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Watershed Management Tool for Selection and Spacial Allocation of Non-Point Source Pollution Control Practices
Citation:
Arabi, M., R. S. Govindaraju, AND M. M. HANTUSH. Watershed Management Tool for Selection and Spacial Allocation of Non-Point Source Pollution Control Practices. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-08/036, 2008.
Impact/Purpose:
In this study, a computational method is presented to determine the significance of modeling uncertainties in assessing the effectiveness of best management practices (BMPs) in two small watersheds in Northeastern Indiana with the Soil and Water Assessment Tool (SWAT).
Description:
Distributed-parameter watershed models are often utilized for evaluating the effectiveness of sediment and nutrient abatement strategies through the traditional {calibrate→ validate→ predict} approach. The applicability of the method is limited due to modeling approximations. In this study, a computational method is presented to determine the significance of modeling uncertainties in assessing the effectiveness of best management practices (BMPs) in two small watersheds in Northeastern Indiana with the Soil and Water Assessment Tool (SWAT). The uncertainty analysis aims at (i) identifying the hydrologic and water quality processes that control the fate and transport of sediments and nutrients within watersheds, and (ii) establishing uncertainty bounds for model simulations as well as estimated effectiveness of BMPs. The SWAT model is integrated with a Monte-Carlo based methodology for addressing model uncertainties. The results suggested that fluvial processes within the channel network of the study watersheds control sediment yields at the outlets, and thus, BMPs that influence channel degradation or deposition are the more effective sediment control strategies. Conversely, implementation of BMPs that reduce nitrogen loadings from uplands areas such as parallel terraces and field borders appeared to be more crucial in reducing total N yield at the outlets. The uncertainty analysis also revealed that the BMPs implemented in the Dreisbach watershed reduced sediment, total P, and total N yields by nearly 57%, 33%, and 31%, respectively. Finally, a genetic algorithm (GA)-based optimization methodology is developed for selection and placement of BMPs within watersheds. The economic return of the selected BMPs through the optimization model was nearly three fold in comparison to random selection and placement of the BMPs.