A fluorescence index-informed δ¹⁸Oₚ-based Bayesian mixing framework for quantifying riverine phosphorus sources.

Area of Science:

• Environmental Chemistry
• Isotope Geochemistry
• Water Resource Management

Background:

• Eutrophication control requires accurate identification of phosphorus (P) sources.
• Phosphate oxygen isotopes (δ¹⁸Oₚ) are used for P source discrimination but often have high uncertainty due to overlapping signatures.
• Existing methods lack precision for watershed-scale P source apportionment.

Purpose of the Study:

• To develop and validate a fluorescence index (FI)-informed Bayesian mixing model for δ¹⁸Oₚ to improve P source discrimination.
• To reduce uncertainty in P source allocation at the watershed scale.
• To investigate seasonal and flow-dependent shifts in P sources.

Main Methods:

• Developed a Bayesian mixing model integrating δ¹⁸Oₚ and FI data.
• Utilized FI to construct period-specific Dirichlet priors for the model.
• Applied the model to the Yongan River watershed, China, analyzing data across different flow conditions.
• Conducted stress tests to assess model robustness.

Main Results:

• The FI-informed model significantly reduced uncertainty (11-20% reduction in 90% posterior intervals) compared to δ¹⁸Oₚ alone.
• Distinct seasonal shifts in P sources were identified, with riparian soils and agricultural runoff dominant during high flow, and groundwater during low flow.
• Model results showed consistency with hydrological and hydrochemical data.
• The model proved robust under plausible data alteration scenarios.

Conclusions:

• Integrating FI with δ¹⁸Oₚ in a Bayesian framework enhances the precision of P source apportionment.
• This approach provides a pragmatic tool for watershed management and eutrophication control.
• FI serves as a valuable complementary indicator for hydro-biogeochemical assessments.