Forest Fire Events Research Articles

Infiltration and Hydrophobicity in Burnt Forest Soils on Mediterranean Mountains

Forest fires are a major global environmental problem, especially for forest ecosystems and specifically in Mediterranean climate zones. These fires can seriously impact hydrologic processes and soil erosion, which can cause water pollution and flooding. The aim of this work is to assess the effect of forest fire on the hydrologic processes in the soil, depending on soil properties. For this purpose, the infiltration rate has been measured by ring infiltration tester, and the hydrophobicity has been quantified by the “water drop penetration time” method in several soils of burnt and unburnt forest areas in the Mediterranean mountains. The infiltration rates obtained are higher in burnt than in unburnt soils (1130 and 891 mm·h−1, respectively), which contradicts most of the research in Mediterranean climates in southeast Spain with calcareous soils. Burnt soils show no hydrophobicity on the surface, but it is there when the soil is excavated by 1 cm. Additionally, burnt soils reveal a low frequency of hydrophobicity (in less than 30% of the samples) but more severe hydrophobicity (above 300 s); whereas, in unburnt soils, the frequency is higher (50%) but the values of hydrophobicity are lower. The results obtained clearly show the infiltration processes modified by fire, and these results may be useful for land managers, hydrologists, and those responsible for decision-making regarding the forest restoration of burnt land.

Concentration of particulate matter and atmospheric pollutants in the residential area of Kathmandu Valley: A case study of March–April 2021 forest fire events

Forest fires have become more intense and frequent in recently changing climates. The wide variety of pollutants released by forest fire include greenhouse gases, photochemically reactive compounds, and fine and coarse particulate matter. This study investigated the impact of forest fire events on air quality in the Kathmandu Valley during March–April 2021 using ground air quality monitoring stations and satellite data. The three fire periods were studied (a) Pre-fire from 21st - 23rd March (b) first-fire episode from 24th −27th March and (c) second fire episode from 1st – 5th April of 2021. The concentrations of PM2.5 reached to maximum 199 μg/m3 during pre-fire period, 371 μg/m3 and 280 μg/m3 during first and second fire event respectively. The second fire episode had lower PM2.5 concentration despite higher fire counts (449) compared to the first episode suggesting influence of fire activities near to vicinity of Kathmandu valley during second fire episode. There was a two-day lag between the beginning of forest fire events and an increase in PM2.5 levels in Kathmandu. Satellite observation showed varying patterns for different pollutants. HCHO levels responded quickly to fire activity, while AOD and CO levels increased after a few days. Also, low wind speed, low temperature, and low relative humidity additionally elevated these pollutants in Kathmandu. This study emphasizes the extent of the impact of forest fires on air quality and the importance of considering meteorological and satellite data to understand the distribution of pollutants during such events.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Comparison of different geometry trees in fire simulation

AbstractWildfire simulations can help to better understand the dynamics and effects of forest fires. The basis of wildfire simulation is the tree-burning simulation. In this paper, the fire simulation of 7 different geometry Hungarian trees in the case of arson is presented. It was observed that the trees were burned down fast. The maximum mass loss rate and maximum heat release rate were larger as the tree was larger. The largest intensity fire could be observed in the case of the smallest tree. The maximum temperature was higher in the case of a large crown diameter. The maximum aerosol reached high pollutant concentrations. In the case of large crown height, the maximum CO2 concentration was higher. The results presented in this paper can be the basis of the following forest fire simulations.

Vegetation, Soil, and Livelihoods: The Complex Effects of Forest Fires on Eastern India's Dry Deciduous Forests

Vegetation, Soil, and Livelihoods: The Complex Effects of Forest Fires on Eastern India's Dry Deciduous Forests

Unlocking Nature’s Potential: Modelling Acacia melanoxylon as a Renewable Resource for Bio-Oil Production through Thermochemical Liquefaction

