S1089: Multi-State Hatch project entitled S1089: Advanced understanding and prediction of pollutants in critical landscapes in watershed
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abstract
Effective mitigation of nonpoint source (NPS) pollution is difficult because of the spatial and temporal variability of sources and pollutant fate and transport pathways. The cost associated with preventing and/or mitigating NPS pollution is ever increasing. Multiple barrier approaches are now in use by large water authorities to reduce and remove contaminants in upstream watersheds, reservoirs, and groundwater systems. Presently, conservation programs designed to install best management practices (BMPs) to mitigate NPS have not been targeted to those areas of the landscape that contribute to NPS pollution disproportionately. Large reductions in watershed-level nutrient loads could be achieved through coordinated placement of BMPs on high-contributing areas.
In general, selection and placement of BMPs is constrained by several factors, which include a myriad of pollutant transport pathways, heterogeneous landscape features, and variable landscape management. In addition, in agricultural settings, BMPs are often adopted at the farm scale, while desired water quality goals function best at a watershed scale. The objective of BMP implementation plans should be to achieve the maximum pollutant load reduction and minimize the costs. BMP experiments, when properly designed and implemented across a range of spatial scales, provide useful information about drivers controlling BMP design, placement, and maintenance. However, empirical information is intrinsically limited to the specific conditions of the field study and this often hinders generalization of the information gained. Mechanistic models, based on first principles, when properly tested with empirical data help to generalize our understanding of how BMPs may perform under a range of conditions, including extreme events that cannot be directly measured.
Therefore, the placement and optimization of BMPs for controlling NPS requires a suite of individual and coupled mechanistic models that effectively capture a range of factors and watershed processes. The goal of this multistate research project proposal is to explore effective solutions to predict BMP performance (individually and cumulatively) at the various spatial and temporal scales. Tools developed through this effort can be used to inform watershed management decisions and investments in the presence of pervasive and unavoidable uncertainty to achieve water quality while minimizing investment.
