U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009-5007

Spatially Referenced Statistical Assessment 

of Dissolved-Solids Load Sources and 

Transport in Streams of the Upper Colorado 

River Basin

Prepared in cooperation with the U.S. Department of the Interior Bureau of Reclamation and 

Bureau of Land Management
Cover: Photograph to the northwest showing Gunnison Butte and agricultural land along 

Green River near Green River, Utah. Photograph by Steven J. Gerner, September 2004.
Spatially Referenced Statistical 

Assessment of Dissolved-Solids Load 

Sources and Transport in Streams of the 

Upper Colorado River Basin 

By Terry A. Kenney, Steven J. Gerner, Susan G. Buto, and Lawrence E. Spangler 

Scientific Investigations Report 2009-5007 

U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the U.S. Department of the Interior Bureau of 

Reclamation and Bureau of Land Management
U.S. Department of the Interior

KEN SALAZAR, Secretary 

U.S. Geological Survey

Suzette M. Kimball, Acting Director 

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

Kenney, T.A., Gerner, S.J., Buto, S.G., and Spangler, L.E., 2009, Spatially referenced statistical assessment of 

dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2009-5007, 50 p. Available at http://pubs.usgs.gov/sir/2009/5007. 
iii

Contents

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

Introduction............................................................................................................................................................. 1

Purpose and Scope...................................................................................................................................... 3

Description of Study Area........................................................................................................................... 3

Previous Studies........................................................................................................................................... 3

Conceptual Model of Dissolved Solids in the UCRB ....................................................................................... 4

Sources of Dissolved Solids ...................................................................................................................... 4

Landscape Transport of Dissolved Solids ................................................................................................ 5

Instream Evolution of Dissolved Solids..................................................................................................... 6

SPARROW Model Description............................................................................................................................. 6

UCRB Dissolved-Solids SPARROW Model ........................................................................................................ 8

Selection of Water Year 1991 .................................................................................................................... 9

Dataset Development ............................................................................................................................... 11

Stream Reach Network and Associated Catchments................................................................ 11

Removal of Flux by Water Diversion and Reservoir Storage..................................................... 15

Dissolved-Solid Loads at Water-Quality Monitoring Sites ........................................................ 16

Source Variables............................................................................................................................... 16

Geology ..................................................................................................................................... 16

Irrigated Agricultural Lands................................................................................................... 21

Point Sources of Dissolved Solids........................................................................................ 21

Springs ............................................................................................................................ 21

Reservoirs ....................................................................................................................... 23

Landscape Transport Characteristics ........................................................................................... 24

Calibration of UCRB Dissolved-Solids SPARROW Model.................................................................... 24

Model Results ............................................................................................................................................. 26

UCRB Dissolved-Solids SPARROW Model Coefficients ...................................................................... 29

Prediction of Dissolved-Solid Loads ....................................................................................................... 30

Dissolved-Solids Loads in Selected Rivers of the UCRB ........................................................... 31

Colorado River.......................................................................................................................... 31

Green River............................................................................................................................... 33

San Juan River......................................................................................................................... 33

Gunnison River......................................................................................................................... 34

Dolores River............................................................................................................................ 34

Distribution of Dissolved-Solids Loads by Source at Selected Locations in the 

UCRB..................................................................................................................................... 34

Grand Division.......................................................................................................................... 34

Green Division.......................................................................................................................... 38

San Juan Division.................................................................................................................... 38
iv

Figures

1. Map showing study area and stream-monitoring sites in the Upper Colorado River Basin 

(UCRB) .............................................................................................................................................. 2

2. Conceptual model illustrating the processes by which the dissolved-solids loads 

are generated and transported to streams in the Upper Colorado River Basin 

(UCRB) .............................................................................................................................................. 5

3. Schematic representation of a calibration reach made up by a series of incremental reaches, defined by colors, and bound by an upstream monitoring site 

and headwater reaches ................................................................................................................ 8

4. Number of USGS streamflow-gaging stations with dissolved-solids monitoring 

data in the Upper Colorado River Basin (UCRB) for the period 1974 through 2003 ............. 9

5. Annual deviation of precipitation from the 30-year average in the Upper 

Colorado River Basin (UCRB) for water years 1987, 1988, and 1991 .................................... 10

6. Annual deviation of precipitation for 4-kilometer cells in the Upper Colorado 

River Basin (UCRB) for water years 1987, 1988, and 1991 ..................................................... 11

7. Representation of geologic units of differing scales for a select location in the 

Upper Colorado River Basin (UCRB) ......................................................................................... 18

8. Irrigated lands in the Upper Colorado River Basin (UCRB) and associated 

lithologic classification group .................................................................................................... 22

9. Map of the 11 defined sources of dissolved solids in the Upper Colorado River 

Basin (UCRB) that were input to the SPARROW model ......................................................... 25

10. Relation between predicted and observed dissolved-solids loads for 218 monitoring sites in the Upper Colorado River Basin (UCRB) used to calibrate the UCRB 

dissolved-solids SPARROW model ............................................................................................ 26

11. Standardized residual values for 218 monitoring sites in the Upper Colorado River 

Basin (UCRB) used to calibrate the UCRB dissolved-solids SPARROW model ................. 28

12. Predicted downstream accumulation of dissolved solids for the Colorado River, 

from the Upper Colorado River Basin (UCRB) SPARROW model of dissolved-solids 

transport, for 1991 ......................................................................................................................... 32

13. Predicted downstream accumulation of dissolved solids for the Green River, from 

the Upper Colorado River Basin (UCRB) SPARROW model of dissolved-solids 

transport, for 1991 ......................................................................................................................... 32

14. Predicted downstream accumulation of dissolved solids for the San Juan River, 

from the Upper Colorado River Basin (UCRB) SPARROW model of dissolvedsolids transport, for 1991 ............................................................................................................. 33

15. Irrigated lands along the San Juan River near Farmington, New Mexico, that 

appear to be related to center pivot sprinklers ....................................................................... 35

16. Predicted downstream accumulation of dissolved solids for the Gunnison 

River, from the UCRB SPARROW model of dissolved-solids transport, for 1991 ............... 36

Comparison of Dissolved-Solids Load Predictions with other Studies at Selected 

Locations in the UCRB ....................................................................................................... 38

Application of Results from Water year 1991 to other Years .............................................................. 46

Limitations and Uncertainty ..................................................................................................................... 46

Summary................................................................................................................................................................ 47

References Cited.................................................................................................................................................. 48 

Appendices .......................................................................................................................................................... 51
v

Tables

1. Transbasin diversions that, on average, export more than 2,000 acre-feet of water per 

year from the Upper Colorado River Basin ................................................................................... 4

2. Sources and landscape transport characteristics and associated datasets used 

in the SPARROW model ................................................................................................................. 12

3. Data sources used to compute sources and landscape transport characteristics ............ 14

4. Reservoirs in the Upper Colorado River Basin with capacities greater than 25,000 acrefeet not associated with transbasin diversion projects that were examined for storage 

changes during water year 1991 .................................................................................................. 17

5. King and Beikman (1974) geologic units and names and geologic units from state 

geology maps for the Upper Colorado River Basin ................................................................... 20

6. Dissolved-solids source groups of Upper Colorado River Basin irrigated agricultural 

lands by lithologic classification .................................................................................................. 23

7. Estimated water year 1991 dissolved-solids load for selected springs in the Upper 

Colorado River Basin ...................................................................................................................... 24

8. Results of nonlinear least squares calibration and resampled bootstrap analysis for 

1991 Upper Colorado River Basin dissolved-solids SPARROW model .................................. 27

9. State Soil Geographic Data Base (STATSGO) soil characteristic code descriptions. ........ 30

10. Dissolved-solids loads and contributions from natural and agricultural sources 

for the 1914–1957 period adjusted for 1957 and 1991 for the Grand Division above the 

Gunnison River, Colorado. .............................................................................................................. 41

11. Dissolved-solids loads and contributions from natural and agricultural sources 

for the 1914–1957 period adjusted for 1957 and 1991 for the Green Division of the 

Upper Colorado River Basin .......................................................................................................... 42

12. Dissolved-solids loads and contributions from natural and agricultural sources 

for the 1914–1957 period adjusted for 1957 and 1991 for the San Juan Division of the 

Upper Colorado River Basin .......................................................................................................... 43

13. Water year 1991 total precipitation and average annual total precipitation for 

1914–1957 at index stations in the Grand, Green and San Juan Divisions of the Upper 

Colorado River Basin ...................................................................................................................... 44

14. Dissolved-solids loads and contributions from natural and agricultural sources 

for 1974–2003 and 1991 for selected locations in the Upper Colorado River Basin ............. 45

Appendices

1. Dissolved-solids load at selected water-quality monitoring sites in the Upper 

Colorado River Basin, water year 1991 ....................................................................................... 53

2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model .................. 62

17. Predicted downstream accumulation of dissolved solids for the Dolores River, 

from the Upper Colorado River Basin (UCRB) SPARROW model of dissolvedsolids transport, for 1991 ............................................................................................................. 36

18. Distribution of dissolved-solids by source for selected streams in the 

Grand Division, from the Upper Colorado River Basin (UCRB) SPARROW model 

of dissolved solids transport, for 1991 ....................................................................................... 37

19. Distribution of dissolved-solids by source for selected streams in the 

Green Division, from the Upper Colorado River Basin (UCRB) SPARROW model 

of dissolved solids transport, for 1991 ....................................................................................... 39

20. Distribution of dissolved-solids by source for selected streams in the San 

Juan Division, from the Upper Colorado River Basin (UCRB) SPARROW model 

of dissolved solids transport, for 1991 ....................................................................................... 40 
vi

Conversion Factors and Datums 

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm) 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 2.590 square kilometer (km2)

Volume

acre-foot (acre-ft) 1,233 cubic meter (m3

)

Flow rate

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3

/s)

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

/yr)

tons per year (tons/yr) 0.9072 metric tons per year (metric tons/ yr)

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

Mass

ton 0.9072 metric ton

Yield

tons per square mile (tons/mi2

) 350 kilograms per square kilometer (kg/km2

)

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25 °C).

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88); 

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83). 

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

°F = (1.8 × °C) + 32.

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

°C = (°F – 32) / 1.8. 
vii

Abbreviations and Acronyms Used in the Text

(Clarification or additional information given in parentheses) 

Assign_hydseq Assign Hydrologic Sequence computer program 

BOR U.S. Bureau of Reclamation 

DEM Digital Elevation Model 

GIS geographic information system 

GUG geologic unit group 

HUC Hydrologic Unit Code 

KB King and Beikman 

LOADEST Load Estimator computer program 

mKB modified King and Beikman 

NED National Elevation Dataset (USGS) 

NHD National Hydrography Dataset (USGS)

NSRS National Spatial Reference System 

NWIS National Water Information System (USGS) 

PRISM Precipitation-elevation Regressions on Independent Slopes Model (Oregon 

State University) 

RSME root mean square error 

SPARROW Spatially Referenced Regressions on Watershed attributes (USGS) 

STATSGO State Soil Geographic Data Base 

UCRB Upper Colorado River Basin 

U.S. United States 

USGS U.S. Geological Survey 
viii
Spatially Referenced Statistical Assessment of DissolvedSolids Load Sources and Transport in Streams of the 

Upper Colorado River Basin

By Terry A. Kenney, Steven J. Gerner, Susan G. Buto, and Lawrence E. Spangler

Abstract

The Upper Colorado River Basin (UCRB) discharges 

more than 6 million tons of dissolved solids annually, about 

40 to 45 percent of which are attributed to agricultural activities. The U.S. Department of the Interior estimates economic 

damages related to salinity in excess of $330 million annually in the Colorado River Basin. Salinity in the UCRB, as 

measured by dissolved-solids load and concentration, has been 

studied extensively during the past century. Over this period, 

a solid conceptual understanding of the sources and transport 

mechanisms of dissolved solids in the basin has been developed. This conceptual understanding was incorporated into 

the U.S. Geological Survey Spatially Referenced Regressions 

on Watershed Attributes (SPARROW) surface-water quality 

model to examine statistically the dissolved-solids supply and 

transport within the UCRB. Geologic and agricultural sources 

of dissolved solids in the UCRB were defined and represented in the model. On the basis of climatic and hydrologic 

conditions along with data availability, water year 1991 was 

selected for examination with SPARROW.

Dissolved-solids loads for 218 monitoring sites were 

used to calibrate a dissolved-solids SPARROW model for the 

UCRB. The calibrated model generally captures the transport 

mechanisms that deliver dissolved solids to streams of the 

UCRB as evidenced by R2

 and yield R2 values of 0.98 and 

0.71, respectively. Model prediction error is approximated at 

51 percent. Model results indicate that of the seven geologic 

source groups, the high-yield sedimentary Mesozoic rocks 

have the largest yield of dissolved solids, about 41.9 tons per 

square mile (tons/mi2). Irrigated sedimentary-clastic Mesozoic 

lands have an estimated yield of 1,180 tons/mi2, and irrigated 

sedimentary-clastic Tertiary lands have an estimated yield of 

662 tons/mi2. Coefficients estimated for the seven landscape 

transport characteristics seem to agree well with the conceptual understanding of the role they play in the delivery of 

dissolved solids to streams in the UCRB. 

Predictions of dissolved-solids loads were generated 

for more than 10,000 stream reaches of the stream network 

defined in the UCRB. From these estimates, the downstream 

accumulation of dissolved solids, including natural and agricultural components, were examined in selected rivers. Contributions from each of the 11 dissolved-solids sources were 

also examined at select locations in the Grand, Green, and 

San Juan Divisions of the UCRB. At the downstream boundary of the UCRB, the Colorado River at Lees Ferry, Arizona, 

monitoring site, the dissolved-solids contribution of irrigated 

agricultural lands and natural sources were about 45 and 57 

percent, respectively. Finally, model predictions, including the 

contributions of natural and agricultural sources for selected 

locations in the UCRB, were compared with results from two 

previous studies. 

Introduction

The economic effects of increased salinity in the Colorado River have prompted a number of water-quality related 

legislative actions. In particular, the Colorado Salinity Control 

Act and its amendments provide the means and authority for 

Federal agencies to implement or assist local entities with 

projects that mitigate the discharge of dissolved solids to the 

Colorado River. Salinity in streams of the Upper Colorado 

River Basin (UCRB), as measured by dissolved-solids concentration and load varies substantially. Geologic and land cover 

characteristics, land-use practices, and precipitation are some 

of the sources and controlling mechanisms in the production and delivery of dissolved solids to rivers and streams. 

Management and(or) mitigation of salinity in UCRB streams 

requires an improved understanding of the spatial distribution 

of salinity sources, load accumulation, and transport mechanisms. 

Streamflow, dissolved-solids concentration, and specific 

conductance have been measured regularly at more than 200 

U.S. Geological Survey (USGS) stream-monitoring sites in the 

UCRB (fig. 1). River streamflow and chemistry are controlled 

by the geology, land cover, land use, and precipitation characteristics of the drainage basin. Coupling measurements of 

discharge, dissolved-solids concentration, and(or) specific 

conductance at stream-monitoring sites with geology, land 

cover, land use, climate, and other physical geospatial data 

within a spatially referenced statistical model can provide a 

tool for assessing the sources and transport of dissolved solids 

throughout the UCRB. 
2 Spatially referenced statistical assessment of dissolved-solids load sources 

EXPLANATION

UCRB boundary

Stream-monitoring site

Major reservoir

Point-source import

114° 108°

42°

39°

36°

WYOMING

UTAH

COLORADO

ARIZONA

NEW MEXICO

IDAHO

111°

0

0

50

50

100 Miles

100 Kilometers

Salt Lake City

Evanston

Green River

Boulder

Price

Grand

Junction

Blanding

Kayenta Farmington

Page Durango

Blue Mesa Reservoir

Glenwood

Springs

Sinbad Valley

Paradox Valley

Meeker Dome

Steamboat Springs

Dotsero Warm

Springs

Navajo Reservoir

Gallup

Pagosa Springs

Lake Powell

Lake Granby

Fontenelle

Reservoir

Flaming Gorge

Reservoir

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990 

Geology modified from USGS and Utah Geologic Survey digital data, 1:500,000 scale 1992–2006

Agricultural land from Bureau of Reclamation digital data 1990 to 1995

Albers Equal Area Conic projection, central meridian at -96°, standard parallels at 29.5° and 45.5°, 

latitude of origin at 23°, North American Datum of 1983

Colorado

River

Colorado

River

Green

River

San Juan

River

Study area

Figure 1. Study area and stream-monitoring sites in the Upper Colorado River Basin (UCRB).
Introduction 3

The USGS Spatially Referenced Regressions on Watershed Attributes (SPARROW) surface-water quality model 

relates measured chemical constituent transport at monitoring 

stations to upland catchment attributes including contributing upstream reaches (Smith and others, 1997). A large scale, 

dissolved-solids SPARROW model has been developed for the 

southwestern United States (U.S.), encompassing the Upper 

and Lower Colorado and Rio Grande River Basins along with 

the Great Basin and portions of southern California (Anning 

and others, 2007). Using similar methods, the USGS, in 

cooperation with the U.S. Department of the Interior Bureau 

of Reclamation (BOR) and the U.S. Department of the Interior 

Bureau of Land Management (BLM), developed a dissolvedsolids SPARROW model of the UCRB to assess the sources 

and transport mechanisms of dissolved-solids loads in streams 

throughout the basin. 

Purpose and Scope

This report documents the methods and data used to 

develop a dissolved-solids SPARROW model for the UCRB 

for water year 1991. Model calibration data that includes 

annual dissolved-solids load estimates from 218 USGS waterquality monitoring sites, are from the 1991 water year, and all 

catchment attributes were computed from geospatial data representative of conditions during this same time period. Results 

from this model, including estimates of dissolved-solids load 

for all defined stream reaches with incremental catchments 

ranging from less than 1 mi2 to a maximum of 78 mi2 within 

the UCRB, are presented. A discussion of model-generated 

coefficients specific to their role in producing and transporting dissolved solids to streams in the UCRB is also presented. 

The applicability of these results to other periods is discussed, 

and the limitations and uncertainties associated with the model 

results and interpretation are outlined.

Description of Study Area

The Colorado River Basin, which drains portions of 

seven states, is the largest river basin in the southwestern U.S.. 

The UCRB, for this study, is defined as the drainage basin 

upstream of USGS streamflow-gaging station 09380000, 

Colorado River at Lees Ferry, Arizona. The UCRB has a contributing drainage area of about 108,000 mi2

 and includes parts 

of Wyoming, Colorado, Utah, New Mexico, and Arizona 

(fig. 1). The UCRB drains a large portion of the Rocky 

Mountains west of the continental divide, from the Wind River 

Mountains in Wyoming, south to the San Juan Mountains in 

Colorado. Major river drainages of the UCRB include the 

Colorado, Green, and San Juan Rivers. The landscapes of the 

UCRB are varied and consist of high alpine, arid badlands, 

and slickrock canyonlands. The annual precipitation ranges 

from about 40 in., mostly as snow, near the continental divide 

to less than 10 in. on the Colorado Plateau (PRISM Group, 

Oregon State University, 2007). The Great Divide Basin is a 

closed basin in southwestern Wyoming, adjacent to the Green 

River drainage, which is formed from a bifurcation of the continental divide. This portion of the UCRB does not contribute 

runoff to the remainder of the basin and has been excluded 

from the study area. 

The main stem of the Colorado River and many river systems in the UCRB have perennial streamflow; however, many 

tributary streams, particularly in low elevation reaches, have 

intermittent or ephemeral streamflow as a result of climate, 

hydrogeology, stream regulation, and(or) water diversions. 

Water development has substantially altered streamflow in the 

Colorado River drainage. Streamflow in the UCRB is controlled at numerous locations by reservoirs. Many of the larger 

reservoirs in the Colorado River system alter the seasonal 

patterns of flow by storing water from snowmelt runoff, a substantial component of the total annual flow, and then releasing 

it at lower magnitude discharges for longer durations during 

the remainder of the year. Transbasin diversions of water from 

the UCRB to the Missouri River Basin (Denver area), the 

Great Basin (Utah Wasatch Front), Rio Grande Basin (New 

Mexico), and others account for nearly 5 percent of the virgin 

streamflow of the UCRB, which is more than 730,000 acre-ft/

yr (Liebermann and others, 1989) (table 1). Most waters 

diverted through these transbasin diversions generally contain 

a small dissolved-solids load, less than 1 percent of the load 

at Lees Ferry, Arizona, (Iorns and other, 1965; Anning and 

others, 2007), because the diversions are in the headwater 

reaches where there are minimal sources of dissolved solids. 

While these diversions do not remove a large amount of dissolved solids from the UCRB, diverting these waters leads to 

an increase of dissolved-solids concentrations during baseflow 

periods. 

Previous Studies

The occurrence and distribution of dissolved solids 

in surface and ground water of the UCRB has been extensively studied and characterized with a number of significant 

investigations completed in the 1970s and 1980s. The first 

comprehensive evaluation of dissolved solids in the basin was 

made by Iorns and others (1965) who developed many of the 

dissolved-solids load estimates currently used in models and 

management plans. Increasing dissolved-solids concentrations in the Lower Colorado River Basin and their associated 

adverse economic impact led to the enactment of the Colorado 

River Basin Salinity Control Act in 1974 and the establishment of water-quality criteria for salinity in the Colorado 

River system (Colorado River Basin Salinity Control Forum, 

2005). This in turn, spurred many studies of dissolved solids 

in the UCRB (U.S. Department of the Interior, 2003). For 

example, BOR investigated the feasibility of implementing 

salinity-control measures in agricultural areas such as the 

Grand Valley of Colorado (U.S. Bureau of Reclamation, 1978) 

and assessed specific point sources such as Glenwood Springs 

(Eisenhauer, 1983). Regional studies completed in the 1970s 
4 Spatially referenced statistical assessment of dissolved-solids load sources 

Table 1. Transbasin diversions that, on average, export more than 2,000 acre-feet of water per year from the Upper Colorado River Basin.

[CO, Colorado; UT, Utah; WY, Wyoming; NA, not available]

Conveyance name State of origin Stream origin Mean annual export

(acre-feet per year) 1

Water year 1991 total export

(acre-feet)

Azotea Tunnel CO Navajo River 106,600 119,000

H.D. Roberts Tunnel CO Blue River 67,720 2 66,000

Moffat Water Tunnel CO Fraser River 59,720 2 64,900

Homestake Tunnel CO Eagle River 34,310 2 638

C.H. Boustead Tunnel CO Frying Pan River 41,470 2 61,100

Busk-Ivanhoe Tunnel CO Frying Pan River 5,850 2 5,660

Twin Lakes Tunnel CO Roaring Fork River 42,330 2

 42,980

A.P. Gumlick Tunnel CO Williams Fork NA 2 3,870

Wurtz Ditch CO Eagle River 2,910 2

2,260

Alva B. Adams Tunnel CO Grand Lake, Colorado River 247,200 2 199,000 

Grand River Ditch CO Colorado River 17,540 2 18,400

Hoosier Pass Tunnel CO Blue River 8,000 2 12,400

Strawberry Tunnel UT Strawberry River 67,820 3 88,900

Duchesne Tunnel UT Duchesne River 18,180 3 21,100

Fairview Tunnel UT Huntington Creek 2,340 3,460

Ephraim Tunnel UT Cottonwood Creek 4,350 2,750

Spring City Tunnel UT Cottonwood Creek 2,210 2,150

Cheyenne Diversion WY Little Snake River 7,050 4 17,600

1

 From Lieberman and others, 1989.

2

 From Colorado Division of Water Rights, 2006.

3

 From Central Utah Water Conservancy District (written commun. Aug. 17, 2006).

4

 From State of Wyoming Engineer (written commun. Nov. 3, 2006). 

and 1980s characterized dissolved solids in many subbasins 

of the UCRB such as the Dirty Devil (Mundorf, 1979) and 

San Rafael (Lindskov, 1986) River Basins. An evaluation 

of the ground-water contributions to salinity of the UCRB 

was completed by Warner and others (1985). Liebermann 

and others (1989) characterized the occurrence and trends of 

streamflow and dissolved solids in the UCRB. More recently, 

the results of implementing salinity-control projects, such as 

those in the Grand Valley (Champion and others, 2004), have 

been studied as well as trends in dissolved-solids concentrations in surface waters of the UCRB (Vaill and Butler, 1999). 

Finally, the regional study of dissolved solids in surface water 

of the southwestern U.S. by Anning and others (2007), which 

included a dissolved-solids SPARROW model, provided a 

framework for this finer scale SPARROW modeling effort 

specific to the UCRB.

Conceptual Model of Dissolved Solids 

in the UCRB 

The SPARROW surface-water quality model uses a 

mass-balanced approach to examine the transport of instream 

constituent mass, or flux, on the basis of a nonlinear weighted 

least squares regression technique. Flux is modeled by simplifying the constituent transport process into diffuse, or nonpoint, source variables, and landscape and aquatic transport 

characteristics that act upon individual source variables. The 

first step in developing a dissolved-solids SPARROW model 

for the UCRB was to refine the conceptual understanding of 

how dissolved solids are generated, transported, and evolve 

within the UCRB and incorporate this into the framework of 

the SPARROW model. Previous studies have identified the 

significant sources, transport mechanisms, and geochemistry of dissolved solids in the UCRB. The general conceptual 

model of the sources and transport of dissolved solids within 

the UCRB is shown in figure 2.

Sources of Dissolved Solids 

Major sources of dissolved solids in the UCRB are generally categorized as either natural or agricultural. Iorns and others (1965) found municipal sources to be negligible. Bedrock 

geology, particularly sedimentary rock, is the largest natural 

source of dissolved solids to streams in the UCRB (Iorns and 

others, 1965; Liebermann and others, 1989; U.S. Department 

of the Interior, 2003; Anning and others, 2007). Dissolved solids are produced from various bedrock lithologies through the 
Conceptual Model of Dissolved Solids in the UCRB 5

Dissolved Dissolved solids

in UCRB streams in UCRB streams

Saline springs

Ground water

Reservoirs

Infiltration

Precipitation

Natural land

Mineral dissolution by

surface runoff

Diffuse ground-water discharge

storage

releases

Dissolution of geologic source

minerals

Irrigated agricultural land

Dissolution of geologic

source

minerals

by

surface runoff of

irrigation water

Evaporation

•Geology

•Soil characteristics

•Physiographic characteristics

Localized ground-water 

discharge

Infiltration

Dissolution of 

geologic source 

minerals 

Water applied for irrigation •Geology

•Soil characteristics

•Physiographic characteristics

Evaporative transpiration

Evaporative transpiration

Transport of water and dissolved solids

Transport of water only Transbasin diversions

process of dissolution of mineral salts by both surface runoff 

and ground-water flow. Saline springs, large point sources of 

ground-water discharge, are natural sources of dissolved solids. Dissolved solids discharged from the seven largest springs 

in the UCRB—Dotsero, Glenwood, Meeker Dome, Paradox 

Valley, Steamboat, Pagosa, and Sinbad Valley—have been 

estimated at 800,000 tons/yr (U.S. Department of the Interior, 2003). Irrigation of agricultural lands, particularly those 

derived from sedimentary rocks, is the major anthropogenic 

source of dissolved solids in the UCRB, accounting for about 

40 percent of the dissolved-solids load (Iorns and others, 1965; 

Liebermann and others, 1989; U.S. Department of the Interior, 

2003). Application of irrigation water to arid lands alters the 

natural rate at which solids are dissolved and transported to 

streams. 

Landscape Transport of Dissolved Solids

The major land-to-water transport mechanism associated with natural sources of dissolved solids is precipitation. 

Chemical weathering of geologic materials high in dissolvable minerals, either as surface runoff or ground-water flow, 

is highly correlated with precipitation. Evaporative transpiration in the context of the SPARROW framework, is another 

mechanism that can enhance the transport of dissolved solids 

to streams. Evaporative transpiration is the process of transferring water to the atmosphere through evaporation of water and 

transpiration from plants. Vegetation consumes water containing dissolved solids from within the soil zone and transpires 

pure water leaving behind the dissolved minerals. Over time, 

these minerals are concentrated within the upper portion of the 

soil, a zone that is readily accessible for dissolution through 

precipitation and surface runoff. Evaporation on bare soils also 

removes pure water and precipitates evaporite minerals on the 

soil surface, which are immediately available for dissolution 

Figure 2. Conceptual model illustrating the processes by which the dissolved-solids loads are generated and transported to streams in 

the Upper Colorado River Basin (UCRB). 
6 Spatially referenced statistical assessment of dissolved-solids load sources 

through precipitation and surface runoff. Other mechanisms 

that allow for the transport of dissolved solids from landscape 

sources to streams in the UCRB are generally not very well 

defined or understood particularly because they modify the 

effects of precipitation. For example, an equal amount of 

rainfall transports dissolved solids differently in steep terrain 

than in flat terrain, and in vegetated soils compared with bare 

soils. Physical parameters associated with the landscape, such 

as basin slope, soil characteristics, or land cover, conceptually 

can play a role in either enhancing or impeding the transport of 

dissolved solids to streams. 

Instream Evolution of Dissolved Solids

In general, dissolved solids in surface waters of the UCRB 

are geochemically conservative. Thus, it can be assumed that 

under steady streamflow conditions, the dissolved-solids load 

will remain constant and will not change under the natural 

temperature range or as a result of geochemical reactions 

with other natural constituents contained within the water, 

open-water evaporation, or reactions with streambed materials. Applying these assumptions to the SPARROW framework 

essentially eliminates the consideration of changes in the 

dissolved-solids load within the streams in the UCRB.

