Wildfire connectivity under drought-induced impacts and landscape management strategies in a Mediterranean region.

Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through multi-task deep learning models. We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire monitoring, and next-day wildfire prediction. Covering wildfire events in the contiguous U.S. from January 2017 to October 2021, the dataset includes 3552 surface reflectance images and auxiliary data such as weather, topography, land cover, and fuel information, totalling 71 GB. Each wildfire's lifecycle is documented, with labels for active fires (AF) and burned areas (BA), supported by manual quality assurance of AF and BA test labels. The dataset supports three tasks: a) active fire detection, b) daily burned area mapping, and c) wildfire progression prediction. Detection tasks use pixel-wise classification of multi-spectral, multi-temporal images, while prediction tasks integrate satellite and auxiliary data to model fire dynamics. This dataset and its benchmarks provide a foundation for advancing wildfire research using deep learning.

Forest and grassland fire behavior prediction is increasingly critical under climate change, as rising fire frequency and intensity threaten ecosystems and human societies worldwide. This paper reviews the status and future development trends of wildfire behavior modeling and prediction technologies. It provides a comprehensive overview of the evolution of models from empirical to physical and then to data-driven approaches, emphasizing the integration of multidisciplinary techniques such as machine learning and deep learning. While conventional physical models offer mechanistic insights, recent advancements in data-driven models have enabled the analysis of big data to uncover intricate nonlinear relationships. We underscore the necessity of integrating multiple models via complementary, weighted fusion and hybrid methods to bolster robustness across diverse situations. Ultimately, we advocate for the creation of intelligent forecast systems that leverage data from space, air and ground sources to provide multifaceted fire behavior predictions in regions and globally. Such systems would more effectively transform fire management from a reactive approach to a proactive strategy, thereby safeguarding global forest carbon sinks and promoting sustainable development in the years to come. By offering forward-looking insights and highlighting the importance of multidisciplinary approaches, this review serves as a valuable resource for researchers, practitioners, and policymakers, supporting informed decision-making and fostering interdisciplinary collaboration.

Background Effective forest fuel reduction treatments reduce hazardous fuel conditions, wildfire behavior, and severity. It has been suggested and partially quantitatively analyzed that these treatments may also reduce future wildfire emissions, but this potential is debated. We apply a previously published, encompassing modeling approach to assess the potential of forest fuel reduction treatments to reduce future wildfire emissions. Aims Evaluate the effectiveness of four fuel treatment types at reducing future wildfire greenhouse gas (GHG) emissions across a range of forest types and initial fire hazard levels. Methods Forest growth, fire behavior, fire spread, and emissions models were used to simulate fuel treatments and their potential impacts. Key results The “underburn only” and “thin from below + pile burn” treatments had a minimum annual fire probability (AFP) 5-35% lower than other treatment types to achieve reduced GHG emissions. When AFP was high, the “SDI thin + underburn” treatment reduced GHG emissions 13-54% more than the next best treatment. Conclusions AFP, forest type, and initial hazard level should be primary considerations when selecting a fuel treatment type for reducing future GHG emissions. Implications These results provide decision support when selecting a fuel treatment type for reducing future GHG emissions.

Wildfire behavior and post-fire effects are strongly modulated by terrain, yet the relative influence of individual topographic factors on burn severity remains incompletely quantified at landscape scales. The Composite Burn Index (CBI) provides a field-calibrated measure of severity, but large-area analyses have been hampered by limited plot density and cumbersome data extraction workflows. In this study, we paired 6150 CBI plots from 234 U.S. wildfire events (1994–2017) with 30 m SRTM DEM, extracting mean elevation, slope, and compass aspect within a 90 m buffer around each plot to minimize geolocation noise. Topographic variables were grouped into ecologically meaningful classes—six elevation belts (≤500 m to >2500 m), six slope bins (≤5° to >25°), and eight aspect octants—and their relationships with CBI were evaluated using Tukey HSD post hoc comparisons. Our findings show that all three factors exerted highly significant influences on severity (p < 0.001): mean CBI peaked in the 1500–2000 m belt (0.42 higher than lowlands), rose almost monotonically with steepness to slopes > 20° (0.37 higher than <5°), and was greatest on east- and northwest-facing slopes (0.19 higher than south-facing aspects). Further analysis revealed that burn severity emerges from strongly context-dependent synergies among elevation, slope, and aspect, rather than from simple additive effects. By demonstrating a rapid, reproducible workflow for terrain-aware severity assessment entirely within GEE, the study provides both methodological guidance and actionable insights for fuel-management planning, risk mapping, and post-fire restoration prioritization.

