A lesson in preparedness: Assessing the effectiveness of low-cost post-wildfire flood protection measures for the catastrophic flood in Kineta, Greece
Bibliographic record
Abstract
Abstract. Climate change–driven wildfires, especially in the Mediterranean, are not only becoming more frequent and severe but also amplifying flood risks by altering catchment hydrology. Yet, post-fire flood risk management remains inadequately addressed. In response, we develop an integrated simulation framework that combines meteorological, hydrological, hydraulic-hydrodynamic models and remote sensing techniques to represent post-wildfire flood hazards and support the design of Post-wildfire Flood Protection Treatments (PFPTs). We utilize the framework to accurately represent a post-wildfire flash flood event in a Mediterranean catchment in Greece. The flood event is simulated under three scenarios: pre-wildfire, post-wildfire without any PFPTs in place (reality), and post-wildfire with PFPTs. The results show that the wildfire's impact on flood extent was around a 24.1 % increase, but the PFPTs could have counterbalanced this impact. Moreover, we present an economic model for estimating the cost of the recommended PFPTs and the flood damage direct costs, combining an accounting and a semi-automated AI-based approach. The cost comparison reveals that the protection would have cost around € 3.45 mill (just the 13.7 % of the flood damage costs, € 25.2 mill) potentially saving € 6.37 mill in flood damage. By filling critical knowledge gaps, our study offers insights into the dynamics of post-wildfire flood events and provides policymakers with valuable insights for timely risk mitigation amidst escalating fire-related disasters.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".