Simulating Hydrologic Budgets for Three Illinois Watersheds

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ELSEVIER

Journal

Journal of Hydrology 176 (1996) 57-77

Estimating hydrologic budgets for three Illinois watersheds

J.G. Arnold”‘*, P.M . Allenb

alJSDA-Agricultural Research Service, 808 East Blackland Road, Temple, TX 76502, USA

‘Baylor University, Department of Geology, Waco, TX 76798, USA

Received 11 November 1994; revision accepted 10 April 199 5

Abstract

It is important to simulate the major co mponen ts of the hydrologic budget to determine the

impacts of proposed land management, vegetative changes, groundwater withdrawals, and

reservoir managem ent on water su pply and wate r quality. A s acquisition of field data is costly

and time consuming, mode ls have been create d to test various land use practices and their

concomitant effects on the hydrologic budget o f watersheds. To simulate such management

scenarios realistically, a mode l should be able to simulate the individual compon ents of the

hydrologic budget. However, most field studies at the watershed scale attempt to measure only

one componen t (e.g. total stream flow, evapotranspiration (ET) , etc.) and are not suitable for

validating individual compon ents of a comp rehensive mode l. A field study was com pleted in the1950 s to estimate several major hydrolog ic compon ents including surface runoff, groundwater

flow, groundwater ET, ET in the soil profile, groundwater recharge, and groundwater heights

from measured data from three watersheds in Illinois. These data were used to validate a

multicomponent water budget model called SWAT . Comparison of measured and predicted

values demonstrated that each component of the model gave reasonable output and that the

interaction among com ponents was realistic. This fact should allow more realistic appraisal of

various land use managem ent practices on a basin-wide scale.

1. Introduction

Numerous models have been developed over the past several years to assist in

understanding the hydrologic system. Such models provide a framework in which

to analyze data and test hypotheses. Mod els are also used as a predictive tool to test

changes in the hydrologic regime such as water yield and pollution brought on by

changes in basin attributes such as land use or irrigation (Beasley et al., 1980; Arnold

* Corresponding author.

0022-16 94/96/$15 .00 0 1996 - Elsevier Science B.V. All rights reservedSSDI 0022-1694(95)02782-3



58 J.G. Arnol d, P.M. A l l en / Journal of Hy drol ogy 176 (1996) 57-77

et al., 1990; Bultot et al., 1990; Refsgaard et al., 1992). Problems inherent to building

such models have been enumerated by Klemes (1986), Beven (1989) and Grayson et

al. (1992). Briefly, these may be summ arized. First, there is the presumption that

physical processes in the basin can be represented in deterministic ways and that

linking of these solutions is possible. Second, it is often assum ed that input datafrom the field are available for estimation of all model param eters or that the

model can accurately derive such parameters from manipulation of related field

data. Finally, there is the assumption that such inputs derived from selective

sampling in the field adequately represent the spatial and/or temporal variability in

the field.

Although many models have successfully synthesized one or more parameters of

the hydrologic budget (Freeze, 1972; Beven and Kirkby, 1979; Leavesley, 1983; Hata

and Anderson, 1983; Loague and Freeze, 1985; Hebbert and Smith, 1990), data

availability and calibration of such models is still an arduous task (Cary, 1991).

Such calibration is often based on assessment of the goodness of fit of the model to

gaged daily or monthly discharge for the watershed. Calibration is achieved often

through a combination of trial and error adjustments and limited optimization.

Following calibration, and based on the assessed accuracy of the predicted to

simulated discharges, many models are being used to evaluate other com ponents of

the hydrologic system such as evaporation, ground water flow and storage (Peuntes

and Atkins, 1989). Assessing the validity of these models to predict such components

of the hydrologic system is usually not done. T his is problematic as measurement of

components of the water balance alw ays involves errors. T he only components of the

water balance that are regionally observed from a number of stations are precipita-

tion, streamflow, and to a lesser extent pan evaporation. Except for a limited numb er

of experimental watersheds, soil moisture, evaporation and transpiration, waterstorage and infiltration are usually estimated from empirical formulae. Here, the

accuracy of the model depends on the input requirements and the degree to which

the structure of the model approximates the physical process. Winter (198 1) discussed

