Environmental and Earth Sciences Proceedings

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Open AccessEditorial

Statement of Peer Review

by Theodore M. Giannaros and Roman Berčak

Environ. Earth Sci. Proc. 2026, 46(1), 17; https://doi.org/10.3390/eesp2026046017 - 6 Aug 2026

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Abstract

In submitting conference proceedings to Environmental and Earth Sciences Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and [...] Read more.

In submitting conference proceedings to Environmental and Earth Sciences Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...] Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

4 pages, 282 KB

Open AccessEditorial

Preface of the 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026)

by Theodore M. Giannaros and Roman Berčak

Environ. Earth Sci. Proc. 2026, 46(1), 18; https://doi.org/10.3390/eesp2026046018 - 6 Aug 2026

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Abstract

The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026) was held on 23–25 June 2026 at the Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Czech Republic [...] Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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5 pages, 475 KB

Open AccessProceeding Paper

Interpretable Machine Learning-Based Wildfire Susceptibility Mapping in a Mediterranean Landscape: The Muğla Case

by Ilknur Alpak, Bedri Kurtuluş and Sevim Seda Yamaç

Environ. Earth Sci. Proc. 2026, 46(1), 1; https://doi.org/10.3390/eesp2026046001 - 1 Jul 2026

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Abstract

Extreme wildfire events are increasingly shaping Mediterranean fire regimes under the combined influence of climatic variability, vegetation stress, and growing anthropogenic pressure, posing critical risks to ecosystem stability and landscape resilience. Understanding the spatial determinants of wildfire susceptibility is therefore essential for advancing [...] Read more.

Extreme wildfire events are increasingly shaping Mediterranean fire regimes under the combined influence of climatic variability, vegetation stress, and growing anthropogenic pressure, posing critical risks to ecosystem stability and landscape resilience. Understanding the spatial determinants of wildfire susceptibility is therefore essential for advancing evidence-based fire risk assessment in fire-prone Mediterranean environments. This ongoing doctoral research investigates the environmental controls of wildfire occurrence in Muğla Province (Türkiye) through the integration of multi-source remote sensing data, geospatial analysis, and interpretable machine learning techniques. Burned-area reference data for 2021–2024 were derived from the MODIS MCD64A1 product within the Google Earth Engine environment and represented using a binary burned/non-burned classification. Predictor variables include ERA5-Land meteorological indicators, SRTM-derived topographic parameters, MODIS-based NDVI vegetation condition, and WorldPop population density as a proxy for human exposure, harmonized at a common 1 km spatial resolution. A Random Forest model was implemented to examine wildfire susceptibility patterns with emphasis on model interpretability and variable contribution rather than predictive optimization. Preliminary results indicate that vegetation condition, wind-related dynamics, and population density are dominant contributors to wildfire occurrence, reflecting coupled ecological vulnerability and human influence. Extreme fire conditions observed during 2021 are intentionally reserved for subsequent validation and stress-testing analyses. The proposed framework provides a transparent and transferable methodological basis for analyzing extreme wildfire susceptibility in Mediterranean landscapes and supports future development of interpretable, data-driven wildfire risk assessment approaches. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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5 pages, 409 KB

Open AccessProceeding Paper

Assessing Wildfire Susceptibility Utilizing Geospatial Datasets and Machine Learning Methods

by Suresh Babu KV, Apostolos Sarris and Dimitris Stagonas

Environ. Earth Sci. Proc. 2026, 46(1), 2; https://doi.org/10.3390/eesp2026046002 - 2 Jul 2026

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Abstract

This study investigates wildfire susceptibility in Cyprus, defined as the spatial probability of fire occurrence influenced by environmental factors and historical fire incidents. We integrated topographic variables, indicators of anthropogenic ignition (such as population density and proximity to infrastructure), and spectral vegetation indices [...] Read more.