This study is focused on the modelling of the production of bio-oil by thermochemical liquefaction. Species Acacia melanoxylon was used as the source of biomass, the standard chemical 2-Ethylhexanol (2-EHEX) was used as solvent, p-Toluenesulfonic acid (pTSA) was used as the catalyst, and acetone was used for the washing process. This procedure consisted of a moderate acid-catalysed liquefaction process and was applied at 3 different temperatures to determine the proper model: 100, 135, and 170 °C, and at 30-, 115-, and 200-min periods with 0.5%, 5.25%, and 10% (m/m) catalyst concentrations of overall mass. Optimized results showed a bio-oil yield of 83.29% and an HHV of 34.31 MJ/kg. A central composite face-centred (CCF) design was applied to the liquefaction reaction optimization. Reaction time, reaction temperature, as well as catalyst concentration, were chosen as independent variables. The resulting model exhibited very good results, with a highly adjusted R-squared (1.000). The liquefied products and biochar samples were characterized by Fourier-transformed infrared (FTIR) and thermogravimetric analysis (TGA); scanning electron microscopy (SEM) was also performed. The results show that invasive species such as acacia may have very good potential to generate biofuels and utilize lignocellulosic biomass in different ways. Additionally, using acacia as feedstock for bio-oil liquefaction will allow the valorisation of woody biomass and prevent forest fires as well. Besides, this process may provide a chance to control the invasive species in the forests, reduce the effect of forest fires, and produce bio-oil as a renewable energy.

Effect of Forest Fire on Community Structure in Sal (<i>Shorea robusta</i> Gaertn.) Dominated Forests of Dehradun

Effect of Forest Fire on Community Structure in Sal (<i>Shorea robusta</i> Gaertn.) Dominated Forests of Dehradun

Temporal variations in burn severity among various vegetation layers in subtropical Pinus Roxburghii (Chir Pine) forest of Hindu Kush mountain range

The Sub-tropical forests of Pinus roxburghii (chir pine) provide various ecosystem services and act as watershed for low lying regions. However, this species is prone to human induced fire primarily due to local communities' dependence for various resources exacerbated by the current dry conditions. The impact of fire across various vegetation layers and developmental stages has not been thoroughly studied. Bearing to this, the present study was conducted using composite burn index to assess the severity on various layers of vegetation and their long-term impact through a chronological approach. The impact of fire on 40 representative circular plots with a radius of 30 m, categorized into five forest strata: large and intermediate trees, seedlings/saplings, pole stage, shrubs, and soil were investigated and compared across four different time interval: unburnt (B0), burnt two years ago (B2), burnt five years ago (B5), and burnt 15 years ago (B15). The results were statistically proved using Kruskal–Wallis followed by Dunn's Post Hoc and Friedman test with the Holm correction in R Language. The study revealed significant variations in the average burn severity for each treatment, with shrubs having the highest average score of burn severity (average = 1.4) and soil showing the lowest (average = 0.408). The results of the Friedman test indicated non-uniform distribution of burn severity across different ecological treatments. This study is contributing significant insights into the effects of forest fires and their severity on different vegetation layers, which can be instrumental in devising and executing successful restoration strategies.

Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden

Fires can strongly change the vegetation structure and the availability of resources for wildlife, but fire suppression has long affected the natural role of fire in shaping boreal ecosystems in northern Europe. Recently, wildfires have increased in frequency, possibly due to global warming. In contrast to the boreal systems in North America, there have been few studies on responses of wildlife to wildfires in northern Europe. Based on the findings from North America, we predict that responses of wildlife to wildfire vary among wildlife species: where mammalian herbivores, such as moose Alces alces and mountain hare Lepus timidus, will be attracted to burnt areas following an increase in food availability, other species, such as reindeer Rangifer tarandus, are negatively impacted due to fire reducing their preferred food. We then tested our predictions by contrasting wildlife utilization of sites that burnt by wildfire in 2006 with nearby unburnt control sites in three areas in northern Sweden. To measure wildlife utilization, we used 72 camera traps, equally divided between the burnt and control sites, with two placement strategies: random and on wildlife trails. The cameras recorded 27 mammal and bird species during summer 2018. Species assemblage differed between burnt and control sites. Fieldfare Turdus pilaris used burnt sites more than control sites, while pine marten Martes martes and western capercaillie Tetrao urogallus used control sites more than burnt sites. We however did not find support for a positive effect of past forest fires on any of the observed wild mammals. We discuss how, due to the impact of forestry, forage‐rich habitat may not be as limiting in Scandinavia as in the North‐American context, potentially leading to recently burnt sites being less attractive to herbivores such as moose.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Evaluating crown scorch predictions from a computational fluid dynamics wildland fire simulator

