97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe River

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Guadalupe

Bay

Hynes Bay

San Antonio

Bay

Cibolo Creek

San Antonio River

San Antonio River

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2014–5182

Prepared in cooperation with the San Antonio River Authority

Simulation of Hydrologic Conditions and SuspendedSediment Loads in the San Antonio River Basin 

Downstream from San Antonio, Texas, 2000–12
Front cover, Collection of a suspended-sediment sample by hydrologic technicians at U.S. Geological

Survey streamflow-gaging station 08185000 Cibolo Creek at Selma, Texas, on May 25, 2013. Photograph by 

Chiquita Lopez, U.S. Geological Survey.

Back cover, U.S. Geological Survey streamflow-gaging station 08181800 San Antonio River near

Elmendorf, Texas, on February 18, 2012.
Simulation of Hydrologic Conditions and 

Suspended-Sediment Loads in the San 

Antonio River Basin Downstream from San 

Antonio, Texas, 2000–12

J. Ryan Banta and Darwin J. Ockerman

Prepared in cooperation with the San Antonio River Authority

Scientific Investigations Report 2014–5182

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

Suggested citation:

Banta, J.R., and Ockerman, D.J., 2014, Simulation of hydrologic conditions and suspended-sediment loads in the San 

Antonio River Basin downstream from San Antonio, Texas, 2000–12: U.S. Geological Survey Scientific Investigations 

Report 2014–5182, 46 p., http://dx.doi.org/10.3133/sir20145182. 

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

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

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

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-3860-9
iii

Contents

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

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

Purpose and Scope .............................................................................................................................4

Description of the Study Area ...........................................................................................................4

Simulation of the Hydrologic Conditions and Suspended-Sediment Loads .......................................5

Functional Description of Hydrological Simulation Program—FORTRAN .................................5

Model Development ..........................................................................................................................12

Model Configuration .................................................................................................................12

Input Data for the Hydrological Simulation Program—FORTRAN Model .......................12

Spatial Data ......................................................................................................................14

Meteorological Data .......................................................................................................14

Streamflow Data ..............................................................................................................16

Estimated Suspended-Sediment Loads .......................................................................17

Model Calibration ...............................................................................................................................18

Hydrology ...................................................................................................................................18

Suspended Sediment ...............................................................................................................32

Parameter Calibration Values .................................................................................................33

Sensitivity Analysis ............................................................................................................................33

Model Limitations ...............................................................................................................................34

Simulated Hydrologic Conditions in the Study Area in the San Antonio River Basin ............35

Simulated Suspended-Sediment Loads and Yields in the Study Area in the San 

Antonio River Basin .............................................................................................................38

Summary .......................................................................................................................................................38

References ...................................................................................................................................................39

Appendix 1. Hydrological Simulation Program—FORTRAN Parameter Definitions and 

Values ..............................................................................................................................................43

Figures

1. Map showing location of sites where Hydrological Simulation Program—

FORTRAN (HSPF) model input data were obtained or HSPF model simulation 

results were output in the study area, San Antonio River Basin downstream 

from San Antonio, Texas, 2000–12 ............................................................................................2

2. Map showing surface geology in the study area, San Antonio River Basin 

downstream from San Antonio, Texas .....................................................................................6

3. Map showing relative soil infiltration rates in the study area, San Antonio River 

Basin downstream from San Antonio, Texas ..........................................................................7

4. Map showing land-cover classification in the study area, San Antonio River 

Basin downstream from San Antonio, Texas ..........................................................................8

5. Diagram showing Hydrological Simulation Program—FORTRAN (HSPF) 

flowchart for hydrologic processes on A, impervious land segments and 

B, pervious land segments .......................................................................................................10
iv

6. Diagram showing Hydrological Simulation Program—FORTRAN (HSPF) 

flowchart for sediment processes on A, impervious land segments; 

B, pervious land segments; and C, stream reaches ............................................................11

7. Map showing watershed and stream/reservoir reach delineation for the 

Hydrological Simulation Program—FORTRAN model of the study area, San 

Antonio River Basin downstream from San Antonio, Texas ...............................................13

8. Map showing location of National Weather Service meteorological stations 

and associated Thiessen rainfall areas in the study area, San Antonio River 

Basin downstream from San Antonio, Texas ........................................................................15

9. Graphs showing comparison of A–C, Measured and simulated daily mean 

streamflow at U.S. Geological Survey streamflow-gaging station 08183500, 

San Antonio River near Falls City, Texas (SAR Falls City gage), 2006–12; 

D–F, Estimated and simulated daily mean suspended-sediment loads at the 

SAR Falls City gage, 2006–12 ...................................................................................................24

10. Graphs showing comparison of A–C, Measured and simulated daily mean 

streamflow at U.S. Geological Survey streamflow-gaging station 08186000, 

Cibolo Creek near Falls City, Texas (Cibolo Falls City gage), 2006–12; 

D–F, Estimated and simulated daily mean suspended-sediment loads at the 

Cibolo Falls City gage, 2006–12 ................................................................................................26

11. Graphs showing comparison of A–C, Measured and simulated daily mean 

streamflow at U.S. Geological Survey streamflow-gaging station 08186500 

Ecleto Creek near Runge, Texas (Ecleto Runge gage), 2006–12; D–F, Estimated 

and simulated daily mean suspended-sediment loads at the Ecleto Runge gage, 

2006–12 ........................................................................................................................................28

12. Graphs showing comparison of A–C, Measured and simulated daily mean 

streamflow at U.S. Geological Survey streamflow-gaging station 08188500, 

San Antonio River at Goliad, Texas (SAR Goliad gage), 2006–12; D–F, Estimated 

and simulated daily mean suspended-sediment loads at the SAR Goliad gage, 

2006–12 ........................................................................................................................................30

13. Map showing measured and simulated daily mean streamflow and daily mean 

suspended-sediment loads at selected sites in the study area, San Antonio 

River Basin downstream from San Antonio, Texas, 2006–12 .............................................36

Tables

1. Sites where Hydrological Simulation Program—FORTRAN (HSPF) model input 

data were obtained or HSPF model simulation results were output in the study 

area, San Antonio River Basin downstream from San Antonio, Texas, 2000–12 ..............3

2. National Land Cover Dataset categories and percent coverage in the study 

area, San Antonio River Basin downstream from San Antonio, Texas ..............................9

3. Wastewater discharges included in the Hydrological Simulation Program—

FORTRAN (HSPF) model of the study area, San Antonio River Basin downstream 

from San Antonio, Texas, 2000–12 ..........................................................................................16

4. Evaluation criteria for Hydrological Simulation Program—FORTRAN (HSPF) 

model simulation results based on Donigian (2002) and Moriasi and others 

(2007) ............................................................................................................................................18

5. Streamflow and suspended-sediment load calibration and testing results, 

Hydrologic Simulation Program—FORTRAN (HSPF) model of the study area in 

the San Antonio River Basin downstream from San Antonio, Texas, 2000–12 ................20
v

6. Rainfall and measured evapotranspiration from rangeland/shrubland on the 

Carrizo Sand, in Medina County, Texas, compared with rainfall and modelsimulated evapotranspiration from rangeland and shrubland on the Carrizo 

Sand in the study area, San Antonio River Basin downstream from San Antonio, 

Tex., 2007–09 ...............................................................................................................................32

7. Reported and simulated ranges of soil erosion rates for land-cover types in 

south Texas and the study area, San Antonio River Basin downstream from 

San Antonio, Texas, 2006–12 ....................................................................................................33

8. Changes in simulated streamflow and suspended-sediment loads as a result 

of changes in selected Hydrological Simulation Program—FORTRAN (HSPF) 

process-related parameter values, 2006–12 .........................................................................34

9. Simulated hydrologic and suspended-sediment conditions upstream from and 

within the study area, San Antonio River Basin downstream from San Antonio, 

Texas, 2006–12 ............................................................................................................................37

10. Simulated groundwater recharge by water-budget zone in the study area, San 

Antonio River Basin downstream from San Antonio, Texas, 2006–12 ..............................37

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

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

)

Flow rate

cubic foot per second (ft3

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

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

ton, short (2,000 lb) 0.9072 megagram (Mg) 

ton per day (ton/d) 0.9072 metric ton per day

ton per day (ton/d) 0.9072 megagram per day (Mg/d)

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

°F=(1.8×°C)+32

Concentrations of suspended sediment in water are reported in milligrams per liter (mg/L).

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29) and the North American Vertical Datum of 1988 (NAVD 88).

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

Simulation of Hydrologic Conditions and SuspendedSediment Loads in the San Antonio River Basin 

Downstream from San Antonio, Texas, 2000–12

By J. Ryan Banta and Darwin J. Ockerman

Abstract

Suspended sediment in rivers and streams can 

play an important role in ecological health of rivers and 

estuaries and consequently is an important issue for 

water-resource managers. To better understand suspendedsediment loads and transport in a watershed, the U.S. 

Geological Survey (USGS), in cooperation with the 

San Antonio River Authority, developed a Hydrological 

Simulation Program—FORTRAN model to simulate 

hydrologic conditions and suspended-sediment loads 

during 2000–12 for four watersheds, which comprise 

the overall study area in the San Antonio River Basin 

(hereinafter referred to as the “USGS–2014 model”). 

The study area consists of approximately 2,150 square 

miles encompassing parts of Bexar, Guadalupe, Wilson, 

Karnes, DeWitt, Goliad, Victoria, and Refugio Counties. 

The USGS–2014 model was calibrated for hydrology 

and suspended sediment for 2006–12. Overall, modelfit statistics and graphic evaluations from the calibration 

and testing periods provided multiple lines of evidence 

indicating that the USGS–2014 model simulations of 

hydrologic and suspended-sediment conditions were 

mostly “good” to “very good.” Model simulation 

results indicated that approximately 1,230 tons per day 

of suspended sediment exited the study area and were 

delivered to the Guadalupe River during 2006–12, of 

which approximately 62 percent originated upstream from 

the study area. Sample data and simulated model results 

indicate that most of the suspended-sediment load in the 

study area consisted of silt- and clay-sized particles (less 

than 0.0625 millimeters). The Cibolo Creek watershed 

was the largest contributor of suspended sediment from 

the study area. For the entire study area, open/developed 

land and cropland exhibited the highest simulated soil 

erosion rates; however, the largest contributions of sediment 

(by land-cover type) were pasture and forest/rangeland/

shrubland, which together composed approximately 

80 percent of the land cover of the study area and generated 

about 70 percent of the suspended-sediment load from the 

study area.

Introduction

Suspended sediment in rivers and streams can play an 

important role in ecological health of rivers and estuaries 

and consequently is an important issue for water-resource 

managers. The quantity and type of suspended sediment can 

affect the biological communities (Wood and Armitage, 1997), 

the concentration and movement of natural constituents and 

anthropogenic contaminants (Moran and others, 2012), and 

the amount of sediment deposition in coastal environments 

(Milliman and Meade, 1983). A better understanding of the 

quantity and timing of suspended-sediment loads delivered to 

the Guadalupe and San Antonio Bays is particularly important 

because these sediment inflows are critical for maintaining 

shallow water habitats and wetlands (Texas Commission on 

Environmental Quality, 2011). 

To better understand suspended-sediment loads and 

transport in a watershed, Hydrological Simulation Program—

FORTRAN (HSPF) models can be developed to simulate 

hydrologic and suspended-sediment conditions. The HSPF 

models have been used successfully in south and south-central 

Texas to represent complex hydrologic processes in the San 

Antonio River Basin (Lizárraga and Ockerman, 2010), simulate 

groundwater recharge to the Trinity and Edwards aquifers 

(Ockerman, 2002, 2007), and simulate suspended-sediment 

concentrations and loads in the lower Nueces River watershed 

(Ockerman and others, 2013).

The U.S. Geological Survey (USGS) has collected 

streamflow and suspended-sediment data at some USGS 

streamflow-gaging stations within the San Antonio River Basin 

(fig. 1, table 1) for decades (Crow and others, 2014). However, 

much of the historical suspended-sediment data were collected 

during routine conditions (often during base-flow, or low-flow 

conditions). Because a substantial part of the annual suspendedsediment load can be transported during a small number of 

floods (Markus and Demissie, 2006), historical sampling might 

not have adequately characterized sediment transport during 

high-flow events. Further, much of the historical data did not 

include an analysis of the particle-size distribution, which 

is necessary to accurately simulate the suspended-sediment 

transport using an HSPF model. Recent (2011–13) sediment 

sampling was focused on sampling larger flow events (Crow 

and others, 2014). 
C

C

C

C

C

C

C

C

C

C

C

11

7

6

5

2

4

10

9

8

3

1

17

16

12

14

15

13

29

20

22

23

24

27

25

19 28

21

26

18

31

30

32

33

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

87

183

281

87

281

77

90

59

77

281

181

77

72

181

37

35

35

35

410

35

37

10

410

Poth

Falls 

 City

Floresville

Sutherland

Springs

Stockdale

Nixon

Cibolo

San

Antonio

Goliad

McFaddin

Kenedy

Karnes Runge City

Yorktown

Saint 

Hedwig

Elmendorf

Selma

La Vernia

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe River

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Bay

Cibolo Creek

San Antonio River

San Antonio River

EXPLANATION

Model watershed of San Antonio R

San Antonio River upstream from C

 Cibolo Creek Ecleto Creek San Antonio River downstream froStudy area boundaryU.S. Geological Survey streamflow and map identifier—See table 1National Weather Service meteoro and map identifier—See table 1 Air temperature and rainfall RainfallWastewater treatment plant discha map identifier—See table 3Watershed model outlet and map id table 1

19

C

8

14

12

32

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Sa

R

290810099

SW Med

meteorolo

station n

D’Hanis, 

Figure 1. Location of sites where Hydrological Simulation Program—FORTRAN (HSPF) model input data were obtained or HSPF model simulation re

study area, San Antonio River Basin downstream from San Antonio, Texas, 2000–12.
Introduction 3

Table 1. Sites where Hydrological Simulation Program—FORTRAN (HSPF) model input data were obtained or HSPF model simulation 

results were output in the study area, San Antonio River Basin downstream from San Antonio, Texas, 2000–12.

