ...........................................................227

 9. Time series of annual yields of constituents that best characterize erosion 

and the carbon cycle, eastern Puerto Rico ..................................................................228

 10. Annual runoff compared with annual yields of silica, chloride, sodium, 

calcium, and bedrock-derived sodium and calcium for five rivers, eastern 

Puerto Rico .........................................................................................................................229

 11. Annual runoff compared with annual yields of magnesium, potassium, 

sulfate, nitrate, ammonia, and phosphate for five rivers, eastern Puerto Rico ......230

 12. Annual runoff compared with annual yields of constituents that best 

characterize erosion and the carbon cycle, eastern Puerto Rico ............................231

 13. A model-based comparison of annual runoff compared with annual yields 

of bedrock, eastern Puerto Rico .....................................................................................235

Table

1. X-ray fluorescence and wet-chemical analysis of geologic materials and 

water from the Luquillo Mountains, eastern Puerto Rico..................................................214

2. Discharge-weighted average concentrations for each percentile class 

estimated by using LOADEST and hourly discharge from study watersheds, 

eastern Puerto Rico..................................................................................................................215

3. Average annual net, atmospheric, and bedrock inputs to study watersheds, 

1991 to 2005, eastern Puerto Rico ..........................................................................................216

4. Parameters and coefficients used in modeling equilibrium erosion in study 

watersheds, eastern Puerto Rico, by using Na-Si and Ca-Si pairs..................................223

5. Analysis of loss on ignition for a discharge-weighted average sample of solid 

load from study rivers, eastern Puerto Rico.........................................................................224

6. Coefficients of power-law model relating annual suspended-bedrock yield to

runoff in study watersheds, eastern Puerto Rico................................................................225

7. Coefficients of model relating annual suspended-bedrock yield to runoff and 

landslide day runoff in study watersheds, eastern Puerto Rico.......................................232
8. Coefficients that predict landslide-day runoff given annual runoff in study 

watersheds, eastern Puerto Rico ..........................................................................................232

9. Convolution information used to represent interannual variability..................................233

10. Yields in study watersheds as observed and adjusted to a common 

intermediate runoff, eastern Puerto Rico .............................................................................236

11. Interpretation of changes in yields of study watersheds as observed and 

adjusted to a common intermediate runoff, eastern Puerto Rico ....................................237

12. Summary of landscape equilibrium properties in study watersheds, eastern 

Puerto Rico ................................................................................................................................238

13. Observed mean-annual runoff compared with equilibrium runoff for power-law 

and landslide day erosion models in study watersheds, eastern Puerto Rico ..............238

14. Total denudation compared with equilibrium denudation for current mean-annual 

runoff and projected to a common runoff in study watersheds, eastern Puerto Rico..238

15. Excess particulate organic carbon yields from the equilibrium model of study 

watersheds, eastern Puerto Rico ..........................................................................................238

Abbreviations Used in This Report

* calculated bedrock-derived constituent

< less than

> greater than

μm micrometer

μmol L−1 micromoles per liter

eq mol−1 equivalents of charge per mole

h hour

kmol kilomole

kmol km−2 yr−1 thousand moles per square kilometer per year

m meter

mg L−1 milligrams per liter

mm millimeter

mm h−1 millimeters per hour

mm k.y.−1 millimeters per thousand years

mm yr−1 millimeters per year

pg yr−1 petagrams per year (1 petagram=1 gram15)

ppm parts per million

t km−2 yr−1 metric tons per square kilometer per year

DBrx dissolved bedrock

DIC dissolved inorganic carbon 

DOC dissolved organic carbon 

LOI loss on ignition
PIC particulate inorganic carbon

POC particulate organic carbon

SBrx suspended bedrock

SSol suspended solids

WEBB Water, Energy, and Biogeochemical Budgets

ZBrx total bedrock-derived cation charge

Conversion Factors

Multiply By To obtain

Length

micrometer (μm) 0.00003937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

Flow rate

millimeters per hour (mm h−1) 0.03937 inches per hour (in. h−1) 

millimeters per year (mm yr−1) 0.03937 inches per year (in. yr−1) 

millimeters per 1,000 years 

(mm k.y.−1) 0.03937 inches per 1,000 year (in. k.y.−1) 

Mass

milligrams per liter (mg L−1) 0.05841 grains per gallon (gr gal−1)

Other

petagrams per year 1.102 billions of short tons per year (t yr−1)9

metric tons per square kilometer per 

year (t km−2 yr−1) 2.855 short tons per square mile per year 

(tons mi−2 yr−1)

Weathering, Landscape Equilibrium, and Carbon in Four 

Watersheds in Eastern Puerto Rico

By Robert F. Stallard

Abstract

The U.S. Geological Survey’s Water, Energy, and Biogeochemical Budgets (WEBB) program research in eastern Puerto 

Rico involves a double pair-wise comparison of four montane 

river basins, two on granitic bedrock and two on fine-grained 

volcaniclastic bedrock; for each rock type, one is forested and 

the other is developed. A confounding factor in this comparison 

is that the developed watersheds are substantially drier than the 

forested (runoff of 900–1,600 millimeters per year compared 

with 2,800–3,700 millimeters per year). To reduce the effects 

of contrasting runoff, the relation between annual runoff and 

annual constituent yield were used to estimate mean-annual 

yields at a common, intermediate mean-annual runoff of 1,860 

millimeters per year. Upon projection to this intermediate 

runoff, the ranges of mean-annual yields among all watersheds 

became more compact or did not substantially change for 

dissolved bedrock, sodium, silica, chloride, dissolved organic 

carbon, and calcium. These constituents are the primary 

indicators of chemical weathering, biological activity on the 

landscape, and atmospheric inputs; the narrow ranges indicate 

little preferential influence by either geology or land cover. The 

projected yields of biologically active constituents (potassium, 

nitrate, ammonium ion, phosphate), and particulate constituents (suspended bedrock and particulate organic carbon) were 

considerably greater for developed landscapes compared with 

forested watersheds, consistent with the known effects of 

land clearing and human waste inputs. Equilibrium rates of 

combined chemical and physical weathering were estimated 

by using a method based on concentrations of silicon and 

sodium in bedrock, river-borne solids, and river-borne solutes. 

The observed rates of landscape denudation greatly exceed 

rates expected for a dynamic equilibrium, except possibly for 

the forested watershed on volcaniclastic rock. Deforestation 

and agriculture can explain the accelerated physical erosion 

in the two developed watersheds. Because there has been no 

appreciable deforestation, something else, possibly climate or 

forest-quality change, must explain the accelerated erosion in 

the forested watersheds on granitic rocks. Particulate organic 

carbon yields are closely linked to sediment yields. This 

relation implies that much of the particulate organic carbon 

transport in the four rivers is being caused by this enhanced 

erosion aided by landslides and fast carbon recovery. The 

increase in particulate organic carbon yields over equilibrium 

is estimated to range from 300 kilomoles per square kilometer 

per year (6 metric tons carbon per square kilometer per year) 

to 1,700 kilomoles per square kilometer per year (22 metric 

tons carbon per square kilometer per year) and is consistent 

with human-accelerated particulate-organic-carbon erosion and 

burial observed globally. There is no strong evidence of human 

perturbation of silicate weathering in the four study watersheds, 

and differences in dissolved inorganic carbon are consistent 

with watershed geology. Although dissolved organic carbon is 

slightly elevated in the developed watersheds, that elevation is 

not enough to unambiguously demonstrate human causes; more 

work is needed. Accordingly, the dissolved organic carbon and 

dissolved inorganic carbon yields of tropical rivers, although 

large, are of secondary importance in the study of the anthropgenically perturbed carbon cycle.

Introduction

The humid tropics presently occupy about 25 percent of 

the Earth’s land surface, and the warm temperatures, moist 

conditions, and luxuriant vegetation promote especially rapid 

biological and chemical processes—photosynthesis, respiration, decay, and chemical weathering—making the region 

particularly important at a global scale from the perspective 

of weathering, erosion, and carbon cycling. Tropical forests 

alone cover only 12 to 13 percent of the Earth’s land surface, but they contain 45 to 52 percent of terrestrial biomass 

carbon and 11 to 17 percent of soil carbon, and they account 

for 23 to 35 percent of net primary productivity (Prentice and 

others, 2001). This productivity equates to between 780 and 

1,250 metric tons per square kilometer per year of carbon 

(t km−2 yr−1 C). Other widespread humid-tropical land cover 

includes grasslands, wetlands, and various types of agricultural lands. According to Meybeck (1979), rivers in the humid 

tropics supply about 65 percent of the dissolved silica and 

38 percent of the ionic load delivered by rivers to the ocean. 

Data compiled by Milliman and Meade (1983) and Milliman 

and Syvitski (1992) indicate that the humid tropics contribute 

about 50 percent of the total solid load delivered by rivers to 

the ocean. These and related studies demonstrate that about 

30 percent of the solid load comes from steep, undammed, 
206 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

coastal rivers that drain active orogenic belts and island arcs 

in the tropics (Lyons and others, 2002). These rivers are an 

especially strong source of particulate organic carbon (POC) 

in the ocean (Ver and others, 1999; Lyons and others, 2002; 

Syvitski and others, 2005; Galy and others; 2007; Hilton and 

others, 2008a,b). 

When examined at both human and geologic time scales, 

today’s world is in a state of exceptionally rapid change 

caused by human activities (Science Magazine, 2011). For the 

last several thousand years, we humans have been modifying 

the Earth’s landscape with fire and tools for food and agriculture, and for the past few hundred years, we humans have 

remodeled the Earth’s surface and altered biogeochemical 

cycles with a technology fueled by geologic and biospheric 

energy sources, a time period commonly referred to as the 

Anthropocene. Associated with this remodeling is a vast 

mobilization and redistribution of soils, sediments, and rock 

at a scale comparable to that of natural erosion (Hooke, 1994, 

2000). Rivers and materials transported by rivers have been 

profoundly affected by human activities related to hydraulic 

engineering, water consumption, waste introduction, and landcover modification (Meybeck, 2003).

Human impacts motivate one of the great developing 

themes of contemporary biogeochemistry, that of describing 

the anthropogenically modified carbon cycle and understanding how it relates to changes in atmospheric and seawater 

composition, land use, and land cover. The ongoing accumulation of carbon dioxide in the atmosphere, derived from 

fossil-fuel burning, deforestation, and land clearing, is now 

a major concern, because of potentially severe consequences 

on climate (Schneider, 2009). Examination of natural and 

technological mechanisms that limit CO2

 accumulation in 

the atmosphere through sequestration has become a global 

research focus. This theme is now a major thrust of the current U.S. Geological Survey Strategic Plan (U.S. Geological 

Survey, 2007). For reference, current characterizations of the 

global carbon budget (Denman and others, 2007) estimate that 

the combined emissions from fossil fuels and cement release 

7.2±0.3 petagrams of carbon per year (pg C yr−1). Of this total, 

4.1±0.1 pg C yr−1 accumulate in the atmosphere; 2.2±0.5 pg 

C yr−1 enter the ocean, and 0.9±0.6 pg C yr−1 are stored on 

land. The large error in the terrestrial carbon sink reflects the 

relative difficulty in accounting for all carbon on land.

Studies of terrestrial CO2

 sequestration normally take 

place at a landscape scale, because the size of the technological perturbation of the carbon cycle is so large that any 

substantial sequestration must be either quite strong or quite 

widely distributed. For a suite of processes to be prominent 

in the technologically perturbed carbon cycle, it must affect 

about 1 pg C yr−1 globally. Distributed over the Earth’s land 

surface (149•106

 km2), this amount corresponds with about 

6.7 t km−2 yr−1 C. Forest regrowth and densification of woody 

vegetation are clearly important at this scale (Pacala and 

others, 2001; summaries in Denman and others, 2007). The 

land area of humid tropical mountains is not large, about 

4•106

 km2 (United Nations Environment Programme World 

Conservation Monitoring Centre, 2002). If a process capable 

of sequestering 1 pg C yr−1 were to be confined to just humid 

tropical mountains, it would need to sequester 250 t km−2 yr−1

C; this mass is about one third to one fifth of tropical forest 

net primary productivity.

Weathering and erosion have a smaller effect on the 

carbon cycle than do plant growth and decay. The combined 

fluvial transport to the ocean of dissolved inorganic carbon 

(DIC), dissolved organic carbon (DOC), and POC is less than 

1 pg C yr−1 (Sarmiento and Sundquist, 1992). It is difficult to 

envision changes caused by human activities that could alter 

current dissolved-carbon fluxes on the order of 1 pg C yr−1. 

Particulate carbon fluxes to the ocean have increased somewhat because of human activities despite the interception of 

considerable masses of sediment behind artificial reservoirs 

(Syvitski and other, 2005), and vast quantities of POC are 

stored on land as soils, colluvium, alluvium, and lacustrine 

deposits. Stallard (1998) estimated that globally averaged 

upland physical erosion caused by human activities has 

increased twofold to sixfold as compared with preagricultural 

rates and has increased erosion by 10 to 50 pg yr−1 sediment; 

most of this eroded sediment is stored in terrestrial sediments 

rather than being transported to the ocean. This deposition 

results in long-term storage of carbon in terrestrial sediments 

on the order of 0.6 to 1.5 pg C yr−1. Stallard (1998) asserted 

that much of this carbon is being replaced through the creation 

of new POC at the sites of erosion, and that carbon stores 

in depositional sites may also be augmented with additional 

POC from autochthonous production in surface waters and 

wetlands. Considerable work has gone into estimating the 

POC fluxes in the landscape and testing whether the deposited 

carbon is stable and whether the deposited carbon is being 

replaced by newly produced POC (Berhe and others, 2007; 

Van Oost and others, 2007, 2008; Harden and others, 2008; 

Lal and Pimentel, 2008; Quinton and others, 2010). The 

most recent estimate of the amount of carbon being removed 

from the atmosphere by erosion, deposition, and replacement is about 0.5 pg C yr−1; increased erosion produces about 

35 pg yr−1 sediment (Quinton and others, 2010). This estimate 

does not include the contribution of carbon to fluvial and 

lacustrine sediments by autochthonous production.

The U.S. Geological Survey’s Water Energy and Biogeochemical Budgets (WEBB) program research in eastern 

Puerto Rico, described herein, provided a unique opportunity 

to examine major biogeochemical processes in headwater river systems in humid tropical mountains, to examine 

their role in the carbon cycle, and to judge whether climate 

change or human activities have pushed these systems out 

of equilibrium. Up to now, research into the biogeochemical functioning of headwater river systems in such settings 

has been limited despite their obvious importance in erosion 

and biogeochemical processes. The fieldwork is quite challenging. To assess the importance of interannual variations, 

years of monitoring are required, and huge storms, which are 

extremely difficult and can be dangerous to sample, have a 

dominant role in transport processes.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 207

Research Setting

The U.S. Geological Survey initiated the WEBB program 

in 1991. The WEBB site in eastern Puerto Rico (Larsen and 

others, 1993), one of five sites, was chosen because it is in a 

montane humid-tropical environment having a bedrock geology that is representative of many parts of the world. Puerto 

Rico is part of the volcanically inactive Greater Antilles island 

arc (fig. 1) that has been moderately to deeply eroded in a 

humid tropical climate. The qualities of geology, climate, land 

cover, and land use make Puerto Rico similar to vast swaths 

of the humid tropics, including the remainder of the Greater 

Antillean island arc, much of Central America, many island 

arcs extending from the southwest Pacific to the eastern Indian 

Ocean such as the Indonesian Archipelago, and considerable 

portions of the Southeast Asian mainland. These landscapes are 

agriculturally productive and support large human populations 

(Bot and others, 2000; Webb, 2006; Foley and others, 2011).

Puerto Rico has undergone a rapid transformation in 

the past several centuries from pre-European conditions of 

relatively undisturbed forest, to intensive agriculture in the 

19th and early 20th century, to a post-1950 industrial economy. In the past 60 years land cover has shifted from almost 

entirely deforested to forest covering about half of the island 

(Brandeis, 2007; Kennaway and Helmer, 2007). For further 

background, see Murphy and others (2012).

The Puerto Rico WEBB work involves a double pair-wise 

comparison of montane streams on granitic bedrock compared 

with fine-grained volcaniclastic bedrock and of forested compared with agricultural landscapes ( Murphy and others, 2012, 

their table 1). Unlike some island-arc regions, Puerto Rico has 

not had active volcanoes for tens of millions of years. Its soils 

lack abundant allophane, which is commonly derived from 

tephras in younger volcanic landscapes. Allophane stabilizes 

soil organic matter and is associated with especially productive 

agricultural soils (Parfit, 2009). The selection of the two types 

of geology was predicated on the idea that the abundance of 

coarse-grained quartz in the granitic rocks would result in soils 

with sufficiently different properties from soils on the quartzpoor volcanic rocks that the differences would be manifested in 

the erosional styles, water flow paths, and solute-load chemical composition. The soils of the volcanic watersheds are, for 

instance, richer in secondary clay minerals and in base cations. 

For each bedrock type, one watershed is covered with mature 

rainforest (drained by Río Icacos and Río Mameyes), and 

the other watershed is affected by land use typical of eastern 

Puerto Rico (drained by Río Cayaguás and Río Canóvanas). 

Two watersheds are located on coarse-grained granitic rocks 

(Icacos and Cayaguás), and two are located on fine-grained 

volcanic rocks and volcaniclastic sedimentary rocks derived 

from closely allied magmas (Mameyes and Canóvanas). Data 

from the Quebrada Guabá, a tributary of the Icacos which 

was monitored for 10 years (1993–2002), is included in many 

calculations and tables as a surrogate for the Icacos (the Guabá 

streamgaging station failed in April 2003 during a large storm 

(appendix 1 of this report; Murphy and Stallard, 2012)).

The double pair-wise comparison of physical and chemical weathering processes has been a major objective of the 

Puerto Rico WEBB program since its inception (Larsen and 

others, 1993). To this end, the program depended upon the collection of time-series of meteorologic, hydrologic, soil-water, 

and groundwater samples and data. Long-term monitoring of 

biogeochemical budgets of the four major watersheds included 

water, major dissolved constituents, nutrients, carbon, and 

sediment. Weathering and mass-wasting processes were to 

be examined in each rock type and land cover by comparing 

budgets calculated for each. Sampling major hydrologic events 

was especially important. A benefit of working in Puerto Rico 

is that at a given altitude and rainfall, similar natural vegetation exists on all types of bedrock (see Murphy and others, 

2012), presumably because as an island, Puerto Rico lacks 

high floral diversity (Ashton and others, 2004). A confounding 

factor in this comparison, however, is that both agricultural 

watersheds are substantially drier than the forested watersheds 

(runoff of 900–1,600 millimeters per year (mm yr−1) as compared with 2,800–3,700 mm yr−1).

An initial snapshot of a four-way comparison, covering 

1991 to 1995, was published by Larsen and Stallard (2000). 

Four-way sediment budgets have been examined by Larsen 

(1997, 2012). Detailed descriptions of the regional geography (Murphy and others, 2012), atmospheric inputs (Stallard, 

2012), the study watersheds and intercomparisons of their 

hydrology (Murphy and Stallard, 2012), physical erosion 

(Larsen, 2012), and water quality (Stallard and Murphy, 2012) 

are found elsewhere in this volume.

Purpose and Scope

The present chapter focuses on a four-way comparison 

of the rates of chemical and physical erosion as related to 

carbon transport and in turn to rates of runoff. Simple models 

are used to project annual constituent-yield data for each river 

basin to a single intermediate runoff, thereby circumventing 

the confounding factor of having different mean-annual runoff 

between forested and developed pairs. The same models are 

also used to consider the consequences of climate change on 

chemical and physical weathering and on fluvial carbon transport. Six questions are asked: 

1. What are the measured rates of chemical and physical 

denudation and how do these relate to runoff in each 

watershed?

2. How do watersheds compare in weathering, erosion, 

carbon export, and nutrient export with regard to geology 

and land cover? Do geology and land cover matter?

3. Is the forested landscape in a steady state? In essence, are 

the Luquillo Mountains a model of a quiescent, stable, 

natural landscape, or are they changing?

4. What does the present dynamic state of the landscape imply 

regarding past and future response to climate change?
208 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Area shown in B

A

B

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

L e s s e r

A n

t i l l e s

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_01H.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

LUQUILLO

MOUNTAINS

Mameyes

Icacos

Guabá

Río Grande de Loíza Watershed

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Watershed boundary

Study watershed

 boundary

Figure 1. Location of Puerto Rico and study watersheds.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 209

5. What are the rates of export of carbon in its various 

forms; how do these rates relate to runoff, and is this 

exported carbon being replaced by new photosynthetically 

fixed carbon?

6. Are rates of carbon processing through the landscape notable on the time scales of human-induced climate change?

The models used in this paper with minor modifications, 

as noted, were developed in parallel with the WEBB datacollection program. Stallard (1995a,b) introduced the concept 

of dissolved and solid bedrock and how major-element compositional information can be rigorously used to characterize 

steady-state erosion. Brown and others (1995, 1998) expanded 

on the concept of steady state and perturbed landscapes as 

determined by measurements of cosmogenic 10Be in bedrock, 

soils, and sediments. Stallard (1998) developed tools that link 

physical erosion that has been greatly accelerated by human 

activities to the anthropologically modified carbon cycle. 

Carbon dioxide is removed from the atmosphere through 

accelerated soil loss and subsequent deposition and burial of 

the eroded soil and associated particulate organic carbon. The 

buried soil carbon is replaced by new soil carbon at the site of 

erosion, thereby creating a net carbon-dioxide sink. Stallard 

(1999) and Stallard and Kinner (2005) formulated models 

to include landslide-producing events in characterizations of 

annual sediment yields. 

Weathering, Erosion, and the Carbon 

Cycle

Soils, river-borne solutes, and sediments are all derived 

from weathering processes that produce both dissolved and 

solid products. Most parent material must weather before 

becoming soil or being transported by flowing water. During chemical weathering, some minerals react congruently 

to produce only dissolved weathering products. Common 

minerals that always weather congruently are halite, anhydrite, gypsum, aragonite, calcite, dolomite, and quartz. 

Halite, anhydrite, and gypsum are so unstable that they 

almost never occur in the solid load of rivers. Calcite and 

dolomite sometimes occur in alkaline rivers that are supersaturated with respect to these minerals; for example, rivers 

within the Yukon River system in Alaska (Striegl and others, 

2007). Quartz (crystalline SiO2

) is the most persistent of all 

common primary minerals and is typically abundant in river 

sediments. Most silicate and sulfide minerals weather incongruently to produce both dissolved cations (Na+, K+, Mg2+, 

Ca2+), silica (Si(OH)4), sulfate (SO4

2−) and sometimes minor 

constituents along with solid products that are depleted in 

cations and usually in silica. Minerals that contain aluminum 

and iron typically weather incongruently to produce clays 

and aluminum/iron sesquioxides (oxides and hydroxides of 

Al3+ and Fe3+). Magnesium, potassium and, to a lesser extent, 

calcium and sodium are retained in cation-rich clays such 

as the smectites, vermiculites, illites, and chlorites (Stallard, 

1985, 1988; Stallard and Edmond, 1983, 1987; Stallard and 

others, 1991; Stallard, 1995a).

To this point, most studies of weathering associated with 

the WEBB program in eastern Puerto Rico have focused on 

the Río Icacos watershed, because of its homogenous granitic 

bedrock. As White and Blum (1995) state, “the pristine Icacos 

watershed in the Luquillo Mountains in eastern Puerto Rico 

has the fastest documented weathering rate of silicate rocks on 

the Earth’s surface.” The earliest biogeochemical work in the 

Icacos watershed was associated with the Luquillo Long-Term 

Ecological Research program (McDowell and others, 1992; 

McDowell and Asbury, 1994; McDowell, 1998; Bhatt and 

McDowell, 2007). Brown and others (1995) examined denudation rates using cosmogenic 10Be in exposed bedrock, soils, and 

fluvial quartz and concluded that denudation rates calculated 

from 10Be match those determined from solid and solute loads. 

Various aspects of chemical weathering have been examined 

by White and Blum (1995), Dong and others (1998), Murphy 

and others (1998), White and others (1998, 1999a,b), Schulz 

and White (1999), White (2002), Turner and others (2003), 

Buss and others (2004, 2005, 2008), Derry and others (2006); 

Fletcher and others (2006), Pett-Ridge and others (2008, 2009), 

Pett-Ridge (2009), and Buss and White (2012). Water quality 

of the measured river-borne constituents is discussed in chapter 

E of this report (Stallard and Murphy, 2012). McDowell and 

Asbury (1994) compared mass balances of the Icacos with two 

other rivers in the Luquillo Mountains that drain volcaniclastic 

rocks, the Sonadora and Toronja. Peters and others (2006) compared mass balances for 1991 to 1996 for the Icacos with rivers 

from each of the other four WEBB sites in the conterminous 

United States. Shanley and others (2011) examined 25 years of 

data from the Icacos starting in 1983.

Solute Loads and Chemical Erosion in Eastern 

Puerto Rico

Virtually all dissolved constituents in surface waters 

either arrive in rainfall or are acquired as water falls through 

vegetation and moves through soil and bedrock. Soils on the 

quartz-rich granitic rocks tend to be considerably thicker than 

soils on the volcanic and volcaniclastic rocks (see Murphy 

and others, 2012). Infiltration in all soils tends to be slow, and 

water infiltration to depths below 0.5 meters (m) takes many 

hours to several days, although rates are about three times as 

fast in soils on the granitic rocks as in soils on volcaniclastic 

rocks (Simon and others, 1990). At the lowest rainfall rates, 

water in all soils flows vertically into deeper soils, through 

deeper riparian zones, and then as hyporheic discharge. Water 

moving through these deep-soil environments typically flows 

past some unweathered primary minerals and relatively little 

organic matter. Studies of weathering in the granitic Icacos 

watershed demonstrate the importance of deep-flowing water 

in the weathering process (Turner and others, 2003; Buss and 

others, 2004, 2005, 2008; Fletcher and others, 2006; Buss 

and White, 2012). Silicon and strontium isotopes confirm 
210 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

that deep-flowing water, emerging as base flow, interacts 

with primary minerals (Ziegler and others, 2005; Derry and 

others, 2006; Pett-Ridge and others, 2009). Shallow flow paths 

increase in relative importance with increasing rates of runoff 

(Derry and others, 2006; Stallard and Murphy, 2012). Similar patterns are recorded in volcaniclastic rocks (Larsen and 

others, 1999; Schellekens and others, 2004). At intermediate 

runoff rates, water also moves through shallow-soil pathways, 

and riparian zones are typically saturated. Shallow flow paths 

commonly have abundant organic matter and plant roots, but 

little unweathered primary minerals except for quartz. Finally, 

at the highest runoff rates, overland flow can become increasingly important, reducing the overall interactions of water 

with plant roots and deeper soil while also entraining higher 

particulate loads (Larsen and others, 1999; Schellekens and 

others, 2004; Larsen, 2012; Stallard and Murphy, 2012).

The relation between runoff and concentration of the 

various measured constituents is discussed in Stallard and 

Murphy (2012). Briefly, constituents that are not particularly bioactive and are derived from bedrock weathering or 

precipitation decrease in concentration with increasing runoff, 

reflecting the decreasing relative importance of flow paths 

that pass deep into the soil. Water that enters deeper soil generally passes through shallow-soil roots and is subject to more 

transpiration. Such constituents include sodium (Na+

), magnesium (Mg2+), calcium (Ca2+), silica (Si(OH)4

), alkalinity, 

and (Cl−). Strongly bioactive constituents commonly display 

steady or increasing concentration with increasing runoff 

rates. These constituents include dissolved organic carbon 

(DOC), potassium (K+), nitrate (NO3

−

), ammonium ion 

(NH4

+

), and phosphate (PO4

3−). The concentrations of many 

of the bioactive constituents eventually decrease at runoff 

rates greater than 3 to 10 millimeters per hour (mm h−1), 

presumably reflecting an increased dilution by rain and lessconcentrated overland flow; DOC consistently increases and 

then decreases with increasing runoff rates. Note, however, 

that water interactions with vegetation (throughfall and 

stemflow) affect stream composition even in intense storms 

(Heartsill-Scalley and others, 2007). Sulfate (SO4

2−) behaves 

like a bedrock-derived constituent in the Canóvanas, Cayaguás, and Mameyes rivers but like a bioactive constituent in 

the Icacos and Guabá (Stallard and Murphy, 2012).

Solid Loads and Physical Erosion in Eastern 

Puerto Rico

The most stable primary minerals are usually physically 

eroded before they have a chance to chemically decompose. 

Minerals that decompose contribute to the dissolved load in rivers, and their solid chemical-weathering products contribute to 

the secondary minerals in the solid load. The secondary minerals and the more stable primary minerals are the most important 

constituents of clastic sedimentary rocks. If physical erosion 

processes are weak, most of the erosion products are dissolved, 

and stable primary minerals and secondary minerals accumulate 

on top of bedrock to form soil (Stallard, 1985, 1988).

Landslides are a dominant component of physical erosion in steeper landscapes of eastern Puerto Rico (see Larsen, 

2012), and the volume moved by landslides exceeds the volume moved by surficial erosion through mechanisms such as 

direct impaction by raindrops on soil, sheet wash, rill formation, and gullying. On the basis of 60 years of aerial photography, shallow landslides appear to affect approximately 

1 percent of the forested eastern Puerto Rican landscape per 

century (Guariguata, 1990; Larsen and Torres-Sánchez, 1998). 

This rate is equivalent to a recurrence interval of about 10,000 

years for landslides at a given site. Landslides erode remarkable quantities of solids in an exceedingly short time, typically 

in association with extreme rainfall events. Landslides in the 

Luquillo Mountains occupy a continuum from simple slumps 

to slides and debris flows, and most are entirely composed of 

soil produced by chemical weathering of bedrock.

Human activities greatly exacerbate landsliding. Aerial 

photographs demonstrate that landslides are much more common on deforested lands in eastern Puerto Rico (Larsen and 

Torres-Sánchez, 1998; Larsen and Santiago-Román, 2001). 

Of particular note is the agriculturally developed basin of 

the Río Cayaguás, on granodiorite. Here the thick soils 

that had developed under forest conditions were consumed 

by massive, regional, shallow-soil landsliding related to 

land clearing that lowered the landscape on average about 

660 millimeters (mm) since 1820 (Larsen and SantiagoRomán, 2001). This study of aerial photographs suggests 

that optimal factors promoting landslides are moderate to 

steep (>12°) slopes, sufficient rainfall, typically greater than 

some threshold, and nonnatural land cover, notably construction sites and roads. Road building alone increases rates of 

landslides sixfold to eightfold in an 85-m swath bordering 

the roads (Larsen and Parks, 1997). 

Typical prerequisites for landslides are steep slopes and 

bedrock that is structurally unstable (relatively noncohesive, 

sheared, or fractured) or, in the case of stable bedrock, the 

formation of unstable soils through chemical or physical 

weathering. Landslides typically come in clusters that are 

triggered by events such as large amounts of precipitation, 

typically exceeding some regional slide-inducing threshold 

(Starkel, 1972; Caine, 1980; Scatena and Larsen, 1991; Larsen 

and Simon, 1993; Montgomery and Dietrich, 1994; Crosta, 

1998; Reid, 1998; Molnia and Hallam, 1999; Montgomery and 

others, 2000; Larsen and others, 2001) or earthquakes (Simonett, 1967; Garwood and others, 1979; Keefer, 1984).

Work in the Puerto Rico WEBB program (Larsen, 

2012) has focused on the landslide process and emphasizes the importance of shallow-soil landslides in physical 

erosion. Tropical disturbances and northern-hemisphere, 

wintertime cold fronts trigger most of the landslides in the 

Puerto Rico WEBB watersheds (Scatena and Larsen, 1991; 

Larsen and Simon, 1993; Larsen and Torres-Sánchez, 1998). 

Larsen and Simon (1993) derived an empirical landslide 

threshold relation, relating rainfall, P (in millimeters), to 

storm duration and to T (in hours) that must be exceeded for 

landslides to happen:
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 211

P 1≥ 91.46•T 0.18. (1)

The essence of this relation is that if more than the requisite rain falls in specified time, and if the terrain is sufficiently 

steep (>12° slope), then there may be landslides. This relation 

was field tested in Puerto Rico after Hurricane Hugo in 1989 

and found to be valid (Larsen and Torres-Sánchez, 1992). For 

storms with a duration of 24, 48, and 72 hours, the threshold 

rainfall is 162, 184, and 197 mm respectively, or roughly 

200 mm. Stallard (1999) and Stallard and Kinner (2005) note 

that in the Panama Canal watershed (where runoff variation 

among rivers was similar to that of the Puerto Rico WEBB 

rivers), the transition to landslide-driven erosion is in the 

range of 800 mm to 1,000 mm mean-annual runoff, but that 

this transition might be lower in basins subjected to intense 

storms such as hurricanes. Accordingly, attempts to predict 

solid yields for wetter and drier conditions need to explicitly 

account for the presence or absence of landslide-producing 

storms at higher runoff regimes.

Without chemical weathering, there would be few 

landslides. Shallow-soil landslides typically occur near the 

interface between hard and soft saprolite, commonly on zones 

of weakness formed on former exfoliation surfaces that had 

become conduits for soil water (Simon and others, 1990). 

The thickness of the soil that slides is about 1 to 4 m (Simon 

and others, 1990; Larsen and Torres-Sánchez, 1992; Larsen 

and Simon, 1993; Larsen, 1997, 2012; Larsen and SantiagoRomán, 2001). Landslide erosion can enrich the solute load 

in cations that are largely leached out of hard saprolite (Na+

and Ca2+) compared with those that partially leach (K+

 and 

Mg2+) (Stallard, 1985, 1988). In the Icacos watershed, which is 

underlain by granitic rocks, an analysis of in-place-produced 

cosmogenic 10Be in quartz as a function of grain size shows 

that the fine material in the river is largely derived from surficial erosion, whereas the coarse material is derived from deep 

erosion, presumably landslides (Brown and others, 1995). 

Sediment-discharge calculations indicate that about half of the 

river-borne quartz is derived from deep erosion.

Vegetation regrowth and succession following a landslide 

are quite rapid—perhaps 100 to 200 years (Guariguata, 1990; 

Walker and others, 1996), starting with the bare slip face. Soon 

after, a characteristic sequence of vegetation grows on the slide 

(lichens and moss, then ferns and grass, then second-growth 

trees, and finally mature forest), anchoring the soil. Similarly, 

soil development and restoration of soils to preslide nutrient 

conditions is on the same time scale (Zarin, 1993; Zarin and 

Johnson, 1995a,b). Conditions in Puerto Rico appear to be 

especially favorable to the rapid regrowth of vegetation; in the 

nearby Blue Mountains of Jamaica, plant succession appears to 

be much slower (Dalling and Tanner, 1995).

In addition to delivering sediment directly to rivers during storms, landslides are strong secondary sources of sediments. First, landslides normally deliver far more debris to a 

valley than rivers can remove in a single flood. This debris is 

then eroded by subsequent storms. Second, the bare slip face 

itself becomes a source of sediment (Larsen, 1997, 2012). 

Initially, sediment yield from the slip face was more than tenfold as great as yield from the adjacent forest, but after 4 to 5 

years, sediment yields dropped to less than those of the forest.

Steady-State Erosion

Erosion under steady-state conditions, or dynamic equilibrium, is a useful frame of reference for studying a river 

system (Stallard, 1995a,b, 1998), especially in a landscape 

with such varied and intense physical erosion as eastern 

Puerto Rico. The core idea is that the geomorphic properties 

of the landscape are not changing substantially through time. 

