Abstract
Groundwater is the primary source of water in Madhesh Province, Nepal, yet the province lacks a reliable forecasting framework. This study develops a provincial-scale groundwater forecasting system by integrating satellite-derived groundwater storage (GWS), machine learning, and CMIP6 climate forcing. A Long Short-Term Memory (LSTM) model was trained using two decades of GRACE–GLDAS anomalies, observed climate data, and land use–land cover (LULC) information, and was forced with bias-corrected MIROC6 projections under the SSP5-8.5 scenario. Results reveal a persistent historical GWS decline of approximately 50 mm over 20 years (2.5 mm yr⁻¹), driven primarily by rainfall variability and intensified by urban expansion and surface water loss. Two LSTM setups were tested: a climate-driven model using precipitation and temperature, which achieved higher predictive accuracy but underrepresented human influences, and a multi-parameter model incorporating LULC, soil moisture, groundwater-irrigated area, and domestic demand, which captured more realistic depletion dynamics despite slightly lower statistical performance. Both the climate-driven and multi-parameter LSTM models indicate a monsoon-dependent, highly seasonal recharge with weakening peaks and continued decline through 2045. Furthermore, scenario-based demand sensitivity showed that mean GWS increased from 635.20 mm to 639.82 mm under demand − 10%, but declined to 623.91 mm under total demand + 30%, showing a mean 95% confidence interval width of 9.24 mm and a mean model spread of 7.78 mm, indicating stable projection responses across model structures. The bias-corrected Machine Learning-driven GWS and predictor dataset provides a reproducible basis for groundwater assessments and model benchmarking in data scare regions. These findings provide actionable insights for groundwater management, supporting climate-resilient water resource planning, sustainable irrigation practices, and policy development in Madhesh Province and similar data-scarce alluvial regions.
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Mahto, A.K., Dahal, V., Regmi, R.K. et al. Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70415-7
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DOI: https://doi.org/10.1038/s41598-026-70415-7
Keywords
- Groundwater storage
- Madhesh Province
- Machine learning
- Climate change
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