While dissolved-solids loads in the streams in the UCRB 

conceptually do not change, reservoirs, specifically their 

management and the process of evaporation of their water, can 

affect the fate of dissolved solids within the aquatic environment. Increased storage of water, and therefore, increased 

dissolved-solids load within a reservoir leads to a smaller load 

at the outflow than the sum of reservoir inflows. A decrease in 

reservoir storage, either from outflows exceeding inflows or 

from evaporation, conceptually increases the dissolved-solids 

load at the outflow relative to the sum of reservoir inflows.

SPARROW Model Description

The SPARROW surface-water quality model relates 

instream constituent mass, or flux, at monitoring sites in a 

basin to upstream catchment attributes using a nonlinear 

regression technique that can be described as a hybrid statistical and process-based approach (Schwarz and others, 2006). 

Through a defined interconnected stream reach network, a 

SPARROW model is able to take advantage of the spatial 

referencing of catchment attributes and monitoring data. The 

connectivity of the network allows for routing of flux through 

the basin and thus, is capable of providing estimates of flux at 

all defined reaches using a mass-balance approach. 

The interconnected stream reach network defines the 

surface streamflow paths for the basin of interest and spatially connects the sources and landscape characteristics to the 

monitoring sites (Schwarz and others, 2006). The network is 

comprised of uniquely numbered reaches that are connected 

to one another by upstream and downstream nodes, termed 

the “from-node” and “to-node,” respectively. All nodes are 

uniquely numbered and can take the form of both from- and 

to-nodes within the network. For example, the to-node for 

a reach becomes the from-node in the next downstream 

reach. The confluence of two or more reaches is represented 

by a shared to-node. Using this infrastructure, a hydrologic 

sequence of flow in the downstream direction can be defined. 

It is this sequence that the model, as shown in equation 1, is 

applied.

By combining the stream reach network with digital 

elevation models (DEMs), catchments can be defined for each 

unique stream reach. These catchments represent the contributing drainage area, or incremental drainage area, for each 

individual reach as defined by the from- and to-nodes. The 

total drainage area for any location within the network can be 

obtained by summing all incremental drainage areas upstream 

of the location of interest. Using geographic information system (GIS) tools, geospatial data to be evaluated in the SPARROW model as sources, landscape transport variables, or 

aquatic transport variables can be computed for each defined 

catchment of the stream reach network. 

The SPARROW modeling framework classifies catchment attributes into three terms: diffuse or nonpoint sources, 

landscape transport, and aquatic transport. Diffuse-source 

terms represent the sources of a chemical constituent that are 

distributed throughout the basin, such as mineral salts in a 

specific shale unit. Environmental processes that affect the 

release and transport of the constituent mass from the sources 

to the streams are represented by the landscape transport 

characteristics to which the landscape transport function is 

applied, such as the precipitation. Likewise, instream decay 

or attenuation processes of the constituent of interest as it is 

transported within streams and(or) reservoirs are represented 

by the aquatic transport characteristics to which the aquatic 

transport function is applied. It is the sources and processrelated characteristics that coefficients are estimated for during 

model calibration. The mathematical representation of the 

SPARROW model is given by the equation (Schwarz and others, 2006): 
SPARROW Model Description 7

S R D S R

S R , D S R

( ) 1

Load component of upstream reaches Load component of the incremental reach

( , ; , ) ( ; ) ( , ; , )

Ns

i j i i i n i n n i i i

j J i n

L L A S D A  

                   Z Z θ θ  Z θ Z Z θ θ

 

(1)

where

Li is the load leaving reach i, in units of mass/time,

L'j is the load that leaves upstream reaches and is delivered to reach i, in units of mass/time,

δi is the fraction of upstream flux delivered to the 

incremental reach, dimensionless,

A(·) is the aquatic transport function,

ZS is the vector of stream characteristics associated 

with aquatic transport, 

ZR is the vector of reservoir characteristics associated 

with aquatic transport,

θS are the estimated coefficient vectors of stream characteristics associated with aquatic transport,

θR are the estimated coefficient vectors of reservoir 

characteristics associated with aquatic transport,

Sn is source n, in units of mass, area or other property, 

αn is the estimated source coefficient for source n, 

Dn(·) is the landscape transport function,

ZD

i are the vector environmental characteristics associated with landscape transport,

θD are the estimated coefficient vectors of environmental characteristics associated with landscape 

transport, and

A'(·) is the aquatic transport function as applied to the 

incremental reach.

The load leaving a given reach, Li, is comprised of an 

upstream component and the incremental reach component 

(equation 1). The upstream component is a summation of the 

calculated incremental loads from upstream reaches, with 

attenuation functions for aquatic transport of mass applied, 

A(ZS

i

, ZR

i

, θS, θR), which is then multiplied by the fraction of 

upstream flux delivered value, δi, of the given reach. 

The load of the incremental reach is computed by applying the estimated source coefficients to the sources contained 

within the incremental reach, applying the specified landscape transport function for each source, which includes the 

landscape transport characteristics of the incremental reach 

and their estimated coefficients, and finally, applying the 

specified attenuation function, which includes the aquatic 

transport characteristics of the incremental reach and their 

estimated coefficients. Water diversions, which conceptually 

remove constituent mass from the system, can be accounted 

for in the stream network by defining the fraction of upstream 

flow delivered, which is assumed equal to the fraction of 

upstream flux delivered, δi

, to the next downstream reach. In 

most cases, the amount of constituent mass that is diverted 

is unknown, but an estimate of the fraction of water that is 

diverted is likely more readily available. The total load leaving 

a given reach, Li, is the sum of the upstream and incremental 

load components. 

This nonlinear model structure with additive sources and 

multiplicative transport terms is conceptually consistent with 

the mechanisms that explain contaminant supply and transport 

(Richard Alexander, U.S. Geological Survey, oral commun., 

October 25, 2006). Of particular importance to modeling flux 

through a stream network, is that this nonlinear approach 

preserves mass balance. For a more detailed and technical 

discussion on the SPARROW surface-water quality model, see 

Schwarz and others (2006).

The calibration routine for a SPARROW model utilizes 

the spatial referencing and connectivity of the stream network. The network infrastructure to which all stream reaches 

are linked allows for unique calibration reaches specific to 

each monitoring site. The size and composition of calibration 

reaches, in terms of upstream catchment attributes, are determined by the location of monitoring sites within the stream 

network. Each monitoring site represents a unique calibration 

reach that is bound upstream by either headwater reaches, a 

combination of headwater reaches and upstream monitoring 

site(s), or solely by upstream monitoring site(s). A schematic 

representation of a calibration reach comprised of a series of 

individual catchments is shown in figure 3. This compartmentalizing of independent calibration reaches facilitates a massbalanced calibration. By following this approach throughout 

the calibration process, the model separates the amount of flux 

delivered between monitoring sites (the difference of measured flux between the calibration reach bounds) and relates 

it to the attributes of the catchment defined by the reach. By 

using the unique calibration reaches, mass remains balanced 

and independence between observations is preserved. 

Through the calibration process, source-specific coefficients, αn, are determined for each significant diffuse source, 

Sn. The landscape transport function, Dn(ZD

i

 ; θD), is applied 

to each of these sources. Landscape transport is a source-specific function of a vector of delivery characteristics, ZD

i

, and 

an associated vector of estimated coefficients, θD (Schwarz 

and others, 2006), determined during model calibration. As a 

decay or attenuation function, aquatic transport, A(ZS

i

, ZR

i

, θS, 

θR), accounts for mass changes over time spent in the stream 

or reservoir environment. A'(ZS

i

, ZR

i

, θS, θR) represents the 

aquatic transport function describing decay or attenuation of 

the incremental reach load, which differs slightly from A(ZS

i

, 

ZR

i

, θS, θR) because flux associated with incremental reaches 

receives the square root of the reaches instream decay because 

it is assumed to be delivered at the midpoint of the reach 

and thus, experiences only half the travel time of the reach 

(Schwarz and others, 2006). Contained within the aquatic 
8 Spatially referenced statistical assessment of dissolved-solids load sources 

transport function are stream and reservoir characteristics, 

denoted by the vectors ZS

i

 and, ZR

i respectively, and estimated vectors of coefficients θS and θR. During calibration, 

coefficients are estimated for each diffuse source, landscape 

transport characteristic, and aquatic transport characteristic. 

For this reason, these coefficients represent an average condition of the role each term and characteristic play throughout 

the basin of interest, assuming an unbiased distribution of the 

monitoring sites used in model calibration. 

UCRB Dissolved-Solids SPARROW 

Model

The first application of the SPARROW modeling 

framework to the transport of dissolved solids was done with 

median annual dissolved-solids loads for the period 1974–

2003 in the southwestern U.S., which includes the Upper and 

Lower Colorado and Rio Grande River Basins along with the 

Great Basin and portions of southern California (Anning and 

others, 2007). The conceptual sources and transport mechanisms of dissolved solids in the southwestern U.S. parallel 

those of the UCRB. The southwestern U.S. effort provided a 

regional understanding for a 30-year period of how dissolved 

solids are produced and transported. By adopting similar 

methods to those of Anning and others (2007), this investigation of the UCRB was able to examine dissolved-solids 

transport at a finer scale, in terms of the stream reach network 

and geospatial data used, within a smaller basin. Different 

Stream reach segment

Downstream monitoring site, X

Point source

Reach

contributing

area

Reservoir

Upstream

monitoring site, Y

Modified from Schwarz and others, 2006

Figure 3. Schematic representation of a calibration reach made 

up by a series of incremental reaches, defined by colors, and 

bound by an upstream monitoring site and headwater reaches. 

from Anning and others (2007), and further described below, a 

single water year was selected in an effort to provide resource 

managers with time-specific, dissolved-solids conditions in 

streams of the UCRB. 

The conservative behavior of dissolved solids in streams 

of the UCRB was verified in Anning and others (2007) in 

that the only aquatic transport characteristics dealing with the 

removal of loads were associated with decreases in streamflow: changes in reach discharge and percent Quaternary basin 

fill. While an aquatic transport function was applied to these 

characteristics, it was not used to account for geochemical 

constituent decay or attenuation processes. The geology of the 

UCRB indicates little Quaternary fill and minimal streamflowlosing reaches. In general, most decreases in downstream 

streamflow for the 1991 UCRB stations are related to reservoirs and to large diversions, such as the Government Highline 

Canal and Grand Valley water diversion structures near Grand 

Junction, Colorado. Large diversions and flow decreases 

associated with reservoir storage were accounted for using the 

fraction of upstream flux delivered, δi

, reach characteristic. 

In considering the temporal resolution selected for this 

modeling effort of a single water year, together with the quantity of flux being modeled versus the influence of processes 

that could affect dissolved solids in the aquatic environment, 

the assumption that any dissolved solids that enter streams in 

the UCRB are transported through the system, unless diverted 

or stored in reservoirs, is justified. Conceptually, this conservative behavior suggests that there are no attenuation or decay 

processes associated with dissolved-solids loads in UCRB 

streams or reservoirs that need to be accounted for. For these 

reasons, along with available data, aquatic transport processes 

were assumed minimal enough to discount and were not considered in this modeling effort. Hence, the aquatic transport 

terms of the SPARROW modeling framework were not considered for the UCRB. Eliminating the aquatic transport terms 

from equation 1 and specifying it to represent dissolved-solids 

sources and transport in the UCRB generates the equation: 

D

, D

( ) 1

Load component of Load component of the incremental reach

upstream reaches

( ; )

Ns

i j i n i n n i

j J i n

L L S D  

                   Z θ

 

(2)

where

Li is the dissolved-solids load leaving reach i, in units 

of mass/time, 

L’j is the dissolved-solids load that leaves upstream 

reaches and is delivered to reach i, in units of mass/

time, 

δi is the fraction of upstream flux delivered to incremental reach, dimensionless, 

Sn is the dissolved-solids source n, in units of mass, 

area or other property, 
Selection of Water Year 1991 9

αn is the estimated source coefficient for dissolvedsolids source n,

Dn(·)is the landscape transport function,

ZD

i are the vector environmental characteristics associated with landscape transport of dissolved solids, 

and

θD are the estimated coefficient vectors of environmental characteristics associated with landscape 

transport of dissolved solids.

Selection of Water Year 1991 

 The SPARROW surface-water quality model allows for 

a mass-balanced examination of the spatial and statistical relation that exist between measured flux, and flux sources and 

transport characteristics for large basins. Conventionally, the 

SPARROW model has been applied to study the transport of 

constituent mass using monitoring data from longer periods of 

record, often 10 or more years, adjusted for desired conditions 

such as a target year. This approach has been used for a number of reasons, including the desire to understand time-averaged conditions of contaminant transport, or simply because 

of data availability. The southwestern U.S. dissolved-solids 

SPARROW model (Anning and others, 2007) was calibrated 

on median annual loads computed for monitoring sites with 

periods of record generally greater than 10 years that occurred 

within a defined 30-year period (1974–2003). Using these 

criteria, the median statistic was assumed temporally representative of recent years (Anning and others, 2007). 

Dissolved solids have been studied extensively in the 

UCRB over the past 40 years. These efforts have provided 

resource managers with a conceptual understanding of the 

sources and major landscape transport mechanisms associated 

with dissolved solids. Anning and others (2007) described the 

general time-averaged conditions of dissolved-solids transport 

for the entire southwestern U.S., which included the UCRB. 

For this investigation of dissolved solids in the UCRB, a 

single water year, 1991, was chosen. 

Modeling a single year has a number of advantages. A 

principal goal of this investigation was to provide resource 

managers with a statistical assessment of dissolved-solids 

transport in the UCRB for use in water and salinity management decisions. An analysis of dissolved-solids transport in 

the UCRB for water year 1991 provides a temporal reference 

point to which conditions in the basin for other periods can be 

readily compared. Utilizing the annual dissolved-solids load 

at water-quality-monitoring sites for a single year reduces the 

influence of multi-year climatic variability inherent in models 

that contain data from multiple years. Estimated coefficients 

for source variables and landscape transport characteristics are 

specific to the conditions experienced in the basin for that year 

rather than average conditions over a longer period.

Dissolved-solids monitoring data consisting of dissolvedsolids concentrations and specific-conductance measurements 

were available annually at more than 195 USGS streamflowgaging stations with periods of record of 10 or more years during the period of 1984 through 1991 (fig. 4; Anning and others, 2007). Much less data are available in the UCRB for other 

periods. For these 8 years, annual streamflow and precipitation 

were compared with long-term averages to select a year that 

was similar to normal climatic and hydrologic conditions. 

Thirty-year average precipitation estimates for the UCRB 

for the period 1974–2003 were computed at a 4-km resolution from annual precipitation estimates obtained from the 

Precipitation-elevation Regressions on Independent Slopes 

Model (PRISM) Group (PRISM Group, Oregon State University, 2007). Precipitation estimates for the years 1984–1991, 

the period with maximum dissolved-solids data available, 

were compared with the 30-year average precipitation, and 

water years 1987, 1988, and 1991 appeared to be most similar 

to the 30-year average. The spatial distribution of the deviation of the annual precipitation from the average precipitation 

for these years was further examined graphically (fig. 5) and 

numerically (fig. 6). Total annual streamflow for water years 

1984–1991 were compared with mean annual streamflow for 

selected streamflow-gaging stations in the UCRB with periods 

of record of 10 or more years. This analysis indicated that 

streamflows in water years 1987 and 1991 were nearest to 

average conditions in the UCRB for the periods of record of 

the streamflow-gaging stations. 

The final decision to model water year 1991, after analyzing the meteorologic and hydrologic data for 1984–1991, the 

period with maximum dissolved-solids data available, was 

influenced by the availability of geospatial datasets. Many 

landscape transport characteristics to be tested as predictors 

of dissolved-solids loads in the UCRB were computed from 

geospatial data that is time dependent. The UCRB has experienced a large population growth over the past quarter century, NUMBER OF SITES

200

150

100

50

0

197419801985199019952000

WATER YEAR

19752003

Figure 4. Number of USGS streamflow-gaging stations with 

dissolved-solids monitoring data in the Upper Colorado River 

Basin (UCRB) for the period 1974 through 2003. 
10 Spatially referenced statistical assessment of dissolved-solids load sources 

1987 1988

114° 111° 108°

EXPLANATION

Difference from 30-year average, in inches

 -25 to -15

 -15 to -10

 -10 to -5

 -5 to -1

 -1 to 1

 1 to 3

 3 to 10

 10 to 15

UCRB Subbasin boundary

0 50 100 150 200 Miles

0 50 100 150 200 Kilometers

114° 111° 108°

42°

39°

36°

42°

39°

36°

1991

42°

39°

36°

114° 111° 108°

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990 

HUC boundaries from USGS digital data, 1:250,000 scale, 1994

Precipitation data from PRISM 2.5 arc-minute annual average data

Albers Equal Area Conic projection, North American Datum of 1983

Figure 5. Annual deviation of precipitation from the 30-year average in the Upper Colorado River Basin (UCRB) for water years 1987, 

1988, and 1991.
Selection of Water Year 1991 11

which has had an effect on land use. Most available geospatial 

datasets are from the 1990s, and it was determined to be most 

beneficial to select water year 1991, which was hydrologically near normal, but also closer in time to the geospatial data 

available. 

Dataset Development 

A comprehensive dataset for the UCRB was assembled 

for input into the SPARROW model. These data consisted of a 

high resolution stream reach network, annual dissolved-solids 

load data from monitoring sites, and a variety of geospatial 

data related to the conceptual sources and landscape transport 

mechanisms. Generally, most data were readily available 

from their developing institutions or agencies; however, some 

unique datasets, as described below, were constructed or modified from readily available datasets to meet the needs of this 

modeling effort. 

SPARROW is a predictive statistical model, and fundamental to predictive statistical modeling is the testing of a 

number of independent parameters for significance in predicting the dependent variable—for this study, annual dissolvedsolids load. Thirty-seven parameters were tested as either 

sources or landscape transport characteristics. Table 2 contains 

the unique parameters tested and their associated dataset(s). 

References for the datasets used are contained in table 3. 

Tested sources and landscape transport characteristics that 

were found to be significant predictors of annual dissolvedsolids loads in the streams of the UCRB, and therefore, 

remained in the final calibrated model, are described in more 

detail below. 

Stream Reach Network and Associated 

Catchments

The SPARROW model requires a hydrologically connected representation of a stream network through which 

loads are transported from an upstream reach to the next reach 

downstream (Schwarz and others, 2006; Moore and others, 

2004). Each stream reach or segment within this synthetic 

stream network has an associated local drainage area or catchment. The synthetic stream reach network and associated 

catchments created for the UCRB SPARROW model were 

assembled using a DEM and a vector-based representation 

of the major streams in the UCRB. The data were processed 

using ArcInfo Workstation Grid methods (Environmental Systems Research Institute, 1999). Processing steps are outlined 

below.

A DEM is a representation of topographic elevation 

that uses a grid of square cells each with an associated value 

that is the average elevation of the area covered by the cell. 

The DEM used for development of the UCRB SPARROW 

catchments was the 1/3 arc-second National Elevation Dataset 

(NED; U.S. Geological Survey, 2002). A single grid cell in the 

1/3 arc-second NED has a spatial resolution of approximately 

70

60

40

50

0

30

10

20

-25 -20 -15 -10 -5 0 252015105

1987

PERCENT OF 4-KILOMETER CELLS IN UCRB

70

60

40

50

0

30

10

20

-25 -20 -15 -10 -5 0 252015105

1988

70

60

40

50

0

30

10

20

-25 -20 -15 -10 -5 0

DEVIATION OF PRECIPITATION, IN INCHES

2015105

1991

25

Figure 6. Annual deviation of precipitation for 4-kilometer cells 

in the Upper Colorado River Basin (UCRB) for water years 1987, 

1988, and 1991. 
12 Spatially referenced statistical assessment of dissolved-solids load sources 

10 m and occupies 100 m2. The vector-based stream data used 

in processing was the 1:100,000-scale National Hydrography 

Dataset (NHD; U.S. Geological Survey, 1999). The NHD is a 

dataset that interconnects and uniquely identifies representations of the stream segments that make up the nation’s surfacewater drainage system (U.S. Geological Survey, 1999). 

The hydrologic network and catchments were developed in 

discrete parts on the basis of 8-digit Hydrologic Unit Code 

(HUC) subbasins (Seaber and others, 1987) that contribute 

flow within the UCRB. These parts were later merged together 

to create a single dataset for the entire study area.

The source DEM was first passed through a function 

that exaggerated the height or created walls at each subbasin 

boundary. The height was exaggerated everywhere except at 

the outlet, or pour-point, of the subbasin. Building a walled 

DEM ensured that flow would remain within the boundary 

and would exit the subbasin at a single pour-point during 

subsequent processing. The walled DEM was passed through a 

surface reconditioning algorithm known as Agree (Hellweger, 

1997). This created a new DEM into which stream segments 

derived from the NHD were deeply incised. The Agree method 

ensured that the stream network derived from the DEM would 

more accurately represent the flow paths mapped by the NHD. 

The NHD stream segments used during the Agree process 

were selected on the basis of NHD attributes and in a way that 

man-made structures and flow diversions such as pipelines and 

canals were removed before incision into the DEM. 

The final phase of DEM processing created three derivatives of the Agree DEM that were used to create the final 

stream network and associated reach catchments. The three 

datasets, in the order that they were produced, are a DEM with 

depressions filled, a dataset defining the flow direction for 

each grid cell, and a flow accumulation grid in which each cell 

receives a value equal to the total number of cells that drain 

into it (Jenson and Dominque, 1988). 

Stream lines were delineated from the elevation derivatives by a grid modeling process that located and connected 

consecutive grid cells where the flow accumulation was above 

a predetermined threshold limit. Cells above this limit were 

assigned a value of one and remaining cells were assigned 

values of “no data.” The cells with values equal to one were 

then merged into distinct stream reaches by applying a link 

code that changed values where two stream reaches formed a 

confluence. The individual catchments for each stream reach 

were defined using the linked stream reach cells in conjunction with the flow direction grid to define the spatial extent 

of the cells that flowed through each unique stream segment 

(Maidment, 2002). 

Tested model parameters Datasets used

Dissolved-solids sources

Point-source imports, in tons USGS NWIS, BOR sources of salt loading, EPA STORET

Crystalline and volcanic rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

High-yield sedimentary Cenozoic rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

Low-yield sedimentary Cenozoic rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

High-yield sedimentary Mesozoic rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

Low-yield sedimentary Mesozoic rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

High-yield sedimentary Paleozoic and Precambrian rocks, in square 

miles

Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

Low-yield sedimentary Paleozoic and Precambrian rocks, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming

Irrigated sedimentary-clastic Tertiary lands, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming. BOR irrigated lands data

Irrigated sedimentary-clastic Mesozoic lands, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming. BOR irrigated lands data

Irrigated lands of other lithologies, in square miles Geologic map of United States, 1:500,000-scale digital geologic maps of Arizona, 

Colorado, New Mexico, Utah, and Wyoming. BOR irrigated lands data

Table 2. Sources and landscape transport characteristics and associated datasets used in the SPARROW model.

[USGS NWIS, U.S. Geological Survey National Water Information System; BOR, U.S. Bureau of Reclamation; EPA STORET, Environmental Protection 

Agency Storage and Retrieval database; DLG, Digital Line Graph; NED, National Elevation Dataset; NHD, National Hydrography Dataset; PRISM, ParameterElevation Regressions on Independent Slopes Model; NLCDe, Enhanced National Land Cover Dataset; STATSGO, State Soil Geographic Database] 
Selection of Water Year 1991 13

Tested model parameters Datasets used

Landscape transport characteristics

Drainage density, dimensionless 1:24,000-scale NHD

Density of all roads, dimensionless 1:100,000 DLG Roads

Density of improved roads, dimensionless 1:100,000 DLG Roads

Density of unimproved roads, dimensionless 1:100,000 DLG Roads

Catchment maximum elevation, in feet 30-meter NED

Catchment minimum elevation, in feet 30-meter NED

Catchment relief 30-meter NED

Reach slope, dimensionless 30-meter NED

Total 1991 precipitation, in inches PRISM

Total 1991 evapotranspiration, in inches 30-minute total evapotranspiration estimates for water year 1991 (Wilmott and 

Matsurra, 2001)

1991 total precipitation, total evapotranspiration ratio, dimensionless PRISM and 30-minute total evapotranspiration estimates for water year 1991 

(Wilmott and Matsurra, 2001)

Total 1991 precipitation, catchment maximum elevation ratio, 

dimensionless

PRISM and 30-meter NED

Total 1991 precipitation, catchment minimum elevation ratio, 

dimensionless

PRISM and 30-meter NED

Available water capacity, in inches per hour STATSGO

Clay content, in percent by weight STATSGO

Organic matter content, in percent by weight STATSGO

Permeability, in inches per hour STATSGO

Mean cumulative thickness of soil, in inches STATSGO

Mean hydrologic soil characteristic code, dimensionless STATSGO

Percentage of area covered by forest NLCDe 1992

Percentage of area covered by urban NLCDe 1992

Percentage of area covered by agriculture NLCDe 1992

Percentage of area covered by rangeland NLCDe 1992

Percentage of area covered by barren land NLCDe 1992

Percentage of area covered by water area NLCDe 1992

Percentage of area covered by wetland NLCDe 1992

Table 2. Sources and landscape transport characteristics and associated datasets used in the SPARROW model.—Continued 
14 Spatially referenced statistical assessment of dissolved-solids load sources 

Table 3. Data sources used to compute sources and landscape transport characteristics.

Dataset name Source description

30-meter National Elevation Dataset (NED) U.S. Geological Survey, 1999, National Elevation Dataset: U.S. 

Geological Survey Fact Sheet 148-99, accessed September 22, 2006, 

at http://erg.usgs.gov/isb/pubs/factsheets/fs14899.html

Enhanced National Land Cover Data 1992 (NLCDe 1992) Nakagaki, N., Price, C.V., Falcone, J.A., Hitt, K.J., and Ruddy, B.C., 

2005, Enhanced National Land Cover Data 1992, version 1.0: U.S. 

Geological Survey, accessed September 22, 2006, at http://water.usgs.

gov/GIS/metadata/usgswrd/XML/nlcde92.xml

Parameter-elevation Regressions on Independent Slopes Model 

(PRISM) climate mapping system 

Daly, C., Nielson, R.P., and Phillips, D.L., 1994, A statistical-topographic 

model for mapping climatological precipitation over mountainous 

terrain: Journal of Applied Meteorology, v. 33, no. 2, p. 140–158, 

accessed July 31, 2006, at http://prism.oregonstate.edu/products

Geologic Map of United States King, P.B., and Beikman, H.M., 1974, Geologic map of the United 

States: U.S. Geological Survey Professional Paper 901, 40 p., 2 pl., 

accessed August 15, 2006, at http://pubs.usgs.gov/dds/dds11/kb.html

1:500,000-scale digital geologic map of Arizona Hirschberg, D.M., and Pitts, G.S., 2000, Digital geologic map of 

Arizona: A digital database derived from the 1983 printing of the 

Wilson, Moore, and Cooper 1:500,000-scale Map, version 1.0: U.S. 

Geological Survey Open-File Report 00-409, accessed June 21, 2006, 

at http://geopubs.wr.usgs.gov/open-file/of00-409/

1:500,000-scale digital geologic map of Colorado Green, G.N., 1992, The digital geologic map of Colorado in Arc/Info 

format: U.S. Geological Survey Open-File Report 92-507, accessed 

June 21, 2006 at http://pubs.usgs.gov/of/1992/ofr-92-0507/

1:500,000-scale digital geologic map of New Mexico Green, G.N., and Jones, G.E., 1997, The digital geologic map of New 

Mexico in Arc/Info format, version 1.0: U.S. Geological Survey OpenFile Report 97-0052, accessed June 21, 2006 at http://pubs.usgs.gov/

of/1997/ofr-97-0052/

1:500,000-scale digital geologic map of Utah Ludington, S., Moring, B.C., Miller, R.J., Stone, P.A., Bookstrom, A.A., 

Bedford, D.R., Evans, J.G., Haxel, G.A., Nutt, C.J., Flyn, K.S., and 

Hopkins, M.J., 2006, Preliminary integrated geologic map databases 

for the United States. Western States: California, Nevada, Arizona, 

Washington, Oregon, Idaho, and Utah, version 1.0: U.S. Geological 

Survey Open-File Report 2005-1305, accessed June 21, 2006 at 

http://pubs.usgs.gov/of/2005/1305/

1:500,000-scale digital geologic map of Wyoming Green, G.N., and Drouillard, P.H., 1994, The digital geologic map of 

Wyoming in Arc/Info format: U.S. Geological Survey Open-File 

Report 94-0425, accessed June 21, 2006 at http://pubs.usgs.gov/

of/1994/ofr-94-0425/

1:250,000-scale State Soil Geographic Database (STATSGO) 

soil characteristics

Wolock, D.M., 1997, STATSGO soil characteristics for the conterminous 

United States: U.S. Geological Survey Open-File Report 656, accessed 

October 10, 2006 at http://water.usgs.gov/GIS/metadata/usgswrd/

XML/ussoils.xml

1:24,000-scale National Hydrography dataset U.S. Geological Survey, 1999, The National Hydrography Dataset, U.S. 