[SAR, San Antonio River; USGS, U.S. Geological Survey; NWS, National Weather Service; AFB, Air Force Base; --, not applicable; WWTP, wastewater 

treatment plant]

Map 

identifier

(fig. 1)

Short name Site name Type of data Period of 

record used

1 SAR Elmendorf USGS station 08181800 San Antonio River near Elmendorf, 

Texas

Streamflow, suspended 

sediment

2000–12

2 SAR Floresville USGS station 08183200 San Antonio River near Floresville, 

Texas

Streamflow, rainfall 2006–12

3 SAR Falls City USGS station 08183500 San Antonio River near Falls City, 

Texas

Streamflow, suspended 

sediment

2000–12

4 Cibolo Selma USGS station 08185000 Cibolo Creek at Selma, Texas Streamflow, suspended 

sediment

2000–12

5 Cibolo St. Hedwig USGS station 08185065 Cibolo Creek near Saint Hedwig, 

Texas

Streamflow 2006–12

6 Martinez St. Hedwig USGS station 08185100 Martinez Creek near Saint Hedwig, 

Texas

Streamflow 2006–12

7 Cibolo Sutherland 

Springs

USGS station 08185500 Cibolo Creek at Sutherland Springs, 

Texas 

Streamflow, rainfall 2006–12

8 Cibolo Falls City USGS station 08186000 Cibolo Creek near Falls City, Texas Streamflow, suspended 

sediment

2000–12

9 Ecleto Runge USGS station 08186500 Ecleto Creek near Runge, Texas Streamflow, suspended 

sediment

2006–12

10 SAR Goliad USGS station 08188500 San Antonio River at Goliad, Texas Streamflow, suspended 

sediment

2000–12

11 SAR McFaddin USGS station 08188570 San Antonio River near McFaddin, 

Texas

Streamflow, rainfall 2006–12

12 NWS Stinson NWS station 418653 San Antonio Stinson Municipal 

Airport, Texas

Rainfall 2000–12

13 NWS Floresville NWS station 413201 Floresville, Texas Rainfall, air temperature 2000–12

14 NWS Randolph NWS station 417422 Randolph AFB, Texas Rainfall, air temperature 2000–12

15 NWS Stockdale NWS station 418658 Stockdale 6N, Texas Rainfall, air temperature 2000–12

16 NWS Runge NWS station 417836 Runge, Texas Rainfall, air temperature 2000–12

17 NWS Goliad NWS station 413618 Goliad, Texas Rainfall, air temperature 2000–12

18 -- Floresville WWTP Effluent 2000–12

19 -- Milam Street WWTP Effluent 2000–12

20 -- OJ Reidel WWTP Effluent 2000–12

21 -- Salatrillo WWTP Effluent 2000–12

22 -- Upper Martinez WWTP Effluent 2000–12

23 -- Martinez II WWTP Effluent 2000–12

24 La Vernia WWTP Effluent 2000–12

25 -- Stockdale WWTP Effluent 2000–12

26 -- Karnes City WWTP Effluent 2000–12

27 -- Kenedy WWTP Effluent 2000–12

28 -- Runge WWTP Effluent 2000–12

29 -- Goliad WWTP Effluent 2000–12

30 -- Upper San Antonio River watershed model outlet Model output 2000–12

31 -- Cibolo Creek watershed model outlet Model output 2000–12

32 -- Ecleto Creek watershed model outlet Model output 2000–12

33 -- Lower San Antonio River watershed model outlet Model output 2000–12
4 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

In 2010, the USGS, in cooperation with the San 

Antonio River Authority, the Evergreen Underground Water 

Conservation District, and the Goliad County Groundwater 

Conservation District, developed an HSPF model for the part 

of the San Antonio River Basin that is downstream from San 

Antonio, hereinafter referred to as the “USGS–2010 model” 

(Lizárraga and Ockerman, 2010). The purpose of the model 

was to provide a better understanding of the hydrologic 

conditions in the San Antonio River Basin. The USGS–

2010 model simulated hydrologic conditions (streamflow, 

evapotranspiration, and groundwater recharge) during 

2000–2007. In addition, the USGS–2010 model was designed 

to accommodate future modifications, such as the addition 

of model components to simulate various water-quality 

constituents. 

In 2012, URS Corporation developed an updated version 

of the San Antonio River Basin HSPF model based on the 

USGS–2010 model (URS Corporation, 2012) (hereinafter 

referred to as the “URS–2012 model”). The URS–2012 model 

was calibrated by using available data for 2000–10. Changes 

to the USGS–2010 model included extending the model 

simulation period of 2000–2007 to 2000–10, updating input 

time-series data to include 2008–10, updating land-cover 

information (previously based on 2001 National Land Cover 

Database [NLCD]) with data from the 2006 NLCD (MultiResolution Land Characteristics Consortium, 2013), and 

reconfiguring subwatersheds and stream reaches for increased 

spatial resolution. 

As part of the USGS “Strategic Directions for U.S. 

Geological Survey Water Science, 2012–2022,” one of the 

identified priorities was to use USGS “core capabilities 

in water science” to “conduct, research, and modeling” in 

support of various goals (Evenson and others, 2012, p. 1, 2). 

One goal was the “development and application of models 

to predict potential changes in population, land use, climate, 

and management practices upon future water availability” 

(Evenson and others, 2012, p. 17). Hence, to simulate 

hydrologic conditions and suspended-sediment loads in the 

San Antonio River Basin during 2000–12, the USGS, in 

cooperation with the San Antonio River Authority, developed 

an HSPF model based on the URS–2012 model and the USGS 

sediment characterization study (Crow and others, 2014). The 

model documented by this report is hereinafter referred to as 

the “USGS–2014 model.” 

Purpose and Scope

The purpose of this report is to describe the simulation 

of hydrologic conditions and suspended-sediment loads 

in the San Antonio River Basin, downstream from San 

Antonio during 2000–12 by using an HSPF model. The 

USGS–2014 model was composed of four watershed models 

that represent the overall study area in the San Antonio 

River Basin. The functionality of the USGS–2014 model is 

described, followed by the model development, calibration, 

and sensitivity analysis. The hydrologic conditions and 

suspended-sediment loads in the four watersheds in the San 

Antonio River Basin are summarized. Limitations of modelsimulated hydrologic conditions and suspended-sediment 

loads are described. Selected sections in this report are 

modified from Lizárraga and Ockerman (2010), Ockerman 

and others (2013), or Crow and others (2014). 

Description of the Study Area

The study area and discussion pertaining to the study 

area are modified from Crow and others (2014), which 

characterized sediment concentrations and loads in the 

San Antonio River Basin downstream from San Antonio, 

Tex. The study area (fig. 1) is part of the San Antonio 

River Basin and consists of approximately 2,150 square 

miles (mi2) encompassing parts of Bexar, Guadalupe, 

Wilson, Karnes, DeWitt, Goliad, Victoria, and Refugio 

Counties. The upstream boundary of the study area 

coincides with USGS streamflow-gaging station 08181800 

San Antonio River near Elmendorf, Tex. (hereinafter 

referred to as the “SAR Elmendorf gage”) and USGS 

streamflow-gaging station 08185000 Cibolo Creek at Selma, 

Tex. (hereinafter referred to as the “Cibolo Selma gage”) 

(fig. 1, table 1) (in this report, each gage is referred to by 

its short name [table 1]). Upstream from the study area, the 

San Antonio River Basin consists of 1,743 mi2 upstream 

from the SAR Elmendorf gage and 274 mi2 upstream from 

the Cibolo Selma gage (streamflow and suspended-sediment 

loads at both gages were considered inputs to the study 

area). The downstream boundary of the study area is the 

San Antonio River confluence with the Guadalupe River 

(fig. 1). 

The San Antonio River extends about 190 river miles 

(mi) in the study area from the SAR Elmendorf gage to the 

confluence with the Guadalupe River. From the upstream 

boundary of the study area at the Cibolo Selma gage, 

Cibolo Creek extends about 75 river mi downstream to the 

confluence with the San Antonio River in Karnes County. 

Ecleto Creek extends about 55 river mi from northern 

Wilson County to the confluence with the San Antonio River 

in Karnes County (fig. 1).
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 5

The northern part of the study area overlies the EdwardsTrinity aquifer system (fig. 2). The remainder of the study 

area overlies the Texas Coastal Uplands and Texas Coastal 

Lowlands aquifer systems (Ryder, 1996). The Cretaceousage rocks of the Edwards-Trinity aquifer system primarily 

consist of limestone and sandstone (Ashworth and Hopkins, 

1995) (fig. 2). The Tertiary-age rocks of the Texas Coastal 

Uplands aquifer system is primarily composed of formations 

of the Midway Group through Whitsett Formation, with the 

sediments distributed as relatively uniform sequences of 

predominantly fine- or course-grained material (Ryder, 1996). 

The Tertiary- and Quaternary-age rocks of the Texas Coastal 

Lowlands aquifer system is primarily composed of formations 

of Catahoula Formation through Deweyville Formation that 

dip and thicken towards the Gulf of Mexico, with sediments 

that exist in complex, overlapping mixtures of sand, silt, and 

clay as a result of numerous oscillations of ancient shorelines 

(Kasmarek and Robinson, 2004; Lizárraga and Ockerman, 

2010). Previous studies have indicated that the upper part 

of the study area exhibits significant streamflow gains and 

losses, which might be attributed to soil types (Lizárraga and 

Wehmeyer, 2012). County soils data (Soil Survey Geographic 

database [SSURGO]) indicate that relative infiltration rates in 

the study area range from “rapid to very rapid” (for example, 

where the surface geology is characterized as the Carrizo 

Sand) to “slow to very slow” in other parts of the study area 

(Natural Resources Conservation Service, 2013) (figs. 2 

and 3). 

In addition to geology and soil types, land cover and 

topography can affect hydrologic conditions and sediment 

loads in the study area. The land-cover composition consists 

of forest (8.3 percent), rangeland and shrubland (36.3 percent), 

pasture (35.4 percent), developed and open space (8.1 

percent), cropland (7.6 percent), and water and wetlands (4.4 

percent) (Multi-Resolution Land Characteristics Consortium, 

2013) (fig. 4, table 2). The study area is composed of gently 

sloping, rolling terrain; the coastal uplands are somewhat more 

dissected and rolling compared to the coastal lowlands (Ryder, 

1996). Elevation in the study area ranges from about 2 feet 

(ft) to 1,033 ft above North American Vertical Datum of 1988 

(NAVD 88) (U.S. Geological Survey, 2014a). The slope of 

the land surface is generally low, mostly less than 5 percent. 

Overall, the stream-channel slope of the San Antonio River 

is about 2 feet per mile (ft/mi) over approximately 190 mi 

from the model boundary at the SAR Elmendorf gage to the 

confluence with the Guadalupe River. 

Simulation of the Hydrologic 

Conditions and Suspended-Sediment 

Loads

The text in this section and the model description were 

modified from Ockerman and others (2013). Hydrologic 

conditions and suspended-sediment concentrations and 

loads were simulated by using a Hydrological Simulation 

Program—FORTRAN (HSPF) model (Bicknell and others, 

2001). The HSPF model is one of the most comprehensive 

watershed models, can simulate a variety of stream and 

watershed conditions with reasonable accuracy, and enables 

flexibility in adjusting the model to simulate alternative 

conditions or scenarios (Donigian and others, 1995). Previous 

HSPF models of the lower San Antonio River (Lizárraga and 

Ockerman, 2010; URS Corporation, 2012) were available and 

were modified to simulate hydrologic conditions and sediment 

transport for this study. 

To simulate the watershed hydrologic and sediment 

processes, different data sources are used as input to the HSPF 

model including rainfall data, potential evapotranspiration, 

streamflow data, land cover, and soil characteristics. 

The outputs of an HSPF model are simulated time series 

of streamflow and other hydrologic conditions such as 

evapotranspiration (ET) and groundwater recharge, as well as 

sediment concentrations, sediment loads, or both. The model 

time series are for a user-specified interval, or time step. A 

1-hour time step was used for this study. 

The HSPF model also can simulate other water-quality 

constituents, including nutrients, metals, and organic 

compounds. Simulations for this study were limited to 

hydrologic processes (streamflow, ET, and groundwater 

recharge) and suspended-sediment concentrations and loads.

Functional Description of Hydrological 

Simulation Program—FORTRAN

The HSPF model uses a continuous, semilumped 

parameter design (Singh, 1995) that can provide continuous 

water and mass balance by tracking rainfall and waterquality constituents through the conceptual pathways of 

the hydrologic cycle in a watershed. In HSPF, a watershed 

is represented by a group of hydrologically distinct areas 

referred to as “hydrologic response units (HRUs)” that 

drain to a stream segment, lake, or reservoir referred to 

as a “reach reservoir (RCHRES).” HRUs are areas in a 

subwatershed that have distinct hydrologic and water-quality 

characteristics that are determined on the basis of land 

use, surficial geology, and other factors that are deemed to 

produce similar hydrologic responses to rainfall and potential 

evapotranspiration. HRUs are categorized as pervious or 

impervious land segments, termed pervious land (PERLND) 

or impervious land (IMPLND), respectively. A PERLND is 

represented conceptually within HSPF by three interconnected 

water-storage zones—an upper zone, a lower zone, and a 

groundwater zone. An IMPLND is represented by a surface 

storage zone, subject to evaporation and runoff processes. 

Each RCHRES is associated with a particular drainage area, 

referred to as a “subwatershed,” and receives the runoff 

and sediment from the PERLNDs and IMPLNDs in the 

subwatershed. The hydraulics of a RCHRES are simulated by 

a storage routing method (Donigian and others, 1995).
TEXAS COASTAL LOWLANDS

AQUIFER SYSTEM

TEXAS COASTAL UPLANDS

AQUIFER SYSTEM

EDWARDS-TRINITY

AQUIFER SYSTEM

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Zone 4

Zone 3

Zone 8

Zone 9

Zone 2

Zone 1

Zone 5

Zone 6

Zone 7

Mg

Ql

Mo

Oc

Em

Ecm

Ey

Ecd

Eqc

Es

Ec

Ew

Qle

Er

Emi

Ewi

Kknm

Qt

Qal

Qd Qbs

Qbc

PoW

Mf

Eca

Ewb

Kpg

Kau

Qt

Qt

Qt

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe River

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Bay 

Cibolo Creek

San Antonio River

San Antonio River

 Whitsett Format Manning Format Wellborn Format Caddell Formatio Yegua Formation Cook Mountain F Sparta Sand Weches Formati Queen City Sand Recklaw Format Carrizo Sand Wilcox Group Midway Group Navarro Group Pecan Gap Chal Austin ChalkWater bodyHydrological Sim Program—FOR budget zone boStudy area boundAquifer system bo 1996; KasmareU.S. Geological S station and ma

Surface geology Alluvium Fluviatile terrace deposits Deweyville Formation Beaumont Formation (sand) Beaumont Formation (clay) Lissie Formation Leona Formation Willis Formation Goliad Formation Fleming Formation Oakville Sandstone Catahoula Formation

SYSTEM

QUATERNARY TERTIARY

Qal

Qt

Qd

QbsQbc

Ql

Qle

PoW

Mg

Mf

Mo

EXPLANATION

TERTIARY UPPER 

CRETACEOUS

Ecd

Em

Ewb

Eca

Ey

Ecm

Es

Ew

Eqc

Er

Ec

EwiEmi

Kknm

KpgKau

Oc

5

C

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator projection, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010;

Surficial geology modified from Proctor and others, 1974;

Brown and others, 1976, 1983; and Aronow and others, 1987

Figure 2. Surface geology in the study area, San Antonio River Basin downstream from San Antonio, Texas.
C

C

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11

7

6

5

2

4

10

9

8

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97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe 

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Bay 

Cibolo Creek

San Antonio River

San Antonio River

Zone 4

Zone 3

Zone 8

Zone 9

Zone 2

Zone 1

Zone 5

Zone 6

Zone 7

EXPLANATION

Relative soil infiltration rate Rapid to very rapid Moderate to moderately rapid Moderate to moderately slow Slow to very slowWater bodyHydrological Simulation Program—FOR water-budget zone boundary (zones 1Study area boundaryU.S. Geological Survey streamflow-gag

5 station and map identifier—See tabl

C

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010;

Soil data modified from Natural Resources

Conservation Service, 2008

Figure 3. Relative soil infiltration rates in the study area, San Antonio River Basin downstream from San Antonio, Texas.
11

7

6

5

2

4

10

9

8

3

1

C

C

C

C

C

C

C

C

C

C

C

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe 

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Bay 

Cibolo Creek

San Antonio River

San Antonio River

Zone 4

Zone 3

Zone 8

Zone 9

Zone 2

Zone 1

Zone 5

Zone 6

Zone 7

EXPLANATION

Model land-cover classification— Water Open space Developed Forest Rangeland and shrubland Pasture Cropland WetlandHydrological Simulation Program— water-budget zone boundary (zoStudy area boundaryU.S. Geological Survey streamflow

5 station and map identifier—See 

C

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator projection, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010;

Land-cover data modified from Multi-Resolution

Land Characteristics Consortium, 2006

Figure 4. Land-cover classification in the study area, San Antonio River Basin downstream from San Antonio, Texas.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 9

The HSPF model is composed of a series of 

computational routines that simulate processes of the 

hydrologic cycle. Specifically, HSPF simulates the hydrologic 

cycle as an interconnected series of storage (and processing) 

segments with water fluxes (volume per unit area per unit 

time) and constituent fluxes (mass [weight] per unit area per 

unit time) moving between the various storages. A flowchart 

of HSPF hydrologic processes for IMPLNDs and PERLNDs 

is shown on figure 5. A flowchart of HSPF sediment processes 

for IMPLNDs, PERLNDs, and RCHRESs is shown on 

figure 6. The movement of water and suspended sediment 

from IMPLNDs and PERLNDs and between storage zones is 

controlled by various process-related parameters. Although 

some parameters are directly measurable, most are determined 

during model calibration (Martin and others, 2001). The 

definitions of selected HSPF model process parameters used in 

the study area are listed in appendix 1. A complete description 

of the computational processes and required input model 

parameters is provided in the HSPF user’s manual (Bicknell 

and others, 2001).

