U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2015-1007

Prepared in cooperation with the Bureau of Reclamation

Geospatial Datasets for Assessing the Effects of Rangeland 

Conditions on Dissolved-Solids Yields in the Upper Colorado 

River Basin
FRONT COVER:

Grazing density (billed animal unit months per acre) on land managed by the Bureau of Land Management in the Upper 

Colorado River Basin in 2010. See figure 4 in report.
Geospatial Datasets for Assessing the 

Effects of Rangeland Conditions on 

Dissolved-Solids Yields in the Upper 

Colorado River Basin

By Fred D Tillman, Marilyn E. Flynn, and David W. Anning

Prepared in cooperation with the Bureau of Reclamation

Open-File Report 2015-1007

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Tillman, F.D, Flynn, M.E., and Anning, D.W., 2015, Geospatial datasets for assessing the effects of rangeland conditions on dissolved-solids yields in the Upper Colorado River Basin: U.S. Geological Survey Open-File Report 2015-

1007, 21 p., http://dx.doi.org/10.3133/ofr20151007.

ISSN 2331-1258 (online)
 iii

Acknowledgments

This study was supported by the Colorado River Salinity Control Forum. Cole Rossi of the 

Bureau of Land Management (BLM) and Kossi Nouwakpo of the University of Nevada-Reno 

provided information and discussion on the impact of rangeland practices and conditions on 

dissolved-solids loading to streams and rivers. BLM GIS and rangeland specialists who assisted 

in the compilation of grazing information on BLM-managed land in the Upper Colorado River 

Basin include Lynnda Jackson, Bill Coulloudon, David Wilson, Jim Holden, David Sjaastad, 

John Pittman, Scott Clarke, Tara De Valois, Michael Jensen, Lynae Rogers, Angela Losasso, 

David Sinton, Matt Dupire, Roger Cumpian, Jeff Tafoya, Larry Lichthardt, Mark Goertel, Ron 

Dietz, George Soehn, Lisa Dilley, and Caleb Hiner. U.S. Forest Service (USFS) GIS and rangeland specialists who assisted in the compilation of grazing information on USFS-managed land 

in the Upper Colorado River Basin include Brian Goldberg, Paul Archual, David Cottle, Tom 

McClure, Dennis Cleary, Teresa Rhoades, Chad Horman, Candace Bogart, and Kevin Sanchez.

Contents

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

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

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

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

Conceptual Model of Rangeland Contributions to Dissolved Solids in UCRB Streams.....................6

Geospatial Datasets Describing UCRB Rangeland Conditions..............................................................7

Bureau of Land Management Grazing ..............................................................................................7

U.S. Forest Service Grazing ................................................................................................................7

Land Disturbance..................................................................................................................................9

Existing Vegetation Type and Cover ..................................................................................................9

2010 Roads ...........................................................................................................................................17

Rainfall-Runoff Erosivity ....................................................................................................................17

Summary and Conclusions.........................................................................................................................20

References Cited..........................................................................................................................................20
iv 

Tables

 1. Description of select attributes, yearly disturbance grids, 

1999-2010_UCRB_LandDisturbance.zip ..................................................................................11

 2. Description of select attributes, composite vegetation disturbance grid, 

1999-2010_UCRB_LandDisturbance.zip ..................................................................................12

 3. Description of select attributes, existing vegetation cover 2010, 

2010_UCRB_VegTypeCover.zip.................................................................................................14

 4. Description of select attributes, existing vegetation type 2010, 

2010_UCRB_VegTypeCover.zip.................................................................................................15

 5. Description of select attributes, 2010 UCRB Roads, 2010_UCRB_Roads.zip ........................17

Figures

 1. Maps of the Upper Colorado River Basin showing its location, major streams, average 

annual precipitation, and major land-cover classifications..................................................2

 2. Map of the study area showing standardized residual values from the 2009 SPARROW 

model for 218 monitoring sites in the Upper Colorado River Basin......................................4

 3. Maps of Upper Colorado River Basin rangelands classified by the National Resources 

Inventory and Forest Inventory and Analysis ..........................................................................5

 4. Maps showing animal unit months (AUMs) per grazing allotment acre for lands managed 

by the Bureau of Land Management and by the U.S. Forest Service in the Upper 

Colorado River Basin in 2010 ......................................................................................................8

 5. Maps depicting disturbance severity and disturbance type for Upper Colorado River 

Basin composite-vegetation disturbance in 1999–2010 from the land-disturbance 

dataset..........................................................................................................................................10

 6. Map showing existing vegetation types in 2010 in the Upper Colorado River Basin from 

the vegetation type and cover dataset ...................................................................................13

 7. Map showing four-wheel-drive vehicle roads in the Upper Colorado River Basin 

in 2010 ...........................................................................................................................................18

 8. Map showing average annual rainfall-runoff erosivity factor (R-factor) for the Upper 

Colorado River Basin in 1971–2000 ..........................................................................................19
 v

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2

)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2

) 

acre 0.004047 square kilometer (km2

)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Mass

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

Energy

kilowatt hour (kWh) 3,600,000 joule (J)

Horizontal coordinate information is referenced to the North American Datum of 

1983 (NAD 83).

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

Concentrations of chemical constituents in water are given either in milligrams 

per liter (mg/L) or micrograms per liter (μg/L).
Page intentionally left blank
Geospatial Datasets for Assessing the Effects of 

Rangeland Conditions on Dissolved-Solids Yields in the 

Upper Colorado River Basin

By Fred D Tillman, Marilyn E. Flynn, and David W. Anning

Abstract

In 2009, the U.S. Geological Survey (USGS) developed 

a Spatially Referenced Regressions on Watershed Attributes 

(SPARROW) surface-water quality model for the Upper 

Colorado River Basin (UCRB) relating dissolved-solids sources 

and transport in the 1991 water year to upstream catchment 

characteristics. The SPARROW model focused on geologic and 

agricultural sources of dissolved solids in the UCRB and was 

calibrated using water-year 1991 dissolved-solids loads from 218 

monitoring sites. A new UCRB SPARROW model is planned 

that will update the investigation of dissolved-solids sources and 

transport in the basin to circa 2010 conditions and will improve 

upon the 2009 model by incorporating more detailed information 

about agricultural-irrigation and rangeland-management practices, 

among other improvements. Geospatial datasets relating to 

circa 2010 rangeland conditions are required for the new UCRB 

SPARROW modeling effort. This study compiled geospatial 

datasets for the UCRB that relate to the biotic alterations and 

rangeland conditions of grazing, fire and other land disturbance, 

and vegetation type and cover. Datasets representing abiotic 

alterations of access control (off-highway vehicles) and sediment 

generation and transport in general, were also compiled. These 

geospatial datasets may be tested in the upcoming SPARROW 

model to better understand the potential contribution of rangelands 

to dissolved-solids loading in UCRB streams. 

