HYDROLOGICAL PROCESSESHydrol. Process. 25, 915–925 (2011)Published online 8 October 2010 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.7876

Development and application of a physically based landscapewater balance in the SWAT model

Eric D. White,1 Zachary M. Easton,1* Daniel R. Fuka,1 Amy S. Collick,2 Enyew Adgo,2

Matthew McCartney,3 Seleshi B. Awulachew,3 Yihenew G. Selassie4 and Tammo S. Steenhuis1

1 Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA2 Department of Water Resource Engineering, Bahir Dar University, Bahir Dar, Ethiopia

3 International Water Management Institute, Nile Basin and East Africa Office, Addis Ababa, Ethiopia4 Amhara Regional Agricultural Institute, Bahir Dar, Ethiopia

Abstract:

Watershed scale hydrological and biogeochemical models rely on the correct spatial-temporal prediction of processes governingwater and contaminant movement. The Soil and Water Assessment Tool (SWAT) model, one of the most commonly usedwatershed scale models, uses the popular curve number (CN) method to determine the respective amounts of infiltration andsurface runoff. Although appropriate for flood forecasting in temperate climates, the CN method has been shown to be lessthan ideal in many situations (e.g. monsoonal climates and areas dominated by variable source area hydrology). The CNmodel is based on the assumption that there is a unique relationship between the average moisture content and the CN forall hydrologic response units (HRUs), and that the moisture content distribution is similar for each runoff event, which is notthe case in many regions. Presented here is a physically based water balance that was coded in the SWAT model to replacethe CN method of runoff generation. To compare this new water balance SWAT (SWAT-WB) to the original CN-based SWAT(SWAT-CN), two watersheds were initialized; one in the headwaters of the Blue Nile in Ethiopia and one in the CatskillMountains of New York. In the Ethiopian watershed, streamflow predictions were better using SWAT-WB than SWAT-CN[Nash–Sutcliffe efficiencies (NSE) of 0Ð79 and 0Ð67, respectively]. In the temperate Catskills, SWAT-WB and SWAT-CNpredictions were approximately equivalent (NSE >0Ð70). The spatial distribution of runoff-generating areas differed greatlybetween the two models, with SWAT-WB reflecting the topographical controls imposed on the model. Results show that awater balance provides results equal to or better than the CN, but with a more physically based approach. Copyright 2010John Wiley & Sons, Ltd.

KEY WORDS SWAT model; water balance; Ethiopia; monsoonal climate; New York; runoff; variable source area

Received 19 April 2010; Accepted 19 August 2010

INTRODUCTION

Non-point source runoff can contribute significant quan-tities of nutrients, chemicals, and sediments to streamand water bodies. To locate these ‘non-point’ sources ofpollution in a landscape, many watershed managers andresearchers frequently use distributed watershed models.One of the most commonly used watershed scale modelsis the US Department of Agriculture (USDA) Soil andWater Assessment Tool (SWAT) (Arnold et al., 1998).SWAT, like any water quality model, must first accu-rately simulate the hydrologic processes before it canrealistically predict pollutant transport. Many differentapproaches to modelling the hydrologic processes havebeen presented in the scientific literature over the pastseveral decades, but SWAT currently uses two meth-ods to model surface runoff: the curve number (CN)(USDA–SCS, 1972) or the Green–Ampt routine (Greenand Ampt, 1911). The Green–Ampt method is a physi-cally based infiltration excess, rainfall–runoff model, and

* Correspondence to: Zachary M. Easton, Department of Biological andEnvironmental Engineering, Cornell University, Ithaca, NY, USA.E-mail: zme2@cornell.edu

is therefore not suitable for regions where the rainfallrate seldom exceeds the saturated conductivity of thesoil, such as in the northeastern part of the USA (Wal-ter et al., 2003). As a result, the empirically based CNmethod is the most commonly used runoff routine in theSWAT model (King et al., 1999; Gassman et al., 2007).SWAT and other CN-based models are frequently used inwatersheds around the world with little recognition thatthe underlying runoff calculations were neither developednor validated for these regions (Gassman et al., 2007).

Indeed, CN models have commonly been applied inthe Blue Nile basin of Africa. Located in the monsoonalclimate of the Ethiopian Highlands, the temporal runoffdynamics in the Blue Nile basin are poorly captured bythe CN method, which assumes that the moisture contentdistribution of the watershed is similar for each runoffevent (Collick et al., 2009; Steenhuis et al., 2009). Pre-vious work in the Blue Nile basin has shown that for agiven amount of rain, runoff volumes will vary through-out the rainy season. Liu et al. (2008) demonstrated thatin the Ethiopian Highlands, less runoff was generated atthe beginning of the rainy season as compared to thesame rain volume at the end of the season. Liu et al.(2008) showed that when a 10-day watershed discharge

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916 E. D. WHITE ET AL.

is plotted against effective precipitation (i.e. precipitationminus potential evapotranspiration) there was no relation-ship until approximately 500 mm of effective precipita-tion. Once 500 mm of effective precipitation had fallen,there was a relatively strong, linear relationship, indicat-ing that the proportion of the rainfall that became runoffwas constant during the remainder of the rainy season.Of course, this relationship can be partially explainedby channel losses in the early monsoonal period, or bythe filling of landscape depressions that must becomelinked prior to the outlet hydrograph responding. How-ever, in another, similar Blue Nile watershed, Ashagre(2009) found that the 5-min rainfall intensity very rarelyexceeded the measured soil infiltration capacity, andthus assumed excess saturation runoff was the dominaterunoff-generating mechanism. These dynamics cannot bepredicted by the CN, and are, in fact, in direct con-trast to the CN approach, which assumes no correlationbetween antecedent precipitation and a watershed’s maxi-mum retention beyond 5 days (USDA–Natural ResourceConservation Service (NRCS), 2004).

Many have attempted to modify the CN model towork better during monsoonal climates, by proposingvarious temporally based values and initial abstractions.For instance, Bryant et al. (2006) suggested that a water-shed’s initial abstraction should vary as a function ofstorm size. Although this is a valid argument, the intro-duction of an additional variable reduces the appealof the one-parameter CN model. Kim and Lee (2008)found that SWAT was more accurate when CN valueswere averaged across each day of simulation, rather thanusing a CN that described moisture conditions only atthe start of each day. White et al. (2009) showed thatSWAT model results improved when the CN was changedseasonally to account for watershed storage variationdue to plant growth and dormancy. Wang et al. (2008)improved SWAT results by using a different relation-ship between antecedent conditions and watershed stor-age. Although these variable CN methods improve runoffpredictions, they are not easily generalized for use out-side the watershed they are tested mainly because theCN method is a statistical relationship and not physicallybased.

