Previous optimization based watershed decision making approaches suffer two major limitations.First of all,none of these approaches provide a systematic way to prioritize the implementation schemes given uncertainties in the watershed systems and the optimization models;Secondly,as actual decision environment and uncertainty space evolve with the implementation processes and new data availability,no efficient method exists to guide an optimal adaptive decision making,particularly at a watershed scale.This paper presents an optimal guided adaptive decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale.The approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner,hence mimicking the real-world decision environment where information availability and uncertainty evolves with time.The proposed approach consists of multiple components,including a risk explicit interval linear programming (REILP) modeling framework,a systematic method for prioritizing implementation schemes,and an iterative process for adapting the core optimization model for updated optimal solutions.The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of Lake Qionghai Watershed,China.The results demonstrated that the proposed guided adaptive approach is able to efficiently incorporate uncertainty into the formulation and solution of the optimization model,and prioritize implementation schemes based on risk and return tradeoff,hence produce more reliable and efficient management outcome than traditional non-adaptive approaches.