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Record W4321502251 · doi:10.5194/egusphere-egu23-4547

High-resolution modeling of historical forest fires in the Canton of Bern

2023· preprint· en· W4321502251 on OpenAlexaboutno aff
Renuka Prakash Shastri, Stefan Brönniman, Daniel Steinfeld

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWeather Research and Forecasting ModelHigh resolutionPrecipitationFire hazardClimatologyGeographyPhysical geographyForestryMeteorologyEnvironmental protectionGeologyArchaeology

Abstract

fetched live from OpenAlex

Forest fires are considered an important hazard in forested areas and a serious threat to forest ecosystem and buildings. The combination of drought, high temperatures, and wind increases the risk of forest fires. To better understand the fundamental causes and consequences of fire, we need to study the historical fire regimes. In this study, the meteorological conditions were simulated with the WRF model (Weather Research and Forecasting; Skamarock et al. 2008) for three historical forest fires, in the Canton of Bern, Switzerland (La Neuveville, April 1893, Simmenflueh, August 1911, Kirchberg, April 1915). In terms of area, these are the largest fires in the canton of Bern in the Swiss fire database. The "Twentieth Century Reanalysis" version 3 (20CRv3, Slivinski et al. 2019) was used as a boundary condition. 20CRv3 has a spatial resolution of about 75 km and a temporal resolution of three hours. Using WRF version 4.1.2 20CRv3 has now been gradually downscaled to a resolution of 1x1 km^2. Simulations suggest that the soil had dried out in the previous week and soil moisture had reached low values on the day the fire broke out. High-resolution fire weather indices are also calculated. A lack of precipitation and high temperatures led to high forest fire index values and a high to very high risk of forest fires. References[1] Slivinski, L. C.et al. (2019), Towards a more reliable historical reanalysis: Improvements to theTwentieth Century Reanalysis system. , Q. J. Roy. Meteorol. Soc. 145, 2876-2908.[2] Pfister, L. , S. Brönnimann, M. Schwander , FA Isotta , P. Horton, and C. Rohr, (2020) StatisticalReconstruction of Daily Precipitation and Temperature Fields in Switzerland back to 1864, Clim.past 16, 663-678.[3] Skamarock, WC, et al. (2008) A Description of The Advanced Research WRF Version 3. NCARTechnical Note.[4] Van Wagner, C.E. (1987): Development and Structure of the Canadian Forest Fire Weather IndexSystem, Forestry Technical Report, Canadian Forestry Service Headquarters, Ottawa.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.229
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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