Geological Survey Fact Sheet 109-99, accessed September 21, 2006 at 

http://erg.usgs.gov/isb/pubs/factsheets/fs10699.html

U.S. Bureau of Reclamation irrigated lands data David Eckhart, Bureau of Reclamation Remote Sensing and Geographic 

Information Group, 1990–1995, Potentially irrigated lands in 

Colorado, New Mexico, Utah, and Wyoming, unpublished data
Selection of Water Year 1991 15

Dataset name Source description

U.S. Bureau of Reclamation quantified sources of salt loading U.S. Department of the Interior, 2003, Quality of water—Colorado River 

Basin, progress report no. 21: U.S. Department of the Interior, 90 p., 

accessed October 19, 2004, at http://usbr.gov/uc/progact/salinity/pdfs/

PR21Final08042004.pdf 

1:100,000-scale Digital Line Graph transportation data U.S. Geological Survey, 1996, US GeoData Digital Line Graphs: U.S. 

Geological Survey Fact Sheet 078-96, accessed September 29, 2006 at 

http://erg.usgs.gov/isb/pubs/factsheets/fs07896t.pdf

30-minute resolution evapotranspiration estimates for the 

conterminous United States

Willmott, C.J., and Matsuura K., 2001, Terrestrial water budget data 

archive: monthly time series (1950–1999), version 1.02, accessed 

September 20, 2007, at http://climate.geog.udel.edu/~climate/html_

pages/README.wb_ts2.html

Environmental Protection Agency Storage and Retrieval 

(STORET) database 

U.S. Environmental Protection Agency, 2008, U.S. Environmental 

Protection Agency STOrage and RETrieval (STORET) database, 

accessed January 15, 2008, at http://www.epa.gov/storet/

U.S. Geological Survey National Water Information System 

(NWIS) 

U.S. Geological Survey, 2006, NWISWeb data for the nation, accessed 

August 13, 2006, at http://water.usgs.gov/nwis

Table 3. Data sources used to compute sources and landscape transport characteristics.—Continued 

The linked stream cells were merged to a single dataset 

and converted to a vector-based synthetic stream network in 

which flow direction and location approximated real-world 

conditions. The network was edited to ensure proper flow 

direction where necessary. The catchment grids for each subbasin were merged together to form a single dataset for the 

UCRB. The merged catchment grid was converted to a vector 

dataset, inspected for accuracy, and edited where necessary. 

Each stream segment and associated catchment was assigned 

a unique identification code for use by the SPARROW model. 

The final step in preparing the interconnected stream reach 

network for use in SPARROW is the definition of the downstream order of the reaches, or hydrologic sequence, that 

allows SPARROW to accumulate flux in the downstream 

direction. The Assign Hydrologic Sequence (Assign_hydseq) computer program (Richard Alexander, U.S. Geological 

Survey, written commun., 2003) was used to determine the 

hydrologic sequence and total drainage area for each reach 

from reach characteristics defined in the interconnected network. The network consisted of 10,813 unique stream reaches 

that ranged in size from less than 1 mi2

 to a maximum of 78 

mi2. The average incremental drainage area for the network 

was 10 mi2. 

Removal of Flux by Water Diversion and 

Reservoir Storage

Extensive water development for municipal, industrial, 

and agricultural uses has occurred in the UCRB over the past 

century and a half. This development has affected the natural 

routing of water in the streams of the basin. Aquatically transported contaminant mass, such as dissolved solids, is affected 

in a similar manner. Water diversions, which remove water 

along with dissolved materials, are abundant and can be found 

on most rivers and streams in the UCRB. For example, the 

Grand Valley in western Colorado diverts a substantial amount 

of Colorado River water through the Government Highline 

Canal and Grand Valley diversion structures, approximately 

620,000 acre-ft in 1991. There are also a number of large 

transbasin diversions that divert water to the Arkansas, Rio 

Grande, and North Platte River Basins, and the Great Basin. 

Table 1 is a list of transbasin diversions that divert on average 2,000 acre-ft or more of water per year out of the UCRB 

(Liebermann and others, 1989). The assembled stream reach 

network described above did represent transbasin diversions 

and therefore, an effort was made to account for losses of flux, 

often using flow as a surrogate, caused by large transbasin 

diversions. 

The fraction of upstream flux delivered to an incremental 

reach, δi

, (equation 1) of the SPARROW model allows for 

losses in constituent mass from water diversions. Term δi is 

unitless and, assuming that contaminants are removed proportionally to the amount of water removed, is often computed 

from quantified or estimated streamflow data. For this study, 

an effort was made to account for the large transbasin diversions, those contained in table 1, along with the large water 
16 Spatially referenced statistical assessment of dissolved-solids load sources 

diversions such as those in Grand Valley, Colorado mentioned 

above. The term δi was computed for most of the transbasin 

diversions using reported diversion flows and river streamflows. Most river streamflows were obtained from nearby 

downstream USGS streamflow-gaging stations; however, 

estimates were required for a few transbasin diversions lacking nearby gaging stations. The fraction of upstream flux for 

affected incremental catchments was computed by dividing 

the river streamflow by the total nondiverted streamflow, often 

obtained by summing the available downstream gaged source 

streamflow and the diverted amount. Diversion records for 

the Government Highline Canal and Grand Valley Diversion 

were obtained from the State of Colorado (Judy Sappington, 

Colorado Division of Water Resources, written commun., 

December 11, 2007). Because these diversions are associated 

with multiple uses and a substantial amount of flow is returned 

to the Colorado River, the fraction of upstream flux for the 

two affected incremental catchments was computed using the 

diversion records and the difference in annual streamflow at 

USGS streamflow-gaging stations 09095500, Colorado River 

near Cameo, Colorado, and 09106150, Colorado River below 

Grand Valley Diversion near Palisade, Colorado.

While the assumption that dissolved solids act conservatively within the streams and reservoirs of the UCRB over the 

period of a single water year is warranted, the management of 

reservoirs over this same temporal scale can affect the mass 

of dissolved solids transported throughout the basin. Conceptually, reservoirs with increases in storage over a given time 

period are a mechanism that removes flux from a basin, albeit 

temporarily. For this reason, δi

 values were computed for 

reaches immediately downstream of the 18 reservoirs shown 

in table 4 with net increases in storage for water year 1991. 

These values were computed by dividing the total annual 

streamflow released from a given reservoir by the sum of the 

total annual streamflow released and net increase in storage. 

Dissolved-Solid Loads at Water-Quality 

Monitoring Sites 

The dependent variable of the UCRB dissolved-solids 

SPARROW model consisted of water year 1991 dissolvedsolids loads computed at 218 water-quality monitoring sites 

(appendix 1). Data from 192 of these sites were available 

from Anning and others (2007) and data from 26 sites were 

determined using the Load Estimator (LOADEST) computer 

program of Runkel and others (2004) adapted for use with 

S-Plus (Insightful Corporation, 2005) statistical software 

(Dave Lorenz, U.S. Geological Survey, written commun., 

2005), based on the methods of Runkel and others (2004) from 

data obtained from the USGS National Water Information 

System (NWIS). 

Source Variables

Dissolved-solids sources in the UCRB are generally 

categorized as either natural or agricultural. The major natural 

sources include geologic units high in dissolvable minerals 

and saline springs. The largest agricultural dissolved-solids 

source is attributed to irrigation of agricultural lands. Methods 

used for representing these sources within the SPARROW 

model are described below. 

Geology

The largest source of naturally generated dissolved 

solids in streams in the southwestern U.S., including the 

UCRB, is derived from the rocks underlying stream basins, 

particularly those high in dissolvable minerals. In the southwestern dissolved-solids SPARROW model (Anning and 

others, 2007), statistically significant source coefficients were 

associated with nine groupings of rock types derived from the 

King and Beikman (1974) 1:2,500,000-scale bedrock geology map of the U.S. For the UCRB model, the geology was 

best represented at a scale compatible with the selected NHD 

stream reach network. The 1:2,500,000 scale of the King and 

Beikman (1974) map lacks the desired detail when compared 

with the catchments of the modified NHD stream network 

(fig. 7A). However, the number of geologic units (34) from the 

map for the UCRB is desirable for model-required simplification. Available state geologic maps at scales of 1:500,000, 

while lacking consistency of unit names and continuity across 

state lines, represent a similar resolution to the NHD stream 

network (fig. 7B). With more than 270 defined geologic 

units for the UCRB, the five state geologic maps do not lend 

themselves easily to the simplification methods of grouping 

rock types for this modeling effort. In an effort to exploit the 

benefits the two available geologic map scales present to our 

modeling methodologies, a geologic map was developed that 

combines the resolution of the state geologic maps with the 34 

defined units from King and Beikman (1974) for the UCRB 

(fig. 7C). The boundaries of the state geologic units were analyzed for assignment of King and Beikman (KB) unit names, 

as described below. 

To determine the boundaries of the modified King 

and Beikman (mKB) geologic units, a digital KB map was 

overlaid with digital state geology maps within a GIS, and 

a system intersect tool was used to compute the percentage 

of each KB unit associated with a state unit. This relation 

was then used to determine the KB unit that is most closely 

associated (areally) with the state geologic unit. Very few of 

the KB units corresponded on a 1:1 basis with state geologic 

units, primarily because of scale differences between the KB 

and state geologic maps. Consequently, the KB unit associated 

with each state geologic unit was determined using a stepwise 

method that considered the lithologies of the KB and state 

geologic units as well as the geologic unit groups (GUGs) that 

the KB and state geologic units fit into. The method involves 

the following steps, which were completed in order until a KB 
Selection of Water Year 1991 17

Table 4. Reservoirs in the Upper Colorado River Basin with capacities greater than 25,000 acre-feet not associated with transbasin diversion projects that were examined for storage changes during water year 1991. [BOR, U.S. Bureau of Reclamation; DBW, Denver Board of Water; RMP, Rocky Mountain Power; NA, none associated; USGS, U.S Geological Survey; EPA, U.S. Environmental Protection Agency; mg/L, milligrams per liter; NA, not applicable] 

Reservoir Authority Associated stream Maximum 

capacity 

of storage 

volume

(acre-feet)

Reservoir 

storage 

volume, Oct. 1, 

1990

(acre-feet)

Reservoir storage 

volume, Sept. 30, 

1991

(acre-feet)

1991 reservoir 

storage volume 

change

(acre-feet)

Dissolved-solids 

concentration, 

from nearest 

available location

(mg/L)

Calculated fraction 

of upstream flux 

delivered to 

incremental reach 

(δi), dimensionless

Apparent import of 

dissolved solids, 

in tons, asociated 

with storage 

volume decrease

Source of dissolved-solids 

concentration and (or) 

specific conductance data

Lake Powell BOR Colorado River 28,820,000 15,720,450 14,699,143 –1,020,000 521 NA 723,000 USGS; Colorado River 

at Lees Ferry, Arizona

Flaming Gorge 

Reservoir

BOR Green River 4,002,700 3,081,486 3,390,465 309,000 NA 0.74 0 NA

Navajo Reservoir BOR San Juan River 1,986,200 1,363,928 1,586,397 222,000 NA 0.70 0 NA

1Strawberry Reservoir BOR Strawberry River 1,127,200 475,188 486,740 11,600 NA 0.12 0 NA

Blue Mesa Reservoir BOR Gunnison River 940,700 617,241 700,364 83,100 NA 0.89 0 NAFontenelle Reservoir BOR Green River 405,160 278,673 325,076 46,400 NA 0.95 0 NAMcPhee Reservoir BOR Dolores River 381,000 239,400 305,828 66,428 NA 0.75 0 NAStarvation Reservoir BOR Strawberry River,

2Duchesne River

189,000 72,267 98,468 26,200 NA 0.79 0 NA

Vallecito Reservoir BOR Los Pinos River 139,200 80,107 74,579 –5,530 3 52 NA 391 4USGS; Los Pinos 

River above Vallecito Reservoir

Morrow Point 

Reservoir

BOR Gunnison River 121,300 115,016 111,520 –3,500 116 NA 552 USGS; Gunnison River 

below Gunnison Tunnel

Taylor Park Reservoir BOR Taylor River 119,970 80,194 85,495 5,300 NA 0.96 0 NA

Ruedi Reservoir BOR Frying Pan River 119,000 92,302 90,810 –1,490 131 NA 265 USGS; Frying Pan River 

near Ruedi, Colorado

Scofield Reservoir BOR Price River 111,300 7,410 9,318 1,910 NA 0.97 0 NAWilliams Fork 

Reservoir

DBW Williams Fork 

River

109,000 79,968 82,107 2,140 NA 0.98 0 NA

Joes Valley Reservoir BOR Seely Creek 71,860 25,238 34,263 9,020 NA 0.73 0 NABig Sandy Reservoir BOR Big Sandy River 54,400 12,084 15,492 3,410 NA 0.96 0 NAMoon Lake BOR Lake Fork River 50,700 9,236 11,918 2,680 NA 0.96 0 NALemon Reservoir BOR Florida River 48,600 17,303 26,291 8,990 NA 0.80 0 NASteinaker Reservoir BOR 2Ashley Creek 40,350 2,025 17,591 15,600 NA 0.78 0 NA

Vega Reservoir BOR Plateau Creek 40,300 2,207 8,758 6,550 NA 0.33 0 NA

Meeks Cabin 

Reservoir

BOR Blacks Fork 

River

38,720 1,926 12,718 10,800 NA 0.77 0 NA

Electric Lake RMP Huntington Creek 35,500 22,221 20,413 –1,810 3 126 NA 310 EPA (2008)

Red Fleet Reservoir BOR Big Brush Creek 33,000 14,404 20,212 5,810 NA 0.78 0 NACrystal Reservoir BOR Gunnison River 30,700 16,827 16,151 -676 116 NA 107 USGS; Gunnison River 

below Gunnison Tunnel

1 Also associated with transbasin diversion project.2 Reservoir is offstream of indicated stream.3 Estimated from specific-conductance measurements.4 Data from 1996–2002.
18 Spatially referenced statistical assessment of dissolved-solids load sources 

King and Beikman 1:2,500,000-scale geology 1:500,000-scale state geologic units

Upper Colorado 1:500,000-scale geologic units

109°30' 108°30'

41°00'

40°30'

EXPLANATION

0 5 10 15 20 25 Kilometers

0 5 10 15 20 25 Miles

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990

King and Beikman geology from USGS digital data, 1:2,500,000 scale, 1998 

Colorado geology from USGS digital data, 1:500,000 scale, 1992

Wyoming geology from USGS digital data, 1:500,000 scale, 1994 

Utah geology from Utah Geological Survey digital data, 1:500,000 scale, 2006 

Albers Equal Area Conic projection, North American Datum of 1983

Utah

Wyoming

Colorado

109°30' 109°00' 108°30'

41°30'

40°30'

Utah

Wyoming

Colorado

109°30' 108°30' 41°30'

40°30'

Utah

Wyoming

Colorado

41°30'

109°00'

109°00'

41°00' 41°00'

King and Beikman and Upper Colorado 

geology associations

Tmc - Miocene continental

Toc - Oligocene continental

Tec - Eocene continental

Tel - Eocene lacustrine

Txc - Paleocene continental

uK4 - Navarro Group

uK3 - Taylor Group

uK2 - Austin and Eagle Ford Groups

uK1 - Woodbine and Tuscaloosa Groups

J - Jurassic

JTr - Lower Jurassic and Upper Triassic

lMz - Lower Mesozoic

Tr - Triassic

uPz - Upper Paleozoic

lPz - Lower Paleozoic

C - Cambrian

Y - Y sedimentary rocks

X - X metasedimentary rocks

Xg - X granitic rocks

A B

C

Note: The explanation below applies only to maps ‘A’ and ‘C’

Study area

Detail

location

Figure 7. Representation of geologic units of differing scales for a select location in the Upper Colorado River Basin (UCRB). 
Selection of Water Year 1991 19

unit was associated with the state geologic unit being considered:

1. If the lithology of a state geologic unit was similar to the 

lithology of the KB unit with the largest corresponding 

area to the state geologic unit (the dominant KB unit), 

and both were contained in the same GUG, then those 

two units were associated. This situation occurred in 79 

percent of the UCRB. 

2. Where the state geologic unit indicated a surficial cover 

such as alluvium, colluvium, landslide deposits, or water, 

the associated KB unit was assumed to be the bedrock 

unit underlying the surficial cover with the largest 

corresponding area. The corresponding KB unit may or 

may not be in the same GUG as the state geologic unit. 

This situation occurred in 6 percent of the UCRB. 

3. If the lithology of a state geologic unit was not similar 

to the lithology of the dominant KB unit, and the KB 

unit with the second largest area or another KB unit in a 

different GUG did correspond with the state geologic unit 

but did not consist of a larger area than the dominant KB 

unit, then the state unit was associated with the dominant 

KB unit. This situation occurred in 5 percent of the 

UCRB. 

4. If the lithology of a state geologic unit was not similar 

to the lithology of the dominant KB unit, but the KB 

unit with the second largest area in a different GUG did 

correspond with the state geologic unit and also exceeded 

40 percent of the total area, then those two units were 

associated. This situation occurred in 1 percent of the 

UCRB. 

5. If the lithology of a state geologic unit was not similar to 

the lithology of the dominant KB unit, but was similar 

to the KB unit with the second largest corresponding 

area within the same GUG, then those two units were 

associated. This situation occurred in 1 percent of the 

UCRB. 

6. If the lithology of a state geologic unit was not similar to 

the lithology of the dominant KB unit, but was similar to 

the KB unit with the second largest corresponding area in 

a different GUG, and the cumulative area of all the KB 

units within that GUG exceeded the area of the dominant 

KB unit, then those two units were associated. This 

situation occurred in 1 percent of the UCRB. 

7. If the lithology of a state geologic unit was not similar 

to the lithology of the dominant KB unit and no other 

KB unit corresponded with the state geologic unit (no 

percentage was assigned to the corresponding unit), then 

the assigned KB unit was the dominant unit. This situation 

occurred in 8 percent of the UCRB. 

The 70 geologic units in the southwestern U.S. from the 

King and Beikman (1974) 1:2,500,000-scale bedrock geology 

map were aggregated into nine rock type groups in the southwestern U.S. SPARROW model (Anning and others, 2007). 

The groupings of Anning and others (2007) were first based 

on lithology: crystalline (plutonic and metamorphic) rocks, 

mafic volcanic rocks, felsic volcanic rocks, eugeosynclinal 

rocks, and sedimentary rocks. Sedimentary rocks were further 

broken down by age: Cenozoic, Mesozoic, and Paleozoic and 

Precambrian; and by dissolved-solids yield: low, medium, or 

high. The determination of dissolved-solids yield classes for 

the sedimentary rocks was done by individually testing each 

geologic unit with a calibrated preliminary dissolved-solids 

SPARROW model. Anning and others (2007) individually 

transferred each unit to the corresponding sedimentary age 

high-yield group, ran the preliminary model, and examined 

model output. Geologic units were reassigned to the high-yield 

group if, as a result of the transfer, (1) the source coefficient 

for the low-yield group decreased, and the source coefficient 

for the high-yield group increased, (2) the probability value of 

the source coefficients remained about the same or decreased, 

and (3) the R2

 of the model remained about the same or 

increased (Anning and others, 2007). This grouping scheme 

simplified the model input data, yet resulted in coefficients 

that made distinctions between significant geologic dissolvedsolids sources. 

 This study used a methodology of grouping geologic 

units similar to that used by Anning and others (2007). Seven 

geologic source groups were defined for the UCRB 

(table 5), six of which were associated with sedimentary rocks 

of three different ages. The three ages of sedimentary rocks 

were divided into high- and low-yield groups. To determine 

which sedimentary geologic units from each age group 

belonged in respective high- and low-yield classifications, a 

preliminary SPARROW model was calibrated with the pointsource imports, irrigated agricultural lands and four geologic 

source groups—crystalline and volcanic rocks, and three agerelated sedimentary rock groups. 

Using the defined source groups and statistically significant landscape transport characteristics from this model, 

sedimentary geologic units from each group were tested 

individually as a unique source through iterative model recalibration. After testing each unit individually, the unit from 

each age group with the lowest probability (p-value) that the 

estimated coefficient is equal to zero as a unique source group 

became the initial member of the respective age-related, highyield group. Beginning first with the sedimentary Cenozoic 

rocks, units were individually added to the high-yield group 

in a stepwise fashion by the lowest p-value, and the model 

was recalibrated. The unit remained in the high-yield group 

if (1) the p-value of the high-yield group decreased and both 

low- and high-yield source coefficients changed, and (2) the 

p-value remained the same or slightly increased but the lowyield source coefficient decreased and the high-yield source 

coefficient increased. Once the high-yield groups were set, 

each unit of the low-yield groups was added back into the 

high-yield groups and examined again for meeting the above 

criteria. Following calibration of the final model, with all sig-
20 Spatially referenced statistical assessment of dissolved-solids load sources 

King and 

Beikman (1974) 

geologic units

Area

(square 

miles)

Name (King and Beikman, 1974) 1 Geologic units from state geology maps (Green and Drouillard, 1994; Green and 

Jones, 1997; Green, 1992; Hirschberg and Pitts, 2000; Ludington and others, 2006) 

assigned to King and Beikman (1974) units. 1

Crystalline and volcanic rocks—9,680 mi2 total area

 Ti 610 Tertiary intrusive rocks Ti, TKdi, Tmi, Tui

 Tpv 1,200 Pliocene volcanic rocks Tbb, Tbbi, Tmv, Tpb

 ITv 1,460 Lower Tertiary volcanic rocks Te, Tpl, Tv

 ITf 1,190 Lower Tertiary felsic volcanic 

rocks

Taf, Tbr, Tbrt, Tial, Tiql

 Kg3 110 Latest Cretaceous granitic rocks TKi

 Cg 7.98 Cambrian granitic rocks _am

 Yg1 838 Older Y granitic rocks Xgy, Xqd, Xsv, Yg, Yxg

 Xg 36 X granitic rocks PCm

 Xm 3,480 X orthogneiss and paragneiss Jmw, KJdw, Qd, Qdo, _s, Xb, Xfh, Xg, Xlc, Xm, Xq, Yam, @cc, @ch

 Wg 345 W granitic rocks Wg, Wgd, Ws

 Wgn 377 W orthogneiss and paragneiss Ksb, shear, Tgc, Ugn, Ugn +, Wgn, WVg, WVsv, !W

 X 30.5 X metasedimentary rocks Xdl

High-yield sedimentary Cenozoic rocks—12,300 mi2

 total area 

 Tel 7,950 Eocene lacustrine T2, Tg, Tgl, Tglm, Tglu, Tgp, Tgt, Tgw, Tgwt, Twn, 

 Txc 4,340 Paleocene continental Kmw, T1, Tc, Tf, Tfu, Tgv, TK, Tmu, Tn, Toa

Low-yield sedimentary Cenozoic rocks—25,700 mi2

 total area 

 Q 8.82 Quaternary KJ, Tdb, Tep, TKp

 Tpc 98.1 Pliocene continental Tov, Tvm

 Tmc 2,710 Miocene continental Mz, QTg, T4, T5, Tbi, Tbp, Tm, Tt

 Toc 129 Oligocene continental Toe, Twru

 Tec 22,800 Eocene continental Qa, Ql, Qs, QT, QTa, Qu, T3, Tb, Tbs, Tglw, Tgrw, Th, Tp, Tsj, Tu, Tw, 

Twa, Twc, Twd, Twg, Twlc, Twm, Two

High-yield sedimentary Mesozoic rocks—14,000 mi2

 total area 

 uK3 9,450 Taylor Group K3, Kal, Kbl, Kch, Ke, Kh, Ki, Kle, Kls, Kmf, Kmgs, Kmp, Kmv, Kmvu, 

Kp, Kpcl, Kpl, Kr, Ks, Ksc, Kw, Qi, Tbf

 TR 4,570 Triassic J@gc, Tr1, Tr2, TRc, TRcs, TRm, @d, @kc, @m, @wc 

Low-yield sedimentary Mesozoic rocks—36,200 mi2

 total area 

 uK1 4,600 Woodbine and Tuscaloosa 

Groups

J, Jmc, K1, Kd, Kdb, Kfd, Kjde, KJdm, Kmfm, Ku, Pzr, Tos

 uK2 10,500 Austin and Eagle Ford Groups K2, Kav, Kba, Kc, Kcc, Km, Kmj, Kml, Kmm, Kms, Kmu, Kmvl, Kn, 

Kph, Kss, MzPz, Qao, Qgo, Td, Tii, TKe, TKec

 uK4 1,950 Navarro Group Kfl, Kkf, Kl, Kpc, Tka

 IK, IK1 305 Lower Cretaceous Ka, Kbb, Kbr, Kf, Kft, KJg

 J 11,800 Jurassic J1, J2, Jm, Jmce, Jme, Jmj, Jms, Jmse, Jmwe, Jsr, J@g, J@mc, J@mg, 

KJdj, KJds, O, Qe, Qls, @Pcp, @Pcs, @Pr 

 JTR 6,730 Lower Jurassic and Upper 

Triassic

JTRgc, Jg, @, @rp

 IMz 400 Lower Mesozoic Jsg, Jst, J@n, J@nd, Kg, KJs, Kmt, @ad, @cd

Table 5. King and Beikman (1974) geologic units and names and geologic units from state geology maps for the Upper Colorado River Basin.

[mi2, square miles] 
Selection of Water Year 1991 21

King and 

Beikman (1974) 

geologic units

Area

(square 

miles)

Name (King and Beikman, 1974) 1 Geologic units from state geology maps (Green and Drouillard, 1994; Green and 

Jones, 1997; Green, 1992; Hirschberg and Pitts, 2000; Ludington and others, 2006) 

assigned to King and Beikman (1974) units. 1

High-yield sedimentary Paleozoic and Precambrian rocks—4,210 mi2 total area

 uPz 3,660 Upper Paleozoic IP, M_ml, M1, M2, M3, Mm, P&m, P&w, P&wm, P2

, Pc, PIP, PM, Pp, &b, 

&e, &ee, &h, &m, &mb, &mr, &rh, @c, @Pdc, @Pjs, @Ps

 IPz 553 Lower Paleozoic M_, MD, MD_, MDO, O_, Qb

Low-yield sedimentary Paleozoic and Precambrian rocks—5,940 mi2

 total area

 C 29.7 Cambrian C1, _l

 P 2,480 Permian P1, Pct, @Pmc

 P2a 82.4 Lower part of Leonardian Series Pdc

 Y 3,350 Y sedimentary rocks C2, PCs, Qg, Wr, Yu, YXu

Table 5. King and Beikman (1974) geologic units and names and geologic units from state geology maps for the Upper Colorado River 

Basin.—Continued

1

 See References Cited for the complete reference of these citations.

nificant source groups and landscape transport characteristics, 

this process was repeated again, starting with four geologic 

source groups to verify the grouping.

Irrigated Agricultural Lands

Past investigations have estimated the contribution of 

dissolved solids from agricultural activities in the UCRB to be 

about 40 percent of the dissolved-solids load at the Lees Ferry, 

Arizona gage (Iorns and others, 1965; U.S. Department of 

Interior, 2003). Dissolved solids are derived from agricultural 

lands in the UCRB as a result of the application of irrigation 

water, soil disturbance, and to a lesser degree, the application of soluble fertilizers. In general, unconsolidated aquifers 

in agricultural regions are artificially recharged by irrigation 

water. The deeper the percolation of water into these aquifers, 

the greater the amount of available minerals for dissolution 

and potential transport to streams. The mineralogic characteristics of the soils, together with the quantity of water applied, 

determine the amount of dissolved solids that can be produced by specific irrigated lands. For arid lands with minimal 

organic soil horizons, the soil mineralogy is most similar to 

the mineralogy of the bedrock geology from which they were 

generated. 

In an attempt to evaluate how irrigated lands throughout 

the UCRB differ as dissolved-solids sources, and assuming that the mineralogy of the soils for these irrigated lands 

are associated with the local bedrock geology they overlay, 

irrigated lands were classified into six distinct lithologies on 

the basis of bedrock geology they overlay from the modified 

geology map described previously. There are about 2,700 mi2

of irrigated lands in the UCRB, as determined from BOR’s 

“Potentially irrigated lands in Colorado, New Mexico, Utah, 

and Wyoming” dataset (David Eckhart, U.S. Bureau of Reclamation, written commun., September 28, 2006). Because of 

the small amount of irrigated lands compared with the total 

drainage area of the UCRB, along with the disproportionate 

amount of irrigated lands distributed between the six lithologic 

classifications, three irrigated agricultural lands source groups 

were defined for input into the SPARROW model: (1) irrigated 

sedimentary-clastic Tertiary lands, (2) irrigated sedimentaryclastic Mesozoic lands, and (3) irrigated lands of other lithologies (fig. 8). Table 6 contains the groups of irrigated lands 

defined as sources of dissolved solids in streams in the UCRB. 

Whereas it is well documented that different irrigation water 

delivery practices, such as flood or sprinkler irrigation, have a 

noticeable effect on the production and delivery of dissolved 

solids from agricultural lands to streams, this modeling effort 

was unable to distinguish between different irrigation practices 

because of a lack of uniform irrigation practice data across the 

states within the UCRB. This lack of irrigation practice data 

may result in over prediction of dissolved-solids loads in areas 

that are predominately irrigated by sprinkler systems. 

