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Embargado hasta 15/11/2025, Wind speed, which is significantly affected by terrain and vegetation, is one of the most crucial factors in terms of fire spread. Fire simulators compute wind speed as open 10-m wind speed multiplied by the Wind Adjustment Factor (WAF). In forested sites, the sub-canopy wind speed plays a fundamental role in low intensity fire or prescribed burn, and therefore, in prescribed burn plan. This research aims to estimate a WAF model based on in-stand wind speed at 2-m above ground and forest characteristics. We use Chi-square Automatic Interaction Detection to classify WAF according to the most influential stand variables.

Fifty-seven sampling sites were established for WAF training and testing. WAF ranged from 0.03 to 0.84, showing significant differences due to stand characteristics. Our findings showed that canopy cover is the variable with the greatest influence on WAF. On the one hand, the non-linear WAF model reached a coefficient of determination (R2) of 90 %. On the other hand, a decision tree performed four decision nodes based on canopy cover, stand height, and stand density. These approaches propose a novel method to the identification of WAF for fuel treatment and prescribed burning implementation, avoiding the errors that could be generated using fixed WAF for each fuel model. The proposed model can be used to simulate the effect of different canopy management alternatives, both fuel treatments and timber harvesting, in wind speed at 2-m height.

Background. Handcrews dig handlines to bare mineral soil for fire containment. Increasing the amount of firefighting resources is insufficient to mitigate wildfire damage or decrease the number of large fires. Aims. This study aims to empirically assess handcrew fireline production rates through direct monitoring of suppression actions on active wildfires. Methods. A database was created from information gathered by crew supervisors during wildfires in southern Spain between 2014 and 2019. Fireline production rates were calculated from working time and handline length. Key results. Mean fireline production rate during direct attack in chaparral was 0.33 m min−1 firefighter−1, whereas production in timber litter was 1.06 m min−1 firefighter−1. However, fireline production rate was considerably reduced during indirect attack, in fuel types with high fuel loading, on wildfires larger than 50 ha, after 3 h of sustained suppression action,
with crews of more than nine firefighters, in unsuccessful fire containment, and when the ground crews lacked aerial support. Conclusions. Our results suggest mean fireline production rates need to be modified by working conditions and psychological variables to better inform efficient acquisition and allocation of resources. Implications. Knowing the operating capability of firefighting resources is important to fire managers for reducing uncertainty and guaranteeing the safety and effectiveness of suppression

[Methods] We first defined eight large ecoregions based on their environment and vegetation: Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. These ecoregions were defined by aggregating 81 WWF ecoregions with the help of the bioregions (https://www.oneearth.org/bioregions-2020/). We provide the shape files with these ecoregions.

Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. Specifically, we computed the following fire statistics for each ecoregion and year (2001-2019): area burnt; mean fire size; fire intensity; fire season; fire patchiness (CV of the fire intensity in each fire); fire recurrence and pyrodiversity. This data was estimated based on individual-fire data provided in GlobFire (Artés et al. 2019) except fire intensity that was estimated using MODIS hotspots (Collection 6 Active Fire Products from Terra and Aqua satellites, dataset MCD14ML; downloaded from the University of Maryland, USA; period 2001-2021). Fire recurrence for each ecoregion was estimated as the number of times each patch was burnt. The pyrodiversity of each ecoregion (i.e., fire-caused landscape heterogeneity) was estimated as the Shannon diversity of fire patches, that is, considering the relative abundance (sizes) of fire-produced patches in each ecoregion. The data provided is the average by ecoregion and year, except for patchiness we provide the area of each patch in each ecoregion, and the number of times the patch burned. More details are provided in the original article.