In many regions, surface runoff is produced by only asmall portion of a watershed that expands with an increas-ing amount of rainfall. This concept is often referred toas a variable source area (VSA), a phenomenon actuallyenvisioned by the original developers of the CN method(Hawkins, 1979), but never implemented in the originalCN method as used by the Natural Resource Conser-vation Service (NRCS). Since the methods’ inception,numerous attempts have been made to justify its usein modelling VSA-dominated watersheds. These adjust-ments range from simply assigning different CNs for wetand dry portions to correspond with VSAs (Sheridan andShirmohammadi, 1986; White et al., 2009) to full rein-terpretations of the original CN method (Hawkins, 1979;Steenhuis et al., 1995; Schneiderman et al., 2007; Eastonet al., 2008).

To determine what portion of a watershed is produc-ing surface runoff for a given precipitation event, thereinterpretation of the CN method presented by Steen-huis et al. (1995) and incorporated into SWAT by Eastonet al. (2008) assumes that rainfall infiltrates when thesoil is unsaturated or runs off when the soil is satu-rated. It has been shown that this saturated contributingarea of a watershed can be accurately modelled spatiallyby linking this reinterpretation of the CN method witha topographic index (TI), similar to those used by thetopographically driven TOPMODEL (Beven and Kirkby,1979; Lyon et al., 2004). This linked CN–TI methodhas since been used in multiple models of watersheds inthe northeastern part of the USA, including the general-ized watershed loading function (GWLF) (Schneidermanet al., 2007) and SWAT (Easton et al., 2008). Althoughthe reconceptualized CN model is applicable in temper-ate US climates, it is limited by the fact that it imposesa distribution of storages throughout the watershed thatneeds to fill up before the runoff occurs. Although thislimitation does not seem to affect results in temperateclimates, it results in poor model results in monsoonalclimates.

SWAT–VSA, the CN–TI adjusted version of SWAT(Easton et al., 2008), returned hydrological simulationsas accurate as the original CN method; however, thespatial predictions of runoff producing areas and as aresult the predicted phosphorus export were much moreaccurate. Although SWAT–VSA is an improvement uponthe original method in watersheds where the topographydrives flows, ultimately, it still relies on the CN to modelrunoff processes and therefore is limited when applied tothe monsoonal Ethiopian Highlands.

Water balance models are relatively simple to imple-ment and have been used frequently in the Blue Nilebasin (Johnson and Curtis, 1994; Conway, 1997; Ayenewand Gebreegziabher, 2006; Kim and Kaluarachchi, 2008;Liu et al., 2008; Collick et al., 2009; Steenhuis et al.,2009). Despite their simplicity and improved watershedoutlet predictions, they fail to predict the spatial loca-tion of the runoff-generating areas. Collick et al. (2009),and to some degree Steenhuis et al. (2009), present semi-lumped conceptualizations of runoff producing areas inwater balance models. SWAT, a semi-distributed model,can predict these runoff source areas in greater detail,assuming that the runoff processes are correctly mod-elled.

In this study, we develop and test a CN-free versionof SWAT based on a simple water balance approach.We utilize the spatial adjustments as proposed by Lyonet al. (2004) and implemented by Easton et al. (2008),into SWAT–VSA, and replace the CN method with awater balance to predict runoff. This new version ofSWAT, SWAT-WB, calculates runoff volumes based onthe available soil storage capacity of a given soil, andthen partitions excess moisture to runoff and infiltratingfractions. This can lead to a more accurate simulation ofwhere and when the runoff occurs in watersheds dom-inated by saturation-excess processes. Both the original

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

SWAT LANDSCAPE WATER BALANCE 917

CN method used by SWAT and the new water balance(SWAT-WB) method are tested on two watersheds thatvary widely in climate, geology, and data availability:one in the monsoonal Blue Nile basin in Ethiopia andone in the Catskill Mountains of New York.

MODEL OVERVIEW

SWAT is a basin-scale model designed to simulate hydro-logical processes, nutrient cycling, and sediment trans-port throughout a watershed. SWAT has been appliedto catchments ranging from 0Ð015 km2 (Chanasyk et al.,2003) to as large as 491 700 km2 (Arnold et al., 2000).To initialize the model, SWAT requires soils data,land use/management information, and elevation data todrive flows and direct sub-basin routing. The hydrologicresponse unit (HRU) is the smallest unit in the SWATmodel and is used to simulate processes such as runoff,infiltration, plant dynamics (including uptake of waterand nutrients, biomass, etc), erosion, nutrient cycling, andleaching of pesticides and nutrients. Traditionally, HRUsare defined by the coincidence of soil type (HydrologicSoil Group, USDA 1972) and land use. The predictionsfrom each HRU are aggregated for each sub-basin, androuted through the internal channel network. Simulationsrequire meteorological input data including precipitation,temperature, wind, humidity, and solar radiation. Allthese inputs are initialized using the ARCSWAT (9Ð2)interface (Olivera et al., 2006). More details on SWATcan be found at http://www.brc.tamus.edu/swat/doc.html.

Original CN approach

Historically, when initializing SWAT, a CN is assignedfor each specific land use/soil combination in the water-shed, and these values are read into the model. SWATthen calculates the upper and lower limits for each CNfollowing a probability function described by the NRCSto account for varying antecedent moisture conditions(CN–AMC) (USDA–NRCS, 2004). SWAT determines aCN for each simulated day by using this CN–AMC dis-tribution in conjunction with daily soil moisture valuesdetermined by the model. This daily CN is then used todetermine a theoretical storage capacity, S, of the water-shed for each day. This storage, S, is then indirectly usedto calculate runoff volume, Q, via

Q D �P � Ia�2

�P � Ia� C S�1�

where S is the watershed storage, P the precipitation, andIa the initial abstraction. All terms are in millimetre ofwater, and by convention Ia is assumed to be equal to0Ð2 ð S. The problem with SWAT, and other CN-basedmodels, is that runoff is calculated via Equation (1) priorto any infiltration occurring in the watershed; thus themodel directly assumes an excess infiltration approach tothe runoff generation.

