A framework for uncertainty and risk analysis in Total Maximum Daily Load applications.

Area of Science:

• Environmental Science
• Water Resource Management
• Bayesian Statistics

Background:

• Total Maximum Daily Loads (TMDL) in the U.S. require a Margin of Safety (MOS) for uncertainty.
• Current TMDL practices often lack explicit uncertainty analysis, leading to subjective MOS estimations.
• This subjectivity hinders study replication and inter-study comparisons.

Purpose of the Study:

• To propose a Bayesian framework for computing TMDLs and MOSs with explicit uncertainty and risk evaluation.
• To introduce a method for calculating TMDL based on an equation of allowable risk for water quality standards.
• To demonstrate the framework's application in both synthetic and real-world case studies.

Main Methods:

• Development of a Bayesian statistical framework for TMDL and MOS calculation.
• Integration of Predictive Uncertainty to quantify risks associated with water quality standards.
• Application of the framework to a synthetic dataset and a nutrient TMDL study for Sawgrass Lake, Florida.

Main Results:

• The proposed Bayesian framework provides a robust method for incorporating uncertainty into TMDL and MOS computations.
• Explicit risk assessment enables a more objective and defensible estimation of the MOS.
• The framework's utility is demonstrated through successful application in a practical water quality management scenario.

Conclusions:

• The Bayesian approach offers a significant improvement over traditional methods for TMDL and MOS determination.
• This framework enhances the reliability and comparability of water quality assessments.
• The method supports informed decision-making in water resource management by quantifying compliance risks.