This study investigates wildfire susceptibility in Cyprus, defined as the spatial probability of fire occurrence influenced by environmental factors and historical fire incidents. We integrated topographic variables, indicators of anthropogenic ignition (such as population density and proximity to infrastructure), and spectral vegetation indices (including NDVI, LAI, NDWI, and NDMI) using eight machine learning models trained on a historical wildfire dataset. Advanced ensemble models (XGBoost, Random Forest, and LightGBM) showed superior performance, achieving an accuracy of 0.82 and an AUC of 0.87. These non-linear classifiers successfully capture complex human–environmental interactions, delivering a high-fidelity spatial tool for targeted wildfire mitigation and forest management. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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6 pages, 4527 KB

Open AccessProceeding Paper

Impact of Increased Resolution on FWI in High-Resolution Meso-NH Simulations

by Cátia Campos, Flavio T. Couto, Nuno Guiomar and Rui Salgado

Environ. Earth Sci. Proc. 2026, 46(1), 3; https://doi.org/10.3390/eesp2026046003 - 6 Jul 2026

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Abstract

Traditionally, fire danger is assessed using daily values of Fire Weather Index (FWI). The methodology used in this study consists of atmospheric modelling for a more accurate representation of the FWI. To this end, two simulations designed with two nested domains of horizontal [...] Read more.

Traditionally, fire danger is assessed using daily values of Fire Weather Index (FWI). The methodology used in this study consists of atmospheric modelling for a more accurate representation of the FWI. To this end, two simulations designed with two nested domains of horizontal resolution of 2500 m and 500 m were carried out covering two periods of active fires in Portugal: October 2017 (central region) and August 2018 (southern region). The study shows that high-resolution simulations are able to capture local circulations and accurately represent the diurnal cycle and fire management services can therefore overcome the limitations of a single daily value, leading to a more detailed assessment of fire danger, particularly in topographically complex regions or areas influenced by coastal dynamics. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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6 pages, 926 KB

Open AccessProceeding Paper

Seasonal Variability of Fire Weather Index (FWI) Across Italy (2007–2024): A Reproducible Climate-Driven Assessment Framework

by Luis Angel Espinosa, João Pedro Pêgo and Giorgio Vacchiano

Environ. Earth Sci. Proc. 2026, 46(1), 4; https://doi.org/10.3390/eesp2026046004 - 6 Jul 2026

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Abstract

Wildfires are an increasing environmental and civil protection challenge across Southern Europe under intensifying climate variability and extreme heat conditions. This study presents a reproducible, climate-informed framework for analysing seasonal fire weather variability across Italy using the Canadian Fire Weather Index (FWI) system. [...] Read more.

Wildfires are an increasing environmental and civil protection challenge across Southern Europe under intensifying climate variability and extreme heat conditions. This study presents a reproducible, climate-informed framework for analysing seasonal fire weather variability across Italy using the Canadian Fire Weather Index (FWI) system. The work was conducted during a Short-Term Scientific Mission funded by the NERO COST Action CA22164 at the University of Milan in September 2025. A harmonised Seasonal FWI dataset for Italy covering 2007–2024 was developed using Copernicus Climate Data Store products, Climate Data Operators (CDO), and R-based statistical workflows. Seasonal FWI metrics were spatially aggregated across 35 Italian ecoregions to evaluate temporal variability and regional fire danger patterns. Results reveal pronounced interannual variability, with mean FWI values ranging from approximately 16 in 2020 to 24 in 2016. Although no statistically significant monotonic trend was detected during 2007–2024, seasonal FWI values consistently exceeded the historical 1970–2000 baseline (~10–15), indicating a shift towards a higher fire danger regime. Figures illustrate the analysed ecoregions, regional burned area trends, and relationships between mean seasonal FWI and burned area. The openly available dataset and reproducible workflow provide a foundation for future climate-informed fire danger assessment and civil protection decision-support systems. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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5 pages, 2044 KB

Open AccessProceeding Paper

Fire Behavior Driver Classification from Geospatial Features

by Antonia Bartulović, Ljiljana Šerić and Oscar Grégoire

Environ. Earth Sci. Proc. 2026, 46(1), 5; https://doi.org/10.3390/eesp2026046005 - 7 Jul 2026

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Abstract

Wildfire behavior can roughly be described as wind-, fuel-, or topography-driven, but these labels usually rest on expert judgment and post-fire analysis rather than on simple, reusable rules. Here, we build a small, interpretable classifier that predicts the dominant fire behavior driver: fuel-driven [...] Read more.