Water balance approach

A daily soil–water balance was used to determine thesaturation deficit (and by extension the runoff volume)of each HRU by SWAT instead of the CN method.Although SWAT’s soil moisture routine greatly simplifiesprocesses that govern water movement through porousmedia (in particular, partly saturated regions), for a dailybasin scale model the approach is generally acceptable(Guswa et al., 2002). In SWAT, the soil moisture levelsare estimated by considering several processes: plantuptake of water, macropore and micropore drainage,evaporation, redistribution between soil layers, lateraldrainage, and drainage of groundwater. A full descriptionof the equations used and their general applicability isprovided in the official SWAT documentation (Neitschet al., 2005) as well as in numerous articles in thescientific literature. Thus, the model already providesa convenient platform that can be expanded upon inorder to determine the surface runoff via a water balance.SWAT’s existing soil moisture routines are then used bySWAT-WB to determine the degree of saturation deficitfor each soil profile for each day of simulation. Thissaturation deficit (in mm H2O) is termed the availablesoil storage, �i and is a function of the soil propertiesand watershed moisture state

�i D �EDCiεi � �it�di �2�

where EDCi is the effective depth of a given soil profilei (unitless), εi the soil porosity (mm) of a given soili, �it the volumetric soil moisture of a given soil i, foreach day, t (mm), and di the soil profile depth of soili (mm). The porosity, εi, is a constant value for eachsoil type, whereas �it varies in time and is determinedby SWAT soil moisture routines. The effective depthcoefficient, EDCi, a parameter ranging from 0 to 1,is used to partition soil moisture in excess of εi intoinfiltrating (groundwater) and runoff fractions (includingrapid shallow interflow). By including this adjustmentto the available storage, the amount of water able toinfiltrate each day is controlled by the EDCi. EDCi isspatially varied based on a saturation probability definedby a soil wetness index (Easton et al., 2008). EDCi

values approaching 1 are assigned to regions expectedto produce little saturation-excess runoff, whereas valuesapproaching 0 indicate an area likely to produce largesaturation-excess runoff volumes. This spatially adjustedavailable storage is then used to determine what portionof rainfall events will infiltrate and what portion willrunoff, qi (mm)

qi D{

0 if P < �i

P � �i if P > �i�3�

The available storage, �i, is calculated each day prior tothe start of any rain event. Once precipitation starts, aportion of the rain, equal in volume to �i, will infiltratethe soil. If the rain event is larger in volume than �i, thesoil profile will saturate and surface runoff will occur.If the rainfall is less than �i, the soil is unsaturated and

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

918 E. D. WHITE ET AL.

there will be no surface runoff and SWATs’ internal soilmoisture routing will calculate the flux.

The model is available online at http://soilandwater.bee.cornell.edu/research/swatwb/swatwb.html.

HRU definition

Traditionally, HRUs are defined in SWAT as beingunique occurrences of soil type, land cover, and slopeclass. Any parcels of land within one sub-basin that sharethe same combination of these three features will be con-sidered one HRU. SWAT models all landscape processesfor each unique HRU in the watershed independently ofposition within each sub-basin. In basins dominated byVSA hydrology, this HRU definition has been shown tobe less than ideal for describing the spatial and tempo-ral evolution of hydrological processes (Schneidermanet al., 2007; Easton et al., 2008). In VSA watersheds,runoff-generating areas are likely to occur in portions ofthe landscape with shallow, low conductive soils, largecontributing areas, mild slopes, or any combination ofthe three. Although SWATs’ inclusion of slope classesin HRU delineation begins to address these issues, thereis currently no way to include upslope contributing areawhile defining HRUs. To correct for this, a TI was inte-grated with existing soils data to create a soil TI (STI),which is then used in the SWAT-WB HRU definitionprocess (Easton et al., 2008).

TIs and their various derivatives have been used tomodel runoff-contributing areas for quite some time (e.g.TOPMODEL; Beven and Kirkby, 1979). Recently, soilTIs have been incorporated into CN-based watershedmodels for use in VSA-dominated regions (Lyon et al.,2004; Schneiderman et al., 2007; Easton et al., 2008).SWAT–VSA integrated a STI into SWAT in order toimprove delineation of runoff-generating areas and thesubsequent nutrient loads in the Catskills Mountains ofNew York (Easton et al., 2008). SWAT–VSA providedmore accurate predictions of runoff source areas (asvalidated by distributed measures in the watershed) thanthe original SWAT; thus, we included an HRU definitionprocess similar to SWAT–VSA in SWAT-WB.

To initialize SWAT-WB, the first step was to createa soil TI for the watershed being modelled. The STI isdefined as (Beven and Kirkby, 1979; Beven, 1986)

� D ln(

˛

T0 tan ˇ

)�4�

The upslope contributing area, ˛, and the slope, tan(ˇ),are both obtained from a DEM, whereas the lateraltransmissivity of the soil profile, T0, when water tableintersects the soil surface (Beven, 1986) is a functionof the soil layer depth, D, and soil layer saturatedhydraulic conductivity, Ks, (e.g. T0 D Ks0 ð D0), and areobtained from soil information. We utilized the D-infinityalgorithm in TauDEM (Tarboton, 1997) to determine the˛ parameter. We assume that the STI values relate toa location’s likelihood of saturation and therefore thelikelihood to contribute surface runoff. Higher STI values

Figure 1. Wetness classes for Gumera, Ethiopia

Figure 2. Wetness classes for Town Brook, located in the CatskillMountains of New York State

are the result of either a large contributing area or smallvalues for slope, soil depth, or saturated conductivity, andtherefore are indicative of areas with a higher probabilityfor saturation.

Following the process outlined for SWAT–VSA (Eas-ton et al., 2008), an areally weighted STI (e.g. wetnessclasses) is used to represent a location’s likelihood to sat-urate. Although using the individual STI values directlyis, in theory, possible, in practice this would introduce fartoo much complexity and computational time to modelinitialization, calibration, and scenario runs. The wetnessclasses determined for the two watersheds used in thisstudy are shown in Figure 1 for the Ethiopian watershedand Figure 2 for the New York watershed. This wetnessclass map is then substituted for the soils map in the HRUdefinition process. Although the wetness classes can beused in HRU delineation instead of a soil map, SWAT stillrequires specific soil properties associated with the soilsmap (e.g. SSURGO database). Thus, in SWAT-WB, soilproperties required by SWAT were areally weighted andaveraged for each wetness class. This practice will notdrastically affect model results for two reasons. First, in

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

SWAT LANDSCAPE WATER BALANCE 919

Figure 3. Elevation of the Gumera basin, located east of Lake Tana inthe Ethiopian Highlands

Ethiopia, soil survey information is rare or nonexistent,and, to our knowledge, no defined database exists thatwould contain the parameters needed by SWAT. Thus,SWAT-WB utilized the UNFAO’s World’s Soil map (sup-plemented by literature values) as the base map (FAO-AGL, 2003), which classifies only five distinct soil typesin all of the 1270 km2 Blue Nile sub-catchment that wasmodelled. We assume that these soils data, with such acourse spatial resolution, will not be adversely affectedby the averaging process used in SWAT-WB. Second,in New York, where information on soils is more read-ily available, soil formation (in glaciated areas) is at leastpartially driven by topography (Page et al., 2005; Sharmaet al., 2006; Thompson et al., 2006; Easton et al., 2008).Therefore, averaging across topographic features with thewetness index should not pose any problems.

