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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
2
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
120
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
132
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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
6
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
366
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
15
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