Point Sources of Dissolved Solids

Springs 

Saline springs represent the largest natural point sources 

of dissolved solids to streams in the UCRB. It is estimated that 

the seven springs listed in table 7 annually discharge as much 

as 800,000 tons of dissolved solids (U.S. Department of the 

Interior, 2003). To represent these point sources in the SPARROW model, an estimate of the water year 1991 dissolvedsolids load was assigned to the stream reach(s) or catchment(s) 

associated with each spring discharge point(s). In general, 

there are sparse dissolved-solids load monitoring data available for springs in the UCRB other than the annual loading 

estimates provided by BOR (U.S. Department of the Interior, 

2003). Fortunately, four of the seven major springs discharge 

directly to streams with monitoring sites located upstream 

and downstream of the spring orifice(s). Data from these 
22 Spatially referenced statistical assessment of dissolved-solids load sources 

0

0

50

50

100 Miles

100 Kilometers

114° 111° 108°

42°

39°

36°

Base from U.S Census Bureau digital data, 1:100,000-scale, 1990 

Geology modified from USGS and Utah Geologic Survey digital data, 1:500,000-scale 1992–2006

Agricultural land from Bureau of Reclamation digital data 1990 to 1995

Albers Equal Area Conic projection, North American Datum of 1983

EXPLANATION

WYOMING

UTAH

COLORADO

ARIZONA

NEW MEXICO

IDAHO

Irrigated agricultural lands

Other lithologies

Sedimentary, clastic - Mesozoic lands

Sedimentary, clastic - Tertiary lands

Figure 8. Irrigated agricultural lands in the Upper Colorado River Basin (UCRB) and associated lithologic classification group. 
Selection of Water Year 1991 23

Table 6. Dissolved-solids source groups of Upper Colorado River Basin irrigated agricultural lands by lithologic classification. 

Source group Total area of source 

group (square miles)

Lithologic classifications included in source group Total area of irrigated 

lands by lithologic 

classification

(square miles)

Irrigated sedimentary-clastic Tertiary lands 1,380 Sedimentary, clastic—Tertiary (continental) 1,380

Irrigated sedimentary-clastic Mesozoic lands 900 Sedimentary, clastic—Mesozoic 900

Irrigated lands of other lithologies 420 Igneous and Metamorphic lithologies 72.2

Sedimentary carbonate (marine) 27.8

Sedimentary mixed (continental and marine) 320

Sedimentary basin fill (continental) 0.03

monitoring sites allowed for an indirect means to estimate the 

water year 1991 dissolved-solids load for Glenwood Springs, 

Dotsero Springs, Meeker Dome, and Paradox Valley (table 

7). The computed upstream-downstream differences indicate 

that for water year 1991, the BOR estimates were between 

24 and 39 percent high. For the three springs without indirect 

monitoring data—Steamboat Springs, Pagosa Springs, and 

Sinbad Valley—the average percentage difference, 32 percent, 

between the BOR estimates and the indirect monitoring data 

was applied to the BOR estimates shown in table 7. 

Reservoirs

A net storage decrease in a reservoir during a water year 

indicates that more water was removed from it than flowed 

into it. The removal of water occurs through a combination 

of outflows exceeding inflows and evaporation. Both of these 

processes lead to an increase in dissolved solids downstream 

of the reservoir. The total annual releases exceeding total 

annual inflows, assuming that the quantity of constituent mass 

and flow is proportional, would increase the dissolved-solids 

load immediately downstream of the reservoir. Excess releases 

from one water year introduces dissolved solids downstream 

that were not generated or transported from upstream in that 

given water year. The evaporation process removes pure water, 

causing dissolved-solid concentrations in the reservoir to 

increase, which would then increase concentrations and thus, 

loads immediately downstream as well. 

Twenty-four reservoirs with normal capacities of 25,000 

acre-ft or more in the UCRB not associated with transbasin 

diversions were examined for changes in net storage in an 

effort to account for changes in dissolved-solids loads downstream. Of those 24 reservoirs, 6 were found to have a net 

decrease in storage, suggesting an increase in dissolved-solids 

loads downstream for the 1991 water year (table 4). For these 

reservoirs, an estimated apparent load was computed using 

nearest available dissolved-solids concentration data and the 

computed reservoir net storage decreases. These load increases 

were treated as point sources of flux and were assigned to 

the stream reach, or catchment, immediately downstream of 

the reservoir. Table 4 shows the reservoirs with net storage 

decreases for water year 1991 and the data used to estimate the 

apparent import of dissolved solids. The process of evaporation and its role in increasing dissolved-solids loads is not 

directly accounted for using the method described above. For 

most of the six reservoirs with net storage decreases, evaporation is a small consideration. However, evaporation on Lake 

Powell, the largest reservoir in the UCRB, is large. Unfortunately, evaporation data were not readily available, and the 

method used to determine the apparent dissolved-solids load 

only considered the net storage decrease and dissolved-solids 

concentrations, which introduces uncertainty in the estimates. 

The point-source load for Lake Powell was computed using 

the median dissolved-solids concentration at USGS streamflow-gaging station 09380000, Colorado River at Lees Ferry, 

Arizona, immediately downstream of the reservoir. As mentioned above, excess releases in a given water year introduce 

dissolved solids from a different time period; however, to balance the model using the best available data, it was important 

to include a parameter to represent the reservoir management 

practices of 1991. The apparent dissolved-solids load of Lake 

Powell was removed from the results presented for locations 

below Lake Powell. The apparent dissolved-solids loads for 

the other five reservoirs were minor enough not to be removed 

from the results presented. 

The apparent dissolved-solids loads for the six reservoirs 

were grouped with the dissolved-solids loads of the saline 

springs to define the point-source imports source group. There 

is uncertainty related to the estimated point source dissolvedsolids loads of the saline springs and reservoirs in the UCRB. 

Fortunately, by treating these increases in dissolved solids as 

point sources within the SPARROW modeling framework, the 

model has flexibility in assigning the coefficient. Assuming an 

accurate representation of all other sources and their specified 

landscape transport functions, the SPARROW calibration routine will adjust the estimates of the point sources by assigning 

a representative coefficient. For example, if on average, the 

point-source load estimates were too high, the point-source 

coefficient would be expected to be less than one. Conversely, 

if the point source load estimates were too low, the point-
24 Spatially referenced statistical assessment of dissolved-solids load sources 

Table 7. Estimated water year 1991 dissolved-solids load for selected springs in the Upper Colorado River Basin. 

[NA, not available]

Saline spring point source Estimated annual 

dissolved-solids load 

(U.S. Department of the 

Interior, 2003)

(tons)

Estimated water year 1991 

dissolved-solids load computed 

from indirect monitoring data 

(tons)

Percent difference of 

estimated dissolvedsolids load for 1991 

and estimated annual 

dissolved-solids load

(percent)

Estimated dissolvedsolids load for water 

year 1991 computed 

from average percent 

difference

(tons)

Dotsero Springs 182,600 138,000 24 NA

Glenwood Springs 335,000 205,000 39 NA

Meeker Dome 57,000 37,100 35 NA

Paradox Valley 205,000 148,000 28 NA

Steamboat Springs 8,500 NA NA 5,770

Pagosa Springs 7,300 NA NA 4,950

Sinbad Valley 6,500 NA NA 4,400

source coefficient would be expected to be greater than one. 

For obvious conceptual reasons, point sources are not specified any landscape transport function. The distribution of the 

11 defined sources of dissolved solids in the UCRB is shown 

in figure 9.

Landscape Transport Characteristics

Twenty-six landscape transport characteristics were 

computed for statistical evaluation as significant predictors 

of dissolved-solids loads for streams in the UCRB during the 

calibration process (table 2). Conceptually, climatic characteristics such as precipitation and evaporative transpiration, play 

a large role in the delivery of dissolved solids from sources 

to streams. Physical drainage basin characteristics, along 

with land cover and soil characteristics, are other potential 

significant landscape transport characteristics. All landscape 

transport characteristics examined were computed for each 

catchment from readily available geospatial datasets (table 3) 

using GIS tools. 

Within the SPARROW modeling framework, landscape 

transport is a source-specific function, which means that landscape transport characteristics are applied to specified sources. 

This application should adhere to the conceptual understanding of how dissolved solids are transported to streams in the 

UCRB. Point sources of dissolved solids are not affected by 

landscape transport mechanisms and therefore, no landscape 

transport was specified for the point-source imports dissolvedsolids source. The transport of dissolved solids from irrigated 

agricultural lands is related to irrigation practices, specifically the quantity of water applied. Comprehensive data on 

the application of water to agricultural lands throughout the 

UCRB are not readily available. Growing seasons and climate 

vary in different agricultural locations within the UCRB. 

Conceptually, climate and growing season, and the amount of 

irrigation water needed, are dependent on elevation. Therefore, 

in an effort to capture a distinguishing characteristic related 

to the amount of water used for irrigating crops in different 

locations within the basin, catchment minimum and maximum 

elevation were considered individually as landscape transport 

characteristics for the irrigated agricultural lands sources. The 

remaining 24 landscape transport characteristics were specified for the seven geologic sources and statistically tested 

during model calibration. 

Calibration of UCRB Dissolved-Solids 

SPARROW Model

Dissolved-solids loads for water year 1991 at 218 monitoring sites were used to calibrate the UCRB dissolved-solids 

SPARROW model. This dataset equated to 218 unique calibration reaches that were examined for the defined dissolved 

solids sources and their specified landscape transport characteristics. Eleven sources of dissolved solids were tested during 

model calibration: seven geologic groups, three irrigated agricultural lands groups, and the point-source imports. Appendix 

2 shows how the 11 source groups were represented within 

each of the 218 calibration reaches. In accordance with the 

conceptual understanding that these are sources of dissolved 

solids, coefficients for the source terms were constrained to be 

positive. 

Landscape transport functions were specified for all 

sources except the point source imports. Coefficients for the 

landscape transport characteristics were not constrained and 

were allowed to be either positive or negative. After individually testing the landscape transport characteristics catchment 

minimum and maximum elevation specified for irrigated 

agricultural lands, the t-test statistic for catchment minimum 

elevation indicated it to be a better predictor. Numerous 

combinations of the remaining landscape transport characteristics, each specified to the seven geologic source groups, 

were explored. Model diagnostics indicated that six of these 

characteristics, as applied to the landscape transport function 

specified for the geologic source groups, were significant, 
Selection of Water Year 1991 25

114° 108°

42°

39°

36°

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990 

Geology modified from USGS and Utah Geologic Survey digital data, 1:500,000 scale 1992–2006

Hydrography from USGS Enhanced River Reach,1:500,000 scale, File 2.0, 2002

Albers Equal Area Conic projection, North American Datum of 1983

WYOMING

UTAH

COLORADO

ARIZONA

NEW MEXICO

IDAHO

111°

0

0

50

50

100 Miles

100 Kilometers

Glenwood

Springs

Sinbad Valley

Paradox Valley

Meeker Dome

Steamboat Springs

Dotsero Warm

Springs

Pagosa Springs

EXPLANATION

Irrigated lands of other lithologies

Irrigated sedimentary-clastic - Mesozoic lands

Irrigated sedimentary-clastic - Tertiary lands

Crystalline and volcanic rocks

High-yield sedimentary Cenozoic rocks

Low-yield sedimentary Cenozoic rocks

High-yield sedimentary Mesozoic rocks

Low-yield sedimentary Mesozoic rocks

High-yield sedimentary Paleozoic and 

Precambrian rocks

Low-yield sedimentary Paleozoic and 

Precambrian rocks

Water

UCRB boundary

Point-source import

Figure 9. Map of the 11 defined sources of dissolved solids in the Upper Colorado River Basin (UCRB) that were input to the SPARROW 

model. 
26 Spatially referenced statistical assessment of dissolved-solids load sources 

with probabilities of the coefficient being zero much less than 

0.001 for all but one (table 8). Of these six characteristics, two 

were climate related—total precipitation and total evaporative 

transpiration; two were soil characteristics—mean cumulative 

thickness of soil and mean hydrologic soil characteristic code; 

one was related to land cover—fraction of catchment area covered by forest; and one was a combination of a climatic metric 

with a physical characteristic—the ratio of total precipitation to 

maximum elevation.

In general, statistical evaluation metrics indicate a good 

fit of the nonlinear least squares model to the observed data. 

Figure 10 shows the relation between observed loads and 

model predicted loads for the 218 sites. Residuals appear 

uniformly scattered about the correlation line, which suggests 

unbiased predictions throughout the range in observed dissolved-solids loads. The R2

 value of 0.98 and the yield R2 value 

of 0.71 suggest that the model generally represents the sources 

and transport of dissolved solids to streams in the UCRB. For 

spatially referenced regression models, such as SPARROW, 

there is a high correlation between drainage area and flux. For 

such models, the yield R2

 metric is a better representation of 

the variance explained by the model fit because the area-flux 

correlation is removed (Schwarz and others, 2006). Yield R2 is 

computed with the equation: 

   

2

2 1

Yield 2

1

1

N

i

i

N

i i

i

R

y y d d







 

      





(3)

Where

R2

Yield is the yield R2

, 

ε are the model residuals,

y is variance in observations, and 

d is the drainage areas of basins or catchments. 

The spatial distribution of standardized residuals (fig. 11)

does not show any obvious patterns that would indicate location related bias in the model. Root mean square error (RMSE) 

for the calibrated model was 0.51, from which a prediction 

error of about 51 percent can be approximated. This compares 

favorably with the RMSE of the southwestern U.S. dissolvedsolids SPARROW model of 0.71, which equates with a prediction error of approximately 71 percent (Anning and others, 

2007).

Following the initial nonlinear least squares calibration 

procedure, a 200 iteration resampled bootstrap analysis was 

done. Resampled bootstrapping is a technique that estimates 

unbiased coefficients, which then can be used to assess the 

validity of the nonlinear least squares estimates that are generated from a finite sample size. Nonlinear least squares coefficient estimates and their standard errors are valid only asymptotically (Schwarz and others, 2006). Assumptions that sample 

sizes are large enough that coefficient estimates are unbiased 

with standard normal distributions are inherent in the nonlinear least squares technique. The resampled bootstrapping 

technique generates coefficient estimates that are obtained 

from a large artificial sample population developed from the 

distribution implied by the available sample data (Schwarz and 

others, 2006). The concept is that if the finite sample population meets the assumptions associated with large sample sizes, 

sufficient resampling will generate mean coefficient estimates 

that are similar to those of the nonlinear least squares calibration. A sufficient sample size that agrees with the assumptions associated with the nonlinear least squares technique is 

validated if coefficient estimates generated from the nonlinear 

least squares calibration and the resampled bootstrap analysis 

are agreeable. 

Model Results

The results of the nonlinear least squares calibration and 

resampled bootstrap analysis are shown in table 8. As shown, 

the mean coefficients determined from the resampled bootstrap analysis are very similar to the nonlinear least square 

coefficients except for the irrigated lands of other lithologies 

parameter. The p-value is an estimate of the probability that 

parameter coefficients are equal to zero, or statistically insignificant in describing the dependent variable. In most predictive statistical modeling, parameters with high p-values are 

usually disregarded as being non-explanatory. This approach 

of eliminating model parameters was followed when testing 

landscape transport characteristics; however, for conceptual 

reasons, all tested sources were left in the model regardless of 

the p-value. As shown in table 8, the irrigated lands of other 

lithologies source group had p-values of 0.96 and 0.305, and PREDICTED 1991 DISSOLVED- SOLIDS LOAD, IN TONS

106

105

104

103

101

102

OBSERVED 1991 DISSOLVED-SOLIDS

LOAD, IN TONS

106 105 104 103 101 102

Figure 10. Relation between predicted and observed dissolvedsolids loads for 218 monitoring sites in the Upper Colorado River 

Basin (UCRB) used to calibrate the UCRB dissolved-solids SPARROW model. 
Selection of Water Year 1991 27

Nonlinear least squares calibration Resampled bootstrap analysis

Model parameters Coefficient

units

Coefficient Standard 

error

p-value Lower bound 

90-percent 

confidence 

interval

Mean 

coefficient

Upper bound 

90-percent 

confidence 

interval

Standard 

error

p-value

Dissolved-solids sources

 Point-source imports D 0.94 0.56 0.09 0.67 0.81 1.10 1.41 0.015

 Crystalline and volcanic 

rocks

ton/mi2 4.47 1.27 <0.001 0.99 4.17 6.08 1.52 0.015

 Sedimentary rocks

 High-yield Cenozoic ton/mi2 36.2 11.4 0.002 15.0 35.3 57.0 14.9 0.02

 Low-yield Cenozoic ton/mi2 16.9 4.76 <0.001 5.94 16.0 25.5 6.04 0.005

 High-yield Mesozoic ton/mi2 41.9 12.4 <0.001 18.14 43.4 66.0 14.6 0.005

 Low-yield Mesozoic ton/mi2 2.65 1.41 0.06 –2.09 2.01 4.55 2.46 0.1

 High-yield Paleozoic 

and Precambrian

ton/mi2 25.6 6.96 <0.001 6.93 23.7 35.4 9.33 0.01

 Low-yield Paleozoic 

and Precambrian

ton/mi2 1.26 0.55 0.02 –0.13 1.05 1.88 0.93 0.045

 Irrigated lands of other 

lithologies

ton/mi2 22.8 473 0.96 –1,520 –295 45.6 558 0.305

 Irrigated sedimentaryclastic Mesozoic landston/mi2 1,180 281 <0.001 636 1,200 1,680 343 0.005

 Irrigated sedimentaryclastic Tertiary landston/mi2 662 254 0.01 273 779 1,320 349 0.01

Landscape transport characteristics

 Minimum catchment 

elevation

1/ft –0.0006 0.0002 0.002 –0.0009 –0.0005 –0.0002 0.0007 0.005

 Mean catchment total 

precipitation

yr/in. 0.16 0.02 <0.001 0.12 0.17 0.23 0.03 0

 Mean catchment total 

precipitation, maximum 

catchment elevation ratio

yr –171 29.9 <0.001 –247 –178 –115 38.9 0

 Mean catchment total 

evapotranspiration

yr/in. 0.11 0.03 <0.001 0.05 0.11 0.17 0.03 0

 Mean catchment 

cumulative thickness 

of soil

1/in. –0.05 0.01 <0.001 –0.08 –0.05 –0.03 0.02 0

 Mean catchment 

hydrologic soil 

characteristic code

D –1.6 0.26 <0.001 –2.54 –1.68 –1.19 0.43 0

 Fraction of catchment 

area covered by forest

D –0.76 0.34 0.03 –1.39 –0.77 –0.15 0.37 0.025

R2 (1)Yield R2 Mean square error Root mean square error Number of observations

.98 .71 .26 .51 218

1

 Indicates variance of model after removing correlation between drainage area and flux. 

Table 8. Results of nonlinear least squares calibration and resampled bootstrap analysis for 1991 Upper Colorado River Basin 

dissolved-solids SPARROW model. 

[D, dimensionless; tons/mi2, tons per square mile; <, less than; yr/, years per; yr, year; in., inch; ft, feet; mi2, square miles]
28 Spatially referenced statistical assessment of dissolved-solids load sources 

114° 108°

42°

39°

36°

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990

Hydrography from USGS Enhanced River Reach,1:500,000 scale, File 2.0, 2002

Albers Equal Area Conic projection, North American Datum of 1983

WYOMING

UTAH

COLORADO

ARIZONA

NEW MEXICO

IDAHO

111°

0

0

50

50

100 Miles

100 Kilometers

EXPLANATION

Green

Colorado

San

Animas

Gunnison

Muddy

Fremont

River

Green

River

River

Creek

River

River

River

River

Standardized residual values

Predicted load is greater than 

observed load

Less than -1.5

-1.5 to 0

Predicted load is less than observed 

load

0 to 1.5

Greater than 1.5

UCRB boundary

Colorado

River

Juan

Figure 11. Standardized residual values for 218 monitoring sites in the Upper Colorado River Basin (UCRB) used to calibrate the UCRB 

dissolved-solids SPARROW model. 
Selection of Water Year 1991 29

accordingly, the nonlinear least squares coefficient differed 

greatly from the mean coefficient of the bootstrap analysis. 

While this suggests that irrigated lands of other lithologies do 

little to explain dissolved-solids loads in streams of the UCRB, 

for the purposes of this modeling effort, and possibly at the 

expense of increasing model uncertainty, it was important that 

all sources, including irrigated lands, be represented. 

UCRB Dissolved-Solids SPARROW Model 

Coefficients

Dissolved-solids source and landscape transport characteristic coefficients, as determined from the nonlinear least 

squares calibration, provide insight into the role to which 

each defined source, or source group, and landscape transport 

characteristic play in dissolved-solids loading to streams in the 

UCRB. Source coefficients approximate the average delivery 

of the source to the streams. As shown in table 8, the coefficients of the geologic and irrigated lands source groups represent estimated yields, in tons/mi2

, to streams in the UCRB. 

The dimensionless coefficient estimated for the point source 

imports was 0.94. While statistical metrics indicate some 

uncertainty in this estimate, as evidenced by a standard error 

of 0.56 and a p-value of 0.09, the coefficient near one suggests 

that the estimated loads for the saline springs and six reservoirs 

with net storage decreases for water year 1991 are generally 

accurate and certainly not statistically different from one. 

The high-yield sedimentary rock groups possess the 

highest delivery rates of the seven rock groups. The high-yield 

sedimentary Mesozoic rocks have the largest coefficient, and 

thus, the highest yield of dissolved solids, which is 41.9 tons/

mi2. However, the high-yield sedimentary Cenozoic rocks have 

a very similar yield, and when the standard errors associated with the coefficients are considered, the yields of the 

groups are statistically equivalent. As would be expected, the 

low-yield sedimentary rock groups, except for the low-yield 

sedimentary Cenozoic rocks, have small yields and(or) high 

p-values, greater than 0.01, that suggest there is a high probability that they are not all that different from zero and likely 

contribute very little dissolved solids to streams in the UCRB. 

Estimated coefficients for the irrigated lands source 

groups indicate large dissolved-solids yields associated with 

irrigated agriculture. Other studies have estimated that irrigated 

lands make up roughly 40 percent of the dissolved-solids load 

at USGS streamflow-gaging station 09380000, Colorado River 

at Lees Ferry, Arizona (Iorns and others, 1965; U.S. Department of the Interior, 2003). Irrigated lands, as computed for 

1991 (David Eckhart, Bureau of Reclamation Remote Sensing 

and Geographic Information Group, written commun., September 28, 2006), occupy less than 3 percent, or about 2,700 mi2

, 

of the UCRB. Irrigated sedimentary-clastic Mesozoic lands 

have an approximate yield of 1,180 tons/mi2, nearly twice that 

of irrigated sedimentary-clastic Tertiary lands. Figure 8 shows 

locations of these irrigated lands in the UCRB. The high yields 

predicted by the SPARROW model for these two irrigated 

lands groups tend to agree with the conceptual model of dissolved solids in the UCRB. The coefficient associated with 

the irrigated lands of other lithologies source group was quite 

small compared with the other irrigated lands groups. This 

coefficient has a large standard error and the largest p-value 

of any source group, which suggests irrigated lands of other 

lithologies are likely not significant sources of dissolved solids 

to streams in the UCRB. 

Coefficients associated with the landscape transport 

characteristics do not lend themselves as easily to interpretation as the source coefficients. Recall from equations 1 and 2

that the landscape transport characteristics are applied to the 

landscape transport function, which is then multiplied by the 

source terms. The transport function, as applied to the sources, 

is exponential. Landscape transport is source-specific, and as 

specified in this model, single characteristics are applied to 

numerous sources. For example, the seven geologic source 

groups have six landscape transport characteristics applied, 

and minimum catchment elevation is specified for the three 

irrigated lands source groups. The estimated coefficients for 

the landscape transport characteristics, which are shown to be 

the reciprocals of the units of the specific landscape transport 

characteristics in table 8, can be interpreted as follows: 100 

times the coefficient of a specific landscape transport characteristic equates to the percentage change in the delivered 

source(s) it is applied to from one unit change in the landscape transport characteristic (Greg Schwarz, U.S. Geological 

Survey, written commun., November 16, 2007). For example, 

the coefficient for mean catchment total precipitation is 0.16 

yr/in. Multiplying this coefficient by 100 equates to a 16-percent change in the sources it is applied to, which are the seven 

geologic source groups. If within a given catchment, the total 

precipitation increased by 1 in/yr, a 16-percent increase in the 

predicted dissolved-solids load associated with each of the 

seven source groups would be expected to occur. 

A more simplified, albeit qualitative, means to interpret 

the coefficients associated with the landscape transport characteristics is to examine the conceptual meaning of the sign 

predicted for their coefficients. Mean catchment total precipitation was assigned a positive coefficient. An increase in 

precipitation on the seven geologic source groups increases the 

availability of water to dissolve minerals from rock. Similarly, 

the estimated coefficient for mean catchment total evaporative 

transpiration was positive. Evaporative transpiration concentrates minerals within the upper portion of the soil horizon. 

The positive coefficient suggests that increased evaporative 

transpiration augments the amount of dissolved solids transported from source rocks to streams in the UCRB. 

Coefficient estimates for the remaining landscape 

transport characteristics were negative. Recall that landscape 

transport is applied as an exponential function to the specified 

sources. Larger values of a landscape transport characteristic 

multiplied by a negative coefficient that is less than one, such 

as –1.5, will generate a smaller yield; and larger values of a 

landscape transport characteristic when multiplied by a negative landscape transport characteristic coefficient greater than 
30 Spatially referenced statistical assessment of dissolved-solids load sources 

one, such as –0.5, will generate a larger yield. The negative 

coefficient assigned to the ratio of mean catchment total precipitation to maximum catchment elevation suggests that for 

identical source rocks, under similar precipitation conditions, 

catchments at higher elevations would produce more dissolved 

solids. Prior to incorporating this ratio into the model, which 

adjusts the effect of precipitation on rock sources as a function 

of elevation (David Anning, U.S. Geological Survey, written 

commun., June 2, 2008), predicted dissolved-solids loads, 

when compared with monitoring data, were generally too large 

at the downstream reaches of the larger rivers. This parameter 

appears to represent a variety of characteristics, such as slope, 

and stream density, that conceptually enhance transport of dissolved solids. However, these characteristics when represented 

in the model alone, either do not appear to be significant landscape transport characteristics or have a tendency to enhance 

transport at lower elevations, in a similar manner as at higher 

elevations. In general, higher elevations are more likely to 

have steeper slopes and shorter transport paths, and thus, rock 

sources at higher elevations should produce more dissolved 

solids than at lower elevations.

The soil related characteristics, cumulative thickness of 

soil and mean hydrologic soil characteristic code, were each 

negative. There are four hydrologic soil characteristic codes as 

defined in the State Soil Geographic Data Base (STATSGO) 

(Wolock, 1997) (table 9). From the 1:250,000-scale map of 

soil characteristics, a mean value was computed for each 

catchment. The negative coefficients assigned to both soil 

landscape transport characteristics, a –0.05 for cumulative soil 

thickness and a –1.60 for mean hydrologic soil characteristic 

code, suggest that in general, the presence of soils inhibits the 

amount of dissolved solids delivered to streams. In regards 

to the cumulative thickness characteristic, there are a variety 

of plausible interpretations of the negative coefficient such 

as thinner soils indicate less weathered rock and thus more 

available solids, thicker weathered soils impede access to 

unweathered rocks, and thicker soils may act as a reservoir for 

long-term storage of solids. As shown in table 9, the highest 

hydrologic soil characteristic code, 4, is associated with low 

infiltration rates and a high water table. The lowest characteristic code, 1, describes deep soils with high infiltration rates. 

Conceptually and according to the model results, catchments 

with lower hydrologic soil characteristic code values enhance 

the transport of dissolved solids compared with catchments 

with higher values. The –0.76 coefficient assigned to the fraction of catchment area covered by forest characteristic, which, 

unless the entire catchment is forested, are less than one, 

indicates that less forest increases dissolved solids. 

The coefficient of the minimum catchment elevation 

characteristic specified for the irrigated lands source groups 

was estimated to be –0.0006. This value indicates that an 

increase in elevation for those lands leads to less dissolved 

solids generated from irrigated lands. Conceptually, higher 

elevations have a shorter growing season, which shortens the 

time that water can be applied. Also, precipitation increases 

and temperature decreases with elevation, which decreases the 

amount of irrigation water needed. If application of irrigation 

water is the transport mechanism leading to increased dissolved solids, this negative coefficient is in agreement. 

Prediction of Dissolved-Solid Loads

Following the nonlinear least squares calibration and 

validation of the inherent least squares assumptions by the 

resampled bootstrap analysis, a parametric bootstrap analysis 

was performed to generate unbiased predictions of dissolvedsolids loads for all reaches of the stream reach network. If the 

coefficient estimates of the nonlinear least squares calibration are asymptotically normally distributed, the technique of 

parametric bootstrapping can be used to remove bias in model 

predictions. Parametric bootstrapping iteratively generates 

random coefficient estimates from a multivariate normal 

distribution with the mean and covariance equal to those of the 

nonlinear least squares coefficient estimates. For each set of 

artificial coefficient estimates, predictions are made at all sites, 

and residuals are computed at observation sites. The set of iterative bootstrap predictions is used to estimate the distribution 

of the model component of the model predictions, and the set 

of iterative bootstrap residuals at observation sites is used to 

estimate the distribution of the estimated mean re-transformation factor for model residuals. These distributions can be used 

to unbias the predictions on the basis of nonlinear least squares 

coefficient estimates and to determine the standard error and 

confidence interval of each prediction (Schwarz and others, 

2006). Because the predictions of dissolved-solids loads for 

the reaches of the stream network were generated using a parametric bootstrapping technique in which strict mass balance 

is lost, it is possible that the sum of the dissolved-solids load 

attributed to each of the individual sources for a given location 

in the UCRB may not exactly equal the predicted dissolvedsolids load.

