Research on Wildfire and Soil Water: A Bibliometric Analysis from 1990 to 2023
Bibliographic record
Abstract
In the context of climate change, wildfires occur more frequently and significantly impact the vegetation–soil–water continuum. Soil water is a critical factor for understanding wildfire occurrence and predicting wildfire hazards. However, there is a lack of specific bibliometric analysis of the research on the mechanisms by which soil water influences wildfire occurrence. Therefore, this study conducted a bibliometric analysis of wildfire and soil water, aiming to understand their relationship, research characteristics, and future development trends. We used the Bibliometrix software package in R 4.4.0, which provides different methods for analyzing bibliometric data. A total of 1585 publications were analyzed from 1990 to 2023. The results of the study showed that the number of publications showed an overall growth trend during the period, with an average annual increase rate of 4.4%. The average annual citations per paper exhibited a pattern of rapid increase, followed by slow growth, and then rapid decrease. Ten highly productive authors in the field contributed 12.2% of the total publications during this period. Over the past 30 years, the University of Aveiro has consistently ranked first in terms of paper quantity. Most of the top ten productive institutions are in the United States, Australia, and several European countries. Fifty-eight countries engage in research related to wildfires and soil water, with close collaboration observed between the United States, Canada, and Spain. The four most frequently used keywords are “wildfire”, “fire”, “water repellency”, and “runoff” (with a total frequency of 1385). Water properties relevant to soil characteristics in the word cloud primarily include hydrophobicity, runoff, erosion, and infiltration. Erosion, wildfires, and runoff are crucial in the field but have yet to receive substantial development. The correlation of post-wildfire soil water properties with infiltration, runoff, and erosion processes is most likely to be addressed in future research. The findings will help researchers assess the post-wildfire disaster chain and its impact on the ecological environment, with clear trends, gaps, and research directions in the areas.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.087 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".