Prepared in cooperation with the Texas State Soil and Water Conservation Board and the Upper 

Guadalupe River Authority

Simulation of Streamflow and the Effects of Brush 

Management on Water Yields in the Upper Guadalupe 

River Watershed, South-Central Texas, 1995–2010

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2012–5051
Cover: North Fork Guadalupe River upstream from Highway 39, near Hunt, Texas, June 17, 2011.
Simulation of Streamflow and the Effects of 

Brush Management on Water Yields in the 

Upper Guadalupe River Watershed, SouthCentral Texas, 1995–2010 

By Johnathan R. Bumgarner and Florence E. Thompson

Prepared in cooperation with the Texas State Soil and Water Conservation Board 

and the Upper Guadalupe River Authority

Scientific Investigations Report 2012–5051

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

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

This and other USGS information products are available at http://store.usgs.gov/

U.S. Geological Survey

Box 25286, Denver Federal Center

Denver, CO 80225

To learn about the USGS and its information products visit http://www.usgs.gov/

1-888-ASK-USGS

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

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Bumgarner, J.R., and Thompson, F.E., 2012, Simulation of streamflow and the effects of brush management on water 

yields in the upper Guadalupe River watershed, south-central Texas, 1995–2010: U.S. Geological Survey Scientific 

Investigations Report 2012–5051, 25 p.
iii

Contents

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

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

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

Description of the Upper Guadalupe River Watershed .................................................................2

Model Development .....................................................................................................................................6

Functional Description of the Soil and Water Assessment Tool ..................................................6

Model Input Data .................................................................................................................................7

Model Calibration ...............................................................................................................................12

Model Limitations ...............................................................................................................................15

Simulation of Streamflow and the Effects of Brush Management on Water Yields ........................15

Simulation Methods ...........................................................................................................................17

Simulation Results .............................................................................................................................17

Summary .......................................................................................................................................................22

References Cited .........................................................................................................................................22

Figures

1. Location of the Guadalupe River watershed, including the upper Guadalupe River 

watershed study area, south-central Texas ...........................................................................3

2. Locations of U.S. Geological Survey streamflow-gaging stations and the National 

Weather Service and U.S. Geological Survey meteorological stations providing 

data for the upper Guadalupe River watershed model, south-central Texas ...................4

3. Subbasin delineation for the upper Guadalupe River watershed model, southcentral Texas ................................................................................................................................8

4. Slope categories used for the upper Guadalupe River watershed model, southcentral Texas ................................................................................................................................9

5. Relative soil infiltration rates in the upper Guadalupe River watershed, southcentral Texas ..............................................................................................................................10

6. National Land Cover Database 2006 land-cover categories in the upper 

Guadalupe River watershed, south-central Texas ...............................................................11

7. Calibration plots of the A, temporal distribution and B, relation of simulated 

monthly streamflow and measured monthly streamflow at U.S. Geological Survey 

streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Texas, 

1995–2010 ....................................................................................................................................14

8. Delineation of the brush-management subbasins for the upper Guadalupe River 

watershed model brush-management simulations, south-central Texas ........................16

9. Texas Ecological Systems Dataset land-cover classes used for brushmanagement simulations for the upper Guadalupe River watershed model, 

south-central Texas ...................................................................................................................18
iv

Tables

1. Data-collection sites providing data for the upper Guadalupe River watershed, 

south-central Texas .....................................................................................................................5

2. Summary of the calibrated values for selected process-related parameters for the 

Soil and Water Assessment Tool watershed model of the upper Guadalupe River 

watershed, south-central Texas, 1995–2010 .........................................................................13

3. Hydrologic calibration results for the Soil and Water Assessment Tool watershed 

model of the upper Guadalupe River watershed at U.S. Geological Survey 

streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Texas, 

1995–2010 ....................................................................................................................................15

4. Percent areal coverage of unmodified National Land Cover Database 2006 landcover categories for the brush-management subbasins used for the upper 

Guadalupe River watershed model brush-management simulations, south-central 

Texas ............................................................................................................................................19

5. Percent areal coverage of modified National Land Cover Database 2006 landcover categories for the brush-management subbasins used for the upper 

Guadalupe River watershed model brush-management simulations, south-central 

Texas ............................................................................................................................................20

6. Effects of brush management on water yields simulated by the Soil and Water 

Assessment Tool watershed model of the upper Guadalupe River watershed, 

south-central Texas, 1995–2010 ...............................................................................................21

Conversion Factors, Datums, and Water-Quality Units

Inch/Pound to SI

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 0.4047 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

gallon (gal) 3.785 liter (L)

gallon (gal) 3.0689 x 10-6 acre-foot

cubic foot (ft3) 0.02832 cubic meter (m3) 

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3) 

Flow rate

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

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

gallon per day (gal/d) 0.003785 cubic meter per day (m3/d)
v

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

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

°C=(°F-32)/1.8

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

(NAVD 88).

Horizontal coordinate information is referenced to the Texas Centric Mapping System–Albers 

Equal Area Projection, North American Datum of 1983 (NAD 83).

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

Abstract 

The U.S. Geological Survey, in cooperation with the 

Texas State Soil and Water Conservation Board and the Upper 

Guadalupe River Authority, developed and calibrated a Soil 

and Water Assessment Tool watershed model of the upper 

Guadalupe River watershed in south-central Texas to simulate 

streamflow and the effects of brush management on water 

yields in the watershed and to Canyon Lake for 1995–2010. 

Model simulations were done to quantify the possible change 

in water yield of individual subbasins in the upper Guadalupe 

River watershed as a result of the replacement of ashe juniper 

(Juniperus ashei) with grasslands. The simulation results will 

serve as a tool for resource managers to guide their brushmanagement efforts.

Model hydrology was calibrated with streamflow data 

collected at the U.S. Geological Survey streamflow-gaging 

station 08167500 Guadalupe River near Spring Branch, Tex., 

for 1995–2010. Simulated monthly streamflow showed very 

good agreement with measured monthly streamflow: a percent 

bias of -5, a coefficient of determination of 0.91, and a Nash–

Sutcliffe coefficient of model efficiency of 0.85.

Modified land-cover input datasets were generated for 

the model in order to simulate the replacement of ashe juniper 

with grasslands in 23 brush-management subbasins in the 

watershed. Each of the 23 simulations showed an increase in 

simulated water yields in the targeted subbasins and to Canyon 

Lake. The simulated increases in average annual water yields 

in the subbasins ranged from 6,370 to 119,000 gallons per 

acre of ashe juniper replaced with grasslands with an average 

of 38,900 gallons. The simulated increases in average annual 

water yields to Canyon Lake from upstream subbasins ranged 

from 6,640 to 72,700 gallons per acre of ashe juniper replaced 

with grasslands with an average of 34,700 gallons.

Introduction

The selective removal of woody (nonherbaceous or succulent) plants in an effort to increase water yields to downstream water resources is a brush-management conservation 

practice currently (2012) used in Texas (Natural Resources 

Conservation Service, 2009; Texas State Soil and Water 

Conservation Board, 2011). Generally, brush-management 

conservation practices (hereinafter referred to as “brush 

management”) include the removal of woody plants for the 

purpose of (1) creating desired plant communities, (2) controlling erosion, (3) improving water quality, (4) enhancing 

streamflow or water yield, (5) improving fish and wildlife habitat, (6) improving forage accessibility, and (7) managing fuel 

loads (Natural Resources Conservation Service, 2009). Woody 

plants have encroached into semiarid grasslands and savannas 

in Texas (Humphrey, 1958; Archer and others, 1988; Archer, 

1989), and their potential to decrease groundwater recharge 

and streamflow via processes such as increased evapotranspiration, which reduces infiltration to the water table, and 

canopy interception of rainfall, which reduces both infiltration and streamflow, is well documented (Archer and others, 

1995; Dugas and others, 1998; Van Auken, 2000; Wilcox, 

2002; Huxman and others, 2005; Wilcox and Thurow, 2006; 

Musgrove and others, 2010). Ashe juniper (Juniperus ashei) 

is a woody plant species that has spread beyond its historical 

range in the understories of small stands of prairie oak (motte) 

and in sheltered canyons in Texas because of overgrazing and 

fire suppression (Smeins, 1980; Fuhlendorf, and others, 1996; 

Fuhlendorf and others, 1997). Studies of the water use of ashe 

juniper indicate that they might intercept and use more water 

than do native grasses (Young and others 1984; Thurow and 

others, 1987; Owens, 1996; Baxter, 2009; Saleh and others, 

2009). As a result, the replacement of ashe juniper with grasslands is used in south-central Texas in an effort to increase 

water yields (Natural Resources Conservation Service, 2009; 

Texas State Soil and Water Conservation Board, 2011). 