Steady-state erosion also implies a chemical mass balance, 

such that the sum of the fluxes of all solid and dissolved 

weathering products leaving a landscape should equal the 

mass of bedrock being degraded by weathering. When rates 

of physical erosion are less than rates of the production 

of loose material by weathering, soils thicken. When the 

opposite holds true, there is net soil erosion. Human activities, mostly land clearing and agriculture, have substantially 

accelerated upland erosion as compared with presumed 

steady-state rates, causing greatly increased carbon burial 

in sediments (Stallard, 1998). When chemical weathering 

reactions control the rate of production of erodible solid 

materials, rates of chemical weathering and the composition 

of weathering products can be used to calculate yields of 

dissolved constituents and thus predict equilibrium yields of 

solids (Stallard, 1995a,b).

For the Icacos watershed, Brown and others (1995) 

compared rates of erosion estimated by using cosmogenic 

10Be to rates estimated by using chemical and sediment data 

for 1983–1986 from McDowell and Asbury (1994) and from 

the earliest years of the Puerto Rico WEBB program. The 

data indicated that the Icacos landscape is near a dynamic 

equilibrium. Chemical mass balance calculations by White 

and others (1998) supported this assertion. In contrast, studies of 10Be in the sediments transported by the Río Cayaguás 

indicate that erosion rates today are considerably greater than 

they were in the past (Brown and others, 1998).

Carbon, Weathering, and Erosion

The largest export of carbon from watersheds is not 

fluvial; it is the conversion of carbon fixed into organic matter by net primary productivity back into atmospheric carbon 

dioxide by respiration and decay. Fluvial transport typically 

exports the next largest amount of carbon from a watershed (excluding agricultural and timber harvest), and this 

transport is tightly linked to weathering and erosion. Rivers export carbon from watersheds in four broad classes of 

material (Meybeck, 1993): dissolved inorganic carbon (DIC), 

dissolved organic carbon (DOC), and particulate organic 

carbon (POC) are important in the streams of Puerto Rico, 

whereas particulate inorganic carbon (PIC), which consists 

of calcium-magnesium carbonates, is not typically present in 
212 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

tropical rivers because tropical waters are typically strongly 

undersaturated with respect to these minerals (Stallard and 

Edmond, 1987; Stallard and others, 1991; Stallard, 1995a). 

The fluvial export of carbon may be related to either inventory changes or new transfers from the atmosphere.

Carbon transport by rivers is tightly coupled with 

chemical and physical erosion processes. Dissolved inorganic carbon is derived from dissolved carbon dioxide that 

is converted to bicarbonate and carbonate ions through the 

chemical weathering of most silicate rocks and all carbonate 

rocks. In these weathering reactions, hydrogen ions are consumed and soluble cations (Na+, K+, Mg2+, Ca2+) are released 

(Garrels and Mackenzie, 1971). The weathering of sulfide 

minerals, which generates mostly insoluble sesquioxides and 

sulfuric acid, has the reverse effect, converting bicarbonate 

and carbonate ions into dissolved carbon dioxide (Garrels 

and Lerman, 1984).

The weathering of carbonate rocks is a source of nonatmospheric DIC (Garrels and Mackenzie, 1971; Garrels 

and Lerman, 1984; Meybeck, 1993). Minor carbonates are 

present in the granitic and volcaniclastic rocks in the study 

watersheds (see Murphy and others, 2012). Trace carbonate 

weathering in the granitic rocks in the Luquillo Mountains 

appears to be limited by the general advance of weathering 

fronts into the rocks (White and others, 1999b). My own 

observations suggest that no geomorphic evidence indicates 

preferential weathering of extensive carbonate rocks in the 

Luquillo Mountains, such as one sees in karst limestone 

landscapes of north-central Puerto Rico. 

Igneous rocks do not contain fossil carbon, a conspicuous research advantage in constructing carbon budgets in 

the Puerto Rico WEBB watersheds. Many rivers in island 

arcs and orogenic belts erode organic-rich shales, and 

in determining POC loads, it is important to distinguish 

between eroded fossil carbon (kerogen) and young (soil) 

carbon, because the reburial of fossil carbon does not affect 

atmospheric carbon budget (Leithold and Blair, 2001; Blair 

and others, 2003; Galy and others, 2007; Hilton and others, 2008a). Carbon budget calculations for such rivers are 

further complicated by the oxidation of fossil carbon and 

pyrite during weathering, which effectively releases carbon 

dioxide to the atmosphere (Petsch and others 2000, 2001). In 

the study WEBB watersheds, we can assume that all eroded 

carbon is geologically young.

The export of DIC from a watershed is operationally 

defined and is best compared with the export of alkalinity, which is commonly equated with the bicarbonate ion 

in streamwater. Alkalinity is strictly the amount of anion 

charge per unit volume that can be titrated with a strong 

acid to reach the first equilibrium endpoint for the carbon 

dioxide system (Stumm and Morgan, 1981). Another name 

for alkalinity is acid-neutralization capacity. Alkalinity is a 

conservative property and is also equal to the total charge 

of nontitratable cations (which include ammonium ion 

at pH<9.3) minus the total charge of nontitratable anions 

(including NO3

−). Alkalinity is also independent of the pH 

and the carbon-dioxide vapor pressure (PCO2

) of the sample. 

If a strong base is required to titrate to the endpoint, then the 

alkalinity is negative and is sometimes is referred to as acidity. In this report DIC is treated as equal to alkalinity.

Dissolved organic carbon (DOC) is derived from carbon 

dioxide that has been “fixed” into soluble organic compounds 

though photosynthesis by plants and has been released as 

plant, fungal, or algal exudates or by the decay of biomass 

(Meybeck, 1993; Hedges and others, 2000; Aufdenkampe and 

others, 2001, 2007; Striegl and others, 2007).

Particulate organic carbon (POC) comes mostly from 

fragments of plant material in various stages of decay and 

from DOC that has become bound to surfaces of mineral 

grains (Hedges and others, 2000; Aufdenkampe and others, 

2001, 2007). Grain-surface POC and fine-grained POC form 

a large component of total POC. Clay-size (<0.2 micrometer 

(μm)) mineral grains, including aggregated grains, have an 

especially large surface-area–to–volume ratio; they are most 

important in stabilizing organic carbon as POC. In headwater streams, such as those described here, organic carbon 

is largely allochthonous (soil and plant debris). In eastern 

Puerto Rico, surface soils in the agricultural watershed are 

1 to 3 percent carbon, whereas soil carbon in the forested 

landscape is somewhat greater (Beinroth and others, 1992). 

The estimated POC yields for many mountainous rivers 

having high runoff and smaller drainage areas greatly exceed 

the global-average POC yield of 2–3 t km−2 yr−1 C (Stallard, 

1998). Organic-carbon molecules range considerably in 

size, so the transition between DOC and POC is operational, 

based on the effective pore size of the filters that were used 

to process the sample. Most Puerto Rico WEBB samples 

were filtered through 0.2-μm membrane filters that rapidly 

clogged (appendix 2). 

Landslides, Erosion, and Carbon

Landslides, including those induced by tropical disturbances, probably have a major role in the erosional export 

of young organic carbon to the ocean (Hilton and others, 

2008a,b). Landslide erosion has many parallels with accelerated agricultural erosion, which is a substantial component of 

the technologically modified carbon cycle, provided that carbon eroded from agricultural lands is buried and then replaced 

at the site of erosion with new photosynthetic carbon (Stallard, 1998). Landslides are even more effective than agricultural erosion at removing the entire soil profile along with all 

of its organic carbon (Simon and others, 1990; Zarin, 1993; 

Zarin and Johnson, 1995a,b; Walker and others, 1996). Zarin 

(1993) and Zarin and Johnson (1995a,b) have documented the 

regeneration of soils in chronosequences of slide scars in and 

around the Icacos watershed. A zero-order accumulation, firstorder-loss model of soil carbon for soils (Stallard, 1998, equation 5) can be applied to the soil organic carbon data of Zarin 

(1993) (fig. 2). It is assumed that little physical erosion occurs 

on the slide scar once it is stabilized by pioneer plants. It is 
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 213

also assumed that the age of old soils in this dataset is a large 

fraction of 10,000 years, the approximate landslide recurrence 

interval calculated for the Luquillo Mountains. The variability 

of the old-soil carbon values reflects the several types of forest that eventually develop following landslides (Guariguata, 

1990). The initial model soil-carbon accumulation rate for 

landslide scars is about 80 t km−2 yr−1 C or 6,700 kilomoles 

moles per square kilometer per year (kmol km−2 yr−1) C, if one 

assumes minor initial carbon on the fresh scars. If the fresh 

slide faces are assumed to be carbon-free, initial rates are 

150 t km−2 yr−1 C or 12,000 kmol km−2 yr−1 C. These fluxes 

represent upper limits on rates of POC generation for the 

eastern Puerto Rico landscape. This rate is 10 to 20 percent of 

net primary productivity for tropical forests, in general. The 

rapid regeneration of soil carbon, about half in 80 years, indicates that all POC removed from the watersheds by landslides 

and subsequent fluvial transport is rapidly being replaced by 

new carbon.

Methods Used to Assess the Effects of 

Weathering and Erosion in the Carbon 

Cycle

An analysis of weathering and erosion of bedrock and 

its relation to fluvial-carbon transport requires the introduction of several properties derived from the concentrations of 

measured constituents. For simplicity, these are referred to 

as “derived constituents.” Two important derived constituents are dissolved bedrock and suspended bedrock, concepts 

that were introduced by Stallard (1995b). Dissolved bedrock 

(DBrx) is derived using the concentrations of various solutes 

to determine the quantity of original bedrock that this represents, which is then expressed as a concentration. Similarly, 

suspended bedrock (SBrx) is the concentration of original 

bedrock represented by solids in suspension. To determine 

DBrx and SBrx, one models atmospheric contributions to the 

dissolved and solid load using mass balances and reconstructions of weathering reactions. These and other derived 

constituents can then be used to formulate erosion models 

and yield-estimation procedures that can be compared among 

the four watersheds.

Chemical Analyses of Dissolved and Solid 

Phases

During 1991 to 2005 in the five rivers of the Puerto 

Rico WEBB program, automated samplers collected data on 

507 hydrologic events from 263 storms, including all major 

hurricanes (Stallard and Murphy, 2012; appendix 1 of this 

report). A total of 4,894 samples were sufficiently analyzed 

to determine dissolved and solid denudation rates, and 860 of 

these were analyzed for a comprehensive suite of chemical 

constituents. Of samples analyzed for comprehensive chemistry and sediment, 543 were collected at runoff rates greater 

than 1 mm h−1, 256 at rates exceeding 10 mm h−1, and three 

that exceed 90 mm h−1. Globally, few samples have been 

collected and analyzed for chemistry and suspended solids 

at runoff rates exceeding 1 mm h−1 (appendix 1). In terms of 

process studies, these data represents a new realm of waterquality data.

The analysis of dissolved constituents and sediment is 

discussed by Stallard and Murphy (2012) and in appendix 2 of 

this report. 

Samples of soil, sediment, and bedrock, selected on the 

basis of freshness of exposure, were analyzed for chemical 

composition. Solid samples were ground using a chrome 

steel grinder such that they could pass through a 250 mesh 

(65 micrometer) stainless steel sieve. Samples were analyzed 

as a lithium-metaborate fused disk by X-ray fluorescence 

and reported as oxides (table 1). Analyses included SiO2

, 

Al2O3, CaO, MgO, Na2O, K2O, Fe2O3, MnO, TiO2, and P2O5, 

and loss on ignition (LOI) with a detection limit of 0.01 

percent, and Ba, Nb, Rb, Sr, Y, and Zr with a detection limit 

of 10 parts per million (ppm). Cr2O3 was analyzed to check 

for grinder contamination, which proved negligible. Ba and 

Nb were generally below detection and are not included in 

table 1. In addition, some samples were analyzed for ferrous 

iron (FeO in table 1), CO2, H2O+, and H2O− by wet chemistry 

and for sulfur and chloride by pressed-pellet X-ray fluorescence. Analyses were performed at XRAL Activations 

Services, Ann Arbor, Michigan.

\chapter_H_figures\BW_figures\BWfigure_02H Soil carbon, in kilograms per square meter

Carbon uptake, in grams per 

square meter per year

10

0

0 50 100 150 200 5,000

20

30

0

50

100

150

Soil age, in years

EXPLANATION

Soil carbon

Carbon-uptake rate

Data from Zarin (1993)

Figure 2. A zero-order accumulation, first-order-loss model of 

soil carbon accumulation on a chronosequence of landslides 

from the Luquillo Mountains based on data from Zarin (1993). This 

version of the model includes initial carbon on the slide face.
Table 1. X-ray fluorescence and wet-chemical analysis of geologic materials and water from the Luquillo Mountains, eastern Puerto Rico. [Units are percent unless otherwise noted; LOI, loss on ignition; --, not analyzed] Description SiO2 Al2O3 CaO MgO Na2O K2O Fe2O3 MnO TiO2 P2O5 S Cl1 LOI H2O+ H2O– CO2 

Solids Quartz diorite, Icacos Route 191, lower road 61.1 17.6 7.18 2.50 3.36 1.23 6.85 0.14 0.56 0.09 -- -- 0.69 -- -- -- 

Quartz diorite, landslide 43 2 64.2 16.6 6.17 2.13 3.56 1.39 5.96 0.13 0.44 0.09 -- -- 0.60 -- -- -- 

Quartz diorite, landslide 43 63.4 16.9 6.04 2.10 3.53 1.25 6.53 0.14 0.471 0.12 0.06 0.05 0.34 0.8 0.2 0.01 

Quartz diorite, landslide 43 62.4 16.8 6.05 2.27 3.42 1.22 6.60 0.14 0.47 0.12 0.1 0.05 0.46 0.7 0.2 0.0 

Xenolith in quartz diorite, landslide 43 55.2 12.0 6.19 6.00 2.11 1.97 14.70 0.36 1.156 0.24 0.06 0.09 0.72 1.4 0.2 0.02 

Xenolith in quartz diorite, landslide 43 56.6 15.3 7.21 5.30 3.30 1.20 9.17 0.27 0.78 0.21 0.2 0.06 0.82 1.2 0.2 0.0 

Aplite vein in quartz diorite, landslide 43 64.4 20.1 3.01 0.14 5.72 4.56 1.19 0.02 0.097 0.02 0.22 0.02 0.39 0.3 0.2 0.01 

Volcanic rock, landslide 8 3 49.1 17.4 8.93 6.64 3.84 0.52 9.78 0.14 0.83 0.11 -- -- 3.47 -- -- -- 

Volcanic rock, landslide 8 50.9 14.4 9.82 7.82 2.00 0.43 8.11 0.12 0.78 0.14 0.1 0.01 4.27 4.1 0.4 0.6 

Volcanic rock, landslide 8 51.4 14.1 9.19 7.78 2.10 0.45 8.02 0.12 0.822 0.16 0.09 0.01 4.00 3.9 0.3 0.50 

Volcanic rock, landslide 8 50.4 14.1 10.10 7.80 1.99 0.47 8.27 0.12 0.76 0.15 0.1 0.01 4.46 3.8 0.3 1.1 

Granitic rock, surface soil, on divide, landslide 43 70.3 11.7 0.03 0.12 0.08 0.25 0.91 0.03 0.48 0.03 -- -- 14.35 -- -- -- 

Granitic rock, surface soil, mid-slope, landslide 43 63.2 14.4 0.25 0.20 0.00 0.23 4.65 0.03 0.52 0.04 0.0 0.02 15.34 10.0 1.8 0.0 

Icacos gage, sand sample 79.3 6.9 0.98 1.00 0.33 0.41 7.55 0.10 0.54 0.03 -- -- 2.99 -- -- -- 

Icacos gage, overbank sediment 36.6 28.1 0.58 0.76 0.22 0.46 12.90 0.17 0.890 0.13 -- -- 20.40 -- -- -- 

Volcaniclastic rock, surface soil, landslide 8 46.8 19.6 0.34 0.44 0.04 0.27 10.25 0.02 1.527 0.09 0.03 0.02 20.64 13.2 3.1 0.00 

Dissolved bedrock oxide concentrations, calculated as described in the text Canóvanas, dissolved bedrock 43.6 -- 30.5 13.2 9.45 2.49 -- -- -- 0.06 0.69 -- -- -- -- -- 

Cayaguás, dissolved bedrock 54.3 -- 18.0 5.67 15.1 5.20 -- -- -- 0.09 1.67 -- -- -- -- -- 

Mameyes, dissolved bedrock 54.0 -- 28.8 6.39 7.25 2.35 -- -- -- 0.04 1.17 -- -- -- -- -- 

Icacos, dissolved bedrock 64.9 -- 17.5 5.13 9.65 2.84 -- -- -- 0.02 0.00 -- -- -- -- -- 

Guabá, dissolved bedrock 71.0 -- 12.9 3.30 9.92 2.88 -- -- -- 0.02 0.0 -- -- -- -- -- 

1Parts per million.2Landslide 43 of Guariguata and Larsen (1990) is located in the Icacos watershed, at the first large stream crossing on Route 191, west side.3Landslide 8 of Guariguata and Larsen (1990) is located on Route 988, about 1 kilometer west of the Mameyes gage site.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 215

Estimation of Constituent Yields

The loads and yields (loads divided by watershed area) 

of most constituents, including derived constituents, were 

calculated using LOADEST (load estimator; Runkel and 

others, 2004) as described in Stallard and Murphy (2012) 

and in appendix 1. The process involved merging thousands 

of chemical measurements with nearly continuous discharge 

Table 2. Discharge-weighted average concentrations of each percentile class estimated by using LOADEST and hourly discharge 

from study watersheds, eastern Puerto Rico.

[DIC, dissolved inorganic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon; LOI, loss on ignition; mm h−1, millimeters per hour; 

mg L−1, milligrams per liter; μmol L−1, micromoles per liter]

Sample

percentile

class

Runoff

rate

(mm hr–1)

SiO2

(mg L–1)

Bedrock

Na2

O

(mg L–1)

Dissolved

bedrock

(mg L–1)

Suspended

bedrock

(mg L–1)

DIC

(μmol L–1)

DOC

(μmol L–1)

POC

(μmol L–1)

LOI 

(percent of

suspended

solids)

POC

(percent of

suspended

solids)

Canóvanas

0–10 percent 0.021 34.8 9.9 90 8 2,044 164 19 23 2.3

>10–25 percent 0.061 29.3 6.8 70 24 1,450 232 51 20 2.1

>25–50 percent 0.158 24.7 4.9 55 72 1,035 299 139 18 1.9

>50–75 percent 0.765 17.5 3.2 37 380 586 414 647 15 1.7

>75–90 percent 4.49 12.4 2.5 26 1,050 349 420 1,786 15 1.7

>90–95 percent 15.3 9.6 2.3 20 1,489 282 374 2,669 17 1.8

>95–99 percent 32.0 8.3 2.3 18 1,669 265 323 3,141 18 1.8

>99–100 percent 59.2 7.1 2.3 16 1,723 254 266 3,457 20 1.9

Cayaguás

0–10 percent 0.041 40.9 18.2 83 12 1,395 123 14 11 1.3

>10–25 percent 0.095 35.6 11.7 67 37 997 168 37 10 1.1

>25–50 percent 0.195 29.6 7.6 53 128 669 221 116 8 1.0

>50–75 percent 0.860 18.9 3.4 31 995 250 312 806 6 0.9

>75–90 percent 4.35 11.2 1.8 19 3,237 80 324 2,580 6 0.9

>90–95 percent 12.1 7.6 1.4 13 5,366 48 278 4,287 6 0.9

>95–99 percent 26.2 5.5 1.2 10 6,727 38 231 5,391 6 0.9

>99–100 percent 41.0 4.7 1.1 9 6,484 33 212 5,015 7 0.9

Mameyes

0–10 percent 0.068 34.3 5.1 61 1 914 80 5 35 3.5

>10–25 percent 0.179 22.3 3.2 41 3 615 133 13 32 3.2

>25–50 percent 0.324 17.8 2.3 32 9 469 178 30 28 2.8

>50–75 percent 0.887 12.3 1.4 22 43 297 245 111 23 2.4

>75–90 percent 2.81 8.3 0.9 15 176 173 308 362 19 2.0

>90–95 percent 7.26 5.9 0.8 11 479 106 358 835 15 1.8

>95–99 percent 21.2 4.2 0.8 8 1,101 61 347 1,692 14 1.6

>99–100 percent 45.6 3.2 0.7 6 1,778 42 320 2,540 13 1.5

Icacos

0–10 percent 0.115 28.6 3.7 41 3 426 75 9 30 2.8

>10–25 percent 0.252 19.9 2.7 30 8 332 125 24 27 2.7

>25–50 percent 0.429 15.4 2.2 24 21 265 186 58 24 2.5

>50–75 percent 1.197 9.3 1.6 15 139 165 299 269 18 1.9

>75–90 percent 3.96 5.2 1.1 9 728 79 368 983 12 1.4

>90–95 percent 9.04 3.6 0.8 6 1,901 41 349 1,998 10 1.1

>95–99 percent 20.8 2.4 0.7 4 5,291 17 285 3,830 6 0.8

>99–100 percent 51.6 1.5 0.5 3 21,372 –2 149 7,811 3 0.4

Guabá

0–10 percent 0.104 26.6 3.2 36.1 4 321 79 14 29 2.9

>10–25 percent 0.250 18.8 2.4 26.1 12 235 120 38 27 2.7

>25–50 percent 0.383 15.8 2.0 22.1 28 194 142 76 25 2.5

>50–75 percent 0.950 10.28 1.5 15.0 161 130 205 326 19 2.0

>75–90 percent 4.48 5.00 1.0 8.0 1,275 59 265 1,801 13 1.5

>90–95 percent 14.48 3.03 0.8 5.3 2,956 30 264 3,621 11 1.3

>95–99 percent 33.1 2.03 0.8 3.9 4,484 16 259 4,998 9 1.2

>99–100 percent 68.6 1.50 0.9 3.1 6,299 8 223 6,667 8 1.2

measurements calculated from stage recordings. LOADEST 

was used to estimate constituent yields for hourly and 

yearly intervals and for the entire study period (15 years in 

the Canóvanas, Cayaguás, Mameyes, and Icacos rivers and 

10 years in the Guabá; tables 2, 3; figs 3–6).

Alkalinity can have negative values because of addition of organic acids from soil process and sulfuric and 

nitric acids from rain. LOADEST, because it works with 
Table 3. Average annual net, atmospheric, and bedrock inputs in 1991 to 2005 to study watersheds, eastern Puerto Rico. [DOC, dissolved organic carbon; POC, particulate organic carbon; DBrx, dissolved bedrock; SBrx, suspended bedrock; mm yr−1, millimeters per year; kmol km−2 yr−1, kilomoles 

kEq km−2 yr−1, kiloequivalents per square kilometer per year; t km−2 yr−1, metric tons per square kilometer per year; -- not analyzed] Watershed Runoff (mm yr–1) Na+ K+ Mg2+ Ca2+ Si(OH)4 Cl– SO42– NO3– NH4+ –PO43– Alkalinity (kEq km–2 yr–1) DOC 

(kmol km−2 yr−1) (kmo

Annual net yields Canóvanas 970 417 31.8 193 280 357 324 43.8 43.5 3.6 0.39 918 313 

Cayaguás 1,620 672 88.1 136 249 662 411 78.1 81.5 4.4 0.94 865 389 

Mameyes 2,750 634 50.7 174 439 739 541 84.5 26.1 2.1 0.41 1,137 573 

Icacos 3,760 733 58.1 150 264 853 585 54.4 44.5 3.7 0.18 800 837 

Guabá 3,630 657 54.0 109 173 779 623 42.7 38.9 2.6 0.18 502 576 

Annual atmospheric inputs Canóvanas 970 276 5.8 31 13 0 324 32.8 18.8 7.2 -- –32 313 

Cayaguás 1,620 350 7.4 40 14 0 411 41.7 23.9 9.1 -- –44 389 

Mameyes 2,750 461 9.7 52 17 0 541 54.9 31.5 12.0 -- –61 573 

Icacos 3,760 498 10.5 57 17 0 585 59.3 34.0 13.0 -- –67 837 

Guabá 3,630 531 11.2 60 18 0 623 63.2 36.3 13.8 -- –73 576 

Annual bedrock-derived/non-rain-derived yields1 Canóvanas 970 141 26.0 162 267 357 0 11.0 24.7 –3.6 -- 950 0 

Cayaguás 1,620 322 80.8 96 235 662 0 36.4 57.6 –4.7 -- 908 0 

Mameyes 2,750 173 41.0 122 422 739 0 29.6 –5.4 –9.9 -- 1,199 0 

Icacos 3,760 235 47.7 93 246 853 0 –4.9 10.5 –9.3 -- 867 0 

Guabá 3,630 126 42.9 49 154 779 0 –20.6 2.6 –11.2 -- 574 0 

Watershed Runoff (mm yr–1) Na+ K+ Mg2+ Ca2+ Si(OH)4 Cl– SO42– NO3– NH4+ –PO43– Alkalinity DOC 

(percent) Atmospheric inputs as percentage of net yield

Canóvanas 970 66 18 16 4 0 100 75 43 201 -- –3 100 

Cayaguás 1,620 52 8 29 6 0 100 53 29 207 -- –5 100 

Mameyes 2,750 73 19 30 4 0 100 65 121 582 -- –5 100 

Icacos 3,760 68 18 38 7 0 100 109 76 353 -- –8 100 

Guabá 3,630 81 21 55 11 0 100 148 93 534 -- –14 100 

1Negative values indicate that atmospheric input exceeds river export.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 217

logarithmic transforms of the data, cannot handle situations 

with legitimate negative values. Instead, alkalinity loads 

and average concentrations were calculated using the loads 

and concentrations of other constituents as estimated by 

LOADEST (concentrations in mole units):

alkalinity = Na++K++NH4

+

+2•(Mg2++Ca2++Sr2+)−

(Cl−+NO3

−

+2•SO4

2−+ZDOC•DOC) (2)

where ZDOC is the negative charge per carbon atom in DOC. 

For each river, ZDOC was calculated by adjusting predicted 

alkalinity to measured alkalinity such that the ratio of averages 

was 1:1. The range of estimated values was 0.5 equivalents of 

charge per mole (eq mol−1) to 0.8 eq mol−1 (appendix 1). For 

reference, Tardy and others (2005) found an optimal ZDOC of 

0.64 eq mol−1 for Amazonian lowland rivers.

Data Manipulation and Processing of Dissolved 

Constituents

One of the main motivations for introducing derived 

constituents is the need to subtract atmospherically derived 

contributions to the dissolved load, much of it seasalt, from 

bedrock-derived contributions. This subtraction is also termed 

a cyclic-salt correction (Stallard and Edmond, 1981, 1983). 

Once such a subtraction is accomplished, it is possible to 

calculate DBrx, which when combined with SBrx, can be used 

to accurately estimate erosion and determine whether erosion 

is in disequilibrium. 

Correction for Atmospherically Derived 

Contributions to the Dissolved and Solid Load

A substantial portion of the dissolved load in rivers 

is derived from the atmosphere. Historically, estimates of 

denudation have not corrected for this component. One atmospheric source is precipitation; another is direct transfer from 

the atmosphere to plant and ground surfaces by impaction 

and dry deposition. Another pathway is “fixation” from the 

gas phase into a solid or soluble form by biological processes, 

mostly photosynthesis and nitrogen fixation. Finally, chemical weathering involves fixation of water and carbon dioxide 

from the atmosphere into dissolved ionic forms and into soil 

minerals as structural or bound water. In the eastern Puerto 

Rico watersheds, which are underlain almost entirely by 

igneous rocks, virtually all the inorganic and organic carbon 

is atmospherically derived. The same is true for all forms of 

nitrogen. Almost all river-borne Cl−

 has a seasalt origin, and 

almost all ions that are abundant in surface seawater make 

considerable atmospheric contributions to the dissolved load. 

Biogenic gases and pollution contribute SO4

2−, NO3−

, and 

NH4

+

. Desert dust contributes notable amounts of soluble 

calcium (see Stallard, 2012).

Atmospheric sources also contribute to the solid load. 

All of the organic carbon and much of the water bound into 

clays in river-borne sediment has an atmospheric origin (table 

1, LOI, H2O+, H2O−). For reference, kaolinite and halloysite 

(Al2Si2O5(OH)4) are 14.0 percent by weight water. Clays and 

other minerals in desert dust also make a minor contribution 

to the solid load (Pett-Ridge, 2009; Stallard, 2012).

In order to estimate bedrock contributions to the dissolved load, the major cations (Na+

, K+, Mg2+, Ca2+), and SO4

2−

require corrections for atmospheric inputs. For Na+, K+, and 

Mg2+, the correction involves subtracting a seasalt contribution 

on the basis of the ratio of that ion to Cl− either in rain or in 

seasalt. If we use Na+ as an example, the Na+:Cl− mole ratio 

for seasalt is 0.8525:

Na* = Na2+ − (Na+:Cl−)•Cl−. (3)

The “*” designates a calculated bedrock-derived 

constituent. The Mg2+:Cl− ratio in rain is 0.09689, less than 

the seasalt ratio of 0.10145 (Stallard, 2012). The seasalt 

SO4

2−:Cl− ratio is 0.052, and in some storms, especially 

high-chloride events, the rain and runoff are near this ratio 

(Stallard and Murphy, 2012, their fig. 10). However, additional sulfur comes from pollution and emissions of organic 

sulfur compounds (dimethyl sulfide and methyl sulfide) from 

the marine environment. To further complicate matters, the 

forest environment may also emit organic sulfur compounds 

back to the atmosphere (Stallard and Edmond, 1981; Stallard 

and Murphy, 2012). The overall SO4

2−:Cl− ratio is 0.10614 

in rain, and this ratio was used in calculations. The K+:Cl−

ratio in rain is 0.02306, whereas the seasalt ratio is 0.0179. 

Because throughfall is so rich in K+ (Heartsill-Scalley and 

others, 2007), the greater rain ratio is seen as reflecting local, 

minor plant contamination, so the seasalt ratio was used. The 

Ca2+:Cl− ratio is 0.01876, and seasalt is a minor contributor 

to the calcium budget.

In individual samples, the atmospheric-correction calculation occasionally yields a negative value. This result may be 

due to real processes, such as cation exchange in soil that can 

alter cation proportions, or it may be due to true variations in 

the ratios to Cl− for individual storms, as happens with SO4

2−. 

In constructing annual and long-term budgets, the atmospheric 

corrections incorporate calculated yields using the Cl−

 yield 

as the basis for the correction. The calculation was checked 

against LOADEST calculations of Na* and Ca*.

Constituent yields estimated using LOADEST were 

compared with atmospheric inputs (Stallard, 2012) (table 3). 

Several constituents (Na+, Cl−, SO4

2−, and NO3−

 plus NH4

+

, 

DOC, and POC) are largely atmospheric in origin. Potassium 

and Mg2+ receive a contribution of about 10 to 50 percent 

from the atmosphere. Calcium, silica, dissolved bedrock, and 

suspended bedrock receive minor atmospheric contributions, 

0 to about 10 percent. Alkalinity has a small negative atmospheric contribution; in essence, acidity in rain is titrating the 

landscape. The atmospheric contribution for phosphate, which 

is quite large (Pett-Ridge, 2009), was not included in table 3; 

phosphate is biologically and chemically quite reactive, and 

without more information it would be hard to apportion atmospheric inputs to the dissolved or solid load. 
218 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A. Dissolved bedrock silicon dioxide, all data B. Dissolved bedrock silicon dioxide, averages

C. Dissolved bedrock sodium oxide, all data D. Dissolved bedrock sodium oxide, averages

E. Dissolved bedrock, all data F. Dissolved bedrock, averages

0.01

Runoff rate, in millimeters per hour

Constituent concentration, in milligrams per liter

0.01 0.1 1 0.1 1 10 100

0.1

1

10

100

200

10 100

0.1

1

10

100

200

0.1

1

10

100

200

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 219

Figure 3. (facing page) The concentration of dissolved silicate

bedrock (DBrx) and two dissolved constituents, bedrock Na2O and 

silicon dioxide, Na2

O, used in calculating DBrx and in estimating 

equilibrium physical denudation rates in eastern Puerto Rico. 

Bedrock Na2

O is dissolved sodium ion, Na+, corrected for seasalt 

sodium, and then expressed as an oxide. SiO2

 is dissolved silica, 

Si(OH)4

, expressed as an oxide. DBrx is the sum of all bedrockderived cations, expressed as oxides or sulfide, after correction 

for atmospheric inputs. The left panels represent the WEBB 

dataset. The right panels represent averages calculated from 

hourly estimates determined using LOADEST. For a number of 

samples, additional sample characteristics are indicated with 

black symbols. These categories are discussed in detail in 

Stallard and Murphy (2012): calcite, samples supersaturated 

with respect to calcite; high chloride, samples with exceptionally 

high chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from the 

sample-average curves represent the averages of the classes of 

sample indicated by the superimposed character. These samples 

are not included in LOADEST models.

Sahara dust brings in additional Ca2+ and insoluble clays 

(Stallard, 2012). Because this Ca2+ is also a minor part of 

the overall Ca2+ budget, and because the dust fallout comes 

from variable long-range transport, a uniform dust correction (6.4 kmol km−2 Ca) was subtracted from the final Ca2+

budget for each watershed. All silicon, aluminum, and iron in 

Sahara dust were assumed to reside in nonreactive clays and 

sesquioxides and therefore to be insoluble (Stallard, 2012; 

Stallard and Murphy, 2012). Sahara dust deposition rates 

of 21 t km−2 yr−1 estimated by Pett-Ridge and others (2009) 

were subtracted equally from the solid bedrock yields for all 

study watersheds.

Total Dissolved Bedrock

Solute denudation rates were calculated using the 

approach of Stallard (1995a,b) that expresses the concentrations of most bedrock-derived constituents as dissolved 

oxides for mass-balance calculations. Silica and each cation, 

after correction for atmospheric inputs, were converted into 

a mass concentration of the corresponding bedrock oxide. 

For example, the calculation of dissolved sodium concentration expressed as an oxide (Na2

O[d]) (table 2) from bedrockderived sodium, Na*, was performed as follows:

Na2O[d] (mg L−1) = 0.03098 • Na* (μmol L−1), (4)

where mg L−1 is milligrams per liter, and μmol L−1 is micromoles per liter.

Dissolved SO4

2− was converted into sulfide. The concentrations of the dissolved oxides and sulfide were summed to 

get total dissolved bedrock (DBrx) (fig. 3; tables 2, 3). If the 

bedrock of a watershed were to contain abundant carbonate 

rocks, for which the charge of Ca2+ and Mg2+ is balanced by 

carbonate ions (CO3

2−) rather than oxygen (O2−), the amount of 

dissolved bedrock would be markedly underestimated.

In addition to calculating DBrx of samples with complete 

chemistry (about 870 samples), DBrx was also estimated in 

all samples for which conductivity, Cl−, and Si(OH)4 were 

measured (about 3,780 samples). This calculation involved 

three steps. First, the bedrock contribution to conductivity was 

estimated by subtracting an atmospheric contribution, which 

was calculated by using a factor determined by estimating 

the conductivity of water having ions in the atmosphericcorrection ratios to Cl−

 just indicated, and by using the specific 

conductivities of each ion (appendix 1):

conductivity* = conductivity − 0.13028 • Cl−. (5)

The relation between conductivity* and total bedrockderived cation charge, ZBrx, is estimated through a regression 

that used the 1,040 completely analyzed samples. A single set 

of regression coefficients worked for all Puerto Rico WEBB 

rivers:

ZBrx = a • (conductivity*)b , (6)

where a (1.08612) and b (0.83239) were regression coefficients 

(coefficient of determination (r2) = 0.96). Using ZBrx, an estimated total dissolved bedrock, DBrx′, was determined by

DBrx′ = c • (ZBrx) d + SiO2[d] , (7)

where c and d are regression coefficients determined for each 

river (table 3 in appendix 1), and SiO2[d] is the dissolved silica 

concentration expressed as an oxide (table 2, fig. 3):

SiO2

[d] (mg L−1) = 0.06008 • Si(OH)4 (μmol L−1). (8)

The correlation between DBrx′ and DBrx for all 1,040 

samples is r2 = 0.990; between log(DBrx′) and log(DBrx), r2

= 0.986. About 4,650 samples were used in load calculations 

for both DBrx and DBrx′, providing a rigorous estimate of dissolved bedrock erosion (table 2).