various types of errors inherent in measurement and computation of the various

components of the water balance. He found that long-term averages had less error

than short-term values. Errors in annual estimates of precipitation, streamflow a nd

evaporation ranged from 2 to 15% whereas m onthly estimates ranged from 2 to 30%

For w orst possible estimates of the error, the sum of these errors should probably be

considered. Given these inherent problems in the water balance computations, it is

still an extremely useful approach to assessing the interrelationships between the

components of the hydrologic system. The only way to begin to assess the usefulness

of a model is to test it against actual data and other independently modeled

interpretations. Grayson et al. (1992) suggested that the following procedures be

part of analyzing any model. First, the model must be tested and calibrated over a

wide variety of watersheds under a wide range of conditions. Second, b oth positive as

well as negative results should be reported, and the uncertainty surrounding the

model predictions should be discussed. Finally, the source and precision of the

input data should be presented. The purpose of this paper is to compare the water

balance o utput of the SWA T (Soil and Water A ssessment Tool) model (Arnold et al.,



J.G. Ar nol d, P.M . A l l en / Journal of Hy drol ogy 176 (1996) 57-77 59

iJ

+- II

I

1t

SCALENYILES

Fig. 1. Location of the simulated basins (from Schicht and Walton, 196 1).



60 J.G. Arnol d, P.M. A l l en / Journal of Hy drol ogy 176 (1996) 57-77

1994) with the measured and modeled results of Schicht and W alton (1961) for three

watersheds in Illinois. Regarding the first two points of Grayson et al. (1992), the

model h as been calibrated over a wide variety of terrains and climates (Arnold and

Williams, 1987).

Table 1

Com parison of characteristics of basins

Characteristics Basin

Panther Creek Goose Creek Hadley Creek

Pysiographic Province

Relief

Twwwhy

Average stream gradient

(m m-‘)

Vegetal cover

Soil

Unconsolidated deposits

Bedrock formations

Av. depth to north

latitude water table

(m below land surface)

North latitude

Mean annual temperature

(“C)

Mean annual precipitation

(cm)

Basin area (km*)

Till plains section of

central lowlands

Divide to stream outlet Divide to stream outlet

30.5 m; average land 18.3 m; average land

slope 0.0038 m m-’ slope 0.0019 m m-l

Gently undulating

uplands

0.0009

Level uplands

0.0009

80% corn, oats, and

soybeans; 20% pasture,

woodland, and farmlots

86% corn, oats,

soybeans, alfalfa, hay,

wheat, rye; 14%pasture, woodland,

and farm lots

Upland prairie silt

loams

30.5 m of glacial till

Drummer silty clay

loam and flanagan

silt loam

53 m of glacia l till

Shale of Pennsylvanian

age

2.1

Shale of Pennsylvanian

ag e

2.4

40” 44’-4oY4

11

85.3

24 6

Till plains section of

central lowlands

40”05’-40”13’

._IL

94.0

122

Northern half in till

plains and southern

half in Lincoln Hills

section of Ozark

PlateauDivide to stream

outlet 120 m; land

slope ranges from

0.0076-0.0273 m m-’

Rugged uplands

0.0009

40% row crops, small

grain, and hay; 60%

pasture woodland,and farm lots

Upland prairie and

timber silt loams

7.6 m of loess and

15.2 m of glacial till

Shale of Mississippian

and Pennsylvanian age

6.1

39”41’-39”50’

13

91.4

18 8



J.G. Ar nol d, P.M. Al l en / Journal of Hy drol ogy 176 (1996) 57-77 61

2. Study area

The three watersheds are located within the till plains of central and western Illinois

(Fig. 1). The general characteristics of each watershed are summ arized in Table 1,

after Schicht and Walton (1961).

3. Historical water budget calculations

Schicht and Walton (1961) used precipitation, stream flow and groundwater level

data to ascertain groundwater recharge, runoff, and evapotranspiration for three

basins in Illinois. They used a simple balance equation that contains basic elements

of the water budget,

P = R + E T + U f A S s * A S g (1 )

where P is precipitation, R is stream flow, E T is evapotranspiration, U is subsurface

underflow, ASS is change in soil moisture, and AS g is change in groundwater storage.