The Texas State Soil and Water Conservation Board 

(TSSWCB) Water Supply Enhancement Program (WSEP) provides funding for brush management in an effort to increase 

water yields to water bodies in Texas used for water supply 

(Texas State Soil and Water Conservation Board, 2011). Canyon Lake, a run-of-river reservoir (water is passed through the 

reservoir without being stored long term) in the upper Guadalupe River watershed in south-central Texas, is the water 

resource identified by the WSEP for this project that might 

receive more water as a result of brush management. In an 

Simulation of Streamflow and the Effects of Brush 

Management on Water Yields in the Upper Guadalupe 

River Watershed, South-Central Texas, 1995–2010

By Johnathan R. Bumgarner and Florence E. Thompson
2 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

effort to effectively manage brush in the upper Guadalupe 

River watershed and potentially increase water yields to Canyon Lake, the U.S. Geological Survey (USGS), in cooperation 

with the TSSWCB and the Upper Guadalupe River Authority (UGRA), developed a Soil and Water Assessment Tool 

(SWAT) (Arnold and others, 1998) watershed model of the 

upper Guadalupe River watershed to simulate the effects of the 

replacement of ashe juniper with grasslands and estimate the 

effects of brush management on water yields to the reservoir. 

Simulation results will be used as part of a WSEP feasibility 

study to quantify possible water-yield changes in subbasins 

in the upper Guadalupe River watershed as a result of brush 

management.

The use of watershed models, which are designed specifically to simulate surface and near-surface processes, has 

been applied previously to brush-management studies and can 

provide insights into the possible effects of brush management 

on water yields. SWAT was used previously in the TSSWCB 

brush-management feasibility studies of eight watersheds in 

Texas (Bednarz and others, 2000). The studies predicted average annual water-yield increases ranging from about 6,650 to 

about 172,000 gallons per acre of simulated brush management. Wu and others (2001) used the Simulation of Production 

and Utilization of Rangelands (SPUR–91) model to evaluate brush management in the Cusenbary Draw Basin in the 

Edwards Plateau in Texas. Their simulations showed increased 

water yields for a variety of brush-management scenarios. 

SWAT was used by Arrington and others (2002) to demonstrate the potential for brush management to increase water 

yields in conjunction with the retention of ecological value in 

rangelands. A USGS investigation of brush management in the 

Upper Seco Creek watershed using the Hydrological Simulation Program—FORTRAN (HSPF) demonstrated increased 

water yields immediately following brush management for 

several subbasins in the watershed (Brown and Raines, 2002). 

However, even though the study at Seco Creek showed that 

initial removal of brush led to increased water yields, 3 years 

later, after re-vegetation with native grasses, there was less 

aquifer recharge than on a control site that remained covered 

by ashe juniper. Lemberg and others (2002) interfaced SWAT 

with the hydrologic-based plant growth simulation model 

PHYGROW to assess the effects of brush management on 

water yields in the Frio River Basin, Tex. The simulations 

indicated that brush management would result in increased 

water yields but that brush management was not cost effective at that time. Finally, Afinowicz and others (2005) showed 

water-yield increases for multiple brush-management scenarios by using a SWAT model of the 140-square-mile (mi2

) 

North Fork Guadalupe River watershed in the western part of 

the upper Guadalupe River watershed.

Purpose and Scope

This report documents the development and calibration 

of a SWAT watershed model of the upper Guadalupe River 

watershed to simulate streamflow and the effects of brush 

management on water yields for 1995–2010. Model simulations were done to quantify the change in water yield of 

individual subbasins in the upper Guadalupe River watershed 

as a result of the replacement of ashe juniper with grasslands. 

Limitations of the model are described.

Description of the Upper Guadalupe River 

Watershed

The Guadalupe River extends about 230 miles (mi) from 

its headwaters in the Edwards Plateau in south-central Texas to 

San Antonio Bay near Tivoli, Tex., and has a drainage area of 

about 10,200 mi2 (fig. 1). The upper Guadalupe River watershed study area is 1,432 mi2 of the Guadalupe River Basin 

upstream from Canyon Dam in Comal County, Tex. Canyon 

Lake is a run-of-river reservoir formed by Canyon Dam that 

supplies water for municipal water supply, irrigation, and 

industrial uses, as well as for operation of several small hydroelectric plants. At conservation pool the lake has a capacity of 

about 382,000 acre-feet (acre-ft), a surface area of about 13 

mi2, and about 80 mi of shoreline (U.S. Army Corps of Engineers, 2011). The study area encompasses parts of Bandera, 

Blanco, Comal, Gillespie, Kendall, Kerr, and Real Counties 

in south-central Texas. The largest city in the study area is 

Kerrville, Tex., which had a human population of 22,347 in 

2010 (U.S. Census Bureau, 2011).

The study area has a subtropical, subhumid climate 

characterized by hot summers and mild winters (Larkin and 

Bomar, 1983). Average daily low temperatures for 1995–2010 

ranged from 40 degrees Fahrenheit (oF) in the month of January to 73oF in August at the Canyon Dam National Weather 

Service (NWS) station (cooperative station identification 

[COOP ID] 411429) (site 5, fig. 2, table 1); average daily high 

temperatures ranged from 61oF in January to 95oF in August 

at that same station (National Climatic Data Center, 2011b). 

Average annual rainfall for 1995–2010 ranged from 27 inches 

at the Hunt 10 W NWS station (COOP ID 414375) (site 17, 

fig. 2, table 1) in the western part of the watershed to 39 

inches at the Canyon Dam NWS station (COOP ID 411429) 

(site 5, fig. 2, table 1) in the eastern part of the watershed 

(National Climatic Data Center, 2011b). Daily rainfall equal 

to or greater than 1 inch for 1995–2010 occurred, on average, 

40 days per year at the Hunt 10 W NWS station (COOP ID 

414375) (site 17, fig. 2, table 1) and 53 days per year at the 

Canyon Dam NWS station (COOP ID 411429) (site 5, fig. 2, 

table 1; National Climatic Data Center, 2011a). Asquith and 

Roussel (2003) reported that daily rainfall equal to or greater 

than 1 inch occurs, on average, every 52 days at the western 

boundary of the watershed to every 36 days in the eastern part 

of the watershed. Although most rainfall occurs in spring, 

early summer, and fall, daily rainfall amounts greater than 1 

inch occur anytime during the year (Larkin and Bomar, 1983).

The study area is located in a karst topographic region 

formed by the dissolution of Cretaceous-age carbonate rocks 
Figure 1. Location of the Guadalupe River watershed, including the upper Guadalupe River watershed study area, south-central Texas. 

Fredericksburg

San

Antonio

Austin

Kerrville

San

Marcos

New

Braunfels

Seguin

Victoria

§ ̈¦10

§ ̈¦35

§ ̈¦35

§ ̈¦10

§ ̈¦37

97°

98°

99°

100°

30°

29°

TRAVIS

BLANCO COUNTY

COUNTY

GILLESPIE

COUNTY

BASTROP

COUNTY

HAYS

COUNTY

KERR

COUNTY

FAYETTE

COUNTY

KENDALL

COUNTY

CALDWELL

COUNTY

REAL

COUNTY

BANDERA

COUNTY

GUADALUPE

COUNTY

GONZALES

COUNTY

LAVACA

COUNTY

WILSON

COUNTY

DEWITT

COUNTY

ARANSAS COUNTY

BEXAR

COUNTY

KARNES

COUNTY

GOLIAD

COUNTY

VICTORIA COUNTY

REFUGIO COUNTY

COMAL

 COUNTY

Guadalupe Blanco River

Guadalupe

River

River River

Guadalupe

River

Gulf of Mexico San Antonio Bay

Upper

Canyon

Lake

San Marcos

Tivoli

CALHOUN

COUNTY

TEXA

Study area 

Edwards 

Plateau

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Equal Area Projection North American Datum of 1983

EXPLANATION

Upper Guadalupe River watershedGuadalupe River watershed boundary (Natural Resources Conservation Service, 2012)City boundary

0 35 70 MILES

0 35 70 KILOMETERS
Figure 2. Locations of U.S. Geological Survey streamflow-gaging stations and the National Weather Service and U.S. Geological Survey meteorologfor the upper Guadalupe River watershed model, south-central Texas. 

§ ̈¦10

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£¤16

£¤27

£¤16

£¤281

£¤290

98°30'

99°

99°30'

30°10'

30°

29°50'

# #

#

#

#

#

#

## # #36 34

33

32

31

30

29

28 27

35 37

!

!

!

!

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! !

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!

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25 12

23

7

8

4

19 24 10

21

2

3

14

20

15

17

26

1 11

22

18

16

39

#

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33

EXPLANATION

Upper Guadalupe River watersheU.S. Geological Survey streamflo for model calibration and site U.S. Geological Survey streamflo and site identifier—See table Meteorological station and site id

00

10 20 MILES

10 20 KILOMETERS

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

North Fork Guadalupe River North Fork Guadalupe River

South Fork

Guadalupe

River

South Fork

Guadalupe

River

River

Guadalupe

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

Fredericksburg

Kerrville

Ingram

Boerne

Bulverde

Blanco

Comfort

Spring

Branch

Hunt

Center

Point

Bandera

Medina

Harper

Sisterdale

Kendalia

Camp

Verde

Cottonwood

Fredericksburg

Kerrville

Ingram

Boerne

Bulverde

Blanco

Comfort

Spring

Branch

Hunt

Center

Point

Bandera

Medina

Harper

Sisterdale

Kendalia

Camp

Verde

Cottonwood Ranch

Bankersmith

REAL

COUNTY
 5

Table 1. Data-collection sites providing data for the upper Guadalupe River watershed model, south-central Texas.—Continued

[NWS, National Weather Service; COOP ID, cooperative station identification; no., number; max, maximum; min, minimum; temp, temperature; USGS, U.S. 

