基于不确定分析的改进灰水足迹模型及应用

鉴于传统的灰水足迹模型难以处理具有不确定性的灰水足迹评价问题,基于不确定性分析理论,提出了改进的灰水足迹模型,并将其应用于太湖流域的水质性缺水研究中。结果表明,与传统的灰水足迹模型相比,改进的灰水足迹模型通过引入不确定性分析理论,能更准确地计算污染物的灰水足迹期望和水质性缺水风险概率,从而量化研究区域的综合水质性缺水程度;2010~2014年太湖流域的水质性缺水风险概率分别为1.00、1.00、0.93、0.97、0.82,其中总氮的灰水足迹期望依次为676.7×10^8、592.6×10^8、576.3×10^8、463.9×10^8、503.7×10^8 m^3,为太湖流域水质性缺水的主要诱因;改进的灰水足迹模型与水功能区水质达标率模型评价结果基本一致,能准确有效地量化研究区域的水质性缺水状况。

Improved Grey Water Footprint Model and Its Application Based on Uncertainty Analysis

The traditional grey water footprint model is hard to deal with grey water footprint with uncertainty evaluation problem. In order to solve this problem, this study proposed an improved grey water footprint model based on the uncertainty analysis theory, and applied to the water scarcity study in Taihu basin. Compared with the traditional grey water footprint model, the results show that the improved grey water footprint model by introducing the theory of uncertainty analysis can calculate the grey water footprint expectation of pollutants and the risk probability of water quality water shortage more accurately so as to quantify the comprehensive water quality water shortage degree in the research area; From 2010 to 2014, the risk probability of water shortage in Taihu basin was 1.00, 1.00, 0.93, 0.97 and 0.82, respectively; The expected grey water footprint of the total nitrogen is 67.67, 59.26, 57.63, 46.39, and 50.37 billion cubic meters, respectively, which is the main cause of water shortage in this region; The improved grey water footprint model is basically consistent with the evaluation results of water quality compliance rate model in the functional areas, which can accurately and effectively quantify the water quality and water shortage in the study area.