Precipitation was measured for each basin with rain gage densities ranging from

17.12 krr2 per gage for Hadley Creek to 20.20 km2 per gage for Goose Creek and

27.84 km2 per gage for Panther Creek. S treamflow was monitored at each basin outlet

for the study period. Groun dwater levels were monitored with continuously recording

gages for three w ells on Goose Creek, and for five wells on Hadley and Panther

Creeks. At times as many as 16-21 wells were monitored in the last two basins to

verify the quality of the data being recorded at the continuous mon itoring wells. Soil

moisture was not measured. Evapotranspiration was solved from the water budget

equation assuming no significant change in soil moisture during the year. Subsurfaceunderflow was calculated from a modified form of the Darcy equation, Q = T I L ,

where Q is underflow (in 1day-‘), T is coefficient of transm issibility (in 1day-’ ft-‘), Z

is hydraulic gradient of the water table (in m m-l), and L is width of the cross-section

of the deposits in meters. The change in soil moisture was assumed to be zero. The

change in groundwater storage wa s estimated from the change in mean groundw ater

stage from the observation wells and the gravity yield of the wells,

ASg = AH( Yg) (2)

where A Sg is change in groundwater storage, AH is mean chang e in groundwater

stage, and Yg is gravity yield of the deposits described by the equation

Yg =P - R - E T - U

AH(3)

This equation was assumed valid for periods when soil moisture change was not

significant. It was assumed that evapotranspiration averaged 90 mm per month,

soil moisture change w as eliminated, and the equation was solved for inventory

periods during winter and early spring when the water table was rising.

The groundwater budget was stated as

P g = R g + E T g + U f A S g (4 )



62 J.G. Ar nol d, P.M. A l l en / Journal of Hy drol ogy 176 (1996) 57-77

where P g is groundwater recharge, R g is groundwater runoff, E T g is groundwater

evapotranspiration, U is subsurface underflow, and ASg is change in groundwater

storage. Groundwater runoff was estimated based on standard hydrograph separa-

tion techniques assuming that surface runoff is complete within 5 days after rainfall

and the reminder of fair weather flow is all groundwater runoff. R ating curves ofmean groundwater runoff corresponding to a groundwater stage were prepared for

both periods of high groundwater evapotranspiration (April-October) and low

groundwater evapotranspiration (November-M arch). The difference in ground-

water runoff between the two curves was specified as ground water evapotranspira-

tion. Recharge w as estimated from the groundwater budget equation where

groundwater runoff and evapotranspiration were determined from the mean ground-

water stage-runoff rating curves. Groundw ater storage was computed from gravity

yield and average water level declines as previously described .

Panther Creek a nd Goose Creek are each underlain by glacial till and dis-

continuous sand and gravel lenses. Such glacial deposits are marked by their great

variability in hydraulic conductivities, which may range from 1.3 x lop7 cm s-i to

3.81 x 10e5 cm s-l depending on clay content, mode of deposition, degree of

weathering, and surficial fractures (Norris, 1963; Fetter, 1988; Hendry, 1988; Ruland

et al., 1991). Although the 30-50 m thick deposits vary over the thinly bedded

sandstone, shale, and limestone bedrock, the groundwater flow is fairly uniform.

These latter depos its act as a barrier to deep percolation.

Hadley Creek watershed differs in subsurface geology from the other two basins.

Up to 7.5 m of silt and clay loess overlies up to 15 m of glacial till. The till consists of

unstratified clays with a few discontinuous sand lenses which range from a few

centimeters up to 1 5 m in thickness. The till is underlaid by less permeable limestone

and coal bedrock.

4. Hydrologic model

It is important to simulate the major components of the hydrologic balance so that

the impacts of proposed land management such as vegetative changes, reservoir

managem ent, groundwater withdraw als, an d stream and reservoir withdrawals can

be determined over various climate cycles. However, to simulate these management

scenarios realistically, the model must be able to simulate rea listically the individu al

components of the hydrologic budg et. The model selected for validation in this study

is the Soil and Water A ssessment Tool (SW AT) (Arnold et al., 1994).

