role of watershed subdivision on modeling the ...assessment tool (swat) model was calibrated and...

ABSTRACT: Distributed parameter watershed models are oftenused for evaluating the effectiveness of various best manage-ment practices (BMPs). Streamflow, sediment, and nutrientyield predictions of a watershed model can be affected by spa-tial resolution as dictated by watershed subdivision. The objec-tives of this paper are to show that evaluation of BMPs using amodel is strongly linked to the level of watershed subdivision;to suggest a methodology for identifying an appropriate subdi-vision level; and to examine the efficacy of different BMPs atfield and watershed scales. In this study, the Soil and WaterAssessment Tool (SWAT) model was calibrated and validatedfor streamflow, sediment, and nutrient yields at the outlet ofthe Dreisbach (623 ha) and Smith Fry (730 ha) watersheds inMaumee River Basin, Indiana. Grassed waterways, grade stabi-lization structures, field borders, and parallel terraces are theBMPs that were installed in the study area in the 1970s. Sedi-ment and nutrient outputs from the calibrated model were com-pared at various watershed subdivision levels, both with andwithout implementation of these BMPs. Results for the studywatersheds indicated that evaluation of the impacts of theseBMPs on sediment and nutrient yields was very sensitive to thelevel of subdivision that was implemented in SWAT. An optimalwatershed subdivision level for representation of the BMPs wasidentified through numerical simulations. For the study water-sheds, it would appear that the average subwatershed area cor-responding to approximately 4 percent of total watershed areais needed to represent the influence of these BMPs when usingthe SWAT model.KEY TERMS: SWAT; modeling; nonpoint source pollution; sedi-ment; nutrients; best management practices (BMPs); water-shed subdivision; sensitivity analysis.)

Arabi, Mazdak, Rao S. Govindaraju, Mohamed M. Hantush, andBernard A. Engel, 2006. Role of Watershed Subdivision onModeling the Effectiveness of Best Management Practices WithSWAT. Journal of the American Water Resources Association(JAWRA) 42(2):513-528.

INTRODUCTION

Section 303(d) of the Clean Water Act requires allstates to develop and implement a total maximumdaily load (TMDL) for their impaired water bodiesand for water bodies that are likely to join this list. ATMDL is an estimate of the maximum pollution load(from point and nonpoint sources) that can be intro-duced into a water body without exceeding specifiedwater quality standards, and it further allocates pol-lutant loadings among point and nonpoint sources.Controlling pollution from nonpoint sources is essen-tial to the successful implementation of TMDLs,“which is now considered to be pivotal in securing thenation’s water quality goals” (NRC, 2001, p. 1). Eventhough the TMDL program is based on maximumdaily concentrations, management strategies are typi-cally evaluated over a longer time scale.

Implementation of BMPs is a conventionalapproach for controlling nonpoint sources of sedi-ments and nutrients. However, installation of BMPsis rarely followed by long term data monitoring tostudy how effective BMPs have been in meeting theoriginal goals. Long term data on flow and water

1Paper No. 04223 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open untilOctober 1, 2006.

2Respectively, (Arabi) Graduate Research Assistant and (Engel) Professor, Purdue University, Department of Agricultural and BiologicalEngineering, Purdue University, 225 South University Street, West Lafayette, Indiana 47907; (Govindarajue) Professor, School of Civil Engi-neering, Purdue University, 550 Stadium Mall Drive, West Lafayette, Indiana 47907; and (Hantush) Hydrologist, U.S. Environmental Protec-tion Agency, National Risk Management Laboratory, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268 (E-Mail/Arabi: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 513 JAWRA

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONAPRIL AMERICAN WATER RESOURCES ASSOCIATION 2006

ROLE OF WATERSHED SUBDIVISION ON MODELING THE EFFECTIVENESSOF BEST MANAGEMENT PRACTICES WITH SWAT1

Mazdak Arabi, Rao S. Govindaraju, Mohamed M. Hantush, and Bernard A. Engel2

quality within watersheds before and after placementof BMPs is not generally available. Therefore, evalua-tion of BMPs (especially new ones that have had littleor no history of use) must be conducted with the helpof watershed models. In this regard, various water-shed and field scale models have been used to assesthe effectiveness of BMPs (Batchelor et al., 1994; Parket al., 1994; Griffin, 1995; Edwards et al., 1996;Mostaghimi et al., 1997). Numerous studies have beenperformed with the SWAT model to examine theeffects of different BMPs on sediment and nutrienttransport within watersheds (Saleh et al., 2000; San-thi et al., 2001b; Kirsch et al., 2002; Saleh and Du,2004; Vache et al., 2002; and Santhi et al., 2003).

