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Rising global fire activity is increasing the prevalence of repeated short‐interval burning (reburning) in forests worldwide. In forests that historically experienced frequent‐fire regimes, high‐severity fire exacerbates the severity of subsequent fires by increasing prevalence of shrubs and/or by creating drier understory conditions. Low‐ to moderate‐severity fire, in contrast, can moderate future fire behavior by reducing fuel loads. The extent to which previous fires moderate future fire severity will powerfully affect fire‐prone forest ecosystem trajectories over the next century. Further, knowing where and when a wildfire may act as a landscape‐scale fuel treatment can help direct pre‐ and post‐fire management efforts. We leverage satellite imagery and fire progression mapping to model reburn dynamics within forests that initially burned at low/moderate severity in 726 unique fire pair events over a 36‐year period across four large fire‐prone Western US ecoregions. We ask (1) how strong are the moderating effects of low‐ to moderate‐severity fire on future fire severity, (2) how long do moderating effects last, and (3) how does the time between fires (a proxy for fuel accumulation) interact with initial fire severity, day‐of‐burning weather conditions, and climate to influence reburn severity. Short‐interval reburns primarily occurred in dry‐ and moist‐mixed conifer forests with historically frequent‐fire regimes. Previous fire moderated reburn severity in all ecoregions with the strongest effects occurring in the California Coast and Western Mountains and the average duration of moderating effects ranging from 13 years in the Western Mountains to >36 years in the California Coast. The strength and duration of moderating effects depended on climate and initial fire severity in some regions, reflecting differences in post‐fire fuel accumulation. In the California Coast, moderating effects lasted longer in cooler and wetter forests. In the Western Mountains, moderating effects were stronger and longer lasting in forests that initially burned at higher severity. Moderating effects were largely robust to fire weather, suggesting that previous fire can mediate future fire severity even under extreme conditions. Our findings demonstrate that low‐ to moderate‐severity fire buffers future fire severity in historically frequent‐fire forests, underlining the importance of wildfire as a restoration tool for adapting to global change. [ABSTRACT FROM AUTHOR]

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Dry lightning is a prevalent episodic natural ignition source for wildfires, particularly in remote regions where such fires can escalate into uncontrollable events, burning extensive areas. In this study, we aimed to understand the interplay of environmental, fuel, and geographical factors in evaluating the probability of fire initiation following dry lightning strikes in Tasmania, Australia. We integrated dry lightning, active fire records, and gridded data on fire weather, fuel, and topography into a binary classification framework for both fire‐initiating and non‐fire‐causing lightning strikes. Employing statistical and machine learning techniques, we quantified the likelihood of fire initiation due to dry lightning, with the resampled Random Forest model exhibiting notable performance with an ROC‐AUC value of 0.98. Our findings highlight how fuel characteristics and moisture content associated with particular vegetation types influence fire initiation and provide an objective approach for identifying susceptible regions of dry lightning ignitions, informing associated fire management responses. Plain Language Summary: Lightning strikes occurring with minimal rainfall, known as dry lightning strikes, often ignite wildfires in dry vegetation, especially in remote areas. These fires can remain undetected for hours to days and quickly grow out of control under favorable conditions, causing extensive damage and burning large areas. Our study investigated how environmental factors such as weather, terrain, and fuel variables (fuel load, fuel type, and fuel moisture) affect the probability of a fire starting after a dry lightning strike in Tasmania, Australia. We analyzed data on lightning strikes, active fires, topography, fuel (via vegetation and fire history), and weather conditions, to build advanced machine learning models to predict the chances of a fire started following a dry lightning strike. Our findings indicate a high probability of ignition in scenarios characterized by high fuel loads and low fuel moisture content, particularly in treeless vegetation types such as buttongrass sedgelands. This information can help fire authorities identify areas or times most at risk of dry lightning‐ignited wildfires, allowing for better fire prevention and response strategies. Key Points: Random Forest modeling accurately identified fires ignited by dry lightning strikes in TasmaniaWestern Tasmania exhibits heightened vulnerability to wildfires ignited by lightning strikesTreeless fuel types have higher risk than forests of dry lightning ignitions [ABSTRACT FROM AUTHOR]

Background: As fire seasons in the Western US intensify and lengthen, fire managers have been grappling with increases in simultaneous, significant incidents that compete for response resources and strain capacity of the current system. Aims: To address this challenge, we explore a key research question: what precursors are associated with ignitions that evolve into incidents requiring high levels of response personnel? Methods: We develop statistical models linking human, fire weather and fuels related factors with cumulative and peak personnel deployed. Key results: Our analysis generates statistically significant models for personnel deployment based on precursors observable at the time and place of ignition. Conclusions: We find that significant precursors for fire suppression resource deployment are location, fire weather, canopy cover, Wildland–Urban Interface category, and history of past fire. These results align partially with, but are distinct from, results of earlier research modelling expenditures related to suppression which include precursors such as total burned area which become observable only after an incident. Implications: Understanding factors associated with both the natural system and the human system of decision-making that accompany high deployment fires supports holistic risk management given increasing simultaneity of ignitions and competition for resources for both fuel treatment and wildfire response. The intensification of Western US fire seasons has resulted in heightened competition for suppression resources as simultaneous incidents strain capacity. We present statistical models identifying characteristics (including region, fire weather, canopy cover, Wildland–Urban Interface category, and history of past fire) associated with ignitions which evolve to garner high personnel deployment. [ABSTRACT FROM AUTHOR]

Wildfire is one of the primary natural disturbance agents in the forests of China. The forecast of fire danger is critically important to assist stakeholders to avoid and mitigate wildfire-induced hazards and losses to both human society and natural ecosystems. Currently, fire danger rating methods often focus on fire weather classification based on fixed thresholds, which has shortcomings in generalizability and robustness. Based on historical fire occurrence data and meteorological data of Northeastern China from 2004 to 2015, we proposed a forest fire danger rating classification and forecasting model by combining the advantages of the Canadian Fire Weather Index (FWI) system and two machine learning models such as the Long Short-Term Memory (LSTM) network and Random Forest (RF) model. The method is divided into two stages. The first stage is the LSTM-based FWI system indexes prediction. In the first stage, the future FWI system indexes are obtained through the LSTM-based prediction model, and the RMSE and MAE of the prediction results are calculated to verify the prediction performance of the model. The second stage is random forest-based fire danger rating prediction method. In the second stage, we use the random forest method to get the fire danger occurrence probability and present the fire danger rating classification scheme. Then we verify the reliability of the fire danger rating classification scheme by using the forest fire danger data in Qipan Mountain. Our method predicts two randomly selected future intervals, and the prediction accuracy is 87.5%. The experimental results show that our machine learning-based forest fire danger rating classification method can provide a new idea for forest fire danger warnings. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai'i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. [ABSTRACT FROM AUTHOR]

Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at 10.5281/zenodo.10037629 (He et al., 2023). [ABSTRACT FROM AUTHOR]

Wildfire agencies use fire danger rating systems (FDRSs) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian fire weather index (FWI) system, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly fire radiative power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI's ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system's value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior. [ABSTRACT FROM AUTHOR]

Increased Arctic air temperatures and evaporative fluxes have coincided with more frequent and destructive high‐latitude wildfires. Arctic fires impact ecosystems and people, especially at the community‐level by degrading air quality, destroying agriculture, and threatening life and property. Central Eastern Interior (CEI) Alaska is one such region that has recently experienced the effects of wildfire activity related to warming air temperatures. To improve our ability to identify fire weather events and assess their potential for extreme outbreaks at actionable lead times relevant to fire weather forecasters and managers, new metrics and approaches need to be established and applied toward understanding the physical mechanisms underlying such wildland fire characteristics. Our study uses a new, regional atmospheric circulation metric, the Alaska Blocking Index (ABI), to describe midtropospheric air pressure around Alaska, which is subsequently related to CEI fire weather conditions at the Predictive Service Area (PSA) scale in climatological and extreme events frameworks. Of note, during years of high fire activity, Build‐Up Index (BUI) values tend to be anomalously high during the duff and drought phases across the CEI PSAs, though comparatively lower BUI values are still associated with high fire activity in the Tanana Zone‐South (AK03S) PSA. Likewise, extreme BUI values are strongly tied to high ABI values and well‐defined upper‐air ridging circulation patterns in the duff and drought periods. The statistical skill of mean daily ABI values in the 6–10 day period preceding extreme duff period BUI values is modest (τ2 > 14%) in the Upper Yukon Valley (AK02) PSA, a hotbed of wildland fire activity. Extremes in ABI and CEI BUI often occur in tandem, yielding regional predictability of upper‐air weather patterns and extremes and underlying surface weather conditions, by statistical and/or dynamical forecast models, imperative for local community and governmental organizations to effectively manage and allocate Alaska's fire weather resources. [ABSTRACT FROM AUTHOR]

Background: Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting. Aims: In this study, we assess the impact of synoptic-scale upper-air patterns on the occurrence of large wildfires and widespread fire outbreaks in the US Pacific Northwest. Additionally, we examine how discrete upper-air map types can augment subregional models of wildfire risk. Methods: We assess the statistical relationship between synoptic map types, surface weather and wildfire occurrence. Additionally, we compare subregional fire danger models to identify the predictive value contributed by upper-air map types. Key results: We find that these map types explain variation in wildfire occurrence not captured by fire danger indices based on surface weather alone, with specific map types associated with significantly higher expected daily ignition counts in half of the subregions. Conclusions: We observe that incorporating upper-air map types enhances the explanatory power of subregional fire danger models. Implications: Our approach provides value to operational wildfire management and provides a template for how these methods may be implemented in other regions. This study assesses the predictive value of synoptic-scale upper-air map types in near-term fire weather forecasting in the US Pacific Northwest. A set of 13 patterns of 500-hPa geopotential heights are associated with spatially heterogeneous variation in wildfire activity within the region. [ABSTRACT FROM AUTHOR]

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy. [ABSTRACT FROM AUTHOR]

Background: The rising occurrence of simultaneous large wildfires has put strain on United States national fire management capacity leading to increasing reliance on assistance from partner nations abroad. However, limited analysis exists on international resource-sharing patterns and the factors influencing when resources are requested and deployed. Aims: This study examines the drivers of international fire management ground and overhead personnel deployed to the United States. Methods: Using descriptive statistics and case examples data from 2008 to 2020, this study investigates the conditions under which international personnel are deployed to the United States and their relationship to domestic resource strain. Factors such as fire weather, fire simultaneity, and the impact on people and structures are analysed as potential drivers of demand for international resources. Additionally, barriers to resource sharing, including overlapping fire seasons between countries are examined. Key results: The findings indicate that international personnel sharing is more likely when the United States reaches higher preparedness levels, experiences larger area burned, and when fires pose a greater impact on people and structures. However, overlapping fire seasons can limit the ability to share resources with partner nations. Conclusions and implications: Understanding the factors influencing resource sharing can help improve collaboration efforts and enhance preparedness for future wildfire seasons. This study explores drivers of international resources sent to aid with US fire management, focusing on ground and overhead personnel. Using 2008–2020 data, this study investigates conditions for international sharing and potential barriers. Findings show sharing is more likely with higher preparedness, larger areas burned, and greater impact on people and structures. [ABSTRACT FROM AUTHOR]

Background. The record number of wildfires in the United States in recent years has led to an increased focus on developing tools to accurately forecast their impacts at high spatial and temporal resolutions. Aims. The Warn-on-Forecast System for Smoke (WoFS-Smoke) was developed to improve these forecasts using wildfire properties retrieved from satellites to generate smoke plumes in the system. Methods. The WoFS is a regional domain ensemble data assimilation and forecasting system built around the concept of creating short-term (0-6 h) forecasts of high impact weather. This work extends WoFS-Smoke by ingesting data from the GOES-16 satellite at 15-min intervals to sample the rapidly changing conditions associated with wildfires. Key results. Comparison of experiments with and without GOES-16 data show that ingesting high temporal frequency data allows for wildfires to be initiated in the model earlier, leading to improved smoke forecasts during their early phases. Decreasing smoke plume intensity associated with weakening fires was also better forecast. Conclusions. The results were consistent for a large fire near Boulder, Colorado and a multi-fire event in Texas, Oklahoma, and Arkansas, indicating a broad applicability of this system. Implications. The development of WoFS-Smoke using geostationary satellite data allows for a significant advancement in smoke forecasting and its downstream impacts such as reductions in air quality, visibility, and potentially properties of severe convection. [ABSTRACT FROM AUTHOR]