Equilibrium Model: Calculating Steady-State 

Erosion from Dissolved Bedrock

When chemical weathering reactions control the rate 

of production of solid materials, the yields of dissolved 

constituents can be used to predict equilibrium yields of 

solids (Stallard, 1995a,b). The sodium-to-silica ratio of the 

dissolved and solid load compared with the ratio in bedrock 

(after correction for atmospheric inputs) is especially useful. 
220 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A. Dissolved inorganic carbon, all data B. Dissolved inorganic carbon, averages

C. Dissolved organic carbon, all data D. Dissolved organic carbon, averages

E. Particulate carbon, all data F. Particulate carbon, averages

0.01

Runoff rate, in millimeters per hour

Constituent concentration, in micromoles per liter

0.01 0.1 1 0.1 1 10 100

1

10

100

1,000

10,000

100,000

10 100

1

10

100

1,000

10,000

100,000

1

10

100

1,000

10,000

100,000

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 221

Figure 4. (facing page) The concentration of the three major 

components of carbon transport in tropical rivers in eastern 

Puerto Rico: dissolved inorganic carbon (DIC = alkalinity), 

dissolved organic carbon (DOC), and particulate organic 

carbon (POC). The left panels represent the WEBB dataset. 

The right panels represent averages calculated from hourly 

estimates determined using LOADEST. For a number of 

samples, additional sample characteristics are indicated with 

black symbols: calcite, samples supersaturated with respect 

to calcite; high chloride, samples with exceptionally high 

chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from 

the sample-average curves represent the averages of the 

classes of sample indicated by the superimposed character. 

These samples are not included in LOADEST models.

Figure 5. The concentration of an operationally derived

constituent, suspended bedrock (SBrx) used in calculating 

physical denudation rates in eastern Puerto Rico. SBrx is the 

mass of the sample after heating to 550°C. For a number of 

samples, additional sample characteristics are indicated with 

black symbols: calcite, samples supersaturated with respect 

to calcite; high chloride, samples with exceptionally high 

chloride concentrations collected during huge storms; high 

potassium, samples with high potassium but not high chloride; 

low silica, Icacos and Guabá samples with unusually low silica 

concentrations for the runoff rate. The points separated from the 

sample-average curves represent the averages of the classes of 

sample indicated by the superimposed character. These samples 

are not included in LOADEST models.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_05H.ai

A. Suspended bedrock, all data

B. Suspended bedrock, averages

0.01

Runoff rate, in millimeters per hour

Suspended bedrock, in milligrams per liter Suspended bedrock, in milligrams per liter

0.1 1 10 100

0.01 0.1 1

0.1

10

1

1

100

1,000

10,000

100,000

0.1

10

100

1,000

10,000

100,000

10 100

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
222 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

In headwater streams, such as those studied in eastern Puerto 

Rico, neither sodium nor silica are particularly bioactive; 

igneous and metamorphic rocks have a narrow range of 

sodium-to-silica ratios, and sodium weathers out of bedrock 

sufficiently early in the weathering process, such that the 

sodium-to-silica ratios of solid weathering products are quite 

low (table 1) throughout the soil profile and as river-borne 

sediments. Calcium shares most of these characteristics, 

except that the calcium-to-silica ratio ranges more widely in 

igneous and metamorphic rocks and is influenced by carbonate rocks (Stallard, 1995a).

To calculate the proportions of dissolved and solid weathering products that are being produced, one needs to express 

the compositions of bedrock, river-borne solids, and bedrockderived solutes as weight-percent oxides. This goal is simple 

for the solid phases, because bulk analyses are normally 

reported this way (table 1). For solutes, the concentration of 

dissolved bedrock oxides, calculated as described earlier (for 

example, equation 4), must be normalized to total 100 percent 

(table 4):

Na2

O[d′] = 100 • Na2O[d] / DBrx . (9)

Following Stallard (1995a, equations 3 and 5 of that 

report), the leaching efficiency of bedrock, W, is the ratio of 

the yield of bedrock weathered into solution, YDBrx, to the yield 

of total bedrock weathered, YDBrx + YSBrx:

W = YDBrx / (YDBrx +YSBrx) . (10)

The hypothetical yield of solid weathering products, 

Y′SBrx, expressed in terms of the observed yield of dissolved 

weathering products, YDBrx, is given by

Y′SBrx = YDBrx • (1−W )/ W . (11)

Similarly, the efficiency of leaching of silica, WSi, is given 

by

WSi = W • SiO2

[d′] / SiO2[b] , (12)

where [d’] refers to the weight percent oxide in the dissolved 

load and [b] to the weight percent oxide in bedrock. When 

one has a constituent pair, such as sodium paired with silica, 

WSi can also be expressed in terms of concentrations of both 

constituents ([s] refers to weathered solids):

WSi(Si,Na) = ((Na2

O[b] / SiO2[b]) − (Na2O[s] / SiO2[s]))

/ ((Na2

O[d′] / SiO2[d′]) − (Na2O[s] / SiO2[s])) . (13)

With this equation, Y′

SBrx is expressed in terms of measureable or easily estimated parameters, all of which are 

concentration ratios. The use of ratios limits the effect of 

many shared errors, such as errors in discharge measurements. 

Finally, from equations 11 and 13,

Figure 6. Percent loss on ignition (LOI). For a number of samples,

additional sample characteristics are indicated with black symbols: 

calcite, samples supersaturated with respect to calcite; high 

chloride, samples with exceptionally high chloride concentrations 

collected during huge storms; high potassium, samples with high 

potassium but not high chloride; low silica, Icacos and Guabá 

samples with unusually low silica concentrations for the runoff rate. 

The points separated from the sample-average curves represent the 

averages of the classes of sample indicated by the superimposed 

character. These samples are not included in LOADEST models.

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_06H.ai

A. Loss on ignition, all data

B. Loss on ignition, averages

0.01

Runoff rate, in millimeters per hour

Loss on ignition, in percent

0.1 1 10 100

0.01 0.1 1

0

20

40

60

80

100

Loss on ignition, in percent

0

20

40

60

80

100

10 100

EXPLANATION

Mameyes

High chloride 

Icacos

Calcite

Low silica

Guabá

Cayaguás

High potassium

Canóvanas Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 223

Table 4. Parameters and coefficients used in modeling equilibrium erosion in study watersheds, eastern Puerto Rico, by using sodium-silicon and calcium-silicon pairs.[A and 

B are dimensionless coefficients defined in equations 16–18]

Description

Bedrock Sediment Dissolved Sodium model Calcium model

SiO2 Na O2 CaO SiO2 Na O2 CaO SiO2 Na O2 CaO

A

B

A

B (percent) (percent) (percent)

Canóvanas, dissolved bedrock 53.8 3.31 8.12 50.3 0.03 1.31 43.6 9.45 30.5 30.5 0.018 14.9 0.388

Cayaguás, dissolved bedrock 62.9 3.84 6.07 58.0 0.27 0.78 54.3 15.09 18.0 28.3 0.134 19.1 0.258

Mameyes, dissolved bedrock 53.8 3.31 8.12 50.3 0.03 1.31 54.0 7.25 28.8 30.5 0.018 14.9 0.388

Icacos, dissolved bedrock 61.4 3.45 6.90 58.0 0.27 0.78 64.9 9.65 17.5 31.6 0.150 16.5 0.222

Guabá, dissolved bedrock 61.4 3.45 6.90 58.0 0.27 0.78 71.0 9.92 12.9 31.6 0.150 16.5 0.222

Y′SBrx = YDBrx • (SiO2[d′] / SiO2[b]) • (1−WSi(Si,Na)) 

/ WSi(Si,Na) . (14)

When the observed weathered bedrock yield equals the 

steady-state weathering yield, YSBrx = Y′SBrx, the denudation of 

the landscape is in equilibrium. If YSBrx >Y′SBrx, solid weathering products are accumulating on the landscape as soils, 

alluvium, and colluvium, and if YSBrx<Y′SBrx, physical erosion 

exceeds the generation of solids, and there must be net loss of 

soil or stored sediment.

Equations 13 and 14 have a number of terms that, in a 

given watershed, are constant for all calculations, such as 

the bedrock and solid-load compositions. These constants 

allow the equations to be simplified into a form that involves 

either measured or easily estimated parameters. For example, 

the concentrations of dissolved bedrock oxides (equation 9), 

expressed as percent, can also be calculated from solute yields. 

Using sodium as an example:

Na2

O[d′] = 100 • YNa2

O[d] / YDBrx . (15)

Accordingly,

Y′SBrx = (A • YNa2

O[d] – B • YSiO2[d]) − YDBrx , (16)

where

A = 100 • SiO2[s] / (Na2O[b] • SiO2[s] − 

Na2O[s] • SiO2[b]), (17)

and

B = 100 • Na2O[s] / (Na2O[b] • SiO2[s] − 

Na2O[s] • SiO2[b]). (18)

Note that the denominators (the terms in parentheses) 

in equations 17 and 18 are the same; accordingly, A is more 

sensitive to errors in estimating SiO2

[s], and B, which is quite 

a small term, is more sensitive to errors in Na2O[s]. In fact, 

so long as Na2O[s] is small, even with considerable error, this 

equation is quite robust. Moreover, in the equations for A and B, 

the numerator represents a concentration of river-borne solids 

and the difference terms in the denominator are multiplied by 

concentrations of river-borne solids. This combination of terms 

eliminates any need to correct for LOI, despite the assumption 

that LOI is atmospherically derived water and organic matter.

By using equation 16, LOADEST-based yield estimates 

were used to calculate equilibrium denudation rates and test 

whether erosion is in equilibrium and how equilibria may 

respond to changes in average long-term runoff; this method 

is referred to as the equilibrium model. Denudation rates were 

calculated using calcium, as a check, but because of the caveats regarding variability of bedrock CaO[s]:SiO2

[s] ratios and 

the influence of possible calcite inputs, the sodium calculations are much preferred.
224 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

sediments to the net sediment load is very small (Stallard and 

Murphy, 2012). At highest runoff rates, LOI values of sediment in rivers that drain granitic rocks are low because of the 

large contribution of quartz sand to these samples.

POC was not measured on most of the sediment samples; 

instead, LOI (fig. 6) is used as a surrogate measurement. LOI 

is commonly equated with organic matter; however, the study 

samples are rich in kaolinite, halloysite, or iron and aluminum 

sesquioxides. Instead, it is assumed here that POC is a fixed 

fraction of LOI for all study samples. This fraction was determined such that the 15-year yield of SSol for the Río Icacos 

consisted of 1 percent carbon, a value that is typical of world 

rivers (Stallard, 1998). The result is that LOI for the study rivers consists of 11 percent carbon. For reference, pure organic 

matter is about 50 percent carbon by mass (Stallard, 1998). 

The water contribution to average LOI never exceeds the 

value of 14.0 percent of pure kaolinite (table 5). The average 

POC of forested rivers, at low runoff rates of less than 1 mm 

h−1, is 2 to 3.5 percent SSol (table 2), similar to values reported 

elsewhere (McDowell and Asbury, 1994). For estimates of 

LOI, POC, and organic matter, see table 5.

Table 4 presents the concentrations of solids and solutes 

needed to calculate equilibrium yields, using either sodium or 

calcium, along with respective coefficients A and B. In addition to analyses of samples collected for this study (table 1), 

table 4 is based on averaged data that includes published analyses of volcanic rocks from other sources. For the bedrock of 

the Mameyes and Canóvanas watersheds, analyses of volcanic 

rocks from Jolly and others (1998; samples DG–12, DG–14, 

DG–16; FG–17, FG–7, FG–9; INF–7, J–77, LOM–12) were 

averaged with the volcanic samples in table 1. For the Icacos and Guabá, data from the Río Blanco stock from Smith 

and others (1998, Río Blanco samples 6513, PRP–4, PRP–5, 

PRP–6, PRP–12, PRP–14, PRP–16, PRP–17) were averaged 

with quartz-diorite samples from table 1, excluding xenoliths 

and aplite. For the Cayaguás, data from Smith and others 

(1998; San Lorenzo samples PP–1, PRP–1, PRP–2, PRP–10, 

PRP–11; PRP–100, PRP–103, PRP–104, PRP–105; SL–1) 

were averaged.

Suspended Bedrock, Loss on Ignition, and 

Particulate Organic Carbon

River-borne solids are composed of material from three 

sources: primary and secondary mineral matter derived from 

the weathering of bedrock, water that has been incorporated 

into secondary minerals during weathering, and organic matter. 

For about two-thirds of the measurements of suspended sediment (see Stallard and Murphy, 2012, and appendices 1, 2), we 

also measured loss on ignition (LOI), which involves measuring the mass loss upon heating to 550°C samples that have 

been dried at temperatures of 105°C. Studies summarized in 

Mackenzie (1957) and Mackenzie and Caillère (1979) show 

that in heating to 550°C, all of the clays and sesquioxides lose 

nonstructural water (most below 200°C). In addition, all sesquioxides, most kaolins, and substantial illites and vermiculites 

lose lattice water below 550°C; however, smectites, talcs, and 

chlorites do not. Nonmineral organic carbon and most mineral 

carbon (coal and graphite) are also oxidized in this range. Carbonate rocks are generally stable.

In this work, the mass of the sample after heating to 

550°C was equated with SBrx (fig. 5). The sample dried to 

105°C is referred to as suspended solids or suspended sediment 

(SSol). For samples in which suspended bedrock was not measured, regressions for each river related SBrx to SSol and LOI:

SBrx = SSol • (a + b • log(SSol)) , (19)

and

LOI = SSol − SBrx , (20)

where a and b are regression coefficients. Values for a and b

for each river are given in appendix 1. At lowest runoff rates, 

LOI is elevated in the forested rivers (Mameyes, Icacos, and 

Guabá; fig. 6). Given that LOI for kaolinite and halloysite is 

14.0 percent, values greater than 14.0 percent likely reflect 

organic matter. At low runoff rates, the overall contribution of 

Table 5. Analysis of loss on ignition for a discharge-weighted

average sample of solid load from study rivers, eastern Puerto Rico.

[LOI, loss on ignition; POC, particulate organic carbon]

River LOI

Weight percent of suspended solids

Water Organic

matter1 POC

Canóvanas 16.3 12.8 3.5 1.8

Cayaguás 6.1 4.3 1.8 0.9

Mameyes 16.9 13.2 3.7 1.9

Icacos 7.5 5.5 2.0 1.0

Guabá 11.7 8.9 2.8 1.4 1

Organic matter is assumed to be twice POC.

Projection to a Common Intermediate Yield

The principal confounding issue in the comparison of 

constituent yields of the various Puerto Rico WEBB watersheds is that among them, the long-term, mean-annual runoff 

differs markedly. The forested watersheds are considerably 

wetter (Mameyes—2,750 mm yr−1, Icacos—3,760 mm yr−1, 

Guabá—3,630 mm yr−1) than the developed watersheds 

(Canóvanas—970 mm yr−1, Cayaguás—1,620 mm yr−1). To 

compare sites, regressions between annual runoff and constituent yields were used to project annual yields to an annual 

intermediate runoff, 1,860 mm yr−1, about halfway between 

the runoff for the Canóvanas and the Mameyes. This value is 

within the observed range of annual runoffs for the Mameyes 

and Cayaguás and represents an extrapolation for the Canóvanas, Icacos, and Guabá. These relations were also used to 

establish whether these landscapes are in a state of dynamic 

equilibrium such that the yield of solid bedrock is the same as 

the rates at which solid weathering products are being generated (equation 16).
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 225

Annual constituent yields, including derived constituents, 

were calculated using LOADEST (appendix 1). Annual runoff 

varied considerably from year to year (fig. 7). The highs and 

lows were mirrored by constituent yields (figs. 7–9).

The relation between annual runoff and annual dissolvedconstituent yields was a simple linear relation for all dissolved constituents (figs. 10–12). Many rivers produce linear 

relations such as these (6 rivers, White and Blum, 1995; 57 

rivers, Godsey and others, 2009). The buffering or “chemostatic” behavior can be explained by many mechanisms, such 

as ion exchange, buffering with amorphous phases, large soil 

reservoirs of solutes, or weathering reactions that respond proportionally to the water flux passing through the soil. Godsey 

and others (2009) presented a “permeability-porosity-aperture 

model” that predicts that in a given watershed, the relation 

between log(concentration) and log(runoff) should be linear 

and with the same slope for all bedrock-derived constituents. 

The authors allow that differences for slopes among various 

constituents could be attributable to differences in the depth 

distribution of reactive minerals throughout soil profiles.

Suspended Bedrock and Runoff

Unlike solute erosion, for which a linear relation between 

annual runoff and annual solute yield agrees well with the 

data, physical erosion is decidedly nonlinear, and concentrations of solid constituents typically increase markedly with 

discharge. Two issues must be addressed with respect to solid 

loads. First is selecting the relation between runoff and solid 

yield for individual years. Landslides add complexity, because 

landslide-producing storms do not occur every year. The 

second is the problem of averaging the effects of variation in 

interannual runoff. Long-term averages reflect a mix of drier 

and wetter years. For linear processes the average solute yield 

of many years is the same as the solute yield of the average 

runoff for all the same years. For solid yields, years that have 

high runoff and abundant high-runoff storms heavily influence 

long-term averages, and computational procedures need to 

capture this increased weighting of wet-year contributions.

Solid Loads for Individual Years

Surficial erosion, the combined effects of direct impaction by raindrops on soil, sheet wash, rill formation, and 

gullying, is driven by flowing water and tends to increase 

exponentially with increasing discharge. Physical erosion is 

most simply described using power-law formulations relating 

annual suspended solids yield, YSSol, to annual runoff, R:

YSSol(year) = a • R(year)

b

 , (21)

where a and b are regression coefficients. For the eastern 

Puerto Rico rivers, the coefficients were estimated iteratively 

by means of nonlinear regressions as opposed to regressions 

on log-transformed data (table 6). Landslides, which typically 

are caused by only the largest storms, distort this relation. 

Stallard (1999) and Stallard and Kinner (2005) developed an approach for quantifying annual sediment yields in 

landslide-dominated watersheds; this approach empirically 

distinguishes surficial and landslide erosion by using datasets 

for rivers in the Panama Canal watershed that had a decade or 

more of daily sediment data. The Puerto Rico WEBB dataset 

does not have such daily measurements, but because of the 

emphasis on storm sampling, the yield estimations done using 

LOADEST are a reasonable substitute. A key assumption of 

this approach is that annual sediment discharge from each 

watershed is considerably greater than interannual storage. 

The volcaniclastic watersheds do not store fine-grained sediments in their riverbeds, so the assumption is valid in these 

watersheds. For the granitic watersheds, flood plain storage 

can be extensive (Brown and others, 1995, 1998; Larsen and 

Santiago-Román, 2001; Larsen, 2012). My repeated visits to 

many sites along the channels of the Río Icacos and the Quebrada Guabá during this project revealed little change, suggesting little net storage or loss, and that Quebrada Guabá has 

no floodplain. The Río Cayaguás channel, however, changed 

considerably in appearance, but it would be difficult to assess 

storage or loss.

As discussed earlier, rainfall must exceed some threshold before landslides occur (equation 1). Rather than use 

rainfall, which requires interpolation and is especially ambiguous in the Canóvanas watershed (see Murphy and Stallard, 

2012), this analysis follows Stallard (1999) and Stallard and 

Kinner (2005) and uses daily runoff, R, calculated from the 

daily discharge (D).

A day is defined as a landslide day if 

R (24, 48, or 72 h) > F • 91.46 • D0.18

(D = 24, 48, or 72 h), (22)

where h is hour, F is an empirical coefficient between 0 

and 1 that adjusts the threshold for three factors that reduce 

runoff relative to local rainfall, in essence stating R = F • 

precipitation for big storms. These three factors are evapotranspiration, infiltration, and rainfall patchiness, where in 

parts of a watershed rainfall will exceed the threshold but 

in other parts not do so. Stallard (1999) and Stallard and 

Kinner (2005) determined that F=0.85 for the Panama Canal 

watershed by using 10 to 16 years of daily sediment data on 

six rivers.

Table 6. Coefficients of power-law model relating annual 

suspended-bedrock yield to runoff (Yield=a•Runoffb) in study 

watersheds, eastern Puerto Rico.

River Coefficient Percent variance

explained Years

a b

Canóvanas 0.002 1.72 43 15

Cayaguás 9.2 • 10–5 2.21 79 15

Mameyes 5.6 • 10–11 3.64 54 15

Icacos 4.4 • 10–8 2.93 40 15

Guabá 1.9 • 10–6 2.52 48 10
226 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_07H.ai

A. Annual runoff B. Chloride

C. Sodium D. Bedrock sodium

E. Calcium F. Bedrock calcium

1990

Year

Constituent yields, in kilomoles per square kilometer per year

Constituent yields, in kilomoles per square kilometer per year

Annual runoff, in millimeters

1990 1995 1995 2000 2005

0

100

200

300

400

500

600

0

100

200

300

400

500

600

2000 2005

0

1,000

3,000

2,000

4,000

5,000

6,000

0

0

200

400

600

800

1,000

1,200

200

400

600

800

1,000

1,200

0

200

400

600

800

1,000

1,200

EXPLANATION

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 7. Time series of annual runoff (upper left) and the annual yields of chloride, sodium, calcium, and bedrockderived sodium and calcium, eastern Puerto Rico.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 227

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_08H.ai

A. Magnesium B. Potassium

C. Sulfate D. Nitrate

E. Ammonium ion F. Phosphate

1990

Year

Constituent yields, in kilomoles per square kilometer per year

1990 1995 1995 2000 2005

0

2

4

6

8

10

2000 2005

0

100

200

300

400

0

0

0

0

0.5

1.0

1.5

50

100

150

200

250

50

100

150

200

50

100

150

EXPLANATION

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 8. Time series of the annual yields of magnesium, potassium, sulfate, nitrate, ammonium ion, and phosphate, eastern Puerto 

Rico. Magnesium is mostly influenced by weathering and atmospheric inputs. The remaining constituents in this graph are strongly 

influenced by biological activities and in some cases by human activities such as fertilizer applications and domestic waste.
228 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_H_figures\BW_figures\BWfigure_09H.ai

A. Silica B. Disolved inorganic carbon

C. Dissolved organic carbon D. Particulate carbon

E. Dissolved bedrock F. Suspended bedrock

1990

Year

Constituent yields, in kilomoles per square kilometer per year Bedrock yields,

in metric tons per square kilometer per year

1990 1995 1995 2000 2005

0

50

100

150

2000 2005

0

200

600

1,000

1,200

0

0

10

100

1,000

10,000

500

1,000

1,500

500

1,000

1,500

400

800

10

100

1,000

10,000

EXPLANATION

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 9. Time series of the annual yields of important constituents in characterizing erosion and the carbon cycle in eastern 

Puerto Rico: silica, dissolved inorganic carbon, dissolved organic carbon, particulate organic carbon, dissolved bedrock, and 

suspended bedrock.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 229

A. Silica B. Chloride

C. Sodium D. Bedrock sodium

E. Calcium F. Bedrock calcium

0

Observed annual yield, in millimeters

Constituent yields, in kilomoles per square kilometer per year

1,000 2,000 3,000 4,000 5,000

0

100

200

300

400

500

600

0

200

400

600

800

1,000

1,200

0

200

400

600

800

1,000

1,200

0 1,000 2,000 3,000 4,000 5,000

EXPLANATION

Projected runoff

Linear regression

Observed annual

 yield

Long-term yield

Projected yield

Mameyes

Icacos

Guabá

Cayaguás

Canóvanas

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 10. Annual runoff compared with the annual yields of silica, chloride, sodium, calcium, and bedrock-derived sodium and 

calcium for the five rivers studied, eastern Puerto Rico. The vertical gray line corresponds with 1,860 millimeters per year, and the solid 

squares with black plus symbols correspond with the projected annual yields. The points are observed annual yields; lines are linear 

regressions based on the assumption that annual yield responds linearly with changing runoff.
230 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_11H.ai

A. Magnesium B. Potassium

C. Sulfate D. Nitrate

E. Ammonium ion F. Phosphate

0

Observed annual yield, in millimeters

Constituent yields, in kilomoles per square kilometer per year

1,000 2,000 3,000 4,000 5,000

0 0.0

0.5

1.0

1.5

0

50

100

150

200

250

0

50

100

150

200

2

3

6

8

10

0

50

100

150

0

100

200

300

400

0 1,000 2,000 3,000 4,000 5,000

EXPLANATION

Projected runoff

Linear regression

Observed annual

 yield

Long-term yield

Projected yield

Mameyes

Icacos

Guabá

Cayaguás

Canóvanas

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 11. Annual runoff compared with the annual yields of magnesium, potassium, sulfate, nitrate, ammonium ion, and phosphate

for the five rivers studied, eastern Puerto Rico. The vertical gray line corresponds with 1,860 millimeters per year, and the solid 

squares with black plus symbols correspond with the projected annual yields. The points are observed annual yields; lines are linear 

regressions based on the assumption that annual yield responds linearly with changing runoff. Except for magnesium, the constituents 

in this graph are strongly influenced by biological activities and in some cases by human activities such as fertilizer applications and 

domestic waste. Potassium, sulfate, and phosphate have bedrock sources. Yields of potassium, nitrate, ammonium ion, and phosphate 

are especially elevated in the developed watersheds indicating additional agricultural or domestic sources.
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 231

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_E_figures\BW_figures\BWfigure_12H.ai

A. Silica B. Dissolved inorganic carbon

C. Dissolved organic carbon D. Particulate carbon

E. Dissolved bedrock F. Suspended bedrock

300

Observed annual yield, in millimeters

Constituent yields, in kilomoles per square kilometer per year Bedrock yields, in metric tons

per square kilometer per year

1,000 10,000

10

100

1,000

10,000

10

100

1,000

10,000

0

500

100

1,000

1,500

0

200

600

1,000

1,200

0

50

150

0

500

1,000

1,500

0 1,000 2,000 3,000 4,000 5,000

0 1,000 2,000 3,000 4,000 5,000

800

400

EXPLANATION

Projected runoff

Linear regression

Observed annual

 yield

Long-term yield

Projected yield

Mameyes

Icacos

Guabá

Cayaguás

Canóvanas

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos

Figure 12. Annual runoff compared with annual yields of important constituents in characterizing erosion and the carbon cycle in 

eastern Puerto Rico: silica, dissolved inorganic carbon, dissolved organic carbon, particulate organic carbon, dissolved bedrock 

and suspended bedrock. The vertical gray line corresponds with 1,860 millimeters per year, and the solid squares with black plus 

symbols correspond with the projected annual yields. The points are the observed annual yields. The lines in parts A−C and E are 

linear regressions based on the assumption that annual yield responds linearly with changing runoff. The lines in parts D and F were 

produced by power-law models, on the basis of equation 21, which plot as lines on graphs with log-log scales. Because these powerlaw curves are being used to predict trends for a changing climate, and because of the variability of climate, a convolution, equation 26, 

was used to smooth the power-law curves. Such convolutions have no effect on linear models.
232 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

For each river and each year of the dataset, the number 

of landslide days, as defined by equation 22, were totaled as 

landslide days per year, Ns(year); successive landslide days 

were assigned to a single day. The resulting combined surface 

erosion–landslide erosion model is

YSSol(year) = a • R(year)b + c • Ns(year) , (23)

where a, b, and c are regression coefficients. This first 

term on the right is a traditional sediment transport model 

(equation 21). The second term is a statement that each landslide day dumps a certain quantity of sediment into the river.

Ns(year) does not distinguish between small and large 

landslide-producing events, so in the present paper, the net 

runoff for landslide days is totaled to determine the landslideday runoff per year, Rs(year). 

YSSol (year) = a • R(year)b + c • Rs(year) (24)

where a, b, and c are regression coefficients. The second term 

on the right side of equation 24 assumes that the amount of 

slide material dumped into rivers on landslide days is directly 

proportional to the runoff on those days.

There are four adjustable parameters in this surfaceerosion–landslide-erosion model (equation 23). The landslide 

threshold adjustment parameter, F, is assumed to be the same 

for all of the watersheds. Because daily sediment data are not 

available for eastern Puerto Rico, a value of 0.85, as determined by Stallard (1999) and Stallard and Kinner (2005), was 

used. The remaining coefficients were estimated iteratively by 

means of nonlinear regressions. The first terms, a and b, were 

estimated for each river using data for years lacking landslide 

days; then the second term, c, was estimated using only those 

years with landslide days (table 7). When compared with the 

power-law model (equation 21) and on the basis of an F-test of 

model variance (Bevington and Robinson, 2003), the models 

that used landslide runoff, Rs(year), generally were statistically more significant (tables 6, 7), except for the Mameyes 

watershed, where the landslide model and the power-law 

model were indistinguishable. From 1991 through 2005, we 

observed landslides in the Mameyes watershed associated with 

large storms. The similarity of the two models may imply that, 

currently, landslides make a relatively small contribution to 

the sediment load of the Mameyes.

To adapt the landslide model to a continuum of runoff it 

is necessary to predict landslide-producing runoff, Rs

(annual 

runoff), as a function of annual runoff, Rannual. The form used 

here was the simplest possible, a linear relation. Only years 

having landslide days are considered. The relation is:

Rs(Rannual) = a•(Rannual − Rs0), Rannual > Rs0 , (25)

where a and a•Rs0 are regression coefficients (table 8). This 

equation defines an annual runoff threshold, Rs0 = 0, below 

which landslides should not occur. This threshold is Rs0 = 

1,272 mm yr−1 for the Canóvanas, Rs0 = 1,656 mm yr−1 for the 

Cayaguás, Rs0 = 2,440 mm yr−1 for the Mameyes, Rs0 = 2,857 

mm yr−1 for the Icacos, and Rs0 = 3,098 mm yr−1 for the Guabá. 

The increasing threshold with increasing runoff among the five 

watersheds appears reasonable, because differences in interannual runoff appear to be primarily driven by orographic precipitation and numerous small storms, which do not produce 

landslides, and not by the number or size of the biggest storms 

(see Murphy and Stallard, 2012).

Table 7. Coefficients of model relating annual suspended-bedrock yield to runoff and landslide day runoff in study watersheds, eastern 

Puerto Rico.

[Yield=a•Annual runoff b+c •Slide-day runoff]

River Coefficient Percent variance

explained Years F-test compared topower-law model1 a b c

Canóvanas 1.6 0.67 1.77 91 15 98

Cayaguás 8.2 • 10–4 1.87 2.84 94 15 86

Mameyes 1.2• 10–10 3.53 0.13 74 15 52

Icacos 8.7• 10–8 2.63 4.13 86 15 93

Guabá 0.059 1.17 3.20 84 10 78 1

F-test gives the probability that this model is better than the power-law model (table 6); 50 percent implies that the models are indistinguishable.

Table 8. Coefficients used to predict landslide-day runoff given 

annual runoff in study watersheds, eastern Puerto Rico.

[Landslide-day runoff=a•(annual runoff minus runoff threshold), provided 

annual runoff is greater than the runoff threshold]

River Runoff

threshold

Coefficient

a

Percent

variance

explained

Years

Canóvanas 1,272 1.18 73 15

Cayaguás 1,656 0.58 85 15

Mameyes 2,440 0.32 68 15

Icacos 2,857 0.31 80 15

Guabá 3,098 0.45 77 10
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 233

Calculating the Long-Term Average for Nonlinear 

Sediment-Yield Models

Because river-borne-solid yields are strongly nonlinear 

with respect to runoff, the effects of interannual variation must 

be assessed and adjusted in order to estimate average yields.

Only one river in eastern Puerto Rico, the Río Fajardo, has 

a sufficiently long record, 45 years, to characterize the range 

and nature of interannual variation (see Murphy and Stallard, 

2012). The annual discharge distribution is log normal with a 

standard deviation of 0.142 log units. For the purposes of data 

smoothing, this same log-normal distribution was assumed 

to apply to all the rivers, and to speed calculation, the effects 

of interannual variations were handled computationally as a 

convolution (see appendix 1). The Fajardo runoff data were 

ranked by increasing annual runoff and separated into seven 

roughly equivalent bins of 7, 6, 6, 7, 6, 6, and 7 years, successively. The average of log(runoff) for all the years of data was 

subtracted from each log(runoff), and the resultant values of 

log(runoff) minus average(log(runoff) were, in turn, averaged 

for each bin (table 9). A 7-point, B-spline convolution (Jupp, 

1976, that report’s table 6, m=4, k=2) was used such that the 

values of the model function were averaged using the B-spline 

coefficients for the runoff under consideration and three equally 

spaced log(runoff) values before and after (table 9). SBrx(r) is 

suspended bedrock as predicted by either the power-law model 

(equation 21) or the landslide model (equations 21, 24, 25). The 

convolved SBrx(r) at a given runoff, r, SBrxc(r), is given by

SBrxc(r) = w1

•SBrx(c1•r)+w2•SBrx(c2•r)+w3•

SBrx(c3•r)+w4•SBrx(c4•r)

 +w5•SBrx(c5•r)+w6•SBrx(c6•r)+w7•SBrx(c7•r), (26)

where c1...c7 are logarithmic convolution factors and w1...w7

are weighting factors (table 9). The log(runoff) interval, 0.069, 

was adjusted so that the seven convolution points best matched 

the Río Fajardo bin averages (table 9). Presumably, SBrxc(r) 

captures the effect of interannual variation.

The relation between annual runoff and particulate 

constituents is best portrayed using log-log graphs (fig. 12, 

POC and SBrx). Power-law (equation 21) and landslide (equations 21, 24, 25) models, after convolution (equation 26), are 

represented separately (fig. 13). Note that the landslide models 

display a distinct hump in the Canóvanas and Icacos. These 

are the rivers for which the landslide model best represents 

the annual sediment-yield data when compared to power-law 

representations (tables 6, 7).

Interpretation of Projections to a Common Yield

 It is assumed that constituents with yields that become 

more alike when projected to a common intermediate runoff 

are not strongly controlled by either geology or land cover. 

Atmospheric inputs, such as chloride, fall into this category 

because the source is external to the watersheds. Accordingly, 

to characterize the significance of projecting mean-annual 

yields to a common intermediate runoff (table 10), tests were 

designed to characterize the compactness of the projection 

and the degree of overlap in the two pair-wise comparisons: 

forested compared with developed landscape and granitic 

compared with volcanic bedrock. For each constituent, the 

compactness of the range of yields was compared though a 

simple test involving the minimum and maximum yields for 

constituent X, YX:min, and YX:max, respectively:

Range ratio as a percent = 200 • (YX:max − YX:min) 

/ (YX:max + YX:min). (27)

The more compact the data range, the smaller this ratio. 

This range ratio for the averaged observed annual-mean 

yields was compared with the ratio of the projected yields for 

a common intermediate mean-annual runoff. Range reduction or no change was assumed to indicate that differences in 

geology or land cover did not strongly influence yields. Five 

rivers were too few to develop strong statistical comparisons; however, range is a reasonable surrogate for standard 

deviation and confidence intervals (Dean and Dixon, 1951). 

