Multi-model and Multi-scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

This exploratory project aims at developing a new approach of global sensitivity analysis to identify controlling processes of complex systems such as groundwater-surface water transition zones, in which intricate hydrologic, microbiologic, and geochemical processes occur and interact to affect the hydrobiogeochemical behaviors at multiple scales (e.g., from local to reaches and to reach networks). The identification of controlling processes is necessary for the development and improvement of mechanistically-based hydro-biogeochemical models. Taking spatial variability of hydrofacies (a geological process) and river stage dynamics (a hydrological process) as examples, if the former is more important than the latter, limited research resources (money and time) should be spent to better characterize spatial variability of hydrofacies. The identification of controlling processes faces the theoretical and computational challenges as follows: (1) How to take into account of the inherent uncertainty in conceptualizing and modeling individual processes? (2) How to quantitatively and explicitly measure the relative importance of a large number of individual processes? (3) How to identify controlling processes in a computationally efficient manner for large-scale, computationally expensive models? The goal of this project is to address these three challenges by developing a new method of global sensitivity analysis for identifying controlling processes at multiple scales to support development and improvement of mechanistically-based models. The overarching scientific question to be answered in this project is as follows: If we are not certain about the choice of process models and model parameters, can we correctly identify the controlling processes of a complex system? To answer this question, this project introduces the concept of multiple working hypotheses into the identification of controlling processes to explicitly take into account the uncertainty in conceptualizing and simulating individual processes. The proposed global sensitivity analysis defines a new process sensitivity index as a summary measure of relative process importance for individual processes. The new index is evaluated using the state-of-the-art methods of sparse-grid collocation approaches that have been demonstrated to be computationally efficient for sensitivity analysis. The new index lays out a foundation for integrated model-and-data analysis in future studies. By collaborating with scientists at the Pacific Northwest National Laboratory (PNNL), this project will be focused on answering scientific questions at the PNNL scientific focus area (SFA). By collaborating with scientists working at the Naturita Site in Colorado (a DOE UMTRA Title I site) and the Shaver's Creek Site in Pennsylvania (the extended site of the NSF Susquehanna Shale Hills Critical Zone Observatory), this project will also provide broader perspectives for other sites of importance to DOE and the nation. This project is complementary to the previous and on-going research conducted at the PNNL SFA, and the research results of this project can be used to confirm existing insights and to gain new insights for advanced scientific understanding of groundwater-surface water transition zones.