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DEVELOPMENT AND APPLICATION OF A PHYSICALLY BASED 1 LANDSCAPE WATER BALANCE IN THE SWAT MODEL 2

3 Eric D White1, Zachary M. Easton1, Daniel R. Fuka1, Amy S.Collick2, Enyew 4 Adgo3,4, Matthew McCartney5, Seleshi B. Awulachew5, Yihenew G. Selassie3,4, 5

and Tammo S. Steenhuis1,2 6 7

1Dept. of Biological and Environmental Engineering, Cornell University, Ithaca, 8 NY, USA 9

2 Faculty of Engineering, Bahir Dar University, Bahir Dar, Ethiopia 10 3 Faculty of Agriculture, Bahir Dar University, Bahir Dar, Ethiopia 11

4Amhara Regional Agricultural Institute, Bahir Dar, Ethiopia 12 5 International Water Management Institute, Nile Basin and East Africa Office, 13

Addis Ababa, Ethiopia 14

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ABSTRACT 30

Watershed scale hydrological and biogeochemical models rely on the correct 31

spatial-temporal prediction of processes governing water and contaminant 32

movement. The Soil and Water Assessment Tool (SWAT) model, one of the most 33

commonly used watershed scale models, uses the popular Curve Number (CN) 34

method to determine the respective amounts of infiltration and surface runoff. While 35

appropriate for flood forecasting in temperate climates, the CN method has been 36

shown to be less than ideal in many situations (e.g., monsoonal climates and areas 37

dominated by variable source area hydrology). The CN model is based on the 38

assumption that there is a unique relationship between the average moisture content 39

and the CN for all hydrologic response units, and that the moisture content 40

distribution is similar for each runoff event, which in many regions is not the case. A 41

physically based water balance was developed and coded in the SWAT model to 42

replace the CN method of runoff generation. To compare this new water balance 43

SWAT (SWAT-WB) to the original CN based SWAT (SWAT-CN), two watersheds 44

were initialized: one in the headwaters of the Blue Nile in Ethiopia and one in the 45

Catskill Mountains of New York State. In the Ethiopian watershed streamflow 46

predictions were significantly better using SWAT-WB than SWAT-CN (Nash-Sutcliffe 47

efficiencies (NSE) of 0.76 and 0.67, respectively). In the temperate Catskills, SWAT-48

WB and SWAT-CN predictions were approximately equivalent (NSE>0.5). 49

Interestingly, and perhaps more importantly, the spatial distribution of runoff 50

generating areas differed greatly between the two models, with SWAT-WB providing 51

a more realistic distribution of saturated and thus runoff source areas. These results 52

suggest that the addition of a water balance in SWAT significantly improves model 53

predictions in monsoonal climates, and provides equally acceptable levels of 54

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accuracy in stream flow prediction under temperate northeastern USA conditions. 55

Spatially distributed watershed areas are predicted realisticly with SWAT-WB 56

1

Introduction 57

Non-point source runoff can contribute significant quantities of nutrients, 58

chemicals, and sediments to stream and water bodies. To locate these “non-59

point” sources of pollution in a landscape, many watershed managers and 60

researchers frequently use watershed scale models. One of the most 61

commonly used watershed scale models is the US Department of Agriculture 62

(USDA) Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998). 63

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SWAT, like any water quality model, must first accurately simulate hydrologic 65

processes before it can realistically predict pollutant transport. Many different 66

approaches to modeling hydrologic processes have been presented in the 67

scientific literature over the past several decades, but SWAT currently uses 68

two methods to model surface runoff: the Curve Cumber (CN) (USDA-SCS, 69

1972) and the Green-Ampt routine (Green and Ampt, 1911).The Green-Ampt 70

method is a physically-based infiltration excess, rainfall-runoff model. 71

Therefore, Green-Ampt is not suitable for regions where the rainfall rate 72

seldom exceeds the saturated conductivity of the soil, such as in the 73

Northeastern USA (Walter et al., 2000). As a result, the empirically based CN 74

method, due to its ease of use and simplifying assumptions, is the most 75

commonly used runoff routine in the SWAT model (King et al., 1999; 76

Gassman, 2005). 77

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SWAT and other CN-based models are frequently used on watersheds around 79

the world where the climate and landscape vary greatly from that of the United 80

States, where the empirical relationships used in the CN were developed. 81

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SWAT has been applied in areas ranging from China, India, Australia, the UK, 82

France, Belgium, Algeria, Tunisia, Italy, and Greece with little recognition that 83

the underlying runoff calculations were neither developed nor validated for 84

these regions (Gassman et al., 2007). 85

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Another region where CN models have been applied is in the Blue Nile Basin 87

of Africa. Located in the monsoonal climate of the Ethiopian highlands, the 88

temporal runoff dynamics in the Blue Nile Basin are poorly captured by the CN 89

method, which assumes that the moisture content distribution of the watershed 90

is similar for each runoff event (Steenhuis et al., 2009; Collick et al., 2009). 91

