Using the Soil and Water Assessment Tool (SWAT) to 

Simulate Runoff in Mustang Creek Basin, California

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5031
Cover: Horses at Mustang Ranch, California. (Photograph by Dina Saleh, U.S. Geological 

Survey, 2004) 
Using the Soil and Water Assessment Tool 

(SWAT) to Simulate Runoff in Mustang 

Creek Basin, California

By Dina K. Saleh, Charles R. Kratzer, Colleen H. Green, and David G. Evans

Scientific Investigations Report 2009–5031

U.S. Department of the Interior

U.S. Geological Survey
iii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Saleh, D.K., Kratzer, C.R., Green, C.H., and Evans, D.G., 2009, Using the Soil and Water Assessment Tool (SWAT) to 

simulate runoff in Mustang Creek Basin, California: U.S. Geological Survey Scientific Investigations Report 2009–

5031, 28 p. 
iv

Figures

Figure 1. Location of the study area within the Mustang Creek Basin, 

 California.....................................................................................................................................2

Figure 2. Monthly temperature at Mustang Creek Basin, obtained from Denair 

 weather station .........................................................................................................................3

Figure 3. Monthly precipitation from Denair weather station in Stanislaus County, 

 California, 1988–2005.................................................................................................................4

Figure 4. Mean monthly precipitation, Denair weather station 1984–2005, and 

 water year 2004..........................................................................................................................5

Figure 5. Soil data for Mustang Creek Basin, California, showing the soil formations and 

 mapped soil series in the study area .....................................................................................6

Figure 6. Relative depth to hardpan in the Mustang Creek Basin, California ..................................7

Figure 7. Detailed agricultural land use in the upper Mustang Creek Basin, California................8

Figure 8. Precipitation and 15-minute streamflow data for Mustang Creek at 

 Monte Vista Avenue near Montpelier, California.................................................................9

Contents

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

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

Purpose and Scope .............................................................................................................................1

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

Climate ..........................................................................................................................................2

Physiography, Geology, and Soils .............................................................................................4

Land Use........................................................................................................................................5

Hydrology and Available Streamflow Data..............................................................................5

Soil and Water Assessment Tool (SWAT) Watershed Model.................................................................6

Model History ........................................................................................................................................6

Previous Applications of SWAT Watershed Model .......................................................................8

Hydrologic Studies ......................................................................................................................8

Calibration Technique Studies.................................................................................................10

Comparison of SWAT with other Models........................................................................................10

Runoff Model Description .................................................................................................................11

Model Data Structure and Inputs ....................................................................................................13

Evaluation of Model Performance ...................................................................................................14

SWAT Model Application to Mustang Creek Basin ...............................................................................15

Input Data for Mustang Creek Simulations ....................................................................................15

Uncalibrated Model Results .............................................................................................................16

Model Calibration................................................................................................................................19

Model Validation .................................................................................................................................19

Conclusions...................................................................................................................................................25

References Cited .........................................................................................................................................25
v

Figure 9. Topographic relief of Mustang Creek Basin used for sub-watershed delineation 

 in the Soil and Water Assessment Tool (SWAT) watershed simulation.........................16

Figure 10. Sub-watershed delineation of the Mustang Creek Basin, California.............................17

Figure 11. Results from an uncalibrated Soil and Water Assessment Tool (SWAT) 

 model run for Mustang Creek Basin, California, for February 2004 ................................18

Figure 12. Distribution of the curve number assigned after calibration to 

 sub-watersheds in the study area........................................................................................20

Figure 13. Changes in the streamflow curve due to changes in the Soil Conservation 

 Service curve number CN value ...........................................................................................21

Figure 14. February 2004 streamflow for Mustang Creek Soil and Water Assessment Tool 

 (SWAT) 2005 calibration simulation results.........................................................................22

Figure 15. Simulated streamflow for Mustang Creek using Soil and Water Assessment 

 Tool (SWAT) 2005 validation results for January and February 2005..............................24

Tables

Table 1. Names, description, and type of data available for sites in the 

 Mustang Creek Basin, California ..............................................................................................3

Table 2. The main soils series and formations present in the 

 Mustang Creek Basin, California ............................................................................................17

Table 3. Statistics from the SWAT2005 uncalibrated simulation run for streamflow in 

 Mustang Creek, California........................................................................................................18

Table 4. Description and distribution of Soil Conservation Service curve number 

 assigned to the Mustang Creek Basin, California................................................................20

Table 5. Statistic results for Soil Conservation Service curve number sensitivity analysis ........21

Table 6. Calibrated values of adjusted parameters for discharge calibration of the 

 SWAT2005 model .......................................................................................................................21

Table 7. The annual hydrologic budget of the Mustang Creek Basin, California, using 

 SWAT2005 simulations with and without the hardpan layer..............................................22

Table 8. Statistics for calibrated simulation of streamflow in Mustang Creek, California ..........23

Table 9. Statistics for validation simulation of streamflow in Mustang Creek, California ...........24
vi

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

square mile (mi2) 259.0 hectare (ha)

Volume

cubic foot (ft3) 28.32 cubic decimeter (dm3) 

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

Flow rate

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

Temperature can be converted between degrees Celsius (°C) and degrees Fahrenheit (°F) by the 

formulas:

°F=(1.8×°C)+32 and °C=(°F-32)/1.8

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

(NAVD 88).
vii

Abbreviations

AVSWAT ArcView extension in SWAT 

CN Curve Number

DEM Digital Elevation Model Data

DWSM Dynamic Watershed Simulation Model 

ET evapotranspiration 

GIS Geographic Information System 

HRUs Hydrologic Response Units 

HSPF Hydrological Simulation Program–FORTRAN 

HYMO Hydrologic Model 

MID Modesto Irrigation District 

NSE Nash-Sutcliffe model efficiency coefficient 

NAWQA National Water-Quality Assessment

NED National Elevation Dataset 

NLCD National Land-Cover Data 

NOAA National Oceanic and Atmospheric Administration 

NRCS U.S. Natural Resource Conservation Service 

SCS Soil Conservation Service 

SD standard deviation 

SEA SSURGO Extension in AVSWAT 

SSURGO Soil Survey Geographic 

SWAT Soil and Water Assessment Tool 

SWRRB Simulator for Water Resources in Rural Basins 

USDS–ARS U.S. Department of Agriculture, Agricultural Research Service 

USGS U.S. Geological Survey 
Abstract

This study is an evaluation of the calibration and validation of the Soil and Water Assessment Tool (SWAT) version 

2005 watershed model for the Mustang Creek Basin, San Joaquin Valley, California. The study is part of a national study 

on the process of agricultural chemical movement through the 

hydrologic system, which is being done by the U.S. Geological Survey (USGS) National Water-Quality Assessment program. The SWAT model was used to simulate streamflow in 

the Mustang Creek Basin on the basis of a set of model inputs 

derived and modified from various data sources. 

The 2005 version of the model was calibrated for 29 

days in February 2004, and validated for 58 days in January 

and February 2005. Measured streamflow for a USGS gaging 

station was used for model calibration and validation. Results 

of the simulated monthly streamflow had a Nash Sutcliffe efficiency value of 0.72 during the calibration period. The 2005 

version of the model was unsuccessful in simulating streamflow during the validation period, as indicated by a Nash 

Sutcliffe efficiency value of 0.33. This lack of a successful 

simulation probably is due to the limited amount of measured 

streamflow data available for calibration, the ephemeral nature 

of flows in Mustang Creek, and the fact that the SWAT model 

was developed primarily for long time periods (2 years and 

more) simulations and not for limited monthly simulations as 

used in Mustang Creek Basin. 

Introduction

The U.S. Geological Survey (USGS) started a national 

study of the transport of agricultural chemicals in 2002 

(Agricultural Chemicals: Sources, Transport, and Fate; ACT) 

as part of its National Water-Quality Assessment (NAWQA) 

program. The objective of that study was to understand the 

effect of environmental processes and agricultural practices 

on the transport and fate of selected nutrients and pesticides 

in the hydrologic systems of nationally important agricultural 

settings (http://in.water.usgs.gov/NAWQA_ACT/). The national 

study focused on five study sites located in varied agricultural and hydrologic settings across the nation (in Maryland, 

Indiana, Nebraska, Washington, and California). One of these 

study sites is the Mustang Creek Basin, located in the San 

Joaquin Valley in central California. The Mustang Creek Basin 

was selected by the USGS because it is a small agricultural 

watershed within the Merced River Basin that has been monitored by NAWQA since 1993 (Gronberg and Kratzer, 2006). 

The San Joaquin Valley is one of the most productive agricultural areas in the world (Gronberg and Kratzer, 2006). Many 

fertilizers, such as manure, urea, and anhydrous ammonia, and 

pesticides are applied to the great diversity of crops grown 

in the valley. Relatively high concentrations of nitrate and 

pesticides have been found in the San Joaquin River (Panshin 

and others, 1998; and Kratzer and others, 2004). Therefore, it 

is important to understand the relation between the application 

of fertilizers and pesticides in the valley and the concentration of nutrients and pesticides in the surface water therein. 

One way to assess the relation between nutrient and pesticide 

application in agricultural areas and resultant nutrient and 

pesticide concentrations in streamflow is to use a watershed 

model capable of simulating streamflow and chemical transport. Because of its widespread use in the agricultural research 

community, the U.S. Department of Agriculture (USDA) Soil 

and Water Assessment Tool (SWAT) was chosen by ACT as 

the watershed model to apply to each of the five study areas 

(Neitsch and others, 2005; Capel and others, 2008).