ABSTRACTThe catastrophic Los Angeles Fires of January 2025 underscore the urgent need to understand the complex interplay between hydroclimatic variability and wildfire behavior. This study investigates how sequential wet and dry periods, hydroclimatic rebound events, create compounding environmental conditions that culminate in extreme fire events. Our results show that a cascade of moisture anomalies, from the atmosphere to vegetation health, precedes these fires by around 6–27 months. This is followed by a drying cascade 6 months before ignition that results in anomalously high and dry fuel loads conducive to fires. These patterns are confirmed when analyzing recent (2012–2025) extreme fire events in Mediterranean and Desert Californian biomes. We find hydroclimatic rebound as a key mechanism driving extreme wildfire risk, where moisture accumulation fuels vegetation growth that later dries into highly flammable fuel. In contrast, extreme fires in the fuel‐rich Forested Mountain regions are less influenced by the moistening cascade and more impacted by prolonged drought conditions, which typically persist up to 11 months prior to fire occurrence. These insights improve fuel‐informed operational fire forecasts for the January 2025 Los Angeles fires, particularly when year‐specific fuel conditions are included. This underscores the value of incorporating long‐memory variables to better anticipate extreme events in fuel‐limited regions.

Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation—issues that are especially critical in safety-sensitive fields such as fire safety engineering (FSE). To address these concerns, physics-informed surrogate modelling (PISM) integrates physical laws into machine learning models, enhancing their accuracy, robustness, and interpretability. This systematic review synthesises existing applications of PISM in FSE, classifies the strategies used to embed physical knowledge, and outlines key research challenges. A comprehensive search was conducted across Google Scholar, ResearchGate, ScienceDirect, and arXiv up to May 2025, supported by backward and forward snowballing. Studies were screened against predefined criteria, and relevant data were analysed through narrative synthesis. A total of 100 studies were included, covering five core FSE domains: fire dynamics, wildfire behaviour, structural fire engineering, material response, and heat transfer. Four main strategies for embedding physics into machine learning were identified: feature engineering techniques (FETs), loss-constrained techniques (LCTs), architecture-constrained techniques (ACTs), and offline-constrained techniques (OCTs). While LCT and ACT offer strict enforcement of physical laws, hybrid approaches combining multiple strategies often produce better results. A stepwise framework is proposed to guide the development of PISM in FSE, aiming to balance computational efficiency with physical realism. Common challenges include handling nonlinear behaviour, improving data efficiency, quantifying uncertainty, and supporting multi-physics integration. Still, PISM shows strong potential to improve the reliability and transparency of machine learning in fire safety applications.

Abstract Invasion by non‐native annual grasses poses a serious threat to native vegetation in California, facilitated through interaction with wildfires. Our work is the first attempt to use the coupled fire‐atmosphere model, WRF‐Fire, to investigate how shifts from native, shrub‐dominated vegetation to invasive grasses could have affected a known wildfire event in southern California. We simulate the Mountain Fire, which burned >11,000 ha in July 2013, under idealized fuel conditions representing varying extents of grass invasion. Expanding grass to double its observed coverage causes fire to spread faster due to the lower fuel load in grasses and increased wind speed. Beyond this, further grass expansion reduces the simulated spread rate because lower heat release partially offsets the positive effects. Our simulations suggest that grass expansion may generally promote larger faster‐spreading wildfires in southern California, motivating continued efforts to contain and reduce the spread of invasive annual grasses in this region.

