A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Abstract

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance‐based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. For demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.

On the Sensitivity of the Precipitation Partitioning Into Evapotranspiration and Runoff in Land Surface Parameterizations

“…Dai et al () have made encouraging progress to investigate the parameterization sensitivity in methodology. They proposed a method based on the Sobol' variance analysis (Sobol', ) to measure the parameterization sensitivity with considerations of parameter uncertainties.…”

Parametric and Structural Sensitivities of Turbine‐Height Wind Speeds in the Boundary Layer Parameterizations in the Weather Research and Forecasting Model

“…The impacts of tuning PBL parameter values or switching PBL scheme options on the simulated wind flow features have been separately examined by previous studies (e.g., Carvalho et al, 2012;Draxl et al, 2014;Nielsen-Gammon et al, 2010;Siuta et al, 2017;Yang et al, 2013;Yang et al, 2017). Structural and parametric problems, however, are often linked, which means the discrepancies in model outputs using different schemes are jointly contributed by the structural differences among them and parameter uncertainties within each scheme (Dai et al, 2017;McNeall et al, 2016). Therefore, conclusions regarding the strengths or limitations of different schemes may not be the same when different parameter values are applied in the schemes (e.g., Clark et al, 2008;Leung et al, 1999;Williamson et al, 2015).…”

Global Sensitivity Analysis for Multiple Interpretive Models With Uncertain Parameters

“…In this sense, our definition of sensitivity is more relevant for factor fixing aspects (i.e., with reference to questions of the kind “How does a parameter affect a given statistical moment of the output pdf ?” and/or “Is it possible to fix its value arbitrarily?”), rather than for the uncertainty apportioning aspect. A comparison between the metrics at heart of our GSA approach and those proposed by Dai and Ye () and Dai et al () is provided in Appendices B and C.…”