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Water 2015, 7(6), 2924-2951; doi:10.3390/w7062924

Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model

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State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, No.8 Donghu South Road, Wuhan 430072, China
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Hubei Collaborative Innovation Center for Water Resources Security, Wuhan University, Wuhan 430072, China
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Authors to whom correspondence should be addressed.
Academic Editor: Athanasios Loukas
Received: 28 February 2015 / Revised: 8 June 2015 / Accepted: 8 June 2015 / Published: 17 June 2015
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Abstract

Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1) Nash–Sutcliffe efficiency (ENS); (2) water balance coefficient (WB); (3) peak discharge efficiency (EP); and (4) time to peak efficiency (ETP) were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior. View Full-Text
Keywords: sensitivity analysis; interaction analysis; correlated parameters; objective functions; flood magnitudes; Sobol’ method sensitivity analysis; interaction analysis; correlated parameters; objective functions; flood magnitudes; Sobol’ method
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wan, H.; Xia, J.; Zhang, L.; She, D.; Xiao, Y.; Zou, L. Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model. Water 2015, 7, 2924-2951.

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