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Groundwater levels estimation from GRACE/GRACE-FO and hydro-meteorological data using deep learning in Ganga River basin, India

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Abstract

Accurate prediction and continuous monitoring of spatiotemporal variations in groundwater levels (GWL) are essential for sustainable groundwater resource management as it is a critical natural resource for human survival and ecosystem health. In this study, deep learning-based models, convolutional neural network (CNN), long short-term memory (LSTM), and CNN-LSTM were developed to predict GWL changes from 2003 to 2020 by integrating the gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) data with hydro-meteorological data sets (evapotranspiration, precipitation, and temperature). To assess the effectiveness of the developed models, ~ 1115 in-situ wells located in various parts of the Ganga River basin were chosen due to their importance in India and their reported decline in groundwater levels. Comparison of predicted GWL from deep-learning models with the in-situ data reveals that the LSTM model shows a relatively higher Pearson’s correlation coefficient (PR) (0.681 on testing) and lower normalized root mean squared error (NRMSE) (0.292 on testing) values compared to the CNN-LSTM and CNN models. Further trend and seasonal characteristics of GWL predicted by the LSTM model also match well with the in-situ groundwater wells, except few parts of the Ganges basin. The negative GWL trend in the Ganga River basin suggests that the basin has been experiencing a continuous decline in the GWL due to anthropogenic groundwater withdrawal for irrigation. Sensitivity analysis of the deep-learning models suggests that GRACE played a significant role in estimating GWL prediction compared to precipitation and temperature.

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Data availability

The data are available from the corresponding author on reasonable request.

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Acknowledgements

GSR acknowledges the Department of Science & Technology for the FIST grant (DST-FIST/197/2018-19/580).

Funding

This work was supported by the Department of Science & Technology (DST-FIST/197/2018-19/580) Government of India.

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Authors and Affiliations

  1. Department of Applied Geophysics, IIT (ISM) Dhanbad, Jharkhand, 826004, India

    Pragay Shourya Moudgil & G. Srinivasa Rao

  2. Department of Earth Science, IIT Bombay, Powai, Mumbai, 400076, India

    G. Srinivasa Rao

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  1. Pragay Shourya Moudgil
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Contributions

All the authors contributed to the study conception and design. PSM: algorithm development, data analysis, and preparation of the first draft of the manuscript. GSR: conceptualization, supervision, methodology, validation, and writing—review and editing. All the authors read and approved the final manuscript.

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Correspondence to G. Srinivasa Rao.

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Moudgil, P.S., Rao, G.S. Groundwater levels estimation from GRACE/GRACE-FO and hydro-meteorological data using deep learning in Ganga River basin, India. Environ Earth Sci 82, 441 (2023). https://doi.org/10.1007/s12665-023-11137-1

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