Introduction

More than 35 million people in the United States and 3 

million people in Mexico depend on the Colorado River to supply 

their domestic and industrial water needs (Bureau of Reclamation, 

2011; Colorado River Basin Salinity Control Forum, 2013). 

The Colorado River also supplies irrigation water for more than 

4.5 million acres of land in the United States and Mexico, and 

generates about 12 billion kilowatt hours annually of hydroelectric 

power along the river and its tributaries (Colorado River Basin 

Salinity Control Forum, 2011). From its headwaters in the Rocky 

Mountains through seven states and Mexico, the Colorado River 

traverses more than 1,400 mi to the Gulf of California (fig. 1A). 

Dissolved-solids concentrations in the river increase from about 

50 mg/L at the river headwaters to about 500 mg/L at Lees Ferry, 

Arizona, to about 850 mg/L where it crosses the United States 

border with Mexico (Anning and others, 2007). Annually, more 

than 9 million tons of dissolved solids flow past Hoover Dam 

(Anning and others, 2007). 

It has been estimated that approximately 55–60 percent 

of the salinity (as measured by dissolved-solids load and 

concentration) in the Colorado River is from natural sources—

primarily from saline spring discharge and the erosion of saline 

geologic formations that were deposited from ancient inland seas 

and waterways (Colorado River Basin Salinity Control Forum, 

2013; Kenney and others, 2009). Dissolved-solids concentrations 

can also increase through human activities that increase loading 

(primarily irrigation of agricultural land, but also municipal and 

industrial development, as well as mining and drilling operations) 

and through accumulation (evaporation from reservoir operations). 

The Bureau of Reclamation estimates that high-salinity Colorado 

River water causes damages of more than 300 million dollars per 

year to users in the United States (Colorado River Basin Salinity 

Control Forum, 2013), largely from reduced agricultural crop 

yields and corrosion and plugging of pipes and water fixtures in 

housing and industry (Bureau of Reclamation, 2011).

In order to provide land and water managers in the area 

more information about the sources of dissolved-solids in 

Upper Colorado River Basin (UCRB) streams, in 2009 the U.S. 

Geological Survey (USGS) developed a Spatially Referenced 

Regressions on Watershed Attributes (SPARROW) surfacewater quality model to relate dissolved solids loads from the 

1991 water year to upland catchment attributes (Kenney and 

others, 2009). The 2009 UCRB SPARROW model focused 

on geologic and agricultural sources of dissolved solids in the 

basin and was calibrated to dissolved-solids loads estimated by 

Anning and others (2007) from 218 water-quality monitoring 

sites. The 2009 UCRB SPARROW model was developed by 

testing 37 parameters for significance in predicting annual 

dissolved-solids loads, including sources (geologic source 

groups, irrigated agricultural land groups, saline springs) and 

landscape transport characteristics (for example, catchment 

relief, percentage of area covered by barren land, precipitation; 
2 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Figure 1. Maps of the Upper Colorado River Basin showing (A) its location, (B) major streams, (C) average annual precipitation (PRISM 

Climate Group, 2012), and (D) major land-cover classifications (Fry and others, 2011).

Water and wetlands

Developed

Barren

Forest

Shrubland

Grassland

Cultivated

Average Annual 

Precipitation (in)

61.6

4.8

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115°00' 110°00'

40°00'

35°00'

30°00'

Lees Ferry

! ! !

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Moab

Durango

Kayenta

Montrose

Shiprock

Telluride

Farmington

Rock Springs

Grand Junction

Glenwood Springs

Steamboat Springs

Page

USGS Streamgage 09380000 

Colorado River at Lees Ferry, Ariz.

Green River

Colorado River

San Juan River

Yampa River

Gunnison River

White River

Dolores River

0 100 MILES

0 150 KILOMETERS

UTAH

COLORADO

NEVADA

CALIF.

WYOMING

NEW 

MEXICO

Southern Rocky 

Mountains

ARIZONA

MEXICO

Upper Colorado 

River Basin

Lower Colorado 

River Basin

Paria 

RiverColorado River

A B

D C

Gulf of 

California
Description of Study Area 3

Kenney and others, 2009). Rangeland conditions were not 

among the parameters tested in the model. Standardized 

residuals from the 2009 UCRB SPARROW model are 

an indication of where predicted and observed dissolvedsolids loads differ (fig. 2). Elevated residuals imply that the 

SPARROW model did not include adequate explanatory 

variables to accurately simulate observed dissolved-solids 

loads. Although 2009 SPARROW model residuals appear 

to be spatially unbiased in general, some patterns emerge 

when looking at residuals by 4-digit Hydrologic Unit Code 

(HUC4’s; fig. 2). HUC4’s with three of the four highest median 

negative residuals (where the SPARROW-predicted load was 

greater than the observed load) are also the three HUC4’s 

with the greatest percentage of area described as Rangeland or 

Transitional Rangeland using the National Resources Inventory 

(NRI; Natural Resources Conservation Service, 1997) definition 

described below (figs. 2, 3). Three of the four HUC4’s with the 

most sites with negative residuals are in the top four HUC4’s 

in terms of NRI-defined Rangeland or Transitional Rangeland 

percent area (figs. 2, 3).

A new UCRB SPARROW model is planned to provide 

land and water managers with information on potential 

contributions to dissolved-solids loads and concentrations 

in UCRB streams from UCRB rangelands. The new UCRB 

SPARROW model will test additional parameters that include 

more detailed information on irrigation methods and indicators 

of rangeland conditions, among other improvements. Geospatial 

datasets relating to circa 2010 rangeland conditions are required 

for the new UCRB SPARROW modeling effort.

Purpose and Scope

This report documents the compilation of geospatial 

data on UCRB rangeland conditions that potentially affect 

dissolved-solids concentrations and loads in UCRB streams. 

Such geospatial information can be digitally processed to 

represent catchment attributes and therefore will directly 

support future efforts to update and improve the UCRB 

dissolved-solids SPARROW model. The study area for this 

investigation of geospatial datasets on rangeland conditions 

is the Upper Colorado River Basin, and the temporal focus is 

on circa 2010 conditions.

Description of Study Area

The Colorado River Basin drains parts of Wyoming, 

Utah, Colorado, New Mexico, Arizona, Nevada, California, 

and Mexico, and is divided into upper and lower basins at 

the compact point of Lees Ferry, Arizona, a location 1 mi 

downstream of the mouth of the Paria River (fig. 1A, 1B; 

Anderson, 2004). The Upper Colorado River Basin (UCRB) 

is defined for this study as the 113,406 mi2 drainage area 

upstream of USGS streamflow-gaging station 09380000, 

Colorado River at Lees Ferry, Arizona (Hydrologic Region 

14; fig. 1B). Major tributaries to the Colorado River in 

the Upper Basin include the Dolores, Green, Gunnison, 

San Juan, White, and Yampa Rivers (fig. 1B). Average 

annual precipitation ranges from less than 10 in. in low 

elevation areas to 39 in. or more in high elevation areas in 

the Southern Rocky Mountains (fig. 1C; PRISM Climate 

Group, 2012). UCRB land cover is predominately shrub/

scrub and evergreen forest (Fry and others, 2011), with few 

high-density population centers (fig. 1D). Major dissolved 

constituents in UCRB streams are the cations calcium, 

magnesium, sodium, and potassium; the anions sulfate, 

chloride, and bicarbonate; and neutral silica (Liebermann 

and others, 1989). Important geologic sources of dissolved 

solids in the UCRB include the Upper Cretaceous Mancos 

Shale, the Paradox Member of the Pennsylvanian Hermosa 

Formation, and the Eocene Green River Formation 

(Liebermann and others, 1989).

