#*

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Fena Valley

Reservoir

Asan 

Point

Orote

Peninsula

Apra Harbor

Pago Bay

Ylig Bay

Talofofo Bay

Inarajan Bay

Fonte River

Taleyfac River

Agfayan Bay

Sagua River

Ajayan River

Togcha River

Asolonso River

Facpi

Point

Pauliluc River

Imong River

Maulap River

Ylig River

Talofofo River

Ugum River

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2017–5093

Version 1.1, February 2019

Prepared in cooperation with the U.S. Department of Defense Strategic Environmental 

Research and Development Program (SERDP)

Fena Valley Reservoir Watershed and Water-Balance 

Model Updates and Expansion of Watershed Modeling 

to Southern Guam
Cover. Map showing modeled regions, model stream segments, and gaged drainage-basin areas in the PRMS_2016 model. 

Top photo: Looking upstream from the U.S. Geological Survey (USGS) streamflow-gaging station 16865000 on the Pago River 

near Ordot. Photograph by Marcael Jamison, USGS, 2007. Center photo: Looking west from the Fena Valley Reservoir dam near 

USGS streamflow-gaging station 16849000 Fena Dam Spillway near Agat. Photograph by Sarah N. Rosa, USGS, February 22, 2014. 

Bottom photo: Looking upstream from USGS streamflow-gaging station 16854500 at Ugum River above Talofofo Falls near Talofofo. 

Photograph by Richard Castro, USGS, April 13, 2012.
Fena Valley Reservoir Watershed and Water-Balance 

Model Updates and Expansion of Watershed Modeling 

to Southern Guam

By Sarah N. Rosa and Lauren E. Hay

Prepared in cooperation with the U.S. Department of Defense Strategic Environmental 

Research and Development Program (SERDP)

Scientific Investigations Report 2017–5093

Version 1.1, February 2019

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Acting Director

U.S. Geological Survey, Reston, Virginia

First release: 2017

Revised: February 2019 (ver. 1.1)

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit https://store.usgs.gov.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

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

Suggested citation:

Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion 

of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations 

Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors wish to thank Steve Markstrom, Steve Regan, and Roland Viger from the 

USGS Modeling of Watershed Systems group for their assistance during the watershed-modeling phase of the study. This work was supported by the U.S. Department of Defense Strategic 

Environmental Research and Development Program, Project ID RC-2340.
iv

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................2

Description of Study Area ............................................................................................................................2

Climate....................................................................................................................................................5

Geology...................................................................................................................................................7

Soils.........................................................................................................................................................7

Vegetation ..............................................................................................................................................7

Streamflow Characteristics ..............................................................................................................11

Precipitation-Runoff Modeling System IV ...............................................................................................11

Description of PRMS-IV.....................................................................................................................11

Background Data................................................................................................................................15

Climate Data................................................................................................................................15

Rainfall................................................................................................................................15

Temperature.......................................................................................................................20

Streamflow Data ........................................................................................................................20

Evaporation and Solar Radiation Data ...................................................................................20

Physiographic Data ...................................................................................................................23

Model Development ...........................................................................................................................23

Watershed and Hydrologic Response Unit Delineation......................................................23

Model Parameterization ....................................................................................................................26

Model Calibration, Verification, and Results..................................................................................26

Energy-Budget Calibration.......................................................................................................26

Water-Budget Calibration.........................................................................................................26

Energy-Budget Verification and Simulation Results............................................................32

Water-Budget Verification and Simulation Results .............................................................32

Model Uncertainties...........................................................................................................................50

Comparison to Previous Fena Valley Reservoir Water Model ....................................................52

Fena Valley Reservoir Water-Balance Model.........................................................................................52

Description of Model..........................................................................................................................52

Model Development ...........................................................................................................................53

Model Calibration, Verification, and Results..................................................................................54

Calibration and Verification Results .......................................................................................55

Model Uncertainties...........................................................................................................................55

Two-Step Modeling Procedure for Fena Valley Reservoir ...................................................................58

Model Development ...........................................................................................................................58

Model Verification ..............................................................................................................................60

Summary and Conclusions.........................................................................................................................61

Acknowledgments.......................................................................................................................................62

References Cited..........................................................................................................................................62
v

Figures

1. Map of Guam showing locations of selected rain gages and weather stations ...............3

2. Diagram of the two-step modeling procedure for the Fena Valley watershed, Guam .....4

3. Graph showing daily maximum and minimum air temperatures, Naval Air 

Station Agana................................................................................................................................5

4. Map showing mean annual rainfall and locations of U.S. Geological Survey 

streamflow-gaging stations, Guam............................................................................................6

5. Geologic map of Guam ................................................................................................................8

6. Map showing distribution of soils by general type on Guam documented by 

Young in 1988 ................................................................................................................................9

7. Map showing land cover on Guam..........................................................................................10

8. Graphs showing daily mean streamflow at the Almagosa, Imong, Maulap, and 

Ugum U.S. Geological Survey streamflow-gaging stations, Guam, 2014 ..........................12

9. Graphs showing monthly rainfall at Almagosa and Fena Pump, January 2006 to 

December 2014; and monthly total streamflow and monthly mean stage of Fena 

Valley Reservoir, Guam, January 2006 to December 2014 ...................................................13

10. Flow-duration curves for the Maulap, Almagosa, Imong Rivers and the 

Almagosa River plus spring diversions, Guam .....................................................................14

11. Diagram of the conceptual hydrologic system used in the Precipitation-Runoff 

Modeling System for southern Guam......................................................................................14

12. Graph showing periods with rain-gage and minimum and maximum air 

temperature data, from 1951 to 2014, used in the development of the 

Precipitation-Runoff Modeling System models for southern Guam ..................................17

13. Graphs showing daily rainfall at the U.S. Geological Survey Almagosa, Fena 

Pump, Windward Hills, Mount Chachao, and Umatac rain gages, Guam, 2014...............18

14. Graph showing periods with streamflow data, from 1951 to 2014, used in the 

development of the Precipitation-Runoff Modeling System models for 

southern Guam............................................................................................................................19

15. Graph showing mean monthly pan evaporation at station WSMO for months 

with at least one daily record from 1958 to 1998, and the mean potential 

evapotranspiration calibration and verification results for the Ugum and the 

Fena Valley watersheds.............................................................................................................22

16. Graph showing mean monthly solar radiation at station WSMO from 1961 to 

1990 and the mean solar radiation calibration and verification results for the 

Ugum and the Fena Valley watersheds...................................................................................22

17. Map showing hydrologic response units, modeled stream segments, and 

gaged drainage basin areas in the PRMS_2016 model........................................................25

18. Graphs showing simulated basin rainfall for the calibration period, Maulap River 

watershed, Guam, from October 1, 2003 to September 30, 2014; and measured and 

simulated daily streamflow for the calibration period, Maulap River watershed, 

Guam, from October 1, 2003 to September 30, 2014..............................................................36

19. Graphs showing simulated basin rainfall for the calibration period, Almagosa River 

watershed, Guam, from October 1, 2001 to September 30, 2014; and measured 

and simulated daily streamflow for the calibration period, Almagosa River 

watershed, Guam, from October 1, 2001 to September 30, 2014.........................................37

20. Graphs showing simulated basin rainfall for the calibration period, Imong River 

watershed, Guam, from October 1, 2009 to September 30, 2014; and measured and 

simulated daily streamflow for the calibration period, Imong River watershed, Guam, 

from October 1, 2009 to September 30, 2014. .........................................................................38
vi

21. Graphs showing simulated basin rainfall for the calibration period, Ugum River above 

Talofofo Falls, Guam, from October 1, 2005 to September 30, 2014; and measured 

and simulated daily streamflow for the calibration period, Ugum River above 

Talofofo Falls, Guam, from October 1, 2005 to September 30, 2014 ....................................39

22. Graphs showing simulated basin rainfall for the verification period, Maulap River 

watershed, Guam, from October 1, 1999 to September 30, 2001; and measured and 

simulated daily streamflow for the verification period, Maulap River watershed, 

Guam, from October 1, 1999 to September 30, 2001..............................................................40

23. Graphs showing simulated basin rainfall for the verification period, Almagosa River 

watershed, Guam, from October 1, 1999 to September 30, 2001; and measured 

and simulated daily streamflow for the verification period, Almagosa River 

watershed, Guam, from October 1, 1999 to September 30, 2001.........................................41

24. Graphs showing simulated basin rainfall for the verification period, Imong River 

watershed, Guam, from October 1, 2007 to September 30, 2009; and measured 

and simulated daily streamflow for the verification period, Imong River watershed, 

Guam, from October 1, 2007 to September 30, 2009..............................................................42

25. Graphs showing simulated basin rainfall for the verification period, Ugum River 

above Talofofo Falls, Guam, from October 1, 1998 to September 30, 2001; and 

measured and simulated daily streamflow for the verification period, Ugum River 

above Talofofo Falls, Guam, from October 1, 1998 to September 30, 2001 ........................43

26. Map showing modeled regions, model stream segments, and gaged drainagebasin areas in the PRMS_2016 model.....................................................................................51

27. Diagram showing components of the water-balance model for the Fena Valley 

Reservoir, Guam..........................................................................................................................53

28. Graph showing water level-surface area and water level-capacity curves for 

the Fena Valley Reservoir, Guam..............................................................................................54

29. Graphs showing measured and simulated month-end reservoir volume for the 

calibration periods, Fena Valley Reservoir, Guam, for September 2006 to 

September 2007 and September 2008 to September 2009...................................................56

30. Graphs showing measured and simulated month-end reservoir volume for the 

verification periods, Fena Valley Reservoir, Guam, for September 2003 to 

September 2004 and September 2007 to September 2008...................................................57

31. Graphs showing monthly reservoir volume and water budget, Fena Valley 

Reservoir, Guam, from October 2006 to September 2008.....................................................59

32. Graphs showing measured and simulated month-end reservoir volume for the 

evaluation periods, Fena Valley Reservoir, Guam, from September 2006 to 

September 2007 and September 2008 to September 2009...................................................60

Tables

1. Rainfall and climate stations in the PRMS_2016 model data file .......................................16

2. Streamflow-gaging stations in the PRMS_2016 model data file ........................................21

3. Descriptions of modules used for the PRMS_2016 model..................................................24

4. List of parameters used in the PRMS_2016 model................................................................27

5. Selected physical characteristics of hydrologic response units for the Fena

 Valley watersheds and Ugum watershed in the PRMS_2016 model ................................29

6. Calibration procedure using the Luca software for the PRMS_2016 model ....................31

7. Selected parameter and coefficient values for daily mode runoff calculations for

the Fena Valley watersheds and Ugum watershed in the PRMS_2016 model.....................33

8. Selected monthly parameter and coefficient values for calculations for the 

Fena Valley watersheds and Ugum watershed in the PRMS_2016 model .......................35
vii

9. Errors in simulated daily mean streamflow for gaged watersheds used to 

calibrate the PRMS_2016 model ..............................................................................................45

10. Errors in simulated monthly mean streamflow for gaged watersheds used to 

calibrate the PRMS_2016 model ..............................................................................................46

11. Summary of measured and simulated cumulative streamflow for the gaged 

watersheds in the Fena Valley watershed, Guam.................................................................47

12. Summary of measured and simulated dry season monthly mean discharge and 

associated error for the gaged watersheds in the Fena Valley watershed, Guam.........48

13. Summary of measured and simulated dry season streamflow for the gaged 

watersheds in the Fena Valley watershed, Guam.................................................................50

14. Errors in simulated month-end storage volume in the Fena Valley Reservoir, Guam .....58

15. Water budget for the Fena Valley Reservoir during calibration and verification 

periods..........................................................................................................................................58

Datum

Vertical coordinate information is referenced to local mean sea level. 

Horizontal coordinate information is referenced to the World Geodetic System of 1984 (WGS 84).

Elevation, as used in this report, refers to distance above the vertical datum.

Abbreviations

COOP cooperative network

DEM digital elevation model

DOD U.S. Department of Defense

ENSO El Niño Southern Oscillation

EPA U.S. Environmental Protection Agency

FVR Fena Valley Reservoir

GIS geographic information system

GWA Guam Waterworks Authority

HRU hydrologic response unit

NAS Naval Air Station

NCDC National Climatic Data Center

NOAA National Oceanic and Atmospheric Administration

NRMSE normalized root mean square error

NS Nash-Sutcliffe efficiency

NWIS National Water Information System

PET potential evapotranspiration

PRMS Precipitation Runoff Modeling System

SERDP Strategic Environmental Research and Development Program

SR solar radiation

USGS U.S. Geological Survey 
viii

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2

)

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

acre-feet 0.32573 million gallons (Mgal)

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3

) 

million gallons (Mgal) 3,785 cubic meter (m3

)

Flow rate

acre-foot per day (acre-ft/d) 0.01427 cubic meter per second (m3

/s)

acre-foot per year (acre-ft/yr) 1,233 cubic meter per year (m3

/yr)

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

cubic foot per second (ft3

/s) 448.8 gallon per minute

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3

/s)

Flow rate—Continued

inch per hour (in/h) 0.0254 meter per hour (m/h)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

mile per hour (mi/h) 1.609 kilometer per hour (km/h) 

Potential evapotranspiration

inch per day (in/d) 25.4 millimeter per day (mm/d)

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as 

°C = (°F – 32) / 1.8.
Fena Valley Reservoir Watershed and Water-Balance 

Model Updates and Expansion of Watershed Modeling to 

Southern Guam

By Sarah N. Rosa and Lauren E. Hay

Abstract

In 2014, the U.S. Geological Survey, in cooperation with 

the U.S. Department of Defense’s Strategic Environmental 

Research and Development Program, initiated a project to 

evaluate the potential impacts of projected climate-change on 

Department of Defense installations that rely on Guam’s water 

resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model 

for the Fena Valley Reservoir. The southern Guam watershed 

model provides a physically based tool to estimate surfacewater availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was 

used to construct the watershed model. The PRMS-IV code 

simulates different parts of the hydrologic cycle based on a set 

of user-defined modules. The southern Guam watershed model 

was constructed by updating a watershed model for the Fena 

Valley watersheds, and expanding the modeled area to include 

all of southern Guam. The Fena Valley watershed model was 

combined with a previously developed, but recently updated 

and recalibrated Fena Valley Reservoir water-balance model. 

Two important surface-water resources for the U.S. 

Navy and the citizens of Guam were modeled in this study; 

the extended model now includes the Ugum River watershed 

and improves upon the previous model of the Fena Valley 

watersheds. Surface water from the Ugum River watershed is 

diverted and treated for drinking water, and the Fena Valley 

watersheds feed the largest surface-water reservoir on Guam. 

The southern Guam watershed model performed “very good,” 

according to the criteria of Moriasi and others (2007), in the 

Ugum River watershed above Talofofo Falls with monthly 

Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 

1.0 represents perfect model fit). In the Fena Valley watershed, 

monthly simulated streamflow volumes from the watershed 

model compared reasonably well with the measured values 

for the gaging stations on the Almagosa, Maulap, and Imong 

Rivers—tributaries to the Fena Valley Reservoir—with NashSutcliffe efficiency values of 0.87 or higher. The southern 

Guam watershed model simulated the total volume of the 

critical dry season (January to May) streamflow for the entire 

simulation period within –0.54 percent at the Almagosa River, 

within 6.39 percent at the Maulap River, and within 6.06 percent 

at the Imong River. 

The recalibrated water-balance model of the Fena Valley 

Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume 

ranged from 6.04 percent (284.6 acre-feet or 92.7 million 

gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million 

gallons). Monthly simulation bias ranged from –0.48 percent 

for the calibration period to 0.87 percent for the verification 

period; relative error ranged from –0.60 to 0.88 percent for the 

calibration and verification periods, respectively. The small 

bias indicated that the model did not consistently overestimate 

or underestimate reservoir storage volume. 

In the entirety of southern Guam, the watershed model 

has a “satisfactory” to “very good” rating when simulating 

monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam 

watershed model uses a more sophisticated climate-distribution 

scheme than the older model to make use of the sparse climate 

data, as well as includes updated land-cover parameters and 

the capability to simulate closed depression areas. 

The new Fena Valley Reservoir water-balance model is 

useful as an updated tool to forecast short-term changes in 

the surface-water resources of Guam. Furthermore, the now 

spatially complete southern Guam watershed model can be 

used to evaluate changes in streamflow and recharge owing to 

climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed 

and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 

2017; DOI:10.5066/F7HH6HV4).
2 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Introduction

Guam is a small, remote island with limited water 

resources that are vulnerable to climate change (Schroeder 

and others, 2012). Although 80 percent of the potable water 

in Guam comes from groundwater (Jocson and others, 2002), 

surface-water resources are important because of the potential increased demand for freshwater as a result of population 

growth and proposed military expansion (Gingerich, 2013). 

For over two decades the U.S. Geological Survey (USGS), in 

cooperation with the U.S. Navy, has provided periodic projections of water availability for the Fena Valley Reservoir (FVR) 

in south-central Guam (fig. 1). Constructed in 1951 by the 

U.S. Navy, the FVR is the largest surface-water reservoir on 

Guam and an important source of potable water for the U.S. 

Navy and the citizens of Guam. 

 A severe drought in 1993, associated with an El Niño 

Southern Oscillation (ENSO) event, prompted the USGS, 

in cooperation with the U.S. Navy, to develop a two-step 

modeling procedure using a Precipitation Runoff Modeling 

System (PRMS) watershed model—from here on referred 

to as PRMS_1994—and a generalized reservoir waterbalance model (Nakama, 1994)—from here on referred to as 

FVR_1994—to manage water levels in the FVR (fig. 2). In 

2004, as a result of the availability and improvement of physiographic and climate data, the watershed model and generalized reservoir water-balance model were updated by Yeung 

(2004) to address limitations identified in the PRMS_1994 

model (Nakama, 1994). The calibrated watershed model of the 

Fena Valley watershed (Yeung, 2004)— from here on referred 

to as PRMS_2004—and a water-balance model of the FVR—

from here on referred to as FVR_2004—were used by the 

USGS to provide quarterly projections of water availability for 

the reservoir for the upcoming 12 months to the Navy Public 

Works Center on Guam. 

In 2014, the USGS initiated a study with the U.S. Department

of Defense’s (DoD) Strategic Environmental Research and 

Development Program (SERDP) to evaluate the potential 

impacts of climate change on DoD installations that depend 

on Guam’s water resources. One of the major tasks of that 

study was to update the PRMS_2004 model and FVR_2004 

water-balance model for the FVR (Yeung, 2004) and expand 

the watershed model to all of southern Guam, using new data 

on the FVR storage-capacity (Marineau and Wright, 2015) 

and the latest version of PRMS, PRMS-IV (Markstrom and 

others, 2015). These new models are documented in this 

report. From here on, the new version of the Guam PRMS-IV 

model will be referred to as PRMS_2016 and the FVR model 

as FVR_2016. The PRMS_2016 model was not extended to 

northern Guam because measurable surface runoff does not 

occur in the northern part of the island owing to karst systems 

(Ward and others, 1965; fig. 1). The new FVR_2016 model 

is useful as an updated tool to forecast short-term changes in 

the surface-water resources of Guam. Furthermore, the now 

spatially complete PRMS_2016 model can be used to evaluate 

changes in streamflow and recharge as a result of climate or 

land-cover changes. These are substantial improvements to the 

FVR_2004 and PRMS_2004 models.

Purpose and Scope

The purpose of this report is to document the watershed 

and water-balance models developed by (1) discussing the 

data and methods used to develop the models; (2) describing 

the calibration and verification of the PRMS_2016 model for 

southern Guam; (3) describing the recalibration and verification of the updated FVR_2016 water-balance model; (4) comparing the PRMS_2016 model and the PRMS_2004 model and 

how output from these models affects results of the FVR_2016 

generalized reservoir water balance; and (5) identifying the 

uncertainties in the models. Potential impacts of projected 

climate change on Guam’s surface-water resources were not 

simulated as part of this report. 

Data from 1951 to 2015 were used to develop the 

PRMS_2016 and FVR_2016 models. The watershed model 

covers nearly all of southern Guam (92.5 square miles [mi2

]). 

The PRMS_2016 model was calibrated using 18 of 21 gaged 

watersheds, and these 18 watersheds encompass 49.7 mi2

(54 percent) of southern Guam. The calibrated model parameters of 11 gaged areas (corresponding to the areas with best 

model performance) were applied to nearby or physiographically similar ungaged areas to complete the model. 

Description of Study Area

Guam is the largest island in Micronesia, with an area of 

approximately 211 mi2

, and is the southernmost of the Mariana 

Islands in the western Pacific Ocean (fig. 1). Guam is divided 

into two topographically, geologically, and hydrologically 

distinct areas by the Adelup Fault, which extends across the 

center of the island from Pago Bay to Adelup Point (fig.1). 