[Usage Notes] The ecoregion map is in "shape" format and can be opened with most GIS softwares (e.g., QGIS). The data is provided as comma-delimited files (csv; ASCII) and can be opened with most softwares for numerical analysis (e.g. in R using the function read.csv) or with a spreadsheet (e.g., LibreOffice Spreadsheet)., We characterised fire regimes and estimated fire regime parameters (area burnt, size, intensity, season, patchiness, pyrodiversity) at broad spatial scales using remotely sensed individual-fire data. Specifically, we focused on the western part of the Palearctic realm, i.e., Europe, North Africa, and the Near East. We first divided the study area into eight large ecoregions based on their environment and vegetation (ecoregions): Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed us to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence, and pyrodiversity for each ecoregion. We then related those fire parameters with the ecoregions’ climate and analysed the temporal trends in fire size. The results suggest that fire regime parameters vary across different environments (ecoregions). The Mediterranean had the largest, most intense, and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for fires to become larger during the last two decades, especially in Arid and Continental environments. In conclusion, fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments, and for increasingly large fires in recent decades., European Commission, Award: GA 101003890 (fireUrisk)., Peer reviewed

The ecoregion map is in "shape" format and can be opened with most GIS softwares (e.g., QGIS). The data is provided as comma-delimited files (csv; ASCII) and can be opened with most softwares for numerical analysis (e.g. in R using the function read.csv) or with a spreadsheet (e.g., LibreOffice Spreadsheet)., We characterised fire regimes and estimated fire regime parameters (area burnt, size, intensity, season, patchiness, pyrodiversity) at broad spatial scales using remotely sensed individual-fire data. Specifically, we focused on the western part of the Palearctic realm, i.e., Europe, North Africa, and the Near East. We first divided the study area into eight large ecoregions based on their environment and vegetation (ecoregions): Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed us to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence, and pyrodiversity for each ecoregion. We then related those fire parameters with the ecoregions' climate and analysed the temporal trends in fire size. The results suggest that fire regime parameters vary across different environments (ecoregions). The Mediterranean had the largest, most intense, and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for fires to become larger during the last two decades, especially in Arid and Continental environments. In conclusion, fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments, and for increasingly large fires in recent decades., Funding provided by: European Commission, Award Number: GA 101003890 (fireUrisk)., Peer reviewed

[Aim] The aim was to characterize fire regimes and estimate fire regime parameters (area burnt, size, intensity, season, patchiness and pyrodiversity) at broad spatial scales using remotely sensed individual-fire data., [Location] Western part of the Palaearctic realm (i.e., Europe, North Africa and the Near East)., [Time period] 2001–2021., [Methods] Initially, I divided the study area into eight large ecoregions based on their environment and vegetation: Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental and Tundra. Next, I intersected each predefined ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed me to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence and pyrodiversity for each ecoregion. I related those fire parameters to the climate of the ecoregions and analysed the temporal trends in fire size., [Results] Fire regime parameters varied across different environments (ecoregions). The Mediterranean had the largest, most intense and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for large fires to become larger during the last two decades, especially in Arid and Continental environments., [Main conclusion] The fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments. In addition, fires have been increasing in size during recent decades., This work was inspired by meetings of the FIRELink Cost Action (European Commission CA18135) and performed under the framework of the fireUrisk project (European Commission GA 101003890; European Union's Horizon 2020 research and innovation programme)., Peer reviewed

This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk., This paper is part of the FirEUrisk project research funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101003890. MS and EK acknowledge the support of the HE project EXHAUSTION (grant 820655) and the Academy of Finland HEATCOST (grant 334798) to the global-scale developments. FAF acknowledges the support of the Spanish Ministerio de Ciencia, Innovación y Universidades (grant #PRE2019-089208).

© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., Interactions between humans and wildfires have increased in many regions over the last decades driven by climate and land-use changes. A shift towards more adaptive fire management and policies is urgently needed but remains difficult to achieve. Better understanding of public interest in wildfire can facilitate this transition, as the public is a key driver for policy decisions. We used Google Trends to assess temporal patterns (2004–2020) in public interest on wildfires worldwide and in five case study countries (Australia, Canada, Indonesia, Portugal, USA). Public interest consistently shows a cyclic pattern with low background and short-lasting spikes during fire seasons and catastrophic events. Wildfires that receive the most interest worldwide are located in Western countries, especially the USA. There is usually high demand for news on wildfires when spikes in interest happen. Overall global interest in wildfire has risen twice: first for a short period in 2007–2008, concomitant to catastrophic wildfires in California, and again since 2017, probably triggered by a series of catastrophic fire events around the globe. Nevertheless, public interest in wildfire is low when compared with socioeconomically more costly earthquakes or hurricanes. The short and seasonal interest in wildfire may present an important obstacle to the implementation of wildfire mitigation policies that require year-round approaches. However, the fact that the public uses the internet to obtain basic knowledge about wildfire functioning and impacts, especially during the interest spikes, can facilitate targeting awareness campaigns. These could be not only about wildfires but also about broader related environmental issues., This work was supported by the European Cooperation in Science and Technology (FIRElinks COST Action grant no. CA18135). C. S. received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska‐Curie Grant Agreement 663830 and the Spanish ‘Ramon y Cajal’ programme, Ref. N. RYC2018–025797-I. S.D. was supported by the H2020 grant FirEUrisk, Ref. 101003890., Peer reviewed

© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., The mobilisation of potentially harmful chemical constituents in wildfire ash can be a major consequence of wildfires, posing widespread societal risks. Knowledge of wildfire ash chemical composition is crucial to anticipate and mitigate these risks.

Here we present a comprehensive dataset on the chemical characteristics of a wide range of wildfire ashes (42 types and a total of 148 samples) from wildfires across the globe and examine their potential societal and environmental implications. An extensive review of studies analysing chemical composition in ash was also performed to complement and compare our ash dataset.

Most ashes in our dataset had an alkaline reaction (mean pH 8.8, ranging between 6 and 11.2). Important constituents of wildfire ash were organic carbon (mean: 204 g kg−1), calcium, aluminium, and iron (mean: 47.9, 17.9 and 17.1 g kg−1). Mean nitrogen and phosphorus ranged between 1 and 25 g kg−1, and between 0.2 and 9.9 g kg−1, respectively. The largest concentrations of metals of concern for human and ecosystem health were observed for manganese (mean: 1488 mg kg−1; three ecosystems > 1000 mg kg−1), zinc (mean: 181 mg kg−1; two ecosystems > 500 mg kg−1) and lead (mean: 66.9 mg kg−1; two ecosystems > 200 mg kg−1). Burn severity and sampling timing were key factors influencing ash chemical characteristics like pH, carbon and nitrogen concentrations. The highest readily dissolvable fractions (as a % of ash dry weight) in water were observed for sodium (18 %) and magnesium (11.4 %). Although concentrations of elements of concern were very close to, or exceeded international contamination standards in some ashes, the actual effect of ash will depend on factors like ash loads and the dilution into environmental matrices such as water, soil and sediment. Our approach can serve as an initial methodological standardisation of wildfire ash sampling and chemical analysis protocols., C. Sánchez-García, C. Santín, J. Neris and S. Doerr acknowledge funding by Natural Environment Research Council grant (NE/R011125/1) and a European Union’s Horizon 2020 research and innovation programme grant (FirEUrisk; 101003890). S. Doerr also acknowledges funding by Leverhulme Trust (Grant RPG-2014-095). C. Santín was also supported by the Spanish “Ramon y Cajal” programme (RYC2018-025797-I). C. Belcher was supported by Natural Environment Research Council grant UK-FDRS (NE/T003553/1). A. Marcotte was supported by a European Union’s Horizon 2020 research and innovation programme MSCA-ITN-2019 – Innovative Training Networks under grant agreement no. 860787., Peer reviewed

© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited., Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires and highlights the need for more field data to further validate this initial assessment., Antonio Girona-García is recipient of the Grant RYC2021-031262-I funded by MICIU/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”; and was also funded by the Spanish Research Council (Consejo Superior de Investigaciones Científicas, CSIC) through the project 20208AT007. Stefan Doerr was supported by Natural Environment Research Council grant UK-FDRS (NE/T003553/1) and the project FirEURisk, funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 101003890., Peer reviewed

The R code for carrying out the meta-analysis is provided in file "global_fire_analysis_240325.R".

The database is provided in the file "global_fire_DB_240325.csv". The columns in the file are described below:

study_id: study identification, composed of the name of the first author and the year of publication.

study_num: study number.

ES_num: number of effect size within study.

country: country where the study was carried out.

latitude/longitude: coordinates indicating where the study was carried out.

FR_hist: type of historical fire regime (surface fire, crown fire, or non fire prone).

