representation of agricultural conservation practices with swat · 2011. 6. 14. · swat (arnold et...

HYDROLOGICAL PROCESSESHydrol. Process. (2007)Published online in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6890

Representation of agricultural conservation practices withSWAT

Mazdak Arabi,1* Jane R. Frankenberger,2 Bernie A. Engel2 and Jeff G. Arnold3

1 Colorado State University, Department of Civil and Environmental Engineering, 1372 Campus Delivery, Fort Collins, Colorado 80523, USA2 Purdue University, Department of Agricultural and Biological Engineering, 225 S. University Street, West Lafayette, Indiana 47907, USA

3 USDA, ARS, Grassland Soil and Water Research Laboratory, 808 East Blackland Road, Temple, Texas 76502, USA

Abstract:

Results of modelling studies for the evaluation of water quality impacts of agricultural conservation practices depend heavilyon the numerical procedure used to represent the practices. Herein, a method for the representation of several agriculturalconservation practices with the Soil and Water Assessment Tool (SWAT) is developed and evaluated. The representationprocedure entails identifying hydrologic and water quality processes that are affected by practice implementation, selectingSWAT parameters that represent the affected processes, performing a sensitivity analysis to ascertain the sensitivity of modeloutputs to selected parameters, adjusting the selected parameters based on the function of conservation practices, and verifyingthe reasonableness of the SWAT results. This representation procedure is demonstrated for a case study of a small agriculturalwatershed in Indiana in the Midwestern USA. The methods developed in the present work can be applied with other watershedmodels that employ similar underlying equations to represent hydrologic and water quality processes. Copyright 2007 JohnWiley & Sons, Ltd.

KEY WORDS modelling; pollution prevention; best management practice; water quality; sediment; SWAT

Received 11 August 2006; Accepted 16 August 2007

INTRODUCTION

Agricultural conservation practices, often called bestmanagement practices or BMPs, are widely used as effec-tive measures for preventing or minimizing pollutionfrom nonpoint sources within agricultural watersheds.Because their effectiveness cannot be tested in all sit-uations, watershed managers rely on models to providean estimate of their impact on improving water qual-ity at the watershed scale. Many watershed managementprogrammes (e.g. EPA, 2005) have suggested modellingstrategies for development and implementation of water-shed management plans. In the absence of a standardprocedure for representing agricultural conservation prac-tices with watershed models, the results of modellingstudies are subject to modellers’ potentially inconsistentdecisions in evaluating practice performance. Establish-ing a standard procedure for representation of conserva-tion practices with a selected watershed model would:(i) reduce potential modeler bias; (ii) provide a roadmapto be followed; (iii) allow others to repeat the study; and(iv) improve acceptance of model results.

The Soil and Water Assessment Tool (SWAT; Arnoldand Fohrer, 2005) is often used to evaluate water qual-ity benefits of agricultural conservation practices. Kalinand Hantush (2003) reviewed key features and capa-bilities of widely cited watershed scale hydrologic and

* Correspondence to: Mazdak Arabi, Colorado State University, Depart-ment of Civil and Environmental Engineering, 1372 Campus Delivery,Fort Collins, Colorado 80523, USA.E-mail: [email protected]

water quality models with emphasis on the ability of themodels to represent practices and total maximum dailyload (TMDL) development. The review indicated thatthe SWAT model offers the greatest number of manage-ment alternatives for modelling agricultural watersheds.Additionally, the model has also been adopted as part ofthe USEPA Better Assessment Science Integrating Pointand Nonpoint Sources (BASINS) software package forapplications including support of TMDL analyses. SWATis also being used by many US federal and state agen-cies, including the US Department of Agriculture withinthe Conservation Effects Assessment Project (CEAP), toevaluate the effects of conservation practices.

SWAT already has an established method for modellingseveral agricultural practices including changes in fer-tilizer and pesticide application, tillage operations, croprotation, dams, wetlands, and ponds. The model also hasthe capacity to represent many other commonly usedpractices in agricultural fields through alteration of itsinput parameters. A number of previous modelling stud-ies have used SWAT to evaluate conservation practicesaround the globe. Vache et al. (2002) used the modelto evaluate the water quality benefits of crop rotation,riparian buffer strips, and strip-cropping practices in twowatersheds in central Iowa (50–100 km2). Representa-tion of filter strips, nutrient management plans, riparianforest buffers, critical area planting, grade stabilizationstructures, and trees and shrub planting with SWAT wasexamined by Santhi et al. (2003) in two segments ofthe Big Cypress Creek watershed in Texas with a totaldrainage area of 1674 km2. Chu et al. (2005) used SWAT

Copyright 2007 John Wiley & Sons, Ltd.

M. ARABI ET AL.

to study water quality impacts of tillage operations ina 3Ð5 km2 watershed in Maryland. However, Bracmortet al. (2006) is the only study, to our knowledge, that pro-vides detailed description of the procedure used for therepresentation of field borders, parallel terraces, grassedwaterways, and grade stabilization structures.

Lack of numerical guidelines for the representationof management practices is not limited to the SWATmodel. For example, Mostaghimi et al. (1997) used theAgricultural Nonpoint Source Pollution (AGNPS) modelto evaluate water quality benefits of several agriculturalconservation practices. Although the authors specifiedthe model parameters to be altered, no numerical pro-cedure was presented. Nietch et al. (2005) developed aframework, centred on using conservation practices, foraddressing critical needs for managing sediments withinwatersheds. The numerical representation of practice per-formance was identified as a vital research need. Like-wise, Shields et al. (2006) suggested that representationmethods should be developed to examine water qualityeffects of agricultural conservation practices with existingmodels.

Most previous work on the evaluation of conserva-tion practices has been done through applying a pri-ori empirical load reduction coefficients (ASCE-AWRI,2001). Application of this approach is limited becausethe performances of practices are site-specific, greatlyinfluenced by landscape characteristics and interactionsbetween practices. Process-based approaches should bedeveloped where water quality impacts of practices areevaluated based on their physical characteristics and spa-tial location. Such methodologies are important for theevaluation of management practices at the watershedscale, especially in ungauged basins with no/little moni-toring data, which are commonplace.

The objective of this study is to present a stepwiseprocedure for the representation and evaluation of hydro-logic and water quality impacts of several agriculturalconservation practices with the SWAT2005 model. Tothis end, we have focused on representation of the prac-tices for which SWAT does not offer an establishedmethod. These include seven practices that are installedin upland areas (contour farming, strip cropping, parallelterraces, cover crops, residue management, field borders,filter strips) and three practices that are implementedwithin small channels (grassed waterways, lined water-ways, and grade stabilization structures). The hydrologicand water quality processes affected by each practiceare reviewed, and the sensitivity of the SWAT outputsto the proposed representation is evaluated. Applicationof the methods for evaluation of impacts of these prac-tices on water quality is demonstrated for a small agri-cultural watershed in Indiana. Given that the practicesdiscussed in the present work have a long history ofuse around the world, we expect the methods will bewidely applied for selection and implementation of agri-cultural NPS pollution control strategies at the watershedscale.