Purpose and Scope 

The objective of this study was to determine how well 

the 2005 version of the SWAT model could simulate runoff 

from storm events in the Mustang Creek Basin. This report 

describes the results of the calibration and validation of the 

SWAT model for various storm runoff events during the 2004 

and 2005 water years in the Mustang Creek Basin. 

Using the Soil and Water Assessment Tool (SWAT) to 

Simulate Runoff in Mustang Creek Basin, California

By Dina K. Saleh1, Charles R. Kratzer1, Colleen H. Green2, and David G. Evans3

1

U.S. Geological Survey, California Water Science Center, 6000 J Street, Placer Hall, Sacramento, California 95819.

2

U.S. Department of Agricuture, Temple, Texas.

3

California State University Sacramento, Department of Geology, 6000 J Street, Placer Hall, Sacramento, California 95819. 
2 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Description of Study Area 

Mustang Creek is a tributary of the Merced River in the 

San Joaquin River Basin (fig. 1). Mustang Creek is an ephemeral creek, flowing only during large precipitation events. It 

flows into Highline Canal, which flows into the Merced River. 

Mustang Creek slopes from the northeast to the southwest, 

with elevations ranging from 155–338 ft above sea level 

(Gronberg and Kratzer, 2006). The drainage basin for Mustang 

Creek is 21 mi2, although the study area covers only the upper 

6.75 mi2 of the Mustang Creek Basin (fig. 1). Only the upper 

part of the Mustang Creek Basin is modeled as part of this 

study. The defined stream channel is 1.86 river-miles long in 

the study area of the basin, flowing in a semi-natural setting. 

There are few anthropogenic effects on the study area except 

for the uppermost stream channel, which has been plowed for 

vineyards and orchards. A manmade flood-control reservoir 

is downstream of the study area and Mustang Creek becomes 

more channelized and controlled (fig. 1). Five USGS streamflow and water-quality sampling sites are on Mustang Creek 

(table 1). Streamflow data used in this study were obtained 

from the USGS gaging station located at site 2 (fig. 1). 

Climate 

Mustang Creek Basin has an arid-to-semiarid climate 

characterized by hot, dry summers and mild, moderately wet 

winters. Temperature and precipitation data that are considered 

representative of the Mustang Creek Basin are available for 

the 1988 through 2005 period from the National Oceanic and 

Atmospheric Administration (NOAA) Denair weather station, 

located about 11 miles west of the basin in Stanislaus County 

(fig. 1). Mean low air temperatures, in degrees Fahrenheit, 

range from the low 30’s in the winter to the upper 50’s in the 

summer. Mean high air temperatures, in degrees Fahrenheit, 

range from the mid 50’s in the winter to the mid 90’s in the 

summer. Mean daily low and high air temperatures for the 

2004 water year are similar to the mean daily lows and highs 

for the 1988–2005 periods (fig. 2). This gaging station and a 

weir were installed at that location in December 2003. However; high flows during the storm on February 25, 2004, cased 

great damage to the weir leading to it’s subsequent removal.

Figure 1. Map showing location of the study area within the Mustang Creek Basin, California.

0 5 10 MILES

0 5 10 KILOMETERS

1

2

3

4

5

EXPLANATION

Main Roads

Streams

Study Area

1 USGS Monitoring Sites

Reservoir Los Angeles

San

Francisco

San Joaquin

Basin

Modesto

Weather Station

Denair

Weather Station

Highline Canal

120° 36'

120° 40'

37° 31'

37° 29'

120° 10'

120° 40'

37° 30'

37° 50'

119° 30'

Merced River

San Joaquin River

0 1 MILE

0 1 KILOMETER

Mustang Creek

Keyes Road Bledsoe Road

Monte Vista Avenue
Introduction 3

Table 1. Names, description, and type of data available for sites in the Mustang Creek Basin, California.

[See figure 1 for site locations. U.S. Geological Survey]

Site USGS site number Site name Site description

Data at site 

(used for this 

study)

1 373115120382801 Culvert Discharge to Mustang Creek at Monte 

Vista Road

USGS sampling site Streamflow; water 

quality

2 373112120382901 Mustang Creek at Monte Vista Avenue near 

Montpelier

USGS sampling site Streamflow; water 

quality

3 373020120385201 Mustang Creek at 1.1 mile south of Monte Vista 

Avenue near Montpelier

USGS sampling site Water quality

4 373012120393401 Mustang Creek Reservoir at Oakdale Road near 

Montpelier

USGS sampling site Water quality

5 372839120413901 Mustang Creek at Bifurcation Structure near 

Ballico

USGS sampling site Streamflow; water 

quality

Denair Weather Station

0

10

20

30

40

50

60

70

80

90

100

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

WATER YEARS 1988-2005, 2004

TEMPERATURE, IN DEGREES FAHRENHEIT

Mean Low 1988-2005

Mean High 1988-2005

Mean Low 2004

Mean High 2004

Figure 2. Monthly temperature at Mustang Creek Basin, obtained from Denair weather station. 

Mean annual precipitation in Mustang Creek Basin is 

about 10 in/yr. Monthly precipitation from 1988 through 2005 

is shown in figure 3. Most of the precipitation in the Mustang 

Creek Basin occurs during the months of November through 

March (Gronberg and Kratzer, 2006). The monthly precipitation during the 2004 water year compared to long-term 

(1988–2005) monthly precipitation is shown in figure 4. 

Precipitation was considerably higher in December 2003 

and lower in January 2004 than the long-term mean monthly 

values. 
4 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Physiography, Geology, and Soils

Mustang Creek Basin is located in the flat-surfaced 

structural basin of the San Joaquin Valley. The Mustang Creek 

Basin is hilly in the upper part of the basin and generally flat 

in the lower parts near the outlet to Highline Canal. Unconsolidated deposits in the study area include Quaternary continental deposits contained within the Turlock Lake Formation, 

moderate amounts of the Riverbank and Mehrten Formations 

and traces of the Modesto and Laguna Formations (fig. 5) 

(Davis and Hall, 1959; Burow and others, 2004). Soil surveys 

performed by Arkley (1964) for Merced County and by McElhiney (1992) for eastern Stanislaus County identify different 

soil series in the study area, mapped and grouped by permeability classes. There are three soil series in the Riverbank 

Formation—San Joaquin, Snelling, and Atwater; three in the 

Turlock Lake Formation—Montpellier, Rocklin, and Whitney; 

and three in the Mehrten Formation—Pentz, Raynore, and 

Keyes (fig. 5). 

The Turlock Lake Formation, the predominant formation in Mustang Creek Basin, consists mainly of arkosic sand, 

gravel, and silt that coarsen upward into coarser pebbly sand 

or gravel. Sand and silt-sized sediments are mostly quartz, 

feldspar, biotite, and minor amounts of heavy minerals. 

Coarser-grained materials consist of andesite, rhyolite, quartz, 

greenstone, schist, and granidiorite (Marchand and Allwardt, 

1981; Burow and others, 2004). The thickness of the Turlock 

Lake Formation is variable and appears to increase toward the 

valley. In eastern Stanislaus County, the Turlock Lake Formation is estimated to be about 300–850 ft thick (Davis and Hall, 

1959; Marchand and Allwardt, 1981). 

Riverbank Formation soils that were eroded during glacial secession are represented by a shallow hardpan, a reddish 

paleosol that defines the stratigraphic boundary between the 

Riverbank Formation and the upper Turlock Lake Formation (Weissmann and others, 2002; Burow and others, 2004). 

The hardpan layer is about 2–3 ft thick, is apparent on the 

surface in the upper northeast part of the study area, and is 

about 2 ft or less below the surface at the lower southwest part 

of the study area. Soil permeability is controlled strongly by 

the depth and existence of the hardpan in the basin. Farming 

activities in the Mustang Creek Basin have altered the location 

and distribution of the hardpan. Figure 6 shows the location 

and depth of the hardpan in the study area as described in 

the soil surveys performed by Arkley (1964) and McElhiney 

(1992). The hardpan layer is located at a shallow depth along 

Figure 3. Monthly precipitation from Denair weather station in Stanislaus County, California, 1988–2005.

0

1

2

3

4

5

6

7

8

9

10

1988198919901991199219931994199519961997199819992000200120022003200420052006

DATE

PRECIPITATION, IN INCHES PER MONTH

Monthly Precipitation
Introduction 5

the main channel of Mustang Creek and to the east side of the 

basin (fig.6). On the west side of the basin the soil is generally 

well drained and of moderate permeability (0.8 to 2.5 in/hr) 

(Arkley, 1964) where the soils are thick and the hardpan layer 

is absent. 

Soils of the Mehrten Formation are dark sandstone, siltstone, claystone, conglomerate, and andesitic breccia and tuff. 

The thickness of the formations ranges from 190 to 1,200 ft 

(Marchand and Allwardt, 1981; Burow and others, 2004). 

Traces of other formations such as the Modesto Formation and 

Laguna Formation generally consist of sand, gravel, and silt, 

and are present in minimal amounts in the study area. 

Land Use

Mustang Creek Basin is an agricultural basin dominated 

by orchards and vineyards. On the basis of the 30-m National 

Land-Cover Characteristics Database (NLCD), 42 percent 

of the agricultural land in Mustang Creek Basin is used for 

almond orchards, 20 percent for vineyards, 20 percent for corn 

and grains, and the remaining 18 percent is native vegetation 

and rangelands (Gronberg and Kratzer, 2006). The study area, 

which is in the upper part of the basin, is composed of 50 

percent vineyards, 34 percent almond orchards, 12 percent row 

crops (such as oats and beans), and 3 percent native vegetation 

(fig. 7). The land use was interpreted from Landsat Thematic 

Mapper data acquired between 1990 and 1994 and was field 

checked and verified by the USGS in 2003–04. 