1

Hydrologic soil 

characteristic 

code

Hydrologic soil characteristic description

1 High infiltration, deep soils, well drained to excessively 

drained sands and gravels.

2 Moderate infiltration rates, deep and moderately 

deep, moderately well and well drained soils with 

moderately coarse textures.

3 Slow infiltration rates, soils with layers impeding 

downward movement of water, or soils with 

moderately fine or fine textures.

4 Very slow infiltration rates, soils are clayey, have a high 

water table, or are shallow to an impervious layer.

1

 From Wolock (1997).

Table 9. State Soil Geographic Data Base (STATSGO) soil characteristic code descriptions. 
Selection of Water Year 1991 31

The 1991 dissolved-solids load predictions for streams in 

the UCRB are presented in this report as predicted dissolvedsolids loads and adjusted predicted dissolved-solids loads. 

The predicted dissolved-solids loads are those generated from 

the parametric bootstrap analysis described above. These 

predictions provide a statistically unbiased assessment of 

dissolved-solids load sources and transport in streams of the 

UCRB, which is important for basin-wide questions related 

to dissolved solids. As would be expected, the predicted 

dissolved-solids loads do not always match those from streammonitoring sites, and resource managers are often interested in 

predictions at specific locations in the UCRB. In an effort to 

provide the most accurate predictions of dissolved-solids loads 

throughout the basin, a second set of predictions was generated—adjusted predicted dissolved-solids loads. The residuals 

between the predicted and observed loads were computed at 

all monitoring sites and applied to the incremental dissolvedsolids loads for each catchment of the associated calibration 

reach upstream to the next monitoring site(s) or headwater(s). 

The total incremental dissolved-solids load, along with the 

incremental dissolved-solids load attributed to each of the 11 

sources, were adjusted in this manner. These incremental loads 

were then accumulated using the interconnected stream reach 

network, with proper consideration to the fraction of upstream 

flux-delivered values assigned to each catchment. These 

results are presented as the adjusted predicted dissolved-solids 

loads. At most locations, the adjusted predicted dissolvedsolids loads more favorably match the loads of the monitoring 

sites. However, at some locations, large differences still exist. 

Dissolved-Solids Loads in Selected Rivers of the 

UCRB

Predicted and adjusted predicted dissolved-solids loads 

for the reaches of the stream reach network were generated 

using the UCRB dissolved solids SPARROW model. Using 

the interconnected stream reach network representing streams 

in the UCRB, the downstream accumulation of dissolved 

solids in some of the major rivers were examined. Along with 

the predicted and adjusted predicted dissolved-solids load, the 

adjusted predicted natural and agricultural load components 

were plotted as well. The natural component includes dissolved solids associated with the point-source imports and 

seven geologic source groups. The agricultural load component is made up of the dissolved-solids loads from the three 

irrigated lands source groups. The locations of major river 

confluences, monitoring sites, reservoirs, saline springs, and 

other pertinent information along the river courses are noted. 

From these plots, dissolved-solids load increases that are associated with inflows from other rivers are visible. Decreases in 

dissolved-solids load, such as those related to the Government 

Highline Canal and Grand Valley diversions on the Colorado 

River and Navajo Reservoir on the San Juan River, are evident 

as well. 

These cumulative plots are useful in assessing how well 

the model is capturing dissolved-solids load sources and 

transport in the major streams of the UCRB. The plots also 

allow for an examination of how the natural and agricultural 

components of dissolved-solids loads in the UCRB accumulate along the courses of the major rivers. As discussed below, 

there are many locations along these selected rivers where the 

predicted and adjusted predicted dissolved-solids loads do not 

agree with the loads associated with monitoring sites. In most 

cases, the differences between the predicted and observed 

values are well within the approximated prediction error of 

the model, which is 51 percent. The standard errors associated with the estimated coefficients also need to be considered 

when evaluating differences in the predicted and observed 

dissolved-solids loads. 

Colorado River

Generally, the adjusted predicted cumulative dissolvedsolids load for the Colorado River (fig. 12) agrees with monitoring data at most locations upstream of the Gunnison River. 

Large differences between monitoring data and the predicted 

and adjusted predicted loads along the Colorado River are 

noticed downstream from the Gunnison and Dolores Rivers, and at Lees Ferry, Arizona. At these locations, the model 

appears to be under-predicting the dissolved-solids load. Some 

of the diverted water from the two major diversions of the 

Grand Valley is known to return to the Colorado River below 

monitoring site 09106150, Colorado River below Grand Valley Diversion near Palisade, Colorado. The dissolved-solids 

load associated with this water could not be represented in 

the model because of a lack of data. As discussed below, the 

adjusted predicted dissolved-solids load for the Gunnison 

River at monitoring site 09152500, Gunnison River near 

Grand Junction, Colorado, was about 252,000 tons, or 23 

percent, less than the monitored load of 1,080,000 tons. At the 

Lees Ferry, Arizona, monitoring site, the adjusted predicted 

dissolved-solids load was 463,000 tons, or 8 percent, less than 

the monitored load. As previously mentioned, the apparent 

dissolved-solids load associated with Lake Powell, contained 

in the point source imports source, was removed from the 

presented results and accordingly from the monitored data as 

well. 

As shown in the cumulative dissolved-solids load plot for 

the Colorado River, for the first 250 mi along the mainstem, 

the natural component accounts for nearly the entire dissolvedsolids load. At the confluence with the Gunnison River, the 

natural and agricultural components become about 50 percent 

each. This nearly even distribution of the two dissolved-solids 

load components continues until the confluence with the 

Green River when the natural load becomes dominant at about 

55 percent. This 55 percent natural and 45 percent agricultural 

distribution continues to the bottom of the UCRB, represented 

by the Lees Ferry, Arizona, monitoring site. 
32 Spatially referenced statistical assessment of dissolved-solids load sources DISSOLVED SOLIDS, IN MILLIONS OF TONS

8

6

4

2

0

09010500

09034500

09058000

09071750

09085100

Dolstero Springs

09093700

09095500

09106150

09163500

09180500

Lake Powell

Glenwood Springs

Roaring Fork River

Gunnison River

Government Highline Canal

and Grand Valley diversions

Colorado-Utah Stateline

Dolores River

Green River

Dirty Devil River

San Juan River

Utah-Arizona Stateline

09380000

0 100 200 300 500 700

APPROXIMATE DOWNSTREAM DISTANCE, IN MILES

400 600

Stream-monitoring site

Predicted total load

Adjusted predicted total load

Adjusted predicted agricultural load

Adjusted predicted natural load 09070500

Colorado River

Figure 12. Predicted downstream accumulation of dissolved solids for the Colorado River, from the Upper Colorado River Basin (UCRB) 

SPARROW model of dissolved-solids transport, for 1991. 

2.5

1.5

1.0

0.5

0

2.0

DISSOLVED SOLIDS, IN MILLIONS OF TONS

0 100 200 300 500 700

APPROXIMATE DOWNSTREAM DISTANCE, IN MILES

400 600

09209400

Fontenelle Reservoir

09211200

Big Sandy River

09217000

Flaming Gorge Reservoir Blacks Fork River Wyoming-Utah Stateline 09234500

Utah-Colorado Stateline

Yampa River

Colorado-Utah Stateline

09261000

Duchesne River

White River

Price River

09315000

San Rafael River

Stream-monitoring site

Predicted total load

Adjusted predicted total load

Adjusted predicted agricultural load

Adjusted predicted natural load

800

Green River

Figure 13. Predicted downstream accumulation of dissolved solids for the Green River, from the Upper Colorado River Basin (UCRB) 

SPARROW model of dissolved-solids transport, for 1991. 
Selection of Water Year 1991 33

Green River

Predicted and adjusted predicted dissolved-solids loads 

for the Green River generally agree favorably with monitored 

loads (fig. 13). The largest differences between the predicted 

and adjusted predicted loads occur at the two most downstream monitoring sites. At monitoring site 09261000, Green 

River near Jensen, Utah, the adjusted predicted total load of 

886,000 tons is about 170,000 tons, or 16 percent, less than 

the monitored load of 1,056,000 tons. The predicted load is 8 

percent less than the monitored load. At the 09315000, Green 

River at Green River, Utah, monitoring site, the adjusted predicted load is 16,900 tons, or 1 percent greater than the monitored load whereas the predicted load is 14 percent greater. 

These results suggest that the model is accurately representing 

the sources and transport of dissolved solids throughout the 

Green River drainage basin. 

From the adjusted predicted total dissolved-solids load 

for the entire Green River Basin, the natural component is 

about 63 percent, and the agricultural component is about 37 

percent. The natural component becomes dominant at the confluence with the Big Sandy River and continues through the 

course of the Green River. The largest increases in the natural 

load occur at the confluences of the Yampa and White Rivers with the Green River. The Duchesne River appears to be 

related to the largest increase of agricultural-related dissolved 

solids. The decrease in dissolved solids associated with the 

net storage increase for Flaming Gorge Reservoir is evident in 

figure 13 at downstream mile 345.

San Juan River

The cumulative predicted and adjusted predicted dissolved-solids loads for the San Juan River (fig. 14) at most 

locations are substantially greater than dissolved-solids loads 

measured at monitoring sites. Below Navajo Reservoir at monitoring site 09355500, San Juan River near Archuleta, New 

Mexico, the predicted and adjusted predicted dissolved-solids 

loads are 121,000 and 64,000 tons greater than the monitored 

load of 116,000 tons. The adjusted predicted dissolved-solids 

load is on average 35 percent higher than the monitored load 

at the three most downstream monitoring sites on the San Juan 

River. Below the confluence of the Animas River, the adjusted 

predicted agricultural load steadily increases, and McElmo 

Creek contributes about 115,000 tons of agricultural-related 

dissolved solids. It is difficult to determine a reason for the 

overprediction throughout the San Juan River. However, the 

large difference found below Navajo Reservoir together with 

some irrigated lands that appear to be associated with sprinkler 

irrigation, may provide some explanation. 

The decrease in dissolved solids associated with the net 

storage increase for Navajo Reservoir is evident in figure 14

at downstream mile 100. As can be seen, the predicted and 

adjusted predicted dissolved-solids loads at this location are 

greater than the load at the San Juan River near Archuleta, 

New Mexico, monitoring site by about 51,000 and 11,000 

tons. It appears that either the dissolved-solids load is being 

overpredicted between monitoring site 09346400, San Juan 

River near Carracas, Colorado, and the Archuleta site, or 

1,000

800

400

200

0

600

0 100 200 300

APPROXIMATE DOWNSTREAM DISTANCE, IN MILES

400

09339900

09342500

09346400

Navajo Reservoir

09355500

09365000

Animas River

09368000

09371010

New Mexico-Colorado Stateline

Colorado-Utah Stateline

McElmo Creek

09379500

Stream-monitoring site

Predicted total load

Adjusted predicted total load

Adjusted predicted agricultural load

Adjusted predicted natural load

DISSOLVED SOLIDS, IN THOUSANDS OF TONS

San Juan River

Figure 14. Predicted downstream accumulation of dissolved solids for the San Juan River, from the Upper Colorado River Basin (UCRB) 

SPARROW model of dissolved-solids transport, for 1991. 
34 Spatially referenced statistical assessment of dissolved-solids load sources 

the determined fraction of upstream flux-delivered value for 

Navajo Reservoir was too large and did not remove enough of 

the predicted dissolved solids. 

Irrigation practices, such as flood or sprinkler irrigation, 

were not differentiated between the irrigated lands source 

groups. Flood irrigation is a less efficient method of irrigating 

crops and generally causes increased dissolved-solids loading 

to streams when compared with sprinkler irrigation. Below 

the confluence of the San Juan and Animas Rivers, there are 

approximately 35 mi2 of irrigated sedimentary-clastic Mesozoic lands (fig. 15). A map of these irrigated lands indicates 

that many of the fields are circular in shape, which is consistent with fields irrigated with center pivot sprinkler systems. 

The adjusted predicted dissolved-solids load associated with 

these fields is about 49,000 tons. There also are 42 mi2 of irrigated sedimentary-clastic Tertiary lands upstream, which also 

are the same circular shape common to center pivot sprinklers. 

These lands, which have a yield roughly 56 percent of the irrigated sedimentary-clastic Mesozoic lands, are associated with 

an adjusted predicted dissolved-solids load of about 33,300 

tons. The loads attributed to these sprinkler irrigated lands are 

likely to be much less than predicted by this model because of 

the efficiencies of sprinkler irrigation. 

Gunnison River

The model predicted that the cumulative dissolved-solids 

load in the Gunnison River (fig. 16) is dominated by agricultural activities in the North Fork Gunnison and Uncompahgre 

Rivers, which are predicted to contribute a total of about 

438,000 tons. The dissolved-solids loads for monitoring sites 

on the Gunnison River generally agree well with predicted 

loads upstream of the North Fork Gunnison River and the 

adjusted predicted dissolved-solids loads at downstream monitoring sites 09144250, Gunnison River at Delta, Colorado, and 

09152500, Gunnison River near Grand Junction, Colorado, are 

179,000 and 252,000 tons, respectively, less than the monitored loads. The adjusted predicted load for the most downstream monitoring site on the Uncompahgre River, 09149500, 

Uncompahgre River at Delta, Colorado, is 310,000 tons, about 

26,000 tons more than the predicted load. These results suggest that the large differences between monitored loads and 

predicted loads at the two most downstream monitoring sites 

on the Gunnison River are likely due to an under prediction of 

the dissolved-solids load associated with the North Fork Gunnison River. No monitoring data were available for the North 

Fork Gunnison River for water year 1991. 

Dolores River

The Dolores River traverses Paradox Valley, which was 

formed by a collapsed salt dome (U.S. Department of the Interior, 2003). Prior to 1996, when the Paradox Valley project to 

intercept saline ground water for deep re-injection commenced 

(Chafin, 2002), brine waters naturally discharged to the 

Dolores River as it passed through the valley. Monitoring data 

from sites located on the upstream and downstream boundaries of the valley were used to estimate a load of 148,000 tons 

of dissolved solids for water year 1991 (table 7). As previously described, this load was treated as a point-source import. 

The cumulative dissolved-solids load plot (fig. 17) shows 

substantial increases associated with Paradox Valley and the 

San Miguel River, which enters the Dolores River less than 

10 mi downstream of Paradox Valley. The adjusted predicted 

dissolved-solids load for the San Miguel was 116,000 tons, 

of which 71,300 tons were associated with agriculture. From 

the cumulative dissolved-solids load plot, it appears that the 

model is accurately representing dissolved-solids loads in the 

Dolores River. 

Distribution of Dissolved-Solids Loads by Source 

at Selected Locations in the UCRB

Aside from the predicted and adjusted predicted dissolved-solids load for each reach in the stream reach network, 

the contribution of the total load by each of the 11 defined 

sources is also available. Resource managers of the UCRB 

concerned with dissolved solids are often interested in the 

apportioning of the total load by source at specific locations 

in the basin. Plots showing the percentage of the adjusted 

predicted dissolved-solids load attributed to each of the 11 

sources of dissolved solids examined with the SPARROW 

model are shown for selected locations in each of the three 

divisions of the UCRB, as defined by Iorns and others (1965) 

(figs. 18 through 20). 

Grand Division

The contribution of dissolved solids by each of the 11 

defined sources is shown in figure 18 for selected streams in 

the Grand Division. The most downstream location, which 

is just above the confluence with the Green River, indicates 

that irrigated sedimentary-clastic Mesozoic lands are the most 

dominant source of dissolved solids in the Grand Division, 

contributing 40 percent. Irrigated sedimentary-clastic Tertiary 

lands contribute about 10 percent of the dissolved-solids load. 

The agricultural areas along the Colorado River, specifically 

Grand Valley, the Gunnison River, and the Uncompahgre 

River, are dominated by sedimentary rocks of Mesozoic age. 

The impact these lands have on dissolved-solids loading is 

evident in the distribution of loading sources below the confluence of the Gunnison and Colorado Rivers. Upstream of this 

confluence at monitoring site 09093700, Colorado River near 

DeBeque, Colorado, 40 percent of the load is associated with 

the saline springs, represented by the point-source imports, 

and high-yield sedimentary Paleozoic and Precambrian rocks 

account for 26 percent. Saline springs contribute 18 percent 

of the dissolved-solids load for the Grand Division. Of the 18 

percent, about 11 percent are discharged from Dotsero and 

Glenwood Springs. Saline ground-water discharge associated 

with Paradox and Sinbad Valleys is the dominant load source 

for the Dolores River, representing 43 percent of its total load. 
Selection of Water Year 1991 35

0 5 10 Kilometers

0 5 10 Miles

108°30' 108°20' 108°10'

36°50'

36°40'

36°30'

36°20'

EXPLANATION

Irrigated agriculture

Irrigated sedimentary-clastic - Mesozoic lands

Irrigated sedimentary-clastic - Tertiary lands

Geologic sources

High-yield sedimentary Cenozoic rocks

Low-yield sedimentary Cenozoic rocks

High-yield sedimentary Mesozoic rocks

Low-yield sedimentary Mesozoic rocks

San Juan

Farmington

River

Animas River

Geology modified from USGS and Utah Geologic Survey digital data, 1:500,000 scale 1992–2006

Agricultural land from Bureau of Reclamation digital data 1990 to 1995

Albers Equal Area Conic projection, North American Datum of 1983

Study area

Detail

location

Figure 15. Irrigated lands along the San Juan River near Farmington, New Mexico, that appear to be related to center pivot sprinklers. 
36 Spatially referenced statistical assessment of dissolved-solids load sources 09107000 09110000

Uncompagre River

09144250

North Fork Gunnison River

09152500

09128000

0 50 100 150

APPROXIMATE DOWNSTREAM DISTANCE, IN MILES

200

1,200

800

400

200

0

600

1,000

09114500

Stream-monitoring site

Predicted total load

Adjusted predicted total load

Adjusted predicted agricultural load

Adjusted predicted natural load

DISSOLVED SOLIDS, IN THOUSANDS OF TONS

Gunnison River

Figure 16. Predicted downstream accumulation of dissolved solids for the Gunnison River, from the UCRB SPARROW model of 

dissolved-solids transport, for 1991.

Stream-monitoring site

Predicted total load

Adjusted predicted total load

Adjusted predicted agricultural load

Adjusted predicted natural load

0 50 100 150

APPROXIMATE DOWNSTREAM DISTANCE, IN MILES

200

DISSOLVED SOLIDS, IN THOUSANDS OF TONS

400

300

200

100

0

250

350

150

50

250

09165000

09166500

San Miguel River

09171100

Paradox Valley

09169500

09180000

Colorado-Utah Stateline

Dolores River

Figure 17. Predicted downstream accumulation of dissolved solids for the Dolores River, from the Upper Colorado River Basin (UCRB) 

SPARROW model of dissolved-solids transport, for 1991. 
Selection of Water Year 1991 37

Sinbad Valley

Paradox Valley

Glenwood Springs

Blue Mesa

Reservoir

Lake Granby

Dotsero Warm Springs

40°

110° 108° 106°

EXPLANATION

38°

River

Colorado

0

0

50 100 Miles

50 100 Kilometers

Adjusted predicted dissolved-solids load

Point-source imports

Crystalline and volcanic rocks

High-yield sedimentary Cenozoic rocks

Low-yield sedimentary Cenozoic rocks

High-yield sedimentary Mesozoic rocks

Low-yield sedimentary Mesozoic rocks

High-yield sedimentary Paleozoic and 

Precambrian rocks

Low-yield sedimentary Paleozoic and 

Precambrian rocks

Irrigated lands of other lithologies

Irrigated sedimentary-clastic Mesozoic lands

Irrigated sedimentary-clastic Tertiary lands

Drainage Line

Stream-monitoring site

Pie charts are sized relative to the 

percentage of the adjusted predicted 

total dissolved-solids load for the 

Grand Division. Percentage shown 

below pie charts

River

Dolores

River

Gunnison

River

Uncompahgre

River

San Miguel

09058000

4.74%

09070500

11.8%

09085100

39.7%

09093700

46.2%

09152500

33.1%

09144250

17.1%

09149500

12.4%

09171100

7.53%

09169500

1.86%

09180000

12.9%

09180500

98.8%

09163500

84.1%

Grand Division

100%

Study area

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990

Hydrography from USGS Enhanced River Reach File 2.0, 1:500,000 scale, 2002 

and USGS National Hydrography dataset, 2008

Albers Equal Area Conic projection, North American Datum of 1983

Figure 18. Distribution of dissolved-solids loads by source for selected streams in the Grand Division, from the Upper Colorado River 

Basin (UCRB) SPARROW model of dissolved-solids transport, for 1991. 
38 Spatially referenced statistical assessment of dissolved-solids load sources 

Green Division

Thirty-seven percent of the dissolved-solids load for 

the Green Division, defined as the Green River drainage 

basin upstream of the confluence with the Colorado River, is 

associated with irrigated agricultural lands (fig. 19). Different from the Grand Division, irrigated sedimentary-clastic 

Tertiary lands are the largest contributing agricultural source at 

22 percent, and irrigated sedimentary-clastic Mesozoic lands 

make up the remaining 15 percent. Dissolved-solids loads 

from the Yampa and White Rivers provide most of the load in 

the Green Division associated with natural geological sources. 

Low-yield sedimentary Cenozoic rocks are the most dominant 

source in the Yampa River, and high-yield sedimentary Paleozoic and Precambrian rocks are the most dominant source in 

the White River. 

San Juan Division

The San Juan Division, which terminates at monitoring 

site 09380000, Colorado River at Lees Ferry, Arizona, is comprised of the San Juan River and the Colorado River below 

the confluence with the Green River (fig. 20). Below the 

confluence, the dissolved-solids load in the Colorado River is 

apportioned as 30 percent irrigated sedimentary-clastic Mesozoic lands, 15 percent irrigated sedimentary-clastic Tertiary 

lands, 12 percent point-source imports, and 47 percent associated with the seven geologic source groups. This distribution 

of load between the 11 sources is nearly identical for the 

Lees Ferry, Arizona, monitoring site. The load at Lees Ferry, 

Arizona, is comprised of about 45 percent irrigated agriculture 

and 57 percent natural sources, which does not include the 

apparent reservoir point-source load for Lake Powell. 

Comparison of Dissolved-Solids Load Predictions 

with other Studies at Selected Locations in the 

UCRB

Iorns and others (1965) determined the probable amounts 

of dissolved solids, computed from the annual average of 

1914–1957 and adjusted to 1957 watershed development conditions, from natural sources and from human activities (Iorns 

and others, 1965), or agricultural sources, for selected stream 

locations in the three divisions of the UCRB. The locations in 

the UCRB presented in Iorns and others (1965) by Division 

are contained in tables 10–12. For each of these locations, 

where available, dissolved-solids loads from Iorns and others 

(1965), and the 1991 UCRB model, are presented along with 

the contributions associated with natural and agricultural 

sources for the two periods. Associated irrigated acres for each 

location and period along with associated streamflows provide 

further information when comparing the two load components. 

Table 13 contains measured precipitation at locations within 

the UCRB presented in Iorns and others (1965) for the two 

periods. Because dissolved-solids loads in streams are variable 

from year to year, the precipitation, streamflow, and watershed 

conditions, specifically the amount of irrigated acres, should 

provide perspective when comparing the dissolved-solids 

loads for the two periods. The period of 1914–1957 predates the large-scale reservoir projects of the UCRB, such as 

Fontenelle and Flaming Gorge Reservoirs on the Green River, 

Navajo Reservoir on the San Juan River, and Lake Powell 

on the Colorado River, which as discussed earlier, affect the 

transport of dissolved solids through the basin. 

For the selected locations in the Grand Division, the 

dissolved-solids loads for 1991 are generally less than those 

for the period 1914–1957 (table 10). Precipitation at the locations presented in Iorns and others (1965) indicates that 1991 

was less than the average for the period 1914–1957. Similarly, 

the annual average streamflows for the period 1914–1957 

are larger than the annual total for 1991 at all but one of the 

locations. The total load at Colorado River below Grand 

Valley Divide, near Palisade, Colorado, in 1991 is affected 

by the Government Highline Canal and Grand Valley diversions; however, the percentage of load apportioned between 

natural and agricultural sources is representative of the flows 

upstream of the diversions because of the manner in which 

the SPARROW model treats diversions. In general, the 1991 

results attribute more of the dissolved-solids loads for the 

Grand Division above the Gunnison River to natural sources 

than those of Iorns and others (1965). 

Table 11 contains the stream locations presented in 

Iorns and others (1965) for the Green Division, along with 

corresponding dissolved solids and streamflow information 

for the two periods. Dissolved-solids loads were greater for 

the 1914–1957 period at all but one location, and the average streamflows for 1914–1957 were greater than 1991 at all 

locations. Precipitation differences between the 1914–1957 

period and 1991 in the Green Division ranged from –2.64 in. 

to +4.45 in. at the selected locations. For all locations of the 

Green Division examined by Iorns and others (1965), with the 

exception of the White River near Watson, Utah, and the San 

Rafael River near Castle Dale, Utah, the amount of irrigated 

acres increased in 1991, on average by about 50 percent. 

Interestingly, when compared with Iorns and others (1965), 

the predicted contribution of dissolved solids attributed to 

agriculture for 1991 for locations on the Green River remained 

about the same, aside from the most upstream location, Green 

River near La Barge, Wyoming, which increased from 38 to 

60 percent. The amount of irrigated acres at the most downstream location represented in table 11, Green River at Green 

River, Utah, increased by more than 35 percent by 1991, and 

the contribution of dissolved solids associated with irrigated 

agriculture was predicted to be between 37 and 42 percent, 

roughly equal to the 39 percent from Iorns and others (1965). 

Improved irrigation delivery practices, such as sprinklers, 

along with water development projects, likely play a role in 

balancing the irrigated agricultural contribution.

Dissolved-solids data for selected locations in the San 

Juan Division for the 1914–1957 period (Iorns and others) and 

1991 are contained in table 12. The number of irrigated acres 

in the San Juan Division, as measured at locations contained 
Selection of Water Year 1991 39

111° 108°

42°

39°

0 50 100 Kilometers

0 50 100 Miles

Fontenelle

Reservoir

Flaming

Gorge

Reservoir

Steamboat

Springs

Meeker Dome

Green

River

EXPLANATION

Adjusted predicted dissolved-solids load

Point-source imports

Crystalline and volcanic rocks

High-yield sedimentary Cenozoic rocks

Low-yield sedimentary Cenozoic rocks

High-yield sedimentary Mesozoic rocks

Low-yield sedimentary Mesozoic rocks

High-yield sedimentary Paleozoic and 

Precambrian rocks

Low-yield sedimentary Paleozoic and 

Precambrian rocks

Irrigated lands of other lithologies

Irrigated sedimentary-clastic Mesozoic lands

Irrigated sedimentary-clastic Tertiary lands

Drainage Line

Stream-monitoring site

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990

Hydrography from USGS Enhanced River Reach File 2.0, 1:500,000 scale, 2002 

and USGS National Hydrography dataset, 2008

Albers Equal Area Conic projection, North American Datum of 1983

Pie charts are sized relative to the 

percentage of the adjusted predicted 

total dissolved-solids load for the 

Green Division. Percentage shown 

below pie chart

Big

Sandy

River

Blacks Fork

Little

Snake

River

River

Yampa

White

River

Duchesne River

Price

River

San Rafael

River

09315000

94.3%

09261000

50.3%

09302000

10.3%

09234500

28.4%

09217000

24.6%

09224700

5.74%

09209400

15.0%

09260000

4.77%

09304500

7.73%

09216050

4.31%

Green River

Division

100%

San Rafael

River Basin

5.32%

White River

Basin

14.5%

Yampa River

Basin

19.1%

Green River above

Warren Bridge

1.06%

Study area

Figure 19. Distribution of dissolved-solids loads by source for selected streams in the Green Division, from the Upper Colorado River 

Basin (UCRB) SPARROW model of dissolved-solids transport, for 1991. 
40 Spatially referenced statistical assessment of dissolved-solids load sources River

Devil

Dirty

Muddy

Creek

0 50 100 Kilometers

0 50 100 Miles

EXPLANATION

Adjusted predicted dissolved-solids load

Point-source imports

Crystalline and volcanic rocks

High-yield sedimentary Cenozoic rocks

Low-yield sedimentary Cenozoic rocks

High-yield sedimentary Mesozoic rocks

Low-yield sedimentary Mesozoic rocks

High-yield sedimentary Paleozoic and 

Precambrian rocks

Low-yield sedimentary Paleozoic and 

Precambrian rocks

Irrigated lands of other lithologies

Irrigated sedimentary-clastic Mesozoic lands

Irrigated sedimentary-clastic Tertiary lands

Drainage Line

Stream-monitoring site

36°

38°

40°

112° 110° 108°

Pagosa Springs

Navajo Reservoir

San Juan

River

River Colorado

Lake

Powell

Fremont River

Base from U.S Census Bureau digital data, 1:100,000 scale, 1990

Hydrography from USGS Enhanced River Reach File 2.0, 1:500,000 scale, 2002 

and USGS National Hydrography dataset, 2008

Albers Equal Area Conic projection, North American Datum of 1983

River

Escalante

09380000

09333500 09330000

09346400

09379500 09371010 09365000

From Grand and

Green Divisions

Muddy Creek

Basin

Escalante River

Basin

Study area

Figure 20. Distribution of dissolved-solids loads by source for selected streams in the San Juan Division, from the Upper Colorado 

River Basin (UCRB) SPARROW model of dissolved-solids transport, for 1991. 
Selection of Water Year 1991 41

Annual streamflow Dissolved-solids load

Natural 1Agricultural

Location Drainage 

area

(square 

miles)

2Acres 

irrigated, 

1957

Acres 

irrigated, 

1991

3Average 

1914–1957

(acre-feet)

1991 total

(acre-feet)

3Average 

1914–1957 

total

(tons)

Monitored 

1991 total

(tons)

4Average 

1914–1957

(percent)

Adjusted 

predicted

[predicted]

1991

(percent)

4Average 

1914–1957

(percent)

Adjusted

predicted 

[predicted]

1991

(percent)

Colorado River at Hot Sulphur 

Springs, CO

825 15,700 15,400 176,800 118,000 18,300 13,400 62 91

[90]

38 9

[10]

5Colorado River near 

Glenwood Springs, CO

4,556 83,700 83,500 1,738,000 1,990,000 639,200 519,000 81 93

[92]

19 8

[9]

Roaring Fork at Glenwood 

Springs, CO

1,451 31,400 34,200 980,200 741,000 299,900 267,000 69 90

[92]

31 12

[11]

Colorado River near Cameo, 

CO

8,050 163,400 172,000 2,998,000 2,170,000 1,578,000 1,330,000 76 88

[88]

24 14

[13]

Plateau Creek near Cameo, CO 592 29,100 31,300 170,200 71,400 66,100 36,600 43 50

[52]

57 60

[62]

6Colorado River below Grand 

Valley Divide, near Palisade, CO 

8,753 192,500 205,000 3,168,200 1,550,000 1,644,100 828,000 76 86

[86]

24 16

[16]

1 Man-caused in Iorns and others (1965).2 From Iorns and others (1965).3 Average adjusted for 1957 watershed development conditions from Iorns and others (1965).4 Computed from Iorns and others (1965).5 Colorado River above Glenwood Springs in Iorns and others (1965).6 Colorado River Basin above Gunnison River in Iorns and others (1965).

