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02-04-2021 | Paper

Hydraulic tomography analysis of municipal-well operation data with geology-based groundwater models

Authors: Xin Tong, Walter A. Illman, Steven J. Berg, Ning Luo

Published in: Hydrogeology Journal | Issue 5/2021

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Abstract

The sustainable management of groundwater resources is essential to municipalities worldwide due to increasing water demand. Planning for the optimized use of groundwater resources requires reliable estimation of hydraulic parameters such as hydraulic conductivity (K) and specific storage (S_s). However, estimation of hydraulic parameters can be difficult with dedicated pumping tests while municipal wells are in operation. In this study, the K and S_s of a highly heterogeneous, multi-aquifer/aquitard system are estimated through the inverse modeling of water-level data from observation wells collected during municipal well operations. In particular, four different geological models are calibrated by coupling HydroGeoSphere (HGS) with the parameter estimation code PEST. The joint analysis of water-level records resulting from fluctuating pumping and injection operations amounts to a hydraulic tomography (HT) analysis. The four geological models are well calibrated and yield reliable estimates that are consistent with previously studies. Overall, this research reveals that: (1) the HT analysis of municipal well records is feasible and yields reliable K and S_s estimates for individual geological units where drawdown records are available; (2) these estimates are obtained at the scale of intended use, unlike small-scale estimates typically obtained through other characterization methods; (3) the HT analysis can be conducted using existing data, which leads to substantial cost savings; and (4) data collected during municipal well operations can be used in the development of new groundwater models or in the calibration of existing groundwater models, thus they are valuable and should be archived.

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Title: Hydraulic tomography analysis of municipal-well operation data with geology-based groundwater models
Authors: Xin Tong
Walter A. Illman
Steven J. Berg
Ning Luo
Publication date: 02-04-2021
Publisher: Springer Berlin Heidelberg
Published in: Hydrogeology Journal / Issue 5/2021
Print ISSN: 1431-2174
Electronic ISSN: 1435-0157
DOI: https://doi.org/10.1007/s10040-021-02320-4

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