The behaviour of wildfires and their occurrence are changing worldwide. This change is especially notable in areas where these events were not common and are now gaining strength, such as in Northern Europe. Norway has suffered unexpected periods of dryness and high temperatures, causing a considerable change in the probability of wildfire occurrence. A clear example of this trend was 2018 when unusual weather conditions caused numerous fires to spread nationwide. This changing trend highlights the need to understand and analyse the current situation to mitigate future impacts and losses. This paper examines recent wildfires in Norway by analysing the events that have happened from 2016 to 2023, the period when data is available. While acknowledging that this period may not be extensive enough to predict future patterns, this analysis provides valuable insights into recent trends and occurrences. During this timeframe, Norway experienced an annual average of 1217 wildfires, burning 2019 hectares per year. Wildfires peak in April and May. Southern Norway, particularly the Southeast, experiences more wildfires due to drier conditions and denser populations, while Northern regions have fewer fires. This study also evaluated climatic conditions, highlighting a strong correlation between the Palmer Drought Severity Index (PDSI) anomalies and the severe drying conditions in 2018, along with other climatic factors such as land surface temperature, precipitation, and wind. Additionally, the normative and operational situation is detailed to show the framework around these events. It provides reflections and recommendations to avoid future disasters, emphasizing the need for improved fire safety measures and proactive fire management.

Reliability of satellite-based vegetation maps for planning wildfire-fuel treatments in shrub steppe: Inferences from two contrasting national parks.

Wildfires propagate through interactions between wind, fuel, and terrain, resulting in complex behaviors that challenge accurate predictions. This study investigates the interaction between wind velocity topology and wildfire dynamics, aiming to enhance our understanding of wildfire spread patterns through a simplified nonlinear convection–diffusion–reaction wildfire model, adopting a fundamental reactive flow dynamics perspective. We revisited the non-dimensionalizion of the governing combustion model by incorporating three distinct time scales. This approach revealed two new non-dimensional numbers, contrasting with the conventional non-dimensionalization that considers only a single time scale. Through scaling analysis, we analytically identified the critical determinants of transient wildfire behavior and established a state-neutral curve, indicating where initial wildfires extinguish for specific combinations of the identified non-dimensional numbers. Subsequently, a wildfire transport solver was developed, integrating upwind compact schemes and implicit–explicit Runge–Kutta methods. We explored the influence of stable and unstable manifolds in wind topology on the transport of wildfire under steady wind conditions defined using a saddle-type fixed point flow, emphasizing the role of the non-dimensional numbers. Additionally, we considered the benchmark unsteady double-gyre flow, examined the effect of unsteady wind topology on wildfire propagation, and quantified the wildfire response to varying wind oscillation frequencies and amplitudes using a transfer function approach. The results were compared to Lagrangian coherent structures (LCS) used to characterize the correspondence of manifolds with wildfire propagation. The approach of utilizing the wind flow manifolds provides valuable insight into wildfire dynamics across diverse wind scenarios, offering a potential tool for improved predictive modeling and management strategies.

Wildfire behavior is shaped by complex fire dynamics, with firebrands playing a critical role in spot fire ignition and fire spread. While previous studies have explored firebrand generation and transport, the specific role of towers and troughs from wildland fires in the lofting of firebrands remains unquantified. This study addresses that gap by using physics-based coupled fire-atmosphere model simulations to examine how wildfire towers (updrafts) and troughs (downdrafts) influence firebrand lofting. Our results show that the majority of firebrands (78.85%) are lofted from towers, where strong updrafts drive long-range transport. In contrast, only 21.15% of firebrands are lofted within troughs, where downdrafts cause most firebrands to fall near the fireline. We also find that firebrand size significantly influences lofting behavior, with smaller particles (1 mm radius) exhibiting the strongest correlation with updraft intensity. These findings highlight the dominant role of wildfire towers in promoting long-distance firebrand dispersal—an essential factor in rapid wildfire growth and wildland-urban interface (WUI) fire risks. By quantifying the relationship between firebrand lofting and fire-induced atmospheric features, this study provides critical insights to improve spot fire modeling, support mitigation planning, and enhance firefighter and WUI community safety in spot fire-prone regions.