Accordingly, extreme examples of range reduction or extension are probably meaningful. 

Overlap was characterized by using a simple ranking 

in which the maximum yields for a class of geology or land 

cover are compared to the minimum yields of the alternative 

class: forest compared to developed and granitic compared 

to volcanic (table 11). Increasing degrees of overlap were 

interpreted as reinforcing the effects of range reduction, and 

loss of overlap or major overlap reversals was interpreted as 

reinforcing the effects of range extension.

Table 9. Convolution information used to represent interannual 

variability.

[7-point convolution around a reference runoff such that SBrxc(r)=w1•SBrx

(c1•r)+w2•SBrx(c2•r)+w3•SBRX(c3•r)+w4•SBrx(c4•r)+w5•SBrx(c5•r)+w6•SBrx

(c6•r)+w7•SBrx(c7•r), where r is runoff; w is runoff weighting factor; and c is 

convolution weighting factor]

Convolution

point

Relative

position

Normalized

runoff bin for

Río Fajardo

w c

1 –3 0.60 0.62 0.010

2 –2 0.77 0.73 0.083

3 –1 0.88 0.85 0.240

4 0 0.99 1 0.333

5 1 1.16 1.17 0.240

6 2 1.31 1.37 0.083

7 3 1.68 1.61 0.010
234 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

A. Dissolved, equilirium model B. Suspended, equilibrium model

C. Suspended, power-law model D. Suspended, landslide model

E. Power law/equilibrium yields F. Landslide/equilibrium yields

300

Observed annual yield, in millimeters

0 1,000 10,000

Long-term

equilibrium yield

0.01

0.1

1

10

100

300 1,000 10,000

10

100

1,000

10,000

10

100

1,000

10,000

Observed and model bedrock yields compared in tonnes per square kilometer per year Ratio of model bedrock yields to equilibrium yields

EXPLANATION

Equilibrium

Projected runoff

Linear regression

Observed annual

 yield

Long-term yield

Projected yield

Mameyes

Icacos

Guabá

Cayaguás

Canóvanas

Canóvanas

Cayaguás

Mameyes

Guabá

Icacos
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 235

Results, Comparison of Constituent 

Yields, and Assessment of Landscape 

Equilibrium

Constituent-Yield Comparisons

Projection of annual yields to a common runoff, 

1,860 mm yr−1, brings several features to the fore (figs. 10–13, 

table 11). A characteristic of the linear relations for dissolved 

constituents (figs. 10–12) is that all have positive slopes and 

most have positive intercepts. In contrast to Si(OH)4 in the 

study rivers, the linear relations between Si(OH)4 and runoff 

presented by White and Blum (1995) all had negative intercepts. Annual solute yields must go to zero if annual runoff 

goes to zero (which would mean a profound desert), so these 

linear relations with nonzero intercepts must eventually reorganize as annual runoff approaches zero. For developed rivers, 

for which mean-annual runoff was below the projected runoff, 

all yields increased for all constituents following projection; 

the reverse was true for the forested rivers. For some constituents such as Si(OH)4

, however, the projected yields 

formed a more compact group than did the averaged observed 

mean-annual yields; for other constituents, such as POC, the 

projected yields became more widely dispersed.

The ranges for Na+

, Si(OH)4, Cl−, and dissolved bedrock 

became more compact upon projection to a common intermediate runoff (figs. 10, 12; table 11). This observation also 

applied to Na* (fig. 10), which is not included in the table. 

The wider ranges for DOC and calcium were slight and not 

significant (Dean and Dixon, 1951). The elevated DOC yield 

on the Canóvanas might be an anthropogenic effect; more 

research is needed. The constituents associated with compact 

ranges are also the principal constituents used to describe and 

assess inputs from chemical weathering, atmospheric sources, 

and biological processes. This compactness indicates that 

these three classes of inputs operated in a similar way in all 

four study watersheds. In particular, overall rates of chemical weathering cannot be strongly distinguished when they 

are compared in terms of land cover or bedrock type. Some 

slight differences may relate to bedrock geology. Yields of 

Na* and Si(OH)4

 from the Cayaguás watershed are distinctly 

greater than in the other three rivers, and Ca* yields from the 

Canóvanas and Mameyes watersheds were elevated. These latter watersheds are both on volcaniclastic rocks, which contain 

more calcium-bearing silicate minerals than do the granitic 

rocks, and the watersheds might also leach some calcium from 

minor sedimentary carbonate bedrock.

The ranges for the remaining constituents (Mg2+, 

DIC, bioactive constituents, and particulate constituents) 

increased, most by large amounts (table 11; figs. 11, 12). In 

this group of constituents, developed watersheds tended to 

demonstrate a large increase in mean-annual yield relative 

to the forested watersheds once data were projected to a 

common mean-annual runoff. The only exception was DIC, 

for which developed and nondeveloped projected yields 

were intermingled. With the exception of SO4

2−, Mg2+, and 

DIC, the behavior of this group of constituents was consistent with our understanding of developed landscapes. 

Fertilizers and domestic wastes can contribute additional 

nutrients to developed watersheds, and increased erosion of 

deforested and agricultural landscapes can contribute to the 

particulate load.

Differences among watersheds for yields of sulfate, 

Mg2+, and DIC (alkalinity) may have geologic causes. 

DIC is elevated in the Canóvanas watershed (figs. 4, 12). 

Note the similarity in diagrams of Mg2+ (fig. 11) and DIC 

(fig. 12). Carbonate rocks, which weather more rapidly than 

most silicate rocks, are a potential source of elevated alkalinity. A small amount of carbonate rock is found in the volcaniclastic rocks within the Canóvanas watershed (Murphy 

and others, 2012), and Ca* yields (fig. 10) are not especially 

elevated; however, Mg2+ yields (fig. 11) are exceptionally 

high. Perhaps bedrock contains abundant, easily weathered 

mafic minerals, such as serpentine. Serpentinites are found 

in Puerto Rico, but none have been described within the 

Figure 13. A model-based comparison of annual runoff 

compared with the annual yields of dissolved bedrock (part A) 

and suspended bedrock (parts B, C, D), eastern Puerto Rico. The 

vertical gray line in parts A−D corresponds with 1,860 millimeters 

per year, solid squares with black plus symbols correspond to 

the projected annual yields, and points are the observed annual 

yields. The curves in part A are linear regressions based on the 

assumption that the annual yield of dissolved bedrock responds 

linearly with changing runoff. The curves in part B relate dissolved 

bedrock to suspended bedrock through an equilibrium model 

discussed in the section Calculating Steady-State Erosion from 

Dissolved Bedrock. This relation is derived from the curves in 

figures 13A, 10A, and 10D using equation 16. The relation is almost 

linear and there has been no convolution smoothing. The lines 

in part C are power-law models, based on equation 21, which 

plot as lines on graphs with log−log scales. The curves in part 

D are derived from the landslide model, equations 24 and 25. 

Curves in parts C and D were smoothed by using equation 26 to 

account for climate variability. The ratio of the power-law model 

of suspended bedrock to equilibrium model and landslide model to 

the equilibrium model are presented in parts E and F, respectively. 

Note that observed suspended-bedrock yields are typically far 

greater than yields calculated by equilibrium models (part B). 

Compared with expected equilibrium yield, yield in the Mameyes 

watershed is slightly elevated and in the Canóvanas more so; the 

Cayaguás, Icacos, and Guabá are physically eroding at rates that 

greatly exceed expected equilibrium (parts E and F).
Table 10. Watershed yields as observed in eastern Puerto Rico and adjusted to a common intermediate runoff (1,860 mm yr−1), on the basis of the rel

annual yield. [DIC, dissolved inorganic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon; DBrx, dissolved bedrock; SBrx, suspended bedrock; mm yr−1, millimeters per y

square kilometer per year; *, derived bedrock contribution] Watershed Runoff (mm yr–1) Na+ K+ Mg2+ Ca2+ Si(OH)4 Cl– SO42– NO3– NH4+ PO43– DIC DOC POC DBrx 

(kmol km–2 yr–1) (t k

Annual net yields Canóvanas 970 426 32 193 280 357 323 44 43 3.6 0.39 927 313 627 47 

Cayaguás 1,620 707 88 143 250 662 411 78 82 4.4 0.94 913 389 1,595 73 

Mameyes 2,750 654 51 183 439 739 542 85 26 2.1 0.41 1,174 573 503 87 

Icacos 3,760 745 58 157 264 854 585 54 45 3.7 0.18 868 837 1,788 78 

Guabá 3,630 735 51 113 170 778 615 43 39 2.6 0.18 587 574 1,954 67 

Annual net yields, adjusted to a common runoff Canóvanas 1,860 713 63 312 448 607 578 80 94 8.5 0.47 1,424 605 1,729 74 

Cayaguás 1,860 786 106 156 271 748 476 92 97 5.4 1.03 967 475 1,910 82 

Mameyes 1,860 515 35 148 366 552 393 62 15 1.3 0.35 1,023 380 155 65 

Icacos 1,860 486 32 104 183 652 342 25 19 1.9 0.09 666 323 253 55 

Guabá 1,860 504 27 95 152 645 411 21 20 1.0 0.10 541 227 196 49 

Projected change in net annual yields from current mean-annual runoff to a common mean-annual runoff Canóvanas 888 287 31 119 168 250 254 36 51 4.9 0.08 498 292 1,102 27 

Cayaguás 271 78 18 13 22 87 65 13 15 1.0 0.09 54 86 315 9 

Mameyes –891 –139 –16 –35 –73 –187 –149 –23 –11 –0.8 –0.06 –151 –194 –348 –22 

Icacos –1,891 –259 –26 –53 –81 –202 –243 –29 –25 –1.8 –0.09 –203 –514 –1,535 –23 

Guabá –1,531 –231 –24 –18 –18 –133 –204 –21 –19 –1.6 –0.08 –47 –346 –1,758 –18 

Watershed Runoff (mm yr–1) Na+ K+ Mg2+ Ca2+ Si(OH)4 Cl– SO42– NO3– NH4+ –PO43– DIC DOC POC DBrx 

(percent)

Percent change in net annual yields from current mean-annual runoff to a common mean-annual runoff

Canóvanas 91 67 97 62 60 70 79 82 117 137 21 54 93 176 58 

Cayaguás 17 11 21 9 9 13 16 17 19 23 10 6 22 20 13 

Mameyes –32 –21 –31 –19 –17 –25 –27 –27 –44 –38 –16 –13 –34 –69 –25 

Icacos –50 –35 –45 –34 –31 –24 –42 –54 –56 –48 –51 –23 –61 –86 –29 

Guabá –45 –31 –47 –16 –10 –17 –33 –50 –48 –61 –45 –8 –60 –90 –28 
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 237

Canóvanas watershed. The high SO4

2− in the Mameyes, 

Canóvanas, and Cayaguás rivers is likely influenced by 

bedrock, because SO4

2− yields of these watersheds exceed 

atmospheric inputs (table 3). Sulfide ores are found in the 

region (see Murphy and others, 2012). Additional inputs 

from human activities cannot be discounted, however, in the 

developed watersheds.

Landscape Equilibrium

The relation between annual runoff and annual yields 

of suspended bedrock are modeled in three ways: a standard 

power-law physical-erosion model (equation 21), a landslide physical-erosion model (equations 22, 24, 25), and as 

the result of equilibrium erosion (the equilibrium model, 

equation 16) (fig. 13). The landslide model provides a superior representation of erosion in the Icacos and Canóvanas 

watersheds, compared with the power-law model, and it is a 

somewhat more accurate model in the Cayaguás (fig. 13E,F). 

Neither model is superior in describing the Mameyes watershed. The equilibrium model bears little resemblance to 

observations or other models (fig. 13B). Observed annual 

suspended-bedrock yields were considerably in excess 

of expected equilibrium yields, except for the Mameyes 

(table 12, fig. 13B). 

Contemporary observed suspended-bedrock yields in all 

study watersheds exceeded equilibrium rates calculated by 

using Na2O (table 12). In the Mameyes watershed, physical erosion is modeled to be twice equilibrium when Na2

O 

is paired with SiO2, which is the preferred pairing, but it is 

modeled to be at equilibrium when CaO is paired with SiO2 

(table 12). The two models of physical erosion can be used to 

estimate the mean-annual runoff for which yields would be 

in equilibrium (fig. 13, table 13). In all watersheds, current 

mean-annual runoff is in excess of the equilibrium runoff. 

Such an excess could imply either that climate has become 

wetter or stormier, pushing the watersheds out of equilibrium, or that erosion processes have changed owing to landcover changes.

In the case of the volcanic watersheds, the Canóvanas and 

the Mameyes, the current mean-annual runoff deviates from 

equilibrium by less than 600 mm (table 13), and suspendedbedrock yields are considerably smaller than in the granitic 

watersheds (table 12). This smaller runoff is within the range 

of annual runoff variation for the Mameyes, and a climatic 

explanation is possible, although it is contrary to presumed 

long-term drying trends (Zack and Larsen, 1993). Although 

substantial parts of the Canóvanas are now in second growth 

(Murphy and others, 2012), much of the watershed has been 

deforested, farmed, and grazed, and its somewhat larger 

deviation from equilibrium could be caused by this history of 

land-cover change.

The Cayaguás and Icacos watersheds, both on granitic 

rocks, are much further from equilibrium. We know that 

deforestation and agriculture have accelerated physical eroTable 11.

sion in the Cayaguás; however, the Icacos, which has similar 

Interpretation of changes in watershed yields as observed in eastern Puerto Rico and adjusted to a common intermediate mean-annual runoff.

[*, calculated bedrock contribution; DIC, dissolved inorganic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon; DBrx, dissolved bedrock; SBrx, suspended bedrock; <, less than]

Runoff Na+ K+ Mg2+ Ca2+ Si(OH)4 Cl– SO42– NO3

– NH4

+ PO43– DIC DOC POC DBrx SBrx Na* Ca*

Percent change in data ranges normalized to midrange (for all watersheds), before and after projection of data to a common mean-annual runoff

Before 118 55 94 52 89 82 62 66 103 73 135 67 91 118 60 118 82 94

After 0 47 118 107 99 30 51 125 147 158 168 90 91 170 51 170 85 102

Change –100 –14 26 106 11 –63 –17 89 43 117 24 35 –0 44 –14 44 4 8

Degree of overlap of forested and developed watersheds1

Before –1 0 0 0 0 –1 –1 0 0 0 0 0 –1 0 0 0 0 0

After 0 1 1 1 0 0 1 1 2 2 1 0 1 2 1 2 1 0

Change –1 1 1 1 0 –1 2 1 2 2 1 0 2 2 1 2 1 0

Degree of overlap of granitic and volcanic watersheds2

Before 0 1 1 –1 –1 0 0 0 0 0 0 –1 0 2 0 2 1 –1

After 0 0 0 0 –1 1 0 0 0 0 0 –1 0 0 0 0 0 –1

Change 0 –1 –1 –1 1 1 0 0 0 0 0 1 0 –1 0 –1 –1 1 1Before, after: −2, 2•maximum forest<minimum developed; −1, maximum forest<minimum developed; 0, range overlap; 1, maximum developed<minimum forest; 2, 2•maximum developed<minimum 

forest. Change: −1, from nonoverlap to overlap; 0, overlap before and after; 1, similar lack of overlap before and after; 2, a reversal or loss in lack of overlap.

2Before, after: −2, 2•maximum granitic<minimum volcanic; −1=maximum granitic<minimum volcanic; 0, range overlap; 1, maximum granitic<minimum volcanic; 2, 2•maximum granitic< minimum 

volcanic. Change: −1, from nonoverlap to overlap; 0, overlap before and after; 1, similar lack of overlap before and after; 2, a reversal or loss in lack of overlap.
238 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 12. Summary of landscape equilibrium properties, eastern Puerto Rico.

[DBrx, dissolved bedrock; SBrx, suspended bedrock; mm yr−1, millimeters per year; t km−2 yr−1, metric tons per square kilometer per year]

River

Observed

mean annual

runoff

(mm yr–1)

Observed

DBrx yield

 (t km–2 yr–1)

Observed

SBrx yield

 (t km–2 yr–1)

Power-law

SBrx yield

 (t km–2 yr–1)

Landslideday SBRx

yield

 (t km–2 yr–1)

Equilibrium

SBrx yield

based on

Na2

O

 (t km–2 yr–1)

Ratio of observed

to equilibrium

SBrx yields based

on Na2

O

Equilibrium

SBrx yield

based on CaO

 (t km-2 yr-1)

Ratio of

observed to

equilibrium

SBrx yields

based on CaO

Canóvanas 970 47.0 355 299 184 89 3.99 154 2.30

Cayaguás 1,620 72.7 1,981 1,219 1,031 233 8.48 162 12.22

Mameyes 2,750 87.3 270 230 228 106 2.56 265 1.02

Icacos 3,760 78.1 1,985 1,555 1,481 154 12.91 130 15.22

Guabá 3,630 67.0 1,590 1,648 1,428 137 11.58 60 26.69

Table 13. Observed mean-annual runoff compared with equilibrium runoff for power-law and landslide day erosion models in study 

watersheds, eastern Puerto Rico.

[mm yr−1, millimeters per year]

River

Observed mean

annual runoff

(mm yr–1)

Equilibrium runoff,

power-law model

(mm yr–1)

Equilibrium runoff,

landslide model

(mm yr–1)

Runoff in excess

of equilibrium for

power-law model

(mm yr–1)

Runoff in excess

of equilibrium for

landslide model

(mm yr–1)

Canóvanas 970 430 270 540 700

Cayaguás 1,620 670 690 950 930

Mameyes 2,750 2,220 2,240 530 510

Icacos 3,760 1,540 2,370 2,220 1,390

Guabá 3,630 820 200 2,810 3,430

Table 15. Excess particulate organic carbon yields from the equilibrium model of study watersheds, eastern Puerto Rico.

[DOC, dissolved organic carbon; POC, particulate organic carbon; mm yr−1, millimeters per year; kmol km−2 yr−1, kilomoles per square kilometer per year]

River

Observed mean

annual runoff

(mm yr–1)

Observed

DOC yield

 (kmol km–2 yr–1)

Observed

POC yield

 (kmol km–2 yr–1)

Equilibrium

POC yield

 (kmol km–2 yr–1)

POC yield in

excess of equilibrium

 (kmol km–2 yr–1)

POC yield in

excess of equilibrium

(percent)

Canóvanas 970 313 627 157 470 300

Cayaguás 1,620 389 1,595 188 1,407 750

Mameyes 2,750 573 503 197 306 160

Icacos 3,760 837 1,788 139 1,650 1,190

Guabá 3,630 574 1,954 169 1,786 1,060

Table 14. Total denudation compared with equilibrium denudation for current mean-annual runoff and projected to a common runoff in 

study watersheds, eastern Puerto Rico.

[mm yr−1, millimeters per year; m Ma−1, meters per mega-annum (106 years)]

River

Observed mean

annual runoff

(mm yr–1)

Observed total

denudation

(m m.y.–1)

Equilibrium

denudation,

modeled

(m m.y.–1)

Projected total denudation 

at 1,860 mm yr–1

annual runoff

(m m.y.–1)

Projected equilibrium

denudation at 1,860 mm yr–1

annual runoff

(m m.y.–1)

Equilibrium denudation

at equilibrium

annual runoff

(m m.y.–1)

Canóvanas 970 152 51 614 72 36

Cayaguás 1,620 775 116 643 125 84

Mameyes 2,750 135 73 45 65 68

Icacos 3,760 779 87 36 66 72

Guabá 3,630 625 77 157 49 72
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 239

bedrock and is forested, is even further from equilibrium. 

For both rivers, the current mean-annual runoff deviates 

from equilibrium by 900 mm to 2,200 mm. Denudation rates 

(table 14) that are projected to a common runoff value produce equilibrium rates for the forested watersheds that exceed 

the predicted denudation rate; these conditions are those 

needed to develop thicker soils. 

Excess POC yields are calculated by assuming that the 

equilibrium POC yield is in the same proportions compared 

with equilibrium solid yield as the observed POC yield is to 

the observed solid yield (table 15). The excess POC yield, 

which reflects presumed landscape disequilibrium, is the 

largest part of the organic carbon budget in all study watersheds except the Mameyes.

Summary and Conclusion: General 

Landscape Disequilibrium and the 

Carbon Cycle

The biogeochemistry and physical erosion of four 

research watersheds were examined through the development 

of simple models that relate observed annual runoff to annual 

yields for various constituents. Several derived constituents 

were developed, including the bedrock-derived yields of all 

measured constituents, dissolved silicate bedrock, suspended 

silicate bedrock, and the equilibrium production of bedrockderived solids. Several constituents are largely atmospheric 

in origin (table 3)—Na+, Cl−, SO4

2−, NO3− plus NH4+, DOC, 

and POC. Between 10 and 50 percent of K+ and Mg2+ originate in the atmosphere. Less than 10 percent of Ca, Si(OH)4

, 

dissolved bedrock, and suspended bedrock originates in the 

atmosphere. Conductivity, Cl−, and Si(OH)4 proved to be an 

excellent surrogate suite of measurements to use for estimating dissolved bedrock. 

At the beginning of the paper, six questions are asked; 

they can now be answered.

What are the measured rates of chemical and 

physical denudation and how do these relate to 

mean-annual runoff in each watershed?

Rates of chemical and physical denudation are summarized in table 14. For dissolved constituents, the relation 

between annual runoff and annual yields is linear (figs. 10–12). 

This relation is consistent with results of other studies (White 

and Blum (1995) and Godsey and others (2009)). Intercepts 

do not intersect zero, an indication that processes must change 

as mean-annual runoff approaches zero. Suspended bedrock 

and particulate organic carbon can be modeled either by a 

power-law relation or by a model based on accounting for the 

total runoff of all days having storms large enough to produce 

landslides (fig. 13C, D).

How do watersheds compare in weathering, 

erosion, carbon export, and nutrient export with 

regard to geology and land cover? Do geology 

and land cover matter?

Watersheds were compared in two ways, at their current 

conditions of mean-annual runoff and by using the relation 

between annual runoff and annual yield to estimate meanannual yields at a common, intermediate, mean-annual runoff 

of 1,860 mm yr−1. Current mean-annual runoffs differ widely 

among the watersheds, because the forested watersheds are relatively wet and the developed watersheds are considerably drier.

Before projection to a common runoff, dissolved bedrock 

yields, about 47 to 87 t km−2 yr−1, differed by a factor of less 

than 2 among all watersheds but were greater for the wetter watersheds. Silica, Cl−

, and DOC also demonstrated little 

relation to land cover, mean-annual runoff, or bedrock type. 

Before projection to a common yield, suspended-bedrock 

yields, ranging from 600 to 2,350 t km−2 yr−1, were threefold to 

fourfold as great for the watersheds draining granitic rocks.

Upon projection to a common mean-annual runoff, 

the yield ranges of dissolved bedrock, Na+, Si(OH)4, and 

Cl− became more compact, whereas the yield ranges of 

DOC and Ca2+ changed little. These constituents are primary 

measures of chemical weathering, seasalt input, and biological activity on the landscape. Narrow ranges indicate that 

differences in bedrock and land cover did not exert a strong 

influence on how these constituents were processed in this 

landscape. DIC, Mg2+, SO4

2−, and Na* showed systematically greater yields in a few watersheds (figs. 10–12), 

probably for geological reasons. The projected yields of 

biologically active constituents (K+, NO3

−, NH4+, PO43−), and 

particulate constituents (suspended bedrock and POC) were 

considerably greater on developed landscapes (table 10, figs. 

11–13), consistent with the effects of land clearing, fertilizer 

application, and minor domestic or industrial waste inputs. 

DOC may show a slight human effect in that concentrations in the developed watersheds were greater than in the 

forested ones; the difference is not as pronounced as with 

the previous constituents.

Is the landscape in a state of dynamic 

equilibrium? In essence, are the Luquillo 

Mountains a model of a quiescent natural 

landscape or are they changing?

The calculation of equilibrium denudation rates by using 

measurements of silica, sodium, and total dissolved bedrock 

appears to be robust (Stallard, 1995a). A notable feature of the 

approach was the use of conductivity, Cl−, and Si(OH)4 measurements, when they were appropriately calibrated, to estimate total dissolved bedrock in samples for which it could not 

be measured because of staff and laboratory limitations. This 

use of surrogates allowed thousands of additional estimates of 

total dissolved bedrock. Adding Na+ to this suite, which now 
240 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

also includes suspended sediment, K+, and DOC, would allow 

especially rigorous characterizations of chemical and physical erosion and carbon export from certain watersheds—those 

where chemical weathering generates erodible solids, and 

those solids have low ratios of Na2

O to SiO2 compared with 

ratios in bedrock. Such settings would be environments with 

moderate to intense chemical weathering. Use of total dissolved solids rather than total dissolved bedrock erroneously 

doubles estimates of solute erosion, and use of suspended sediments (dried at 105°C) rather than suspended bedrock (heated 

to 550°C) overestimates physical erosion by about 10 percent.

Except possibly for the Mameyes watershed, the current 

landscape is not in a state of dynamic equilibrium as estimated 

using the method of Stallard (1995a,b), based on concentrations of silicon and sodium in bedrock, in river-borne solids, 

and in solution (fig. 12, tables 12–14). The estimates of equilibrium rates are consistent, but slightly greater than, estimates 

obtained by using cosmogenic 10Be for the Icacos (Brown and 

others, 1995) and for the Cayaguás (Brown and others, 1998). 

Observed suspended-bedrock yields are fivefold to tenfold 

as great as equilibrium yields on the granitic rocks and only 

slightly elevated on the volcanic rocks. The Mameyes watershed appears to be relatively close to equilibrium. 

Brown and others (1995) observed that the Icacos 

watershed is in a state of dynamic equilibrium, on the basis of 

a cosmogenic 10Be denudation rate of 43 millimeters per thousand years (mm k.y.−1) that matched an estimate of denudation of 75 mm k.y.−1, based on limited data. The 75 mm k.y.−1

denudation rate is clearly too low, given rates estimated from 

the Puerto Rico WEBB dataset of 780 mm k.y.−1. It is more 

likely that the Icacos recently departed from equilibrium and 

that the cosmogenic 10Be in sediments and ridge-crest soils of 

the Icacos record its former equilibrium denudation rate much 

as Brown and others (1998) demonstrated for the Cayaguás, 

where the preagricultural equilibrium rate recorded in sediments is estimated to be 85 mm k.y.−1.

The equilibrium denudation rates at the annual runoff 

rates where the physical-erosion models match the equilibrium rates of sediment production in the Icacos (table 12) 

are somewhat greater than those based on cosmogenic 10Be, 

indicating that the denudation rates recorded by 10Be for the 

Icacos were established under even drier or more quiescent 

conditions. Clearly, the ridgetops in the Icacos watershed 

that were sampled for 10Be in soils must still reflect the old 

equilibrium conditions as evidenced by the clean bioturbation 

profiles reported in Brown and others (1995). The watershed 

as a whole, however, is not in this quiescent state.

What does the present dynamic state of the 

landscape imply regarding past and future 

response to climate change?

Deforestation and agriculture can easily explain disequilibrium erosion in the two developed watersheds. The 

disequilibrium is so great for the forested watershed on 

granitic rock (the Icacos) that other explanations are needed. 

The equilibrium denudation rates at the annual runoff rates 

where the models predict equilibrium (table 14) are somewhat 

greater than those based on cosmogenic 10Be for the Icacos, 

indicating that the thick soils of this watershed were developed 

under especially quiescent conditions of physical erosion, 

compared with today. If the rate change is to be attributed to 

human activities, trails to mines have crossed the landscape for 

perhaps 500 years, and paved roads were built into the Icacos 

watershed in the middle 20th century (Larsen, 2012; Murphy 

and others, 2012). Alternatively, the forest in the Icacos 

may be degrading, perhaps from increased nitrogen loading (Stallard, 2012) or invasion by species such as bamboo 

(Murphy and others, 2012), thereby failing to inhibit erosion, 

particularly through an increasing rate of landsliding. Climate 

change, perhaps by increasing the frequency of huge landslide-generating storms, could have caused such a change in 

erosional style in the forested granitic watersheds; the weather 

record is not long enough to test this suggestion.

What are the rates of export of carbon in its 

various forms; how do these rates relate to 

runoff, and is this carbon being replaced by new 

photosynthetically fixed carbon?

Rates of carbon export as dissolved-inorganic, dissolvedorganic, and particulate-organic carbon are summarized as 

yields in table 15. The POC yields exceed combined dissolved 

carbon yields in the granitic watersheds, but they are considerably less than the dissolved yields in the volcanic watersheds. 

Annual yields of DIC and DOC relate linearly to annual 

runoff, whereas POC has a steep power-law-style increase 

with increasing runoff. The POC yields are tied to sediment 

yields that are, in turn, far in excess of equilibrium yields. This 

excess implies that much of the present carbon transport in 

eastern Puerto Rico is the result of erosion in excess of equilibrium rates as hypothesized by Stallard (1998). In essence, 

soils that have developed for thousands of years are eroding 

more rapidly than they are now forming. Carbon accumulation is quite rapid, however, and in a landslide scar half of 

the carbon is regenerated in 80 years and all of it in about 

200 years (fig. 2). Thus, eroding carbon is being replaced with 

new photosynthetic carbon from the atmosphere. 

Are rates of carbon processing through the 

landscape notable on the time scales of humaninduced climate change? 

Estimates of the combined yields of all forms of carbon 

from the WEBB watersheds (table 3) range from about 

1,800 kmol km−2 yr−1 (22 t km−2 yr−1 C) to 3,300 kmol km−2

yr−1 (40 t km−2 yr−1 C). The largest observed yield of organic 

carbon (DOC+POC), 2,600 kmol km−2 yr−1 (31 t km−2 yr−1 C), 

is 2.5 to 4 percent of net primary productivity for tropical 
Weathering, Landscape Equilibrium, and Carbon in Four Watersheds in Eastern Puerto Rico 241

forests, an amount that is relatively small and sustainable. 

For reference, these POC yields from the granitic watersheds are about half of those estimated by Hilton and others 

(2008a) for a landslide-prone shalely landscape in New 

Zealand. For a process to be important to the carbon cycle, 

it would have to scale to about 6.7 t km−2 yr−1 C for a global 

terrestrial phenomenon, or about 250 t km−2 yr−1 C if confined to humid tropical mountains. The perturbed POC yields 

(table 15), 300 kmol km−2 yr−1 (6 t km−2 yr−1 C) to 1,700 kmol 

km−2 yr−1 (22 t km−2 yr−1 C), are close to and greater than 

6.7 t km−2 yr−1 C but far less than 250 t km−2 yr−1 C.

Human-accelerated POC burial is a global phenomenon (Stallard, 1998), and these excess POC yields are 

consistent with it. The lack of evidence in eastern Puerto 

Rico of human effects for either DOC production or silicate 

weathering suggests that although the DIC and DOC yields 

of tropical rivers are large, they are of secondary interest to 

research of the anthropogenically perturbed carbon cycle at 

a century time scale.

Eastern Puerto Rico contains a fascinating landscape 

for examining biogeochemical processes operating at maximal rates. The models presented here, however, represent 

considerable simplifications of these complex interacting 

processes. It is hoped that these results inspire others to 

develop physically based models and geochemical tests that 

better define the processes and how they respond to environmental and climate change. It is a setting that may have 

been important to the climate history of our planet, and it 

probably is representative of the sources of a huge fraction of the clastic sediments that form the geologic record. 

From the perspective of observation, this landscape, except 

perhaps for the Mameyes watershed, is far from equilibrium 

and probably is more representative of conditions during 

major erosion cycles in the Earth’s history than the more 

quiescent times in between.

Acknowledgments

This work was supported through the United States Geological Survey Water, Energy, and Biogeochemical Budgets 

program (Larsen and others, 1993; Lins, 1994). This chapter 

benefited from reviews by Troy Baisden, Alex Blum, and 

Jamie Shanley. During the last 22 years, management support 

of this long-term study, from concept to integrated product, 

has been greatly appreciated.

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Weathering Processes in the Icacos and 

Mameyes Watersheds in Eastern Puerto 

Rico

By Heather L. Buss and Arthur F. White

Chapter I of

Water Quality and Landscape Processes of Four Watersheds in 

Eastern Puerto Rico

Edited by Sheila F. Murphy and Robert F. Stallard

Professional Paper 1789–I

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract.......................................................................................................................................................253

Introduction.................................................................................................................................................253

Weathering Processes at the Bedrock Interface ................................................................................256

Weathering Processes in the Saprolite and Soil .................................................................................260

Summary......................................................................................................................................................260

References..................................................................................................................................................260

Figures

1. Map showing location and geology of Icacos and Mameyes watersheds....................255

2. Photograph showing a corestone of Río Blanco quartz diorite that is 

weathering spheroidally..........................................................................................................256

3. Schematic diagram of the weathering profile in the Icacos watershed.........................257

4. Backscattered electron images of thin sections taken from a corestone-rindlet 

complex, Icacos watershed....................................................................................................258

Abbreviations Used in This Report

≈ nearly equal to

30Si ratio of stable isotopes of silicon, 30Si/28Si, relative to a standard

Å angstrom

μmol mol–1 micromoles per mole

cm centimeter

km2 square kilometer

m meter

Ma million years old

m m.y.–1 meters per million years

mol m–2 s–1 moles per square meter per second

ICP–OES inductively coupled plasma–atomic emission spectroscopy

WEBB Water, Energy, and Biogeochemical Budgets

XRD X-ray diffraction
Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

Area

square kilometer (km2

) 0.3861 square mile (mi2)

Weathering rate

meters per million years (m m.y.−1) 3.281 feet per million years (ft m.y.−1)

Weathering Processes in the Icacos and Mameyes 

Watersheds in Eastern Puerto Rico

By Heather L. Buss and Arthur F. White

Abstract

Streams draining watersheds of the two dominant lithologies (quartz diorite and volcaniclastic rock) in the Luquillo 

Experimental Forest of eastern Puerto Rico have very high 

fluxes of bedrock weathering products. The Río Blanco quartz 

diorite in the Icacos watershed and the Fajardo volcaniclastic 

rocks in the Mameyes watershed have some of the fastest 

documented rates of chemical weathering of siliceous rocks 

in the world. Rapid weathering produces thick, highly leached 

saprolites in both watersheds that lie just below the soil and 

largely isolate subsurface biogeochemical and hydrologic 

processes from those in the soil. The quartz diorite bedrock 

in the Icacos watershed weathers spheroidally, leaving large, 

relatively unweathered corestones that are enveloped by 

slightly weathered rock layers called rindlets. The rindlets 

wrap around the corestones like an onionskin. 

Within the corestones, biotite oxidation is thought to 

induce the spheroidal fracturing that leads to development 

of rindlets; plagioclase in the rindlets dissolves, creating 

additional pore spaces. Near the rindlet-saprolite interface, 

the remaining plagioclase dissolves, hornblende dissolves to 

completion, and precipitation of kaolinite, gibbsite, and goethite becomes pervasive. In the saprolite, biotite weathers to 

kaolinite and quartz begins to dissolve. In the soil layer, both 

quartz and kaolinite dissolve. The volcaniclastic bedrock of 

the Mameyes watershed weathers even faster than the quartz 

diorite bedrock of the Icacos watershed, leaving thicker saprolites that are devoid of all primary minerals except quartz. The 

quartz content of volcaniclastic bedrock may help to control 

watershed geomorphology; high-quartz rocks form thick saprolites that blanket ridges. 

