The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in ...The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in peatlands.However,the control mechanisms for changes in wildfire activity in peatlands during this period remain unclear.In this study,evidence from the Gzhelian in the Ordos Basin,such as the inertinite/vitrinite(Ⅰ/Ⅴ)ratio,indicated varying wildfire frequencies.Climate indicators(CaO/MgO and CaO/MgO·Al_(2)O_(3))revealed that high-frequency wildfires mainly occurred in warm and humid climates.Based on former age constraints,we deduced that orbital cycles(long eccentricity)controlled the climate influence on peatland wildfires during the Gzhelian.Higher eccentricity brought more sunshine and rainfall,creating warmer,wetter peatlands conducive to vegetation growth,which increased fuel loads and led to more wildfires.Global Gzhelian wildfire records show that wildfires occurred mainly in tropical regions with abundant vegetation,reinforcing the idea that fuel loads drove fire activity.While wildfires can release mercury(Hg),the frequent volcanic activity during this period likely contributed significantly to Hg enrichment.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
Fires have historically played a natural role in shaping ecosystems,contributing to biodiversity and ecological renewal.However,in the Anthropocene,the interplay of climate change and human activities has exacerbated ...Fires have historically played a natural role in shaping ecosystems,contributing to biodiversity and ecological renewal.However,in the Anthropocene,the interplay of climate change and human activities has exacerbated fire frequency and intensity,with cascading impacts on soil health,biodiversity,and ecosystem resilience.This study highlights the complex effects of fire on soil ecosystems,particularly in Mediterranean environments,by analysing the aftermath of the 2021 wildfire in Aspromonte National Park.The results of this research reveal the multifaceted impact of fire on soil composition and biological activity.Burned areas exhibited altered microbial communities,characterized by a higher biomass of bacteria and actinomycetes but reduced fungal presence,aligning with findings that fungi are more sensitive to heat than other microorganisms,particularly under moist conditions.Changes in enzyme activity,such as decreased oxidoreductase and hydrolase activities but elevated catalase activity,suggest significant metabolic adjustments among surviving microbial strains.Additionally,increased potassium,magnesium,sulphates,and total phenols in burned areas point to shifts in nutrient dynamics driven by the combustion of organic matter.Fire also impacted microarthropod communities but the rapid recovery of microarthropod communities that has been recognized by numerous authors suggests that fire may not universally impair soil biodiversity in Mediterranean environments.The transition zone played a critical intermediate role,retaining a higher organic matter content than the unburned zone,suggesting its potential as a buffer or recovery zone in post-fire dynamics.Microarthropod communities,while initially affected,demonstrated resilience in line with previous research,indicating that Mediterranean soils might possess adaptive mechanisms to recover from low-to moderate-severity wildfires.Importantly,the incorporation of ashes and partially burned organic material in such fires may lead to enhanced soil fertility,fostering bacterial and actinomycetes proliferation and facilitating ecosystem recovery.展开更多
Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,y...Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.展开更多
In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts ...In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts of 20thcentury wildfires on North American climate and hydrology. Summer represents the peak wildfire season in North America, with the Gulf of Mexico and Midwest regions experiencing the most severe effects. Wildfires not only damage vegetation during the fire season but also extend prolonged impacts into non-fire periods, showing distinct seasonal variations. In spring, wildfires increase surface albedo, triggering a cooling effect through enhanced snow cover and delayed snowmelt. Conversely, summer and autumn surface warming stems primarily from wildfire-suppressed vegetation transpiration. Warming near the Gulf of Mexico enhances moisture transport and precipitation, particularly in summer and autumn. Reduced evaporation and increased precipitation from the Gulf of Mexico significantly altered the hydrological cycle across North America, leading to increased runoff continent-wide.展开更多
Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(b...Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(burned area:36.3 ha)event occurred in Souesmes(Loiret-Cher,Sologne,France),and its plume spread out over 200 km on the following day as observed by the MODIS satellite.Based on measurements at a suburban site(~50 km northwest of the fire location)in Orléans and backward trajectory analysis,young wildfire plumes were characterized.Significant increases in gaseous pollutants(CO,CH_(4),N_(2)O,VOCs,etc.)and particles(including black carbon)were found within the wildfire plumes,leading to a reduced air quality.Emission factors,defined as EF(X)=ΔX/ΔCO(where,X represents the target species),of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies.Changes in the ambient ions(such as ammonium,sulfate,nitrate,chloride,and nitrite in the particle-and gasphase)and aerosol properties(e.g.,aerosol water content,aerosol p H)were also quantified and discussed.Moreover,we estimated the total carbon and climate-related species(e.g.,CO_(2),CH_(4),N_(2)O,and BC)emissions and compared them with fire emission inventories.Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions.For instance,we found that the Global Fire Assimilation System(GFAS)may underestimate emissions(e.g.,CO)of this small wildfire while other inventories(GFED and FINN)showed significant overestimation.Considering that it is the first time to record wildfire plumes in this region,related atmospheric implications are presented and discussed.展开更多
