Comparison of sensitivity analysis techniques: A case study with the rice model WARM

Confalonieri, Roberto; Bellocchi, Gianni; Bregaglio, Simone; Donatelli, Marcello; Acutis, Marco

doi:10.1016/j.ecolmodel.2010.04.021

Cited by 180 publications

(132 citation statements)

References 25 publications

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“…Pappenberger et al (2008) flood inundation model 6 Different methods lead to completely different results. Confalonieri et al (2010) water accounting rice model 11 Morris method, the simplest among the SA methods used, produced results comparable to those obtained by methods more computationally expensive. Massmann and Holzmann (2012) rainfall-runoff model 11…”

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confidence: 50%

Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

Duan

Gong

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2-8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.

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confidence: 50%

Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

Duan

Gong

et al. 2013

Hydrol. Earth Syst. Sci.

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“…Given the availability of different SA techniques, selecting an appropriate technique, monitoring the convergence, and estimating the uncertainty of the SA results are crucial for hydrological models, especially distributed models, due to their nonlinearity, nonmonotonicity, highly correlated parameters, and intensive computational requirements (Yang, 2011). Many previous studies have examined the reliability of SA results in complex models, such as Yang (2011), Pappenberger et al (2008), and Confalonieri et al (2010). These investigations also showed that no SA method is ideal and they explicitly stated that it is important to avoid erroneous interpretations of the model outputs' sensitivity to the parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Given the wide range of SA methods, practitioners require adequate resource to better understand the methods that are appropriate for a specific application (Ratto et al, 2007;Tang et al, 2007b;Pappenberger et al, 2008;Confalonieri et al, 2010;Yang, 2011;Sun et al, 2012;Gan et al, 2014). Different types of SA methods can be selected based on: (a) the objective of the analysis, (b) the number of uncertain input factors, (c) the degree of complexity of the model, (d) the computing time for a single model simulation, and (e) the analyst's time available to perform a SA (Cacuci et al, 2013;Saltelli et al, 2005Saltelli et al, , 2012Wallach et al, 2006;Zajac, 2010).…”

Section: Selection Of Sa Methods For Hydrological Modelsmentioning

confidence: 99%

“…Thus, the dimensionality of the parameter space must be limited to avoid model over-parameterization. For efficient parameter identification, SA is useful in providing the qualitative and quantitative indices needed to identify important and non-important parameters (Yang, 2011;Pappenberger et al, 2008;Confalonieri et al, 2010). It might be difficult to estimate these parameters efficiently when there are many parameters with no clearly identifiable influence on output variables, or when many parameters have similar effects (or interactions) on the output variables.…”

Section: Sa and Parameter Identificationmentioning

confidence: 99%

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

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

470

273

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s u m m a r y 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.

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“…Global SA approaches can be classified into screening methods, regression-based methods and variance-based methods [28]. Among them, variance-based methods are most widely used due to their reliable performance.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Optimization and statistical analysis of Au-ZnO/Al2O3 catalyst for CO oxidation

Yan

Jia

Chen

et al. 2013

Journal of Energy Chemistry

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Comparison of sensitivity analysis techniques: A case study with the rice model WARM

Cited by 180 publications

References 25 publications

Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

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

Optimization and statistical analysis of Au-ZnO/Al2O3 catalyst for CO oxidation

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