HYDROLOGIC AND WATER QUALITYPROCESSES IN SWAT2005

SWAT (Arnold et al., 1998; Neitsch et al., 2005) isa process-based distributed-parameter simulation model,operating on a daily time step. The model was originallydeveloped to quantify the impact of land managementpractices in large, complex watersheds with varying soils,land use, and management conditions over a long periodof time. SWAT uses readily available inputs and hasthe capability of routing runoff and chemicals throughstreams and reservoirs, and allows for the addition offlows and the inclusion of measured data from pointsources. Moreover, SWAT has the capability to evaluatethe relative effects of different management scenarios onwater quality, sediment, and agricultural chemical yield inlarge, ungauged basins. Major components of the modelinclude weather, surface runoff, return flow, percolation,evapotranspiration (ET), transmission losses, pond andreservoir storage, crop growth and irrigation, groundwaterflow, reach routing, nutrient and pesticide loads, andwater transfer.

For simulation purposes, SWAT partitions the water-shed into subunits including subbasins, reach/main chan-nel segments, impoundments on the main channelnetwork, and point sources to set up a watershed.Subbasins are divided into hydrologic response units(HRUs) that are portions of subbasins with unique landuse/management/soil attributes. The geographical infor-mation system (GIS) interface of the model (AvSWAT;Di Luzio et al., 2002) enables users to specify a criticalsource area (CSA) that controls the number of subbasinsand the density of the channel network in the study area.This critical source area is the minimum area that isrequired for initiation of channel flow. The number ofsubbasins and the density of the channel network increasewith decreased CSA (Di Luizo et al., 2002; Arabi et al.,2006).

Hydrologic component

SWAT uses a modification of the SCS curve numbermethod (USDA Soil Conservation Service, 1972) tocompute surface runoff volume for each HRU. Peakrunoff rate is estimated using a modification of theRational Method. Daily or sub-daily rainfall data is usedfor calculations. Flow is routed through the channelusing a variable storage coefficient method developed byWilliams (1969) or the Muskingum routing method. Inthis study, SCS curve number and Muskingum routingmethods along with daily climate data, were used forsurface runoff and streamflow computations.

Sediment component

Sheet erosion is estimated for each HRU usingthe Modified Universal Soil Loss Equation (MUSLE)(Williams, 1975):

S D 11Ð8 ð �Q ð q ð A�0Ð56 ð K ð C ð P ð LS ð F�1�

Copyright 2007 John Wiley & Sons, Ltd. Hydrol. Process. (2007)DOI: 10.1002/hyp

REPRESENTATION OF AGRICULTURAL CONSERVATION PRACTICES WITH SWAT

where S is the sheet erosion on a given day (metric tons),Q is the surface runoff volume (mm water), q is the peakrunoff rate (m3 s�1), A is the area of the HRU (ha), Kis the USLE soil erodibility factor, C is the USLE coverand management factor, P is the USLE support practicefactor, LS is the USLE topographic factor, and F is thecoarse fragment factor.

Sediment deposition and channel degradation (i.e.channel erosion) are the two dominant channel processesthat affect sediment yield at the outlet of the water-shed. Whether channel deposition or channel degrada-tion occurs depends on the sediment loads from uplandareas and transport capacity of the channel network. Ifsediment load in a channel segment is larger than its sed-iment transport capacity, channel deposition will be thedominant process. Otherwise, channel degradation occursover the channel segment. SWAT estimates the transportcapacity of a channel segment as a function of the peakchannel velocity:

Tch D a ð vb �2�

where Tch (ton m�3) is the maximum concentration ofsediment that can be transported by streamflow (i.e.,transport capacity), a and b are user-defined coefficients,and v (m s�1) is the peak channel velocity. The peakvelocity in a reach segment at each time step is calculatedfrom:

v D ˛

nð Rch

2/3 ð Sch1/2 �3�

where ˛ is the peak rate adjustment factor with a defaultvalue of unity, n is Manning’s roughness coefficient, Rch

is the hydraulic radius (m), and Sch is the channel invertslope (m m�1). Channel degradation (Sdeg) and deposition(Sdep) in tons are computed as:

Sdep D{

�Si � Tch� ð Vch ; Si > Tch

0 ; Si � Tch�4�

Sdeg D{

0 ; Si ½ Tch

�Tch � Si� ð Vch ð Kch ð Cch ; Si < Tch�5�

where Si is the initial sediment concentration in thechannel segment (ton m�3), Vch is the volume of water inthe channel segment (m3), Kch is the channel erodibilityfactor (cm h�1 Pa�1), and Cch is the channel cover factor.The total amount of sediment that is transported out ofthe channel segment (Sout) in tons is computed as:

Sout D �Si C Sdeg � Sdep� ð Vout

Vch�6�

In Equation (6), Vout is the volume of water leavingthe channel segment (m3) at each time step.

Nutrient and pesticide components

Movement and transformation of several forms ofnitrogen and phosphorus and pesticides over the water-shed are accounted for within the SWAT model. Nutrientsare introduced into the main channel through surface

runoff and lateral subsurface flow, and transported down-stream with channel flow. It is worth mentioning that inthe current version of the model (SWAT2005), in-streamnutrient processes are not linked with sediment channelprocesses.

Pesticides loadings from land areas to streams andwater bodies are simulated in soluble or sorbed forms.Transport and transformation of pesticides in the channelnetwork is modelled with a simple mass balance analysis.The current version of the model (SWAT2005) has thecapacity to route only one pesticide through the channelnetwork.

METHODS AND MATERIAL

Ten important agricultural conservation practices wereselected for representation with the SWAT2005 model,based on their relatively common use in water qualityprojects. These include contour farming, strip-cropping,parallel terraces, cover crops, residue management, fieldborders, filter strips, grassed waterways, lined water-ways, and grade stabilization structures. A representationmethodology for the selected practices was developed andthen applied in the Smith Fry watershed in Indiana. Thewater quality variables of interest included sediment, totalP, total N, and pesticide (i.e. atrazine) yields. All compu-tations in this study were performed on a monthly basisfor the 2001–2025 time horizon.

Study area

The Smith Fry watershed located in Allen County,north-east Indiana, is a 7Ð3 km2 watershed in the MaumeeRiver basin in the midwestern portion of the USA(Figure 1). The major soil series in the watershed ishydrologic group C with moderate to low drainagecharacteristics. Land use in the watershed based on NASS2000 data (USDA-NASS, 2000) is comprised of 30%corn, 30% soybean, 29% pasture, 7% forested areas, and4% other covers.

For computational purposes, the watershed was sub-divided into 97 subwatersheds corresponding to a crit-ical source area (CSA) of 3 ha. Major soils and landuse were used to characterize each subwatershed. Thus,each subwatershed was comprised of only one hydrologicresponse unit (HRU). The overall land use in the water-shed changed by only 2% as a result of this watershedconfiguration.