Rangeland can be defined in a variety of ways and 

is often described using land cover, land use, potential 

vegetation, or administrative characteristics (Lund, 2007). 

A study by Reeves and Mitchell (2011) identifies more 

than 300 definitions of rangelands that have been used 

internationally. Reeves and Mitchell (2011) quantify 

rangeland area in the coterminous United States using two 

methods: (1) by applying the definition used by the U.S. 

Department of Agriculture (USDA) Natural Resources 

Conservation Service (NRCS) in its National Resources 

Inventory (NRI; Natural Resources Conservation Service, 

1997), and (2) by applying the definition used by the U.S. 

Forest Service through the Forest Inventory and Analysis 

(FIA) program (U.S. Forest Service, 2010). The NRI model 

estimates area of three rangeland classes: rangelands, 

afforested rangelands, and transitory rangelands. 

Rangelands in the NRI definition are dominated by shrub 

or herbaceous vegetation and must contain suitable species 

for browsing or grazing (Reeves and Mitchell, 2011). 

Afforested rangelands are dominated by herbaceous or 

shrub species but are being encroached upon by forest 

(>25 percent tree cover). Transitory rangelands are 

dominated by herbs or shrubs, but the potential vegetation 

is classified as forest capable of supporting >25 percent 

tree cover (Reeves and Mitchell, 2011). The simpler FIA 

definition of rangeland, essentially nonforest areas that are 

not cultivated, produces only one rangeland class. Some 

differences are seen in the UCRB between the NRI and 

FIA models of rangeland. The NRI classification results 

in about 63,500 mi2 of UCRB rangeland or transitional 

rangeland, whereas the FIA classification results in about 

53,600 mi2 of UCRB rangeland, with the largest difference, 

in Colorado, of about 36 percent (fig. 3). Owing to the 

lack of a universally accepted definition for rangelands, 

geospatial datasets potentially relevant to rangeland 

conditions that are documented in this report were not 

limited to particular UCRB rangeland areas, but were 

assembled for all available areas in the UCRB.
4 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Figure 2. Map of the study area 

showing standardized residual 

values from the 2009 SPARROW 

model (Kenney and others, 2009) 

for 218 monitoring sites in the 

Upper Colorado River Basin.

Predicted load is greater than observed load

Predicted load is less than observed load

Less than -1.5

-1.5 to 0

0 to 1.5

Greater than 1.5

EXPLANATION

 Standardized residual values

Hydrologic subregion boundary

Green R.

Colorado R.

San Juan R.

Yampa R.

White R.

Dolores R.

Gunnison R.

0 40 80 MILES

0 50 100 KILOMETERS
Description of Study Area 

5

Figure 3. Maps of Upper Colorado River Basin rangelands classified by the National Resources Inventory (NRI; Natural Resources Conservation Service, 1997) and Forest Inventory and Analysis (FIA; U.S. Forest Service, 2010) definitions (Reeves and Mitchell, 2011).

National Resources Inventory (NRI) Forest Inventory and Analysis (FIA)

WYOMING

UTAH COLORADO

ARIZONA NEW MEXICO

WYOMING

UTAH COLORADO

ARIZONA NEW MEXICO

RangelandTransitional Rangeland (NRI only)Afforested Rangeland (NRI only)

EXPLANATION

0 80 40 MILES

0 100 50 KILOMETERS
6 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Conceptual Model of Rangeland 

Contributions to Dissolved Solids in 

UCRB Streams

Dissolved solids may be transported from sources 

to streams by a combination of wind and (or) water 

processes. Dust from wind erosion of salt-laden sediments 

may be deposited directly on lakes and streams or be 

deposited on soil surfaces and subsequently transported 

to lakes and streams by runoff (Weltz and others, 2014). 

Dissolved solids may enter streams and rivers through 

groundwater, either as point sources (for example, saline 

springs) or as a diffuse source (base flow), or through 

surface runoff (Kenney and others, 2009). Water in 

groundwater and surface runoff sources of dissolved solids 

originates as either precipitation or irrigation water.

It is a common assumption that controlling soil 

erosion and sediment transport will reduce dissolvedsolids transport to lakes and streams. Tillman and Anning 

(2014) investigated this hypothesis by developing 

multiple linear regression (MLR) models for dissolvedsolids concentrations in UCRB streams and rivers. Data 

from more than 480 water-quality and streamgaging 

sites in the UCRB were evaluated, and MLR models of 

dissolved-solids concentrations were developed at 164 

sites that had sufficient data. MLR models included a 

suspended-sediment explanatory variable to determine 

if it had significant predictive power on dissolved-solids 

concentrations. Results revealed 29 sites (18 percent) that 

indicate strong evidence of a relation between suspended 

sediment and dissolved solids and 39 sites (24 percent) 

that indicate moderate evidence of such a relation (Tillman 

and Anning, 2014). Dissolved-solids models of most of 

the sites showing strong or moderate evidence indicated 

a positive relation between suspended-sediment and 

dissolved-solids concentrations, and most of the sites 

showing strong or moderate evidence drain regions 

dominated by sedimentary rocks (Tillman and Anning, 

2014). The evidence of a statistical relation between 

dissolved solids and suspended sediment in Tillman 

and Anning (2014) indicates that control of suspended 

sediment might result in a decrease in dissolved-solids 

concentrations in streams and rivers at some UCRB sites, 

but have little or no impact at others.

In 2014, scientists from the USDA’s Agricultural 

Research Service and Natural Resources Conservation 

Service, the University of Nevada, Reno, and the Bureau 

of Land Management (BLM) compiled published studies 

on salinity mobilization and transport on rangelands 

(Gagnon and others, 2014) and produced a synthesis of 

findings from the 768 unique citations in the literature 

review (Weltz and others, 2014). Although the goal of 

the literature review and synthesis was to improve the 

understanding of dissolved-solids sources and transport 

mechanisms in UCRB rangelands, relevant research 

conducted outside the UCRB area, including international 

studies, was also included. Few published studies were 

found that directly addressed the impact of rangeland 

conditions on dissolved-solids delivery to surface 

waters. Most studies included in the literature review 

and summarized in the synthesis were investigations that 

addressed the impact of rangeland conditions on runoff, 

erosion, and sediment generation and transport. 