The suspended-sediment concentrations and loads were 

simulated by using the appropriate HSPF modules: SEDMNT 

for simulation of production and removal of sediment 

from PERLNDs, SOLIDS for simulation of accumulation 

and removal of solids from IMPLNDs, and SEDTRN for 

simulation of transport of sediment in RCHRESs. For each 

PERLND, the processes of detachment of sediment from 

the soil matrix and washoff of this sediment were simulated 

on the basis of rainfall intensity, surface runoff, and model 

parameters that control the accumulation, detachment, 

and transport of soils. For each IMPLND, the processes 

of accumulation and washoff of sediment are based on the 

amount of sediment available and the transport capacity of the 

overland flow. Sediment transport processes in a RCHRES 

included deposition and scour, which are functions of 

sediment size, settling velocity, density, erodibility, bed depth, 

and critical shear stress. Sediment transport for RCHRESs is 

computed separately for each sand, silt, and clay fraction of 

sediment size.

Table 2. National Land Cover Dataset categories and percent coverage in the study area, San Antonio River Basin downstream from 

San Antonio, Texas.

[NLCD, National Land Cover Dataset; HSPF, Hydrological Simulation Program—FORTRAN]

NLCD 

code NLCD classification HSPF model classification

Square 

miles

Land cover 

(percent)1

11 Open water Water 11.3 0.5

21 Developed, open space Open space 110.4 5.1

31 Barren land (rock/sand/clay) Open space 7.8 0.4

22 Developed, low intensity Developed 38.9 1.8

23 Developed, medium intensity Developed 12.6 0.6

24 Developed, high intensity Developed 3.6 0.2

41 Deciduous forest Forest 152.2 7.1

42 Evergreen forest Forest 14.0 0.7

43 Mixed forest Forest 9.8 0.5

52 Shrub/scrub Rangeland/shrubland 688.9 32.0

71 Grassland/herbaceous Rangeland/shrubland 91.9 4.3

81 Pasture/hay Pasture 762.1 35.4

82 Cultivated crops Cropland 163.1 7.6

90 Woody wetlands Wetland 75.4 3.5

95 Emergent herbaceous wetlands Wetland 8.4 0.4

1

Land-cover percentages do not sum to 100 percent because of rounding.
Interception Storage 

Direct Infiltration Rainfall Potential ET Rainfall Potential ET Surface Retention Storage Surface Detention Storage Actual Evaporation Actual ET ETETETETET Overland FlowTo stream Potential Runoff RETSC* CEPSC* LZETP* LZSN* Lower Zone Storage INFILT* INFILD* INFEXP* INTFW* AGWETP* BASETP* DEEPFR* UZSN* LSUR* NSUR* SLSUR* 

IR

Delayed Infiltration Upper Zone Storage Inactive Groundwater Active Groundwater Storage Surface Detention Storage Interflow Storage 

KVA

AGW

A. Impervious land segments B. Pervious land segments Mo

StoOuDeDe    

* EXP

ET Ev

Modified from Lizárraga and Ocke

Figure 5. Hydrological Simulation Program—FORTRAN (HSPF) flowchart for hydrologic processes on A, impervious land segments and B, pervious 

from Ockerman and others, 2013).
ModStorOutfDes H P p a

*

EXPL

Accumulation of solids

Solids storage Lateral input of solids 

To

stream

ACCSDP*

SLSLD* Washoff

of solids 

KEIM*, JEIM*

Washoffof solids 

REMSDP*

Net vertical

sediment input

Detached sediment

storage

Lateral input 

of sediment

To

stream

NVSI*

SLSED*

Total removal

of sediment 

KSER*, JSER*

Soil matrix

Detachment of soil by rainfall

KRER*, JRER*

Washoff of detached

sediment by water 

Sediment attachment

AFFIX*

Scour of matrix

soil by water 

KGER*, JGER*

Suspended

sediment

storage

Sediment inflow

to RCHRES

Sediment outflow

from RCHRES

Bed

sediment

storage

Deposition TAUCD*, TAUCS*

Scour

sand 

silt

clay 

clay and silt

sand

KSAND*, EXPSND*

A. Impervious land segments

B. Pervious land segments

C. Stream reaches

Modified from Ockerman and ot

Figure 6. Hydrological Simulation Program—FORTRAN (HSPF) flowchart for sediment processes on A, impervious land segments; B, pervious land s

reaches (modified from Ockerman and others, 2013).
12 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Model Development

The USGS–2014 model was developed by (1) 

configuring the model to represent the study area in the 

San Antonio River Basin and (2) compiling and processing 

required meteorological, streamflow, and sediment input data. 

The USGS–2014 model was configured by defining model 

watersheds (including subwatersheds and HRUs), waterbudget zones, and RCHRES segments for the study area on 

the basis of surficial geology, land cover, and location of 

meteorological stations. Because of the relatively large size of 

the study area, the study area in the USGS–2014 model was 

divided into four watersheds, each with an associated HSPF 

model (fig. 7): (1) San Antonio River upstream from Cibolo 

Creek, hereinafter referred to as the “upper SAR model”; 

(2) Cibolo Creek, hereinafter referred to as the “Cibolo 

model”; (3) Ecleto Creek, hereinafter referred to as the “Ecleto 

model”; and (4) San Antonio River downstream from Cibolo 

Creek, hereinafter referred to as the “lower SAR model.”

Model Configuration

The upper SAR model includes the drainage area 

extending from the upstream model boundary at the SAR 

Elmendorf gage to the confluence of the San Antonio River 

and Cibolo Creek. The Cibolo model includes the drainage 

area from the Cibolo Selma gage to the confluence of Cibolo 

Creek and the San Antonio River. The Ecleto model includes 

the entire Ecleto Creek watershed, upstream from the 

confluence of Ecleto Creek and the San Antonio River. The 

lower SAR model includes the drainage area extending from 

the confluence of the San Antonio River and Cibolo Creek 

to the confluence of the San Antonio and Guadalupe Rivers, 

excluding the Ecleto Creek watershed. The most downstream 

watershed model (lower SAR model) receives as input the 

simulated streamflow and sediment from the outlets of the 

three other HSPF models. A simulation of the overall study 

area involves running simulations of the three upstream 

watershed HSPF models, then running the lower SAR model.

The drainage areas of each of the four watershed models 

(upper SAR, Cibolo, Ecleto, and lower SAR models) were 

further divided into subwatersheds, with associated stream 

reaches (RCHRESs). Considerations in delineating the 

subwatersheds and stream reaches included (1) defining 

reaches with streamflows such that travel times through 

RCHRESs approximate the model simulation time step and 

(2) locating outlets of RCHRESs at strategic points, such 

as streamflow-gaging stations, tributary confluences, and 

geologic outcrop boundaries (Donigian and others, 1984). 

The USGS–2010 model (Lizárraga and Ockerman, 

2010) included 162 subwatersheds with an average size of 

13.3 mi2

. The URS–2012 model revised the subwatershed 

and stream reach delineation, increasing the spatial resolution 

of the model(s) to include a total of 339 subwatersheds 

(and associated RCHRESs) with an average size of 6.3 mi2

(URS Corporation, 2012). The URS–2012 model stream 

delineation was adopted for use in this modeling study and 

further modified by the addition of a single subwatershed at 

the most downstream boundary of the model, at the confluence 

of the San Antonio and Guadalupe Rivers. The subwatershed 

and stream-reach delineation is shown in figure 7. Each 

subwatershed is further subdivided into PERLND and 

IMPLND HRUs based on surficial geology, land-cover type, 

and source of input meteorological data.

The model(s) configuration also included the delineation 

of nine water-budget zones, determined on the basis of 

surficial geology, as shown in figure 2. For each zone, model 

outputs were created to be able to provide simulation results 

of water-budget information (runoff, evapotranspiration, and 

groundwater recharge).

The hydraulic response of each RCHRES was simulated 

based on channel geometry (channel width and depth, 

and stream discharge as a function of water volume in the 

RCHRES) (Bicknell and others, 2001). For RCHRESs 

that included a USGS streamflow-gaging station, channelgeometry and stream discharge data (depth and width for 

a range of stream discharge conditions) were based on 

available USGS measurement data collected at the station 

(U.S. Geological Survey, 2014b). Channel depth and width 

values measured at a USGS streamflow-gaging station were 

assumed constant for the entire reach length to calculate 

RCHRES surface area and water volume associated with a 

measured stream discharge. For ungaged RCHRESs, where 

channel geometry data were not available, the channel 

depths and widths were assumed to be similar as nearby 

gaged RCHRESs, for the same stream discharge. During 

model calibration, the hydraulic characteristics for selected 

RCHRESs were adjusted to improve model fit.

Input Data for the Hydrological Simulation 

Program—FORTRAN Model

Input data for the HSPF model included spatial and 

time-series data. Spatial data included geology, soils, landcover, topography, and drainage characteristics such as 

subwatershed boundaries and stream-reach length and crosssection data. Spatial data were used to define and configure 

model subwatersheds, water-budget zones, RCHRESs, and 

HRUs (PERLNDs and IMPLNDs). Time-series data included 

meteorological data (rainfall and potential evapotranspiration), 

streamflow data, and suspended-sediment load data. 

Meteorological time-series data were used to drive the model 

simulation. Streamflow and suspended-sediment time-series 

data generally were used as input data at the upstream model 

boundaries and also used to calibrate and test the model at 

selected locations in the model study area.
98

170

166

158

156

108

110

96

102

76 92

88

94

86

80

28

20

12

32 56

54 60

72

68

70

62

34

50

37

2 4

168

162

14

58

42

46

64

202 204

246

232

594

620

618

586 590

592

576

574

542

534

532

536 540 526530

410 524 408

412

416

406

402 404

414 418

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442

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436

434 432

430

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398

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366 364 340

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390

448 450

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538 574

570 572

566 556

522 558

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568

646

644

642

288 560

286

280

276 274

284

282

582

578

550

580

588 584

612

614 610

608

720

706

674 676 722

724

734

728

736

738 748

672

740

6

16 18

22

24

26

30

8

10

44

206

208

210

122

124 118

116

126

128

134 132

136

142

130

146

148

150

138

140

152

154

144

160

120 112

106

103 104

114 214

218

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220

230 224

226

228

216

212

708

710

712

714

716

718

694

248

100

730

732

666

664

658

640

656 638

632

630

626

628

624

622 616

636

354

742

746

744

750

751

66

52

48

726

74 90

84

40

39 78

82

47

49

38

36

670 668

662

660

654

652

634

702

698

690 700 650

688

686

682

680 684

678

648

604

598

596

606 602 600

692

696

704

552

554 518

516 514 506

512

508

504

502

510 270

266

256

254

252 164 244

250

242

236

234

240

268

264

278

272

262

238 260

258

544 548

520

C

C

C

C

C

C

C

C

C

C

C

11

7

6

5

2

4

10

9

8

3

1

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

San Antonio

River

Manahuilla 

Creek

San Antonio

River

Cibolo Creek

EcletoCreek

CabezaCreek

Martinez

Creek

Calaveras 

Lake

Guad

San Antonio River

Medina

Cibolo Creek

 River

Hynes 

EXPLANATION

Model watershed of San Antonio  San Antonio River upstream fro Cibolo Creek Ecleto Creek San Antonio River downstream Study area boundaryStream/reservoir reach (RCHRES drainage area, identified by nU.S. Geological Survey streamflo station and map identifier—S

5

12

C

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010

Figure 7.

Watershed and stream/reservoir reach delineation for the Hydrological Simulation Program—FORTRAN model of the study area, San Ant

downstream from San Antonio, Texas.
14 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Spatial Data

Surficial geology data (fig. 2) were obtained from four 

areal geologic maps published in the Geologic Atlas of Texas 

by the University of Texas Bureau of Economic Geology: 

Beeville-Bay City sheet (Aronow and others, 1987), Crystal 

City-Eagle Pass sheet (Brown and others, 1976), Seguin sheet 

(Proctor and others, 1974), and San Antonio sheet (Brown and 

others, 1983). The surficial geology data were used to help 

classify HRU types and to define general geologic outcrop 

areas, referred to in this report as “water-budget zones” 

(fig. 2). The HSPF model outputs were created for each of 

the water-budget zones to evaluate simulated hydrologic 

conditions by zone and by watershed.

County soils data (Soil Survey Geographic database 

[SSURGO]) from the Natural Resources Conservation Service 

(2013) were compiled for the study area. Soils data were 

not used explicitly to define PERLND types. Instead, soil 

attributes of relative infiltration and erodibility were used to 

determine selection of initial values for the HSPF parameters 

of soil-infiltration rate (INFILT) and soil erodibility/

detachability (KRER).

The USGS–2010 model used the 2001 National Land 

Cover Database (NLCD) (Homer, and others, 2007) as the 

source for land-cover data. The URS–2012 model (URS 

Corporation, 2012) incorporated the 2006 NLCD (Fry and 

others, 2011). The 2006 NLCD data also were used for the 

USGS–2014 version of the model. In the study area, there 

were 15 NLCD land-cover classes. To simplify the model 

configuration, the 15 classes were reclassified into eight landcover categories (fig. 4, table 2). The area of each land-cover 

type and percent coverage of the study area are also listed in 

table 2. Developed land was principally classified as lowintensity development, of which 25 percent was simulated 

as impervious area. Barren land was a small percentage of 

the study area (0.4 percent) and was grouped with developed 

open space, and the resulting acreage was considered to be 

15 percent impervious (Multi-Resolution Land Characteristics 

Consortium, 2013). Areas classified as open water were 

not part of a PERLND or IMPLND HRU; these areas were 

modeled as part of the stream RCHRESs. Digital elevation 

data for the study area were obtained from the USGS National 

Elevation Dataset (U.S. Geological Survey, 2014a). The 

digital elevation data were used to delineate subwatersheds as 

part of the HSPF model development. Spatial data for streams 

(location and reach length) were obtained from the National 

Hydrography Dataset (U.S. Geological Survey, 2014c). 

Meteorological Data

Daily precipitation totals and daily maximum and 

minimum temperatures used as input for the models were 

obtained from the DAYMET climate database, distributed 

through the Oak Ridge National Laboratory Distributed Active 

Archive Center (Thornton and others, 2012). DAYMET 

algorithms and computer software are designed to interpolate 

and extrapolate from observed daily meteorological 

observations to produce gridded values of daily precipitation 

and maximum and minimum temperature on a 1 kilometer 

(km) × 1 km gridded surface over the conterminous United 

States, Mexico, and Southern Canada. These data have an 

advantage of using consistent calculation methodology and 

data gap filling, which are often problematic when using 

discrete National Weather Service (NWS) or USGS rainfall 

gage data. To maintain a consistent meteorological input 

configuration as the earlier versions of the models, DAYMET 

data were obtained from the five NWS meteorological stations 

in the model study area (fig. 1, table 1). Besides daily rainfall 

from the DAYMET locations, available hourly rainfall data 

from the NWS station 417422 Randolph Air Force Base, Tex. 

(hereinafter referred to as the “NWS Randolph station”), also 

were compiled for use with the model simulations (National 

Oceanic and Atmospheric Administration, 2013). Consistent 

with the URS–2012 model, rainfall was spatially distributed 

between watershed models using the Thiessen method 

(Linsley and others, 1982). Each Thiessen area (polygon) was 

named for the NWS station in that polygon (fig. 8, table 1). 

