Moment Independent Importance Measures: New Results and Analytical Test Cases

Borgonovo, Emanuele; Castaings, William; Tarantola, S.

doi:10.1111/j.1539-6924.2010.01519.x

Cited by 147 publications

(124 citation statements)

References 60 publications

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“…A key limitation of a variance-based GSA is that the uncertainty of the output is implicitly considered to be fully characterized by its variance. Relying solely on this criterion can provide an incomplete picture of a system response to model parameters, also considering that probability densities of typical hydrological quantities can be characterized by highly skewed and tailed distributions (e.g., Borgonovo et al, 2011). Recent studies (e.g., KrykaczHausmann, 2001;Borgonovo, 2007;Borgonovo et al, 2011) introduced a sensitivity metric grounded on the complete probability density function, pdf, of the model output.…”

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confidence: 99%

Moment-based metrics for global sensitivity analysis of hydrological systems

Dell’Oca

Riva

Guadagnini

2017

Hydrol. Earth Syst. Sci.

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Abstract. We propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher-order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized polynomial chaos expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. The approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment.

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mentioning

confidence: 99%

Moment-based metrics for global sensitivity analysis of hydrological systems

Dell’Oca

Riva

Guadagnini

2017

Hydrol. Earth Syst. Sci.

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“…Thus a straightforward solution consists in computing Sobol' sensitivity indices for independent groups of dependent variables. First introduced by Sobol' (1993), this idea is exploited in practice by Jacques et al (2006), Borgonovo (2007), Borgonovo and Tarantola (2008) and Borgonovo et al (2011). In addition, other studies have also applied SA with correlated parameters (e.g., Elston, 1992;Helton et al, 1995;Fang et al, 2004;Jacques et al, 2006;Pan et al, 2011).…”

Section: Analysis Of Correlated Parameters In Hydrological Modelsmentioning

confidence: 99%

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

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

474

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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“…a moment of the model output distribution), it is considered moment-independent (Borgonovo et al, 2011). A global sensitivity approach was used for this study.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Uncertainty and sensitivity in a bank stability model: implications for estimating phosphorus loading

Lammers

Bledsoe

Langendoen

2016

Earth Surf Processes Landf

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UNCERTAINTY AND SENSITIVITY IN A BANK STABILITY MODEL: IMPLICATIONS FOR ESTIMATING PHOSPHORUS LOADINGEutrophication of aquatic ecosystems is one of the most pressing water quality concerns in the U.S. and around the world. Bank erosion has been largely overlooked as a source of nutrient loading, despite field studies demonstrating that this source can account for the majority of the total phosphorus budget of a watershed. Substantial effort has been made to develop mechanistic models to predict bank erosion and instability in stream systems; however, these models do not account for inherent natural variability in input values. Providing only single output values with no quantification of associated uncertainty can complicate management decisions focused on reducing bank erosion and nutrient loading to streams. To address this issue, uncertainty and sensitivity analyses were performed on the Bank Stability and Toe Erosion Model (BSTEM), a mechanistic model developed by the USDA-ARS that simulates both mass wasting (stability) and fluvial erosion of streambanks. Sensitivity analysis results indicate that variable influence on model output can vary depending on assumed input distributions.Generally, bank height, soil cohesion, and plant species were found to be most influential in determining stability of clay (cohesive) banks. In addition to these three inputs, groundwater elevation, stream stage, and bank angle were also identified as important in sand (non-cohesive) banks. Slope and bank height are the dominant variables in fluvial erosion modeling, while erodibility and critical shear stress are relatively unimportant. However, the threshold effect of critical shear stress (determining whether erosion occurs) was not explicitly accounted for, ii possibly explaining the relatively low sensitivity indices for this variable. Model output distributions of sediment and phosphorus loading rates corresponded well to ranges published in the literature, helping validate both model performance and selected ranges of input values. In addition, a probabilistic modeling approach was applied to data from a watershed-scale sediment and phosphorus loading study on the Missisquoi River, Vermont to quantify uncertainty associated with these published results. While our estimates indicated that bank erosion was likely a significant source of sediment and phosphorus to the watershed in question, the uncertainty associated with these predictions indicates that they should probably be considered order of magnitude estimates only.iii ACKNOWLEDGEMENTS

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Moment Independent Importance Measures: New Results and Analytical Test Cases

Cited by 147 publications

References 60 publications

Moment-based metrics for global sensitivity analysis of hydrological systems

Moment-based metrics for global sensitivity analysis of hydrological systems

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

Uncertainty and sensitivity in a bank stability model: implications for estimating phosphorus loading

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