Wildfire behavior can roughly be described as wind-, fuel-, or topography-driven, but these labels usually rest on expert judgment and post-fire analysis rather than on simple, reusable rules. Here, we build a small, interpretable classifier that predicts the dominant fire behavior driver: fuel-driven (plume/convection dominated), wind-driven, or topography-driven, from basic environmental information. The classifier is built using ERA5 10 m wind and relative humidity, summary elevation metrics, and fuel descriptors for a 14-event dataset of coastal Croatian wildfires. We compare the performance of multinomial logistic regression, random forest, and decision tree-based classifiers, focusing on agreement between their coefficients, feature importances, and splits rather than on formal optimization. All three models converge on a simple common rule set. Relative humidity, mean elevation, and elevation range emerge as the main axes of variation, consistent with basic fire behavior physics and published fire type schemes. Despite the small dataset, the classifier formalizes expert intuition in a transparent way and offers a template for scaling larger datasets, where it could evolve into a quick diagnostic of the dominant spread driver for ongoing fires. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 175 KB

Open AccessProceeding Paper

Integrating Scientific Risk Assessment and Policy Instruments in European Wildfire Risk Management: A Multilevel Comparative Perspective

by Todor Stoyanov

Environ. Earth Sci. Proc. 2026, 46(1), 6; https://doi.org/10.3390/eesp2026046006 - 6 Jul 2026

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Abstract

The increasing frequency, intensity, and spatial extent of wildfires across Europe require a paradigm shift from reactive fire suppression toward integrated wildfire risk management grounded in scientific evidence and adaptive governance. This article examines how scientific risk assessment methodologies and policy instruments can [...] Read more.

The increasing frequency, intensity, and spatial extent of wildfires across Europe require a paradigm shift from reactive fire suppression toward integrated wildfire risk management grounded in scientific evidence and adaptive governance. This article examines how scientific risk assessment methodologies and policy instruments can be effectively aligned across European, regional, national, and local governance levels. The study develops a multilevel comparative analytical framework linking technical risk assessment tools with strategic policy objectives for fire-adapted societies. Particular attention is paid to vertical policy coherence (from EU to local level), horizontal coordination among sectors, and the integration of spatial risk modelling into planning, prevention, and climate adaptation strategies. The article contributes to the field of wildfire risk management by proposing a structured multilevel integration model that connects scientific assessment, ecosystem-based management, and adaptive policy instruments, offering practical implications for strengthening resilience in European forest landscapes under accelerating climate change. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

14 pages, 3169 KB

Open AccessProceeding Paper

Assessment of Fire Risk near Linear Infrastructure: Corridor-Based Evaluation

by Jan Hora, Zdeněk Hanuška, Dana Chudová, Tereza Česelská, Izabela Šudrichová and Jan Pergl

Environ. Earth Sci. Proc. 2026, 46(1), 7; https://doi.org/10.3390/eesp2026046007 - 7 Jul 2026

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Abstract

Standard protection zones and uniform maintenance along linear infrastructure, especially railways, do not reliably capture spatial variability in a real landscape matrix. A more suitable approach is segment-based corridor evaluation grounded in local hazard and exposure assessment. In a model railway corridor in [...] Read more.

Standard protection zones and uniform maintenance along linear infrastructure, especially railways, do not reliably capture spatial variability in a real landscape matrix. A more suitable approach is segment-based corridor evaluation grounded in local hazard and exposure assessment. In a model railway corridor in Central Europe, a screening workflow is developed to combine fuel and vertical structure, terrain-driven spread amplifiers, weather, fuel-moisture conditions, and a provisional infrastructure exposure/sensitivity layer. In addition to these environmental layers, geocoded fire and response records are used to build an event layer for segment profiling and historical plausibility checking. The railway corridor was divided into analytical segments, and an environmental and incident profile was compiled for each segment. The pilot application shows observed concentrations of fire incidents in short-track sections and provides a framework for segment typology based on fuel, topography, moisture, weather, and operational factors. The analysis supports targeted vegetation management, future detection-node planning, and a scalable decision chain for other linear critical infrastructure elements. The proposed workflow should be understood as a corridor-scale screening and prioritization framework, not as a statistically validated hotspot model or a component-specific infrastructure risk score. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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8 pages, 2586 KB

Open AccessProceeding Paper

Fire Patterns in the Himalaya and Their Meteorological Drivers from Km-Scale ICON-CLM Simulations

by Prashant Singh and Bodo Ahrens

Environ. Earth Sci. Proc. 2026, 46(1), 8; https://doi.org/10.3390/eesp2026046008 - 7 Jul 2026

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Abstract

Mountain regions are highly sensitive to climate warming, and the Himalayas are among the most vulnerable. Rising temperatures and changing hydroclimatic conditions are expected to increase forest fire risk, particularly in the Himalayan foothills and potentially at higher elevations. To investigate the meteorological [...] Read more.

