Sensitivity Analysis for Chemical Models

Saltelli, Andrea; Ratto, Marco; Tarantola, Stefano; Campolongo, Francesca

doi:10.1021/cr040659d

Cited by 543 publications

(286 citation statements)

References 80 publications

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“…The method itself consists of multiple, random one‐factor‐at‐a‐time (OAT) experiments, also termed “elementary effects,” that are repeated across many paths through the parameter space in order to account for individual parameter influence and interactions [ Saltelli et al ., 2005]. For a single parameter x i , an elementary effect EE i is calculated as the change in parameter value with model output along a trajectory with a grid of size Δ i .…”

Section: Sensitivity Analysismentioning

confidence: 99%

Model‐based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments

Kelleher

Wagener

McGlynn

2015

Water Resources Research

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Ungauged headwater basins are an abundant part of the river network, but dominant influences on headwater hydrologic response remain difficult to predict. To address this gap, we investigated the ability of a physically based watershed model (the Distributed Hydrology‐Soil‐Vegetation Model) to represent controls on metrics of hydrologic partitioning across five adjacent headwater subcatchments. The five study subcatchments, located in Tenderfoot Creek Experimental Forest in central Montana, have similar climate but variable topography and vegetation distribution. This facilitated a comparative hydrology approach to interpret how parameters that influence partitioning, detected via global sensitivity analysis, differ across catchments. Model parameters were constrained a priori using existing regional information and expert knowledge. Influential parameters were compared to perceptions of catchment functioning and its variability across subcatchments. Despite between‐catchment differences in topography and vegetation, hydrologic partitioning across all metrics and all subcatchments was sensitive to a similar subset of snow, vegetation, and soil parameters. Results also highlighted one subcatchment with low certainty in parameter sensitivity, indicating that the model poorly represented some complexities in this subcatchment likely because an important process is missing or poorly characterized in the mechanistic model. For use in other basins, this method can assess parameter sensitivities as a function of the specific ungauged system to which it is applied. Overall, this approach can be employed to identify dominant modeled controls on catchment response and their agreement with system understanding.

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Section: Sensitivity Analysismentioning

confidence: 99%

Model‐based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments

Kelleher

Wagener

McGlynn

2015

Water Resources Research

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“…given system: the sensitivity (SEN) measures the ratio of output noise to input noise 930 [95,96]. This approach allowed to develop sensitivity expressions for multi-way 931 calibration based on PARAFAC [94], MCR-ALS [97] and PLS/RML (RML indicates 932 residual multi-linearization, and includes RBL, RTL, RQL, etc.)…”

Section: Multi-way Analytical Figures Of Merit 888mentioning

confidence: 99%

Second- and higher-order data generation and calibration: A tutorial

Escandar

Goicoechea

Peña

et al. 2014

Analytica Chimica Acta

155

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“…Equation (2) has no general explicit solution, and its numerical solution is too costly to compute in most cases, especially when M s exceeds a few units. One has then resort to a simulation method to generate sample trajectories from the distribution that is the solution of Eq.…”

Section: A Stochastic Chemical Systemsmentioning

confidence: 99%

“…From this expansion, one can easily establish the expression of the variance of the conditional expectation, 2 + hot, where hot stands for higher order terms involving higher order moments in (B − µ B ), and that are assumed to vanish faster than σ 2 B when CoV B → 0. Therefore, it follows that…”

Section: Comparison With Local Sensitivity Estimatesmentioning

confidence: 99%

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Global sensitivity analysis in stochastic simulators of uncertain reaction networks

Jimenez

Maître

Knio

2016

The Journal of Chemical Physics

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Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

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Sensitivity Analysis for Chemical Models

Cited by 543 publications

References 80 publications

Model‐based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments

Model‐based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments

Second- and higher-order data generation and calibration: A tutorial

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

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