Uncertainty Analysis for Coupled Watershed and Water Quality Modeling Systems

Abstract

A series of uncertainty analysis methods was applied to investigate the propagation of parameter uncertainty within a coupled model system and to evaluate the effects of uncertainty on model outputs and decision-making processes. First-order error analysis showed that among a large number of model parameters, only a few significantly affected the variation in pollutant loads at the watershed outlets and concentrations in the receiving body of water, and the variation in pollutant concentrations is greater than the variation in pollutant loads. The uncertainty analysis regarding the loads and concentrations showed different patterns, underscoring the importance of a complete uncertainty analysis and the need for an explicit quantification of the errors associated with the predicted loads. Monte Carlo simulation showed that best management practice scenarios considered as a safe scheme based on a deterministic model could actually lead to a significant risk of violating the water quality standards when model uncertainty is considered. With a modeling framework that considers uncertainty, feasible alternatives can be evaluated and ranked based on their risks of exceeding the target water quality criteria.