A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

Abstract

Abstract. Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. 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 solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most 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. However, most previous works 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. In this study, 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 with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction of 30%, from the baseline with a BMP implementation cost of $18 million. 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.

An Overview of China’s Agricultural Non-point Source Issues and the Development of Related Research

“…China has been the world's largest consumer of pesticides for more than 10 years [4]. The latest research focus has changed from the fate and transport simulation into the source tracking and management measures [20][21][22]. However, Chinese pesticide research is mainly focused on determining the extent of pesticide residues in soil and water.…”

Watershed modeling using large-scale distributed computing in Condor and the Soil and Water Assessment Tool model

“…8 The application of a calibrated watershed model to evaluate the effectiveness of various BMPs in improving water quality can also be computationally cumbersome. For example, Maringanti et al 14 have reported that optimizing BMPs in agricultural watersheds using the SWAT model may require a very large number of model runs (10 300 runs for an agricultural watershed having 500 farms and four BMPs possible for each farm). This computational burden is one of the primary reasons why the SWAT model has not been used to optimize BMPs in large agricultural watersheds.…”

Evaluation of Modeling Approach for Suspended Sediment Yield Reduction by Surface Cover Material using Rice Straw at Upland Field

“…또한 시험포나 경작지에서 평가된 최적관리기법에 의한 비점오염원의 저감 효과는 다른 지점이나 유역에 적 용하기 위해서나 혹은 유역 단위에서의 저감 효과를 분석 하기 위해서는 최적관리기법에 의한 비점오염원의 정량화 가 필요하다 (Jang et al, 2010;Lee et al, 2014). 볏짚에 의한 지표피복 등과 같은 최적관리기법에 대한 평가를 위해서는 모니터링뿐만 아니라, 수문 모형에 의한 평가도 필요하다 (Bishop et al, 2005;Jang et al, 2010;Kim et al, 2006;Lee et al, 2014;Lee et al, 2012;Lee et al, 2000;Maringanti et al, 2009;Maringanti et al, 2008;Park et al, 2014;Srivastava et al, 2002). 2 )가 0.91로 나타났 으며, USLE P 인자를 추정하기 위한 회귀식(식 (1))과 수 문 모형을 통한 볏짚매트에 의한 유사량 저감 모의 방법을 제안한 바 있다 (Jang et al, 2010 (Ahn et al, 2012;Park et al, 2013;Park et al, 2008).…”