Geological Survey]

Site 

identifier 

(fig. 2)

Station name, 

number, and location

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

Type of data 

(period of record available)

1 NWS station COOP ID 410509, Bankersmith, Gillespie County, Tex. 30.14111 98.82000 Daily precipitation (1947–2010)

2 NWS station COOP ID 410832, Blanco, Blanco County, Tex. 30.10583 98.42861 Daily max and min air temp 

(1946–2010)

3 NWS station COOP ID 410902, Boerne, Kendall County, Tex. 29.79778 98.73473 Daily precipitation; daily max and 

min air temp (1946–2010)

4 NWS station COOP ID 411395, Camp Verde, Kerr County, Tex. 29.89472 99.10500 Daily precipitation (1961–2010)

5 NWS station COOP ID 411429, Canyon Dam, Comal County, Tex. 29.87056 98.19666 Daily precipitation; daily max and 

min air temp (1961–2010)

6 NWS station COOP ID 411431, Canyon Dam no. 1, Comal County, Tex. 29.86167 98.29195 Daily precipitation (1961–2002)

7 NWS station COOP ID 411433, Canyon Dam no. 3, Comal County, Tex. 29.94639 98.39694 Daily precipitation (1961–2010)

8 NWS station COOP ID 411434, Canyon Dam no. 4, Comal County, Tex. 29.91111 98.37139 Daily precipitation (1961–2010)

9 NWS station COOP ID 411438, Canyon Dam no. 7, Comal County, Tex. 29.91667 98.21667 Daily precipitation (1961–1993)

10 NWS station COOP ID 411920, Comfort 2, Kendall County, Tex. 29.96139 98.89472 Daily precipitation (1961–2010)

11 NWS station COOP ID 412040, Cottonwood, Gillespie County, Tex. 30.16055 99.13556 Daily precipitation (1946–2010)

12 NWS station COOP ID 412830, Northington Rch, Kendall County, Tex. 29.88333 98.65000 Daily precipitation (1963–2010)

13 NWS station COOP ID 413156, Fischers Store, Comal County, Tex. 29.97556 98.26472 Daily precipitation (1947–2010)

14 NWS station COOP ID 413329, Fredericksburg, Gillespie County, Tex. 30.23917 98.90889 Daily max and min air temp 

(1946–2010)

15 NWS station COOP ID 413954, Harper 1w, Gillespie County, Tex. 30.30111 99.26806 Daily precipitation; daily max and 

min air temp (1947–2010)

16 NWS station COOP ID 414374, Hunt 3 SW, Kerr County, Tex. 30.02944 99.36139 Daily precipitation (1947–2000)

17 NWS station COOP ID 414375, Hunt 10 W, Kerr County, Tex. 30.06278 99.50500 Daily precipitation; daily max and 

min air temp (1976–2010)

18 NWS station COOP ID 414458, Ingram no. 2, Kerr County, Tex. 30.07361 99.23861 Daily precipitation (1947–2010)

19 NWS station COOP ID 414757, Kendalia, Kendall County, Tex. 29.96583 98.51167 Daily precipitation (1961–2010)

20 NWS station COOP ID 414782, Kerrville 3 NNE, Kerr County, Tex. 30.07444 99.10861 Daily precipitation; daily max and 

min air temp (1974–2010)

21 NWS station COOP ID 415742, Medina 1ne, Bandera County, Tex. 29.81000 99.24973 Daily max and min air temp 

(1959–2010)

22 NWS station COOP ID 416257, Nelson Rch, Kerr County, Tex. 29.95000 99.51667 Daily precipitation (1962–1983)

23 NWS station COOP ID 416448, Northington Rch, Kendall County, Tex. 29.86417 98.65806 Daily precipitation (1963–2010)

24 NWS station COOP ID 418358, Sisterdale, Kendall County, Tex. 29.97667 98.73000 Daily precipitation (1961–2010)

25 NWS station COOP ID 418544, Spring Branch 2se, Comal County, Tex. 29.86555 98.38194 Daily precipitation (1956–2010)

26 NWS station COOP ID 419904, Worlds End Rch, Kerr County, Tex. 29.96667 99.43333 Daily precipitation (1947–1983)

27 USGS streamflow-gaging station 08165300 North Fork Guadalupe 

River near Hunt, Tex.

30.06410 99.38699 Streamflow (1967–2010)

28 USGS streamflow-gaging station 08165500 Guadalupe River at Hunt, 

Tex.

30.06993 99.32171 Streamflow (1941–2010)

29 USGS streamflow-gaging station 08166000 Johnson Creek near Ingram, 

Tex.

30.10021 99.28310 Streamflow (1941–2010)

30 USGS streamflow-gaging station 08166140 Guadalupe River above 

Bear Creek at Kerrville, Tex.

30.06965 99.19532 Streamflow (1978–2010)

31 USGS streamflow-gaging station 08166200 Guadalupe River at 

Kerrville, Tex.

30.05327 99.16338 Streamflow (1986–2010)

Table 1. Data-collection sites providing data for the upper Guadalupe River watershed model, south-central Texas.

[NWS, National Weather Service; COOP ID, cooperative station identification; no., number; max, maximum; min, minimum; temp, temperature; USGS, U.S. 

Geological Survey]
6 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

(U.S. Geological Survey, 2006). Karst systems are generally 

characterized by groundwater–surface-water connections, 

such as losing streams, sinkholes, springs, and fracture and 

conduit connections between surface water and groundwater (Katz and others, 1997; Winter and others, 1998). Land 

cover in the watershed is dominated by shrubland and evergreen forest (German and others, 2009). Soils generally are 

shallow, calcareous clay and clay loam with rocky outcrops 

(Natural Resources Conservation Service, 2011a). Elevation in the watershed ranges from about 900 to 2,400 feet (ft) 

(Gesch, 2007), and land slopes varied from 0 to greater than 

60 percent.

Model Development

Functional Description of the Soil and Water 

Assessment Tool

The SWAT watershed model is a process-based, semidistributed water balance model designed to predict the 

effects of management decisions on water, sediment, and 

agricultural chemical yields (Arnold and others, 1998). As 

summarized in part by Garcia (2009), in SWAT, a delineated 

watershed is divided into subbasins, each identified by a 

single reach. Each subbasin is further divided into hydrologic 

response units (HRUs) that consist of unique combinations of 

land cover, soil characteristics, land slope, and land-management criteria. Processes including, but not limited to, surface 

runoff, evapotranspiration, base flow, channel transmission 

losses, the life cycle of plants, nutrient cycling, and constituent 

transport can be simulated for each HRU and are determined 

by the process-related parameter values uniquely defined for 

each HRU. During model development, default values are 

assigned to the model parameters by the modeling software 

based on the unique HRU characteristics. The simulated water 

and constituent loads are aggregated within their corresponding subbasins, are allocated to the subbasin reach, and exit a 

subbasin via outlet points on the stream network that define 

the subbasin. The model accounts for in-stream processes 

while discharge and constituent fluxes are kinematically 

routed downstream from upstream subbasins to the watershed 

outlet. Model output includes streamflow for any subbasin 

outlet, including the delineated watershed outlet. The input 

data and user-specified parameters determine the model that 

is developed by delineating HRUs and assigning default and 

calibrated parameter values. A complete description of the 

SWAT model and its simulated processes can be found in 

Neitsch and others (2011a).

The hydrologic component of SWAT uses the Natural 

Resources Conservation Service (NRCS) runoff curve-number 

(CN) equation (Soil Conservation Service, 1986). The CN 

equation is empirically based and relates runoff potential to 

land-cover and soil characteristics. A high CN translates into 

Table 1. Data-collection sites providing data for the upper Guadalupe River watershed model, south-central Texas.—Continued

[NWS, National Weather Service; COOP ID, cooperative station identification; no., number; max, maximum; min, minimum; temp, temperature; USGS, U.S. 

Geological Survey]

Site 

identifier 

(fig. 2)

Station name, 

number, and location

Latitude 

(decimal 

degrees)

Longitude 

(decimal 

degrees)

Type of data 

(period of record available)

32 USGS streamflow-gaging station 08166250 Guadalupe River near 

Center Point, Tex.

29.98778 99.11000 Streamflow (2008–2010)

33 USGS streamflow-gaging station 08167000 Guadalupe River at 

Comfort, Tex.

29.96524 98.89717 Streamflow (1939–2010)

34 USGS streamflow-gaging and meteorological station 08167347 

Unnamed Tributary Honey Creek Site 1C near Spring Branch, Tex.

29.85531 98.48483 Streamflow (2000–2010); daily 

precipitation (2000–2010)

35 USGS streamflow-gaging and meteorological station 08167350 

Unnamed Tributary Honey Creek Site 1T near Spring Branch, Tex.

29.85039 98.47289 Streamflow (1999–2010); daily 

precipitation (1999–2010)

36 USGS streamflow-gaging and meteorological station 08167353 

Unnamed Tributary Honey Creek Site 2T near Spring Branch, Tex.

29.85624 98.48008 Streamflow (2000–2010); daily 

precipitation (2000–2010)

37 USGS streamflow-gaging station 08167500 Guadalupe River near 

Spring Branch, Tex.

