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Record W6964469254 · doi:10.26233/heallink.tuc.100376

Future climate change impact on wildfire danger over the Mediterranean: the case of Greece

2024· other· en· W6964469254 on OpenAlexaboutno aff

Bibliographic record

VenueTechnical University of Crete · 2024
Typeother
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeMediterranean climateEffects of global warmingGlobal warmingFire regimeClimate model

Abstract

fetched live from OpenAlex

This thesis studies the anticipated impact of climate change on wildfire danger in Greece, using meteorological variables and both atmospheric and land modelling techniques. The combined insights from this thesis underline the multifaceted challenges posed by climate change and wildfires in Mediterranean regions, and particularly in Greece. The projected increase in fire danger due to climate change necessitates robust fire management strategies and adaptive policies. Integrating advanced atmospheric and land modeling simulations, such as with WRF-Chem and JULES-INFERNO, can provide valuable predictions and guide effective interventions. As climate conditions continue to evolve, continuous monitoring and adaptive management will be essential to mitigate the adverse impacts of wildfires on ecosystems, human health, and economies. The first paper “Future climate change impact on wildfire danger over the Mediterranean: the case of Greece” focuses on three Representative Concentration Pathways (RCPs): RCP2.6 (optimistic), RCP4.5 (moderate), and RCP8.5 (pessimistic) to study their effect on future fire danger. Findings indicate a significant increase in fire danger across Greece, particularly in high-risk areas such as Crete, the Aegean Islands, the Attica region, and parts of the Peloponnese. Projections show that, under the RCP8.5 scenario, these regions could experience up to 40 additional days of critical fire danger by the late 21st century compared to the late 20th century. The study highlights the importance of localized FWI thresholds due to varying climatic conditions across regions. The extension of the fire season is also anticipated, with some areas experiencing an increase of up to one month under the worst-case scenario. This could lead to higher frequencies of wildfires and associated health impacts from particulate emissions. The second study "Wildfire Aerosols and Their Impact on Weather: A Case Study of the August 2021 Fires in Greece Using the WRF-Chem Model" investigates the impact of aerosols emitted from the August 2021 catastrophic wildfires in Greece on local weather patterns using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The study focuses on how wildfire aerosols affect temperature amongst other atmospheric variables. The results demonstrate that wildfire aerosols significantly modify atmospheric conditions, leading to decreased solar radiation reaching the surface, which in turn reduces surface temperatures. Moreover, the study finds that aerosols in the smoke plume can absorb solar radiation leading to the creation of rising air movement as well as a remote atmospheric circulation feedback away from the plume where pressure increases to counteract the lower pressure above the smoke plume, resulting in higher surface temperatures. The study underscores the importance of integrating aerosol data into weather forecasting models to improve the accuracy of weather predictions during wildfire events and enhance emergency response strategies. Connecting the first two studies, the third one titled “Automatic smoke plume detection using satellites” investigates the detection and analysis of aerosols released by wildfires in Greece using satellite products such as thermal anomalies and aerosol optical depth (AOD), in addition to the Canadian Fire Weather Index (FWI) that is produced from the widely used MERRA reanalysis weather data. This dataset was used as it provides long-term continues high temporal and spatial resolution essential for regional climate studies. It also incorporates obseravations from satellites and ground stations resulting in a reliable dataset that uses a consistent methodology essential for capturing climate data trends. The research attempts to separate fire-related aerosols and establish a relationship between AOD levels and wildfire activity by utilizing a variety of filtering techniques. The findings show that a dependable technique for tracking wildfire emissions is to combine thermal anomaly detection with AOD filtering based on the 99th percentile of readings. The results highlight how integrating satellite data might enhance air quality monitoring and wildfire identification, especially in areas where fires occur often. The forth research titled "Estimating future burnt area changes over Greece using the JULES-INFERNO model" explores the role of changing climate conditions as well as the evolution of different types of vegetation cover to examine future burnt area trends over Greece using the Joint UK Land Environment Simulator (JULES) combined with the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO) wildfire scheme. The study highlights that when keeping a static vegetation cover, burnt area is projected to increase everywhere in Greece in response to dryer climatological conditions. When the vegetation is allowed to change dynamicaly, however, the overall burning is overall smaller, with the main agricultural areas of the country actually experiencing a reduction in burned area. With the potential for up to 1000 additional big burnt area incidents in a future period of 20 years (2080-2090) when compared with the period (2030-2040) across Greece, high emission scenarios greatly raised the probability of wildfires, with eastern continental Greece being especially susceptible. Future predictions using static vegetation resulted in an average increase of 0.8 km² in burnt area over Greece. On the other hand, because burnt areas were less likely to burn again, dynamic vegetation simulations projected a lesser increase of 0.3 km², showcasing that vegetation dynamics have a substantial impact on the future of wildfire activity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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.

Study designNot applicable
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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