Baseline simulation

The baseline values for the input parameters could beselected by (i) a model calibration procedure; or (ii) a‘suggested’ value obtained from the literature, previousstudies in the study area, or prior experience of the ana-lyst. In this analysis, baseline values were selected froma manual calibration exercise (Arabi et al., 2004, 2006).Specific management operations used for the baselinesimulation include: 10 May planting (‘plant begin, begin-ning of the growing season’) for corn and soybean, and 15


M. ARABI ET AL.

Figure 1. Location and elevation maps for the Smith Fry watershed

October for ‘harvest and kill’ operation for corn and soy-bean. A generic spring ploughing operation and fertilizerapplications were scheduled 7 days and 5 days beforethe beginning of the growing season for crops. Pesti-cide (atrazine) application was scheduled 3 days after thebeginning of the growing season only for corn-plantedareas. No crop rotation was considered in the baselinescenario. Rates of N-fertilizer and P-fertilizer application(in kg ha�1) were set, respectively, to 150 and 60 for corn,zero and 50 for soybean, 75 and 50 for winter wheat, and50 and 35 for pasture lands. Rate of application of thepesticide atrazine in corn areas was set to 1Ð5 kg ha�1.SWAT default values were used for other managementoperations.

Representation of conservation practices

Based on the function of a conservation practice, amethod was suggested for representing the practice withSWAT. This included a discussion of specific parametersthat need to be changed. Definition and purpose of prac-tices were obtained from national conservation practicestandards—NHPS (USDA-NRCS, 2005). Various hydro-logic and water quality processes that were consideredinclude: infiltration; surface runoff (peak and volume);upland erosion (sheet and rill erosion); gully and chan-nel erosion; nutrient and pesticide loadings from uplandareas; and within-channel processes.

Contour farming. Implementation of contour farmingpractices in a field will result in: (1) reduction of surfacerunoff by impounding water in small depressions; and(2) reduction of sheet and rill erosion by reducing erosivepower of surface runoff and preventing or minimizingdevelopment of rills. SCS curve number (CN ) and USLE

practice factor (USLE P ) were modified to simulate theseimpacts.

Neitsch et al. (2005) provide a table with recommen-dations for curve number in fields with different land useand soil characteristics under various hydrologic condi-tions adapted from Wischmeier and Smith (1978). Therecommendations also include impacts of contour farm-ing, strip-cropping, terracing, and residue management oncurve number.

However, curve number is a primary parameter usedfor calibration of the hydrologic component of the SWATmodel (Santhi et al., 2001), and thus the use of these val-ues directly from the table will not represent adequatelythe effect of the conservation practice. Therefore, the rec-ommendations were used to establish a more general rela-tionship between curve number before and after imple-mentation of contour farming, terraces, and residue man-agement. Figure 2 illustrates the impact of these practiceson curve number, using all the values in the table inNeitsch et al. (2005), which recommends curve numbervalues under various conditions. For contour farming,curve number was reduced from the default/calibratedvalue by 3 units. Table I presents USLE support prac-tice factor (USLE P ) for fields under contouring, strip-cropping, and terraced conditions, and these values wereused to simulate the erosion reduction due to implemen-tation of the corresponding practices.

Strip-cropping. Implementation of strip-cropping prac-tices in a field will result in: (1) reduction of sur-face runoff by impounding water in small depressions;(2) reduction of peak runoff rate by increasing surfaceroughness and slowing surface runoff; and (3) reductionof sheet and rill erosion by preventing development of



Figure 2. Effect of contouring, terracing, and residue management oncurve number

Table I. USLE P factor values for contouring, strip-cropping, andterracing (adapted from Wischmeier and Smith, 1978)

Land USLE Pslope (%)

Contour Strip- Terracingfarming cropping

Type1a Type2b

1 to 2 0Ð60 0Ð30 0Ð12 0Ð053 to 5 0Ð50 0Ð25 0Ð1 0Ð056 to 8 0Ð50 0Ð25 0Ð1 0Ð059 to 12 0Ð60 0Ð30 0Ð12 0Ð05

13 to 16 0Ð70 0Ð35 0Ð14 0Ð0517 to 20 0Ð80 0Ð40 0Ð16 0Ð0621 to 25 0Ð90 0Ð45 0Ð18 0Ð06

a Type1: Graded channels sod outlets.b Type2: Steep backslope underground outlets.Refer to ASAE (2003) for description of these types of terracing.

rills. SCS curve number (CN ), USLE support practicefactor (USLE P ), USLE cover factor (USLE C ) and Man-ning’s roughness coefficient for overland flow (OV N )should be modified for representation of strip-croppingpractices. Renard et al. (1997) suggest that impacts of astrip-cropping system on movement of runoff and thedeposition of sediment are taken into account in theUSLE practice factor. However, this does not reflect theprotection given to the soil by surface cover. This impactmust be represented through the USLE cover factor.

Similar to contour farming, in fields where strip-cropping is practised, curve number was reduced fromthe calibrated value by 3 units. Table I provides rec-ommendations for USLE P value under strip-croppingconditions. USLE C and OV N were adjusted based onweighted average values for the strips in the system. Theweighted average can be computed based on the area ofeach strip in the field.

Parallel terraces. Implementation of parallel terracesin a field will result in: (1) reduction of surface runoff

volume by impounding water in small depressions;(2) reduction of peak runoff rate by reducing length ofthe hillside; and (3) reduction of sheet and rill erosion byincreased settling of sediments in surface runoff, reduc-ing erosive power of runoff, and preventing formation ofrills and gullies. SCS curve number (CN ), USLE supportpractice factor (USLE P ), and slope length of the hillside(SLSUBBSN ) were modified for representation of parallelterraces.

Curve number value (CN ) was reduced by 6 unitsfrom its calibrated value to represent the impact ofparallel terraces on surface runoff volume (Figure 2).Also, Table I provides recommended USLE P values forterraced condition with two types of outlets. Slope length(SLSUBBSN ) was modified to (ASAE, 2003):

SLSUBBSN D �x ð SLOPE C y� ð 100

SLOPE�7�

where x (dimensionless) is a variable with values from0Ð12–0Ð24, y (dimensionless) is a variable influenced bysoil erodibility, cropping systems, and crop managementpractices, and SLOPE is average slope of the field.Variable x can be determined from ASAE standardS268Ð4 FEB03 (ASAE, 2003) based on its geographicallocation in the USA. Variable y can take values of 0Ð3,0Ð6, 0Ð9, or 1Ð2. The low value (i.e. 0Ð3) is used for highlyerodible soils with conventional tillage and little residue,while the high value (i.e. 1Ð2) is used for soils with verylow erodibility and no-till/residue (residue more than 3Ð3t ha�1) management condition.

The USLE topographic factor (LS ) in the MUSLEEquation (1) is determined as a function of slope(SLOPE ) and slope length (SLSUBBSN ) of the field:

LS D(

SLSUBBSN

22Ð1)m

ð �65Ð41 ð sin2 ˛ C 4Ð56 ð sin ˛ C 0Ð065�;

m D 0Ð6[1 � exp��35Ð835 ð SLOPE�];

˛ D tan�1�SLOPE� �8�

As evident in Equation (8), slope length (SLSUBBSN )has a more significant impact on the LS factor insubbasins with higher slope (SLOPE ).

