A framework for uncertainty and risk analysis in Total Maximum Daily Load applications. (March 2018)

• Record Type: Journal Article
• Authors: Camacho, Rene A.; Martin, James L.; Wool, Tim; Singh, Vijay P.
• Is Part Of: Environmental modelling & software. Volume 101(2018)
• Page Start: 218
• Page End: 235
• Publication Date: 2018-03
• Subjects: Total Maximum Daily Load -- Margin of safety -- Uncertainty analysis -- Risk assessment -- Bayesian analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.12.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In the United States, the computation of Total Maximum Daily Loads (TMDL) must include a Margin of Safety (MOS) to account for different sources of uncertainty. In practice however, TMDL studies rarely include an explicit uncertainty analysis and the estimation of the MOS is often subjective and even arbitrary. Such approaches are difficult to replicate and preclude the comparison of results between studies. To overcome these limitations, a Bayesian framework to compute TMDLs and MOSs including an explicit evaluation of uncertainty and risk is proposed in this investigation. The proposed framework uses the concept of Predictive Uncertainty to calculate a TMDL from an equation of allowable risk of non-compliance of a target water quality standard. The framework is illustrated in a synthetic example and in a real TMDL study for nutrients in Sawgrass Lake, Florida. Highlights: The study proposes a framework to compute TMDLs including explicit assessment of risk and uncertainty. An novel equation to compute the risk of failure of the TMDL conditional on predictions of a model is introduced. The risk of failure is the probability of violating a water quality standard if the TMDL is implemented. The TMDL is computed using Bayesian Monte Carlo Analysis once an acceptable risk of failure is defined. The margin of safety is directly computed from the results of the Bayesian Analysis.