Global Insight into Extreme Events and Land Subsidence: Understanding Drivers, Interplay, and Impacts
Bibliographic record
Abstract
Land subsidence (LS) or the relative lowering of the Earth’s ground surface is a critical concern that warrants global attention. LS is a chronic hazard in many areas that has adverse effects on built infrastructure, people, and natural systems. As global atmospheric temperatures rise and the water cycle intensifies, climatic extreme events (e.g., droughts, wildfires, heatwaves, floods) are expected to become more severe. We must therefore better understand the impact of interactions and feedbacks among extreme events, LS, human activities, and their effects around the world. Notably, our global study highlights that LS can alter the potential impacts of extreme events, and extreme events can contribute to LS. We also identify a variety of LS drivers, both natural and anthropogenic (e.g., natural compaction, urbanization, extraction of fossil fuels and groundwater from the subsurface), and corresponding LS rates throughout a variety of climatic zones and environments from the coastline inland. This study presents analysis of anthropogenic-related activities and natural processes that cause LS, but can also enhance climate change as greenhouse gases are released from the soil into the atmosphere (e.g., via permafrost thawing or peatland and wetland removal). Through our synthesis of process-driven relationships and examples, we underscore the interplay of climatic extremes and LS that damages infrastructure and enhances the vulnerability of large populations to floods and other natural hazards. Our study provides guidance for future policies and adaptation and mitigation approaches that account for the critical connections between the land surface, environmental change, and extreme events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".