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To capture the dependence in the upper tail of a time series, we develop non‐negative regularly varying time series models that are constructed similarly to classical non‐extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly varying time series via a measure of pairwise extremal dependencies, the tail pairwise dependence function (TPDF). We state consistency requirements among the finite‐dimensional collections of the elements of a regularly varying time series and show that the TPDF's value does not depend on the dimension of the random vector being considered. So that our models take non‐negative values, we use transformed‐linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDFs. We fit models to hourly windspeed and daily fire weather index data, and we find that the fitted transformed‐linear models produce better estimates of upper tail quantities than a traditional ARMA model, classical linear regularly varying models, a max‐ARMA model, and a Markov model. [ABSTRACT FROM AUTHOR]

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day‐to‐day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near‐surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1‐day R2 = 0.39–0.48, 2‐day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models. Plain Language Summary: Increasing wildfire smoke is undoing decades of air quality progress, yet air quality forecasts often miss the most intense smoke events. This is because forecasted smoke is released at constant rates whereas the rate of smoke release from real wildfires varies in time. In this work we teach a machine learning algorithm to predict the daily change in fire heat output, a quantity that is used to calculate wildfire emissions. The machine learning algorithm uses information regarding weather, fuel moisture and amount, and firefighting efforts to make its predictions. We also tried to predict the daily change in fire heat output using only weather information but found the machine learning method to be more successful. Many federal agencies have their own ways of tracking fire weather and fuel moisture, and in this paper, we show that we can apply machine learning to the data from any of several agencies and get the same level of forecasting skill. Key Points: Random forest models trained on weather, fuel, and firefighting data surpass persistence and weather‐based methods to predict fire energyRandom forest models beat persistence across states, for most days in the 2020 fire season, and across levels of fire severityFire weather and latest fire energy predictors add most skill to the random forest; models using agency weather data perform similarly [ABSTRACT FROM AUTHOR]

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: Autumn and winter Santa Ana Winds (SAW) are responsible for the largest and most destructive wildfires in southern California. Aims: (1) To contrast fires ignited on SAW days vs non-SAW days, (2) evaluate the predictive ability of the Canadian Fire Weather Index (CFWI) for these two fire types, and (3) determine climate and weather factors responsible for the largest wildfires. Methods: CAL FIRE (California Department of Forestry and Fire Protection) FRAP (Fire and Resource Assessment Program) fire data were coupled with hourly climate data from four stations, and with regional indices of SAW wind speed, and with seasonal drought data from the Palmer Drought Severity Index. Key results: Fires on non-SAW days were more numerous and burned more area, and were substantial from May to October. CFWI indices were tied to fire occurrence and size for both non-SAW and SAW days, and in the days following ignition. Multiple regression models for months with the greatest area burned explained up to a quarter of variation in area burned. Conclusions: The drivers of fire size differ between non-SAW and SAW fires. The best predictor of fire size for non-SAW fires was drought during the prior 5 years, followed by a current year vapour pressure deficit. For SAW fires, wind speed followed by drought were most important. Fires ignited on Santa Ana Wind days are the largest and most destructive fires but other fires are more numerous and account for greater area burned. Fire indices on days after ignition are important predictors of fire size, but drought and wind speed are most closely tied to fire size. [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]

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content ( m f ) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of m f based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region. [ABSTRACT FROM AUTHOR]

Prescriptions for fuel management are universally applied across the forest types in British Columbia, Canada, to reduce the fire behaviour potential in the wildland–urban interface. Fuel thinning treatments have been advocated as a means of minimizing the likelihood of crown fire development in conifer forests. We hypothesized that these types of prescriptions are inappropriate for the coastal rainforests of the Whistler region of the province. Our study examined the impact of fuel thinning treatments in four stands located in the Whistler community forest. We measured several in-stand microclimatic variables beginning with snow melt in the spring up to the height of fire danger in late summer, at paired thinned and unthinned stand locations. We found that the thinning led to warmer, drier, and windier fire environments. The difference in mean soil moisture, ambient air temperature, and relative humidity between thinned and unthinned stands was significant in the spring with approximate p-values of 0.000217, 9.40 × 10−5, and 4.33 × 10−8, respectively, though there were no discernible differences in the late summer. The difference in mean solar radiation, average wind speed, and average cross wind between thinned and unthinned locations are significant in the spring and late summer (with approximate p-values for spring of 9.54 × 10−7, 0.02101, 1.92 × 10−9, and for late summer of 2.45 × 10−7, 4.08 × 10−6, and 2.45 × 10−5, respectively). [ABSTRACT FROM AUTHOR]