29.86050 98.38363 Streamflow (1922–2010)

38 USGS meteorological station 08167700 Canyon Lake near New 

Braunfels, Tex.

29.86883 98.19890 Daily precipitation (1998–2010)

39 USGS meteorological station 295040098283701 Honey Creek Rain 

Gage No. 1, Tex.

29.84451 98.47699 Daily precipitation (1999–2010)
Model Development 7

greater runoff. For example, forested land cover, such as ashe 

juniper, has a lower CN than grasslands and produces less 

runoff (Soil Conservation Service, 1986). Daily CN values 

were calculated in the model as a function of plant evapotranspiration instead of the standard calculation, which is as a 

function of soil moisture (model parameter ICN=1) (Neitsch 

and others, 2011b). Daily CNs calculated as a function of soil 

moisture tended to overestimate runoff in watersheds with 

shallow soils (Neitsch and others, 2011b), and the upper Guadalupe River watershed generally has shallow soils (Natural 

Resources Conservation Service, 2011a).

The SWAT watershed model version used for this study 

was the SWAT2009.exe revision 445. The SWAT model can be 

executed from within a geographic information system (GIS), 

which incorporates spatially distributed data. For this study, 

ArcGIS 9.3.1 (Environmental Research Systems Institute, 

Inc., Redlands, Calif.) and the ArcGIS extension ArcSWAT 

version 2009.93.5 (Winchell and others, 2010) were used to 

execute SWAT. The model and simulations presented in this 

report can be replicated by following the model-development 

steps described in the ArcSWAT user’s manual (Winchell and 

others, 2010) and the user input and calibration specifications 

described in this report.

Model Input Data

Input data for the upper Guadalupe River watershed 

model included spatial and temporal datasets. The spatial 

data consisted of elevation, soils, land cover, and locations 

of streamflow-gaging stations and meteorological stations. 

Temporal data consisted of streamflow, precipitation, and air 

temperature and were used for model calibration and model 

input. 

Ten-meter (one elevation value derived for every 

10-meter [m] by 10-m pixel) digital elevation models (DEMs) 

from the National Elevation Dataset were obtained to generate 

the topographic inputs for the model (Gesch and others, 2002; 

Gesch, 2007). Di Luzio and others (2005) and Cotter and others (2003) found that the resolution of the DEM was the most 

critical input parameter when developing a SWAT model. The 

study area watershed was delineated, and a stream network 

was created by using the DEM and a streamflow accumulation 

process in the ArcSWAT application. The 1,436-mi2 domain of 

the SWAT watershed model extends about 2 mi downstream 

from Canyon Dam. Ninety-three subbasins were created by 

using the elevation data and the user-selected subbasin outlets 

on the stream network (fig. 3). The model subbasins define 

model output locations and are used to generate HRUs for 

model simulations and calibration. Many of the subbasin 

outlets were selected on the basis of their proximity to USGS 

streamflow-gaging stations and to the upper Guadalupe River 

inlet to Canyon Lake. A slope raster, which is used in HRU 

generation, also was created from the elevation models and 

classified into five categories: less than or equal to 5 percent, 

greater than 5 percent to 10 percent, greater than 10 percent to 

30 percent, greater than 30 percent to 60 percent, and greater 

than 60 percent (fig. 4).

Soils data used for HRU generation were obtained from 

the Soil Survey Geographic (SSURGO) database (Natural 

Resources Conservation Service, 2011a). The SSURGO 

database was selected because it is the highest resolution soils 

product available that can be input to a SWAT model with 

minimal data processing. Some processing of the SSURGO 

data was required because ArcSWAT is developed for using 

the lower resolution State Soil and Geographic Survey 

(STATSGO) database (Natural Resources Conservation Service, 2011b). An ArcMap tool developed by Sheshukov and 

others (2009) was used to process the SSURGO database into 

a modified STATSGO format that is readable by ArcSWAT. 

The processing of the SSURGO data maintains the resolution of the data. One of the more important database attributes 

assigned to a soil by the NRCS is the hydrogroup, which is the 

infiltration rate of a soil (high, moderate, low, and very low) 

and is used to determine the CN of an HRU (fig. 5) (Natural 

Resources Conservation Service, 2011b). 

Land-cover data used to generate the HRUs were 

obtained from two sources: the National Land Cover Dataset 

(NLCD) 2006 (Fry and others, 2011) (fig. 6) and the Texas 

Ecological Systems Dataset (TESD) (German and others, 

2009). The NLCD 2006 was selected because its land-cover 

classifications can be directly input to a SWAT model for HRU 

generation. The NLCD 2006 was used in the initial model 

runs to develop the HRUs and calibrate the model, and the 

TESD was used during the brush-management simulations 

to delineate areas where ashe juniper could be replaced with 

grasslands in the NLCD 2006. In general, the inherent model 

properties for forest land-cover categories, including those 

that might contain ashe juniper, account for less runoff, more 

canopy interception of rainfall, and more evapotranspiration 

than range categories. The parameter values associated with 

these model properties vary by HRU and are too numerous to 

reproduce in this report (on file at the USGS office in Austin, Tex., and available upon request). The model generated 

6,357 HRUs as a result of intersecting the 93 model subbasins 

(fig. 3), 5 slope categories, soils data, and land-cover data.

USGS streamflow-gaging station locations for the study 

area and mean daily streamflow data for 1995–2010 were 

obtained from the USGS National Water Information System (NWIS) Web interface (U.S. Geological Survey, 2011). 

There are 11 USGS streamflow-gaging stations in the study 

area that recorded streamflow during the simulation period 

(fig. 2, table 1). The upper Guadalupe River watershed model 

was calibrated with streamflow data recorded continuously at 

USGS streamflow-gaging station 08167500 Guadalupe River 

near Spring Branch, Tex. (hereinafter the Guadalupe River 

near Spring Branch gage) (site 37, fig. 2, table 1), for 1995–

2010. The streamflow data collected at the Guadalupe River 

near Spring Branch station were chosen for model calibration 

because there are no structures or hydraulic modifications 

near the station—including, but not limited to, dams, floodcontrol structures, retention basins, and changes to channel 
Figure 3. Subbasin delineation for the upper Guadalupe River watershed model, south-central Texas.

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

72

37#

EXPLANATION

Model subbasin and numUpper Guadalupe River wModel stream reachU.S. Geological Survey st

station and site identif

0 10 20 MILES

0 10 20 KILOMETERS

North Fork Guadalupe River North Fork Guadalupe River

South Fork

Guadalupe

River

South Fork

Guadalupe

River

River

Guadalupe

# #

#

#

#

#

#

## # # 37 36 34

33 32

31

30 29 28

27

35

72

52

1

46

56 48

8

45 28

37

29

38

44

83

6

93

24

75

35

42

3

49

22

14

31

7

16

71

53

57

60

55

5

34

12

61 47

64

92

33

50

25

89

9

88

51

10

26

23

43

39

5

27

32

30

54

15

11

40

41

19

20

85

5

36

76

13

21

4

18 17

2

72

52

1

46

56 48

8

45 28

37

29

38

44

83

6

93

24

75

35

42

3

49

22

14

31

7

16

71

53

57

60

55

5

34

12

61 47

64

92

33

50

25

89

9

88

51

10

26

23

43

39

5

27

32

30

54

15

11

40

41

19

20

85

5

36

76

13

21

4

18 17

2
Figure 4. Slope categories used for the upper Guadalupe River watershed model, south-central Texas. 

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

Slope from USGS National El

EXPLANATION

Slope category 0 to 5 percent (%) Greater than (>) 5% to 1 > 10% to 30% > 30% to 60% > 60%Upper Guadalupe River watershed boundary

0 10 20 MILES

0 10 20 KILOMETERS
Figure 5. Relative soil infiltration rates in the upper Guadalupe River watershed, south-central Texas.

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

Soil data from soil survey geog

Blanco, Comal, Gillespie, Ken

(Natural Resources 

EXPLANATION

Relative soil infiltration r High infiltration rate Moderate infiltration rat Low infiltration rate Very low infiltration rateUpper Guadalupe River watershed boundary

0 10 20 MILES

0 10 20 KILOMETERS
Figure 6. National Land Cover Database 2006 land-cover categories in the upper Guadalupe River watershed, south-central Texas. 

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

Land cover d

Da

Land cover category Open water Developed, open space Developed, low intensity Developed, medium intensity Developed, high intensity Barren land Deciduous forest

 Evergreen fo Mixed forest Shrubland Grassland/he Pasture/hay Cultivated cr Woody wetlaUpper Guadalu watershed 

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS
12 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

geometry—that might affect streamflows and the contributing 

drainage area (1,315 mi2) of the station is more than 91 percent of the total watershed area. Minimum monthly streamflow at the Guadalupe River near Spring Branch station for 

1995–2010 was 4.85 cubic feet per second (ft3/s) in July 2009, 

maximum monthly streamflow was 10,534 ft3/s in July 2002, 

mean monthly streamflow was 505 ft3/s, and median monthly 

streamflow was 224 ft3/s (U.S. Geological Survey, 2011).

