Institute: North Carolina
Year Established: 2020 Start Date: 2020-03-01 End Date: 2021-02-28
Total Federal Funds: $12,000 Total Non-Federal Funds: $4,704
Principal Investigators: Daniel R. Obenour
Abstract: Anthropogenic nutrient loading is a critical driver of water quality throughout North Carolina and much of the world. Nutrient loading has increased over the last century due to fertilization of crops and green spaces, as well as waste from humans, pets, and livestock. The most salient outcome of nutrient loading is increased eutrophication (organic matter accumulation in surface waters), often leading to harmful algal blooms and hypoxia, which jeopardize water supplies and public recreation. As such, developing nutrient criteria and management strategies is a timely objective for state water resources managers. While sources of nutrients have been identified and many nutrient control measures have been proposed, there remains a need to quantitatively assess these sources and controls, particularly at the watershed scale. In this study, we propose a modern, data-driven approach to update our knowledge of the magnitudes of various sources and the effectiveness of various nutrient control strategies. The approach leverages large databases of water quality, hydro-meteorology, and watershed attributes, which have been developed by federal, state, and local governments over the last few decades. The approach will also leverage and advance a sophisticated â€œhybridâ€ watershed model that combines a mechanistic representation of nutrient fate and transport within a probabilistic (Bayesian) framework where prior knowledge of loading and transport rates is updated through data-driven inference, and where uncertainty is rigorously quantified. Our project will focus on the Falls and Jordan Lake watersheds of North Carolina, for which preliminary models and data are already available. Key objectives of the proposed research include (1) development of an integrated geospatial database on watershed development, (2) adaptation of the hybrid watershed model to assess watershed development practices, and (3) application of the model to assess future management scenarios. Expected outcomes include quantitative guidance for developing nutrient reduction goals and watershed management strategies.