Because the HSPF models were designed to simulate on an 

hourly time step, DAYMET daily precipitation totals from 

four precipitation stations were disaggregated to hourly 

totals based on the temporal distribution of rainfall measured 

at one or more nearby meteorological stations in the study 

area (NWS station 418653 Stinson San Antonio Municipal 

Airport, Tex. [hereinafter referred to as the “NWS Stinson 

station”], the NWS Randolph station, and three USGS rainfall 

stations colocated with USGS streamflow-gaging stations: 

SAR Floresville, Cibolo Sutherland Springs, SAR McFaddin) 

(fig. 1, table 1). 

USGS rainfall gages in the study area were calibrated 

to provide 15-minute rainfall data (U.S. Geological Survey, 

2009), and when aggregated to hourly totals, were considered 

reliable temporal precipitation records that could be used for 

disaggregating the DAYMET daily rainfall totals to hourly 

totals. If hourly rainfall data were not available from the 

nearby meteorological stations, hourly values were obtained 

by using a triangular distribution of the daily rainfall value 

with a peak at the middle of the day (Hummel and others, 

2001). Disaggregation of daily rainfall data was performed 

through the WDMUtil program, a utility program that is 

part of the Better Assessment Science Integrating Point and 

Nonpoint Sources (BASINS) software package (version 

4.0) provided by the U.S. Environmental Protection Agency 

(EPA), and used to run the HSPF model (U.S. Environmental 

Protection Agency, 2013). Hourly data from the NWS 

Randolph station were used directly for model simulations. 

DAYMET annual rainfall for 2000–12 (Thornton and others, 

2012) at the locations of the five NWS stations in the study 

area ranged from 13.8 inches in 2008 (NWS Randolph) to 

54.9 inches in 2007 (NWS station 418658 Stockdale 6N, Tex. 

[hereinafter referred to as the “NWS Stockdale station”]), with 

an average rainfall of approximately 33 inches per year.
C

C

C

C

C

C

C

C

C

C

C

11

7

6

5

2

4

10

9

8

3

1

17

16

12

14

15

13

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalupe Riv

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Bay

Cibolo Creek

San Antonio River

San Antonio River

EXPLANATION

Model watershed of the San Anto San Antonio River upstream from  Cibolo Creek Ecleto Creek San Antonio River downstream frHydrological Simulation Program water-budget zone boundary (zonThiessen rainfall area boundary aStudy area boundaryStream/reservoir reach (RCHRES)  area boundaryNational Weather Service (NWS)  station and map identifier—See  Air temperature and rainfall RainfallU.S. Geological Survey streamflow

5 station and map identifier—See 

14

12

C

Zone 4

Zone 3

Zone 8

Zone 9

Zone 2

Zone 1

Zone 5

Zone 6

Zone 7

NWS Randolph

NWS Floresville

NWS Stockdale

NWS Runge

NWS Goliad

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman (2010)

Figure 8. Location of National Weather Service meteorological stations and associated Thiessen rainfall areas in the study area, San Antonio River 

San Antonio, Texas.
16 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

DAYMET daily maximum and minimum temperature 

data were used to calculate daily potential evapotranspiration 

at the five NWS stations located in the study area. The Hamon 

method (Bidlake, 2002) was used to calculate daily potential 

evapotranspiration from minimum and maximum daily 

temperatures by using the EPA BASINS 4.0 software (U.S. 

Environmental Protection Agency, 2013). Daily potential 

evapotranspiration was converted to hourly time series by 

using the disaggregation algorithms in the BASINS 4.0 

software (U.S. Environmental Protection Agency, 2013). Daily 

potential evaporation is disaggregated on the basis of latitude 

and time of year (Hummel and others, 2001).

Streamflow Data

As stated in the Description of the Study Area section of 

this report, most of the streamflow in the study area originates 

upstream from the study area. These inflows are measured at 

the SAR Elmendorf and Cibolo Selma gages (fig. 1, table 1), 

which account for streamflow into the model area (U.S. 

Geological Survey, 2014b). Other USGS streamflow-gaging 

stations in the study area provided data that were used for 

calibration and testing of the model simulations. Location 

information, type of data collected, and period of record 

for gages that provided streamflow data for the model are 

listed in table 1. The streamflow data used for this report are 

available from the USGS National Water Information System 

(NWISWeb) (U.S. Geological Survey, 2014b).

Wastewater discharges upstream from the study area have 

an appreciable effect on streamflow in the San Antonio River. 

Simulated treated wastewater accounted for about 20 percent 

of the San Antonio River annual mean streamflow entering 

the study area upstream from the SAR Elmendorf gage during 

1997–2001 (Ockerman and McNamara, 2003). Within the 

Cibolo Creek watershed, wastewater from four relatively 

large treatment plants (OJ Riedel, Salatrillo, Upper Martinez, 

and Martinez II) flows into Martinez Creek or Cibolo Creek 

(fig. 1, table 3). Each of the four plants discharges more than 

1 million gallons of treated wastewater each day. Monthly 

mean discharge data from these four plants for 2000–2007 

were reported by Lizárraga and Ockerman (2010) and 

were included as model inputs for the USGS–2010 model. 

Discharge data were updated through 2012 for this version 

of the model (Daniel Flores, San Antonio River Authority, 

written commun., 2013; Douglas Vaughn and Robert Dabney, 

Cibolo Creek Municipal Authority, written commun., 2013). 

Monthly discharge totals were disaggregated and input to the 

model as hourly mean discharges. Locations and daily mean 

discharge for eight additional smaller wastewater treatment 

plants (less than 1 million gallons per day) (fig. 1, table 3) 

were obtained from the U.S. Environmental Protection Agency 

database (2013).

As described in Lizárraga and Ockerman (2010), 

inflow to Cibolo Creek from springs upstream from USGS 

streamflow-gaging station 08185500 Cibolo Creek at 

Sutherland Springs, Tex. (hereinafter referred to as the “Cibolo 

Sutherland Springs gage”) (fig. 1, table 1), was simulated in 

the model as a time-series input to stream reaches (RCHRESs) 

14, 34, 62, 64, 76, 92, and 98 of the Cibolo Creek model 

(fig. 7). The time series representing springflow was developed 

from data collected from streamflow gain-loss measurements 

during 2006–10 (Lizárraga and Wehmeyer, 2012). Streamflow 

gains, attributed to springs, were observed from the differences 

between measurements made at the USGS streamflowgaging station 08185065 Cibolo Creek near St. Hedwig, Tex., 

(hereinafter referred to as the “Cibolo St. Hedwig gage”) and 

Cibolo Sutherland Springs gage. During four sets of synoptic 

Table 3. Wastewater discharges included in the Hydrological Simulation Program—FORTRAN (HSPF) model of the study area, San 

Antonio River Basin downstream from San Antonio, Texas, 2000–12.

[WWTP, wastewater treatment plant]

Map identifier

(fig. 1)

Wastewater 

treatment plant Receiving stream Average discharge(million gallons per day)

18 Floresville WWTP San Antonio River upstream from Cibolo Creek 0.57

19 Milam Street WWTP San Antonio River upstream from Cibolo Creek 0.12

20 OJ Riedel WWTP Cibolo Creek 6.0

21 Salatrillo WWTP Martinez Creek 6.0

22 Upper Martinez WWTP Martinez Creek 2.7

23 Martinez II WWTP Martinez Creek 2.2

24 La Vernia WWTP Cibolo Creek 0.05

25 Stockdale WWTP Cibolo Creek 0.13

26 Karnes City WWTP San Antonio River downstream from Cibolo Creek 0.06

27 Kenedy WWTP San Antonio River downstream from Cibolo Creek 0.99

28 Runge WWTP San Antonio River downstream from Cibolo Creek 0.06

29 Goliad WWTP San Antonio River downstream from Cibolo Creek 0.26
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 17

measurements made during base-flow conditions (April 2006, 

August 2006, February 2007, and October 2007), streamflow 

gains between the Cibolo St. Hedwig and Cibolo Sutherland 

Springs gages ranged between 4.8 and 29 cubic feet per 

second (ft3/s) (Lizárraga and Wehmeyer, 2012). To develop 

a continuous daily time series of springflow for input to the 

model, the observed streamflow gains were related to daily 

water-level readings at the J–17 index well (Edwards Aquifer 

Authority, 2014), made on the same day as the streamflow 

measurements from which the streamflow gains are 

determined. The J–17 index well (State well number AY–68–

37–203) (not shown in fig. 1) is located in San Antonio, 

approximately 27 miles northwest of Sutherland Springs. A 

regression equation relating measured streamflow gains to 

J–17 daily water levels (ranging from approximately 635 to 

702 feet above the National Geodetic Vertical Datum of 1929 

[NGVD 29], Edwards Aquifer Authority, 2014) was developed 

and used to calculate daily springflow input to Cibolo Creek 

for 2000–12. The resulting regression equation was

Qspringflow = 2×10-77 × L27.521, (3)

where 

Qspringflow is the daily mean spring discharge to Cibolo 

Creek, in cubic feet per second; and 

L is daily water level of the J–17 well, in feet 

above NGVD 29.

The coefficient of determination (R2) between springflow 

and the daily water level of the J–17 well was 0.88. 

Monthly surface-water withdrawals for irrigation from 

the San Antonio River and Cibolo Creek were based on data 

provided by the Texas Commission on Environmental Quality 

(TCEQ) (Lizárraga and Ockerman, 2010). The monthly 

surface-water withdrawals were disaggregated into hourly 

values. Monthly mean withdrawals for each TCEQ stream 

reach during 2000–2005 were used to estimate monthly values 

for the period when withdrawal data were not available (2006–

12). Also, TCEQ-defined stream reaches do not correspond 

directly with the HSPF RCHRESs; each TCEQ reach 

typically includes several RCHRESs. Total withdrawals from 

each TCEQ stream reach were allocated to the appropriate 

RCHRES in proportion to the amount of cropland included 

in the area draining to each RCHRES. Cropland acreage was 

assumed constant for the simulation period, 2000–12.

Estimated Suspended-Sediment Loads

Estimated suspended-sediment loads at selected stream 

locations were used (1) to account for sediment inflows at 

the model upstream boundaries and (2) to provide time-series 

data for model calibration and testing at selected in-stream 

locations. The study area, which is in the San Antonio River 

Basin, receives streamflow and sediment from two upstream 

watersheds that are not included as part of the model (the 

inflow from these watersheds is gaged at the SAR Elmendorf 

and Cibolo Selma gages). To account for these upstream 

contributions of suspended sediment entering the lower SAR 

model, estimated suspended-sediment load time series were 

developed for the SAR Elmendorf and Cibolo Selma gages 

(fig. 1) to represent inflows to the upstream model boundaries. 

Similarly, estimated suspended-sediment time-series were 

developed for the USGS streamflow-gaging station 08183500 

San Antonio River near Falls City, Tex., (hereinafter referred 

to as the “SAR Falls City gage”), the USGS streamflowgaging station 08186000 Cibolo Creek near Falls City, Tex., 

(hereinafter referred to as the “Cibolo Falls City gage”), the 

USGS streamflow-gaging station 08186500 Ecleto Creek near 

Runge, Tex., (hereinafter referred to as the “Ecleto Runge 

gage”), and the USGS streamflow-gaging station 08188500 

San Antonio River at Goliad, Tex., (hereinafter referred to 

as the “SAR Goliad gage”) (fig. 1, table 1). Streamflow data 

collected at these gages were used as calibration and testing 

datasets. Time series of the estimated suspended-sediment 

loads for 2000–12 were developed by using regression 

equations to estimate suspended-sediment concentration as a 

function of streamflow (Crow and others, 2014). A regression 

equation to estimate suspended-sediment concentrations at 

the Cibolo Selma gage was not developed by Crow and others 

(2014) because few measurements of suspended-sediment 

concentration data were available. To represent potential 

suspended-sediment load input to the USGS–2014 model at 

the model boundary, a time series of estimated suspendedsediment loads at the Cibolo Selma gage was developed 

based on a relation between streamflow and three available 

suspended-sediment concentration samples (hereinafter 

referred to as “estimated suspended-sediment loads”). The 

resulting equation (when retransformed, accounting for 

potential transformation bias; Helsel and Hirsch, 2002) was

SSC = 85.1 × Q0.208 (3)

where

SSC is the estimated suspended-sediment 

concentration, in milligrams per liter, at the 

Cibolo Selma gage; and

Q is the streamflow, in cubic feet per second.

Annual sediment loads entering the system at the 

Cibolo Selma gage were highly variable because streamflow 

is relatively infrequent at this gage. From the suspendedsediment particle size samples collected at the SAR Elmendorf 

gage, Crow and others (2014) reported a distribution of about 

4 percent sand, 23 percent silt, and 73 percent clay. This 

distribution was used to apportion the estimated sediment 

loads entering the model study area at the SAR Elmendorf 

gage between sand, silt, and clay. Analysis of suspendedsediment particle size data at the Cibolo Selma gage indicated 

a distribution of about 2 percent sand, 19 percent silt, and 

79 percent clay (Crow and others, 2014). This distribution was 

used to apportion the estimated sediment loads entering the 

model study area at the Cibolo Selma gage between sand, silt, 

and clay.
18 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Model Calibration

The USGS–2014 model was calibrated in two steps. First, 

the parameters related to hydrologic processes were calibrated. 

Next, the parameters related to suspended-sediment processes 

were calibrated. The USGS–2014 model was calibrated by 

using data collected during 2006–12. After the USGS–2014 

model was calibrated, the model was then tested by using 

data collected during 2000–2005 (not part of the calibration 

process). The testing period was used to evaluate the ability of 

the model to accurately simulate hydrologic and suspendedsediment processes within the study area. 

Hydrology

A primary goal of hydrologic model calibration is to 

adjust process-related parameters such that model-simulated 

streamflow matches observed streamflow at the USGS 

streamflow-gaging stations in the study area. In addition, 

model parameters were set so that the simulated hydrologic 

conditions, such as evapotranspiration and groundwater 

recharge rates in study area, were similar to those reported 

in literature. The model calibrations were evaluated by 

following guidelines by Donigian and others (1984), Donigian 

(2002), U.S. Environmental Protection Agency (2000, 2006), 

and Moriasi and others (2007). These guidelines include 

comparing observed and simulated results (for example, 

streamflow volumes), as well as examining model-fit statistics 

on daily to annual time steps. For example, model calibration 

is classified as “very good” when the monthly or annual 

mean streamflow percent difference ([simulated–measured]/

measured × 100) is less than 10 percent, “good” when the 

difference is 10–15 percent, and “fair” when the difference 

is 15–25 percent (Moriasi and others, 2007; Donigian, 2002) 

(table 4). In addition, generation and analysis of model 

simulation scenarios (GenScn) for watersheds (Kittle and 

others, 1998) were used to develop model-fit statistics between 

the observed and simulated hydrologic conditions. Modelfit statistics include (1) R2

 of the linear regression between 

measured and simulated variable (in this section, streamflow); 

(2) Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 

(Nash and Sutcliffe, 1970); and (3) root mean squared error 

(RMSE) to observations standard deviation (SD) ratio, 

hereinafter referred to as the “RSR.” The R2 and NSE describe 

the variation in the dependent variable that is explained by 

the independent variable. Donigian (2002) also provides a 

continuum of R2 values for evaluating the simulation of daily 

mean streamflow. For this report, simulated mean streamflow 

with an R2 greater than 0.8 is classified as “very good,” 0.70 

to 0.80 as “good,” 0.60 to 0.70 as “fair,” and less than 0.60 as 

“unsatisfactory” (table 4). The RSR is the ratio of the RMSE 

to SD, which provides an indication of the model fit. Moriasi 

and others (2007) provide performance rating guidelines of 

“very good” to “unsatisfactory” for the monthly NSE and RSR 

statistics (table 4). 

Table 4. Evaluation criteria for Hydrological Simulation Program—FORTRAN (HSPF) model simulation results based on Donigian (2002) 

and Moriasi and others (2007).