FR_comp: fire regime component (frequency or severity).

fire_type: type of fire (wildfire or prescribed fire).

habitat: type of habitat (broadleaf forest, conifer forest, mixed forest, grassland, shrubland, or woodland).

response: broad response variable measured (abundance, diversity, or fitness).

response_detailed: more detailed response variable measured (e.g., size or biomass, density or frequency, Shannon or Simpson indices, gamma diversity, survival).

TSF_months: time since fire in months.

TSF_factor: factor variable of time since fire (short, 24 months or less, or long, more than 24 months).

PLF: plant life form studied (woody plant, herb, or bryophyte).

climate: climate prevalent in the study region (arid, cold, temperate with a dry season, temperate without a dry season, or tropical).

Xtreat1_type: type of crossed factor number one.

Xtreat1_fact: category for crossed factor number one.

Xtreat2_type: type of crossed factor number two.

Xtreat2_fact: category for crossed factor number two.

surface-crown: type of change in fire behaviour with intensification of fire regime (surface to crown, surface to surface, or crown to crown).

eff_n: effective sample size.

d_Hedges: Hedges’ d.

var_Hedges: variance of Hedges’ d.

imputed: whether the effect size was imputed or not.

source: source of the data used to calculate the effect size (e.g., Table1, Figure2, main text).

'NA' indicate missing values, e.g., because they were not reported in the source study, or because the variable does not apply to that study., Database of responses of plant abundance, diversity, and fitness to increased fire frequency or severity, collected from published scientific articles or reports. The database includes 394 studies published worldwide between 1962 and 2023. Information on the following variables are also provided: fire regime component (fire frequency or severity), time since the last fire, fire type (wildfire or prescribed fire), historical fire regime type (surface or crown fire), plant life form (woody plant, herb, or bryophyte), habitat type, and climate. The database underpins the meta-analysis 'Global plant responses to intensified fire regimes', published in Global Ecology and Biogeography., Generalitat Valenciana: Prometeo/2021/040 European Union: Horizon 2020 - 101003890, Peer reviewed