Peak runoff rate is also affected by changing slopelength. SWAT uses the modified Rational Method forcomputing the peak flow rate for each HRU:

q D ˛tc ð Q ð A

3Ð6 ð tc�9�

where tc is time of concentration, and ˛tc reflects thefraction of daily rainfall that occurs during the time ofconcentration. Time of concentration is computed as:

tc D SLSUBBSN0Ð6 ð OV N0Ð6

18 ð SLOPE�10�

where OV N is the overland Manning’s roughness coef-ficient. Thus, the total impact of slope length on upland


M. ARABI ET AL.

erosion can be estimated as:

S / SLSUBBSN�m�0Ð336� �11�

where S is the sheet erosion computed for the HRU(Equation (1)). In fields where m is less than 0Ð336 orslope is less than 0Ð023, SWAT-estimated upland erosionis inversely correlated to slope length (Figure 3). Thus,reducing slope length to represent parallel terraces willresult in higher erosion estimates for these conditions. Forsuch areas, adjustment of slope length (SLSUBBSN ) forthe representation of parallel terraces with SWAT shouldbe skipped.

Residue management. Implementation of residue man-agement practices in a field will result in: (1) slowingdown surface runoff and peak runoff by increasingland cover and surface roughness; (2) increasing infiltra-tion/reducing surface runoff by decreasing surface sealingand slowing down the overland flow; and (3) reducing

Figure 3. Impact of interplay between slope and slope length on SWATupland erosion estimation

Figure 4. USLE C ð USLE P before and after representation of residuemanagement with USLE C0orig = 0Ð2, and rsd D 500 kg ha�1

sheet and rill erosion by reducing surface flow volume,overland flow rate, raindrop impact, providing more sur-face cover, and preventing development of rills. SCScurve number (CN ), Manning’s roughness coefficient foroverland flow (OV N ), and USLE cover factor (USLE C )were modified for the representation of residue manage-ment practices with SWAT.

In fields with residue management practices, curvenumber value was reduced by 2 units from its default/calibrated value, as demonstrated in Figure 2. The directimpact of surface residue on erosion estimates is reflectedin computation of USLE cover factor. SWAT updatesUSLE cover factor for each field on a daily basis as afunction of residue cover (SOL COV ) on the surface:

USLE C D 0Ð8k ð USLE C0�1�k�;

k D exp��0Ð00115 ð SOL COV� �12�

where USLE C0 is the original minimum of the USLEcover factor that is typically obtained from calibration.USLE C decreases as plant residue increases during thegrowing season.

Users can define a harvest efficiency value for eachHRU that specifies the amount of residue biomass thatis removed from the HRU in the harvest operation.The current version of the SWAT model does notincorporate the impact of residue biomass on sheeterosion and transport of nutrients from upland fields.Thus, an alternative procedure for representation of theimpact of residue biomass on sheet erosion was applied.The alternative procedure included a manipulation offactors in the MUSLE equation as follows:

(i) Adjust USLE practice factor (USLE P ) for the field:

USLE P D USLE C

USLE C0D 0Ð8�k0�1� ð USLE C0�1�k0�;

k0 D exp��0Ð00115 ð rsd� �13�

where rsd reflects the residue biomass left on thesurface. For rsd D 500 kg ha�1 and USLE C0 D 0Ð2,USLE P would be 0Ð55.

(ii) Adjust minimum USLE cover factor (USLE C0 ):since users can define only one USLE practice factor(USLE P ) for a given field, minimum USLE coverfactor (USLE C ) were altered such that the product ofUSLE C and USLE P for the growing season remainsthe same. This is because residue biomass is left onthe surface to reduce upland erosion when there is nocrop growing. The USLE C0 was adjusted as:

USLE C0mod D USLE C0orig

USLE P�14�

where USLE C0orig and USLE C0mod are the originaland modified minimum USLE cover factor, respec-tively. The original minimum USLE cover factor iseither the SWAT default value or obtained from cali-bration.



Table II. Values of Manning’s roughness coefficient for overlandflow (Neitsch et al., 2005)

Characteristics of land surface OV N

No till, no residue 0Ð14No till, 0Ð5–1 t ha�1 residue 0Ð20No till, 2–9 t ha�1 residue 0Ð30

Table III. An example of a corn–soybean rotation practice

Year Operation Crop Date

month day

1 Tillage CORN May 31 N-fertilizer CORN May 51 P-fertilizer CORN May 51 Plant begin CORN May 101 Pesticide application CORN May 131 Harvest and kill CORN October 151 Tillage SOYB May 32 N-fertilizer SOYB May 52 P-fertilizer SOYB May 52 Plant begin SOYB May 102 Harvest and kill SOYB October 15

Figure 4 shows how the representation procedureworks. For the period of the year when no crop is grow-ing, the residue biomass is effective in reducing theupland erosion (left asymptotic behaviour). The impactdiminishes as crop biomass increases during the growingseason (right asymptotic behaviour).

Residue management influences surface roughness ofthe field. Recommended OV N values for crop lands withresidue are provided in Table II.

Conservation crop rotation. SWAT contains a manage-ment feature for representation of crop rotation practices.The management input files (.mgt) for HRUs accommo-date crop rotation in successive years. An example of acorn–soybean rotation is provided in Table III. Opera-tions in bold are required management operations.

Cover crops. Cover crops were represented with SWATby scheduling a crop rotation within a single year. Anexample of management operations for a winter wheat(WWHT) cover in a corn field is provided in Table IV.Operations in bold are required management operations.Notice that SWAT does not allow growing of two cropsin a single HRU simultaneously. Therefore, ‘plant begin,beginning of the growing season’ and ‘harvest and kill’operations were scheduled for the cover crop both inspring and winter covering the time the main crop isnot growing.

Field borders. Field borders are installed along theperimeter of a field to reduce sediment, nutrients, pesti-cides, and bacteria in surface runoff as it passes throughthe edge-of-the-field vegetative strip. Pollutant loads insurface runoff are trapped in the strip of vegetation.

Table IV. An example of a winter wheat-cover in a corn field

Year Operation Crop Date

month day

1 Plant begin WWHT March 11 Harvest and kill WWHT May 21 Tillage May 31 N-fertilizer May 51 P-fertilizer May 51 Plant begin CORN May 101 Pesticide application CORN May 131 Harvest and kill CORN October 151 Plant begin WWHT October 201 Harvest and kill WWHT December 31

Figure 5. Effect of strip width on trapping efficiency of vegetative strips

SWAT provides a specific method to incorporate edge-of-field filter strips through the FILTERW parameter thatreflects the width of the strip. The trapping efficiency forsediment, nutrients and pesticides (trapef sed) is calcu-lated from:

trapef sed D 0Ð367 ð FILTERW0Ð2967 �15�

Equation (15) implicitly incorporates the higher effi-ciency of the front portion of the strips in trapping sedi-ments, nutrients, and pesticides (Figure 5). For bacteria,the trapping efficiency (trapef bac) is calculated:

trapef bac D �11Ð8 C 4Ð3 ð FILTERW�

100�16�

While SWAT uses the same trapping efficiency(Equation (15)) for sediments, nutrients, and pesticides,users can manipulate the FILTERW parameter in orderto modify both linear and exponential coefficientsin the equation. For example, if the desired formof Equation (15) for phosphorus is trapef p D c ðFILTERWk , a modified width of the field bordercan be computed as FILTERWmod D ck�0Ð367 ðFILTERW0Ð2967��k , where FILTERW reflects the actualwidth.