5. Model operation

SW AT is a continuous time model that operates on a daily time step. The objective

in model development was to predict the impact of managem ent on water, sediment

and agricultural chemical yields in large ungaged basins. To satisfy the objective, the

model (a) is physically based (calibration is not possible on ungaged basins), (b) uses



J.G. Ar nol d, P.M. Al l en / Journal of Hy drol ogy 176 (1996) 57-77 63

readily available inpu ts, (c) is comp utationally efficient to operate on large basins in a

reasonable time, and (d) is a continuous time model and is capable of simulating long

periods for computing the effects of managem ent changes.

SW AT uses a comm and structure for routing runoff and chemicals through a

watershed similar to the structure of HY MO (Williams and Hann, 1973). Com mand sare included for routing flows through streams an d reservoirs, adding flows, and

inputting measured data or point sources. Using a routing com mand language, the

model can simulate a basin subdivided into grid cells or subwatersheds. Additional

comm ands have been developed to allow measured and point source data to be input

to the model an d routed with simulated flows. Also, output data from other simula-

tion models can be input to SW AT. Using the transfer comm and, water can be

transferred from any reach or reservoir to any other reach or reservoir within the

basin. T he user can specify the fraction of flow to divert, the minim um flow remaining

in the channel or reservoir, or a daily amount to divert. The user can also apply w ater

directly to a subbasin for irrigation. Although the model operates on a daily time step

and is efficient enough to run for many years, it is intended as a long-term yield model

and is not capable of detailed, single-even t, flood routing.

5.1 . Subbasin components

The components of SWA T can be placed into eight major divisions-hydrology,

weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and

agricultural managem ent. A detailed description of the SWA T components has

been given by Arnold (1992) and Arnold et al. (1994). A brief description of the

hydrology components is presented here.

5 .2 . Su r face runo f hyd ro l ogy

Surface runoff from daily rainfall is predicted using a procedure similar to the

CRE AM S runoff model, option one (Knisel 198 0; Williams and Nicks, 19 82). Like

the CRE AM S model, runoff volume is estimated with a modification of the SCS curve

number method (USD A Soil Conservation Service, 1972). The curve number varies

non-linearly from the 1 (dry) condition at wilting point to the 3 (wet) condition at field

capacity, and approaches 100 at saturation. The SW AT model also includes a

provision for estima ting runoff from frozen soil.

Peak runoff rate predictions are based on a modification of the Rational Formula.The runoff coefficient is calculated as the ratio of runoff volume to rainfall. The

rainfall intensity during the watersh ed time of concentration is estimated for each

storm as a function of total rainfall using a stochastic technique. The watershed time

of concentration is estimated u sing M anning’s Formula considering both overland

and channel flow.

5 .3 . Pe rco la t ion

The percolation component of SW AT uses a storage routing technique to predict



64 J.G. A rnol d, P.M. Al l en 1 Journal of Hy drol ogy 176 (1996) 57-77

f low through each soil layer in the root zone. Do wnw ard flow occurs when field

capacity of a soil layer is exceeded if the layer below is not satu rated. The dow nward

flow rate is governed by the saturated conductivity of the soil layer. Upw ard flow may

occur when a lower layer exceeds field capacity. Movement from a lower layer to an

adjoining upper layer is regulated by the soil water to field capacity ratios of the twolayers. Percolation is also affected by soil tempe rature. If the tempera ture in a

particular layer is 0°C or below, no percolation is allowed from that layer.

5.4. Lateral subsurface flow

Lateral subsurface flow in the soil profile (O-2 m) is calculated simultaneously with

percolation. A nonlinear function of lateral flow travel time is used to simulate the

horizontal component of subsurface flow. The magnitudes of the vertical and

horizontal components are determined by a simultaneous solution of the two

governing equations.

5.5. Groundwaterflow

Groundwater flow contribution to total streamflow is simulated by creating a shallow

aquifer storage (Arno ld et al., 1993). Percolate from the bottom of the root zone is

recharge to the shallow aqu ifer. A recession constant, derived from daily streamflow

records, is used to lag flow from the aquifer to the stream. Other com ponents include

evaporation, pum ping withdraw als, and seepage to the deep aquifer.

5.6. Evapotranspiration

The model offers three options for estimating potential ET-Hargreaves

(Hargreaves and Sam ani, 1985), Priestley-Taylor (Priestley and Taylor, 1972) and

Penman -Monteith (Mon teith, 1965). The Penman-M onteith method was used in

this study and requires solar radiation, air temperature, wind speed, and relative

humidity as input. Daily values of wind speed, relative hum idity, and solar radiation

were generated from average monthly values.