Previous work has shown that for a given amount of rain, runoff volumes will 92

vary throughout the rainy season. Liu et al. (2008) demonstrated that in the 93

Ethiopian Highlands less runoff was generated at the beginning of the rainy 94

season as compared to the same rain event at the end of the season. Lui et al. 95

(2008) showed that when watershed discharge is plotted against effective 96

precipitation (i.e., precipitation minus potential evapotranspiration) there is a 97

relatively strong, linear relationship, indicating that the proportion of the rainfall 98

that became runoff was constant during the remainder of the rainy season. 99

These dynamics cannot be predicted by the CN, and are, in fact, in direct 100

contrast to the official method’s literature, which states that there is no 101

correlation between antecedent precipitation and a watershed’s maximum 102

retention beyond five days (NRCS, 2004). 103

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In order to correct the CN to better work in monsoonal climates, various 105

temporally-based values and initial abstractions have been suggested. For 106

instance, Bryant et al. (2006) suggest that a watershed’s initial abstraction 107

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should vary as a function of storm size. While this is a valid argument, the 108

introduction of an additional variable reduces the appeal of the one-parameter 109

CN model. Kim and Lee (2008) found that SWAT was more accurate when CN 110

values were averaged across each day of simulation, rather than using a CN 111

that described moisture conditions only at the start of each day. White et al. 112

(2009) showed that SWAT model results improved when the CN was changed 113

seasonally to account for watershed storage variation due to plant growth and 114

dormancy. Wang et al. (2008) improved SWAT results by using a different 115

relationship between antecedent conditions and watershed storage. While 116

these variable CN methods improve runoff predictions, they are not easily 117

generalized for use outside of the watershed they are tested for due to the fact 118

that the CN method is a statistical relationship and is not physically based. 119

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In many regions, surface runoff is produced by only a small portion of a 121

watershed that expands with an increasing amount of rainfall. This concept is 122

often referred to as a variable source area (VSA); a phenomenon actually 123

envisioned by the original developers of the CN method (Hawkins, 1979), but 124

never implemented in the original CN method as used by the NRCS. Since the 125

method’s inception, numerous attempts have been made to justify its use in 126

modeling VSA-dominated watersheds. These adjustments range from simply 127

assigning different CNs for wet and dry portions to correspond with VSAs 128

(Sheridan and Shirmohammadi, 1986; White et al., 2009), to full 129

reinterpretations of the original CN method (Hawkins, 1979; Steenhuis et al., 130

1995; Schneiderman et al., 2008; Easton et al., 2008). 131

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To determine what portion of a watershed is producing surface runoff for a 133

given precipitation event, the re-interpretation of CN method presented by 134

Steenhuis et al. (1995) and incorporated into SWAT by Easton et al. (2008) 135

assumes that rainfall infiltrates when the soil is unsaturated or runs off when 136

the soil is saturated. It has been shown that this saturated contributing area of 137

a watershed can be accurately modeled spatially by linking this re-138

interpretation of the CN method with a topographic index (TI), similar to those 139

used by the topographically driven TOPMODEL (Beven and Kirkby, 1979; 140

Lyon et al., 2004). This linked CN-TI method has since been used in multiple 141

models of watersheds in the northeastern US, including the Generalized 142

Watershed Loading Function (GWLF) (Schneiderman et al., 2007) and SWAT 143

(Easton et al., 2008). While the re- conceptualized CN model is applicable in 144

temperate US climates, it is limited by the fact that it imposes a distribution of 145

storages throughout the watershed that need to fill up before runoff occurs. 146

While this limitation does not seem to affect results in temperate climates, it 147

results in poor model results in monsoonal climates. 148

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SWAT-VSA, the CN-TI adjusted version of SWAT (Easton et al., 2008), 150

returned hydrologic simulations as accurate as the original CN method, 151

however the spatial predictions of runoff producing areas and as a result the 152

predicted phosphorus export were much more accurate. While SWAT-VSA is 153

an improvement upon the original method in watersheds where topography 154

drives flows, ultimately, it still relies upon the CN to model runoff processes 155

and therefore is limited when applied to the monsoonal Ethiopian highlands. 156

Water balance models are relatively simple to implement and have been used 157

frequently in the Blue Nile Basin (Johnson and Curtis, 1994; Conway, 1997; 158

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Ayenew and Gebreegziabher, 2006; Liu et al., 2008; Kim and Kaluarachchi, 159