Distributed models partition the watershed intosmaller units to represent heterogeneity within thewatershed. The SWAT model partitions watershedsinto subunits (subwatersheds or hydrologic responseunits) for computations. Delineation of the watershed,identification of the stream network, and partitioningof the study area into smaller units is generallyaccomplished through geographic information system(GIS) databases that help automate this process andmake it convenient for modeling purposes. However,division into subwatersheds and identification ofstream networks are extremely sensitive to spatialscale. The number and size of computational unitsvaries with a user defined critical source area (CSA),the minimum area required for channel initiation.Previous studies by Bingner et al. (1997), Mamillapal-li (1998), FitzHugh and MacKay (2000), and Jha etal. (2004) indicate that the SWAT model sedimentand nutrient simulations vary quite dramaticallywith the number and size of subwatersheds. Becausemodel outputs are affected by geomorphologic resolu-tion, the predicted performance of BMPs will be influ-enced as well. Thus, examination of the efficacy ofBMPs must be conducted in conjunction with studiesperformed at multiple spatial scales. Previousresearch on evaluation of the effectiveness of BMPshas not incorporated the effects of geomorphologicresolution.

The objectives of this study are to show that spatialresolution effects resulting from watershed subdivi-sion have a strong influence on model-based evalua-tion of long term impacts of BMPs on fate andtransport of sediments and nutrients within water-sheds; to suggest a methodology for identifying anappropriate spatial scale for evaluating BMPs; and toexamine, using this appropriate level of subdivision,the efficacy of different BMPs at field and watershedscales. This is done by using the SWAT model in thecontext of two small watersheds in Indiana. Theresearch methodology is presented next, followed by adiscussion of results and conclusions of the study.

METHODOLOGY

For the objectives of this research to be accom-plished successfully, a study area with adequatewater quality data and information on BMPs wasselected. Then, streamflow, sediment, and nutrientcomponents of the SWAT model were calibrated andvalidated for the study area. It has been widelyacknowledged that a good correlation between mea-sured and simulated quantities such as streamflow,sediment, and nutrient yields alone is not sufficientfor the validity of physically based model predictions(e.g., Beven, 1989). Further validation can beachieved through performing paired watershed stud-ies. Here, the modeling approach has been applied totwo watersheds, Dreisbach and Smith Fry in theMaumee River Basin, Indiana. The BMPs that wereinstalled in the study area are represented in themodel by altering corresponding model parameters.Model simulations were performed at various water-shed subdivision levels. Comparisons of sediment andnutrient predictions with and without implementa-tion of the BMPs were used to determine the efficien-cy of the BMPs at each watershed subdivision level.

The Study Area and Available Data

The Dreisbach (6.23 km2) and Smith Fry (7.30km2) watersheds lie in Allen County, northeasternIndiana (Figure 1). Several BMPs have been imple-mented in these watersheds, and adequate hydrologicand water quality data including daily rainfall,streamflow, and sediment and nutrient yields areavailable at the outlet of these two watersheds. Theavailable data for the study area are summarized inTable 1. The types and locations of the BMPs in theDreisbach and Smith Fry watersheds are depicted inFigure 1.

Land use in the Dreisbach watershed is mostly pas-ture in the upper portion, while cropland iswidespread in the remainder of the watershed. Landuse in the Smith Fry watershed is mostly cropland.Table 2 presents land use distributions for the water-sheds. The dominant hydrological soil group of soilseries in both watersheds is type C. The number andareas under the influence of each BMP in the studywatersheds are shown in Table 3.

Model Description

The SWAT model integrated with USEPA’s model-ing framework, Better Assessment Science Integrat-ing Point and Nonpoint Sources (BASINS), was used

JAWRA 514 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

ARABI, GOVINDARAJUE, HANTUSH, AND ENGEL

in this study to examine the role of watershed subdi-vision for assessment of the impacts of BMPs on sedi-ment and nutrient yields. SWAT model development,operation, limitations, and assumptions have beendiscussed by Arnold et al. (1998). Sirinivasan et al.(1998) reviewed applications of the SWAT model for

streamflow prediction, transport of sediments andnutrients, and effects of management practices onwater quality. The Soil and Water Assessment ToolUser’s Manual and theoretical documentation areavailable at the SWAT website (USDA-ARS, 2002). Inthis paper, only the relevant sediment and nutrient


ROLE OF WATERSHED SUBDIVISION ON MODELING THE EFFECTIVENESS OF BEST MANAGEMENT PRACTICES WITH SWAT

Figure 1. Locations of the Dreisbach and Smith Fry Watersheds Along With theLocations and Types of the Installed Best Management Practices (BMPs).

TABLE 1. Available Data and Sources.

Data Type Source Date Description

DEM National Elevation Data 2001 30 m resolution, U.S. Geological Survey (USGS)

Soils Soil Survey Geographic Database 2002 Digital representation of County Soil Survey mapspublished by the USDA-NRCS

Land Use USDA-NRCS 2003 Digitized into GIS from aerial photos

Land Use Black Creek Project 1975 Digitized into GIS from aerial photos

Weather Black Creek Project1 1974 to 1977 Daily precipitation groups

Weather Purdue Applied Meteorology Group 1902 to 2002 Minimum and maximum daily temperature anddaily precipitation

Crop Management Engel and Lim (2001) 1975 Management scenarios for crops

Streamflow Black Creek Project2 1975 to 1978 Daily streamflow

Water Quality Black Creek Project3 1974 to May 1977 Daily Suspended Solids, Mineral P, Total P,NO3-N, and Total N

1Lake and Morrison, 1977a.2Lake and Morrison, 1977b.3Morrison and Lake, 1983.

channel routing components of the SWAT model arediscussed briefly.

