报告题目：Numerical modeling for integrated water resources management – uncertainty, information and decision-making
My primary research interest lies in linking numerical modeling with water resources management. I am particularly interested in understanding uncertainty and value of information which are both critical for making cost-effective decision of integrated water resources management.
In this presentation, first I will briefly review my research experience related to water resources modeling and management. Next, I will present my PhD research as an example of integrating stochastic modeling with large-scale water resources management. In this research, a framework for sources of uncertainty and their interactions was built in the context of watershed management. Based on this framework, Generalized Likelihood Uncertainty Estimation (GLUE) was initially evaluated as a potential approach for conducting stochastic simulation and uncertainty analysis for complex watershed models. The limitations of GLUE became evident, which led to the development of a new Bayesian approach, Management Objectives Constrained Analysis of Uncertainty (MOCAU). The concept Compliance of Confidence (CC) was then introduced to bridge the gap between modeling uncertainty and degree of water quality protection. An optimization model was also developed for cost-minimized management plans. The methodologies developed were implemented to study the diazinon pollution in the Newport Bay watershed (southern California). This research has significant contributes to the theory of stochastic watershed water quality modeling, as well as to the practices of managing watershed water quality.
At the end, I will talk about my potential research in the Department of Energy and Resource Engineering.