This study describes spatial and temporal patterns in fire across the US Western Great Plains over the last 40 years. Although pyrogeographic studies have explored the nexus of fire patterns in relation to the bio-physical environment and socio-ecological trends, most of this research has focused on forested ecosystems and regions long known for conflict between wildfires and human development, especially at the wildland–urban interface. But evidence suggests large wildfire activity is increasing in the US Great Plains, and the Western Great Plains—a Land Resource Region comprised of four ecoregions, Northwestern Plains, High Plains, Nebraska Sandhills, and Southwestern Tablelands—not only contains some of the largest areas of rangeland in the US but also the highest concentration of public land in the Great Plains. As such, the Western Great Plains provides an opportunity to explore fire activity in primarily rural landscapes with a combination of public and private ownership, all dominated by rangeland vegetation. We combined several publicly-available datasets containing fire records between 1992 and 2020 to create two databases, one with georeferenced point data on 60,575 wildfire events in the region, and another with georeferenced perimeter data for 2665 fires. Ignition by humans was the dominant cause of fires. No ecoregion showed a statistically significant trend towards either increasing or decreasing the annual burned area. The Northwestern Plains had the most burned area and the greatest number of incidents—consistently around or above 1000 incidents per year since 1992—with the majority in July. The High Plains showed the greatest increase in annual fire incidence, never reaching more than 200–300 per year 1992–2009, and averaging above 1000 incidents per year since 2010. Few long-term trends in human population, weather, or fuel metrics appear strongly associated with fire patterns in any ecoregion, although the years 2006, 2012, and 2017 stood out for their levels of fire activity, and these years often frequently logged extreme values in wildland fuel metrics. These relationships merit much closer examination in the Western Great Plains, because like other rangeland-dominated landscapes, the fine fuels that comprise these wildland fuelbeds are much more responsive to fine-scale changes in moisture conditions. Rural Western Great Plains landscapes are a mosaic of public and private land ownership, and an increasing impact of wildfires on public grazing lands—which are often situated within other jurisdictions or ownership—will likely have an impact on rural livelihoods. [ABSTRACT FROM AUTHOR]

A key uncertainty in understanding climate change effects on wildfires in western North America is the role of mountain pine beetle (MPB) outbreaks in driving wildfire occurrence and severity. In this study, we investigated the complex relationship between MPB outbreaks, other environmental factors, and wildfire occurrence in British Columbia (BC), Canada. We adopted a fire risk analysis method developed for fire occurrence prediction to separate the effect of changing fuel conditions on wildfires in BC when neither post‐outbreak fuel conditions, climate, nor management is stationary. Using lasso‐logistic regression and a novel variable ranking procedure, we determined that MPB‐affected areas had 1.7 times more large lightning‐caused fires (≥100 ha), as the likelihood of large lightning‐caused fires increased by 40% in these areas and likely contributed to the increased burned areas in BC. Meanwhile, the likelihood of large human‐caused fires decreased in MPB‐affected areas. Fire weather factors were most influential for both lightning‐ and human‐caused fires, while anthropogenic factors were most influential for human‐caused fires. Fuel dynamics following MPB outbreaks vary across the wide distribution of a host species such as lodgepole pine, at stand and landscape levels. Furthermore, the expression of the effects of MPB and other disturbances on wildfire is also conditional on, as well as confounded with, many other environmental factors and management activities that vary across western North America. Therefore, a lack of consensus on the impacts of MPB on wildfire is not surprising. [ABSTRACT FROM AUTHOR]

Forest fires in Türkiye, like in other regions, have detrimental effects on wildlife habitats, water quality, air pollution, climate change, and the economy. These fires become particularly concerning during the dry summer months. In 2021, forest fires affected over 150 thousand hectares of land across the country, with the Manavgat district in Antalya province alone witnessing the burning of approximately 60 thousand hectares of forest area. This study aims to assess the applicability and suitability of Fire Weather Index (FWI) data derived from meteorological station data in the Antalya region, as well as EFFIS FWI data generated using satellite-based meteorological information, for fire danger mapping during the Manavgat forest fire that occurred between 28 July and 6 August 2021. Additionally, correlation analyses were performed between the two FWI datasets and other relevant variables, including the difference in Normalized Burn Ratio (dNBR), the difference in Land Surface Temperature (dLST), and Fire Radiative Power (FRP) data detected from MODIS and VIIRS satellites. The results of the correlation analysis indicated that the FWI values obtained using in-situ meteorology station data showed much higher correlations than FWI values obtained from EFFIS, with the highest correlation (73%) observed with dLST data. Consequently, the fire danger map was created using the in-situ meteorological data, given its stronger correlation. The results prominently revealed a widespread high-risk level across the entire Antalya province, with the Manavgat district classified into the "Extreme" and "Very Extreme" FWI classes, emphasizing the critical importance of utilizing in-situ meteorological data for precise fire danger assessments and proactive fire management strategies. [ABSTRACT FROM AUTHOR]

Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting fire occurrence, and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into earth system models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling, and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (∼9 km, daily), is the first of its kind and will be regularly updated. [ABSTRACT FROM AUTHOR]

Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past two decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a Global Forest Burn Severity (GFBS) database of information on the amounts of biomass that were consumed by fire between 2003 and 2016. To build it, we used the Fire Atlas product to determine when and where forest fires occurred during that period and then overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. Using the CONUS-wide Composite Burn Index (CBI) as a ground truth, we evaluated the performance of GFBS relative to the performance of the existing MODIS-based global burn severity dataset (MOSEV). The results showed that dNBR of GFBS was more strongly correlated with CBI (R2 = 0.4) than dNBR of MOSEV (R2 = 0.08). RdNBR of GFBS also exhibited better agreement with CBI (R2 = 0.31) than RdNBR of MOSEV (R2 = 0.04). At global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS were similar to those of MOSEV, MOSEV tended to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. [ABSTRACT FROM AUTHOR]

This study explored, for the first time, the drivers shaping large fire size and high severity of forest fires classified as level-2 in Spain, which pose a great danger to the wildland–urban interface. Specifically, we examined how bottom-up (fuel type and topography) and top-down (fire weather) controls shaped level-2 fire behavior through a Random Forest classifier at the regional scale in Galicia (NW Spain). We selected for this purpose 93 level-2 forest fires. The accuracy of the RF fire size and severity classifications was remarkably high (>80%). Fire weather overwhelmed bottom-up controls in controlling the fire size of level-2 forest fires. The likelihood of large level-2 forest fires increased sharply with the fire weather index, but plateaued at values above 40. Fire size strongly responded to minimum relative humidity at values below 30%. The most important variables explaining fire severity in level-2 forest fires were the same as in the fire size, as well as the pre-fire shrubland fraction. The high-fire-severity likelihood of level-2 forest fires increased exponentially for shrubland fractions in the landscape above 50%. Our results suggest that level-2 forest fires will pose an increasing danger to people and their property under predicted scenarios of extreme weather conditions. [ABSTRACT FROM AUTHOR]