Hydrology and Available Streamflow Data

Mustang Creek is an ephemeral creek that, on average, 

flows only 2–3 months of the year as a result of winter storms. 

A continuous-recording (data collected every 15-minutes) 

water-level gage was installed at the USGS partial-record, 

water-quality station, Mustang Creek at Monte Vista Avenue 

(373112120382901) in December 2003. Periodic measurements of stream discharge, together with recorded water levels 

(stage) at the time of discharge measurement, were used to 

develop a stage-discharge relation that was used to compute 

discharge for every recorded value of stage. Hourly streamflow and precipitation for February 2004 are shown in figure 8. 

The stage-discharge relation was changed significantly during 

a flood on February 25, 2004, and daily mean discharge for 

February 25 and subsequent days of runoff were estimated 

based on the instantaneous peak discharge for the flood and 

the recorded stage data. The peak discharge on February 

25, 2004, was determined to be 207 ft3/s, on the basis of an 

indirect discharge computation using the slope-area method 

described by Dalrymple and Benson (1967). Figure 8 shows 

that little surface-water runoff resulted from a significant 

rainfall on February 2, a large hourly runoff value resulted 

0

1

2

3

4

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

MEAN MONTHLY PRECIPITATION, IN INCHES

Long-term (1988-2005) Monthly Precipitation

2004 Monthly Precipitation

WATER YEARS 1988-2005, 2004

Figure 4. Mean monthly precipitation, Denair weather station 1984–2005, and water year 2004.
6 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

from a storm on February 18, and an even larger runoff value 

occurred during the February 25 storm. 

Surface water generally is not available for irrigation 

in the Mustang Creek Basin, and extensive ground-water 

pumping for irrigation has lowered the water table over time 

to an average depth of about 140 ft. (Burow and others, 2004; 

Gronberg and Kratzer, 2006; Steve Phillips, U.S. Geological 

Survey, written commun., 2006). Ground-water recharge takes 

place mostly on the west side of Mustang Creek where the 

hardpan layer is absent. On the east side of Mustang Creek, 

precipitation water infiltrates through the soil until it reaches 

the hardpan layer, where it then travels laterally toward Mustang Creek. 

Soil and Water Assessment Tool 

(SWAT) Watershed Model

Model History

The SWAT model was developed in the early 1990’s by 

the U.S. Department of Agriculture, Agricultural Research 

Service (USDA–ARS), and it has undergone many changes 

and improvements since its formation. The SWAT model is 

a direct descendant of the Simulator for Water Resources in 

Rural Basins (SWRRB) model (Williams and others, 1985), 

which was designed to simulate management effects on water 

and sediment movement for ungaged rural basins across the 

United States. The SWAT 2000 version (SWAT2000) added 

components for the simulation of nutrient, pesticide, and 

EXPLANATION

Soil Series

120° 38'

37° 33'

37° 32'

120° 37'

120° 36'

Atwater loamy sand

Greenfield Soil Series 

Keyes Soil Series

Montpellier Soil Series

Pentz Soil Series

Raynore Soil Series

Redding Soil Series

Rocklin Soil Series

San Joaquin Soil Series

Snelling Soil Series

Whitney Soil Series

Riverbank Formation

Turlock Lake Formation

Mehrten Formation

Modesto Formation

Laguna Formation

Roads

Monte Vista Avenue Bledsoe Road

Keyes Road

0 1 MILE

0 1 KILOMETER

Mustang Creek

Mustang Creek

Figure 5. Soil data for Mustang Creek Basin, California, showing the soil formations and mapped soil series in the study area.
Soil and Water Assessment Tool (SWAT) Watershed Model 7

bacteria transport; incorporated a subdaily time step into the 

model; and, allowed for potential evapotranspiration (ET), 

daily solar radiation, relative humidity, and wind speed in a 

watershed to be added as input variables to the model. 

Hydrological processes simulated by the SWAT model 

include precipitation, infiltration, surface runoff, evapotranspiration, lateral flow, and percolation. SWAT uses a command 

structure similar to the structure of the Hydrologic Model 

(HYMO) (Williams and Hann, 1978) for routing runoff and 

chemicals through a watershed. Commands are included for 

routing flows through streams and reservoirs, adding flows, 

and using measured data for point sources. 

SWAT2000 was incorporated into a Geographic Information System (GIS) platform using the ArcView and SWAT 

(AVSWAT) interface tool (Di Luzio and others, 2004). This 

platform provides the user with a complete set of GIS tools for 

developing, running, and editing hydrologic and management 

inputs, and finally for calibrating the model. The SWAT2000 

version was integrated into the 2005 version (SWAT2005), 

which incorporated additional changes. Some of these changes 

included: a revised bacteria transport routine; ability to use 

weather forecast scenarios; use of a subdaily precipitation generator; and allowing the retention parameter used in the daily 

Curve Number (CN) calculation to be a function of either soil 

water content or plant ET (Neitsch and others, 2005). 

SWAT2005 version of SWAT uses an upgraded version of 

AVSWAT, termed AVSWAT-X. This GIS tool contains several 

added extensions, including SSURGO extensions that allow 

for the U.S. Department of Agriculture (1:24,000 scale) soil 

survey data to be used in the model; auto calibration tools; and 

land-use/land-cover class splitting tools (Arnold and Fohrer, 

2005; Green and others, 2006). SWAT also includes a soilwater routing modification, which assigns a maximum watertable depth by assigning a specific depth to an impermeable 

soil layer (Du and others, 2005; Green and others, 2006). 

Very shallow depth, surface to 21 inch

Shallow depth, 18 to 30 inch

Medium depth, 24 to 54 inch

Very deep, 42 to 60 inch

No hardpan

Roads

Mustang Creek

Depth to Hardpan

EXPLANATION

120° 38'

37° 33'

37° 32'

120° 37'

120° 36'

Monte Vista Avenue Bledsoe Road

Keyes Road

0 1 MILE

0 1 KILOMETER

Mustang Creek

Figure 6. Relative depth to hardpan in the Mustang Creek Basin, California.
8 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Previous Applications of SWAT 

Watershed Model 

Hydrologic Studies

Hydrologic components (surface runoff, ET, recharge, 

and streamflow) in SWAT have been developed and validated 

worldwide on a variety of watershed scales in an attempt 

to address different hydrological and environmental issues. 

Through the many applications of SWAT, the model generally has proven to be an effective tool for assessing water 

resources. For example, Bingner (1996) simulated reasonable 

values of runoff for daily, monthly, and annual time steps for 

the Goodwin Creek watershed in the upper Mississippi Basin 

for a 10-year time period. Van Liew and Garbrecht (2003) 

evaluated SWAT’s ability to predict streamflow under varying climatic conditions for three nested watersheds in Little 

Washita River Experimental Watershed in Oklahoma. They 

found that the model performed better in drier years than in 

wetter years. Sun and Cornish (2005) used SWAT to estimate 

recharge in the headwaters of the Liverpool Plains in Australia. The study used water balance modeling at the catchment 

scale to derive parameters for long-term recharge estimation. 

Results showed that recharge occurs only in wet years and 

recharge primarily could be explained by the climatic factor 

rather than by land use changes. Peterson and Hamlet (1998) 

found that SWAT was better suited for long periods of simulation and suggested that the snowmelt routine be improved 

(Gassman and others, 2007). 

2 Monte Vista Avenue Bledsoe Road

Keyes Road

0 1 MILE

0 1 KILOMETER

EXPLANATION

Vineyards

Almonds

Row Crops

Native vegetation

Homestead

Land use

Roads

Mustang Creek

Mustang Creek at Monte Visa Avenue near 

 Montpelier, CA

2

Mustang Creek

120° 38'

37° 33'

37° 32'

120° 37'

120° 36'

Figure 7. Detailed agricultural land use in the upper Mustang Creek Basin, California.
Soil and Water Assessment Tool (SWAT) Watershed Model 9

On the other hand, some SWAT applications were less 

successful in simulating hydrologic processes. For example, 

Chu and Shirmohammadi (2004) used 6 years of data to 

calibrate and validate SWAT’s capability to calculate surface 

flow for a small watershed in Maryland, and found that SWAT 

was unable to simulate an extremely wet year within that time 

period. Spruill and others (2000) calibrated and validated 

a SWAT model to determine daily streamflow for a small 

karst-influenced watershed in central Kentucky over a 2-year 

period, and found that the model poorly predicted peak flows 

and hydrograph recession rates. Rosenthal and others (1995) 

linked GIS to SWAT and, with no calibration, simulated 

10 years of streamflow for the lower Colorado River Basin 

in Texas. SWAT underestimated extreme events, and yet the 

relationship between measured and simulated streamflow 

(R2 = 0.75) was significant. Hernandez and others (2000) 

applied the SWAT hydrologic model to a small semi-arid 

watershed in southeast Arizona. They developed a continuous 

SWAT model with a daily time step by using existing data sets 

(State Soil Geographic soil data [STATSGO], accessed September, 30, 2006, and the USGS Land Cover Institute 

classification data, accessed March 16, 2006). The SWAT 

model overestimated soil water in dry soil conditions and 

underestimated soil water in wet soil conditions, yet the 

SWAT model was successful in simulating soil-water patterns 

in the watershed on a daily time step (Gassman and others, 

2007). 

