A new process sensitivity index to identify important system processes under process model and parametric uncertainty. Issue 4 (13th April 2017)

• Record Type: Journal Article
• Authors: Dai, Heng; Ye, Ming; Walker, Anthony P.; Chen, Xingyuan
• Is Part Of: Water resources research. Volume 53:Issue 4(2017)
• Page Start: 3476
• Page End: 3490
• Publication Date: 2017-04-13
• Subjects: process sensitivity index -- variance decomposition -- model averaging -- model uncertainty -- parametric uncertainty -- groundwater reactive transport modeling; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR019715
• Languages: English
• ISSNs: 0043-1397

Abstract

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. Plain Language Summary: If we have only one model, we always know how to identify the important factors of the models. However, if there are multiple models, it is not always clear how to identify the important factors. The factors important to one model may not be important to another model. It is necessary to develop a method that can identify important factors not for a single model but for multiple models. This study aims at resolving this problem by developing a mathematically rigorous method to provide a single summary measure for identifying important factors in the face of competing models. This is called multimodel process sensitivity analysis, and the mathematical measure is called process sensitivity index. The new index is demonstrated using a numerical example of groundwater reactive transport modeling with two recharge models and two geology models. The multimodel process sensitivity analysis has a wide range of applications in hydrologic and environmental modeling. Key Points: A new process sensitivity index is defined to identify important system processes when multiple competing process models are available The new index is derived by integrating the concept of model averaging into the framework of variance‐based global sensitivity analysis The new index allows for interactions between process models and/or between process model parameters in the global sensitivity analysis