As wildfires become increasingly perilous amidst Pakistan's expanding population and evolving environmental conditions, their global significance necessitates urgent attention and concerted efforts toward proactive measures and international cooperation. This research strives to comprehensively enhance wildfire prediction and management by implementing various measures to contribute to proactive mitigation in Pakistan. Additionally, the objective of this research was to acquire an extensive understanding of the factors that influence fire patterns in the country. For this purpose, we looked at the spatiotemporal patterns and causes of wildfires between 2000 and 2023 using descriptive analysis. The data analysis included a discussion on density-based clustering as well as the distribution of the data across four seasons over a period of six years. Factors that could indicate the probability of a fire occurrence such as weather conditions, terrain characteristics, and fuel availability encompass details about the soil, economy, and vegetation. We used a convolutional neural network (CNN) to extract features, and different machine learning (ML) techniques were implemented to obtain the best model for wildfire prediction. The majority of fires in the past six years have primarily occurred during the winter months in coastal locations. The occurrence of fires was accurately predicted by ML models such as random forest (RF), which outperformed competing models. Meanwhile, a CNN with 1D and 2D was used for more improvement in prediction by ML models. The accuracy increased from an 86.48 to 91.34 accuracy score by just using a CNN 1D. For more feature extraction, a CNN 2D was used on the same dataset, which led to state-of-the-art prediction results. A 96.91 accuracy score was achieved by further tuning the RF model on the total data. Data division by spatial and temporal changes was also used for the better prediction of fire, which can further be helpful for understanding the different prospects of wildfire. This research aims to advance wildfire prediction methodologies by leveraging ML techniques to explore the benefits and limitations of capturing complex patterns and relationships in large datasets. Policymakers, environmentalists, and scholars studying climate change can benefit greatly from the study's analytical approach, which may assist Pakistan in better managing and reducing wildfires. [ABSTRACT FROM AUTHOR]

Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change. [ABSTRACT FROM AUTHOR]

Landscape fires emit smoke that contains particulate matter (PM) that can be harmful to human health. Prescribed fires or hazard reduction burns (HRBs) and wildfires can substantially reduce air quality in populated areas. While HRBs reduce the size and PM output of future wildfires, they also produce PM. There is a critical question of whether conducting HRBs adds to or reduces the total PM (prescribed + wildfire PM) exposure of populations, which has important community health implications. We modelled mean 24 h PM2.5 in Sydney from fire type, 24 h active regional fire area and weather predictors. We compared HRB and wildfire area effects via an interaction between fire area and fire type, and we found that a non-linear effect of fire area was most appropriate. We conducted a trade-off analysis by using the model to predict increased HRB area scenarios and calculated the number of HRB and wildfire days with 'Fair' or worse air quality under each scenario. Regional HRBs and wildfire areas had similar effects on PM2.5 in Sydney. Increasing regional HRB area produced substantial increases in HRB exceedance days (>12.5 µgm−3) in Sydney but only a small reduction in wildfire exceedance days. Our results indicate that small fires in the region have a higher per-hectare impact on PM levels in Sydney, and consequently, increasing regional HRB area would result in more poor air quality days overall (HRB + wildfire days) in Sydney. [ABSTRACT FROM AUTHOR]

Although broadleaf tree species of the boreal biome have a lower flammability compared to conifers, there is a period following snow melt and prior to leaf flush (i.e., greenup), termed the "spring window" by fire managers, when these forests are relatively conducive to wildfire ignition and spread. The goal of this study was to characterize the duration, timing, and fire proneness of the spring window across boreal Canada and assess the link between these phenological variables and the incidence of springtime wildfires. We used remotely sensed snow cover and greenup data to identify the annual spring window for five boreal ecozones from 2001 to 2021 and then compared seasonality of wildfire starts (by cause) and fire‐conducive weather in relation to this window, averaged over the 21‐year period. We conducted a path analysis to concomitantly evaluate the influence of the spring window's duration, the timing of greenup, and fire‐conducive weather on the annual number and the seasonality of spring wildfires. Results show that the characteristics of spring windows vary substantially from year to year and among geographic zones, with the interior west of Canada having the longest and most fire‐conducive spread window and, accordingly, the greatest springtime wildfire activity. We also provide support for the belief that springtime weather generally promotes wind‐driven, rather than drought‐driven wildfires. The path analyses show idiosyncratic behavior among ecozones, but, in general, the seasonality of the wildfire season is mainly driven by the timing of the greenup, whereas the number of spring wildfires mostly responds to the duration of the spring window and the frequency of fire‐conducive weather. The results of this study allows us to better understand and anticipate the biome‐wide changes projected for the northern forests of North America. [ABSTRACT FROM AUTHOR]

Background: The Iberian Peninsula is recurrently affected by severe wildfires resulting from an interplay of human activities, landscape features and atmospheric conditions. Aims: The role played by atmospheric conditions on wildfire activity in 2001–2020 is assessed in four pyroregions of the Iberian Peninsula. Methods: Wildfire activity is characterised by Fire Radiative Power (FRP) and meteorological danger is rated by the Fire Weather Index (FWI). The distribution of log10FRP in each pyroregion consists of a truncated lognormal central body with Generalised Pareto distributions as tails, and the model is improved using FWI as covariate. Synthetic time series of total annual FRP are generated using the models with and without FWI as covariate, and compared against observed FRP. Key results: Pyroregions NW, N, SW and E present increases of 1, 5, 6 and 7% in interannual explained variance of FRP when progressing from the model without to that with FWI as covariate. Conclusions: The models developed characterise the role of meteorological conditions on fire activity in the Iberian Peninsula, and are especially valuable when comparing expected impacts for different scenarios of climate change. Implications: The largest effects of atmospheric conditions on fire activity are in regions of the IP where the strongest impact of climate change is expected. A statistical model is used to assess the role played by atmospheric conditions on wildfire activity in the Iberian Peninsula as measured by the Fire Radiative Power (FRP) released by vegetation fires. The largest effects are observed in the regions where climate change is expected to have a pronounced impact. [ABSTRACT FROM AUTHOR]

This paper aims to contribute to the field of green environment and meteorological signal processing by exploring methods for analyzing and predicting environmental factors. The focus is on developing an intelligent approach for preventing and predicting wildfires that may arise due to changes in atmospheric temperature or other conditions. The proposed solution involves using machine learning and evolutionary deep learning to create a neural model that can interact with the Internet of Things (IoT) and respond in real-time to minimize potential damage. The experiments were carried out in the forests of Jandouba, Tunisia, using Python software. The results demonstrate that this approach offers significant advantages over the most ranked existing Canadian method (FWI) for fire weather index. [ABSTRACT FROM AUTHOR]