Table 10. Dissolved-solids loads and contributions from natural and agricultural sources for the 1914–1957 period adjusted for 1957 and 1991 for the Grand Division above the Gunnison River, Colorado. [An explanation of adjusted predicted and predicted 1991 dissolved-solids loads can be found on page 31. Sum of 1991 natural and agricultural dissolved-solids percent may not always equal 100. CO, Colorado] 
42 Spatially referenced statistical assessment of dissolved-solids load sources Annual streamflow Dissolved-solids load Natural 1Agricultural Location Contribution drainage area (square miles)

2Acres 

irrigated 1957

Acres 

irrigated, 1991

3Average 

1914–1957

(acre-feet)

1991 total

(acre-feet)

3Average 

1914–1957 total

(tons)

Monitored 1991 

total

(tons)

4Average 

1914–1957

(percent)

Adjusted 

predicted 

[predicted] 

1991

(percent)

4Average 

1914–1957

(percent)

Adjusted 

predicted 

[predicted] 

1991

(percent)

Green River near La Barge, WY 3,910 NA 242,000 NA 1,003,418 NA 263,000 NA 41

[41]

NA 59

[60]

Green River near Fontenelle, WY 23,970 131,600 NA 1,166,000 NA 294,000 NA 62 NA 38 NA

Green River near Green River, WY 9,740 151,600 269,000 1,295,000 973,736 504,000 424,000 63 61

[63]

37 39

[37]

Green River near Greendale, UT 15,390 255,400 397,000 1,645,000 895,547 847,400 593,000 63 62

[62]

37 38

[38]

5Green River Basin above Yampa River 16,850 258,400 399,000 NA NA 967,100 543,000 67 64

[64]

33 36

[36]

6Yampa River near Maybell, CO 3,410 51,300 69,300 1,152,000 872,380 218,800 251,000 86 68

[68]

14 33

[33]

7Little Snake River near Lily, CO 3,730 20,400 32,200 450,600 237,968 120,500 76,500 75 81

[81]

25 19

[19]

5Yampa River Basin 28,000 73,700 106,000 NA NA 405,800 336,000 85 72

[74]

15 28

[26]

Duchesne River near Randlett, UT 4,250 135,700 145,000 555,700 86,152 460,200 125,000 29 47

[29]

71 55

[74]

White River near Watson, UT 4,020 29,900 25,400 553,500 418,236 330,600 254,000 50 85

[85]

50 16

[15]

5Green River at Ouray, UT 31,540 530,100 714,000 4,508,000 NA 2,407,000 1,010,000 61 61

[62]

39 39

[39]

Green River at Green River, UT 40,890 550,600 757,000 4,558,000 2,432,529 2,652,000 1,670,000 61 63

[59]

39 37

[42]

5San Rafael River near Castle Dale, UT 2927 36,000 27,900 96,350 NA 171,300 83,600 33 55

[48]

77 45

[52]

1 Man-caused in Iorns and others (1965).2 From Iorns and others (1965).3Average adjusted for 1957 watershed development conditions from Iorns and others (1965).4 Computed from Iorns and others (1965).5 No gage at location in 1991; used adjusted predicted load.6 Yampa River at bridge on county road near Maybell, CO; in Iorns and others (1965).7 Little Snake River at bridge on State Highway 318, near Lily, CO; in Iorns and others (1965).

Table 11. Dissolved-solids loads and contributions from natural and agricultural sources for the 1914–1957 period adjusted for 1957 and 1991 for the Green Division of the Upper Colorado River Basin.[An explanation of adjusted predicted and predicted 1991 dissolved-solids loads can be found on page 31. Sum of 1991 natural and agricultural dissolved-solids percent may not always equal 100. CO, Colorado; UT, Utah; WY, Wyoming; NA, data not available]
Selection of Water Year 1991 43

Table 12. Dissolved-solids loads and contributions from natural and agricultural sources for the 1914–1957 period adjusted for 1957 and 1991 for the San Juan Division of the Upper Colorado River Basin.[An explanation of adjusted predicted and predicted 1991 dissolved-solids loads can be found on page 31. Sum of 1991 natural and agricultural dissolved-solids percent may not always equal 100. CO, Colorado; NM, New Mexico; UT, Utah; WY, Wyoming; NA, data not available] 

Annual streamflow Dissolved-solids load

Natural 1Agricultural

Location

Contribution 

drainage 

area

(square 

miles)

2Acres 

irrigated 

1957

Acres 

irrigated, 

1991

3Average 

1914–1957

(acre-feet)

1991 total

(acre-feet)

3Average 

1914–1957 

total

(tons)

Monitored 

1991 total

(tons)

4Average 

1914–1957

(percent)

Adjusted 

predicted 

[predicted]

(percent)

4Average 

1914–1957

(percent)

Adjusted 

predicted 

[predicted] 

1991

(percent)

San Juan River near Carracas, CO 1,230 NA 15,800 NA 464,000 NA 86,200 NA 82

[84]

NA 18

[17]

San Juan River near Arboles, CO 21,340 13,300 NA NA NA 77,000 NA 83 NA 17 NA

San Juan River near Archuleta, NM 3,260 NA 73,100 NA 519,000 NA 116,000 NA 73

[66]

NA 27

[34]

San Juan River near Blanco, NM 23,560 61,600 NA 1,100,000 NA 187,000 NA 79 NA 21 NA

San Juan River near Bluff, UT 23,000 206,400 307,000 2,028,000 1,080,000 997,000 655,000 71 55

[51]

29 46

[49]

5San Juan River Basin 224,900 206,400 307,000 NA NA 1,073,000 915,000 73 56

[52]

27 45

[49]

Dirty Devil River above Poison Spring 

Wash, near Hanksville, UT

4,160 NA 29,000 NA 41,000 NA 67,700 NA 58

[59]

NA 43

[42]

Dirty Devil River near Hite, UT 24,360 23,300 NA 673,890 NA 197,600 NA 72 NA 28 NA

5Escalante River at mouth, near 

Escalante, UT

22,010 7,000 3,800 761,720 NA 25,200 16,200 68 51

[53]

32 49

[48]

Colorado River at Lees Ferry, AZ 108,000 1,410,000 1,727,026 12,710,000 87,080,000 8,642,000 85,760,000 60 857

[59]

40 845

[43]

1 Man-caused in Iorns and others (1965).2 From Iorns and others (1965).3Average adjusted for 1957 watershed development conditions from Iorns and others (1965).4 Computed from Iorns and others (1965).5 No gage at location in 1991; used adjusted predicted load.6 Average 1949–1957.7 Average 1951–1955.8 1991 apparent reservoir point-source load and storage decrease-related streamflow for Lake Powell removed.
44 Spatially referenced statistical assessment of dissolved-solids load sources 

in table 12, has increased substantially since 1957, except for 

the Escalante River at mouth near Escalante, Utah. The 1991 

results indicate a slight increase in the agricultural component of the dissolved-solids load upstream of the San Juan 

River near Blanco, New Mexico, and considerable increases 

below. The 1991 dissolved-solids loads were less than those 

for the 1914–1957 period at all locations except the San Juan 

River near Carracas, Colorado. The agricultural component 

of dissolved solids for the Dirty Devil and the Escalante 

Rivers increased by about 15 percent. However, the average 

1914–1957 and 1991 monitored total loads for the Dirty Devil 

River differed considerably, which makes the results difficult 

to compare. 

Iorns and others (1965) attributed about 60 percent of 

the total load for the UCRB as measured at Lees Ferry, Arizona, to natural sources, and 40 percent to agriculture. After 

removing the apparent reservoir point-source load attributed 

to Lake Powell, the adjusted predicted 1991 results suggest 

that 57 percent of the total load at the Colorado at Lees Ferry, 

Arizona, site is from natural sources and 45 percent is from 

agricultural sources. The predicted 1991 results are nearly 

identical with 59 percent from natural sources and 43 percent 

from agricultural sources. Because of the bootstrap prediction 

routine applied by the SPARROW model, the sum of mass, 

in this case dissolved-solids load, attributed to the individual 

sources may exceed the total predicted load. For the entire 

UCRB, the amount of irrigated land has increased by more 

than 315,000 acres, or 22 percent, from 1957 to 1991 (David 

Eckhart, Bureau of Reclamation Remote Sensing and Geographic Information Group, written commun., September 28, 

2006). However, the dissolved-solids load associated with irrigated agriculture has only increased by about 2 percent. The 

apparent reservoir point-source load attributed to Lake Powell 

was determined to be 723,000 tons (table 4) and was included 

in the point-source imports parameter, which was also used to 

represent the saline springs, in an effort to retain balance in the 

model. This apparent increase in dissolved solids is not related 

to 1991 conditions of dissolved-solids sources and transport 

in the UCRB, and therefore, was removed from the results. 

The minor apparent reservoir point-source loads associated 

with the other five reservoirs (table 4) were very small, and no 

effort was made to remove them from the results presented. 

Results from the southwestern U.S. dissolved-solids 

SPARROW model (Anning and others, 2007), which included 

the UCRB, were compared with results generated from this 

study (table 14). The predicted dissolved-solids loads at 

selected locations generally are similar except for the Colorado River above the Gunnison River. The 1991 adjusted 

predicted load of 886,000 tons generally agrees with the 

nearest upstream monitored load of 828,000 tons at Colorado 

River below Grand Valley Diversion, near Palisade, Colorado, 

which is approximately 15 mi upstream. The southwestern 

U.S. model predicts the natural component of the dissolvedsolids load to be greater at all but two of the selected locations: 

Colorado River above Gunnison River, and White and Yampa 

Rivers above Green River. For the entire UCRB, as measured 

Table 13. Water year 1991 total precipitation and average annual 

total precipitation for 1914–1957 at index stations in the Grand, 

Green and San Juan Divisions of the Upper Colorado River Basin.

[AZ, Arizona; CO, Colorado; NM, New Mexico; UT, Utah; WY, Wyoming; 

NA, data not available]

1

1991 total 

precipitation

(inches)

2

Average annual total 

precipitation, 1914–1957

(inches)

Grand Division

Spicer, CO 14.1 13.02

Estes Park, CO 13.33 17.55

Fraser, CO NA 18.82

Idaho Springs, CO NA 15.12

Dillon, CO 13.1 18.29

Leadville, CO 10.4 19.47

Shosone, CO 22.23 17.78

Collbran, CO 14.33 15.24

Grand Junction, CO 8.89 8.86

Cedaredge, CO 12.79 11.94

Paonia, CO 12.82 15.47

Montrose, CO 9.53 9.62

Gunnison, CO NA 10.79

Pitkin, CO NA 16.4

Ames, CO NA 25.57

Rico, CO 30.72 26.35

Moab, UT 6.57 9.32

Green Division

Lander, WY 14.52 13.84

Encampment, WY 12.06 13.78

Border, WY 16.71 12.98

Evanston, WY 14.43 12.09

Green River, WY 10.94 8.34

Bedford, WY 24.34 19.89

Hayden, CO 17.97 16.08

Steamboat Springs, CO 23.22 24.19

Meeker, CO 16.22 16.55

Vernal, UT 7.8 8.46

Elkhorn Ashley, UT NA 13.73

Duchesne, UT 9.21 9.45

Snake Creek, UT 21.06 23.09

Spanish Fork, UT 19.13 17.54

Moroni, UT 7.94 10.58

Green River, UT 7.24 6.22

San Juan Division

Silverton, CO 28.12 23.52

Hermit, CO 15.85 16.19

Ignacio, CO 13.63 15.4

Fort Lewis, CO 15.69 18.56

Aztec Ruins, NM 9.54 9.56

Regina, NM NA 16.3

Crownpoint, NM NA 10.57

Blanding, UT 11.47 12.95

Emery, UT NA 7.48

Hanksville, UT 3.47 5.17

Piute Dam, UT NA 8.17

Orderville, UT 12.43 14.58

Lees Ferry, AZ NA 5.94

1

 From Western Region Climate Center (2008).

2

 From Iorns and others (1965).
Selection of Water Year 1991 45

at Lees Ferry, Arizona, the southwestern U.S. model attributes 

approximately 68 percent of the dissolved-solids load to natural sources, which is about 10 percent higher than the results 

of the 1991 UCRB model. 

Although both of these efforts developed a dissolvedsolids SPARROW model following a similar conceptual 

model of the sources and transport of dissolved solids, there 

are fundamental differences associated with the spatial extent, 

the datasets used, and periods of the studies that should be 

considered when comparing their results. The UCRB was 

represented by more than 10,000 stream reaches in the UCRB 

model. The network of the southwestern U.S. model contained 

about 5,200 reaches, 1,986 of which were within the UCRB. 

The finer scale of the UCRB model allowed for a more 

detailed representation of the various sources and landscape 

transport characteristics within each incremental catchment. 

Geology in the southwestern U.S. model was represented 

Dissolved-solids load

Natural Agricultural

Location

Area

(square 

miles)

Median 

1974–2003 

total

(tons)

Monitored 

1991 total

(tons)

Adjusted 

predicted 

[predicted]

1991 total

(tons)

1

Median 

1974–

2003

(percent)

Adjusted 

predicted 

[predicted] 

1991

(percent)

1

Median 

1974–

2003

(percent)

Adjusted 

predicted 

[predicted] 

1991

(percent)

2 Colorado River above Gunnison River 9,860 3,170,000 828,000 886,000

[690,000]

75 85

[86]

25 18

[17]

3 Gunnison River above Colorado River 8,020 1,173,000 1,080,000 830,000

[544,000]

55 32

[45]

45 72

[61]

Colorado River above Green River 26,100 3,421,000 NA 2,510,000

[1,800,000]

7

68 52

[62]

7

32 50

[42]

4 Green River above Yampa River 16,850 931,000 NA 543,000

[556,000]

79 64

[64]

21 36

[36]

5 White and Yampa Rivers above Green 

River

13,350 685,000 NA 591,000

[634,000]

61 77

[78]

39 23

[22]

Green River above Colorado River 44,500 2,478,000 NA 1,760,000

[2,050,000]

7

73 63

[58]

7

27 38

[42]

6 San Juan River below Mancos River 13,740 574,000 530,000 695,000

[715,000]

67 64

[59]

33 37

[42]

San Juan River above Colorado River 25,120 744,000 NA 915,000

[972,000]

7

61 56

[52]

7

39 45

[49]

Upper Colorado River Basin 108,000 8

6,393,000 95,760,000 95,300,000

[9

4,930,000]

7,868 9

57

[9

59]

7,832 9

45

[9

43]

1

 From Anning and others (2007).

2

 Colorado headwaters hydrologic accounting unit 140100 in Anning and others (2007). 

3

 Gunnison hydrologic accounting unit 140200 in Anning and others (2007). 

4

 Upper Green hydrologic accounting unit 140401 in Anning and others (2007). 

5

 White-Yampa hydrologic accounting unit 140500 in Anning and others (2007). 

6

 Upper San Juan hydrologic accounting unit 140801 in Anning and others (2007). 

7

 Computed from Anning and others (2007). 

8

 Includes Paria River. 

9

 1991 apparent reservoir point-source load for Lake Powell removed.

Table 14. Dissolved-solids loads and contributions from natural and agricultural sources for 1974–2003 and 1991 for selected locations 

in the Upper Colorado River Basin. 

[An explanation of adjusted predicted and predicted 1991 dissolved-solids loads can be found on page 31. Sum of 1991 natural and agricultural dissolved-solids 

percent may not always equal 100. NA, data not available] 

by the King and Beikman (1974) 1:2,500,000-scale bedrock 

geology map of the United States. For the UCRB model, 

the geologic unit names of the King and Beikman (1974) 

1:2,500,000-scale bedrock geology map were assigned to the 

finer scale units defined by the various 1:500,000-scale state 

geologic maps. Both modeling efforts incorporated a similar 

method for grouping the geologic units into distinct geologic 

source groups; however, this did not ensure that units of the 

same name were placed in the same geologic source groups. 

The UCRB model contained three sources of irrigated agricultural lands, classified by lithology, and the agricultural lands 

in southwestern U.S. model were represented by two sources: 

cultivated and pasture lands. The UCRB model was developed 

from monitored dissolved-solids loads from water year 1991 

and the southwestern U.S. model was developed from median 

annual dissolved-solids loads from stations with about 10 or 

more years of record during the period 1974–2003. Results 
46 Spatially referenced statistical assessment of dissolved-solids load sources 

from the southwestern U.S. effort generally describe the conditions of dissolved solids for the period 1974–2003 (Anning 

and others, 2007). Results from the UCRB model describe the 

conditions of dissolved solids in water year 1991. 

Application of Results from Water year 1991 to 

other Years

The UCRB dissolved-solids SPARROW model was 

calibrated using monitoring data along with precipitation 

and evaporative transpiration estimates for water year 1991. 

Potential irrigated lands data for the period 1990–1995 (David 

Eckhart, Bureau of Reclamation Remote Sensing and Geographic Information Group, written commun., September 28, 

2006) were used in defining the irrigated lands sources. The 

remaining parameters contained in this model are not specific 

to any period. As previously described, 1991 was selected 

on the basis of available monitoring data, precipitation that 

was similar to the 30-year average, streamflows similar to 

period of record averages in the basin, and the vintage of 

various geospatial data. A single year was chosen to provide 

a temporal reference point of dissolved-solids sources and 

transport in the UCRB to resource managers. Because the 

hydrologic conditions for water year 1991 were near normal, 

as determined through comparison with the 30-year average 

precipitation and selected long-term streamflow records, the 

results of this modeling effort can be considered representative 

of an average water year. Although this is generally the case 

when considering the entire UCRB, there are locations within 

the basin during water year 1991 that deviated from average 

precipitation conditions as shown in figure 5. When evaluating predicted dissolved-solids loads under the assumption of 

average climatic conditions for specific locations, particularly 

in smaller basins, landscape transport characteristics, particularly the 1991 precipitation estimate, should be examined. 

When comparing predicted dissolved-solids loads from this 

model with current conditions in the UCRB, consideration of 

any changes made within watersheds since 1991, specifically 

to the irrigated lands and point sources, is required. The results 

generated from this model are also influenced by the manner 

in which reservoirs were managed during the 1991 water year. 

As further described below, the estimated coefficients generated for the various sources and landscape transport characteristics during calibration of the 1991 SPARROW model are 

representative of basin-averaged conditions. 

Limitations and Uncertainty 

When interpreting the results and(or) findings of this 

investigation, as with any modeling exercise, specific limitations and uncertainties associated with the methodologies, data, and techniques used, need to be considered. This 

report documents the development of a model describing the 

sources and transport of dissolved solids in the UCRB using 

a nonlinear weighted least squares regression technique. The 

results of the model include predictions of water year 1991 

dissolved-solids loads for more than 10,000 unique stream 

reaches with catchment sizes ranging from 1 to 78 mi2. The 

estimated coefficients, which represent basin-averaged conditions generated during the nonlinear least-squares calibration, 

provide understanding into the role the 11 defined sources and 

7 defined landscape transport characteristics play in generating 

and transporting dissolved solids to streams in the UCRB. 

The UCRB is a large regional scale drainage. Simplification of real world complexity, especially with natural processes, is necessary when modeling at nearly all scales. This 

dissolved-solids SPARROW model examined the contributing 

108,000 mi2 drainage of the UCRB and required a number 

of simplifications, such as grouping 34 geologic units into 

seven geologic source groups. Moreover, statistically related 

assumptions were required to apply the techniques of the 

SPARROW surface-water quality model. In general, there 

are three sources of uncertainty to consider when evaluating 

the results of this modeling effort: (1) parameter uncertainty 

attributed to finite sample size, (2) model uncertainty attributed to unaccounted factors affecting contaminant transport, 

and (3) measurement error. 

SPARROW is a statistically based model used for examining constituent sources, transport, and fate. Because it is not 

a physical process model, there are some required assumptions 

related to the behavior of the contaminant being modeled. 

The nonlinear mathematical representation of the SPARROW 

model, given by equation 1, is assumed to represent mathematically the means by which the contaminant of interest is 

produced and transported, and how it evolves. From the results 

of this modeling effort, particularly the yield R2

 and RMSE 

values, the model appears to accurately represent a large portion of these processes as related to dissolved solids; however, 

model uncertainty, evident in the results, indicates there are 

complexities that are not entirely captured. Model uncertainty 

theoretically decreases with additional statistically significant 

explanatory parameters. There are issues related to scale, as 

describe below, that have some effect on model uncertainty. 

Statistical techniques often assume that datasets used in the 

analysis possess statistical characteristics, such as normal 

unbiased distributions, that are fundamental to applying the 

technique. The nonlinear weighted least squares technique of 

SPARROW contains a number of such assumptions. Observation data used for calibration are assumed to be spatially 

distributed in an unbiased manner, that is, the network of 

monitoring sites represents all sources of the contaminant 

completely and in an equal manner. Although the 218 monitoring sites used in this investigation, as shown in figure 1, do 

not appear to fit this assumption completely, appendix 2 can 

be used to identify how the sources are represented within 

the 218 unique calibration reaches. As previously discussed, 

the coefficient estimates generated in the nonlinear least 

squares calibration are valid only asymptotically. Parameter 

uncertainty decreases with larger sample sizes. Therefore, it is 

assumed that large sample sizes generate results that are unbiased and possess standard normal distributions. The resampled 
Summary 47

bootstrap analysis indicated a sufficiently large enough sample 

size to meet these criteria. For a more detailed discussion of 

the statistical assumptions embedded in the statistical techniques of the SPARROW model, refer to Schwarz and others 

(2006). 

Scale limitations are important to consider when examining the estimated coefficients and predicted loads generated 

from this modeling effort. Model parameters used in model 

calibration were computed from geospatial data of varying 

scales as shown in table 3. When analyzing model parameters 

for specific locations, it is important to consider the scale from 

which they were generated. Catchments generated from the 

modified NHD stream reach network ranged from 1 to 78 mi2. 

Calibration catchments, defined as reaches bound by monitoring sites or a combination of monitoring sites and headwater 

reaches, ranged from 4 to 14,200 mi2. Predictions of dissolvedsolids loads were made for the more than 10,000 stream 

reaches of the network. Generally, predictions for basins with 

total drainage areas and source representations within the 

range of the calibration reaches, as shown in appendix 2, and 

assuming an unbiased spatial distribution of monitoring sites, 

possess less uncertainty in the predicted loads than basins outside the range of calibration data, including independent variables. Uncertainty also would be expected to increase as basin 

size decreases and model required simplifications become 

less valid. At finer scales, local influences on dissolved-solids 

loading to streams not represented in the model, such as smallscale water developments that are common in many areas, can 

cause large differences between model-generated predicted 

loads and actual loads. When considering limitations related to 

scale, model required simplifications become increasingly sensitive especially when examining small watersheds. Because 

coefficients are basin-wide averages, finer scales increase the 

potential for local geology to possess higher or lower yields 

than those represented by the coefficients assigned to the 

seven geologic source groups. 

Uncertainties associated with measurement error are 

inherently contained within both the dependent and independent variables. Measurement errors are difficult to quantify 

and generally cannot be removed with more observations or 

variables. Measurement errors associated with the independent 

variables can lead to biased coefficients. Measurement errors 

associated with the dependent variable, water year 1991 dissolved-solids loads for the 218 monitoring sites, are difficult 

to quantify fully because there are a number of error sources. 

Dissolved-solids loads are derived from analyses of dissolvedsolids concentrations, specific-conductance measurements, 

daily mean streamflow computations, and statistical modeling. Data from all 218 monitoring sites were measured and 

analyzed by USGS personnel in accordance with USGS standards and techniques as outlined in Rantz and others (1982), 

U.S. Geological Survey (variously dated), and Fishman and 

Friedman (1989). Using the measurements and lab analyses 

obtained at the 218 monitoring sites, annual loads were determined statistically as described in Anning and others (2007), 

and by using the LOADEST computer program of Runkel and 

others (2004) adapted for use with S-Plus (Insightful Corporation, 2005) statistical software (Dave Lorenz, U.S. Geological 

Survey, written commun., 2005) based upon the methods of 

Runkel and others (2004). The amount of uncertainty associated with measurement error is difficult to quantify; however, 

because standard procedures were adhered to for the measurements and lab analyses, measurement errors can be assumed to 

be generally equivalent for each site. 

As has been stressed throughout this report, the results of 

this dissolved-solids SPARROW modeling effort are representative of basin-averaged conditions, as defined by the 218 

calibration reaches. Homogeneous yields and properties of the 

defined sources are assumed. The specified landscape transport characteristics for the various sources provide a means 

to differentiate between similar sources in different locales. 

However, it is possible that geologic sources in some basins 

do not yield the same amount of dissolved solids as in other 

basins even under similar landscape transport characteristic 

conditions for a variety of reasons. The presence of dissolvable minerals varies within geologic units, and the natural 

routing of water in certain landscapes is not necessarily equal 

in other landscapes. This is especially true when the efficiency 

of transport in mountainous areas versus areas dominated by 

mesas or incised canyons is considered. 

Summary

The sources and transport mechanisms of dissolved solids 

in the UCRB have been studied extensively and generally 

are well understood. The conceptual understanding derived 

from past investigations of dissolved solids in the UCRB was 

applied to the approach of the SPARROW surface-water quality model to examine dissolved-solids supply and transport. 

SPARROW is a spatially referenced regression model that 

examines the statistical relation between observed contaminant 

mass, or flux, at monitoring sites within an interconnected 

stream network to upstream watershed attributes. Attributes 

consist of sources, landscape transport characteristics, and 

aquatic transport characteristics. From the well-developed 

conceptual model of dissolved solids in the UCRB, 11 sources 

of dissolved solids were defined for examination with the 

SPARROW model; seven geologic source groups, three irrigated agricultural lands groups, and one point-source associated with saline springs. Twenty-four landscape transport 

characteristics were statistically examined for significance 

in predicting dissolved-solids loads in streams of the UCRB 

through iterative model calibration. From these 24 characteristics, seven were found to be valid predictors: precipitation, 

evaporative transpiration, soil thickness, hydrologic soil characteristic code, precipitation—maximum catchment elevation ratio, area covered by forest, and minimum catchment 

elevation. Minimum catchment elevation was specified to the 

three irrigated agricultural lands sources, and the remaining 

six characteristics were specified for the seven geologic source 

groups. The saline springs point source was not specified any 

landscape transport characteristics. 
48 Spatially referenced statistical assessment of dissolved-solids load sources 

Dissolved-solids loads for 218 monitoring sites were 

used to calibrate a dissolved-solids SPARROW model for the 

UCRB representative of water year 1991 conditions. The calibrated model generally captures the transport mechanisms that 

deliver dissolved solids to streams in the UCRB as evidenced 

by R2 and yield R2 values of 0.98 and 0.71, respectively. 

Model prediction error is approximated at 51 percent. Model 

results indicate that of the seven geologic source groups, the 

high-yield sedimentary Mesozoic rocks have the largest yield 

of dissolved solids, about 41.9 tons/mi2. Irrigated sedimentaryclastic Mesozoic lands have an estimated yield of 1,180 tons/

mi2, about two times greater than the irrigated sedimentaryclastic Tertiary lands. Coefficients estimated for the seven 

landscape transport characteristics seem to agree well with the 

conceptual understanding of the role they play in the delivery 

of dissolved solids to streams in the UCRB. 

