Risk-based decision making to evaluate pollutant reduction scenarios.

Area of Science:

• Environmental science
• Water resource management
• Computational hydrology

Background:

• Water bodies in the U.S. often require Total Maximum Daily Loads (TMDLs) to meet water quality standards.
• Computational watershed models are crucial for developing pollutant reduction scenarios but are subject to inherent uncertainty.
• Addressing model uncertainty is vital for robust decision-making in water quality management.

Purpose of the Study:

• To present a risk-based framework for evaluating alternative pollutant allocation scenarios.
• To incorporate reliability in achieving water quality goals into the decision-making process.
• To demonstrate the application of Generalized Likelihood Uncertainty Estimation (GLUE) to Hydrological Simulation Program-FORTRAN (HSPF) for uncertainty analysis.

Main Methods:

• Application of a generic routine for Generalized Likelihood Uncertainty Estimation (GLUE).
• Utilized Hydrological Simulation Program-FORTRAN (HSPF) with existing software.
• Evaluated two bacteria reduction scenarios for a TMDL in a mixed land use watershed in Virginia, U.S.

Main Results:

• Probabilistic analysis showed similar exceedance rates for TMDL and full reduction scenarios at reliability levels <50%.
• The full reduction scenario performed better at higher reliability levels but still could not meet water quality criteria.
• Achieving water quality goals with very high reliability was not possible, even with extreme pollutant reductions.

Conclusions:

• The presented risk-based framework effectively propagates watershed model uncertainty.
• The framework enables assessment of alternative pollutant reduction scenarios' performance.
• Decision-makers can better understand the reliability of scenarios in achieving water quality goals.