Watershed descriptions

Gumera watershed, Blue Nile basin, Ethiopia. SWAT-WB was tested on the Gumera River watershed, aheavily cultivated region in the Ethiopian Highlands.Located approximately 30 km northeast of Bahir Dar(11Ð83 °N, 37Ð63 °E), this 1270 km2 watershed drainsinto Lake Tana, the headwaters of the Blue Nile River(Figure 3). Land use in the Gumera watershed consistsof 96% mixed agriculture and 4% brush (or pasture).Elevation determined from a 75 m DEM ranged from1797 to 3708 m above sea level with slopes rangingfrom 0 to 79%. Temperatures in the basin show largeelevation (9–25 °C) and diurnal variation, but smallseasonal changes. The annual average temperature is18 °C (Conway, 2000). The climate of the basin is tropicalhighland monsoonal with the majority of the rain fallingbetween June and October. Rainfall amounts decreasefrom the southwest to the northeast with approximately80% occurring between June and October. Predominantsoils were gathered from the FAO World Soil map andwere classified as haplic and chromic luvisols (58 and22%, respectively). Other soils present in the basin wereeutric fluvisols (8%), eutric leptosols (8%), and eutric

vertisols (3%), with minimal areas classified as urban(>1%) (FAO-AGL, 2003).

Precipitation and temperature data were gathered fromthe National Meteorological Agency of Ethiopia forthe Debre Tabor, Bahir Dar, Addis Zeman, and NefasMewcha stations, the closest gauges to the Gumerabasin. The average annual precipitation from 1994 to2005 was 1470 mm (data courtesy of the EthiopianMinistry of Water Resources; MoWIR, 2002), withaverage potential evapotranspiration losses of 1220 mm.Daily precipitation and temperature data from 1992 to2003 was used for model calibration and validation. Otherrequired climatic data included relative humidity, windspeed, and solar radiation. These data were obtainedfor the nearby city of Bahir Dar through the UnitedStates National Climatic Data Center (NCDC, 2007). TheSWAT-WB model included 656 HRUs in 25 sub-basins,whereas the SWAT-CN model included 117 HRUs in 24sub-basins.

Town Brook watershed, Catskills, New York. SWAT-WB was also tested on the Town Brook watershed(Figure 2) in the USA, a 37 km2 sub-catchment ofthe Cannonsville reservoir basin. The region is typifiedby steep-to-moderate hillslopes of glacial origins withshallow permeable soils, underlain by a restrictive layer.The climate is humid with an average annual temperatureof 8 °C and average annual precipitation of 1123 mm.Elevation in the watershed ranges from 493 to 989 mabove mean sea level. The slopes are quite steep with amaximum of 91%, and a mean of 21%. Soils are mainlysilt loam or silty clay loam with soil hydrological groupC ratings (USDA–NRCS, 2000). Soil depth ranges fromless than 50 cm to greater than 1 m and is underlainby a fragipan restricting layer (e.g. coarse-loamy, mixed,active, mesic, to frigid Typic Fragiudepts, Lytic or TypicDystrudepts common to glacial tills) (Schneidermanet al., 2002). The lowland portion of the watershedis predominantly agricultural, consisting of pasture androw crops (20%) or shrub land (18%), whereas theupper slopes are forested (60%). Water and wetlandscomprise 2%. Impervious surfaces occupy <1% of thewatershed and were thus excluded from consideration inthe model. Several studies in this watershed or nearbywatersheds have shown that VSAs control overlandflow generation (Frankenberger et al., 1999; Mehta et al.,2004; Lyon et al., 2006; Schneiderman et al., 2007;Easton et al., 2008) and that infiltration-excess runoffis rare (Walter et al., 2003). The SWAT-WB modelincluded 180 HRUs in three sub-basins, whereas theSWAT-CN model included 172 HRUs in the threesub-basins.

Model calibration

To calibrate both the SWAT-WB and SWAT-CN mod-els for Gumera and Town Brook, we utilized the dynam-ically dimensioned search (DDS) algorithm (Tolson andShoemaker, 2005). The DDS calibration routine allows

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920 E. D. WHITE ET AL.

for parameters to be calibrated at the watershed, sub-basin, HRU, or wetness class level, which in turn allowedfor EDC to be calibrated separately for each wetnessclass. Because there is no information at the wetness classlevel on runoff generation, we forced the calibration tomaintain a pre-imposed distribution of EDC values thatroughly correlated with the STI (e.g. higher STI wet-ness classes had lower EDC values, whereas lower STIwetness classes had higher EDC values). For SWAT-CN,the CN was calibrated for each unique soil/land use typeinstead of the EDC. Eleven other parameters were cal-ibrated in both models. These 11 parameters relate tosurface and groundwater interaction, soil water, evapo-ration and plant uptake, and soil water holding capacity.Models were calibrated to minimize the root mean squareerror (RMSE).

Streamflow at the Gumera watershed outlet was cali-brated over a period of 8 years, from 1996 to 2003, andstreamflow in Town Brook was calibrated from 1998 to2002.

Model validation

Streamflow data from 1992 to 1995 was used to val-idate the Gumera model. For Town Brook, streamflowdata from 2002 to 2004 was used to validate the model.To test SWAT-CN’s and SWAT-WB’s abilities to cor-rectly predict distributed hydrology, we used measure-ments by Lyon et al. (2006) of height of water table abovethe restricting layer for a section of the Town Brookwatershed. Briefly, 44 pieziometric data loggers, installedto depths of 50 cm, recorded the water table depth in15 min intervals from April 2004 to September 2004.The field site encompassed five wetness index classes(Figure 3) and three land use types: pasture (PAST),shrub (RNGB), and mixed forest (FRST). To comparethe measured and SWAT–VSA water table heights, thepiezometer data were averaged across index classes; therewere between 2 and 32 piezometers per index (i.e. two

piezometers on index class six, 32 on index class 10, etc).To compare measured water table heights with SWAT-CN water table heights, we averaged across land use;there were 4–32 measurements per land use. SWAT (andSWAT-WB) reports soil water in millimetre of waterintegrated over the soil profile (i.e. cumulative waterdepth for all soil layers). Thus, we converted the model-predicted soil water to an equivalent depth by dividingby the SSURGO reported porosity and assuming theSSURGO reported soil depth represented the depth to therestricting layer. According to the SSURGO data base,the depth of the local restricting layer is 1Ð2–1Ð4 m.