Northern Guam is mostly composed of karst which absorbs 

rainfall quickly, and thus the region has no defined streams 

(Ward and others, 1965). Southern Guam, the focus of the 

surface-water availability model, consists primarily of rugged 

volcanic uplands dissected by streams and gently sloping 

foothills. A mountain chain running from north to south along 

the west edge of southern Guam contains Mount Lamlam, the 

highest peak in southern Guam, with an altitude of 1,332 feet 

above mean sea level. Terrain from the ridgeline to the western 

coast is characterized by steep dissected slopes that transition 

into coastal lowland alluvial-valley floors from Hagåtña Bay 

to Facpi Point. To the east of the ridgeline the terrain is steeply 

sloped, but at lower elevations the terrain transitions into 

gently sloping foothills and limestone plateaus near the coast. 

Older limestone units overlay the volcanic units in the mountainous areas and in the interior basin. The interior basin is 

a structural depression that starts at the headwaters of the Maagas River and terminates near the convergence of the Mahlac 

River with the Talofofo River (Tracey and others, 1964). The 

interior basin includes the FVR, and the rolling hills and valleys consisting of volcanic, limestone, and alluvial units. 
Description of Study Area 3

*#

914727

914226

132617144423366

132234144441966

131105144405166

132132144422366

131729144393766

914468

914278

914275

914870

914193

914828

914001

914885

914827

132128144421201

EXPLANATION

Limestone plateau

Flat-lying plateau

Dissected plateau underlain by argillaceous limestone

Volcanic uplands

Steep dissected slopes

Gently sloping foothills

Dissected limestone cap

Interior basin

Coastal lowlands and alluvial valley floors

Closed depression—With surface area greater 

 than 0.099 acre

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

144°55’

144°50’

144°45’

144°40’

13°35’

13°30’

13°25’

13°20’

13°15’

Base modified from U.S. Geological Survey 

10-meter digital elevation model, Universal 

Tranverse Mercator projection, zone 55, 

WGS84 datum.

Adelup

Point Asan

Point

Pati 

Point

Ritidian 

Point

Fena Valley

Reservoir

Umatac Bay

Inarajan Bay

Talofofo Bay

Ylig Bay

Pago Bay

Apra

Harbor

Orote

Peninsula

Facpi

Point

Tumon

Bay

Mount

 Lamlam

ADELUP FAULT

Hagåtña

Swamp

Hagåtña

Bay

Imong River

Maulap River

Talofofo River

Almagosa River

Mahlac River

Maagas

.

. . .

.

.

.

.

. .

. . . . .

. . .

.

.

.

.

.

.

. .

. .

.

. . . .

. .

. . .

.

.

.

.

.

..

.

. .

.

...

. .

.

.

.

.

.

.

.

. .

. . . . . .

.

. .

. .

. . .

. . . . .

. .

.

.

.

.

.

.

.

.

.

PACIFIC OCEAN

Mariana 

Guam Islands

Australia

Federated

States of

 Micronesia

Hawaiʻi

5000 1,000 KILOMETERS

0 500 1,000 MILES

20° N 

0°

20° S 

140° E 160° E 180° 160° W

001

226

Rain gage and identification number—Gages with 15-digit 

 numbers are USGS gages; all other gages have National 

 Weather Service Cooperative Station identification 

 numbers. All 3-digit numbers begin with prefix 914

Weather station and National Weather Service 

 Cooperative Station identification number—

 All 3-digit numbers begin with prefix 914

Model stream segments

Figure 1. Locations of selected rain gages and weather stations (modified from Johnson, 2012).
4 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

STEP TWO: ESTIMATE FENA VALLEY RESERVOIR RESPONSE

STEP ONE: ESTIMATE STREAMFLOW 

 INPUT DATA: 

RAINFALL AND AIR TEMPERATURE ESTIMATES

 Streamflow

Time Time Time

PRMS_2016

Maulap

watershed

Imong

watershed

Almagosa

watershed

Time

Water level

INFLOWS OUTFLOWS

WATER-BALANCE MODEL

STORAGE = INFLOWS – OUTFLOWS

Projected

rainfall

Evaporation

Water withdrawal

(various pumping

 scenarios)

Streamflow

Streamflow

7 Watershed boundary 

EXPLANATION

Streamflow

(from PRMS_2016)

Time

Streamflow

Ungaged

area

4

8 6

1

3

2

57

3

6

5 4

2

1

5 4 8

9

2

1 3

6 7

7

11

2 10

1

Figure 2. Diagram of the two-step modeling procedure for the Fena Valley watershed, Guam (modified from Yeung, 2004; PRMS, 

Precipitation Runoff Modeling System).
Description of Study Area 5

The FVR is currently the primary source of water for 

Naval Base Guam and the nearby civilian residents in villages near the southern Guam base (Marineau and Wright, 

2015). The FVR has a total storage capacity of approximately 

6,915 acre-feet (acre-ft) or 1,936 million gallons (Mgal) and 

a surface area of 192.6 acres (0.30 mi2

) when at full capacity (Marineau and Wright, 2015). Streamflow from the Fena 

Valley Watershed’s three gaged tributaries, the Maulap, 

Almagosa, and Imong Rivers, and one ungaged area, a watershed totaling approximately 3,768 acres (5.89 mi2

; only 15 

percent of the FVR catchment area is ungaged), is captured 

by the Fena Valley Reservoir. Over time sediment carried by 

streamflow into the FVR has reduced the storage capacity of 

the reservoir, resulting in a change in the relation between 

reservoir stage and storage capacity. Marineau and Wright 

(2015) surveyed the FVR in February 2014 as part of the 

SERDP study to produce new reservoir stage-surface area and 

reservoir stage-storage capacity curves needed for this study 

to update and recalibrate the water-balance model. Prior to the 

2014 survey, the storage capacity of FVR was last determined 

in 1990 (Nakama, 1992). FVR total storage capacity decreased 

about 3.7 percent or 265 acre-ft from 1990 to 2014 as a result 

of sediment input (Nakama, 1992; Marineau and Wright, 

2015).

Climate

Guam’s climate is characterized by warm temperatures 

and humid conditions year-round. Temperatures typically 

range from the middle or high 80s (in °F) in the afternoon 

to the 70s at night (fig. 3). Average annual relative humidity 

typically ranges from 71 percent during the late afternoon to 

86 percent during the early morning (Guard and others, 1999). 

Rainfall, however, varies both seasonally and geographically 

due to the orographic effect (increasing rainfall with altitude) 

of the mountain range running from north to south along the 

western edge of southern Guam (fig. 4). Mean annual rainfall 

ranges from about 85 inches (in.) near the coastal lowland 

areas to greater than 115 in. over the mountainous areas in 

southern Guam (Lander and Guard, 2003). The distinct differences in Guam’s wet and dry seasons are defined by the 

variability in wind and rainfall. Trade winds, blowing from 

the east, are episodic in Guam (Keener and others, 2012). 

The temperature of Guam is expected to increase by the end 

of the century by 3.6 °F and the annual rainfall is expected to 

increase moderately (8.7 in/year), based on the Intergovernmental Panel on Climate Change’s RCP8.5 scenario (Intergovernmental Panel on Climate Change, 2013).

Periodically Guam experiences severe periods of drought 

due to the highly seasonal rainfall and occurrences of ENSO 

events. Mean annual rainfall on Guam is about 100 in. with 

about 70 percent of the annual total occurring in the wet 

season (July through December) and the remaining 30 percent 

occurring in the dry season (January through June). Tropical 

cyclones, or typhoons, during the rainy season can bring 

high intensity rainfall distributed across the island due to the 

nature of the storm’s structure (Lander and Guard, 2003). 

Thirty percent of the rainfall on Guam comes from tropical 

cyclones (Kubota and Wang, 2009), which are less frequent 

the year after an El Niño event and result in a dry year (Lander 

and Guard, 2003). Monthly rainfall totals can range from 

less than 1 in. during February to April to more than 20 in. 

during August to November, while rainfall during the transitional months of December and June varies from year to year 

(Lander, 1994).

60

65

70

75

80

85

90

95

100

1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012

Minimum

Maximum

EXPLANATION

Air temperature, in degrees Fahrenheit

Figure 3. Daily maximum and minimum air temperatures, Naval Air Station Agana (National 

Climatic Data Center), Guam, 1952–2014.
6 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

0 1 2 3 4 MILES

0 2 3 4 KILOMETERS 1

EXPLANATION

USGS streamflow-gaging station

and abbreviated number—

Complete numbers begin with prefix 16

Hydrologic response units

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

Universal Tranverse Mercator projection zone 55, 

WGS84 datum.

Fena Valley

Reservoir

Asan 

Point

Tumon

Bay

Hagåtña

Bay

Orote

Peninsula

Apra Harbor

Pago Bay

Ylig Bay

Talofofo Bay

Inarajan Bay

Fonte River

Taleyfac River

Agfayan Bay

Sagua River

Ajayan River

Togcha River

Asolonso River

Facpi

Point

Pauliluc River

Imong River

Maulap River

Ylig River

Talofofo River

Ugum River

P H I L I P P I N E

S E A

P A C I F I C

O C E A N

807650

809600

816000

848500

808300

809400

848100

821000

Mean annual rainfall, in inches

96918681 101 106 111 118

Almagosa River

865000

855000

858000

846000

847000

845000

850000

854500

849000

862000

835000

835000

840000

144°50'

144°45'

144°40'

13°30'

13°25'

13°20'

13°15'

Figure 4. Mean annual rainfall (modified from Heitz, 2015) and locations of U.S. Geological Survey streamflowgaging stations, Guam.
Description of Study Area 7

Geology

 The geology underlying the watersheds in southern 

Guam is predominantly low permeability water-laid volcanic 

deposits of the Umatac, Alutom, and Facpi Formations (Tracey 

and others, 1964). Older high-elevation limestone-capped interior areas, as well as lower coastal regions with Hagåtña and 

Mariana limestones (Siegrist and Reagan, 2007) cover much 

of the rest of southern Guam and have high porosity and high 

permeability (fig. 5). The older Alifan Limestone underlies 

approximately 20 and 31 percent of the Maulap and Almagosa 

River watersheds, respectively (Nakama, 1994). Near the 

western drainage divide of the Fena Valley watershed, the 

older limestone units that overlie the volcanic uplands contain 

perched groundwater (Mink, 1976). This perched groundwater 

discharges at the contact with low permeability volcanic rocks 

in areas like the Almagosa Springs, which is the largest spring 

in southern Guam. The Navy historically diverted as much 

as 3.9 cubic feet per second (ft3

/s; 2.5 million gallons per day 

[Mgal/d]) from Almagosa Springs (Nakama, 1992). However, 

recent data from the Navy indicate much less of a reliance on 

water diverted from Almagosa Springs and many days when 

no water is diverted (at the time of publication, data had not 

been published by the U.S. Navy). The karst geology of southern Guam also creates many closed and internally drained 

surface depressions created by dissolution or collapse of the 

terrain (Taborosi and others, 2004; Taborosi, 2006). Some of 

the largest closed depressions in southern Guam can be found 

in the Bonya Limestone at the Naval Magazine, the Alifan 

Limestone capping the southern mountain ridgeline, and the 

Mariana Limestone flanking the southeastern coast (Taborosi 

and others, 2004).

Soils

The soil survey completed by the Soil Conservation Service (Young, 1988) was updated and digitized by the Natural 

Resources Conservation Service (U.S. Department of Agriculture, 2009). The main physiographic regions primarily dictate 

how the soil types, and therefore permeability, vary throughout 

southern Guam (fig. 6). The soil properties below were characterized by Young (1988) and summarized by Johnson (2012). 

Most of the soils covering the limestone plateaus and bordering coastal plains have moderately rapid to rapid permeability 

of 2–20 inches per hour (in/h). Soils covering the argillaceous 

limestone areas are less permeable owing to their higher clay 

content. Volcanic uplands are covered by soils that generally 

have moderately slow to moderate permeability of 0.2–2 in/h. 

Older limestone in the southern highlands of the Fena Valley watersheds is covered by soils with moderately rapid to 

rapid permeability. Soils covering the valley floors and coastal 

lowlands that are derived from volcanic alluvium have slow to 

moderate permeability of 0.01–2 in/h.

Soil thickness across southern Guam tends to vary 

according to topography and parent material. Soils covering 

the limestone and volcanic uplands are spatially variable and 

are typically thicker than 10 in. Soils in closed depressions, 

valley floors, or coastal lowlands could be several feet thick. 

The Almagosa watershed closed depression is filled with relatively deep, moderately low-permeability, and high available 

water holding capacity soils (Nakama, 1994). 

Vegetation

According to the U.S. Department of Agriculture (2006) 

vegetation map (fig. 7), most of southern Guam’s land surface 

is covered by forests and grasslands with some smaller pockets of urban cultivated and urban built-up land. Forest landcover categories “limestone forest” and “scrub forest” cover 

much of the rural areas underlain by limestone. Trees within 

the forests of southern Guam tend to have small diameters 

owing to a long history of disturbances and typhoon activity 

(Donnegan and others, 2004). The volcanic uplands in southern Guam are mostly covered by “ravine forest,” “limestone 

forest,” or “savanna complex.” Barren regions of eroding soil 

are also present in southern Guam and are of concern for many 

agencies involved in the management of Guam’s nearshore 

reef environment. 
8 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Fena Valley 

watershed

and Reservoir 

Tumon 

Bay

Haputo

Bay

Almagosa !

Springs

Beach deposits, reef deposits, alluvium, and artificial fill

Limestone

 Mariana Limestone

 Hagåtña argillaceous member 

 Barrigada Limestone

 Older limestone—Includes Bonya Limestone, Alifan 

Limestone, and Janum Formation

Volcanic rocks

 Umatac Formation

 Alutom Formation 

 Facpi Formation

EXPLANATION

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

144°55’

144°50’

144°45’

144°40’

13°35’

13°30’

13°25’

13°20’

13°15’

Base modified from U.S. Geological Survey 

10-meter digital elevation model, Universal 

Tranverse Mercator projection, zone 55, 

WGS84 datum.

Figure 5. Geologic map of Guam (modified from Tracey and others, 1964; Siegrist and others, 2007; Johnson, 2012). Geologic 

nomenclature from Siegrist and others (2007). 
Description of Study Area 9

EXPLANATION

Akina-Agfayan

Soil type

Land cover

Akina-Atate

Akina-Togcha-Ylig

Guam

Inarajan-Inarajan Variant

Pulantat-Kagman-Chacha

Ritidian-Rock outcrop

Shioya

Troposaprists

Urban land

Water

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

144°55’

144°50’

144°45’

144°40’

13°35’

13°30’

13°25’

13°20’

13°15’

Base modified from U.S. Geological Survey 

10-meter digital elevation model, Universal 

Tranverse Mercator projection, zone 55, 

WGS84 datum.

Figure 6. Distribution of soils by general type on Guam documented by Young in 1988 (modified from U.S. Department 

of Agriculture, 2009).
10 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

0 1 2 3 4 MILES

0 1 2 3 4 KILOMETERS

144°55’

144°50’

144°45’

144°40’

13°35’

13°30’

13°25’

13°20’

13°15’

Base modified from U.S. Geological Survey 

1:24,000-scale digital data, Universal 

Tranverse Mercator projection zone 55, 

WGS84 datum.

Urban cultivated

Urban builtup

Other shrub/grass

Barren land

Golf course

Agriculture field

Wetland

Water

Scrub forest

Limestone forest

Ravine forest

Leucaena stand

Coconut plantation

Casuarina thicket

Acacia plantation

Savanna complex

EXPLANATION

Land cover

Figure 7. Land cover on Guam (modified from U.S. Department of Agriculture, 2006). Acacia plantation, barren land, and 

casuarina thicket land covers are difficult to see at this scale (Johnson, 2012).
Precipitation-Runoff Modeling System IV 11

Streamflow Characteristics

Southern Guam’s geology and rainfall distribution heavily influence the streamflow characteristics of each watershed. 

Streamflow that is not diverted from the Fena Valley watershed is captured by the Fena Valley Reservoir. The three gaged 

FVR tributaries, the Maulap, Almagosa, and Imong Rivers, 

along with the gaged Ugum River represent a substantial 

amount of the surface-water resources currently being used in 

southern Guam. These watersheds have similar rainfall patterns and geology, which are reflected in the general shape and 

timing of their hydrographs (fig. 8). The distinctive climatic 

wet and dry seasons directly translate into seasonal streamflow patterns. For the Fena Valley watersheds these seasonal 

streamflow patterns also influence the reservoir response 

(fig. 9). In years when less water is diverted from the FVR 

system (2011–14) the reservoir is not as dependent on these 

seasonal flows to replenish it to its full capacity and sharp 

declines in reservoir levels are not evident.

Streamflow during the dry season is sustained by base 

flow, which is composed entirely of groundwater discharge 

from the limestones of southern Guam. In the wet season, 

streamflow is much flashier, owing to higher rainfall rates in 

combination with the steep volcanic terrain of these small 

watersheds, creating direct runoff (Ward and others, 1965). 

Average streamflow for the four gaged watersheds (fig. 8) 

during the wet season (July to December) typically ranges 

from 68 to 78 percent of total annual streamflow for these 

gages. At these same gages, dry season streamflow (January to 

June) is typically 19 to 23 percent of annual streamflow, but in 

general, the streamflow during the dry season varies substantially from stream to stream (Ward and others, 1965). 

Flow-duration curves of streamflow for the three gaged 

basins in the Fena Valley watershed (Maulap, Almagosa, and 

Imong Rivers) from 2002–14 are shown in figure 10. The 

discharge data in figure 10 were normalized by dividing discharge by the drainage area at the streamflow-gaging stations 

so that the data could be directly compared. A flow-duration 

curve shows the percentage of time a specific discharge is 

equaled or exceeded in a given period. The shape of a flowduration curve provides an indication of how the underlying 

geology, physiography, or rainfall patterns of the watershed 

affect streamflow. The steep slope and shape of the flowduration curves for the high to median flows for all three 

watersheds in the FVR are very similar. These higher flows 

tend to be controlled by rainfall patterns, physiography, and 

the land cover of the watershed, which are similar across the 

three watersheds. However, at the lower-end of the flow range 

the flow-duration curves diverge. Differences in the low-flow 

characteristics reflect differences in the underlying geology 

controlling the base-flow component of flow in the watershed. 

Even though the Almagosa and Maulap watersheds have 

very similar underlying geology, the lower flows in Maulap 

are much smaller than those of Almagosa. For this period of 

record, the steep slope at the lower-end of Maulap’s flow-duration curve is most likely a result of the inclusion of estimated 

data for a particularly dry period with many extreme low-flow 

days from February to June 2010. Daily flow from the Almagosa spring diversion (at the time of publication, data had not 

been published by the U.S. Navy) was added to the daily flow 

measured at the Almagosa gaging station at the outlet of the 

basin to account for the effects of the flow diversions. The 

difference between the two curves reflects the effects of the 

spring diversions on Almagosa River streamflow. The spring 

diversion affects low to medium flows in Almagosa, but at 

higher flows the effects of the diversion are small relative to 

the discharge. 

Precipitation-Runoff Modeling 

System IV

The PRMS_2004 model for the Fena Valley watershed 

(Yeung, 2004) was updated and expanded to include all of 

southern Guam. The updated version of the USGS PRMS-IV 

model code (Markstrom and others, 2015), was used to construct the PRMS_2016 model. PRMS-IV simulates different 

parts of the hydrologic cycle based on a set of user-defined 

modules. Model development requires the delineation and 

parameterization of watersheds and hydrologic response units 

(HRUs), and the selection of appropriate PRMS-IV modules 

suitable for simulating the southern Guam environment.

Description of PRMS-IV

PRMS is a modular, deterministic, distributed-parameter, 

physical-process simulation code that is used to simulate landsurface hydrologic processes, including evapotranspiration, 

runoff, and infiltration, and subsurface occurrences of water, 

including groundwater and soil moisture, based on inputs of 

distributed daily precipitation and maximum and minimum 

temperature. PRMS-IV was developed as a tool to evaluate the 

impact of changing watershed characteristics on surface-water 

runoff and recharge of a watershed. A detailed description of 

PRMS-IV can be found in the report by Markstrom and others 

(2015). PRMS-IV was run using available daily datasets for 

southern Guam and calibrated and verified based on the watershed model’s ability to simulate daily and monthly runoff.

PRMS-IV simulates different parts of the hydrologic 

cycle through a set of inter-connected user-defined modules. 

