U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5126

National Water-Quality Assessment (NAWQA) Program

Identifying Hydrologic Processes in Agricultural Watersheds 

Using Precipitation-Runoff Models

Saturation excess runoff

Infiltration excess runoff

Unsaturated lateral preferential flow

Saturated lateral preferential flow

Groundwater flow

Observed flow

Lateral flow

Direct runoff

Groundwater flow

Observed flow

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WEBMOD 

Saturation excess runoff

Infiltration excess runoff

Unsaturated lateral preferential flow

Saturated lateral preferential flow

Groundwater flow

Observed flow

Lateral flow

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Oct–01 Oct–02 Oct–03 Oct–04

SWAT 

WEBMOD 

National Water-Quality Assessment (NAWQA) Program

Identifying Hydrologic Processes 

in Agricultural Watersheds Using 

Precipitation-Runoff Models 

By Joshua I. Linard, David M. Wolock, Richard M.T. Webb, and 

Michael E. Wieczorek

Scientific Investigations Report 2009–5126

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Linard, J.I., Wolock, D.M., Webb, R.M.T., and Wieczorek, M.E., 2009, Identifying hydrologic processes in agricultural 

watersheds using precipitation-runoff models: U.S. Geological Survey Scientific Investigations Report 2009–5126, 

22 p.
iii

Contents

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

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

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

Precipitation-Runoff Models...............................................................................................................2

Methods...........................................................................................................................................................3

Performance of the Models .........................................................................................................................8

Performance of Initial Models............................................................................................................8

Performance of Calibrated Models ...................................................................................................8

The Maryland Watershed...........................................................................................................8

The Indiana Watershed ............................................................................................................11

The Nebraska Watershed ........................................................................................................13

Hydrologic Processes in Agricultural Watersheds Identified Using 

Precipitation-Runoff Models ........................................................................................................14

Limitations of Study and Needs for Future Research............................................................................16

Summary and Conclusions.........................................................................................................................16

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

Figures

1. Maps showing the Soil and Water Assessment Tool (SWAT) hillslopes and 

 Water, Energy, and Biogeochemical MODel (WEBMOD) subbasins for the 

(A) Maryland watershed, (B) Indiana watershed, and (C) Nebraska watershed..............4

2–7. Graphs showing:

2. Observed and simulated streamflow in the Maryland watershed for 

 water year 1998...........................................................................................................................12

3. Observed and simulated streamflow in the Indiana watershed for 

 water year 1998...........................................................................................................................13

4. Observed and simulated streamflow in the Nebraska watershed for 

 water year 1997...........................................................................................................................15

5. Contributions to streamflow from validated Soil and Water Assessment 

 Tool (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Maryland watershed .......................................................................................17

6. Contributions to streamflow from validated Soil and Water Assessment Tool 

 (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Indiana watershed...........................................................................................18

7. Contributions to streamflow from validated Soil and Water Assessment 

 Tool (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Nebraska watershed.......................................................................................19
iv

Tables

1. Spatial representation of the Maryland, Indiana, and Nebraska watersheds 

 for the Soil and Water Assessment Tool (SWAT) and Water, Energy, and 

 Biogeochemical MODel (WEBMOD) models...........................................................................7

2. Statistical results of initial Soil and Water Assessment Tool (SWAT) and 

 Water, Energy, and Biogeochemical MODel (WEBMOD) simulations ................................9

3. Parameters changed during calibration for SWAT models developed for 

 the Maryland, Indiana, and Nebraska watersheds ................................................................9

4. Parameters changed during calibration for WEBMOD models developed for 

 the Maryland, Indiana, and Nebraska watersheds ..............................................................10

5. Statistical evaluation of calibrated and validated Soil and Water Assessment 

 Tool (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Maryland watershed .......................................................................................12

6. Statistical evaluation of calibrated and validated Soil and Water Assessment 

 Tool (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Indiana watershed...........................................................................................14

7. Statistical evaluation of calibrated and validated Soil and Water Assessment 

 Tool (SWAT) and Water, Energy, and Biogeochemical MODel (WEBMOD) 

 models for the Nebraska watershed.......................................................................................15

8. Contributions to streamflow from Soil and Water Assessment Tool (SWAT) and 

 Water, Energy, and Biogeochemical MODel (WEBMOD) simulations for 

 the validation period...................................................................................................................16
v

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch 

millimeter (mm) 0.03937 inch 

meter (m) 1.094 yard

Area

square kilometer (km2

) 247.1 acre

square kilometer (km2

) 0.3861 square mile 

Flow rate

centimeter per hour (cm/hr) 0.3937 inch per hour (in/hr)

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

°F=(1.8×°C)+32

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

(NAVD 88).

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

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

Water Year (WY) is defined as beginning October 1 and continuing through September 30 of the 

following year.

Abstract

Understanding the fate and transport of agricultural 

chemicals applied to agricultural fields will assist in designing the most effective strategies to prevent water-quality 

impairments. At a watershed scale, the processes controlling 

the fate and transport of agricultural chemicals are generally 

understood only conceptually. To examine the applicability 

of conceptual models to the processes actually occurring, two 

precipitation-runoff models—the Soil and Water Assessment 

Tool (SWAT) and the Water, Energy, and Biogeochemical 

Model (WEBMOD)—were applied in different agricultural 

settings of the contiguous United States. Each model, through 

different physical processes, simulated the transport of water 

to a stream from the surface, the unsaturated zone, and the 

saturated zone. Models were calibrated for watersheds in 

Maryland, Indiana, and Nebraska. The calibrated sets of input 

parameters for each model at each watershed are discussed, 

and the criteria used to validate the models are explained.

The SWAT and WEBMOD model results at each watershed conformed to each other and to the processes identified in 

each watershed’s conceptual hydrology. In Maryland the conceptual understanding of the hydrology indicated groundwater 

flow was the largest annual source of streamflow; the simulation results for the validation period confirm this. The dominant source of water to the Indiana watershed was thought to 

be tile drains. Although tile drains were not explicitly simulated in the SWAT model, a large component of streamflow 

was received from lateral flow, which could be attributed to 

tile drains. Being able to explicitly account for tile drains, 

WEBMOD indicated water from tile drains constituted most 

of the annual streamflow in the Indiana watershed. The 

Nebraska models indicated annual streamflow was composed 

primarily of perennial groundwater flow and infiltration-excess 

runoff, which conformed to the conceptual hydrology developed for that watershed. The hydrologic processes represented 

in the parameter sets resulting from each model were comparable at individual watersheds, but varied between watersheds. The models were unable to show, however, whether 

hydrologic processes other than those included in the original 

conceptual models were major contributors to streamflow. 

Supplemental simulations of agricultural chemical transport 

could improve the ability to assess conceptual models. 

Identifying Hydrologic Processes in Agricultural 

Watersheds Using Precipitation-Runoff Models

By Joshua I. Linard, David M. Wolock, Richard M.T. Webb, and Michael E. Wieczorek

Introduction

Agricultural chemical transport from point and nonpoint 

sources is one of the leading impairments to water quality in 

rivers and lakes (Parry, 1998). To alleviate the impairments 

induced by agricultural chemicals, much research has focused 

on improving the understanding of relations between the application areas, the media through which the chemicals travel, 

and the final receptor where the chemicals can impair beneficial uses. A receptor can consist of any reservoir of water, 

such as a stream, lake, aquifer, or the unsaturated zone. It has 

become apparent that understanding the spatial distribution of 

agricultural chemical sources and the resulting drainage to a 

receptor is essential to the design or implementation of practices that will effectively improve water quality (Edwards and 

others, 1997; Leu and others, 2005). Factors that complicate 

the understanding of agricultural chemical transport include 

the inherent spatial variability of the media through which the 

agricultural chemicals travel and anthropogenic factors associated with agricultural areas (Carluer and De Marsily, 2004; 

Moussa and others, 2002; Green and others, 2006). 