Selection forestry sustains timber production and stand structural complexity via partial harvesting. However, regeneration initiated by harvesting may function as fuel ladders, providing pathways for fire to reach the forest canopy. We sought potential mitigation approaches by simulating stand growth and potential wildfire behavior over a century in stands dominated by coast redwood (Sequoia sempervirens (Lamb. ex. D. Don) Endl.) on California’s north coast. We used the fire and fuels extension to the forest vegetation simulator (FFE-FVS) to compare group selection (GS) to single-tree selection silviculture with either low-density (LD) or high-density (HD) retention on a 20-year harvest return interval. These three approaches were paired with six options involving vegetation management (i.e., hardwood control or pre-commercial thinning (PCT)) with and without fuels treatments (i.e., prescribed fire or pile burning), or no subsequent vegetation or fuel treatment applied after GS, HD, or LD silviculture. Fuel treatment involving prescribed fire reduced hazardous fuel loading but lowered stand density and hence productivity. Hardwood control followed by prescribed fire mitigated potential wildfire behavior and promoted dominance of merchantable conifers. PCT of small young trees regenerating after selection harvests, followed by piling and burning of these cut trees, sustained timber production while reducing potential wildfire behavior by approximately 40% relative to selection silviculture without vegetation/fuel management, which exhibited the worst potential wildfire behavior.

Abstract This study presents a comprehensive approach to assessing wildfire behavior in a Mediterranean landscape using the FlamMap fire moelling software. We employed geospatial tools to map and categorize land cover types, followed by field visits to validate the remote data and collect detailed vegetation and fuel load information. Key physical parameters, including slope, elevation, and historical weather data, were integrated into the FlamMap model to simulate wildfire behavior under two wind scenarios: one with an average wind speed of 10 km/h and another with a maximum wind speed of 23 km/h. The simulations provided detailed insights into fire behavior parameters such as fireline intensity, flame length, and rate of spread (ROS), emphasizing the critical roles of wind speed, vegetation type, and topography. Results showed significant variations in fire behavior across different vegetation types, with the highest fireline intensities and rates of spread occurring in areas dominated by Aleppo pine and dense shrublands, particularly under high wind conditions. The overlay of historical fire data with simulation results revealed that regions with dense shrub stands and Aleppo pine wooded shrub are most prone to wildfires, underscoring the need for targeted fire management strategies. This study demonstrates the utility of FlamMap in predicting fire behavior and guiding wildfire management efforts in high-risk areas.