Model evaluation

Criteria used to assess the ability of the models topredict discharge in Gumera and Town Brook includeda visual comparison between the modelled and theobserved hydrographs, Nash-Sutcliffe model efficiencies(NSE) (Nash and Sutcliffe, 1970) and the RMSE.

RESULTS

Both the SWAT-WB and SWAT-CN models were ini-tialized with the same input data, and calibrated andvalidated using the same automatic procedure for boththe Gumera and Town Brook watersheds. Thus, a com-parison of the model outputs is warranted.

Gumera basin

The SWAT-WB-predicted streamflow was significantlydifferent (based on paired t-test) from the SWAT-CN-predicted streamflow, with SWAT-WB returning sig-nificantly more accurate results than the SWAT-CNmodel of the Gumera watershed (Figure 4). SWAT-WB predicted streamflow with a daily NSE D 0Ð77 andRMSE D 2Ð42 for the calibration period and NSE D 0Ð81and RMSE D 2Ð47 for the validation period (Table I

0

5

10

15

20

25

30

Dis

char

ge (

mm

)

Observed

Water Balance

0

5

10

15

20

25

30

Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03

Dis

char

ge (

mm

)

Observed

Curve Number

(a)

(b)

y = 0.87x -0.065r2 = 0.64

0

10

20

30

0 10 20 30

Obs

erve

d

SWAT-CN

y = 0.86x + 0.084R2 = 0.74

0

10

20

30

0 10 20 30

Obs

erve

d

SWAT-WB

Figure 4. Observed and modelled streamflow for Gumera using (A) SWAT-WB and (B) SWAT-CN

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

SWAT LANDSCAPE WATER BALANCE 921

Table I. Model statistics for daily streamflow in Gumera basin

SWAT-WB SWAT-CNa

Calibration Validation Calibration Validation

NSE 0Ð77 0Ð81 0Ð64 0Ð63RMSE 2Ð42 2Ð47 2Ð67 2Ð72Percent difference �6Ð7 �16Ð5a Same input as SWAT-WB.

Figure 5. Spatial distribution of surface runoff in Gumera modelled by (A) SWAT-CN and (B) SWAT-WB

and Figure 4). SWAT-CN was less accurate predictingstreamflow, with a daily NSE D 0Ð64 and a RMSE D2Ð67 for the calibration period and NSE D 0Ð63 anda RMSE D 2Ð72, for validation (Table I and Figure 4).The percent difference between measured and mod-elled flows was substantially smaller for SWAT-WB(�6Ð7%) than for SWAT-CN (�16Ð5%). These SWAT-CN results are similar to those of Setegn et al. (2008)who modelled streamflow of the Gumera watershed withSWAT-CN. Their model predicted streamflow with anNSE of 0Ð61. Validation results for their model returnedan NSE of 0Ð61 similar to our results for SWAT-CN.

Perhaps more interesting is how SWAT-WB andSWAT-CN differ in the predicted distribution of runoff inthe watershed. For one storm in October 1997 (104 mmof rain), SWAT-CN predicted that all HRUs within thewatershed would contribute runoff, with a minimumdepth of 17 mm of runoff and a maximum of 71 mm(Figure 5A). For the same storm, SWAT-WB predictedthat some HRUs would produce no runoff, whereas oth-ers produced as much as 97 mm of runoff (Figure 5B).Both models predicted some surface runoff for someupland areas, but SWAT-CN predicted much less runoffbeing generated in the low-lying, flatter areas near thewatershed outlet, where SWAT-WB predicted the mostrunoff. At the watershed outlet, 16Ð5 mm of flow weremeasured. SWAT-WB predicted the outlet flows to be15Ð9 mm, whereas SWAT-CN substantially under pre-dicted the flows (9Ð42 mm).

Town Brook watershed

The SWAT-WB results for Town Brook were comparedwith the results from SWAT-CN. SWAT-WB-predictedstreamflow agreed well with measured flow, the cali-bration NSE was 0Ð77 and the RMSE was 2Ð09, thevalidation NSE was 0Ð79 and RMSE was 2Ð07. Pre-dicted streamflow for the SWAT-CN Town Brook modelresulted in a daily NSE of 0Ð75 and a RMSE of 2Ð12for calibration, and a NSE of 0Ð78 and a RMSE of 2Ð14for validation. The SWAT-WB and SWAT-CN-predictedstreamflows were not statistically different (based on apaired t-test). The percent difference between measuredand modelled flows was similar for both SWAT-WB(5Ð6%) and SWAT-CN (6Ð2%). Visual comparison ofSWAT-WB’s hydrograph with the measured hydrograph(Figures 4 and 6) indicates that the model performs wellfor the Town Brook watershed, a fact supported by thereasonably high daily NSE values of 0Ð77 and 0Ð79 for thecalibration and validation periods, respectively (Table II).

Comparing the SWAT-CN and SWAT-WB-predictedwater table heights with those measured by Lyonet al. (2006) shows that the SWAT-WB-predicted soilwater table height agreed with measurements across themonitored hill side in the watershed with r2 D 0Ð68(Figure 7A). There was a slight tendency for SWAT-WB to under predict water table height for large watertable heights (Figure 7A), and slightly under predictsmall water table heights. SWAT-CN, however, systemat-ically under-predicted water table height for all conditions(Figure 7B).

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

922 E. D. WHITE ET AL.

0

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Figure 6. Observed and modelled streamflow in the Town Brook watershed using (A) SWAT-WB and (B) SWAT-CN

Table II. Model statistics for daily streamflow in Town Brook

SWAT-WB SWAT-CNa

Calibration Validation Calibration Validation

NSE 0Ð77 0Ð79 0Ð75 0Ð78RMSE 2Ð09 2Ð07 2Ð12 2Ð14Percent difference 5Ð6 6Ð2a Same input as SWAT-WB.