Weltz and others (2014) summarize the published 

literature on impacts of rangeland conditions on dissolved 

solids or sediment transport and divide those impacts 

into two categories: abiotic alterations and biotic 

alterations. Abiotic alterations include practices such as 

contour furrowing, gully plugs, soil amendments, and 

access controls. Contour furrowing involves creating a 

series of ridges and furrows along the contours of the 

landscape, which may be effective in reducing runoff and 

enhancing infiltration (Weltz and others, 2014). Gully 

plugs are small earthen dams constructed in erosional 

drainages that provide grade control and sediment 

retention (Weltz and others, 2014). Soil amendments

include superabsorbent polymers and gypsum, among 

other materials, and have been shown to improve soil 

hydrologic properties by increasing infiltration and soilwater storage and decreasing runoff. Access controls

in rangelands refers mainly to the exclusion of offhighway vehicles (OHVs) from areas in order to reduce 

soil disturbance, land degradation, and erosion. Abiotic 

alterations that enhance infiltration or reduce runoff are 

assumed to reduce dissolved-solids loading to receiving 

waters, although infiltrating water that encounters easily 

dissolvable geologic material and then becomes base 

flow may increase loading. Biotic alterations summarized 

in Weltz and others (2014) include chaining, grazing, 

and the effects of fire. Chaining is the removal of brittle 

brush and woody plants by dragging a large, heavy chain 

from behind two tractor-type vehicles. No consistent 

differences in salt concentrations in surface waters 

draining chained and unchained areas were noted in the 

literature, although the ancillary effect of replacement 

of woody plants by grasses has been shown to reduce 

surface runoff. Grazing may influence runoff and sediment 

transport through the alteration of vegetation and through 

direct soil modification resulting from animal trampling. 

Heavy grazing that results in bare soil patches or in the 

replacement of tall- and mid-size grasses with short grasses 

will increase surface runoff and soil erosion. Increase 

in soil bulk density by animal compaction may lead to 

reduced infiltration and increased runoff and erosion. Fire

can have different short- and long-term effects on runoff, 

soil erosion, and sediment transport. Loss of vegetation 

immediately following a fire may increase the soil’s 

vulnerability to erosion, but the increased erosion potential 

wanes as vegetation is reestablished. A net decrease in 

runoff, erosion, and sediment transport may result if 
Geospatial Datasets Describing UCRB Rangeland Conditions 7

woody plant communities destroyed by fire are replaced by 

grasses or shrubs that increase ground cover and hydraulic 

roughness over prefire conditions.

Geospatial Datasets Describing UCRB 

Rangeland Conditions 

Geospatial datasets were investigated to represent 

rangeland conditions described in Weltz and others (2014) 

for testing in the forthcoming SPARROW model. The 

search for suitable geospatial datasets was not limited to 

only rangeland areas but was extended to cover as much 

of the UCRB as possible. Geospatial information relevant 

to circa 2010 was prioritized, because this time period will 

be the focus of the updated SPARROW model. Geospatial 

datasets were sought that directly address a rangeland 

condition described in Weltz and others (2014) or that 

indirectly address the underlying process(es) associated 

with a rangeland condition (for example, potential 

increased runoff). Each dataset is discussed separately 

in this section and is available from the website for this 

report (http://pubs.usgs.gov/of/2015/1007). All datasets 

include metadata and spatial reference information.

Basinwide geospatial datasets were not located for all 

rangeland conditions described in Weltz and others (2014), 

specifically the abiotic processes of contour furrowing, 

gully plugs, and soil amendments. These activities are 

probably constrained to areas that are small relative to 

the size of UCRB catchments and would most likely be 

considered site-scale conditions or practices.

Bureau of Land Management Grazing

File Name: 2010_UCRB_BLM_Grazing_projected.zip

Description: This shapefile dataset includes information 

on the amount and location of grazing on land managed 

by the Bureau of Land Management (BLM) in the UCRB 

during 2010, and is directly related to the “grazing effects” 

biotic alteration in Weltz and others (2014). The shapefile 

contains 2,367 polygons of BLM grazing allotments within 

or bordering the UCRB (fig. 4). Attributes for the allotment 

polygons include the allotment name (ALLOT_NAME) and 

number (ST_ALLOT), the authorized number of “animal 

unit months” for the allotment (AUTH_AUMS), and the 

area of the allotment in both acres (AREA_acres) and square 

kilometers (AREA_km2).

Source and Processing of Geospatial Data: BLM does not 

make an annual count of the actual number of livestock that 

graze on BLM-managed lands because that number may vary 

from day to day and livestock are often moved around (http://

www.blm.gov/wo/st/en/prog/grazing.html). Instead, BLM 

compiles “animal unit months” (AUMs) that take into account 

the number of livestock and the amount of time they spend 

grazing. An AUM is the amount of forage needed to sustain one 

cow and calf, one horse, or five sheep or goats for one month 

(http://www.blm.gov/wo/st/en/prog/grazing.html).

A report on billed AUMs was obtained from the 

BLM Rangeland Administration System (RAS) for 

grazing year 2010, which runs from March 1, 2010 

to February 28, 2011 (Lynnda Jackson, BLM, written 

communication, 2014). Nationwide BLM grazing 

allotment spatial data (shapefiles) were obtained from 

the BLM Geocommunicator website (http://www.

geocommunicator.gov/GeoComm/services.htm#Download). 

A subset of UCRB grazing allotments was created from 

grazing allotments within the UCRB or intersecting the 

UCRB boundary. Billed AUMs were joined to UCRB 

grazing allotments in ArcGIS by the combination of 

2-character state and 5-digit allotment numbers (for 

example, AZ05336). Several 2010 billed AUMs did not 

have corresponding allotments; each of these unassociated 

billed AUMs was investigated with assistance from 

BLM field offices in each of the five upper basin states. 

If the unmatched bill was within the UCRB, then either 

an allotment shapefile was obtained or the allotment 

number was corrected. In the spatially joined billed 

AUM shapefiles, the area of each grazing allotment 

was calculated using the ArcGIS “Calculate Geometry” 

function (AREA_acres and AREA_km2).

U.S. Forest Service Grazing

File Name: 2010_UCRB_USFS_Grazing_protected.zip

Description: This shapefile dataset includes information 

on the amount and location of grazing on land managed 

by the U.S. Forest Service (USFS) in the UCRB during 

2010, and is directly related to the “grazing effects” 

biotic alteration in Weltz and others (2014). The shapefile 

contains 444 polygons of USFS grazing allotments 

within or bordering the UCRB (fig. 4). Attributes for the 

allotment polygons include the allotment name (RMU_

NAME) and number (RMU_CN), the authorized number of 

animal unit months for the allotment (AUTH_AUMS), and 

the area of the allotment in both acres (AREA_acres) and 

square kilometers (AREA_km2). USFS-billed grazing is 

referred to as the “authorized” amount and is equivalent to 

BLM’s “billed” grazing (U.S. Government Accountability 

Office, 2005).