BackgroundCrown scorch—the heating of live leaves, needles, and buds in the vegetative canopy to lethal temperatures without widespread combustion—is one of the most common fire effects shaping post-fire canopies. Despite the ability of computational fluid dynamic models to finely resolve fire activity and buoyant plume dynamics including heterogenous 3D distributions of forest canopy heating, these models have had only limited use in simulating fire effects and have not been used to evaluate crown scorch. Here, we demonstrate a method of evaluating crown scorch using a computational fluid dynamics model, FIRETEC, and validate this approach by simulating the experiments that were used to develop Van Wagner’s 1973 crown scorch model.ResultsThe average scorch height prediction from FIRETEC compares well with the empirical model derived by Van Wagner, which is the most widely used empirical model for crown scorch. We further find that the 3D buoyant plume dynamics from a steady and homogeneous idealized heat source on the ground results in a spatially heterogenous crown scorch pattern reflecting complex heating dynamics that are best represented by percent scorch rather than height of scorch.ConclusionsThe ability of the computational fluid dynamics model to capture variation in crown scorch due to 3D buoyant plume dynamics provides direct links between forest structure, fire behavior, and fire effects that can be used by forest managers and researchers to better understand how fires result in crown damage under various environmental and management scenarios.

Fire behavior simulation of Xintian forest fire in 2022 using WRF-fire model

IntroductionThe behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province.MethodsBased on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data.ResultsThe study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well.DiscussionThe results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management.

Differences in the intensity of past forest fire events inferred from stable oxygen isotope analysis of charred bark

Understanding past fire regimes requires reliable proxy data that record fire conditions and preserve them over time. The objective of this study was to determine if the oxygen isotope composition of charred bark samples (pyrogenic organic matter) could be used as proxy data to differentiate wildfires based on burn intensity. We collected charred and uncharred bark samples from three fire sites in northern Ontario, Canada that represented a gradient of fire intensity as depicted by Fire Weather Index (FWI) data. We hypothesized that the mean Δ18Obark-char (the difference between δ18O of uncharred bark and a charred sample) would be greater for fires with higher intensities. Analysis of variance of Δ18Obark-char indicated a significant effect of fire event ( F = 73.6, p < 0.001), which explained 57.0% of the variance. A prescribed surface fire treatment (mean FWI = 9.5) had significantly lower Δ18Obark-char than two natural crown fires (FWI = 21 and 27). These results demonstrate that Δ18Obark-char differentiated moderate from high intensity fires in a similar manner to the FWI data.

Modelling postfire recovery of snow albedo and forest structure to understand drivers of decades of reduced snow water storage and advanced snowmelt timing

AbstractForest fires darken snow albedo and degrade forest structure, ultimately reducing peak snow–water storage, and advancing snowmelt timing for up to 15 years following fire. To date, no volumetric estimates of watershed‐scale postfire effects on snow–water storage and snowmelt timing have been quantified over decades of postfire recovery. Using postfire parameterizations in a spatially‐distributed snow mass and energy balance model, SnowModel, we estimated postfire recovery of forest fire effects on snow–water equivalent (SWE) and snowmelt timing over decades following fire. Using this model, we quantified volumetric recovery of forest fire effects on snow hydrology across a chronosequence of eight sub‐alpine forests burned between 2000 and 2019 in the Triple Divide of western Wyoming. We found that immediately following fire, forest fire effects reduced snow–water storage by 6.8% (SD = 11.2%) and advanced the snow disappearance date by 31 days (SD = 9 days). Across the 15‐year recovery following fire, forest fire effects reduced snow–water storage by 4.5% (SD = 11.4%). Postfire effects on snow hydrology generally recovered over time, but still persisted beyond 15‐years following fire due to the observed postfire shift from forest to open meadow. Estimates of postfire reductions on peak SWE summed over the entire 15‐year postfire recovery period were 18 times greater than the immediate losses in the first winter following fire alone. These lasting effects of forest fires on snow hydrology decades following fire highlight the importance of postfire parameterizations for more accurate watershed‐scale volumetric estimates of forest fire effects on snow–water resources.