Each component of the hydrologic cycle in PRMS-IV (fig. 11) 

is calculated by empirical relations or process algorithms (note 

that snow processes are not used in this model). By taking into 

account watershed characteristics, including slope, aspect, 

altitude, land cover, soil type, and temperature and rainfall 

distribution, the watershed can be divided into homogeneous 

units called hydrologic response units (HRUs). These HRUs 

are assumed to be homogeneous with regard to their physical 

properties and hydrologic response. PRMS-IV utilizes these 

HRUs to have distributed-parameter capabilities and account 
12 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

0.1

1

10

100

1,000

Maulap River (station 16848500)

0.1

1

10

100

1,000

Almagosa River (station 1648100)

0.1

1

10

100

1,000

Imong River (station 16847000)

0.1

1

10

100

1,000

Ugum River above Talofofo Falls (station 16854500)

Daily mean streamflow, in cubic feet per second

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Dry season Wet season

Figure 8. Daily mean streamflow at the Almagosa, Imong, Maulap, and Ugum U.S. Geological 

Survey streamflow-gaging stations, Guam, 2014.
Precipitation-Runoff Modeling System IV 13

50

40353025201510

Almagosa

Fena

403020100–10–20 10

100

1,000

10,000

2006 2007 2008 2009 2010 2011 2012 2013 2014

StreamflowReservoir stage

Monthly rainfall, in inches Monthly streamflow, in cubic feet per second

Monthly mean reservoir stage, in feet above spillway

A

B

EXPLANATIONEXPLANATION

Figure 9. A. Monthly rainfall at Almagosa and Fena Pump, January 2006 to December 2014. B. Monthly total streamflow (from Almagosa, Maulap, and 

Imong Rivers) and monthly mean stage of Fena Valley Reservoir (from Fena Dam spillway), Guam, January 2006 to December 2014.
14 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Percentage of time indicated flow is equaled or exceeded

0.01 0.1 1 10 30 50 70 90 99 99.9 99.99

Unit discharge, in cubic feet per second per square mile

0.01

0.1

1

10

100

1,000

Imong River (station 16847000)

Almagosa River plus spring diversions

Almagosa River (station 16848100)

Maulap River (station 16845000)

EXPLANATION

Figure 10. Flow-duration curves for the Maulap, Almagosa, Imong Rivers and the Almagosa River 

plus spring diversions, Guam (2002–14).

Surface runoff

Interflow

Groundwater flow

Evaporation and

transpiration

Sublimation

Precipitation

Solar radiation

Air temperature

Rain

Depression

storage

Snowmelt

Recharge

Groundwater sink

Rain Snow

Snowmelt

Upslope surface

runoff and interflow

Plant canopy interception

Soil Zone

Groundwater

Snowpack

Impervious

 dna wolfmaertS ituor ekal gn

Figure 11. Diagram of the conceptual hydrologic system used in the Precipitation-Runoff Modeling System 

for southern Guam (modified from Regan and LaFontaine, 2017).
Precipitation-Runoff Modeling System IV 15

for the spatial variability within a watershed. To account for 

heterogeneity within a given HRU, area-weighted averages 

are calculated for each of the appropriate basin characteristics within a HRU (for example an area-weighted average to 

determine the HRU’s slope is not calculated). For each of the 

HRUs, an energy and water balance is calculated and then the 

HRU water-budget components are summed or routed to produce the entire watershed’s hydrologic response (Markstrom 

and others, 2015). 

PRMS-IV distributes precipitation and minimum and 

maximum air-temperature data to each HRU as well as 

estimates of daily shortwave radiation for each HRU. Precipitation in PRMS-IV can either be intercepted by vegetation, 

evaporated, or continue to the land surface as throughfall that 

reaches the soil. PRMS-IV also estimates potential evapotranspiration (PET) for each HRU, and actual evapotranspiration 

is calculated by PRMS-IV as a function of PET, the HRU 

soil type, the HRU plant-cover type, the water available in 

the soil zone, and the water-storage capacity of the soil zone. 

Any rainfall reaching the land surface may then be stored in 

the impervious-zone reservoir or closed surface-depression 

areas, infiltrate into the soil zone, evaporate, or contribute to 

surface runoff. 

Surface runoff and infiltration for each HRU are calculated and once water infiltrates into the soil zone, PRMS-IV 

uses a conceptual three-reservoir system to model soil-zone 

water content (refer to Markstrom and others [2015] for 

a detailed description and figure). The capillary reservoir 

models soil water between the wilting-point and field-capacity 

thresholds. The gravity reservoir models soil-water content 

between field capacity and the preferential-flow threshold, and 

accounts for slow lateral interflow and drainage to the groundwater reservoir. The preferential-flow reservoir also models 

soil-water content over field capacity, but instead accounts for 

fast lateral interflow. PRMS-IV calculates the water inflows 

and outflows of each soil zone for each HRU. Groundwater 

in PRMS-IV is simulated using another conceptual reservoir 

called the groundwater reservoir. Any excess infiltration from 

the soil zone can directly enter the groundwater reservoir as 

direct recharge or go through the gravity reservoir and be 

partitioned into gravity drainage from the soil zone or slow 

interflow. Groundwater may leave the groundwater reservoir 

but stay in the watershed as groundwater flow to a stream, or 

leave the groundwater reservoir and be designated to leave the 

system through a groundwater sink. PRMS-IV calculates the 

water and energy balances for each HRU and the total for each 

watershed (Markstrom and others, 2015).

Background Data

Available climate data from October 1, 1951, to February 

28, 2015, were used as input to the PRMS_2016 model. Additionally, daily streamflow was used to calibrate the model. 

Mean monthly pan-evaporation and solar-radiation data were 

used to calibrate the model’s energy budget as an approximation for the seasonal patterns of potential evapotranspiration 

and solar radiation. Physiographic data were used to parameterize the HRUs.

Climate Data

Daily rainfall, and daily minimum and maximum air 

temperature data were compiled with the USGS Downsizer 

application (Ward-Garrison and others, 2009) from available climate-station records from the National Oceanic and 

Atmospheric Administration’s (NOAA) National Weather 

Service Cooperative Observer Program (National Oceanic and 

Atmospheric Administration, 2015) into a data file recognized 

by PRMS-IV. The USGS Downsizer program, which is no 

longer updated or supported by the USGS, automates the 

quality-assurance/quality-control (QA/QC) checks while 

formatting the data into an appropriate file that can be used 

with PRMS-IV. The data file compiled by the USGS Downsizer application was then supplemented with USGS rainfall 

data from the National Water Information System (NWIS) that 

were not in the Downsizer database (U.S. Geological Survey, 

2015). Stations are listed in table 1 and periods of record are 

shown in figure 12.

Rainfall

For the PRMS_2016 model, 11 stations with daily rainfall 

data with various periods of record ranging from January 1954 

to February 2015 (National Oceanic and Atmospheric Administration, 2015) were compiled into the PRMS-IV data file 

format with the USGS Downsizer (Ward-Garrison and others, 

2009). These data were supplemented with data from NWIS 

(U.S. Geological Survey, 2015) from six additional USGS rain 

gages (table. 1). The timing of rainfall collected at 5 of the 6 

USGS rain gages used in the PRMS_2016 model is similar 

although the rainfall volumes vary among sites (fig. 13). 

Data from 2 of the 17 gages considered for the analysis were 

determined to be unsuitable or insufficient to be used as input 

to the model and were coded within the model’s parameter 

file to be ignored by the model. Twelve of the 15 remaining 

stations were located in southern Guam (fig. 1). The other 

three remaining stations were located in northern Guam. These 

stations were either located in close proximity to some of the 

southern Guam watersheds or were the only source of climate 

data for some watersheds that only had streamflow data in the 

mid-1950s available for calibration (fig. 14). The elevations 

of the rain gages used as input to the model ranged from 10 to 

830 ft (3 to 253 meters[m]) covering almost the entire elevation range of southern Guam. The daily rainfall data from 

the rain gages are then distributed to each HRU based on the 

distribution algorithm designated in PRMS-IV described in the 

model development section of the report. 
16 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 1. Rainfall and climate stations in the PRMS_2016 model data file.

[The National Oceanic and Atmospheric Adminitration’s Cooperative Observer Program rainfall and climate stations are indicated below according to 

their six-digit station numbers. U.S. Geological Survey rainfall stations are listed below according to their fifteen-digit station numbers. PRMS, Precipitation Runoff Modeling System; ID, identifier; nr, near; Ag., Agricultural; NASA, National Aeronautics and Space Administration; WSMO, Weather Service 

Meteorological Office; NAS, Naval Air Station; RG at Res., rain gage at reservoir]

Station number Station name Elevation,

in feet Longitude1 Latitude1 Station ID in PRMS 

data file

914275 Inarajan Ag. Station 30 13.28530 144.75470 1

914885 Umatac 190 13.28330 144.66670 2

914278 Inarajan NASA 280 13.31140 144.73640 3

914828 Talofofo Village 299 13.35000 144.75000 4

914193 Fena Filter Plant 367 13.36670 144.70000 5

914001 Agat 10 13.38940 144.65750 6

914468 Mangilao 60 13.45280 144.79810 7

914827 Talofofo2 250 13.36470 144.73890 8

914870 Guam WSMO 361 13.55970 144.83750 9

914226 NAS-Tiyan 254 13.48330 144.80000 10

914727 Pirates Cove 10 13.35220 144.76690 11

131729144393766 Umatac Rain Gage at 

Umatac 180 13.29178 144.66220 12

132105144405166 Almagosa Rain Gage 

nr Santa Rita 600 13.35292 144.68320 13

132128144421201

Fena Dam Rain Gage 

at Spillway near 

Agat2

113 13.35936 144.70580 14

132132144422366 Fena RG at Res. 

Pump Station 70 13.30625 144.70890 15

132617144423366 Mount Chachao Rain 

Gage near Piti 830 13.43947 144.71220 16

132234144441966

Windward Hills 

Rain Gage near 

Talofofo

365 13.37717 144.73780 17

1

Latitude and longitude coordinates in decimal degrees north of the equator and east of the prime meridian and World Geodetic System of 1984.

2

Station data were not used as input to the model.
Precipitation-Runoff Modeling System IV 17

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

Inarajan AG Station (RG)Umatac (RG)Inarajan NASA (T, RG)Talofofo Village (RG)Fena Filter Plant (T, RG)Agat (RG)Mangilao (T, RG)Talofofo (RG)

WSMO (T)

NAS Tiyan (T, RG)Pirates Cove (RG)Umatac USGS (RG)Almagosa USGS (RG)Fena Dam USGS (RG)Fena at Pump USGS (RG)Mount Chachao USGS (RG)

Windward Hill USGS (RG)

EXPLANATION

Figure 12. Periods with rain-gage (RG) and minimum and maximum air temperature (T) data, from 1951 to 2014, used in the development of the PrecipitationRunoff Modeling System models for southern Guam. AG, Agricultural; NASA, National Aeronautics and Space Administration; USGS, U.S. Geological Survey.
18 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Daily rainfall, in inches

0

2

4

6

8

10

Almagosa

2014 total rainfall, 137 inches 

0

2

4

6

8

10

Fena Pump

2014 total rainfall, 120 inches 

0

2

4

6

8

10

Windward Hills

2014 total rainfall, 105 inches 

0

2

4

6

8

10

Mount Chachao

2014 total rainfall, 145 inches 

0

2

4

6

8

10

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Umatac

2014 total rainfall, 81 inches 

Figure 13. Daily rainfall at the U.S. Geological Survey Almagosa, Fena Pump, Windward 

Hills, Mount Chachao, and Umatac rain gages, Guam, 2014.
Precipitation-Runoff Modeling System IV 19

AplachoFinileCettiLa Sa FuaUmatacGeusInarajanTinagaTolaeyuus nr AgatTolaeyuus nr mouthImongAlmagosa SpringsAlmagosaMaulapFena DamTalofofoUgum abv Talofofo FallsUgum nr TalofofoYligLonfitPago

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

EXPLANATION

Figure 14. Periods with streamflow data, from 1951 to 2014, used in the development of the Precipitation-Runoff Modeling System models for southern Guam. 

Abv, above; nr, near.
20 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Temperature

The air temperature in Guam varies very little (fig. 3). 

According to Daly and Halbleib (2006), the spatial variability, in any given month, of modeled estimates of average 

minimum and maximum air temperature is less than 7 °F. The 

required daily minimum and maximum air temperatures were 

available at 5 of the 17 climate stations (table. 1; fig. 12).

Streamflow Data

USGS streamflow data stored in NWIS for the 21 available southern Guam streamflow-gaging stations were also 

compiled using the USGS Downsizer and used to calibrate 

PRMS_2016. The USGS established a network of streamflowgaging stations in southern Guam in 1951. During the interval 

from 1951 to 2015, 21 streamflow-gaging stations were established to collect continuous streamflow data across southern 

Guam. Three of the available streamflow-gaging stations in 

southern Guam were not used due to either an insufficient 

amount of data or streamflow regulation that affected the 

usability of the data for model calibration (table 2). Measured 

daily streamflow data were used directly with no adjustments 

for 17 of the 18 gages used to calibrate the PRMS_2016 

model. Five of the 17 streamflow-gaging stations with daily 

streamflow data that were used directly with no adjustments 

may have been affected by an upstream diversion at some time 

during the station’s period of record, which could affect lower 

flows. However, because diversion data were not available 

the records were left unadjusted, and instead the calibration 

scheme was adjusted to avoid periods with regulated flows and 

put more weight on the calibration of monthly flows, which 

will be discussed later in the report.

The Maulap, Almagosa, and Imong River gages provide 

a continuous record of inflow to the FVR. These gages in the 

Fena Valley watershed have been concurrently operated by the 

USGS since 1972. The Almagosa River gaging station’s data, 

however, do not reflect natural streamflow conditions owing to 

the Almagosa Springs diversion. Because daily flow data for 

the Almagosa Springs diversion were available from the U.S. 

Navy (at the time of publication, data had not been published 

by the U.S. Navy), the daily diverted flow was added back 

into the Almagosa streamflow record to account for the effects 

of the diversion and reconstruct natural-flow conditions. This 

record reconstruction assumes that the amount of flow diverted 

at the springs causes an equal amount of reduction of flow at 

the gaging station located near the outlet of the watershed.

The streamflow records for the 18 gages used to calibrate 

the PRMS_2016 model ranged in accuracy from “good” to 

“poor” ratings. A “good” and “fair” rating indicates that about 

95 percent of the daily discharges are within 10 and 15 percent 

of their actual values, respectively. Records that are considered to be less accurate are rated “poor.” The accuracy of the 

Almagosa Springs diversion data supplied by the U.S. Navy is 

not known. 

Evaporation and Solar Radiation Data

Daily PET can be estimated in PRMS-IV using either 

pan-evaporation data or minimum and maximum air-temperature data. The PRMS_1994 model used pan-evaporation data 

to estimate PET because of the availability of pan-evaporation 

data for the duration of the modeled period (Nakama, 1994). 

However, the PRMS_2004 model used one of the empirical 

methods in PRMS that estimates PET using minimum and 

maximum air temperature and possible hours of sunshine, the 

Hamon (1961) method (Yeung, 2004). For the PRMS_2016 

model, the Jensen and Haise method (1963) was used to 

estimate PET. The Jensen-Haise method uses minimum and 

maximum temperature, and simulated daily solar radiation 

(SR) to estimate PET (Markstrom and others, 2015), which 

is more accurate than the Hamon method. The required daily 

minimum and maximum air temperatures were available at 5 of

the 17 climate stations (table. 1; fig. 12). Even though there 

is a sparse distribution of gages both spatially and throughout 

time (figs. 1, 12) the available data were sufficient to estimate 

PET across southern Guam. 

The model’s energy budget was calibrated using mean 

monthly pan-evaporation data from 1958 to 1998 at station 

WSMO (table 1, fig. 15) as an approximation for the seasonal 

pattern of potential evapotranspiration. Accuracies associated 

with the pan-evaporation data and air-temperature data are not 

known, but the data were adjusted downward by 30 percent 

to be consistent with the values used by Nakama (1994). The 

National Weather Service provides data-quality flags which 

were used to either manually or automatically set the flagged 

data to “missing” as part of the internal QA/QC process in the 

USGS Downsizer. Measured mean monthly solar-radiation 

values were calculated from hourly solar-radiation data for 

station WSMO (table 1) from 1961 to 1990, obtained from 

the National Solar Radiation Database (National Renewable 

Energy Laboratory, 2007; fig. 16) to calibrate the daily solarradiation model. The calibration process using the available 

solar-radiation and pan-evaporation data is discussed later in 

the Energy-Budget Calibration section of the report.
Precipitation-Runoff Modeling System IV 21

Table 2. Streamflow-gaging stations in the PRMS_2016 model data file. 

[PRMS, Precipitation Runoff Modeling System; ID, identifier; nr, near]

Station number Station name Elevation,

in feet Longitude1 Latitude1 Calibrated Station ID in PRMS data file

16807650 Aplacho River at 

Apra Heights 250 13.4040 144.6897 yes 1

16808300 Finile Creek at Agat 20 13.3791 144.6596 yes 2

16809400 Cetti River near 

Umatac 10 13.3175 144.6592 yes 3

16809600 La Sa Fua River 

near Umatac 120 13.3069 144.6644 yes2 4

16816000 Umatac River at 

Umatac 8 13.2980 144.6652 yes2 5

16821000 Geus River nr 

Merizo 60 13.2714 144.6793 yes2 6

16835000 Inarajan River nr 

Inarajan 15 13.2795 144.7399 yes2 7

16840000 Tinaga River nr 

Inarajan 15 13.2876 144.7534 yes 8

16845000 Tolaeyuus River 

near Agat 90 13.3669 144.7142 yes 9

16846000 Tolaeyuus River at 

mouth near Agat 60 13.3598 144.7109 no2 10

16847000 Imong River near 

Agat 120 13.3390 144.7015 yes 11

16848000 Almagosa Springs 

near Agat 620 13.3469 144.6816 no2 12

16848100 Almagosa River 

near Agat 155 13.3467 144.6955 yes2 13

16848500 Maulap River near 

Agat 130 13.3554 144.6979 yes 14

16849000 Fena Dam Spillway 

near Agat 111 13.3594 144.7057 no2 15

16850000 Talofofo River near 

Talofofo 20 13.3531 144.7330 yes2 16

16854500 Ugum River above 

Talofofo Falls 130 13.3224 144.7361 yes 17

16855000 Ugum River near 

Talofofo 3 13.3354 144.7509 yes 18

16858000 Ylig River near 

Yona 20 13.3926 144.7538 yes 19

16862000 Lonfit River near 

Ordot 30 13.4364 144.7556 yes 20

16865000 Pago River near 

Ordot 25 13.4368 144.7560 yes 21

1

Latitude and longitude coordinates in decimal degrees north of the equator and east of the prime meridian and World Geodetic System of 1984.

2

Possible low-flow alteration during part of station’s period of record.
22 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Mean monthly pan evaporation, in inches per day

Measured

Calibrated

Verified

EXPLANATION

Figure 15. Mean monthly pan evaporation at station WSMO for months 

with at least one daily record from 1958 to 1998, and the mean potential 

evapotranspiration calibration and verification results for the Ugum and the 

Fena Valley watersheds.

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Measured

Calibrated

Verified

EXPLANATION

200

300

400

500

600

Mean monthly solar radiation, in langleys per day

Figure 16. Mean monthly solar radiation at station WSMO from 1961 to 

1990 (National Renewable Energy Laboratory, 2007) and the mean solar 

radiation calibration and verification results for the Ugum and the Fena Valley 

watersheds.
Precipitation-Runoff Modeling System IV 23

Physiographic Data

Distributed parameters for physical characteristics for the 

PRMS_2016 model, including basin area, slope, aspect, and 

elevation, were estimated from a 5-m digital elevation model 

(DEM) derived by Johnson (2012) from the Joint Airborne 

LIDAR (light detection and ranging) Bathymetry Technical 

Center of Expertise topobathy data (National Oceanic and 

Atmospheric Administration, 2007). Johnson (2012) used the 

procedures described in Taylor and Nelson (2008) to derive 

the DEM and delineate internally drained areas in the karst 

topography, or areas with closed depressions and their surface 

drainage basins that contribute runoff to the closed depression 

(fig. 1). These internally drained areas were defined as separate 

HRUs and modeled as closed depressions in the PRMS_2016 

model. Losses from closed depressions in the PRMS_2016 

model include seepage to the groundwater reservoirs as well 

as evaporative losses based on the PET (Markstrom and 

others, 2015). Land cover for the PRMS_2016 model was 

determined using the U.S. Department of Agriculture (2006) 

detailed vegetation dataset (fig. 7). Soil properties for the 

model were determined using the updated digital Natural 

Resources Conservation Service (U.S. Department of Agriculture, 2009) soils dataset originally documented by Young in 

1988 (fig. 6). General geologic information was derived from 

the geologic map of Guam produced by Tracey and others 

(1964; fig. 5). 