Source and Processing of Geospatial Data: UCRB 

grazing and allotment data were obtained by contacting 

USFS personnel in the three Forest Service regions that 

cover the UCRB. Shapefiles of grazing allotments were 

joined to Microsoft Excel reports of the amount of 2010 

grazing by allotment number. As with the BLM, the USFS 
8 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields < 0.10 > 0.10 to 0.20 > 0.20 to 0.30 > 0.30 to 0.50 > 0.50 to 1.99 AUMs per Acre Hydrologic subregion boundary U.S. Forest Service Bureau of Land Management EXPLAN

ATION

0 80 40 MILES

0 100 50 KILOMETERS

Figure 4. Maps showing animal unit months (AUMs) per grazing allotment acre for lands managed by the Bureau of Land Management (left) and by the U.S. Forest Service (right) in the Upper Colorado River Basin in 2010.
Geospatial Datasets Describing UCRB Rangeland Conditions 9

does not make an annual count of the actual number of 

livestock that graze on USFS-managed lands and instead 

compiles grazing information based on AUMs, using the 

same definition as BLM.

Land Disturbance

File Name: 1999-2010_UCRB_LandDisturbance.zip

Description: These layers include temporal and spatial 

information on disturbances to the landscape as a result 

of management activities or natural events. Two types of 

grids are presented: yearly disturbance grids for 1999–

2010 and a composite grid of the yearly disturbance grids 

that summarizes vegetation disturbance for 1999–2010. 

Spatially, all grids cover the entire UCRB and have a 

30-meter pixel resolution.

The disturbance grids include attribute values that 

are directly related to the “fire effects” biotic alteration in 

Weltz and others (2014), as well as attribute values that 

describe other types of landscape disturbances that may 

affect the generation and transport of sediment. Yearly 

disturbance grid attributes include the year in which the 

disturbance occurred, disturbance type, and the severity 

of the disturbance (table 1; LANDFIRE, 2014b). The 

composite vegetation disturbance grid attributes include a 

generalized disturbance type category, a disturbance level 

classification, and time from 2010 since the disturbance 

(table 2; LANDFIRE, 2014c).

Source and Processing of Geospatial Data: All land 

disturbance grids were obtained from LANDFIRE, a 

U.S. Department of Agriculture Forest Service and U.S. 

Department of the Interior shared program that provides 

landscape scale geo-spatial products (LANDFIRE, 

2014a). The data were downloaded and processed using 

the LANDFIRE Data Access Tool (LFDAT), an ArcGIS 

toolbar that allows users to download selected LANDFIRE 

data layers as zipped files using a defined extent directly 

from ArcMap (LANDFIRE, 2014d). The size of the 

extent rectangle necessary to cover the UCRB required 

the requested layers to be divided into multiple pieces for 

downloading and thus resulted in multiple zipped files 

per layer. LFDAT’s Smart Assembler tool was used to 

batch unzip the files, merge the multiple-piece layers into 

a single layer, and reattach attribute fields to the output 

raster attribute tables (fig. 5).

Existing Vegetation Type and Cover

File Name: 2010_UCRB_VegTypeCover.zip

Description: These layers include information on the 

vegetation type and vegetation cover in 2010 in the UCRB. 

The 2010 existing vegetation cover (EVC) layer represents 

the vertically projected percent cover of the live canopy layer. 

The 2010 existing vegetation type (EVT) layer represents the 

species composition. Spatially, both grids cover the entire 

UCRB and have a 30-meter pixel resolution.

Although vegetation layers are not directly related 

to biotic alterations in Weltz and others (2014), the layers 

may be useful in the new UCRB SPARROW model 

because they provide a detailed view of 2010 vegetation 

conditions. Areas with minimal vegetation cover (for 

example, barren) may be combined with information 

on precipitation intensity (see description of rainfallrunoff erosivity factor below) to infer areas that may 

be susceptible to sediment generation and transport. 

The EVC layer includes a land-cover classification 

attribute that describes the percent of canopy cover 

separately for tree, shrub, and herbaceous life forms 

(table 3; LANDFIRE, 2014g). The EVT layer includes 

an attribute that contains the terrestrial ecological 

systems classification values developed by NatureServe 

for the Western Hemisphere; this classification system 

defines groups of plant community types (associations) 

that tend to co-occur within landscapes with similar 

ecological processes, substrates, and (or) environmental 

gradients (LANDFIRE, 2014e). In addition, the EVT 

layer contains attributes that have been cross-referenced 

to existing vegetation classifications. The EVT_ORDER, 

EVT_CLASS, and EVT_SUBCL attributes are based 

on the Federal Geographic Data Committee Vegetation 

Subcommittee’s vegetation classification standard and 

pertain to upper physiognomic levels of the National 

Vegetation Classification System hierarchy (LANDFIRE, 

2014a). The SAF_SRM attribute is based on the floristic 

composition of each EVT and contains either the Society 

of American Foresters (SAF) and the Society of Range 

Management (SRM) cover type classifications for forested 

and rangeland types, respectively, or a general LANDFIRE 

“cover class” where pre-existing categories do not apply 

(table 4; LANDFIRE, 2014e; LANDFIRE, 2014f).

Source and Processing of Geospatial Data: Both the 

vegetation cover and vegetation type grids were obtained 

from LANDFIRE, a U.S. Department of Agriculture 

Forest Service and U.S. Department of the Interior 

shared program that provides landscape scale geospatial 

products (LANDFIRE, 2014a). The data were downloaded 

and processed using the LANDFIRE Data Access Tool 

(LFDAT), an ArcGIS toolbar that allows users to download 

selected LANDFIRE data layers as zipped files using a 

defined extent directly from ArcMap (LANDFIRE, 2014d). 

The size of the extent rectangle necessary to cover the 

UCRB required the requested layers to be divided into 

multiple pieces for downloading and thus resulted in 

multiple zipped files per layer. LFDAT’s Smart Assembler 

tool was used to batch unzip the files, merge the multiplepiece layers into a single layer, and reattach attribute fields 

to the output raster attribute tables (fig. 6).
10 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Figure 5. Maps depicting disturbance severity and disturbance type for Upper Colorado River Basin composite-vegetation disturbance 

in 1999–2010 from the land-disturbance dataset.

High

Moderate

Low

No disturbance

Disturbance Severity

Development

Chemical

Fire

Insects-disease

Disturbance Type

Mechanical add

Mechanical remove

Windthrow

No disturbance

Hydrologic subregion 

boundary

EXPLANATION

0 40 80 MILES

0 50 100 KILOMETERS
Geospatial Datasets Describing UCRB Rangeland Conditions 11

Table 1. Description of select attributes, yearly disturbance grids, 1999-2010_UCRB_LandDisturbance.zip

Attribute name and description Value Description

DIST_YEAR – 

Year of disturbance 1999–2010 Approximate year in which the disturbance occurred.