Figure 8. Precipitation and 15-minute streamflow data for Mustang Creek at Monte Vista Avenue near Montpelier, California. STREAMFLOW, IN CUBIC FEET PER SECOND

0

2 4 6 8 10 12 14 16 18 20 22 24 26 28

50

100

150

200

250

FEBRUARY 2004

0

0.1

0.2

0.3

0.4

0.5

Missing

 Data

Precipitation

15-minute streamflow from gage

Instantaneous streamflow measurement

PRECIPITATION, IN INCHES
10 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Calibration Technique Studies

SWAT’s input parameters are physically based and can be 

varied for calibration within a given uncertainty range defined 

in the SWAT tool input and output file documentation, version 

2005 (Neitsch and others, 2005). SWAT model calibrations can 

be completed in two ways: manual and (or) autocalibration. 

Manual calibration requires the user to compare measured data 

to simulated data, and to use judgment to determine whether 

simulated data are acceptable. Statistical methods can be used 

to assist in the evaluation of simulation results and to help 

adjust model parameters. Santhi and others (2001), and Coffey 

and others (2004) used manual calibration and validation of 

SWAT for streamflow, sediment, nitrogen, and phosphorus loss 

simulation for different watersheds. They recommended using 

two statistical measures, the Nash-Sutcliff Index (NSE) and the 

square of the correlation coefficient (R2), to assess the simulation results for monthly data. Spruill and others (2000) also 

used manual calibration and performed a sensitivity analysis 

of simulated data using SWAT to show that saturated hydraulic 

conductivity, alpha base-flow factor, drainage area, channel 

length, and channel width were the most sensitive hydrologic 

parameters. Holvoet and others (2005) used a manual calibration of SWAT2000 and performed a sensitivity analysis for 

hydrologic parameters and pesticide transport toward the river 

in Nil-catchment, a small, hilly basin located in the central part 

of Belgium. They found that the moisture condition II curve 

number (CN2), the surface runoff lag coefficient (surlag), the 

deep aquifer percolation fraction (rchrg_dp), and the threshold 

depth of water in the shallow aquifer required for return flow 

to occur (GWQMN) were the parameters to which the model 

was the most sensitive. The second method of calibration is an 

autocalibration procedure embedded in the SWAT2005 model. 

The autocalibration procedure uses an optimization scheme to 

adjust various model parameters within a specific and realistic 

range of possible values. Applications of the complex optimization scheme are described by Van Griensven and Brauwens 

(2001; 2003; 2005). The user inputs calibration parameters 

and ranges with measured daily flow and pollutant data. The 

automated calibration scheme controls thousands of model 

runs to find the best dataset (Gassman and others, 2007). 

Many sensitivity analyses have been completed to determine the effects of sub-watershed delineation and other inputs 

on SWAT’s prediction. Bingner and others (1997), FitzHugh 

and Mackay (2000), and Chen and Mackay (2004) all found 

that SWAT flow predictions were insensitive to Hydrologic 

Response Units (HRUs); portions of a sub-watershed that 

possess unique land use, management, and soil attributes, and 

sub-watershed delineations. However, Bingner and others 

(1997) found that the number of sub-watersheds in the basin 

affects the predicted sediment yield for a watershed. Jha and 

others (2004) found that SWAT nitrate predictions were sensitive to HRUs and sub-watershed configurations. Bosch and 

others (2004) found that SWAT streamflow estimates were 

more accurate when using high-resolution topographic data, 

land-use data, and soil data. Cotter and others (2003) and 

Di Luzio and others (2005) found that the resolution of the 

Digital Elevation Model (DEM) was the most critical input 

parameter when developing a SWAT model (Gassman and 

others, 2007). 

Comparison of SWAT with other Models

Van Liew and others (2003) compared the streamflow 

predictions of SWAT and the Hydrological Simulation Program–FORTRAN (HSPF) model developed by the U.S. Environmental Protection Agency on eight nested agricultural subwatersheds within the Washita River Basin in southwestern 

Oklahoma. They found that differences in model performance 

mainly were attributed to the runoff production mechanisms 

of the two models. Van Liew and others (2003) concluded that 

SWAT gave more consistent results than HSPF in estimating 

streamflow for agricultural watersheds under various climatic 

conditions and, thus, may be better suited for investigating 

the long-term effects of climate variability on surface-water 

resources. Saleh and Du (2004) calibrated SWAT and HSPF 

with daily flow, sediment, and nutrients measured at five 

stream sites of the Upper North Bosque River watershed in 

central Texas. They concluded that the simulations of average daily flow and sediment and nutrient loading from SWAT 

were closer to measured values than were the corresponding 

simulated values from HSPF for the calibration and 

verification periods (Gassman and others, 2007).

Borah and Bera (2004) compiled 17 SWAT, 12 HSPF, and 

18 Dynamic Watershed Simulation Model (DWSM) applications and concluded that both SWAT and HSPF were: (1) suitable for predicting yearly flow volumes, sediment loads, and 

nutrient losses; (2) adequate for monthly predictions, except 

for months with extreme storm events and hydrologic conditions; and (3) poor in simulating daily extreme-flow events. 

In contrast, DWSM (developed by the Illinois State Water 

Survey) reasonably predicted distributed flow hydrographs and 

concentration or discharge graphs of sediment, nutrients, and 

pesticides at small time intervals (Gassman and others, 2007).
Soil and Water Assessment Tool (SWAT) Watershed Model 11

Runoff Model Description

The SWAT model is a watershed scale, continuous-time 

model with a daily time step. SWAT is capable of simulating 

long-term yields for determining the effect of land-management practices (Arnold and Allen, 1999). There are many 

components of SWAT, including hydrology, weather, soil 

temperature, crop growth, nutrients, pesticides, sediment yield, 

and agricultural management practices (Chanasyk and others, 

2003). The hydrologic components of SWAT are based on the 

water balance equation applied to water movement through 

soil: 

SWt = SW + day surf a seep gw

i

t

t

R Q E w Q

SW

0

where 

= soil w

( − − − − ) =

∑1

ater content at time ;

SW = initial soil water content;

 

t

t

0

= time (in days);

R = amount of precipitation on day i; day

Q i

E

surf

a

= amount of surface runoff on day ;

 a = mount of evapotranspiration on day ;

= water percolation to the 

i

Wseep bottom of the soil 

profile on day ; and

= amount of wat

i

Qgw er returning to the ground

water on day i.

 

(1)

This equation takes into account several different processes—precipitation, surface runoff, ET, recharge, and soil 

water storage. The Soil Conservation Service (SCS) curve 

number (CN) equation is used to estimate surface runoff. This 

method was developed from many years of streamflow records 

from agricultural watersheds in many parts of the United 

States. CN is a function of soil group, land cover complex, and 

antecedent moisture conditions. The curve number method 

was adopted in the SWAT model because it (1) is used widely 

throughout the United States, (2) has been tested on watersheds of varying sizes, and (3) requires only easily available 

input data. The SCS curve number method uses two equations 

for runoff. The first relates runoff to rainfall and retention 

parameter as:

Q

R S

R S

Q

= ( ) −

+

0 2

0 8

2

.

. 0.2

where

 = daily surface runoff 

 , R > S

(in mm);

 = daily rainfall (in mm); 

 = retention paramete

R

S r, the maximum potential 

difference between rainfall and runoff (in mm)

starting at the time the storm begins.

(2)

The second equation relates retention parameter to curve 

number as:

S

CN = − 



 

 25 4  1 000 . 10 ,

where 

CN = curve number ranging from 0 ≤ ≤ CN 100. (3)

A complete description of the SCS curve number method 

is explained by Neitsch and others (2005). The SCS curve 

number is a measure of the infiltration characteristics of a soil. 

In general, soils are divided into four major classes of infiltration and runoff characteristics: 

1. Low runoff potential and high infiltration rate even 

when thoroughly wetted. These soils mainly consist 

of excessively drained sand and gravel. These soils 

have a high water transmission rate.

2. Moderate infiltration rate when thoroughly wetted. 

These soils mainly consist of well-drained fine-tomoderately fine textures. They have a moderate 

water transmission rate. 

3. These soils have a slow infiltration rate when thoroughly wetted. These soils mainly have a layer that 

impedes downward movement of water and have a 

slow water transmission rate. 