As Moore and others (1991) indicate, the transport of 

agricultural chemicals through an area of interest (for example, from a hillslope to a receptor such as a stream or groundwater system) is controlled almost exclusively by the movement of water. A conceptual model of the processes governing 

that movement of water can be developed on the basis of field 

observations. Generally, the number of processes or paths by 

which water can reach a stream is small. Surface runoff is 

generated when the precipitation rate exceeds the infiltration 

rate of the surface soil. Surface runoff also is generated when 

precipitation falls on a saturated soil. Prior to reaching the 

water table, water can enter a stream by preferential lateral 

flow or by lateral return flow from a perched, saturated soil 

matrix. Groundwater can contribute to streamflow through the 

streambed or where the water table intersects the streambank 

and the water seeps from the banks before entering the stream. 

Although some studies have linked agricultural chemicals to 

specific water-quality impairment of receptors, few studies 

have holistically assessed the function of the transport media 

in the processes used to transport chemicals to the receptor 

(Fenelon and Moore, 1998; Hyer and others, 2001; Leu and 

others, 2004).
2 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Due to the difficulties associated with accounting for the 

spatial variability of agricultural chemical transport through 

field measurements, numerical models for numerous environmental processes have been developed as an alternative 

means of accounting for spatial variability (National Research 

Council, 2002; Shaffer and Delgado, 2001). For example, 

an environmental process that helps regulate the transport of 

agricultural chemicals is the spatially variable response of the 

land surface to precipitation. A common method of accounting 

for this variability is the curve number (CN) method (McCuen, 

1982), which partitions received rainfall into water available 

for either infiltration or direct runoff to the stream. Various 

CNs can be designated to different areas of a watershed and 

are generally based on land cover and soil type. Areas with 

greater soil moisture are commonly predicted to produce 

more runoff than drier soils. By simulating the response to 

rainfall of different CNs throughout a watershed, the spatial 

variability of soil moisture can be accounted for. Although 

this type of empirical relation is a means of accounting for the 

spatially variable response of the land surface to precipitation, 

when applied to an entire watershed it does not account for 

the generally accepted variable source area concept of runoff 

generation made known by Whipkey (1965) and Hewlett and 

Hibbert (1967). 

Beven and Kirkby (1979) introduced the TOPography 

based hydrological MODEL (TOPMODEL), which mathematically represents the variable source area concept. In 

TOPMODEL, points in a watershed with similar upslope 

contributing areas and slopes are assumed to have similar 

depths to the water table and underlying aquifer materials with 

similar water-transmitting capacities. TOPMODEL algorithms 

have commonly been applied in humid, pristine watersheds 

where the water table is near land surface. Although applications of the algorithm to agricultural areas have been few, the 

ability of the TOPMODEL algorithm to simulate hydrologic 

processes is appealing for the purpose of determining the 

dominant hydrologic processes in agricultural landscapes. To 

improve understanding of how different hydrologic processes 

transport water through agricultural watersheds to streams, the 

U.S. Geological Survey (USGS) designed a study using two 

available precipitation-runoff models.

Purpose and Scope

This report describes applications of models developed 

for three different agricultural watersheds in different parts of 

the United States: the Maryland Watershed, the Indiana Watershed, and the Nebraska Watershed. Two precipitation-runoff 

(PR) models were used to identify different hydrologic processes that transport water to streams; one model used the CN 

method and the other used the TOPMODEL algorithm. The 

hydrologic processes identified by the models were then compared to corresponding processes in the conceptual models at 

each watershed. Through this comparison, a realistic picture 

of the hydrologic system, in terms of the relative importance 

of different pathways by which water was transported to the 

watershed outlet, was obtained. Inclusive to this discussion 

was the contrasting of the methods used by each model to 

simulate the hydrologic processes governing the movement 

of water to a stream from the land surface and through the 

unsaturated zone, saturated zone, and preferential flow paths. 

The methods used by each model to account for different agricultural management practices were also discussed.

Precipitation-Runoff Models

The Soil and Water Assessment Tool (SWAT) (version 

SWAT2000) (Neitsch and others, 2002; also see http://brc.

tamus.edu/swat/ accessed April 28, 2009) uses the CN method 

to partition precipitation into either infiltration, which can then 

reach a stream by several flow paths, or to overland runoff, 

which flows directly to a stream. As the CN increases, a 

greater amount of precipitation is apportioned to direct runoff. 

Infiltrated water can reach a stream through shallow lateral 

return flow or through tile drains. Water percolating from 

the soil profile to the saturated zone can travel to a stream as 

groundwater flow or be lost to a deeper, regional groundwater 

system. 

The Water, Energy, and Biogeochemical MODel (WEBMOD) (Webb and others, 2006) uses the TOPMODEL 

algorithm as its means of accounting for the spatially variable 

response of a watershed to precipitation. In addition to precipitation-excess runoff, infiltration-excess water that becomes 

overland flow also is simulated in WEBMOD. Within the 

WEBMOD structure, soil water that exceeds field capacity can 

move vertically downward and(or) flow laterally to a stream 

as preferential flow. Water that percolates from the soil profile 

that recharges the shallow aquifer can reach a stream through 

saturated preferential flow paths (for example, tile drains), by 

exfiltration, and by direct groundwater flow to a stream. Exfiltration represents water from the shallow aquifer that reaches 

the stream after it emerges on a hillslope as seeps where the 

water table intersects the land surface. A head-dependent loss 

parameter is used to account for ground-water losses from the 

saturated zone. 

The differences in the way SWAT and WEBMOD 

account for hydrologic processes and land cover reflect the 

original goals in their model design. SWAT was designed to 

simulate the water draining from relatively flat areas with 

patches of distinct crops, whereas WEBMOD was designed to 

simulate streamflow generation in forested areas of high relief. 

As noted in Capel and others (2008), each model divides the 

study watershed into subbasins. Within SWAT each subbasin 

is further subdivided into Hydrologic Response Units (HRUs) 

based on differences in land cover or soils. Although HRUs 

lack spatial attributes, a unique hydrologic response is simulated from each.

WEBMOD isolates a subbasin into right and left hillslopes to be able to simulate different insolation on northfacing slopes compared to that on south-facing slopes. The 
Methods 3

hillslope hydrology is simulated, in part, on the basis of the 

dominant vegetation type, log-normal models for decreasing hydraulic conductivity and tranmissivity with depth, and 

discrete areas of similar topographic wetness index (TWI). 

The TWI areas on a hillslope are the smallest hydrologic units 

in WEBMOD. Although not explicitly connected, TWI values 

will be smallest on hilltops and greatest in riparian areas and 

wetlands. For each hillslope, computations of infiltration, 

evapotranspiration, recharge, and water-table fluctuations were 

carried out for 30 areas of similar TWI. Vegetation types in 

WEBMOD affect evapotranspiration and are limited to bare 

ground, grass, shrubs, and forest; the major distinction is the 

available canopy interception in summer compared to winter. 

In all WEBMOD models, crops were simulated as deciduous trees by using the “leaf-on” period typical of the modeled 

region.

Methods

SWAT and WEBMOD models were developed for each 

of the Maryland, Indiana, and Nebraska study watersheds 

by using the same spatial data inputs: a digital elevation 

model (DEM), a land-use grid, and a grid of soil map units. 

Thirty-meter resolution DEMs were obtained from the USGS 

Seamless Server (U.S. Geological Survey, 2006) and were 

hydrologically corrected using high-resolution stream coverages from the National Hydrography dataset (U.S. Geological 

Survey, 1999). For the Nebraska study site, a 33-m resolution 

DEM was used because of geographic information system 

(GIS) limitations. Subbasins (fig. 1) were created within each 

model (table 1) with the outlet of each subbasin being identical in the two models. Basin boundary delineations differed 

slightly due to the way the data preprocessors conditioned the 

DEM. Between the two models the areas of the individual 

subbasins and the total basin area were within 0.5 percent of 

each other. 

