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Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria

1
College of Hydrology and Water Resources, Hohai University, NO.1 Xikang Road, Nangjing 210098, China
2
Chair Group of Hydroinformatics, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, The Netherlands
3
Water Resources Section, Delft University of Technology, Mekelweg 5, 2628CD Delft, The Netherlands
4
Water Problems Institute, Russian Academy of Sciences, Gubkina 3, Moscow 117735, Russia
*
Authors to whom correspondence should be addressed.
Water 2019, 11(5), 1062; https://doi.org/10.3390/w11051062
Received: 29 April 2019 / Revised: 16 May 2019 / Accepted: 17 May 2019 / Published: 22 May 2019
(This article belongs to the Section Hydrology)
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Abstract

Currently, practically no modeling study is expected to be carried out without some form of Sensitivity Analysis (SA). At the same time, there is a large number of various methods and it is not always easy for practitioners to choose one. The aim of this paper is to briefly review main classes of SA methods, and to present the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST (extended Fourier Amplitude Sensitivity Test), Morris, LH-OAT, RSA (Regionalized Sensitivity Analysis), and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod, and HBV) applied to the case study of Bagmati basin (Nepal). The methods are compared with respect to effectiveness, efficiency, and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, and thus it can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability, which we think are due to the ways Cumulative Distribution Functions (CDFs) are built, and using Kolmogorov–Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, and availability of software. View Full-Text
Keywords: Global Sensitivity Analysis; hydrological model; bootstrapping resample Global Sensitivity Analysis; hydrological model; bootstrapping resample
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Wang, A.; Solomatine, D.P. Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria. Water 2019, 11, 1062.

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