The SWAT model uses a modification of the U.S.Department of Agriculture-Soil Conservation Service(SCS) curve number method (USDA-SCS, 1972) orGreen and Ampt infiltration method (Green andAmpt, 1911) to compute surface runoff volume foreach hydrologic response unit (HRU). Sheet erosionand sediment yield from upland areas are estimatedfor each HRU with the Modified Universal Soil LossEquation (MUSLE) (Williams, 1975). Sediment depo-sition and degradation are the two dominant channelprocesses that affect sediment yield at the outlet ofthe watershed. Whether channel deposition orchannel degradation occurs depends on sedimentloadings from upland areas and transport capacity ofthe channel network. If sediment load in a channelsegment is larger than its sediment transport capaci-ty, channel deposition will be the dominant process.Otherwise, channel degradation (i.e., channel erosion)occurs over the channel segment. The SWAT modelestimates the transport capacity of a channel segmentas a function of the peak channel velocity,

Tch = α x vb

where Tch (ton/m3) is the maximum concentration ofsediment that can be transported by streamflow(transport capacity), a and b are user defined coeffi-cients, and v (m/s) is the peak channel velocity. Thepeak velocity in a reach segment is calculated as

where α is the peak rate adjustment factor with adefault value of unity, n is Manning’s coefficient, Rchis the hydraulic radius (m), and Sch is the channelinvert slope (m/m).

Channel degradation (Seddeg) and deposition (Seddep) in tons are computed as

sedi > Tch : seddep = (sedi - Tch) x Vch

& seddeg = 0

sedi < Tch : seddeg = (Tch - sedi) x Vch x Kch x Cch

& seddep = 0

where sedi is the initial sediment concentration in thechannel segment (ton/m3), Vch is the volume of waterin the channel segment (m3), Kch is the channel erodi-bility factor (cm/hr/Pa), and Cch is the channel coverfactor. The total amount of sediment that is transport-ed out of the channel segment (sedout) in tons is com-puted as

In Equation (5), Vout is the volume of water leavingthe channel segment (m3) at each time step.

The soil eroded from upland areas contains nutri-ents resulting from humus, fertilizers, or cropresidues. Thus, nutrient, total phosphorus (P) andtotal nitrogen (N), loadings from upland areas areusually highly correlated to sheet erosion. The SWATmodel uses nutrient cycles to account for movement



TABLE 2. Historical Land Use in the Dreisbachand Smith Fry Watersheds in 1975.

Dreisbach Smith FryLand Use (percent area) (percent area)

Pasture 37.5 8.7

Corn 23.4 33.6

Winter Wheat 17 14.3

Soybean 7.2 31.8

Forest 5.8 8.9

Residential 9.1 2.7

TABLE 3. BMPs Installed in the Dreisbach and Smith Fry Watersheds.

Dreisbach Smith FryLength/Area Length/Area

BMP Number (unit) Number (unit)

Field Border 7 2,600 (m) 1 1,800 (m)

Parallel Terrace 4 2,130 (m) 2 480 (m)

Grassed Waterway 5 3.50 (ha) 1 0.95 (ha)

Grade Stabilization Structure 10 – 2

vn

R Sch ch= α 2 3 1 2/ / (2)

(3)

(4)

sed sed sed sedVVout i dep

out

ch= + −( ) ×deg (5)

(1)

and transformation of various forms of nutrients.Nutrients are introduced into the main channel andtransported downstream through surface runoff andlateral subsurface flow.

BMP Representation

Four types of structural BMPs, namely grassedwaterways, field borders, parallel terraces, and gradestabilization structures, were installed in the Dreis-bach and Smith Fry watersheds in the early to mid-1970s (Table 3). Figure 1 depicts the locations of theseBMPs in the watersheds. For representation of theseBMPs, appropriate model parameters (Table 4) wereselected and altered based on their functionality anda detailed sensitivity analysis at HRU and/or water-shed scales. For instance, parallel terraces and fieldborders decrease soil loss from upland areas. Thus, asensitivity analysis at an HRU scale was performed toexamine the sensitivity of model outputs to the select-ed model parameters. On the other hand, the appro-priate scale for validation of the sensitivity of modeloutputs to the parameters selected for representationof grassed waterways and grade stabilization struc-tures is a watershed scale, because these BMPs affectthe performance of the channel network. Table 4includes information on the representative parame-

ters for each BMP along with its modified values forthe HRUs or channel segments where the BMP islocated. More discussion on representation of theBMPs in the study watersheds with the SWAT modelis provided in Bracmort (2004).