2008; Steenhuis et al., 2009). Despite their simplicity and improved watershed 160

outlet predictions they fail to predict the spatial location of the runoff 161

generating areas. Collick et al. (2009), and to some degree Steenhuis et al. 162

(2009), present semi-lumped conceptualizations of runoff producing areas in 163

the water balance model. SWAT, a more distributed (semi-distributed) model 164

can predict these runoff source areas in greater detail, assuming the runoff 165

processes are correctly modeled. 166

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We propose incorporating elements from the spatially adjusted water balance 168

models, as proposed by Lyon et al. (2004) and implemented by Easton et al. 169

(2008), into SWAT-VSA. To accomplish this, we develop and test a CN-free 170

version of SWAT. This new version of SWAT, SWAT-WB, calculates runoff 171

volumes based on the available soil storage capacity of a soil, and then 172

partitions excess moisture to runoff and infiltrating fractions. This can lead to 173

more accurate simulation of where runoff occurs in watersheds dominated by 174

saturation-excess processes. Both the original CN method used by SWAT and 175

the new, water balance (SWAT-WB) method are tested on two watersheds 176

that vary widely in climate, geology, and data availability: one in the 177

monsoonal Blue Nile Basin in Ethiopia, and one in the Catskill Mountains of 178

New York State. 179

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Model Overview 180

Summarized SWAT Description 181

SWAT is a basin-scale model designed to simulate hydrologic processes, 182

nutrient cycling, and sediment transport throughout a watershed. SWAT has 183

been applied to catchments ranging from 0.15 km2 (Chaney et al., 2003) to as 184

large as 491,700 km2 (Arnold et al., 2000). To initialize the model, SWAT 185

requires soils data, land use/management information, and elevation data to 186

drive flows and direct sub-basin routing. The hydrologic response unit (HRU) 187

is the smallest unit in the SWAT model and is used to simulate processes 188

such as rainfall, runoff, infiltration, plant dynamics (including uptake of water 189

and nutrients, biomass, etc.), erosion, nutrient cycling, and leaching of 190

pesticides and nutrients. Traditionally, HRUs are defined by the coincidence of 191

soil type (Hydrologic Soil Group, USDA 1972) and land use. The predictions 192

from each HRU are aggregated for each subbasin, and routed through the 193

internal channel network. Simulations require meteorological input data 194

including precipitation, temperature, wind, humidity and solar radiation. All of 195

these inputs are initialized using a GIS system (ArcGIS 9.2). More detail on 196

SWAT can be found at http://www.brc.tamus.edu/swat/doc.html. 197

Model Development 198

Original Curve Number Approach 199

Historically, when initializing SWAT a CN is assigned for each specific 200

landuse/soil combination in the watershed, and these values are read into the 201

model. SWAT then calculates upper and lower limits for each CN following a 202

probability function described by the NRCS to account for varying antecedent 203

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moisture conditions (CN-AMC) (USDA-NRCS, 2004). SWAT determines a CN 204

for each simulated day by using this CN-AMC distribution in conjunction with 205

daily soil moisture values determined by the model. This daily CN is then used 206

to determine a theoretical storage capacity, S, of the watershed for each day 207

the model is run. The storage is then indirectly used to calculate runoff 208

volume, Q, via: 209

( )( )

2

a

a

P IQ

P I S

−=

− + eq. 1 210

where S is watershed storage, P is precipitation, and Ia is initial abstraction. All 211

terms are in mm of water, and by convention Ia is assumed to be equal to 212

0.2*S. 213

Water Balance Approach 214

A daily soil water balance was used to determine the saturation deficit of each 215

hydrologic response unit (HRU) in SWAT, which was then used, instead of the 216

CN method, to determine daily runoff volume. To replace the CN, a simple soil 217

profile water balance was calculated for each day of simulation. While SWAT’s 218

soil moisture routine greatly simplifies processes that govern water movement 219

through porous media (in particular, partly-saturated regions), for a daily basin 220

scale model the approach is generally acceptable (Guswa et al., 2002). Thus 221

the model already provides a convenient platform on which to incorporate a 222

water balance. SWAT’s inherent soil moisture routines are then used by 223

SWAT-WB to determine the degree of saturation-deficit for each soil profile for 224

each day of simulation. This saturation-deficit (in mm H2O) is termed the 225

available soil storage, τ: 226

227

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( )EDCτ ε θ= −

eq. 2 228

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where EDC is the effective depth of the soil profile (unitless), ε is the total soil 230

porosity (mm), and θ is the volumetric soil moisture for each day (mm). The 231

porosity is a constant value for each soil type, whereas θ varies by the day 232

and is determined by SWAT’s soil moisture routines. The effective depth 233

coefficient, EDC, a parameter ranging from zero to one, is used to partition soil 234

moisture in excess of ε into infiltrating and runoff fractions. By including this 235

adjustment to the available storage, the amount of water able to infiltrate each 236

day is controlled by the EDC. EDC is spatially varied based on a saturation 237

risk. EDC values approaching one are assigned to regions expected to 238

produce little saturation excess runoff while values approaching zero indicate 239

an area likely to produce large saturation excess volumes. This spatially 240

adjusted available storage is then used to determine what portion of rainfall 241

events will infiltrate and what portion will runoff: 242

243

ττ τ

<=

− ≥

0, if

, if

PQ

P P eq. 3 244

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The available storage, τ, is calculated each day prior to the start of any rain 246

event. Once precipitation starts, a portion of the rain, equal in volume to τ, will 247