Hydrologic flow paths within the weathering profiles 

vary with total fluid flux, and they influence the chemistry of 

streams. Under low-flow conditions, the Río Icacos and its 

tributaries are fed by rainfall and by groundwater from the 

fracture zones; during storm events, intense rainfall rapidly 

raises stream levels and water is flushed through the soil as 

shallow flow. As a result, weathering constituents that shed 

into streamwaters are dominated by rindlet-zone weathering 

processes during base flow and by soil weathering processes 

during stormflow. The upper reaches of the Mameyes watershed are characterized by regolith more than 35 meters thick 

in places that contains highly fractured rock embedded in its 

matrix. Weathering contributions to stream chemistry at base 

flow are predicted to be more spatially variable in the Mameyes watershed than in the Icacos watershed owing to the 

more complex subsurface weathering profile of the volcaniclastic bedrocks of the Mameyes watershed. 

Introduction

Biogeochemical and physical weathering processes in tropical watersheds produce most of the solutes and sediments discharged to the oceans. These watersheds contribute 50 percent 

of the water, 38 percent of the dissolved ions, and 65 percent of 

the dissolved silica in the oceans, despite covering only about 

25 percent of the terrestrial surface (Stallard and Edmond, 1983; 

Meybeck, 1987). Thus, relative to their area, tropical systems 

are disproportionately important in terms of weathering, erosion, and global CO2

 cycles. In addition to influencing stream 

and ocean chemistry and sediment loads, weathering processes 

exert control over chemical transport through soils and regolith 

(such as saprolite), soil and saprolite formation rates, mineral 

nutrient availability, and microbial growth rates. Weathering 

processes are complex phenomena comprising coupled chemical, physical, and microbial processes, driven by water and 

oxygen fluxes. These coupled processes also control the shape, 

position, stability, and rate of retreat of the bedrock weathering 

interface, which in turn affect topography and erosion. 

To date (2012), the majority of research into weathering 

processes in the four U.S. Geological Survey Water, Energy, 

and Biogeochemical Budgets program (WEBB) watersheds 

in eastern Puerto Rico has focused on the Icacos watershed. This watershed is an ideal site for studying tropical 

weathering processes, because it is relatively pristine, small 

(3.3 square kilometers (km2

)), and lithologically homogeneous (99 percent Río Blanco quartz diorite; Murphy and 

others, 2012). Thick saprolite profiles on ridge tops allow 

analysis of deep weathering and the cycling of chemical 

elements—processes that are not directly coupled with the 

surficial environment. The homogeneity of the quartz diorite 

(relative to the volcaniclastic units that dominate the Mameyes watershed) also considerably simplifies interpretation 

of the weathering profiles. Currently, a subwatershed of the 
254 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Mameyes watershed, Bisley 1 (fig. 1), is being investigated 

and compared with the Icacos watershed in order to better 

understand the influence of lithology on weathering processes, weathering fluxes, and mineral nutrient availability. 

Weathering processes within the Bisley 1 watershed are 

spatially more variable than in the Icacos watershed owing 

to the more varied lithology of the parent rock. The Bisley 1 

watershed is underlain by the Fajardo Formation, a marinebedded volcaniclastic bedrock that is mapped as having 

several units: an upper thin-bedded tuffaceous siltstone and 

sandstone, an upper thick-bedded tuff, a lower thin-bedded 

tuffaceous sandstone and siltstone, a lower thick-bedded 

tuff, and an undivided unit (Briggs and Aguilar-Cortés, 

1980). Bed thicknesses differ, and coarse tuff, breccias, and 

cherty or calcareous siltstone beds are found in some units. 

Although the few bedrock exposures in the Bisley watershed 

appear homogeneous, the watershed is underlain by the upper 

thick-bedded tuff unit, which includes some breccias, lithic 

andesitic clasts, calcareous siltstone, and some pumice and red 

scoria. Spheroidally weathered, marine-bedded, volcaniclastic 

sandstones are occasionally exposed by landslides. Weathered, 

quartz-rich clasts, which may be relict quartz veins, are found 

in the Bisley regolith. 

In contrast to the high quartz content of the Río Blanco 

quartz diorite (≈20–30 percent by weight; White and others, 1998; Turner and others, 2003; Buss and others, 2008), 

which creates a weathering-resistant matrix that supports 

isovolumetric weathering to form saprolite, the bluish-gray 

tuffaceous rock commonly found in the Bisley streambeds 

contains very little quartz (≈7 percent, as analyzed by X-ray 

diffraction (XRD) and quantified using the program RockJock as described by Eberl, 2003). Without a quartz matrix 

to support the volume during chemical weathering, these 

volcaniclastic rocks likely dissolve completely or collapse in 

volume during weathering. However, quartz-rich saprolites (as 

much as 65 percent) greater than 35 meters (m) deep are found 

in the Bisley watershed, indicating formation from a parent 

rock with a higher quartz content than the rocks commonly 

encountered in the Bisley watershed today. Similar rocks, but 

with higher quartz content (≈22–27 percent) may be exposed 

after relatively deep landslides in the Bisley 1 watershed and 

within a few kilometers to the east. These rocks may have 

been hydrothermally altered by infiltrating Si-rich fluids during intrusion of the Río Blanco stock. Thus, saprolite-mantled 

ridges in the Bisley watershed likely formed from these relatively quartz-rich rocks. 

Both the quartz-rich and quartz-poor volcaniclastic rocks 

in the Bisley watershed contain similar amounts of chlorite 

(20–25 percent) and pyroxene (5–10 percent), but they diverge 

in feldspar content: about 35 and 50 percent, respectively. 

The Bisley saprolite contains ≈15–65 percent microcrystalline quartz into which are embedded sparse, small, weathered, 

quartz-rich clasts (as much as ≈80 percent quartz) that may 

represent relict quartz veins. Typically, quartz content by 

weight is higher in weathered materials, such as saprolite, 

relative to the parent rock. When other minerals weather and 

elements such as Ca, Na, Mg, and K are lost from the system, 

quartz makes up a larger proportion of the remaining mass 

than it did originally. Similarly, elements that are relatively 

immobile during chemical weathering, such as Ti and Zr, are 

also more abundant in saprolite relative to parent rock in both 

the Bisley watershed (bulk chemistry measured by inductively 

coupled plasma–atomic emission spectroscopy (ICP–OES) 

after lithium metaborate fusion) and Icacos watershed (White 

and others, 1998; Buss and others, 2005, 2008).

The Fajardo Formation, which underlies the Bisley watershed, is older than the granitic intrusion underlying the Icacos 

watershed, ≈100 million years old (Ma) as compared with 

47 Ma, respectively (Jolly and others, 1998; Smith and others, 

1998). Regolith in the Bisley 1 watershed is also thicker than 

in the Icacos watershed: about 10–20 m and 5–9 m on ridge 

tops in the Bisley and Icacos watersheds, respectively (White 

and others, 1998; Schellekens and others, 2004; Buss and others, 2005). Borehole drilling on a road in the Bisley 1 watershed recovered regolith cores more than 35 m deep, within 

which were embedded highly fractured rock in varying stages 

of weathering. These cores may contain a combination of 

transported material (such as from old landslides) and saprolite 

(which is formed in place, by definition). A 29-m-deep borehole on a road in the Icacos watershed passed through 5 m of 

saprolite, followed by a fractured-bedrock zone less than 1 m 

thick, and finally through 23 m of solid, unfractured, quartz 

diorite bedrock. A saprolite-production rate (or bedrockweathering rate) of 211 meters per million years (m m.y.–1) 

has been estimated for a Bisley ridgetop (Dosetto and others, 

2011), which is considerably faster than the 43–58 m m.y.–1

rate estimated for the Icacos watershed (Brown and others, 

1995; White and others, 1998; Riebe and others, 2003, 2004). 

The bedrock weathering rates of both watersheds are also 

high relative to weathering rates of siliceous bedrock (such 

as basalts, andesites, granitoids, and schists) in watersheds 

around the world (White and others, 1998; Freyssinet and 

Farah, 2000; Navarre-Sitchler and Brantley, 2007; Dosseto 

and others, 2011). 

The Bisley saprolite is about 60–80 weight percent clay 

(primarily microcrystalline, disordered kaolinite) and is completely devoid of primary minerals except quartz, indicating an 

advanced stage of weathering (mineralogy analyzed by XRD). 

The kaolinite contains impurities of Mg, Fe, and K that are 

released as the kaolinite itself dissolves with time. The Icacos 

saprolite contains primary biotite, which releases appreciable 

Mg, K, and Fe during transformation to kaolinite throughout 

the saprolite (Murphy and others, 1998). 

The remainder of this chapter discusses weathering 

processes in the Icacos watershed (fig.1), which has been 

studied in much greater detail than the Mameyes watershed. 

The Río Blanco quartz diorite weathers in place along joint 

planes, forming rounded bedrock corestones (as much as 

about 2 m diameter), which then weather spheroidally (fig. 2). 

Onionskin-like layers (rindlets) form on the corestones during 

spheroidal weathering; continued weathering of the corestones produces a thick (about 2–8 m) saprolite overlain by 
Weathering Processes in the Icacos and Mameyes Watersheds in Eastern Puerto Rico 255

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_I_figures\BW_figures\BWfigure_01I.ai

0

0

10 20 KILOMETERS

10 20 MILES

0

0 0.5

0.5 1 KILOMETER

1 MILE

EXPLANATION

Luquillo Experimental Forest

Study watershed boundary

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

San Juan

65°48'

18°20'

18°19'

18°18'

18°17'

65°47' 65°46' 65°45' 65°44'

Study watershed boundary

Note: Geology modified from U.S. Geological Survey

 Open-File Report 98–38, Bawiec, 1998

EXPLANATION

Metamorphosed volcaniclastic rock

Río Blanco quartz diorite

Volcaniclastic rock

Icacos

Mameyes

Icacos

Guabá

Bisley 1

67°00’ 66°30’

18°30’

18°00’

66°00’

C A R I B B E A N S E A

A T L A N T I C O C E A N

Mameyes

Geology

PUERTO RICO

Figure 1. Location and geology of Icacos and Mameyes watersheds. (Geology adapted from Bawiec (1998); metamorphic contact 

from Seiders (1971) and Briggs and Aguilar-Cortés (1980), with digital interpretation by F.N. Scatena and Miguel Leon, University of 

Pennsylvania Luquillo Critical Zone Observatory; Bisley 1 watershed boundary courtesy Miguel Leon, University of Pennsylvania; 

Guabá watershed boundary courtesy Sheila Murphy, U.S. Geological Survey.)
256 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_I_figures\BW_figures\BWfigure_02I.ai

Figure 2. Sampled roadcut 600 meters southeast of the mouth of the Icacos 

watershed showing a 2-meter diameter corestone of Río Blanco quartz 

diorite that is weathering spheroidally. A 49-centimeter-thick zone of partially 

weathered rock layers called rindlets surrounds the corestone. The string grid 

(at an angle to the camera) partitions the outcrop into 1-square-meter blocks.

thin (about 0.5–1 m) soils. The resulting weathering profile is 

characterized by multiple interfaces that define distinct zones 

in which weathering processes differ and, in some cases, operate independently of the processes operating within the other 

zones. Here we will discuss weathering processes in individual 

zones from the fresh bedrock upward to the surface soil. 

Weathering Processes at the Bedrock 

Interface

Weathering of fresh, intact bedrock is the initial step in 

the soil-forming process and is the primary contributor of 

chemical solutes to the hydrosphere—groundwater, streams, 

precipitation, and oceans. Interfaces where intact bedrock 

weathers to disaggregated material, such as saprolite, are controlled by coupled chemical, physical, and microbial processes 

driven by the flux of reactants to the fresh rock. The rate at 

which the bedrock weathers determines the rate at which the 

interface moves downward, the rate of formation of the saprolite, the flux of mineral nutrients to the biosphere, and the flux 

of solutes to the hydrosphere.

A spheroidally weathering interface, such as in the Río 

Blanco quartz diorite, provides a model environment for 

studying the coupling of physical and chemical processes 

involved in rock weathering and saprolite formation. Spheroidal weathering is a well-known, but poorly understood, form 

of exfoliation that has been linked to chemical weathering 

(for example, Farmin, 1937; Ollier, 1971; Fletcher and others, 

2006; Buss and others, 2008). Spheroidal weathering begins 

when bedrock joint planes are exposed to weathering, which 

rounds off corners in place to form boulders called corestones. 

Corestones then fracture parallel to and just below their surface, forming concentric layers called rindlets (Bisdom, 1967; 

Ollier, 1971; Fritz and Ragland, 1980; Turner and others, 

2003; Buss and others, 2005, 2008). A complete set of rindlets 

(from corestone to saprolite) comprise a rindlet zone (fig. 3). 
Weathering Processes in the Icacos and Mameyes Watersheds in Eastern Puerto Rico 257

Rindlets are slightly weathered, but most are still relatively 

hard. The hand-friable outer rindlets (at the saprolite interface) 

are sometimes referred to as saprock, a material intermediate between saprolite and bedrock. Where rindlet zones are 

exposed by erosion (which is dominated by landslides; Larsen 

and Torres-Sánchez, 1998), they tend to slough off, leaving 

bare corestones. Corestone-rindlet complexes that remain in 

place at depth eventually weather to saprolite.

Expansion of a mineral during chemical weathering has 

been identified as a possible cause of spheroidal fracturing (for 

example, Larsen, 1948; Simpson, 1964; Eggler and others, 

1969; Isherwood and Street, 1976; Begle, 1978; Chatterjee 

and Raymahashay, 1998; Fletcher and others, 2006; Buss and 

others, 2008). Although isovolumetric weathering is required 

to produce the overlying saprolite, Fletcher and others (2006) 

demonstrated quantitatively that only a very small net volume 

change is required to generate sufficient elastic strain energy 

to produce spheroidal fracturing as observed in the Río Blanco 

quartz diorite. 

The rindlet zone in the Río Blanco quartz diorite is 

composed of a sequence of rindlets each about 2.5 centimeters 

(cm) thick, which are slightly weathered relative to the parent 

corestones (fig. 4). Within the rindlet zone, the transformation 

from bedrock to saprolite is accomplished. This transformation 

includes development of pore space, essentially complete loss 

(≈99 percent) of the primary minerals plagioclase, hornblende, 

chlorite, and apatite; oxidation of biotite; and formation of 

the secondary minerals kaolinite, gibbsite, and goethite (Buss 

and others, 2008, 2010). These weathering processes occur 

at different points within the rindlet zone but are interrelated. 

The transformation of bedrock to saprolite begins within the 

corestones. Aqueous oxygen contained in pore waters diffuses into the unweathered corestone and oxidizes Fe(II) in 

the biotite (Buss and others, 2008), which then expands in the 

d(001) direction from 10.0 angstroms (Å) to 10.5 Å (Dong and 

others, 1998; Murphy and others, 1998). This modest expansion builds up elastic strain energy within the rock (Buss and 

others, 2008). When the elastic-strain-energy density reaches 

fracture surface energy, fracturing occurs, creating a rindlet 

(Fletcher and others, 2006). The oxidation of Fe(II) in biotite 

is initiated when dissolved oxygen diffuses into the corestones; thus the weathering rate depends on the concentration 

of oxygen in pore waters. This concentration is expected to 

decrease with increasing saprolite thickness, creating a positive feedback between erosion and weathering rates that may 

maintain profiles at steady state throughout geologic time 

(Fletcher and others, 2006). 

A weathering front is a boundary layer, or zone, that separates weathered material from fresh rock. It is often defined 

in terms of a weathering reaction (for example, dissolution of 

a given mineral) such that the weathering front encompasses 

the entire zone where the weathering reaction occurs. For a 

mineral weathering reaction, the mineral becomes progressively more altered (or changes in abundance) within the 

weathering front. Turner and others (2003) conceptualized the 

spheroidal weathering system in the Río Blanco quartz diorite 

as consisting of many parallel weathering fronts that move 

inward toward each of the rindlet centers from their edges as 

well as inward towards the centers of the corestones. However, 

in detailed analyses of a complete rindlet sequence, Buss and 

others (2008) did not detect a trend in chemical weathering 

intensity within individual rindlets and proposed an alternative 

model. In this model, the entire rindlet zone and corestone is a 

single weathering front controlled by only two interfaces: the 

bedrock-rindlet interface and the rindlet-saprolite interface, 

which are coupled by the pore-space-forming processes of 

mineral dissolution and fracturing. 

hapter_I_figures\BW_figures\BWfigure_03I.ai

Soil

Saprolite

Rindlet

zone

Protosaprolite

Corestone

Figure 3. Generalized view of the weathering profile in the 

Icacos watershed. Quartz diorite bedrock corestones weather 

spheroidally, producing a zone of concentric layers called rindlets. 

Rindlets transform into saprolite in a zone called protosaprolite or 

saprock, where weathering processes differ from those in either 

the rindlet zone or saprolite (Buss and others, 2008).
258 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

N:\Jeff\den11_hwcg00_0224_pp_murphy\chapter_I_figures\BW_figures\BWfigure_04I.ai

A B

C D

Figure 4. Backscattered electron images of thin sections taken from a corestone-rindlet complex exposed at a roadcut 600 meters 

southeast of the mouth of the Icacos watershed. A, Corestones are relatively unweathered and unfractured and are dominated 

by large zoned plagioclase crystals. B, A typical rindlet sampled from about 20 centimeters above the corestone (the entire rindlet 

sequence was 49 centimeters thick) contains microcracks, unaltered hornblende (HB), and pore space within plagioclase (Plag) 

crystals where calcic cores are weathering before sodic edges. C, Within the protosaprolite zone, a rindlet sampled from about 2 

centimeters below the visible rindlet-saprolite interface contains unaltered quartz, highly weathered plagioclase (Plag) with gibbsite 

and kaolinite within the pore spaces, and slightly weathered hornblende (HB). D, Also within the protosaprolite zone, a sample taken 

from about 0.5 centimeter above the visible rindlet-saprolite interface contains quartz and visibly altered biotite; hornblende and 

plagioclase crystals are no longer visible. Large areas are filled with gibbsite and kaolinite (after Buss, 2006; Buss and others, 2008).
Weathering Processes in the Icacos and Mameyes Watersheds in Eastern Puerto Rico 259

In a one-dimensional, steady-state profile, the loss of a 

mobile element or dissolving mineral can be modeled as a 

linear decrease with decreasing depth from the parent rock 

composition to some weathered composition. This linear 

decrease, or weathering gradient, is composed of two vectors: the weathering velocity (which equals rate of advance 

of the weathering front in a solid-state profile or the fluid flux 

in an aqueous profile) and the rate of loss of the individual 

mineral or element that defines the gradient (White, 2002). 

Solid-state gradients in Na, Fe(II), and P within a rindlet 

zone were used to calculate weathering rates for plagioclase 

dissolution (Na), biotite oxidation (Fe(II)), and apatite dissolution (P) (Buss and others, 2008, 2010). 

Within the bulk of the rindlet zone, hornblende is 

relatively stable, whereas plagioclase dissolves at a rate of 

90×10–14 moles per square meter per second (mol m–2 s–1), 

biotite is oxidized at a rate of 3.1×10–14 mol m–2 s–1, and 

apatite dissolves at a rate of 6.8×10–14 mol m–2 s–1 (Buss 

and others, 2008, 2010). Concurrent with these mineral 

weathering reactions, the rindlets are further fractured by 

microcracks. Additional pore space develops by the dissolution of plagioclase; some of the dissolved constituents 

precipitate within the pore space as kaolinite and gibbsite 

(Murphy and others, 1998; Turner and others, 2003; Buss 

and others, 2004, 2008), whereas other constituents, notably 

Na and Ca, are lost from the profile. Sodium and calcium 

are abundant in all stream waters within the Icacos Basin 

(Bhatt and McDowell, 2007), reflecting plagioclase dissolution. The precipitation of kaolinite following plagioclase dissolution leads to an isotopically heavy δ30Si 

signature (+0.8 per mil; ratio of stable isotopes of silicon, 

30Si/28Si, relative to international standard NBS-28) in 

pore waters from the deepest saprolite, because isotopically light Si is preferentially sequestered in the kaolinite 

(Ziegler and others, 2005). Similarly, Ge/Si ratios in deep 

saprolite pore waters are low (≈1.0–2.0 micromoles per 

mole (μmol mol–1)) reflecting the dissolution of plagioclase 

(Ge/Si = 1.5μmol mol–1) and the formation of kaolinite 

enriched in Ge (Ge/Si = 4.8–6.1 μmol mol–1, Lugolobi and 

others, 2010). Because plagioclase weathers only in the 

rindlet zone (White and others, 1998; Turner and others, 

2003) and at the rindlet-saprolite interface (Buss and others, 

2008), weathering at this interface is the primary control 

on Si flux into the deep saprolite pore waters. The low Ge/

Si ratios of the deep saprolite pore waters are also reflected 

in stream waters in the Icacos and Guabá watersheds during base flow (Ge/Si ≈ 0.29 μmol mol–1), indicating that 

weathering at the rindlet zone is the primary contributor 

to stream-water Si during base flow (Lugolobi and others, 

2010; Kurtz and others, 2011). Likewise, the isotopic composition of Si in stream waters in the Icacos watershed (δ30Si 

= +1.2 to +1.5 per mil during base flow) reflects weathering reactions (plagioclase and hornblende dissolution) 

that occur at the bedrock-saprolite interface (Ziegler and 

others, 2005). During storm events, however, stream-water 

Ge/Si ratios are higher (2.3 μmol mol–1) and δ30Si values 

lower (+0.8 per mil), consistent with the shallow saprolite 

and soil zone Ge/Si ratios (2.3 to 3.3 and 4.6 μmol mol–1, 

respectively) and δ30Si values (–0.8 and –1.4 to –1.7 per mil, 

respectively), indicating rapid flow of storm waters overland 

and through shallow subsurface zones (Ziegler and others, 

2005; Kurtz and others, 2011).

The removal of soil and saprolite from bedrock and 

corestone-rindlet assemblages by landslides enhances access 

of reactants (such as oxygen, carbonic acid, and organic acids) 

to primary minerals such as plagioclase, hornblende, biotite, 

and apatite (Frizano and others, 2002; Bhatt and McDowell, 

2007). This effect is more pronounced at higher elevations and 

is revealed in stream waters draining higher elevations (Bhatt 

and McDowell, 2007). Enhanced access to mineral nutrients 

contained in primary minerals also aids in the rapid restoration of vegetation on landslide scarps in the Icacos watershed 

(Frizano and others, 2002).

At the rindlet-saprolite interface, the rindlets finally 

disaggregate and transform into saprolite (figs. 3, 4). The 

narrow zone (≈7 cm at the rindlet sequence studied) surrounding the visible interface has been called the protosaprolite zone (Buss and others, 2008) or saprock zone 

(Minyard and others, 2011). Within this zone, plagioclase 

and apatite completely dissolve and biotite achieves the 

oxidized composition found within the saprolite. Also 

within this zone, hornblende begins to weather and rapidly 

dissolves to completion at a rate of 1.4 ×10–12 mol m–2 s–1, 

calculated from the solid-state gradient in Fe(II) within the 

protosaprolite zone (Buss and others, 2008). Thus hornblende provides the dominant flux of Fe(II) to the saprolite 

and the deep saprolite biota (Buss and others, 2005). 

In the deep saprolite, microbial communities are relatively isolated from the biogeochemical cycling near the 

surface. Nutrients, such as organic carbon and phosphorous, 

do not infiltrate to the deep saprolite (Murphy, 1995; Buss 

and others, 2005, 2010). Therefore, the deep communities 

depend directly upon nutrients and energy sources derived 

from the rock-forming minerals during weathering of the 

rindlets and corestones. Apatite dissolution across the rindlet 

zone and at the rindlet-saprolite interface produces sufficient 

flux of phosphorous to support the needs of the deep microbial community (Buss and others, 2010). This community 

includes autotrophic microorganisms that fix CO2

 to produce 

organic carbon from inorganic carbon. At least some of these 

autotrophs are also iron oxidizers, which derive energy by 

oxidizing the Fe(II) that is rapidly released at the rindletsaprolite interface during hornblende dissolution (Buss and 

others, 2005, 2008). Iron-oxidizing bacteria contribute to 

the formation of secondary Fe(III)-oxide minerals (goethite) 

in the saprolite and potentially affect the rate of spheroidal 

weathering by decreasing the concentration of dissolved 

oxygen in pore water (Buss and others, 2005, 2008).
260 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Weathering Processes in the Saprolite 

and Soil

Saprolite is, by definition, an isovolumetric weathering 

product (Bates and Jackson, 1984), and this characteristic 

has been confirmed for the Icacos saprolite by calculations of 

volumetric strain with respect to Ti (White and others, 1998; 

Buss and others, 2008) that are close to zero. The 50 percent 

porosity and visible primary granitic texture of the saprolite 

is also consistent with isovolumetric weathering (White and 

others, 1998). 

Weathering reactions within the saprolite include the 

replacement of biotite by kaolinite, quartz dissolution, oxidation of Fe(II) and Mn(II), and precipitation of Fe and Mn 

oxides (Murphy and others, 1998; White and others, 1998; 

Buss and others, 2005). These reactions are revealed in gradients in pore-water and solid-state chemistry, in petrographic 

and electron microscopy of thin sections, and in microbiological assays. Concentrations of K, Mg, and Si in Icacos saprolite 

pore waters increase with depth, defining weathering gradients 

(White and others, 1998). Weathering rates for biotite alteration to kaolinite (10–15 mol m–2 s–1) and for quartz dissolution 

(10–14.8 mol m–2 s–1) were calculated from these gradients (Murphy and others, 1998; Schulz and White, 1999).

In the Icacos weathering profile, biotite weathers in two 

stages. As mentioned earlier, biotite in the corestones and 

rindlets oxidizes to form an “altered biotite” phase, which 

is characterized by a d(001) spacing of ≈10.5 Å, and higher 

Al/Si ratios and lower Fe, Mg, and K contents than fresh biotite (Dong and others, 1998; Murphy and others, 1998; Buss 

and others, 2008). In the saprolite, altered biotite weathers 

further to form kaolinite by either of two mechanisms: direct 

epitaxial overgrowth of two layers of kaolinite onto one layer 

of altered biotite, or formation of one layer of kaolinite per 

layer of altered biotite after the formation of an intermediate 

halloysite layer (Dong and others, 1998; Murphy and others, 

1998). The weathering of biotite to kaolinite controls Mg and 

Si concentrations in pore waters in all but the shallowest and 

deepest saprolite.

Quartz dissolution in the soil is reflected in Si concentrations (Schulz and White, 1999) and Si isotope ratios (Ziegler 

and others, 2005) in the pore waters. Quartz dissolution rates 

under natural conditions are extremely slow, and thus residual 

quartz is the major structural component of many weathering 

profiles, including the Icacos saprolite, where quartz abundances and grain size distributions are approximately constant 

with depth. However, these quartz grains are not inert but 

show evidence of dissolution in the form of etch pits (Schulz 

and White, 1999). Quartz dissolution rates, calculated from 

silica concentrations in the pore waters, are fastest at shallow 

depths (2.8×10–15 mol m–2 s–1 at 1.5 m depth), where quartzgrain etch pits are the most abundant (Schulz and White, 

1999). Silica concentrations in the saprolite pore waters 

increase linearly with depth, whereas quartz weathering rates 

decrease with depth. Quartz dissolution also controls the δ30Si 

signature in the soil pore waters (–1.4 to –1.7 per mil, Ziegler 

and others, 2005). Some kaolinite in the shallow soil dissolves 

in association with lower pH and elevated concentrations 

of Al and dissolved organic carbon in the soil pore waters 

(White and others, 1998). Dissolution by organic acids and 

other organic ligands produced by soil microorganisms and 

vegetation are likely responsible for the loss of kaolinite in 

the soil layer, as has been observed in laboratory studies (for 

example, Maurice and others, 2001) and in other tropical soils 

(for example, Chorover and Sposito, 1995). However, despite 

the influence of quartz and kaolinite dissolution, pore-water 

chemistry in the soil and shallow saprolite is dominated by 

atmospheric deposition and biological cycling (White and others, 1998; Pett-Ridge and others, 2009; Lugolobi and others, 

2010) rather than mineral weathering processes. 

Summary

Calculated bedrock weathering rates for the Icacos and 

Mameyes watersheds are high compared with watersheds 

around the world that are underlain by siliceous bedrock. 

Rapid weathering produces thick, highly leached saprolites 

in both watersheds that decouple subsurface processes from 

those in the soil. The quartz diorite bedrock in the Icacos 

watershed weathers spheroidally, forming relatively unweathered bedrock corestones that are surrounded by concentric layers of rock called rindlets. The spheroidal weathering process 

produces two controlling weathering interfaces. First, disaggregation of the intact rock is initiated at the bedrock-rindlet 

interface, where biotite oxidation within the corestones leads 

to spheroidal fracturing. The second controlling interface is the 

rindlet-saprolite boundary, where the rindlets disintegrate into 

saprolite owing to the development of extensive pore space 

and the complete dissolution of plagioclase and hornblende. 

Stream-water chemistry at base flow within the Río Icacos and 

its tributaries reflects these rindlet-zone weathering processes. 

Within the saprolite, biotite is replaced by kaolinite and quartz 

dissolves. The geochemistry of the overlying soil layer is 

dominated by the biological recycling of mineral nutrients, but 

it is also influenced by the dissolution of quartz and kaolinite. 

These shallow weathering processes are reflected in streamwater chemistry during storm events. 

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Introduction

This appendix summarizes procedures used to characterize hydrology and to compare water quality among four 

watersheds studied as part of the U.S. Geological Survey’s 

Water, Energy, and Biogeochemical Budgets (WEBB) 

research program in Puerto Rico and parallel work in 

Panama. The four comparative watersheds were the Canóvanas, Cayaguás, Icacos, and Mameyes (fig. 1). An additional 

small watershed within the Icacos watershed, the Guabá, was 

examined to study scaling effects; therefore, data from five 

stream-gaging stations were available (fig. 1). Data from the 

first 15 years (1991–2005) of the Puerto Rico component 

were processed for this report, an undertaking that entailed 

working with several million discharge measurements, 

thousands of measurements from automated rain gages, and 

several thousand samples analyzed for some aspect of water 

quality (from suspended sediment and electrical conductivity to comprehensive chemical analyses). Discharge records 

from stream-gaging stations and water-quality datasets 

were processed separately to check for errors and to fill in 

discharge-data gaps with estimated values. The hydrologic 

and water-quality datasets were then merged into two types 

of master files. One contains all primary hydrology data and 

interpretive information about hydrologic processes, such 

as calculating trends and tagging recessions and exceptional 

storms; one hydrologic-data file was compiled for each gage 

site. The other file type holds primary water-quality data, 

chemical parameters derived from the primary data, and an 

abbreviated hydrological characterization of each sample; 

analyses of samples from all sites and sources were compiled 

into a single water-quality data file. This water-quality-data 

file, which contains about 6,400 records for 15 years of data, 

can be manipulated in conventional spreadsheets.

This appendix is divided into seven sections. The first discusses the computational procedures used to handle this large 

dataset. The second covers hydrological data processing. The 

third describes processing of the water-quality data, including calculated parameters such as saturation indices, cyclicsalt correction, and dissolved bedrock. The fourth describes 

the constituent-load computations built around the program 

LOADEST (Runkel and others, 2004). The fifth details how 

runoff and yield percentile ranges are calculated. The sixth 

presents summaries of the dataset. The last compares this dataset with other large datasets assembled by the U.S. Geological 

Survey, in terms of the range of process rates measured. 

Computational Procedures to Interpret 

and Merge Hydrology and Chemistry 

Datasets

This data-processing exercise required the design of 

computational procedures to handle files that exceed the 

capacity of many databases and spreadsheets and to link 

data records on the basis of time stamps and sample identifications. Multimillion-record datasets are well beyond the 

size normally handled by conventional spreadsheets. Each 

discharge-data record represents an independent measurement, 

and subsequent modeling and interpretation is typically based 

on watershed conditions at or immediately before the time of 

measurement. A major objective was to prepare the database 

for models presented in Murphy and Stallard (2012), Stallard 

(2012b), and Stallard and Murphy (2012).

For much of this work, the program “gawk,” GNU Operating System’s implementation of a selection program “awk,” 

was used (http://www.gnu.org/software/gawk/gawk.html) 

to process data files. Programs that read and process single 

records at a time are well suited to handling time-series 

datasets; records are read in chronological order, calculations 

are made, statistical information is accumulated, and where 

necessary a short, running history is stored in memory. As 

processing is completed, the modified records, with computed 

statistics, are written into a new file. In this manner, complex 

calculations can be undertaken without having the entire 

dataset resident in computer memory at once. The “gawk” program has excellent text-parsing capabilities and adequate math 

and array processing. These features permit the creation of 

programs that combine data archives in many individual textbased formats into a single standardized text-based format. 

Capabilities include error recognition, complex renaming, unit 

conversions, data testing, and more advanced calculations. The 

“gawk” scripts are compiled when they are called, allowing 

for fast and effective debugging.

An important feature of an integrated computational 

package is its ability to reprocess data when appreciable 

changes are made. The procedures described in this chapter 

are controlled by a small number of batch-processing scripts. 

As new data were added or whenever notable changes were 

made, such as acquisition of better estimates of watershed 

areas, the entire dataset was rebuilt from primary data sources. 

In this manner, partial implementation of changes and retention of errors was avoided. 

Appendix 1. Data Processing and Computation to Characterize Hydrology and 

Compare Water Quality of Four Watersheds in Eastern Puerto Rico

By Robert F. Stallard
264 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

B

Cayaguás

San Juan

18°20’

66°10’ 66°00’ 65°50’ 65°40’

18°10’

A T L A N T I C O C E A N

0

0

10 KILOMETERS

10 MILES

5

5

N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_01apx1.ai

Canóvanas

CAYEY

MOUNTAINS

C A R I B B E A N S E A

Base from U.S. Geological Survey National Atlas, 2002

1:2,000,000, Geographic coordinate system, degrees

North American Datum of 1983 (NAD 1983)

EXPLANATION

Luquillo Experimental

 Forest

Study watershed

 boundary

Stream-gaging station 

 and identifier

CAY

CAN

ICA

MAM

Mameyes

ICA

GUA

Icacos

LUQUILLO

 MOUNTAINS

Area shown in B

A

FLORIDA

PUERTO RICO

25°

85° 80° 75° 70° 65° 60°

0

0

200 400 KILOMETERS

200 400 MILES

20°

15°

10°

BAHAMAS

DOMINICAN

REPUBLIC

HAITI

CUBA

JAMAICA

G r e a t e r

A n t i l l e s

C A R I B B E A N S E A

A T L A N T I C O C E A N

Base from United Nations Digital Soil Map of the World, 2007

1:5,000,000, Geographic coordinate system, degrees

World Geodetic Survey 1984 (WGS 1984)

L e s s e r

A n

t i l l e s

Figure 1. Location of Puerto Rico, study watersheds, and stream-gaging stations (CAN, Río Canóvanas 

near Campo Rico; CAY, Río Cayaguás at Cerro Gordo; GUA, Quebrada Guabá near Naguabo; ICA, Río Icacos 

near Naguabo; MAM, Río Mameyes near Sabana).
Appendix 1 265

Noise Reduction, Smoothing, and Derivatives

All hydrologic data contain measurement noise, which 

complicates calculations such as the rate of change in discharge 

(the derivative of discharge with respect to time, dQ/dt). Time 

derivatives are used to characterize many hydrologic phenomena, such as the description of rises and recessions (Vogel and 

Kroll, 1996; Geyer and others, 2008). In general, these characterizations should not make restrictive assumptions about 

the mathematical functions that are used to describe the data, 

because such assumptions can bias subsequent modeling. The 

huge files evaluated here required that simplified calculation 

tools be used for smoothing data and estimating derivatives. The 

primary assumption made here is that data, which are expected 

to match real-world observations, are smoothly varying; step 

functions and isolated sharp peaks are not physically realistic 

and indicate faulty data. The filling of intermittent or extended 

data gaps is discussed in the section “Filling Data Gaps.” Spline 

functions are ideal computational tools for examining smoothly 

varying data, and two types of spline procedures were used 

in the data processing here: the relaxed cubic spline and the 

B-spline convolution (table 1). 