4. These soils have a high runoff potential and very 

slow to no infiltration when thoroughly wetted. They 

mainly consist of clay soils that have high swelling 

potential, soils that have a permanent water table, 

and soils that have a clay-pan or a clay layer near the 

surface. These soils have a very slow water transmission rate (Neitsch and others, 2005). 
12 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

The curve number also depends on the antecedent soil 

moisture condition. The NRCS (formerly the SCS) defines 

three antecedent soil moisture conditions: I- dry (wilting 

point), II-average moist, and III-wet. The moisture condition I 

curve number is the lowest value the daily curve number can 

be in dry conditions. The standard values of curve number 

shown in NRCS tables for various soils and land-cover conditions are based on antecedent soil moisture condition II. The 

standard values for curve number can be adjusted for drier or 

wetter antecedent conditions using the following equations:

CN CN

CN

CN CN 1 2

2

2 2

20 100

100 2 533 0 0636 100 = − × − ( )

− +  − × ( ) −  

 ( exp . . )

(4)

CN CN CN

CN

3 2 2 = × 0 00673× − ( ) 100  

 exp .

where

1 = moisture condition I curve number;

= moisture condition II curve number; CN2

= moisture condition III curve number. CN3

(5)

SWAT allows the user to select between two methods for 

calculating the retention parameter. The traditional method is 

to allow the retention parameter to vary with soil profile water 

content. An alternative method added in SWAT2005 allows the 

retention parameter to vary with accumulated plant ET. The 

alternative method was added because use of the traditional 

method based on soil moisture often resulted in over prediction of runoff in shallow soils. By calculating daily CN as a 

function of plant ET, the value is less dependent on soil storage and more dependent on antecedent climate (Neitsch and 

others, 2005). The daily retention parameter as a function of 

plant ET can be calculated with this equation:

S S E cncoef S

S R Q

S

prev o

prev = + day surf

 − −









 *exp − −

max

where 

= retention parameter for a given day 

(in millimeters);

Sprev = retention parameter for the previous day 

(in millimeters);

= potential evapotranspiration for the day 

(in millimet

Eo

ers per day); 

cncoef = weighting coefficient used to calculate the 

retention coefficient for daily curve number 

calculations dependent on plant ET;

 = S maximum value the re max tention parameter can

achieve on any given day (in millimeters);

R = rainfall depth for the day (in millimeters);

Q

day

surf = surface runoff (in millimeters).

(6)

The initial value of the retention parameter is defined as 

S S = 0 9. * max (7)

The daily curve number value adjusted for moisture content is calculated by the following equation:

CN

S

CN

S

= ( ) +

25400

254

where

 = curve number on a given day;

 = retention parameter calculated for the moisture 

content of the soil on that day 

(Neitsch and others, 2005).

(8)

SWAT uses typical curve numbers for various soils with 

moisture condition II, and a set slope of 5 percent. These data 

were obtained from the SCS Engineering Division 1986 document (Neitsch and others, 2005). To adjust the curve number 

to different slopes, William (1995) came up with the following 

equation:
Soil and Water Assessment Tool (SWAT) Watershed Model 13

CN

CN CN

slp CN

CN

S

S

2

3 2

2 3 = 1 2 13 86 ( ) − × −  × −( )  

 exp . . +

where

= moist 2 ure condition II curve number adjusted

for the slope;

CN3 = moisture condition III curve number for default 

5 percentslope; 

= moisture condition II curve number for 

defaul

CN2

t 5 percent slope; 

slp = average percent slope of the sub-watershed

(Neitsch and others, 2005).

 

(9)

Model Data Structure and Inputs

SWAT is a comprehensive model that requires a diversity 

of information. The first step in setting up a SWAT watershed 

simulation is to partition the watershed into subunits. SWAT 

allows several different subunits to be defined within a watershed. The first level of subdivision is the sub-watersheds. Subwatersheds possess a geographic position in the watershed and 

are related to one another spatially. For example, outflow from 

upstream sub-watershed number 3 may enter downstream subwatershed number 6. The sub-watershed delineation is defined 

by surface topography so that the entire area within a subwatershed flows to the sub-watershed outlet. The land area in a 

sub-watershed may be divided into HRUs. These portions of a 

sub-watershed possess unique land use/management/soil attributes. The number of HRUs in a sub-watershed is determined 

by the threshold value for land use and soil delineation in the 

sub-watershed (Neitsch and others, 2005). The delineation 

of the HRUs within the sub-watershed is determined using 

AVSWAT-X built-in tools (Di Luzio and others, 2004). The 

use of HRUs generally simplifies a simulation run because 

all similar soil and land-use areas are lumped into a single 

response unit. The user can define the amount of lumping 

depending on the detail required for a particular study. 

The water balance of each HRU in SWAT is represented 

by four storage volumes: snow, soil profile (0–6.5 ft), shallow 

aquifer (typically 6.5–65 ft), and deep aquifer (>65 ft). Flows, 

sediment yield, and nonpoint-source loadings from each HRU 

in a subwatershed are summed, and the resulting flow and 

loads are routed through channels (Neitsch and others, 2005), 

ponds, and (or) reservoirs to the watershed outlet (Arnold 

and Allen, 1993; Di Luzio and others, 2004). The soil profile 

is subdivided into multiple layers that may have differing 

soil-water processes including infiltration, evaporation, plant 

uptake, lateral flow, and percolation to lower layers. The soil 

percolation component of SWAT uses a storage routing technique to predict flow through each soil layer in the root zone. 

Downward flow occurs when field capacity (the water content 

to which a saturated soil drains under gravity) of a soil layer 

is exceeded and the layer below is not saturated. Percolation 

from the bottom of the soil profile recharges the shallow aquifer. When the temperature in a particular layer is equal to or 

below 48°F, no percolation is allowed from that layer. Lateral 

subsurface flow in the soil profile is calculated simultaneously 

with percolation. Ground-water flow contribution to total 

streamflow is simulated by routing a shallow aquifer storage 

component to the stream (Arnold and Allen, 1993; Di Luzio 

and others, 2004).

Input data for SWAT can be defined at different levels of 

detail—watershed, sub-watershed, or HRU. Watershed-level 

inputs are used to model processes throughout the watershed. 

For example, for a watershed–level simulation, the method 

selected to model potential ET will be used in all HRUs in the 

watershed. There are three main watershed-level input files:

1. Watershed configuration file (fig.fig): The watershed configuration file contains information 

used by SWAT to simulate processes occurring 

within the HRU/sub-watershed and to route the 

streamflow and constituent loads through the 

channel network of the watershed. 

2. The master watershed file (file.cio): The master 

watershed file contains information related to 

modeling options, climate inputs, databases, and 

output specifications. Information in this file 

includes number of calendar years simulated, 

beginning year of simulation, the beginning and 

ending Julian day of simulation, and weather 

and rain station information. This file also 

contains links to files needed for the watershed 

definition and delineation, as well as links to the 

files holding the precipitation and temperature 

information needed for the simulation. 

3. The basin input file (basins.bsn): General watershed attributes are defined in the basin input file. 

These attributes control a diversity of physical 

processes at the watershed level. The attributes 

initially are automatically set to “default” values. Examples of attributes in the basins.bsn file 

are specification of the method used for estimating ET, initial soil water storage, and surface 

runoff lag time, and other parameters used in the 

SWAT simulation on the watershed scale. Users 

can use the default values or change them to 

better reflect conditions in a specific watershed.
14 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Sub-watershed or HRU-level input files are used to 

identify unique processes to specific sub-watersheds or HRUs. 

There are many different types of sub-watersheds and HRUscale data files used in the SWAT watershed simulation. A set 

of these sub-watershed and HRU-scale data files are assigned 

to each sub-watershed and HRU in the watershed. Some of the 

more commonly used sub-watershed or HRU-level data files 

are:

• The sub-watershed general input file (.sub) contains 

information related to a diversity of features within 

the sub-watershed to which it is assigned. Data in the 

(.sub) file can be grouped into the following categories: 

sub-watershed size and location; specification of climatic data used within the sub-watershed; the amount 

of topographic relief within the sub-watershed and its 

effect on the climate; properties of tributary channels 

within the sub-watershed; variables related to climate 

change; the number of HRUs in the sub-watershed; 

and, the names of HRU input files.

• The HRU general input file (.hru) contains information 

related to features within a HRU. Data contained in 

the HRU input file can be grouped into the following 

categories: topographic characteristics, water flow, erosion, land cover, and depressional storage areas.

• The HRU management file (.mgt) contains information 

related to land and water management practices taking 

place within the system. This file also contains input 

data for planting, harvest, irrigation applications, fertilizer applications, pesticide applications, and tillage 

operations.

• The soil input file (.sol) defines the physical properties 

of the soils in the watershed. These properties govern 

the movement of water and air through the profile and 

have a major effect on the cycling of water within the 

HRU (Neitsch and others, 2005). 

Evaluation of Model Performance

Simulated data from the SWAT model can be compared 

statistically to observed data to evaluate the predictive capability of the model (Green and others, 2006). Santhi and others 

(2001) and Coffey and others (2004) recommended using the 

correlation coefficient (R2) together with the Nash-Sutcliffe 

model efficiency coefficient (NSE) (Nash and Sutcliffe, 1970) 

as a method to evaluate and analyze simulated monthly data. 

The R2 value is a measure of the strength of the linear correlation between the predicted and observed values. The NSE value, 

which is a measure of the predictive power of the model, is 

defined as:

N

Q Q

Q Q

N

SE

o

t

m

t

t

T

o

t

o

t

T

SE

= −

( ) −

( ) −

=

=

∑

∑

1

2

1

2

1

where

= Nash-Sutcliffe coefficient;

Q = observed discharge;

Q = modeled discharge;

o

m

Q

Q

o

t

= mean observed discharge;

 = discharge at time t.

(10)

A value of 1 for NSE indicates a perfect match between 

simulated and observed data values. A value of 1 for the R2

also indicates a perfect linear correlation between simulated 

and observed data values. 

The overall reliability of model simulations depends on 

factors that vary from study to study. Thus, while the statistical parameters R2

 and NSE provide a means for assessing the 

relative reliability of model simulations for a given input data 

set, there are no standards or range of values for the statistical 

parameters that definitively indicate acceptable model performance (Daniel Moriasi, U.S. Department of Agriculture, 

written commun., 2006). In general, the longer the time-period 

selected for simulation and the more high-quality measured 

data that are available for input for both calibration and validation periods, the more reliable are the simulations. In addition, 

simulations of data averaged over longer time periods, such as 

annual or monthly mean streamflows, are more reliable than 

simulations of daily data (Santhi and others, 2001; Green and 

others, 2006).