M. ARABI ET AL.

Filter strips. The function of filter strips is similar tothe field borders except filter strips are installed along theedge of a channel segment. Therefore, pollutant loadsfrom the area that drains into the channel segment aretrapped in the vegetative strip. For representation of filterstrips, the parameter FILTERW in Equations (15) and(16) for the fields that constitute the drainage area for thechannel segment was adjusted.

Grassed waterways. Grassed waterways will increasesediment trapping in a channel segment by reducing flowvelocity. Also, peak flow rate/flow velocity in the channelsegment will be reduced by increasing roughness of flowin the channel segment. Moreover, gully erosion in thechannel segment will be reduced by establishing chan-nel cover in streambed/banks. Channel width (CH W2 ),channel depth (CH D), channel Manning’s roughnesscoefficient (CH N2 ) and channel cover factor (CH COV )were adjusted in channel segments where grassed water-ways are installed.

Manning’s roughness coefficient for flow in the chan-nel segment (CH N2 ) was adjusted based on the typeand density of vegetation used in the grassed waterway.Fiener and Auerswald (2006) assumed CH N2 rangesbetween 0Ð3 and 0Ð4 over the year. A CH N2 value of 0Ð1was suggested for grassed waterways under poor condi-tions. These values are typical in the case of dense grassesand herbs under non-submerged conditions (Jin et al.,2000; Abu Zreig, 2001). Channel cover factor (CH COV )was adjusted to 0Ð001 (fully covered). Note that 0Ð001 isan arbitrary very low value that is used instead of zero. Ifthis value is set to zero, the default value will be used inSWAT simulations. Channel width and depth are typicallydefined by the design specifications.

Lined waterways/stream channel stabilization. Thefunction of lined waterways and stream channel stabi-lization practices is to cover a channel segment witherosion resistant material to reduce gully erosion. Rep-resentation of these practices was achieved by adjustingchannel width (CH W2 ), channel depth (CH D), channelManning’s roughness coefficient (CH N2 ), and channelerodibility factor (CH EROD).

Channel width and depth are typically defined bythe design specifications. Channel erodibility factor wasadjusted to 0Ð001 (non-erodible). Again, a very smallnumber (i.e. 0Ð001) was used instead of zero to avoidthe use of default values. Channel Manning’s roughnesscoefficient was adjusted according to values in Table V.

Grade stabilization structures. Grade stabilizationstructures (GSS) are used to control the grade and headcutting in natural or artificial channels. Implementationof grade stabilization structures will increase sedimenttrapping by reducing flow velocity in the channel seg-ment. Peak flow rate/flow velocity in the channel segmentwill be decreased by reducing the slope of the channelsegment. Gully erosion will be reduced in the channel

Table V. Manning’s roughness coefficient for lined channels(adapted from USDA-NRCS, 2005)

Lining CH N2

Concrete Trowel finish 0Ð012–0Ð014Float finish 0Ð013–0Ð017Shotcrete 0Ð016–0Ð022Flagstone 0Ð020–0Ð025

1/ Riprap - (angular rock) 0Ð027�D50CH S2�0Ð147

Synthetic turf reinforcement Manufacturer’sFabrics and grid pavers recommendations

1/ Applies on slopes between 2 and 40% with a rock mantle thicknessof 0Ð05 ð D50 where:D50 D median rock diameter (m), CH S2 D lined section slope (m m�1)(0Ð02 � CH S2 � 0Ð4)

segment by reducing channel erodibility and flow veloc-ity. Slope of the channel segment (CH S2 ) and channelerodibility factor (CH EROD) were adjusted for the rep-resentation of grade stabilization structures.

The slope of the upstream channel segment (CH S2 )was adjusted as follows:

CH S2 D CH S2pre � h

CH L2�17�

where CH S2 is the slope of the upstream channel afterimplementation of the GSS, CH S2pre is the slope of theupstream channel before implementation of the GSS, h(m) reflects the height of the GSS, and CH L2 (m) is thelength of upstream channel segment. Channel erodibilityfactor (CH EROD) was adjusted to 0Ð001 (non-erodible).

Sensitivity analysis

Representation of conservation practices with themethod presented in this paper is based on altering appro-priate model parameters. The methodology would behandicapped if the model was not sensitive to the selectedparameters. The SWAT model is a distributed-parametermodel that has hundreds of parameters. Some of theseparameters represent initial or boundary conditions whileothers are forcing factors. Selection of a parameter thatis an insensitive parameter under any given temporal andspatial condition would not be appropriate for represen-tation of practices. Therefore, a sensitivity analysis wasconducted to check that parameters selected for represen-tation of practices are not insensitive parameters.

The Morris One-At-a-Time (OAT) (Morris, 1991) pro-cedure was used for sensitivity analysis of the SWATmodel. Morris OAT is a sensitivity analysis technique thatfalls under the category of screening methods (Saltelliet al., 2000). Each model run involves perturbation ofonly one parameter in turn. In this way, the variation ofmodel output can be unambiguously attributed to pertur-bation of the corresponding factor. For each input param-eter, local sensitivities are computed at different points ofthe parameter space, and then the global (main) effect isobtained by taking their average. The elementary effectof a small perturbation of the ith component of the p-dimensional parameter vector (˛i) at a given point in the



parameter space ˛ D �˛1, . . . , ˛i�1, ˛i, ˛iC1, . . . , ˛p�) is(Morris, 1991):

d�˛ij˛� D[y�˛1, . . . , ˛i�1, ˛i C , ˛iC1, . . . , ˛p�

�y�˛�]

�18�

where y�˛� corresponds to model output. The results arequantitative, elementary, and exclusive to the parame-ter ˛i. However, the elementary effect computed fromEquation (18), i.e. d�˛ij˛�, is only a partial effect anddepends on the values chosen for the other elements ofthe parameter vector (˛j). A finite distribution (Fi) ofelementary effects of parameter ˛i is obtained by sam-pling at different points of the space, i.e. different choicesof parameter set ˛. The mean of the distributions isindicative of the overall influence of the parameter onthe output, while the variance demonstrates interactionswith other parameters and nonlinearity effects.

Sensitivity of each model parameter fromEquation (18) was estimated at 10 different points of theparameter space. Therefore, a total of 20 model simula-tions were performed for each parameter in the sensitivityanalysis. Table VI provides a list of parameters that wereconsidered in the analysis, their definitions, units, andsuggested ranges (lower and upper bounds). The sug-gested range of model parameters were obtained from theSWAT users’ manual (Neitsch et al., 2005) and our previ-ous experience in the same study watershed. The analysiswas conducted for both daily and monthly simulations.

