Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** hypoxia and harmful algal blooms…

Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** hypoxia and harmful algal blooms (HABS), primarily caused by excess nonpoint source (NPS) nitrogen and phosphorus loadings in U.S. Waters, such as lake erie, the chesapeake BAY, and the gulf of mexico, have caused significant economic losses (just one major HAB event can cost local coastal economies tens of millions of dollars) and health issues. Reducing NPS nutrient losses from agricultural areas is the key to solving HAB and hypoxia issues in the U.S.agricultural best management practices (BMPS) are popular approaches to reduce NPS nutrient loadings.given varied functionalities of different types of BMPS, the impacts of BMPS on hydrology and water quality vary. In addition, BMP performance is also significantly affected by local conditions, such as weather conditions (precipitation, temperature, relative humidity, solar radiation, and wind speed) and drainage area features (land use/land cover, in-situ soil, elevation, groundwater, and drainage area size). Therefore, the performance of BMPS with typical BMP designs according to BMP design standards would be determined by their types, quantities, and spatial locations. Computer models that accurately quantify the life cycle effectiveness and costs of commonly used BMPS are vital for developing optimization-based decision support systems to create optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPS) that minimize nutrient loadings at minimum cost. Moreover, computationally efficient optimization methods are needed for applications in large watersheds. However, current tools lack these capabilities.therefore, this project will address the challenges of creating cost-effective and sustainable agricultural bmpimplementation strategies by developing and applying an optimization-based decision support system (SWAT-BMP-OPT) incorporating improved amalgam (a multi-algorithm genetically adaptive multi-objective method, which is a popular optimization method using artificial intelligence techniques), enhanced SWAT (soil and water assessment tool), and modified BMP-cost (BMP cost evaluation tool). The SWAT-BMP-OPT will be able to comprehensively evaluate the effectiveness, cost, and cost-effectiveness of commonly used agricultural BMPS in reducing npsnutrient loadings; and reliably and efficiently develop optimal BMP implementation strategies (optimal types, quantities, and spatial locations of BMPS) to minimize nutrient loadings at minimum cost. The SWAT-BMP-OPT can be appliedinfuture agricultural BMP planning and implementation projects, resulting in increased cost-effectiveness and sustainability in implementing agricultural BMPS.