Within subbasins created for the SWAT models, HRUs 

were created based on dominant land cover and soils. Multiple 

HRUs within a subbasin were determined if a land cover 

within a subbasin occupied more than 20 percent of the 

subbasin area and a soil type consisted of at least 10 percent 

of the subbasin area. These threshold values were chosen on 

the basis of experience with the model development, which 

indicated that when the chosen values exceeded those used 

(the 20 percent and 10 percent), the creation of multiple 

HRUs within a subbasin was unlikely. This upper threshold 

was used because Fitzhugh and Mackay (2000) indicated 

that an excess of HRUs could limit model performance. 

The National Land Cover Dataset (NLCD) (Vogelmann and 

others, 2001) land-cover data were used to determine HRUs 

in the Maryland and Indiana watersheds. For the Nebraska 

watershed the Cropland Data Layer (CDL) (United States 

Department of Agriculture-National Agricultural Statistics 

Service, 2002), which was unavailable for the Maryland 

and Indiana watersheds, was used in determining HRUs 

because it had separate classification for agricultural crops. 

In both the Maryland and Indiana watersheds the only 

land-cover classifications conforming to the criteria were 

row crops and pasture. Due to the more detailed land-cover 

information available for the Nebraska watershed, the row 

crop classification could be divided into classifications for 

corn and soybeans. The State Soil Geographic Database 

(STATSGO) (Schwarz and Alexander, 1995) was used in all 

watersheds for soil classifications. The 10-percent criteria 

for soils left four possible soil types in both the Maryland 

and Indiana watersheds and five in the Nebraska watershed. 

Not every combination of land cover and soil type existed in 

every SWAT subbasin; consequently, for some combinations, 

an HRU was not created. The varied land cover and soils 

yielded 6 HRUs in the Maryland watershed, 7 in the Indiana 

watershed, and 13 HRUs in the Nebraska watershed. 

Default CNs were assigned by the SWAT models to each 

HRU on the basis of the combination of land cover and soil. 

HRUs with row crops in the Maryland watershed received 

a value of 78, and 69 was the value given those classified 

as pasture. Indiana had identical land-cover classifications; 

however, differing soil hydrologic properties produced two 

CNs for row crops (78 and 85) and pasture (69 and 79). The 

different row crops in the Nebraska watershed produced a CN 

of 77 for corn, 78 for soybeans, and 69 for pasture.

The extent of climatic spatial variability in each of the 

study watersheds was evaluated by USGS hydrologists located 

in the State of each watershed. Single weather stations were 

deemed adequate to represent the precipitation and temperature for the Maryland and Indiana watersheds (Chestertown, 

Maryland, and Greenfield, Indiana). Precipitation and temperature data from seven weather stations (fig. 1C) in and around 

the Nebraska watershed were distributed spatially using the 

XYZ method described by Hay and McCabe (2002). This 

method is readily incorporated into models, such as WEBMOD, that have been developed within the framework of the 

USGS Modular Modeling System (MMS) (Leavesley and others, 1998). In SWAT, the climate for each subbasin is assumed 

to be adequately represented by data from the closest weather 

station; therefore, to ensure climatic inputs were consistent 

between models, the WEBMOD simulated precipitation and 

temperature were assigned to the Nebraska SWAT subbasins. 

Precipitation-runoff models were created to simulate 

hydrology for daily time steps by using 19 years of daily 

observed temperature and precipitation from the aforementioned weather stations. Daily temperature and precipitation 

data were obtained from the U.S. Department of Commerce 

National Climatic Data Center (http://gis.ncdc.noaa.gov/

website/ims-cdo/sod/viewer.htm, accessed January 4, 2006). 

Streamflow data corresponding to the time period of the 

precipitation and temperature data were used to calibrate 

and validate the models. Daily streamflow for a stream gage 

located at the outlets of each watershed (Sugar Creek at New 

Palestine, Indiana, station number 03361650; Morgan Creek 

near Kennedyville, Maryland, station number 01493500; 

and Maple Creek near Nickerson, Nebraska, station number 
4 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Figure 1. Water, Energy, and Biogeochemical MODel (WEBMOD) hillslopes and Soil and Water Assessment Tool (SWAT) 

subbasins for the (A) Maryland watershed, (B) Indiana watershed, and (C) Nebraska watershed.

#

"

"

#

0

0 1 2 MILES

1 2 KILOMETERS

76°W 75°55'W

39°20'N

39°15'N

MARYLAND

Map 

location

Soil and Water Assessment Tool subbasins

Water, Energy, and Biogeochemical MODel hillslopes

Chestertown, Maryland, Climate Station

Morgan Creek near Kennedyville, Maryland,

 stream gage 01493500

A
Methods 5

#*

"

85°50' W 85°40' W 85°30' W

40° N

39°50' N

Greenfield, Indiana, Climate Station

Sugar Creek at New Palestine, Indiana, stream gage 03361650

0

0 3 6 MILES

3 6 KILOMETERS

#*

"

INDIANA

Map 

location

Soil and Water Assessment Tool subbasins

Water, Energy, and Biogeochemical MODel hillslopes

B

Figure 1. Water, Energy, and Biogeochemical MODel (WEBMOD) hillslopes and Soil and Water Assessment Tool (SWAT) 

subbasins for the (A) Maryland watershed, (B) Indiana watershed, and (C) Nebraska watershed.—Continued
6 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Figure 1. Water, Energy, and Biogeochemical MODel (WEBMOD) hillslopes and Soil and Water Assessment Tool (SWAT) 

subbasins for the (A) Maryland watershed, (B) Indiana watershed, and (C) Nebraska watershed.—Continued

#

"

"

" "

" "

"

0

0 5 10 MILES

5 10 KILOMETERS

NEBRASKA

Map location

"

#

97°10'W 97°0'W 96°50'W 96°40'W 96°30'W

41°50'N

41°40'N

41°30'N

41°20'N

Dodge

Stanton

Fremont

Columbus Schuyler

West Point

Clarkson

Soil and Water Assessment Tool subbasins

Water, Energy, and Biogeochemical MODel hillslopes

Climate station

Maple Creek near Nickerson, Nebraska, stream gage 06800000

C
Methods 7

06800000) was obtained from the USGS National Water 

Information System (http://waterdata.usgs.gov/nwis/, accessed 

January 4, 2006). Throughout the report, streamflow will be 

reported in terms of depth of water, which is determined as the 

quotient of streamflow divided by basin area. Data for October 

1985 through September 1987 were used to limit the effect 

of poor initial parameter values on simulations results, and 

the models were calibrated using daily data for October 1987 

through September 1995. The models were then validated by 

using daily data for October 1996 through September 2004 

as input parameters. In addition to calibrating the models to 

observed daily streamflow, the simulated mean annual actual 

evapotranspiration was compared to values reported in the 

literature. Mean annual actual evapotranspiration near the 

Maryland watershed was reported to be 770 mm (Sloto, 2002). 

Neitsch and others (2002) reported 700 mm of actual evapotranspiration at the Indiana watershed, and in Nebraska, Dugan 

and Zelt (2000) reported a mean annual evapotranspiration of 

about 500 mm. The simulated evapotranspiration values, averaged over the 8-year calibration period, were deemed adequate 

if they were within 10 percent of the values reported in the 

literature.

Conceptual models included several hydrologic processes that governed the runoff response of each watershed. 

