The estimation and allocation of water environmental capacity （WEC） are essential to water quality management and social-economic interests. However, there is inevitable uncertainty in the capacity estimation due to...The estimation and allocation of water environmental capacity （WEC） are essential to water quality management and social-economic interests. However, there is inevitable uncertainty in the capacity estimation due to model conceptualization, data collection and parameter calibration. An innovative holistic approach was developed, which took both independence and relevance between parameters into account to analyze the uncertainties in WEC calculation and estimate the margin of safety. The Doniang River was taken as the case to demonstrate the method, focusing on the chemical oxygen demand and NH-N that were the two major water quality problems in the river. The results showed that the proposed holistic approach is very promising and applicable compared to traditional methods of uncertainty analysis.展开更多
基金the funding from the National Basic Research Program (973) of China (No.2010CB429004)the Chinese Academy of Sciences (No.KZZD-EW-10-02)+2 种基金the National Natural Science Foundation of China (No.51279196)Nanjing Hydraulic Research Institute (No.Y913017)Ministry of Communication (No.2011328222120)
文摘The estimation and allocation of water environmental capacity （WEC） are essential to water quality management and social-economic interests. However, there is inevitable uncertainty in the capacity estimation due to model conceptualization, data collection and parameter calibration. An innovative holistic approach was developed, which took both independence and relevance between parameters into account to analyze the uncertainties in WEC calculation and estimate the margin of safety. The Doniang River was taken as the case to demonstrate the method, focusing on the chemical oxygen demand and NH-N that were the two major water quality problems in the river. The results showed that the proposed holistic approach is very promising and applicable compared to traditional methods of uncertainty analysis.
