Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation. (September 2021)

• Record Type: Journal Article
• Authors: Ali, Muhammad Zeeshan; Chu, Hone-Jay; Tatas, ; Burbey, Thomas J.
• Is Part Of: Environmental modelling & software. Volume 143(2021)
• Publication Date: 2021-09
• Subjects: Groundwater -- GPS based Deformation -- Time-dependent spatial regression -- Seasonal variation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105123
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Significant subsidence is susceptible to groundwater level variations in aquifer systems. The relation between groundwater level change and global positioning system (GPS) estimated subsidence is spatially variable. Time-dependent spatial regression can be used for the estimation of groundwater level changes using GPS based deformation data. Furthermore, the model can be validated using observed hydraulic head data from available monitoring stations. This study uses GPS station data to estimate the monthly groundwater levels in the west-central Taiwan for the period: 2016–17. Time-dependent spatial regression provides a more realistic estimation of groundwater level changes in response to highly heterogeneous aquifer properties than other methods. The high correlation (r = 0.95) between observed and estimated groundwater levels shows that GPS estimated deformations represent an alternative approach for estimating seasonal groundwater changes. Due to availability of spatially broad/low cost GPS data (compared to the sparse availability groundwater monitoring stations), the use of GPS data represents a powerful solution for future monitoring of estimated seasonal groundwater level changes in areas where only few groundwater observations are available. Highlights: Model determines the relation between GPS deformation and groundwater level. Model provides reliable information for accurate groundwater level mapping. Model does not require extensive physical model calibration. Model identifies various patterns of groundwater level changes in the alluvial fan.