Model Development

PRMS-IV simulates different parts of the hydrologic 

cycle based on a set of user-defined modules. Model development requires the delineation of watersheds and HRUs, and 

the selection of appropriate modules suitable for simulating 

the southern Guam hydrologic cycle. The modules selected for 

the PRMS_2016 model are summarized in table 3 and briefly 

described below. For a more detailed description of each module and the underlying equations used to approximate each 

hydrologic process refer to Markstrom and others (2015). 

Rainfall and minimum and maximum temperature 

data for southern Guam are distributed to each HRU using 

the PRMS-IV inverse distance and elevation-weighting 

scheme, module ide_dist. This is an improvement over the 

PRMS_1994 and PRMS_2004 models of the FVR watersheds 

(Nakama, 1994; Yeung, 2004), which used the arithmetic average of the two closest rainfall stations for the HRU portioned 

rainfall. This method tends to lower rainfall extremes (Yeung, 

2004). However, in order to make it possible to download and 

process data in a timely manner such that the PRMS_2016 

and FVR_2016 models can be used as a forecasting tool for 

surface-water availability (see section below on “Two-Step 

Modeling Procedure for Fena Valley Reservoir”), the parts 

of the PRMS_2016 model covering the Fena Valley watershed (Maulap, Almagosa, Imong, and FVR ungaged areas) 

are limited to using rainfall data from the USGS Almagosa 

(132105144405166) and Fena (132132144422366) rain gages 

and temperature data from the Naval Air Station (914226) in 

Tiyan (table 1).

For the other processes, module selection is straightforward. Daily shortwave radiation is estimated using the soltab 

module and distributed to each HRU using a modified degreeday method, module ddsolrad. Interception in PRMS-IV is 

modeled with the intcp module by calculating the amount of 

rainfall that is intercepted by vegetation, the amount of evaporation of intercepted rain, and the amount of net rain throughfall that reaches the soil. PET for the PRMS_2016 model 

is calculated for each HRU using a modified Jensen-Haise 

approach, module potet_jh. The surface runoff and infiltration 

for each HRU are calculated using the srunoff_smidx module, 

a non-linear variable-source-area method. Detailed descriptions and the equations used for each of these modules can 

be found in the report by Markstrom and others (2015) that 

documents PRMS-IV.

Watershed and Hydrologic Response Unit 

Delineation

HRUs were first delineated based on overland-flow planes 

that contribute to each stream segment using the geographic 

information system (GIS) Weasel application (Viger and 

Leavesley, 2007) and the 5-m DEM derived by Johnson 

(2012). These initial HRUs were then intersected with the 

closed-depression delineations (Johnson, 2012) to define 

HRUs in the model that are internally drained to the subsurface and modeled as closed-depression HRUs, resulting in 

a total of 317 HRUs (fig. 17). Outside of the water-balance 

model (which does not simulate either the water-levels above 

the spillway crest or flow over the spillway) the FVR was 

modeled as an open depression in the PRMS_2016 model to 

simulate flows downstream of the Fena Dam spillway. In the 

PRMS_2004 model only two subsurface and groundwater 

reservoirs per watershed were used to represent subsurface 

hydrogeologic conditions of each basin, however for the 

PRMS_2016 model subsurface and groundwater reservoirs 

were used for every HRU to describe the underlying limestone 

or volcanic units. Yeung (2004) also added an extra HRU to 

the Almagosa watershed in the PRMS_2004 model to account 

for added subsurface and groundwater contributions from the 

Alifan Limestone at the headwaters of the basin (fig. 5) owing 

to the disproportionate contribution of groundwater at the 

Almagosa Springs and the sloping geologic contact favoring 

flow toward the Almagosa River. For the PRMS_2016 model, 

flow from two HRUs delineated as closed depressions that 

originally drained to the west of the mountain range were 

rerouted by reassigning the stream segments of those HRUs so 

that they instead contributed subsurface and groundwater flow 

to the Almagosa watershed (HRUs 5 and 11, fig. 2). These 

additional closed-depression HRUs added approximately 

280 acres of contributing area to the Almagosa watershed. 
24 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 3. Descriptions of modules used for the PRMS_2016 model

[Table modified from Markstrom and others (2015); HRU, hydrologic response unit]

Module name Description

Basin definition process

basin Defines shared watershed-wide and HRU physical parameters and 

variables.

Cascading flow process

cascade Determines computational order of the HRUs and groundwater 

reservoirs for routing flow downslope.

Solar table process

soltab Compute potential solar radiation and sunlight hours for each HRU 

for each day of year; modification of soltab_prms.

Time series data process

obs Reads and stores observed data from all specified measurement 

stations.

Combined climate distribution process

ide_dist

Determines the form of precipitation and distributes precipitation and 

temperatures to each HRU on the basis of measurements at stations 

with closest elevation or shortest distance to the respective HRU.

Solar radiation distribution process

ddsolrad Distributes solar radiation to each HRU and estimates missing solar 

radiation data using a maximum temperature per degree-day relation.

Transpiration period process

transp_tindex Determines whether the current time step is in a period of active 

transpiration by the temperature index method.

Potential evapotranspiration process

potet_jh Computes the potential evapotranspiration by using the Jensen-Haise 

formulation (Jensen and Haise, 1963).

Canopy interception process

intcp Computes volume of intercepted precipitation, evaporation from 

intercepted precipitation, and throughfall that reaches the soil.

Surface runoff process

srunoff_smidx Computes surface runoff and infiltration for each HRU using a 

nonlinear variable-source-area method allowing for cascading flow.

Soil zone process

soilzone

Computes inflows to and outflows from soil zone of each HRU and 

includes inflows from infiltration, groundwater, and upslope HRUs, 

and outflows to gravity drainage, interflow, and surface runoff to 

down-slope HRUs

Groundwater process

gwflow

Sums inflow to and outflow from PRMS groundwater reservoirs; 

outflow can be routed to downslope groundwater reservoirs and 

stream segments.

Streamflow process

strmflow_in_out Routes water between segments in the system by setting the outflow 

to the inflow.

Summary process

basin_sum Computes daily, monthly, yearly, and total flow summaries of 

volumes and flows for all HRUs.
Precipitation-Runoff Modeling System IV 25

144°50'

144°45'

144°40'

13°30'

13°25'

13°20'

13°15'

0 1 2 4 MILES

0 2

3

1 3 4 KILOMETERS

EXPLANATION

Gaged drainage basin

Model stream segments

*# USGS streamflow-gaging station 

and abbreviated number—

Complete numbers begin with

prefix 16

Hydrologic response units

Base modified from U.S. Geological Survey 

10-meter digital elevation model, Universal 

Tranverse Mercator projection zone 55, 

WGS84 datum.

Fena Valley

Reservoir

Asan 

Point

Tumon

Bay

Hagåtña

Bay

Orote

Peninsula

Apra Harbor

Pago Bay

Ylig Bay

Talofofo Bay

Inarajan Bay

Fonte River

Taleyfac River

Agfayan Bay

Sagua River

Ajayan River

Togcha River

Asolonso River

Facpi

Point

Pauliluc River

Imong River

Maulap River

Ylig River

Talofofo River

Ugum River

P H I L I P P I N E

S E A

P A C I F I C

O C E A N

807650

809600

816000

847000

848500

865000

808300

809400

835000

840000

845000

855000

858000

848100

846000

850000

854500

849000

862000

835000

821000

Figure 17. Hydrologic response units, modeled stream segments, and gaged drainage basin areas in the 

PRMS_2016 model.
26 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Model Parameterization

Distributed parameters representing physical HRU characteristics were estimated using the parameterization methods 

in the GIS Weasel application (Viger and Leavesley, 2007) 

and the available datasets described earlier. Descriptions of 

the major distributed and nondistributed parameters used in 

the PRMS_2016 model are listed in table 4. Selected physical 

characteristics of the HRUs for the Fena Valley watersheds 

and the Ugum watershed in the PRMS_2016 model, two of the 

most important surface-water resources in southern Guam, are 

listed in table 5. The remaining physical characteristics of the 

HRUs listed in table 5, as well as the physical characteristics 

of the other southern Guam HRUs can be found in the associated USGS data release (Rosa and Hay, 2017; DOI:10.5066/ 

F7HH6HV4). An average depth of 120 in. (3.0 m) was used 

for each of the HRUs in the model that are internally drained 

to the subsurface and modeled as closed depression HRUs 

(table 5; Taborosi and others, 2004). In the PRMS_2016 model 

the FVR was modeled as an open depression with an average 

depth of 900 in. (22.9 m) to simulate flows below the Fena 

Dam spillway (Marineau and Wright, 2015). Either default 

values within PRMS-IV or values from the previous model 

(Yeung, 2004) were used as initial parameter values for the 

nonphysical parameters. 

Model Calibration, Verification, and Results

The selection of the calibration and verification periods 

was largely driven by the availability of concurrent and continuous streamflow and climate data for each of the watersheds 

modeled. Continuous periods of data eliminated the need to 

reinitialize the model or the need to synthesize input data. 

Selection of a calibration period that covers a wide range of 

flow conditions results in greater confidence that the model 

is capable of being applied over a range of flow conditions. 

An additional consideration for the Fena Valley watersheds 

was that the calibration period needed to include an ENSO 

(El Niño) event because the performance of the model during 

periods of drought was important. In the most recent period of 

data available for calibration (2002–14), Guam experienced 

four weak to moderately strong El Niño events (National Oceanic Atmospheric Administration, 2016) resulting in reduced 

rainfall during the year following the El Niño event. The periods selected for calibration and verification are described later 

in the Simulation Results section of the report.

The PRMS_2016 model was calibrated using an automated parameter-estimation procedure combined with a 

geographically nested approach. Model parameters were 

calibrated in two phases, the energy-budget and waterbudget phases, with a total of six steps (table 6) using the 

multiple-objective stepwise-calibration software Luca (Hay 

and Umemoto, 2007). The PRMS_2016 model was subdivided 

into areas that may be used and analyzed separately, such as 

the FVR basin. The PRMS_2016 model was calibrated to 

obtain the best fit to the data in each of the selected calibration 

periods and the verification period results represent an independent assessment of the model performance.

Energy-Budget Calibration

The first phase in the calibration procedure was to 

adjust the energy-budget using available mean monthly SR 

and PET data. Table 6 lists the parameters used to calibrate 

the PRMS_2016 model during the SR and PET phase of the 

calibration. This phase of the calibration focused on the role of 

these parameters in calculating SR and PET even though they 

also influence other PRMS-IV calculations. The goal of the 

calibration was to minimize the objective function. The objective function used by the calibration software Luca to calibrate 

the mean monthly SR and PET values calculated by PRMS-IV 

is described as

OF abs MSD SIM m= − m m

=

∑1

12

( ), (1)

where

OF is the objective function,

m is the month,

abs is the absolute value,

MSD are the mean monthly measured values of 

either SR or PET, and

SIM are the mean monthly simulated values of 

either SR or PET.

Water-Budget Calibration

In the second phase of model calibration with the Luca 

software, the simulated water budget was first adjusted by 

volume and then by timing, in an attempt to match measured 

streamflow in the 18 gaged watersheds in southern Guam. 

Model calibration for the 18 watersheds was divided into two 

calibration rounds. Data from the Ugum above Talofofo Falls 

(16854500), Imong (16847000), Almagosa (16848000), Maulap (16848500), and Lonfit (16862000) River gages, which are 

located near the headwaters of each basin, were used to calibrate the model in round 1 (table 2). Data from the remaining 

gages were used to calibrate the model in round 2, and parameters that were previously calibrated in round 1 were preserved 

and not recalibrated in this second round of calibration. 
Precipitation-Runoff Modeling System IV 27

Table 4. List of parameters used in the PRMS_2016 model.—Continued

[PRMS, Precipitation-Runoff Modeling System; HRU, hydrologic response unit; GWR, groundwater reservoir; ET, evapostranspiration]

PRMS model parameter Description of parameter

Distributed (HRU-dependent) parameters

CAREA_MAX Maximum possible area contributing to surface runoff (in decimal fraction of HRU area)

COV_TYPE Vegetation cover type for HRU (bare soil, grasses, shrubs, or trees)

COVDEN_SUM Summer vegetation cover density for the major vegetation type in each HRU as a decimal fraction

COVDEN_WIN Winter vegetation cover density for the major vegetation type in each HRU as a decimal fraction

DPRST_AREA Aggregate sum of surface depression areas of each HRU (acres)

DPRST_DEPTH_AVG Average depth of storage depressions at maximum storage capacity (inches)

DPRST_ET_COEF Fraction of unsatisfied potential evapotranspiration to apply to surface depression storage

DPRST_FLOW_COEF Coefficient in linear flow routing equation for open surface depressions for each HRU

DPRST_FRAC_HRU Fraction of surface depression areas of each HRU

DPRST_FRAC_INIT Fraction of maximum surface depression storage that contains water at the start of a simulation

DPRST_FRAC_OPEN Fraction of open surface depression storage area within an HRU that can generate surface runoff as a 

function of storage volume

DPRST_SEEP_RATE_CLOS Coefficient used in linear seepage flow equation for closed surface depressions for each HRU

DPRST_SEEP_RATE_OPEN Coefficient used in linear seepage flow equation for open surface depressions for each HRU

FASTCOEF_LIN Linear coefficient in equation to route preferential-flow storage down slope for each HRU

preferential-flow storage down slope for each HRU

FASTCOEF_SQ Non-linear coefficient in equation to route preferential-flow storage down slope for each HRU

HRU_AREA Area of HRU (acres)

HRU_ASPECT Aspect of HRU (angular degrees)

HRU_ELEV Mean land-surface altitude of HRU (meters)

HRU_PERCENT_IMPERV Decimal fraction of HRU area that is impervious

HRU_SEGMENT Segment index for HRU lateral inflow

HRU_SLOPE Slope of each HRU

HRU_X Longitude (X) for HRU in Albers projection

HRU_Y Latitude (Y) for HRU in Albers projection

IMPERV_STOR_MAX Maximum retention storage for HRU impervious area (inches)

JH_COEF_HRU Air temperature coefficient used in Jensen-Haise potential ET computations for each HRU

OP_FLOW_THRES Fraction of open depression storage above which surface runoff occurs for each timestep

PREF_FLOW_DEN Fraction of the soil zone in which preferential flow occurs for each HRU; used if control parameter

RAD_TRNCF Transmission coefficient for short-wave radiation through winter plant canopy (decimal fraction)

SAT_THRESHOLD Water holding capacity of the gravity and preferential flow reservoirs; difference between field 

capacity and total soil saturation for each HRU (inches)

SLOWCOEF_LIN Linear coefficient in equation to route gravity-reservoir storage down slope for each HRU

SLOWCOEF_SQ Non-linear coefficient in equation to route gravity reservoir storage down slope for each HRU

SMIDX_COEF Coefficient in non-linear contributing area algorithm for each HRU (for computing surface runoff)

SMIDX_EXP Exponent in non-linear contributing area algorithm for each HRU (for computing surface runoff)

SOIL_MOIST_INIT Initial value of available water in the capillary reservoir (inches)

SOIL_MOIST_MAX Maximum available water holding capacity of capillary reservoir from land surface to rooting depth 

of the major vegetation type of each HRU (inches)

SOIL_RECHR_INIT Initial storage for soil recharge zone (upper part of capillary reservoir where losses occur as both 

evaporation and transpiration) for each HRU (inches)
28 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 4. List of parameters used in the PRMS_2016 model.—Continued

[PRMS, Precipitation-Runoff Modeling System; HRU, hydrologic response unit; GWR, groundwater reservoir; ET, evapostranspiration]

PRMS model parameter Description of parameter

Distributed (HRU-dependent) parameters—Continued

SOIL_RECHR_MAX Maximum storage for soil recharge zone (upper portion of capillary reservoir where losses occur as 

both evaporation and transpiration) (inches)

SOIL_TYPE Soil type of each HRU (sand, loam, or clay)

SOIL2GW_MAX Maximum amount of the capillary reservoir excess that is routed directly to the GWR for each HRU 

(inches)

SRAIN_INTCP Summer rain interception storage capacity for the major vegetation type in each HRU (inches)

WRAIN_INTCP Winter rain interception storage capacity for the major vegetation type in each HRU (inches)

SRO_TO_DPRST Fraction of pervious surface runoff that flows into surface depression storage; the remainder flows to 

a stream network for each HRU

SRO_TO_DPRST_IMPERV Fraction of impervious surface runoff that flows into surface depression storage; the remainder flows 

to a stream network for each HRU

VA_CLOS_EXP

Coefficient in the exponential equation relating maximum surface area to the fraction that closed 

depressions are full to compute current surface area for each HRU

VA_OPEN_EXP

Coefficient in the exponential equation relating maximum surface area to the fraction that open 

depressions are full to compute current surface area for each HRU

Selected temperature and rainfall related parameters

ADJUST_RAIN

Monthly factor to adjust measured precipitation on each HRU to account for differences in elevation, 

and so forth

DDAY_INCPT Monthly (January to December) intercept in degree-day equation

DDAY_SLOPE Monthly (January to December) slope in degree-day equation

DIST_EXP Exponent for inverse distance calculations

JH_COEF

Monthly (January to December) air temperature coefficient used in Jensen-Haise potential ET

computations

NDIST_PSTA Number of precipitation measure stations for inverse distance calculations

NDIST_TSTA Number of air temperature-measurement stations for inverse distance calculations

PRCP_WGHT_DIST Monthly (January to December) precipitation weighting function for inverse distance calculations

PSTA_ELEV Altitude of each measurement station that measures precipitation

PSTA_NUSE Defines which measurement stations will be used in the distribution regression of precipitation

PSTA_X Longitude (X) for each precipitation measurement station in Albers projection

PSTA_Y Latitude (Y) for each measurement station that measures precipitation in Albers projection

RADMAX

Maximum fraction of the potential solar radiation that may reach the ground due to haze, dust, smog, 

and so forth

SOLRAD_ELEV Elevation of the solar radiation station used for the degree-day curves to distribute temerature

TEMP_WGHT_DIST Monthly (January to December) temperature weighting function for inverse distance calculations

TMAX_ADJ Adjustment to maximum air temperature for HRU, estimated on basis of slope and aspect

TMIN_ADJ Adjustment to minimum air temperature for HRU, estimated on basis of slope and aspect

TRANSP_TMAX Temperature index to determine the specific date of the start of the transpiration period

TSTA_ELEV Altitude of each measurement station that measures air temperature

TSTA_NUSE Defines which measurement stations will be used in distribution regression of air temperatures

TSTA_X Longitude (X) for each measurement station that measures air temperature in Albers projection

TSTA_Y Latitude (Y) for each measurement station that measures air temperature in Albers projection
Precipitation-Runoff Modeling System IV 29

Table 4. List of parameters used in the PRMS_2016 model.—Continued

[PRMS, Precipitation-Runoff Modeling System; HRU, hydrologic response unit; GWR, groundwater reservoir; ET, evapostranspiration]

PRMS model parameter Description of parameter

Groundwater and subsurface routing related parameters

GWFLOW_COEF Linear coefficient in the equation to compute groundwater discharge for each GWR

GWSINK_COEF Linear coefficient in the equation to compute outflow to the groundwater sink for each GWR

GWSTOR_INIT Storage in each GWR at the beginning of a simulation

SSR2GW_EXP

Non-linear coefficient in equation used to route water from the gravity reservoirs to the GWR for 

each HRU

SSR2GW_RATE

Linear coefficient in equation used to route water from the gravity reservoir to the GWR for each 

HRU

SSSTOR_INIT Initial storage of the gravity and preferential-flow reservoirs for each HRU

Table 5. Selected physical characteristics of hydrologic response units for the Fena Valley watersheds and Ugum watershed in the 

PRMS_2016 model.—Continued

[Parameter definitions are shown in table 4. HRU, hydrologic response unit; HRUs for the Fena Valley watershed are shown in figure 2]

HRU COV_TYPE HRU_AREA 

(acres)

HRU_ELEV 

(feet)

HRU_PERCENT_IMPERV

HRU_SLOPE DPRST_AREA

 (acres)

DPRST_

DEPTH_AVG 

(inches)

SOIL_TYPE

Maulap River watershed

1 Trees 230.22 652.72 0.01 4.71 0.00 0 Clay

2 Trees 522.96 485.44 0.00 3.90 0.00 0 Clay

Almagosa River watershed1

1 Trees 73.31 764.24 0.01 2.92 0.00 0 Loam

2 Trees 106.43 806.88 0.00 4.04 0.00 0 Loam

3 Trees 70.44 357.52 0.02 5.63 0.00 0 Clay

4 Trees 296.43 498.56 0.00 3.46 0.00 0 Clay

5 Trees 277.90 1059.44 0.00 2.54 276.51 120 Loam

6 Trees 49.66 931.52 0.00 0.16 49.41 120 Loam

7 Trees 25.98 918.40 0.00 0.19 25.81 120 Loam

8 Trees 170.95 967.60 0.00 2.00 170.10 120 Loam

9 Trees 126.08 908.56 0.00 2.98 125.45 120 Clay

10 Trees 5.41 790.48 0.00 0.28 5.38 120 Loam

11 Trees 6.20 1243.12 0.00 0.18 6.17 120 Loam

Imong River watershed

1 Trees 44.24 239.44 0.00 4.20 0.00 0 Loam

2 Trees 80.20 298.48 0.00 3.21 0.00 0 Loam

3 Trees 391.86 803.60 0.00 3.26 0.00 0 Loam

4 Trees 191.76 508.40 0.00 3.03 0.00 0 Loam

5 Trees 256.82 534.64 0.00 3.76 0.00 0 Loam

6 Trees 264.97 603.52 0.00 3.25 0.00 0 Loam
30 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 5. Selected physical characteristics of hydrologic response units for the Fena Valley watersheds and Ugum watershed in the 

