Machine-learning-based regional-scale groundwater level prediction using GRACE

• Published in: Hydrogeology journal Vol. 29; no. 3; pp. 1027 - 1042
• Main Authors: Malakar, Pragnaditya, Mukherjee, Abhijit, Bhanja, Soumendra N., Ray, Ranjan Kumar, Sarkar, Sudeshna, Zahid, Anwar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Agriculture; Aquatic Pollution; Aquifers; Data; Depth; Earth and Environmental Science; Earth Sciences; Geology; Geophysics/Geodesy; GRACE (experiment); Gravity; Groundwater; Groundwater data; Groundwater levels; Groundwater storage; Hydrogeology; Hydrology/Water Resources; Learning algorithms; Machine learning; Observation wells; Population density; Predictions; Pumping; River basins; Statistical analysis; Statistical methods; Statistics; Support vector machines; Transboundary waters; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Wells; Satellite imagery; Groundwater level anomaly prediction; Transboundary aquifer; Groundwater exploration; Machine learning
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-021-02306-2

The rapid decline of groundwater levels (GWL) due to pervasive groundwater abstraction in the densely populated (~1 billion) Indus-Ganges-Brahmaputra-Meghna (IGBM) transboundary river basins of South Asia, necessitates a robust framework of prediction and understanding. While few localized studies exist, three-dimensional regional-scale characterization of GWL prediction is yet to be implemented. Here, ‘support vector machine’, a machine-learning-based method, is applied to data from the Gravity Recovery and Climate Experiment (GRACE) and data on land-surface-model-based groundwater storage and meteorological variables, to predict the GWL anomaly (GWLA) in the IGBM. The study has three main objectives, (1) to understand the spatial (observation well locations) and subsurface (shallow vs. deep observation wells) variability in prediction results for in-situ GWLA data for a large number of observation wells ( n  = 4,791); (2) to determine its relationship with groundwater abstraction, and; (3) to outline the advantages and limitations of using GRACE data for predicting GWLAs. The findings, based on individual observation well results, suggest significant prediction efficiency (median statistics: r  > 0.71, NSE > 0.70; p  < 0.05) in most of the IGBM; however, the study identifies hotspots, mostly in the agriculture-intensive regions, having relatively poor model performance. Further analysis of the subsurface depth-wise prediction statistics reveals that the significant dominance of pumping in the deeper depths of the aquifer is linked to the relatively poor model performance for the deep observation wells (screen depth > 35 m) compared with the shallow observation wells (screen depth < 35 m), thus, highlighting the limitation of GRACE in representing spatial and depth-dependent local-scale pumping.