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arXiv AI By Saleh Sakib Ahmed, Rashed Uz Zzaman, Saifur Rahman Jony, Faizur Rahman Himel, Afroza Sharmin, A. H. M. Khalequr Rahman, M. Sohel Rahman, Sara Nowreen

Unraveling Groundwater Stress: A Model Generalizable Across Space and Time Reveals Co-Declining Water Tables and Recharge Across Bangladesh

Read the original on arXiv AI →

The study presents a Teacher-Student machine‑learning framework that reconstructs high‑resolution groundwater depth maps across Bangladesh using sparse in‑situ measurements and GLDAS groundwater storage data. The model achieves high predictive accuracy (R² ≈ 0.93) and generalizes to unseen years and districts, revealing a nationwide decline in groundwater recharge of 1.10 mm/yr and widespread deepening of water tables. The findings challenge the notion of widespread recharge enhancement from pumping and highlight a significant imbalance between recharge declines and increases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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