DIST_TYPE – 

Disturbance type Development Conversion of natural lands into housing, commercial, or industrial building sites. Involves permanent land clearing.

Clearcut The cutting of essentially all trees, producing a fully exposed microclimate for the 

development of a new age class.

Harvest

A general term for the cutting, felling, and gathering of forest timber. This value was 

assigned to events where there was insufficient information available to assign one 

of the two distinct values of Clearcut or Thinning.

Thinning A tree removal practice that reduces tree density and competition between trees in a 

stand.

Mastication Means by which vegetation is mechanically “mowed” or “chipped” into small pieces 

and changed from a vertical to horizontal arrangement.

Other mechanical

Catch-all term for a variety of forest and rangeland mechanical activities related to 

fuel reduction and site preparation including: piling of fuels, chaining, lop and 

scatter, thinning of fuels, Dixie harrow, etc.

Wildfire

An unplanned, unwanted wildland fire including unauthorized human-caused fires, 

escaped wildland fire use events, escaped prescribed fire projects, and all other 

wildland fires where the objective is to suppress or put out the fire.

Wildland fire use

The application of the appropriate management response to naturally ignited wildland 

fires to accomplish specific resource management objectives in pre-defined designated areas outlined in Fire Management Plans.

Prescribed fire Any fire ignited by management actions to meet specific objectives.

Wildland fire

A catch-all term used to describe any non-structure fire that occurs in the wildland. 

This value was assigned to events where there was insufficient information available to assign one of the three distinct values of Wildfire, Wildland Fire Use, or 

Prescribed Fire.

Weather Weather related event, such as blowdown, hurricane, or tornado, that results in loss of 

vegetation.

Chemical

Application of a chemical substance. This value was assigned to events where there 

was insufficient information available to assign one of the two distinct values of 

Herbicide or Insecticide.

Insects Infestations of unwanted insects, such as bark beetle, that can affect vegetative health.

Disease Infestations of disease, such as root rot, that can affect vegetative health.

Insects/Disease

Infestations of insects and (or) disease that can affect vegetative health. This value 

was assigned to events where there was insufficient information available to assign 

one of the two distinct values of Insects or Disease.

Herbicide Application of a chemical substance used to kill or inhibit the growth of plants.

Biological The use of living organisms, such as predators, parasites, and pathogens, to control 

weeds, pest insects, or diseases.

Unknown Sources indicate that a disturbance occurred but causality is uncertain.

NA No disturbance.

SEVERITY – 

Severity of disturbance Unburned/Low General classification level associated with low or unburned landcover.

Low General classification level associated with low fire effect on landcover.

Medium General classification level associated with medium fire effect on landcover.

High General classification level associated with high fire effect on landcover.

Increased green Post-fire image indicates an increase in landcover greenness compared to fire image.

No data Area not mapped.

[Modified from LANDFIRE Disturbance 1999–2012 Attribute Data Dictionary (LANDFIRE, 2014b)]
12 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Table 2. Description of select attributes, composite vegetation disturbance grid, 1999-2010_UCRB_LandDisturbance.zip

[Modified from LANDFIRE Vegetation Disturbance Attribute Data Dictionary (LANDFIRE, 2014c)]

Attribute name and description Value Description

D_TYPE – General disturbance 

type category

No disturbance No disturbance detected or reported.

Fire Any non-structure fire that occurs in the wildland.

Mechanical add Means by which vegetation is mechanically “mowed” or “chipped” into 

small pieces and changed from a vertical to horizontal arrangement.

Mechanical remove A general term for the cutting, felling, and gathering of forest timber.

Windthrow Weather-related event that results in loss of vegetation.

Insects/Disease Infestations of insects and (or) disease that can affect vegetative health.

Chemical Application of a chemical substance.

Biological The use of living organisms, such as predators, parasites, and pathogens to 

control weeds, pest insects, or diseases.

Development Conversion of natural lands into housing, commercial, or industrial building 

sites. Involves permanent land clearing.

D_SEVERITY – Classification 

level of disturbance

Low General classification level associated with low effect on landcover.

Medium General classification level associated with medium effect on landcover.

High General classification level associated with high effect on landcover.

D_TIME – Time from 2010 since 

disturbance

One year One year from 2010 since disturbance.

Two to five years Two to five years from 2010 since disturbance.

Six to ten years Six to ten years from 2010 since disturbance.
Geospatial Datasets Describing UCRB Rangeland Conditions 13

Herbaceous / 

Nonvascular-dominated

2010 Existing Vegetation Type

Shrub-dominated

Tree-dominated

Non-vegetated

No dominant lifeform

Hydrologic subregion 

boundary

EXPLANATION

0 40 80 MILES

0 50 100 KILOMETERS

Figure 6. Map showing existing vegetation types in 2010 in the Upper Colorado River Basin from the vegetation type and cover dataset.
14 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

[Modified from LANDFIRE Existing Vegetation Cover Attribute Data Dictionary (LANDFIRE, 2014g); NASS, National Agricultural Statistics Service; NLCD, 

National Land Cover Database]

Table 3. Description of select attributes, existing vegetation cover 2010, 2010_UCRB_VegTypeCover.zip 

Attribute name and description Value Description (source)

Value–Land cover classification 11 Open water (NLCD class)