Field observations indicated that the mean depth to water at 

the Maryland watershed was 2 to 3 m. Although groundwater 

was the dominant source of water contributing to streamflow 

in the Maryland watershed, infiltration-excess runoff was 

likely the source of observed peaks in streamflow. However, 

this surface runoff was believed to be retarded by the many 

retention ponds within the watershed. In addition, prior 

research by local USGS hydrologists indicates that about 2 

percent (20 mm) of the annual precipitation is lost annually 

from the watershed as groundwater flow because a restrictive 

subsurface layer dips toward an adjacent watershed (Hancock 

and Brayton, 2006). Groundwater was likely to contribute to 

streamflow near the mouth of the Indiana watershed. The main 

source of annual streamflow and peak streamflow, however, 

was likely to be the discharge from tile drains buried at depths 

of 1.0 to 1.5 m beneath many of the fields in the watershed. 

As much as 50 percent of the total volume of water discharged 

from the tile drains is estimated to do so during storms (Stone 

and Wilson, 2006; Lathrop, 2006). In the Nebraska watershed 

the depth to water in the headwaters of the watershed could be 

about 100 m (Fredrick and others, 2006). Sustained periods 

of low flow were observed in intermittent streams of the 

watershed, indicating that perched water tables can develop. 

This conceptualization was substantiated by field observations 

that indicated peaks in streamflow could only occur following an increase in near-surface soil moisture. Consequently, 

the hydrologic process responsible for peak streamflows is 

infiltration-excess runoff. Field observations also indicated 

that contributions to streamflow from regional groundwater 

flow generated perennial streamflow near the mouth of the 

Nebraska watershed. 

The statistics used to evaluate the performance of the 

models included the Nash-Sutcliffe measure of efficiency 

(NSE), the ratio of the root mean square to the standard deviation of the observed data (RSR), and the percent bias (PBIAS) 

of the model (Moriasi and others, 2007). The NSE assesses 

the ability of a model to correctly simulate streamflow during 

periods when observed streamflow deviates largely from the 

mean streamflow and is calculated as:

 NSE Y Y Y Y i

obs

i

sim

i

obs mean

i

n

i

n

= − − ( ) ( ) − ⎡

⎣

⎢ ⎤

⎦

⎥ = =

1 ∑ ∑2 2

1 1

/ , (1)

where Yi

obs is the observed streamflow for the ith timestep, Yisim

is the simulated streamflow at the ith timestep, and Ymean is the 

mean observed streamflow. The total number of timesteps is 

indicated by n. A perfect model would produce an NSE of 1; 

however, Moriasi and others (2007) indicate that the performance of a model is considered to be “good” if the NSE is 

between 0.65 and 0.75 and “satisfactory” when the NSE is 

above 0.5.

Table 1. Spatial representation of the Maryland, Indiana, and Nebraska watersheds for 

the Soil and Water Assessment Tool (SWAT) and Water, Energy, and Biogeochemical MODel 

(WEBMOD) models.

Watershed Watershed area 

(square kilometers)

Number of subbasins

(SWAT/WEBMOD)

Mean SWAT subbasin

in areas 

(square kilometers)

Maryland 33 13/26 2.5

Indiana 249 26/52 9.6

Nebraska 953 28/56 34.0
8 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

The RSR evaluates the error associated with model performance and is determined from the root-mean square error 

(RMSE) and standard deviation (STDEV) in the observed 

data:

RSR RMSE

STDEV

Y Y

Y Y

i

obs

i

sim

i

n

i

obs mean

i

= =

( ) − ⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

( ) −

=

=

∑ 2

1

2

1

n

∑

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

, (2)

where the factors are as defined in equation 1.

A model performing with no error would produce an RSR 

of 0.0. The criteria presented by Moriasi and others (2007) 

would designate model performance as “good” if the RSR 

ranges from 0.5 to 0.6 and “unsatisfactory” if the RSR were 

greater than 0.7. 

To evaluate the ability of the model to reproduce the 

streamflow component of the mass balance for an entire simulation, the PBIAS statistic was used and is calculated as:

PBIAS

Y Y

Y

i

obs

i

sim

i

n

i

obs

i

n =

( ) −

( )

=

=

∑

∑

1

1

 (3)

The PBIAS statistic can be positive or negative, but the 

closer to zero the value is, the closer the agreement between 

simulated and observed streamflow for the period being 

evaluated. A positive PBIAS value indicates the model is 

underpredicting streamflow, whereas negative values indicate 

overprediction. PBIAS values between ±10 and ±15 indicate 

a “good” model simulation, whereas values greater than ±25 

indicate an “unsatisfactory” model simulation (Moriasi and 

others, 2007). 

Performance of the Models

Performance of Initial Models

Input files created by the data preprocessors were used to 

develop initial models for October 1985 through September 

1995. No parameters were adjusted, and no land-management practices were considered in the initial models. When 

determining statistics, the results of the first 2 years of each 

simulation were not included. The statistics determined from 

the initial SWAT and WEBMOD simulations for each watershed are given in table 2. Both models tended to overpredict 

large storm-event peaks and miss peaks resulting from small 

precipitation events. The observed base-flow conditions are 

typically much lower and show less fluctuation than those 

simulated. These tendencies of initial model performance are 

consistent with the results of initial simulations for the Indiana 

and Nebraska watersheds. NSE values did not exceed zero for 

any initial model except for the WEBMOD Indiana model, 

which produced a value of 0.36. Nash-Sutcliffe values below 

zero indicated the mean streamflow for the period of record 

was a better predictor of streamflow than the model. The NSE 

values alone indicated the initial models for each watershed 

were unsatisfactory. 

Performance of Calibrated Models

The SWAT and WEBMOD models were manually 

calibrated to improve their performance statistics. Although 

the parameters in each model represented different hydrologic 

theory, commonly each model required adjustments to better 

simulate the same hydrologic processes. In general, the parameters governing the infiltration of water through the root zone, 

the delay of groundwater movement from the saturated zone 

to the stream, and the evolution and melt of snow required 

changing. Standard changes could be made to each parameter 

on the basis of prior knowledge of the system being modeled. 

WEBMOD contained a parameter indicating the depth to bedrock or other restrictive layer, which could also be set on the 

basis of prior knowledge. Including these parameters, no more 

than 16 SWAT parameters (table 3) and 14 WEBMOD parameters (table 4) required changes to achieve calibrated models 

at each watershed. Simulation results from validated models 

produced statistical values similar to those of the calibration 

period.

The Maryland Watershed

The initial SWAT simulations for the Maryland watershed 

indicated a need to decrease streamflow contributions from 

direct runoff. The magnitude of direct runoff in the SWAT 

model was controlled using the CN value specific to each 

HRU. The CNs for each land cover (row crops and pasture) 

were decreased to the minimum values recommended by 

SWAT (Neitsch and others, 2002). In addition to adjusting the 

CN values, the available water-holding capacities of the soils 

were decreased so that they were similar to those reported by 

Meyer and others (1997). To improve the temporal relation 

between observed and simulated peak streamflows, water in 

stream channels was retained by decreasing the parameter 

controlling the surface runoff lag coefficient by 75 percent. In 

SWAT, decreases in the lag coefficient cause a decrease in the 

fraction of water available to move downstream. The improvement in model performance elicited from this change could 

implicitly represent the retention effects of the ponds within 

the watershed on streamflow generation. Following these 

adjustments, the magnitude and temporal distribution of simulated streamflow peaks were similar to those of the observed 

streamflow. 

After calibrating to the runoff peaks in each model, 

attempts were made to match simulated base flow to observed 

periods of low flow. Complicating the calibration was the 

influence of a restrictive subsurface layer beneath the southern 

half of the watershed, which caused percolating water to drain 
Performance of the Models 9

Table 2. Statistical results of initial Soil and Water Assessment Tool (SWAT) and Water, Energy, and Biogeochemical 

MODel (WEBMOD) simulations.