Introduction Extreme wildfires are increasingly prevalent worldwide, driving significant forest area loss and severe environmental and socioeconomic impacts (Cunningham et al. 2024). The Mediterranean, in particular, is projected to face heightened fire risks due to climate change-induced drier conditions and lower fuel moisture (de Rivera et al. 2020). However, the drivers of extreme wildfires remain poorly understood. Current fire models, typically calibrated on global fire datasets, are primarily designed to estimate annual total burned areas and struggle to capture the unique behaviours of extreme wildfires (Forrest et al. 2024). Furthermore, correlation-based approaches, which dominate current modelling efforts, may fail to identify the underlying causal drivers of these events and are poorly suited for extrapolation to changing conditions. Causal discovery methods, which aim to identify cause-and-effect relationships from observational data, offer a promising pathway to uncover the mechanisms driving extreme wildfires. While increasingly applied in environmental sciences, their use in wildfire prediction remains limited (de Rivera et al. 2020, Zhang et al. 2024, Zhao et al. 2024).This study will use causal discovery to identify key drivers of extreme wildfire in the Mediterranean, and further integrate the causal graphs into a stand-alone model of wildfire spread. This approach aims to move beyond correlation-based models, improve our understanding of extreme wildfire behaviour and inform more robust mitigation strategies. Study Area and Data We will use the Mesogeos dataset (Kondylatos et al. 2023), designed for wildfire modelling in the Mediterranean region. Spanning 17 years (2006–2022) at a 1 km² spatial and daily temporal resolution, it includes meteorological variables (e.g., temperature, wind speed), vegetation indices (e.g., NDVI, LAI), and human activity indicators (e.g., population density, road proximity). Wildfire data include MODIS fire ignitions and burned areas from EFFIS. Methods Extreme Wildfire Definition and Sampling In this study, we define extreme wildfires as those that are exceptionally large in size. To identify these events, we will first extract the final burned areas associated with each fire ignition recorded in the Mesogeos dataset. Since the classification of large fires is inherently subjective and varies by region, we will adopt a data-driven approach based on an absolute quantitative threshold. Specifically, we will define extreme wildfires as those exceeding the 99th percentile of fire sizes, though this threshold may be adjusted to align with extreme fire events documented in national fire reports. While this method provides a straightforward and reproducible way to define extreme events, we acknowledge its limitations. Future work will refine this approach by incorporating region-specific thresholds and additional contextual factors to improve geographic relevance. Phase I: Causal Discovery Using local variables from Mesogeos, averaged over final burned areas and lagged to time t, we will estimate causal graphs for extreme events via Python’s Tigramite library with the PCMCI method (Runge et al. 2019). PCMCI detects time-lagged causal associations in large nonlinear datasets through iterative conditional independence testing. To ensure robustness, we will assess graph stability across hyperparameters and selected drivers, and validate graphs through expert knowledge. Phase II: Causal Fire Spread Model We will develop a fire spread model incorporating causal mechanisms from Phase I. This model will integrate spatiotemporal fire dynamics, causal dependencies constraining fire spread, and dynamic weather and fuel inputs. By explicitly modeling causal interactions, it aims to improve early warning systems and risk assessments under future climate scenarios. The causal model’s performance will be benchmarked against statistical models to evaluate its predictive accuracy and robustness. Expected Results We expect that the data-driven approach proposed in this study will enhance the predictability of extreme wildfires by reducing confounding effects and capturing key drivers of extreme fire events. Compared to purely statistical approaches, incorporating causal structures should lead to more reliable predictions, particularly in out-of-sample applications or under changing environmental conditions. Furthermore, the causal fire spread model will provide insights into how climate, vegetation, and anthropogenic factors interact to drive fire spread, supporting fire prevention and mitigation strategies.

Firebrands or embers are a crucial phenomenon in wildfire behaviour. Firebrands – small, burning or smouldering pieces of wood or other flammable materials – can be carried by wind considerable distances, leading to ignition of new fires ahead of the main fire front. This process, called spotting, significantly contributes to the rapid spread of fires, particularly in wildland–urban interface (WUI) areas. Spot fires pose a severe threat to people and properties. Better understanding the thermal behaviour of firebrands and their ability to ignite various natural fuel beds and structural materials is crucial for developing effective fire prevention and mitigation strategies. This paper presents a comprehensive review of recent studies investigating the thermal behaviour of firebrands and their interaction with natural and structural fuels. These intensive research efforts have focused on predicting firebrand behaviour in spot fires through experimental studies, numerical simulations and statistical modelling to identify factors influencing ignition likelihood. This review explores the mechanisms through which firebrands interact with vegetative and building materials, focusing on ignition and subsequent fire spread. Critical factors, such as material composition, moisture content and firebrand accumulation, are discussed. This study also identifies critical knowledge gaps and proposes future research directions to ultimately contribute to more effective wildfire mitigation and management strategies.