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring. [ABSTRACT FROM AUTHOR]

Climate change contributes to the increased 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 regionalised research efforts. 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 2023–2024 fire season, 3.9×106 km 2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire 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 Hawaii (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, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes 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 to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). 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. New datasets presented in this work are available from 10.5281/zenodo.11400539 (Jones et al., 2024) and 10.5281/zenodo.11420742 (Kelley et al., 2024a). [ABSTRACT FROM AUTHOR]

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper's results will be used to prevent future active fire occurrences in the CC. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we use remotely sensed burnt area products and a common fire weather index calculated from global meteorological reanalysis data to identify and describe geographic variation in fuel moisture as a flammability threshold. This geospatial analysis identified a wide range of flammability thresholds associated with fire activity across 772 ecoregions, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can ignite and sustain wildfires with higher estimated fuel moisture than previously identified; Mediterranean forests, in contrast, tend to sustain fires with consistently low estimated fuel moisture. Statistical modelling showed that flammability thresholds derived from burnt area are primarily driven by climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation–climate–fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatial and temporal fire activity. [ABSTRACT FROM AUTHOR]

Better understanding how fires respond to climate variability is an issue of current interest in light of ongoing climate change. However, evaluating the global‐scale temporal variability of fires in response to climate presents a challenge due to the intricate processes at play and the limitation of fire data. Here, we investigate the links between year‐to‐year variability of burned area (BA) and climate using BA data, the Fire Weather Index (FWI), and the Standardized Precipitation Evapotranspiration Index (SPEI) from 2001 to 2021 at ecoregion scales. Our results reveal complex spatial patterns in the dependence of BA variability on antecedent and concurrent weather conditions, highlighting where BA is mostly influenced by either FWI or SPEI and where the combined effect of both indicators must be considered. Our findings indicate that same‐season weather conditions have a more pronounced relationship with BA across various ecoregions, particularly in climatologically wetter areas. Additionally, we note that BA is also significantly associated with periods of antecedent wetness and coolness, with this association being especially evident in more arid ecoregions. About 60% of the interannual variations in BA can be explained by climatic variability in a large fraction (∼77%) of the world's burnable regions. Plain Language Summary: In the context of changing climatic conditions, it is increasingly important to better understand the relationship between climate and fires. This study proposes a method that evaluates climatic conditions, either antecedent to or coincident with the fire season or the composite effect of both. It shows that the interannual variability of the global burned area for the period 2001 to 2021 is explained by climatic factors by around 60%. Our results also indicate that climatic drivers concurrent to the fire season prevail moderately over the antecedents. This suggests that the expected increase in burned area due to warmer and/or drier conditions can be mitigated where these climate conditions limit fuel availability. Key Points: 2/3 of the interannual variations in area burned are explained by climate in a substantial portion of the world's burnable regionsSame‐fire‐season weather is moderately more important than antecedent‐fire‐season weather for burned area variabilityBurned area is associated with antecedent wet and cool periods especially in the more arid areas [ABSTRACT FROM AUTHOR]