Mountain regions are highly sensitive to climate warming, and the Himalayas are among the most vulnerable. Rising temperatures and changing hydroclimatic conditions are expected to increase forest fire risk, particularly in the Himalayan foothills and potentially at higher elevations. To investigate the meteorological conditions associated with forest fires in complex terrain, we analyzed 10 years (2011–2020) of km-scale (~3.3 km) ICON-CLM simulations together with MODIS/VIIRS fire observations, GFED5 fire emissions, and ERA5 reanalysis. Fire activity was examined across the Himalayan region (25–40° N, 70–115° E), with particular focus on elevation-dependent patterns. GFED5 indicates increasing black carbon and CO₂ emissions from elevations above 1 km, suggesting rising fire activity in Himalayan forests, while MODIS/VIIRS observations show that March–May is the peak fire season. Analysis of meteorological conditions during observed fire events shows that fires are associated with higher temperature, lower relative humidity, stronger winds, and little to no precipitation. A clear elevational shift was identified: compared with fires at 500–1000 m, fire events at 2500–3000 m occurred under relatively cooler and more humid conditions. Both ICON-CLM and ERA5 reproduce this pattern, but ICON-CLM generally represents fire event environments as warmer and drier than ERA5, highlighting the added value of km-scale regional climate modeling for understanding wildfire risk in the complex Himalayan terrain. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 790 KB

Open AccessProceeding Paper

Slovenian Case Study—Wind Impact on 2022 Goriški Kras Wildfire Progression

by Jaša Saražin, Ana Seifert Barba and Gašper Ogrin

Environ. Earth Sci. Proc. 2026, 46(1), 9; https://doi.org/10.3390/eesp2026046009 - 7 Jul 2026

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Abstract

In July 2022, Slovenia was affected by the largest wildfire in its recorded history. The Goriški Kras wildfire lasted from 15 July to 1 August 2022 and continuously covered an area of 4249 ha on Slovenian and Italian sides of the border. Using [...] Read more.

In July 2022, Slovenia was affected by the largest wildfire in its recorded history. The Goriški Kras wildfire lasted from 15 July to 1 August 2022 and continuously covered an area of 4249 ha on Slovenian and Italian sides of the border. Using satellite and incident commanders’ data, we were able to reconstruct the wildfire’s progression for up to three situations per day. The main driving factors of wildfire spread were south and west winds, while the most common (NE) bora wind played only a minor role. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 2459 KB

Open AccessProceeding Paper

Canyon-Induced Fire Acceleration and Its Integration in an Eruptive Fire Early Warning System

by Darko Stipaničev, Antonia Ivanda, Marin Bugarić, Ljiljana Šerić and Damir Krstinić

Environ. Earth Sci. Proc. 2026, 46(1), 10; https://doi.org/10.3390/eesp2026046010 - 9 Jul 2026

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Abstract

Eruptive fire behavior is one of the most hazardous forms of extreme wildfire dynamics and has caused numerous firefighter fatalities. Early identification of high-risk conditions and locations is essential for improving operational safety. The Croatian Advanced Wildfire Surveillance System includes an Eruptive Fire [...] Read more.