Evaluation of conservation practices

The water quality impacts of the conservation practicesdescribed previously were evaluated at the outlet of theSmith Fry watershed. In general, these practices can beclassified into two groups: (1) practices that are installedon upland areas, including conservation crop rotation,

Table VI. SWAT parameters included in the sensitivity analysis with their lower bound (LB) and upper bound (UB). Parametersspecified by Ł were altered as a percentage of the default values

NO SWAT Symbol Definition Units LB UB

1 ALPHA BF baseflow alpha factor for recession constant days 0 12 BIOMIX biological mixing efficiency 0Ð01 13 CH COV channel cover factor 0Ð001 0Ð64 CH K1 Ł effective hydraulic conductivity in tributary channels mm/hr �0Ð5 15 CH K2 effective hydraulic conductivity in the main channel mm/hr 0Ð1 1506 CH N1 Manning’s roughness coefficient for tributary channels 0Ð008 0Ð0657 CH N2 Manning’s roughness coefficient for the main channel 0Ð01 0Ð38 CH S1 Ł average slope for tributary channels �0Ð5 19 CH S2 Ł average slope for the main channels �0Ð5 1

10 CMN rate factor for mineralization of active organic nutrients 0Ð001 0Ð00311 CN Ł SCS runoff curve number �0Ð5 0Ð1512 DAY CORN day of ‘planting/beginning of growing season’ for corn 1 3013 DAY SOYB day of ‘planting/beginning of growing season’ for soybean 1 3014 DAY WWHT day of ‘planting/beginning of growing season’ for winter wheat 1 3015 DDRAIN depth of tile drains mm 0 500016 ESCO soil evaporation compensation factor 0Ð001 117 FILTERW width of edge-of-field filter strip m 0 518 GW DELAY groundwater delay day 0 50019 GW REVAP groundwater ‘revap’ coefficient 0Ð02 0Ð220 GWQMN threshold depth of water in the shallow aquifer for return flow mm 0 500021 HARVEFF harvest efficiency22 LABP initial soluble P in soils mg/kg 1 5023 NPERCO nitrogen percolation coefficient 0Ð001 124 ORGN initial organic N in soils mg/kg 1 10 00025 ORGP initial organic P in soils mg/kg 1 400026 OV N Manning’s roughness coefficient for overland flow 0Ð1 0Ð327 PERCOP pesticide percolation coefficient 0Ð001 128 PPERCO phosphorus percolation coefficient 10 m3/Mg 10 17Ð529 RSDCO residue decomposition coefficient 0Ð02 130 SFTMP snowfall temperature oC �5 531 SLOPE Ł average slope steepness �0Ð5 132 SLSUBBSN Ł average slope length m �0Ð5 133 SOL AWC Ł available soil water capacity m/m �0Ð5 134 SOL K Ł saturated hydraulic conductivity mm/hr �0Ð5 135 SOLN initial NO3 in soils 0Ð1 536 SPCON linear coefficient for in-stream channel routing 0Ð0001 0Ð0137 SURLAG surface runoff lag time day 1 1238 USLE C Ł minimum value of USLE equation cover factor �0Ð5 139 USLE K Ł USLE equation soil erodibility factor �0Ð5 140 USLE P USLE equation support practice factor 0Ð2 1


M. ARABI ET AL.

cover crop, contour farming, strip-cropping, parallel ter-racing, residue management, field border and vegetativefilter strip; (2) practices that are installed within the chan-nel network, including grassed waterway, lined water-way, and grade stabilization structure. It should be notedthat although filter strips are implemented along the edgeof streams, they do not impact within-channel processes.Therefore, filter strips were evaluated with the group ofupland practices. The upland practices were evaluatedwhen implemented in areas with corn land use. Fieldswith corn land use cover nearly 30% of the total water-shed area.

Impacts of within-channel practices were evaluatedwhen installed within streams with different geomorpho-logic characteristics. The channels in the watershed wereclassified based on the Strahler Scheme (Smart, 1972)that is often used for ranking the geomorphologic orderof channel segments:

1. Channels that originate at upland areas are definedas class 1 streams. Class 1 streams do not have anyupstream channels.

2. A stream of class j C 1 is generated when two streamsof class j meet.

3. When two streams of classes i and j meet, the class ofthe immediately downstream channel segment is max(i,j).

4. The class of the watershed is the highest stream class.

The Smith Fry watershed with the subdivision schemeshown in Figure 6 is of class 4. Table VII providesinformation regarding the class of streams in the studywatershed. The impacts of within-channel practices wereevaluated when they were considered for implementationin streams of class 1, streams of classes 2 and lower,streams of classes 3 and lower, and streams of classes4 and lower. The latter case covers the entire channelnetwork.

Figure 6. Class of streams in the computational setup for the Smith Frywatershed with CSA D 3 ha

Table VII. Fraction of stream classes in the Smith Fry watershed

Streamclass

Number ofsegments

Length(m)

Fraction of totaldrainage network (%)

1 47 13 240 452 22 6333 213 25 8570 294 3 1457 5Total 97 29 600 100

The extent of the channel network derived with the GISinterface of the SWAT model based on a Digital ElevationModel (DEM) varies with the choice of the user-specifiedcritical source area (CSA). Selection of smaller valuesfor the CSA will result in a larger number of class 1channel segments with smaller drainage areas. Thus, theclass of channel segments based on the Strahler Schemein a modelling effort is a function of the selected CSA.

One limitation of the SWAT model is that nutrientchannel processes are not linked with sediment channelprocesses. Therefore, evaluation of the impacts of within-channel practices on transport of sediment-bound nutri-ents, especially phosphorus, with the method discussedpreviously will not be meaningful. Here, evaluation ofwithin-channel practices was limited to impacts of sedi-ment yield at the outlet of the study watershed.

RESULTS AND DISCUSSION

The results of SWAT simulations for the baseline sce-nario showed that daily streamflows would range between0 and 6Ð06 m3 s�1 over the 2001–2025 simulationperiod. The average daily streamflow was estimated at0Ð084 m3 s�1 with a standard deviation of 0Ð24 m3 s�1.Baseflow, on average, contributed nearly 75% of the dailystreamflow, while this contribution ranged from 0Ð1% to100%.

Sensitivity analysis

The results of the sensitivity analysis indicated thatparameters selected for representation of conservationpractices were sensitive parameters. Table VIII summa-rizes the Morris’ OAT sensitivity indices fromEquation (18) for SWAT parameters in Table VI. A neg-ative sensitivity index indicates that the parameter andthe output variable are inversely correlated.

