Advances in the study of uncertainty quantification of large-scale hydrological modeling system

被引:26
|
作者
Song Xiaomeng [2 ]
Zhan Chesheng [1 ]
Kong Fanzhe [2 ]
Xia Jun [1 ]
机构
[1] Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[2] China Univ Min & Technol, Sch Resource & Earth Sci, Xuzhou 221008, Jiangsu, Peoples R China
关键词
uncertainty quantification; hydrological model; PSUADE; land-atmosphere coupling model; large scale; FORMAL BAYESIAN METHOD; PARAMETER-ESTIMATION; DATA ASSIMILATION; INPUT UNCERTAINTY; GLUE; OPTIMIZATION; SIMULATION; SCHEME; ALGORITHM; PROJECT;
D O I
10.1007/s11442-011-0881-2
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The regional hydrological system is extremely complex because it is affected not only by physical factors but also by human dimensions. And the hydrological models play a very important role in simulating the complex system. However, there have not been effective methods for the model reliability and uncertainty analysis due to its complexity and difficulty. The uncertainties in hydrological modeling come from four important aspects: uncertainties in input data and parameters, uncertainties in model structure, uncertainties in analysis method and the initial and boundary conditions. This paper systematically reviewed the recent advances in the study of the uncertainty analysis approaches in the large-scale complex hydrological model on the basis of uncertainty sources. Also, the shortcomings and insufficiencies in the uncertainty analysis for complex hydrological models are pointed out. And then a new uncertainty quantification platform PSUADE and its uncertainty quantification methods were introduced, which will be a powerful tool and platform for uncertainty analysis of large-scale complex hydrological models. Finally, some future perspectives on uncertainty quantification are put forward.
引用
收藏
页码:801 / 819
页数:19
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