infiltrate the soil. If the rain event is larger in volume than τ, the soil profile will 248

saturate and surface runoff will occur. If the rainfall is less than τ, the soil is un-249

saturated and there will be no surface runoff and SWAT’s internal soil moisture 250

routing will calculate the flux. 251

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HRU Definition 252

Traditionally, HRUs are defined in SWAT as being unique occurrences of soil 253

type, land cover, and slope class. Any parcels of land within one subbasin that 254

share the same combination of these three features will be considered one 255

HRU. SWAT models all landscape processes for each unique HRU in the 256

watershed, independent of position within each subbasin. In basins dominated 257

by VSA hydrology this HRU definition has been shown to be less than ideal for 258

describing the spatial and temporal evolution of hydrologic processes 259

(Schneiderman et al., 2007; Easton et al., 2008). In VSA watersheds, runoff-260

generating areas are likely to occur in portions of the landscape with shallow, 261

low conductive soils, large contributing areas, mild slopes, or any combination 262

of the three. While SWAT’s inclusion of slope classes in HRU delineation 263

begins to address these issues, there is currently no way to include upslope 264

contributing area while defining HRUs. To correct for this, a soil topographic 265

index (STI) was integrated with existing soils data in the SWAT HRU definition 266

process (e.g., Easton et al., 2008). 267

268

Topographic indices and their various derivatives have been used to model 269

runoff-contributing areas for quite some time [e.g., TOPMODEL (Beven and 270

Kirkby, 1979]. Recently, soil topographic indices have been incorporated into 271

CN-based watershed models for use in VSA dominated regions (Lyon et al., 272

2004; Schneiderman et al., 2007; Easton et al., 2008). SWAT-VSA integrated 273

STIs into SWAT in order to improve determination of runoff-generating areas 274

and the subsequent nutrient loads from these areas in the Catskills Mountains 275

of New York State (Easton et al., 2008). SWAT-VSA provided more accurate 276

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predictions of runoff source areas (as validated by distributed measures in the 277

watershed) than the original SWAT, thus, we included the HRU definition 278

process similar to SWAT-VSA in SWAT-WB. 279

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To initialize SWAT-WB the first step was to create a soil topographic index for 281

the watershed being modeled. The STI is defined as: 282

283

( )β

=

ln

tans

ASTI

DK eq. 4 284

The upslope contributing area, A, and the slope, tan(β), are both obtained 285

from a DEM, while the soil depth, D, and saturated hydraulic conductivity, Ks, 286

are obtained from a soil survey. We assume that STI values relate to a 287

location’s likelihood of saturation, and therefore the likelihood to contribute 288

surface runoff. Higher STI values are the result of either a large contributing 289

area, or small values for slope, soil depth, or saturated conductivity, and 290

therefore are indicative of areas with a higher probability for saturation. 291

Following the process outlined for SWAT-VSA (Easton et. al., 2008), an 292

areally weighted STI (e.g., wetness classes) is used to represent a location’s 293

likelihood to saturate. The wetness classes determined for the two watersheds 294

used in this study are shown in Fig. 1 for the Ethiopian Watershed and Fig. 2 295

for the New York watershed. This wetness class map is then substituted for 296

the soils map in the HRU definition process. While the wetness classes can be 297

used in HRU delineation instead of a soil map, SWAT still requires specific soil 298

properties that are commonly associated with the soils map (e.g., SSURGO 299

Database).Thus, in SWAT-WB soil properties required by SWAT were areally 300

weighted and averaged for each wetness class. This practice will not 301

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drastically affect model results for two reasons. First, in Ethiopia, soil survey 302

information is rare or nonexistent, and, to our knowledge, no defined database 303

exists that would contain the parameters needed by SWAT. Thus SWAT-WB 304

utilized the UN-FAO’s World’s Soil Map (supplemented by literature values) as 305

the base map(FAO-AGL, 2003), which classifies only five distinct soil types in 306

all of the 1270 km2 Blue Nile sub-catchment which was modeled. This soils 307

data, with such a course spatial resolution, will not be adversely affected by 308

the averaging process used in SWAT-WB. Second, in New York State, where 309

soils information is more readily available, soil formation (in glaciated areas) is 310

at least partially driven by topography (Page et al., 2005; Sharma et al., 2006; 311

Thompson et al., 2006; Easton et al., 2008). Therefore, averaging across 312

topographic features with the wetness index should not pose any problems. 313

Watershed Descriptions 314

Gumera Watershed, Blue Nile Basin, Ethiopia 315

SWAT-WB was tested on the Gumera River watershed, a heavily cultivated 316

region in the Ethiopian highlands. Located approximately 30 km northeast of 317

Bahir Dar (11.83°N, 37.63°E); this 1270 km2 watershed drains into Lake Tana, 318

the headwaters of the Blue Nile River (Fig. 3). Land use in the Gumera 319

watershed consists of 96% agriculture and 4% brush (or pasture). Elevation 320

(determined from a 90 meter DEM ranged from 1797 to 3708 meters above 321

sea level with slopes ranging from 0% to 79%. Predominant soils were 322

gathered from the FAO World Soils map and were classified as haplic and 323

chromic luvisols (58% and 22%, respectively). Other soils present in the basin 324

were eutric fluvisols (8%), eutric leptosols (8%), eutric vertisols (3%), with 325

minimal areas classified as urban (>1%) (FAO-AGL, 2003). 326

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Precipitation and temperature data were gathered from the National 328