 In this study, measured streamflow data available for 

model calibration and validation was very limited. In addition, Mustang Creek is a flashy, ephemeral stream that is not 

well suited for continuous streamflow simulation. Finally, the 

model was used to simulate daily values of streamflow rather 

than the more commonly used monthly mean values. For all 

these reasons, simulation results for Mustang Creek 

arbitrarily were assumed reasonable if the values for NSE and 

R2 exceeded about 0.5. 
SWAT Model Application to Mustang Creek Basin 15

SWAT Model Application to 

Mustang Creek Basin

Watershed models contain many parameters, some of 

which cannot be measured and must be estimated. To utilize 

any predictive watershed model for estimating the effectiveness of future management practices, the model needs to be 

calibrated by adjusting some or all of the estimated model 

parameters. Then, using an independent set of measured data, 

the model also needs to be tested and validated without any 

additional change to the model parameters. Model calibration requires reasonable incremental changes to the adjustable 

model parameters until simulated results match measured data 

within some acceptable range of comparison. The validation 

step ensures that the simulated results from the calibrated 

model match measured data from a completely independent 

data set within the acceptable range of comparison. A calibrated and validated watershed model generally is considered 

capable of making reasonable simulations of streamflow 

under widely varying climate or land-use change scenarios. A 

watershed model without a successful validation process still 

may have the capability of providing simulations of streamflow under varying climatic and land-use scenarios that can be 

reasonably compared. In this context, the relative differences 

between simulations may be reasonable, but absolute values of 

simulated streamflow are not likely to be reasonable. 

In this study, the SWAT2005 model was first run without 

calibration (uncalibrated model run) for the February 2004 

time-period to identify the parameters to which the model is 

most sensitive. The model then was calibrated by adjusting 

those parameters to reasonably predict the February 2004 measured streamflow dataset. The model then was validated using 

the January and February 2005 measured streamflow dataset 

to demonstrate the model’s capability of reasonably simulating 

streamflow in the Mustang Creek Basin.

Input Data for Mustang Creek Simulations

Basin topographic information for the Mustang Creek 

watershed was obtained from Digital Elevation Model (DEM) 

data. Initially, DEMs with a 30-m resolution were used. These 

DEMS were derived from the 1:24,000-scale USGS quadrangle sheets that are available from the National Elevation Dataset (NED) (available at: http://seamless.usgs.gov/). Because of 

the limited topographic relief in the Mustang Creek Basin, the 

AVSAWT-X interface in the model was unable to predict the 

correct topography and the streamflow paths for the watershed 

using the 30-m DEMs. Therefore, to improve the reliability of 

the topography for streamflow simulation, DEMs with a 5-m 

resolution were created by manually digitizing the 1:24,000-

scale USGS quadrangle sheets, and the new 5-m DEMs were 

used in the SWAT simulation for Mustang Creek (fig. 9).

The AVSWAT-X automated delineation tool (Di Luzio 

and others, 2004) was used with the 5-m DEM data to divide 

the watershed into 25 sub-watersheds, each of which contained 

one HRU (fig. 10). Information regarding each sub-watershed 

and the unique characteristics for each HRU was stored in a 

set of 25 sub-watershed-scale files (.sub, and .hru files). 

Studies have shown that simulated hydrologic responses 

in SWAT are sensitive to historical land-use changes (Miller 

and others, 2002) and hypothetical land-use changes (Fohrer 

and others, 2001; Eckhardt and Ulbrich, 2003; Heuvelmans 

and others, 2004). The land-use data used for the study area 

were obtained from the NLCD. The land cover was interpreted 

from Landsat Thematic Mapper data acquired between 1990 

and 1994 with a 30-m pixel resolution. This land-use information was validated and updated by the USGS in 2002–04 on 

the basis of field site visits. Mustang Creek Basin is dominated 

by agricultural land. On the basis of the updated and validated 

NLCD data, the study area was divided into four general landcover classes: (1) vineyards, covering about 50 percent of the 

total basin area; (2) almond orchards, covering about 

35 percent of the total basin area; (3) row crops, covering 

about 12 percent of the total basin area; and (4) native vegetation and homestead, covering about 3 percent of the total basin 

area (fig. 7). 

Soils data for the relatively small study area were 

obtained from the Soil Survey Geographic (SSURGO) datasets, which contains detailed information that is well suited 

for SWAT simulations in basins the size of Mustang Creek 

watershed (Tolson and Shoemaker, 2004). A SSURGO extension in AVSWAT (SEA) was applied to process and manage 

these datasets (Di Luzio and others, 2004). Soil properties 

assigned to the model HRUs were based on the most common 

soil series of the SSURGO map unit present in each HRU. 

Studies show the existence of an impermeable soil layer (hardpan) located at different depths throughout the Mustang Creek 

Basin (Burow and others, 2004). Table 2 shows the main soil 

series and formations present in the Mustang Creek Basin and 

the depth to hardpan in each soil profile obtained from the 

SSURGO database.

 Daily precipitation and temperature data for 2002–05 

were obtained from Denair weather station (fig. 1). Weather 

data were input to SWAT at the sub-watershed level. All 

HRUs within the same sub-watershed use the same climate 

data. Daily streamflow data from the USGS measurement site 

Mustang Creek at Monte Vista Avenue (site 2) were used for 

calibration and validation of the Mustang Creek SWAT model. 

The period of available flow record for Mustang Creek was 

very limited and the data were of fair quality, which was a 

direct result of very poor and unstable site conditions at the 

gage location. Fair daily discharge record means that only 

about 5 percent of the recorded daily discharges are within 

15 percent of the true daily discharge values. The streamflow record for 2004 was especially problematic because the 

hydraulic control for low-to-medium flows completely washed 

out during or shortly after the peak flow on February 25, 2004. 

Damage to the weir during the 2004 storm led to its subsequent removal in 2005. Records for 2005 were considered 

more stable and slightly better than those from 2004 
16 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

(C. Parrett, U.S. Geological Survey, written commun., 2006). 

However, records for 2005 still are considered fair because of 

the lack of discharge measurements during high flows owing 

to the fact that all site visits were during low-to-medium flow 

conditions.

Uncalibrated Model Results

Uncalibrated model results were obtained from a SWAT 

simulation using the default SWAT settings for parameter 

values before any calibration was performed. The uncalibrated 

simulation was performed for February 2004, the only time 

period in 2004. On the basis of daily precipitation data from 

the Denair weather station, significant daily rainfall (daily 

total greater than 0.15 in) occurred on February 2 (daily total 

of 0.60 in), February 16-18 (daily totals of 0.45 in, 0.17in, and 

0.59 in, respectively), and February 25 (daily total of 0.87 in). 

Figure 11 shows daily precipitation, and recorded and simulated daily streamflows, for the three storms in the February 

2004 simulation period. 

In this uncalibrated simulation, SWAT2005 overestimated 

daily streamflow values throughout the simulation period. For 

example, on February 25, the day of greatest precipitation, the 

simulated daily discharge was 103.5 ft3/s whereas the recorded 

discharge was only 31 ft3/s. Likewise, the simulated daily discharges on February 18 and February 2 were 59 and 57 ft3

/s, 

respectively, compared to recorded discharges on those two 

days of 17 and 3 ft3/s, respectively.

Table 3 summarizes the statistical data comparing the 

simulated daily discharges with the recorded daily discharges 

for February 2004. As indicated in table 3, the simulated mean 

daily discharge for February 2004 was 9.3 ft3/s, whereas the 

recorded mean daily discharge was only 2.2 ft3/s. The R2 for 

the correlation between simulated and recorded daily discharge was relatively high (0.77), indicating a strong linear 

relationship between simulated and recorded flows. The value, 

however, was -8.4, indicating that the simulated daily discharge very poorly matched the recorded discharge. The negative sign associated with an NSE value far from 1.0 indicates 

large, systematic overprediction. On that basis, calibration of 

the model was required.

Figure 9. Topographic relief of Mustang Creek Basin used for sub-watershed delineation in the Soil and Water Assessment Tool 

(SWAT) watershed simulation.

EXPLANATION

High: 300 feet

Low: 200 feet

Elevation

120° 38'

37° 33'

37° 32'

120° 37'

120° 36'

0 1 MILE

0 1 KILOMETER
SWAT Model Application to Mustang Creek Basin 17

Mustang Creek

SWAT-defined subbasins

 and number

24

120° 38'

37° 33'

37° 32'

120° 36'

0 1 MILE

0 1 KILOMETER

15

9

24

3

11

1

2

4

21

10

7

8

6

19 14

12

18

17

5

22

16

13

23

20

25

EXPLANATION

Figure 10. Sub-watershed delineation of the Mustang Creek Basin, California.

Formation Description Soil series 

mapping unit

Modesto Arkosic sand, gravels, and silts. Thickness from 65 to 120 feet. Hardpan layer not present in these 

soils. 

Greenfield

Riverbank B-horizon soils fairly compacted with considerable clay. Variable thickness from 150 to 250 feet. 

Hardpan present in some soils at depth 0–21 inches.

San Joaquin

Snelling 

Atwater

Turlock Lake Succession of gravel and coarse sand, fine grained sand, silt, and clay of possible lacustrine 

origin. Thickness from 160 to 720 feet. Hardpan present in some soils at depth 18–30 inches.

Montpellier

Rocklin

Whitney

Laguna Alluvial, coarsening upward sequence of gravel, sands, and silt. Discontinuous distribution in 

outcrop. Hardpan present in some soils at depth 18–30 inches.

