Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

By Xiaomeng Song, Jianyun Zhang, Che-sheng Zhan, Yunqing Xuan, Ming Ye, Chonggang Xu. 2015

562 citations recorded by the source corpus.

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance. (C) 2015 Elsevier B.V. All rights reserved.

Subfields: Civil and Structural Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a7567ee97278f7d463f7c889f4058b4db62508fb

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Citations in the corpus

Global Sensitivity Analysis of environmental models: Convergence and validation (2016, methodology): “SA is a diagnostic tool that can guide model calibration and veri ﬁ cation, support the prioritization of efforts for uncertainty reduction, or help with model-based decision-making (Norton, 2015; Pianosi et al., 2016; Song et al., 2015).”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, background): “…and takes into account the interactions between different parameters (van Griensven et al., 2006; Herman et al., 2013; Mishra et al., 2009; Nossent et al., 2011; Pan et al., 2011; Saltelli, 2000; Saltelli et al., 2010; Saltelli and Sobol, 1995; Shi et al., 2014; Song et al., 2015; Yang, 2011).”
Improvement of hydrological model calibration by selecting multiple parameter ranges (2016, unknown): “Furthermore, the range setting of one parameter may influence the calibration of other related parameters (Song et al., 2015).”
Uncertainty Analysis of SWAT Modeling in the Lancang River Basin Using Four Different Algorithms (2021, background): “With the development of computing technology, an increasing number of optimization algorithms have been proposed to solve or reduce model uncertainty [28].”
Implementation and Application of a Root Growth Module in HYDRUS (2018, methodology): “VZJ | Advancing Critical Zone Science p. 8 of 16 An overview of studies using the Sobol¢ method for sensitivity analysis in hydrological modeling was provided by Song et al. (2015).”