Curve number was the most sensitive parameter for alloutput variables but baseflow by a large margin. Chan-nel process parameters were among the most sensitiveparameters for sediment computations. These parametersincluded the parameters that influence transport capac-ity of the channel network, such as CH N2 and SPCON.Moreover, sediment computations were not sensitive tothe channel cover factor (CH COV ). Channel cover isused for estimation of channel erosion as described inEquation (5). These indicated that channel deposition wasthe dominant channel process in the study watershed forthe simulation period. It is worthwhile to re-emphasize



Table VIII. Sensitivity of SWAT parameters in Table VI. Top fiveparameters in each category are highlighted

Parameter StreamFlow

Sediment Total P Total N Pesticide

ALPHA BF 0Ð03 0Ð25 0Ð21 0Ð21 0.35BIOMIX �0Ð03 0Ð07 0Ð09 0Ð07 0Ð06CH COV 0Ð00 0Ð00 0Ð00 0Ð00 0Ð00CH K1 �0Ð01 �0Ð01 �0Ð01 0Ð00 0Ð00CH K2 �0Ð01 �0Ð16 �0Ð12 �0Ð12 �0Ð14CH N1 0Ð00 �0Ð08 �0Ð12 �0Ð09 0Ð00

CH N2 �0Ð01 −0.75 �0Ð14 �0Ð13 �0Ð19CH S1 0Ð00 0Ð10 0Ð13 0Ð08 �0Ð01CH S2 0Ð00 0Ð20 0Ð04 0Ð03 0Ð04CMN 0Ð02 0Ð03 0Ð04 0Ð11 0Ð00

CN 2.39 4.28 4.16 3.26 6.78

DAY CORN 0Ð00 0Ð00 0Ð01 0Ð03 0.45DAY SOYB 0Ð00 0Ð00 0Ð01 0Ð00 0Ð00DAY WWHT 0Ð00 0Ð00 0Ð00 0Ð00 0Ð00

DDRAIN 0.15 0Ð20 0Ð37 0Ð32 0Ð35

ESCO 0.27 0Ð20 0Ð22 0Ð35 0.68

FILTERW 0Ð00 0Ð30 0.74 0.60 0.60GW DELAY �0Ð04 0Ð00 0Ð00 �0Ð08 �0Ð01GW REVAP �0Ð07 0Ð00 0Ð00 �0Ð09 0Ð00

GWQMN −0.24 �0Ð04 �0Ð01 �0Ð21 �0Ð03HARVEFF �0Ð01 �0Ð01 �0Ð12 �0Ð13 �0Ð01LABP 0Ð00 0Ð00 0Ð14 0Ð00 0Ð00NPERCO 0Ð00 0Ð00 �0Ð01 0Ð09 0Ð00

ORGN 0Ð01 0Ð03 0Ð05 0.81 �0Ð01

ORGP 0Ð00 0Ð00 0.85 0Ð00 0Ð00OV N �0Ð01 �0Ð10 �0Ð16 �0Ð11 �0Ð01PERCOP 0Ð00 0Ð00 0Ð03 0Ð01 0Ð81PPERCO 0Ð00 0Ð00 0Ð00 0Ð00 0Ð00RSDCO 0Ð00 0Ð00 0Ð00 0Ð00 0Ð00SFTMP �0Ð01 �0Ð04 �0Ð03 �0Ð02 �0Ð02SLOPE 0Ð07 0Ð40 0Ð68 0Ð54 0Ð21SLSUBBSN �0Ð08 0Ð00 0Ð01 0Ð00 �0Ð02

SOL AWC −0.31 �0Ð29 �0Ð33 �0Ð50 �1Ð01SOL K �0Ð03 �0Ð07 �0Ð07 �0Ð06 �0Ð38SOLN 0Ð00 0Ð00 0Ð00 0Ð00 0Ð00

SPCON 0Ð00 0.67 0Ð00 0Ð00 0Ð00SURLAG 0Ð01 0Ð10 0Ð12 0Ð08 �0Ð03USLE C 0Ð00 0Ð21 0Ð35 0Ð26 0Ð00

USLE K 0Ð00 0.50 0.83 0.65 0Ð01

USLE P 0Ð00 0.49 0.80 0.60 0Ð01

that sediment and nutrient channel processes of the SWATmodel are not linked. This explains why sediment chan-nel process parameters are not as sensitive for total P andtotal N computations.

SWAT uses the user-defined harvest efficiency(HARVEFF ) parameter to update USLE cover factorfor the period after the harvest operation. Interestingly,the results in Table VIII indicated that harvest efficiency(HARVEFF ) had marginal impacts on flow and sedimentcomputations of the SWAT model. Therefore, adjustingHARVEFF would not be adequate for representation ofresidue management practices. In this study an alterna-tive approach based on manipulation of parameters inthe MUSLE equation (Equation (1)) was suggested andapplied to evaluate residue management practices.

Table IX. Estimated effectiveness (r) of upland practices imple-mented within areas with corn land use using the proposed rep-

resentation procedures

Management practice r (%)

Sediment TotalP

TotalN

Pesticide

Corn–soybean rotation inTable III

0 0 0 40

Cover crop (winter wheatcover crop in Table IV)

3 10 14 2

Contouring 5 18 24 16Strip-cropping (50% oat

strips)10 20 29 16

Residue management(rsd D 2000 kg ha�1)

15 23 35 17

Parallel terracing (steepbackslope undergroundoutlet)

15 27 36 27

Field border(FILTERW D 5 m)

3 21 24 32

Demonstration of methods in the Smith Fry Watershed inIndiana, USA

Upland conservation practices. Effectiveness of con-servation practices that are implemented within agricul-tural fields was evaluated by comparing model simula-tions with no practice and simulations with the practiceimplemented in fields under corn land use. Areas withcorn land use cover nearly 30% of the total area of theSmith Fry watershed based on the NASS 2000 land use.Effectiveness of each practice (r) was computed as:

r D y1 � y2

y1ð 100 �19�

where y1 and y2 reflect model outputs before and afterimplementation of the practice, respectively.

Table IX provides a summary of results on the effect ofupland practices on water quality of the study watershed.Corn–soybean rotation for corn, as described in Table III,did not impact sediment and nutrient yields comparedwith continuous corn, but reduced atrazine use by nearly40% over the 25-year simulation period. This was mainlybecause atrazine was applied only in years when cornwas planted. Among all other upland practices, parallelterraces were the most effective for reducing sedimentsand nutrients. Filter strips were considered an uplandpractice because they impact pollutant loads from uplandareas and not within-channel processes. Although waterquality benefits of field borders and filter strips are thesame in this analysis, filter strips have significantly highereffectiveness per unit area than field borders.

Effectiveness of residue management/no till practiceswere evaluated for various residue biomasses left on thesoil surface after the harvest operation. Results in Table Xindicate that effectiveness of residue management prac-tices increased with higher residue biomass (rsd ) left onthe soil surface.