The relaxed cubic spline (Reinsch, 1967) is a piecewise, 

continuous (connecting without gaps) set of cubic polynomials fit through the data, with one polynomial for each data 

segment. These polynomials are also piecewise continuous in 

the first and second derivatives. Moreover, in a relaxed cubic 

spline, the residual error of the regression can be specified 

beforehand. Data points do not need to be equally spaced along 

the ordinate axis (time). The time ordinate must increase monotonically, and no two points can have the same ordinate value. 

The cubic-spline regression uses an array that is seven times as 

large as the number of data pairs, and when the error is specified, the derivation of spline coefficients is iterative, making it 

difficult to apply a relaxed cubic spline to an entire time series 

of millions of measurements.

The B-spline convolution (Jupp, 1976) requires that points 

be evenly spaced. The advantage of a convolution is that only a 

few points in the convolution calculation have to be in memory 

during any computational step. The B-spline both smooths 

the data and calculates derivatives of the smoothed data at the 

central point in the convolution. Smoothing is especially useful 

in reducing measurement noise, which can be recorded during 

low-flow conditions. Accordingly, smoothed data were used 

in low-flow estimates, in describing recession curves, and in 

characterizing data noise. To estimate the derivative of the data 

during nonlow-flow conditions, a fourth-order function was 

forced through the central point and two adjacent points on 

each side, resulting in no smoothing. This procedure was also 

completed as a convolution.

Processing Hydrologic Data

Water-stage levels are recorded every 5 minutes at the 

Río Mameyes stream-gaging station and every 15 minutes 

for the remaining stations (on Río Canóvanas, Río Cayaguás, 

Quebrada Guabá, Río Icacos). Stage levels are subsequently 

converted into real-time discharge by using a stage-discharge 

relation. The real-time data are stored in the U.S. Geological 

Survey’s Automated Data Processing System at the Caribbean 

Water Science Center. For Río Mameyes, the 15-year dataset 

represented roughly 1.6 million measurements. Real-timedischarge measurements were downloaded from this data 

processing system and were converted into a more compact 

format that uses a spreadsheet-style Julian date (days since 

December 30, 1899) and metric units. Four parameters were 

generated (table 1): Julian time, raw stage (in meters), corrected stage (in meters), and discharge (in cubic meters per 

second). Corrected stage refers to documented corrections 

made in processing the data before acquiring data from the 

data processing system; normally this category is blank.

The secondary source of discharge data is the official 

tabulation of daily-mean discharges reported through the 

U.S. Geological Survey’s National Water Information System 

(NWIS), publicly available on the Web site (http://waterdata.

usgs.gov/nwis). NWIS data are calculated by using the data 

in the Automated Data Processing System, and when data 

are missing in the latter, Water Science Center hydrologists 

estimate the NWIS data using data from other stream-gaging 

stations and rainfall. The NWIS data were used to check the 

efficacy of calculations by the programs described here, and 

where primary data from the Automated Data Processing System were missing, estimated NWIS daily mean discharge data 

were used to calculate estimated values to fill those gaps (by 

using procedures described in the next section). 

All data assembly calculations used primary discharge 

(Q) data. Because the drainage areas of the watersheds are 

different, however, subsequent comparisons used areanormalized properties, specifically runoff rates of water 

(R) and yields of constituents (YX), which are calculated 

from water discharge (R = Q / area) and constituent loads 

(YX = LoadX / area), respectively. Annual runoff also differs 

among the sites, and discharge-weighted concentrations are 

used for runoff-normalized comparisons (average concentration of X = average yield of X/average runoff).

Site-Specific Issues

A datum shift for the Río Icacos stream-gaging station 

was identified in 2003 (Matthew Larsen, Caribbean Water Science Center Chief, written commun., January 15, 2003; Díaz 

and others, 2004), whereby the elevation datum (reference 

mark (RM)–1) on an old rock stilling well (the original stage 

measurement recording site, since replaced) shifted downward 

through subsidence of the manmade structure. From 1979 to 

1987, the RM–1 elevation decreased by 0.083 feet (ft); from 

1987 to 1995, by 0.111 ft; and from 1995 to 1999, by 0.268 ft. 

All datums are tied to RM–1, the base reference mark for this 

station. The Caribbean Water Science Center corrected the 

record. As a result of this instability, discharge data had been 

too high (a 0.2-ft change is equal to 10 to 20 cubic feet per 

second (ft3 s−1) at base-flow conditions). All discharge data for 
266 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Table 1. Parameters input and calculated in the real-time discharge-data file used to characterize the hydrology of five rivers, eastern

Puerto Rico.

Field Variable Header Parameter

Primary data fields

1 JULIAN Julian_Time Julian time in spreadsheet format

2 RAW_ST Stage_Raw Raw stage data in meters or coding for missing data. Codes: “--”, good discharge, no stage; “==”, 

estimated discharge. Derivatives and several other derived parameters were not calculated for 

stream samples when discharges were estimated.

3 STAGE Stage_Fin Corrected stage data in meters.

4 QQ Q_cms Discharge, Q, in cubic meters per second. If discharge was estimated, the raw stage (RAW_ST) is 

coded accordingly.

Data fields calculated by the smoothing routine

5 SMTH_QQ Smooth_Q Smoothed discharge, Qs

, in cubic meters per second. Used in low-flow estimates, determining 

recession curves, and in characterization of data noise.

6 DQ_DT dQ/dt The derivative of discharge with respect to time, dQ/dt, in cubic meters per second per hour.

7 SMTH_DQDT Smooth_dQ/dt The derivative of smoothed discharge with respect to time, dQs

/dt, in cubic meters per second 

per hour.

8 DL10Q_DT d(log(Q))/dt The derivative of log(Q) with respect to time, dlog(Q)/dt, in log(fraction) per hour.

9 SM_DL10QT Sm_dlogQ/dt The derivative of log(Qs

) with respect to time, dlog(Qs)/dt, in log(fraction) per hour.

Data fields calculated by the antecedent-conditions routine

10 AVG_Q_1 Avg(Q)_day Running average discharge for the prior day, in cubic meters per second, for characterization of 

antecedent conditions.

11 AVG_Q_7 Avg(Q)_week Running average discharge for the prior week, in cubic meters per second, for characterization of 

antecedent conditions.

12 AVG_Q14 Avg(Q)_window Running average discharge for the prior 2-week period, in cubic meters per second, for characterization of antecedent conditions.

13 MAX_Q14 Max(Q)_window Maximum discharge for the prior 2-week period, in cubic meters per second, for characterization 

of antecedent conditions.

14 MAX_J14 Max(Q)_julian Julian time off the maximum discharge for the prior 2-week period for characterization of antecedent 

conditions.

Baseflow data field calculated by the the baseflow routine

15 BASEFLOW Baseflow Baseflow estimate, in cubic meters per second.

Data fields calculated by the recession and landslide routine

16 RECESS Quick_Recess Quick-recession curve (monotonic decrease of Qs

), in successive measurements.

17 RECSLO Slow_Recess Slow-recession curve (steady drop of AVG_1Q to AVG_1Q to AVG_1Q) in successive measurements. This procedure for calculating recession is similar to most published methods, which are 

based on mean daily discharge.

18 AVQ_1Q One-day_Q Running daily convolution of average flow, in cubic meters per second. For any moment this is an 

average discharge of one-half day back and one-half day forward in time.

19 OVER_TH Over-Threshold If the runoff in the previous 1, 2, or 3 days exceeds the landslide threshold, this runoff is coded in 

binary: +=1 is 1 day; +=2 is 2 days; +=4 is 3 days.

20 PAST_SD Slide-day JT Days since the end of the last period for which runoff in the previous 1, 2, or 3 days exceeds the 

landslide threshold. If this number is zero, then the observation is within the period where the 

threshold has been exceeded.

the Río Icacos station were subsequently revised in the Automated Data Processing System and in the NWIS to correct for 

the datum shift.

The useable record for the Quebrada Guabá stream-gaging 

station is only 10 years long (1993 through 2002) and ends 

with gage failure on April 17, 2003, during a large frontal storm 

that produced several landslides upstream of the gage. A site 

inspection in August 2010 indicated that pulses of coarse sediment derived from these landslides continue to move through 

the channel and create a time-varying cross section. Although 

data for the station were reported by the U.S. Geological Survey after that date, we consider these data to be flawed.

Filling Data Gaps

Each of the real-time-discharge datasets has gaps ranging 

from 30 minutes to several months. Commonly, the longest 

gaps are associated with the loss of a streamgage caused by a 

huge storm; the length of the gap reflects the time needed to 

rebuild the gage. These gaps introduce two problems. First, a 

gap represents a missing water volume that must be estimated 

to complete annual water and constituent budgets. Second, the 

computational procedures require evenly spaced, gap-free data. 

The objective of data interpolation was to calculate estimated 

discharge in gaps in real-time Automated Data Processing 
Appendix 1 267

System data, such that there is either an observed or an estimated discharge for each possible measurement time. Fulfilling 

this objective makes the smoothing algorithms far more rapid, 

because convolutions can be used. Gaps were filled by using 

a cubic spline calculation such that (1) daily mean discharges 

of the estimated data matched the daily mean discharges in 

the NWIS, and (2) estimated data were calculated at the same 

time intervals and are continuous with respect to real data and 

to first and second derivatives. These discharge estimates are 

used in daily, monthly, and annual mass balance calculations, 

but they are flagged as estimated data so that they would not be 

used to determine discharges of individual samples.

For data gaps of less than a day, discharge values were 

linearly interpolated from the adjacent data. For gaps too long 

to be filled by interpolation, both the stage and discharge fields 

are filled with a no-data code, to ensure that there are no time 

gaps. For gaps equal to or longer than a day, discharge values 

were estimated at the same time steps as the real-time data by 

using NWIS mean-daily discharge; on a few occasions when 

NWIS estimates were not available, mean-daily discharge 

values were estimated by using adjacent sites. Daily discharge 

estimates were downloaded from the NWIS and converted to 

a simpler format, similar to that described for Automated Data 

Processing System files. 

For gap-filling calculations, mean-daily-discharge files 

also had to be gap free. The NWIS does not have measured 

or estimated daily values for the full 15-year record for Río 

Canóvanas (743 days missing) or Quebrada Guabá (1,541 days 

missing, including all days after April 17, 2003). For the 

Canóvanas and Guabá gaps, daily values were estimated by 

using relations determined from regressions (Bevington and 

Robinson, 2003) on all combinations of one or more dailymean-discharge data from nearby gages. For Río Canóvanas, 

the Río Cayaguás was selected as the independent variable. 

For Quebrada Guabá, the Río Icacos was the independent variable. The selected regression equations are as follows:

QCanóvanas = 0.573•QCayaguás − 0.068 (r2

=0.806) 

QGuabá = 0.0298•QIcacos − 0.00102 (r2

=0.872),

where Q is the daily mean discharge at the station indicated 

by the subscript of the variable, and r2 is the coefficient of 

determination and is the fraction of the variance accounted for 

by the regression (Bevington and Robinson, 2003). A cubic 

spline was used to fill the gaps. Julian day is the independent 

variable. For each gapless daily-value file, cumulative total 

daily discharge in cubic meters is calculated and assigned to 

the next day; this cumulative total becomes the dependent 

variable. A nonrelaxed cubic spline (nonrelaxed goes through 

the data points) is fit through the cumulative data such that at 

the end of each day, the discharge total calculated by summing 

the mean-daily discharge data matches the value of the cubic 

spline. The derivative of this spline is calculated for each 

time step of the real-time discharge record. This derivative 

is converted to cubic meters per second and, where real-time 

discharge data are lacking, these estimates provide a smoothly 

varying substitute. The estimated real-time data are used for 

discharge-related calculations involving time resolution of a 

day or more, but they are not used to calculate instantaneousflow statistics or instantaneous mass fluxes.

The final file assembled for all discharge-related calculations was constructed by merging the real-time discharge 

data from the Automated Data Processing System with the 

estimated real-time data from the NWIS. These estimates vary 

smoothly and have the same daily mean-discharge values as 

the NWIS dataset. The estimated data have no time or discharge gaps, but there are no estimates of stage. The merging 

process looked for gaps that are greater than a day, because 

shorter gaps were already filled through interpolation. First, 

the appropriate discharge estimates were extracted from the 

smoothed data files derived from daily mean values. A fourthorder polynomial having zero slope at each end and zero area 

was added to the estimated discharge; this addition adjusted 

the estimated data to the real data such that all data connect 

without any abrupt steps or slope breaks. For short gaps of less 

than 1.5 days, a single adjustment curve is used. For longer 

gaps, two adjustment curves (of no more than one day in 

length) were applied, one at each end of the gap. Between the 

two curves the discharge estimates are not adjusted.

For computational simplicity, the fourth-order polynomial is calculated by assuming that the gap, or the part of the 

gap that is being adjusted, is normalized such that it starts at 

zero and has a length of one day. The difference between the 

observed discharge at the beginning of the gap (QO1) and the 

estimated discharge (QE1) is DQ1. DQ2 is calculated the same 

way at the end of the gap.

DQ1 = QO1 – QE1, and DQ2 = QO2 – QE2.

To simplify the calculation, the duration of the adjustment 

period was normalized to 1, and a dimensionless time, X, 

0≤X≤1, is defined (t is the time of the adjusted observation, the 

adjustment start time is t

start, and the adjusted end is tend):

X = (t−t

start)/(tend−tstart).

If the beginning or the end of the polynomial is within the gap, 

then the respective DQ1 or DQ2 is assigned a zero value. The 

fourth-order polynomial is given by the following:

DQ(X) = DQ1 + c•X 2 + d•X 3 + e•X 4, 0 ≤ X ≤ 1

c = −(18•DQ1+12•DQ2)

d = 32•DQ1+28•DQ2

e = −15•(DQ1+DQ2).

X and DQ(X) is calculated for every time step in the adjustment 

period, and DQ(X) is added to the smoothed data.

The resulting gap-filled real-time discharge files are of 

the following form: Julian time, raw stage (in meters), corrected stage (in meters), and discharge (in cubic meters per 

second). If the discharge was estimated for gaps of more than 

a day, a “==” was coded into the raw stage.
268 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Processing Real-Time-Discharge Data

Sixteen parameters were calculated from gap-filled, realtime discharge data. These parameters, in addition to the four 

primary measurements, were recorded in a single flat file for 

subsequent access by other data-characterization programs 

(table 1). These other programs include graphing programs, a 

program that links discharges, runoff rates, and rates of change 

of discharge to individual samples, a program that summarized 

flow statistics, a program that characterizes recurrence, and 

a program that does recession analyses. The procedures for 

estimating these 16 parameters are described below.

The Smoothing Routine

A variety of processes related to whether samples are 

collected on the rising or falling limbs of the hydrograph can 

affect the concentrations of constituents. Whether discharge 

is increasing or decreasing is determined by the derivative 

of discharge with respect to time. Measurement errors can 

produce noise that, if not damped, can make the calculations 

of derivatives quite difficult. To provide such information 

for data interpretation, the entire discharge record, including 

filled gaps, is smoothed by using a B-spline convolution (Jupp, 

1976) to calculate a smoothed discharge (Qs

) and its derivative 

(dQs/dt). Because the errors of log(Q) are approximately normally distributed, the calculations used log-transformed data, 

log(Q). The convolution coefficients (Jupp, 1976, that report’s 

table 6, m=4, k=2) are as follows:

for the smoothing convolution, log(Qs): (1, 8, 23, 32, 23, 

8, 1)/96 

for the derivative, dlog(Qs)/dt: (5, 4, 1, 0, −1, −4, −5)/

(32•seconds per interval).

Smoothed Qs and dQs/dt are calculated from log(Qs) and 

dlog(Qs)/dt, respectively:

Qs = 10log(Qs)

dQs/dt = (Qs•d((log(Qs))/dt ) / ln(10)

The nonsmoothed derivative of the data is also calculated by 

using a five-point convolution that is equivalent to fitting a 

fourth-order polynomial through five consecutive points:

for the derivative, dlog(Q)/dt: (8, 1, 0, –1, –8)/(12•seconds per interval).

Five fields of data are added after the original four fields 

(table 1): Qs

, dQ/dt, dQs/dt, dlog(Q)/dt, and dlog(Qs)/dt to 

produce a smoothed-data file.

Antecedent-Conditions Routine

Antecedent conditions in a watershed are usually 

assumed to influence the chemistry of streams, and more 

recent conditions are assumed to be more important than 

those of the more distant past. To provide information about 

prior watershed wetness, additional discharge information 

is extracted from the antecedent data (table 1). Data are 

summarized for three time windows: one day, one week, 

and two weeks. Five new fields of data are appended to the 

previous nine: a one-day average discharge, a one-week average discharge, a two-week average discharge, the maximum 

discharge in the previous two weeks, and the Julian time of the 

maximum discharge in the previous two weeks. In addition, a 

temporary file of the minimum smoothed discharge, Qs, in the 

previous two weeks, along with the Julian time of this minimum discharge, is generated in order to delineate base flow.

Base Flow Calculation Routines

When a considerable amount of time had passed after 

storm events, the water in the channel was assumed to be 

entirely base flow or else water that was contributed to the 

river from deeper flow paths (Vogel and Kroll, 1996). A lowflow envelope was defined for the dataset (table 1) of the realtime data (including filled gaps). This envelope was developed 

iteratively. The table of minimum discharge in the previous 

two weeks (table 1) and the Julian time of this minimum discharge were used to delineate an envelope of minimum flows. 

For simplicity the minimums were connected by linear interpolation. This envelope was compared with observed flows, 

Qs, and points below the curve that had been defined by linear 

interpolation were added to refine the envelope. This procedure was repeated three times, and the base-flow estimate was 

appended to the data table.

Recession-Curve and Landslide-Day-Extraction Routines

Two types of phenomena examined in this report, recession curves and landslide-producing storms, rely on characterization of hydrographs of storms. Both of these determinations 

represent a characterization of antecedent conditions, recession curves for dry periods following storms, and landslideproducing storms, here termed “landslide days,” for especially 

wet events (slide-day in table 1). These routines identify recession curves and landslide days, but do no further calculations.

Recession calculations normally use daily mean discharge 

(see reviews and model summaries by Hall, 1968; Tallaksen, 

1995; Vogel and Kroll, 1996; and Chapman, 1999). Daily mean 

discharge is used in part because a large body of daily means is 

available, but it was also used because during low flows, at time 

scales of less than a day, processes that produce minor fluctuations in discharge (such as transpiration or floating debris) do 

not substantially affect daily means. Accordingly, two types 

of recession are identified. In a quick-recession curve, the 

smoothed discharge, Qs, steadily decreases. In a slow-recession 

curve, daily mean discharge calculated by the antecedent-conditions routine steadily decreases. Each discharge measurement is 

tagged as being part of neither, one, or both recessions.

Much of the work in the Puerto Rico Water, Energy, and 

Biogeochemical Budgets (WEBB) program has focused on the 

landslide process and emphasizes the importance of shallowsoil landslides in physical erosion (Larsen, 2012). Stallard 

(1999) and Stallard and Kinner (2005) formulated models 

that include landslide-producing events in characterizations of 

annual sediment yields; the models are based on an empirical 
Appendix 1 269

landslide threshold derived by Larsen and Simon (1993). This 

landslide threshold relates a rainfall threshold, P (in millimeters), that must be exceeded for landslides to be initiated, to 

storm duration, D (in hours):

P(millimeters) ≥ 91.46•D(hours)0.18 . (1)

The essence of this relation is that if more than the requisite 

rain falls in the requisite time, and if the terrain is sufficiently 

steep (>12° slope), then landslides may be initiated. For storms 

with durations of 24, 48, and 72 hours (h), the threshold rainfall 

is 162, 184, and 197 millimeters (mm) respectively.

Stallard (1999) and Stallard and Kinner (2005) modified 

this threshold relation to accommodate runoff, rather than 

rainfall, because many river basins lack adequate rain-gage 

networks. These reports describe rivers in the Panama Canal 

watershed for which a decade or more of combined dailydischarge and daily-sediment-load data had been recorded in 

a terrane with exhumed island-arc geology, topography, and 

annual rainfall similar to that of eastern Puerto Rico. The daily 

runoff value, R, needed to exceed the landslide threshold was 

calculated from the daily discharge data. A day was defined as 

a landslide day if the amount of runoff indicates that rainfall 

during that day exceeded the landslide threshold 

R(millimeters in 24, 48, 72 h) > F•91.46•D0.18

(D = 24, 48, 72 h), (2)

where F is a catchall empirical coefficient between 0 and 1 that 

adjusts the threshold for three factors that reduce runoff relative 

to local rainfall. These three factors are (1) evapotranspiration, 

(2) infiltration, and (3) rainfall patchiness, whereby parts of a 

watershed will be over or under the threshold. Because daily 

sediment data were not available for eastern Puerto Rico, an F

value of 0.85, as determined empirically by Stallard (1999) and 

Stallard and Kinner (2005) for central Panama watersheds, was 

used. For storms with a duration of 24, 48, and 72 hours, the 

threshold runoff is 138, 156, and 167 mm respectively. Calculations were based on whole days, because many sites elsewhere 

do not have real-time data, and because we could use estimated 

discharges during real-time data gaps.

Landslide days were identified by comparing runoff totals 

for the previous 24, 48, and 72 h with the threshold values. The 

threshold-exceedence state of the river was coded as a binary 

value. A value of one was added if the threshold is exceeded for 

the previous day; two was added if the threshold is exceeded 

for the previous 2 days, and four was added if thresholds were 

exceeded for the previous 3 days. In addition, the number of 

days since the landslide threshold was last exceeded was tabulated. This value is zero when the discharge measurement was 

within a period in which the threshold was exceeded.

Processing Water-Quality Data

During the eastern Puerto Rico WEBB Program, two 

principal types of samples were collected: grab samples, which 

were manually collected on a regular basis from riverbanks 

at well-mixed cross sections near each gage site, and event 

samples, which were collected by an automated sampler during 

storm events and retrieved when weather conditions permitted. Conductivity, temperature, pH, and dissolved oxygen (O2

) 

of grab samples were measured onsite; only conductivity was 

measured onsite for event samples, because the other field 

parameters are unstable if not measured promptly. Depthintegrated samples for suspended sediment were occasionally 

collected at the same time as a grab sample. All water samples 

were filtered through 0.2- micrometer (μm) filters as soon as 

practicable in the laboratory. Sample collection and processing 

are described in detail in appendix 2. 

Measured Water-Quality Parameters 

This section describes how water-quality measurements 

were processed to characterize each water sample and to 

estimate constituent loads. Constituent measurements include 

the following: 

1. Field properties: sample time, temperature, pH, conductivity, and dissolved oxygen (O2

).

2. Major cations: sodium (Na+), potassium (K+), magnesium (Mg2+), and calcium (Ca2+).

3. Major anions: chloride (Cl–) and sulfate (SO4

2–).

4. Alkalinity: (alkalinity = HCO3

–

 + 2∙CO3

2– + OH–

 – 

H+).

5. Dissolved silica: (Si(OH)4).

6. Dissolved organic carbon (DOC).

7. Nutrient ions: nitrite (NO2

–

), nitrate (NO3

–

), ammonium ion (NH4

+

), and phosphate (–PO4

3−: preceding 

“–” refers to the tendency for phosphate to bind with 

other constituents).

8. Suspended solids: weight of material filtered onto a 

0.2-micrometer (μm) filter per unit volume following drying to 105°C.

9. Suspended bedrock: weight of material filtered onto 

a 0.2-μm filter per unit volume following ashing at 

550°C. This process is referred to as ashing, and the 

weight loss is referred to as loss on ignition (LOI).

10. Suspended sand: weight of sand sieved out of the 

sample using a 65-μm sieve prior to filtration and 

dried at 105°C. The weight of this sand is added 

into measurements of suspended solids. Inspection 

of sand samples indicated very little clay pellets or 

organic matter.

11. Trace constituents: strontium (Sr2+), aluminum 

(–Al3+), manganese ion (Mn2+), reduced iron (Fe2+), 

total iron (Fetotal = Fe2+ + –Fe3+), boric acid (B(OH)3), 

fluoride (F–

) and bromide (Br–). Trace constituents 

were analyzed in a reduced suite of samples.
270 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

12. Dissolved organic nutrients: dissolved organic nitrogen and dissolved organic phosphorus were analyzed 

in some samples in the last years of the program 

(appendix 2).

These constituents were analyzed at several laboratories 

and by several types of analytical equipment (appendix 2). The 

data formats from these laboratories differed, and analyses 

were often reported in different units. For example, SO4

2– was 

reported variously as micromoles per liter, microequivalents 

per liter, milligrams of sulfur per liter, and milligrams of 

sulfate per liter. All constituent concentrations were converted 

to micromoles per liter, except alkalinity, which was converted 

to microequivalents per liter, and suspended solids, sand, and 

ashed solids, which were converted to milligrams per liter. 

Data were standardized iteratively because of typographical 

errors in transcribing field and laboratory data and ambiguous 

reporting units. The tracking of anomalous results commonly 

required consultation of the original field and laboratory notes. 

Data processing was programmed in the “awk” (“gawk”) language because of its portability and its simplicity in handling 

both numeric and character data. The final data files were 

converted into a format suitable for upload into the NWIS.

Derived Water-Quality Parameters

The interpretation of the analytical results required the 

calculation of additional, derived, parameters. Table 2 gives 

constituent properties that were used in deriving some of these 

parameters. Derived parameters used in developing this report 

are grouped as follows:

1. Error checking: Total cation and total anion charge, 

net charge, and predicted conductivity (Miller and 

others, 1988)

2. Thermodynamic calculations: Ionic strength, ionic 

strength correction (gamma), hydrogen ion (H+), 

hydroxide ion (OH–), carbon-dioxide vapor pressure 

(PCO2), dissolved carbon dioxide (H2CO3 = CO2[d] + 

H2

CO3[d]), bicarbonate ion (HCO3

–

), carbonate ion 

(CO3

2–), the saturation index of calcite (SIcalcite), and 

oxygen saturation (SO2).

3. Cyclic salt corrections: Large fractions of some 

constituents are derived from marine solutes blown 

inland, cyclic salts. Because we assumed that all 

chloride is cyclic, a marine-derived cyclic salt 

component was subtracted from various constituents, 

leaving an estimated bedrock-derived concentration. 

Following Stallard and Edmond (1981), estimated 

bedrock-derived constituents are designated with 

a “*” —for example, Na*. Cyclic-salt corrections 

were determined for Na*, K*, Mg*, Ca*, Sr*, Cl*, 

and SO4

*. Other constituents were assumed to have 

no marine inputs.

4. Dissolved bedrock erosion: dissolved bedrock 

(DBrx).

5. Suspended bedrock (SBrx) and particulate organic 

carbon.

Error Checking

Two types of error checking were used in quality control of the water-quality data. Charge balance was calculated from the sum of concentrations times valence states 

(table 2) for the various chemical constituents. To check for 

errors, total positive and negative charges were compared; 

major disparities, greater than 5 percent, indicated potential 

problems. Predicted conductivity was calculated from the 

equivalent conductivity for each ion (table 2). To check for 

errors, observed and predicted conductivities were compared 

(Miller and others, 1988). Because conductivity is a field 

measurement, this check proved a robust way to track down 

improperly recorded or handled samples.

Thermodynamic Calculations

For thermodynamic calculations involving the carbondioxide system, equilibrium constants were tabulated as a 

function of temperature (Stumm and Morgan, 1981). Some 

of these tabulations were in the form of equations, but many 

were table entries. Table entries were converted into equations 

as a function of temperatures and are given below. To correct 

for the effects of ionic strength, the Davies equation was used 

to calculate the ionic-strength correction, gamma. Stumm and 

Morgan (1981) present all equations used in the thermodynamic calculations. Oxygen saturation is given by an equation 

developed by Elmore and Hayes (1960).

Equilibrium constants (K) are given by the following 

equations (K refers to temperature in degrees Kelvin; the 

subscripts of K refer to different reactions, where “W” refers 

to the deprotonation of water, “H” refers to Henry’s law for 

total dissolved carbon dioxide, “1” and “2” refer to the first 

and second deprontonations of carbonic acid, respectively, 

and “sp” refers to the solubility product of calcite; T refers 

to temperatures in degrees Celsius; and mg L−1, milligrams 

per liter):

1. Reaction H2O ↔ H++OH−,

KW = −4470.99/K+6.0875 – 0.01706•K

2. Reaction CO2[g]+H2O ↔ H2CO3*,

KH = 2201.32/K–12.60325+0.01258•K

3. Reaction H2CO3* ↔ H++HCO3

−

,

K1 = –3406.15/K + 14.844 –0.03277•K 

4. Reaction HCO3

–

 ↔ H++CO3

2−,

K2 = –2910.82.15/K+6.54767–0.02386•K

5. Reaction CaCO3 ↔ Ca2++2CO3

2−,

Ksp = –3000/K+13.543–0.04001•K

6. Reaction O2[g] ↔ O2[d],

O2

, saturation in mg L−1 = 14.652–0.41022•T + 

0.007991•T2–0.00007777•T3
Appendix 1 271

Cyclic Salt Corrections

In order to estimate bedrock contributions to the dissolved load, the major cations (Na+

, K+, Mg2+, Ca2+), and 

sulfate (SO4

2−) require corrections for atmospheric inputs. For 

Na+, K+, and Mg2+, the correction involves subtracting a seasalt 

contribution based either on the ratio of that ion to chloride 

(Cl−) in rain or on the seasalt ratio (table 2). If we use sodium 

as an example, the Na+:Cl− mole ratio for seasalt is 0.8525:

Na* = Na2+–(Na+:Cl−)•Cl−. (3)

Following Stallard and Edmond (1981), the “*” designates an estimated bedrock-derived constituent. Note that 

the seasalt correction, based on measured chloride, also 

effectively removes NaCl derived from human activities. The 

Mg2+:Cl−

 ratio in rain is 0.09689, less than the seasalt ratio 

of 0.10145 (see discussion in Stallard, 2012a). The seasalt 

SO4

2−:Cl−

 ratio is 0.052, and in some storms, especially highchloride events, the rain and runoff are near this ratio (Stallard and Murphy, 2012, their fig. 10). However, additional 

sulfur comes from pollution and from emissions of organic 

sulfur compounds (dimethyl sulfide and methyl sulfide) from 

the marine environment (Stallard and Edmond, 1981). To further complicate matters, forests may also emit organic sulfur 

compounds back to the atmosphere and sulfur may also be 

sequestered in vegetation (Stallard and Murphy, 2012). The 

overall rain SO4

2−:Cl−

 ratio is 0.10597, and this ratio was used 

in calculations. Potassium was handled differently. The K+:Cl−

ratio in rain is 0.0231, whereas the seasalt ratio is 0.0179. 

Because plant material is so rich in potassium, the greater 

rain ratio is seen as reflecting local, minor plant contamination, so the seasalt ratio was used. The Ca2+:Cl−

 ratio in rain is 

0.01876, and seasalt is therefore a minor contribution to the 

calcium budget. Sahara dust brings in additional soluble calcium (Stallard, 2012a). Because this calcium is also a minor 

part of the overall calcium budget, and because the dust fallout comes from long-range transport, a uniform dust correction was subtracted from the final calcium budget rather than 

from individual samples. All silicon, aluminum, and iron in 

Sahara dust was assumed to be in nonreactive clays and sesquioxides and is therefore insoluble (Stallard, 2012a). Owing 

Table 2. Measured constituent properties used in calculating interpretive parameters and derived constituents in five rivers, eastern

Puerto Rico.

[g mol−1, grams per mole; μS cm−1 L mEq−1, microsiemens per centimeter per milliequivalent per liter; μmol L−1, micromoles per liter; μEq L−1, microequivalents 

per liter; mg L−1, milligrams per liter; DOC, dissolved organic carbon; --, not relevant]

Constituent Database

units

Molecular

weight

(g mol–1)

Valence

Equivalent

conductivity

(μS cm–1 L mEq–1)

Ratio in 

seawater, 

constituent 

to chloride

Cyclic-saltcorrection ratio 

to chloride

Bedrock

chemical

form

Conversion factor

to bedrock

(g mol–1)

Measured constituents

Na+ μmol L−1 22.9898 1 50.1 0.85251 0.85251 Na2

O 30.99

K+ μmol L−1 39.102 1 73.5 0.01790 0.01790 K2

O 47.1

Mg2+ μmol L−1 24.312 2 53.1 0.10145 0.09689 MgO 40.31

Ca2+ μmol L−1 40.08 2 59.5 0.01876 0.01876 CaO 56.08

Sr2+ μmol L−1 87.62 2 59.4 0.00016 0.00016 SrO 103.62

Si(OH)4 μmol L−1 96.115 0 0 0 0 SiO2 60.08

Alkalinity μEq L−1 61.0173 –1 44.5 0.00714 –0.10992 -- --

Cl– μEq L−1 35.453 –1 76.4 1 1 -- --

SO4

2– μmol L−1 96.06 –2 80 0.052 0.10597 S 32.06

NO3

– μmol L−1 62.0049 –1 71.5 0 0 -- --

NO2

– μmol L−1 46.0055 –1 71.5 0 0 -- --

NH4

+ μmol L−1 18.0388 1 73.4 0 0 -- --

–PO4

3– μmol L−1 94.9714 –2 57.4 0 0 P2

O5 70.97

Oxygen mg L−1 31.9988 0 0 -- -- -- --

DOC μmol L−1 12.0111 –0.07 40.9 0 0 -- --

–Al3+ μmol L−1 26.9815 0 68.4 0 0 Al2

O3 50.98

–Fe3+ μmol L−1 55.847 0 68.4 0 0 FeO+Fe2

O3 71.73

B μmol L−1 10.811 0 -- 0.00076 0.00076 -- --

F– μmol L−1 18.9984 –1 55.4 0.00012 0.00012 -- --

Br– μmol L−1 79.909 –1 78.1 0.00153 0.00153 -- --

Derived constituents

H+ μmol L−1 1.00797 1 349.8 -- -- -- --

OH– μmol L−1 17.0074 –1 198.6 -- -- -- --

H2

CO3 μmol L−1 62.0253 0 0 -- -- -- --

HCO3

– μmol L−1 61.0173 –1 44.5 -- -- -- --

CO3

2– μmol L−1 60.0093 –2 80 -- -- -- --
272 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

to biological activity, surface seawater has low concentrations 

of Si(OH)4, NO2

−

, NO3

−

, NH4

+

, and –PO4

3−, and essentially 

zero ratios of these constituents to Cl−.

For individual samples, the atmospheric-correction calculation occasionally yields a negative value. This value may 

be due to real processes, such as cation exchange in soil that 

can alter cation proportions, or it may be due to true variations 

in the ratios to chloride during individual events, as happens 

with sulfate. With sodium and magnesium, the calculations 

calculate the differences of large numbers, so analytical errors 

can produce negative values. This large-number problem is 

quite severe for high-chloride events (see Stallard, 2012a; 

Stallard and Murphy, 2012), so high-chloride events are not 

included in the bedrock-contribution calculations. Regardless 

of the reason, if the result of the calculation is negative, it was 

reassigned a zero value, essentially indicating that the sample 

contained no effective bedrock contribution of that constituent. 

Mg* and SO4* calculations result in many zero values; Na* 

and K* rarely do; and Ca* never.