National Weather Service (NWS) station locations and 

precipitation and air temperature data for 1980–2010 were 

obtained from the National Climatic Data Center (2011a, 

2011b). The model was run using the 1980–1994 meteorological data and the ending conditions within the model (vegetation, soil moisture conditions, shallow groundwater levels, and 

so on) from that 15-year simulation were used as the initial 

conditions in the model for the 1995–2010 simulations. Total 

daily precipitation, daily maximum temperature, and daily 

minimum temperature were the data used to drive the model. 

Additional sites and precipitation data were obtained from 

the NWIS Web interface (U.S. Geological Survey, 2011). The 

SWAT watershed model automatically selected the 28 nearest 

rain gages and 8 temperature gages for use in the simulations 

(fig. 2, table 1).

Model Calibration 

Hydrology of the upper Guadalupe River watershed 

model was manually calibrated. Manual hydrologic calibration of a SWAT model consisted of adjusting process-related 

parameter values to minimize the differences between simulated and measured streamflows at a USGS streamflow-gaging 

station. Parameter values can vary within a given uncertainty 

range defined in the SWAT tool input and output file documentation (Neitsch and others, 2011b). Model performance 

is evaluated with several criteria for goodness of fit (for 

example, calibration metrics): the percent bias (PBIAS) of 

simulated streamflow to measured streamflow, the coefficient 

of determination (R2) of the linear regression, and the Nash–

Sutcliffe coefficient of model efficiency (NSE) (Nash and 

Sutcliffe, 1970).

To evaluate the ability of a model to produce an unbiased 

estimate of the streamflow component of the mass balance 

for an entire simulation, the PBIAS statistic was used and is 

calculated by

( )

( ) 1

1

PBIAS

obs sim

i i

ob

n

i

n

i

s

i

Y Y

Y

=

=

− = ∑

∑ (1)

where obs Yi is the measured streamflow at the i

th

 time step; 

sim and Yi is the simulated streamflow at the i

th time step.

The total number of time steps is indicated by n. The 

PBIAS statistic can be positive or negative. The closer to 0 the 

PBIAS value is, the more equally balanced are the overpredictions and the underpredictions of streamflow for the period 

being evaluated. A negative PBIAS value indicates generally 

that a model is overpredicting streamflow, whereas a positive 

value indicates underprediction. PBIAS values between 0 and 

plus or minus (+/-) 10 percent indicate a “very good” model 

simulation, values between +/-10 and +/-15 percent indicate a 

“good” model simulation, and values between +/-15 and +/-25 

percent indicate a “satisfactory” model simulation (Moriasi 

and others, 2007). These are the ranges that were used to 

evaluate the model calibration.

The R2 value, which measures how well the simulated 

and measured regression lines approach an ideal linear regression, ranges from 0 to 1, with a value of 0 indicating no correlation and a value of 1 indicating that the simulated values 

equal the corresponding measured values. Gassman and others 

(2007) used 0.5 as a satisfactory fit for R2 values for all time 

steps to compare results across various SWAT applications.

The NSE assesses the ability of a model to correctly 

simulate streamflow during periods when recorded streamflow 

deviates greatly from the measured mean monthly streamflow. 

The NSE is calculated by

( ) ( ) 1

2

1

2

NSE 1 / n n obs sim

i i

obs mean Yi Yi Yi Y = =

  = − − − ∑ ∑  (2)

where 

mean Y is the measured mean monthly streamflow 

(other variables were previously defined).

The NSE can range from negative infinity to 1. An NSE 

of 1 indicates a perfect fit between simulated and measured 

data. An NSE of 0 indicates that the model predictions are as 

accurate as the mean of the measured data, and an NSE of less 

than 0 indicates that the measured mean is a better predictor 

than the model (Nash and Sutcliffe, 1970). 

A recent review of SWAT applications throughout the 

world, including many in Texas, shows monthly NSE values 

ranging from 0.30 to greater than 0.95 (Gassman and others, 

2007). Moriasi and others (2007) proposed that the performance of a model is considered to be “very good” if the 

monthly NSE is greater than or equal to 0.75, “good” if the 

monthly NSE is greater than or equal to 0.65 and less than 

0.75, “satisfactory” if the monthly NSE is greater than or equal 

to 0.5 and less than 0.65, and “unsatisfactory” if the monthly 

NSE is less than 0.5. Gassman and others (2007) used NSE 

values of greater than or equal to 0.5 as indicative of satisfactory fit for all time steps to compare results across various 

SWAT applications. The NSE model-performance criteria 

proposed by Moriasi and others (2007) were used to evaluate 

the model calibration.

Table 2 shows the process-related parameter values that 

were adjusted to calibrate the simulated watershed hydrology. 

The default parameter values, which are described in detail 

in Neitsch and others (2011a), were used for the remaining parameter values. Also, forest and range categories were 
Model Development 13

varied in an equitable way so as to maintain the relative 

parameter differences between the two categories. In other 

words, forest categories generally produced less runoff, had 

higher canopy interception of rainfall, and higher evapotranspiration rates than range categories in the model. Permitted 

surface-water withdrawals (water rights) (Texas Commission 

on Environmental Quality, 2012) and discharges for municipal, industrial, and agricultural purposes (U.S. Environmental 

Protection Agency, 2012) were evaluated but not included 

in the model calibration because they are each an order of 

magnitude less than the average annual flow volumes at the 

calibration gage. Current (2012) permitted annual withdrawals in the watershed upstream from Guadalupe River near 

Spring Branch gage used in the calibration (less than 30,000 

acre-ft) are minimal compared to the average annual water 

yields at the calibration streamflow-gaging station (about 

366,000 acre-ft for 1995–2010). The PBIAS for the monthly 

hydrologic calibration using data collected at the Guadalupe 

River near Spring Branch gage for 1995–2010 was -5 (overprediction), monthly R2

 was 0.91, and monthly NSE was 0.85 

(fig. 7, table 3). These calibration metric values indicate a 

very good fit between the simulated and measured monthly 

streamflow values. Minimum simulated monthly streamflow 

was 11 ft3/s in November 2008, maximum simulated monthly 

streamflow was 12,950 ft3/s in July 2002, mean simulated 

monthly streamflow was 531 ft3/s, and median simulated 

monthly streamflow was 171 ft3/s. Finally, although the model 

performance was primarily evaluated on a monthly time step, 

the daily and annual calibration metrics were calculated as 

a secondary evaluation of model performance. As expected, 

there was variability in the NSE values based on temporal 

resolution. The daily NSE was 0.72, and the annual NSE was 

0.90, which indicate a good fit and very good fit between the 

simulated and measured daily and annual streamflow values, 

respectively.

The calibration results of the upper Guadalupe River 

watershed model are similar to those of other SWAT watershed model applications in Texas watersheds of similar size. 

A study of an approximately 1,900-mi2 part of the Trinity 

River watershed in north Texas (Srinivasan and others, 1998) 

reported monthly NSE values of 0.87 and 0.84. A modeling 

effort completed in the Bosque River watershed in central 

Texas obtained a monthly NSE value of 0.89 for an approximately 1,150-mi2 watershed (Santhi and others, 2001). Moon 

and others (2004) reported monthly NSE values of 0.78 and 

0.82 for the approximately 1,000-mi2 Cedar Creek watershed 

in northeast Texas. A modeling effort in the approximately 

1,750-mi2 West Fork watershed of Trinity River watershed 

in northeast Texas obtained monthly NSE values of 0.12 and 

0.72 (Santhi and others, 2006). A SWAT watershed model 

application to the approximately 1,750-mi2 Lake Travis part of 

the Colorado River watershed in central Texas (Anchor QEA, 

LLC and Parsons Corporation, 2009) reported monthly NSE 

values of 0.47 and 0.82. Finally, Afinowicz and others (2005) 

reported a daily NSE of 0.05 and monthly NSE of 0.50 for a 

model of the approximately 140-mi2 North Fork Guadalupe 

River watershed, which is in the western part of the upper 

Guadalupe River watershed in south-central Texas.

Table 2. Summary of the calibrated values for selected process-related parameters for the Soil and Water Assessment Tool watershed 

model of the upper Guadalupe River watershed, south-central Texas, 1995–2010.

[mm, millimeters; mm/hour, millimeters per hour; --, unitless value; SWAT, Soil and Water Assessment Tool; *, a variable model subbasin or HRU number is 

contained in input file name; **, value varies by HRU and was therefore increased or decreased by a percent or constant value; %, percent; calibration parameters for open-water land cover in .mgt and .hru files were not changed from default values]

Parameter Description (units) SWAT input 

file location

Calibrated 

parameter value

Default 

parameter value

ALPHA_BF Base-flow recession constant (days) *.gw 0.15 0.048

CANMX Maximum canopy storage (mm) *.hru +20 **

CH_K2 Effective hydraulic conductivity in main channel alluvium 

(mm/hour)

*.rte 13 0

CN2 Initial SCS curve number (--) *.mgt All Range and Forest 

categories decreased by 7%

**

GW_DELAY Groundwater delay time (days) *.gw 15 30

GW_REVAP Represents water movement from the shallow aquifer to the 

root zone (--)

*.gw 0.2 0.02

GWQMN Threshold depth for water in the shallow aquifer for return 

flow to occur (mm)

*.gw 60 0

RCHRG_DP Deep aquifer percolation factor (--) *.gw 0.46 0

REVAPMN Threshold depth for water in the shallow aquifer for percolation to the deep aquifer to occur (mm)*.gw 0 1

SURLAG Surface runoff lag coefficient (--) .bsn 1 4
14 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

Figure 7. Calibration plots of the A, temporal distribution and B, relation of simulated monthly streamflow and measured monthly 

streamflow at U.S. Geological Survey streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Texas, 1995–2010. 