Model Calibration and Validation

Calibration procedure plays an important role inwatershed modeling. Utilization of a model withoutcalibration may result in predictions substantially dif-ferent from observed data. A detailed procedure forcalibration of SWAT was presented by Santhi et al.(2001a). The coefficient of determination, R2, and thecoefficient of efficiency, EN-S, (Nash and Sutcliffe,1970) were used to evaluate model predictions. R2

values range from 0 to 1, with an R2 value equal to 1indicating a perfect correlation between measureddata and model predictions. EN-S ranges from -∞ to 1, and higher values indicate a better prediction.If EN-S is negative or very close to 0, the model predic-tion is considered unacceptable (Santhi et al., 2001a).The coefficient of efficiency is indicative of how wellthe plot of observed versus predicted values fit a 1:1line.

Using default values, first the response (i.e.,streamflow, sediment, and nutrient yields) of the



TABLE 4. Representation of Grassed Waterways, Field Borders, Parallel Terraces, and Grade Stabilization Structures in SWAT.

Representing SWAT ParameterVariable Value When BMP

BMP Function (input file) Range Implemented

Field Border Increase sediment trapping FILTERW (.hru) 0 to 5 (m) 5 (m)

Parallel Terrace Reduce overland flow CN(2) (.mgt) 0 to 100 *Reduce sheet erosion USLE_P (.mgt) 0 to 1 0.2 (terraced)Reduce slope length SLSUBBSN (.hru) 10 to 150 **

Grassed Waterway Increase channel cover CH_COV (.rch) 0 to 1 0.0 (fully protected)Reduce channel erodibility CH_EROD (.rch) 0 to 1 0.0 (nonerosive)Increase channel roughness CH_N(2) (.rch) 0 to 0.3 0.24

Grade Stabilization Structure Reduce gully erosion CH_EROD (.rch) 0 to 1 0.0 (nonerosive)Reduce slope steepness CH_S(2) (.rch) – ***

***Estimated based on land use and hydrologic soil group of the HRU where it is installed for terraced condition.***Estimated for each parallel terrace based on its features and SWAT assigned overland slope of the HRU where it is installed:

SLSUBBSN = (A x S + B) x 100/S

***where S is average slope of the HRU; A = 0.21, and B = 0.9 (ASAE, 2003).***Estimated for each grade stabilization structure based on its features and SWAT assigned slope, CH_S(2)old, and length of the channel***segment where it is installed

CH_S(2)new = CH_S (2)old - D/CH_L(2)

***where D is height of the structure (1.2 m) and CH_L(2) is length of the channel segment (in m).

uncalibrated model to different critical source areaswas examined. It was found that a 2 percent CSAspecification within GIS was needed to generatereproducible model responses for the Dreisbach andSmith Fry watersheds. This is in agreement with pre-vious studies (Bingner et al., 1997; FitzHugh andMcKay, 2000; Jha et al., 2004) in which a criticalsource area of about 2 to 5 percent of the watershedarea was found to be sufficient for modeling purposes.This level of subdivision (corresponding to 2 percentCSA) resulted in an average subwatershed area corre-sponding to 4 percent of the total watershed area inthis study. Second, HRU distribution levels for soiland land use areas were set at 0 percent. The SWATmodel predictions are very sensitive to HRU distribu-tion levels (Mamillapalli, 1998). These user specifiedthresholds control the number of HRUs in the water-shed. For example, if a 10 percent soil area is definedin HRU distribution, only soils that occupy more than10 percent of a subwatershed area are considered inHRU distributions. Subsequently, the number ofHRUs in the watershed decreases with increasingthreshold values. A 0 percent HRU distribution levelwas applied for both soil and land use areas toexclude the effects of spatial resolution of input dataincluding soil and land use. Third, BMPs were repre-sented during calibration. Parameters of the HRUsand channel segments where the BMPs have beeninstalled were accordingly set to the values specifiedin Table 4 and were not altered during model calibra-tion.

Both calibration and validation procedures wereperformed manually on a monthly basis. Surfacerunoff and streamflow components of the model werecalibrated for a 30-month period, from January 1975to May 1977, until the following conditions were met:average simulated streamflows were within 15 per-cent of average observed values, R2 ≥ 0.6, and EN-S ≥0.5. The uncalibrated model slightly overpredicted theaverage monthly streamflows. The SCS curve number

(CN2) and threshold depth of water in shallow aquiferfor return flow to occur (GWQMN) were modified toachieve the above mentioned calibration criteria. Forsediment and nutrient yields, calibration requiredthat average simulated quantities were within ± 20 percent of average observed values, R2 ≥ 0.6, andEN-S ≥ 0.5. Sediment and nutrient components of themodel were calibrated for the 12-month periods ofJanuary through December 1974 and Januarythrough December 1975 at the outlets of the Dreis-bach and Smith Fry watersheds, respectively. USLEsupport practice factor (USLE_P), Manning’s valuefor the main channel (CH_N2), channel erodibilityfactor (CH_EROD), and channel cover factor(CH_COV) were altered for sediment calibration. Ini-tial concentration of soluble P (SOL_LABP), organic P(SOL_ORGP), NO3 (SOL_NO3N), and organic N(SOL_ORGN) were also modified for calibration ofnutrient components of the SWAT model. During cali-bration, the ranges of parameter variations wereobtained from Neitsch et al. (2001) and calibration cri-teria from Santhi et al. (2001a). The results of the cal-ibration procedure are summarized in Table 5, whichshows that the above mentioned criteria were met forboth watersheds. The results of model validation aresummarized in Table 6. The error statistics of the val-idation procedure indicated that model parameterswere representative of both Dreisbach and Smith Frywatersheds and that the model performance was sat-isfactory despite a low EN-S coefficient for total P inthe Smith Fry watershed. The observed and simulat-ed monthly streamflow, sediment, total P, and total Nfor the calibration and validation periods at the out-lets of the Dreisbach and Smith Fry watersheds areshown in Figures 2 and 3, respectively. These figuresprovide further indication of model performance.

