Defense Presentation Title: Assessing Spatio-Temporal Variability in Watershed Nutrient Loadings Using Statistical-Mechanistic Modeling

Advisor: Dr. Dan Obenour, faculty fellow and associate professor in the Department of Civil, Construction and Environmental Engineering

Abstract: Nutrient pollution is a global concern that threatens surface water quality. This research focuses on characterizing critical nutrient loading and retention rates to effectively manage nutrient sources. Hybrid watershed models that apply simple, process-based formulations using statistical calibration techniques have been developed to quantify nutrient sources for long-term average conditions, which limits their ability to assess temporal drivers of nutrient loading. The models developed here capture inter-annual variability in precipitation, land use and nutrient inputs. A hybrid Bayesian hierarchical framework is used to (1) characterize phosphorus loading and retention rates in two of North Carolina’s large basins, (2) assess the effectiveness of watershed management strategies in reducing nutrient export and (3) characterize hydrologic phosphorus loading and retention across the continental United States, identifying hotspots of phosphorus loss. Overall, this research showcases the value of hybrid statistical-mechanistic models in enhancing our understanding of nutrient loading factors and supporting effective watershed management across different spatial scales.