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论文类型：期刊论文

发表时间：2021-01-31

发表刊物：JOURNAL OF HYDROLOGY

卷号：540

页面范围：274-286

ISSN号：0022-1694

关键字：GLUE; Multiple metrics; LHS; epsilon-NSGAII; Xinanjiang model

摘要：This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (epsilon-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of epsilon-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the epsilon-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from epsilon-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of epsilon-NSGAII parameter sets; (3) The parameter posterior distributions from epsilon-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with epsilon-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process. (C) 2016 Elsevier B.V. All rights reserved.