2023 journal article
Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model
Water Resources Research.
Nutrient pollution is a widespread environmental problem that degrades water quality worldwide. Addressing this issue calls for characterizing nutrient sources and retention rates, especially in seasons when water quality problems are most severe. Hybrid (statistical-mechanistic) watershed models have been used to quantify nutrient loading from various source categories. However, these models are generally developed for long-term average conditions, limiting their ability to assess temporal drivers of nutrient loading. They also have not been calibrated for season-specific estimates of loading and retention rates. To address these issues, we developed a hybrid watershed model that incorporates interannual variability in land use and precipitation as temporal drivers of phosphorus loading and transport. We calibrate the hybrid watershed model within a Bayesian hierarchical framework on both an annual and summer basis over a multi-decadal period (1982–2017). For our study area in the North Carolina Piedmont region (USA), we find that urban lands developed before 1980 are the largest contributor of phosphorus (per unit area), especially under dry conditions. Seasonally, summer phosphorus export rates are generally found to be lower than corresponding annual rates (kg/ha/mo), while in-stream retention is found to be elevated in summer. In addition, we find that precipitation has a substantially larger influence on phosphorus export from agricultural lands than other source types, especially in summer, and that antecedent (May) precipitation significantly influences summer phosphorus export. Overall, our approach provides a data-driven and probabilistic line of evidence to support watershed phosphorus management across different sources and seasons.