PRMS_2016 model.—Continued

[Parameter definitions are shown in table 4. HRU, hydrologic response unit; HRUs for the Fena Valley watershed are shown in figure 2]

HRU COV_TYPE HRU_AREA 

(acres)

HRU_ELEV 

(feet)

HRU_PERCENT_IMPERV

HRU_SLOPE DPRST_AREA 

(acres)

DPRST_

DEPTH_AVG 

(inches)

SOIL_TYPE

Ungaged watershed areas

1 Trees 97.20 200.08 0.05 0.56 0.00 0 Clay

2 Trees 17.35 160.72 0.00 1.92 0.00 0 Clay

3 Trees 83.21 239.44 0.02 1.61 0.00 0 Loam

4 Trees 127.37 203.36 0.00 0.55 0.00 0 Clay

5 Trees 13.28 193.52 0.01 3.06 0.00 0 Clay

6 Trees 104.25 285.36 0.00 1.86 0.00 0 Clay

7 Trees 8.25 154.16 0.00 3.96 0.00 0 Loam

8 Trees 125.43 288.64 0.00 2.63 0.00 0 Loam

Ugum River watershed

1 Trees 9.03 3.28 0.00 10.00 0.00 0 Clay

2 Trees 16.97 3.28 0.00 8.04 0.00 0 Clay

3 Trees 370.94 180.40 0.00 3.24 0.00 0 Clay

4 Trees 417.56 242.72 0.03 3.55 0.00 0 Clay

5 Trees 699.19 206.64 0.01 2.95 0.00 0 Clay

6 Trees 290.37 419.84 0.00 2.78 0.00 0 Clay

7 Trees 180.15 285.36 0.00 3.44 0.00 0 Sand

8 Trees 376.27 173.84 0.01 3.16 0.00 0 Loam

9 Trees 90.70 364.08 0.00 3.59 0.00 0 Sand

10 Trees 613.68 521.52 0.00 3.25 0.00 0 Loam

11 Trees 56.54 259.12 0.00 3.06 0.00 0 Clay

12 Trees 491.16 449.36 0.00 3.40 0.00 0 Loam

13 Trees 238.29 305.04 0.00 3.34 0.00 0 Clay

14 Grasses 108.16 298.48 0.02 3.92 0.00 0 Loam

15 Trees 11.94 219.76 0.00 8.75 0.00 0 Clay

16 Trees 46.21 265.68 0.00 2.60 0.00 0 Loam

17 Trees 94.99 331.28 0.00 3.58 0.00 0 Clay

18 Trees 153.72 380.48 0.00 2.71 0.00 0 Clay

19 Trees 1321.78 462.48 0.00 2.93 0.00 0 Loam

20 Trees 94.68 328.00 0.00 3.40 0.00 0 Loam

21 Trees 40.71 255.84 0.00 0.12 40.51 120 Loam

22 Trees 7.39 190.24 0.00 0.26 7.35 120 Clay

1

HRUs 5 and 11 for Almagosa River watershed were added to account for additional groundwater flow from limestone areas. 
Precipitation-Runoff Modeling System IV 31

Table 6. Calibration procedure using the Luca software for the PRMS_2016 model.

[Weight of the objective function is shown in parentheses]

Calibration data set Objective function(s) and 

parameter(s) included

Paremeters used to calibrate 

model state Parameter range1

Phase 1

Step 1-Solar radiation

Basin mean monthly solar 

radiation (SR)

Absolute difference: 

1. Mean monthly (1)

dday_intcp 

dday_slope

-60 to 10 

0.2 to 0.9

Step 2-Potential evapotranspiration

Basin mean monthly potential 

evapotranspiration (PET)

Absolute difference: 

1. Mean monthly (1)

jh_coef 0.001 to 0.09

Phase 2

Step 1-Annual and monthly water budget

Annual and monthly streamflow 

volume

Normalized root mean square 

error: 

1. Mean monthly (1/3) 

2. Monthly mean (1/3) 

3. Annual mean (1/3) 

adjust_rain -0.75 to 0.75

Step 2-High flows

High flows Normalized root mean square 

error: 

1. Mean monthly (0.5) 

2. Daily high flows (0.3) 

3. Three-day moving 

average (0.2) 

smidx_coef 

smidx_exp 

0.0001 to 1.0 

0.2 to 0.8

Step 3-Low flows

Low flows Normalized root mean square 

error: 

1. Mean monthly (0.5) 

2. Daily low flows (0.3) 

3. Three-day moving 

average (0.2) 

gwflow_coef 

soil2gw_max 

0.0006 to 0.08 

0.0 to 0.6

Step 4-Daily streamflow timing

Daily streamflow timing Normalized root mean square 

error: 

1. Daily high flows (0.3) 

2. Daily low flows (0.1) 

3. Three-day moving 

average (0.6) 

carea_max 

fastcoef_lin 

pref_flow_den 

slowcoef_sq 

soil_moist_max 

radmax 

tmax_adj 

tmin_adj 

soil_rechr_max 

transp_tmax 

0.2 to 0.8

0.01 to 0.3

0.00 to 0.2

0.005 to 0.3

0.05 to 12

0.05 to 1.0

–3.0 to 3.0

–3.0 to 3.0

0.05 to 4

0 to 1.1

1

The range represents a typical range from other published models.
32 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

The parameters that influence simulated streamflow volume and timing were calibrated during this phase using four 

steps, attempting to match measured (1) annual and monthly 

water budgets, (2) high flows, (3) low flows, and (4) daily 

streamflow timing (table 6). Flow regulation and water use in 

southern Guam potentially impacts the timing and volume of 

the low flows measured at 6 of the 18 gages during some subperiods of the station’s period of record (table 2). Data from 

5 of the 6 streamflow-gaging stations with flow regulation 

during part of their period of record were used directly with 

no adjustments. Because diversion data were not available 

to adjust measured discharge at these gages, the calibration 

scheme was adjusted to only consider periods of nonregulated 

flow. To account for possible unknown periods of flow alteration, less weight was placed on the calibration of daily flows 

and more weight on the calibration of mean monthly flows for 

all gages. The Almagosa streamflow record was adjusted for 

known diversions, as discussed in the data section. The calibration scheme was designed to avoid calibrating natural-flow 

model parameters to reflect regulated-flow conditions.

The calibration scheme to fit simulated streamflow volumes to measured streamflow volumes used monthly mean, 

mean monthly, and annual mean streamflow aggregates of 

the datasets. The PRMS_2016 model was calibrated for highand low-flow conditions using mean monthly, daily high and 

low flows, and a 3-day moving average of daily streamflow. 

A 3-day moving average was used to prevent the optimization process from overcompensating and adjusting the model 

parameters to unrealistic values because of the difference in 

flow timing owing to the possibility of low-flow alterations. 

The goal of the calibration was to minimize the objective function. The final objective function value in the Luca software 

for each step is calculated as

OF w OF i

nOF

= i i

=

∑1

( ) × ,

 

(2)

where 

OFi is the objective function value for the ith 

objective function,

wi is the weight for the ith objective function, 

and

nOF is the total number of objective functions for a 

given step.

The objective function, parameters included in the objective function, and the weight applied to each objective function used for each step in Phase 2 of the calibration are listed 

in table 6. These objective functions were calculated using the 

normalized root mean square error (NRMSE):

NRMSE

MSD SIM

MSD

n

nstep

n n

n

nstep

n

= ( ) −

( ) −

=

=

∑

∑

1

2

1

2

MN , (3)

where

n is the time step,

nstep is the total number of time steps,

MSDn are the measured streamflow values,

SIMn are the simulated streamflow values, and 

MN is the mean of all streamflow values for the 

objective function time period.

If NRMSE = 0, then the measured values are equal to 

the simulated values (MSD=SIM). A value of NRMSE >1 

indicates that the average value of all the measured data is 

as good or better than using the simulated values from the 

PRMS_2016 model. Selected parameter and coefficient values 

for the daily mode streamflow calculations for the Fena Valley 

watersheds and the Ugum watershed in the PRMS_2016 

model are listed in tables 7 and 8. 

Energy-Budget Verification and 

Simulation Results

Mean monthly reference ET values that Johnson (2012) 

calculated to use in conjunction with crop coefficients as an 

approximation for PET in a water-budget model for Guam 

ranged from 0.16 inches/day (in/day) in October to 0.20 in/day 

in April. Johnson (2012) estimated that reference ET is 

about 82 percent of pan evaporation on Guam, which generally agrees with the approximation for the seasonal pattern 

of measured PET used in the calibration process for the 

PRMS_2016 model.

Water-Budget Verification and 

Simulation Results

Simulation results from the PRMS_2016 model were 

examined both graphically and statistically. Graphs of simulated basin rainfall and measured and simulated daily streamflow for the calibration and verification periods used for the 

Fena Valley watersheds and the Ugum watershed are shown 

in figures 18 through 25. The data are plotted using a semilogarithmic scale to emphasize the low flows during the dry 

season, when water availability is of greatest concern for these 

particular areas of surface-water development. The figures 

show PRMS_2016 simulates the volume and timing of streamflow in southern Guam well, however the model is sometimes 

limited in its ability to simulate the complex shape of the baseflow recession hydrographs.
Precipitation-Runoff Modeling System IV 33

Table 7. Selected parameter and coefficient values for daily mode runoff calculations for the Fena Valley watersheds and Ugum watershed in the PRMS_2016 model.—Continued[Parameter definitions are shown in table 4. HRU, hydrologic response unit]

HRU CAREA_ MAX COVDEN_ SUM FASTCOEF_ LIN GW- FLOW_ COEF PREF_ FLOW_ DEN RAD- MAX SLOW- COEF_ SQ SMIDX_ COEF SMIDX_ EXP SOIL_ MOIST_ MAX SOIL_ RECHR_ MAX SOIL- 2GW_ MAX SRAIN_ INTCP SSR2GW_ RATE TMAX_ ADJ TMIN_ ADJ TRANSP_ TMAX

Maulap

1 0.2004 0.7133 0.2527 0.0136 0.0002 0.5329 0.0632 0.0006 0.6169 8.1110 0.7980 0.2266 0.0615 0.0250 2.9830 2.9730 0.3440

2 0.2016 0.9291 0.2527 0.0136 0.0002 0.5329 0.0632 0.0006 0.6169 11.9854 1.0223 0.2266 0.0722 0.0250 2.9830 1.5145 0.3440

Almagosa

1 0.2051 1.0000 0.2028 0.0104 0.0000 0.5233 0.0427 0.0122 0.4512 11.4302 2.5586 0.0000 0.0825 0.0250 2.9953 -0.0724 0.9608

2 0.2011 0.8068 0.2028 0.0104 0.0000 0.5233 0.0427 0.0122 0.4512 11.7985 3.4135 0.0000 0.0731 0.0250 2.9953 1.6774 0.9608

3 0.2065 0.7816 0.2028 0.0104 0.0000 0.5233 0.0427 0.0122 0.4512 1.3792 2.1451 0.0000 0.0714 0.0250 2.9953 1.7711 0.9608

4 0.2016 0.6864 0.2028 0.0104 0.0000 0.5233 0.0427 0.0122 0.4512 11.9972 0.4658 0.0000 0.0665 0.0250 2.9953 -2.0733 0.9608

5 0.0010 0.9423 0.2028 0.0204 0.1999 0.5233 0.2886 0.0010 0.0010 10.0245 2.7347 0.5368 0.0794 0.2000 2.9953 -1.6799 0.9608

6 0.2008 0.6353 0.2028 0.0204 0.1999 0.5233 0.2886 0.0131 0.3363 0.4519 0.8523 0.5368 0.0620 0.2000 2.9953 -0.2147 0.9608

7 0.2008 0.7351 0.2028 0.0204 0.1999 0.5233 0.2886 0.0131 0.3363 0.3405 2.4341 0.5368 0.0701 0.2000 2.9953 -1.3205 0.9608

8 0.2008 0.9306 0.2028 0.0204 0.1999 0.5233 0.2886 0.0131 0.3363 0.2208 3.6527 0.5368 0.0787 0.2000 2.9953 -1.7717 0.9608

9 0.0010 0.7204 0.2028 0.0204 0.1999 0.5233 0.2886 0.0010 0.0010 9.6730 0.2829 0.5368 0.0683 0.2000 2.9953 -0.7089 0.9608

10 0.2008 1.0000 0.2028 0.0204 0.1999 0.5233 0.2886 0.0131 0.3363 0.8923 0.1167 0.5368 0.0825 0.2000 2.9953 -0.8244 0.9608

11 0.2008 0.9010 0.2028 0.0204 0.1999 0.5233 0.2886 0.0131 0.3363 0.7896 0.4132 0.5368 0.0746 0.2000 2.9953 2.7361 0.9608

Imong

1 0.5297 0.2561 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 1.6090 1.5080 0.3809 0.0569 0.0250 2.9776 2.5556 0.6085

2 0.5297 0.8022 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 1.0937 0.5944 0.3809 0.0874 0.0250 2.9776 2.1895 0.6085

3 0.5297 0.3109 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 3.1281 0.4005 0.3809 0.0587 0.0250 2.9776 2.7762 0.6085

4 0.5297 0.5449 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 2.2820 1.5963 0.3809 0.0713 0.0250 2.9776 2.6646 0.6085

5 0.5297 0.5751 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 1.4029 0.7939 0.3809 0.0747 0.0250 2.9776 1.4271 0.6085

6 0.5297 0.4120 0.0334 0.0175 0.0006 0.7186 0.2644 0.0438 0.2056 3.2979 0.3511 0.3809 0.0645 0.0250 2.9776 2.9437 0.6085

Ungaged

1 0.2010 0.6212 0.2527 0.0136 0.0002 0.5329 0.0632 0.0006 0.6169 10.0482 0.9102 0.2266 0.0639 0.0250 2.9830 1.1469 0.3440

2 0.2010 0.5130 0.2527 0.0136 0.0002 0.5329 0.0632 0.0006 0.6169 10.0482 0.9102 0.2266 0.0744 0.0250 2.9830 0.0587 0.3440

3 0.2010 0.9128 0.2527 0.0136 0.0002 0.5329 0.0632 0.0006 0.6169 10.0482 0.9102 0.2266 0.0566 0.0250 2.9830 0.0587 0.3440

4 0.5297 0.5743 0.0334 0.0175 0.0006 0.5329 0.2644 0.0438 0.2056 2.1356 0.8740 0.3809 0.0873 0.0250 2.9830 0.0587 0.6085

5 0.5297 0.7283 0.0334 0.0175 0.0006 0.5329 0.2644 0.0438 0.2056 2.1356 0.8740 0.3809 0.0860 0.0250 2.9830 2.4527 0.6085
34 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Table 7. Selected parameter and coefficient values for daily mode runoff calculations for the Fena Valley watersheds and Ugum watershed in the PRMS_2016 model.—Continued [Parameter definitions are shown in table 4. HRU, hydrologic response unit] HRU CAREA_ MAX COVDEN_ SUM FASTCOEF_ LIN GW- FLOW_ COEF PREF_ FLOW_ DEN RAD- MAX SLOW- COEF_ SQ SMIDX_ COEF SMIDX_ EXP SOIL_ MOIST_ MAX SOIL_ RECHR_ MAX SOIL- 2GW_ MAX SRAIN_ INTCP SSR2GW_ RATE TMAX_ ADJ TMIN_ ADJ TRANSP_ TMAX Ungaged—Continued

6 0.5297 0.6535 0.0334 0.0175 0.0006 0.5329 0.2644 0.0438 0.2056 2.1356 0.8740 0.3809 0.0637 0.0250 2.9830 0.0587 0.6085

7 0.2008 0.9749 0.2028 0.0204 0.1999 0.5329 0.2886 0.0131 0.3363 5.3635 1.7336 0.5368 0.0908 0.0250 2.9830 -0.7031 0.9608

8 0.2008 0.9079 0.2028 0.0204 0.1999 0.5329 0.2886 0.0131 0.3363 5.3635 1.7336 0.5368 0.0717 0.0250 2.9830 0.0587 0.9608

Ugum

1 0.5999 0.8961 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 3.2943 2.6969 0.6000 0.0717 0.0250 -0.0077 1.1528 0.1140

2 0.5999 0.5517 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 2.8128 2.4121 0.6000 0.0585 0.0250 -0.0077 -0.8691 0.1140

3 0.2743 0.5842 0.0243 0.0163 0.0008 0.5511 0.0096 0.0342 0.2004 3.6674 0.6335 0.0564 0.0564 0.0250 2.9665 2.8625 0.4565

4 0.5999 0.7244 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 2.1267 1.7922 0.6000 0.0657 0.0250 -0.0077 -0.8691 0.1140

5 0.5999 0.6317 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 2.0071 1.6681 0.6000 0.0561 0.0250 -0.0077 -0.8691 0.1140

6 0.3670 0.2881 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.6447 0.7451 0.1051 0.0414 0.0250 2.7497 2.7285 0.8643

7 0.3670 0.2907 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.1671 0.3882 0.1051 0.0426 0.0250 2.7497 2.9532 0.8643

8 0.2743 0.6445 0.0243 0.0163 0.0008 0.5511 0.0096 0.0342 0.2004 3.2735 0.4040 0.0564 0.0581 0.0250 2.9665 0.5965 0.4565

9 0.3670 0.2692 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.6226 0.6527 0.1051 0.0375 0.0250 2.7497 0.4816 0.8643

10 0.3670 0.4134 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.1717 0.3520 0.1051 0.0470 0.0250 2.7497 2.7285 0.8643

11 0.3670 0.8932 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.2206 0.3765 0.1051 0.0698 0.0250 2.7497 2.9532 0.8643

12 0.3670 0.5586 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.1681 0.3329 0.1051 0.0550 0.0250 2.7497 0.4816 0.8643

13 0.3670 0.4587 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.2656 0.4350 0.1051 0.0492 0.0250 2.7497 0.4816 0.8643

14 0.3670 0.1716 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 1.9278 0.1930 0.1051 0.0323 0.0250 2.7497 0.4816 0.8643

15 0.3670 0.8829 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.1555 0.2542 0.1051 0.0679 0.0250 2.7497 1.6050 0.8643

16 0.3670 0.4849 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 1.9053 0.1216 0.1051 0.0479 0.0250 2.7497 0.4816 0.8643

17 0.3670 0.7141 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.6283 0.7023 0.1051 0.0629 0.0250 2.7497 2.9532 0.8643

18 0.3670 0.6585 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.6898 0.7322 0.1051 0.0587 0.0250 2.7497 0.4816 0.8643

19 0.3670 0.3999 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.3352 0.4832 0.1051 0.0477 0.0250 2.7497 0.4816 0.8643

20 0.3670 0.4959 0.0609 0.0128 0.0036 0.5818 0.0096 0.0668 0.2000 2.2427 0.3496 0.1051 0.0504 0.0250 2.7497 0.4816 0.8643

21 0.7999 0.9318 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 1.8274 1.5050 0.6000 0.0743 0.2000 -0.0077 0.1419 0.1140

22 0.7999 0.9776 0.0418 0.0227 0.0008 0.8359 0.0095 0.0001 0.2500 2.2217 1.8886 0.6000 0.0690 0.2000 -0.0077 -0.8691 0.1140
Precipitation-Runoff Modeling System IV 35

Table 8. Selected monthly parameter and coefficient values for calculations for the Fena Valley watersheds and Ugum watershed 

in the PRMS_2016 model.