Calibration metrics: Percent bias (PBIAS) of -5, monthly coefficient of determination (R2) was 0.91, and monthly Nash–Sutcliffe 

coefficient of model efficiency (NSE) of 0.85. 

Measured monthly mean streamflow, in cubic feet per second

0 10 100 1,000 10,000 100,000

1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Year

100,000

10,000

1,000

100

10

0

Simulated monthly mean streamflow, in cubic feet per second

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Monthly mean streamflow, in cubic feet per second

A

B

Measured

Simulated

EXPLANATION
Simulation of Streamflow and the Effects of Brush Management on Water Yields 15

Model Limitations

Errors in the model calibration can be classified as systematic or measurement errors (Raines, 1996) and are represented in the model calibration metrics. Systematic errors are 

those that reflect the model’s inability to perfectly represent 

the hydrologic processes of the watershed. As a result of these 

types of errors, there are limits to how well model parameters 

and equations can replicate the complex physical properties of 

streamflow processes, which can affect the accuracy of model 

calibration.

Measurement errors are those that are introduced as a 

result of inaccurate or missing data. The measurement errors 

that most likely affected the performance of the upper Guadalupe River watershed model were as follows: the spatial 

distribution of the land-cover data did not change over time; 

the model was calibrated by using data from one streamflowgaging station; permitted water withdrawal amounts were not 

included; and point measurements of precipitation data were 

distributed in space across the model domain. The model 

simulations for this study did not consider changes in the 

spatial distribution of land cover over time, which might alter 

runoff calculations (Strauch and Linard, 2009). In an attempt 

to compensate, simulations were limited to a 16-year time 

period around the data compilation date of the NLCD 2006. 

The inherent assumption is that watershed land cover did not 

appreciably change during this period of time. 

The model was calibrated to streamflow data collected 

at a single gage because the majority of the other gages in the 

watershed were near structures or hydraulic modifications that 

affected streamflows on a local level (for example, floodcontrol dams). As a result, there is more uncertainty related 

to subbasin streamflow than there would be if the watershed 

calibration included data collected at multiple streamflow-gaging stations. Because permitted withdrawals in the watershed 

upstream from the calibration streamflow-gaging station are 

only about 8 percent of average annual flow measured at the 

calibration streamflow-gaging station (Guadalupe River near 

Spring Branch gage), any uncertainty as a result of not including permitted withdrawals in the model is likely minimal.

In SWAT, precipitation data are used as direct input 

to subbasins from the closest precipitation gage instead of 

applying a gradient based on interpolation between gages, 

which might affect model calibration if gages are distributed 

unevenly throughout the basin (Strauch and Linard, 2009). 

There is an observed gradient of average annual precipitation increase from west to east in the watershed. Additionally, 

precipitation in the study area can be isolated to a small area 

surrounding a precipitation gage, and the model might overpredict the amount of rainfall-produced runoff from an HRU. 

Conversely, a substantially higher amount of precipitation 

might occur between gages than was recorded at the gages, 

and thus, the model might underpredict rainfall-produced 

runoff from an HRU. Finally, the trajectory and speed of a 

storm affect watershed response, and storm paths might not be 

represented by the recorded data, which may also affect model 

calibration.

Simulation of Streamflow and the 

Effects of Brush Management on Water 

Yields

The 93 model subbasins of the calibrated watershed 

model (fig. 3) were aggregated into 23 brush-management 

subbasins for the simulation of the effects of brush management on water yields (fig. 8). Twenty-three modified landcover input datasets were created to estimate the potential for 

brush management in each of the 23 subbasins. HRUs were 

generated in the model by using the modified land-cover input 

datasets. Water-yield changes were calculated as the difference between the water-yield and streamflow outputs from the 

brush-management simulation and the water-yield and streamflow outputs from the unmodified model simulation.

Table 3. Hydrologic calibration results for the Soil and Water Assessment Tool watershed model of the upper Guadalupe River 

watershed at U.S. Geological Survey streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Texas, 1995–2010.

[min, minimum; max, maximum; ft3/s, cubic feet per second; calibration parameters for open-water land cover in .mgt and .hru files were not changed from 

default values]

Calibration 

time 

step

Percent 

bias 

(PBIAS)

Coefficient of 

determination 

(R2)

Nash–

Sutcliffe 

coefficient 

of model 

efficiency 

(NSE)

Simulation Measured

Min 

streamflow 

(ft3/s)

Max 

streamflow 

(ft3/s)

Mean 

streamflow 

(ft3/s)

Median 

streamflow 

(ft3/s)

Min 

streamflow 

(ft3/s)

Max 

streamflow 

(ft3/s)

Mean 

streamflow 

(ft3/s)

Median 

streamflow 

(ft3/s)

Daily -5% 0.83 0.72 8 87,227 531 161 0 69,400 506 197

Monthly -5% .91 .85 11 12,950 531 171 5 10,534 505 224

Annual -5% .91 .90 51 1,654 531 378 76 1,312 506 353
Figure 8. Delineation of the brush-management subbasins for the upper Guadalupe River watershed model brush-management simulations, south-c

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

8 9

1

5

2

3

7

6

18 

4

11

13

19

10

20

17

12 16

15 21

14

22

19 Brush-management subba

 See table 6 for simulatiUpper Guadalupe River w

EXPLANATION

0 10 20 MILES

0 10 20 KILOMETERS

North Fork Guadalupe River North Fork Guadalupe River

South Fork

Guadalupe

River

South Fork

Guadalupe

River

River

Guadalupe
Simulation of Streamflow and the Effects of Brush Management on Water Yields 17

Simulation Methods

The NLCD 2006 was modified to simulate ashe juniper 

replacement with grasslands in each of the 23 brush-management subbasins. The NLCD 2006 categories chosen to 

represent areas that might contain ashe juniper were Deciduous Forest, Evergreen Forest, Mixed Forest, and Shrubland. 

Because the NLCD 2006 categories do not explicitly identify 

vegetation types, a second dataset with more detailed categories of vegetation was required to isolate ashe juniper in 

each subbasin. The TESD was selected for this task because 

it has 43 ecological-system categories in the study area, and 

ashe juniper was identified as present in 13 of them (German 

and others, 2009). The 13 categories with ashe juniper were 

evaluated on the basis of the TESD ecological-system descriptions. Categories in which ashe juniper was listed as one of 

the dominant vegetation types in the category description, 

except those that were also classified as riparian zone or steep 

slope, were selected for use in the brush-management simulations. The categories dominated by ashe juniper that were also 

classified as riparian zone or steep slope were not removed for 

the brush-management simulations because, as explained by 

Afinowicz and others (2005), field applications are unlikely 

to replace ashe juniper with grasslands in these areas. Ultimately, there were three TESD categories chosen to represent 

the location of ashe juniper to be replaced with grassland for 

the brush-management simulations. The “common names” for 

the TESD categories are Edwards Plateau: Ashe Juniper Motte 

and Woodland; Edwards Plateau: Ashe Juniper and Live Oak 

Shrubland; and Native Invasive: Ashe Juniper Shrubland. The 

two TESD land-cover class names for these categories are 

Juniper Forest and Juniper Shrubland, and they are in about 

23 percent of the upper Guadalupe River watershed study area 

(fig. 9; German and others, 2009).

To simulate removal of the ashe juniper, the raster 

calculator in ArcMap 9.3.1 was used to change the NLCD 

2006 values from Deciduous Forest, Evergreen Forest, Mixed 

Forest, and Shrubland to Grassland/Herbaceous if the cells 

in the NLCD 2006 raster intersected cells in the TESD raster 

that represented Juniper Forest and Juniper Shrubland. This 

method is similar to the removal methods used in the previous 

Texas State Soil and Water Conservation Board (TSSWCB) 

feasibility studies (Bednarz and others, 2000) and the maximum removal scenarios used by Afinowicz and others (2005). 

Twenty-three modified land-cover input datasets were created, 

each with the ashe juniper removed from just one subbasin. 

Table 4 shows the areal coverage of unmodified NLCD 2006 

land-cover categories for each brush-management subbasin 

in the upper Guadalupe River watershed. Table 5 shows the 

areal coverage of the NLCD land-cover categories after 

modification of the NLCD 2006 for each brush-management subbasin that was used in the 23 brush-management 

simulations.

