Abstract
Land subsidence as a result of groundwater overpumping in the San Joaquin Valley, California, is associated with the loss of groundwater storage and aquifer contamination. Although the physical processes governing land subsidence are well understood, building predictive models of subsidence is challenging because so much subsurface information is required to do so accurately. For the first time, we integrate airborne electromagnetic data, representing the subsurface, with subsidence data, mapped by interferometric synthetic aperture radar (InSAR), to model deformation. By combining both data sets, we are able to solve for hydrologic and geophysical properties of the subsurface to effectively model the complex spatiotemporal process of deformation. The resulting model reveals that roughly 3 m of subsidence has occurred at one location of our study area from 1990 to 2018. This model also allows us to predict subsidence more accurately under future hydrologic scenarios, which is needed to develop plans for sustainable groundwater use.
Recommended Citation
R. G. Smith and R. Knight, "Modeling Land Subsidence using InSAR and Airborne Electromagnetic Data," Water Resources Research, vol. 55, no. 4, pp. 2801 - 2819, American Geophysical Union (AGU), Apr 2019.
The definitive version is available at https://doi.org/10.1029/2018WR024185
Department(s)
Geosciences and Geological and Petroleum Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
InSAR; Subsidence; Electromagnetics; MCMC; Groundwater
International Standard Serial Number (ISSN)
0043-1397; 1944-7973
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Apr 2019
Comments
This research was funded by the Gordon and Betty Moore Foundation. R. Smith was supported by a National Science Foundation Fellowship (grant DGE‐114747).