Implementación de índices para la evaluación del riesgo de incendios forestales en la cabecera del júcar y análisis de las repercusiones del cambio climático en los incendios
Bibliographic record
Abstract
[EN] For many years, forest fires have been one of the main environmental problems in the province of Cuenca, in the summertime. It is vital that appropriate measures be taken to prevent these fires happening, if the frequent occurrence of serious disasters is to be avoided. To this end, forest managers have a series of aids to help them prevent forest fires, such as fire danger indices. Two fire danger indices have been used in this study: the Forest Fire Weather Index (FWI) developed by the Canadian Forest Service, and the Keetch-Byram Drought Index (KBDI) used by the US Department of Agriculture’s Forest Service, both of which were designed to be used in real time, based on daily weather updates from a given area. To begin with, daily readings were taken at the Júcar’s headwaters, with statistics dating from 1994 to January 2014, with both indices showing an increased risk of fire in the summer months. Readings were then taken for the same period of time, but on a monthly basis. At this point in the study, the indices were modified so that these readings tallied with those taken on a daily basis, thus validating these modifications and providing a model of modification for both indices, on a daily to monthly scale. Once the indices had been modified, there then followed a breakdown of the monthly historical trend data for the period 1940-2012, with findings showing variations between the two indices, making the Canadian FWI the more reliable index, given that it was consistent with forest-fire statistics recorded by the Spanish Ministry of Agriculture, Food and the Environment (MAGRAMA) since 1968. Finally, two general circulation models – the HadGEM2-ES and the MPI-ESM-MR – were used to model climate change scenarios RCP 2.6, 4.5, 6.0, and 8.5, yielding new sets of data, both for temperatures and rainfall during a given period (e.g. short-term, medium-term, long-term). This data formed the basis for calculating projected indices in the future. As a result of applying these indices, it was established that, in the long term (2070-2090), and in the worst-case scenario (RCP 8.5), both indices show values that register as EXTREME, in summer. In other words, the FWI went from a HIGH risk of fires, in the summer, to an EXTREME risk, whilst the variation on the KBDI was even more pronounced, going from MODERATE to EXTREME. It can thus be concluded that, in the event of climate change, the risk of increased occurrence of disasters, in the long term, is quite high
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 teacher head, 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".