Background: Climate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia. Aims: Here we argue that heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought will amplify potential fire behaviour well beyond projections based on calculations of afternoon forest fire danger derived from climate models. Methods: We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves, drawing on examples of dynamical processes driving fire behaviour during the Australian Black Summer bushfires of 2019–20. Results: Key dynamical processes identified include: nocturnal low-level jets, deep, unstable planetary boundary layers and fire–atmosphere coupling. Conclusions: The future scenario we contend is long windows of multi-day fire events where overnight suppression is less effective and fire perimeters will expand continuously and aggressively over multiple days and nights. Implications: Greater overnight fire activity and multi-day events present strategic and tactical challenges for fire management agencies including having to expand resourcing for overnight work, manage personnel fatigue and revise training to identify conditions conducive to unusually active fire behaviour overnight. Effective messaging will be critical to minimise accidental fire ignition during heatwaves and to alert the community to the changing fire environment Heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought are likely to amplify fire behaviour under climate change. We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves using examples from the 2019–20 Australian Black Summer bushfires and examine potential challenges posed for future fire management. [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]

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05). [ABSTRACT FROM AUTHOR]

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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]

This study examines the convergence between traditional and scientific knowledge regarding the use of fire and its potential to trigger wildfires, with possible impacts on ecosystems and human well-being. The research encompasses three distinct natural regions of Ecuador: the coast, the highlands, and the Amazon. Data on traditional fire use were collected through semi-structured interviews with 791 members from five local communities. These data were compared with climatic variables (rainfall (mm), relative humidity (%), wind speed (km/h), and wind direction) to understand the climatic conditions conducive to wildfires and their relationship with human perceptions. Furthermore, the severity of fires over the past 4 years (2019–2022) was assessed using remote sensing methods, employing the Normalized Burn Ratio (NBR) and the difference between pre-fire and post-fire conditions (NBR Pre-fire–NBR Post-fire). The results revealed a significant alignment between traditional knowledge, climatic data, and many fires, which were of low severity, suggesting potential benefits for ecosystems. These findings not only enable the identification of optimal techniques and timing for traditional burns but also contribute to human well-being by maintaining a harmonious balance between communities and their environment. Additionally, they provide valuable insights for the development of more inclusive and effective integrated fire management strategies in these natural areas of Ecuador. [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]

Given the increasing prevalence of wildland fires in the Western US, there is a critical need to develop tools to understand and accurately predict burn severity. We develop a novel machine learning model to predict post-fire burn severity using pre-fire remotely sensed data. Hydrological, ecological, and topographical variables collected from four regions of California — the site of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) — are used as predictors of the differenced normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vecchia's Gaussian approximation will accurately model burn severity. We use a cross-validation study to show that the spatial SL model can predict burn severity with reasonable classification accuracy, including high burn severity events. After fitting and verifying the performance of the SL model, we use interpretable machine learning tools to determine the main drivers of severe burn damage, including greenness, elevation, and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emergency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California. [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]

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short‐interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire‐fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5‐Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short‐interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long‐interval fires; however, all Canadian boreal ecozones experienced short‐interval reburning (1981–2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short‐interval fire spread days was more extreme than the conditions during long‐interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short‐interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981–2021). Our results suggest an ongoing degradation of fire‐fuel feedbacks, which is likely to continue with climatic warming and drying. [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]

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]

Background: In 1986, CSIRO conducted a large program of experimental fires in grassland at Annaburroo Station, Northern Territory, Australia, with the objective of quantifying the effect of fuel condition (load and height) on fire behaviour. Aims: This paper provides the data collected during this program, representing a unique set of observations and measurements of large, free-burning experimental fires conducted in a multi-factor experimental design. Methods: Data are collated by experimental burn plot, providing detailed measurements of weather (wind speed, air temperature, relative humidity), fuel state (load, height, moisture content, curing) and fire behaviour (rate of spread, flame depth, flame height, head fire width), as well as processed information (e.g. steady-state rate of spread). Data availability: The data are made available for free download on the CSIRO Data Access Portal () and include detailed metadata descriptions of the data and their structure, also provided in this article. Conclusions: We have made the data available for fire behaviour researchers around the world to use in their research under the Creative Commons Attributions licence. It is hoped they will analyse these data and extract new and innovative insights to help improve our understanding of wildland fires burning in grass fuels. The 1986 Annaburroo grassland fire experiments conducted in the Northern Territory, Australia, created a unique dataset of fire behaviour across a range of fuel and burning conditions. This dataset, comprising detailed observations of fuels, weather and fire behaviour in 121 plots, is now publicly available via the CSIRO Data Access Portal. [ABSTRACT FROM AUTHOR]