Meteorological Agency of Ethiopia for the Debre Tabor station, the closest rain 329

gauge to the Gumera basin. Daily precipitation data from 1992 through 2003 330

was used for model calibration and validation. Other required climatic data 331

included relative humidity, wind speed, and solar radiation. These data were 332

obtained for the nearby city of Bahir Dar through the United States National 333

Climatic Data Center (NCDC, 2007). 334

Townbrook Watershed, Catskills, New York 335

SWAT-WB was also tested on the Townbrook watershed (Fig. 2) in the United 336

States; a 37 km2 sub-catchment of the Cannonsville Reservoir Basin. The 337

region is typified by steep to moderate hillslopes of glacial origins with shallow 338

permeable soils, underlain by a restrictive layer. The climate is humid with an 339

average annual temperature of 8ºC and average annual precipitation of 1123 340

mm. Elevation in the watershed ranges from 493 to 989 m above mean sea 341

level. The slopes are quite steep with a maximum of 91%, and a mean of 21%. 342

Soils are mainly silt loam or silty clay loam with soil hydrologic group C ratings 343

(USDA-NRCS, 2000). Soil depth ranges from less than 50 cm to greater than 344

1 m and is underlain by a fragipan restricting layer (e.g. coarse-loamy, mixed, 345

active, mesic, to frigid Typic Fragiudepts, Lytic or Typic Dystrudepts common 346

to glacial tills) (Schneiderman et al., 2002). The lowland portion of the 347

watershed is predominantly agricultural, consisting of pasture and row crops 348

(20%) or shrub land (18%) while the upper slopes are forested (60%). Water 349

and wetlands comprise 2%. Impervious surfaces occupy <1% of the 350

watershed and were thus excluded from consideration in the model. Several 351

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studies in this watershed or nearby watersheds have shown that variable 352

source areas control overland flow generation (Frankenberger et al., 1999; 353

Mehta et al., 2004; Lyon et al., 2006a, 2006b; Schneiderman et al., 2007; 354

Easton et al., 2008) and that infiltration-excess runoff is rare (Walter et al., 355

2003). 356

Model Calibration 357

To calibrate both the SWAT-WB and SWAT-CN models for Gumera and 358

Townbrook, we utilized the Dynamically Dimensioned Search (DDS) algorithm 359

(Tolson and Shoemaker,2005). The DDS calibration routine allows for 360

parameters to be calibrated at the watershed, subbasin, HRU, or wetness 361

class level, which in turn allowed for EDC to be calibrated separately for each 362

wetness class. For SWAT-CN the CN was calibrated for each wetness class 363

instead of the EDC. In addition 11 other hydrologic parameters were calibrated 364

in both models. 365

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Streamflow at the Gumera watershed outlet was calibrated over a period of 367

eight years, from 1996 to 2003, and streamflow in Townbrook was calibrated 368

from 1998 to 2002. 369

Model Validation 370

Streamflow data from 1992 through 1995 was used to validate the Gumera 371

model. For Townbrook, streamflow data from 2002 through 2004 was used to 372

validate the model. To test SWAT-CN’s and SWAT-WB’s abilities to correctly 373

predict distributed hydrology, we used measurements by Lyon et al. (2006a) of 374

height of water table above the restricting layer for a section of the Townbrook 375

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watershed. Briefly, 44 pieziometric data loggers, installed to depths of ~50 cm, 376

recorded the water table depth in 15 minute intervals from April 2004 to 377

September 2004.The field site encompassed five wetness index classes (Fig. 378

3) and three land use types, pasture (PAST), shrub (RNGB), and mixed forest 379

(FRST). To compare the measured and SWAT-VSA water table heights the 380

piezometer data were averaged across index classes; there were between two 381

and 32 piezometers per index (i.e., two piezometers on index class six, 32 on 382

index class 10, etc).To compare measured water table heights with SWAT-CN 383

water table heights, we averaged across land use; there were four to 32 384

measurements per land use. SWAT (and SWAT-WB) reports soil water in mm 385

of water integrated over the soil profile (i.e. cumulative water depth for all soil 386

layers). Thus, we converted the model predicted mm of soil water to and 387

equivalent depth by dividing by the SSURGO reported porosity and assumed 388

the SSURGO reported soil depth represented the depth to the restricting layer. 389

According to the SSURGO data base, the depth of the local restricting layer is 390