Predictions of dissolved-solids loads for more than 

10,000 stream reaches of the stream reach network used to 

define the UCRB were generated. From these estimates, 

the downstream accumulation of dissolved solids, including natural and agricultural components, were examined for 

selected rivers. Contributions from each of the 11 dissolvedsolids sources were examined at selected locations in the 

Grand, Green, and San Juan Divisions of the UCRB. At the 

downstream boundary of the UCRB, the Colorado River at 

Lees Ferry, Arizona, monitoring site, the dissolved-solids 

contributions of irrigated agricultural lands and natural sources 

were 45 and 57 percent, respectively. The largest source of 

dissolved solids in the Grand Division was predicted to be 

irrigated sedimentary-clastic Mesozoic lands, which account 

for about 40 percent of the total load above the confluence 

with the Green River. The bulk of the agricultural loading to 

the Colorado River occurs below Grand Valley, much of which 

is attributed to the Gunnison River. The agricultural activities 

in the Gunnison River basin account for nearly 24 percent or 

598,000 tons of the total load for the Grand Division. Irrigated 

sedimentary-clastic Tertiary lands represent the dominant 

dissolved-solids source in the Green Division, with 23 percent 

of the total load above the confluence with the Colorado River.

Model predictions including the contributions of natural 

and agricultural sources for selected locations in the UCRB 

were compared with results from Iorns and others (1965) and 

Anning and others (2007). Generally, dissolved-solids loads 

for the 1991 UCRB model were less than those in Iorns and 

others (1965) and were comparable to those in Anning and 

others (2007). For the Grand Division above the Gunnison 

River, Iorns and others (1965) attributed more dissolved solids 

to agriculture than the results from this study. Anning and others (2007) generally associated a larger natural component of 

the dissolved-solids loads for nearly all selected locations in 

the UCRB than this study. At the Lees Ferry, Arizona, monitoring site, Iorns and others (1965) attributed a smaller amount 

of the dissolved-solids loads to irrigated agriculture than the 

1991 predictions of this study. This appears to be related to 

a substantial increase in the amount of irrigated acres in the 

UCRB since 1957. The agricultural components of the total 

load at Lees Ferry, Arizona, were 40 percent in Iorns and 

others (1965), 32 percent in Anning and others (2007), and 45 

percent in the 1991 UCRB model.

Results generated from this modeling exercise are bound 

by specific limitations and uncertainties associated with the 

methodologies, data, and techniques used. The limitations and 

caveats presented in this report should be considered when 

using or interpreting the results generated. 

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selected model output that is available at http://pubs.usgs.

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available at the provided URL. 
Appendices 1 and 2 51

Appendices 1 and 2
52 Spatially referenced statistical assessment of dissolved-solids load sources 

Appendix 1
Appendix 1 53

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09010500 Colorado River below Baker 

Gulch, near Grand Lake, CO

40°19'33'' 105°51'22'' 14010001 1,610 A 8.4 nd

09024000 Fraser River at Winter Park, CO 39°54'00'' 105°46'34'' 14010001 682 A 20.3 nd

09025000 Vasquez Creek at Winter Park, CO 39°55'13'' 105°47'05'' 14010001 269 A 18.7 nd

09025400 Elk Creek near Fraser, CO 39°55'09'' 105°49'31'' 14010001 48 A 9.5 nd

09026500 St. Louis Creek near Fraser, CO 39°54'36'' 105°52'40'' 14010001 859 A 18.5 nd

09032000 Ranch Creek near Fraser, CO 39°57'00'' 105°45'54'' 14010001 206 A 12.1 nd

09032100 Cabin Creek near Fraser, CO 39°59'09'' 105°44'40'' 14010001 120 A 16.4 nd

09034250 Colorado River at Windy Gap, 

near Granby, CO

40°06'30'' 106°00'13'' 14010001 11,800 A 13.2 nd

09034500 Colorado River at Hot Sulphur 

Springs, CO

40°05'00'' 106°05'15'' 14010001 13,400 A 12.8 nd

09034900 Bobtail Creek near Jones Pass, CO 39°45'37'' 105°54'21'' 14010001 239 A 9.3 nd

09035500 Williams Fork below Steelman 

Creek, CO

39°46'44'' 105°55'40'' 14010001 497 A 15.1 nd

09035700 Williams Fork above Darling 

Creek, near Leal, CO

39°47'50'' 106°01'32'' 14010001 988 A 14.7 nd

09035800 Darling Creek near Leal, CO 39°48'02'' 106°01'33'' 14010001 288 A 7.7 nd

09035900 South Fork of Williams Fork near 

Leal, CO

39°47'45'' 106°01'48'' 14010001 1,110 A 7.7 nd

09036000 Williams Fork near Leal, CO 39°50'02'' 106°03'21'' 14010001 3,130 A 9.6 nd

09037500 Williams Fork near Parshall, CO 40°00'01'' 106°10'45'' 14010001 5,170 A 15.8 nd

09038500 Williams Fork below Williams 

Fork Reservoir, CO

40°02'07'' 106°12'17'' 14010001 8,090 A 16.2 nd

09039000 Troublesome Creek near 

Pearmont, CO

40°13'03'' 106°18'45'' 14010001 1,350 A 10.2 nd

09041090 Muddy Creek above Antelope 

Creek, near Kremmling, CO

40°12'09" 106°25'19" 14010001 7,290 L nd 6.8

09041500 Muddy Creek at Kremmling, CO 40°03'37'' 106°23'51'' 14010001 26,100 A 35.7 nd

09046490 Blue River at Blue River, CO 39°27'21'' 106°01'52'' 14010002 1,440 A 35.5 nd

09046600 Blue River near Dillon, CO 39°34'00'' 106°02'56'' 14010002 6,730 A 25.1 nd

09047500 Snake River near Montezuma, CO 39°36'20'' 105°56'33'' 14010002 2,720 A 29.8 nd

09047700 Keystone Gulch near Dillon, CO 39°35'40'' 105°58'19'' 14010002 236 A 24.9 nd

09050100 Tenmile Creek below North 

Tenmile Creek, at Frisco, CO

39°34'31'' 106°06'36'' 14010002 23,800 A 60.3 nd

09050700 Blue River below Dillon, CO 39°37'32'' 106°03'57'' 14010002 21,600 A 16.6 nd

09051050 Straight Creek below Laskey 

Gulch, near Dillon, CO

39°38'23'' 106°02'23'' 14010002 859 A 28.1 nd

09052000 Rock Creek near Dillon, CO 39°43'23'' 106°07'41'' 14010002 577 A 41.5 nd

09052400 Boulder Creek at upper station, 

near Dillon, CO

39°43'41'' 106°10'22'' 14010002 532 A 37.0 nd

09052800 Slate Creek at upper station, near 

Dillon, CO

39°45'47'' 106°11'31'' 14010002 717 A 30.1 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991. 

[CO, Colorado; UT, Utah; WY, Wyoming; NM, New Mexico; AZ, Arizona; A, from Anning and others (2007); L, from Load Estimator (LOADEST) computer 

program model; nd, none determined] 
54 Spatially referenced statistical assessment of dissolved-solids load sources 

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09054000 Black Creek below Black Lake, 

near Dillon, CO

39°47'57'' 106°16'04'' 14010002 789 A 44.3 nd

09055300 Cataract Creek near Kremmling, 

CO

39°50'07'' 106°18'57'' 14010002 581 A 37.4 nd

09057500 Blue River below Green Mountain 

Reservoir, CO

39°52'49'' 106°20'00'' 14010002 28,500 A 18.3 nd

09057520 Blue River below Spruce Creek, 

near Kremmling, CO

39°57'49" 106°21'35" 14010002 19,900 L nd 10.0

09058000 Colorado River near Kremmling, 

CO

40°02'12'' 106°26'22'' 14010001 110,000 A 14.9 nd

09058500 Piney River below Piney Lake, 

near Minturn, CO

39°42'29'' 106°25'34'' 14010001 589 A 28.0 nd

09058610 Dickson Creek near Vail, CO 39°42'14'' 106°27'25'' 14010001 642 A 9.6 nd

09058700 Freeman Creek near Minturn, CO 39°41'54'' 106°26'42'' 14010001 84 A 21.8 nd

09058800 East Meadow Creek near Minturn, 

CO

39°43'54'' 106°25'34'' 14010001 115 A 24.1 nd

09059500 Piney River near state bridge, CO 39°48'00'' 106°35'00'' 14010001 8,270 A 16.7 nd

09060550 Rock Creek at Crater, CO 39°58'42'' 106°42'34'' 14010001 1,590 A 12.6 nd

09060770 Rock Creek at McCoy, CO 39°54'44'' 106°43'30'' 14010001 7,430 A 18.1 nd

09063000 Eagle River at Red Cliff, CO 39°30'30'' 106°21'58'' 14010003 3,030 A 13.0 nd

09063200 Wearyman Creek near Red Cliff, 

CO

39°31'20'' 106°19'23'' 14010003 1,070 A 16.3 nd

09063400 Turkey Creek near Red Cliff, CO 39°31'22'' 106°20'08'' 14010003 2,810 A 10.7 nd

09063900 Missouri Creek near Gold Park, 

CO

39°23'25'' 106°28'10'' 14010003 110 A 15.6 nd

09064500 Homestake Creek near Red Cliff, 

CO

39°28'24'' 106°22'02'' 14010003 765 A 17.8 nd

09064600 Eagle River near Minturn, CO 39°33'14'' 106°24'07'' 14010003 9,570 A 20.7 nd

09065100 Cross Creek near Minturn, CO 39°34'05'' 106°24'43'' 14010003 974 A 27.5 nd

09065500 Gore Creek at upper station, near 

Minturn, CO

39°37'33'' 106°16'39'' 14010003 659 A 14.8 nd

09066000 Black Gore Creek near Minturn, 

CO

39°35'47'' 106°15'52'' 14010003 1,180 A 52.3 nd

09066100 Bighorn Creek near Minturn, CO 39°38'24'' 106°17'34'' 14010003 249 A 16.3 nd

09066150 Pitkin Creek near Minturn, CO 39°38'37'' 106°18'07'' 14010003 308 A 18.4 nd

09066200 Booth Creek near Minturn, CO 39°38'54'' 106°19'21'' 14010003 445 A 25.4 nd

09066300 Middle Creek near Minturn, CO 39°38'45'' 106°22'54'' 14010003 422 A 15.1 nd

09066310 Gore Creek, lower station, at Vail, 

CO

39°38'28'' 106°23'37'' 14010003 8,540 A 12.5 nd

09066400 Red Sandstone Creek near 

Minturn, CO

39°40'58'' 106°24'03'' 14010003 346 A 13.9 nd

09067000 Beaver Creek at Avon, CO 39°37'47'' 106°31'20'' 14010003 1,330 A 27.0 nd

09067005 Eagle River at Avon, CO 39°37'54'' 106°31'19'' 14010003 33,300 A 13.9 nd

09070000 Eagle River below Gypsum, CO 39°38'58'' 106°57'11'' 14010003 143,000 A 14.8 nd

09070500 Colorado River near Dotsero, CO 39°38'38'' 107°04'38'' 14010001 381,000 A 13.0 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
Appendix 1 55

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09071300 Grizzly Creek near Glenwood 

Springs, CO

39°43'00'' 107°18'35'' 14010001 1,240 A 8.7 nd

09071750 Colorado River above Glenwood 

Springs, CO

39°33'32" 107°17'25" 14010001 519,000 L nd 2.1

09073300 Roaring Fork River above 

Difficult Creek, near Aspen, CO

39°08'28'' 106°46'25'' 14010004 1,790 A 18.5 nd

09073400 Roaring Fork River near Aspen, 

CO

39°10'48'' 106°48'05'' 14010004 3,230 A 16.6 nd

09074000 Hunter Creek near Aspen, CO 39°12'21'' 106°47'49'' 14010004 922 A 21.8 nd

09074800 Castle Creek above Aspen, CO 39°05'15'' 106°48'42'' 14010004 6,650 A 33.8 nd

09075700 Maroon Creek above Aspen, CO 39°07'25'' 106°54'17'' 14010004 13,100 A 27.4 nd

09080400 Frying Pan River near Ruedi, CO 39°21'56'' 106°49'30'' 14010004 18,900 A 19.2 nd

09081600 Crystal River above Avalanche 

Creek, near Redstone, CO

39°13'56'' 107°13'36'' 14010004 44,400 A 16.7 nd

09085000 Roaring Fork River at Glenwood 

Springs, CO

39°32'37'' 107°19'44'' 14010004 267,000 A 10.9 nd

09085100 Colorado River below Glenwood 

Springs, CO

39°33'18'' 107°20'13'' 14010005 991,000 A 14.3 nd

09086000 West Elk Creek near New Castle, 

CO

39°39'59" 107°37'35" 14010005 201 L nd 1.8

09086470 Main Elk Creek near New Castle, 

CO

39°40'41" 107°34'21" 14010005 10,800 L nd 2.7

09089500 West Divide Creek near Raven, 

CO

39°19'52'' 107°34'46'' 14010005 3,780 A 18.6 nd

09093700 Colorado River near De Beque, 

CO

39°21'45'' 108°09'07'' 14010005 1,240,000 A 11.3 nd

09095500 Colorado River near Cameo, CO 39°14'21'' 108°15'56'' 14010005 1,330,000 A 10.7 nd

09105000 Plateau Creek near Cameo, CO 39°11'00'' 108°16'02'' 14010005 36,600 A 14.2 nd

09106150 Colorado River below Grand 

Valley Diversion, near Palisade, 

CO

39°05'55'' 108°21'16'' 14010005 828,000 A 13.5 nd

09107000 Taylor River at Taylor Park, CO 38°51'37'' 106°33'58'' 14020001 5,160 A 11.6 nd

09107500 Texas Creek at Taylor Park, CO 38°50'41" 106°34'12" 14020001 1,100 L nd 2.0

09109000 Taylor River below Taylor Park 

Reservoir, CO

38°49'06'' 106°36'31'' 14020001 9,420 A 14.5 nd

09110000 Taylor River at Almont, CO 38°39'52'' 106°50'41'' 14020001 20,400 A 14.8 nd

09112500 East River at Almont, CO 38°39'52'' 106°50'51'' 14020001 37,800 A 12.3 nd

09114500 Gunnison River near Gunnison, 

CO

38°32'31'' 106°56'57'' 14020002 71,500 A 12.6 nd

09118450 Cochetopa Creek below Rock 

Creek, near Parlin, CO

38°20'08'' 106°46'18'' 14020003 5,160 A 16.2 nd

09119000 Tomichi Creek at Gunnison, CO 38°31'18'' 106°56'25'' 14020003 27,100 A 14.9 nd

09124500 Lake Fork at Gateview, CO 38°17'56'' 107°13'46'' 14020002 16,000 A 16.5 nd

09126000 Cimarron River near Cimarron, 

CO

38°15'26'' 107°32'46'' 14020002 5,410 A 17.1 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
56 Spatially referenced statistical assessment of dissolved-solids load sources 

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09128000 Gunnison River below Gunnison 

Tunnel, CO

38°31'45'' 107°38'54'' 14020002 111,000 A 11.4 nd

09128500 Smith Fork near Crawford, CO 38°43'40'' 107°30'22'' 14020002 2,380 A 22.8 nd

09132500 North Fork Gunnison River near 

Somerset, CO

38°55'33'' 107°26'01'' 14020004 31,900 A 24.4 nd

09134000 Minnesota Creek near Paonia, CO 38°52'12'' 107°30'13'' 14020004 3,500 A 32.4 nd

09135900 Leroux Creek at Hotchkiss, CO 38°47'53'' 107°43'53'' 14020004 5,050 A 32.2 nd

09143000 Surface Creek near Cedaredge, 

CO

38°59'05'' 107°51'13'' 14020005 2,210 A 27.4 nd

09143500 Surface Creek at Cedaredge, CO 38°54'06'' 107°55'14'' 14020005 1,500 A 21.1 nd

09144250 Gunnison River at Delta, CO 38°45'11'' 108°04'40'' 14020005 609,000 A 20.2 nd

09146200 Uncompahgre River near 

Ridgway, CO

38°11'02'' 107°44'43'' 14020006 50,000 A 17.8 nd

09147000 Dallas Creek near Ridgway, CO 38°10'40'' 107°45'28'' 14020006 13,800 A 19.5 nd

09147025 Uncompahgre River below 

Ridgway Reservoir, CO

38°14'17'' 107°45'31'' 14020006 81,600 A 11.9 nd

09147500 Uncompahgre River at Colona, 

CO

38°19'53'' 107°46'44'' 14020006 83,000 A 20.5 nd

09149500 Uncompahgre River at Delta, CO 38°44'31'' 108°04'49'' 14020006 263,000 A 17.0 nd

09152500 Gunnison River near Grand 

Junction, CO

38°59'00'' 108°27'00'' 14020005 1,080,000 A 16.1 nd

09153290 Reed Wash near Mack, CO 39°12'41'' 108°48'11'' 14010005 51,100 A 17.0 nd

09163500 Colorado River near ColoradoUtah State Line39°07'58'' 109°01'35'' 14010005 2,800,000 A 10.0 nd

09165000 Dolores River below Rico, CO 37°38'20'' 108°03'35'' 14030002 13,900 A 16.2 nd

09166500 Dolores River at Dolores, CO 37°28'21'' 108°29'49'' 14030002 46,700 A 12.7 nd

09166950 Lost Canyon Creek near Dolores, 

CO

37°26'46'' 108°28'07'' 14030002 889 A 34.8 nd

09169500 Dolores River at Bedrock, CO 38°18'37'' 108°53'05'' 14030002 41,000 A 37.5 nd

09171100 Dolores River near Bedrock, CO 38°21'25'' 108°49'58'' 14030002 189,000 A 44.1 nd

09172500 San Miguel River near Placerville, 

CO

38°02'33'' 108°07'54'' 14030003 41,100 L nd 3.1

09177000 San Miguel River at Uravan, CO 38°21'26'' 108°42'44'' 14030003 105,000 A 23.4 nd

09180000 Dolores River near Cisco, UT 38°47'50'' 109°11'40'' 14030004 323,000 A 32.9 nd

09180500 Colorado River near Cisco, UT 38°48'38'' 109°17'34'' 14030005 3,080,000 A 3.9 nd

09184000 Mill Creek near Moab, UT 38°33'44'' 109°30'48'' 14030005 1,090 A 16.9 nd

09186500 Indian Creek above Cottonwood 

Creek , near Monticello, UT

37°58'20" 109°31'07" 14030005 333 L nd 17.5

09209400 Green River near La Barge, WY 42°11'34'' 110°09'45'' 14040101 263,000 A 19.5 nd

09211200 Green River below Fontenelle 

Reservoir, WY

42°01'16'' 110°02'57'' 14040103 293,000 A 14.6 nd

09215550 Big Sandy River below Farson, 

WY

42°04'24'' 109°28'43'' 14040104 30,700 A 31.9 nd

09216050 Big Sandy River at Gasson 

Bridge, near Eden, WY

41°56'51'' 109°41'15'' 14040104 123,000 A 19.6 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
Appendix 1 57

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09217000 Green River near Green River, 

WY

41°30'59'' 109°26'54'' 14040106 424,000 A 16.0 nd

09224700 Blacks Fork near Little America, 

WY

41°32'46'' 109°41'34'' 14040107 101,000 A 24.8 nd

09234500 Green River near Greendale, UT 40°54'30'' 109°25'20'' 14040106 593,000 A 5.9 nd

09235600 Pot Creek above Diversions, near 

Vernal, UT

40°46'05'' 109°19'06'' 14040106 111 A 33.1 nd

09237450 Yampa River above Stagecoach 

Reservoir, CO

40°16'09'' 106°52'49'' 14050001 18,900 A 14.0 nd

09237500 Yampa River below Stagecoach 

Reservoir, CO

40°17'07'' 106°49'51'' 14050001 19,800 A 7.7 nd

09238900 Fish Creek at upper station, near 

Steamboat Springs, CO

40°28'30'' 106°47'11'' 14050001 1,020 A 22.7 nd

09239500 Yampa River at Steamboat 

Springs, CO

40°29'01'' 106°49'54'' 14050001 34,900 A 33.5 nd

09240900 Elk River above Clark, CO 40°44'36" 106°51'17" 14050001 5,690 L nd 5.5

09241000 Elk River at Clark, CO 40°43'03" 106°54'55" 14050001 6,570 L nd 20.3

09242500 Elk River near Milner, CO 40°30'53'' 106°57'12'' 14050001 11,500 L nd 4.6

09243700 Middle Creek near Oak Creek, CO 40°23'08'' 106°59'33'' 14050001 888 A 19.9 nd

09243900 Foidel Creek at Mouth, near Oak 

Creek, CO

40°23'25'' 106°59'39'' 14050001 4,220 A 19.2 nd

09245000 Elkhead Creek near Elkhead, CO 40°40'11'' 107°17'04'' 14050001 4,030 A 17.9 nd

09246920 Fortification Creek near 

Fortification, CO

40°44'38" 107°32'25" 14050001 1,170 L nd 10.5

09247600 Yampa River below Craig, CO 40°28'51'' 107°36'49'' 14050001 176,000 A 35.7 nd

09249750 Williams Fork at mouth, near 

Hamilton, CO

40°26'14'' 107°38'50'' 14050001 44,700 A 26.6 nd

09250507 Wilson Creek above Taylor Creek, 

near Axial, CO

40°18'53" 107°47'58" 14050002 1,190 L nd 11.3

09251000 Yampa River near Maybell, CO 40°30'10'' 108°01'45'' 14050002 251,000 A 32.6 nd

09253000 Little Snake River near Slater, CO 40°59'58'' 107°08'34'' 14050003 8,930 A 21.5 nd

09255000 Slater Fork near Slater, CO 40°58'57'' 107°22'56'' 14050003 5,290 A 22.3 nd

09260000 Little Snake River near Lily, CO 40°32'50'' 108°25'25'' 14050003 76,500 A 35.6 nd

09260050 Yampa River at Deerlodge Park, 

CO

40°27'06'' 108°31'28'' 14050002 277,000 A 42.8 nd

09261000 Green River near Jensen, UT 40°24'34'' 109°14'05'' 14060001 1,060,000 A 24.3 nd

09261700 Big Brush Creek above Red Fleet 

Reservoir, near Vernal, UT

40°35'20'' 109°27'53'' 14060002 4,970 A 15.4 nd

09266500 Ashley Creek near Vernal, UT 40°34'39'' 109°37'17'' 14060002 6,490 A 22.5 nd

09275500 West Fork Duchesne River near 

Hanna, UT

40°27'01'' 110°53'01'' 14060003 5,120 A 15.2 nd

09276600 West Fork Duchesne River above 

North Fork, near Hanna, UT

40°27'42" 110°50'10" 14060003 6,650 L nd 7.1

09277500 Duchesne River near Tabiona, UT 40°18'01" 110°36'06" 14060003 30,100 L nd 8.4

09277800 Rock Creek above South Fork, 

near Hanna, UT

40°33'27" 110°41'50" 14060003 521 L nd 7.3

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
58 Spatially referenced statistical assessment of dissolved-solids load sources 

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09278000 South Fork Rock Creek near 

Hanna, UT

40°32'54'' 110°41'37'' 14060003 577 A 17.8 nd

09279000 Rock Creek near Mountain Home, 

UT

40°29'36'' 110°34'39'' 14060003 4,650 A 23.8 nd

09279100 Rock Creek near Talmage, UT 40°18'40'' 110°29'36'' 14060003 8,670 A 20.5 nd

09279150 Duchesne River above Knight 

Diversion, near Duchesne, UT

40°16'14'' 110°26'31'' 14060003 40,000 A 20.7 nd

09285000 Strawberry River near Soldier 

Springs, UT

40°08'00'' 111°01'27'' 14060004 3,830 A 12.0 nd

09285900 Strawberry River at Pinnacles, 

near Fruitland, UT

40°07'38" 110°44'28" 14060004 15,400 L nd 1.9

09286100 Red Creek above reservoir, near 

Fruitland, UT

40°19'48" 110°51'43" 14060004 1,020 L nd 7.2

09286700 Currant Creek below Currant 

Creek Dam, near Fruitland, UT

40°19'51" 111°02'56" 14060004 1,270 L nd 6.3

09288000 Currant Creek near Fruitland, UT 40°12'01'' 110°54'25'' 14060004 6,730 A 10.7 nd

09288180 Strawberry River near Duchesne, 

UT

40°09'17'' 110°33'15'' 14060004 39,200 A 11.4 nd

09288400 Strawberry River below Starvation 

Reservoir, near Duchesne, UT

40°10'26" 110°25'44" 14060004 56,900 L nd 1.8

09289500 Lake Fork River above Moon 

Lake, near Mountain Home,UT

40°36'24" 110°31'35" 14060003 1,450 L nd 21.9

09292500 Yellowstone River near Altonah, 

UT

40°30'43'' 110°20'27'' 14060003 4,900 A 21.5 nd

09295000 Duchesne River at Myton, UT 40°12'01'' 110°03'47'' 14060003 49,000 A 23.1 nd

09299500 Whiterocks River near 

Whiterocks, UT

40°35'37'' 109°55'54'' 14060003 2,490 A 31.5 nd

09302000 Duchesne River near Randlett, UT 40°12'56'' 109°46'58'' 14060003 125,000 A 23.2 nd

09303000 North Fork White River at 

Burford, CO

39°59'15'' 107°36'50'' 14050005 47,100 A 7.1 nd

09303300 South Fork White River at Budges 

Resort, CO

39°50'36'' 107°20'03'' 14050005 8,050 A 14.0 nd

09303400 South Fork White River near 

Budges Resort, CO

39°51'51'' 107°32'00'' 14050005 19,700 A 11.3 nd

09304000 South Fork White River at Buford, 

CO

39°58'28'' 107°37'29'' 14050005 30,000 A 9.5 nd

09304200 White River above Coal Creek, 

near Meeker, CO

40°00'18'' 107°49'29'' 14050005 88,200 A 10.2 nd

09304500 White River near Meeker, CO 40°02'01'' 107°51'42'' 14050005 125,000 A 10.1 nd

09304800 White River below Meeker, CO 40°00'48'' 108°05'33'' 14050005 169,000 A 13.3 nd

09306007 Piceance Creek below Rio Blanco, 

CO

39°49'34'' 108°10'57'' 14050006 4,600 A 8.9 nd

09306200 Piceance Creek below Ryan 

Gulch, near Rio Blanco, CO

39°55'16'' 108°17'49'' 14050006 14,900 A 12.7 nd

09306222 Piceance Creek at White River, 

CO

40°04'39'' 108°14'07'' 14050006 20,300 A 13.7 nd

09306242 Corral Gulch near Rangely, CO 39°55'13'' 108°28'20'' 14050006 357 A 11.2 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
Appendix 1 59

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09306255 Yellow Creek near White River, 

CO

40°10'07'' 108°24'02'' 14050006 6,300 A 6.9 nd

09306290 White River below Boise Creek, 

near Rangely, CO

40°10'47'' 108°33'53'' 14050007 236,000 A 17.3 nd

09306500 White River near Watson, UT 39°58'44'' 109°10'41'' 14050007 254,000 A 19.5 nd

09310000 Gooseberry Creek near Scofield, 

UT

39°42'57'' 111°17'58'' 14060007 4,190 A 35.0 nd

09310500 Fish Creek above reservoir, near 

Scofield, UT

39°46'28'' 111°11'25'' 14060007 6,220 A 19.1 nd

09312600 White River below Tabbyune 

Creek, near Soldier Summit, UT

39°52'33'' 111°02'12'' 14060007 5,880 A 13.9 nd

09313000 Price River near Heiner, UT 39°43'08" 110°51'55" 14060007 22,700 L nd 4.1

09315000 Green River at Green River, UT 38°59'10'' 110°09'02'' 14060008 1,670,000 A 19.5 nd

09326500 Ferron Creek (upper station) near 

Ferron, UT

39°06'15'' 111°12'57'' 14060009 17,100 A 12.4 nd

09328500 San Rafael River near Green 

River, UT

38°51'30'' 110°22'10'' 14060009 78,200 A 27.0 nd

09329050 Seven Mile Creek near Fish Lake, 

UT

38°37'40'' 111°38'50'' 14070003 853 A 14.3 nd

09330000 Fremont River near Bicknell, UT 38°18'25'' 111°31'05'' 14070003 29,400 A 10.0 nd

09330230 Fremont River near Caineville, UT 38°16'45'' 111°03'54'' 14070003 29,700 A 17.6 nd

09333500 Dirty Devil River above Poison 

Spring Wash, near Hanksville, UT

38°05'39'' 110°24'24'' 14070004 67,700 A 27.6 nd

09337000 Pine Creek near Escalante, UT 37°51'45'' 111°38'07'' 14070005 443 A 18.3 nd

09337500 Escalante River near Escalante, 

UT

37°46'41'' 111°34'26'' 14070005 1,970 A 23.4 nd

09339900 East Fork San Juan River above 

Sand Creek, near Pagosa Springs, 

CO

37°23'23'' 106°50'26'' 14080101 6,170 A 11.6 nd

09342500 San Juan River at Pagosa Springs, 

CO

37°15'58'' 107°00'37'' 14080101 23,900 A 21.0 nd

09346000 Navajo River at Edith, CO 37°00'10'' 106°54'25'' 14080101 10,300 A 20.7 nd

09346400 San Juan River near Carracas, CO 37°00'49'' 107°18'42'' 14080101 86,200 A 34.5 nd

09349800 Piedra River near Arboles, CO 37°05'18'' 107°23'50'' 14080102 56,300 A 22.3 nd

09352900 Vallecito Creek near Bayfield, CO 37°28'39'' 107°32'35'' 14080101 4,530 A 15.5 nd

09354500 Los Pinos River at La Boca, CO 37°00'34'' 107°35'56'' 14080101 26,700 A 17.7 nd

09355000 Spring Creek at La Boca, CO 37°00'40'' 107°35'47'' 14080101 8,310 A 23.7 nd

09355500 San Juan River near Archuleta, 

NM

36°48'05'' 107°41'51'' 14080101 116,000 A 14.3 nd

09361500 Animas River at Durango, CO 37°16'45'' 107°52'47'' 14080104 160,000 A 14.5 nd

09363500 Animas River near Cedar Hill, 

NM

37°02'17'' 107°52'25'' 14080104 201,000 A 11.4 nd

09364500 Animas River at Farmington, NM 36°43'17'' 108°12'05'' 14080104 205,000 A 16.3 nd

09365000 San Juan River at Farmington, 

NM

36°43'23" 108°13'33" 14080105 370,000 L nd 6.6

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
60 Spatially referenced statistical assessment of dissolved-solids load sources 