A total of 26 BMPs were implemented in the Dreis-bach watershed, while only six were implemented inthe Smith Fry watershed (see Figure 1 and Table 3).After application of the same method to represent the



TABLE 5. Results of Calibration of SWAT for Average Monthly Streamflow, Sediment,Total Phosphorus, and Total Nitrogen, Dreisbach and Smith Fry Watersheds.

Dreisbach Watershed Smith Fry WatershedVariable Obs Sim R2 EN-S Obs Sim R2 EN-S

Streamflow (m3/s) 0.039 0.04 0.92 0.84 0.054 0.052 0.86 0.73

Surface Runoff (m3/s) 0.035 0.037 0.91 0.80 0.045 0.049 0.84 0.62

Suspended Solids (t/ha) 0.027 0.024 0.97 0.92 0.151 0.16 0.94 0.86

Total P (kg/ha) 0.077 0.094 0.93 0.78 0.075 0.068 0.64 0.51

Total N (kg/ha) 1.35 1.53 0.76 0.54 1.39 1.55 0.61 0.50

Notes: Obs is observed; Sim is simulated.

BMPs in the watersheds, the same set of calibratedparameters was obtained for the Dreisbach and SmithFry watersheds. This provided further corroborationfor the calibration procedure and the method that wasused to represent the BMPs.

Watershed Subdivision

In the previous section, calibration and validationresults of SWAT for the study watersheds were dis-cussed. Once the parameter values have been identi-fied, now the question of the role of watershedsubdivision on evaluation of BMPs is addressed.

SWAT simulations were performed with variouswatershed configurations for a 30-year time horizon,from 1971 through 2000. The characteristics of thewatershed configurations that were used in this studyare summarized in Tables 7 and 8 for the Dreisbachand Smith Fry watersheds, respectively. The tablessummarize different critical source areas (km2) usedin the study and the corresponding number of subwa-tersheds, drainage density (km/km2), and averagesubwatershed area (km2). Drainage density is definedas the ratio of total channel length to the total water-shed area. For brevity, only some of watershed config-urations used for the Dreisbach watershed are shownin Figure 4.

Management Scenarios

At each watershed subdivision level, two scenarioswere compared. Scenario A corresponded to modelresults without BMPs, while Scenario B showed themodel outputs with BMPs in place. All of the inputparameters for the two scenarios were exactly thesame over the study watersheds with the exception ofthe parameters of the HRUs with parallel terracesand field borders and the parameters of the channel

segments with grassed waterways and stabilizationstructures. In Scenario A, these parameters wereassumed to be the same as the rest of the study areafor which calibrated values were available. The valuesspecified for different BMPs in Table 4 were utilizedfor these parameters in Scenario B. A comparison ofmodel predictions for these two scenarios enabled thedetermination of the long-term impacts of the BMPson sediment and nutrient yields at the outlet of theDreisbach and Smith Fry watersheds.

RESULTS AND DISCUSSION

Impacts of BMPs on Sediment Yield

The effect of watershed subdivision on sedimentoutput of the SWAT model at the outlet of the studywatersheds is depicted in Figure 5. Under Scenario A,without the BMPs, sediment yield at the outlets of thewatersheds increased by nearly 200 percent betweenthe coarsest and the finest subdivision levels. Theincrease could be due to two processes: higher erosionfrom upland areas and/or more intense channel ero-sion.

The SWAT model employs MUSLE (Williams,1975) to estimate sheet erosion. All of the parametersin the MUSLE equation are estimated for each HRUwith the exception of USLE topographic factor, LS,which is determined for each subwatershed andapplied to the HRUs contained in the subwatershed.Thus, changing subwatershed size will lead to differ-ent LS factors. The results of this study, shown in Fig-ure 6, revealed that the weighted average USLEtopographic factor was reduced by nearly 25 percentbetween the coarsest and finest subdivision levels.The rate of reduction plateaued at finer subdivisionlevels. Similar trends were observed for the computedsheet erosion from upland areas. Consequently, the



TABLE 6. Results of Validation of SWAT for Average Monthly Streamflow, Sediment,Total Phosphorus, and Total Nitrogen, Dreisbach and Smith Fry Watersheds.

