Global sensitivity measures from given data

Plischke, Elmar; Borgonovo, Emanuele; Smith, Curtis

doi:10.1016/j.ejor.2012.11.047

Cited by 318 publications

(250 citation statements)

References 68 publications

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“…This problem can be avoided by utilizing rank-or log-transformed data; however, variance based methods cannot support this transformation (Borgonovo et al, 2014). For this reason, a density-based method (Plischke et al, 2013) that is compatible with log-transformed data was used for the Toe Erosion model.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…This is done by sorting on an input variable (and associated model output) and dividing this matrix into a fixed number of classes, M. In this case, M was chosen to be 50. Increasing the class number above this value has a negligible effect on estimation accuracy (Plischke et al, 2013).…”

Section: A3 Sensitivity Analysismentioning

confidence: 99%

“…While the density based method of Plischke et al (2013) Given a model that is a function of an array of input variables (Y = f(X 1 , X 2 , …X k ), the variance based first order sensitivity measure for any variable X i is:…”

Section: A3 Sensitivity Analysismentioning

confidence: 99%

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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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Section: Sensitivity Analysismentioning

confidence: 99%

Section: A3 Sensitivity Analysismentioning

confidence: 99%

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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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“…Let Y be the output of interest and X i a generic model input. Pearson's correlation ratio (Plischke et al, 2013) is the first-order variance-based sensitivity index, …”

Section: Uncertainty and Ensemble-based Sensitivity Analysesmentioning

confidence: 99%

“…First, a limited number N of simulations of the TH code are performed and a Finite Mixture Model (FMM) is used to reconstruct the pdf of the model output (Carlos et al, 2013). The natural clustering made by the FMM on the TH code output (McLachlan et al 2000, Di Maio et al 2014a) is exploited to estimate global sensitivity measures using a given data approach (Plischke et al, 2013). As shown in (Borgonovo et al (2014)), in fact, variance-based and distribution-based sensitivity measures rest on a common rationale that allows them to be estimated from the same design of experiments.…”

Section: Introductionmentioning

confidence: 99%

Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

Maio

Nicola

Borgonovo

et al. 2016

Reliability Engineering & System Safety

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Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

Rakovec¹,

et al. 2014

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[1] This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based ''local'' methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative ''bucket-style'' hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km 2 ) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.

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Global sensitivity measures from given data

Cited by 318 publications

References 68 publications

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

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

Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

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