abstract
- In this study, a Bayesian risk-induced interval stochastic modeling framework (BRISF) is proposed for planning effluent trading program among point and nonpoint sources as well as identifying interactions of important trading factors under system risk. BRISF incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian inference with random walk Metropolis algorithm (RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP) within a general framework. Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings; this process provides the random inputs for optimization process. CRTSP is capable of dealing with multiple uncertainties in modeling effluent trading program as well as system risk of environmental allowance violation. BRISF is applied to a real case of Xiangxihe watershed in China for water quality management. Solutions for optimal trading scheme corresponding to different risk levels are generated. Thousands of scenarios are examined to analyze the individual and interactive effects of trading ratios and treatment rates on trading system. Comparison between cross-industry and intra-industry effluent trading scheme is also conducted. It is proved that cross-industry trading would bring about higher benefit with reduced pollution loading; cross-industry effluent trading scheme would be recommended to achieve optimal water quality management and system benefit.