Amplified Risk: How Climate Change is Modifying the Risks from Geological Hazards
Bibliographic record
Abstract
The global climate is changing, and the effects of these changes on natural hazards are increasingly being felt, particularly by the populations in low- and middle-income countries. Consequently, in the last decades, there has been much research examining the extent of these effects, but the focus has largely been on hydrometeorological hazards. The potential effects of climate change on geological hazards, like earthquakes and volcanic activity, is less studied and deserves greater attention.Amplified Risk is a four-year program currently being led by the GeoHazards International (GHI), a non-profit committed to saving lives by empowering at-risk communities worldwide to build resilience ahead of disasters and climate impacts. Funded by the United States Agency for International Development (USAID), the overarching goal of the program is to increase collective understanding of how volcanic and earthquake hazards and their societal impacts may be affected by climate change in at-risk low- and middle-income countries.In line with this, we have thus far explored through literature review and subject matter expert consultations how climate change may alter earthquake and volcanic processes and associated hazards, considering eight climate change signals as the starting point: increased precipitation, decreased precipitation, increased temperature, increased rain-drought cycles, increased free-thaw cycles, increased typhoons, increased wind and sea level rise. Our results show the potential amplifying, cascading, and compounding effects of climate change on geological hazards. In general, climate change can affect earthquake and volcanic hazards in two ways: Firstly, it can directly trigger or contribute to directly triggering the hazards as a result of stress regime change following climate-induced variations of loads on the earth surface, mainly due to changes in the volume of ice and water, e.g., glacier melting. Secondly, climate change prepares the ground so that the occurrence of secondary hazards becomes more likely should an earthquake or volcanic eruption occur. For instance, increased precipitation increases soil saturation, making liquefaction more likely in the event of an earthquake. In this presentation, we discuss the findings to date in more detail. We also present the flowchart that summarizes our result, which we intend to publish online as an interactive informational tool that may be useful to risk managers, authorities, community leaders, and researchers in appraising the range of effects from climate change on local hazards, and therefore in determining and prioritizing intervention measures.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".