Abstract Southwestern Europe faced an extreme wildfire season in 2025, with nearly 700,000 hectares burned in the Iberian Peninsula (IP) alone. Here, we analyze the drivers and impacts of the 2025 wildfire season in the IP and its significance within the ongoing global pyrocrisis. Decades-long declines in burned area, driven by increased fire suppression, ceased after an inflection point in 2022. Fire intensity has escalated over the last two decades, and the energy emitted in 2025 approached that produced annually by a 1,000MW nuclear reactor. Despite a historically wet spring, an extreme summer heatwave triggered a flash drought, dehydrating fuels below critical thresholds. Remarkably, 29-42% of all wildfires spread faster at night than during the day, a seldom-reported phenomenon likely arising from interactions between surface weather, atmospheric instability, and pyroconvective processes. Global change-induced increases in fire intensity facilitated the overwhelming of suppression efforts during simultaneous fire events that may have been manageable decades ago. Fire activity expanded into previously fire-free high-altitude regions, and there was a marked change in fire-size distributions, with the largest wildfire in record and the largest proportion of burned area by megafires (those burning over 5,000ha). Impacts included over 2,000 premature deaths from smoke exposure and significant effects on protected areas. These results indicate shifts in key components of anthropogenic fire regimes, including unprecedented nocturnal fire acceleration and increased burned area and fire intensity, with escalating impacts on human health and ecosystems.

Background Fire simulation frameworks and decision support systems (DSSs) are critical tools in fire response dispatching that need to consider factors influencing fire spread and suppression difficulty while provide easily interpreted indexes.Aims We present a new user-friendly Initial Attack Assessment (IAA) index, ranging from 1 to 5, designed to quickly and efficiently evaluate wildfires from their onset.Methods We used 26,907 California's wildfire ignitions to run automatic simulations. The Fire Behavior Index (FBI), Terrain Difficulty Index (TDI) and IAA were determined using size-based and fire behavior outputs from each simulation. Initial attack success was evaluated by comparing simulations with real fire sizes. Binary models were calibrated and validated to predict success based on IAA, FBI and TDI, and suppression response time.Key results The IAA effectively identified fires exceeding suppression capacity. Higher levels of IAA, FBI and TDI were associated with reduced success odds, IAA=5 giving a 90% decrease in the odds of initial attack success. Response time and its interaction with terrain difficulty were also influential.Conclusions The IAA is a powerful index to feed DSSs, prioritizing fire response and predicting the probability of control at a small size.Implications This ensures practicality for end-users, allowing agencies and utilities to better respond to wildfires.

Background Increasing frequency of large fires in northwest Europe, a region under-represented in fire studies, with different ecosystem processes from those most studied, indicates the need to understand the drivers of hazardous fire behavior. Aims This study characterizes rate of spread variation in the region and delves into vegetation and weather drivers through remote sensing. Methods For 58 large fires, we analyzed phenology (using the temporal variation of satellite-measured vegetation indices) and weather (using as the Canadian Fire Weather Index System). Their relations and capability of predicting fire spread rates were assessed. Key results Low vegetation greenness correlated non-linearly with high rate of spread, and fires in the growing season showed a drastic reduction in spread. Low levels of weather-related danger were correlated with high rate of spread. Conclusions In NW Europe, the integration of phenology into fire behavior analyses helps predict spread rate. Analyzing vegetation indices variation can help estimate times when ignition could generate fast-spreading fires. Contrary to expectations, high danger related to fire weather was associated with low spread. Implications This study highlights the need for including timing of vegetation greenness in wildfire risk modeling and for a fire weather index systems tailored to regional conditions that relate to high-hazard fire behavior.

In recent years fires of greater magnitude have been documented throughout northwest Europe. With several climate projections indicating future increases in fire activity in this temperate area, it is imperative to identify the status of fire in this region. This study unravels unknowns about the state of the fire regime in northwest Europe by characterizing one of the key aspects of fire behavior, the rate of spread (ROS). Using an innovative approach to cluster Visible Infrared Imaging Radiometer Suite (VIIRS) hotspots into fire perimeter isochrones to derive ROS, we identify the effects of land cover and season on the rate of spread of 102 landscape fires that occurred between 2012 and 2022. Results reveal significant differences between land cover types, and there is a clear peak of ROS and burned area in the months of March and April. Median ROS within these peak months is approximately 0.09 km h−1 during a 12 h overpass, and 66 % of the burned area occurs in this spring period. Heightened ROS and burned area values persist in the bordering months of February and May, suggesting that these months may present the extent of the main fire season in northwest Europe. Accurate data on ROS among the represented land cover types, as well as periods of peak activity, are essential for determining periods of elevated fire risk, the effectiveness of available suppression techniques, and appropriate mitigation strategies (land and fuel management).

Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. We reveal significant relationships between the main global CTs and burned area that vary across and within continents and biomes according to both synchronous and lagged signals, and marked regional patterns. Overall, CTs modulate 52.9% of global burned area, the Tropical North Atlantic mode being the most relevant CT. Here, we summarized the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned area across the different world’s biomes. Our findings highlight the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making.

Background. Wildfire simulators allow estimating fire spread and behaviour in complex environments, supporting planning and analysis of incidents in real time. However, uncertainty derived from input data quality and model inherent inaccuracies may undermine the utility of such predictions.Aims. We assessed the performance of fire spread models for initial attack incidents used in California through the analysis of the rate of spread (ROS) of 1853 wildfires.Methods. We retrieved observed fire growth from the FireGuard (FG) database, ran an automatic simulation with Wildfire Analyst Enterprise and assessed the accuracy of the simulations by comparing observed and predicted ROS with well-known error and bias metrics, analysing the main factors influencing accuracy.Key results. The model errors and biases were reasonable for simulations performed automatically. We identified environmental variables that may bias ROS predictions, especially in timber areas where some fuel models underestimated ROS.Conclusions. The fire spread models' performance for California is in line with studies developed in other regions and the models are accurate enough to be used in real time to assess initial attack fires.Implications. This work allows users to better understand the performance of fire spread models in operational environments and opens new research lines to further improve the performance of current operational models.

Natural disasters and epidemics are unfortunate recurring events that lead to huge societal and economic loss. Recent advances in supercomputing can facilitate simulations of such scenarios in (or even ahead of) real-time, therefore supporting the design of adequate responses by public authorities. By incorporating high-velocity data from sensors and modern high-performance computing systems, ensembles of simulations and advanced analysis enable urgent decision-makers to better monitor the disaster and to employ necessary actions (e.g., to evacuate populated areas) for mitigating these events. Unfortunately, frameworks to support such versatile and complex workflows for urgent decision-making are only rarely available and often lack in functionalities. This paper gives an overview of the VESTEC project and framework, which unifies orchestration, simulation, in-situ data analysis, and visualization of natural disasters that can be driven by external sensor data or interactive intervention by the user. We show how different components interact and work together in VESTEC and describe implementation details. To disseminate our experience three different types of disasters are evaluated: a Wildfire in La Jonquera (Spain), a Mosquito-Borne disease in two regions of Italy, and the magnetic reconnection in the Earth magnetosphere.

Wildfire events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfire risk to communities focused on modeling wildfire behavior in the wildland to aid in developing fuel reduction and fire suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfire behavior that fully integrates the wildland’s characteristics and the built environment’s features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfire behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufficient attention. Predicting damage to the built environment is essential in understanding and developing fire mitigation strategies to make communities more resilient to wildfire events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfire-affected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from 58 - 64 % for the historical wildfires tested. From the results, we observe that the modified random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modified degree formulation provides better predictions for nodes with mid-range vulnerability values.

The use of wildfire simulators allows estimating fire spread and behaviour in diverse and complex fire environments, supporting fire planning and analysis of incidents in operational environments. However, uncertainty derived from the spatio-temporal estimation of input variables and model’s inherent inaccuracies related to its limitations and assumptions may undermine the utility of such predictions. Here, we assessed the performance of well-known operational surface and crown fire spread models used in California through the analysis of the rate of spread (ROS) of 1,853 wildfires occurring in the State from 2019 to 2021. For each detected wildfire, we retrieved the observed fire progression using the FireGuard (FG) database which provides the fire progression through geo-spatial polygons every 15 minutes approximately. Based on this data, we developed an algorithm that characterises the ROS for each FG polygon and the average ROS in the first 8 hours of the fire. Also, we ran a fire simulation for each wildfire with Wildfire Analyst Enterprise using high-resolution fuel and weather data to estimate the rate of spread. Then, we assessed 1) the accuracy of the fire simulations by comparing the real and simulated ROS based on the Mean Absolute Error (MAE), Mean Bias Error (MBE) and Mean Absolute Percentage Error (MAPE); 2) the most important factors influencing the accuracy of fire simulations, including the wind speed, and fuel types. Although the models clearly underestimated ROS in timber fuels, especially when no crown fire behaviour was predicted, most of the fire simulations had an acceptable error to be used in operational environments given the new techniques to adjust and calibrate the fires with field data. Finally, we used this analysis to present new approaches to optimise the fire predictions, including the optimisation of new custom fuels through genetic algorithms and enhancement of crown fire spread models.

Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.

Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953–2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation.

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat -5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

In Spain, traditional forest fire management practices have been conducted for many decades, for both prevention- and extinction-oriented purposes. This management model has been forced to shift as a result of changes in fire behavior and has also been adapted to the use of new technologies. The challenge presented by wildfires is amplified due to socioeconomic changes in the last 40 years and inadequate land management in the context of climate change. The principal objective of this work is to establish the most adequate methodology to define the "propagation nodes" in a territory. To do that, the new simulation modes offered by the WildFire AnalystTM simulator (WFA) have been explored to obtain fire behavior data. Likewise, the behavior of large fires in the area has been extrapolated to future scenarios, according to forecasts of different climate change, analyzing extreme weather conditions that can occur in such scenarios (ONU, 2019). The WFA simulator (Tecnosylva, 2014) works efficiently in simulating fire, proving greatly useful in both real suppression operations and fire prevention analysis. It can very accurately generate large wildfires' main pathways without making any kind of adjustments; this is quite useful when planning operations at the head of a fire. It also allows evacuation time evaluation for a given Wildland Urban Interface zone. The area selected for this study is called Sot de Chera, in the Valencia region (Spain). The methodology employed here uses the simulation with WFA setting extreme meteorological and phenological windows associated with wind-driven fires or convection fires dominated with wind, from different starting points looking for the areas where they are grouped. In other words, it is a matter of identifying on the territory the areas where the heads of these higher-intensity fires will arrive, in order to offer realistic control possibilities to the firefighting teams. The results of the simulation identify the heads of the fires with the greatest rate of spread and intensity, exceeding suppression capabilities and efforts, allowing thus to plan for appropriate fuel management strategies to effectively manage emergency responses to fires in these areas.

Fire simulators allow predicting fire spread and behavior and some of which in real-time. Both strategies and tactics to suppress wildland fires depend on fire analysis which is generally based on fire simulations that need to be accurate for a proper decision making. However, limitations and assumptions of the fire models and uncertainties in input data may induce errors in the simulated fire growth and, therefore, fire simulations do not always match actual fire spread. In this paper, we show an innovative method implemented in Wildfire Analyst to adjust fire simulations in real-time. The method determines the adjustment factors needed for the optimal rate of spread by fuel model in order to minimize the arrival time error between the simulated fire and a set of control points where the arrival time of the observed (real) fire is known. Two case studies were used to present the method, showing robust results in reducing the error and fitting the simulated fire growth to the real fire spread, with practical real-time implications. The method presented may be solved in real-time and used with any empirical fire propagation.

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.

Large wildfires can cover millions of hectares of forest every year worldwide, causing losses in ecosystems and assets. Fire simulation and modeling provides an analytical scheme to characterize and predict fire behavior and spread in several and complex environments. Spatial dynamics of large wildfires can be analyzed using satellite active fire data, a cost-effective way to acquire information systematically worldwide. The simulated growth of three large wildland fires from the USA, Chile and Spain with different fire spread pattern, duration and size has been compared to satellite active fire data. Additionally, a new approach to reinitialize fire simulations in near real-time and predict a more accurate fire spread is shown in this work. Discrepancies between the simulated fire growth and satellite active data were measured spatially and temporally in the three fires, increasing along the fire duration. The reinitialization approach meaningfully improved the accuracy of fire simulations in all case studies. Satellite active fire data showed a high potential to be used in real fire incidents, improving fire monitoring and simulation and, therefore, supporting the decision-making process of the fire analyst. The reinitialization approach could be applied by using the current satellite active fire data such as MODIS or VIIRS as well as Unmanned Aerial Vehicles or GPS locations from suppression resources.

Accurately predicting fire spread and behaviour on the fireline, in the field, is highly important in order to prevent the loss of human life, improve the success of initial attack and better understand the potential fire behaviour, minimizing many risks for firefighters. We present the Wildfire Analyst (TM) Pocket Edition application (WFA Pocket), a mobile tool aimed to be used by the fire fighter community. It shows punctual fire characteristics and estimated progression based on user introduced input data in an intuitive 3D map interface in real-time, allowing the user to interactively change parameters and analyze how the fire behaviour changes in relation to the inputs. The mathematical models implemented are all well-known by the scientific community and reported in this article. The application has integrated GIS capabilities, can work online and offline, and can retrieve fuel, weather and canopy data from online servers for the georeferenced ignition point. We describe the background and model foundation of WFA Pocket as well as its system design and main features. We also evaluate its robustness of results and present a case study to show the potential use of this tool in the field. Limitations and assumptions in the use of the application as well as potential improvements for the future are discussed.