[RMSE-SD, root mean squared error to standard deviation of observations ratio, also referred to as “RSR”; <, less than; >, greater than]

Performance

rating

Daily mean 

streamflow 

percent difference

Daily mean 

suspended-sediment 

percent difference

Coefficient of 

determination1

(R2

)

Nash-Sutcliffe 

efficiency2

(NSE)

RMSE-SD2

(RSR)

Very good <10 <15 >0.80 >0.75 0.00–0.50

Good 10–15 15–30 0.70–0.80 0.65–0.75 0.50–0.60

Fair 15–25 30–55 0.60–0.70 0.50–0.65 0.60–0.70

Unsatisfactory >25 >55 <0.60 <0.65 >0.70

1

Applicable to daily mean streamflow values.

2

Applicable to monthly mean streamflow or monthly mean sediment values.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 19

Four USGS streamflow-gaging stations were selected as 

primary calibration sites in the study area (SAR Falls City, 

Cibolo Falls City, Ecleto Runge, and SAR Goliad), with each 

gage representing a separate watershed HSPF model (upper 

SAR, Cibolo, Ecleto, and lower SAR models, respectively). 

During 2006–12, the percent differences between measured 

and simulated daily mean streamflow at each primary 

calibration site were all within 10 percent (table 5) and were 

rated as a “very good” fit. For comparison of daily mean 

streamflows, R2 ranged from 0.77 to 0.94 (“good” to “very 

good” fits), except for the Ecleto Runge gage which was 

0.42 (“unsatisfactory” fit). For monthly mean streamflows 

at each primary calibration site, the NSE was greater than 

0.79 (“good” to “very good” fits), and the RSR ranged from 

0.22 to 0.50 (“very good” fits). In general, peak discharges, 

which are primarily driven by rainfall intensity and timing, 

were often challenging for the model to replicate. In addition 

to evaluating simulated daily streamflow time-series data, 

it is important to evaluate exceedance-probability curves 

and scatter plots (figs. 9–12) to ensure that a few high-flow 

events do not skew the average streamflow values (Moriasi 

and others, 2007). Evaluation of the model-fit statistics and 

graphical comparisons at the most downstream streamflowcalibration gage (SAR Goliad gage), which incorporates 

simulated streamflow from all four watershed HSPF models, 

indicated the streamflow calibration was “very good.” 

Calibration model-fit statistics for the Ecleto Runge gage 

indicated a lower quality fit than at the other gages (table 5). 

This lower quality fit was likely because there were frequent 

periods when the stream did not flow. The NSE was notably 

sensitive to periods of very low streamflow (for example, 

flows less than 1 ft3

/s), and hence, only the R2 was reported 

for daily mean streamflow (R2=0.42); however, on a monthly 

basis, the model fit improved. Another possible reason for 

the lesser quality model fit was lack of rainfall data. The 

Ecleto Creek watershed did not have a rain gage within its 

boundaries, so rainfall data for model input from stations in 

adjacent watersheds were used. In contrast, the other three 

watersheds included at least one NWS daily rainfall station 

and at least one USGS streamflow-gaging station that also 

recorded hourly rainfall. 

After completing the calibration process, post-calibration 

tests of the model fit were performed at each of the calibration 

gages during 2000–2005, except for the Ecleto Runge gage, 

which was not in operation during 2000–2002. During the 

testing period, model parameters were not further adjusted to 

attempt to improve the model fit. Rather, the testing period 

was an independent evaluation of model performance. During 

the testing period, simulated daily mean streamflow at the 

SAR Falls City and SAR Goliad gages was within 10 percent 

of the measured daily mean streamflow (“very good” fit) and 

was 20.5 percent less at the Cibolo Falls City gage (“fair” fit) 

(table 5). Model-fit statistics (R2

 greater than 0.77) for daily 

mean streamflow the SAR Falls City, Cibolo Falls City, and 

SAR Goliad gages during the testing period indicated “good” 

to “very good” fits. Monthly mean streamflow model-fit 

statistics were “very good” fits (NSEs were greater than 0.93, 

and RSRs were less than 0.50 at each gage). As shown in 

table 5, the simulations during the testing period had similar 

model-fit statistics as during the calibration period, with the 

exception of the Cibolo Falls City gage. One possible reason 

for weaker testing results (compared with calibration results) 

was that the quality of available rainfall data for 2000–2005 

was likely poorer than the 2006–12 data. During the 

calibration period, the daily rainfall was disaggregated from 

daily to hourly rainfall using nearby USGS hourly rainfall 

gages located at USGS streamflow-gaging stations (table 

1); however, during the testing period, hourly data were not 

available from the USGS rain gages, and thus available hourly 

data from the NWS Stinson station were used to disaggregate 

the daily rainfall or the WDMUtil program triangular 

distribution method was applied (Hummel and others, 2001). 

The intensity and timing of rainfall are especially important 

during large stormflow events, such as those that occurred 

during 2002 and 2004. 

In addition to the primary calibration sites, testing 

of model streamflow simulation also was done at USGS 

streamflow-gaging station 08183200 San Antonio River 

near Floresville, Tex. (hereinafter referred to as the “SAR 

Floresville gage”); Cibolo St. Hedwig gage; USGS 

streamflow-gaging station 08185100 Martinez Creek near 

Saint Hedwig, Tex. (hereinafter referred to as the “Martinez 

St. Hedwig gage”); and the Cibolo Sutherland Springs and 

SAR McFaddin gages. All of these gages were installed during 

2005 and did not have a sufficient period of record for separate 

calibration and testing periods. Because streamflow at the 

Martinez St. Hedwig gage was likely in backwater conditions 

during periods of high flow in Cibolo Creek (Richard Slattery, 

USGS, oral commun., 2014), this gage was not used for 

testing purposes. The streamflow percent difference during the 

2006–12 testing period was less than 10 percent for the SAR 

Floresville, Cibolo Sutherland Springs, and SAR McFaddin 

gages (“very good” fits). The simulated percent difference 

for the Cibolo St. Hedwig gage was 25 percent. Considering 

streamflow percent differences and model-fit statistics (R2

, 

NSE, and RSR), the test-gage simulations indicated “fair” to 

“very good” model fits (table 5). SAR McFaddin is the most 

downstream gage in the watershed and monitors streamflow 

from about 98.5 percent of the study area watershed. Testing 

results at this gage provide an important evaluation of the 

model’s ability to accurately assess streamflow and suspendedsediment discharges near the outlet of the study watershed. 

Streamflow simulation testing results at the SAR McFaddin 

gage for 2006–12 indicate a 7.2 percent difference in daily 

mean streamflow (“very good” fit), daily mean streamflow R2

of 0.64 (“fair” fit), and monthly mean streamflow R2, NSE, 

and RSR indicating a “very good” fit. 

During the 2000–12 simulation period, the model-fit 

statistics at each of the calibration gages (SAR Falls City, 

Cibolo Falls City, and SAR Goliad) indicate “good” to “very 

good” fits. Overall, model-fit statistics and graphic evaluations 

from the calibration and testing periods, provide multiple lines 

of evidence indicating the USGS–2014 model simulations of 

hydrologic conditions were mostly “good” to “very good.”
20 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Table 5. Streamflow and suspended-sediment load calibration and testing results, Hydrologic Simulation Program—FORTRAN (HSPF) 

model of the study area in the San Antonio River Basin downstream from San Antonio, Texas, 2000–12.

[USGS, U.S. Geological Survey; SAR, San Antonio River; ft3/s, cubic feet per second; SSL, suspended-sediment load; tons/d, tons per day; RMSE, root mean 

squared error; --, not determined] 

Streamflow and suspended-sediment loads at SAR Falls City (USGS station 08183500 San Antonio River near Falls City, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Calibration period 2006–12 511 544 6.5 828 778 -6.0

Testing period 2000–2005 905 905 0.0 1,760 1,530 -13.1

Simulation period 2000–12 693 711 2.6 1,260 1,130 -10.3

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Calibration period 2006–12

Number of days, months, or years 2,557 84 7 2,557 84 7

Coefficient of determination (R2

) 0.94 0.98 1.00 0.80 0.92 1.00

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.93 0.96 0.97 0.73 0.89 0.99

RMSE-observations standard deviation ratio2

 (RSR) -- 0.23 0.18 -- 0.41 0.09

Testing period 2000–2005

Number of days, months, or years 2,192 72 6 2,192 72 6

Coefficient of determination (R2

) 0.97 0.99 1.00 0.95 0.97 0.96

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.97 0.99 1.00 0.86 0.92 0.94

RMSE-observations standard deviation ratio2

 (RSR) -- 0.08 0.06 -- 0.36 0.25

Simulation period 2000–12

Number of days, months, or years 4,749 156 13 4,749 156 13

Coefficient of determination (R2

) 0.96 0.99 0.99 0.92 0.96 0.97

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.96 0.99 0.99 0.84 0.92 0.96

RMSE-observations standard deviation ratio2

 (RSR) -- 0.12 0.11 -- 0.37 0.21

Streamflow and suspended-sediment loads at Cibolo Falls City (USGS station 08186000 Cibolo Creek near Falls City, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Calibration period 2006–12 140 140 0.0 231 242 4.8

Testing period 2000–2005 258 205 -20.5 475 461 -2.9

Simulation period 2000–12 194 170 -12.4 344 343 -0.3

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Calibration period 2006–12

Number of days, months, or years 2,557 84 7 2,557 84 7

Coefficient of determination (R2

) 0.77 0.96 0.99 0.56 0.78 0.96

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.73 0.96 0.98 0.54 0.78 0.96

RMSE-observations standard deviation ratio2

 (RSR) -- 0.22 0.16 -- 0.53 0.19

Testing period 2000–2005

Number of days, months, or years 2,192 72 6 2,192 72 6

Coefficient of determination (R2

) 0.77 0.97 0.95 0.37 0.78 0.79

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.66 0.93 0.77 0.37 0.78 0.77

RMSE-observations standard deviation ratio2

 (RSR) -- 0.23 0.37 -- 0.51 0.43

Simulation period 2000–12

Number of days, months, or years 4,749 156 13 4,749 156 13

Coefficient of determination (R2

) 0.77 0.96 0.93 0.41 0.79 0.86

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.68 0.94 0.88 0.41 0.79 0.85

RMSE-observations standard deviation ratio2

 (RSR) -- 0.22 0.30 -- 0.51 0.36
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 21

Table 5. Streamflow and suspended-sediment load calibration and testing results, Hydrologic Simulation Program—FORTRAN (HSPF) 

model of the study area in the San Antonio River Basin downstream from San Antonio, Texas, 2000–12.—Continued

[USGS, U.S. Geological Survey; SAR, San Antonio River; ft3/s, cubic feet per second; SSL, suspended-sediment load; tons/d, tons per day; RMSE, root mean 

squared error; --, not determined]

Comparison of daily mean streamflow at Ecleto Runge (USGS station 08186500 Ecleto Creek near Runge, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Calibration period 2006–12 27 28 3.7 42 41 -2.4

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Calibration period 2006–12

Number of days, months, or years 2,557 84 7 2,557 84 7

Coefficient of determination (R2

) 0.42 0.79 0.97 0.11 0.59 0.99

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) --3 0.79 0.94 --3 0.58 0.99

RMSE-observations standard deviation ratio2

 (RSR) -- 0.50 0.18 -- 0.97 0.09

Streamflow and suspended-sediment loads at SAR Goliad (USGS station 08188500 San Antonio River at Goliad, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Calibration period 2006–12 757 820 8.3 1,200 1,180 -1.7

Testing period 2000–2005 1,400 1,300 -7.1 2,640 2,190 -17.0

Simulation period 2000–12 1,060 1,040 -1.9 1,860 1,640 -11.8

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Calibration period 2006–12

Number of days, months, or years 2,557 84 7 2,557 84 7

Coefficient of determination (R2

) 0.82 0.98 1.00 0.54 0.92 1.00

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.80 0.93 0.95 0.51 0.86 0.98

RMSE-observations standard deviation ratio2

 (RSR) -- 0.35 0.25 -- 0.52 0.14

Testing period 2000–2005

Number of days, months, or years 2,192 72 6 2,192 72 6

Coefficient of determination (R2

) 0.82 0.99 0.99 0.71 0.95 0.98

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.82 0.98 0.97 0.66 0.91 0.94

RMSE-observations standard deviation ratio2

 (RSR) -- 0.12 0.15 -- 0.39 0.25

Simulation period 2000–12

Number of days, months, or years 4,749 156 13 4,749 156 13

Coefficient of determination (R2

) 0.81 0.97 0.97 0.67 0.94 0.97

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.81 0.97 0.97 0.62 0.89 0.96

RMSE-observations standard deviation ratio2

 (RSR) -- 0.20 0.19 -- 0.42 0.21
22 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Table 5. Streamflow and suspended-sediment load calibration and testing results, Hydrologic Simulation Program—FORTRAN (HSPF) 

model of the study area in the San Antonio River Basin downstream from San Antonio, Texas, 2000–12.—Continued

[USGS, U.S. Geological Survey; SAR, San Antonio River; ft3/s, cubic feet per second; SSL, suspended-sediment load; tons/d, tons per day; RMSE, root mean 

squared error; --, not determined]

Comparison of daily mean streamflow at SAR Floresville (USGS station 08183200 San Antonio River near Floresville, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Testing period 2006–12 553 540 -2.4 -- -- --

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Testing period 2006–12

Number of days, months, or years 2,557 84 7 -- -- --

Coefficient of determination (R2

) 0.97 0.99 1.00 -- -- --

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.97 0.99 0.99 -- -- --

RMSE-observations standard deviation ratio2

 (RSR) -- 0.08 0.07 -- -- --

Comparison of daily mean streamflow at Cibolo St. Hedwig (USGS station 08185065 Cibolo Creek near St. Hedwig, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Testing period 2006–12 28 35 25.0 -- -- --

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Testing period 2006–12

Number of days, months, or years 2,557 84 7 -- -- --

Coefficient of determination (R2

) 0.82 0.91 0.97 -- -- --

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.78 0.75 0.83 -- -- --

RMSE-observations standard deviation ratio2

 (RSR) -- 0.85 0.61 -- -- --
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 23

Table 5. Streamflow and suspended-sediment load calibration and testing results, Hydrologic Simulation Program—FORTRAN (HSPF) 

model of the study area in the San Antonio River Basin downstream from San Antonio, Texas, 2000–12.—Continued

[USGS, U.S. Geological Survey; SAR, San Antonio River; ft3/s, cubic feet per second; SSL, suspended-sediment load; tons/d, tons per day; RMSE, root mean 

squared error; --, not determined]

Comparison of daily mean streamflow at Cibolo Sutherland Springs (USGS station 08185500 Cibolo Creek at Sutherland Springs, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily mean 

SSL

(tons/d)

Percent

difference1

Testing period 2006–12 108 106 -1.9 -- -- --

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Testing period 2006–12

Number of days, months, or years 2,557 84 7 -- -- --

Coefficient of determination (R2

) 0.78 0.95 0.99 -- -- --

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.67 0.94 0.99 -- -- --

RMSE-observations standard deviation ratio2

 (RSR) -- 0.23 0.09 -- -- --

Comparison of daily mean streamflow at SAR McFaddin (USGS station 08188570 San Antonio River near McFaddin, Texas)

Mean streamflow Mean suspended-sediment load

Measured 

daily mean 

streamflow

(ft3

/s)

Simulated 

daily mean 

streamflow

(ft3

/s)

Percent

difference1

Estimated 

daily mean 

SSL

(tons/d)

Simulated 

daily 

mean 

SSL

(tons/d)

Percent

difference1

Testing period 2006–12 801 859 7.2 -- -- --

Model-fit statistics Daily Monthly Annual Daily Monthly Annual

Testing period 2006–12

Number of days, months, or years 2,557 84 7 -- -- --

Coefficient of determination (R2

) 0.64 0.92 1.00 -- -- --

Nash-Sutcliffe coefficient of model-fit efficiency (NSE) 0.63 0.89 0.96 -- -- --

RMSE-observations standard deviation ratio2

 (RSR) -- 0.43 0.22 -- -- --

1

Percent difference = [(simulated - measured)/measured] × 100.