For each of the 23 brush-management simulations, the 

HRUs were generated in the model by using the modified 

land-cover input dataset that corresponded with the simulated 

subbasin, and the model parameters associated with land 

cover were automatically modified by the modeling software 

accordingly (Neitsch and others, 2011b). Following each 

brush-management simulation, the model output was compared to the unmodified model simulation output to calculate 

the change in water yield per acre of ashe juniper replaced 

with grasslands. Change in water yield was calculated in two 

ways: (1) as the difference in annual water yield to the reach in 

a given brush-management subbasin (calculated by using the 

model output parameter WYLD in the output.sub file, which is 

the net amount of water that leaves a given subbasin and contributes to streamflow in the reach via surface runoff, lateral 

flow, and groundwater discharge over a given time step) and 

(2) as the difference in annual water yield from the subbasin 

to Canyon Lake based on the difference in annual streamflow 

at the combined subbasin outlets of model subbasins 58 and 

59 (brush-management subbasin 22; calculated by using the 

model output parameter FLOW_OUT in the output.rch file, 

which is the average streamflow out of a reach over a given 

time step) (figs. 3 and 9). The difference in annual streamflow 

(the second method) represents the simulated change in water 

yield to Canyon Lake for each of the brush-management subbasins except subbasin 23. Subbasin 23 is downstream from 

subbasin 22, and water yield in subbasin 23 (the first method) 

was considered direct drainage to Canyon Lake.

Simulation Results

Annual water yields increased in each of the 23 brushmanagement simulations (table 6). The increases in average annual water yields in the subbasins ranged from 6,370 

to 119,000 gallons per acre of ashe juniper replaced with 

grasslands with an average of 38,800 gallons per acre of ashe 

juniper replaced with grasslands. The increases in average 

annual water yields from upstream subbasins to Canyon Lake 

ranged from 6,640 to 72,700 gallons per acre of ashe juniper 

replaced with grasslands with an average of 34,700 gallons 

per acre of ashe juniper replaced with grasslands. These values 

are within the range of water-yield increases calculated for the 

eight previous TSSWCB brush-management feasibility studies 

(Bednarz and others, 2000). In some cases, the increase in 

streamflow volume was less than the increase in water yield 

because of simulated stream-channel transmission losses that 

occurred between each subbasin and Canyon Lake. There is 

uncertainty in the absolute values of water-yield increases 

from brush-management practices that is represented by 

the calculated uncertainty within the model (the calibration 

metrics). Given the distributed approach to model calibration 

(unique HRU parameter values were treated equally regardless 

of the location of the HRU in the watershed), there is probably 

less uncertainty in the relative differences in subbasin sensitivities to simulated brush management than the absolute values. 

Finally, simulations did not consider potential ashe juniper 

re-growth as this was beyond the scope of the study.
Figure 9. Texas Ecological Systems Dataset land-cover classes used for brush-management simulations for the upper Guadalupe River watershed m

98°30'

99°

99°30'

30°10'

30°

29°50'

GILLESPIE COUNTY 

KERR COUNTY

KENDALL COUNTY

BANDERA COUNTY

BEXAR COUNTY

REAL

COUNTY

8 9

1

5

2

3

7

6

18 

4

11

13

19

10

20

17

12 16

15 21

14

22

Base modified from U.S. Geological Survey 1:100,000-scale digital dataTexas Centric Mapping System–Albers Equal Area Projection North American Datum of 1983

Texas 

Texas Pa

EXPLANATION

19

Juniper forest and junipeBrush-management subb and numberUpper Guadalupe River w

0 10 20 MILES

0 10 20 KILOMETERS
Simulation of Streamflow and the Effects of Brush Management on Water Yields 19

Table 4. Percent areal coverage of unmodified National Land Cover Database 2006 land-cover categories for the brush-management 

subbasins used for the upper Guadalupe River watershed model brush-management simulations, south-central Texas.

[Because of rounding differences, percent areal coverages for a subbasin might not add to 100 percent]

Brush-management 

subbasin 

(fig. 8)

Subbasin 

area 

(acres)

Open 

water 

(percent)

Developed, 

open space 

(percent)

Developed, 

low intensity 

(percent)

Developed, 

medium intensity 

(percent)

Developed, 

high intensity 

(percent)

Barren 

land 

(percent)

Deciduous 

forest 

(percent)

1 45,369 0 0.243 0.022 0.015 0 0.012 9.62

2 42,270 .243 .029 .028 .005 0 0 10.3

3 45,138 .024 1.01 .004 0 0 0 6.28

4 37,717 .320 2.46 .096 .006 .004 0 7.15

5 50,379 .065 1.67 .345 .103 0 0 2.90

6 49,977 .175 4.02 .473 .182 0 .010 6.83

7 44,312 .054 .482 .034 .004 0 0 5.99

8 45,287 .442 9.48 1.35 .540 .142 0 8.55

9 27,095 .559 18.7 3.99 1.81 .861 .056 2.89

10 39,486 .224 6.02 .250 .044 0 .004 3.56

11 31,697 .434 5.81 .194 .057 .015 .096 5.57

12 36,106 .090 4.86 .047 0 0 .027 4.10

13 50,332 .099 5.15 .458 .125 0 .102 6.26

14 41,192 .038 .663 .013 0 0 .028 6.20

15 52,853 .047 5.58 .561 .180 .063 .242 3.48

16 46,664 .068 2.97 .052 .005 0 .139 5.57

17 47,758 .113 1.16 .006 .003 0 .107 7.40

18 35,501 .054 2.08 .071 0 0 0 5.53

19 30,848 .210 1.12 .085 .020 .002 .162 10.4

20 34,697 .019 .833 .019 0 0 .037 8.77

21 28,626 .286 9.04 1.47 .190 .018 .045 13.6

22 30,873 .673 13.2 2.66 .131 .004 .040 12.7

23 24,614 16.5 15.3 5.44 .462 .090 .306 7.65

Brush-management 

subbasin 

(fig. 8)

Evergreen 

forest 

(percent)

Mixed 

forest 

(percent)

Shrubland 

(percent)

Grassland or 

herbaceous 

(percent)

Pasture 

or hay 

(percent)

Cultivated 

crops 

(percent)

Woody 

wetlands 

(percent)

1 17.6 0.006 72.0 0.404 0 0 0

2 34.3 .038 53.5 1.53 0 0 .004

3 31.2 .067 61.0 .379 0 0 .006

4 47.0 .051 41.4 1.55 0 0 .007

5 14.7 .023 78.9 1.19 .054 0 .007

6 34.3 .003 51.2 2.58 .073 .160 .008

7 32.7 .020 58.5 2.07 .185 .000 .009

8 46.7 .019 28.9 3.71 .151 .094 .007

9 31.6 0 32.3 6.32 .677 .230 .035

10 34.8 .047 48.3 6.01 .121 .550 .002

11 30.9 .031 44.3 11.1 .856 .621 .045

12 28.5 .005 42.2 16.4 1.349 2.41 .013

13 15.6 .007 56.9 13.9 .490 .834 .043

14 19.5 0 58.2 14.7 .337 .262 .015

15 19.7 0 46.4 18.7 2.04 3.04 .059

16 37.5 .012 37.5 15.3 .381 .453 .033

17 22.4 .007 50.1 18.2 .320 .177 .040

18 30.9 .017 44.4 16.7 .169 .109 .026

19 39.6 .005 37.6 10.8 .000 .000 .061

20 27.7 0 48.0 13.8 .441 .347 .054

21 27.5 0 29.3 17.7 .114 .633 .238

22 35.7 0 23.5 11.2 .060 0 .199

23 28.6 0 16.0 9.50 .029 0 .149
20 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

Table 5. Percent areal coverage of modified National Land Cover Database 2006 land-cover categories for the brush-management 

subbasins used for the upper Guadalupe River watershed model brush-management simulations, south-central Texas.

[Because of rounding differences, percent areal coverages for a subbasin might not add to 100 percent]

Brush-management 

subbasin 

(fig. 8)

Subbasin 

area 

(acres)

Open 

water

Developed, 

open space 

(percent)

Developed, 

low intensity 

(percent)

Developed, 

medium intensity 

(percent)

Developed, 

high intensity 

(percent)

Barren 

land 

(percent)

Deciduous 

forest 

(percent)

1 45,369 0 0.243 0.022 0.015 0 0.012 4.17

2 42,270 .243 .029 .028 .005 0 0 3.75

3 45,138 .024 1.01 .004 0 0 0 3.18

4 37,717 .320 2.46 .096 .006 .004 0 3.95

5 50,379 .065 1.67 .345 .103 0 0 1.52

6 49,977 .175 4.02 .473 .182 0 .010 3.32

7 44,312 .054 .482 .034 .004 0 0 2.15

8 45,287 .442 9.48 1.35 .540 .142 0 4.82

9 27,095 .559 18.7 3.99 1.81 .861 .056 1.82

10 39,486 .224 6.02 .250 .044 0 .004 1.81

11 31,697 .434 5.81 .194 .057 .015 .096 3.56

12 36,106 .090 4.86 .047 .000 0 .027 2.94

13 50,332 .099 5.15 .458 .125 0 .102 5.65

14 41,192 .038 .663 .013 0 0 .028 5.63

15 52,853 .047 5.58 .561 .180 .063 .242 3.00

16 46,664 .068 2.97 .052 .005 0 .139 4.44

17 47,758 .113 1.16 .006 .003 0 .107 6.59

18 35,501 .054 2.08 .071 0 0 0 4.43

19 30,848 .210 1.12 .085 .020 .002 .162 7.34

20 34,697 .019 .833 .019 .000 0 .037 7.44

21 28,626 .286 9.04 1.47 .190 .018 .045 10.9

22 30,873 .673 13.2 2.66 .131 .004 .040 6.34

23 24,614 16.5 15.3 5.44 .462 .090 .306 4.50

Brush-management 

subbasin 

(fig. 8)

