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The ability of a watershed model to mimic specified watershed processes is assessed through the calibration and validation process. The Soil and Water Assessment Tool (SWAT) watershed model was implemented in the Beaver Reservoir Watershed of Northwest Arkansas. The objectives were to: (1) provide detailed information on calibrating and applying a multisite and multivariable SWAT model; (2) conduct sensitivity analysis; and (3) perform calibration and validation at three different sites for flow, sediment, total phosphorus (TP), and nitrate‐nitrogen (NO3‐N) plus nitrite‐nitrogen (NO2‐N). Relative sensitivity analysis was conducted to identify parameters that most influenced predicted flow, sediment, and nutrient model outputs. A multi objective function was defined that consisted of optimizing three statistics: percent relative error (RE), Nash‐Sutcliffe Coefficient (RNS2), and coefficient of determination (R2). This function was used to successfully calibrate and validate a SWAT model of Beaver Reservoir Watershed at multi‐sites while considering multivariables. Calibration and validation of the model is a key factor in reducing uncertainty and increasing user confidence in its predictive abilities, which makes the application of the model effective. Information on calibration and validation of multisite, multivariable SWAT models has been provided to assist watershed modelers in developing their models to achieve watershed management goals.

Abstract:Implementation of sensitivity analysis (SA) procedures is helpful in calibration of models and also for their transposition to different watersheds. The reported studies on SA of Soil and Water Assessment Tool (SWAT) model were mostly focused on identifying parameters for pruning or modifying during the calibration process. This paper presents a sensitivity and identifiability analysis of model parameters that influence stream flow generation in SWAT. The analysis was focused on evaluating the sensitivity of the parameters in different climatic settings, temporal scales and flow regimes. The global sensitivity analysis (GSA) technique based on classical decomposition of variance, Sobol', was employed in this study. The results of the study indicate that modeled stream flow show varying sensitivity to parameters in different climatic settings. The results also suggest that the identifiability of a parameter for a given watershed is a major concern in calibrating the model for the specific watershed, as it might lead to equifinality of parameters. The SWAT model parameters show varying sensitivity in different years of simulation suggesting the requirement for dynamic updation of parameters during the simulation. The sensitivity of parameters during various flow regimes (low, medium and high flow) is also found to be uneven, which suggests the significance of a multi-criteria approach for the calibration of models.

The objective of this study was to evaluate the impact of rapidly changing land use on erosion and sedimentation in a mixed land use watershed in the Ozark Highlands of the USA. The research combines a geographic information system‐based soil erosion modeling approach with land use change detection to quantify the influence of changing land use on erosion risk. Five land use/land cover maps were generated or acquired for a 20‐year period (1986 through 2006) at approximately 5‐year intervals to assess land use change and to predict a projected (2030) land use scenario for the West Fork White River watershed in Northwest Arkansas. The Unit Stream Power based Erosion/Deposition model was applied to the observed and predicted land use to assess the impact on erosion. Total erosion from urban areas was predicted to increase by a factor of six between 1986 and 2030 based on the projected 2030 land use. Results support previous reports of increased urbanization leading to increased soil erosion risk. This study highlights the interaction of changes in land use with soil erosion potential. Soil erosion risk on a landscape can be quantified by incorporating commonly available biophysical data with geographic information system and remote sensing, which could serve as a land/watershed management tool for the rapid assessment of the effects of environmental change on erosion risk. Copyright © 2011 John Wiley & Sons, Ltd.

[1] Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA-II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade-off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.Citation: Maringanti, C., I. Chaubey, and J. Popp (2009), Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control, Water Resour. Res., 45, W06406,

Abstract:This paper describes the effect of DEM data resolution on predictions from the SWAT model. Measured hydrologic, meteorological, watershed characteristics and water quality data from Moores Creek watershed (near Lincoln, AR, USA) were used in the simulation. The effect of input data resolution was evaluated by running seven scenarios at increasing DEM grid sizes (30 ð 30 m, 100 ð 100 m, 150 ð 150 m, 200 ð 200 m, 300 ð 300 m, 500 ð 500 m, 1000 ð 1000 m). The model was calibrated on an annual basis for flow, NO 3 -N and total P using 30 ð 30 m DEM data. The predicted output at the calibrated scale was used to evaluate output accuracy for the remaining input resolutions. Results of this study showed that DEM resolution affects the watershed delineation, stream network and sub-basin classification in the SWAT model. A decrease in DEM resolution resulted in decreased stream flow and NO 3 -N load predictions. However, model predicted total P did not always decrease with DEM resolution. Results of this study indicate that the choice of input DEM resolution depends on the watershed response of interest. Minimum DEM data resolution ranged from 100 to 200 m to achieve less than 10% error in SWAT output for flow, NO 3 -N and TP predictions.

Resolution of the input GIS data used to parameterize distributed‐parameter hydrologic/water quality models may affect uncertainty in model outputs and impact the subsequent application of model results in watershed management. In this study we evaluated the impact of varying spatial resolutions of DEM, land use, and soil data (30 × 30 m, 100 × 100 m, 150 × 150 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, and 1,000 × 1,000 m) on the uncertainty of SWAT predicted flow, sediment, NO3‐N, and TP transport. Inputs included measured hydrologic, meteorological, and watershed characteristics as well as water quality data from the Moores Creek watershed in Washington County, Arkansas. The SWAT model output was most affected by input DEM data resolution. A coarser DEM data resolution resulted in decreased representation of watershed area and slope and increased slope length. Distribution of pasture, forest, and urban areas within the watershed was significantly affected at coarser resolution of land use and resulted in significant uncertainty in predicted sediment, NO3‐N, and TP output. Soils data resolution had no significant effect on flow and NO3‐N predictions; however, sediment was overpredicted by 26 percent, and TP was underpredicted by 26 percent at 1,000 m resolution. This may be due to change in relative distribution of various hydrologic soils groups (HSGs) in the watershed. Minimum resolution for input GIS data to achieve less than 10 percent model output error depended upon the output variable of interest. For flow, sediment, NO3‐N, and TP predictions, minimum DEM data resolution should range from 30 to 300 m, whereas minimum land use and soils data resolution should range from 300 to 500 m.

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