2

RMSE-observations standard deviation ratio not calculated for daily mean streamflow or daily mean suspended-sediment load.

3

Nash-Sutcliffe coefficient of model-fit efficiency not calculated because of frequent periods of no streamflow.
24 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

1:1 line

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

B

C

A

Calendar year

Exceedance probability, in percent

Measured daily mean streamflow,

in cubic feet per second 

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

10 100 1,000 10,000 100,000

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

Daily mean streamflow, in cubic feet per second Daily mean streamflow, in cubic feet per second 

Simulated daily mean streamflow, in

cubic feet per second 

Figure 9. Comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflow-gaging station 

08183500, San Antonio River near Falls City, Texas (SAR Falls City gage), 2006–12; D–F, Estimated and simulated daily mean suspendedsediment loads at the SAR Falls City gage, 2006–12.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 25

1:1 line

Estimated

Simulated

EXPLANATION

Estimated

Simulated

EXPLANATION

E

F

D

Calendar year

Exceedance probability, in percent

Estimated daily mean suspended-sediment load,

in tons per day

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

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

1

10

100

1,000

10,000

100,000

1

100

10

1,000

10,000

100,000

1,000,000

1,000,000

1

10

100

1,000

10,000

100,000

1,000,000

Daily mean suspended-sediment load,

in tons per day

Daily mean suspended-sediment load,

in tons per day

Simulated daily mean suspended-sediment load,

in tons per day

Figure 9. Graphs showing comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflowgaging station 08183500, San Antonio River near Falls City, Texas (SAR Falls City gage), 2006–12; D–F, Estimated and simulated daily 

mean suspended-sediment loads at the SAR Falls City gage, 2006–12.—Continued
26 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

1:1 line

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

B

C

A

Calendar year

Exceedance probability, in percent

Measured daily mean streamflow,

in cubic feet per second 

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

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

1

10

100

1,000

10,000

100,000

1

10

100

1,000

10,000

100,000

1

10

100

1,000

10,000

100,000

Daily mean streamflow, in cubic feet per second Daily mean streamflow, in cubic feet per second 

Simulated daily mean streamflow, in

cubic feet per second 

Figure 10. Comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflow-gaging station 

08186000, Cibolo Creek near Falls City, Texas (Cibolo Falls City gage), 2006–12; D–F, Estimated and simulated daily mean suspendedsediment loads at the Cibolo Falls City gage, 2006–12.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 27

1:1 line

Estimated

Simulated

EXPLANATION

Estimated

Simulated

EXPLANATION

E

F

D

Calendar year

Exceedance probability, in percent

Estimated daily mean suspended-sediment load,

in tons per day

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

0.1 1 10 100 1,000 10,000 100,000

1

10

100

1,000

10,000

100,000

1

100

10

0.1

0.1

1,000

10,000

100,000

0.1

1

10

100

1,000

10,000

100,000

Daily mean suspended-sediment load,

in tons per day

Daily mean suspended-sediment load,

in tons per day

Simulated daily mean suspended-sediment load,

in tons per day

Figure 10. Graphs showing comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflowgaging station 08186000, Cibolo Creek near Falls City, Texas (Cibolo Falls City gage), 2006–12; D–F, Estimated and simulated daily mean 

suspended-sediment loads at the Cibolo Falls City gage, 2006–12.—Continued
28 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

1:1 line

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

B

C

A

Calendar year

Exceedance probability, in percent

Measured daily mean streamflow,

in cubic feet per second 

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

1 10 100 1,000 10,000

1

10

100

1,000

10,000

1

10

100

1,000

10,000

1

10

100

1,000

10,000

Daily mean streamflow, in cubic feet per second Daily mean streamflow, in cubic feet per second 

Simulated daily mean streamflow, in

cubic feet per second 

Figure 11. Comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflow-gaging station 

08186500 Ecleto Creek near Runge, Texas (Ecleto Runge gage), 2006–12; D–F, Estimated and simulated daily mean suspended-sediment 

loads at the Ecleto Runge gage, 2006–12.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 29

1:1 line

Estimated

Simulated

EXPLANATION

Estimated

Simulated

EXPLANATION

E

F

D

Calendar year

Exceedance probability, in percent

Estimated daily mean suspended-sediment load,

in tons per day

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

0.1 1 10 100 1,000 10,000 100,000

1

10

100

1,000

10,000

100,000

1

100

10

0.1

0.1

1,000

10,000

100,000

0.1

1

10

100

1,000

10,000

100,000

Daily mean suspended-sediment load,

in tons per day

Daily mean suspended-sediment load,

in tons per day

Simulated daily mean suspended-sediment load,

in tons per day

Figure 11. Graphs showing comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflowgaging station 08186500 Ecleto Creek near Runge, Texas (Ecleto Runge gage), 2006–12; D–F, Estimated and simulated daily mean 

suspended-sediment loads at the Ecleto Runge gage, 2006–12.—Continued
30 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

1:1 line

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

B

C

A

Calendar year

Exceedance probability, in percent

Measured daily mean streamflow,

in cubic feet per second 

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

10 100 1,000 10,000 100,000

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

Daily mean streamflow, in cubic feet per second Daily mean streamflow, in cubic feet per second 

Simulated daily mean streamflow, in

cubic feet per second 

Figure 12. Comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflow-gaging station 

08188500, San Antonio River at Goliad, Texas (SAR Goliad gage), 2006–12; D–F, Estimated and simulated daily mean suspended-sediment 

loads at the SAR Goliad gage, 2006–12.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 31

1:1 line

Estimated

Simulated

EXPLANATION

Estimated

Simulated

EXPLANATION

E

F

D

Calendar year

Exceedance probability, in percent

Estimated daily mean suspended-sediment load,

in tons per day

0 10 20 30 40 50 60 70 80 90 100

2006 2007 2008 2009 2010 2011 2012 2013

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

1

10

100

1,000

10,000

100,000

1

100

10

1,000

10,000

100,000

1,000,000

1,000,000

1

10

100

1,000

10,000

100,000

1,000,000

Daily mean suspended-sediment load,

in tons per day

Daily mean suspended-sediment load,

in tons per day

Simulated daily mean suspended-sediment load,

in tons per day

Figure 12. Graphs showing comparison of A–C, Measured and simulated daily mean streamflow at U.S. Geological Survey streamflowgaging station 08188500, San Antonio River at Goliad, Texas (SAR Goliad gage), 2006–12; D–F, Estimated and simulated daily mean 

suspended-sediment loads at the SAR Goliad gage, 2006–12.—Continued
32 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Simulated evapotranspiration (ET) rates were compared 

with available ET data collected in a nearby watershed with 

similar land use and surface geology (ET measurements 

in the study area were not available). During 2007–09, the 

USGS operated meteorological station 290810099212100 

SW Medina County meteorological station near D’Hanis, 

Tex. (hereinafter referred to as the “SW Medina ET site”) 

to measure actual evapotranspiration continuously by using 

the eddy covariance method (Slattery and others, 2011). 

The SW Medina ET site is about 70 miles west of the study 

area (fig. 1); Medina County is not shown. The station was 

located on rangeland/shrubland where the surface geology 

was the Carrizo Sand (Slattery and others, 2011), which 

also outcrops in the study area (zone 3, fig. 2). Measured ET 

(during 2007–09) at the SW Medina ET site was compared to 

model-simulated ET from similar land types in zone 3 of the 

study area (rangeland/shrubland on the Carrizo Sand outcrop) 

(table 6). Because annual rainfall amounts were substantially 

different between the study area watershed and the watershed 

of the SW Medina ET site, it was not possible to directly 

compare the model-simulated ET to the measured ET. Instead, 

ET was evaluated as a percent of rainfall in each watershed. 

During 2000–2009, measured Medina County ET was about 

81 percent of rainfall and USGS–2014 simulations showed ET 

to be about 74 percent of rainfall, thus indicating reasonable 

simulation of ET by the model (table 6).

Suspended Sediment

Selection of initial values and calibration of sedimentrelated process parameters of the USGS–2014 model (app. 1) 

were based on published guidelines (U.S. Environmental 

Protection Agency, 2006). Calibration of sediment-related 

parameters involved the following steps: (1) estimation of 

suspended-sediment loads at upstream model boundaries (at 

the SAR Elmendorf and Cibolo Selma gages); (2) estimation 

of the soil-erosion and sediment-washoff parameters used to 

generate sediment washoff from PERLNDs and IMPLNDs, 

respectively, to match relative sediment erosion rates from 

the various land types in the watershed; and (3) calibration 

of sediment-transport (RCHRES) parameters by comparing 

simulated suspended-sediment loads with estimated 

suspended-sediment loads at the same streamflow-gaging 

stations used for primary streamflow calibration (SAR Falls 

City, Cibolo Falls City, Ecleto Runge, and SAR Goliad 

gages).

The calibration process involves adjusting model process 

parameters that control sediment yields from the various 

land-cover categories simulated in the model. The U.S. 

Environmental Protection Agency (EPA) provides general 

guidance for expected soil erosion rates for selected landcover categories (U.S. Environmental Protection Agency, 

2006) (table 7). Previously reported soil erosion rates of 

specific land-cover types in the south Texas area also are listed 

in table 7 (Baird and others, 1996; Ockerman and others, 

1999; Ockerman and Petri, 2001; Ockerman, 2002; Ockerman 

and Fernandez, 2010; Ockerman and others, 2013). The ranges 

of reported soil erosion rates were used as guidance for model 

calibration of sediment production from different land-cover 

categories. Sediment-related HSPF model parameters were 

adjusted so that simulated soil erosion rates approximated 

the reported soil erosion rates by land-cover type (U.S. 

Environmental Protection Agency, 2006). 

Similar to the hydrology calibration, suspended sediment 

was evaluated by using the calibration period (2006–12) at 

four gages (SAR Falls City, Cibolo Falls City, Ecleto Runge, 

and SAR Goliad). The model-simulated and estimated 

suspended-sediment load (in units of tons per day) during 

the calibration period are depicted in figures 9D, E, F–12D, 

E, F and table 5. General goodness-of-fit metrics (Donigian, 

2002; Moriasi and others, 2007) include (1) percent difference, 

(2) NSE, and (3) RSR. The percent difference between the 

simulated and estimated daily mean suspended-sediment load 

at each of the gages was less than 15 percent (“very good” 

fit). The monthly mean values for NSE ranged from 0.78 

Table 6. Rainfall and measured evapotranspiration from 

rangeland/shrubland on the Carrizo Sand, in Medina 

County, Texas, compared with rainfall and model-simulated 

evapotranspiration from rangeland and shrubland on the Carrizo 

Sand in the study area, San Antonio River Basin downstream from 

San Antonio, Tex., 2007–09.

Calendar 

year

Measured rainfall and evapotranspiration from 

Medina County meteorological station1

Medina 

County 

rainfall 

(inches)

Medina County 

evapotranspiration

(inches)

Evapotranspiration 

as a percentage 

of rainfall

(percent)

2007–09 26.9 21.8 81

Calendar 

year

 Rainfall and simulated evapotranspiration from 

model study area (San Antonio River Basin)

Model 

study area 

rainfall2

(inches)

Simulated 

rangeland/

shrubland 

evapotranspiration3

(inches)

Evapotranspiration 

as a percentage 

of rainfall

(percent)

2007–09 35.2 25.9 74

1

Rainfall and evapotranspiration data collected at U.S. Geological Survey 

station 290810099212100 SW Medina Meteorological Station near D’Hanis, 

Texas.

2

Rainfall from DAYMET in water-budget zone 3 (fig. 2) (Thornton and 

others, 2012). 

3

Model-simulated evapotranspiration from rangeland/shrubland on Carrizo 

Sand in water-budget zone 3 (fig. 2).
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 33

to 0.89 (“very good” fit), with the exception of the Ecleto 

Runge gage which was 0.58 (“fair” fit). The monthly mean 

values for RSR were 0.41 to 0.53 (“good” to “very good”), 

again, with the Ecleto Runge gage being the exception with 

a RSR of 0.97 (“unsatisfactory” fit). The relative weakness 

of the model fit at the Ecleto Runge gage (compared to the 

model fit at other gages in the watersheds) likely reflects the 

accuracy of the streamflow calibration, which in turn, is likely 

affected by the lack of rainfall data available for the Ecleto 

model. At the SAR Goliad gage, testing results provide an 

important evaluation of the model’s ability to accurately assess 

streamflow and suspended-sediment discharges near the outlet 

of the study area. The model-fit statistics indicate a “good” to 

“very good” fit.

Similar to the hydrology calibration, the suspendedsediment load model-fit statistics also were evaluated during 

the testing period at the SAR Falls City, Cibolo Falls City, and 

SAR Goliad gages. The percent differences indicated a slightly 

poorer fit than during the calibration period, ranging from -2.9 

to -17.0 percent (“good” to “very good” fits). The monthly 

mean suspended-sediment NSE values were similar between 

the calibration and testing periods, with “very good” model 

fits for all three sites. The range of monthly mean suspendedsediment RSR values (0.36 to 0.51) indicated model fits of 

“good” to “very good.”

During the 2000–12 simulation period, the suspendedsediment model-fit statistics at each of the calibration gages 

(SAR Falls City, Cibolo Falls City, and SAR Goliad) indicate 

“good” to “very good” fits. Overall, model-fit statistics and 

graphic evaluations from the calibration and testing periods 

provided multiple lines of evidence indicating that the USGS–

2014 model simulations of suspended-sediment conditions 

were mostly “good” to “very good,” except at the Ecleto 

Runge gage, which is considered “unsatisfactory” to “fair.”

Parameter Calibration Values

Calibration of the HSPF models resulted in a final set 

of model parameter values for simulation of streamflow 

and suspended-sediment loads for the study area. Selected 

parameter values (or ranges of values) related to hydrologic 

and suspended-sediment simulation are listed in appendix 1. 

Sensitivity Analysis

Calibrated values of selected HSPF process-related 

parameters were further evaluated by a set of sensitivity 

analyses to determine the effects that changes in selected 

parameters would have on simulated streamflow and 

suspended-sediment loads. Each sensitivity simulation 

was made by adjusting a single parameter of the model by 

relatively large amounts (decreased by 25 percent) while 

keeping other model parameters unchanged. The percentage 

change was made for all values of the single parameter in all 

four watershed models. The sensitivity analyses simulations 

were performed for 2006–12. The resulting changes in 

streamflow and suspended-sediment loads were evaluated 

by comparing initial simulated streamflow (no parameter 

sensitivity changes) with adjusted simulated streamflow for 

Table 7. Reported and simulated ranges of soil erosion rates for land-cover types in south Texas and the study area, San Antonio River 

Basin downstream from San Antonio, Texas, 2006–12.

[(tons/mi2)/yr, tons per square mile per year; tons/yr, tons per year; --, not available]

Land-cover 

type

Area

(square 

miles)

Reported range of soil erosion 

rates from literature

([tons/mi2

]/yr)

Simulated soil 

erosion rates1

([tons/mi2

]/yr)

Total sediment 

production in 

study area2

(tons/yr)

Open/developed 173 128–640 (U.S. Environmental Protection Agency, 2006);

21–125 (Ockerman and others, 1999);

17 (Baird and others, 1996)

167–252 39,400

Forest/rangeland/

shrubland

957 32–256 (U.S. Environmental Protection Agency, 2006) 47–150 80,500

Pasture 763 192–1,152 (U.S. Environmental Protection Agency, 2006);

9 (Ockerman, 2002)

43–175 67,400

Cropland 163 320–2,560 (U.S. Environmental Protection Agency, 2006);

195 (Ockerman and Petri, 2001);

44 and 167 (Ockerman and Fernandez, 2010)

66–224 22,900

Wetland 83 -- 12–23 1,260

1

Range of values of the four model watersheds in the study area.

2

Area weighted average of total sediment production does not equal sediment leaving the study area because of losses from irrigation withdrawals or sediment 

retention in reservoirs.
34 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

each of the parameters. SAR Goliad was the simulation point 

for the evaluation of streamflow and sediment sensitivity. 