The model computes evaporation from soils and plants separately, as described by

Ritchie (1 972). Potential soil water evaporation is estimated as a function of potential

ET and leaf area index (area of plant leaves relative to the soil surface area). Actual

soil water evaporation is estimated by using exponential functions of soil depth and

water content. Plant water evaporation is simulated as a linear function of potential

ET and leaf area index an d can be limited by soil water content. It is assumed that

30% of total plant uptake comes from the upper 10% of the root zone and roots can

compensate for water deficits in certain layers by using more water in layers with

adequate supplies.

5.7. Snow melt

The SWA T snow melt component is similar to that of the CRE AM S model (K nisel,



J.G. Ar nol d, P.M. Al i en / Journal of Hy drol ogy 176 (1996) 57-77 65

1980). If snow is present, it is melted on days when the maxim um temperature exceeds

0°C using a linear function of temperature. Melted snow is treated the same as

rainfall for estimating runoff a nd percolation, but rainfall energy is set to 0.0 and

peak runoff rate is estimated assumin g uniformly distributed rainfall for a 24 h

duration.

5.8 . Tran smi ss ion losses

Many semiarid watersheds have alluvial channels that abstract large volumes of

streamflow (Lane, 1982). The abstractions, or transmission losses, reduce runoff

volumes as the flood wave travels downstream. SWA T uses Lane’s method described

in Chapter 19 of the SCS Hydrology Handbook (USD A Soil Conservation Service,

1983) to estimate transmission losses. Channel losses are a function of channel width

and length and flow duration. Both runoff volume and peak rate are adjusted when

transmission losses occur.

5.9 . Ponds

Farm ponds are small structures that occur within a subbasin. Pond storage is

simulated as a function of pond capacity, daily inflows and outflows, seepage, and

evaporation. Ponds are assumed to have only emergency spillways. Required inputs

are capacity and surface area. Surface area below capacity is estimated as a non-linear

function of storage.

6. Routing components

6 .1 . Channe l f l ood rou t in g

Channel routing uses a variable storage coefficient method developed by Williams

(1969). Channel inputs include the reach length, channel slope, bankfull width and

depth, channel side slope, flood plain slope, and Manning’s n for channel and flood

plain. Flow rate and average velocity are calculated using Man ning’s equation and

travel time is computed by dividing channel length by velocity. Outflow from a

channel is also adjusted for transmission losses, evaporation, diversion, and returnflow.

7. Reservoir routing

7.1. Reser vo i r w a t e r ba l a n ce and rou t i n g

The water balance for reservoirs includes inflow, outflow, rainfall on the surface,



66 J.G. Arno ld , P.M . A l l en { Journal of Hy drol ogy 176 (1996) 57-77

evaporation, seepage from the reservoir bottom, and diversions and return flow.

There are currently three methods to estimate outflow. The first method simply

reads in measured outflow and allows the model to simulate the other com ponents

of the water balance. The second method is for small uncontrolled reservoirs, and

outflow occurs a t a specified release rate when volume exceeds the principal storage.Volume exceeding the emergency spillway is released within 1 day. For larger

manag ed reservoirs, a monthly target volume approach is used.

8. Model inputs and calibration

The SWAT hydrologic model requires input on soils (bulk density, av ailable water

capacity, sand , silt, clay, organic m atter, and saturated conductivity), land use (crop

and rotation), managem ent (tillage, irrigation, nutrient, and pesticide applications),

weather (daily precipitation, temperature, and solar radiation), channels (slope,

length, bankfull width and depth), and the shallow aquifer (specific yield, recession

constan t, and revap coefficient). Revap is defined as water that is extracted from the

shallow aquifer by deep roots or water that travels from the shallow aquifer to the soil

profile and is then lost to soil evaporation or plant root uptake (Arnold et al., 1993). A

complete list of inputs has been given by Arnold (1992).

The watersheds were subdivided to account for differences in soils and land use. No

channel (flood) routing was simulated; thus, it was assumed that all surface runoff

reached the basin outlet on the day of the runoff event. Each basin was subdivided

into three subbasins-one for pasture and woodland, one for a corn-soybean

rotation, and one for a soybean-corn rotation. As most of the cropland is in

corn-soybean rotation, half of the land is in corn one year and soybeans the next,so in any given year half of the land is in corn and the other half in soybeans.