Watershed

Nash-Sutcliffe measure of 

efficiency

Ratio of the root mean square 

to the standard deviation of the 

observed data

Percent bias

SWAT WEBMOD SWAT WEBMOD SWAT WEBMOD

Maryland –0.52 –4.03 1.23 2.24 –22.64 –43.59

Indiana –1.91 0.36 1.71 0.80 –28.13 –10.12

Nebraska –1.05 –0.28 1.43 1.13 –37.81 –78.76

Table 3. Parameters changed during calibration for SWAT models developed for the Maryland, Indiana, and Nebraska 

watersheds. 

[Multiple numbers indicate different values for different hydrologic response units (HRU). Changes are in bold. For more detailed 

parameter descriptions, see Neitsch and others (2002)]

SWAT parameter Initial 

value Maryland Indiana Nebraska

Curve number (CN2) CN0 CN0

–23% CN0–32% CN0–13%

Hydraulic conductivity (SOL_K) K0 K0 K0 K0

*100

Available water capacity (SOL_AWC) AWC0

AWC0+0 

and +0.1

AWC0

+0.06

AWC0

+0.05 

and +0.1

Vertical preferential flow (ICRK) 0 0 1 1

Main channel Manning’s n (CH_N2) 0.014 0.14 0.03 0.014

Subbasin Manning’s n (CH_N1) 0.014 0.014 0.14 0.014

HRU Manning’s n (OV_N) 0.14, 0.15 0.35, 0.55 0.44 0.44, 0.6

Lateral traveltime (LATTIME) 0 1 1.5–4 2

Slope length of the soils (SLSOIL) 0 2 0.25 1

Minimum amount of water in the shallow aquifer 

required for groundwater flow (GWQMN) 0 0 0 0, 200

Minimum amount of water in the shallow aquifer 

required for evaporation to the root zone 

(REVAPMN)

1 0 0 0

Coefficient limiting evaporation to the root zone 

(GW_REVAP) 0.02 0.04 0.07 0.02, 0.04

Base-flow alpha factor (ALPHA_BF) 0.048 0.048, 0.1 0.048, 0.075 0.4, 0.9

Groundwater delay time (GW_DELAY) 31 500 31–61 31, 210

Deep aquifer percolation fraction (RCHRG_DP) 0.05 0–0.2 0.2 0.05

Surface runoff lag coefficient (SURLAG) 4 1 2 1

Snowfall temperature (SFTMP) 1 0 1 1

Snowmelt temperature (SMTMP) 0.5 2 0.5 0.5

Melt factor for snow on June 21 (SMFMX) 4.5 3 4.5 4.5

Melt factor for snow on December 21 (SMFMN) 4.5 1.5 1.5 4.5

Minimum snow water equivalent equal to 100% 

snow cover (SNOCOVMX) 1 1 10 1
10 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

to an adjacent watershed rather than to the stream. By decreasing the CNs in the SWAT model, infiltration was increased 

and more water was made available for groundwater flow. 

Contributions of groundwater to streamflow were decreased 

by increasing the time required for groundwater to reach the 

stream and by increasing the amount of water in the shallow 

aquifer available for evaporation to the root zone to alleviate 

the soil moisture deficit. To account for the groundwater loss 

from the watershed in the SWAT model, recharge to the deep 

aquifer from all subbasins on the southern half of the watershed was increased to 20 percent of water percolation. Losses 

of groundwater from the northern half of the watershed were 

not allowed. This allowed water in the shallow aquifer to be 

removed from the topographical boundary of the watershed, 

consistent with the effects of the restrictive layer. In the SWAT 

model, groundwater moving to a deep, regional aquifer cannot 

directly contribute to streamflow. 

WEBMOD simulations indicated that infiltration-excess 

overland runoff was the source of overly-large peak streamflows. The vertical, saturated hydraulic conductivity (xk0) of 

the soil varied in each hillslope, but in general was increased 

to about 1.2 cm/hr (table 4). When simulated infiltration was 

increased, WEBMOD predicted that the mean depth to the 

water table in the watershed was within 1 m of the land surface. Field observations, in contrast, indicated the mean depth 

to the water table was about 2–3 m. To increase the thickness 

of the unsaturated zone, the average depth to the restrictive 

layer was increased from the default value of 1.5 m to 15 m, 

which is similar to depths measured in the field. Simulations 

made after adjusting the depth to the restrictive layer produced streamflow hydrographs in which the magnitudes of 

the large peaks were similar to observed peaks. The simulated 

responses to small precipitation events still exceeded those 

in the observed hydrograph, while other such events were 

not reflected in simulation results. Commonly, the “missing” 

simulated streamflow peaks occurred early in the growing season when the model simulated excessive groundwater influx 

to the stream. To generate the small peaks in streamflow that 

were not simulated, percolating water was partitioned so that 

additional water was apportioned to lateral preferential flow to 

the stream. The fraction of potential infiltration that was preferentially routed to the saturated zone was also increased to 

further assist in generating the “missing” peaks. Additionally, 

the parameters controlling the exponential decrease of hydraulic conductivity (xk0) and transmissivity (t0) with depth were 

decreased. These decreases caused more water to be retained 

Table 4. Parameters changed during calibration for WEBMOD models developed for the Maryland, Indiana, and Nebraska 

watersheds. 

[Multiple numbers indicate different values for different hillslopes. Changes are in bold. For more detailed parameter, see Webb and others (2006)]

WEBMOD parameter Initial value Maryland Indiana Nebraska

Main channel routing velocity (chv) 100 250 1,500 1,700

Minimum temperature for snowmelt (MBASE) 32 32 38 38

Melt factor for snow on June 21 (MFMAX) 0.6 0.5 1.6 1.6

Melt factor for snow on December 21 (MFMIN) 0.2 0.2 0.3 0.3

Fraction of potential infiltration that is preferentially routed to 

the saturated zone (pmac_sat) 0 0.05 0 0

Fraction of potential infiltration that is preferentially routed to 

the unsaturated zone (pmacro) 0 0.15 0.2 0

Fraction of percolation past the root zone that is preferentially 

routed to the stream (qdffrac) 0 0.28 0.2 0.2, 0.5

Depth to the restrictive layer (s_rock_depth) 1.524 15 10 100

Hydraulic conductivity of preferential flow through the 

saturated zone (s_satpref_k) 0 0 0.013 0

Water table elevation at which preferential flow through the 

saturated zone is maximized (s_satpref_zmax) 0 0 -1.0 0

Water table elevation at which preferential flow through the 

saturated zone can occur (s_satpref_zmin) 0 0 -1.5 0

Soil field capacity (s_theta_fc) FC0 FC0 FC0 1.2 FC0 0.9

Shape factor for exponentially decreasing transmissivity (szm) SZM0 SZM0 1.65 SZM0 SZM0 1.20

Natural log of transmissivity (t0) T0-1 T0-1 –2.25 T0-1 –1 T0-1 ±0.8

Vertical, saturated hydraulic conductivity (xk0) XK0-1 XK0-1 1.2 XK0-1 2 XK0-1 9.2

Unsaturated zone time delay (td) 60 40 300 7; 2,000

Initial soil moisture deficit (sbar0) 0.001 0.69 0.03 0.25

Groundwater loss rate (gw_loss) 11 8.5, 11 11 11
Performance of the Models 11

in the root zone, which facilitated the generation of peaks as 

previously discussed. 

To decrease the amount of groundwater influx to the 

stream in WEBMOD, a head-dependent loss parameter 

(gw_loss) was used to remove water from the shallow aquifer. 

By initiating groundwater losses in subbasins in the southern 

half of the watershed, simulated base-flow conditions matched 

observed base-flow conditions during periods of low streamflow. However, simulated base-flow streamflows during the 

growing season exceeded those observed during the same 

periods. 