Dreisbach Watershed Smith Fry WatershedVariable Obs Sim R2 EN-S Obs Sim R2 EN-S

Streamflow (m3/s) 0.042 0.047 0.87 0.73 0.053 0.069 0.81 0.63

Surface Runoff (m3/s) 0.038 0.045 0.88 0.75 0.051 0.065 0.84 0.63

Suspended Solids (t/ha) 0.032 0.033 0.86 0.75 0.052 0.073 0.85 0.68

Total P (kg/ha) 0.074 0.09 0.90 0.79 0.241 0.159 0.73 0.37

Total N (kg/ha) 1.227 1.20 0.75 0.52 2.59 2.45 0.85 0.72

Notes: Obs is observed; Sim is simulated.

model predicted that variation of upland erosion wasnot the cause for higher sediment yield at the outletdue to finer watershed subdivision.

When the impacts of the BMPs were not included(Scenario A), sediment yield at the outlets of the

watersheds was computed by SWAT to be greaterthan predicted erosion from upland areas, becauseestimated transport capacity of the channel networkexceeded sediment loadings from upland areas. Thus,channel degradation was predicted by the model to be



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the dominant channel process and contributed to thesediment yield at the outlet. Dominance of channeldegradation indicated that sediment yield at the out-let would increase with drainage density, whichincreased with finer subdivision levels (Figure 7). At

finer subdivision levels, higher drainage density pro-vided longer channel networks that would be subjectto channel degradation. This resulted in dramaticallyhigher sediment yields at the outlets. The correlationcoefficient between sediment yield at the outlet and



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drainage density of the Dreisbach and Smith Frywatersheds was 0.98 and 0.97, respectively. The corre-

lation was extremely poor for Scenario B, which simu-lates the presence of BMPs.



TABLE 7. Properties of the Watershed Configurations Used for the Dreisbach Watershed.

Critical Source Area (km2)0.03 0.05 0.10 0.15 0.36 0.38 0.50 1.5 2.5

NS 103 51 29 19 11 8 5 2 1

NH. 647 470 359 301 204 180 138 91 73

DD (km/km2) 3.91 3.05 2.28 1.97 1.39 1.30 1.22 0.94 0.91

ASA (km2) 0.06 0.12 0.22 0.33 0.57 0.79 1.26 3.11 6.23

Notes: NS is number of subwatersheds; NH is number of HRUs; DD is drainage density; and ASA is average subwatershed area.

TABLE 8. Properties of the Watershed Configurations Used for the Smith Fry Watershed.

Critical Source Area (km2)0.03 0.05 0.10 0.15 0.30 0.50 1.5 2.9

NS 089 063 033 020 012 008 004 01

NH 676 577 429 358 278 239 159 95

DD (km/km2) 4.09 3.27 2.54 2.25 1.76 1.45 0.96 0.65

ASA (km2) 0.08 0.12 0.22 0.37 0.61 0.92 1.83 7.30

Notes: NS is number of subwatersheds; NH is number of HRUs; DD is drainage density; and ASA is average subwatershed area.

Figure 4. Examples of How Watershed Subdivision Occurs in the DreisbachWatershed for Different Choices of Critical Source Area (CSA).

Predicted sediment yield at the outlet was compar-atively stable at various subdivision levels whenmodel simulations were performed under Scenario B.Transport capacity of the channel network is a func-tion of the peak channel velocity as indicated in Equa-tion (1). Implementation of grade stabilizationstructures in the watersheds resulted in lower mainchannel slopes, while implementation of grassedwaterways increased channel resistance, both ofwhich lowered the peak channel velocity. Subsequent-ly, transport capacity of the channel network was sig-nificantly lower after implementation of the grassedwaterways and grade stabilization structures. Withthe BMPs, both Dreisbach and Smith Fry watershedsexhibited the characteristics of “transport limited”watersheds. For such watersheds, estimated transportcapacity of the channel network is less than sedimentloading from upland areas, and sediment deposition isthe dominant main channel process. Dominance ofchannel deposition indicated that sediment yield atthe outlet did not increase with drainage density. Theresults presented in Figure 5 confirm that sedimentyield at the outlet was relatively insensitive to finerwatershed subdivision under Scenario B when influ-ence of BMPs was included in model simulations.

As discussed above, several factors contribute todetermine the impact of the BMPs on abatement ofsediment yield at the outlet of watersheds. An overallevaluation was therefore made by estimating BMPefficacy at any particular subdivision level as



Figure 5. Average Annual Sediment Yield (a) at the Outletof Dreisbach Watershed, (b) at the Outlet of Smith Fry

Watershed, and (c) Percent Sediment Reduction forBoth Watersheds. Scenario A: Simulations With NoBMP; Scenario B: Simulations With BMPs in Place.

Figure 6. Weighted Average USLE Topographic Factorfor Various Levels of Watershed Subdivision.

In the Dreisbach Watershed, the efficacy of theBMPs for abating sediment yield was evaluated to beonly 7 percent at the coarsest subdivision level, whilethe efficacy was nearly 70 percent at the finest subdi-vision level. The corresponding efficacy values in theSmith Fry Watershed were nearly 0 and 50 percent,respectively (see Figure 5c).

