Mahto et al. (2026) Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-09-09
• Authors: Anjana Kumari Mahto, Vishan Dahal, Ram Krishna Regmi, Govinda Prasad Poudel, Prakash Chandra Ghimire, Shukra Raj Paudel
• DOI: 10.1038/s41598-026-70415-7

Research Groups

• Environmental Engineering Program, Department of Civil Engineering, Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal
• Department of Civil Engineering, Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal
• Department of Civil Engineering, Institute of Engineering, Tribhuvan University, Thapathali Campus, Kathmandu, Nepal

Short Summary

This study develops a provincial-scale groundwater forecasting system for Madhesh Province, Nepal, by integrating satellite-derived groundwater storage (GWS), machine learning, and CMIP6 climate forcing. The results reveal a persistent historical GWS decline of approximately 50 mm over 20 years.

Objective

• Investigate the impact of climate change, land-use changes, and human-driven factors on groundwater storage in Madhesh Province, Nepal.

Study Configuration

• Spatial Scale: Provincial scale (Madhesh Province, Nepal)
• Temporal Scale: 20-year period (2000-2020) with projections until 2045

Methodology and Data

• Models used: Long Short-Term Memory (LSTM) model
• Data sources:
  • Satellite-derived groundwater storage (GWS) from GRACE–GLDAS anomalies
  • Observed climate data
  • Land use–land cover (LULC) information

Main Results

• A persistent historical GWS decline of approximately 50 mm over 20 years (2.5 mm yr⁻¹)
• Climate-driven and multi-parameter LSTM models indicate a monsoon-dependent, highly seasonal recharge with weakening peaks and continued decline through 2045
• 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%

Contributions

• This study provides 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.

Funding

• Not specified

Citation

@article{Mahto2026Machinelearning,
  author = {Mahto, Anjana Kumari and Dahal, Vishan and Regmi, Ram Krishna and Poudel, Govinda Prasad and Karki, Ramesh and Ghimire, Prakash Chandra and Paudel, Shukra Raj},
  title = {Machine-learning projections of groundwater storage under climate, land-use, and human-driven changes in Madhesh Province, Nepal},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-70415-7},
  url = {https://doi.org/10.1038/s41598-026-70415-7}
}