For the models to be considered accurate representations 

of hydrologic processes occurring at the Maryland watershed, 

it is expected that the statistical evaluations of model 

performance conform to the criteria of Moriasi and others 

(2007). In addition, the simulated actual evapotranspiration 

should be consistent with the long-term means noted 

previously. The performance of the models for October 1, 

1997, to September 30, 1998 (the water year of the simulation 

for which the annual streamflow was closest to the long-term 

mean annual streamflow of 270 mm reported by James and 

others [2003]), is illustrated in figure 2. Streamflow simulated 

by SWAT more closely matches the observed streamflow than 

the WEBMOD simulated streamflow (fig. 2). The statistics 

evaluated over the 8-year calibration and validation periods, 

presented in table 5, confirm that the SWAT model performs 

better than the WEBMOD model. In all statistical evaluations 

for the validation period, however, the performance of both 

models can be considered “good.”

The continuity between the observed and simulated massbalance components was ensured throughout the calibration 

process. Mean annual observed precipitation was 1,076 mm 

during the calibration period, which was drier than the 

1,120 mm mean calculated from the 19 years of input precipitation data. Mean annual observed streamflow during the 

calibration period was 294 mm, indicating that although precipitation during the period was less than the long-term mean 

annual value, more water was entering the stream than is indicated by the long-term mean annual streamflow of 270 mm. 

Mean annual streamflow simulated for the calibration period 

by the SWAT model corresponded to the drier-than-normal 

climate and generated a mean annual simulated streamflow 

of 234 mm, about 60 mm less than the mean annual observed 

streamflow for the calibration period. WEBMOD however, 

produced a mean annual streamflow of 305 mm. These differences in model simulation results are evident when considering the PBIAS statistics in table 5, which indicate that 

SWAT underpredicts and WEBMOD overpredicts streamflow. 

After calibration, both models simulated mean annual actual 

evapotranspiration values similar to the reference evapotranspiration of 770 mm reported by Sloto (2002). SWAT simulated 

a mean annual evapotranspiration of 769 mm, and WEBMOD 

simulated 748 mm. Parameters related to permeability of the 

confining unit were adjusted to approximate a loss of 20 mm/

year from the shallow aquifer to an adjacent watershed. Mean 

annual simulated groundwater losses from the watershed were 

17 mm in SWAT and 25 mm in WEBMOD during the calibration period, and 21 mm and 25 mm, respectively, for the validation period. The “good” statistical values and the continuity 

between simulated and observed mass balances confirmed the 

calibration of both the SWAT and WEBMOD models at the 

Maryland watershed.

The Indiana Watershed

There were large differences in the performances of 

the initial SWAT and WEBMOD models developed for the 

Indiana watershed. As in the calibration of the SWAT model 

for the Maryland watershed, CNs for the Indiana SWAT model 

were decreased to allow more infiltration because the initial 

Indiana model produced peak streamflows exceeding those 

observed. Attempts were made to incorporate tile drains into 

the SWAT model for Indiana. However, analysis of simulation 

results indicated an insufficient lag time of tile flows into the 

stream. As Green and others (2006) note, the new version of 

this model, SWAT2005, is able to better reproduce the effects 

of tile drains on streamflow. To account for the influence of 

tile drains on streamflow with the SWAT2000 version, it was 

necessary to decrease the lateral traveltime and slope length of 

soils for individual HRUs to produce the desired peaks. The 

initial simulations indicated that the storm runoff was reaching 

the watershed outlet too quickly. Therefore, the parameter controlling how much water was retained by the channel, the surface-runoff lag coefficient (SURLAG), was reduced by onehalf to increase the amount of time required for water to reach 

the watershed outlet. Implicitly, this reduction could represent 

an accounting for the influence of stream-channel sinuosity 

on simulated streamflow, which is often lost during model 

preprocessing due to the coarse resolution of DEMs. Following these adjustments, the magnitude and temporal distribution 

of streamflow peaks closely resembled observed streamflow. 

The parameters controlling the influence of groundwater on 

streamflow, ALPHA_BF and GW_DELAY, required minor 

adjustments and consisted of increasing the time required for 

groundwater to reach the stream and increasing the amount of 

water available from the shallow aquifer to the root zone to 

alleviate the soil moisture deficit (REVAPMN). The resulting 

model was able to reliably reproduce streamflow and could 

be termed a “good” model, as indicated by the statistics in 

table 6. 

The initial WEBMOD model developed for the Indiana 

watershed was the only model to produce a positive NSE 

value from initial parameter input values. Improvements to 

model performance were made by assigning values to the 

s_satpref_zmax and s_satpref_zmin parameters that allow the 

model to simulate lateral preferential flow through the saturated zone, which implicitly accounts for the influence of tile 

drains. Additional minor adjustments to the s_theta_fc, t0, and 

xk0 parameters were made to decrease the rate at which water 

reached the saturated zone. Following these changes, model 

performance increased and produced statistics similar to the 

calibrated SWAT model. 
12 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Observed streamflow

Soil and Water Assessment 

 Tool simulated streamflow

Water, Energy, and Biogeochemical 

 MODel simulated streamflow

SWAT AND WEBMOD SIMULATED RUNOFF SHOWN WITH RUNOFF OBSERVED AT MORGAN CREEK

10/01/1997 01/01/1998 04/01/1998 07/01/1998 10/01/1998

STREAMFLOW, IN MILLIMETERS

0

2

4

6

8

10

12

14

16

18

20

Figure 2. Observed and simulated streamflow in the Maryland watershed for water year 1998. Streamflow (millimeters) 

was determined as the quotient of streamflow (cubic meters per second) divided by basin area (square kilometers).

Table 5. Statistical evaluation of calibrated and validated Soil and Water Assessment Tool (SWAT) and Water, Energy, and 

Biogeochemical MODel (WEBMOD) models for the Maryland watershed.

Model evaluation 

statistics

Calibrated Validated

SWAT WEBMOD SWAT WEBMOD

Nash-Sutcliffe efficiency 0.75 0.59 0.84 0.67

Ratio of the root mean square to the standard deviation of the observed data 0.5 0.64 0.4 0.57

Percent bias 7.43 –3.98 0.84 –3.95
Performance of the Models 13

The SWAT and WEBMOD models developed for the 

Indiana watershed were the best performing models of the 

study. Annual streamflow was most similar to the mean annual 

streamflow during the September 30, 1997, to October 1, 

1998, water year, and figure 3 illustrates the performance of 

models during that water year. As the statistics in table 6 indicate, the calibrated models can be considered “good” models. 

The mean annual precipitation for the calibration period was 

1,105 mm, and the mean annual streamflow equaled 383 mm. 

From the PBIAS statistics, it is clear that SWAT overpredicts 

(410 mm) mean annual streamflow, whereas WEBMOD 

underpredicts (300 mm) it. The differences in simulated 

streamflow are translated into the simulated evapotranspiration 

for each model. SWAT produced a mean annual evapotranspiration of 647 mm and WEBMOD produced 722 mm. The 

performance of both models was similar during the validation 

period and confirmed that both models were “good.”

The Nebraska Watershed

Developing calibrated models for the Nebraska watershed proved to be the most difficult. The initial simulations 

from both SWAT and WEBMOD not only did not produce 

large peaks in streamflow when those peaks actually occurred 

but produced large peaks when only base-flow conditions 

were present. Most of the observed peaks that the model did 

not simulate occurred during the winter months, most likely 

resulting from precipitation or snowmelt draining from a 

frozen land surface. Adjustments were made to the WEBMOD 

parameters controlling snowmelt timing to help reproduce the 

“missing” peaks. Parameters specific to the methods controlling water movement once it enters the soil were then adjusted 

in SWAT and WEBMOD. 

To reduce the size of the simulated runoff peaks, changes 

were made in the SWAT parameters to increase infiltration. 