Dissolved-Bedrock Erosion

Conventionally, solute denudation rates are calculated 

by using the concentration of total dissolved solids, defined as 

the total of the mass concentration of all dissolved constituents. This total is multiplied by discharge to get load, and then 

divided by drainage area to get the yield of dissolved solids, 

which is in turn equated with solute denudation. This calculation fails to correct for atmospheric inputs which, in addition 

to all chloride present and a considerable portion of the major 

cations, also includes the bicarbonate and carbonate ions that 

balance most of the bedrock-derived cation charge. Moreover, 

it fails to recognize that silicate rocks, particularly igneous 

and metamorphic rocks, are essentially a mixture of complex 

oxides (table 2), for which the charge of all the soluble cations 

and silicon was once balanced by oxygen (O2−).

Stallard (1995a,b) uses a different approach that expresses 

the concentrations of most bedrock-derived constituents as 

dissolved oxides for mass-balance calculations. Silica and each 

cation, after correction for atmospheric inputs, are converted 

into a mass concentration of the corresponding bedrock oxide 

(table 2). For example, the calculation of dissolved sodium 

concentration expressed as an oxide (Na2O[d]) (table 2) from 

bedrock-derived sodium, Na*, was performed as follows (mg 

L−1, milligrams per liter; μmol L−1, micromoles per liter, 30.98, 

conversion factor in g mol−1 from table 2):

Na2O[d] (mg L−1) = 0.001•30.98 •Na* (μmol L−1). (4)

Dissolved sulfate is converted into sulfide. The concentrations of the dissolved oxides and sulfide are summed to 

get total dissolved bedrock (DBrx). For the WEBB watersheds, DBrx estimates were approximately half of the total 

dissolved solids, an indication of the serious degree of 

error introduced by estimating bedrock solute yields on the 

basis of total dissolved solids. Obviously, if the bedrock 

of a watershed includes substantial carbonates (Stallard, 

1995a), for which the charge of calcium and magnesium is 

balanced by carbonate ions (CO3

2−) rather than by oxygen 

(O2−), DBrx will underestimate the amount of dissolved 

bedrock.

In addition to calculating DBrx for samples with 

complete chemistry (about 870 samples), it was estimated 

in all samples for which conductivity, chloride, and silica 

were measured (about 3,780 samples). High-chloride events 

were excluded, because of the problems of calculating the 

differences of large numbers. This calculation involves 

three steps. First, the bedrock contribution to conductivity is 

estimated by subtracting an atmospheric contribution. The 

atmospheric contribution is calculated by using the equivalent conductivities of each ion (table 2) to estimate conductivity of a solution that has the chloride concentration of the 

sample with each ion in the atmospheric-correction ratio to 

chloride just indicated:

conductivity* = conductivity – 0.13028 •Cl–. (5)

The assumption is that the “acid” rain caused by additional sulfate in the rain is mixed with soil and groundwaters 

that have positive alkalinity. The excess H+ in the rain is 

neutralized by this positive alkalinity, and although a negative alkalinity is used in cyclic-salt correction (table 2), the 

hydrogen-ion contribution to rainwater conductivity is 

ignored. Interestingly, this correction is very close to the seasalt correction (0.13142) without excess sulfate.

The relation between conductivity* and total bedrock-derived cation charge, ZBrx, is estimated through a 

regression involving the 1,040 completely analyzed samples. 

A single set of regression coefficients worked for all Puerto 

Rico WEBB rivers:

ZBrx = a•(conductivity*)b (6)

where a (1.08612) and b (0.83239) are regression coefficients 

(r2 = 0.963). Using ZBrx, estimated total dissolved bedrock, 

DBrx′, is determined:

DBrx′ = c•(ZBrx) d+SiO2

[d] (7)

where c and d are regression coefficients determined for each 

river (table 3), and SiO2[d] is the dissolved silica concentration 

expressed as an oxide:

SiO2[d] (mg L–1) = 0.06008•Si (μmol L–1). (8)

The correlation between DBrx′ and DBrx for all 1,040 

samples is r2 = 0.990, and between log(DBrx′) and log(DBrx), 

r

2

 = 0.986. Accordingly, both DBrx and DBrx′, about 4,650 

samples (figs. 2–6), were used in load calculations. Instantaneous dissolved-bedrock erosion rate is the product of either 

DBrx or DBrx′ and instantaneous runoff rate. The large number of determinations of either DBrx or DBrx′ gives considerably more rigor to estimates of long-term bedrock erosion 

through leaching.
N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_02apx1.ai

EXPLANATION

Daily runoff—MeanDaily runoff—RangeLong-term mean runoff

Suspended solids

Conductivity onlyConductivity, chloride, silica

Major storm

Landslide day—Runoff rate sufficient to permit landslides

Complete chemistryChemistry, with solids

Chloride—Exceptionally high concentrationCalcite—Supersaturated sample

1991

0.005

0.01

0.1

1

10

100

500

1992 1993

Runoff rate, in millimeters per hour

1994 1995 1996 1997 1998 1999

Year

2000 2001 2002 2003 2

Figure 2. Data record for stream-gaging station Río Canóvanas near Campo Rico, Puerto Rico, station number 50061800. Daily range of runoff (realrunoff, and date on which water sample collected and type of analysis (Stallard and Murphy, 2012). 
1991 0.005 0.01 0.1110 100

500

1992 1993

Runoff rate, in millimeters per hour

1994 1995 1996 1997 1998 1999

Year

2000 2001 2002 2003 

EXPLANATION

Daily runoff—MeanDaily runoff—RangeLong-term mean runoff

Suspended solids

Conductivity onlyConductivity, chloride, silica

Major storm

Landslide day—Runoff rate sufficient to permit landslides

Complete chemistry

Chemistry, with solids

Chloride—Exceptionally high concentrationPotassium—High potassium but not high chloride concentration

N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_03apx1.ai

Figure 3. Data record for stream-gaging station Río Cayaguás at Cerro Gordo, Puerto Rico, station number 50051310. Daily range of runoff (real-timand date on which water sample collected and type of analysis (Stallard and Murphy, 2012). 
N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_04apx1.ai

Silica—Exceptionally low concentration

EXPLANATION

Daily runoff—MeanDaily runoff—RangeLong-term mean runoff

Suspended solids

Conductivity onlyConductivity, chloride, silica

Major storm

Landslide day—Runoff rate sufficient to permit landslides

Complete chemistry

Chemistry, with solids

Chloride—Exceptionally high concentrationPotassium—High potassium but not high chloride concentration

1991

0.005

0.01

0.1

1

10

100

500

1992 1993

Runoff rate, in millimeters per hour

1994 1995 1996 1997 1998 1999

Year

2000 2001 2002 2003 2

Figure 4. Data record for stream-gaging station Río Mameyes near Sabana, Puerto Rico, station number 50065500. Daily range of runoff (real-time and date on which water sample collected and type of analysis (Stallard and Murphy, 2012). 
N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_05apx1.ai

1991

0.005

0.01

0.1

1

10

100

500

1992 1993

Runoff rate, in millimeters per hour

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Silica—Exceptionally low concentration

EXPLANATION

Daily runoff—MeanDaily runoff—RangeLong-term mean runoff

Suspended solids

Conductivity onlyConductivity, chloride, silica

Major storm

Landslide day—Runoff rate sufficient to permit landslides

Complete chemistryChemistry, with solids

Chloride—Exceptionally high concentrationPotassium—High potassium but not high chloride concentration

Year

Figure 5. Data record for stream-gaging station Río Icacos near Naguabo, Puerto Rico, station number 50075000. Daily range of runoff (real-time dand date on which water sample collected and type of analysis (Stallard and Murphy, 2012). 
N:\Jeff\den11_hwcg00_0224_pp_murphy\appendix_1_figures\BW_figures\BWfigure_06apx1.ai

1991

0.005

0.01

0.1

1

10

100

500

1992 1993

Runoff rate, in millimeters per hour

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

Silica—Exceptionally low concentration

EXPLANATION

Daily runoff—MeanDaily runoff—RangeLong-term mean runoff

Suspended solids

Conductivity onlyConductivity, chloride, silica

Major storm

Landslide day—Runoff rate sufficient to permit landslides

Complete chemistryChemistry, with solids

Chloride—Exceptionally high concentrationPotassium—High potassium but not high chloride concentration

Year

Figure 6. Data record for stream-gaging station Quebrada Guabá near Naguabo, Puerto Rico, station number 50074950. Daily range of runoff (realrunoff, and date on which water sample collected and type of analysis (Murphy and Stallard, 2012). First 1.6 and final 2.7 years of daily runoff were eIcacos data record.
278 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Suspended Bedrock and Particulate Organic Carbon

River-borne sediment is composed of material from three 

sources: primary and secondary mineral matter derived from 

the weathering of bedrock, water that has been incorporated 

by secondary minerals during weathering, and organic matter. For about two-thirds of the measurements of suspended 

sediment (see Stallard and Murphy, 2012, and appendix 2), 

we also measured loss on ignition (LOI)—the weight loss of 

samples that were dried (at 105°C) upon heating up to 550°C. 

Studies summarized in Mackenzie (1957) and Mackenzie 

and Caillère (1979) show that in heating to 550°C, all of the 

clays and sesquioxides lose nonstructural water (most below 

200°C). In addition, all sesquioxides, most kaolins, and 

substantial illites and vermiculites lose lattice water below 

550°C; however, smectites, talcs, and chlorites do not. Talc 

and chlorites are not reported from soils or sediments; smectites are rare. Nonmineral organic carbon and most mineral 

carbon (coal and graphite) are also oxidized in this range. 

Carbonates, which are absent in humid-tropical soils and sediment, are generally stable.

In this study, the weight of the sample after heating to 

550°C is equated with “suspended bedrock” (SBrx). The 

sample dried to 105°C is referred to as suspended solids or 

suspended sediment (SSol). For samples in which suspended 

bedrock was not measured, regressions for each river relate 

SBrx to SSol:

LOI = SSol–SBrx, (11) 

and

SBrx = SSol•(a+b•log(SSol)), (12)

where a and b are regression coefficients (table 4). 

Particulate organic carbon was not measured on most of 

the sediment samples; instead, LOI was used as a surrogate 

measurement. LOI is often equated with organic matter; 

however, this procedure is not used with samples that are 

rich in kaolinite, halloysite, or iron and aluminum sequioxides such as the WEBB samples. Instead, it was assumed 

here that particulate organic carbon is a fixed fraction of 

LOI for all the WEBB samples. This fraction was determined such that the 15-year yield of SSol for the Río Icacos 

consisted of 1 percent carbon, a value that is typical of rivers 

elsewhere in the world (Stallard, 1998). The result is that 

LOI for the WEBB rivers consists of 11 percent carbon. 

Because pure organic matter is about 50 percent carbon by 

mass (Stallard, 1998), 11 percent of LOI is the noncarbon 

elements in organic matter, and 78 percent is water. For the 

forested rivers, at low runoff rates of less than 1 millimeter per hour, the average particulate organic carbon is 2 to 

3.5 percent of SSol (Stallard, 2012b, its tables 2, 5).

Calculation of Discharge, Constituent 

Loads, Runoff, and Constituent Yields

In order to calculate yields, the real-time discharge and 

water-quality datasets needed to be merged. This merger 

matched timestamps in the water-quality dataset with those 

in the real-time-discharge dataset, and interpolated where 

necessary. In addition to pairing water quality with discharge, 

additional properties were appended to each record in the 

water-quality dataset. These properties included runoff rate 

(R), days since the last landslide day, estimated base flow, and 

dlog(QS)/dt. Runoff rate was typically used for comparison, 

because it is area-normalized.

Of the variety of approaches that could be used to estimate constituent loads, the load estimator program LOADEST (Runkel, and others, 2004) was chosen. LOADEST 

estimates constituent loads in streams and rivers given a time 

series of streamflow, additional independent variables, and 

a constituent concentration. LOADEST assists the user in 

developing a regression model of constituent load (calibration). The explanatory variables normally used in LOADEST 

are discharge, seasonal cycles (sine and cosine of time), and 

time. The formulated regression model is then used to estimate 

loads throughout a user-specified time interval (estimation).

The construction of the input data files used by LOADEST from our two types of datasets was automated. For each 

river, the LOADEST calibration file is derived from relevant 

entries in the water-quality file, and the LOADEST estimation 

file is derived from the real-time discharge-data file. LOADEST requires only minor adaptations of these two datasets. 

First, the shortest time step allowed for the time series of 

discharge and other data in the estimation file is one hour. 

This time step requirement requires subsampling of eastern 

Puerto Rico real-time discharge data into one-hour intervals 

(which creates a small (less than 0.1 percent) difference with 

Table 3. Coefficients of a model relating bedrock-derived cation 

charge, ZBrx, to dissolved bedrock, DBrx (DBrx=c • ZBrxd+SiO2) in 

five watersheds, eastern Puerto Rico.

Watershed Coefficient Correlation1 Count

c d

Canóvanas –1.431 0.955 1.000 163

Cayaguás –1.279 0.914 1.000 127

Mameyes –1.348 0.958 1.000 177

Icacos –1.393 0.941 0.999 249

Guabá –1.398 0.941 0.997 156 1

Correlation between log(DBrx) and predicted log(DBrx).

Table 4. Coefficients for a model relating suspended bedrock, 

SBrx, to suspended solids, SSol (SBrx=SSol • (a+b • log(SSol))) in

five watersheds, eastern Puerto Rico.

Watershed Coefficient Correlation1 Count

a b

Canóvanas 0.809 0.0126 1.000 873

Cayaguás 0.905 –0.0007 1.000 802

Mameyes 0.647 0.0709 1.000 133

Icacos 0.720 0.0477 1.000 646

Guabá 0.651 0.0696 1.000 1,035 1

Correlation between log(SBrx) and log(SSol).
Appendix 1 279

the runoff values calculated using all runoff measurements). 

Second, model run times allowed use of only the simplest 

option for standard-error calculation (defined by LOADEST 

parameter SEOPT).

The SEOPT parameter allows selections of increasingly rigorous calibration options. The calibration and 

estimation procedures within LOADEST are based on three 

statistical estimation methods: adjusted maximum likelihood estimation, maximum likelihood estimation, and least 

absolute deviation, an alternative to maximum likelihood 

estimation that can be used when residuals are not normally 

distributed. The simplest option for calculating standard 

error, SEOPT=1, calculates standard error for an adjustedmaximum-likelihood-estimation−based linear approximation; standard error is not calculated for maximum likelihood 

estimation and least absolute deviation. The data processing 

for the eastern Puerto Rico dataset required one hour of computer time. The next-level option, SEOPT=2, additionally 

calculates standard errors for maximum likelihood estimation 

and least absolute deviation. That option, SEOPT=2, was run 

for 2 weeks on an otherwise unused computer. On the basis 

of the number of calculations that had been completed at the 

end of 2 weeks, more than 150 days of computer time would 

have been required to calculate standard errors for maximum 

likelihood estimation and least absolute deviation for all 

parameters of interest. Therefore, running the model while 

using SEOPT=2 or 3 was not viable.

To examine the eastern Puerto Rico dataset, a variety of 

yield models was tested in LOADEST. The user can define 

the model to be used in LOADEST; alternatively, LOADEST comes with a suite of regression options (defined by 

LOADEST parameter MODNO). If MODNO is set to zero, 

then LOADEST automatically selects the best model from 

a set of nine models (MODNO=1 to 9; Runkel, and others, 

2004). This suite of models combines linear and quadratic fits 

to the natural log of discharge (ln[discharge]) and time and to 

the sine or cosine of time (sin[time] or cos[time]). A regression to time and to time squared implies a systematic drift in 

chemistry. The effects of increased runoff may be adequately 

represented by the ln(discharge) part of the regression. 

Accordingly, five model options were chosen. LOADEST was 

allowed to select the best option (MODNO=0), which could be 

a model with systematic drift. In addition, all options that did 

not include systematic drift (MODNO=1, 2, 4, and 6) were run 

separately and compared with the selected model. In reporting 

average yields, the results of MODNO=0 and the best driftless 

model are tabulated.

LOADEST was used to calculate the loads of Na+

, K+, 

Mg2+, Ca2+, Cl−

, SO4

2−, Si(OH)4

, DOC, NO3

−

, NH4

+

, –PO4

3−, 

and suspended sediment (Stallard and Murphy, 2012). LOADEST was also used to calculate the yields of DBrx, SBrx, Na*, 

and particulate organic carbon (Stallard, 2012b). Alkalinity 

was calculated from the other models as described below.

Fundamentally, LOADEST is a regression, and biases 

produced by the logarithmic transformation of data and nonnormality of log-transformed data produce problems that can 

invalidate results (Runkel and others, 2004). In particular, zero 

values complicate LOADEST calculations. Mg* and SO4

* 

calculations result in many zero values; Na* and K* rarely; 

and Ca* never. Of these, K+

 acts as a nutrient, and Ca2+ is primarily bedrock derived. Accordingly, only Na* yield, derived 

using LOADEST, is used directly in budget calculations. For 

all other bedrock constituents, the atmospheric-input corrections are made afterwards on calculated constituent yields, by 

using the calculated chloride yields, and in the case of Ca*, on 

additional deposition from Saharan dust (Stallard, 2012b).

Alkalinity loads cannot be estimated directly using 

LOADEST, because in samples collected at highest discharge 

values in the Icacos and Guabá watersheds, alkalinity is 

frequently negative (acidity). The primary source of alkalinity is the weathering of silicate and carbonate minerals. This 

weathering consumes hydrogen ions from the water, releasing 

nontitratable cations but essentially no nontitratable anions. 

Organic acids, sulfuric acid, and nitric acid can cause negative 

alkalinities. In load estimates, alkalinity was calculated from 

the concentrations of all other constituents (concentrations in 

mole units):

Alkalinity = (Na+

+K++NH4

+

+2•(Mg2++Ca2++Sr2+)–

(Cl−+NO3

−

+2•SO4

2−+ZDOC•DOC) (9)

where ZDOC is the negative charge per carbon (equivalents 

per mole). For each river, ZDOC was calculated by adjusting predicted alkalinity in the water-quality data file to 

measured alkalinity such that the ratio of averages was 1:1. 

For the Canóvanas, calculated ZDOC = 0.08 equivalents per 

mole (eq mol−1); Cayaguás, ZDOC = 0.06 Eq mol−1; Mameyes, 

ZDOC = 0.07 Eq mol−1; Icacos, ZDOC = 0.05 Eq mol−1, and 

Guabá, ZDOC = 0.05 Eq mol−1. For comparison, Tardy and 

others (2005) estimate an optimum ZDOC of 0.064 Eq mol−1 for 

Amazonian lowland rivers.

Other samples showed anomalous trends on plots of 

concentration against runoff rate (figs. 2–6). Some of these 

anomalies were caused by processes that are exceptional, 

such as high-chloride storms, that are not related to the 

explanatory variables normally used by LOADEST—discharge, seasonal cycles, and time trends. Statistically these 

samples are outside a normal distribution, and they were 

not included in load estimation. High-chloride samples are 

caused when winds during major storms blow exceptionally 

large amounts of seasalt inland (Stallard, 2012a). Samples 

collected during high-chloride events were censored for 

major seawater constituents (Na+, K+, Mg2+, Ca2+, Cl−, SO4

2−). 

These same samples also had exceptionally low NO3

−, perhaps reflecting rainwater composition (Stallard and Murphy, 

2012), and NO3

− was also censored. Following Hurricane 

Georges, a high-chloride event, a number of subsequent 

events demonstrated high potassium, but not high concentrations of chloride and other seasalt constituents. The elevated 

potassium is likely attributed to the decay of downed vegetation (Stallard and Murphy, 2012). High-potassium samples 

were censored only for potassium. A few events affecting the 
280 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

Icacos River during the first several years of the program had 

exceptionally low silica concentrations (Stallard and Murphy, 

2012). Because silica concentrations in most event samples 

during this same time interval were not low, silica concentrations in low-silica samples were also censored.

Calculation of Runoff and Yield 

Percentile Ranges

To compare equivalent samples among rivers, runoff 

rates were ranked by their overall contribution to annual 

runoff. This ranking allowed equivalent classes of samples 

to be compared among the five rivers despite differences 

in annual runoff. All water-quality samples were assigned 

into runoff-rate percentile classes, similar to the approach 

used by the U.S. Geological Survey Water Watch Web site 

(http://water.usgs.gov/waterwatch/) in monitoring flood and 

drought conditions in the United States. Water Watch ranks 

discharge into percentile classes based on the amount of time 

a river flows in a given discharge range and compares daily 

discharge to this ranking. For this report, where the focus is 

on mass balance, percentile classes (0–10, >10–25, >25–50, 

>50–75, >75–90, >90–95, >95–99, and >99 percent) are 

based on runoff and yields. For example, the tenth percentile 

corresponds to a runoff rate below which 10 percent of the 

total runoff has been discharged from the basin. Likewise, 

the tenth percentile in sediment yield corresponds to a runoff 

rate below which 10 percent of the total sediment mass has 

been discharged from the basin. When we compared rivers 

based in percentile classes, our implicit assumption was that 

each class is hydrologically equivalent in each river (see 

Stallard and Murphy, 2012). When base flow is referred to 

in this text, it typically represents the samples in the 0-to10-percent range.

To calculate percentile classes, the volume of water or 

mass of a constituent being discharged at a particular runoff 

was assigned to and totaled in a logarithmic runoff bin, with 

20 bins per log cycle. For runoff, the real-time discharge-data 

files were used, whereas for constituent yields, the individual 

output load files of LOADEST were used. The bins were 

cumulatively totaled and normalized by the total volume of 

water or constituent mass discharged from the watershed for 

the period 1991–2005. Percentile classes for each constituent 

were then calculated from the normalized mass-accumulation 

curve for that constituent. Each sample in the water-quality 

database was then assigned to a runoff percentile class and 

rising and falling stages were distinguished on the basis of 

dlog(Qs

)/dt. Predicted constituent yields and instantaneous 

runoff were totaled for each percentile class, and an average 

concentration for each percentile class was then calculated 

by dividing total yield for each constituent by total runoff 

within that class. 

Dataset Features and Characteristics

The combined datasets are presented in two ways: a 

graphical time series of daily mean runoff (figs. 2–6), simplified sample information (tables 5, 6), and annual summaries 

of the same information. The simple classification is used to 

assess the degree of completeness of sample analyses and 

unusual chemical characteristics. The degree of completeness includes a chemistry characterization and a sediment 

characterization. In terms of increasing degrees of analysis 

for chemistry, we distinguished among (1) samples with only 

conductivity measured, (2) samples with at least conductivity, Cl−, and Si(OH)4

 measured, and (3) samples with 

complete chemistry. We also distinguished the presence or 

absence of suspended sediment data. Special chemical characteristics are included because of the way several of these 

were handled in load-estimation calculations and because 

their biogeochemical significance is discussed in other 

chapters of this report. These special chemical characteristics 

are (1) high-chloride samples, (2) normal-chloride, highpotassium samples, (3) low-silica samples, and (4) samples 

that are supersaturated with respect to calcite.

The sampling design used in the Puerto Rico WEBB 

program is clearly evident by the distribution of sampledata points in figures 2 through 6. Two primary types of 

samples were collected, manual “grab” samples and automated “event” samples (appendix 2). Grab samples were 

collected by researchers or a field crew who visited each 

site at regular intervals. Most samples collected during 

low runoff (figs. 2–6) were grab samples, whereas samples 

collected during high runoff were event samples. At intermediate runoff, relatively fewer samples were collected, an 

artifact of the sampling protocol. Owing to the large volume 

of samples collected by automated sampling, the stage 

threshold for event sampling was adjusted, typically by an 

increase, first in 1994 and again in 1997, with the objective 

of sampling the biggest events. This increase is reflected 

by upward shifts in the event-data distribution evident in 

figures 2 through 6. Samples in the intermediate zone were 

typically collected during multipeak events and during falling stage after single events.

Comparison of Hydrologic Sampling in 

the Eastern Puerto Rico Large-Scale 

Study with Hydrologic Sampling in 

Other Large-Scale Studies

In many settings, rare, high-energy hydrologic events 

play a major, often dominant, role in the mobilization of riverborne materials from hillslopes and stream channels and in the 

transport of these materials downriver (Wolman and Miller, 

1960). In the WEBB watersheds described in this report, work 

on water quality emphasized event sampling. From 1991 
Table 5. Annual hydrologic characteristics, data quality, and sampling effort in five watersheds, eastern Puerto Rico.—Continued[mm, millimeters; mm h−1, millimeters per hour; km2, square kilometers; -- no samples]

Year

Annual

runoff

total

(mm)

Minimum

runoff rate

(mm h−1)

Minimum 

daily

runoff rate

(mm h−1)

Average

runoff rate

(mm h−1)

Maximum

daily

runoff rate

(mm h−1)

Maximum

runoff rate

(mm h−1)

Number of

landslide

day periods

Total of

landslide

day runoff

(mm)

Days

of gaps

Sediment

only

Conductivity

only

Conductivity

and sediment

Conductivity,

chloride,

silica

Conductivit

chloride, 

silica,

sediment

Canóvanas (station 50061800, area 25.49 km2)

1991 471 0.010 0.011 0.054 1.51 8.7 -- -- 28.2 -- -- -- -- -- 

1992 907 0.008 0.013 0.103 1.58 20.6 -- -- 86.3 24 -- -- -- -- 

1993 667 0.014 0.014 0.076 2.79 16.2 -- -- 21.6 -- -- -- -- 13 1994 371 0.009 0.010 0.042 0.71 2.5 -- -- 2.5 -- 8 136 2 16 

1995 810 0.011 0.012 0.092 3.45 14.7 -- -- -- -- 27 258 -- 1 

1996 1,449 0.021 0.021 0.165 16.98 40.5 1 449 25.7 -- 5 38 -- 55 1997 697 0.014 0.015 0.080 2.72 29.0 -- -- -- 2 1 37 6 74 1998 1,547 0.010 0.010 0.177 8.09 69.0 1 370 -- -- -- 4 -- 77 

1999 1,314 0.021 0.022 0.150 2.12 16.2 -- -- -- 7 5 54 -- 176 

2000 600 0.010 0.011 0.068 1.49 7.1 -- -- 1.4 -- -- 20 -- 46 

2001 710 0.011 0.012 0.081 3.85 25.3 -- -- -- -- -- 1 1 99 2002 678 0.017 0.018 0.077 2.98 12.7 -- -- -- -- -- 5 -- 13 

2003 1,416 0.014 0.015 0.162 5.99 20.0 1 242 12.9 -- -- -- -- 28 2004 1,539 0.030 0.032 0.175 6.76 53.6 1 215 -- -- -- 2 -- -- 

2005 1,448 0.015 0.032 0.165 2.62 20.0 -- -- 5.3 -- -- -- -- -- 

Total 974 0.008 0.010 0.111 16.98 69.0 4 1,276 183.8 33 46 555 9 598 

Cayaguás (station 50051310, area 26.42 km2)

1991 652 0.033 0.035 0.074 3.71 27.0 -- -- -- -- -- -- -- -- 

1992 1,546 0.032 0.033 0.176 3.25 27.8 -- -- -- 22 -- -- -- -- 

1993 1,603 0.050 0.051 0.183 4.37 21.2 -- -- -- -- -- -- -- 19 

1994 852 0.030 0.030 0.097 3.55 31.4 -- -- 19.3 -- -- 35 1 15 

1995 939 0.035 0.035 0.107 1.96 13.4 -- -- 10.1 -- 2 44 -- -- 

1996 2,240 0.030 0.049 0.255 19.86 48.7 1 551 41.3 1 -- 19 7 15 1997 1,759 0.026 0.032 0.201 6.79 28.0 1 196 41.0 -- -- 38 4 99 1998 3,297 0.033 0.053 0.376 15.86 43.3 3 1,024 156.5 -- 2 19 -- 133 

1999 2,123 0.020 0.033 0.242 4.03 17.1 -- -- -- -- -- 23 1 89 

2000 1,339 0.037 0.041 0.152 4.61 27.4 -- -- 15.9 3 -- 33 -- 113 

2001 1,061 0.028 0.032 0.121 5.52 54.8 1 42 3.1 -- -- 4 -- 101 

2002 904 0.041 0.042 0.103 2.09 5.5 -- -- 18.5 -- -- 1 -- 24 

2003 1,913 0.042 0.043 0.218 5.55 21.2 1 220 -- -- -- 2 -- 68 

2004 1,768 0.055 0.058 0.201 5.71 37.9 1 105 -- 2 -- 2 -- 28 

2005 2,237 0.027 0.029 0.255 3.72 16.0 2 78 134.0 -- -- -- -- -- 

Total 1,615 0.020 0.029 0.184 19.86 54.8 10 2,217 439.7 28 4 220 13 704 

Mameyes (station 50065500, area 17.82 km2)

1991 2,329 0.051 0.053 0.266 3.66 40.8 -- -- 1.0 -- -- -- -- 2 

1992 2,901 0.071 0.091 0.330 4.29 60.6 1 52 12.1 10 -- 1 -- 1 

1993 2,690 0.071 0.095 0.307 3.02 38.5 -- -- 45.2 -- -- -- 7 74 

1994 1,677 0.041 0.043 0.191 2.90 35.1 -- -- 4.1 -- 17 98 -- 17 

1995 2,323 0.060 0.065 0.265 1.86 18.8 -- -- -- -- 15 42 -- -- 

1996 2,929 0.043 0.068 0.333 10.72 65.2 1 308 47.5 -- 10 53 12 73 

1997 2,428 0.060 0.063 0.277 3.01 65.3 -- -- 64.0 -- 3 1 67 39 

1998 3,646 0.079 0.087 0.416 8.42 72.0 1 316 -- -- 21 14 35 53 

1999 3,247 0.058 0.075 0.371 3.70 63.9 -- -- -- -- 16 13 3 37 2000 2,218 0.059 0.062 0.253 4.04 62.5 -- -- -- -- 5 6 3 91 
Table 5. Annual hydrologic characteristics, data quality, and sampling effort in five watersheds, eastern Puerto Rico.—Continued[mm, millimeters; mm h−1, millimeters per hour; km2, square kilometers; -- no samples]

Year

Annual

runoff

total

(mm)

Minimum

runoff rate

(mm h−1)

Minimum 

daily

runoff rate

(mm h−1)

Average

runoff rate

(mm h−1)

Maximum

daily

runoff rate

(mm h−1)

Maximum

runoff rate

(mm h−1)

Number of

landslide

day periods

Total of

landslide

day runoff

(mm)

Days

of gaps

Sediment

only

Conductivity

only

Conductivity

and sediment

Conductivity,

chloride,

silica

Conductivit

chloride, 

silica,

sediment

Mameyes (station 50065500, area 17.82 km2)—Continued

2001 2,633 0.067 0.072 0.301 4.20 78.8 2 234 -- -- -- 29 13 138 

2002 2,405 0.059 0.063 0.275 3.41 56.2 -- -- 2.3 -- -- -- -- 28 

2003 3,400 0.073 0.077 0.388 9.57 89.8 2 575 -- 3 -- 7 -- 275 2004 3,553 0.053 0.060 0.405 8.01 88.6 2 397 -- -- -- 34 -- 7 

2005 2,929 0.025 0.027 0.334 2.19 26.0 -- -- -- -- -- -- -- -- 

Total 2,753 0.025 0.027 0.314 10.72 89.8 9 1,883 176.3 13 87 298 140 835 

Icacos (station 50075000, area 3.26 km2)

1991 3,089 0.113 0.113 0.353 4.76 30.7 1 147 71.6 -- -- -- -- 5 

1992 3,552 0.066 0.077 0.404 5.04 39.4 -- -- 16.5 1 -- -- 10 -- 

1993 2,935 0.059 0.097 0.335 2.13 12.5 -- -- 11.2 -- -- 6 -- 19 

1994 2,384 0.071 0.088 0.272 3.65 25.8 -- -- 48.3 1 3 57 -- 5 

1995 3,512 0.119 0.134 0.401 3.95 24.6 -- -- -- -- 17 49 -- -- 

1996 4,989 0.109 0.122 0.568 17.93 55.6 2 674 -- 1 5 53 -- 46 

1997 3,934 0.117 0.130 0.449 5.07 32.7 1 135 37.6 -- 1 4 2 84 

1998 4,960 0.100 0.114 0.566 7.48 64.1 2 539 12.6 -- 11 40 1 151 

1999 4,193 0.090 0.099 0.479 5.91 19.7 2 356 -- -- -- 37 1 137 

2000 3,328 0.101 0.126 0.379 4.53 22.7 -- -- -- 1 -- 5 -- 48 

2001 3,395 0.116 0.121 0.388 5.89 37.9 3 483 -- -- -- 23 1 217 

2002 3,102 0.130 0.142 0.354 6.38 35.1 1 180 3.2 -- -- 8 -- 36 

2003 4,169 0.119 0.131 0.476 13.86 86.0 3 807 -- -- -- 1 -- 97 

2004 4,606 0.123 0.134 0.524 10.97 57.5 2 685 -- -- -- 2 -- 74 

2005 4,295 0.067 0.076 0.490 5.11 21.1 1 301 5.9 -- -- -- -- -- 

Total 3,762 0.059 0.076 0.429 17.93 86.0 18 4,307 206.8 4 37 285 15 919 

Guabá (station 50074950, 0.114 km2)

1991 2,701 0.074 0.074 0.308 4.44 5.0 1 149 365.0 1 -- -- -- -- 

1992 4,211 0.068 0.076 0.479 4.71 43.9 1 38 201.7 -- -- -- -- 5 

1993 2,962 0.063 0.104 0.338 3.50 57.2 1 53 67.7 -- -- 1 -- 8 

1994 2,512 0.041 0.079 0.287 5.36 45.6 1 206 9.1 1 -- 24 -- -- 

1995 2,764 0.063 0.096 0.315 2.89 27.6 -- -- 4.2 -- 2 24 -- -- 1996 4,114 0.063 0.090 0.468 20.26 91.0 3 735 18.5 -- 5 31 -- 59 1997 3,484 0.041 0.074 0.398 4.52 82.1 1 107 -- -- 2 11 1 64 

1998 4,247 0.040 0.069 0.485 12.60 93.3 3 714 -- -- 1 10 -- 94 1999 4,117 0.055 0.055 0.470 7.10 73.2 2 475 -- -- -- 26 -- 78 

2000 3,487 0.057 0.061 0.397 4.14 52.0 -- -- -- -- -- 2 -- 17 

2001 3,523 0.094 0.115 0.402 6.79 56.2 3 637 15.9 -- -- 2 -- 152 2002 2,963 0.079 0.084 0.338 7.89 105.1 1 232 20.0 -- -- -- -- 30 2003 4,453 0.090 0.096 0.508 18.57 118.8 3 1052 258.0 -- -- 1 -- 60 

2004 4,588 0.107 0.110 0.522 11.36 13.1 2 769 366.0 -- -- 11 -- 8 2005 4,255 0.047 0.060 0.486 5.27 5.8 2 422 365.0 -- -- -- -- -- 

Total 3,624 0.040 0.055 0.414 20.26 118.8 24 5588 1691.2 2 10 143 1 575 
Appendix 1 283

Table 6. Number and types of samples collected in different discharge classes and special sample types in five rivers, eastern 

Puerto Rico.—Continued

[--, no samples]

Discharge classes

and special

sample types

Number

of samples

Sediment

only

Conductivity

only

Conductivity

and sediment

Conductivity,

chloride,

silica

Conductivity,

chloride,

silica,

sediment

Full

chemistry

Full

chemistry,

sediment

Canóvanas

0–10 percent, rising 26 -- -- 2 -- 15 -- 9

0–10 percent, falling 45 -- -- 9 -- 19 1 16

>10–25 percent, rising 16 -- -- 3 -- 5 -- 8

>10–25 percent, falling 33 1 -- 9 -- 13 1 9

>25–50 percent, rising 53 3 3 38 -- 9 -- --

>25–50 percent, falling 100 3 3 65 1 26 -- 2

>50–75 percent, rising 136 10 11 83 -- 28 -- 4

>50–75 percent, falling 384 12 22 215 2 110 -- 23

>75–90 percent, rising 223 2 2 47 2 146 -- 24

>75–90 percent, falling 307 2 5 78 3 189 -- 30

>90–95 percent, rising 24 -- -- 2 1 13 -- 8

>90–95 percent, falling 19 -- -- 1 -- 12 -- 6

>95–100 percent, rising 3 -- -- -- -- 1 -- 2

>95–100 percent, falling 1 -- -- -- -- -- -- 1

High chloride 29 -- -- 2 -- 11 -- 16

High potassium -- -- -- -- -- -- -- --

Low silica -- -- -- -- -- -- -- --

No runoff 2 -- -- 1 -- 1 -- --

Cayaguás

0–10 percent, rising 60 -- -- 14 -- 40 1 5

0–10 percent, falling 87 -- -- 18 1 56 1 11

>10–25 percent, rising 50 2 -- 11 -- 32 -- 5

>10–25 percent, falling 68 -- -- 17 1 40 -- 10

>25–50 percent, rising 37 -- -- 9 -- 24 -- 4

>25–50 percent, falling 80 2 -- 21 -- 50 1 6

>50–75 percent, rising 55 4 -- 8 1 32 1 9

>50–75 percent, falling 119 11 -- 23 5 69 -- 11

>75–90 percent, rising 130 3 2 32 1 88 -- 4

>75– 90 percent, falling 192 5 -- 41 1 136 -- 9

>90–95 percent, rising 37 -- 1 5 -- 26 -- 5

>90–95 percent, falling 39 -- 1 8 -- 27 -- 3

>95–100 percent, rising 24 -- -- 5 -- 5 -- 14

>95–100 percent, falling 23 1 -- 4 3 10 -- 5

High chloride 1 -- -- -- -- 1 -- --

High potassium 2 -- -- -- -- 1 -- 1

Low silica -- -- -- -- -- -- -- --

No runoff 59 -- -- 4 -- 53 -- 2

Mameyes

0–10 percent, rising 6 -- 3 -- -- 2 -- 1

0–10 percent, falling 50 -- 1 4 -- 11 7 27

>10–25 percent, rising 8 -- 1 1 -- 2 -- 4

>10–25 percent, falling 32 -- -- 3 1 9 -- 19

>25–50 percent, rising 3 -- -- 2 -- 1 -- --

>25–50 percent, falling 33 2 1 6 -- 11 1 12

>50–75 percent, rising 10 -- -- 1 1 7 -- 1

>50–75 percent, falling 39 2 4 19 -- 9 -- 5

>75–90 percent, rising 161 1 10 32 6 93 -- 19

>75–90 percent, falling 446 -- 15 98 17 285 -- 31

>90–95 percent, rising 202 1 22 37 23 113 1 5

>90–95 percent, falling 344 4 20 80 52 179 -- 9

>95–100 percent, rising 86 1 3 7 8 52 -- 15

>95–100 percent, falling 87 2 7 7 9 47 -- 15

High chloride 34 -- -- 1 14 7 -- 12

High potassium 13 -- -- -- 9 4 -- --

Low silica 4 -- -- -- -- 2 -- 2

No runoff 3 -- 1 -- -- 1 -- 1
284 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

through 2005, 507 hydrologic events were sampled by automated samplers in five rivers during 263 storms, including all 

major hurricanes during that period (figs. 2–6). Although the 

climate of eastern Puerto Rico is weakly seasonal (more rain 

in May and in August through December) large storms occur 

throughout the year in response to cold fronts from November 

to May and tropical disturbances from June to October. In this 

study, 332 samples that were analyzed for suspended sediment and chemistry were collected at runoff rates greater than 

20 millimeters per hour (mm h−1), and some chemical samples 

were even collected at runoff rates of almost 100 mm h−1. 