The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire danger at two- to three-week lead times for the period 2003 to 2017: the official Australian climate simulation dynamical model and a statistical model based on climate drivers. We employed linear logistic regression to develop the statistical model, assessing the influence of individual climate drivers using single linear regression. The performance of both models was evaluated through case studies of three significant extreme fire events in Australia: the Canberra (2003), Black Saturday (2009), and Pinery (2015) fires. The results revealed that ACCESS-S2 generally underestimated the spatial extent of all three extreme FBI events, but with accuracy scores ranging from 0.66 to 0.86 across the case studies. Conversely, the statistical model tended to overpredict the area affected by extreme FBI, with high false alarm ratios between 0.44 and 0.66. However, the statistical model demonstrated higher probability of detection scores, ranging from 0.57 to 0.87 compared with 0.03 to 0.57 for the dynamic model. These findings highlight the complementary strengths and limitations of both forecasting approaches. Integrating dynamical and statistical models with transparent communication of their uncertainties could potentially improve accuracy and reduce false alarms. This can be achieved through hybrid forecasting, combined with visual inspection and comparison between the statistical and dynamical forecasts. Hybrid forecasting also has the potential to increase forecast lead times to up to several months, ultimately aiding in decision-making and resource allocation for fire management. [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]

In the long run, ongoing climate change is expected to alter fuel production as well as the frequency and severity of fire weather, which may result in an unprecedented frequency of extreme fire events. In this paper we propose a simplified and spatially explicit method to assess the probability of experiencing large fires, based on topography (slope length) as well as extent and aggregation of the forested area (fuel connectivity). We considered 21 homogeneous pyroregions covering entire Switzerland as a study case and computed the length of the upslope paths within the forested areas, simulating ignition points on a systematic 100 × 100 m square grid. We then compared the obtained path lengths for each pyroregion with selected historical large forest fire statistics (e.g., mean area of the largest 5% of fires, maximum burnt area per fire) collected over the course of the last 30 years. This resulted in rather high R2 values, ranging from 0.558 to 0.651. The proposed approach was shown to allow for an easy identification and geo-localization of potential hotspots in terms of the likelihood for large fires to occur in mountainous regions, which is a prerequisite for a targeted planning of fire management measures aimed at preventing large fires and related post-fire gravitative natural hazards. [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: Historical records of fire weather phenomena provide valuable insights into spatial and temporal trends which can inform further research and are important tools for planning. Aims: We outline a 19-year climatology of a Research Prototype (AFDRSRP), of the new Australian Fire Danger Rating System, documenting its spatial and temporal characteristics. Methods: The analysis utilises the Australian Bureau of Meteorology's high-resolution reanalysis suite (BARRA), together with fuel data provided by Australian fire agencies. We examine the spatial and temporal distribution of the AFDRSRP. Distributions are categorised by fuel type, analysing relative variability across time and space. Key results: The results validate the broad behaviour of the new system and provide insight into the variation of fire danger throughout Australia, adding detail to the understanding of timing of peak fire danger both diurnally and annually. Conclusions: While the AFDRSRP differs from the operational system in its fire danger rating categories and tuning of algorithms, it nonetheless provides useful insights into the operational implementation. Implications: These results will be essential for planning during fire seasons. We present a 19-year climatology of the Australian Fire Danger Rating System: Research Prototype (AFDRSRP), recently implemented operationally throughout Australia. The climatology describes the typical spatial and temporal behaviour of the AFDRSRP. It is important for users to improve familiarity with the new system, and to ensure it performs as expected. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Background: Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims: In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods: The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results: A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions: The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications: The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years. We introduce the Australian Fire Danger Rating System (AFDRS), providing a history of fire danger rating in Australia and requirement for advancements. We describe development, design principles and the supporting framework of the AFDRS as an introduction to subsequent papers in this series. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