Eruptive fire behavior is one of the most hazardous forms of extreme wildfire dynamics and has caused numerous firefighter fatalities. Early identification of high-risk conditions and locations is essential for improving operational safety. The Croatian Advanced Wildfire Surveillance System includes an Eruptive Fire Early Warning System that provides real-time assessment of eruptive fire potential. While earlier versions classified risk based on slope, aspect, meteorological conditions, and vegetation, the new version introduces an additional overlay layer incorporating canyon geometry as a key factor in accelerating fire spread. The proposed operational approach integrates experimentally derived canyon geometry thresholds with terrain analysis from Digital Elevation Model (DEM)-derived GIS layers in order to identify locations with increased eruptive fire potential. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 323 KB

Open AccessProceeding Paper

Image-Based Wildfire Behavior Classification Using Convolutional Neural Networks

by Jakov Bejo, Ljiljana Šerić and Damir Krstinić

Environ. Earth Sci. Proc. 2026, 46(1), 11; https://doi.org/10.3390/eesp2026046011 - 9 Jul 2026

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Abstract

After ignition, fire behavior is governed by a complex interaction of fuel, weather and topography. In practice, fire behavior is labeled as wind, topography or fuel-driven depending on the dominant driver. These labels are typically assigned through expert judgment and post-fire analysis rather [...] Read more.

After ignition, fire behavior is governed by a complex interaction of fuel, weather and topography. In practice, fire behavior is labeled as wind, topography or fuel-driven depending on the dominant driver. These labels are typically assigned through expert judgment and post-fire analysis rather than real-time classification. In this paper, we propose a model based on the ConvNeXtV2 convolutional neural network (CNN), capable of detecting the dominant wildfire driver directly from high-resolution surveillance images. The model is trained using 16 labeled sequences with targeted augmentation, class-balanced sampling and loss weighting to counter strong class imbalance and heavy repetition within sequences. The network shows good results in both fuel- and wind-driven situations but struggles with topography-driven wildfires, even when trained on full-resolution images. Despite the small dataset, our approach illustrates how modern CNNs can complement expert predictions. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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11 pages, 9560 KB

Open AccessProceeding Paper

The Effect of Cold Front Passage on Wildfires: Examples from Greece

by Gavriil Xanthopoulos, Miltiadis Athanasiou and Konstantinos Kaoukis

Environ. Earth Sci. Proc. 2026, 46(1), 12; https://doi.org/10.3390/eesp2026046012 - 13 Jul 2026

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Abstract

The wind shift associated with the passage of a cold front, when it affects a wildfire, is known to change fire spread direction and to cause quick fire growth and serious firefighting problems. A number of historic wildfires in Greece, presented in this [...] Read more.

The wind shift associated with the passage of a cold front, when it affects a wildfire, is known to change fire spread direction and to cause quick fire growth and serious firefighting problems. A number of historic wildfires in Greece, presented in this paper, serve as a learning opportunity. The phenomenon needs to be considered seriously in fire danger prediction and in firefighter training. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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6 pages, 1071 KB

Open AccessProceeding Paper

A Unified Observational Dataset of Extreme Wildfire Events in Europe

by Paula Olivera Prieto, Nieves Fernández Anez, Roman Berčák, Thijs Stockmans, Salini Manoj Santhi, Theodore M. Giannaros and Mario Miguel Valero

Environ. Earth Sci. Proc. 2026, 46(1), 13; https://doi.org/10.3390/eesp2026046013 - 15 Jul 2026

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Abstract

Extreme wildfires represent an escalating socio-economic threat in Europe, yet their understanding remains constrained by a lack of comprehensive observational data. This study introduces a unified and standardized dataset design to support extreme wildfire research and management. Community-contributed fire progression observations, derived from [...] Read more.

Extreme wildfires represent an escalating socio-economic threat in Europe, yet their understanding remains constrained by a lack of comprehensive observational data. This study introduces a unified and standardized dataset design to support extreme wildfire research and management. Community-contributed fire progression observations, derived from satellite imagery and European fire agency records, were compiled, cleaned, and homogenized to capture critical fire behavior variables, including the temporal evolution of burned area and perimeter metrics. Processed and validated within QGIS, the resulting GeoJSON outputs facilitate seamless geographic information system integration. The objective of this work is to improve the observation and understanding of extreme wildfire events across Europe by providing researchers, modelers, and fire managers with a shared and standardized observational resource. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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6 pages, 20344 KB

Open AccessProceeding Paper

Atmospheric Dynamics Associated with the Pyroconvective Wildfires in Canada During the Early 2025 Fire Season

by Georgios Papavasileiou and Theodore M. Giannaros

Environ. Earth Sci. Proc. 2026, 46(1), 14; https://doi.org/10.3390/eesp2026046014 - 17 Jul 2026

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Abstract

This study examines the atmospheric dynamics associated with major pyroconvective wildfires in Canada during the early 2025 fire season (May–June). Using reanalysis and observational atmospheric datasets, including upper-tropospheric, surface, satellite, radar, and sounding data, we investigate the synoptic and mesoscale drivers that supported [...] Read more.