The results of the sensitivity analyses (changes in streamflow 

and suspended-sediment loads resulting from adjustments of 

selected parameters) are listed in table 8.

Simulated streamflow was relatively insensitive to 

adjustments of any of the selected parameters—none of 

the sensitivity simulation scenarios produced changes in 

streamflow greater than 10 percent. One reason is that most 

of the streamflow and sediment loads in the model study 

area originate upstream from the study area; streamflow 

and sediment loads entering the model study area from 

upstream were not affected by the change of any of the model 

parameters. The largest change in streamflow (increase of 

8.8 percent) occurred as a result of changing the fraction of 

groundwater inflow to deep recharge (DEEPFR).

Suspended-sediment loads were more sensitive than 

streamflow to changes in selected process-related parameters. 

The most sensitive parameters were the index to infiltration 

capacity of soil (INFILT) and upper zone nominal storage 

(UZSN). Sensitivity simulations of INFILT, UZSN, and 

JSER resulted in an increase of sediment loads by 20 percent, 

16 percent, and 14 percent, respectively. Other parameter 

adjustments that produced changes in suspended-sediment 

loads greater than 5 percent were lower zone nominal storage 

(LZSN) and the coefficient of detached-sediment washoff 

equation (KSER).

Model Limitations

The lower San Antonio River Basin model represents a 

complex natural system with a set of mathematical equations 

that describe the system. Intrinsic to the model is the error and 

uncertainty associated with the approximations, assumptions, 

and simplifications that must be made. Hydrologic modeling 

errors typically result from a combination of input data, 

representation of the physical processes by the algorithms of 

the model, and parameter estimation during the calibration 

process (Ely and Kahle, 2004).

There were uncertainties associated with model 

inputs such as rainfall, springflow, effluent discharge, and 

withdrawals which contributed to model result uncertainty. 

Uncertainties associated with the rainfall data used for model 

input were likely the most important source of model error. 

During storms, rainfall can vary substantially across the 

study area, and using only five rainfall precipitation stations 

to represent the 2,150-mi2

 study area limits the accuracy 

of the rainfall input to the study area. Rainfall intensity is 

an important factor in estimation of runoff and sediment 

erosion and transport. The primary source of rainfall data 

for the USGS–2014 model were daily totals. Although data 

from several hourly rainfall stations were available to help 

disaggregate the daily totals to hourly values for part of the 

study period, there exists some uncertainty in the spatial and 

temporal representation of rainfall. 

Table 8. Changes in simulated streamflow and suspended-sediment loads as a result of changes in selected Hydrological Simulation 

Program—FORTRAN (HSPF) process-related parameter values, 2006–12.

[SAR Goliad, U.S. Geological streamflow-gaging station 08188500 San Antonio River at Goliad, Texas; ft3/s, cubic feet per second; tons/d, tons per day]

Parameter 

(app. 1)

Initial 

values

Adjusted 

values

Change in 

parameter 

value 

(percent)

Initial 

simulated 

daily mean 

streamflow

at SAR 

Goliad

(ft3

/s)

Adjusted 

simulated 

daily mean 

streamflow

at SAR 

Goliad

(ft3

/s)

Change in 

simulated 

streamflow 

(percent)

Initial 

simulated 

daily mean 

suspendedsediment 

load at SAR 

Goliad

(tons/d)

Adjusted 

simulated 

daily mean 

suspendedsediment 

load at SAR 

Goliad 

(tons/d)

Change in 

simulated 

suspendedsediment 

load 

(percent)

DEEPFR 0.40–0.85 0.30–0.64 -25 820 892 8.8 1,180 1,180 0

INFILT 0.12–0.33 0.09–0.25 -25 820 843 2.8 1,180 1,420 20

LZSN 10.0–11.0 7.50–8.25 -25 820 859 4.8 1,180 1,280 8.5

UZSN 0.93–1.15 0.70–0.86 -25 820 850 3.7 1,180 1,370 16

KRER 0.05–0.45 0.04–0.34 -25 820 820 0 1,180 1,160 -1.7

JRER 2.00 1.50 -25 820 820 0 1,180 1,180 0

KSER 0.52–2.56 0.39–1.92 -25 820 820 0 1,180 1,100 -6.8

JSER 2.50–3.00 1.88–2.25 -25 820 820 0 1,180 1,350 14

M (silt) 0.12 0.09 -25 820 820 0 1,180 1,180 0

M (clay) 0.12 0.09 -25 820 820 0 1,180 1,180 0
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 35

To help address rainfall spatial variability, DAYMET 

rainfall data were used as the input rainfall for the model. 

DAYMET rainfall takes into account rainfall data from 

multiple nearby National Weather Station precipitation 

stations, as available (Thornton and others, 2012). The 

DAYMET data are provided as daily totals. To help address 

the temporal representation of rainfall, the 2006–12 DAYMET 

data were disaggregated to hourly time steps by using 

available data from nearby USGS hourly rainfall gages; 

however, USGS hourly rainfall data for 2000–2005 were not 

available. Hence, hourly rainfall data from NWS Stinson were 

used to disaggregate DAYMET daily data for the NWS station 

413201 Floresville, Tex. (hereinafter referred to as the “NWS 

Floresville station”), NWS Stockdale station, and the NWS 

station 417836 Runge, Tex. (hereinafter referred to as the 

“NWS Runge station”). For the NWS station 413618 Goliad, 

Tex. (hereinafter referred to as the “NWS Goliad station”), the 

daily rainfall data were disaggregated by using the BASINS 

disaggregation program. The resulting hourly rainfall data 

for model statistics during 2000–2005 indicate the model-fit 

statistics are not as good as during 2006–12. The hydrologic 

conditions in the study area are largely driven by the amount, 

timing, and intensity of the rainfall. Hence, without hourly 

rainfall data, the streamflow response is more challenging for 

the model to replicate. The Ecleto model, for example, had the 

weakest calibration statistics, which relied on rain-gage data 

from adjacent watersheds. 

Suspended-sediment simulations also were possibly 

affected by uncertainties associated with rainfall and 

streamflow. For example, overestimation of rainfall intensity 

would increase soil particle detachment and washoff of 

sediment from PERLNDs, thereby possibly overestimating 

runoff and suspended-sediment concentrations and loads. 

Further, the sediment yield from different land-cover types 

as reported in literature can vary significantly based on sitespecific conditions such as soil type, land use, and location. 

Hence, though simulated sediment yields by land-cover type 

compared reasonably well with literature sediment yields, 

there are a limited number of studies in the south-central 

Texas area. Lastly, the daily suspended-sediment loads that 

were used as calibration targets were not measured daily loads 

but estimated based on the relations between streamflow and 

suspended-sediment concentrations (Crow and others, 2014). 

These relations were generally based on a large number of 

samples collected during a range of hydrologic concentrations. 

At the Cibolo Selma gage, however, the relation was based on 

very few samples, although they were collected during a range 

of streamflow conditions. 

Simulated Hydrologic Conditions in the Study 

Area in the San Antonio River Basin

The USGS–2014 model was used to simulate hydrologic 

conditions of the study area within the San Antonio River 

Basin. The individual components of the hydrologic 

budget (precipitation, streamflow, evapotranspiration, and 

groundwater recharge) were simulated for the four watersheds 

during 2006–12 (fig. 13, table 9). During 2006–12, the daily 

mean streamflow at SAR Elmendorf and Cibolo Selma 

gages was 521 and 20 ft3/s, respectively. The daily mean 

streamflow leaving the study area, thus contributing to the 

Guadalupe River, was 865 ft3/s. Annual mean runoff from 

land surfaces were calculated for each model watershed by 

dividing the generated runoff volume of each watershed by the 

corresponding watershed area (table 9). It should be noted that 

the simulated runoff might not equal the streamflow exiting 

the watershed because of the effects of withdrawals from 

streams (irrigation and evapotranspiration) and discharges to 

the stream (wastewater and rainfall on the stream surface). 

Of the four watersheds, the Cibolo Creek watershed had the 

largest annual mean runoff from land surfaces (2.7 inches per 

year). The SAR watershed upstream from Cibolo Creek had 

the smallest annual mean runoff (1.9 inches) (table 9). 

The USGS–2014 model results indicate that after 

rainfall, evapotranspiration is the next largest component of 

the water budget (table 9). Simulated evapotranspiration was 

generally consistent between watersheds, ranging from 23.6 to 

26.6 inches per year. 

The simulated annual mean groundwater recharge varied 

across the study area by watershed, but more notably by 

water-budget zone (table 10). The greatest rate of simulated 

annual mean groundwater recharge (4.2 inches per year) was 

in zone 3, which is the zone with the highest qualitative soil 

infiltration rates (fig. 3), and corresponds to the outcrop of 

the Carrizo Sand geologic unit in the Texas Coastal Uplands 

aquifer system (fig. 2). The lowest simulated annual mean 

groundwater recharge (1.7 inches per year) was in zone 

9, which includes outcrops of the Lissie, Deweyville, and 

Beaumont Formations, and composes the Texas Coastal 

Lowlands aquifer system. These simulated groundwater 

recharge values are in general agreement with groundwater 

recharge values reported by the University of Texas Bureau of 

Economic Geology (Scanlon and others, 2003). The reported 

groundwater recharge rates ranged from 0.1 to 5.8 inches per 

year in the Carrizo-Wilcox aquifer (which is part of the Texas 

Coastal Uplands aquifer system) and 0.0004 to 2 inches per 

year for the Texas Coastal Lowlands aquifer system (Scanlon 

and others, 2003). 
MEAS SIM

Q

757820

L

1,2001,180

MEAS

SIM

Q

511544

L

828778

MEAS

SIM

Q

140140

L

231242

MEAS

SIM

Q

2729

L

4241

MEAS

SIM

Q

2823

MEAS

SIM

Q

108106

MEAS

SIM

Q

553540

MEAS

Q

20

L

22

MEAS

Q

521

L

737

C

C

C

C

C

C

C

C

C

C

C

11

7

6

5

2

4

10

9

8

3

1

17

16

12

14

15

13

29

20

22

23

24

27

25

19 28

21

26

18

31

30

32

97°00'

97°30'

98°00'

29°30'

29°00'

28°30'

BEE COUNTY

LIVE OAK

COUNTY

BEXAR COUNTY

ATASCOSA COUNTY

VICTORIA 

COUNTY

DEWITT

COUNTY

WILSON COUNTY

KARNES COUNTY

GONZALES

COUNTY

GUADALUPE COUNTY

Poth

Falls 

 City

Floresville

Sutherland

Springs

Stockdale

Nixon

Cibolo

San

Antonio

Goliad

McFaddin

Kenedy

Karnes Runge City

Yorktown

Saint 

Hedwig

Elmendorf

Selma

La Vernia

COMAL

COUNTY

GOLIAD

COUNTY

REGUGIO

COUNTY

EXPLANATION

Model watershed of San An

San Antonio River upstrea

 Cibolo Creek Ecleto Creek San Antonio River downstStudy area boundaryU.S. Geological Survey stre and map identifier—See National Weather Service m and map identifier—See  Air temperature and rainfa RainfallWastewater treatment plan

map identifier—See tab

Watershed model outlet an table 1Measured streamflow and  sediment loadSimulated streamflow and  loadDaily mean streamflow, in Daily mean suspended-sed day

1930

8

14

12

MEAS

SIM

Q

L

C

Manahuilla Creek

Ecleto Creek

San Antonio River

Martinez Creek

Calaveras Lake

Guadalup

Medina

Cibolo Creek

San Antonio River

Cabeza Creek

 River

Hynes Ba

Cibolo Creek

San Antonio River

San Antonio River

0 5 10 15 20 MILES0 10 15 20 KILOMETERS 5

Base modified from U.S. Geological Survey 1:100,000-scale digital dataUniversal Transverse Mercator, zone 14North American Datum of 1983

Modified from Lizárraga and Ockerman, 2010

Figure 13.

Measured and simulated daily mean streamflow and daily mean suspended-sediment loads at selected sites in the study area, San Anton

from San Antonio, Texas, 2006–12.
Simulation of the Hydrologic Conditions and Suspended-Sediment Loads 37

Table 9. Simulated hydrologic and suspended-sediment conditions upstream from and within the study area, San Antonio River Basin 

downstream from San Antonio, Texas, 2006–12.

[in/yr, inches per year; ft3/s, cubic feet per second; tons/d, tons per day; (tons/mi2)/yr, tons per square mile per year; --, not applicable; SAR Elmendorf, USGS 

station 08181800 San Antonio River near Elmendorf, Texas; Cibolo Selma, USGS station 08185000 Cibolo Creek at Selma, Texas]

Watershed

Drainage 

area

(square 

miles)

Annual 

mean 

rainfall

(in/yr)

Annual 

mean 

runoff 

from land 

surfaces

(in/yr)

Annual 

mean 

evapotranspiration

(in/yr)

Annual 

mean 

groundwater 

recharge

(in/yr)

Daily 

mean 

streamflow1

(ft3

/s)

Daily 

mean 

suspendedsediment 

load2

(tons/d)

Annual 

mean 

suspendedsediment 

yield3

([tons/mi2

]/yr)

San Antonio River upstream 

from the study area (inflow at 

SAR Elmendorf)

1,743 -- -- -- -- 521 737 154

Cibolo Creek watershed 

upstream from the study area 

(inflow at Cibolo Selma)

274 -- -- -- -- 20 22 30

San Antonio River watershed 

upstream from Cibolo Creek

554 29.7 1.9 24.6 3.1 571 831 62

Cibolo Creek watershed 579 29.3 2.7 23.6 3.1 146 252 145

Ecleto Creek watershed 266 29.0 2.1 24.1 3.2 31 43 59

San Antonio River watershed 

downstream from Cibolo 

Creek

751 30.8 2.1 26.6 2.0 865 1,230 51

San Antonio River Basin study 

area

2,150 29.9 2.2 25.0 2.7 865 1,230 80

1

Daily mean streamflow exiting the watershed during 2006–12, including inflows from upstream watersheds.

2

Daily mean suspended-sediment load exiting the watershed during 2006–12, including inflows from upstream watersheds.

3

Yields calculated as suspended sediment produced in the modeled watershed, which does not include inflows from upstream watersheds.

Table 10. Simulated groundwater recharge by water-budget 

zone in the study area, San Antonio River Basin downstream from 

San Antonio, Texas, 2006–12.

Water-budget 

zone

Simulated annual 

mean groundwater 

recharge rates

(inches)

Zone 1 2.8

Zone 2 3.2

Zone 3 4.2

Zone 4 3.2

Zone 5 3.0

Zone 6 2.2

Zone 7 2.5

Zone 8 2.0

Zone 9 1.7
38 Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12

Simulated Suspended-Sediment Loads and 

Yields in the Study Area in the San Antonio 

River Basin

The USGS–2014 model of the study area in the San 

Antonio River Basin also was used to estimate suspendedsediment loads of the study area during 2006–12 within 

the San Antonio River Basin, which were delivered to the 

confluence of the San Antonio River and the Guadalupe 

River. The 2006–12 simulated mean suspended-sediment 

loads at each of the calibration and testing sites are shown in 

table 5 and figure 13. The estimated daily mean suspendedsediment loads were 737 tons per day (tons/d) and 22 tons/d, 

respectively, at the SAR Elmendorf and Cibolo Selma gages 

during 2006–12 (table 9). Simulated suspended-sediment 

loads (including inflows from upstream watersheds) were 831 

tons/d from the upper SAR model, 252 tons/d from the Cibolo 

model, and 43 tons/d from the Ecleto model. At the outlet of 

the study area, the confluence of the San Antonio River with 

the Guadalupe River, the simulated annual mean suspendedsediment load during 2006–12 was 1,230 tons/d. These model 

results indicate that 759 of the 1,230 tons/d (approximately 

62 percent) of the suspended sediment being delivered to the 

Guadalupe River originates upstream from the study area, 

mainly upstream from the SAR Elmendorf gage. Model results 

indicate that most of the suspended-sediment load in the study 

area consists of silt- and clay-sized particles (less than 0.0625 

millimeters). At the confluence of the San Antonio River with 

the Guadalupe River, approximately 98 percent of the total 

simulated suspended-sediment load consisted of silt and clay.