The Mediterranean region experiences the annual destruction of thousands of hectares due to climatic conditions. This study examines forest fires in Türkiye's Antalya region, a Mediterranean high-risk area, from 2000 to 2023, analyzing 26 fires that each damaged over 50 hectares. Fire danger maps created from fire weather indexes (FWI) indicated that 85.7% of the analyzed fire areas were categorized within the high to very extreme danger categories. The study evaluated fire danger maps from EFFIS FWI and ERA5 FWI, both derived from meteorological satellite data, for 14 forest fires between 2019 and 2023. With its better spatial resolution, it was found that EFFIS FWI had a higher correlation (0.98) with in situ FWIs. Since FWIs are calculated from temperature and fire moisture subcomponents, the correlations of satellite-based temperature (MODIS Land Surface Temperature—LST) and soil moisture (SMAP) data with FWIs were investigated. The in situ FWI demonstrated a positive correlation of 0.96 with MODIS LST, 0.92 with EFFIS FWI, and 0.93 with ERA5 FWI. The negative correlation between all FWIs and SMAP soil moisture highlighted a strong relationship, with the highest observed in in situ FWI (−0.93) and −0.90 and −0.87 for EFFIS FWI and ERA5 FWI, respectively. [ABSTRACT FROM AUTHOR]

Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk‐reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community. [ABSTRACT FROM AUTHOR]

It is crucial to investigate the characteristics of fire danger in the Beijing-Tianjin-Hebei (BTH) region to improve the accuracy of local fire danger monitoring, forecasting, and management. With the use of instrumental observation data from 173 national meteorological stations in the BTH region from 1991 to 2020, the fire weather index (FWI) is first calculated in this study, and its spatiotemporal characteristics are analyzed. The high- and low-fire danger periods based on the FWI occur in April and August, respectively, with significant decreasing and increasing trends throughout the BTH region over the past 30 years. Next, the contributions of different meteorological factors to the FWI are quantified via a detrending technique. Most regions are affected by precipitation during the high-fire danger period. Both the maximum surface air temperature (Tmax) and precipitation, however, notably contribute to the FWI trend changes during the low-fire danger period. Then, we assess the linkage with atmospheric circulation. Abundant water vapor from the Northwest Pacific and local upward motion jointly lead to increased precipitation and, as a consequence, a decreased FWI during the high-fire danger period. A lack of water vapor from the boreal zone and local downward movement could cause adiabatic subsidence and hence, amplify the temperature and FWI during the low-fire danger period. In contrast to shared socioeconomic pathway (SSP) 585, in which the FWI in the BTH region exhibits a north–south dipole during the low-fire danger period, SSP245 yields an east–west dipole during the low-fire danger period. This study reveals that there is a higher-than-expected probability of fire danger during the low-fire danger period. Therefore, it is essential to intensify research on the fire danger during the low-fire danger period to improve our ability to predict summer fire danger. [ABSTRACT FROM AUTHOR]

The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWI peat , was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWI peat should enable improved monitoring of fire risk in boreal peatlands. [ABSTRACT FROM AUTHOR]

Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate. [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]

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The European Centre for Medium range weather forecast (ECMWF) on behalf of the Copernicus Emergency Management Service (CEMS) has recently widened the fire danger data offering in the Climate Data Store (CDS) to include a set of fire danger forecasts with lead times up to 7 months. The dataset incorporates fire danger indices for three different models developed in Canada, United States and Australia. The indices are calculated using ECMWF Seasonal Forecasting System 5 (SEAS5) and verified against the relevant reanalysis of fire danger based on the ECMWF Re-Analysis (ERA5). The data set is made openly available for the period 1981 to 2023 and will be updated regularly providing a resource to assess the predictability of fire weather at the seasonal time scale. The data set complements the availability of seasonal forecast provided by the Copernicus Emergency Management Service in real time. A preliminary analysis shows that globally anomalous conditions for fire weather can be predicted with confidence 1 month ahead. In some regions the prediction can extend to 2 months ahead. In most situations beyond this horizon, forecasts do not show more skill than climatology. However an extended predictability window, up to 6-7 months ahead is possible when anomalous fire weather is the results of large scale phenomena such as the El Niño Southern Oscillation and the Indian Ocean Dipole, often conducive of extensive fire burning in regions such as Indonesia and Australia. [ABSTRACT FROM AUTHOR]

