Modeling Elastic and Inelastic Pumping-Induced Deformation with Incomplete Water Level Records in Parowan Valley, Utah
Abstract
Groundwater extraction causes significant land subsidence in many parts of the world with compressible sediments. InSAR data have been used to estimate the magnitude of this subsidence, and derive hydrologic and geomechanical properties in these areas. In spite of recent advances in InSAR processing methods, several challenges remain in relating surface deformation to hydrologic systems. The relationship between deformation and groundwater levels is non-linear, and existing groundwater level data are often sparsely sampled, so that it is challenging to relate them to InSAR-derived deformation. In addition, historical InSAR datasets are sparsely sampled in time, leaving many gaps in existing deformation estimates. In this study, we present an approach to improve the temporal density of groundwater level and deformation estimates. The study area for this demonstration is Parowan Valley, Utah, a region dependent on groundwater for agricultural use that has seen significant declines in head over the past 70 years. In our approach, we use Theis curves to interpolate seasonal fluctuations in head, and a subsidence model to extend deformation estimates. We find that with this approach, we are able to closely match long-term and seasonal estimates of deformation, estimate seasonal fluctuations in head, and characterize the elastic and inelastic response to changes in head.
Recommended Citation
R. G. Smith and J. Li, "Modeling Elastic and Inelastic Pumping-Induced Deformation with Incomplete Water Level Records in Parowan Valley, Utah," Journal of Hydrology, vol. 601, article no. 126654, Elsevier, Oct 2021.
The definitive version is available at https://doi.org/10.1016/j.jhydrol.2021.126654
Department(s)
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Groundwater; Groundwater Modeling; InSAR; MCMC; Subsidence
Geographic Coverage
Parowan Valley, Utah
International Standard Serial Number (ISSN)
0022-1694
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2021 Elsevier, All rights reserved.
Publication Date
01 Oct 2021