A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

doi:10.1002/2016WR019756

Title: A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

Journal Article · Wed May 31 00:00:00 EDT 2017 · Water Resources Research

DOI:https://doi.org/10.1002/2016WR019756· OSTI ID:1368106

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^[1]; Zachara, John M. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Florida State Univ., Tallahassee, FL (United States)

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. As such, in this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

Grant/Contract Number:: AC05-76RL01830; SC0008272; 1552329; AC02-05CH11231

OSTI ID:: 1368106

Alternate ID(s):: OSTI ID: 1402249

Report Number(s):: PNNL-SA-120000; KP1702030

Journal Information:: Water Resources Research, Vol. 53, Issue 5; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 26 works

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