[Parameter definitions are shown in table 4]

Month ADJUST_

RAIN

DDAY_

INTCP

DDAY_

SLOPE JH_COEF

Maulap

January -0.116 -55.049 0.336 0.014

February -0.116 -55.049 0.621 0.009

March -0.116 -55.049 0.612 0.010

April -0.116 -55.049 0.605 0.010

May -0.116 -55.049 0.476 0.012

June -0.116 -55.049 0.558 0.009

July -0.116 -55.049 0.347 0.010

August -0.116 -55.049 0.322 0.007

September -0.116 -55.049 0.461 0.012

October -0.116 -55.049 0.587 0.010

November -0.116 -55.049 0.424 0.010

December -0.116 -55.049 0.611 0.010

Almagosa

January 0.177 -50.169 0.776 0.007

February 0.177 -50.169 0.667 0.009

March 0.177 -50.169 0.583 0.011

April 0.177 -50.169 0.566 0.009

May 0.177 -50.169 0.455 0.010

June 0.177 -50.169 0.436 0.010

July 0.177 -50.169 0.447 0.010

August 0.177 -50.169 0.468 0.009

September 0.177 -50.169 0.575 0.008

October 0.177 -50.169 0.571 0.010

November 0.177 -50.169 0.701 0.007

December 0.177 -50.169 0.720 0.008

Imong

January -0.008 -57.134 0.405 0.017

February -0.008 -57.134 0.555 0.015

March -0.008 -57.134 0.243 0.012

April -0.008 -57.134 0.536 0.013

May -0.008 -57.134 0.360 0.013

June -0.008 -57.134 0.491 0.009

Month ADJUST_

RAIN

DDAY_

INTCP

DDAY_

SLOPE JH_COEF

Imong—Continued

July -0.008 -57.134 0.550 0.009

August -0.008 -57.134 0.522 0.010

September -0.008 -57.134 0.368 0.012

October -0.008 -57.134 0.547 0.011

November -0.008 -57.134 0.452 0.010

December -0.008 -57.134 0.417 0.015

Ungaged

January -0.116 -55.049 0.336 0.014

February -0.116 -55.049 0.621 0.009

March -0.116 -55.049 0.612 0.010

April -0.116 -55.049 0.605 0.010

May -0.116 -55.049 0.476 0.012

June -0.116 -55.049 0.558 0.009

July -0.116 -55.049 0.347 0.010

August -0.116 -55.049 0.322 0.007

September -0.116 -55.049 0.461 0.012

October -0.116 -55.049 0.587 0.010

November -0.116 -55.049 0.424 0.010

December -0.116 -55.049 0.611 0.010

Ugum

January -0.101 -57.014 0.360 0.011

February -0.101 -57.014 0.531 0.015

March -0.101 -57.014 0.648 0.008

April -0.101 -57.014 0.633 0.010

May -0.101 -57.014 0.469 0.014

June -0.101 -57.014 0.444 0.008

July -0.101 -57.014 0.570 0.009

August -0.101 -57.014 0.421 0.007

September -0.101 -57.014 0.613 0.013

October -0.101 -57.014 0.458 0.010

November -0.101 -57.014 0.565 0.014

December -0.101 -57.014 0.464 0.010
36 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Daily mean streamflow, in cubic feet per second

0.01

0.1

1

10

100

1,000

Measured

Simulated

Basin rainfall, in inches per day

0

2

4

6

8

10

12

14 A

B

EXPLANATION

Figure 18. A, Simulated basin rainfall for the calibration period, Maulap River watershed, Guam, from 

October 1, 2003 to September 30, 2014. B, Measured and simulated daily streamflow for the calibration 

period, Maulap River watershed, Guam, from October 1, 2003 to September 30, 2014.
Precipitation-Runoff Modeling System IV 37

Daily mean streamflow, in cubic feet per second

Measured

Simulated

Basin rainfall, in inches per day

A

B

EXPLANATION

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0.01

0.1

1

10

100

1,000

0

5

10

15

20

25

Figure 19. A. Simulated basin rainfall for the calibration period, Almagosa River watershed, Guam, from 

October 1, 2001 to September 30, 2014. B. Measured and simulated daily streamflow for the calibration period, 

Almagosa River watershed, Guam, from October 1, 2001 to September 30, 2014.
38 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Daily mean streamflow, in cubic feet per second

Measured

Simulated

Basin rainfall, in inches per day

A

B

EXPLANATION

2010 2011 2012 2013 2014

0.01

0.1

1

10

100

1,000

0

2

4

6

8

12

10

Figure 20. A, Simulated basin rainfall for the calibration period, Imong River watershed, Guam, from October 1, 

2009 to September 30, 2014. B, Measured and simulated daily streamflow for the calibration period, Imong River 

watershed, Guam, from October 1, 2009 to September 30, 2014.
Precipitation-Runoff Modeling System IV 39

Daily mean streamflow, in cubic feet per second

Measured

Simulated

Basin rainfall, in inches per day

A

B

EXPLANATION

2006 2007 2008 2009 2010 2011 2012 2013 2014

0.1

1

10

100

1,000

0

2

4

6

8

10

Figure 21. A, Simulated basin rainfall for the calibration period, Ugum River above Talofofo Falls, Guam, from 

October 1, 2005 to September 30, 2014. B, Measured and simulated daily streamflow for the calibration period, 

Ugum River above Talofofo Falls, Guam, from October 1, 2005 to September 30, 2014.
40 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Daily mean streamflow, in cubic feet per second

Measured

Simulated

Basin rainfall, in inches per day

A

B

EXPLANATION

0.1

1

10

100

1,000

0

2

4

6

8

2000 2001

Figure 22. A, Simulated basin rainfall for the verification period, Maulap River watershed, Guam, from 

October 1, 1999 to September 30, 2001. B, Measured and simulated daily streamflow for the verification 

period, Maulap River watershed, Guam, from October 1, 1999 to September 30, 2001.
Precipitation-Runoff Modeling System IV 41

Daily mean streamflow, in cubic feet per second

Measured

Simulated

Basin rainfall, in inches per day

A

B

EXPLANATION

0.1

1

10

100

1,000

0

2

4

6

8

2000 2001

Figure 23. A, Simulated basin rainfall for the verification period, Almagosa River watershed, Guam, from 

October 1, 1999 to September 30, 2001. B, Measured and simulated daily streamflow for the verification 

period, Almagosa River watershed, Guam, from October 1, 1999 to September 30, 2001.
42 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Daily mean streamflow, in cubic feet per second Basin rainfall, in inches per day

A

B

0.1

1

10

100

1,000

0

2

4

6

8

Measured

Simulated

EXPLANATION

2000 2001

Figure 24. A. Simulated basin rainfall for the verification period, Imong River watershed, Guam, from 

October 1, 2007 to September 30, 2009. B. Measured and simulated daily streamflow for the verification 

period, Imong River watershed, Guam, from October 1, 2007 to September 30, 2009.
Precipitation-Runoff Modeling System IV 43

Daily mean streamflow, in cubic feet per second Basin rainfall, in inches per day

A

B

0.1

1

10

100

1,000

0

2

1

3

4

5

6

Measured

Simulated

EXPLANATION

1999 2000 2001

Figure 25. A. Simulated basin rainfall for the verification period, Ugum River above Talofofo Falls, 

Guam, from October 1, 1998 to September 30, 2001. B. Measured and simulated daily streamflow for the 

verification period, Ugum River above Talofofo Falls, Guam, from October 1, 1998 to September 30, 2001.
44 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Statistical summaries of the accuracy of simulated daily 

and monthly mean streamflow for the calibration and verification periods are shown in tables 9 and 10. Models were evaluated using the Nash-Sutcliffe model efficiency index (NSE; 

Nash and Sutcliffe [1970]) and the model bias (Pbias, expressed 

as a percentage) equations below (the terms used in equations 

4 and 5 were previously defined for equation 3): 

NSE

MSD SIM

MSD MN

n

nstep

n n

n

nstep

n

= − ( ) −

( ) −

=

=

∑

∑

1 0 1

2

1

2 . , and (4)

Pbias

n

n

n n

n n

n

step

step

SIM MSD

MSD = ( ) − × =

=

∑

∑

1

1

100

( )

, (5)

The NSE statistic or coefficient of efficiency was examined as a measure of the overall quality of model fit, with a 

value of 1 representing a perfect fit between the measured and 

simulated data. A value of 0 indicates that using the average 

value of all of measured data is as good as the model. A negative NSE statistic indicates that the average value of all of the 

measured data provides a better fit than the simulated model 

results. The coefficient of efficiency is a widely used relative 

measure of a model’s predictive power (Markstrom and others, 2008). Model bias was calculated to determine whether 

the model consistently overestimated or underestimated 

streamflow. A bias of 0 indicates that the model does not have 

a tendency to either overestimate or underestimate streamflow. A positive value indicates a tendency to overestimate 

streamflow, whereas a negative value indicates a tendency to 

underestimate streamflow. Monthly model simulations with a 

coefficient of efficiency equal to or greater than 0.5 and a bias 

between +25 and –25 percent are commonly rated as having a 

satisfactory performance. Monthly model simulations with a 

coefficient of efficiency greater than 0.65 and a bias between 

+15 and –15 percent are commonly rated as having a good 

performance. Finally, monthly model simulations with a coefficient of efficiency greater than 0.75 and a bias between +10 

and –10 percent are commonly rated as having a very good 

performance (Moriasi and others, 2007).

The coefficient of efficiency based on simulated and 

measured daily streamflows in the PRMS_2016 model ranges 

from 0.28 to 0.90 for the calibration period and from 0.26 to 

0.99 for the verification period (table 9). These values indicate that the PRMS_2016 model provides a better estimate of 

daily streamflow than the mean measured value of streamflow 

for each of the 18 watersheds that were used in the calibration. The bias in the PRMS_2016 model ranges from –16.71 

to 4.99 percent for the calibration period and from –17.81 

to 38.06 percent for the verification period (table 9). The 

model generally is expected to perform better in the calibration period than in the verification period because the model 

parameter values are optimized for the calibration period. 

In this study, the most heavily weighted calibration target 

of the PRMS_2016 model was the simulation of monthly 

flows (table 6). Model-evaluation statistics calculated using 

monthly values (determined from aggregated daily values) 

generally indicate better performance than statistics calculated 

using the daily values. The statistics from the daily models 

are rated (table 9) according to the same statistical ranges 

provided by Moriasi and others (2007) for monthly modelevaluation statistics. The monthly coefficient of efficiency 

in the PRMS_2016 model ranges from 0.62 to 0.98 for the 

calibration period and from 0.70 to 0.98 for the verification 

period (table 10). These values indicate that the PRMS_2016 

model provides a better estimate of monthly streamflow than 

the mean monthly measured value of streamflow for each of 

the 18 watersheds that were used in the calibration process. 

The bias in the PRMS_2016 model for monthly values ranges 

from –16.71 to 5.73 percent for the calibration period and 

from –17.81 to 38.06 percent for the verification period (table 

10). According to the criteria of Moriasi and others (2007), the 

PRMS_2016 model has a “satisfactory” to “very good” rating 

for all but two of the gaged watersheds during the verification 

period, the Talofofo (16850000) and Lonfit (16862000) watersheds (table 10). The verification periods for both of these 

watersheds rely on temperature and rainfall data from the only 

available climate station during 1953–56, the Fena Filter Plant 

(fig. 1, table 1). The verification period for the Tolaeyuus near 

Agat (16845000) watershed, which has a good rating, also 

uses data from this climate station as input to the model. However, the Tolaeyuus stream gage is located in closer proximity 

to the Fena Filter Plant station, and therefore the better model 

performance for the Tolaeyuus gaging station is expected. 

The accuracy of monthly simulation results is especially 

important because the FVR water-balance model is run using 

a monthly time step with monthly streamflow totals from the 

Fena Valley watersheds as input. The following discussion 

of the accuracy of model simulations is therefore focused 

primarily on monthly results for the Fena Valley watershed. 

PRMS_2016 simulated streamflow volume on a monthly 

scale for all three gaged Fena Valley watersheds reasonably well. The monthly coefficient of efficiency for both the 

calibration and verification periods was greater than 0.87 for 

all three watersheds. Bias for the calibration and verification 

periods ranged from underestimating flow by 6.96 percent 

in the Almagosa River watershed to overestimating flow by 

0.60 percent in the Maulap River watershed. Overall, simulation errors were greatest in the Almagosa River watershed, 

most likely due to the added complexity of the closed depression area simulations and the reconstructed streamflow record 

to account for the diversion at the Almagosa Springs. Simulation errors for the Imong River watershed were the smallest of 

all three watersheds. A summary of measured and simulated 

streamflow volumes for each simulation period (table 11) indicates that the cumulative streamflow volume for the Almagosa 

River watershed was underestimated by 6.96 percent for the 

verification period, whereas the Maulap watershed was within 

0.57 percent for the same period. The model calculated total 
Precipitation-Runoff Modeling System IV 45

Table 9. Errors in simulated daily mean streamflow for gaged watersheds used to calibrate the PRMS_2016 model.

[abv, above; nr, near; –, not applied to any region]

USGS 

stream 

gaging 

station

Calibration Verification Region 

model 

calibration 

applied2

Period Coefficient 

of efficiency

Bias 

(percent)

Performance 

rating1 Period Coefficient of efficiency

Bias 

(percent)

Performance 

rating1

16807650 

(Aplacho)

10/1/2007–

9/30/2009

0.28 –5.30 Unsatisfactory 10/1/2009–

9/30/2011

0.80 0.04 Very good A

16808300 

(Finile)

10/1/1976–

9/30/1982

0.44 1.19 Unsatisfactory 10/1/1962–

9/30/1976

0.59 –0.36 Satisfactory D

16809400 

(Cetti)

10/1/1964–

9/30/1966

0.70 –1.29 Good 10/1/1961–

9/30/1964

0.26 –3.25 Unsatisfactory –

16809600 

(La Sa Fua)

10/1/2007–

9/30/2013

0.89 0.56 Very good 10/1/2001–

9/30/2007

0.82 –14.04 Good G

16816000 

(Umatac)

10/1/2004–

9/30/2010

0.83 –16.71 Satisfactory 10/1/2010–

9/30/2011

0.39 1.49 Unsatisfactory –

16821000 

(Geus)

10/1/1971–

9/30/1974

0.41 –2.17 Unsatisfactory 10/1/1974–

9/30/1975

0.34 8.65 Unsatisfactory –

16835000 

(Inarajan)

10/1/1972–

9/30/1982

0.64 2.34 Satisfactory 10/1/1970–

9/30/1972

0.55 –17.81 Satisfactory –

16840000 

(Tinaga)

10/1/1975–

9/30/1985

0.74 0.01 Good 10/1/1965–

9/30/1971

0.52 0.74 Satisfactory H

16845000 

(Tolaeyuus 

nr Agat)

10/1/1956–

9/30/1959

0.57 –16.01 Satisfactory 10/1/1954–

9/30/1956

0.99 –13.75 Good –

16847000 

(Imong)

10/1/2009–

9/30/2014

0.88 0.34 Very good 10/1/2007–

9/30/2009

0.72 –0.43 Good Imong

16848100 

(Almagosa)

10/1/2001–

9/30/2014

0.75 –0.67 Very good 10/1/1999–

9/30/2001

0.82 –6.96 Very good Almagosa

16848500 

(Maulap)

10/1/2003–

9/30/2014

0.83 0.60 Very good 10/1/1999–

9/30/2001

0.87 0.57 Very good Maulap

16850000 

(Talofofo)

10/1/1957–

9/30/1961

0.51 4.99 Satisfactory 10/1/1953–

9/30/1957

0.72 35.19 Unsatisfactory E

16854500 

(Ugum abv 

Talofofo 

Falls)

10/1/2005–

9/30/2014

0.87 0.84 Very good 10/1/1998–

9/30/2001

0.79 7.37 Very good F

16855000 

(Ugum nr 

Talofofo)

10/1/1961–

9/30/1969

0.58 1.27 Satisfactory 10/1/1969–

9/30/1970

0.46 –17.27 Unsatisfactory –

16858000 

(Ylig)

10/1/1999–

9/30/2001

0.90 –0.25 Very good 4/1/1998–

9/30/1999

0.68 –15.33 Good C

16862000 

(Lonfit)

10/1/1956–

9/30/1959

0.72 2.43 Good 10/1/1954–

9/30/1956

0.61 38.06 Unsatisfactory –

16865000 

(Pago)

10/1/2008–

9/30/2014

0.85 0.55 Very good 10/1/2006–

9/30/2008

0.82 1.19 Very good B

1

Performance ratings as described by Moriasi and others, 2007.

2

Regions are shown in figure 26.
46 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 10. Errors in simulated monthly mean streamflow for gaged watersheds used to calibrate the PRMS_2016 model.

[abv, above; nr, near; –, not applied to any region]

USGS 

stream gaging station

Calibration Verification Region 

model 

calibration 

applied2

Period Coefficient 

of efficiency

Bias 

(percent)

Performance 

rating1 Period Coefficient of efficiency

Bias 

(percent)

Performance 

rating1

16807650 

(Aplacho)

10/1/2007–

9/30/2009

0.62 –0.65 Satisfactory 10/1/2009–

9/30/2011

0.78 0.04 Very good A

16808300 

(Finile)

10/1/1976–

9/30/1982

0.84 1.19 Very good 10/1/1962–

9/30/1976

0.80 –0.36 Very good D

16809400 

(Cetti)

10/1/1964–

9/30/1966

0.86 –1.29 Very good 10/1/1961–

9/30/1964

0.70 –3.25 Good –

16809600 

(La Sa Fua)

10/1/2007–

9/30/2013

0.95 0.56 Very good 10/1/2001–

9/30/2007

0.92 –14.04 Good G

16816000 

(Umatac)

10/1/2004–

9/30/2010

0.89 –16.71 Satisfactory 10/1/2010–

9/30/2011

0.79 1.49 Very good –

16821000 

(Geus)

10/1/1971–

9/30/1974

0.71 –2.17 Good 10/1/1974–

9/30/1975

0.90 8.65 Very good –

16835000 

(Inarajan)

10/1/1972–

9/30/1982

0.90 2.34 Very good 10/1/1970–

9/30/1972

0.73 –17.81 Satisfactory –

16840000 

(Tinaga)

10/1/1975–

9/30/1985

0.93 0.01 Very good 10/1/1965–

9/30/1971

0.85 0.74 Very good H

16845000 

(Tolaeyuus 

nr Agat)

10/1/1956–

9/30/1959

0.84 –16.01 Satisfactory 10/1/1954–

9/30/1956

0.72 –13.75 Good –

16847000 

(Imong)

10/1/2009–

9/30/2014

0.94 0.50 Very good 10/1/2007–

9/30/2009

0.94 –0.43 Very good Imong

16848100 

(Almagosa)

10/1/2001–

9/30/2014

0.87 –0.67 Very good 10/1/1999–

9/30/2001

0.92 –6.96 Very good Almagosa

16848500 

(Maulap)

10/1/2003–

9/30/2014

0.92 0.60 Very good 10/1/1999–

9/30/2001

0.94 0.57 Very good Maulap

16850000 

(Talofofo)

10/1/1957–

9/30/1961

0.80 5.73 Very good 10/1/1953–

9/30/1957

0.84 35.19 Unsatisfactory E

16854500 

(Ugum abv 

Talofofo 

Falls)

10/1/2005–

9/30/2014

0.97 0.84 Very good 10/1/1998–

9/30/2001

0.93 7.37 Very good F

16855000 

(Ugum nr 

Talofofo)

10/1/1961–

9/30/1969

0.87 1.27 Very good 10/1/1969–

9/30/1970

0.75 –17.27 Satisfactory –

16858000 

(Ylig)

10/1/1999–

9/30/2001

0.98 –0.25 Very good 4/1/1998–

9/30/1999

0.87 –15.33 Good C

16862000 

(Lonfit)

10/1/1956–

9/30/1959

0.94 2.43 Very good 10/1/1954–

9/30/1956

0.75 38.06 Unsatisfactory –

16865000 

(Pago)

10/1/2008–

9/30/2014

0.93 –0.28 Very good 10/1/2006–

9/30/2008

0.98 0.02 Very good B

1

Performance ratings as described by Moriasi and others, 2007.

2

Regions are shown in figure 26.
Precipitation-Runoff Modeling System IV 47

streamflow volume for the Maulap, Almagosa, and Imong 

River watersheds within 0.60, –1.43, and 0.27 percent of measured values, respectively, over the entire simulation period.

Of particular interest are the monthly dry-season flows 

for the Fena Valley watersheds. Measured and simulated 

monthly mean streamflow for the dry season (January to 

Table 11. Summary of measured and simulated cumulative 

streamflow for the gaged watersheds in the Fena Valley 

watershed, Guam.

