How shallow and deep groundwater impact environmental parameters correlated with global heatwaves
Bibliographic record
Abstract
Heatwaves present serious challenges to ecosystems, human health, and a wide range of socioeconomic activities. As the frequency and intensity of heatwaves increase, understanding the mechanisms driving their dynamics and interactions with land surface processes become more important. While extensive research has investigated the influence of various land and atmospheric parameters on heatwaves, less is known about how groundwater depth influences heatwave dynamics through their effects on soil moisture and surface evaporative fluxes (Vogelbacher et al., 2024, Sadeghi et al., 2012). To address this knowledge gap, we investigated how the groundwater depth affects the key parameters controlling heatwave dynamics on a global scale. Specifically, we developed more than 200,000 localized Artificial Intelligence (AI) models to represent the spatial distribution of heatwave frequency over the past 21 years across the world. For each model, a radius of 1.5 degrees (approximately 149 neighboring pixels) is considered in the computation to identify key parameters contributing to heatwaves in that region. We analyzed surface fluxes, as well as atmospheric, hydrological, and local environmental variables, to understand their correlation to heatwaves. Our findings suggest that geopotential height representing atmospheric drivers, is the key predictor of heatwave events in regions with deep groundwater tables (>100 m). In contrast, in areas with shallow groundwater (
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".