Figure 7 shows the effectiveness of filter strips withvarious widths. As width of the vegetative strip increased,


M. ARABI ET AL.

Table X. Estimated effectiveness (r) of residue biomass (rsd ) inthe residue management/no till practice implemented within areas

with corn land use in the Smith Fry watershed

rsd r (%)(kg ha�1)

Sediment Total P Total N Pesticide

500 7 16 28 131000 10 20 32 142000 15 23 35 17

Figure 7. Estimated effect of width of edge-of-the-field filter stripsimplemented within areas with corn land use on sediment and atrazineyields in the Smith Fry watershed. Variable trapef sed is defined in

Equation (15)

higher pollutant load reduction was achieved. The circlesand squares in this figure show the effectiveness of filterstrips computed based on SWAT simulations. The lineswere computed based on the best fit between the trappingefficiency in Equation (15) and the effectiveness basedon SWAT simulations, shown in circles and squares.Interestingly, it is evident that the estimated reductionof atrazine load based on SWAT simulations was lin-early proportional to the trapping efficiency of filter stripsfor sediments, nutrients, and pesticides in Equation (15).The proportionality constant is 69. The SWAT modelassumes 75% application efficiency for pesticide applica-tion. Moreover, the calibrated value for the pesticide per-colation coefficient was 5%. Thus, considering channeldegradation and erosion do not affect atrazine transport,a priori estimate of the proportionality constant betweenatrazine load reduction and the trapping efficiency inEquation (15) would also be nearly 70. Similar trendswith different proportionality constants were observedfor total P and total N. These indicated that comparisonof impacts of filter strips with different widths on nutri-ent and atrazine yields could be simply achieved withoutSWAT simulations, using only Equation (15).

The results for sediment yield, however, were dif-ferent. Sediment reductions estimated by SWAT werenot linearly proportional to the trapping efficiency givenby Equation (15). As the results of sensitivity indicated,channel deposition was the dominant channel process in

Table XI. Estimated effects of within-channel practices installedwithin channels of various classes

Stream class Sediment reduction r (%)

Grassedwaterway1

Linedwaterway2

Grade stab.structure3

1 1 0 11 to 2 — �4 21 to 3 — �3 91 to 4 — �302 74

1 Manning’s roughness coefficient: CH N2 D 0Ð3.2 Concrete-trowel finish: CH N2 D 0Ð014.3 Height of the structure: h D 1Ð2 m.

the study watershed. Therefore, sediment transport in thewatershed channels behaved nonlinearly with the edge-of-the-field vegetative filter strips. Thus, more accurateestimates of the impacts of filter strips on sediment yieldwere achieved by SWAT simulations.

Within-channel conservation practices. Grassed water-ways are typically installed to prevent gully erosion dueto concentrated flow. Thus, the performance of grassedwaterways was evaluated when implemented within class1 streams with drainage areas less than 15 ha. Linedwaterways and grade stabilization structures implementedwithin various stream classes in Table XI were evalu-ated. The class of the streams reflects the location of thepractices within the watershed shown in Figure 6.

The results indicated that implementation of grassedwaterways and grade stabilization structures within class1 channels would not reduce sediment yield at the out-let significantly. The highest water quality benefits fromgrade stabilization structures would be achieved whenimplemented within class 4 streams that are located atthe very downstream part of the watershed, and consti-tute less than 5% of the channel network (Table VII).Conversely, implementation of lined waterways wouldincrease sediment at the outlet. The calibrated value ofManning’s roughness coefficient for a channel segment(CH N2 ) was 0Ð04, which is a typical value for naturalstreams. Lining the channels with concrete finish woulddecrease CH N2 to 0Ð014. As a result, flow velocity inthe channel segments and consequently their transportcapacity would increase. Increasing the transport capac-ity of channel segments, especially class 4 streams at thedownstream part of the watershed, would reduce sed-iment deposition in the channel network. Thus, moresediment would be carried to and out of the watershedoutlet.

Additional analysis was performed to evaluate thesensitivity of the suggested representation method forgrade stabilization structures to the height of the structure.The results depicted in Figure 8 indicate insensitivityof the procedure to values higher than 1Ð25 m in theSmith Fry watershed. The SWAT model provides aspecific option for modelling reservoirs installed withinthe channel network. Stabilization structures should be



Figure 8. Sensitivity of estimated sediment reduction in the Smith Frywatershed to height of grade stabilization structure h

modelled as a reservoir when they function as smalldams.

The water quality benefits of conservation practicesevaluated in this study are site specific and are likelyto vary in other watersheds with different characteristics.However, the representation methodology could be usedin other studies. A standard procedure as recommendedherein could reduce the subjectivity of results to potentialmodellers’ bias and would help watershed managersendorse and apply the results in the decision makingprocess.

Of importance is that evaluation of the effectivenessof management actions, such as agricultural conservationpractices, may be affected by the watershed subdivisionschemes used for parameterization of the watershed. Forexample, a watershed is divided into subwatersheds andchannel segments in SWAT. Arabi et al. (2006) showedthat the estimated effectiveness of practices clearly variedwith the number of subwatersheds. While the methods inthe current study will allow users to model the impactsof practices for a given watershed subdivision scheme,additional analysis similar to the approach by Arabi et al.(2006) would provide complimentary information forpractice evaluation.

Finally, the methods developed in the present studyare based on assessment of impacts of conservationpractices on hydrologic and erosion processes representedin SWAT primarily by the SCS curve number methodand the Modified Universal Soil Loss Equation. Becausethese two methods are the most widely used methods inmathematical representation of watershed models (Nietchet al., 2005), they can be applied with many otherwatershed models besides SWAT at various spatial scales.

CONCLUSIONS

A standard procedure was suggested for the representa-tion of 10 agricultural conservation practices using the

SWAT model. The representation method is based onhydrologic and water quality processes that are modi-fied by the practice. Appropriate process parameters wereidentified and altered to mimic the functionality of thepractices. A global sensitivity analysis was conducted toinvestigate the sensitivity of model outputs to the selectedprocess parameters. The analysis provided herein can beused by (i) watershed modellers and managers to eval-uate water quality impacts of the selected conservationpractices at the watershed scale, and (ii) SWAT modeldevelopers to increase the capacity of the model for rep-resentation of these practices. For example, it becameevident that flow and sheet erosion computations of themodel are not sensitive to the harvest efficiency coef-ficient, which could potentially, be used to representresidue management practices.

The applicability of the recommended procedure wasdemonstrated in a small, primarily agricultural, watershedin Indiana. The results indicated that SWAT simulationswere sensitive to the methods applied for representationof the selected conservation practices. The demonstrationstudy revealed that practices installed within upland areascould potentially reduce sediment, nutrient, and pesticideloadings from agricultural nonpoint sources. Moreover,within-channel practices could reduce the transport ofpollutant loads to the watershed outlet.

The process outlined in this paper could be usedto develop practice representation methods within otherwatershed models, particularly those that employ the SCScurve number method for representation of runoff pro-cesses and the Universal Soil Loss Equation for erosionestimation. However, care should be taken when deal-ing with representation of pollution prevention strategiesat various spatial and temporal scales. The performanceof management actions is likely to vary under differentflow regimes. In this paper, the discussion focused onthe impacts of practices on average monthly pollutantloads based on daily SWAT simulations over a long-term(25 years) simulation period. Similar approaches couldbe employed to investigate whether these conclusionswould be different under varying sub-daily flow con-ditions. Future studies should focus on identification ofappropriate spatial and temporal scales for representa-tion of practices, and assessment of the impacts of modeluncertainty on the evaluation of practices.

ACKNOWLEDGEMENTS

The USDA-Natural Resources Conservation Servicethrough its Environmental Quality Incentive Program(EQIP) provided funding for this study.

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