Wildfires can result from natural causes like lightning,volcanoes,and earthquakes.In Brazil,though,volcanoes and earthquakes are rare.The surge in fires over the past decade is linked to human activities,mainly defore...Wildfires can result from natural causes like lightning,volcanoes,and earthquakes.In Brazil,though,volcanoes and earthquakes are rare.The surge in fires over the past decade is linked to human activities,mainly deforestation driven by foreign interests searching for strategic minerals in low-productivity areas.This extensive mining affects the environment and climate,jeopardizes lives,impacts local birth rates and young women,and ultimately threatens entire populations seeking a dignified life.展开更多
Wildfires can result from natural causes like lightning, volcanoes, and earthquakes. In Brazil, though, volcanoes do not exist, and earthquakes are rare. The surge in fires over the past decade is linked to human acti...Wildfires can result from natural causes like lightning, volcanoes, and earthquakes. In Brazil, though, volcanoes do not exist, and earthquakes are rare. The surge in fires over the past decade is linked to human activities, mainly deforestation driven by foreign interests searching for strategic minerals in low-productivity areas. The aim of those mines is to develop new green technologies;however, those innovative approaches to energy involving lots of minerals are rarely found on Earth. Extensive mining brutally affects the climate and environment and jeopardizes lives, impacting not only the environment but also increasing the pollution and degradation of human lives in the investigated locations.展开更多
基金financially supported by the National Natural Science Foundation of China (Grant Nos.42472166, U24A20595, 42102127, 41972170)the Natural Science Foundation of Shandong Province (Grant No. ZR2021QD087)+2 种基金the Shandong Provincial Postdoctoral Science Foundation (SDCX-ZG-202203053)the Shandong University of Science and Technology (Grant No. 2018TDJH101)the Deep-Time Digital Earth program (DDE) for their support of this work
文摘The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in peatlands.However,the control mechanisms for changes in wildfire activity in peatlands during this period remain unclear.In this study,evidence from the Gzhelian in the Ordos Basin,such as the inertinite/vitrinite(Ⅰ/Ⅴ)ratio,indicated varying wildfire frequencies.Climate indicators(CaO/MgO and CaO/MgO·Al_(2)O_(3))revealed that high-frequency wildfires mainly occurred in warm and humid climates.Based on former age constraints,we deduced that orbital cycles(long eccentricity)controlled the climate influence on peatland wildfires during the Gzhelian.Higher eccentricity brought more sunshine and rainfall,creating warmer,wetter peatlands conducive to vegetation growth,which increased fuel loads and led to more wildfires.Global Gzhelian wildfire records show that wildfires occurred mainly in tropical regions with abundant vegetation,reinforcing the idea that fuel loads drove fire activity.While wildfires can release mercury(Hg),the frequent volcanic activity during this period likely contributed significantly to Hg enrichment.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
基金funding provided by Universitàdegli Studi Mediterranea di Reggio Calabria within the CRUI-CARE Agreementfunded by Calabrian Region,grant number DDL n°16315657 del 13-12-2022,POR CALABRIA FESR-FSE 2014-2020 ASSE I-PROMOZIONE DELLA RICERCA 658 E DELL’INNOVAZIONE.
文摘Fires have historically played a natural role in shaping ecosystems,contributing to biodiversity and ecological renewal.However,in the Anthropocene,the interplay of climate change and human activities has exacerbated fire frequency and intensity,with cascading impacts on soil health,biodiversity,and ecosystem resilience.This study highlights the complex effects of fire on soil ecosystems,particularly in Mediterranean environments,by analysing the aftermath of the 2021 wildfire in Aspromonte National Park.The results of this research reveal the multifaceted impact of fire on soil composition and biological activity.Burned areas exhibited altered microbial communities,characterized by a higher biomass of bacteria and actinomycetes but reduced fungal presence,aligning with findings that fungi are more sensitive to heat than other microorganisms,particularly under moist conditions.Changes in enzyme activity,such as decreased oxidoreductase and hydrolase activities but elevated catalase activity,suggest significant metabolic adjustments among surviving microbial strains.Additionally,increased potassium,magnesium,sulphates,and total phenols in burned areas point to shifts in nutrient dynamics driven by the combustion of organic matter.Fire also impacted microarthropod communities but the rapid recovery of microarthropod communities that has been recognized by numerous authors suggests that fire may not universally impair soil biodiversity in Mediterranean environments.The transition zone played a critical intermediate role,retaining a higher organic matter content than the unburned zone,suggesting its potential as a buffer or recovery zone in post-fire dynamics.Microarthropod communities,while initially affected,demonstrated resilience in line with previous research,indicating that Mediterranean soils might possess adaptive mechanisms to recover from low-to moderate-severity wildfires.Importantly,the incorporation of ashes and partially burned organic material in such fires may lead to enhanced soil fertility,fostering bacterial and actinomycetes proliferation and facilitating ecosystem recovery.
