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Record W4392580754 · doi:10.5194/egusphere-egu24-8691

Development of wind and fire risk indices for climate-mitigation forestry

2024· preprint· en· W4392580754 on OpenAlexaboutno aff
Els Ribbers, Hanna Lee, Priscilla Mooney, Helene Muri, Lei Cai, Jin‐Soo Kim, Lars Nieradzik

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsForestryEnvironmental scienceClimate changeAgroforestryEnvironmental resource managementGeographyEcology

Abstract

fetched live from OpenAlex

Afforestation has long been discussed as a nature-based climate mitigation solution. Although it could be an economic, green, and safe climate mitigation method, several studies suggest the possibility of unforeseen consequences depending on how it is implemented. An important aspect to be taken into account when designing af- and reforestation plans is the risk of damage to the new forest system in the face of climate warming. Recent studies have already shown an increase in both wind and fire damage risks in northern latitudinal forests related to climate warming, with strong winds leading to breakage of individual branches as well as in the knock-over of individual trees or even entire forest areas.However, the forest system is complex, with a high number of feedback loops between different types of damage and between forest structure and ecological parameters. A few examples: Trees that are weakened by damage from pest outbreaks and snowfall are more susceptible to damage from wind and fire; Gaps in the forest that are created by management or damage both increase wind flow due to an eddy effect and create new forest edges with poorly adapted trees, increasing the risk of wind-throw.Due to this complexity, the resilience to damage and therefore ability of forests to mitigate climate on a regional scale are still poorly understood. Understanding this complexity requires model work and extensive literature research, as most studies only focus on a few aspects of the forest system, such as the management type or wind effects. The aim of the study is therefore to develop adequate and future-proof wind- and fire risk indices that boreal forest managers can use to improve management strategies to make climate-mitigation forests more effective, resilient and damage resistant.To do this, output from the Weather Research and Forecasting (WRF) model is used in combination with data on damage, forest management and forest structure to shed some light on possible feedbacks between forest systems and climate on a small-scale basis, in this case 3kmx3km. This information is then used to expand the Canadian Forest Fire Weather Index (FWI) to include ecological, management-related and forest structural parameters. As the structure of the existing FWI is climate-based, the wind risk index will be based on the developed fire risk index.Our preliminary results show that wind damage was most common and extensive in the south-western coastal area of Norway over the last two decades. In contrast, fire damage was most prevalent in the south, with increased damage extent in the south-west of the country. Furthermore, the FWI shows that under an afforestation scenario in Norway, the mountainous region will have the highest frequency of days with medium to high danger of forest fires under climate warming. In this presentation we will discuss these preliminary results, as well as the methodology we will be using to develop the risk indices. Policymakers and forest owners alike will be able to use the risk indices to make the climate-mitigation forests more resilient against damage in a warming climate.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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