Exploring internal phosphorus loads and management interventions through Bayesian reservoir modeling.

Area of Science:

• Environmental Science
• Limnology
• Water Quality Management

Background:

• Phosphorus is a key nutrient driving eutrophication in global lakes and reservoirs.
• Internal phosphorus loading (IPL) from sediments is a significant, yet often underestimated, contributor to eutrophication.
• Existing management strategies primarily focus on reducing external phosphorus inputs, neglecting the complexities of IPL.

Purpose of the Study:

• To develop and validate a novel modeling approach for characterizing internal phosphorus loading dynamics.
• To quantify the contribution of IPL to overall phosphorus loads in a eutrophic reservoir.
• To assess the long-term impacts of climate warming and management interventions on phosphorus dynamics.

Main Methods:

• Utilized multi-decadal monitoring data and a mass-balance model within a Bayesian inference framework.
• Applied the model to Jordan Lake, a segmented eutrophic reservoir in North Carolina, USA.
• Simulated long-term scenarios including lake warming and various management interventions (e.g., capping, dredging).

Main Results:

• The Bayesian model demonstrated good performance in estimating total phosphorus (R ² = 57%, RMSE = 0.032 mg/L).
• Internal phosphorus loading was found to be dominant in summers, contributing nearly twice the external phosphorus load.
• Lake warming intensified sediment-water phosphorus fluxes but had minimal impact on water-column phosphorus concentrations.

Conclusions:

• Internal phosphorus loading is a critical factor in reservoir eutrophication, particularly during summer.
• IPL mitigation strategies, combined with external load reductions, can enhance and sustain water quality improvements.
• The developed modeling approach is transferable to other waterbodies and provides a framework for probabilistic forecasting of phosphorus dynamics.