Evaluation of Hydraulic Conductivity Estimates from Various Approaches with Groundwater Flow Models

• Published in: Ground water Vol. 62; no. 3; pp. 384 - 404
• Main Authors: Sun, Dongwei, Luo, Ning, Vandenhoff, Aaron, McCall, Wesley, Zhao, Zhanfeng, Wang, Chenxi, Rudolph, David L, Illman, Walter A
• Format: Journal Article
• Language: English
• Published: United States Ground Water Publishing Company 01.05.2024
• Subjects: Boreholes; Contaminants; Data integration; Drawdown; Environmental Monitoring - methods; Estimates; Geological surveys; Geology; Grain size; Groundwater; Groundwater flow; Hydraulic conductivity; Hydraulics; Hydrology - methods; Modelling; Models, Theoretical; Parameter estimation; Parameterization; Pollution transport; Predictions; Pumping; Pumping tests; Spatial variations; Surveys; Tomography; Transport processes; Water Movements
• ISSN: 0017-467X; 1745-6584; 1745-6584
• DOI: 10.1111/gwat.13348

Significant efforts have been expended for improved characterization of hydraulic conductivity (K) and specific storage (S ) to better understand groundwater flow and contaminant transport processes. Conventional methods including grain size analyses (GSA), permeameter, slug, and pumping tests have been utilized extensively, while Direct Push-based Hydraulic Profiling Tool (HPT) surveys have been developed to obtain high-resolution K estimates. Moreover, inverse modeling approaches based on geology-based zonations, and highly parameterized Hydraulic Tomography (HT) have also been advanced to map spatial variations of K and S between and beyond boreholes. While different methods are available, it is unclear which one yields K estimates that are most useful for high resolution predictions of groundwater flow. Therefore, the main objective of this study is to evaluate various K estimates at a highly heterogeneous field site obtained with three categories of characterization techniques including: (1) conventional methods (GSA, permeameter, and slug tests); (2) HPT surveys; and (3) inverse modeling based on geology-based zonations and highly parameterized approaches. The performance of each approach is first qualitatively analyzed by comparing K estimates to site geology. Then, steady-state and transient groundwater flow models are employed to quantitatively assess various K estimates by simulating pumping tests not used for parameter estimation. Results reveal that inverse modeling approaches yield the best drawdown predictions under both steady and transient conditions. In contrast, conventional methods and HPT surveys yield biased predictions. Based on our research, it appears that inverse modeling and data fusion are necessary steps in predicting accurate groundwater flow behavior.