[Background]: Wildfires play a significant and complex role in ecosystems, influencing various aspects of their functioning and structure. These natural disturbances can positively and negatively impact ecosystems, shaping landscapes, nutrient cycles, biodiversity, and ecological processes. This study focuses on assessing and integrating the different factors that affect the ecological vulnerability to wildfires at the European scale. Our methodology follows three steps. Firstly, ecological values based on biological distinctiveness and conservation status were estimated to understand pre-fire conditions better. Secondly, we obtain vegetation’s coping capacity (or resistance) to the impacts of fire, considering the functional traits of plants and fire characteristics through a fire extreme scenario. Finally, post-fire recovery time was calculated by considering the species-specific recovery time, recovery starting time, growth recovery rate, and the environmental constraints affecting the optimal vegetation response. These three variables were combined using a dynamic model that assumed the change of value due to wildfires integrated throughout the recovery time., [Results]: Our results indicate that the tundra biome emerges as the most ecologically vulnerable to fire, primarily due to its high ecological values and long recovery time, which outweigh its moderate coping capacity. Following closely, the temperate conifer forests also exhibit high vulnerability driven by their high recovery time, despite moderate ecological and coping capacity values. The boreal forests rank next, with moderate vulnerability due to their long recovery time and moderate coping capacity. The Mediterranean region, although having moderate ecological values and recovery time, shows a notable vulnerability influenced by lower coping capacity. The temperate broadleaf and mixed forests demonstrate relatively lower vulnerability owing to their balanced ecological values, moderate recovery time, and substantial coping capacity. Lastly, the temperate grasslands, savannas, and shrublands are the least vulnerable, benefiting from lower ecological values and the fastest recovery time, alongside moderate coping capacity, which collectively reduce their overall fire vulnerability. Furthermore, we found that coping capacity is the factor that most influenced ecological vulnerability to wildfires., [Conclusions]: The study identifies key zones for European or national policies on fire prevention and post-wildfire regeneration. It offers insights into effective forest management and conservation policies, applicable to current conditions. Additionally, the methods can predict future ecological vulnerability to wildfires based on climatic and socio-economic trends., [Antecedentes]: Los fuegos de vegetación juegan un rol significativo y complejo en los ecosistemas, influenciando varios aspectos de sus estructuras y funcionamiento. Estos disturbios naturales pueden impactar de manera positiva o negativa a los ecosistemas, modelando paisajes, ciclos de nutrientes, biodiversidad, y procesos ecológicos. Este estudio se enfoca a determinar e integrar los diferentes factores que afectan la vulnerabilidad a los incendios a escala europea. Nuestra metodología siguió tres etapas. En primer lugar, los valores ecológicos basados en distintivos biológicos y estatus de conservación fueron estimados para entender mejor las condiciones previas al fuego. Segundo, obtuvimos la capacidad de ajuste (o resistencia) de la vegetación a los impactos del fuego, considerando las características funcionales de las plantas y las características de los fuegos en un escenario de fuegos extremos. Finalmente, el tiempo de recuperación post fuego fue calculado considerando la recuperación específica de cada especie, el tiempo de inicio de la recuperación, la tasa de recuperación del crecimiento y de los condicionantes ambientales que afectan la respuesta óptima de la vegetación. Estas tres variables fueron combinadas usando un modelo dinámico que supuso el cambio de valor debido al fuego, integrado a través del tiempo de recuperación., [Resultados]: Nuestros resultados indicaron que el bioma de la Tundra emerge como el más ecológicamente vulnerable al fuego, debido fundamentalmente a sus valores ecológicos y altos tiempos de recuperación, lo que supera su moderada capacidad de afrontar loe efectos de ese disturbio. A este ecosistema le siguen los bosques templados de coníferas, que también exhiben una alta vulnerabilidad debido a sus extensos tiempos de recuperación y su moderada capacidad de ajuste al fuego. Los bosques boreales se ubican en tercer lugar, con una moderada vulnerabilidad debido también a sus largos períodos de recuperación y moderada capacidad de ajuste a este disturbio. La región Mediterránea, aunque presenta valores ecológicos y tiempos de recuperación moderados, muestra una notable vulnerabilidad debido a su muy baja capacidad de adaptación. Los bosques deciduos y mixtos demostraron una vulnerabilidad relativamente más reducida debido fundamentalmente a sus valores ecológicos más balanceados y a una sustancial capacidad de adaptación. Por último, los pastizales templados, las sabanas y los arbustales, resultaron los menos vulnerables, que se benefician por su bajo valor ecológico y la más alta capacidad de recuperación además de su moderada capacidad de ajuste, lo que colectivamente reducen su vulnerabilidad total al fuego. Además, encontramos que la capacidad de ajuste es el factor que más influencia la vulnerabilidad ecológica a los fuegos de vegetación., [Conclusiones]: Este estudio identifica zonas clave para políticas a nivel nacional o europeo sobre prevención de incendios y en la regeneración post fuego. Ofrece perspectivas para el manejo efectivo de bosques y políticas de conservación aplicables a las condiciones actuales. Adicionalmente, los métodos pueden predecir la vulnerabilidad ecológica a fuegos de vegetación basados en tendencias climáticas y socio-económicas., This research has been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. PRE2019-089208). Moreover, this paper is part of the FirEUrisk project research funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101003890., Peer reviewed

Background. Fire activity in the UK and comparable regions of northwest Europe is generally out of phase with peak fire weather conditions. Aims. Here, we assess the potential effect of phenology on fire occurrence patterns for the UK. Methods. We examined fire occurrence and vegetation phenology in the UK for 2012–2023, mapped onto the main fire-affected vegetation cover types within distinct precipitation regions, allowing the fire occurrence for fuels in different phenological phases to be explored across distinct ‘fuel’ types and regions. Key results. The UK’s fire regime is characterised by burning in semi-natural grasslands and evergreen dwarf shrub ecosystems in early spring when vegetation is still dormant. During the high-greenness phase in late spring and summer, fire activity is reduced by a factor of 5–6 despite typically elevated fire weather conditions within that period. Conclusions and implications. Semi-natural vegetation in the UK is very resistant to burning during the high-greenness phase. However, this ‘fire barrier’ is diminished during severe drought episodes, which are predicted to become more extreme in the coming decades. Incorporating phenology information into models therefore has great potential for improving future fire danger and behaviour predictions in the UK and comparable humid temperate regions., This study was supported by NERC grant UK-FDRS ‘Toward a UK fire danger rating system: understanding fuels, fire behaviour and impacts’ (NE/T003553/1) and the EC project FirEUrisk, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101003890., Peer reviewed