Wildfire spread modeling is governed by a complex system of nonlinear partial differential equations (PDEs) that capture the intricatedynamics of wildfire behavior, including heat transfer and moisture interaction. A comprehensive understanding of these dynamics is critical for developing effective management, mitigation, and intervention strategies. In this study, temperature-dependent diffusion and convection terms are incorporated into the volume fraction of moisture, enriching the model framework and improving its accuracy in representing wildfire spread. To ensure the mathematical robustness of the model, the non-linear PDE system is transformed into a dimensionless form using appropriate dimensionless variables, facilitating the analysis of the equations. The model equations describe the dynamics of combustible forest material (CFM) in terms of the volume fractions of dry organic matter, moisture, coke, heat, and oxygen. The conditions for the existence and uniqueness of solutions to the model equations are rigorously established using the Lipschitz continuity criterion. The results confirm that unique solutions exist when the Lipschitz conditions are satisfied.

Wildfires represent a growing concern worldwide, and their frequency has increased due to climate change and human activities, posing risks to biodiversity and human safety. In the Metropolitan District of Quito (DMQ), the combination of flammable vegetation and steep slopes increases the wildfire susceptibility. Although there are no formally designated firebreaks in these areas, many natural and artificial elements, such as roads, water bodies, and rocky terrain, can effectively function as firebreaks if properly adapted. This study aimed to evaluate the wildfire behavior and assess the effectiveness of both adapted existing barriers and proposed firebreaks using FlamMap simulations. Geospatial and meteorological data were integrated to generate landscape and weather inputs for simulating wildfires in nine high-susceptibility areas within the DMQ. Fuel vegetation models were obtained by matching the national land-cover data with Scott and Burgan fuel models, and OpenStreetMap data were used to identify the firebreak locations. The simulation results show that adapting existing potential firebreaks could reduce the burned area by an average of 42.6%, and the addition of strategically placed firebreaks could further reduce it by up to 70.2%. The findings suggest that implementing a firebreak creation and maintenance program could be an effective tool for wildfire mitigation.

Wildfires in Canada are common occurrences, but as climate change drives rising fire activity and intensity, remote wildland-urban interface (WUI) sites are increasingly under threat. This paper uses a single event—the 2023 Québec wildfires prompting evacuation of Lebel-sur-Quévillon (LSQ)—as a methodological pilot project for visualizing dynamic wildfire behaviour and the traces megafires leave behind. The paper utilizes two methods, moving between general fire behaviour principles at a distance and their specific impacts on the ground. First, we developed physical models that draw from fire science experimentation methods to visualize principles of fire behaviour. Second, we completed fieldwork in LSQ a year post-fire; we then identified and mapped three sites of WUI significance: a firebreak, a power substation and a logging clean-up site. Combined, the work makes legible variables that drive volatile dynamic fire spread while revealing the wide range of conditions that fall within the WUI purview.

Fire regimes are changing across the globe, with new wildfire behaviour phenomena and increasing impacts felt, especially in ecosystems without clear adaptations to wildfire. These trends pose significant challenges to the scientific community in understanding and communicating these changes and their implications, particularly where we lack underlying scientific evidence to inform decision-making. Here, we present a perspective on priority directions for wildfire science research—through the lens of academic and government wildfire scientists from a historically wildfire-prone (USA) and emerging wildfire-prone (UK) country. Key topic areas outlined during a series of workshops in 2023 were as follows: (A) understanding and predicting fire occurrence, fire behaviour and fire impacts; (B) increasing human and ecosystem resilience to fire; and (C) understanding the atmospheric and climate impacts of fire. Participants agreed on focused research questions that were seen as priority scientific research gaps. Fire behaviour was identified as a central connecting theme that would allow critical advances to be made across all topic areas. These findings provide one group of perspectives to feed into a more transdisciplinary outline of wildfire research priorities across the diversity of knowledge bases and perspectives that are critical in addressing wildfire research challenges under changing fire regimes.This article is part of the theme issue ‘Novel fire regimes under climate changes and human influences: impacts, ecosystem responses and feedbacks’.