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.
arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
arXiv:2603.25779v2 Announce Type: replace-cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling c...
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years, combining satellite altimetry and in‑situ gauge data to enable large‑scale spatiotemporal graph imputation. The authors highlight the extreme sparsity of observations—less than 1% of sections per day—and the directed acyclic topology of river networks, which challenge existing imputation methods. They propose a bidirectional selective state‑space model that samples connected subgraphs and uses topology‑aware positional encodings, achieving 18–39% lower RMSE than the current state‑of‑the‑art SWOT‑based approach while providing predictions for all river sections.
arXiv:2606. 23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle.
arXiv:2609.39005v1 Announce Type: new Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During...
The paper introduces AmazonSWE, a dataset covering over 19,000 river sections in the Amazon basin for 10 years (2016‑2026) that integrates satellite altimetry, including SWOT, to enable large‑scale spatiotemporal graph imputation. The dataset is extremely sparse—fewer than 1% of sections are observed daily—and features a directed acyclic river topology that is larger and structurally distinct from existing benchmarks. The authors demonstrate that conventional imputation methods struggle with this topology, scale, and sparsity, and propose a bidirectional selective state‑space model that outperforms prior approaches, reducing RMSE against in‑situ gauges by 18‑39% and providing predictions for every river section. whyItMatters":"AmazonSWE offers a novel, real‑world use case that could improve flood forecasting and water resource management by enabling more accurate and comprehensive water surface elevation estimates across a vast, sparsely monitored river network."
arXiv:2502. 14894v5 Announce Type: replace-cross Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling.
Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come fro...
arXiv:2609.36193v1 Announce Type: new Abstract: Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is...
Autoregressive modelling, successful in language tasks, is applied to mineral‑exploration drillholes where lithology is revealed sequentially from shallow to deep. The authors introduce DrillBench, a benchmark of 49,671 Western Australian drillholes for next‑layer prediction and autoregressive stratigraphic generation across a graded transfer spectrum. Experiments show that spatial conditioning helps locally but fails under strong shift, while autoregressive models transfer more robustly; a backbone‑agnostic recipe combining large‑scale pretraining and spatial retrieval improves generalisation, especially in distant geological splits.
EvoBench is a diagnostic benchmark framework designed to reveal where and when machine‑learning models for environmental variables fail, rather than relying on a single average accuracy score. It tracks model performance over time and across spatial extrapolation, highlighting issues such as non‑stationarity, poor performance at unmonitored sites, and under‑prediction of rare events like floods. In a case study on daily streamflow prediction across 9,000 U.S. basins, EvoBench shows that no model dominates all scenarios and that targeted fixes can mitigate identified weaknesses.
The study trains a convolutional LSTM on ten annual Earth Observation variables to forecast desertification dynamics in Ceará, Brazil, achieving modest improvements over persistence and capturing 53‑69% of the regional degradation trend. While the model slightly outperforms simple baselines one to four years ahead, it explains only a small fraction of cell‑level variance, indicating that EO data are more reliable for regional trends than for individual pixel predictions. The authors also demonstrate that elevation and EVI channels degrade forecast skill, and use Monte Carlo dropout to quantify uncertainty in future degradation maps.