An optimal watershed subdivision level for repre-sentation of the BMPs and their validity could beidentified from Figure 5 at a CSA corresponding to 2percent of the total watershed areas. The average sub-watershed area at this subdivision level was approxi-mately 4 percent of the total watershed area. Thereare two main reasons for this recommendation: first,the estimated erosion from upland areas did not varysignificantly beyond this subdivision level; and sec-ond, the asymptotic behavior of the average slope ofchannel network (Figure 7) indicated that channeldegradation and its contribution to the sediment yield

at the outlet also tended to stabilize at finer subdivi-sion levels. These trends are more apparent in theSmith Fry watershed, where the upstream channelnetwork is relatively flatter than the one in the Dreis-bach watershed.

Impacts of BMPs on Nutrient Yield

Figures 8 and 9 show that total P and total Nyields at the outlet of the watersheds are highly corre-lated to simulated sediment yields in Figure 5. Theeffects of instream processes that could cause changesin the fate of nutrients were negligible for the studywatersheds. Without BMPs (Scenario A), total P pre-dictions by the SWAT model were 200 percent higherat the finest subdivision level in comparison to thecoarsest level used for the watersheds. However, therate of change stabilized at finer subdivision levels.



Re (%)ductionModel output from Scenario A Model output from Scenario B

Model output from Scenario A= − × 100 (6)

Figure 7. Drainage Density (DD) and Average Slope of Channel Networkof (a) Dreisbach Watershed and (b) Smith Fry Watershed.

Total N predictions of the model exhibited similartrends as evidenced in Figures 8 and 9.

The installed BMPs were estimated to effectivelyreduce total P yield at the outlet of the DreisbachWatershed by 30 percent when the finest subdivision



Figure 8. Average Annual Total P Yield (a) at the Outlet ofDreisbach Watershed, (b) at the Outlet of Smith Fry

Watershed, and (c) Percent Total P Reduction for BothWatersheds. Scenario A: Simulations With No BMP;

Scenario B: Simulations With BMPs in Place.

Figure 9. Average Annual Total N Yield (a) at the Outlet ofDreisbach Watershed, (b) at the Outlet of Smith Fry

Watershed, and (c) Percent Total N Reduction forBoth Watersheds. Scenario A: Simulations With NoBMP; Scenario B: Simulations With BMPs in Place.

level was used. The reduction (predicted by the SWATmodel) corresponding to the coarsest subdivision levelwas 0 percent (see Figure 8a). The results presentedin Figure 9a demonstrate that the impact of BMPs inalleviating total N yield at the outlet of the Dreisbachwatershed also depended on the utilized watershedsubdivision level. A 25 percent reduction wasobserved at the finest subdivision level, while thereduction was negligible at the coarsest level. Similartrends were observed for reduction of total P and totalN in the Smith Fry watershed as depicted in Figures8b and 9b, respectively. From Figures 8c and 9c, anoptimal CSA corresponding to 2 percent of total areasof the respective watersheds could be identified forevaluation of effectiveness of the BMPs for reductionof total P and total N. This was partly anticipatedbecause the same optimal subdivision level was iden-tified earlier for sediment yield.

Field Scale Versus Watershed Scale Evaluation

The impacts of the BMPs in the Dreisbach andSmith Fry Watersheds were examined at two spatialscales based on their functionality. Parallel terracesand field borders are implemented to reduce soil lossfrom upland areas. Therefore, their efficacy may beevaluated at an HRU (or field) scale as well as awatershed scale. The effect of grassed waterways andgrade stabilization structures must be discussed at alarger watershed scale because their effects cannot befelt on upland areas. Model predictions at the finestsubdivision level, i.e., critical source area equal to0.03 (km2), were applied to compare the efficacy of theBMPs at watershed and field scales. The sediment,total P, and total N reduction rates determined bycomparing model simulations with and without inclu-sion of parallel terraces and field borders are summa-rized in Table 9. In this table, the presented results atHRU scale correspond to reduction rates of model out-puts averaged over the particular field plots wherethe parallel terraces and field borders have beenimplemented (shown in Figure 1). At a watershedscale, these BMPs did not contribute to appreciablesediment, total P, or total N reductions. This wasanticipated because they have been placed only in avery small portion of the study watersheds. On thecontrary, sediment, total P, and total N loadings fromthe fields where the terraces and field borders wereinstalled decrease by nearly 50, 25, and 45 percent,respectively, which implies that land owners wouldsubstantially benefit from their implementation if theregulation were to be imposed immediately down-stream of the upland area.

Grassed waterways and grade stabilization struc-tures would likely be more beneficial to developmentof a sediment and nutrient TMDL at the outlet of thestudy watersheds. Sediment, total P, and total Nyields at the outlet of the Dreisbach watersheddecreased by nearly 70, 25, and 30 percent as a resultof the installation of the waterways and stabilizationstructures (see Figures 5a, 8a, and 9a). Figures 5b, 8b,and 9b illustrate that the corresponding values in theSmith Fry watersheds were approximately 50, 30,and 35 percent.

