Future impact of climate extremes in the Mediterranean: Soil erosion projections when fire and extreme rainfall meet
Bibliographic record
Abstract
Abstract Climate change projections over the Mediterranean basin point toward an increase in frequency and intensity of extreme events that will directly impact ecosystems resilience. In this study, we evaluated future trends of soil loss in forestland in Catalonia (NE Spain) due to fires and vegetation dynamics, considering the potential future impacts of co‐occurring extreme fire and rainfall events, and assessing how fire suppression can contribute to soil erosion mitigation. The process‐based MEDFIRE model was used to simulate changes in forestland due to climate and fires between 2011 and 2050 under six different future scenarios that resulted from the combination of two climatic scenarios and three fire management policies. Annual projections on landscape changes were used to estimate soil loss using the Universal Soil Loss Equation. Projected annual soil losses for forested land in Catalonia ranged between 15 and 16 tons/ha, with scenarios simulating current levels of fire suppression projecting around −5% soil loss than those assuming more relaxed suppression strategies. On average, fires explained 12–16% of annual soil loss in the region, but in fire‐severe years, they explained up to 90% of the total annual soil loss. Projected mean total soil loss in years where extreme rainfall and fire meet was 150% higher than in years where both events were not contemporary. The estimated annual probability that the two extreme impacts will co‐occur in the future ranged between 0.09 and 0.11 between scenarios. Our results highlight the importance of landscape and fire management in minimizing soil loss and its potential impacts for ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".