Uncertainty assessment in watershed‐scale water quality modeling and management: 2. Management objectives constrained analysis of uncertainty (MOCAU)

Abstract

Watershed‐scale water quality models are increasingly used to support management decision making. However, significant uncertainty in model output remains an unaddressed issue. In our first study, a framework for assessing the uncertainty in watershed modeling and management was developed, and the application of the generalized likelihood uncertainty estimation (GLUE) approach was examined. The influence of subjective choices (especially the likelihood measure) in a GLUE analysis, as well as of availability of observational data, was investigated. On the basis of GLUE, we developed a new Bayesian approach of uncertainty analysis, specifically for management‐oriented watershed water quality modeling, as introduced in this paper. The approach, named management objectives constrained analysis of uncertainty (MOCAU), inherits GLUE's equifinality ideology while explicitly considering management objectives and observational uncertainty. It has many unique features that have not been covered (or have not been explored in great detail) by previous GLUE studies. A series of experiments was conducted to investigate the performance of MOCAU. The results show that MOCAU can be applied efficiently, generating accurate uncertainty estimates for management applications. Subjective assumptions in the uncertainty analysis are explicit and realistic, on the basis of management objectives such as nonattainment frequency of water quality objectives. MOCAU also yields insights into watershed model structure improvement and strategic data collection to reduce uncertainty. Besides water quality modeling, MOCAU can also be applied to other complicated modeling problems where errors are significant, observational data is limited, and management objectives are involved.