Topographic and land use data is taken from Table 1. Upland prairie silt loams

were characterized by the Drummer soil series and timber silt loams by the Flanagan

soil series. Requ ired soil properties for each series were obtained from the Soils-5

data base (USD A Soil Conservation Service, 1992). Daily precipitation and tempera-

ture were collected from the following stations in Illinois: (1) Delan d for Goose

Creek, (2) Barry for Hadley Creek; (3) Minon k, Gridley, and Panola for Panther

Creek.

Inpu ts to the model are physically based (i.e. based on readily observed or

measured information). However, there is often considerable uncertainty in model

inputs owing to spatial variability, measurement error, etc. In this study model, inputs

were allowed to vary within a given realistic uncertainty range to calibrate to annual

measured values. For exam ple, Soils-5 properties are listed as ranges (i.e. available

water capacity might range from 0.11 to 0.13). As the exact values were unknow n, the

model was manually calibrated within the uncertainty ranges for annual streamllow,

and annual surface and groundwater contributions. The input variables used in

calibration were soil properties and the curve numb er. The curve number has

categories for good, fair, and poor hydrologic condition and was allowed to vary

within these ranges for calibration.



J.G. A rnol d, P.M . A l l en 1 Journal of Hy drol ogy 176 (1996) 57-77 67

9. Simulation results and discussion

Table 2 gives measured and predicted hydrologic budget com ponents for selected

years (reported by Schicht and Walton (1961)) for all three basins. Streamllow is

separated into surface runoff and groundwater flow, and ET is divided into surfaceand soil ET and groundwater ET. In SWAT, soil ET is the sum of soil evaporation

and plant root uptake from the crop root zone (approximately 2 m). Groundwater ET

is plant root uptake (trees and shrub s) from soil and rock layers below the crop root

zone or water loss that occurs as the water from the shallow aquifer re-enters the soil

zone. Groundwater recharge is the amount of water that percolates past the soil

Table 2

Com parison of hydrologic budgets for the Illinois basins

Measured

(mm)

Predicted

(mm)

Goose Creeek , 1957

Precipitation

Stream flow

Surface runoff

Groundwater flow

Evapotranspiration

Surface and soil ET

Groundwater ET

Groundwater recharge

Change in groundw ater storage

Underflow

Hadley Creek, 1957

Precipitation

Stream flow

Surface runoff

Groundwater flow

Evapotranspiration

Surface and soil ET

Groundwater ET


Change in groundwater storage

Underflow

Pant her Creek, 1952

Precipitation

Stream flow

Surface runoff

Grotmdwater flow

Evapotranspiration

Surface and soil ET

Groundwater ET


Change in groundwater storage

Underflow

944.4

240.8

144.3

96.5

617.2

535.9

81.3

264.2

+86.4

neg.

1009.1

353.8

305.8

48.0

626.9

604.5

22.4

98.8

+26.7

1.8

822.4

249.4

67.6

181.9

608.1

557.0

51.1

204.0

-28.9

neg.

253.5

145.1

121.2

603.0

521.6

81.4

210.0

+85.0

not simulated

366.4

300.5

65.9

634.6

612.9

21.7

88.8

+38.9

not simulated

239.0

85.6

153.4

594.9

556.1

38.8

191.1

-9.7

not simulated



Pre

pta

o TamisoL

Pwc

oom sk

Re

g

De

Aq

e

Fg2H

ocfowsmuznSWA



J.G. Arnol d, PM . A l l en / Journal of Hy drol ogy 176 (1996) 57-77 69

PantherCreek (1952)

Total Fnxipitation = 822.4 mm

Qmmd~Ilov

HadleyCreek (1957)

Total precipitation= 1009.1 mm

oluuldw~fiow

Actual 6i'redicted

Goose Creek (1957)

_wa,k~otal Fhcipitation = 944.4 mm

Fig. 3. Com ponents of water budget as percentage of total precipitation.