This study examines the atmospheric dynamics associated with major pyroconvective wildfires in Canada during the early 2025 fire season (May–June). Using reanalysis and observational atmospheric datasets, including upper-tropospheric, surface, satellite, radar, and sounding data, we investigate the synoptic and mesoscale drivers that supported deep pyroconvection development. The results highlight the role of jet stream dynamics and the accompanied upper-level ridging/blocking and ridge/block collapsing, mid-tropospheric moisture advection, and dynamic lifting in creating favorable conditions for the development of deep pyroconvection and pyrocumulonimbus (pyroCb) clouds. A well-predicted (>7 days) sequence of atmospheric drivers highlights the early warning potential of synoptic-scale patterns. This pattern-based diagnostic perspective may also support operational early-warning approaches for pyroCb-prone environments, including regions where direct pyroconvection observations are less complete. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 2417 KB

Open AccessProceeding Paper

Development of a Methodology for Daily Fire Exposure Assessment Based on Stochastic Fire Behaviour Simulations

by Hugo Velhinho, Catarina Cabrita, Bruno Aparicio and Akli Benali

Environ. Earth Sci. Proc. 2026, 46(1), 15; https://doi.org/10.3390/eesp2026046015 - 22 Jul 2026

Viewed by 158

Abstract

Wildfires represent a major environmental and socio-economic threat in Mediterranean regions. Increasing wildfire activity highlights the need for timely assessments of wildfire exposure. This study presents a methodology to estimate daily wildfire exposure for rural communities using stochastic fire spread simulations. Daily fire [...] Read more.

Wildfires represent a major environmental and socio-economic threat in Mediterranean regions. Increasing wildfire activity highlights the need for timely assessments of wildfire exposure. This study presents a methodology to estimate daily wildfire exposure for rural communities using stochastic fire spread simulations. Daily fire spread simulations were performed using the FARSITE fire area simulator, integrating terrain, fuel, and weather information. Simulations used ECMWF weather forecasts provided by IPMA, ignition locations sampled from historical wildfire probability surfaces, and 25,000 stochastic realisations for each simulation day. Wildfire exposure for rural settlements was estimated by combining simulated fire behaviour metrics with spatial data on settlement locations. The methodology was tested in three pilot regions located along the Portuguese–Spanish border, representing distinct landscape and fuel conditions. Results from the pilot regions show that the framework captures both spatial and temporal variability in wildfire danger and effectively identifies communities more likely to be exposed to large and intense wildfires. These outputs can support practical applications such as early warning systems, operational planning, and decision-making by civil protection and fire management agencies. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 461 KB

Open AccessProceeding Paper

Wildfire Hazard for Seveso Installations

by Enrico Danzi, Rafał Porowski, Václav Nevrlý, Vojtěch Jankuj and Ernesto Salzano

Environ. Earth Sci. Proc. 2026, 46(1), 16; https://doi.org/10.3390/eesp2026046016 - 4 Aug 2026

Viewed by 371

Abstract

Wildfire events are becoming more frequent across Europe, extending well beyond the traditionally fire-prone Mediterranean countries. Little attention is paid to the threat that wildfires pose to industrial facilities near forested areas, particularly those subject to the Seveso III Directive. In this work, [...] Read more.

Wildfire events are becoming more frequent across Europe, extending well beyond the traditionally fire-prone Mediterranean countries. Little attention is paid to the threat that wildfires pose to industrial facilities near forested areas, particularly those subject to the Seveso III Directive. In this work, the Dow’s Fire and Explosion Index (F&EI) has been extended to account for wildfire hazards. The wildfire-F&EI (W-F&EI) retains the familiar penalty-credit framework while incorporating wildfire-specific factors, interpreted through a bow-tie approach as fire likelihood and severity drivers (vegetation, topography, fuel continuity, wind), local likelihood calibration (fire history) and Natech escalation barriers (safety distance, fire protection). Preliminary results for an LPG installation show an 88% increase in the global risk index when wildfire penalties are applied. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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5 pages, 521 KB