Period Measured1

(inches)

Simulated1

(inches)

Percentage

difference2

Maulap River watershed

Calibration 604.90 608.56 0.61

(10/1/2003–

9/30/2014)

Verification 93.05 93.58 0.57

(10/1/1999–

9/30/2001)

Entire period 697.95 702.14 0.60

 Almagosa River watershed

Calibration 836.84 831.27 –0.67

(10/1/2001–

9/30/2014)

Verification 115.84 107.78 –6.96

(10/1/1999–

9/30/2001)

Entire period 952.68 939.05 –1.43

Imong River watershed

Calibration 365.73 367.57 0.50

(10/1/2009–

9/30/2014)

Verification 121.19 120.66 –0.44

(10/1/2007–

9/30/2009)

Entire period 486.92 488.23 0.27

1

Cumulative streamflow depth= total volume of streamflow/area of 

watershed

2

Percentage difference = 100 x (simulated - measured)/measured

June) for months during which concurrent data are available 

for the three Fena Valley gaged watersheds are summarized 

in table 12. Months with estimated values of streamflow in 

the measured record were not included in the analysis. The 

differences between measured and simulated monthly mean 

streamflow for the Maulap River watershed ranged from 

–38.1 percent (May 2014) to 91.7 percent (March 2012) in the 

calibration period and from –22.0 percent (January 2000) to 

81.6 percent (January 2001) in the verification period. For the 

Almagosa River watershed, the differences ranged from –18.9 

(January 2014) to 48.0 percent (April 2012) in the calibration period and from –15.2 (April 2001) to 45.5 percent (May 

2000) in the verification period. For the Imong River watershed, the differences ranged from –47.3 percent (May 2010) to 

135.6 percent (March 2013) in the calibration period and from 

–18.8 percent (January 2008) to 17.8 percent (May 2009) in 

the verification period. 

The large over-estimation error during March 2013 for 

the Imong River watershed is probably related to error in the 

rainfall estimate. Rainfall input to the model is estimated using 

the inverse distance and elevation module in PRMS-IV with 

data collected from Fena pump and Almagosa rain gages. 

During March 2013, the rainfall distribution from these gages 

probably overestimated rainfall in the Imong River watershed 

located farther south. Measured rainfall records at the Almagosa and Fena rain gages indicated a localized storm occurred 

in the northern part of the study area in March 2013. Monthly 

total rainfall for March 2013 at the Fena pump and Almagosa 

rain gages averaged about 7.4 in.; farther to the south, the 

Umatac rain gage received only about 4.6 in. 

Total measured and simulated streamflow volumes during 

the dry seasons for each simulation period are summarized in 

table 13. The PRMS_2016 model simulated the total volume 

of dry season streamflow for the entire simulation period at 

Almagosa River watershed within 0.54 percent of the measured volume. Simulation errors for the Maulap and Imong 

River watersheds were higher at 6.39 and 6.06 percent, but 

were still within the limit of the accuracy of the measured 

streamflow record. Over-simulation rates (number of overestimated months divided by the total number of monthly 

simulations) for all three watersheds were close to 50 percent, 

which indicated that the positive and negative errors were 

distributed evenly. 
48 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 12. Summary of measured and simulated dry season monthly mean discharge and associated error for the gaged watersheds in 

the Fena Valley watershed, Guam.

[ft3/s, cubic feet per second; percentage difference = 100 x (simulated - measured)/measured; gray shading, months that include estimated measured data; n/a, not 

available]

Maulap watershed Almagosa watershed Imong watershed

Month

Monthly mean discharge Monthly mean discharge Monthly mean discharge

Simulated

ft3

/s

Measured

ft3/s

Percentage

difference

Simulated

ft3/s

Measured

ft3/s

Percentage

difference

Simulated

ft3/s

Measured

ft3/s

Percentage

difference

Calibration period

2010

January 2.91 3.17 –8.3 6.91 5.81 18.8 4.84 4.93 –1.9

February 1.43 1.62 –11.9 3.74 3.35 11.7 2.57 2.99 –13.9

March 1.15 0.55 109.9 2.58 2.56 1.0 1.94 2.53 –23.5

April 1.09 0.27 308.6 2.35 2.11 11.3 1.79 2.75 –34.9

May 0.55 0.04 1,259.0 1.60 1.56 2.4 0.94 1.78 –47.3

2011

January 6.51 5.85 11.2 10.59 9.90 7.0 12.58 11.07 13.6

February 6.54 7.62 –14.2 10.85 11.34 –4.4 12.98 11.62 11.7

March 2.95 2.79 5.6 5.91 4.84 22.3 6.17 5.00 23.5

April 2.92 3.08 –5.0 5.46 6.29 –13.2 6.43 6.55 –1.9

May 1.93 1.35 42.9 4.23 2.87 47.3 2.74 3.01 –8.8

2012

January 5.38 4.93 9.3 9.11 9.95 –8.5 9.57 11.92 –19.7

February 2.63 1.60 64.7 5.14 3.63 41.5 4.73 4.54 4.3

March 1.95 1.02 91.7 3.67 2.72 34.9 3.71 3.78 –2.0

April 1.69 0.90 88.0 3.12 2.11 48.0 2.99 2.70 10.5

May 2.29 1.69 35.2 4.72 4.24 11.5 4.11 5.72 –28.2

2013

January 2.53 1.79 41.6 4.86 4.36 11.4 4.96 3.29 50.7

February 1.69 1.20 40.3 3.39 2.57 32.2 3.44 2.10 63.8

March 2.28 1.67 36.1 4.31 3.61 19.4 5.65 2.40 135.6

April 1.07 0.79 36.0 2.75 2.13 28.9 2.29 1.84 24.8

May 1.82 1.54 18.4 4.04 2.86 41.4 4.06 2.46 65.1

2014

January 7.28 7.33 –0.7 11.49 14.18 –18.9 16.72 12.99 28.7

February 4.52 3.57 26.7 8.64 6.43 34.4 7.45 5.23 42.5

March 1.71 1.68 2.2 4.79 3.38 41.5 4.17 3.32 25.5

April 1.55 2.11 –26.5 3.68 3.41 7.9 2.95 2.85 3.4

May 0.77 1.24 –38.1 2.60 2.12 22.7 1.72 2.27 –24.1

Verification period

2000

January 1.23 1.57 –22.0 3.26 2.62 24.6 N/A N/A N/A

February 2.32 2.13 8.5 4.30 3.98 8.0 N/A N/A N/A

March 1.04 1.33 –21.8 2.56 2.29 11.8 N/A N/A N/A

April 0.72 0.86 –16.9 1.88 1.42 31.7 N/A N/A N/A

May 2.41 2.06 16.9 5.02 3.45 45.5 N/A N/A N/A
Precipitation-Runoff Modeling System IV 49

Table 12. Summary of measured and simulated dry season monthly mean discharge and associated error for the gaged watersheds in 

the Fena Valley watershed, Guam.

[ft3/s, cubic feet per second; percentage difference = 100 x (simulated - measured)/measured; gray shading, months that include estimated measured data; n/a, not 

available]

Maulap watershed Almagosa watershed Imong watershed

Month

Monthly mean discharge Monthly mean discharge Monthly mean discharge

Simulated

ft3

/s

Measured

ft3/s

Percentage

difference

Simulated

ft3/s

Measured

ft3/s

Percentage

difference

Simulated

ft3/s

Measured

ft3/s

Percentage

difference

Verification period—Continued

2001

January 2.40 1.32 81.6 4.74 3.98 18.9 N/A N/A N/A

February 1.54 0.85 80.9 3.04 2.91 4.4 N/A N/A N/A

March 1.03 0.85 22.3 1.95 2.21 -11.9 N/A N/A N/A

April 0.79 0.96 -17.7 1.39 1.64 -15.2 N/A N/A N/A

May 0.67 0.72 -6.9 1.31 1.43 -8.9 N/A N/A N/A

2008

January N/A N/A N/A N/A N/A N/A 6.24 7.68 -18.8

February N/A N/A N/A N/A N/A N/A 5.95 5.87 1.4

March N/A N/A N/A N/A N/A N/A 4.18 4.12 1.5

April N/A N/A N/A N/A N/A N/A 2.23 2.74 -18.6

May N/A N/A N/A N/A N/A N/A 2.11 2.47 -14.4

2009

January N/A N/A N/A N/A N/A N/A 11.82 12.39 -4.6

February N/A N/A N/A N/A N/A N/A 2.64 2.50 5.5

March N/A N/A N/A N/A N/A N/A 2.09 1.99 4.7

April N/A N/A N/A N/A N/A N/A 2.35 2.07 13.6

May N/A N/A N/A N/A N/A N/A 3.90 3.31 17.8
50 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 13. Summary of measured and simulated dry season streamflow 

(January to May) for the gaged watersheds in the Fena Valley 

watershed, Guam.

Period Measured1

(inches)

Simulated1

(inches)

Percentage

difference2

Over-simulation rate3

(percent)

 Maulap River watershed

Calibration 106.18 112.36 5.82 54.5

(10/1/2003–

9/30/2014)

Verification 12.12 13.51 11.42 40.0

(10/1/1999–

9/30/2001)

Entire period 118.31 125.87 6.39 52.3

 Almagosa River watershed

Calibration 152.55 149.53 –1.98 58.5

(10/1/2001–

9/30/2014)

Verification 15.44 17.56 13.67 70.0

(10/1/1999–

9/30/2001)

Entire period 167.99 167.08 –0.54 60.0

 Imong River watershed

Calibration 70.01 76.86 9.78 56.0

(10/1/2009–

9/30/2014)

Verification 26.62 25.63 –3.71 60.0

(10/1/2007–

9/30/2009)

Entire period 96.63 102.49 6.06 57.1

1

Cumulative streamflow depth = total volume of streamflow/area of watershed

2

Percentage difference = 100 x (simulated - measured)/measured

3

Number of overestimated months divided by the total number of monthly 

simulations

For ungaged areas within each modeled region, 

model parameters were determined from a selected and 

representative gaged area with similar physical watershed characteristics within that region. The modeled 

regions, model stream segments, and the gaged drainage basins that were used to calibrate the PRMS_2016 

model are shown in figure 26. The ungaged regions to 

which the parameters from each gaged model calibration were applied are listed in the last column in tables 9 

and 10. The parameters for each of the FVR ungaged 

areas were derived from one of the nearby Fena Valley 

watershed gaged areas. Calibrated parameters from the 

gaged areas were transferred to the ungaged HRUs in 

closest proximity to the outlet of the gaged Fena Valley 

watershed areas (tables 7 and 8). 

Model Uncertainties

PRMS-IV is a practical tool that makes it possible 

to simulate complex natural systems through sets of 

mathematical equations and empirical relations representing and approximating the major components of the 

hydrologic cycle and hydrologic processes involved. As 

a result of the assumptions and simplifications that must 

be made, error and uncertainty are built into the model. 

Even though the 317 HRUs in the PRMS_2016 model 

capture some of the heterogeneity of the watershed 

characteristics, these characteristics still must be simplified into these assumed homogenous units. Models in 

general are also limited by errors associated with the 

input data. The quality and accuracy of time-series data 

for rainfall, temperature, streamflow, diversion, SR, 

and PET impact the accuracy of the simulation results. 

Calibration and verification were done for a specific 

time period and range of streamflow and therefore it 

is uncertain how the PRMS_2016 model will perform 

under different conditions. 
Precipitation-Runoff Modeling System IV 51

WEST EAST

144°50'

144°45'

144°40'

13°30'

13°25'

13°20'

13°15'

*#

*#

*#

*#

*#

*#

*#

#*

*#

*#

*#

*#

#*

*#

*#

#*

#*

#*

0 1 2 4 MILES

0 2

3

1 3 4 KILOMETERS

USGS streamflow-gaging station 

and abbreviated number—

Complete numbers begin with

prefix 16

EXPLANATION

Gaged drainage basin

Model stream segments

#*

Model regions

Base modified from U.S. Geological Survey 

10-meter digital elevation model, Universal 

Tranverse Mercator projection zone 55, 

WGS84 datum.

Fena Valley

Reservoir

Asan 

Point

Tumon

Bay

Hagåtña

Bay

Orote

Peninsula

Apra Harbor

Pago Bay

Ylig Bay

Talofofo Bay

Inarajan Bay

Fonte River

Taleyfac River

Agfayan Bay

Sagua River

Ajayan River

Togcha River

Asolonso River

Facpi

Point

Pauliluc River

Imong River

Maulap River

Ylig River

Talofofo River

Ugum River

P H I L I P P I N E

S E A

P A C I F I C

O C E A N

807650

809600

816000

847000

848500

865000

808300

809400

835000

840000

845000

855000

858000

848100

850000

854500

862000

835000

821000

Almagosa additional HRUs

A

B

C

D

E

F

G

H

FVR ungaged area

Imong

Almagosa

Maulap

Figure 26. Modeled regions, model stream segments, and gaged drainage-basin areas in the PRMS_2016 model. 

FVR, Fena Valley Reservoir; HRU, hydrologic response unit.
52 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Comparison to Previous Fena Valley Reservoir 

Water Model

Many of the limitations identified in the PRMS_1994 

model of the Fena Valley watershed developed by Nakama 

(1994) were addressed in the PRMS_2004 model developed 

by Yeung (2004). Yeung (2004) calibrated the Almagosa River 

watershed model and accounted for the diversion from the 

Almagosa Springs to reflect natural streamflow conditions necessary for model calibration and an area of limestone terrain 

outside of the topographically based divide for the Almagosa 

River watershed that contributes groundwater discharge to the 

watershed. Yeung (2004) improved the geographical representation of rainfall data and modeled the ungaged areas of the 

watershed to improve the estimation of total watershed streamflow draining into the FVR. Furthermore, the PRMS_2004 

model included a period of three moderately strong to severe 

El Niño events, from 1990 to 2001, giving modeled examples 

of these important climate drivers.

As well as modeling all of southern Guam, PRMS_2016 

contains a model of the Fena Valley watersheds that improved 

on the Fena Valley watershed (only) PRMS_2004 model. The 

new model considers the most recent hydrologic conditions in 

the watershed, including ENSO events (weak, moderate, and 

strong) in both the calibration and verification periods; it also 

used a longer period of data (1951–2015) to achieve a more 

representative model of each watershed. The PRMS_2016 

model improved upon the simulated basin rainfall by using the 

inverse distance and elevation (ide_dist) distribution module 

instead of an arithmetic average of the Almagosa and Fena 

rain gages. A small improvement was made in PET calculation accuracy by calibrating to solar radiation data, whereas 

the PRMS_2004 model used possible hours of sunshine 

(Yeung, 2004). Lastly, closed depressions were included in the 

PRMS_2016 model to more appropriately address internally 

drained limestone areas near the headwaters of the Almagosa 

watershed that substantially add to the subsurface and groundwater flow measured at the outlet. These additional closed 

depression HRUs added 280 acres of contributing area to the 

Almagosa watershed (table 5). 

The common verification periods of the Maulap and 

Almagosa watersheds were compared to evaluate the performances of the PRMS_2004 model and PRMS_2016 model 

of the Fena Valley watersheds. The PRMS_2016 model 

simulated cumulative streamflow in the Maulap basin more 

accurately than the PRMS_2004 model (0.57 percent difference compared to 8.2 percent difference). The PRMS_2004 

model simulated cumulative streamflow more accurately in 

the Almagosa watershed (–2.9 percent difference compared 

to –6.96 percent difference). However, the PRMS_2016 

model simulated dry season monthly mean streamflows more 

accurately in the Almagosa watershed and less accurately in 

the Maulap watershed. The discrepancies in the PRMS_2016 

model performance for the Maualp and Almagosa watersheds 

may be due to the decreased number of HRUs in the Maualp 

watershed and inclusion of the closed depressions in the Almagosa watershed.

 Although results of the PRMS_2004 and PRMS_2016 

models were only directly compared for the Maulap and 

Almagosa watersheds, the average monthly coefficients of 

efficiency for the PRMS_2004 model and the coefficients of 

efficiency for the PRMS_2016 model provide another measure of comparison. In the PRMS_2004 model, the average 

monthly coefficients of efficiency for the Maulap, Almagosa, 

and Imong River watersheds’ second verification period were 

0.93, 0.96, and 0.88, respectively (table 9 in Yeung [2004]). 

In the PRMS_2016 model, the average monthly coefficients 

of efficiency for the Maulap, Almagosa, and Imong River 

watersheds’ verification periods were 0.94, 0.92, and 0.94, 

respectively (table 10). Values for the PRMS_2016 model 

were higher for the Maulap and Imong watershed, but slightly 

lower for the Almagosa watershed. 

Fena Valley Reservoir Water-Balance 

Model

This section describes the transformation of the 

FVR_2004 water-balance model from Yeung (2004) into the 

FVR_2016 model. The FVR_2016 model is not a redeveloped 

model structure, but instead the same model structure is used 

with updated reservoir stage-surface area and reservoir stagestorage capacity curves (Marineau and Wright, 2015), and 

the model is recalibrated. Yeung (2004) provides a detailed 

description of the water-balance model and development. The 

sections here describing the model and the model development 

are largely taken from that report. 

Description of Model

The water-balance model developed for FVR simply 

accounts for the interactions between various forms of water 

going into and out of the reservoir over monthly intervals. 

Water levels above spillway crest level were not simulated 

because measured bathymetric information was available only 

up to spillway crest level and flow-over-spillway data were not 

available. Fennessey (1995) determined that a monthly time 

step provides accurate reservoir-yield estimates. The model is 

based upon a simple water-balance equation: 

CHANGE IN STORAGE = GAGED STREAMFLOW 

+ UNGAGED STREAMFLOW + RAINFALL - 

WITHDRAWALS - EVAPORATION

The components of the reservoir water-balance model 

are shown in figure 27. Inflow components include streamflow from the three gaged tributary rivers (draining 80 percent 

of the watershed), streamflow from the ungaged land area 

(15 percent of the watershed area), and direct rainfall on the 
Fena Valley Reservoir Water-Balance Model 53

Rainfall Evaporation

Change

in

storage

Streamflow

(ungaged)

Streamflow

(gaged)

Water

withdrawals

Maximum surface area Spillway level

(full capacity)

Exposed

land areas

above

water surface

Water surface

Figure 27. Components of the water-balance model for the Fena Valley Reservoir, 

Guam (Yeung, 2004).

reservoir water surface (5 percent). Outflows from the reservoir include withdrawals for water supply, and direct evaporation from the water surface of the reservoir. Limited understanding of groundwater exchange in Fena Valley Reservoir 

prevented the estimation of the loss or gain of groundwater. 

Groundwater inflow and outflow were assumed negligible 

relative to other inflow and outflow components. The low 

permeability of the clayey soil and volcanic rocks underlying the reservoir probably do not allow for appreciable water 

exchange between the reservoir and the groundwater body 

(Roumasset and others, 2010). 

Model Development

The data required for an accurate calibration of the 

water-balance model are reservoir bathymetry, reservoir-wide 

average evaporation, reservoir-wide average rainfall, monthly 

water levels, tributary inflows, and water-withdrawal data. 

Recent relations between reservoir water level and 

water-surface area, and water level and storage volume were 

developed (fig. 28) on the basis of the new bathymetric survey 

of the FVR (Marineau and Wright, 2015). The FVR_2016 

water-balance model incorporated these relations to convert 

simulated month-end storage volume to water level and to 

determine month-end reservoir surface area. 

Information pertaining to evaporation losses from the 

Fena Valley Reservoir is limited. Because pan-evaporation 

data are no longer collected in the vicinity of the reservoir, 

PET generated from the PRMS_2016 model, using the Jensen 

and Haise (1963) method, was applied in the water-balance 

model. Measured PET data were adjusted downward by 

30 percent and these adjusted values were 

used as an approximation for the seasonal 

pattern and absolute magnitude of measured PET during the calibration process. 

The simulated average monthly PET 

rate was multiplied by the corresponding month-start reservoir surface area to 

estimate volume of reservoir evaporation 

loss for the month. 

Rain falling on the water surface of 

the reservoir was considered a component of inflow. The simulated rainfall in 

the PRMS_2016 model of the Almagosa 

watershed was used to estimate rainfall inflow in the water-balance model. 

Monthly rainfall was estimated using the 

PRMS_2016 simulated basin rainfall with 

a rainfall adjustment factor applied. Direct 

inflow to the reservoir from rainfall was 

calculated as the monthly average rainfall 

times the maximum water-surface area 

of the reservoir. Maximum water-surface 

area was assigned the area when the water 

level in the reservoir was at the spillway crest. The watersurface area was held constant in the calculations because 

according to field observations during dry seasons, reservoir 

land area above the water surface and below spillway level 

remained nearly saturated. It was assumed that all rain falling 

on these saturated areas ran off and contributed directly to 

reservoir inflow.

Data for the remainder of the inflow and outflow components in the water-balance model, including gaged and 

ungaged streamflow and water withdrawals, were applied 

without adjustment. Gaged streamflow was calculated as the 

recorded streamflow from the three gaged rivers. Adjustment was not necessary for the Almagosa River data because 

recorded discharge at the gage reflected actual streamflow 

going into the reservoir. Streamflow from the ungaged land 

area was estimated using the PRMS_2016 model. Monthly 

total withdrawal data were metered and available from the 

U.S. Navy, (at the time of publication, data had not been published by the U.S. Navy). Accuracy of the recorded withdrawal 

data is not known. 