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Mixed ForestPastureShrub

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Figure 7. Relationship between (A) SWAT-WB and (B) SWAT-CN predicted water table heights above the restricting layer by index class (SWAT-WB)or land use (SWAT-CN) and the measured water table heights for March–September 2004 from Lyon et al. (2006). Individual measured points within

an index class or land use represent the average of the pieziometic measurement within the respective classes for a single day

Similar to the Gumera results, differences in thespatial distribution of runoff are evident when an eventfrom November 2003 is compared between the TownBrook models (Figure 8A and B). As expected, SWAT-CN predicts some surface runoff from the majority ofthe watershed and it is clearly driven by differencesin land use, whereas SWAT-WB predicts substantialportions of the watershed producing no surface runoff,not surprising considering the emphasis the model placeson topographic position as it pertains to runoff generation.For this particular storm, SWAT-WB predicted that most

of the wetness classes in the low lying areas of thewatershed would be saturated at the start of this event,leading to these low-lying wet areas producing nearlyidentical volumes of runoff (i.e. almost the entire volumeof precipitation).

DISCUSSION

Clear improvements were made to SWAT in the Ethiopianwatershed by removal of the CN; however, the results are

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

SWAT LANDSCAPE WATER BALANCE 923

not as definitive for the Town Brook watershed in NewYork State. Although SWAT-WB has substantially highermodel accuracy for the calibration period, it has similaraccuracy during validation as SWAT-CN. By comparingthe hydrograph from the Town Brook outlet (Figure 6)and the model statistics, it is clear that SWAT-WB gen-erally performs as well as SWAT-CN. Thus, in caseswhere rainfall is evenly distributed throughout the yearboth models perform equally well in predicting discharge.However, we would argue that the water balance methodcaptures the processes controlling runoff generation real-istically. In Town Brook, Easton et al. (2008) showed thatthe inclusion of the STI-based wetness index better cap-tured the spatial distribution of water table depths and, byextension, runoff producing areas. SWAT-WB provided asimilar level of accuracy in predicting water table heights(Figure 7). While we have no distributed runoff data foreither of the watersheds, including the wetness index inSWAT-WB resulted in what appears to be more a realis-tic distribution of runoff generating areas than SWAT-CN.Indeed, in VSA-dominated watersheds, runoff generationis closely related to soil moisture levels (as controlled byperched water table levels), which in turn is governed, toa large extent, by topographic position. In many SWAT-CN applications, the location of an HRU within eachsub-basin is not a concern and thus, any locations thatshare land use and soil are treated identically, regardlessof its topographic position and the corresponding likeli-hood to produce runoff. In SWAT-WB, STIs were usedto link HRUs by similar topographic position, provid-ing model users the capability to examine intra-watershedrunoff dynamics.

This difference in spatial distribution of runoff-generating areas predicted by SWAT-WB and SWAT-CNis clearly demonstrated for both Gumera (Figure 5) andTown Brook (Figure 8). For the same large storm event inthe Gumera basin (Figure 5), SWAT-WB did not generatesurface runoff for all HRUs, whereas SWAT-CN pre-dicted that the entire watershed would contribute surfacerunoff more or less evenly. Due to the imposed topo-graphical controls, SWAT-WB predicted that the wettestportions of the watershed would contribute more runoff

than drier areas. In addition to the fact that SWAT-CNpredicts a nearly uniform runoff volume for the entirewatershed, there are two other points of interest thatshould be discussed. First is the fact that SWAT-CNpredicts that the area nearest Gumera’s outlet producesthe least amount of surface runoff, exactly opposite ofSWAT-WB’s results, which predict that this area pro-duces high runoff volumes. These differences betweenthe models can be explained by the inclusion of slopein the HRU delineation (and therefore EDCi calibration).Again, holding with VSA principles, SWAT-WB assumesthat these flat, near-stream regions will wet up and con-tribute the most runoff, because of reduced lateral flow(due either to shallower slope reducing the hydraulic gra-dient, or perhaps more importantly the accumulation ofinterflow from upslope areas), whereas SWAT-CN treatsthese HRUs the same as any upland region with the samesoil and land cover. The second interesting point is thatboth models predict that certain upland regions generatea significant portion of surface runoff from the test storm,but for different reasons. These soils have a low saturatedconductivity; for SWAT-CN, this results in a high CN andfor SWAT-WB an increase in STI values, both of whichincrease runoff.

SWAT-CN and most other watershed models havebeen developed for temperate climates where rainfall isgenerally well distributed throughout the year. Utilizingmodels developed during a temperate climate for Ethiopiaconditions, with a monsoonal climate, is problematic.Temperate models assume that there is a nearly uniquerelationship between precipitation amounts or intensityand runoff generated. This is not the case for Ethiopia asdemonstrated by the results of Liu et al. (2008) where forthree watersheds with more than 16 years of record, therainfall relationship was far from unique. The first rainsafter the dry season all infiltrate and nearly no runoffis generated. As the rainfall season progresses more andmore rainfall becomes runoff. Because the intensity of therain did not affect the runoff amounts for a given storm,Liu et al. (2008) concluded that the runoff mechanismwas dominated by excess saturation processes.

Figure 8. Spatial distribution of surface runoff in Town Brook modelled by (A) SWAT-WB and (B) SWAT-CN

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

924 E. D. WHITE ET AL.

Water balance models are consistent with saturationexcess runoff processes because the runoff is related tothe available watershed storage capacity and the amountof precipitation. The implementation of water balancesinto runoff calculations in the Blue Nile basin is not anovel concept and others have shown that water balancetype models often perform better than more complicatedmodels in Ethiopian type landscapes (Johnson and Curtis,1994; Conway, 1997; Ayenew and Gebreegziabher, 2006;Liu et al., 2008). However, these water balance modelsare typically run on monthly or yearly time steps becausethe models are generally not capable of separating base-,inter-, and surface-runoff flows. To truly model erosionand sediment transport (of great interest in the Blue Nilebasin), large events must be captured by the model anddaily simulations are required to do so. Thus, SWAT-WBnot only maintains a water balance but also calculatesthe interflow and the base flow component, and givesa reasonable prediction of peak flows. SWAT-WB istherefore more likely to be capable of predicting erosionsource areas and sediment transport than either SWAT-CN or water budget models with monthly time steps.Indeed, Tebebu et al. (2010) found gully formation anderosion in the Ethiopian Highlands to be related to watertable levels and saturation dynamics, which SWAT-WBreliably predicts (Figure 7 for Town Brook).

During initial development of SWAT-WB, we encoun-tered the same issue of fractionating excess moisture intorunoff and base flow components. When the entire soilprofile depths were used to calculate the available stor-age, �i, almost all precipitation became base flow. If onlythe uppermost soil layer was used nearly all precipitationbecame surface runoff. To address these issues, and toallow for SWAT-WB to be applied at a daily time step, itbecame evident that a new parameter, in addition to soildepth and moisture content, would need to be added to theequation used to calculate �i. Although this new parame-ter, EDC, is calibrated, it should be noted that, in theory,this parameter is physically measureable in the field orperhaps from base flow-separated runoff (Easton et al.,2010), whereas the CN remains largely a theoretical cal-ibration parameter in saturation-excess runoff-dominatedareas.