1.2 – 1.4 m. 391

Model Evaluation 392

Criteria used to assess the ability of the models to predict discharge in 393

Gumera and Townbrook included: a visual comparison between the modeled 394

and the observed hydrographs, Nash-Sutcliffe Efficiencies (NSE) (Nash and 395

Sutcliffe, 1970), and coefficient of determination, R2. 396

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Results 397

Model Comparison 398

To determine if SWAT-WB was indeed a more accurate than the standard CN-399

based SWAT, the two models were calibrated and validatedusing to same 400

automatic procedure for Gumera antiTown brook watersheds 401

Gumera Basin 402

Following calibration, SWAT-WB returned more accurate results than both of 403

the SWAT-CN models of the Gumera watershed(based on statistics and visual 404

comparison of hydrographs, Fig. 4) than SWAT-CN.. A daily NSE value of 405

0.70 for the calibration period was achieved, with an R2 of 0.71. SWAT-WB 406

accuracy increased for the validation period, with NSE and R2 values of 0.76 407

and 0.81, respectively (Table 1 and Fig. 4). SWAT-CN model of Gumera 408

previously published by Stegen et al (2008) can be used s a benchmark. They 409

used a twelve year calibration period that resulted in a model with a daily NSE 410

of 0.61 and a R2 of 0.71. Validation results for their model returned a NSE of 411

0.61 and an R2 of 0.70 similar to our results for SWAT-CN. Additionally, intra-412

watershed runoff producing areas were modeled with higher spatial resolution 413

than SWAT-CN due to the inclusion of the STI-based HRU delineation 414

process as introduced in SWAT-VSA (Easton et al., 2008). 415

Perhaps more interesting is how SWAT-WB and SWAT-CN differ in the 416

predicted distribution of runoff in the watershed. For one large storm in 417

October, 1997 (104 mm of rain), SWAT-CN predicted that all HRUs within the 418

watershed would contribute runoff; with a minimum depth of 17 mm of runoff 419

and a maximum of 71 mm (Fig. 5A). For the same storm, SWAT-WB predicted 420

that some HRUs would produce no runoff, while others produced as much as 421

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97 mm of runoff (Fig. 5B). Both models predicted some surface runoff for 422

some upland areas, but SWAT-CN predicted much less runoff being 423

generated in the low-lying, flatter areas near the watershed outlet, where 424

SWAT-WB predicted the most runoff. 425

Townbrook Watershed 426

SWAT-WB results for Townbrook were compared to results from SWAT-CN. 427

Predicted streamflow for the SWAT-CN Townbrook model resulted in a daily 428

NSE and R2 of 0.43 and 0.59, respectively, for calibration, and 0.62 and 0.69, 429

respectively, for validation. Thus in the Townbrook watershed, SWAT-WB 430

outperformed SWAT-CN during the calibration period. However, SWAT-WB’s 431

validation period was not as accurate as its calibration period, while the CN 432

based model performed better during validation. A visual comparison of 433

SWAT-WB’s hydrograph with the measured hydrograph (Figs. 4 and 6) 434

indicates that the model performs fairly well for the Townbrook watershed, a 435

fact supported by the reasonably high daily NSE values of 0.64 and 0.52 for 436

the calibration and validation periods, respectively (Table 2). 437

438

Similar to the Gumera results, differences in spatial distribution of runoff is 439

evident when the same event from November 2003 is compared between the 440

Townbrook models (Figs. 7A and B). As expected, SWAT-CN predicts some 441

surface runoff from the majority of the watershed and it is clearly driven by 442

differences in landuse, whereas SWAT-WB predicts substantial portions of the 443

watershed producing no surface runoff, not surprising considering the 444

emphasis the models place on topographic position as it pertains to runoff 445

generation. For this particular storm SWAT-WB predicted that most of the 446

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wetness classes in the low lying areas of the watershed would be saturated at 447

the start of this event, leading to these low-lying wet areas producing nearly 448

identical volumes of runoff (i.e. almost the entire volume of precipitation). 449

450

Comparing the SWAT-CN and SWAT-WB predicted water table heights to 451

those measured by Lyon et al. (2006) shows that the SWAT-WB predicted soil 452

water table height agreed with measurements across the monitored hill side in 453

the watershed with R2 = 0.68 (Fig. 7a). There was a slight tendency for SWAT-454

WB to under predict water table height for large water table heights (Fig. 7a), 455

and slightly under predict small water table heights. SWAT-CN, however, 456

systematically under predicted water table height for all conditions (Fig. 7b). 457