基金the National Natural Science Foundation of China(42377170,42407212)the National Funded Postdoctoral Researcher Program(GZB20230606)+3 种基金the Postdoctoral Research Foundation of China(2024M752679)the Sichuan Natural Science Foundation(2025ZNSFSC1205)the National Key R&D Program of China(2022YFC3005704)the Sichuan Province Science and Technology Support Program(2024NSFSC0100)。
文摘Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.
基金National Natural Science Foundation of China(42175022)。
文摘In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts of 20thcentury wildfires on North American climate and hydrology. Summer represents the peak wildfire season in North America, with the Gulf of Mexico and Midwest regions experiencing the most severe effects. Wildfires not only damage vegetation during the fire season but also extend prolonged impacts into non-fire periods, showing distinct seasonal variations. In spring, wildfires increase surface albedo, triggering a cooling effect through enhanced snow cover and delayed snowmelt. Conversely, summer and autumn surface warming stems primarily from wildfire-suppressed vegetation transpiration. Warming near the Gulf of Mexico enhances moisture transport and precipitation, particularly in summer and autumn. Reduced evaporation and increased precipitation from the Gulf of Mexico significantly altered the hydrological cycle across North America, leading to increased runoff continent-wide.
基金supported by the VOLTAIRE project (ANR-10-LABX-100-01)funded by the ANR and the PIVOTS project provided by the Region Centre−Val de Loire (ARD 2020 program and CPER 2015−2020).
文摘Wildfire events are increasing globally which may be partly associated with climate change,resulting in significant adverse impacts on local,regional air quality and global climate.In September 2020,a small wildfire(burned area:36.3 ha)event occurred in Souesmes(Loiret-Cher,Sologne,France),and its plume spread out over 200 km on the following day as observed by the MODIS satellite.Based on measurements at a suburban site(~50 km northwest of the fire location)in Orléans and backward trajectory analysis,young wildfire plumes were characterized.Significant increases in gaseous pollutants(CO,CH_(4),N_(2)O,VOCs,etc.)and particles(including black carbon)were found within the wildfire plumes,leading to a reduced air quality.Emission factors,defined as EF(X)=ΔX/ΔCO(where,X represents the target species),of various trace gases and black carbon within the young wildfire plumes were determined accordingly and compared with previous studies.Changes in the ambient ions(such as ammonium,sulfate,nitrate,chloride,and nitrite in the particle-and gasphase)and aerosol properties(e.g.,aerosol water content,aerosol p H)were also quantified and discussed.Moreover,we estimated the total carbon and climate-related species(e.g.,CO_(2),CH_(4),N_(2)O,and BC)emissions and compared them with fire emission inventories.Current biomass burning emission inventories have uncertainties in estimating small fire burned areas and emissions.For instance,we found that the Global Fire Assimilation System(GFAS)may underestimate emissions(e.g.,CO)of this small wildfire while other inventories(GFED and FINN)showed significant overestimation.Considering that it is the first time to record wildfire plumes in this region,related atmospheric implications are presented and discussed.
文摘Wildfires can result from natural causes like lightning,volcanoes,and earthquakes.In Brazil,though,volcanoes and earthquakes are rare.The surge in fires over the past decade is linked to human activities,mainly deforestation driven by foreign interests searching for strategic minerals in low-productivity areas.This extensive mining affects the environment and climate,jeopardizes lives,impacts local birth rates and young women,and ultimately threatens entire populations seeking a dignified life.
文摘Wildfires can result from natural causes like lightning, volcanoes, and earthquakes. In Brazil, though, volcanoes do not exist, and earthquakes are rare. The surge in fires over the past decade is linked to human activities, mainly deforestation driven by foreign interests searching for strategic minerals in low-productivity areas. The aim of those mines is to develop new green technologies;however, those innovative approaches to energy involving lots of minerals are rarely found on Earth. Extensive mining brutally affects the climate and environment and jeopardizes lives, impacting not only the environment but also increasing the pollution and degradation of human lives in the investigated locations.
