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Best management practices (BMPs) have been proven to effectively reduce the Nonpoint source (NPS) pollution loads from agricultural areas. Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Indiana, exceeding the 3 ppb threshold for drinking water on a number of days. Candidate BMPs that could effectively control the movement of atrazine include buffer strips and land management practices such as tillage operations. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solutions that satisfies the given objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP section and placement problem. However, most of the previous works done have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. Most of the optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. In the present work the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage has been overcome through the development of BMP tool, a novel technique to estimate the pollution reduction efficiciencies of BMPs a priori. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (USGS 8 digit [05120107] HUC), Indiana, for atrazine reduction. The most ecologically effective solution from the model had a reduction of 30%, in atrazine concentration, from the base scenario with a BMP implementation cost of $10 million in the watershed per annum. The pareto-optimal fronts generated between the two optimized objective functions can be used to achieve desired water quality goals with minimum BMP implementation cost for the watershed..

Development of a Multi-Objective Optimization Tool for the Selection and Placement of BMPs for Pesticide Control