Evergreen 

forest 

(percent)

Mixed 

forest 

(percent)

Shrubland 

(percent)

Grassland or 

herbaceous 

(percent)

Pasture 

or hay 

(percent)

Cultivated 

crops 

(percent)

Woody 

wetlands

(percent)

1 8.13 0.005 57.3 30.1 0 0 0

2 12.4 .024 41.7 41.8 0 0 .004

3 17.5 .058 53.5 24.7 0 0 .006

4 24.5 .037 31.3 37.4 0 0 .007

5 8.83 .018 71.2 16.2 .054 0 .007

6 20.0 .002 41.2 30.4 .073 .160 .008

7 14.3 .019 45.9 36.8 .185 0 .009

8 27.3 .016 20.3 35.4 .151 .094 .007

9 20.2 0 24.4 26.7 .677 .230 .035

10 23.8 .043 37.1 30.0 .121 .550 .002

11 20.9 .023 34.2 33.2 .856 .621 .045

12 21.1 .005 33.5 33.8 1.35 2.41 .013

13 12.9 .007 48.9 25.3 .490 .834 .043

14 16.1 0 49.8 27.2 .337 .262 .015

15 16.6 0 41.5 27.1 2.04 3.04 .059

16 30.8 .011 31.7 29.0 .381 .453 .033

17 18.2 .007 44.0 29.3 .320 .177 .040

18 23.5 .015 39.6 29.9 .169 .109 .026

19 24.9 .003 31.0 35.1 0 0 .061

20 21.8 0 44.1 25.0 .441 .347 .054

21 18.4 0 25.3 33.3 .114 .633 .238

22 16.3 0 15.1 45.4 .060 0 .199

23 14.6 0 11.9 30.7 .029 0 .149
Simulation of Streamflow and the Effects of Brush Management on Water Yields 21

In general, the highest water-yield increases were calculated for the brush-management subbasins in the eastern half 

of the watershed (7 of the top 10), which is the part of the 

watershed that receives the most precipitation. Previous studies indicated that brush management had the greatest effect on 

water yields in areas that received at least 18 inches of annual 

precipitation (Thurow, 1998); Bednarz and others (2000) 

reported a correlation between water-yield increase and precipitation (R2

 = 0.75). Investigation of the correlation between 

the amount of precipitation and watershed response to brush 

management is beyond the scope of this study. Future studies are needed to determine if there are correlations between 

water-yield increases and precipitation, watershed attributes, 

or simulation methodology.

Table 6. Effects of brush management on water yields simulated by the Soil and Water Assessment Tool watershed model of the upper 

Guadalupe River watershed, south-central Texas, 1995–2010.

[gal, gallons; --, value is not applicable because the flow from the subbasin is direct drainage to the lake]

Brushmanagement 

subbasin 

(fig. 8)

Subbasin 

area 

(acres)

Total area modified 

for brush management simulation 

(acres)

Percent of 

subbasin 

modified

Increased average annual 

water yield to the subbasin 

reach per acre of ashe juniper 

replaced with grasslands 

(gal)1

Increased average annual 

water yield to Canyon Lake per 

acre of ashe juniper 

replaced with grasslands 

from each subbasin

(gal)2

1 45,369 13,475 30 21,000 18,200 

2 42,270 17,035 40 29,200 28,500 

3 45,138 10,969 24 20,600 18,600 

4 37,717 13,540 36 20,600 19,800 

5 50,379 7,557 15 17,800 17,600 

6 49,977 13,894 28 20,200 19,900 

7 44,312 15,376 35 22,800 22,500 

8 45,287 14,356 32 42,100 41,600 

9 27,095 5,527 20 71,500 70,600 

10 39,486 9,477 24 45,800 45,700 

11 31,697 7,005 22 6,370 6,640 

12 36,106 6,282 17 61,900 60,800 

13 50,332 5,738 11 27,400 27,300 

14 41,192 5,149 13 15,200 15,000 

15 52,853 4,493 9 18,700 19,100 

16 46,664 6,393 14 33,200 33,200 

17 47,758 5,301 11 29,300 29,200 

18 35,501 4,686 13 56,000 54,700 

19 30,848 7,496 24 73,300 72,700 

20 34,697 3,886 11 58,500 58,500 

21 28,626 4,494 16 45,400 44,700 

22 30,873 10,558 34 38,100 38,100 

23 24,614 5,218 21 119,000 -- 

1

The difference in annual water yield to the reach in a given brush-management subbasin is calculated by using the model output parameter WYLD in the 

output.sub file, which is the net amount of water that leaves a given subbasin and contributes to streamflow in the reach via surface runoff, lateral flow, and 

groundwater discharge over a given time step.

2

The difference in annual water yield from the brush-management subbasin to Canyon Lake, based on the difference in annual streamflow to Canyon Lake, 

which is calculated by using the model output parameter FLOW_OUT in the output.rch file for the combined subbasin outlets of model subbasins 58 and 59 

(brush-management subbasin 22); FLOW_OUT is the average streamflow at a subbasin outlet over a given time step.
22 Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed

Summary

The U.S. Geological Survey (USGS), in cooperation 

with the Texas State Soil and Water Conservation Board 

(TSSWCB) and the Upper Guadalupe River Authority 

(UGRA), developed a Soil and Water Assessment Tool 

(SWAT) watershed model of the upper Guadalupe River 

watershed in south-central Texas to simulate the effects of 

brush management on water yields in the watershed for 1995–

2010. In general, brush management is the removal of woody 

plants for the purpose of (1) creating desired plant communities, (2) controlling erosion, (3) improving water quality, 

(4) enhancing streamflow or water yield, (5) improving fish 

and wildlife habitat, (6) improving forage accessibility, and 

(7) managing fuel loads. Ashe juniper (Juniperus ashei) is 

a woody plant species that has spread beyond its historical 

range in the understories of small stands of prairie oak (motte) 

and in sheltered canyons in Texas because of overgrazing and 

fire suppression, and studies of the water use of ashe juniper 

indicate that they might intercept and use more water than do 

native grasses. As a result, the replacement of ashe juniper 

with grasslands is used in south-central Texas in an effort to 

increase water yields.

The SWAT watershed model simulations were done to 

quantify the possible change in water yield of individual subbasins in the upper Guadalupe River watershed as a result of 

the replacement of ashe juniper with grasslands. The evaluation of possible reductions in water-yield increases as ashe 

juniper re-growth occurs was beyond the scope of the study. 

Resource managers, such as TSSWCB and UGRA, plan to use 

the model results as a tool to guide brush management in the 

upper Guadalupe River watershed.

A monthly calibration of the SWAT model, which consisted of adjusting process-related parameter values to minimize the differences between simulated and measured streamflows, was done by using streamflow data collected at the 

USGS streamflow-gaging station 08167500 Guadalupe River 

near Spring Branch, Tex., for 1995–2010. Forest categories 

generally produced less runoff, had higher canopy interception of rainfall, and higher evapotranspiration rates than range 

categories in the model. Simulated monthly streamflow was 

a “very good” fit to measured monthly data (percent bias of 

-5, coefficient of determination of 0.91, and a Nash–Sutcliffe 

coefficient of model efficiency of 0.85). Model calibration 

was limited by systematic and measurement errors, which 

are represented in the model calibration metrics. These errors 

included, but were not limited to, the spatial distribution of 

the land-cover data did not change over time; the model was 

calibrated by using data from one streamflow-gaging station; 

permitted water withdrawal amounts were not included; and 

point measurements of precipitation data were distributed in 

space across the model domain.

The calibrated SWAT watershed model was used to 

complete brush-management simulations. The National Land 

Cover Dataset 2006, which was the land-cover dataset used to 

develop the watershed model, was modified to simulate ashe 

juniper replacement with grasslands in each of the 23 brushmanagement subbasins, and the model parameters associated 

with land cover were automatically modified by the modeling 

software accordingly. A simulation was completed for each of 

the 23 brush-management subbasins, and the results indicated that brush management in the watershed would increase 

water yields. The simulated increases in average annual water 

yields in the subbasins (calculated by using the model output parameter WYLD in the output.sub file, which is the net 

amount of water that leaves a given subbasin and contributes 

to streamflow in the reach via surface runoff, lateral flow, and 

groundwater discharge over a given time step) ranged from 

6,370 to 119,000 gallons per acre of ashe juniper replaced 

with grasslands with an average of 38,800 gallons per acre. 

The simulated increases in average annual water yields from 

upstream subbasins to Canyon Lake (calculated by using the 

model output parameter FLOW_OUT in the output.rch file, 

which is the average streamflow out of a reach over a given 

time step) ranged from 6,640 to 72,700 gallons per acre of 

ashe juniper replaced with grasslands with an average of 

34,700 gallons. The highest simulated water-yield increases 

occurred in the eastern half of the watershed, which receives 

more rainfall than does the western half of the watershed. 

Future studies are needed to determine if there are correlations 

between water-yield increases and precipitation, watershed 

attributes, or simulation methodology.

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Bumgarner and Thompson—Simulation of Streamflow and Effects of Brush Management on Water Yields in the Upper Guadalupe River Watershed—SIR 2012–5051

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