摘要
To tackle China’s pervasive water pollution, tremendous efforts are needed to achieve more and better information. However, resources for information collection (e.g., water quality monitoring, field experiments, etc.) are very limited for large watersheds with significant nonpoint source pollution. Thus, it is crucial to identify the priority of information acquisition. Based on the theory of value of information (VOI), a stochastic optimization approach was developed in this study to evaluate the importance of information. The approach was applied to several key polluted water bodies in China (e.g., Lake Taihu, Lake Chaohu, and Lake Dianchi). The major findings include: (1) because of the severe pollution and large uncertainty, the VOI for the targeted water bodies is substantial; (2) when the uncertainty is significant, a stricter regulation would result in a higher VOI, and therefore provide more incentives for data collection; (3) due to the interaction among different information sources, collecting multiple types of information simultaneously could be more valuable than collecting one after another; and (4) the importance of a specific type of information could vary significantly across watersheds. The proposed approach can be readily extended to more complex models and more sophisticated watershed cases. It could effectively support watershed management in China, as well as in other countries.