Background. Crown fires are an ecologically necessary but hazardous process in conifer forests. Prediction of their behaviour in Canada has largely depended on the Canadian Forest Fire Behaviour Prediction System, in which fire weather indices drive primarily fixed fuel type models. The Crown Fire Initiation and Spread (CFIS) system presents a more flexible approach to predicting crown fire occurrence than fixed fuel type models. Aims. Using a multi-decadal database of experimental fires carried out in conifer plots (1960–2019, n = 113), our aim was to develop updated models based on the CFIS system approach, fitting crown fire occurrence models to fire environment variables using logistic regression. Methods. We tested alternative fuel moisture estimates and compared various model forms using repeated cross-validation. In two-storeyed stands, crown fire occurrence was defined as the involvement of lower canopy stratum fuels. Key results. Final models based on wind speed, fuel strata gap, litter moisture and surface fuel consumption predicted crowning events correctly in up to 92% of cases in training data (89% in cross-validation). Conclusions and implications. These new models offer improved accuracy and flexibility that will help users assess how competing environmental factors interact under different fuel treatments and wildfire scenarios. [ABSTRACT FROM AUTHOR]

Since one of the important steps in the management of natural areas is to know when and where the wildfire is more likely to occur or where it has a more negative effect, examining and preparing a fire risk map of sensitive and high-risk areas is crusial. In terms of wildfires, this is one of the main necessities in wildfires control and management. Wildfire risk indicators can be very useful for predicting areas with wildfire risk potential. One of the most widely used wildfire risk indices is the Forest service fire Weather Index (FWI), which has been based on the Canadian forest service fire risk rating system since 1971. In order to evaluate the effectiveness of FWI indicators in identifying wildfire-prone areas in the vegetation areas of Iran from the climate variables of temperature, relative humidity, wind speed, and precipitation of 53 meteorological stations were monitored on a daily basis for the period of 1981-2020. In order to analyze the climate of the vegetation areas under investigation, combined satellite product of burned areas and FWI indices during the period (1981-2020) were used. After downloading and an initial processing, the examined indicators were coilculated for the border of Iran. Then, their time series was analyzed, using zonal statistics. Than the area-averaged of each of Iran's forest regions was calculated, and their seasonal maps were prepared by the kriging interpolation method. Finally, the Pearson correlation coefficient method was used to determine the relationship and correlation between FWI indices and burned areas. The results of the analysis of the combined satellite product of the burned areas showed that the maximum extent of wildfire in the forest regions of Iran is related to the summer season. In this season, a large part of the country's forests, especially in the forest regions of Arsbaran, that has the occurrence of wildfires, and the minimum amount of wildfire was related to the spring season, which was observed in a limited area in the southwest of Iran. The values of atmospheric fire indicators vary according to the different characteristics of forest regions such as vegetation, topography, and climate. The drier parts of the south, east, and southeast of Iran mainly have the highest values of FWI indices, which shows that the increase in temperature and drought affects the moisture content and, as a result, increases the occurrence and spread of fire. The FWI indices in spring for all forest regions of Iran have a high efficiency for identifying areas with wildfire occurrences. Also, in the hot season of the year, when most wildfires occur in the vegetation areas of Iran, FFMC and FWI indices had the best performances. According to the climate variability and topography of Iran's forests, it is difficult to control the occurrence of fire. Hence identifying wildfire critical areas, and determining and preparing a map of fire-risk areas is an effective step in helping forest managers to plan and manage high-risk areas in terms of fire. In addition to this, wildfire atmospheric indices obtained with high horizontal resolution can help better understand the spatiotemporal variability of fire risk, especially in Iran with its complex and diverse topography. [ABSTRACT FROM AUTHOR]

Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting fire occurrence and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into Earth-System models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (~9 km, daily), is the first of its kind and will be regularly updated. [ABSTRACT FROM AUTHOR]

Accurate predictions of daily wildfire growth rates are crucial, as extreme wildfires have become increasingly frequent in recent years. The factors which determine wildfire growth rates are complex and depend on numerous meteorological factors, topography, and fuel loads. In this paper, we have built upon previous studies that have mapped daily burned areas at the individual fire level around the globe. We applied several Machine Learning (ML) algorithms including XGBoost, Random Forest, and Multilayer Perceptron to predict daily fire growth rate based on meteorological factors, topography, and fuel loads. Our best model on the entire dataset obtained a 1.15 km2 MAE. The ML model obtained a 90% accuracy when predicting whether a fire's growth rate will increase or decrease the following day, compared to 61% using a logistic regression. We discuss the central factors that determine wildfire growth rate. To the best of our knowledge, this study is the first to perform such analyses on a global dataset. [ABSTRACT FROM AUTHOR]

Seismic lines are cleared corridors for the location mapping of subsurface bitumen. After use, the lines can be left to regenerate naturally with varying success. Wildfires, another prominent disturbance in the Boreal region, are propagated by continuous fuel distribution (coarse/fine), meteorological variables (e.g., wind speed, temperature, and precipitation), and the moisture content of the fuel and soil. However, little is known about seismic lines and the potential risk and severity of wildfires. This work presents a case study of wildfire variables on two paired (seismic line and adjacent natural area) sites near Fort McMurray, Alberta, Canada. Wind speed was increased on seismic lines, and the dominant wind direction changed. Higher precipitation, air temperature, and soil moisture and reduced water table depths were observed on seismic lines. Coarse fuel distribution was not continuous on seismic lines; however, fine fuels were. Although the Fire Weather Index (FWI) indicated an enhanced wildfire potential on one line (NS orientation), peat smouldering and ignition models (Hcomb/Hign) showed increased smouldering potential on both seismic lines compared to adjacent natural areas. Future work should focus on expanding the diversity of seismic line characterization, working towards the landscape-scale modelling of these variables. [ABSTRACT FROM AUTHOR]

When tropical cyclones in the western North Pacific transition into midlatitude cyclones, it often perturbs the jet stream, resulting in amplified flow conditions in the north Pacific and various weather extremes in North America. Thus far, however, the climatological impacts of extratropical transitioning cyclones (ETCs) on North American fire weather are mostly undocumented. In this study, we group ETCs by the characteristics that are important for their interaction with the jet stream and document the response in North American fire weather. We find that ETCs are consistently associated with broad swaths of suppressed fire weather along with smaller areas of enhanced fire weather in North America, mostly through anomalous upper-level circulation and near-surface temperatures. While the chaotic nature of the ETC and jet stream interaction means that ETCs grouped by similar characteristics and locations can result in varying downstream responses, the composite analysis reveals some areas of consistently altered fire weather for ETCs which recurve at certain longitudinal ranges, including the Pacific Northwest and northern Intermountain West. At a time in which the risk and extent of wildfires in the Western United States is an issue of growing concern, this study represents the first holistic understanding of how ETCs' downstream perturbations impact fire weather. [ABSTRACT FROM AUTHOR]

