A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

ABSTRACT

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.

KEYWORDS:

• hydrological simulation
• distributed model
• sensitivity analysis
• SHETRAN
• Morris analysis
• calibration

Editor A. Castellarin Associate editor A. Petroselli

Editor A. Castellarin Associate editor A. Petroselli

Acknowledgments

The authors acknowledge the Directorate of Economics and Statistics, Karnataka, and the India Meteorological Department for sharing rainfall and meteorological data for this study; the Central Water Commission for providing streamflow data; and Dr Birkinshaw, School of Civil Engineering and Geosciences; Newcastle University, UK, for great assistance in clearing all the SHETRAN-related queries. The first author acknowledges Dr Suraj Harshan, former Research Assistant, Department of Geography, National University of Singapore, for helpful discussions on sensitivity analysis. The national supercomputing facility PARAM Yuva at C-DAC, Pune, is acknowledged for providing the computational facilities. The authors acknowledge the editorial board and anonymous reviewers for their critical comments which improved the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

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