Despite the abundant firefighting resources deployed to reinforce the fire exclusion policy, extreme events continue to cause substantial losses in Mediterranean regions. These catastrophic wildfires question the merely-reactive response, while science-based decision-making advocates for a paradigm shift towards a long-term solution to coexist with fire. Comprehensive management solutions integrate multiple efforts to minimize the number of escaped wildfires in fire ignition hotspots, restrict large fire spread across the landscape, and prevent losses to valued resources and assets. This study develops a wildfire management zone (WMZ) delineation framework to inform decision-making in fire-prone Mediterranean landscapes. First, we combined modeling outcomes of wildfire occurrence, initial attack success, and wildfire transmission to communities to segment the landscape in WMZ blocks. We assumed the worst-case scenario in terms of fire simultaneity and weather conditions to implement the models. The geospatial outcomes were assembled and classified into four primary archetypes, and we then designated the most suitable risk mitigation strategies for each management unit. The WMZs included (1) comprehensive management, (2) human ignition prevention, (3) intensive fuel management, and (4) fire reintroduction areas. Finally, we downscaled within zones to assign specific management prescriptions to the different areas. The results were presented in a set of cross-scale maps to assist in designing risk management plans and raise social awareness. The methodological framework developed in this study may be valuable to help mitigate risk in fire-prone Mediterranean areas, but also in other regions in which similar total suppression policies fail to reduce catastrophic wildfire losses. © 2021 The Author(s)

Background. Lightning is the most common origin of natural fires, being strongly linked to specific synoptic conditions associated with atmospheric instability, such as dry thunderstorms; dry
fuels are required for ignition to take place and for subsequent propagation. Aims. The aim was to predict the daily probability of ignition by exploiting a large dataset of lightning and fire data to anticipate ignition over the entire Iberian Peninsula. Methods. We trained and tested a machine learning model using lightning strikes (>17 million) in the period 2009–2015. For each lightning
strike, we extracted information relating to fuel condition, structural features of vegetation, topography, and the specific characteristics of the strikes (polarity, intensity and flash density).
Key results. Naturally triggered ignitions are typically initiated at higher elevations (above 1000 m above sea level) under conditions of low dead fuel moisture (<10–13%) and moderate live moisture
content (Drought Code > 300). Negative-polarity lightning strikes (−10 kA) appear to trigger fires more frequently. Conclusions and implications. Our approach was able to provide ignition forecasts at multiple temporal and spatial scales, thus enhancing forest fire risk assessment systems.

Wildfire is a common phenomenon in Mediterranean countries but the 2022 fire season has been extreme in southwest Europe (Portugal, Spain and France). Here we provide a preliminary but comprehensive analysis of 2022's wildfire season in southwest Europe. Burned area has exceeded the 2001–2021 median by a factor of 52 in some regions and large wildfires (>500 ha) started to occur in June–July, earlier than the traditional fire season. These anomalies were associated with record-breaking values of fuel dryness, atmospheric water demand and pyrometeorological conditions. Live fuel moisture content was below the historical minima for almost 50 % of the season in some regions. A few large wildfires were responsible for 82 % of the burned area and, in turn, 47 % of the area burned occurred in protected areas. Shrublands, transitional woodlands and conifer forests (but not eucalypt plantations) were the land cover types most affected by extreme fires. As climate change intensifies, we can expect such fire seasons to become the new normal in large parts of the continent, potentially leading to major negative impacts on rural economies. These results highlight the need for landscape level fuel management also in protected areas, to avoid fire-induced biodiversity losses and landscape scale degradation. Our results have important policy implications and indicate that fire prevention should be explicitly addressed within continental forest legislation and strategies.