Changes included reducing the CN, increasing the available water capacity of the soil (SOL_AWC), increasing the 

hydraulic conductivity of the soil (SOL_K), and increasing the 

percentage of pore volume in the soil available for preferential 

flow to the saturated zone (ICRK) (table 3). Following these 

changes, it was evident that an increase in streamflow peak 

duration was needed. Due to the coarseness of the DEM, the 

modeled stream channel is more linear than the actual channel; 

thus, water in the stream was reaching the outlet too quickly. 

The influence of this more sinuous stream channel on simulated streamflow was implicitly accounted for by decreasing, 

by 75 percent, the surface-runoff lag coefficient (SURLAG), 

Observed streamflow

Soil and Water Assessment 

 Tool simulated streamflow

Water, Energy, and Biogeochemical 

 MODel simulated streamflow

SWAT AND WEBMOD SIMULATED RUNOFF SHOWN WITH RUNOFF OBSERVED AT SUGAR CREEK NEAR PALESTINE, INDIANA

10/01/1997 01/01/1998 04/01/1998 07/01/1998 10/01/1998

STREAMFLOW, IN MILLIMETERS

0

2

4

6

8

10

12

14

16

18

20

Figure 3. Observed and simulated streamflow in the Indiana watershed for water year 1998. Streamflow (millimeters) was 

determined as the quotient of streamflow (cubic meters per second) divided by basin area (square kilometers).
14 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

which controls the time required for water received by the 

stream to leave the watershed. To account for the effects of the 

perched water tables in the headwaters of the watershed, the 

lateral traveltime and slope length of the soils were decreased. 

Improvements in the model’s ability to match the base-flow 

conditions of the stream were made by increasing the time 

required for water leaving the soil profile to reach the stream 

(ALPHA_BF and GW_DELAY). 

The performance of the Nebraska WEBMOD model 

was improved by enabling more infiltration. The parameters 

controlling the log-normal decreases of hydraulic conductivity (szm) and transmissivity (t0) with depth were adjusted. An 

additional benefit of these changes was that more water was 

available for groundwater flow, and simulation results better 

matched the observed perennial contribution of groundwater 

to the stream. In addition, 20 to 50 percent of water leaving the 

root zone was routed to the stream as lateral preferential flow 

through the unsaturated zone. 

The resulting performances of the calibrated Nebraska 

models were considerably poorer than that of the models for 

the Maryland and Indiana watersheds (table 7). Neither of the 

Nebraska models could be considered “good” models, but 

both were “satisfactory” according to the criteria described 

by Moriasi and others (2007). An example of streamflow 

simulated by SWAT and WEBMOD for a water year in which 

annual streamflow was closest to mean annual streamflow is 

illustrated in figure 4. The ability of the models to conform 

to the mass balance of the system could improve the confidence in the parameter sets for each model. Mean annual 

precipitation for the calibration period was 691 mm, and mean 

annual observed streamflow was 95 mm. The mean annual 

streamflow simulated by the two models was in near agreement: 95 mm by the SWAT model and 92 mm by WEBMOD. 

The mean actual evapotranspiration from the SWAT model 

was 579 mm; WEBMOD’s result was 602 mm. Both of 

these values are nearly 100 mm greater than that reported by 

Dugan and Zelt (2000) for Nebraska. However, the estimate 

of 500 mm of evapotranspiration was derived from a simple 

mass-balance calculation that limited evapotranspiration based 

on the availability of soil water and considered a mean annual 

precipitation of 609 mm. The additional 82 mm of precipitation received during the calibration period could be expected 

to alleviate the soil moisture deficit and allow for the higher 

simulated values of evapotranspiration. This conclusion does 

not make the models acceptably reliable, but their ability to 

reproduce observed streamflow should allow for the processes 

governing streamflow contributions to be realized. The difficulties of calibrating the Nebraska model could be associated 

with the hydrologic complexity of the modeled watershed; the 

models had to account for a spatially variable climate, seasonally frozen soils, and groundwater contributions from perched 

and regional systems. 

Hydrologic Processes in Agricultural 

Watersheds Identified Using 

Precipitation-Runoff Models

Once the models were calibrated it was possible to examine the different hydrologic processes by which both SWAT 

and WEBMOD conveyed water to a stream. The calibrated 

models introduced water into hillslopes by different means 

and yet generated streamflow through similar processes. By 

examining how each process contributed to streamflow, a better understanding of the hydrologic processes occurring could 

be achieved.

The conceptual understanding of the hydrology of the 

Maryland watershed indicated that groundwater flow was the 

largest annual source of streamflow, and as table 8 indicates, 

the simulation results for the validation period confirm this. 

In SWAT and in WEBMOD, lateral flow contributes nearly 

equal percentages to annual streamflow. The streamflow peaks 

that correspond to these lateral flows could actually represent 

the peak retardation resulting from the multitude of manmade 

ponds in the watershed. In general, it can be stated that, on an 

annual basis, groundwater constitutes the majority of stream 

water. 

The influence of the different methods of accounting for 

soil moisture at the Maryland watershed is evident in figure 5. 

Due to the increased potential for runoff with shallow water 

tables implicit in the TOPMODEL-based WEBMOD, the 

responsiveness of the groundwater system to precipitation is 

better realized. Conceptually, peaks of runoff generated from 

precipitation were believed to be due primarily to infiltration excess runoff. However, WEBMOD results indicate that 

lateral preferential flow through the unsaturated zone was 

the primary means of conveying stormwater to the stream. 

Table 6. Statistical evaluation of calibrated and validated Soil and Water Assessment Tool (SWAT) and Water, Energy, and 

Biogeochemical MODel (WEBMOD) models for the Indiana watershed.

Model evaluation statistics Calibrated Validated

SWAT WEBMOD SWAT WEBMOD

Nash-Sutcliffe efficiency 0.78 0.77 0.65 0.79

Ratio of the root mean square to the standard deviation of the observed data 0.47 0.47 0.59 0.45

Percent bias –6.98 21.81 –13.38 0.24
Performance of the Models 15

Table 7. Statistical evaluation of calibrated and validated Soil and Water Assessment Tool (SWAT) and Water, Energy, 

and Biogeochemical MODel (WEBMOD) models for the Nebraska watershed.

Model evaluation statistics Calibrated Validated

SWAT WEBMOD SWAT WEBMOD

Nash-Sutcliffe efficiency 0.57 0.50 0.32 0.55

Ratio of the root mean square to the standard deviation of

 the observed data 0.65 0.70 0.82 0.66

Percent bias –0.18 3.72 10.23 –2.78

Observed streamflow

Soil and Water Assessment 

 Tool simulated streamflow

Water, Energy, and Biogeochemical 

 MODel simulated streamflow

SWAT AND WEBMOD SIMULATED RUNOFF SHOWN WITH RUNOFF OBSERVED AT MAPLE CREEK NEAR NICKERSON, NE

0

2

4

6

8

10

10/01/1996 01/01/1997 04/01/1997 07/01/1997 10/01/1997

STREAMFLOW, IN MILLIMETERS

Figure 4. Observed and simulated streamflow in the Nebraska watershed for water year 1997. Streamflow (millimeters) was 

determined as the quotient of streamflow (cubic meters per second) divided by basin area (square kilometers).
16 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Table 8. Contributions to streamflow from Soil and Water Assessment Tool (SWAT) and Water, Energy, 

and Biogeochemical MODel (WEBMOD) simulations for the validation period.

