Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical...

Area of Science:

• Environmental Science
• Hydrology
• Geochemistry

Background:

• Anthropogenic phosphorus inputs cause water quality issues via riverine export.
• Estimates of hydrologic phosphorus export are often unavailable at large spatial scales.
• Nutrient budgets quantify watershed nutrient import/export but lack broad-scale export data.

Purpose of the Study:

• To develop a Bayesian hierarchical model for estimating annual phosphorus export across the contiguous U.S.
• To identify key drivers of phosphorus loss, including agricultural, urban, and geogenic sources.
• To map hotspots of phosphorus loss and understand factors influencing riverine export.

Main Methods:

• Developed a Bayesian hierarchical model to estimate annual phosphorus export.
• Incorporated agricultural, urban, and geogenic phosphorus sources.
• Utilized an extensive calibration dataset of riverine loadings and considered varying precipitation conditions.

Main Results:

• Precipitation significantly influences hydrologic export rates of anthropogenic phosphorus, particularly from agriculture.
• Geogenic phosphorus sources are important contributors to overall riverine export.
• Mapped U.S. hotspots of phosphorus loss, highlighting key contributing factors.

Conclusions:

• The Bayesian hierarchical model provides robust estimates of national-scale phosphorus export.
• Precipitation is a critical factor modulating phosphorus export from various sources.
• Understanding geogenic phosphorus contributions is vital for comprehensive water quality management.