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

Pragnaditya Malakar, Abhijit Mukherjee, Soumendra N. Bhanja, Ranjan Kumar Ray, Sudeshna Sarkar, Anwar Zahid

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1027-1042
Number of pages16
JournalHydrogeology Journal
Volume29
Issue number3
DOIs
StatePublished - May 2021
Externally publishedYes

Funding

The authors acknowledge the Central Ground Water Board (Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation) of the Government of India and Bangladesh Water Development Board of the Government of Bangladesh for data support. The authors also acknowledge the India Meteorological Department (IMD) and Climatic Research Unit (CRU). GRACE land data were processed by Sean Swenson, supported by the NASA MEaSUREs Program. The GLDAS data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The authors are thankful to Matt Rodell of NASA and Srimanti Duttagupta IIT Kharagpur. The authors acknowledge the use of ArcGIS software (version 10.2.1), Origin software (version 2015), R statistical software, and Ferret program (Pacific Marine Environmental Laboratory NOAA) for analysis. The authors acknowledge the Central Ground Water Board (Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation) of the Government of India and Bangladesh Water Development Board of the Government of Bangladesh for data support. The authors also acknowledge the India Meteorological Department (IMD) and Climatic Research Unit (CRU). GRACE land data were processed by Sean Swenson, supported by the NASA MEaSUREs Program. The GLDAS data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The authors are thankful to Matt Rodell of NASA and Srimanti Duttagupta IIT Kharagpur. The authors acknowledge the use of ArcGIS software (version 10.2.1), Origin software (version 2015), R statistical software, and Ferret program (Pacific Marine Environmental Laboratory NOAA) for analysis.

Keywords

  • Groundwater exploration
  • Groundwater level anomaly prediction
  • Machine learning
  • Satellite imagery
  • Transboundary aquifer

Fingerprint

Dive into the research topics of 'Machine-learning-based regional-scale groundwater level prediction using GRACE'. Together they form a unique fingerprint.

Cite this