Abstract
For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.
| Original language | English |
|---|---|
| Article number | e2021WR029812 |
| Journal | Water Resources Research |
| Volume | 58 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2022 |
Funding
The first two authors were supported in part by the U.S. Department of Energy Grant DE‐SC0019438 and the National Science Foundation Grant EAR‐1552329. The third author was supported by the River Corridor Science Focus Area (SFA) project at the Pacific Northwest National Laboratory (PNNL), as part of the Subsurface Biogeochemical Research Program (SBR) of the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER). PNNL is operated for the DOE by Battelle Memorial Institute under contract DE‐AC05‐76RL01830. ORNL is managed by UT‐Battelle, LLC, for the DOE under contract DE‐AC05‐1008 00OR22725. This study describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the study do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The authors would like to thank Associate Editor Thorsten Wagener, Alberto Guadagnini, and the other anonymous reviewer for their insightful comments and suggestions, which helped improve this manuscript. This manuscript has been co‐authored by UT‐Battelle, LLC under Contract no. DE‐AC05‐00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non‐exclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). All computer codes and data used in this study are available online at https://github.com/jyangfsu/total-effect and at Zenodo via https://zenodo.org/record/4542541#.YCtnm2gzZPY with DOI:10.5281/zenodo.4542541. The first two authors were supported in part by the U.S. Department of Energy Grant DE-SC0019438 and the National Science Foundation Grant EAR-1552329. The third author was supported by the River Corridor Science Focus Area (SFA) project at the Pacific Northwest National Laboratory (PNNL), as part of the Subsurface Biogeochemical Research Program (SBR) of the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER). PNNL is operated for the DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-1008 00OR22725. This study describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the study do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The authors would like to thank Associate Editor Thorsten Wagener, Alberto Guadagnini, and the other anonymous reviewer for their insightful comments and suggestions, which helped improve this manuscript.
Keywords
- process importance
- process influence
- process interactions
- process screening
- process-based modeling
- uncertainty reduction