Development of data-driven, reduced-complexity watershed simulation models to address agricultural non-point source sediment pollution in southern Minnesota

Agricultural nonpoint source sediment pollution and management has been a subject of intensive modeling and economic research. Important challenges remain for the scientific and regulatory communities in understanding, not only the mechanisms of pollution, but also different options for environmental management. To better understand nonpoint source pollution at the watershed scale and to evaluate the impacts of various management options, a data-driven, reduced-complexity modeling framework is developed through a collaborative process involving multiple scientists, engineers, and economists, as well as local stakeholders in Southern Minnesota. The models in this framework are developed by making the most effective use of abundant information on soils, topography, and sediment loading in a platform that is transparent and accessible for decision-making. The models’ simulation outputs are within the constraints provided by observation and tested against independent data, thereby providing reliable and robust predictions about management impact on water quality. This modeling framework can support evaluation of different conservation scenarios to address nonpoint source sediment pollution in an agricultural watershed with simulation outputs that are relevant to environmental and social needs