Redding

Mehrten Dark sandstone, siltstone, claystone, conglomerate, and andesitic breccia and tuff. Thickness 

from 190 to 1,200 feet. Hardpan present in some soils at depth 18–30 inches.

Pentz

Raynore

Keyes

Table 2. The main soils series and formations present in the Mustang Creek Basin, California.

[Revised from Burow and others, 2004]
18 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Statistics 

February 2004 monthly streamflow

Measured Simulated SWAT 

uncalibrated run

Mean 2.2 ft3/s 9.3 ft3/s

Median 0 ft3/s 0.069 ft3/s

Standard deviation 6.4 23.6

Minimum 0 ft3/s 0.069 ft3/s

Maximum 31 ft3/s 103.5 ft3/s

Total precipitation 2.9 in.

Correlation coefficient (R2) 0.77

Nash Sutcliffe NSE –8.4

0

20

40

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

FEBRUARY 2004

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PRECIPITATION, IN INCHES PER DAY

Precipitation from Denair weather station 

Simulated streamflow (SWAT2005)

Measured streamflow from USGS gage

Storm 1

Storm 2

Storm 3

STREAMFLOW, IN CUBIC FEET PER SECOND

Figure 11. Results from an uncalibrated Soil and Water Assessment Tool (SWAT) model run for Mustang Creek Basin, California, for 

February 2004.

Table 3. Statistics from the SWAT2005 uncalibrated simulation run for streamflow in Mustang Creek, California.

[NSE, Nash-Sutcliffe model efficiency coefficient; SWAT, Soil and Water Assessment Tool. ft3/s, cubic foot per second; in., inch]
SWAT Model Application to Mustang Creek Basin 19

Model Calibration

With a complex watershed model having a large number 

of model input parameters, a sensitivity analysis is a process 

of varying model input parameters over a reasonable range 

and observing the change in the model output. The magnitude of change in model output for various changes in input 

parameters provides a key for determining which parameters 

may need adjustment for model calibration. For this study, a 

preliminary sensitivity analysis based on all available climatic 

and hydrologic input data for water year 2004 was performed 

on four parameters (SCS curve number, CN; depth to impermeable layer, depimp; soil evaporation compensation factor, 

ESCO; and surface runoff lag coefficient, surlag). The model 

was found to be sensitive to only two of these parameters: CN 

and depimp. 

SWAT simulations were sensitive to changes in the CN 

value. The default value for CN value was determined in 

SWAT and assigned to each HRU, depending on the soils type 

and land-use cover information. In this study, three CN values 

were distributed among the sub-watersheds by the SWAT 

model. Figure 12 and table 4 show the distribution of the 

default CN by soil and land-cover category in the study area. 

Table 4 shows that 90 percent of the study area was assigned 

a default CN value of 83, 6 percent was assigned a default 

CN value of 84, and the remaining 4 percent was assigned 

a default CN value of 79. The calibration was performed by 

changing the CN value for each sub-watershed by increments 

of -5 from the default CN value. Figure 13 illustrates the 

changes in the simulated streamflow due to changes in the CN 

value. Decreasing the CN value increases the soil infiltration 

capability and, therefore, decreases the resulting simulated 

surface runoff. 

Statistical results from the systematic adjustments to CN 

are displayed in table 5. Decreasing all values of CN by –15 

resulted in the closest match of measured and simulated mean 

discharge for February 2004 and the best value of NSE (0.77). 

Changes to CN had only a small effect on the value for R2. 

Other studies have cautioned that CN probably should not be 

changed from the calculated default value by more than about 

10 percent (Lenhart and others, 2002). On this basis, the final 

calibrated values for CN were determined to be the default 

values minus 10. The change from the default values to these 

decreased values was only slightly greater than 10 percent, 

and the simulated mean discharge for February 2004 still was 

close to the measured value with an NSE of 0.72. Table 6 lists 

values of the significant watershed parameters used in the final 

calibration simulation and the range of values commonly used 

in SWAT simulations. 

The only other model parameter for which SWAT simulations were sensitive was the depth to impermeable layer 

(depimp) parameter, the model was considered to have only 

two possible states: (1) with the hardpan soil layer present 

and (2) with the hardpan soil layer removed from the watershed. Information available in the SSURGO soils dataset 

was used to determine the location of the hardpan for each 

sub-watershed in the Mustang Creek Basin. Table 7 shows 

the simulated hydrologic budget for Mustang Creek Basin for 

February 2004 with and without the hardpan layer, and with 

the calibrated values of CN (default CN –10). The presence of 

a hardpan layer significantly affects the Mustang Creek Basin 

water-yield components (ground-water flow, lateral flow, 

surface runoff, water percolation out of soil, and total aquifer 

recharge). The comparison of simulated surface runoff with 

recorded runoff clearly indicates that the presence of a hardpan 

layer results in more reasonable values of simulated surface 

runoff compared to the measured data (table 7). On that basis, 

all subsequent calibration and validation simulations were 

based on the presence of a hardpan layer. 

Results from the calibrated simulation of daily discharges 

for February 2004 are compared to recorded daily discharges 

in figure 14. In this calibration run, SWAT2005 was successful 

in predicting flow events in the Mustang Creek Basin through 

the calibration time-period, but SWAT2005 was unsuccessful in simulating the magnitude of runoff during those events. 

In this calibration run, SWAT2005 overestimated discharge 

values for February 2 and 3, and February 16 underestimated 

discharge values for February 18 and 27. However, the model 

successfully simulated discharge values for the February 25 

storm. Table 8 summarizes the comparison statistics for the 

simulated and recorded daily mean discharges for the calibrated SWAT model for February 2004. Overall, the R2

 (0.77) 

indicates a strong linear correlation between recorded and 

simulated daily discharge, and the NSE value (0.72) indicates a 

good match between recorded and simulated daily discharge 

for the calibration period. 

Model Validation

Streamflow data collected during January and February 2005 at site 2 were used for validation of the predictive 

capability of the SWAT model applied to Mustang Creek. As 

shown in figure 15, runoff was recorded during four general 

storm periods during January 10–11, January 28, February 

15–19, and February 26. Figure 15 also indicates that the 

simulated discharge for the four storm events was significantly 

less than the recorded discharges. The comparison statistics 

for recorded and simulated daily discharges for the validation 

period are shown in table 9. 

As shown in table 9, recorded mean daily discharge for 

the January-February 2005 validation period was 2.9 ft3/s 

compared to a simulated value of 0.73 ft3/s. The R2 for the 

validation period was only 0.50, indicating a poor linear 

correlation between simulated and recorded daily discharge. 

Likewise, the NSE was only 0.33, indicating a poor match 

between simulated and recorded values. The NSE value of 0.33 

is significantly smaller than the 0.50 value initially considered 

to provide a reasonable match between simulated and recorded 

data for Mustang Creek. On this basis, the SWAT model is 

considered to be not validated for simulation of daily mean 

discharge for Mustang Creek.
20 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Figure 12. Distribution of the curve number assigned after calibration to sub-watersheds in the study area.

Category Area

(mile2) Land-use type Soil description

Default SWAT 

assigned curve 

number

1 6.1 Orchards and vineyards Grayish-brown loam, hard. Moderate permeability and runoff. Well suited for orchards and vineyards when 

irrigation available.

83

2 0.42 Oats and row crops Grayish-brown loam, hard. Moderate permeability and 

runoff.

84

3 0.23 Rangeland Brown coarse sandy loam, slightly hard. Slow permeability 

and slow to moderate runoff.

79

Table 4. Description and distribution of Soil Conservation Service curve number assigned to the Mustang Creek Basin, California.

[SWAT, Soil and Water Assessment Tool]

9

24

3

11

1

2

4

21

10

7

8

6

19 14

12

18

17

5

22

16

13

23

20

15

25

Mustang Creek

Category 1 (CN =83)

Category 3 (CN =79)

Numbers are assigned by the model

Category 2 (CN = 84)

 4

18

 2

0 1 MILE

0 1 KILOMETER

120° 38'

37° 33'

37° 32'

120° 36'

EXPLANATION
SWAT Model Application to Mustang Creek Basin 21

Figure 13. Changes in the streamflow curve due to changes in the Soil Conservation Service curve number CN value.

Model run R2 Measured mean Calculated mean Nash Sutcliffe 

Curve number set at default value 0.78 2.2 4.37 0.11

Curve number set at default value –5 0.78 2.2 3.50 0.53

Curve number set at default value –10 0.77 2.2 2.82 0.72

Curve number set at default value –15 0.77 2.2 2.32 0.77

Table 5. Statistic results for Soil Conservation Service curve number sensitivity analysis.

[R2, correlation coefficient]

Original CN

Original CN-5

Original CN-10

Original CN-15

FEBRUARY 2004

STREAMFLOW, IN CUBIC FEET PER SECOND

60

40

20

0

0 4 6 8 10 12 14 16 18 20 22 24 26 28

Parameter Description Range Calibrated value

ESCO Soil evaporation compensation factor 0.01–1.0 0.01

FFCB Initial soil water storage expressed as a fraction 

of field capacity water content

0–1.0 0.01

Surlag Surface runoff lag coefficient (days) 0–4 2

ICN Based on the Soil Conservation Service runoff 

curve number procedure and a soil moisture 

accounting technique

0 or 1 0 (calculate daily curve number value as a function of soil 

moisture)

CNcoef Curve number coefficient 0.0–2.0 0.0

CN Initial Soil Conservation Service runoff curve 

number for moisture condition II

30–100 Set to original curve number 

value –10 (see table 5)

Depimp Depth to impermeable boundary layer (in mm) Unlimited depth below the 

surface1

0–16 ft (4,570 mm)

Table 6. Calibrated values of adjusted parameters for discharge calibration of the SWAT2005 model.