Spatially explicit data on forest canopy fuel parameters provide critical information for wildfire propagation modelling, emission estimations and risk assessment. LiDAR observations enable accurate retrieval of the vertical structure of vegetation, which makes them an excellent alternative for characterising forest fuel structures. In most cases, fuel parameterisation has been based on Airborne Laser Scanning (ALS) observations, which are costly and best suited for local research. Spaceborne LiDAR acquisitions overcome the limited spatiotemporal coverage of airborne systems, as they can cover much wider geographical areas. However, they do not provide continuous geographical data, requiring spatial interpolation methods to obtain wall-to-wall information. We developed a two-step, easily replicable methodology to estimate forest canopy fuel parameters for the entire European territory, based on data from the Global Ecosystem Dynamics Investigation (GEDI) sensor, onboard the International Space Station (ISS). First, we simulated GEDI pseudo-waveforms from discrete ALS data about forest plots. We then used metrics derived from the GEDI pseudo-waveforms to estimate mean canopy height (Hm), canopy cover (CC) and canopy base height (CBH), for which we used national forest inventory data as reference. The RH80 metric had the strongest correlation with Hm for all fuel types (r = 0.96?0.97, Bias = ?0.16-0.30 m, RMSE = 1.53?2.52 m, rRMSE = 13.23?19.75%). A strong correlation was also observed between ALS-CC and GEDI-CC (r = 0.94, Bias = ?0.02, RMSE = 0.09, rRMSE = 16.26%), whereas weaker correlations were obtained for CBH estimations based on forest inventory data (r = 0.46, Bias = 0 m, RMSE = 0.89 m, rRMSE = 39.80%). The second stage was to generate wall-to-wall maps for the continent of Europe of canopy fuel parameters at a resolution of 1 km using a spatial interpolation of GEDI-based estimates for within-fuel polygons covered by GEDI footprints. GEDI observations were not available for some of the polygons (mainly Northern latitudes, above 51.6°N). In these cases, the parameters were estimated using random forest regression models based on multispectral and SAR imagery and biophysical variables. Errors were higher than from direct GEDI retrievals, but still within the range of previous results (r = 0.72?0.82, Bias = ?0.18-0.29 m, RMSE = 3.63?4.18 m and rRMSE = 28.43?30.66% for Hm; r = 0.82?0.91, Bias = 0, RMSE = 0.07?0.09 and rRMSE = 10.65?14.42% for CC; r = 0.62?0.75, Bias = 0.01?0.02 m, RMSE = 0.60?0.74 m and rRMSE = 19.16?22.93% for CBH). Uncertainty maps for the estimated parameters were provided at the grid level, for which purpose we considered the propagation of individual errors for each step in the methodology. The final outputs, which are publicly available (https://doi.org/10.21950/KTALA8), provide a wall-to-wall estimation for the continent of Europe of three critical parameters for modelling crown fire propagation potential and demonstrate the capacity of GEDI observations to improve the characterisation of fuel models., Ministerio de Ciencia, Innovación y Universidades, Universidad de Alcalá, European Commission

We thank the global Lancet Countdown and the Wellcome Trust (grant number 209734/Z/17/Z) for their financial and technical support. We acknowledge funding from the European Union's Horizon Europe research and innovation programme under grant agreement number 101057131 (Horizon Europe project CATALYSE) and grant agreement number 101057554 (Horizon Europe project IDAlert). For the development of the Leishmaniasis indicator, we acknowledge support from Climate Monitoring and Decision Support Framework for Sand Fly-borne Disease Detection and Mitigation with Cost-benefit and Climate Policy Measures (CLIMOS; 101057690) and UK Research and Innovation (10038150 and 10039289). We thank the EU Climate Change and Health Cluster, which includes the CATALYSE, IDAlert, and CLIMOS grants. SD and EJZR report support from COST Action PROCLIAS (PROcess-based models for CLimate Impact Attribution across Sectors), funded by COST (European Cooperation in Science and Technology). MSp and JP report funding from the Horizon Europe project SYLVA (grant 101086109) and Academy of Finland project ALL-IMPRESS, grant 329215. MSp reports funding from the Wellcome Trust (205212/Z/16/Z & 225318/Z/22/Z). RH, MSo, and RK report funding from HEATCOST (Academy of Finland grant number 334798), Horizon projects FirEUrisk (101003890), EXHAUSTION (grant 820655), and ENBEL (grant 101003966).