Interdependencies Between Wildfire‐Induced Alterations in Soil Properties, Near‐Surface Processes, and Geohazards
Bibliographic record
Abstract
Abstract The frequency, severity, and spatial extent of destructive wildfires have increased in several regions globally over the past decades. While direct impacts from wildfires are devastating, the hazardous legacy of wildfires affects nearby communities long after the flames have been extinguished. Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapes. The interplay and feedback between geohazards and wildfire‐induced changes to soil properties, land cover conditions, and near‐surface and surface processes are still poorly understood. Here, we synthesize wildfire‐induced processes that can affect the critical attributes of burned soils and their conditioning of subsequent geohazards. More specifically, we discuss the state of knowledge pertaining to changes in mineralogical, hydraulic, mechanical, and thermal properties of soil due to wildfire with a focus on advances in the past decade. We identify how these changes in soil properties alter evapotranspiration, interception, sediment transport, infiltration, and runoff. We then link these alterations to the evolution of different geohazards, including dry raveling, erosion, rockfalls, landslides, debris flows, and land subsidence. Finally, we identify research gaps and future directions to advance knowledge on how wildfires control the evolution of various earth surface processes and geohazards over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".