Interestingly, the EDCi solution to these issues issimilar to a recent water balance model developed forthe Blue Nile by Kim and Kaluarachchi (2008) thatcombines a water balance with a traditional tank model.To differentiate between surface and various subsurfaceflows, Kim and Kaluarachchi (2008) developed a modelusing two ‘tanks’. The upper tank, described by an upperzone soil moisture term, was used to calculate surfacerunoff, and a lower zone term was used to model baseflow. The upper layer produced no surface runoff untila ‘runoff orifice’ depth was filled by rainfall. This upperzone soil layer with its runoff orifice depth is analogous toSWAT-WB’s EDCi term; both parameters acknowledgethat in saturation-excess-dominated areas only a certainportion of the soil profile plays a role in runoff generation.

CONCLUSION

Daily modelling of stream flow and surface runoff in amonsoonal watershed was improved by the replacementof the CN method with a simplified water balanceroutine in the SWAT model. SWAT-WB uses calculatedsaturation-deficit values with an EDC to partition rainfallinto surface runoff and infiltrating water. This EDC-basedwater balance method is analogous to other tank modelsthat have been successfully applied in monsoonal regions

SWAT-WB was as accurate in predicting dischargeat the outlet as the CN method in a watershed thatexperiences evenly distributed rainfall throughout theyear (e.g. for conditions the CN was developed and testedunder). However, SWAT-WB predicted the distributionof water table heights on a hillslope in the watershedsignificantly better than SWAT-CN, giving us increasedconfidence that the spatial distribution of runoff dynamicsare more realistically captured.

These results indicate that SWAT performs betterin saturation-excess-controlled areas when a simplesaturation-deficit water balance model is used to calculaterunoff volumes. With this physically based and easy-to-use model, effective water and land management schemeswill be easier to successfully implement in watershedsdominated by saturation-excess runoff generation, partic-ularly data-poor regions where use of the CN method-ology has not been validated. For instance, the modelhas direct application to identification and delineation forhydrologically sensitive areas in watersheds dominatedby VSA hydrology.

ACKNOWLEDGEMENTS

We thank the Amhara Regional Agricultural ResearchInstitute (ARARI), the Ethiopain Ministry of WaterResources (MoWR), the International Water manage-ment Institute (IWMI), and the Eastern Nile TechnicalRegional Office (ENTRO) for providing data.

REFERENCES

Arnold JG, Muttiah RS, Srinivasan R, Allen PM. 2000. Regionalestimation of base flow and groundwater recharge in the upperMississippi basin. Journal of Hydrology 227(1–4): 21–40.

Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large areahydrologic modeling and assessment part I: model development.Journal of the American Water Resources Association 34(1): 73–89.

Ashagre BB. 2009. Formulation of best management option for awatershed using SWAT (Anjeni watershed, Blue Nile Basin, Ethiopia).MS thesis, Cornell University, Ithaca, NY.

Ayenew T, Gebreegziabher Y. 2006. Application of a spreadsheethydrological model for computing long-term water balance of LakeAwassa, Ethiopia. Hydrological Sciences Journal 51(3): 418–431.

Beven KJ. 1986. Runoff production and flood frequency in catchmentsof order n: an alternative approach. In Scale Problems in Hy-drology , Gupta VK, Rodriguez-Iturbe I (eds). Reidel, Dordrecht: TheNetherlands; 107–131.

Beven KJ, Kirkby MJ. 1979. Towards a simple physically based variablecontributing model of catchment hydrology. Hydrological SciencesBulletin 24(1): 43–69.

Bryant RD, Gburek WJ, Veith TL, Hively WD. 2006. Perspectives onthe potential for hydropedology to improve watershed modeling ofphosphorus loss. Geoderma 131: 299–307.

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

SWAT LANDSCAPE WATER BALANCE 925

Chanasyk DS, Mapfum E, Williams W. 2003. Quantification andsimulation of surface runoff from fescue grassland watersheds.Agricultural Water Management 59(2): 137–153.

Collick AS, Easton ZM, Adgo E, Awulachew SB, Gete Z, Steenhuis TS.2009. Application of a physically based water balance model on fourwatersheds throughout the upper Nile basin in Ethiopia. HydrologicalProcesses 23: 3718–372. DOI: 10.1002/hyp.7517.

Conway D. 1997. A water balance model of the upper Blue Nile inEthiopia. Hydrological Sciences Journal 42(2): 265–286.

Conway D. 2000. The climate and hydrology of the upper Blue NileRiver. Geographical Journal 166: 49–62.

Easton ZM, Fuka DR, Walter MT, Cowan DM, Schneiderman EM,Steenhuis TS. 2008. Re-conceptualizing the Soil and Water AssessmentTool (SWAT) model to predict runoff from variable source areas.Journal of Hydrology 348(3–4): 279–291.

Easton ZM, Walter MT, Fuka DR, White ED, Steenhuis TS. 2010. Asimple concept for calibrating runoff thresholds in quasi-distributedvariable source area watershed models. Hydrological Processes . Inreview.

FAO-AGL. 2003. World reference base: map of world soil resources.United Nations, Food and Agriculture Organization.

Frankenberger JR, Brooks ES, Walter MT, Walter MF, Steenhuis TS.1999. A GIS-based variable source area model. Hydrological Processes13(6): 804–822.

Gassman PW, Reyes MR, Green CH, Arnold JG. 2007. The soil andwater assessment tool: historical development, applications, andfuture research directions. Transactions of the American Society ofAgricultural and Biological Engineers 50(4): 1211–1250.

Green WH, Ampt G. 1911. Studies of soil physics: part I—the flow ofair and water through soils. Journal of Agricultural Science 4: 1–24.

Guswa AJ, Celia MA, Rodriguez-Iturbe I. 2002. Models of soil dynamicsin ecohydrology: a comparative study. Water Resources Research38(9): 1166–1181.

Hawkins RH. 1979. Runoff curve numbers from partial area watersheds.Journal of Irrigation and Drainage Engineering-ASCE 105(4):375–389.

Johnson PA, Curtis PD. 1994. Water balance of Blue Nile river basinin Ethiopia. Journal of Irrigation and Drainage Engineering-ASCE120(3): 573–590.

