Sobol' sensitivity analysis of a complex environmental model

By Jiri Nossent, Pieter Elsen, Willy Bauwens. Environmental Modelling & Software, 2011

697 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7400f92d3319d733f041f6ab1568de12fbd128ab

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Citations in the corpus

Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification (2019, methodology): “Recently, various GSA methods have been developed for use with hydrological and environmental models and have shown improved ability to quantify model sensitivity to parameters compared with the OAT method (Confalonieri et al., 2010; Foscarini et al., 2010; Nossent et al., 2011; Tang et al., 2007).”
Application of a normalized Nash-Sutcliffe efficiency to improve the accuracy of the Sobol' sensitivity analysis of a hydrological model (2012, methodology): “In our application on a SWAT model of the Kleine Nete catchment (Belgium) (Nossent et al., 2011), this is indeed valid for flow predictions, as the mean NSE for all model evaluations is -0.73.”
Characterising performance of environmental models (2013, methodology): “For more information on sensitivity analysis, see Saltelli et al. (2000), Norton (2008), Frey and Patil (2002), Saltelli and Annoni (2010), Shahsavani and Grimvall (2011), Makler-Pick et al. (2011), Yang (2011), Nossent et al. (2011), Ratto et al. (2012) and Ravalico et al. (2010).”
A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015, positive): “…environmental modelling domains (see for example older and more recent applications in Pastres et al. (1999); Pappenberger et al. (2008); van Werkhoven et al. (2008); Nossent et al. (2011); Ziliani et al. (2013); Baroni and Tarantola (2014), and Saltelli et al. (2008) for a general discussion).”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, positive): “Hydrological models have developed significantly over the past three decades (Beven, 2009) in terms of their greater complexity (from rational methods to distribution models) and their diverse uses in many applications (Nossent et al., 2011), such as land use (Park et al.”
Global Sensitivity Analysis of environmental models: Convergence and validation (2016, background): “Some authors use the confidence intervals of the sensitivity indices for a more quantitative assessment of their convergence (e.g. Campolongo and Saltelli, 1997; Nossent et al., 2011).”
Variance-based sensitivity analysis of a forest growth model (2012, methodology): “However, as it relies on Monte Carlo simulation to sample over the entire parameter space, a major barrier to the use of this method is its high computational cost (Nossent et al., 2011; Saltelli et al., 2010).”
Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis (2013, methodology): “Methods: The presentation of methods is interesting but it may be interesting to shortly discuss the existing applications of each of them to complex models (see e.g. Nossent et al., 2011; Zhang et al., 2013, for the Sobol’s method).”
A sensitivity study of the WRF model in wind simulation for an area of high wind energy (2012, background): “Although more physical options are available in the model (for cumulus, radiation, microphysics, etc.), it is not feasible or necessary to include all the model configuration options in the sensitivity analysis to obtain an efficient model configuration optimization (Nossent et al., 2011).”
Sensitivity analysis of environmental models: A systematic review with practical workflow (2014, background): “…including: to support model calibration, veri ﬁ cation, diagnostic evaluation or simpli ﬁ cation (e.g. Sieber and Uhlenbrook, 2005; Harper et al., 2011; Nossent et al., 2011; Kelleher et al., 2013; Shin et al., 2013; Butler et al., 2014); to prioritize efforts for uncertainty reduction (e.g. Hamm…”