Weather and climate play an important role in shaping global wildfire regimes and geographical distributions of burnable area. As projected by the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6), in the near future, fire danger is likely to increase in many regions due to warmer temperatures and drier conditions. General circulation models (GCMs) are an important resource in understanding how fire danger will evolve in a changing climate, but, to date, the development of fire risk scenarios has not fully accounted for systematic GCM errors and biases. This study presents a comprehensive global evaluation of the spatiotemporal representation of fire weather indicators from the Canadian Forest Fire Weather Index System simulated by 16 GCMs from the sixth Coupled Model Intercomparison Project (CMIP6). While at the global scale, the ensemble mean is able to represent variability, magnitude and spatial extent of different fire weather indicators reasonably well when compared to the latest global fire reanalysis, there is considerable regional and seasonal dependence in the performance of each GCM. To support the GCM selection and application for impact studies, the evaluation results are combined to generate global and regional rankings of individual GCM performance. The findings highlight the value of GCM evaluation and selection in developing more reliable projections of future climate-driven fire danger, thereby enabling decision makers and forest managers to take targeted action and respond to future fire events. [ABSTRACT FROM AUTHOR]

Wildfire is a critical ecological disturbance in terrestrial ecosystems. Australia, in particular, has experienced increasingly large and severe wildfires over the past two decades while globally fire risk is expected to increase significantly due to the projected increase in fire weather severity and drought condition. Therefore, understanding and predicting fire severity is critical for evaluating current and future impacts of wildfires on ecosystems. Here, we firstly introduce a vegetation16 type specific fire severity classification applied on satellite imagery, which is further used to predict fire severity using antecedent drought conditions, fire weather, and topography of the fire season. Based on a 'leave-one-out' cross-validation experiment, we demonstrate high accuracy for both the fire severity classification and the regression using a suite of performance metrics: determination coefficient (R²), mean absolute error (MPE) and root mean square error (RMSE), which are 0.89, 0.05, and 0.07, respectively. Our results also show that the fire severity prediction results using the vegetation-type specific thresholds could better capture the spatial patterns of fire severity, and has the potential to be applicable for seasonal fire severity forecasting due to the availability of seasonal forecasts of the predictor variables. [ABSTRACT FROM AUTHOR]

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression. Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports. Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset. Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R 2 = 0.95) and VIIRS (R 2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available. Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring. We developed a fire mapping method that uses active fire data from the MODIS and VIIRS instruments on board satellites to develop a high-resolution wildfire growth database. The satellite-derived fire events show excellent agreement with agency records (R 2 ≥ 0.95). This method can enable many fire-weather modelling and real-time fire applications. [ABSTRACT FROM AUTHOR]

Background: Satellite fire radiative power (FRP) products are a potential source of wildland fire growth measurements that could be used with gridded or observed weather data to study meteorological influences on wildfire behaviour. There is little research to date examining how FRP relates to weather. Aims: The goal of this study is to explore the relationship between satellite wildland fire FRP measurements and wind speed. Methods: We examine GOES-16 FRP data from the summer of 2018 in California, comparing it with winds from the CANSAC reanalysis to understand the relationship between wind and FRP. Examination focuses on statistical distribution of the data and results of two different approaches to relating FRP and wind speed. Key results: FRP is log-normally distributed, and there are significant consequences to using conventional statistical techniques that assume normality. We propose an alternative framing of the wind speed–FRP relationship to preserve more information about the FRP distribution. Conclusions: Use of FRP data for meaningful understanding of fire–weather interactions requires attention to the nature of the FRP empirical distribution. Implications: Applying our alternative approach to relating wildland fire FRP to weather has the potential to retain much more information than conventional approaches, but requires careful examination of the FRP data in each study. Fire radiative power (FRP) is non-Gaussian, yet is often treated as such. We look at the actual distribution and how it relates to wind speed using a novel approach that preserves the distribution of FRP, rather than reducing it to a single (mean) value for a given wind speed. [ABSTRACT FROM AUTHOR]

Downslope wind‐driven fires have resulted in many of the wildfire disasters in the western United States and represent a unique hazard to infrastructure and human life. We analyze the co‐occurrence of wildfires and downslope winds across the western United States (US) during 1992–2020. Downslope wind‐driven fires accounted for 13.4% of the wildfires and 11.9% of the burned area in the western US yet accounted for the majority of local burned area in portions of southern California, central Washington, and the front range of the Rockies. These fires were predominantly ignited by humans, occurred closer to population centers, and resulted in outsized impacts on human lives and infrastructure. Since 1999, downslope wind‐driven fires have accounted for 60.1% of structures and 52.4% of human lives lost in wildfires in the western US. Downslope wind‐driven fires occurred under anomalously dry fuels and exhibited a seasonality distinct from other fires—occurring primarily in the spring and fall. Over 1992–2020, we document a 25% increase in the annual number of downslope wind‐driven fires and a 140% increase in their respective annual burned area, which partially reflects trends toward drier fuels. These results advance our understanding of the importance of downslope winds in driving disastrous wildfires that threaten populated regions adjacent to mountain ranges in the western US. The unique characteristics of downslope wind‐driven fires require increased fire prevention and adaptation strategies to minimize losses and incorporation of changing human‐ignitions, fuel availability and dryness, and downslope wind occurrence to elucidate future fire risk. Plain Language Summary: Downslope mountain winds bring locally strong winds along with dry and often warm air to downwind slopes and are a critical fire weather pattern when such winds co‐occur with receptive fuels. Fires associated with downslope winds were primarily ignited by humans, exhibited distinct peaks in the shoulder seasons across the western United States, and had geographic hotspots in the front range of the Rockies, southwestern California, and eastern slope of the Washington Cascades. While wildfires typically burn upslope and away from populated regions, downslope wind events allow fires to rapidly spread downhill and often toward human settlements. Fires coincident with downslope winds accounted for most of the cumulative losses in both human lives and structures during 1999–2020. While winds are a key driver of these fires, large downslope wind‐driven fires preferentially occurred with anomalously dry fuels, suggesting that conditions leading up to ignitions coincident with downslope winds play an important role in enabling large fires. Lastly, we found an increase in downslope wind‐driven fires and their burned area over the past three decades. Such changes in downslope fire activity partially reflect a drying‐driven extension of the fire weather season into the spring and fall when downslope winds are more common. Key Points: Downslope wind‐driven fires accounted for 13.4% of fire occurrence and 11.9% of total burned area in the western United States during 1992–2020Most structure losses and fatalities in fires 1999–2020 were during downslope windsDownslope wind‐driven fires primarily occurred in the spring and fall, coincident with anomalously dry fuels [ABSTRACT FROM AUTHOR]

