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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-78
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-78
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Feb 2018

Research article | 28 Feb 2018

Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Anqi Wang1 and Dimitri P. Solomatine2,3,4 Anqi Wang and Dimitri P. Solomatine
  • 1College of Hydrology and Water Resources, Hohai University, NO.1 Xikang Road, Nanjing, 210098, China
  • 2Chair of Hydroinformatics, IHE Delft Institute for Water Education, Westvest 7, Delft, 2611AX, The Netherlands
  • 3Water Problems Institute, Russian Academy of Sciences, Leninsky prospekt 14, Moscow, 119991, Russia
  • 4Water Resources Section, Delft University of Technology, Postbus 5, Delft, 2600AA, The Netherlands

Abstract. Sensitivity Analysis (SA) and Uncertainty Analysis (UA) are important steps for better understanding and evaluation of hydrological models. The aim of this paper is to briefly review main classes of SA methods, and to presents the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST, Morris, LH-OAT, RSA 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), and also initially tested on the case of Dapoling-Wangjiaba catchment in China. 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, thus 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 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, as well as availability of software.

Anqi Wang and Dimitri P. Solomatine
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Anqi Wang and Dimitri P. Solomatine
Anqi Wang and Dimitri P. Solomatine
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Short summary
This paper presents a brief review and classification of sensitivity analysis (SA) methods. Six different global SA methods: Sobol, FAST, Morris, LH-OAT, RSA and PAWN are tested on the three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV), with respect to effectiveness, efficiency and convergence. Practical framework of selecting and using the SA methods is presented, which may be of assistance for practitioners assessing reliability of their models.
This paper presents a brief review and classification of sensitivity analysis (SA) methods. Six...
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