IO

Table 3

J.G. A rnol d, P.M . A l l en / Journal of Hy drol ogy 176 (19%) 57-77

Com parison of measu red a nd predicted m onthly stream flow components (values in mm )

Mean SD R2 Slope

Goose Creek, 1955-1957Surface

Measured 6.16

Predicted 6.84

Groundwater

Measured 4.89

Predicted 5.50

Total

Measured 11.6

Predicted 11.3

Hadley Creek, 1956-1958

Surface

Measured 14.7Predicted 15.8

Groundwater

Measured 2.54

Predicted 3.90

Total

Measured 17.2

Predicted 19.6

Pant her Creek, 1951-1952

Surface

Measured 15.9

Predicted 15.1

GroundwaterMeasured 13.9

Predicted 13.0

Total

Measured 29.8

Predicted 27.7

13.0

11.7

9.33

4.90

17.6

14.8

30.225.8

4.24

3.16

34.4

28.2

24.0

25.0

13.9

7.3

31.0

29.3

0.19 0.99

0.38 1.18

0.63 0.94

0.94 1.14

0.51 0.96

0.95 1.18

0.82

0.40

0.78

0.87

1.19

0.94

profile-root zone and recharges the shallow aquifer (F ig. 2). Change in groundwater

storage is the sum of the change in soil water in the root zone and the change in the

shallow aquifer. Underflow is not currently simulated by SW AT as it would require

information on well level fluctuations and transmissivity which is normally not

available for rural watersheds. In general, the simulation model results compared

well with the historical water budget calculations. Mo st components are within 5%

and nearly all are within 25% . T his error is of the same order of magnitude as that

found by Gerhart (1984), who applied a three-dimensional model to simulate flows in

two layers in the Lower S usquehana River Basin in Pennsylvania and Maryland. Pie

charts of the major hydrologic components (as a percentage of rainfall) for all three

basins are shown in Fig. 3. The pie charts allow easy visualization of the relative

magnitude of the various com ponents and show the close match of predicted to actual

values in the water budgets.



J.G. Arnol d, P.M . A l i en / Journal of Hy drol ogy 176 (1996) 57-77 71

Table 3 shows the results of measured and simulated surface runoff, groundwater

flow, and total w ater yields for the three basins. It is important for simulation models

to produce frequency distributions that are similar to measured frequency distribu-

tions. Close agreement between means and standard deviations indicates that the

frequency distributions are similar. Generally, simulated values compare well withmeasured values considering that the basin characteristics utilized are relatively crude

for estimating model input p arameters (Table 1). A common criticism of simulation

models is that they do not simulate extremes w ell and thus underpredict standard

deviations. In this case, measured and predicted standard deviations compare well for

all flows. Regression line slopes and R2 values near unity also indicate a close relation-

ship between m easured and predicted yields. Statistics are valuable criteria but often a

graph gives considerable insight into the goodness-of-fit. Measured vs. predicted

monthly streamflow for Hadley Creek is plotted in Fig. 4. Regression lines and

lines-of-perfect-fit (1: 1) are plotted with the regression points.

Seasonal trends can easily be visualized by plotting measured and predicted

150

25

R-squared 0.94560 stddevmeas 34.35 .’

M eesM ean 17.20

25 50 75 100 125

M easured M onthly St~amflow(mm)

Fig. 4. Mea sured vs. predicted monthly total streamflow for Hadley Creek.



12 J.G. Ar nol d, P.M. Al l en / Journal of Hy drol ogy 176 (1996) 57-77

monthly values against time. Fig. 5 shows measured and predicted monthly stream-

flow for the Hadley Creek watershed during a 22 month period in 1954-1956 . These

graphs show if the model tends to over- or underpredict during certain seasons of the

year. Although the timing of modeled groundwater flow and volume parallels the

measured flow, there is some apparent deviation from the peak values (Fig. 6).Groundwater flow in the model is a function of three variables: (1) percolation or

recharge; (2) recession factor; (3) groundwater evaporation. Although it is assumed

that groundw ater discharge is a linear function of recharge, deviation is possible if the

aquifer behaves in a nonlinear manner as described by Rushton and Tomlinson

(1979), or if two aquifers are present in which tw o different recession factors would

have to be input as described by Riggs (1985). So, with the knowledge that a more

complex model m ay come a little closer to actual catchm ent processes, it appears

justifiable to adopt this simpler m odel ba sed on the results to date. A similar approach

has been advocated by Nathan and McM ahon (1990). Sensitivity analysis of these last

two factors will be analyzed in the future.