Peatland ecosystems are of global conservation and environmental importance storing globally significant amounts of ancient carbon, regulating regional temperatures and hydrological regimes, and supporting unique biodiversity. Livestock grazing, land‐use change, drainage, nutrient and acid deposition, and wildfire threaten the composition and function of many peatlands including those in the uplands of the United Kingdom. Presently, little is known about either the short‐ or long‐term effects of wildfires within these systems in the UK. Our study aimed to evaluate how plant communities respond to wildfires across a range of vegetation communities, soil types, and burn severities. We evaluated wildfire burn severity using the ground‐based Composite Burn Index adapted for treeless peatlands. Using paired burned–unburned plots, we quantified differences in the abundance of plant families and functional groups, vegetation diversity, and community composition. Multivariate differences in composition between burned and unburned areas were used as an index of community resilience to fire. Plots in heathland communities with shallow organic soils burned at the highest severities and had the greatest reductions in plant diversity and richness. There were significant declines in plot‐scale species richness and diversity with increasing burn severity. Graminoids were resilient to fire whilst Ericaceae tended to increase with higher severity. Bryophyte composition was substantially altered—pleurocarpous species declined and acrocarpous species increased with greater burn severity. Community resilience was related to ground layer burn severity with higher burn severity driving greater changes in communities. Wildfire effects on temperate peatlands are a function of fire weather and site environmental and ecological characteristics. Management policy should ensure that the risk of severe wildfires is mitigated to protect ecosystem function and biodiversity. This will require system‐specific fire management prescriptions across the gradient of peatland soil and vegetation types. [ABSTRACT FROM AUTHOR]

Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial and temporal distribution patterns of forest fires in Heilongjiang Province, as well as the ability of satellite remote sensing to detect these fires using VIIRS 375 m fire point data, ground history forest fire point data, and land cover dataset. The study also investigated the occurrence patterns of lightning-caused forest fires and the factors affecting satellite identification of these fires through case studies. Results show that April has the highest annual number of forest fires, with 77.6% of forest fires being caused by lightning. However, less than 30% of forest fires can be effectively detected by satellites, and lightning-caused forest fires account for less than 15% of all fires. There is a significant negative correlation between the two. Lightning-caused forest fires are concentrated in the Daxing'an Mountains between May and July, and are difficult to monitor by satellites due to cloud cover and lack of satellite transit. Overall, the trend observed in the number of forest fire pixels that are monitored by satellite remote sensing systems is generally indicative of the trends in the actual number of forest fires. However, lightning-caused forest fires are the primary cause of forest fires in Heilongjiang Province, and satellite remote sensing is relatively weak in monitoring these fires due to weather conditions and the timing of satellite transit. [ABSTRACT FROM AUTHOR]

Wildfire smoke can be transported far from its origin, adversely impacting human health. The height of the atmospheric mixing layer, the near-surface layer of the troposphere in which turbulent convection leads to vertical mixing, is called the mixing height. Mixing height is a critical input in the smoke dispersion and air quality models used by agencies that monitor wildfires and air pollution. These models, coupled with forecaster expertise, are also used to determine if it is safe to execute a prescribed burn. In this paper, we derive mixing heights from two satellite datasets in order to assess mixing height forecasts produced by the NationalWeather Service (NWS) Fire Weather Program. Namely, we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Masks (VFM) and vertical water vapor profiles from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our comparison indicates that NWS forecasts tend to underestimate CALIOPmixing heights with a median relative error of --13% and a mean relative error of --3.34%. Although MODIS and NWS mixing heights showed some agreement below 3 km, the lower vertical resolution of the MODIS estimates hindered a full comparison. We examine the discrepancies among mixing heights over wildfire smoke plumes determined by these methods and discuss biases and limitations. This work provides insight into potential bias patterns present in current mixing height forecasts and provides directions for future improvements in both NWS mixing height forecasts and satellite-based measurements of mixing height. [ABSTRACT FROM AUTHOR]

The Fire Weather Index (FWI) is used to assess meteorological fire danger worldwide. It has been argued that it lacks an atmospheric instability term. A new enhanced FWI (FWIe) was recently developed incorporating atmospheric instability in the form of the Continuous Haines Index (CHI). Here, the first climatological and evolution analysis of these indexes was performed using ERA5 data for the 1980–2020 period. There was a prevalence of higher values over central Iberia; these were heavily modulated by the climate types, topography, and land cover. Southwest and east Iberia suffered the greatest decadal increases in all three indexes. Relating both indexes to occurrences detected by satellite, through fire radiative power (FRP), showed that FWIe provided an improved meteorological fire danger assessment in higher-risk conditions. This showed that greater-risk observations were more prone to be affected by atmospheric instability than lower-danger observations. Case studies for the 2017 central Portugal and 2003 and 2018 Monchique wildfires were additionally conducted to verify these conclusions. This work points to the usefulness of FWIe when/where atmospheric instability may play a critical role in the development of wildfires, which may contribute to a more focused deployment of suppression mechanisms by the authorities. [ABSTRACT FROM AUTHOR]

Fuel and wildfire management decisions related to fuel break construction, maintenance, and use in fire suppression suffer from limited information on fuel break success rates and drivers of effectiveness. We built a dataset of fuel break encounters with recent large wildfires in Southern California and their associated biophysical, suppression, weather, and fire behavior characteristics to develop statistical models of fuel break effectiveness with boosted regression. Our results suggest that the dominant influences on fuel break effectiveness are suppression, weather, and fire behavior. Variables related to fuel break placement, design, and maintenance were less important but aligned with manager expectations for higher success with wider and better maintained fuel breaks, and prior research findings that fuel break success increases with accessibility. Fuel breaks also held more often if burned by a wildfire during the previous decade, supporting the idea that fuel breaks may be most effective if combined with broader fuel reduction efforts. [ABSTRACT FROM AUTHOR]

