Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia

Malakar, Pragnaditya; Bhanja, Soumendra N.; Dash, Adya Aiswarya; Saha, Dipankar; Ray, Ranjan Kumar; Sarkar, Sudeshna; Zahid, Anwar; Mukherjee, Abhijit

doi:10.1021/acsestwater.2c00220

Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia

Journal Article · Wed Aug 31 00:00:00 EDT 2022 · ACS ES&T Water

DOI:https://doi.org/10.1021/acsestwater.2c00220· OSTI ID:1885320

Malakar, Pragnaditya ^[1]; ^[2]; Dash, Adya Aiswarya ^[3]; Saha, Dipankar ^[4]; Ray, Ranjan Kumar ^[5]; Sarkar, Sudeshna ^[3]; Zahid, Anwar ^[6]; ^[3]

Institute of Technology Kharagpur (India); University of Alabama, Tuscaloosa, AL (United States); Jadavpur University, Kolkata (India)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Indian Institute of Technology Kharagpur (India)
Central Ground Water Board, Faridabad (India); Manav Rachna International Institute for Research and Development, Faridabad (India)
Central Ground Water Board, Faridabad (India)
Bangladesh Water Development Board (BWDB), Dhaka (Bangladesh)

We report the groundwater depletion in South Asia’s Himalayan, transboundary Indus-Ganges-Brahmaputra-Meghna (IGBM) rivers basin is among the highest globally. Given the high irrigation demand and population, groundwater sustainability requires an improved understanding of groundwater systems for the accurate prediction of groundwater levels (GWLs). However, the prediction of groundwater system behaviors is a significant challenge since it is dominated by spatiotemporal and subsurface depth-dependent drivers. Earlier studies that address the challenges are mainly based on the short spatial and temporal extent and/or do not separate the renewable (i.e., shallow) vs nonrenewable (i.e., deeper) groundwater signals. Here, we first identified the variable importance of spatial and depth-dependent drivers on GWL in the IGBM basin. Our results indicate a greater influence of anthropogenic factors (i.e., widespread pumping and increased population) in most parts of the IGBM basin, except in the precipitation-dominated basin of the Brahmaputra. Our next purpose was to delineate a multifactorial approach for GWL prediction using the two most used machine learning models (i.e., support vector machine and feed-forward neural network) in the literature. In general, the machine learning model outputs show a good match in comparison to the GWL from the observation wells (n = 2303 distributed across India and Bangladesh) with some limitations in areas with increased groundwater irrigation. We separately compared the results from shallow (<35 m) and deep (>35 m) observation wells, emphasizing the significance of deep groundwater pumping. Our approach highlights the importance of spatiotemporal to multidepth factors in GWL prediction and can be adopted in other parts of the globe to predict GWLs.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1885320

Journal Information:: ACS ES&T Water, Journal Name: ACS ES&T Water Journal Issue: 6 Vol. 3; ISSN 2690-0637

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Delineating Variabilities of Groundwater Level Prediction Across the Agriculturally Intensive Transboundary Aquifers of South Asia

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