Actual month-end reservoir volumes were compared 

with the simulated month-end reservoir volumes to assess 

the accuracy of the water-balance model. Reservoir volume 

(water storage) was calculated using month-end water-level 

measurements collected at the USGS reservoir water-level 

gage (16849000; fig. 17) at the Fena Dam spillway and the 

documented relation between water level and reservoir storage 

volume (Marineau and Wright, 2015; fig. 28). The accuracy 

ratings of the measured water-level data ranged from “poor” to 

“good.” Records were usually rated “good” except for periods 

of estimated data that were rated “poor.”
54 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

The monthly reservoir water-balance calculations are 

summarized in the following steps:

1. Given the reservoir water level at the beginning of a 

month, determine the initial reservoir volume (Vi

) and 

surface area for the month using relations illustrated in 

figure 28.

2. Calculate the reservoir evaporation volume for the 

month (E) based on initial surface area times the estimated evaporation rate for the month as calculated by 

the PRMS_2016 model.

3. Determine the volume of water withdrawn (W) from the 

reservoir during the month based on data recorded by the 

U.S. Navy, Guam.

4. Estimate the volume of rain falling directly on the reservoir (R) during the month based on the surface area of 

the reservoir at spillway level times the monthly average 

rainfall.

5. Determine the volume of streamflow input to the reservoir from the gaged watersheds using data from the three 

gaged rivers (Sg

). 

6. Determine the volume of streamflow input to the reservoir from the ungaged areas using output from the 

PRMS_2016 model (Su

).

7. Determine volume of water in the reservoir at the end of 

the month (Vf

) using the reservoir water-balance equation: Vf

 = Vi + R + Sg + Su - E - W.

Model Calibration, Verification, and Results

The selection of periods used for calibration and verification of the water-balance model were dictated by the availability of concurrent data, similar to procedures used for the 

PRMS_2016 model. Periods of missing streamflow data and 

periods of questionable withdrawal data (owing to pumpmeter failure) limited model calibration and verification to 

four discontinuous periods between 2003 and 2009. Data for 

the periods September 2006 through September 2007 and 

September 2008 through September 2009 were used for model 

calibration, and data for the periods September 2003 through 

September 2004 and September 2007 through September 2008 

were used for model verification. Calibration and verification 

periods were selected so that each included a drought period, 

200 180 160 140 120 100 80 60 40 20 0

40

50

60

70

80

90

100

110

120

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Reservoir water volume, in acre-feet

Reservoir water level, in feet above mean sea level

Reservoir water surface area, in acres

EXPLANATION

Surface area

Volume

Figure 28. Water level-surface area and water level-capacity curves for the Fena Valley 

Reservoir, Guam (Marineau and Wright, 2015).
Fena Valley Reservoir Water-Balance Model 55

as the ability of the water-balance model to accurately simulate steep water-level declines during extreme dry conditions 

is critical to its utility as a management tool. 

A relatively simple process was used to calibrate the 

water-balance model. Among all the water-balance components, direct evaporation and rainfall were the most uncertain 

and difficult to estimate. Because other components were 

comparatively well known, calibration involved adjusting only 

the correction coefficients associated with direct water-surface 

evaporation and rainfall. The coefficients were adjusted 

manually in a trial-and-error manner to fit simulated reservoir 

volumes to measured reservoir volumes. 

Calibration and Verification Results

Calibration results indicated a rainfall correction coefficient of 1.17, which is similar to the adjustment applied in the 

Almagosa watershed model (table 8). A coefficient of 0.7 was 

applied to PET to estimate reservoir evaporation. The coefficient was expected to be close to 1.0 because evaporation 

from a large water surface is considered to be approximately 

equivalent to PET (Jones, 1992). 

Monthly results for the calibration and verification periods 

are presented graphically in figures 29 and 30. The water-balance 

model generally simulated monthly reservoir storage volume with 

reasonable accuracy. For the calibration periods, errors associated 

with simulated month-end reservoir-storage volume for individual 

months ranged from 6.04 percent (284.6 acre-feet or 92.7 Mgal) 

to –5.70 percent (–240.8 acre-feet or –78.5 Mgal). For the 

verification periods, errors for individual months ranged from 

4.22 percent (280.2 acre-feet or 91.3 Mgal) to 0.08 percent 

(5.7 acre-feet or 1.9 Mgal). Simulated month-end volumes 

for March 2008 to June 2008 were not included in the error 

analysis because reservoir water levels from the USGS gaging 

station for those months were estimated. Bias and relative 

errors also were calculated to evaluate model performance 

during both calibration and verification periods. Results are 

summarized in table 14. Monthly simulation bias ranged from 

–0.48 percent for the calibration period to 0.87 percent for 

the verification period; relative error ranged from –0.60 to 0.88 

percent for the calibration and verification periods, respectively. Relatively small bias indicated that the model did not 

consistently overestimate or underestimate reservoir storage 

volume. Out of the 44 monthly simulations, 26 (59 percent) of 

the simulations resulted in overestimation and 10 (23 percent) 

resulted in underestimation. Water-balance model errors did 

not display any systematic patterns, therefore supporting the 

assumption that groundwater inflows and outflows were negligible relative to other model components. Because water levels above spillway crest level were not simulated, the model 

assumed any simulated volume greater than the reservoir 

capacity to be 6,915 acre-feet (2,253 Mgal).

Model Uncertainties

Although the water-balance equation is fairly simple, 

measures of accuracy regarding hydrologic phenomena are 

uncertain. The difference between simulated and measured 

reservoir volume reflects errors associated with measuring 

tributary inflow, change in storage, and water withdrawal; estimating streamflow from ungaged areas, rainfall, and reservoir 

evaporation; and neglecting flow between the reservoir and 

the groundwater body. Lacking meteorological measurements 

over the reservoir surface, evaporation was the most difficult 

component to quantify and had the highest uncertainty among 

all the water-balance components. Because evaporation is 

typically less than 7 percent of total outflow (table 15), errors 

associated with evaporation, however, are considered to have a 

relatively small effect on overall simulation results. 

In terms of magnitude, the two largest water-budget 

components in the water-balance model are total watershed 

streamflow and water withdrawal (table 15). The magnitudes 

of these two components vary seasonally (fig. 31). Total watershed streamflow, the sum of gaged tributary inflow and simulated ungaged area streamflow, is much greater than the other 

components during the wet season. As a result, inflow during 

the wet season is overwhelmingly greater than outflow and 

the reservoir remains at full capacity during most of the wet 

season. The accuracies of rainfall and evaporation estimates 

during the wet season are therefore not very critical from a 

reservoir management perspective. From the onset of the dry 

season, reservoir volumes start to decline as total streamflow 

decreases and water withdrawals become more significant, 

around 94 percent of the outflow (fig. 31). A small error in the 

water-withdrawal data could have a substantial effect on the 

simulation results (table 15). This implies that the accuracy of 

the model calibration during the dry season is highly dependent on the accuracy of the water-withdrawal data. 
56 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam Reservoir water volume, in acre-feet

2,500

3,500

4,500

5,500

6,500

7,500

2006−07

Measured

Simulated

2,500

3,500

4,500

5,500

6,500

7,500

Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

2008−09

Measured

Simulated

EXPLANATION

EXPLANATION

A

B

Figure 29. Measured and simulated month-end reservoir volume for the calibration periods, Fena Valley 

Reservoir, Guam, for (A) September 2006 to September 2007 and (B) September 2008 to September 2009.
Fena Valley Reservoir Water-Balance Model 57

Reservoir water volume, in acre-feet

2003−04

Measured

Simulated

Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

2007−08

Measured

Simulated

EXPLANATION

EXPLANATION

4,500

5,000

5,500

6,000

6,500

7,000

7,500

4,500

5,000

5,500

6,000

6,500

7,000

7,500

A

B

Figure 30. Measured and simulated month-end reservoir volume for the verification periods, Fena Valley 

Reservoir, Guam, for (A) September 2003 to September 2004 and (B) September 2007 to September 2008.
58 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

Table 14. Errors in simulated month-end storage volume 

in the Fena Valley Reservoir, Guam.

Period

Relative

error1

(percent)

Bias2

(percent)

Oversimulation

rate3

(percent)

Calibration –0.60 –0.48 42

Verification 0.88 0.87 80

Entire period 0.08 0.18 59

1

Relative error: i

N

i i i SIM MSD MSD N =

∑ − 1

(( )/ )/ ×100

where,

SIMi is simulated month-end storage 

volume for month i

MSDi is measured month-end storage 

volume for month i

 N is number of observed values

2

Bias, as a percentage of mean observed storage volume, 

 = ( ) − 















× =

=

∑

∑

i

N

i i

i i

N

SIM MSD

MSD

1

1

100

( )

3

Number of overestimated months divided by the total number of 

monthly simulations

Table 15. Water budget for the Fena Valley Reservoir during 

calibration and verification periods

Period

Inflows (percentage 

of total inflow)

Outflows (percentage of 

total outflows)

Total 

streamflow

Directrainfall Evaporation Water 

withdrawals

Calibration period

September 

2006– 2007

88.39 11.61 5.65 94.35

September 

2008– 2009

88.69 11.31 6.41 93.59

Verification period

September 

2003– 2004

92.68 7.32 5.97 94.03

September 

2007– 2008

89.25 10.75 7.14 92.86

Two-Step Modeling Procedure for Fena 

Valley Reservoir

The two-step modeling procedure (fig. 2) is used as a 

management tool by the U.S. Navy to estimate the response of 

the FVR to a variety of rainfall and water-withdrawal scenarios. The PRMS_2016 model of the Fena Valley watersheds 

can be used in conjunction with the FVR_2016 model, which 

incorporates a reservoir water balance (Yeung, 2004) and 

updated FVR capacity curves (Marineau and Wright, 2015), 

to estimate reservoir water levels. The first step involves 

application of the PRMS_2016 model to estimate monthly 

total streamflow for the three gaged watersheds and ungaged 

areas in the Fena Valley watershed. The second step involves 

use of the monthly streamflow and potential evapotranspiration estimates from the PRMS_2016 model as input to the 

FVR_2016 model to estimate water-level changes in the 

reservoir. Estimates of future water availability in the reservoir 

vary depending on the range of rainfall and water-withdrawal 

scenarios being evaluated.

Model Development

Rainfall scenarios commonly applied in the PRMS_2016 

and FVR_2016 models are long-term average monthly rainfall 

and current ENSO rainfall projections provided by the Pacific 

ENSO Application Center at the University of Hawaii(http://

www.weather.gov/peac/update). ENSO rainfall projections are 

reported as a percentage of long-term average monthly rainfall. The long-term average monthly rainfall for the updated 

model was calculated using the entire period of record for the 

Almagosa (1992–2015) and Fena (1993–2015) rain gages. 

The long-term average monthly rainfall values can be used 

directly in the water-balance model because the model is run 

using a monthly time step. The PRMS_2016 model, however, 

is applied on a daily time step and requires daily rainfall data 

to calculate monthly streamflow and potential evapotranspiration. Monthly rainfall projections are partitioned into daily 

time steps by following historical rainfall patterns recorded at 

the Almagosa and Fena rain gages. The pattern used to partition rainfall projections for the month of June, for example, is 

based on the June during which the recorded rainfall at each 

gage was the closest to the long-term average June rainfall for 

each respective rain gage. The same technique is applied to 

each of the remaining months. This method assumes that the 

historical rainfall patterns at the Almagosa and Fena Pump 

rain gages are reasonable representations of daily rainfall 

patterns for the monthly rainfall projections being modeled. 

The historical daily rainfall pattern is used to distribute the 

monthly ENSO rainfall projections for both the Almagosa and 

Fena rain gages and then used as input to the PRMS_2016 

model to produce the streamflow estimates to evaluate future 

water availability. The long-term average minimum and 

maximum daily temperature data needed as input to run the 
Two-Step Modeling Procedure for Fena Valley Reservoir 59

Dry season Wet season Dry season

Water volume, in acre-feet Reservoir water volume, in acre-feet

3,000

4,000

5,000

6,000

7,000

8,000

–1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

October

November

December

January

February

March

April

May

June

July

August

September

October

November

December

January

February

March

April

May

June

July

August

September

Change in reservoir volume

Total watershed streamflow

Direct rainfall

Evaporation

Water withdrawls

EXPLANATION

Figure 31. Monthly reservoir volume and water budget, Fena Valley Reservoir, Guam, from 

October 2006 to September 2008.
60 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

watershed model were from the NAS-Tiyan station using data 

from the last 30 years (1986–2015).

Model Verification

Earlier parts of this report focus on the accuracies of 

the PRMS_2016 model and FVR_2016 models individually. 

Measured daily rainfall was used as input in the evaluation 

of the PRMS_2016 model, and measured monthly rainfall 

and streamflow data were used as input in the evaluation of 

the FVR_2016 model. To evaluate the performance of the 

two-step modeling procedure as a whole, streamflow simulated using the PRMS_2016 model was used as input in the 

FVR_2016 model. Daily rainfall applied in the PRMS_2016 

model was simulated by distributing the measured monthly 

total using the method as discussed above. 

Periods in 2006–07 and 2008–09, each including a moderate ENSO event, were selected for the evaluation of the twostep modeling procedure. Monthly reservoir storage volumes 

computed using both measured and simulated daily rainfall 

were compared to measured reservoir storage volumes (fig. 32). 

Differences between calculated results using measured and 

simulated rainfall were used to indicate errors associated 

with using simulated rainfall patterns in the modeling procedure. The modeling procedure performed reasonably well 

when measured rainfall patterns were used. Results based on 

simulated rainfall using the ENSO forecasts (as updated every 

three months), however, consistently overestimated reservoir 

storage volumes in both evaluation periods. Overall in the 

two evaluation periods, monthly reservoir storage volumes 

computed from measured and simulated rainfall patterns were 

within 13.21 percent (622.7 acre-feet or 202.9 Mgal) and 

Reservoir water volume, in acre-feet

Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

EXPLANATION

3,000

4,000

5,000

6,000

7,000

8,000

3,000

4,000

5,000

6,000

7,000

8,000

Measured

Simulated (measured rainfall)

Simulated (simulated rainfall-ENSO forecast)

Simulated (simulated rainfall-percent normal)

A

B

Figure 32. Measured and simulated month-end reservoir volume for the evaluation periods, Fena 

Valley Reservoir, Guam, from (A) September 2006 to September 2007 and (B) September 2008 to 

September 2009. ENSO, El Niño Southern Oscillation.
Summary and Conclusions 61

40.20 percent (1462.2 acre-feet or 476.5 Mgal), respectively. 

The water balance was re-run using the monthly values of 

observed percent normal of the long-term rainfall in the ENSO 

forecast bulletins instead of the ENSO forecast percent normal 

to eliminate the accuracy of the ENSO forecast and only 

evaluate the simulation of daily rainfall patterns. Daily rainfall 

values are still being estimated for input to PRMS_2016, however the observed percent normal are being used to represent 

the monthly rainfall values instead of the ENSO forecasts. 

Overall in the two evaluation periods, monthly reservoir storage volumes computed from the simulated rainfall patterns 

using the observed percent normal from the ENSO bulletin 

were estimated within 26.90 percent (1268.0 acre-feet or 

413.2 Mgal) and 11.04 percent (560.0 acre-feet or 156.8 Mgal) 

of the measured reservoir storage volumes. It should be noted, 

however, that this does not measure the actual forecast capabilities of the procedure because the accuracy of a forecast 

is heavily dependent on the accuracy of rainfall and waterwithdrawal projections being applied. 

Summary and Conclusions

A two-step modeling procedure developed by the U.S. 

Geological Survey will allow estimates of monthly water 

levels in the FVR in response to various combinations of projected water-withdrawal rates and rainfall conditions. The first 

step in this predictive modeling procedure involves the use of 

the USGS PRMS_2016 model, a physically based, distributedparameter watershed model designed to analyze the effects of 

rainfall, temperature, and land use on watershed streamflow. 

The second step of the procedure is to use streamflow estimates from the PRMS_2016 model as input to the FVR_2016 

model to estimate changes in water levels in the reservoir. 

The PRMS_2004 model (Yeung, 2004) was updated 

and recalibrated for the Maulap, Almagosa, and Imong River 

watersheds, and also expanded to all of southern Guam. 

The parameters from gaged watersheds were applied to 

the ungaged areas by transferring model parameters from 

nearby representative watersheds. The PRMS_2016 model 

also improved upon the simulated basin rainfall by using the 

inverse distance and elevation (ide_dist) distribution module. 

A small improvement was made in PET calculation accuracy 

by calibrating to solar radiation data, whereas the PRMS_2004 

model used possible hours of sunshine (Yeung, 2004). Lastly, 

closed depressions were included in the PRMS_2016 model to 

more appropriately address internally drained limestone areas.

In the entirety of southern Guam, the watershed model 

has a “satisfactory” to “very good” rating, according to 

the criteria of Moriasi and others (2007), when simulating 

monthly mean streamflow for all but one of the gaged watersheds during the verification period. Statistical analyses of 

monthly measured and simulated streamflow for the three 

gaged rivers in the Fena Valley watershed and the Ugum River 

watershed indicated “very good” model performance for the 

PRMS_2016 model in these valuable surface-water resource 

areas. Model performance in the ungaged areas could not be 

evaluated. For the Fena Valley watersheds, bias in simulating 

monthly mean streamflow ranged from –0.67 to 0.60 percent 

for the calibration period and from –6.96 to 0.57 percent for 

the verification period; the coefficient of efficiency ranged 

from 0.87 to 0.94 percent for the calibration period and from 

0.92 to 0.94 percent for verification period. The total streamflow-volume error for the entire simulation period ranged 

from -1.43 to 0.60 percent for the Fena Valley watersheds. 

For the Ugum River above Talofofo Falls watershed, bias in 

simulating monthly mean streamflow was 0.84 percent for the 

calibration period and 7.37 percent for the verification period; 

the coefficient of efficiency was 0.97 for the calibration period 

and 0.93 for the verification period. These statistics indicate 

that the PRMS_2016 model performance is “very good” in the 

Ugum River watershed. The only area where the PRMS_2016 

model’s monthly performance is rated “unsatisfactory” is the 

Talofofo River watershed, owing to the large bias during the 

verification period, which is most likely caused by the limited 

data available in southern Guam to run the model in the 

early 1950s. 

Because reliable simulations of monthly dry season flows 

were of greatest concern, the focus of the PRMS_2016 model 

calibration was on monthly dry season conditions. Dry season 

model results for the entire simulation period indicated that 

streamflow can be predicted within 6.39 percent in the Maulap 

River watershed, within -0.54 percent in the Almagosa River 

watershed, and within 6.06 percent in the Imong River watershed. Although model error for the Maulap and Imong River 

watersheds was higher than for the other gaged watersheds, 

the results were still within the limits of the accuracy of the 

streamflow records used to develop the model. 

The month-end reservoir volumes simulated by the 

FVR_2016 model for both calibration and verification periods 

compared closely with measured reservoir volumes. For the 

calibration periods, errors associated with month-end reservoir-storage simulation for individual months ranged from 

6.04 percent (284.6 acre-feet or 92.7 Mgal) to –5.70 percent 

(–240.8 acre-feet or –78.5 Mgal). For the verification periods, 

errors for individual months ranged from 4.22 percent 

(280.2 acre-feet or 91.3 Mgal) to 0.08 percent (5.7 acre-feet or 

1.9 Mgal). Monthly simulation bias ranged from –0.48 percent 

for the calibration period to 0.87 percent for the verification 

period; relative error ranged from –0.60 to 0.88 percent for 

the calibration and verification periods. Relatively small bias 

indicated that the model did not consistently overestimate or 

underestimate reservoir volume. 

The PRMS_2016 model of the Fena Valley watersheds 

developed as part of this study represents a reliable tool 

for estimating streamflow contributions to the FVR. The 

PRMS_2016 model, in conjunction with the reservoir waterbalance model, which was also recalibrated in this study, 

provide accurate and reliable estimates of future availability 

of water supply in the FVR for a broad range of projected 

climatic and water-withdrawal scenarios. This information is 
62 Fena Valley Reservoir Watershed and Water-Balance Model Updates and Expansion of Watershed Modeling to Southern Guam

critical to the U.S. Navy to make timely decisions regarding 

the amount of water that can reliably be withdrawn from the 

reservoir. The updated and expanded PRMS_2016 model is 

a useful tool to evaluate changes in streamflow and recharge 

owing to climate change or land-cover change in other 

watersheds of interest in southern Guam, such as the Ugum 

River. Datasets of projected climate could be used as input to 

PRMS_2016 to evaluate potential climate-change impacts to 

Guam’s surface-water resources.

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Rosa and Hay—Fena Valley Reservoir W

. 1.1 Scientific Investigations Report 2017–5093, ver

atershed and W -Balance Model Updates and Expansion of W ater atershed Modeling to Southern Guam—

ISSN 2383-0328 (online)

https://doi.org/10.3133/sir20175093