Forest fires, though part of a natural forest renewal process, when frequent and on a large -scale, have detrimental impacts on biodiversity, agroforestry systems, soil erosion, air, and water quality, infrastructures, and the economy. Portugal endures extreme forest fires, with a record extent of burned areas in 2017. These complexes of extreme wildfire events (CEWEs) concentrated in a few days but with highly burned areas are, among other factors, linked to severe fire weather conditions. In this study, a comparison between several fire danger indices (named 'multi-indices diagnosis') is performed for the control period 2001–2021, 2007 and 2017 (May–October) for the Fire Weather Index (FWI), Burning Index (BI), Forest Fire Danger Index (FFDI), Continuous Haines Index (CHI), and the Keetch–Byram Drought Index (KBDI). Daily analysis for the so-called Pedrógão Grande wildfire (17 June) and the October major fires (15 October) included the Spread Component (SC), Ignition Component (IC), Initial Spread Index (ISI), Buildup Index (BUI), and the Energy Release Component (ERC). Results revealed statistically significant high above-average values for most of the indices for 2017 in comparison with 2001–2021, particularly for October. The spatial distribution of BI, IC, ERC, and SC had the best performance in capturing the locations of the two CEWEs that were driven by atmospheric instability along with a dry environment aloft. These results were confirmed by the hotspot analysis that showed statistically significant intense spatial clustering between these indices and the burned areas. The spatial patterns for SC and ISI showed high values associated with high velocities in the spread of these fires. The outcomes allowed us to conclude that since fire danger depends on several factors, a multi-indices diagnosis can be highly relevant. The implementation of a Multi-index Prediction Methodology should be able to further enhance the ability to track and forecast unique CEWEs since the shortcomings of some indices are compensated by the information retrieved by others, as shown in this study. Overall, a new forecast method can help ensure the development of appropriate spatial preparedness plans, proactive responses by civil protection regarding firefighter management, and suppression efforts to minimize the detrimental impacts of wildfires in Portugal. [ABSTRACT FROM AUTHOR]

CMIP6 models suggest that extreme fire weather associated with the April 2021 Cape Town wildfire has become 90% more likely in a warmer world. [ABSTRACT FROM AUTHOR]

Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25 ∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios. [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of vegetation fires is essential for understanding changes in the climate and ecosystem in mountainous regions. MODIS Collection 6 active fire products indicate that the area burned by vegetation fires declined globally from over 4.27 million km2 to less than 3.52 million km2 annually during 2001–2021. In contrast, global high mountains higher than 3000 m have experienced an overall increase in their burned area and suffered record‐breaking wildfires from August to December 2020. Although high mountains accounted for less than 0.03% of the global burned area during 2001–2021, this proportion had more than tripled by 2020. This unprecedented wildfire record in 2020 could be mainly caused by favorable fire weather conditions such as low relative humidity, low soil water and high temperature. [ABSTRACT FROM AUTHOR]

Wildland fires have become a major research subject among the national and international research community. Different simulation models have been developed to prevent this phenomenon. Nevertheless, fire propagation models are, until now, challenging due to the complexity of physics and chemistry, high computational requirements to solve physical models, and the difficulty defining the input parameters. Nevertheless, researchers have made immense progress in understanding wildland fire spread. This work reviews the state-of-the-art and lessons learned from the relevant literature to drive further advancement and provide the scientific community with a comprehensive summary of the main developments. The major findings or general research-based trends were related to the advancement of technology and computational resources, as well as advances in the physical interpretation of the acceleration of wildfires. Although wildfires result from the interaction between fundamental processes that govern the combustion at the solid- and gas-phase, the subsequent heat transfer and ignition of adjacent fuels are still not fully resolved at a large scale. However, there are some research gaps and emerging trends within this issue that should be given more attention in future investigations. Hence, in view of further improvements in wildfire modeling, increases in computational resources will allow upscaling of physical models, and technological advancements are being developed to provide near real-time predictive fire behavior modeling. Thus, the development of two-way coupled models with weather prediction and fire propagation models is the main direction of future work. [ABSTRACT FROM AUTHOR]

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps. [ABSTRACT FROM AUTHOR]

Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA's FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden's geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application. [ABSTRACT FROM AUTHOR]

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Coupled atmosphere‐fire modeling is recognized as a relevant approach for the representation of the interaction between a wildland fire and local meteorology at landscape scales. The atmospheric model component used in the coupled system is based on several approximations, which are adopted for computational efficiency or physical processes representation, including the widely used anelastic approximation. The validity domain of the anelastic approximation may be questioned in the context of high‐resolution wildland fire modeling due to the large fire‐induced heat releases near the surface. This study aims to study this question with the MesoNH anelastic model coupled with the Blaze fire model. A compressible version of the MesoNH‐Blaze coupled model has been developed for comparison with the anelastic system. The FireFlux I experimental fire is used for this comparative study conducted at a 10‐m and a 25‐m horizontal atmospheric resolution. Results show significant anelastic/compressible differences at a 10‐m resolution on the physical processes occurring near the fire with higher horizontal velocities and the presence of gravity waves downstream of the fire. This is in addition to the fire plume with realistic larger vertical velocities. Differences at a 25‐m resolution are much smaller in all evaluated processes. The compressible system only enriches the physics underlying fire‐atmosphere interactions at a very high resolution, which means that the anelastic approximation remains relevant for large‐scale coupled atmosphere‐fire simulations, considering the significant economy concerning numerical costs. Plain Language Summary: Wildfires burn large amounts of forests each year, destroying the living habitat of many species, endangering human settlements and provoking cross‐continent smoke events leading to air quality issues. Wildfires result from complex physical, chemical and biological processes. Understanding the fundamental processes driving wildfire behavior is a key point for the prediction of fire spread across the landscape and the induced atmospheric dynamics. Numerical models coupling the atmospheric dynamics (wind, temperature, air density and pressure, etc.) and fire front evolution are efficient tools in this scientific process. This paper evaluates how such model simulations are affected by the way in which the effects of high air density gradients induced by the very large amount of heat released into the atmosphere are modeled. When the spatial resolution of the atmospheric model is as high as 10 m, an exact – compressible – formulation leads to the formation of gravity waves downstream of the fire, and to stronger fire‐induced wind, fire propagation and larger vertical velocities than an approximate (and computationally faster) approach. At a resolution of 25 m, which is still very high, the approximate approach leads to results, which are as good as the exact one. This can then be used to simulate wildland fire behavior at landscape‐to‐meteorological scales. It also paves the way for future accurate wildfire forecast systems. Key Points: A compressible version of the MesoNH atmospheric model has been developed to evaluate equation system approximations in wildfire simulationHorizontally buoyancy‐driven effects induce turbulent movements, waves ahead of the fire, and a bending of the plumeThe anelastic approximation for the atmosphere model is suitable to represent wildland fire effects, except for resolutions of 10 m or finer [ABSTRACT FROM AUTHOR]