It is also important that the model sim ulates an nual variations in the hydrologic

components, although model inputs are static (not updated annually) during the

simulation. The Panther Creek watershed provides an excellent example of the

potential magnitude of the annual variability. In 1951, surface runoff w as greater

than groun dwater flow with a measured ratio of groundwater flow to total flow of

0.33 (Table 4 ). However in 1952, the ratio of groundwater flow to total flow was 0.73.

140

120

Bl w

Lo

18x60

B

40

20

r- Measured

---predicted

Fig. 5. Mea sured and predicted total flow by month for Hadley C reek.



J.G. Ar nol d, P.M . A l l en / Journal of Hy drol ogy 176 (1996) 57-77 73

16

14 -- Measured

-g 12 - ---Predicttxl

z 10 -

” Jul AugSep CktNo~ aC JM FcbMarAprMayJ~n Jul AugSep OetNovDec Jan FebMarApr

1954 1955 1956

Fig. 6. Meas ured and predicted grou ndwater flow by month for Hadley C reek

Considering the large variation, the model did a reasonable job, simulating a ratio of

0.37 in 1951 and 0.64 in 1952 (Table 4).

Another important output of the SW AT model is prediction of groundwater levels.

High groundw ater levels are a principal cause of slope instability and other geo-

technical problems (Sangrey et al., 1984). Fig. 7 plots measured and predicted

groundwater levels (mean stage below the land surface) in the Hadley Creek

watershed. The model does relatively w ell in predicting monthly trends in ground-

water levels, including tracking the rise in levels during the winter and spring of 1955

and the subsequent decline in the autumn of 1955 (Fig. 7).

Although the SWAT model operates on a daily time step, it was designed to predict

Table 4

Annu al variation in surface and groundwa ter flow at Panther Creek

1951

Measured

(mm)

Predicted

(mm)

1952

Measured

(mm)

Predicted

(mm)

Surface runoff 313 276 68 86

Groundwater flow 152 159 182 153

Ratio of groundwater/ 0.33 0.31 0.73 0.64

total flow



14 J.G. Arnold, P.M. All en 1 Journal of Hydrology 176 (1996) 57-77

-Measured

---Predicted

Jul AugSep OctNovDcc Jan FcbMarA prA4ayJun Jul AugSep OctNovD cc Jan FebMarA ~

1954 1955 1956

Fig. 7. Mea sured and predicted ground water height by month for Hadley Creek.

14

12 - -Predicted

h

ii

----M easured.10 - R

E

; :,

1 8-

2 6-

g 4-

2-

0150 155 160 165 170 175 180 185

J ulianDay - 1955

Fig. 8. Measu red and predicted daily streamflow for the month of June 195 5 at Goose Cree k.



J.G. Ar nol d, P.M. Al l en 1 Journal of Hy drol ogy I 76 (1996) 57-77 15

accurately monthly or annual hydrologic parameters. As the model operates on a

daily time step, there is considerab le uncertainty within a day that is not accounted for

(i.e. rainfall intensity, tempe rature variability, rainfall events crossing midnight, etc.).

However, it is important that the model realistically predict daily peak characteristics

over time and daily hydrograph recessions. Fig. 8 shows daily measured and predictedflows after a large runoff event (the largest in 1955 -1957) in the Goose Creek

watershed. Although this is only one large event, it does show that the model can

adequately simulate the daily hydrograph recession.

10. Sum mary and conclusions

A multicomponent water budget model (SWA T) has been tested for three

watersheds in Central Illinois. The model appears to be able to simulate all com-

ponents of the budget w ithin acceptable limits on both an annual and monthly timestep. Comp arison of the modeled results with measured water budgets allows com-

parison of the accuracy of the different components of the model. In this particular

case, it demonstrates that each component of the model gives reasonable output. This

fact should allow more realistic appraisal of various land use managem ent practices

on a basin-wide scale. It should also better pinpoint exactly how each alternative will

affect the water budget, thu s allowing for more innovative managem ent practices to

be tested a priori and their effects traced through each hydrologic component of the

watershed.

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Simulating Hydrologic Budgets for Three Illinois Watersheds