Aquifer System Deformation in the San Luis Valley: A New Framework for Modeling Subsidence in Agricultural Regions
Abstract
The San Luis Valley (SLV), Colorado, is challenged with implementing sustainable groundwater management in the face of increasing surface water scarcity due to climate change. Groundwater extraction in unconsolidated aquifers such as the SLV can cause cm-scale subsidence and rebound. This study utilizes Interferometric Synthetic Aperture Radar (InSAR) data, validated by Global Navigation Satellite System (GNSS) measurements, to measure subsidence and analyze the groundwater dynamics that cause it. Addressing the challenges of phase decorrelation and data gaps, notably from September 2018 to April 2019, we adopted a modified DS-interpolation algorithm, alongside a Singular Spectrum Analysis (SSA)-based gap filling technique. Furthermore, we enhanced the temporal resolution of groundwater level data through the Theis curve interpolation. These methodologies enabled the integration of observational well data with satellite measurements to calibrate a one-dimensional deformation model, capturing both the elastic and inelastic responses of the aquifer system. Our investigation, spanning 2015 to 2021, reveals both seasonal and long-term subsidence, with the confined aquifer section experiencing up to 1 cm/year of subsidence alongside notable seasonal fluctuations. The methodology presented here provides a path to model subsidence in regions with sparse groundwater level and noisy InSAR data. It also provides valuable insights for developing effective water management strategies in the SLV.
Recommended Citation
S. Vajedian et al., "Aquifer System Deformation in the San Luis Valley: A New Framework for Modeling Subsidence in Agricultural Regions," Journal of Hydrology, vol. 642, article no. 131876, Elsevier, Oct 2024.
The definitive version is available at https://doi.org/10.1016/j.jhydrol.2024.131876
Department(s)
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Aquifer storage parameters; Data gap filling; Groundwater dynamics; InSAR time series analysis; Land subsidence; MCMC; San Luis valley (SLV)
International Standard Serial Number (ISSN)
0022-1694
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Oct 2024
Comments
European Space Agency, Grant 5R01ES032612-03