12 Snow/Ice (NLCD class)

13 Developed-upland deciduous forest (NLCD class)

14 Developed-upland evergreen forest (NLCD class)

15 Developed-upland mixed forest (NLCD class)

16 Developed-upland herbaceous (NLCD class)

17 Developed-upland shrubland (NLCD class)

21 Developed-open space (NLCD class)

22 Developed-low intensity (NLCD class)

23 Developed-medium intensity (NLCD class)

24 Developed-high intensity (NLCD class)

25 Developed-roads (NLCD class)

31 Barren (NLCD class)

32 Quarries-strip mines-gravel pits (NLCD class)

61 NASS-vineyard (NASS class)

63 NASS-vow crop-close grown crop (NASS class)

64 NASS-vow crop (NASS class)

100 Sparse vegetation canopy (NLCD class)

101 Tree cover >= 10 and < 20% (LANDFIRE class)

102 Tree cover >= 20 and < 30% (LANDFIRE class)

103 Tree cover >= 30 and < 40% (LANDFIRE class)

104 Tree cover >= 40 and < 50% (LANDFIRE class)

105 Tree cover >= 50 and < 60% (LANDFIRE class)

106 Tree cover >= 60 and < 70% (LANDFIRE class)

107 Tree cover >= 70 and < 80% (LANDFIRE class)

108 Tree cover >= 80 and < 90% (LANDFIRE class)

109 Tree cover >= 90 and < 100% (LANDFIRE class)

111 Shrub cover >= 10 and < 20% (LANDFIRE class)

112 Shrub cover >= 20 and < 30% (LANDFIRE class)

113 Shrub cover >= 30 and < 40% (LANDFIRE class)

114 Shrub cover >= 40 and < 50% (LANDFIRE class)

115 Shrub cover >= 50 and < 60% (LANDFIRE class)

116 Shrub cover >= 60 and < 70% (LANDFIRE class)

117 Shrub cover >= 70 and < 80% (LANDFIRE class)

118 Shrub cover >= 80 and < 90% (LANDFIRE class)

119 Shrub cover >= 90 and < 100% (LANDFIRE class)

121 Herb cover >= 10 and < 20% (LANDFIRE class)

122 Herb cover >= 20 and < 30% (LANDFIRE class)

123 Herb cover >= 30 and < 40% (LANDFIRE class)

124 Herb cover >= 40 and < 50% (LANDFIRE class)

125 Herb cover >= 50 and < 60% (LANDFIRE class)

126 Herb cover >= 60 and < 70% (LANDFIRE class)

127 Herb cover >= 70 and < 80% (LANDFIRE class)

128 Herb cover >= 80 and < 90% (LANDFIRE class)

129 Herb cover >= 90 and < 100% (LANDFIRE class)
Geospatial Datasets Describing UCRB Rangeland Conditions 15

Attribute Name and Description Value

EVT_ORDER—describes the dominant life forms (tree, shrub, dwarf 

shrub, herbaceous, or nonvascular) within the Vegetated Division of the 

hierarchy

Herbaceous / Nonvascular-dominated

No dominate lifeform

Non-vegetated

Shrub-dominated

Tree-dominated

EVT_CLASS—describes the level in the classification hierarchy defined 

by the relative percent canopy cover of the tree, shrub, dwarf shrub, 

herb, and nonvascular life form in the uppermost strata during the peak 

of the growing season (EVT_CLASS)

Herbaceous-grassland

Closed tree canopy

Dwarf-shrubland

Herbaceous-shrub-steppe

No dominant lifeform

Non-vegetated

Open tree canopy

Shrubland

Sparse tree canopy

Sparsely vegetated

EVT_SUBCL—describes the predominant leaf phenology of classes 

defined by tree, shrub, or dwarf shrub stratum (evergreen, deciduous, 

mixed evergreen-deciduous)

Graminoid/Forb

Deciduous open tree canopy

Deciduous shrubland

Developed

Evergreen closed tree canopy

Evergreen dwarf-shrubland

Evergreen open tree canopy

Evergreen shrubland

Evergreen sparse tree canopy

Herbaceous-grassland

Mixed evergreen-deciduous open tree canopy

Mixed evergreen-deciduous shrubland

Non-vegetated

Perennial graminoid grassland

Perennial graminoid steppe

Sparsely vegetated

SAF_SRM—describes a floristic-based composition using the combined 

classifications of the Society of American Foresters (SAF) and the 

Society of Range Management (SRM) cover type classifications for 

forested and rangeland types, respectively, or LANDFIRE “cover 

classes” where pre-existing categories do not apply

LF 20: Developed

LF 33: Sparsely vegetated

LF 41: Deciduous shrubland

LF 42: Great Plains riparian

LF 54: Introduced upland vegetation-herbaceous

LF 58: Introduced woody wetlands and riparian vegetation

LF 80: Agriculture

No dominant lifeform

Non-vegetated

SAF 201: White spruce

SAF 206: Engelmann spruce-subalpine fir

SAF 208: Whitebark pine

SAF 209: Bristlecone Pine

SAF 210: Interior Douglas-Fir

SAF 211: White Fir

SAF 213: Grand Fir

SAF 217: Aspen

Table 4. Description of select attributes, existing vegetation type 2010, 2010_UCRB_VegTypeCover.zip

[Modified from LANDFIRE Existing Vegetation Type Attribute Data Dictionary (LANDFIRE, 2014e); LF, LANDFIRE; SAF, Society of American Foresters; 

SRM, Society of Range Management]
16 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Attribute Name and Description Value

SAF_SRM—describes a floristic-based composition using the combined 

classifications of the Society of American Foresters (SAF) and the 

Society of Range Management (SRM) cover type classifications for 

forested and rangeland types, respectively, or LANDFIRE “cover 

classes” where pre-existing categories do not apply

SAF 218: Lodgepole Pine

SAF 219: Limber Pine

SAF 235: Cottonwood-Willow

SAF 237: Interior Ponderosa Pine

SAF 238: Western Juniper

SAF 241: Western Live Oak

SRM 106: Bluegrass Scabland

SRM 107: Western Juniper-Big Sagebrush-Bluebunch Wheatgrass

SRM 203: Riparian Woodland

SRM 212: Blackbush

SRM 311: Rough Fescue-Bluebunch Wheatgrass

SRM 312: Rough Fescue-Idaho Fescue

SRM 314: Big Sagebrush-Bluebunch Wheatgrass

SRM 402: Mountain Big Sagebrush

SRM 403: Wyoming Big Sagebrush

SRM 405: Black Sagebrush

SRM 406: Low Sagebrush

SRM 409: Tall Forb

SRM 410: Alpine Rangeland

SRM 412: Juniper-Pinyon Woodland

SRM 413: Gambel oak

SRM 414: Salt Desert Shrub

SRM 415: Curlleaf Mountain-Mahogany

SRM 418: Bigtooth Maple

SRM 421: Chokecherry-Serviceberry-Rose

SRM 422: Riparian

SRM 501: Saltbush-Greasewood

SRM 502: Grama-Galetta

SRM 503: Arizona Chaparral

SRM 504: Juniper-Pinyon Pine Woodland

SRM 505: Grama-Tobosa Shrub

SRM 506: Creosotebush-Bursage

SRM 507: Palo Verde-Cactus

SRM 508: Creosotebush-Tarbush

SRM 601: Bluestem Prairie

SRM 604: Bluestem-Grama Prairie

SRM 605: Sandsage Prairie

SRM 606: Wheatgrass-Bluestem-Needlegrass

SRM 611: Blue Grama-Buffalograss

SRM 612: Sagebrush-Grass

SRM 701: Alkali Sacaton-Tobosa Grass

SRM 704: Blue Grama-Western Wheatgrass

SRM 713: Grama-Muhly-Threeawn

SRM 720: Sand Bluestem -Little Bluestem Dunes

SRM 729: Mesquite

SRM 730: Sand Shinnery Oak

SRM 735: Sideoats Grama-Sumac-Juniper

Table 4.—Continued
Geospatial Datasets Describing UCRB Rangeland Conditions 17

Attribute

name and description Value Description (source)

MTFCC—a 5-digit code to classify 

and describe geographic objects or 

features.

S1100 Primary Roads—Primary roads are generally divided, limited-access highways within the Interstate Highway system or under state management, and are distinguished by the presence of 

interchanges. These highways are accessible by ramps and may include some toll highways.

S1200 Secondary Roads—Secondary roads are main arteries, usually in the U.S. Highway, State 

Highway, or County Highway system. These roads have one or more lanes of traffic in each 

direction, may or may not be divided, and usually have at-grade intersections with many 

other roads and driveways.