[—, not calculated in the model]

State

Infiltration 

excess runoff 

(percent)

Saturation 

excess runoff 

(percent)

Unsaturated 

lateral preferential flow 

(percent)

Saturated 

lateral preferential flow 

(percent)

Groundwater 

flow (percent)

WEBMOD

Maryland 12 2 23 0 63

Indiana 12 1 12 50 25

Nebraska 23 2 19 0 56

SWAT

Direct runoff 

(percent)

Lateral flow 

(percent)

Groundwater 

flow (percent)

Maryland 22 — 25 — 53

Indiana 46 — 30 — 24

Nebraska 28 — 54 — 18

This unsaturated lateral preferential flow could be contained 

within the direct runoff produced by the SWAT model. The 

CN simply partitions water between that which enters the 

stream and water that enters the soil matrix; therefore, SWAT 

is unable to explicitly account for lateral preferential flow in 

the unsaturated zone. This could account for the underprediction of the SWAT model indicated in figure 5; the percolating 

water, unable to contribute to streamflow, simply alleviates the 

soil moisture deficit. 

Annual flows from tile drains at the Indiana watershed 

were thought to contribute most of the streamflow. In the 

SWAT model, it was assumed that tile-drain flow could be 

represented by lateral flow; however, most of the streamflow 

was contributed by direct runoff, as noted in table 8. Field 

observations indicated the presence of vertical cracks in the 

land surface, which could conduct direct runoff to the tile 

drains. Therefore, it is reasonable to assume that at least some 

of the direct runoff could be routed to the stream through the 

tile drains. With WEBMOD, preferential lateral flow from the 

saturated zone (tile drains) constituted most of the annual flow 

of the stream. Contributions to streamflow from groundwater 

in SWAT were similar in timing (fig. 6) and volume (table 

8) to WEBMOD. The simulation results confirm that lateral 

preferential flow, predominantly from tile drains, is the major 

contributor to streamflow at the Indiana watershed. 

The performance of the model for the Nebraska watershed was not as good as the models for the Indiana and 

Maryland watersheds. This result was due, for the most part, 

to the high spatial variability of precipitation in this watershed; 

however, some conclusions can be made about the dominant hydrologic processes in Nebraska. Infiltration-excess 

runoff generated the largest simulated peaks in each model, 

and groundwater flow contributed to streamflow year-round 

(table 8, figure 7). The flows from perched water tables in the 

headwater parts of the watershed could be accounted for by 

the lateral flow in SWAT and by the lateral preferential flow 

through the unsaturated zone in WEBMOD. 

Limitations of Study and Needs for 

Future Research

This study was limited principally because no chemical transport simulations were conducted. Different chemical 

species and concentrations have been shown to be indicative 

of hydrologic processes. For example, nitrate concentrations 

in groundwater can be correlated to nitrate concentrations 

observed at a watershed outlet if groundwater is a major component of streamflow. Results of these simulations could have 

been compared to chemicals measured in groundwater and at 

the watershed outlet. Once the hydrologic processes that are 

major sources of streamflow have been identified, the next 

step would be to determine if there were any relations between 

those processes and the applied agricultural chemicals. 

Because both SWAT and WEBMOD can simulate chemical 

transport, these models could be used in any future efforts to 

link environmental processes to in-stream chemistry. 

Summary and Conclusions

Agricultural chemical transport from point and nonpoint 

sources is one of the leading impairments to water quality 

in rivers and lakes. To alleviate the impairments induced by 

agricultural chemicals, much research has focused on improving the understanding of relations between application areas, 

the media through which the chemicals travel, and the receptor 

to which the chemicals are transported. Almost exclusively 

this transport is controlled by the hydrologic processes moving 
Summary and Conclusions 17

Infiltration excess runoff

Saturation excess runoff

Unsaturated lateral preferential flow

Saturated lateral preferential flow

Groundwater flow

Observed flow

Direct runoff

Lateral flow

Groundwater flow

Observed flow

0

20

40

60

80

100

120

140

160

STREAMFLOW, IN MILLIMETERS STREAMFLOW, IN MILLIMETERS

0

20

40

60

80

100

120

140

160

Oct–01 Oct–02 Oct–03 Oct–04

SWAT 

WEBMOD 

Figure 5. Contributions to streamflow from validated Soil and Water Assessment Tool (SWAT) and 

Water, Energy, and Biogeochemical MODel (WEBMOD) models for the Maryland watershed.
18 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

Saturation excess runoff

Infiltration excess runoff

Unsaturated lateral preferential flow

Saturated lateral preferential flow

Groundwater flow

Observed flow

Lateral flow

Direct runoff

Groundwater flow

Observed flow

0

20

40

60

80

100

120

140

160

STREAMFLOW, IN MILLIMETERS STREAMFLOW, IN MILLIMETERS

0

20

40

60

80

100

120

140

160

Oct–01 Oct–02 Oct–03 Oct–04

SWAT 

WEBMOD 

Figure 6. Contributions to streamflow from validated Soil and Water Assessment Tool (SWAT) and 

Water, Energy, and Biogeochemical MODel (WEBMOD) models for the Indiana watershed. 
Summary and Conclusions 19

Saturation excess runoff

Infiltration excess runoff

Unsaturated lateral preferential flow

Saturated lateral preferential flow

Groundwater flow

Observed flow

Lateral flow

Direct runoff

Groundwater flow

Observed flow

0

5

10

15

20

25

30

35

40

45

50

STREAMFLOW, IN MILLIMETERS STREAMFLOW, IN MILLIMETERS

0

5

10

15

20

25

30

35

40

45

50

Oct–01 Oct–02 Oct–03 Oct–04

SWAT 

WEBMOD 

Figure 7. Contributions to streamflow from validated Soil and Water Assessment Tool (SWAT) and 

Water, Energy, and Biogeochemical MODel (WEBMOD) models for the Nebraska watershed. 
20 Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

water through the environment. A conceptual model of the 

processes governing the movement of water can be developed 

on the basis of field observations. An alternative to a conceptual model—an alternative that can quantitatively describe the 

hydrologic processes controlling chemical transport—is a distributed precipitation-runoff model. Through such a model’s 

ability to account for the spatial variability of hydrologic 

processes, a more quantitative understanding of agricultural 

chemical transport can be obtained. 

This study used two precipitation-runoff models—the 

Soil and Water Assessment Tool (SWAT) and the Water, 

Energy, and Biogeochemical Budgets Model (WEBMOD)—to 

identify the hydrologic processes that move water through 

three different agricultural watersheds: the Maryland watershed, the Indiana watershed, and the Nebraska watershed. 

The hydrologic processes identified by the models were then 

compared to corresponding processes in the conceptual models developed at each watershed. Through this comparison, 

a realistic picture of the hydrologic system, in terms of the 

relative importance of different processes by which water was 

transported to the watershed outlet, was obtained. The SWAT 

and WEBMOD models were manually calibrated to improve 

their performance statistics. Although the parameters in each 

model represented different hydrologic theory, each model 

commonly required adjustments to better simulate the same 

hydrologic processes. In general, the parameters governing 

the infiltration of water through the root zone, the delay of 

groundwater movement from the saturated zone to the stream, 

and the timing of snowmelt required changing. Simulation 

results from validated models produced statistical values similar to those of the calibration period.

The SWAT and WEBMOD model results at each watershed conformed to each other and to the processes identified in 

each watershed’s conceptual hydrology. In Maryland the conceptual understanding of the hydrology indicated groundwater 

flow was the largest annual source of streamflow; the simulation results for the validation period confirm this. The dominant source of water to the Indiana watershed was thought to 

be tile drains. Although tile drains were not explicitly simulated in the SWAT model, a large component of streamflow 

was received from lateral flow, which could be attributed to 

tile drains. Being able to explicitly account for tile drains, 

WEBMOD indicated water from tile drains constituted most 

of the annual streamflow in the Indiana watershed. The 

Nebraska models indicated annual streamflow was composed 

primarily of perennial groundwater flow and infiltration-excess 

runoff, which conformed to the conceptual hydrology developed for that watershed. The hydrologic processes represented 

in the parameter sets resulting from each model were comparable at individual watersheds but varied between watersheds. The models were unable to show, however, whether 

hydrologic processes other than those included in the original 

conceptual models were major contributors to streamflow.

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