Discussion 458

While the CN, a loosely constrained calibration parameter in most applications 459

was removed from SWAT; the WB routine adds another calibration parameter 460

(EDC), negating the potential reduction in calibrated parameters. The need to 461

calibrate EDC became evident when the saturation deficit for each soil profile 462

was calculated. For instance, if the entire soil profile was included in the 463

calculation of the available storage, τ, the model would not simulate any 464

surface runoff, all precipitation would infiltrate. If only the uppermost soil 465

horizon were used to determine τ, then essentially all precipitation would 466

runoff, resulting in no infiltration. By examining a range of soil depths used to 467

calculate τ, it became clear that the total depth used to determine surface 468

runoff had to be adjusted; hence the introduction of EDC¸ the effective depth 469

coefficient. This issue has been realized in previous iterations of water balance 470

models, and many previous water balance models of the Blue Nile were 471

18

limited to application at a monthly time-step due to inabilities to successfully 472

partition moisture between baseflow, interflow, and surface runoff (Johnson 473

and Curtis,1994; Conway, 1997). When no EDC was used in SWAT-WB, 474

these same issues were present; high τ values resulted in all moisture as 475

baseflow, and when τ was too high, all moisture in excess of soil capacity 476

became surface runoff with minimal baseflow contributions. 477

478

Interestingly, the EDC solution to these issues is remarkably similar to a recent 479

water balance model developed for the Blue Nile by Kim and Kaluarachchi 480

(2008) that combines a water balance with a traditional tank model. To 481

differentiate between surface and various subsurface flows, Kim and 482

Kaluarachchi (2008) developed a model using two ‘tanks’. The upper tank, 483

described by an upper zone soil moisture term, was used to calculate surface 484

runoff, and a lower zone term was used to model baseflow. The upper layer 485

would produce no surface runoff until a “runoff orifice” depth was filled by 486

rainfall. This upper zone soil layer with its runoff orifice depth is analogous to 487

SWAT-WB’s EDC term; both parameters acknowledge that in saturation 488

excess dominated areas only a certain portion of the soil profile plays a role in 489

runoff generation. 490

491

Clear improvements were made to SWAT in the Ethiopian watershed by 492

removal of the CN, however the results are not as definitive for the Townbrook 493

watershed in New York State. While SWAT-WB has substantially higher model 494

accuracy for the calibration period, it does not perform as well during validation 495

as SWAT-CN. By comparing the hydrograph from the Townbrook outlet (Fig. 496

6) and the model statistics, it is clear that SWAT-WB performs at least as well 497

19

as SWAT-CN. Thus in case where rainfall is evenly distributed throughout to 498

year both models perform equally well in predicting discharge as would have 499

been expected from earlier studies ( Steenhuis it al Easton 500

In Townbrook, Easton et al. (2008) showed that the inclusion of the STI based 501

wetness index better captured the spatial distribution of water table depths 502

and, by extension, runoff producing areas. SWAT-WB provided a similar level 503

of accuracy in predicting water table heights (Fig. 7). While we have no 504

distributed runoff data for either of the watersheds, including the wetness 505

index in SWAT-WB resulted in what appears to be more a realistic distribution 506

of runoff generating areas than SWAT-CN. Indeed, in VSA dominated 507

watersheds, runoff generation is closely related to soil moisture levels (as 508

controlled by perched water table levels), which on turn is governed, to a large 509

extent, by topographic position. In many SWAT-CN applications the location of 510

an HRU within each subbasin is not a concern and thus, any locations that 511

share landuse and soil are treated identically, regardless of its topographic 512

position and the corresponding likelihood to produce runoff. In SWAT-WB, 513

STIs were used to link HRUs by similar topographic position, giving model 514

users the capability to examine intra-watershed runoff dynamics. 515

516

This difference in spatial distribution of runoff generating areas predicted by 517

SWAT-WB and SWAT- CN is clearly demonstrated for both Gumera (Fig. 5) 518

and Townbrook in Fig. 8. For the same large storm event in the Gumera basin 519

(Fig. 5), SWAT-WB did not generate surface runoff for all HRUs, whereas 520

SWAT-CN predicted that the entire watershed would contribute surface runoff 521

more or less evenly. Due to the imposed topographical controls, SWAT-WB 522

predicted that the wettest portions of the watershed would contribute more 523

20

runoff than drier areas. In addition to the fact that SWAT-CN predicts a nearly 524

uniform runoff volume for the entire watershed, there are two other points of 525

interest that should be discussed. First, is the fact that SWAT-CN predicts that 526

the area nearest Gumera’s outlet produces the least amount of surface runoff, 527

exactly opposite of SWAT-WB’s results which predict that this area produces 528

the highest runoff volumes. These differences between the models can easily 529

be explained by the inclusion of slope in the HRU delineation (and therefore 530

EDC calibration). Again, holding with VSA principles, SWAT-WB assumes that 531

these flat, near-stream regions will wet up and contribute the most runoff, 532

because of reduced lateral flow whereas SWAT-CN treats these HRUs the 533

same as any upland region with the same soil and land cover. The second 534

interesting point is that both models predict that certain upland regions 535

generating a significant portion of surface runoff from the test storm. These 536

soils have a low saturated conductivity. For SWAT-CN this result in a high 537

curve number and for SWAT-WB an increase into STI value. Both increase 538

runoff. 539

540

SWAT-CN and most other watershed models have been developed for 541

temperate climates where rainfall is generally well distributed throughout the 542

year. Utilizing models developed in a temperate climate for Ethiopia 543

conditions, with a monsoonal climate, is problematic. Temperate models 544

assume that there is a nearly unique relationship between precipitation 545

amounts or intensity and runoff generated. This is not the case for Ethiopia as 546

demonstration by the results of Liu et al. (2008) where for three watersheds 547

with more than 16 years of record, the rainfall relationship was far from unique. 548