Surface-water-quality monitoring site Location

Dissolvedsolids load

(tons)

Dissolvedsolids 

load 

source

Residual error, 

as a percent of 

median daily 

dissolvedsolids 

concentration

Standard error 

of prediction, 

as a percent 

of daily 

dissolvedsolids load

Site number Site name Latitude

(deg/min/sec)

Longitude

(deg/min/sec)

Hydrologic 

accounting 

unit

09367500 La Plata River near Farmington, 

NM

36°44'23" 108°14'51" 14080105 13,100 L nd 29.2

09368000 San Juan River at Shiprock, NM 36°47'32'' 108°43'54'' 14080105 475,000 A 28.6 nd

09371000 Mancos River near Towaoc, CO 37°01'39'' 108°44'27'' 14080107 22,900 A 29.4 nd

09371002 Navajo Wash near Towaoc, CO 37°12'03" 108°41'50" 14080107 12,600 L nd 8.7

09371010 San Juan River at Four Corners, 

CO

37°00'20'' 109°02'00'' 14080201 530,000 A 20.2 nd

09371500 McElmo Creek near Cortez, CO 37°19'22'' 108°40'21'' 14080202 79,400 A 15.6 nd

09372000 McElmo Creek near ColoradoUtah state line37°19'27'' 109°00'54'' 14080202 84,700 A 13.2 nd

09378170 South Creek above reservoir, near 

Monticello, UT

37°50'48" 109°22'08" 14080203 79 L nd 4.4

09378200 Montezuma Creek at golf course 

at Monticello, UT

37°51'38'' 109°20'30'' 14080203 63 A 44.3 nd

09378630 Recapture Creek near Blanding, 

UT

37°45'20'' 109°28'33'' 14080201 37 A 45.4 nd

09379500 San Juan River near Bluff, UT 37°08'49'' 109°51'51'' 14080205 655,000 A 24.1 nd

09380000 Colorado River at Lees Ferry, AZ 36°51'53'' 111°35'15'' 14070006 6,480,000 A 7.6 nd

Appendix 1. Dissolved-solids load at selected water-quality monitoring sites in the Upper Colorado River Basin, water year 1991.—

Continued 
Appendix 2 61

Appendix 2
62 Spatially referenced statistical assessment of dissolved-solids load sources Calibration reach labeled by downstream monitoring site number Drainage area of calibration reaches (square miles) Point source imports (tons) Irrigated sedimentary- clastic Tertiary lands (square miles) Irrigated sedimentary- clastic Mesozoic lands (square miles) Irrigated lands of other lithologies (square miles) Crystalline and volcanic rocks (square miles) High-yield sedimentary Cenozoic rocks (square miles) Low-yield sedimentary Cenozoic rocks (square miles) High-yield sedimentary Mesozoic rocks (square miles) Low-yield sedimentary Mesozoic rocks (square miles) High-yield sedimentary Paleozoic and Precambrian rocks (square miles) Low-yield sedimentary Paleozoic and Precambrian rocks (square miles) 09010500 53.8 0 0 0 0 52.7 0 0.173 0.920 0 0 0 09024000 27.6 0 0 0 0 27.6 0 0 0 0 0 0 09025000 28.2 0 0 0 0 28.2 0 0 0 0 0 0.078 09025400 7.25 0 0.034 0 0.081 7.11 0 0.139 0 0 0 0 09026500 32.9 0 0 0 0 32.2 0 0 0 0.787 0 0.001 09032000 20.0 0 0 0 0 19.8 0 0.222 0 0 0 0 09032100 6.28 0 0 0 0 6.28 0 0 0 0 0 0 09034250 614 0 14.8 2.64 5.42 332 147 85.5 24.9 8.90 0 15.9 09034500 36.0 0 0.934 0.011 0.043 0.737 32.6 2.44 0.146 0 0 0.097 09034900 6.12 0 0 0 0 6.12 0 0 0 0 0 0 09035500 10.3 0 0 0 0 10.3 0 0 0 0 0 0 09035700 18.9 0 0 0 0 18.9 0 0 0 0 0 0 09035800 8.86 0 0 0 0 8.86 0 0 0 0 0 0 09035900 27.7 0 0 0 0 27.7 0 0 0 0 0 0 09036000 17.8 0 0 0 0.106 17.8 0 0 0 0 0 0 09037500 94.3 0 0.876 0.272 0.988 75.7 2.99 10.3 0 3.76 0 1.54 09038500 9.02 0 0.191 0 0 1.23 1.65 6.14 0 0 0 0

09039000 44.7 0 0 0 0.004 39.8 4.45 0.530 0 0 0 0

09041090 148 0 0.207 6.11 0.257 22.9 11.1 19.5 54.9 37.8 0 2.02

09041500 146 0 0.875 6.10 2.01 36.1 5.07 26.3 39.3 35.5 0.658 3.45

09046490 42.5 0 0 0 0 24.3 0 0 0.461 1.93 15.8 0

09046600 80.9 0 0 0 0 54.3 0 0 11.0 9.98 3.57 2.03

09047500 58.1 0 0 0 0 57.1 0 0 1.04 0 0 0

09047700 9.39 0 0 0 0 9.39 0 0 0 0 0 0

09050100 86.6 0 0 0 0 56.7 0.407 0 0 0 29.6 0

09050700 51.3 0 0 0 0 33.9 5.54 0 7.59 3.66 0 0.602

09051050 19.3 0 0 0 0 18.3 0 0 0.985 0.028 0 0

09052000 16.0 0 0 0.014 0 15.3 0 0 0.005 0.756 0 0

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model. 
Appendix 2 63

Calibration 

reach 

labeled by 

downstream 

monitoring 

site number

Drainage area 

of calibration 

reaches

(square miles)

Point source 

imports

(tons)

Irrigated 

sedimentaryclastic 

Tertiary lands

(square miles)

Irrigated 

sedimentaryclastic 

Mesozoic 

lands

(square miles)

Irrigated 

lands of other 

lithologies

(square miles)

Crystalline 

and volcanic 

rocks

(square miles)

High-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

High-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

High-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

Low-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

09052400 8.66 0 0 0 0 8.54 0 0 0 0.124 0 009052800 14.5 0 0 0 0 12.4 0 1.91 0.191 0.058 0 009054000 15.3 0 0 0 0 14.5 0.123 0.669 0 0 0 009055300 12.0 0 0 0 0 10.6 0.275 0.546 0 0.573 0 009057500 163 0 1.75 2.93 2.85 77.5 3.00 9.66 50.4 18.5 0 3.8109057520 61.6 0 0 1.95 0 16.2 0 0.945 10.5 32.7 1.19 0

09058000 379 0 21.7 5.25 4.92 80.5 134 105 16.6 32.9 0.090 9.60

09058500 12.8 0 0 0 0 10.8 0 0 0 0 2.05 0

09058610 3.56 0 0 0 0 0.148 0 0.100 0 0.008 3.31 0

09058700 3.58 0 0 0 0 0.620 0 0.188 0 0 2.77 0

09058800 3.60 0 0 0 0 3.25 0 0 0 0.038 0.312 0

09059500 70.5 0 0.146 0 0.269 10.7 0 7.63 4.14 19.9 28.1 0

09060550 72.9 0 0 0 0 56.4 0 1.58 0 8.42 6.52 0

09060770 128 0 1.19 9.35 1.05 22.9 0 10.6 0 78.1 16.6 0

09063000 76.0 0 0 0 0 28.0 0.055 0 0 0 45.4 2.50

09063200 9.69 0 0 0 0 0.585 0 0.828 0 0 8.27 0

09063400 14.1 0 0 0 0 0.937 0 0.487 0 0 12.7 0

09063900 6.48 0 0 0 0 6.48 0 0 0 0 0 0

09064500 51.9 0 0 0 0 51.0 0.433 0 0 0 0.390 0

09064600 34.4 0 0 0 0 21.4 0 0 0 0 13.0 0

09065100 34.8 0 0 0 0.008 33.5 0 0 0 0 1.30 0

09065500 14.6 0 0 0 0.003 14.3 0 0.006 0 0 0.307 0

09066000 12.5 0 0 0 0 3.55 0 0.842 0 0 8.09 0

09066100 5.65 0 0 0 0.050 5.63 0 0 0 0 0.026 0

09066150 6.19 0 0 0 0.059 4.83 0 0 0 0 1.13 0.237

09066200 6.12 0 0 0 0.021 4.07 0 0 0 0 1.99 0.061

09066300 6.01 0 0 0 0.019 1.79 0 0 0 0 4.18 0.04309066310 26.0 0 0 0 0.956 1.90 0 4.39 0 0 18.5 1.15

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
64 Spatially referenced statistical assessment of dissolved-solids load sources Calibration reach labeled by downstream monitoring site number Drainage area of calibration reaches (square miles) Point source imports (tons) Irrigated sedimentary- clastic Tertiary lands (square miles) Irrigated sedimentary- clastic Mesozoic lands (square miles) Irrigated lands of other lithologies (square miles) Crystalline and volcanic rocks (square miles) High-yield sedimentary Cenozoic rocks (square miles) Low-yield sedimentary Cenozoic rocks (square miles) High-yield sedimentary Mesozoic rocks (square miles) Low-yield sedimentary Mesozoic rocks (square miles) High-yield sedimentary Paleozoic and Precambrian rocks (square miles) Low-yield sedimentary Paleozoic and Precambrian rocks (square miles) 09066400 8.49 0 0 0 0 4.05 0 0 0 0 4.44 0 09067000 14.8 0 0 0 0 8.68 0 0 0 0 6.16 0 09067005 73.7 0 0.082 0 0.843 7.32 0 5.50 0 0 58.8 2.09 09070000 549 0 0.057 1.45 14.8 91.4 0 24.6 35.9 77.0 296 24.6 09070500 773 0 1.81 10.5 4.31 209 0.183 89.8 35.5 191 247 0.615 09071300 6.83 0 0 0 0 0.320 0 0 0 0 6.51 0 09071750 137 123,000 0 0 0.147 17.0 0 5.89 0 0.096 114 0 09073300 76.3 0 0 0 0.029 76.3 0 0 0 0 0 0 09073400 29.8 0 0 0 0.158 27.2 0 0 0 0 2.64 0 09074000 41.7 0 0 0 0.04 39.8 0 0 0 0 1.93 0 09074800 32.3 0 0 0 0 18.1 0 0 0 0 14.2 0 09075700 35.8 0 0 0 0 7.92 0 0 0 0 27.9 0 09080400 223 237 0.062 0 0.211 146 2.85 2.40 0 0 72.5 0 09081600 169 0 0.004 0.132 0.118 33.7 0 6.56 21.2 57.2 50.0 0 09085000 833 0 3.93 16.1 32.6 197 0.207 109 19.9 172 296 39.2 09085100 24.4 183,000 0.016 0 0.077 5.25 0 0.277 0 0 18.9 0 09086000 9.53 0 0 0.002 0.011 0 0 0 0 1.07 8.46 0

09086470 91.3 0 0 0 0.022 1.13 0 0 0 0 90.2 0

09089500 64.5 0 0.217 0 0 0.123 0 59.5 4.91 0 0 0

09093700 1,180 0 47.1 7.97 16.6 16.6 144 630 49.7 101 194 49.0

09095500 623 0 10.2 0 3.18 0 285 307 16.7 6.75 0 7.38

09105000 592 0 22.1 3.80 23.0 184 77.3 286 10.7 14.6 0 19.5

09106150 172 0 1.19 0.559 0.236 4.86 1.04 34.8 124 5.54 0 0.916

09107000 129 0 0 0 0.239 118 0.045 0 0 0 6.81 3.60

09107500 44.5 0 0 0 0 42.9 0.110 0 0 0 0.279 1.19

09109000 81.9 0 0 0 0.360 62.8 2.66 0 0 0 9.52 6.91

09110000 223 0 0 0.232 0.709 155 0 0 0 8.53 59.3 0.794

09112500 290 0 2.14 4.57 10.4 68.1 0 17.3 21.9 108 67.1 6.87

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
Appendix 2 65

Calibration 

reach 

labeled by 

downstream 

monitoring 

site number

Drainage area 

of calibration 

reaches

(square miles)

Point source 

imports

(tons)

Irrigated 

sedimentaryclastic 

Tertiary lands

(square miles)

Irrigated 

sedimentaryclastic 

Mesozoic 

lands

(square miles)

Irrigated 

lands of other 

lithologies

(square miles)

Crystalline 

and volcanic 

rocks

(square miles)

High-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

High-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

High-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

Low-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

09114500 245 0 15.9 9.35 5.87 96.6 0 73.4 28.7 39.6 0 6.74

09118450 334 0 5.93 0 9.27 298 0 24.4 0 6.41 0 5.28

09119000 727 0 15.4 4.90 8.72 505 0.044 48.9 0 135 38.5 0

09124500 340 0 0.649 0 1.65 310 0.592 24.9 0 0 0 4.47

09126000 67.2 0 0 0 0 53.8 0 13.5 0 0 0 0

09128000 1,490 588 8.87 4.53 8.17 1,220 22.4 141 2.00 98.3 0 4.34

09128500 43.6 0 0.001 0.232 0 20.9 0 5.60 0 17.1 0 0

09132500 525 0 11.1 0.344 1.44 104 3.27 299 98.0 16.1 0 5.28

09134000 41.8 0 0 0.142 0 8.28 0 5.67 15.7 12.1 0 0

09135900 66.9 0 0.069 2.38 2.17 15.8 2.86 13.4 9.58 18.5 0 6.78

09143000 27.7 0 0 0 0 19.0 1.17 7.36 0.002 0 0 0.114

09143500 14.9 0 0 0.156 1.25 5.89 0.736 0.717 1.99 0.163 0 5.39

09144250 951 0 13.9 73.4 39.0 91.3 9.71 156 103 518 0 73.1

09146200 149 0 0.274 3.29 4.25 87.6 0 12.0 0 29.8 10.1 9.81

09147000 97.6 0 2.30 4.55 0.161 23.7 0 20.1 0 53.7 0 0.001

09147025 18.1 0 0.096 1.40 0.0475 0.208 0 1.91 0 16.0 0 0

09147500 183 0 1.44 3.63 1.30 55.7 0 50.2 0 72.6 1.14 3.26

09149500 667 0 33.0 39.0 30.9 1.49 0.290 128 1.86 489 0 47.1

09152500 1,170 0 3.89 14.0 13.9 51.1 2.91 50.5 139 899 6.78 20.1

09153290 17.7 0 0 9.67 0 0 0 0 0 17.7 0 0

09163500 1,170 0 11.4 53.5 35.5 20.6 51.2 119 346 575 0 60.0

09165000 105 0 0 0 0 13.7 0 5.01 6.72 41.8 37.7 0.529

09166500 399 0 0 2.76 0.540 16.1 0.520 0 64.1 286 32.2 0

09166950 69.3 0 0 0.241 0 0.166 0.263 0 0 68.8 0 0

09169500 1,450 0 2.88 6.91 0.027 4.53 0 41.5 140 1,240 17.3 10.6

09171100 122 132,000 4.34 0.086 0.427 0 0 37.0 23.9 44.1 8.06 8.81

09172500 309 0 0.196 5.51 1.06 80.8 0.111 20.1 13.1 182 12.4 0

09177000 1,190 0 0 43.4 0 16.7 0.482 15.1 24.7 1,130 5.18 0.042

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
66 Spatially referenced statistical assessment of dissolved-solids load sources Calibration reach labeled by downstream monitoring site number Drainage area of calibration reaches (square miles) Point source imports (tons) Irrigated sedimentary- clastic Tertiary lands (square miles) Irrigated sedimentary- clastic Mesozoic lands (square miles) Irrigated lands of other lithologies (square miles) Crystalline and volcanic rocks (square miles) High-yield sedimentary Cenozoic rocks (square miles) Low-yield sedimentary Cenozoic rocks (square miles) High-yield sedimentary Mesozoic rocks (square miles) Low-yield sedimentary Mesozoic rocks (square miles) High-yield sedimentary Paleozoic and Precambrian rocks (square miles) Low-yield sedimentary Paleozoic and Precambrian rocks (square miles) 09180000 928 3,940 2.60 1.35 0.635 71.3 0.116 37.2 252 489 7.51 71.3 09180500 1,530 0 2.44 2.74 0.998 36.4 235 94.9 407 758 0.029 0 09184000 74.5 0 0 0.496 0 3.46 0 1.68 0 65.0 0 4.31 09186500 30.8 0 0 0 0 4.97 0 7.42 0 18.4 0 0 09209400 3,830 0 365 0.274 13.4 610 149 2,190 0.413 341 154 392 09211200 370 0 7.50 0.716 0 0 118 111 14.8 111 9.22 5.43 09215550 1,130 0 21.0 0 0.288 83.6 456 550 0.891 0.198 0 44.5 09216050 600 0 13.0 0 0 0 492 107 0 0 0 0 09217000 3,800 0 0.215 0 0 0 1,470 1,360 702 269 0 0 09224700 2,980 0 144 3.94 0.350 0 209 2,020 149 384 21.6 206 09234500 2,400 0 37.4 8.38 4.24 0.418 619 1,150 13.2 157 56.5 402 09235600 24.7 0 0 0 0 0 0 11.4 0 1.84 0.066 11.4 09237450 207 0 5.01 14.9 3.05 26.0 0.492 83.0 0.346 93.7 0 3.67 09237500 20.5 0 0.255 0 0 2.86 0 16.5 0 1.08 0 0 09238900 26.0 0 0 0 0 26.0 0.066 0 0 0 0 0 09239500 313 2,570 5.40 0.810 4.32 186 0.623 87.1 8.78 24.9 0 6.20 09240900 123 0 0 0.026 0.210 112 0 0.271 0 8.67 0 2.67

09241000 93.3 0 1.12 0.018 0.206 37.6 0 40.6 0 14.1 0 1.07

09242500 243 0 6.25 6.66 1.59 103 0 45.5 15.1 74.8 0 5.21

09243700 24.7 0 0 0.238 0 0 0 0 21.9 2.86 0 0

09243900 17.7 0 0 0.058 0 0 0 0 17.7 0 0 0

09245000 67.8 0 0 0.094 0 0.986 0 14.7 24.3 27.8 0 0

09246920 40.3 0 1.10 0 0 0.116 0 40.1 0 0 0 0

09247600 952 2,570 19.0 12.0 0.015 38.6 78.4 260 379 195 0 1.24

09249750 458 0 0.207 10.7 0 23.9 0 53.4 161 220 0 0

09250507 20.1 0 0 0.193 0 0 0 0 19.2 0.910 0 0

09251000 776 0 6.28 8.72 0.031 0.149 31.6 269 288 180 5.67 0.991

09253000 252 0 1.86 0.561 0.710 124 0 96.0 14.2 14.6 0 2.53

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
Appendix 2 67

Calibration 

reach 

labeled by 

downstream 

monitoring 

site number

Drainage area 

of calibration 

reaches

(square miles)

Point source 

imports

(tons)

Irrigated 

sedimentaryclastic 

Tertiary lands

(square miles)

Irrigated 

sedimentaryclastic 

Mesozoic 

lands

(square miles)

Irrigated 

lands of other 

lithologies

(square miles)

Crystalline 

and volcanic 

rocks

(square miles)

High-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

High-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

High-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

Low-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

09255000 151 0 2.19 1.94 0 7.78 14.5 106 7.45 15.4 0 0

09260000 3,630 0 39.2 3.61 0.133 89.5 668 2,350 370 109 5.43 38.6

09260050 517 0 6.98 0.099 0 0 30.8 369 20.7 60.0 26.5 9.68

09261000 2,430 0 4.41 0.244 0.425 13.2 428 944 39.2 167 401 435

09261700 87.5 0 0 0.108 0 0 0 30.8 7.53 7.86 30.0 11.3

09266500 101 0 0 0 0 0 0 42.0 0 0.382 11.6 47.3

09275500 63.0 0 0 0.013 0 0 0 23.5 11.1 28.4 0 0

09276600 26.9 0 0 0.134 0 0 0 5.17 2.90 0.490 14.8 3.57

09277500 268 0 9.06 0.417 0.677 0 0 64.3 22.3 51.0 45.5 85.1

09277800 101 0 0 0 0 0 0 0.143 0 0.327 0 100

09278000 16.3 0 0 0 0 0 0 0.093 0 0.261 5.20 10.7

09279000 35.8 0 0.075 0 0 0 0 7.02 1.85 0.294 12.0 14.7

09279100 83.2 0 0.160 0 0 0 0 47.8 3.64 18.2 7.11 6.44

09279150 30.3 0 1.88 0 0 0 0 30.3 0 0.020 0 0

09285000 214 0 0 0 0 0 10.9 149 9.98 31.5 11.1 0.918

09285900 159 0 0.144 0 0 0 25.3 134 0 0 0 0

09286100 31.4 0 0 0 0 0 0 8.00 13.3 10.1 0 0

09286700 47.1 0 0 0 0 0 0 12.5 16.7 17.9 0 0

09288000 119 0 2.61 0 0 0 0 112 6.64 0.989 0 0

09288180 350 0 3.21 0.948 0 0 115 214 0.087 20.9 0 0

09288400 140 0 0.045 0.002 0 0 0 118 0 21.9 0 0

09289500 77.8 0 0 0 0 0 0 0 0 0 0 77.8

09292500 139 0 0.189 0 0 0 0 22.3 0 0.297 4.85 112

09295000 763 0 33.2 14.8 10.6 0 41.5 547 0.747 75.7 7.78 90.3

09299500 124 0 0.167 0 0.058 0 0 16.0 0 0.610 3.55 104

09302000 1,010 0 87.8 30.8 29.9 0 0 544 8.97 118 13.4 322

09303000 259 0 0.010 0 1.29 93.0 0.601 69.7 0 37.6 52.7 5.57

09303300 52.6 0 0 0 0 30.0 0.114 0.208 0 0 22.2 0

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
68 Spatially referenced statistical assessment of dissolved-solids load sources Calibration reach labeled by downstream monitoring site number Drainage area of calibration reaches (square miles) Point source imports (tons) Irrigated sedimentary- clastic Tertiary lands (square miles) Irrigated sedimentary- clastic Mesozoic lands (square miles) Irrigated lands of other lithologies (square miles) Crystalline and volcanic rocks (square miles) High-yield sedimentary Cenozoic rocks (square miles) Low-yield sedimentary Cenozoic rocks (square miles) High-yield sedimentary Mesozoic rocks (square miles) Low-yield sedimentary Mesozoic rocks (square miles) High-yield sedimentary Paleozoic and Precambrian rocks (square miles) Low-yield sedimentary Paleozoic and Precambrian rocks (square miles) 09303400 75.8 0 0 0 0 22.6 0 0.211 0 0 53.0 0 09304000 51.7 0 0.060 0 1.87 8.91 0 16.0 0 0 22.5 4.34 09304200 208 0 0.588 0.458 1.83 3.21 0.863 11.2 0 34.0 158 0.704 09304500 113 33,100 1.88 5.91 0 0 0 6.38 18.6 83.5 4.54 0 09304800 264 0 6.88 5.75 0 0 31.5 78.7 85.2 51.7 16.6 0 09306007 177 0 1.23 0.093 0 0 28.8 119 21.1 7.46 0.984 0 09306200 328 0 3.57 0 0 0 7.27 321 0 0 0 0 09306222 146 0 1.37 0 0 0 11.0 135 0 0 0 0 09306242 31.7 0 0 0 0 0 14.3 17.4 0 0 0 0 09306255 231 0 0.285 0 0 0 34.8 196 0 0 0 0 09306290 593 0 4.04 0.301 0.284 0 151 190 79.4 125 43.1 4.39 09306500 1,370 0 2.11 1.25 0 0 382 219 607 137 20.8 0 09310000 17.0 0 0 0 0 0 13.0 1.72 2.30 0 0 0 09310500 47.3 0 0 0 0 0 24.7 0 22.3 0.309 0 0 09312600 93.7 0 0 0 0 0 84.2 8.24 1.28 0 0 0 09313000 302 0 0 0.678 0 0 175 0 123 4.33 0 0 09315000 6,800 0 61.3 62.9 0.03 0 2,790 1,850 474 1,530 69.6 76.6

09326500 141 0 0 0 0 0 90.2 0.684 34.4 2.84 0 13.1

09328500 1,520 277 20.5 26.4 0 0 199 121 360 773 8.73 59.2

09329050 24.7 0 0 0 0 11.4 0.249 9.93 0 0.006 0 3.10

09330000 739 0 12.5 10.7 2.56 444 1.44 206 4.92 66.8 0 15.9

09330230 447 0 2.90 1.56 0.643 33.7 0 72.9 86.1 225 9.79 19.5

09333500 2,950 0 12.0 2.54 0 30.3 73.9 229 394 2,190 0.095 30.5

09337000 67.8 0 0 0 0 37.4 0 19.3 0 10.5 0 0.610

09337500 258 0 1.35 0.480 0 15.8 18.4 96.5 8.25 118 0 0.622

09339900 65.8 0 0.135 0.291 0.064 54.0 0 3.31 2.08 5.07 0 1.37

09342500 215 2,180 1.78 4.68 0.416 139 0 30.7 38.2 7.89 0 009346000 176 0 1.24 2.21 0.042 66.3 0 46.7 38.0 25.3 0 009346400 797 2,180 2.98 10.8 0 80.5 0 102 276 338 0 0

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
Appendix 2 69

Calibration 

reach 

labeled by 

downstream 

monitoring 

site number

Drainage area 

of calibration 

reaches

(square miles)

Point source 

imports

(tons)

Irrigated 

sedimentaryclastic 

Tertiary lands

(square miles)

Irrigated 

sedimentaryclastic 

Mesozoic 

lands

(square miles)

Irrigated 

lands of other 

lithologies

(square miles)

Crystalline 

and volcanic 

rocks

(square miles)

High-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Cenozoic 

rocks

(square miles)

High-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

Low-yield 

sedimentary 

Mesozoic 

rocks

(square miles)

High-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

Low-yield 

sedimentary 

Paleozoic and 

Precambrian 

rocks

(square miles)

09349800 654 0 1.23 9.27 2.25 132 0 59.4 70.3 348 36.7 7.06

09352900 80.3 0 0 0 0 51.8 0 0.036 0 0 5.40 23.1

09354500 439 349 18.0 24.1 5.06 105 4.82 72.9 20.5 173 48.4 14.0

09355000 58.6 0 1.60 14.8 0.159 0 0 21.3 0 36.7 0 0.609

09355500 748 0 12.3 0.664 0 0 9.66 719 0 18.9 0 0

09361500 701 0 0.003 0.473 8.20 271 1.43 5.05 25.0 42.9 311 44.9

09363500 401 0 14.6 25.8 7.62 34.3 43.7 67.0 63.3 134 45.5 13.3

09364500 269 0 10.1 0.017 0 0 161 105 0 2.30 0 0

09365000 2,590 0 41.8 0.039 0 0 880 1,700 0 8.48 0 0

09367500 586 0 6.89 15.5 19.1 8.05 89.9 41.9 285 104 9.29 47.8

09368000 5,070 0 19.2 42.9 0 4.32 583 262 2,510 1,720 1.63 0

09371000 528 0 0 14.3 4.49 4.31 0.231 0 337 176 0.746 9.38

09371002 25.5 0 3.93 0.102 0 1.51 0 9.19 2.86 11.9 0 0

09371010 1,130 0 1.38 3.41 0 22.2 17.4 21.2 218 847 0 3.08

09371500 230 0 8.09 46.2 0 0.589 0.052 16.6 9.90 203 0 0

09372000 116 0 0 3.03 0 4.99 0 2.10 4.53 105 0 0

09378170 8.46 0 0 0 0 2.33 0 0.055 0 6.07 0 0

09378200 19.3 0 0 0 0 2.70 0 0.454 0 16.2 0 0

09378630 4.52 0 0 0 0 0.756 0 0 0 3.77 0 0

09379500 8,110 0 1.81 67.4 0 49.3 169 187 1,530 5,410 88.1 681

09380000 14,200 646,000 2.36 6.91 0.03 102 46.2 546 1,660 9,850 285 1,750

Appendix 2. Sources of dissolved solids in Upper Colorado River Basin streams by bounded calibration reaches as defined for Upper Colorado River Basin SPARROW model.—Continued
70 Spatially referenced statistical assessment of dissolved-solids load sources 

Kenney, Gerner, Buto, and Spangler—Spatially Referenced Statistical Assessment of Dissolved-Solids Load Sources and Transport in Streams of the Upper 

Colorado River Basin—Scientific Investigations Report 2009–5007