Open AccessProceeding Paper

Understanding Fire Rate of Spread Drivers Using a Machine Learning Approach

by Rafael Oliveira, Akli Benali and Ana Russo

Environ. Earth Sci. Proc. 2026, 46(1), 19; https://doi.org/10.3390/eesp2026046019 - 11 Aug 2026

Viewed by 205

Abstract

Wildfires often lead to extreme environmental impacts, as well as to loss of life, injuries, and widespread destruction of homes and critical infrastructure. Regardless of their increasing impact, these complex and rapidly evolving events are still not fully understood. Better understanding of the [...] Read more.

Wildfires often lead to extreme environmental impacts, as well as to loss of life, injuries, and widespread destruction of homes and critical infrastructure. Regardless of their increasing impact, these complex and rapidly evolving events are still not fully understood. Better understanding of the drivers of fire behaviour is essential for fire danger prediction and to support operational decision-making. This study analyses the main environmental factors influencing the rate of spread (ROS) using a machine learning approach applied to the PT-FireSprd database. Fire progression polygons were complemented with meteorological and landscape variables, and fire danger indices. Results indicate that near-surface wind speed and dead fuel moisture content are key drivers of ROS, while the Hot-Dry-Windy Index shows the strongest correlation. The predictive model explains 55.6% of ROS variability. Clustering the main drivers reveals six fire propagation patterns characterised by different combinations of environmental conditions. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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5 pages, 1285 KB

Open AccessProceeding Paper

Approaching Terminal-Velocity of Large Firebrands for Extreme Fire Events

by Fabian Brännström, Misarah Abdelaziz, Ha-Ninh Nguyen, Alexander Filkov, Andrew L. Sullivan and Jean-Baptiste Filippi

Environ. Earth Sci. Proc. 2026, 46(1), 20; https://doi.org/10.3390/eesp2026046020 - 17 Aug 2026

Viewed by 185

Abstract

Wildfire spread on a large scale is heavily driven by the transport of firebrands, as it leads to short- and long-range spotting. Detailed knowledge of terminal velocity can help predict firebrand transport and therefore overall wildfire spread modelling. The current work evaluates an [...] Read more.

Wildfire spread on a large scale is heavily driven by the transport of firebrands, as it leads to short- and long-range spotting. Detailed knowledge of terminal velocity can help predict firebrand transport and therefore overall wildfire spread modelling. The current work evaluates an approach to estimating terminal velocity based on CFD simulations with a detailed firebrand shape and assesses its impact on orientation. For the selected firebrand and simulation setup, a range of terminal velocities of approximately 5 to 11 m/s is calculated. This is based on 352 RANS simulations for one particular detailed firebrand at 11 different orientations and two turbulence models, with varying inlet velocities and two mesh refinements. DES and LES turbulence models are considered as a next step for a detailed comparison with wind tunnel measurements. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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4 pages, 2342 KB

Open AccessProceeding Paper

Deep Learning Wildfire Scenario Modelling: A Case Study for iFire 2.0

by Renhao Huang, Ali Asadipour, Dennis Del Favero and Yang Song

Environ. Earth Sci. Proc. 2026, 46(1), 21; https://doi.org/10.3390/eesp2026046021 - 20 Aug 2026

Viewed by 152

Abstract

This study presents a deep learning–based wildfire behaviour model developed for iFire 2.0, an immersive visualisation project by iCinema, UNSW. The model integrates fuel conditions, ignition points, and weather conditions to simulate fire spread and crown fire activity. It demonstrates the ability to [...] Read more.

This study presents a deep learning–based wildfire behaviour model developed for iFire 2.0, an immersive visualisation project by iCinema, UNSW. The model integrates fuel conditions, ignition points, and weather conditions to simulate fire spread and crown fire activity. It demonstrates the ability to generate unforeseen dynamic scenarios under changing conditions, marking an important step toward modelling and visualising extreme wildfire behaviour, allowing users to modify variables and experience resulting changes in wildfire dynamics. Full article

(This article belongs to the Proceedings of The 2nd International Workshop on Extreme Wildfire Events (X-Fire 2026))

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