The first rains after the dry season all infiltrate and nearly no runoff is 549

21

generated. As the rainfall season progresses more and more rainfall becomes 550

runoff. Since the intensity of the rain did not affect the runoff amounts for a 551

given storm, Liu et al.(2008) concluded that the runoff mechanism was 552

dominated by saturation excess processes. 553

554

Water balance models are consistent with saturation excess runoff process 555

because the runoff is related to the available watershed storage capacity and 556

the amount of precipitation. The implementation of water balances into runoff 557

calculations in the Blue Nile Basin is not a novel concept and others have 558

shown that water balance type models often perform better than more 559

complicated models in Ethiopian type landscapes (Johnson and Curtis, 1994; 560

Conway, 1997; Ayenew and Gebreegziabher, 2006; and Liu et al., 2008). 561

However, these water balance models are typically run on a monthly or yearly 562

time steps because the models are generally not capable of separating base- 563

inter- and surface runoff flow. To truly model erosion and sediment transport 564

(of great interest in the Blue Nile Basin), large events must be captured by the 565

model and daily simulations are required to do so. Thus SWAT-WB not only 566

maintains a water balance but also calculates the interflow and the base flow 567

component, and gives a reasonable prediction of peak flows. SWAT-WB is 568

therefore more likely to be capable of predicting sediment transport than either 569

SWAT-CN or water budget models with monthly time steps. 570

Conclusion 571

Daily modeling of stream flow and surface runoff in a monsoonal watershed 572

was substantially improved by replacement of the CN method with a simplified 573

water balance routine in the SWAT model. SWAT-WB uses calculated 574

22

saturation-deficit values with an effective depth coefficient, EDC, to partition 575

rainfall into surface runoff and infiltrating water. This EDC-based water 576

balance method is analogous to other tank models that have been 577

successfully applied in monsoonal regions 578

579

SWAT-WB was as accurate in predicting discharge at the outlet as the CN 580

method in a watershed that experiences evenly distributed rainfall throughout 581

the year (New York). SWAT-WB also predicted the distribution of water table 582

heights on a hillslope in the watershed significantly better than SWAT-CN, 583

giving us increased confidence that the spatial distribution of runoff dynamics 584

are more realistically captured. 585

586

These results indicate that SWAT performs better in saturation-excess 587

controlled areas when a simple saturation-deficit water balance model is used 588

to calculate runoff volumes. With this physically-based, and easy-to-use 589

model, effective water and land management schemes will be easier to 590

successfully implement in watersheds dominated by saturation excess runoff 591

generation, particularly data-poor regions where use of the CN methodology 592

has not been validated. 593

594

23

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749

750

751

27

Tables and Figures 752

753

Table 1. Model statistics for daily streamflow in Gumera Basin. 754

755 756 757

758

28

Table 2. Model statistics for daily streamflow in Townbrook. 759

760

29

761 Figure 1. Wetness classes for Gumera, Ethiopia. 762

763

30

764

765

766

Figure 2.Wetness classes for Townbrook, located in the Catskill Mountains of 767

New York State. 768

769

31

770

771

772 Figure 3. Elevation of the Gumera Basin, located east of Lake Tana in the 773 Ethiopian Highlands. 774

775

32

776

777

778 Figure 4. Observed and modeled streamflow for Gumera using (a) SWAT-WB, 779 and (b) SWAT-CN. 780

781

33

782 Figure 5. Spatial distribution of surface runoff in Gumera modeled by: A) 783 SWAT-CN and B) SWAT-WB. 784

785

34

786 Figure 6. Observed and modeled streamflow in the Townbrook watershed 787 using (a) SWAT-WB and (b) SWAT-CN. 788

789

790

791

792

793

794

795

35

796

797

798

R² = 0.68

500

700

900

1100

1300

1500

500 700 900 1100 1300 1500

SWAT-WB Water Table (mm)

Measured Water Table (mm)

1098765

R2 = 0.57

500

700

900

1100

1300

1500

500 700 900 1100 1300 1500SWAT-CN Water Table (mm)

Measured Water Table (mm)

Mixed Forest

Pasture

Shrub

a b

799 800 Figure 7. Relationship between SWAT-WB (a) and SWAT-CN (b) predicted 801 water table heights above the restricting layer by index class (SWAT-WB) or 802 landuse (SWAT-CN) and the measured water table heights for March 2004 – 803 September 2004 from Lyon et al. (2006). Individual measured points within an 804 index class or landuse represent the average of the pieziometic measurement 805 within the respective classes for a single day. 806

807

36

808

809

810 Figure 8. Spatial distribution of surface runoff in Townbrook modeled by (a) 811 SWAT-WB, and (b) SWAT-CN. 812

813

814