Different land-cover types resulted in different simulated 

soil erosion rates contributing to the suspended-sediment 

loads in the study area (fig. 4). Open space and developed 

land-cover categories were combined in the model output 

of sediment (hereinafter referred to as “open/developed”) 

because open space is a subcategory of developed and 

the barren land was a relatively small percent of the total 

land-cover area. Open/developed and cropland land-cover 

categories exhibited the highest simulated soil erosion rates 

(table 7). Simulated soil erosion rates from the open/developed 

land-use category ranged from 167 to 252 tons per square mile 

per year ([tons/mi2

]/yr) (2006–12). Cropland simulated soil 

erosion rates ranged from 66 to 224 (tons/mi2)/yr. Together, 

cropland and open/developed categories composed about 16 

percent of the study area (table 2) and contributed about 29 

percent of the total sediment produced within the study area 

(table 7). Model output of sediment for forest (8.2 percent 

of the study area) and rangeland and shrubland (36.3 percent 

of the study area) were combined (hereinafter referred to as 

“forest/rangeland/shrubland”) because they exhibited similar 

hydrologic and sediment simulation response. The forest/

rangeland/shrubland category generated about 38 percent of 

the total sediment produced within the study area. Pasture land 

(35.3 percent of the study area) produced about 32 percent of 

the total sediment generated within the study area (table 7). 

Wetlands (3.9 percent of the study area) contributed less than 

1 percent of the total sediment produced within the study area 

(table 7). It should be noted that the total sediment production 

does not equal the suspended-sediment loads leaving the study 

area because of sediment losses associated with withdrawals 

or sediment retention in reservoirs. 

Simulated suspended-sediment yields by watershed 

(which only includes suspended sediment produced within 

that watershed) are shown in table 9. Simulated annual mean 

suspended sediment yields were 62 (tons/mi2

)/yr from the 

upper SAR model, 145 (tons/mi2)/yr from the Cibolo model, 

59 (tons/mi2)/yr from the Ecleto model, and 51 (tons/mi2)/yr in 

the lower SAR model during 2006–12. Overall, the simulated 

annual mean suspended-sediment yield in the study area was 

80 (tons/mi2)/yr. The higher annual mean suspended-sediment 

yields in the Cibolo Creek watershed are likely because of 

steeper topography, more developed and cropland land cover, 

and more runoff compared to the other watersheds in the study 

area. 

Summary

Suspended sediment in rivers and streams can play an 

important role in ecological health of rivers and estuaries 

and consequently is an important issue for water-resource 

managers. To better understand suspended-sediment loads 

and transport in a watershed, a Hydrological Simulation 

Program—FORTRAN (HSPF) model was developed to 

simulate hydrologic conditions and suspended-sediment 

loads in the San Antonio River Basin, downstream from 

San Antonio, Texas. In 2010, the U.S. Geological Survey 

(USGS), in cooperation with the San Antonio River Authority, 

the Evergreen Underground Water Conservation District, 

and the Goliad County Groundwater Conservation District, 

developed an HSPF model for the San Antonio River Basin 

to simulate hydrology only (hereinafter referred to as the 

“USGS–2010 model”). In 2012, URS Corporation developed 

an updated version of the San Antonio River Basin HSPF 

model (hereinafter referred to as the “URS–2012 model”), 

which was based on the USGS–2010 model. For this study, the 

USGS, in cooperation with the San Antonio River Authority, 

developed an updated version of the HSPF model, based on 

the URS–2012 model, to simulate hydrologic conditions and 

suspended-sediment loads in the San Antonio River Basin 

during 2000–12 (hereinafter referred to as the “USGS–2014 

model”). The USGS–2014 model used available historical 

suspended-sediment data, as well as recently collected data 

(2011–13) at selected locations in the study area to calibrate 

the model sediment simulation.

The study area consists of approximately 2,150 square 

miles encompassing parts of Bexar, Guadalupe, Wilson, 

Karnes, DeWitt, Goliad, Victoria, and Refugio Counties. The 

upstream boundary of the study area coincides with USGS 

streamflow-gaging station 08181800 San Antonio River 
References 39

near Elmendorf, Tex. (hereinafter referred to as the “SAR 

Elmendorf gage”) and USGS streamflow-gaging station 

08185000 Cibolo Creek at Selma, Tex. (hereinafter referred 

to as the “Cibolo Selma gage”). The downstream boundary 

of the study area is the confluence with the Guadalupe 

River. The HSPF model of the study area was developed by 

(1) configuring the model to represent the study area in the 

San Antonio River Basin and (2) compiling and processing 

required meteorological, streamflow, and sediment input data. 

Because of the large size of the study area, the study area in 

the San Antonio River Basin was divided into four watershed 

models: (1) San Antonio River upstream from Cibolo Creek, 

(2) Cibolo Creek, (3) Ecleto Creek, and (4) San Antonio River 

downstream from Cibolo Creek. Input data for the HSPF 

model included spatial and time-series data. Spatial data 

included geology, soils, land-cover, topography, and drainage 

characteristics such as subwatershed boundaries and streamreach length and cross-section data. Time-series data included 

meteorological data (rainfall and potential evapotranspiration), 

streamflow data, and suspended-sediment load data.

The calibrated model of the study area in the San Antonio 

River Basin was used to simulate hydrologic conditions and 

suspended-sediment loads for 2000–12, as well as to simulate 

sediment production by various land types within the study 

area. Taking into consideration the model-fit statistics and 

graphical comparisons, the watershed models simulated 

the observed streamflow within the “good” to “very good” 

categories during the calibration period (2006–12), with the 

exception of the Ecleto model. The calibration process also 

included a separate, post-calibration test of the model fit 

during 2000–2005. Simulations during the testing period had 

similar model-fit statistics as during the calibration period, 

with the exception of the Cibolo Falls City gage. One possible 

reason for weaker testing results (compared with calibration 

results) is that the quality of available rainfall data for 2000–

2005 was likely poorer than the 2006–12 data. During the 

entire 2000–12 simulation period, the model-fit statistics at 

each of the primary calibration gages; the USGS streamflowgaging station 08183500 San Antonio River near Falls City, 

Tex., (hereinafter referred to as the “SAR Falls City gage”),

the USGS streamflow-gaging station 08186000 Cibolo Creek 

near Falls City, Tex., (hereinafter referred to as the “Cibolo 

Falls City gage”), and the USGS streamflow-gaging station 

08188500 San Antonio River at Goliad, Tex., (hereinafter 

referred to as the “SAR Goliad gage”) indicate “good” to 

“very good” fits. Overall, model-fit statistics and graphic 

evaluations from the calibration and testing periods provided 

multiple lines of evidence indicating the USGS–2014 model 

simulations of hydrologic conditions were mostly “good” to 

“very good.”

Similar to the hydrology calibration, the suspendedsediment load model-fit statistics also were evaluated during 

the testing period at the SAR Falls City, Cibolo Falls City, 

and SAR Goliad gages, resulting in “good” to “very good” 

fits. Overall, model-fit statistics and graphic evaluations from 

the calibration and testing periods, provided multiple lines 

of evidence indicating the USGS–2014 model simulations 

of suspended-sediment conditions were mostly “good” to 

“very good,” except for Ecleto Runge, which is considered 

unsatisfactory to fair. The ranges of expected soil erosion 

rates were used as guidance for establishing targets for model 

simulation of sediment yields for different land-cover types. 

Sediment-related HSPF model parameters were adjusted so 

that simulated soil erosion rates approximated the reported soil 

erosion rates by land-cover type.

 The daily mean estimated suspended-sediment loads 

were 737 tons per day (tons/d) and 22 tons/d, respectively, 

at the SAR Elmendorf and Cibolo Selma gages during 

2006–12. At the outlet of the study area, the confluence of the 

San Antonio River with the Guadalupe River, the simulated 

daily mean suspended-sediment load during 2006–12 was 

1,230 tons/d. These model results indicate that 759 of the 

1,230 tons/d (approximately 62 percent) of the suspended 

sediment being delivered to the Guadalupe River originates 

upstream from the study area, mostly upstream from the 

SAR Elmendorf gage. Sample analyses and model results 

indicate that most of the suspended-sediment load in the 

study area consists of silt- and clay-sized particles (less than 

0.0625 millimeters). At the confluence of the San Antonio 

River with the Guadalupe River, approximately 98 percent of 

the total simulated suspended-sediment load was composed of 

silt and clay. 

The Cibolo Creek watershed was the largest contributor 

of suspended sediment from the study area. The higher 

suspended-sediment yields in the Cibolo Creek watershed 

are likely because of steeper topography, more developed 

and cropland land cover, and more runoff than the other 

watersheds. For the entire study area, open/developed land 

and cropland exhibited the highest simulated soil erosion 

rates; however, the largest sources of sediment from the study 

area (by land-cover type) were pasture and forest/rangeland/

shrubland, which account for about 80 percent of the study 

area and contributed about 70 percent of the suspendedsediment load.

References

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Barnes, V.E., 1987, Geologic atlas of Texas, GA0005 

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Appendix 1—Hydrological Simulation Program—FORTRAN 

Parameter Definitions and Values

Appendix 1. Summary of calibrated values for selected hydrologic and sediment-related parameters for the Hydrological Simulation Program—FORbudget zone in the study area, San Antonio River Basin downstream from San Antonio, Texas.—Continued [PERLND, pervious land surface; IMPLND, impervious land surface; RCHRES, stream/reservoir reach; --, not applicable; /, per] Parameter Module unit Description Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 

Hydrologic parameters

AGWETP PERLND Fraction of available 

evapotranspiration from active groundwater

0.04–0.04 0.04–0.05 0.04–0.05 0.04–0.05 0.04–0.05 0.04–0.05 0.04–0.05 0.04–0.

AGWRC PERLND Base groundwater recession rate 0.92 0.92 0.92 0.92 0.92 0.92 0.92–0.94 0.94 

BASETP PERLND Fraction of available 

evapotranspiration from base flow

0.05–0.08 0.05–0.08 0.05–0.08 0.05–0.08 0.05–0.08 0.05–0.08 0.05–0.08 0.04 

CEPSC PERLND Interception storage capacity 0.22–0.22 0.22–0.22 0.20–0.22 0.22–0.22 0.22–0.22 0.22–0.22 0.22–0.22 0.22–0.

DEEPFR PERLND Fraction of groundwater inflow to 

deep recharge

0.69–0.69 0.69–0.71 0.81–0.85 0.68–0.7 0.65–0.67 0.40–0.50 0.59 0.5 

INFEXP PERLND Infiltration equation exponent 1.9–2.0 1.9–2.0 1.9–2.0 1.9–2.0 1.9–2.0 1.9–2.0 1.9–2.0 2.0 

INFILD PERLND Ratio of maximum to mean 

infiltration rate

2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 

INFILT PERLND Index to infiltration capacity of soil 0.16 0.20–0.23 0.30–0.33 0.25–0.28 0.16–0.27 0.16–0.22 0.18–0.22 0.17 

INTFW PERLND Index to interflow 1.0 1.0 1.0 1.0 1.0 1.0 1.0–1.5 1.5 

IRC PERLND Interflow recession coefficient 0.5 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.5–0.7 0.7 

KVARY PERLND Groundwater outflow modifier 3 3 2–3 2–3 2–3 2–3 2–3 3 

LSUR PERLND or 

IMPLND

Average length of assumed overlandflow plane 250 250 250 250 250 250 250–300 250–30

LZETP PERLND Lower-zone evapotranspiration 0.6–0.7 0.6–0.7 0.6–0.7 0.6–0.7 0.6–0.7 0.6–0.7 0.6–0.7 0.6–0.

LZSN PERLND Lower-zone nominal storage 10 10–11 10–11 10–11 10–11 10–11 10–11 11 

NSUR PERLND or 

IMPLND

Manning’s n for assumed overlandflow plane 0.15 0.15–0.20 0.15–0.25 0.15–0.25 0.15–0.25 0.15–0.25 0.15–0.25 0.20 

RETSC PERLND Retention storage capacity of 

impervious areas

0.10–0.20 0.10–0.20 0.10–0.20 0.10–0.20 0.10–0.20 0.10–0.20 0.10–0.20 0.10–0.

SLSUR PERLND or 

IMPLND

Average slope of assumed overlandflow plane 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 

UZSN PERLND Upper-zone nominal storage 0.93–0.93 0.93–1.15 0.93–1.15 0.97–1.15 0.97–1.15 0.97–1.15 0.97–1.15 1.10 

Sediment parameters

KRER PERLND Coefficient of soil-detachment 

equation

0.33–0.41 0.33–0.45 0.25–0.45 0.30–0.45 0.30–0.45 0.30–0.45 0.30–0.45 0.33–0.

JRER PERLND Exponent of soil-detachment equation 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 

KSER PERLND Coefficient of detached-sediment 

washoff equation

0.52–2.46 0.52–2.56 0.52–2.56 0.52–2.56 0.52–2.56 0.52–2.56 0.52–2.56 0.67–2.

Appendix 1. Summary of calibrated values for selected hydrologic and sediment-related parameters for the Hydrological Simulation Program—FORbudget zone in the study area, San Antonio River Basin downstream from San Antonio, Texas. [PERLND, pervious land surface; IMPLND, impervious land surface; RCHRES, stream/reservoir reach; --, not applicable; /, per]
Appendix 1. Summary of calibrated values for selected hydrologic and sediment-related parameters for the Hydrological Simulation Program—FORbudget zone in the study area, San Antonio River Basin downstream from San Antonio, Texas.—Continued [PERLND, pervious land surface; IMPLND, impervious land surface; RCHRES, stream/reservoir reach; --, not applicable; /, per] Parameter Module unit Description Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 

Sediment parameters—Continued JSER PERLND Exponent of detached-sediment washoff equation 2.9 2.9–3.0 2.5–3.0 2.5–3.0 2.5–3.0 2.5–3.0 2.5–3.0 3.0 

KGER PERLND Coefficient of matrix scour equation 0.00–0.20 0.00–0.20 0.00–0.20 0.00–0.20 0.00–0.20 0.00–0.20 0.00–0.20 0.00–0.

JSER PERLND Exponent of matrix scour equation 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 

KEIM IMPLND Coefficient of solids washoff equation 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 

JEIM IMPLND Exponent of solids washoff equation 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 

AFFIX PERLND Fraction by which detached sediment decreases daily through soil compaction 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 

COVER PERLND Fraction of land surface shielded from rainfall erosion1, 2 0.10–0.80 -- -- -- -- -- -- -- 

NVSI PERLND Rate at which sediment enters detached-sediment storage from atmosphere 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 

ACCSDP IMPLND Solids accumulation rate 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 

RHO RCHRES Density of sediment particle 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 

M (silt) RCHRES Erodibility coefficient of sediment 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 

M (clay) RCHRES Erodibility coefficient of sediment 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 

W (silt and clay) RCHRES Settling velocity of sediment particle in still water 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 

TAUCD (silt) RCHRES Critical bed shear stress for sediment deposition2 0.001–0.95 -- -- -- -- -- -- -- 

TAUCS (silt) RCHRES Critical bed shear stress for sediment scour2 0.001–1.35 -- -- -- -- -- -- -- TAUCD (clay) RCHRES Critical bed shear stress for sediment deposition2 0.001–0.26 -- -- -- -- -- -- -- TAUCS (clay) RCHRES Critical bed shear stress for sediment scour2 0.002–1.30 -- -- -- -- -- -- -- 

1Range represents monthly values.2Range represents values from zones 1 through 9.
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Banta and Ockerman—Simulation of Hydrologic Conditions and Suspended-Sediment Loads in the San Antonio River Basin, 2000–12—SIR 2014–5182

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