[SWAT, Soil and Water Assessment Tool; mm, millimeter]

1

 Parameter assigned by the user.
22 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California

Hydrologic component Water year 2004 with hardpan

(in inches)

Water year 2004 without hardpan 

(in inches)

Precipitation 10.0 10.0

Surface runoff 0.65 (234 acre-feet) 0.095 (34 acre-feet)

Lateral flow 0.017 0.003

Ground-water accretion 0.00 0.00

Total aquifer recharge 0.00 1.63

Percolation 0.00 1.69

Evapotranspiration 58.25 56.30

Potential evapotranspiration 76.43 76.43

Table 7. The annual hydrologic budget of the Mustang Creek Basin, California, using SWAT2005 simulations with and without the 

hardpan layer.

[SWAT, Soil and Water Assessment Tool]

Figure 14. February 2004 streamflow for Mustang Creek Soil and Water Assessment Tool (SWAT) 2005 calibration simulation results.

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

FEBRUARY 2004

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PRECIPITATION, IN INCHES PER DAY

Storm 1 Storm 2

Storm 3

Precipitation from Denair weather station

Simulated streamflow (SWAT2005)

Measured streamflow from USGS gage

STREAMFLOW, IN CUBIC FEET PER SECOND
SWAT Model Application to Mustang Creek Basin 23

The reasons for the poor simulation performance are 

varied. The SWAT model was developed for use in basins 

with perennial flow, and has been used with most success in 

simulating monthly or annual mean discharge rather than daily 

discharge. Mustang Creek drains a small basin and flows only 

in response to relatively large precipitation events. Accurately 

simulating relatively infrequent and highly variable daily 

flows rather than monthly or annual flows is problematic for 

any continuous flow simulation model. 

 In addition, the period of available flow record for 

Mustang Creek was very limited. Having a much longer 

period of daily flow record for both calibration and validation likely would have resulted in better comparisons between 

recorded and simulated daily flows, because a longer record 

would not be so affected by a few anomalous high values of 

discharge as a short record. Not only was the daily flow record 

for Mustang Creek short, the quality of the recorded daily 

discharges was fair. The fair-quality streamflow data were 

the result of very fair and unstable site conditions at the gage 

location. Fair daily discharge record means that only about 5 

percent of the recorded daily discharges are within 15 percent 

of the true daily discharge values. Such considerable scatter 

in the recorded daily discharges makes reasonable agreement 

between simulated and recorded discharges even harder to 

achieve.

Finally, owing to the fact that precipitation data used for 

the Mustang Creek model calibration and validation only were 

available from the Denair weather station located 11 miles 

west of the study area, and storms are isolated in this location, 

data obtained from the Denair weather station may not be representative of weather conditions in the Mustang Creek Basin. 

This will reflect on the occurrence of runoff in Mustang Creek 

Basin during the time period when precipitation data were 

available. For example, during the February 2004 calibration 

period, runoff occurred on February 14 when no precipitation had been recorded from February 8 through February 15 

(fig.14).

Although the SWAT model was not considered validated 

and suitable for simulating daily discharge on Mustang Creek 

for specific land-use or climatic conditions, the model was 

considered usable for assessing the relative difference in daily 

discharge in Mustang Creek in response to two representative 

storm patterns (Saleh, 2006). To ensure that future applications of the SWAT model or some other hydrologic simulation 

model will lead to better simulation results in Mustang Creek, 

or a similar ephemeral stream, a streamflow gage with a stable 

hydraulic-control section would need to be established and 

operated for at least several seasons. In addition, at least one 

precipitation gage would need to be established within the 

basin and operated concurrently with the streamflow gage. 

Statistics Parameters 

February 2004 monthly streamflow

Measured

(ft3/s)

Simulated SWAT

(ft3/s)

Mean 2.2 ft3/s 2.8 ft3/s

Median 0 ft3/s 0.1 ft3/s

Standard deviation 6.4 7.0

Minimum 0 ft3/s 0 ft3/s

Maximum 31 ft3/s 32.3 ft3/s

Total precipitation 2.9 inches

Correlation coefficient (R2) 0.77

Nash Sutcliffe NSE 0.72

Table 8. Statistics for calibrated simulation of streamflow in Mustang Creek, California. 

[NSE , Nash-Sutcliffe model efficiency coefficient; SWAT, Soil and Water Assessment Tool. ft3/s, cubic feet per second]
24 Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California STREAMFLOW, IN CUBIC FEET PER SECOND

PRECIPIT

A

TIO

N, IN IN

C

HES PER

D

A

Y

0

5

10

15

20

25

30

35

40

45

1/1 1/5 1/9 1/13 1/17 1/21 1/25 1/29 2/2 2/6 2/10 2/14 2/18 2/22 2/26

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Storm 1

Storm 2

Storm 3

Storm 4

DATE (2005 WATER YEAR)

Precipitation from Denair weather 

 station 

Simulated streamflow (SWAT2005)

Measured streamflow from USGS 

 gage

Figure 15. Simulated streamflow for Mustang Creek using Soil and Water Assessment Tool (SWAT) 2005 validation results for January 

and February 2005.

Statistics Parameters 

January and February 2005 monthly streamflow

Measured

(ft3/s)

Simulated SWAT

(ft3/s)

Mean 2.9 ft3/s 0.73 ft3/s

Median 0 ft3/s 0 ft3/s

Standard deviation 7.5 3.0

Minimum 0 ft3/s 0 ft3/s

Maximum 41 ft3/s 21.3 ft3/s

Total precipitation 5.2 inches

Correlation coefficient (R2) 0.50

Nash Sutcliffe NSE 0.33

Table 9. Statistics for validation simulation of streamflow in Mustang Creek, California. 

[NSE , Nash-Sutcliffe model efficiency coefficient; SWAT, Soil and Water Assessment Tool. ft3/s, cubic feet per second]
References Cited 25

Conclusions

The SWAT2005 model was used to simulate streamflow 

in Mustang Creek Basin, California, as part of the USGS 

NAWQA program. The study objective was to determine 

whether the SWAT model could be used to simulate streamflow for a basin with an arid-to-semiarid climate, ephemeral 

flows, and whose land use was predominantly agriculture. The 

SWAT2005 model was modified to include a depth-to-impermeable-layer component to account for the impermeable hardpan layer underlying the study area. This hardpan layer is distributed at different depths and locations throughout the basin. 

Data from a USGS gaging station were used to investigate the 

overall hydrologic simulation capability of SWAT2005. 

Streamflow data available for calibration and validation 

were limited — February 2004 (29 days) for calibration and 

January and February 2005 (58 days) for validation. During 

the calibration period there were three storm events with precipitation amounts of 0.60, 0.58, and 0.87 in/d, respectively. 

Results from the calibration period for Mustang Creek showed 

a reasonably good match between simulated and recorded 

daily discharge, with Nash-Sutcliffe efficiency (NSE) value of 

0.72. 

For the validation period, simulated daily discharge 

was significantly less than the recorded discharge, and the 

NSE value was an unacceptably poor 0.33. As a result, it was 

concluded that the SWAT model could not be used to reliably 

simulate daily discharge for various climatic and land-use 

scenarios in this particular agricultural basin. 

The reasons for the poor simulation performance are 

varied. The SWAT model was developed for use in basins with 

perennial flow, and has been used with the most success in 

simulating monthly or annual mean discharge rather than daily 

discharge. Mustang Creek drains a small basin and flows only 

in response to relatively large precipitation events. Accurately 

simulating relatively infrequent and highly variable daily 

flows rather than monthly or annual flows is problematic for 

any continuous flow simulation model. 

 In addition, the period of available flow record for Mustang Creek was very limited. Having a much longer period 

of daily flow record for both calibration and validation likely 

would have resulted in better comparisons between recorded 

and simulated daily flows, because a longer record would not 

be so affected by a few anomalous high values of discharge 

as a short record. Not only was the daily flow record short for 

Mustang Creek, the quality of the recorded daily discharges 

was poor. The poor-quality streamflow data were the result 

of very poor and unstable site conditions at the gage location. 

Poor daily discharge record means that only about 5 percent 

of the recorded daily discharges are within 15 percent of 

the true daily discharge values. Such considerable scatter in 

the recorded daily discharges makes reasonable agreement 

between simulated and recorded discharges even harder to 

achieve.

Finally, owing to the fact that precipitation data used for 

the Mustang Creek model calibration and validation only were 

available from the Denair weather station located 11 miles 

west of the study area, and storms are isolated in this location, data obtained from the Denair weather station may not 

be representative of weather conditions in the Mustang Creek 

Basin. This will reflect on the occurrence of runoff in Mustang 

Creek Basin during the time period when precipitation data 

were available. 

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Manuscript approved for publication, June 19, 2008

Prepared by the USGS Publishing Network,

Publishing Service Center, Sacramento, California

For more information concerning the research in this report, contact the

California Water Science Center Director,

U.S. Geological Survey, 6000 J Street

Sacramento, California 95819

http://ca.water.usgs.gov
Saleh and others—Using the Soil and Water Assessment Tool (SWAT) to Simulate Runoff in Mustang Creek Basin, California—SIR 2009–5031

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