Interestingly, although the number of the BMPsimplemented in the Smith Fry Watershed was signifi-cantly lower than that for the Dreisbach Watershed,the estimated sediment and nutrient reduction rateswere comparable. This indicates that not only thenumber of BMPs but also their locations in the water-shed play a significant role. The assessment of theimpact of individual BMPs revealed that the twograde stabilization structures at the downstream por-tion of the channel network in the Smith Fry Water-shed were the primary reason for such reductionrates. These structures lowered the transport capacityof upstream channel segments that resulted in depo-sition of a large amount of sediments and nutrients inthe channel segments. Thus, simulated sediment andnutrient yields at the outlet were dramaticallyreduced. This would imply that for maximum bene-fits, the BMPs should be placed as close upstream aspossible to where the regulation will be imposed. Italso suggests that with proper implementation ofBMPs, managers are able to exert enough control toconvert a capacity limited watershed to a transport-limited one.



TABLE 9. Reduction of Sediment, Total P, and Total NLoadings Resulting From Implementation of

Parallel Terraces and Field Borders.

Percent ReductionWatershed Scale Sediment Total P Total N

Dreisbach HRU* 50 25 45Watershed 02 02 02

Smith Fry HRU* 48 24 45Watershed 01 01 01

*Obtained by averaging the reduction rates over the HRUs (i.e., *fields) where the parallel terraces and field borders have been *installed (Figure 1).

CONCLUSIONS

In this paper, the interplay of watershed subdivi-sion and evaluation of BMPs was examined for twosmall watersheds in Indiana. Conclusions about theappropriate level of watershed subdivision for evalua-tion of BMPs are limited to similar sized watershedsin the Midwest. However, the developed methodologycan be utilized in other watersheds.

For the study watersheds, sediment, total P, andtotal N outputs of the SWAT model were highly influ-enced by watershed subdivision before representationof the BMPs. Predicted dominance of channel degra-dation in simulations without BMPs resulted in anincrease of these outputs with drainage density, whichincreased with finer subdivision levels.

The implemented grassed waterways and gradestabilization structures appreciably reduced thetransport capacity of the channel network of thewatersheds. After implementation of the BMPs, sedi-ment deposition was the dominant channel process inthe study watersheds. The predicted sediment yield atthe outlet of the study watersheds was relatively sta-ble and did not vary with finer subdivision.

The predicted reductions of sediment and nutrientyields as a result of implementation of the BMPs wereinsignificant when more coarse levels of subdivisionwere applied. Utilization of finer subdivision levelsresulted in apparent sediment and nutrient reduc-tions. An optimal subdivision level at a critical sourcearea corresponding to 2 percent of the total watershedarea was identified to be adequate for representationof the BMPs and assessment of their validity. Studyresults indicated that a proper assessment of the effi-cacy of the BMPs must be conducted in conjunctionwith multiple watershed subdivision levels.

The management implications of this study werefound to be scale dependent. Implementation of paral-lel terraces and field borders significantly alleviatedestimated sediment and nutrient loadings from thefield where they have been installed. The reductionwas negligible at the outlets of the study watersheds.While landowners may identify parallel terraces andfield borders as being very effective for controllingdownstream discharges, watershed managers may notappreciate their impact on water quality at the out-lets of the Dreisbach and Smith Fry Watersheds.Based on SWAT model predictions, at a watershedscale, grassed waterways and grade stabilizationstructures appeared to reduce sediment and nutrientyields at the outlets more effectively than field bor-ders and parallel terraces. In particular, grassedwaterways and grade stabilization structures in thedownstream portion of the channel network increasedchannel deposition in upstream segments. It may beconcluded that placement of the BMPs plays an

important role in improving the water quality at theoutlets of the watersheds. Identification of the mostappropriate locations for implementation of abate-ment strategies requires a better understanding ofcontrol processes in a watershed.

Since different sets of calibrated parameters maybe obtained from the calibration procedure, applyingsensitivity and uncertainty analysis techniques wouldbe valuable for identification of control processes andkey management actions such as sheet erosion, chan-nel degradation, and channel deposition within awatershed. In a watershed where channel degrada-tion is the dominant main channel process, implemen-tation of grassed waterways and grade stabilizationstructures would be highly successful in reducing sed-iment and nutrient loads to the extent of converting acapacity-limited watershed to a transport limited one.Application of BMPs such as parallel terraces andfield borders would be more successful for watershedswhere upland areas are the dominant sources of sedi-ments and nutrients. Their role in changing the over-all nature of the watershed is likely to be minimal.

The results of this study, which was conducted onsmall watersheds, should be verified by other studiesfocused on evaluation of effectiveness of BMPs at var-ious watershed subdivision levels. Sediment andnutrient yields from larger watersheds may exhibitdifferent trends with watershed subdivision. Themethod presented in this paper for evaluation of effec-tiveness of BMPs at various subdivision levels is rec-ommended for other watershed studies becauseuncertainties resulting from spatial resolutiondeserve more attention than has been devoted tothem in the past.

ACKNOWLEDGMENTS

The U.S. Environmental Protection Agency through its Office ofResearch and Development funded the research described herethrough contract number 3C-R289-NAEX. It has not been subjectedto Agency review and therefore does not necessarily reflect theviews of the agency, and no official endorsement should be inferred.

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