S1400 Local Neighborhood Road, Rural Road, City Street—Generally a paved non-arterial street, 

road, or byway that usually has a single lane of traffic in each direction. Roads in this feature 

class may be privately or publicly maintained. Scenic park roads would be included in this 

feature class, as would (depending on the region of the country) some unpaved roads. 

S1500 Vehicular Trail (4WD)—An unpaved dirt trail where a four-wheel drive vehicle is required. 

These vehicular trails are found almost exclusively in very rural areas.

S1630 Ramp—A road that allows controlled access from adjacent roads onto a limited access highway, 

often in the form of a cloverleaf interchange.

S1640 Service Drive usually along a limited-access highway—A road, usually paralleling a limited 

access highway, that provides access to structures along the highway.

S1710 Walkway/Pedestrian Trail—A path that is used for walking, being either too narrow for or 

legally restricted from vehicular traffic.

S1730 Alley—A service road that does not generally have associated addressed structures and is usually unnamed. It is located at the rear of buildings and properties and is used for deliveries. 

S1740 Private Road for service vehicles (such as for logging, oil fields, ranches)—A road within private property that is privately maintained for service, extractive, or other purposes. 

S1750 Internal U.S. Census Bureau Use

S1780 Parking Lot Road—The main travel route for vehicles through a paved parking area. 

S1820 Bike Path or Trail—A path that is used for manual or small, motorized bicycles, being either too 

narrow for or legally restricted from vehicular traffic. 

Table 5. Description of select attributes, 2010 UCRB Roads, 2010_UCRB_Roads.zip

[Modified from U.S. Census Bureau, 2012b]

2010 Roads

File Name: 2010_UCRB_Roads.zip

Description: This layer contains information about the location 

and type of roads in the UCRB in 2010. One value in the MAF/

TIGER Feature Class Code (MTFCC) attribute field in the roads 

layer is S1500, named “Vehicular Trail (4WD)”, and is described 

as “an unpaved dirt trail where a four-wheel drive vehicle is 

required” (table 5). The Vehicular Trail (4WD) attribute presents 

potential UCRB locations of off-highway vehicle use—an activity 

directly related to the “access controls” abiotic alteration in Weltz 

and others (2014) (table 5; fig. 7). The 2010 roads layer covers the 

entire UCRB.

Source and Processing of Geospatial Data: The 2010 roads layer 

files were obtained from the U.S. Census Bureau as individual 

county-based TIGER/Line® shapefiles (U.S. Census Bureau, 

2012a).

Data in this layer are supplied as individual county-based 

zipped shapefiles. File names are in the format of tl_2010_#####_

roads.zip, where #### is the two-digit state Federal Information 

Processing Series (FIPS) code followed by a three-digit county 

FIPS code (for code definitions, see http://www.census.gov/geo/

reference/codes/cou.html).

Rainfall-Runoff Erosivity

File Name: UCRB_R-factor.zip

Description: This tabular dataset presents the 1971–2000 

average annual rainfall-runoff erosivity factor (R-factor) for the 

UCRB. The R-factor is a measure of the cumulative erosive 

force of individual precipitation events (Daly and Taylor, 

2002). All other factors being constant, sediment generation 

from precipitation is directly proportional to the product of 

the total kinetic energy of a storm and the storm’s maximum 

30-minute intensity. The mean annual R-factor is a sum of 

this product for all storms in a year, averaged over all years of 

record (Daly and Taylor, 2002). Although not directly related 

to biotic alterations in Weltz and others (2014), information on 

rainfall-runoff erosivity may be used with land-use information 

to evaluate areas with elevated sediment generation and transport 

potential. The unique COMID attribute in the R-factor tabular 

dataset was related to NHDPlus Catchments Version 1.1 (U.S. 

Environmental Protection Agency and U.S. Geological Survey, 

2007) for the Upper Colorado River (fig. 8).
18 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

56037

49037

35045

08081

56035

49047

49015

04001

49019

49017

08077

49013

08051

08103

08045

49025

49055

08107

56007

08085

56023

08083

35039

08049

04005

35031

08037

08067

56041

49007

08113

08007

08029

04017

08033

08097

49009

08109

49051

08117

49041

08091

49043

08053

35043

08111

08053

49039

08079

49049

56013

49031

56039

08105

08021

Four-wheel drive vehicular 

trail

County boundary

County code

State boundary

49019

Hydrologic subregion 

boundary

EXPLANATION

0 40 80 160 MILES

0 50 100 200 KILOMETERS

Figure 7. Map showing four-wheel-drive vehicle roads in the Upper Colorado River Basin in 2010 (U.S. Census Bureau, 2012a).
Geospatial Datasets Describing UCRB Rangeland Conditions 19

Figure 8. Map showing average annual rainfall-runoff erosivity factor (R-factor) for the Upper Colorado River Basin in 1971–2000.

32 to 100

>100 to 200

>200 to 300

>300 to 600

>600 to 1249

Hydrologic subregion 

boundary

Estimated mean R-factor

hundreds of foot-tons-force inches

per hour per acre per year ( ) × 10

EXPLANATION

0 40 80 160 MILES

0 50 100 200 KILOMETERS
20 Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields

Source and Processing of Geospatial Data: This tabular 

dataset was created by the USGS National Water-Quality 

Assessment Program from the source data of Daly and Taylor 

(2002) to estimate the average annual R-factor per watershed 

segment (Wieczorek and LaMotte, 2010).

Summary and Conclusions

High dissolved-solids concentrations in the Colorado 

River cause more than 300 million dollars per year in damages 

to water users in the United States (Colorado River Basin 

Salinity Control Forum, 2013). A 2009 study by the USGS 

used the Spatially Referenced Regressions on Watershed 

Attributes (SPARROW) surface-water-quality model to 

examine dissolved-solids supply and transport within the 

Upper Colorado River Basin. A planned update to the 2009 

UCRB SPARROW model will examine potential contributions 

to dissolved-solids loading from irrigation and rangeland 

practices. Weltz and others (2014) detail rangeland conditions 

and practices that contribute to dissolved solids or sediment 

loading, including biotic and abiotic alterations. The present 

study compiled geospatial datasets for the UCRB that relate 

to the biotic alterations and conditions of grazing, fire and 

other land disturbance, and vegetation type and cover, the 

abiotic alteration of access control (off-highway vehicles), and 

sediment generation and transport in general. These geospatial 

datasets may be tested in the upcoming SPARROW model to 

better understand the potential contribution of rangelands to 

dissolved-solids loading in UCRB streams and rivers.

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Menlo Park Publishing Service Centers, California 

Manuscript approved for publication January 13, 2015

Edited by Peter Stauffer

Design and Layout by Cory Hurd
Tillman and others—Geospatial Datasets for Assessing the Effects of Rangeland Conditions on Dissolved-Solids Yields—Open-File Report 2015-1007

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20151007