Without comparison with other sampling programs, it is 

difficult for the reader to assess sampling effort necessitated 

Table 6. Number and types of samples collected in different discharge classes and special sample types in five rivers, eastern 

Puerto Rico.—Continued

[--, no samples]

Discharge classes

and special

sample types

Number

of samples

Sediment

only

Conductivity

only

Conductivity

and sediment

Conductivity,

chloride,

silica

Conductivity,

chloride,

silica,

sediment

Full

chemistry

Full

chemistry,

sediment

Icacos

0 to 10 percent, rising 19 -- -- 1 -- 9 -- 9

0 to 10 percent, falling 55 -- -- 5 1 19 1 28

>10 to 25 percent, rising 12 -- -- 2 -- 9 -- 1

>10 to 25 percent, falling 49 1 -- 10 -- 22 4 12

>25 to 50 percent, rising 12 -- -- 2 -- 5 1 4

>25 to 50 percent, falling 47 1 -- 14 2 16 1 13

>50 to 75 percent, rising 9 1 -- -- -- 7 -- 1

>50 to 75 percent, falling 22 -- -- 6 -- 13 -- 3

>75 to 90 percent, rising 171 -- 1 44 1 112 -- 13

>75 to 90 percent, falling 330 1 11 65 2 229 -- 22

>90 to 95 percent, rising 222 -- 16 53 3 134 -- 16

>90 to 95 percent, falling 196 -- 5 49 3 126 -- 13

>95 to 100 percent, rising 83 -- 2 17 3 45 1 15

>95 to 100 percent, falling 67 -- 2 13 -- 44 1 7

High chloride 62 -- -- -- -- 42 -- 20

High potassium 24 -- -- 1 -- 20 -- 3

Low silica 89 -- -- 1 -- 28 1 59

No runoff 32 -- 1 2 -- 27 -- 2

Guabá

0–10 percent, rising 16 -- -- 1 -- 13 1 1

0–10 percent, falling 43 -- -- 12 -- 13 -- 18

>10–25 percent, rising 9 -- -- 1 -- 6 -- 2

>10–25 percent, falling 49 -- 1 15 1 18 1 13

>25–50 percent, rising 19 -- -- 8 -- 8 -- 3

>25–50 percent, falling 43 -- -- 12 -- 20 -- 11

>50–75 percent, rising 18 -- 4 3 -- 9 -- 2

>50–75 percent, falling 23 -- -- 4 -- 16 -- 3

>75–90 percent, rising 63 -- 1 16 -- 42 -- 4

>75–90 percent, falling 22 1 -- 5 -- 14 -- 2

>90–95 percent, rising 104 -- 2 22 -- 74 1 5

>90–95 percent, falling 126 -- 2 22 -- 93 -- 9

>95–100 percent, rising 57 -- -- 3 -- 44 -- 10

>95–100 percent, falling 56 -- -- 1 -- 50 -- 5

High chloride 50 -- -- -- -- 42 -- 8

High potassium 40 -- -- -- -- 40 -- --

Low silica 83 -- -- -- -- 25 1 57

No runoff 72 -- -- 18 -- 48 -- 6

by the need to accurately describe storm-related processes 

at the eastern Puerto Rico WEBB site. For such a comparison, a large data collection assembled by Alexander and 

others (1996a, 1996b, 1998) was chosen. This collection 

contains data describing rivers from the U.S. Geological 

Survey Hydrologic Benchmark Network for 1962 to 1995 

and from the U.S. Geological Survey National Stream 

Quality Accounting Network (NASQAN) for 1973 to 1995. 

This combined database is referred to here as Water Quality 

Network–1996 (WQN96). The average record duration per 

site in the WQN96 database is 18.36 years, comparable to 

the 15 years of the WEBB dataset. Watersheds from these 

networks represent a broad cross section of rivers in the 
Appendix 1 285

Table 7. Number of samples in three river-hydrology datasets 

that have sufficient data to calculate total denudation rates.

[WQN96, database that combines data of the Hydrologic Benchmark and 

National Assessment of the Quality of Water Networks (Alexander and others, 

1996b); WEBB, Water, Energy, and Biogeochemical Budgets; <, less than; 

>, more than; mm h−1, millimeters per hour]

Runoff rate of 

sample

Number

of rivers

in class

Number

of samples

Samples 

per river

Samples

per river

per year

All rivers in WQN96 with chemistry or sediment data

Total 679 211,281 311 16.73

All rivers in WQN96 with sufficient data 

to calculate total denudation rates

<1 mm h−1 548 30,187 55 2.96

>1 mm h−1 43 96 2 0.12

>10–20 mm h−1 0 0 – –

>20 mm h−1 0 0 – –

Total 548 30,283 55 2.97

All tropical rivers in WQN96 with sufficient data 

to calculate total denudation rates

<1 mm h−1 15 980 65 3.51

>1 mm h−1 11 35 3 0.17

>10–20 mm h−1 0 0 – –

>20 mm h−1 0 0 – –

Total 15 1,015 68 3.64

Eastern Puerto Rico WEBB samples with sufficient data 

to calculate total denudation rates

<1 mm h−1 5 922 184 13

>1 mm h−1 5 3,398 680 49

>10–20 mm h−1 5 932 186 13

>20–30 mm h−1 5 303 61 4.3

>30–40 mm h−1 5 124 25 1.8

>40–50 mm h−1 5 53 11 0.76

>50–60 mm h−1 3 32 11 0.76

>60–70 mm h−1 3 20 7 0.48

>70–80 mm h−1 2 11 6 0.39

>80–90 mm h−1 1 5 5 0.36

>90 mm h−1 1 3 3 0.21

Total 5 4,320 864 62

United States, including 8 in Hawaii, 6 in Puerto Rico, and 1 

in Guam. Most of the rivers are described by a set of chemical and physical measurements that match those sampled in 

the Puerto Rico WEBB rivers. The programs that produced 

the WQN96 database sampled extreme flows in many of the 

rivers. To compare sampling efforts with the eastern Puerto 

Rico WEBB program, all sites lacking discharge measurements or having ambiguous drainage areas (lakes, canals, 

and diversion—total of 24) and all sites without a body of 

comparable chemical and physical measurements (less than 

4 suspended-sediment measurements or less than 10 complete chemical analyses for a total of 20) were eliminated 

from comparison. A total of 53 rivers were excluded from the 

comparison, leaving 626 rivers.

When compared with the 626 rivers in the WQN96 

database, the eastern Puerto Rico rivers are among those 

that are wettest and have the greatest runoff. The watersheds 

with the greatest mean-annual runoff in our study are the 

Mameyes, Icacos, and Guabá, which are forested. Only three 

rivers in the WQN96 database had comparable mean-annual 

runoff (the Queets River and North Fork of the Quinault, both 

in Washington; and Honolii Stream, Hawaii). Mean annual 

runoff of the Cayaguás is exceeded by 11 rivers from California, Hawaii, and Washington. Mean annual runoff for the Río 

Canóvanas is exceeded by 26 other rivers. 

A runoff rate of 1 mm h−1 was set as the dividing line 

between low- and high-runoff samples in the eastern Puerto 

Rico WEBB and the WQN96 databases. In the WEBB rivers, 30 to 37 percent of the total annual runoff occurs when 

runoff rates exceed 1 mm h−1 (0.2778 cubic meters per 

second per square kilometer (m3

 s−1 km−2), or 25.41 cubic 

feet per second per square mile (ft3

 s−1 mi−2). A higher runoff 

rate might have been chosen, but very few samples in the 

combined WQN96 database were collected at high runoff 

rates. Only samples that had enough chemical analyses 

to calculate bedrock denudation (table 7) or a charge balance (comprehensive chemistry, table 8) were included. For 

samples with a full charge balance, the WEBB dataset also 

recorded suspended solids; the WQN96 samples usually 

did not. The sampling effort for comprehensive chemistry, 

expressed as samples per river per year (table 8), was about 

five times as great for eastern Puerto Rico WEBB rivers as 

compared with the overall combined WQN96 database. For 

tropical rivers, the WEBB effort was less than three-fold as 

great. For samples collected at rates less than 1 millimeter 

per hour (mm h−1), there was a comparable effort; however, 

for runoff rates greater than 1 mm h−1, the eastern Puerto 

Rico effort was 50-fold as great; 543 samples were collected 

as compared with 149 for the entire combined database or 38 

for the tropical rivers in the database. The maximum runoff 

rates sampled for chemistry in the combined WQN96 database were 12.79 mm h−1 for Biscuit Brook, New York, and 

9.77 mm h−1 for Honolii Stream, Hawaii. The eastern Puerto 

Rico database has 125 samples with runoff rates that exceed 

20 mm h−1 and 3 that even exceed 90 mm h−1. Basin area 

might be a factor in this comparison, and in terms of basin 

area, the WQN96 rivers overlap all the WEBB rivers, except 

the Guabá which has a 0.114-km2

 watershed at the streamgaging station.

Globally, few other event samples of streams have been 

collected and analyzed for sediment and chemistry at runoff 

rates of greater than 5 mm h−1. In the tropics, two samples 

were collected at about 16 mm h−1 by the U.S. Geological 

Survey in Puerto Rico in 1970 during a tropical depression 

(Haire, 1970). Goldsmith and others (2008) collected samples 

in Taiwan during Typhoon Mindulle at runoff rates of as 

much as 7.3 mm h−1, and Hicks and others (2000) collected 

samples in New Zealand during Cyclone Bola at runoff rates 

of as much as 4.8 mm h−1. 

The eastern Puerto Rico WEBB sampling program 

represents the opportunity to assess how physical and biogeochemical processes affect small watersheds at extremely 

high runoff conditions. Because of the extensive sampling of 
286 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

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[WQN96, combined dataset of the Hydrologic Benchmark and National 

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Runoff rate

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Number

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in class

Number

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Samples

per river

Samples

per river

per year

All rivers in WQN96 with chemistry or sediment data

All 679 211,281 311 16.73

All rivers in WQN96 with runoff and comprehensive chemistry

<1 mm h−1 548 26,568 48 2.61

>1 mm h−1 54 149 3 0.15

>10–20 mm h−1 1 1 1 0.05

>20 mm h−1 0 0 – –

All 548 26,717 49 2.62

All tropical rivers in WQN96 with runoff 

and comprehensive chemistry

<1 mm h−1 15 1,167 78 4.18

>1 mm h−1 13 38 3 0.16

>10–20 mm h−1 0 0 – –

>20 mm h−1 0 0 – –

All 15 1,205 80 4.32

Eastern Puerto Rico WEBB samples with runoff 

and comprehensive chemistry

<1 mm h−1 5 314 63 4.5

>1 mm h−1 5 543 109 7.8

>10–20 mm h−1 5 256 51 3.7

>20–30 mm h−1 5 125 25 1.8

>30–40 mm h−1 5 56 11 0.80

>40–50 mm h−1 5 24 5 0.34

>50–60 mm h−1 3 18 6 0.43

>60–70 mm h−1 3 9 3 0.21

>70–80 mm h−1 2 6 3 0.21

>80–90 mm h−1 1 3 3 0.21

>90 mm h−1 1 3 3 0.21

All 5 857 171 12

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Appendix 2. Methods Used to Analyze Water Quality of Four Watersheds in 

Eastern Puerto Rico

By Sheila F. Murphy and Robert F. Stallard

Introduction

The U.S. Geological Survey’s Water, Energy, and Biogeochemical Budgets (WEBB) program in Puerto Rico analyzed a 

core suite of chemical and physical measurements that would 

indicate the major sources and processes affecting water chemistry of the rivers. More than 6,600 samples from both routine 

sampling and storm events were collected and analyzed for 

chemical or physical parameters (or both) from 1991 to 2005 

(appendix 1, its table 5). Standard methods of sample collection, processing, field measurements, and laboratory analysis 

were used throughout the project and are described here.

Field Sampling

Two principal types of samples, grab and storm event, 

were collected during the study. Grab samples were initially 

collected quarterly, but sampling frequency varied depending 

on staff availability. Onsite measurements included pH, conductivity (microsiemens per centimeter, μS cm−1), temperature 

(degrees Celsius, °C), and dissolved oxygen (O2; milligrams 

per liter, mg L−1). Water chemistry samples were collected 

from riverbanks at well-mixed cross sections near each gage 

site by using dip bottles and gloved hands, following methods 

described by Edwards and Glysson (1999). Three 1-liter (L) 

high-density polyethylene (HDPE) bottles were rinsed four 

times sample water and then filled with sample water and 

labeled MIN (for sediment mineralogy), IC (for inorganic 

chemistry), or OC (for organic carbon). One 250-milliliter 

(mL) clear HDPE bottle was similarly rinsed four times, filled 

with sample water, and labeled ARC (for archive). Depth-integrated samples for suspended sediment were periodically collected at the same time as a grab sample, following the method 

of Wilde and others (1999). This sample was collected in a 

preweighed 125-mL, wide-mouth HDPE bottle labeled TSM 

(for total suspended mass). Samples were transported on ice to 

the U.S. Geological Survey Caribbean Water Science Center 

laboratory as soon as possible, typically within a few hours. 

Storm event samples were collected by an ISCO®

sampler, which was automatically triggered when the river 

reached a specified stage height. Once triggered, all 24 bottles 

were filled following a preset program calibrated by using 

sample storm hydrographs. The sampler was then emptied 

and restarted manually. Because a small storm could prevent 

the sampling of a subsequent larger storm, and the number of 

samples generated by the initial stage was often too great to 

process in a reasonable time, threshold stages were gradually 

raised during the study (appendix 1, its figures 2–6). ISCO®

bottles were retrieved and transported to the laboratory for 

processing as soon as the site could be accessed after a storm. 

Conductivity was measured in the laboratory, but because the 

samples could not be measured immediately, temperature, pH, 

and dissolved oxygen were not analyzed.

Sample Filtration and Preservation

Before filtration, storage bottles and quartz-fiber filters 

were cleaned. For major ions (MAJ) and nutrients (NUT), 

250-mL clear high-density polyethylene (HDPE) and 

250-milliliter (mL) amber HDPE bottles, respectively, were 

leached with hot deionized water overnight, then rinsed three 

times with deionized water. For trace elements (TE), 250-mL 

HDPE bottles were soaked overnight with 2 percent hydrochloric acid, followed by three rinses with deionized water 

and drying at 60°C. For dissolved organic carbon (DOC), 

125-mL amber glass bottles were rinsed with deionized water 

and baked for 1 hour at 500°C. Quartz-fiber filters (used to 

capture DOC and particulate organic carbon) were soaked 

overnight in 10 percent hydrochloric acid, rinsed thoroughly 

in deionized water, and baked for 1 hour at 500°C.

For grab samples, weights of all sample bottles were 

recorded upon return to the Caribbean Water Science Center 

laboratory, and the archive (ARC) bottle was frozen. Water in 

the remaining sample bottles was filtered as soon as practicable. 

The 1-liter (L) sediment mineralogy (MIN) sample was filtered 

through a 0.2-micrometer (μm) cellulose acetate (Millipore®) 

filter in a polycarbonate filter apparatus. The filter and funnel 

were rinsed with sample water four times, and the filtrate was 

used to rinse the MAJ and NUT storage bottles four times (the 

final rinse was retained in the bottles for later use, as described 

later in this paragraph). After the entire MIN bottle was filtered, 

the weight of the empty bottle was recorded, and the filter was 

air dried and saved for later mineralogical analysis. A 0.2-μm 

polycarbonate (Nucleopore®) filter was then placed in the same 

polycarbonate filtration apparatus and rinsed three times using 

the rinse water stored in the MAJ and NUT bottles. Approximately 50 mL of sample water from the inorganic chemistry 

(IC) bottle was then filtered and discarded; this step serves to 

“clog” the filter, which ensures that the filtrate is subjected to 

a consistent filter pore size. Sample from the IC bottle was 

then filtered into the MAJ bottle and refrigerated. If the sample 
290 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

filtered rapidly, the filter was left in the holder, but if the MAJ 

sample was excessively slow to filter, the filter was replaced 

with a new 0.2-μm polycarbonate filter, and the filter and 

filter apparatus were rinsed three times with rinse water and 

“clogged” as described above. The IC sample was then filtered 

into the emptied NUT bottle, preserved with 0.25 mL chloroform, and refrigerated. 

Simultaneously, the 125-mL total suspended mass (TSM) 

sample bottle was agitated and filtered through a preweighed 

0.2-μm polycarbonate filter in a Teflon filtration funnel. The 

TSM bottle and filtration apparatus were then rinsed three 

times with rinse water from the MAJ or NUT bottles to wash 

any remaining particulate matter onto the filter. Most of the 

filters were dried at 105°C and then weighed. The dried weight 

minus the prefilter weight represented total suspended sediment. Some filters were placed in a preweighed crucible and 

ashed at 550°C. The final weight minus the prefilter weight 

represented total suspended bedrock.

Sample from the IC bottle was then filtered through 

a new 0.2-μm Nucleopore® filter and the Teflon® filtration 

apparatus. Rinse water from the MAJ and NUT bottles was 

passed through the filter and filter funnel three times into a 

waste container. Approximately 50 mL of sample water from 

the IC bottle was filtered to “clog” the filter, and all filtrate was 

discarded. Sample water from the IC bottle was then filtered 

into the TE bottle and was treated to 1 percent hydrochloric 

acid with ultrahigh purity hydrochloric acid. 

Sample water from the OC (organic carbon) bottle was 

then filtered. In the first 6 years of the study, OC samples were 

pressure filtered through a silver-membrane filter. After 1996 

(because the manufacturer discontinued these filters), organic 

carbon (OC) samples were passed through an acid-washed, 

prebaked, quartz-fiber filter in the Teflon® filtration apparatus. 

The bottle was agitated during filtration. At least half of the 

bottle’s contents was filtered through the quartz-fiber filter into 

a waste container and discarded. The next volume was filtered 

into the DOC bottle and preserved with 0.25 mL concentrated 

phosphoric acid (44 Normal). The remaining OC sample was 

filtered and discarded. The quartz-fiber filter was air dried and 

stored for later particulate organic carbon analysis. 

For event samples, the 1-L ISCO® bottles were refrigerated 

upon return to the laboratory. The sample bottles were weighed 

and processed as soon as possible. Polycarbonate filters and prebaked, rinsed, quartz-fiber filters were weighed and stacked in 

the polycarbonate filtration apparatus, with the quartz-fiber filter 

on top. The filters and filtration apparatus were rinsed twice 

with sample water, and filtrate was fed into a waste container. 

Sample water was then filtered into a NUT bottle, followed by 

a MAJ bottle (both rinsed twice with filtrate), followed by a TE 

bottle (which was not rinsed). The remainder of the 1-L bottle 

was agitated and filtered into a clean glass beaker. A DOC bottle 

was filled from the beaker, and the remaining filtrate was poured 

back into the 1-L ISCO® bottle to rinse remaining sediment onto 

the filter. The storage bottles were preserved in the same manner 

as grab samples. The empty 1-L sample bottle was weighed, 

and this weight was subtracted from the initial bottle weight to 

obtain water-sample weight. Filters were placed in a petri dish 

and dried as pairs; when dry, the filters were weighed together, 

then separately. The weight of sediment on the filters was 

obtained by subtracting filter dry weights from final weights; 

it was then divided by the sample weight to obtain suspendedsediment concentrations. 

Owing to the enormous volume of event samples (even 

after threshold stages were increased), analyses performed on 

storm event samples were eventually reduced to conductivity, 

silica, chloride, and sometimes potassium and suspended sediment. For these analyses, sample water was filtered through 

a 0.2-μm polycarbonate (Nucleopore®) filter into a 30- or 

60-mL “Si/Cl” clear HDPE bottle (which had been leached by 

hot deionized water). The bottle was rinsed three times with 

sample water, filled, and refrigerated. 

Samples were periodically shipped, on ice, to the U.S. 

Geological Survey National Research Program laboratory in 

Boulder, Colo., for laboratory analyses.

Chemical Analyses

Most analyses were performed at the U.S. Geological 

Survey National Research Program laboratory in Boulder, 

Colo. A subset of samples was analyzed at the Kiowa Environmental Chemistry Laboratory of the University of Colorado, 

Boulder; at the Department of Geosciences of the University of Montana, Missoula; or at the U.S. Geological Survey 

Panola Laboratory in Atlanta, Ga., as described below. 

Alkalinity

Alkalinity was determined at the U.S. Geological Survey 

National Research Program laboratory in Boulder, Colo. From 

1991 to 1998, alkalinity was determined on an autotitrator 

that used Gran titration. After 1998, a new autotitrator was 

obtained, and an incremental inflection point titration was used. 

For both methods, a blank, a sample replicate, three standards 

(50, 500, and 1,000 milliequivalents per liter (μeq L−1)), and 

a U.S. Geological Survey standard reference water sample 

(SRWS) (U.S. Geological Survey, 2012) were analyzed every 

14 samples. Samples were reanalyzed if the regression coefficient (r2

) of the slope of the standards was less than 0.99, 

or if replicate samples deviated by more than 10 percent. If 

alkalinity was greater than the highest standard, the sample 

was diluted and reanalyzed. When the incremental inflection 

point titration was used, if alkalinity was below the detection 

limit, then the sample was spiked with 1 mL of 1,000 μeq L−1

KHCO3, reanalyzed, and the spike was then subtracted. 

Major Cations

Major cations (sodium, potassium, magnesium, and calcium) were analyzed with inductively coupled plasma (ICP) 

spectroscopy. From 1991 to 1995, samples were analyzed at 
Appendix 2 291

the Department of Geosciences of the University of Montana. U.S. Geological Survey SRWSs were run approximately every seven samples. If measured concentrations did 

not agree within specified limits of certified values (limits 

varied from element to element, but were always within 

10 percent), samples were reanalyzed. Spike recoveries and 

replicates were also analyzed on 10 percent of the samples, 

randomly selected. 

From 1995 to 2005, major cations were analyzed at the 

U.S. Geological Survey National Research Program laboratory in Boulder, Colo., with a Perkin Elmer Optima 3300 DV®

inductively coupled plasma–atomic emission spectroscopy 

(ICP–AES) by using techniques described by Garbarino and 

Taylor (1979). Five or six calibration standards were analyzed 

for each analysis run; each had an r2 value greater than 0.999. 

Samples with concentrations above the highest standard 

were diluted and reanalyzed. Twelve to 17 laboratory blanks, 

interspersed throughout each analysis run, were used to 

calculate detection limits for each analyte for each analysis 

run. SRWSs, analyzed at a frequency exceeding 30 percent 

of samples, were interspersed throughout each analysis run. 

Each sample was analyzed in triplicate, and the mean of 

the three concentration values was reported. Precision was 

calculated as the standard deviation of the three values; if one 

of the replicates substantially disagreed with the other two, 

it was discarded and the average and standard deviation of 

the remaining two were used to calculate concentration and 

precision. When all three values substantially disagreed, the 

sample was reanalyzed.

Silica

Silica was analyzed in more than 4,600 grab and event 

samples, all at the U.S. Geological Survey National Research 

Program laboratory in Boulder, Colo. Silica was measured 

either by an ultraviolet/visible (UV/Vis) spectrophotometer 

or by ICP–AES, and sometimes by both. For spectrophotometer analyses, a blank, four standards, a SRWS, and a 

laboratory replicate were analyzed on a Perkin Elmer® UV/

Vis spectrophotometer for every nine samples. A regression 

of the standards calculated sample concentration. Samples 

were reanalyzed if blanks were greater than the instrument 

detection limit (1 micromole per liter (μmol L−1)) of silica, if 

absorbance values of replicates differed by more than 0.025 

in the 0.100 to 0.500 absorbance range or by more than 0.050 

in the 0.500 to 1.000 absorbance range, or if the observed 

value of the SRWS was greater than 5 percent of its most 

probable value.

For ICP–AES analyses, silica was analyzed on a Leeman Labs Direct Reading Echelle® dual view, sequential 

multielement ICP spectrometer. The detection limit was 

0.05 milligrams per liter (mg L−1) SiO2. Blanks, SRWSs, and 

laboratory replicates were analyzed several times during each 

analytical run. Replicate determinations were performed and 

were within 2 percent.

Potassium

Potassium was determined either on an atomic adsorption spectrophotometer or by ICP–AES at the U.S. Geological 

Survey National Research Program laboratory in Boulder, 

Colo. For atomic absorption, samples were analyzed on a Perkin Elmer 5000® atomic adsorption spectrophotometer using 

a three-point standard curve. Replicates and several SRWSs 

were analyzed every 10 samples. If SRWS concentrations 

deviated more than 5 percent from certified values, the instrument was restandardized and analyses were repeated.

For ICP–AES analyses, potassium was analyzed on a Leeman Labs Direct Reading Echelle® dual view, sequential multielement, ICP spectrometer. The detection limit was 0.04 mg 

L−1 potassium. Blanks, SRWSs, and laboratory replicates were 

analyzed several times during each analytical run. Replicate 

determinations were within 3 percent of the original value.

Major Anions

When complete chemistry was analyzed, nitrate, sulfate, and chloride were determined by ion chromatography. 

Samples were usually analyzed at the U.S. Geological Survey 

National Research Program laboratory in Boulder, Colo., on 

a Dionex® ion chromatograph. Three blanks, six standards, a 

SRWS, and a laboratory replicate were analyzed every ninth 

sample. In 1996, owing to a high volume of samples and to 

problems with instruments, samples were analyzed at the U.S. 

Geological Survey’s Panola Laboratory in Atlanta, Ga., by ion 

chromatography. Six standards and two SRWSs were analyzed 

at the beginning of each run. After every 10 samples, a replicate sample and a random selection of 2 reference samples, 

including SRWSs and blanks, were analyzed.

In many samples chloride was analyzed, but nitrate and 

sulfate were not. Like silica, chloride was analyzed in more 

than 4,600 grab and event samples. It was analyzed either by 

ion chromatography, as described above, or with a Buchler 

Chloridometer®, which operates by coulometric titration with 

silver ions, at the U.S. Geological Survey National Research 

Program laboratory in Boulder, Colo. Four calibration standards were analyzed at the beginning and end of each run and 

were used to calculate sample concentrations. Blanks and 

SRWSs were analyzed every 10 samples. All samples were run 

in duplicate, and if the relative standard deviation was greater 

than 5 percent, they were reanalyzed. A 70 μmol L−1 chloride 

spike was added to every sample to ensure a clean titration end 

point for samples with low chloride concentrations.

Nitrite

Most samples were analyzed for nitrite at the U.S. 

Geological Survey National Research Program laboratory 

in Boulder, Colo., on a Perkin-Elmer® spectrophotometer 

by using a four-point calibration curve. Blanks and intermediate standards were analyzed throughout each run, and 
292 Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico

approximately 10 percent of samples were run in duplicate. 

Samples were reanalyzed if the blank concentration was 

greater than the instrument detection limit (0.2 μmol L−1 of 

nitrogen), if the observed value of the SRWS was greater 

than 5 percent of its most probable value, or if absorbance 

values of replicates differed by more than 0.005 in the 

0.030 to 0.100 absorbance range, by more than 0.020 in the 

0.100 to 0.500 absorbance range, or by more than 0.030 in 

the 0.500 to 1.000 absorbance range. Some samples were 

analyzed at the U.S. Geological Survey Panola Laboratory 

in Atlanta, Ga., on an ALPKEM® nutrient analyzer; samples 

were analyzed in triplicate, and a seven-point calibration 

curve was run before and after each analytical run.

Phosphate

Phosphate was determined on a Perkin Elmer® UV/Vis 

spectrophotometer at the U.S. Geological Survey National 

Research Program laboratory in Boulder, Colo., by using a 

four-point calibration curve. A blank, four standards, a SRWS, 

and a replicate were analyzed every nine samples. Samples 

were reanalyzed if blanks were greater than the instrument 

detection limit (0.03 μmol L−1 phosphate), if absorbance values 

of replicates differed by more than 0.010 in the 0.030 to 0.100 

absorbance range or by more than 0.020 in the 0.100 to 0.500 

absorbance range, or if the observed value of the SRWS was 

greater than 5 percent of its most probable value.

Ammonium Ion

Ammonium ion was determined on a Perkin Elmer®

UV/Vis spectrophotometer at the U.S. Geological Survey 

National Research Program laboratory in Boulder, Colo., by 

using a four-point calibration curve. A blank, four standards, 

a U.S Geological Survey SRWS, and a laboratory replicate were analyzed for every nine samples. Samples were 

reanalyzed if the absorbance of the blank was greater than 

0.030, if the observed value of the SRWS was greater than 5 

percent of its most probable value, or if absorbance values of 

replicates differed by more than 0.010 in the 0.030 to 0.100 

absorbance range, by more than 0.020 in the 0.100 to 0.500 

absorbance range, or by more than 0.030 in the 0.500 to 

1.000 absorbance range.

Dissolved Organic Carbon

Dissolved organic carbon was determined on an OI 

Analytical TOC analyzer model 700® at the U.S. Geological 

Survey National Research Program laboratory in Boulder, 

Colo. The instrument utilizes persulfate oxidation to produce 

CO2 gas, which is then trapped and detected with infrared 

light. Samples were run in replicate, with a four- or five-point 

calibration curve. Calibration standards were run every 10 to 

12 samples. If replicate sample analysis did not agree within 

5 percent of the original, samples were reanalyzed.

Particulate Organic Carbon

Particulate organic carbon analyses were made on 

overbank sediments and on sediment collected on quartz fiber 

filters. Samples were analyzed for total carbon on a Coulometrics Total Carbon Combustion Apparatus Model 5120® at the 

U.S. Geological Survey National Research Program laboratory 

in Boulder, Colo. This method measures both CaCO3 and total 

organic carbon. The assumption was made that all carbon in 

samples was organic. One or two CaCO3 standards, instrument blanks, and quartz-fiber filter blanks were run each day 

of analysis. Owing to the small amount of sample, replicates 

were not analyzed.

Additional Analyses

In 2003 and 2006, a review of analyses found some 

gaps in analytes, and a subset of 170 samples was analyzed 

at the Kiowa Environmental Chemistry Laboratory of the 

University of Colorado. Analyses included alkalinity (by 

titration), major cations (by atomic absorption), major anions 

(by ion chromatography), and ammonia, phosphate, silica, 

and nitrogen species (by colorimetric methods). Methods 

and detection limits are described at the Kiowa Environmental Chemistry Laboratory Web site (Kiowa Environmental 

Chemistry Laboratory, 2011). Replicates and SRWSs were 

submitted with water samples.

References

Edwards, K.E., and Glysson, G.D., 1999, Field methods for 

measurement of fluvial sediment, in National field manual 

for the collection of water-quality data: U.S. Geological 

Survey Techniques of Water-Resources Investigations, 

book 3, ch. C2, 89 p.

Garbarino, J.R., and Taylor, H.E., 1979, An inductively 

coupled plasma atomic-emission spectrometric method for 

routine water quality testing: Applied Spectroscopy, v. 33, 

p. 220–226.

Kiowa Environmental Chemistry Laboratory, 2011, Kiowa 

Environmental Chemistry Laboratory procedure manual, 

accessed March 29, 2011, at http://snobear.colorado.edu/

Kiowa/Kiowaref/procedure.html.

U.S. Geological Survey, 2012, Standard Reference Sample 

Project: U.S. Geological Survey, accessed January 24, 2012 

at http://bqs.usgs.gov/srs/.

Wilde, F.D., and Radtke, D.B., 1998, Field measurements, in

National field manual for the collection of water-quality 

data: U.S. Geological Survey Techniques of WaterResources Investigations, book 9, ch. A6, p. 3–20.
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U.S. Geological Survey

436 National Center

Reston, Virginia 20192

Or visit the web site at:

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Murphy and Stallard, eds.—Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico—Professional Paper 1789