A string of fierce fires broke out in Chile in the austral summer 2023, just six years after the record-breaking 2017 fire season. Favored by extreme weather conditions, fire activity has dramatically risen in recent years in this Andean country. A total of 1.7 million ha. burned during the last decade, tripling figures of the prior decade. Six of the seven most destructive fire seasons on record occurred since 2014. Here, we analyze the progression during the last two decades of the weather conditions associated with increased fire risk in Central Chile (30°–39° S). Fire weather conditions (including high temperatures, low humidity, dryness, and strong winds) increase the potential for wildfires, once ignited, to rapidly spread. We show that the concurrence of El Niño and climate-fueled droughts and heatwaves boost the local fire risk and have decisively contributed to the intense fire activity recently seen in Central Chile. Our results also suggest that the tropical eastern Pacific Ocean variability modulates the seasonal fire weather in the country, driving in turn the interannual fire activity. The signature of the warm anomalies in the Niño 1 + 2 region (0°–10° S, 90° W–80° W) is apparent on the burned area records seen in Central Chile in 2017 and 2023. [ABSTRACT FROM AUTHOR]

Forestation can contribute to climate change mitigation. However, an increasing frequency and intensity of climate extremes is posed to have profound impact on forests, and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different Integrated Assessment Models (IAMs) by show-casing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation. [ABSTRACT FROM AUTHOR]

Background: Prescribed burning (PB) is becoming relevant in fuel reduction and thus fre hazard abatement in fre-prone ecosystems of southern Europe. Yet, empirical evidence on the efectiveness of this practice to mitigate wildfre severity in Mediterranean shrublands is non-existent, despite being the focus of PB eforts in this region. Here, we intended to quantify the protective efect of PB treatment units (2005-2021) to subsequent wildfre severity in shrublands across mainland Portugal, as well as the relative contribution and complex interactions between drivers of wildfre severity in PB-treated areas and untreated neighboring counterparts through Random Forest regression. We leveraged cloud-computing remote sensing data processing in Google Earth Engine to estimate fre severity (PB and wildfre) as the Relativized Burn Ratio (RBR) using Landsat data catalog. Results: PB treatment was particularly efective at mitigating wildfre severity at the frst PB-wildfre encounter in shrublands, with a mean reduction of around 24% in RBR units. Fuel age (i.e., time since prescribed burning) in PB-wildfre intersection areas overwhelmed to a large extent the efect of fre weather, burning probability, and PB severity. The mitigating efect of PB on wildfre severity persisted for a fuel age of around 5 years. However, this efect decreased with increasingly adverse fre weather conditions, such that variation in wildfre severity was somewhat insensitive to fuel age under extreme fre weather. Similarly, the lowest wildfre severity experienced in sites with high burning probability, along with the interaction efect observed between burning probability and fuel age, suggest that repeated PB treatments may be useful in controlling fuel accumulation and mitigating wildfre severity. The relative contribution of fre weather in explaining wildfre severity was exceedingly high in untreated areas, doubling that of the other variables in the model in the absence of PB treatment variables. Conclusions: Our results suggest that the implementation of PB treatments at intervals of less than 5 years is of paramount importance to control fuel build-up and fre hazard under extreme fre weather in productive Mediterranean shrublands. Further research on this topic is warranted in other shrublands worldwide, namely in Mediterranean-type climate regions. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [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 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]

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]

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]

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]