Kim U, Kaluarachchi JJ. 2008. Application of parameter estimation andregionalization methodologies to ungauged basins of the upper BlueNile river basin, Ethiopia. Journal of Hydrology 362: 39–56.

Kim NW, Lee J. 2008. Temporally weighted average curve numbermethod for daily runoff simulation. Hydrological Processes 22:4936–4948.

King KW, Arnold JG, Binger RL. 1999. Comparison of Green–Amptand curve number methods on Goodwin creek watershed using SWAT.Transactions of the American Society of Agricultural and BiologicalEngineers 42(4): 919–925.

Liu BM, Collick AS, Zeleke G, Adgo E, Easton ZM, Steenhuis TS.2008. Rainfall-discharge relationships for a monsoonal climate in theEthiopian highlands. Hydrological Processes 22(7): 1059–1067.

Lyon SW, Seibert J, Lembo AJ, Walter MT, Steenhuis TS. 2006.Geostatistical investigation into the temporal evolution of spatialstructure in a shallow water table. Hydrology and Earth System Sciences10(1): 113–125.

Lyon SW, Walter MT, Gerard-Marchant P, Steenhuis TS. 2004. Using atopographic index to distribute variable source area runoff predictedwith the SCS curve-number equation. Hydrological Processes 18:2757–2771.

Mehta VK, Walter MT, Brooks ES, Steenhuis TS, Walter MF, John-son M, Boll J, Thongs D. 2004. Evaluation and application of SMRfor watershed modeling in the Catskill Mountains of New York State.Environmental Modeling & Assessment 9(2): 77–89.

MoWR (Ministry of Water Resources). 2002. Ethiopian Water SectorStrategy . MoWR: Addis Ababa.

Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptualmodels: Part I a discussion of principles. Journal of Hydrology 10:282–290.

Neitsch SL, Arnold JG, Kiniry JR, Williams JR. 2005. Soil andWater Assessment Tool: Theoretical Documentation—Version 2005 .

Grassland, Soil and Water Research Laboratory, Agricultural ResearchService: Temple, TX.

NCDC. 2007. Global summary of the day. National Climatic DataCenter’s Climate Data Online. 24 November 2007.

Olivera F, Valenzuela M, Srinivasan R, Choi J, Cho H, Koka S,Agrawal A. 2006. ArcGIS-SWAT: a geodata model and GIS interfacefor SWAT. Journal of the American Water Resources Association 42:295–309.

Page T, Haygarth PM, Beven KT, Joynes A, Butler T, Keeler C, Freer J,Owens PN, Wood GA. 2005. Spatial variability of soil phosphorus inrelation to the topographic index and critical source areas: samplingfor assessing risk to water quality. Journal of Environmental Quality34: 2263–2277.

Setegn SG, Srinivasan R, Dargahi B. 2008. Hydrological modeling inthe Lake Tana basin, Ethiopia using SWAT model. Open HydrologyJournal 2: 49–62.

Schneiderman EM, Pierson DC, Lounsbury DG, Zion MS. 2002. Model-ing the hydrochemistry of the Cannonsville watershed with generalizedwatershed loading functions (GWLF). Journal of the American WaterResources Association 38(5): 1323–1347.

Schneiderman EM, Steenhuis TS, Thongs DJ, Easton ZM, Zion MS,Neal AL, Mendoza GF, Walter MT. 2007. Incorporating variablesource area hydrology into a curve-number-based watershed model.Hydrological Processes 21: 3420–3430.

Sharma SK, Mohanty BP, Zhu JT. 2006. Including topography andvegetation attributes for developing pedotransfer functions. SoilScience Society of America Journal 70: 1430–1440.

Sheridan JM, Shirmohammadi A. 1986. Application of curve numberprocedure on coastal plain watersheds. Proceedings from the 1986Winter Meeting of the American Society of Agricultural Engineers.Paper No. 862505 . ASAE: Chicago, IL.

Steenhuis TS, Collick AS, Easton ZM, Leggesse ES, Bayabil HK,White ED, Awulachew SB, Adgo E, Ahmed AA. 2009. Predictingdischarge and erosion for the Abay (Blue Nile) with a simple model.Hydrological Processes 23: 3728–3737.

Steenhuis TS, Winchell M, Rossing J, Zollweg JA, Walter MF. 1995.SCS runoff equation revisited for variable-source runoff areas. Journalof Irrigation and Drainage Engineering-ASCE 121(3): 234–238.

Tarboton DG. 1997. A new method for the determination of flowdirections and contributing areas in grid digital elevation models. WaterResources Research 33(2): 309–319.

Tebebu TY, Abiy AZ, Dahlke HE, Easton ZM, Zegeye AD, TilahunSA, Collick AS, Kidnau S, Moges S, Dadgari F, Steenhuis TS. 2010.Surface and subsurface flow effects on permanent gully formation andupland erosion near Lake Tana in the northern Highlands of Ethiopia.Hydrology and Earth System Sciences Discussions 7: 3837–3878.

Thompson JA, Pena-Yewtukhiw EM, Grove JH. 2006. Soil landscapemodeling across a physiographic region: topographic patterns andmodel transportability. Geoderma 133: 57–70.

Tolson BA, Shoemaker CA. 2005. Dynamically dimensioned searchalgorithm for computationally efficient watershed model calibration.Water Resource Research 43: W01413–W01429.

USDA-SCS. (Soil Conservation Service). 1972. Part 630 hydrology inthe national engineering handbook. Section 4, Chapter 10.

USDA-NRCS. 2000. Soil survey geographic (SSURGO) database forDelaware county. New York.

USDA-NRCS. 2004. Estimation of direct runoff from storm rainfall.National Engineering Handbook. Part 630: Hydrology.

Walter MT, Mehta K, Marrone AM, Boll J, Gerard-Merchant P, Steen-huis TS, Walter MF. 2003. A Simple estimation of the prevalence ofHortonian flow in New York city’s watersheds. Journal of HydrologicEngineering 8(4): 214–218.

Wang X, Shang S, Yang W, Melesse AM. 2008. Simulation of anagricultural watershed using an improved curve number method inSWAT. Transactions of the American Society of Agricultural andBiological Engineers 51(4): 1323–1339.

White ED, Feyereisen GW, Veith TL, Bosch DD. 2009. Improving dailywater yield estimates in the Little River watershed: SWAT adjustments.Transactions of the American Society of Agricultural and BiologicalEngineers 52(1): 69–79.

Copyright 2010 John Wiley & Sons, Ltd. Hydrol. Process. 25, 915–925 (2011)

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