A stochastic modeling approach for risk management of water resources systems

This study presents a stochastic modeling approach for quantifying and analyzing various uncertainties associated with water resources systems. Based on probabilistic chaos expansion (PCE) and multivariate analysis, a probabilistic simulation method is developed to construct a surrogate for complex hydrological models and to support efficient stochastic simulations. Stepwise cluster analysis (SCA) is used to establish complex nonlinear relationships among various system components. PCEs are established based on the probabilistic collocation method to generate probabilistic runoff time series. The proposed approach is demonstrated using the climatic and hydrological data from two watersheds, i.e., the Grand River Watershed in Canada and the Xiangxi River Watershed in China. Results show that the proposed approach is effective in tackling the complexities in water resources systems, particularly with respect to the inherent uncertainties involved in hydrological modeling. This work can provide robust decision support for risk management of water resources systems, such as hydraulic infrastructure design, reservoir operation, and floodplain planning. The developed modeling framework will help decision makers not only to improve their effectiveness in managing water risks, but also to enhance their preparedness for and response to water related hazards.