Evaluating the Performance of Open-type Composite Material Check Dams during Forest Fire Events

Evaluating the Performance of Open-type Composite Material Check Dams during Forest Fire Events

Amazon Wildfires and Respiratory Health: Impacts during the Forest Fire Season from 2009 to 2019.

The Brazilian Amazon, a vital tropical region, faces escalating threats from human activities, agriculture, and climate change. This study aims to assess the relationship between forest fire occurrences, meteorological factors, and hospitalizations due to respiratory diseases in the Legal Amazon region from 2009 to 2019. Employing simultaneous equation models with official data, we examined the association between deforestation-induced fires and respiratory health issues. Over the studied period, the Legal Amazon region recorded a staggering 1,438,322 wildfires, with 1,218,606 (85%) occurring during August-December, known as the forest fire season. During the forest fire season, a substantial portion (566,707) of the total 1,532,228 hospital admissions for respiratory diseases were recorded in individuals aged 0-14 years and 60 years and above. A model consisting of two sets of simultaneous equations was constructed. This model illustrates the seasonal fluctuations in meteorological conditions driving human activities associated with increased forest fires. It also represents how air quality variations impact the occurrence of respiratory diseases during forest fires. This modeling approach unveiled that drier conditions, elevated temperatures, and reduced precipitation exacerbate fire incidents, impacting hospital admissions for respiratory diseases at a rate as high as 22 hospital admissions per 1000 forest fire events during the forest fire season in the Legal Amazon, 2009-2019. This research highlights the urgent need for environmental and health policies to mitigate the effects of Amazon rainforest wildfires, stressing the interplay of deforestation, climate change, and human-induced fires on respiratory health.

Impacts of Forest Fires on Landscape Patches

The interrelationships between landscape and forest fires are synthesized by going through relevant books and reviewing related literature. Various effects of forest fires on landscape patches are described, while the interference of landscape on forest fires is illustrated, and some applications of forest fires in landscape ecology, such as firebreaks, are enumerated to provide scientific and reasonable bases for the prevention and control of forest fires.

Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye’s history

In the summer of 2021, Türkiye experienced unprecedented forest fire events. Throughout that fire season, a total of 291 fire incidents, covering an area of 202,361 hectares, dominated the public agenda. This study aimed to document and analyze the 30 large fires (affecting over 100 hectares) of 2021 using remote sensing and GIS techniques. A comprehensive fire database was established, encompassing information on burned areas, fire severity, and fuel types, determined from forest-stand types and topographical properties including slope, elevation, and aspect (in eight directions). Sentinel-2 satellite images were utilized to calculate dNBR values for assessing fire severity, analyzed in the Google Earth Engine platform. Three GIS-integrated Python scripts were developed to construct the fire database. In total, 164,658 hectares were affected by these large fires, occurring solely in three regions of Türkiye: the Mediterranean, Aegean, and Eastern Anatolian. The majority of the burned area was situated in the Mediterranean region (59%), with only 3% in Eastern Anatolia. The burned areas ranged from a minimum of 150 hectares to a maximum of 58,798 hectares. Additionally, 679 hectares of residential areas and 22,601 hectares of agricultural land were impacted by the fire events. For each fire, 21 fuel types and their distribution were determined. The most prevalent fire-prone class, “Pure Turkish pine species (Pr-Çz),” accounted for 59.56% of the total affected area (99,516 hectares). Another significant fire-prone pine species, the “Pure Black pine species (Pr-Çk),” covered 7.67% (12,811 hectares) of the affected area. Fuel types were evaluated by considering both forest-stand development stages and canopy closure. Regarding forest-stand development stages, the largest area percentage burned belonged to the “Mature” class (26.48%).

The effect of growth, deforestation, forest fires, and volcanoes on Indonesian regional air quality

As a rapidly developing country, Indonesia faces a challenge in improving air quality that is made more difficult by frequent forest fires linked to deforestation and volcanic eruptions. This paper analyses the link between development and air pollution in 30 Indonesian cities from 2002 to 2019. Air quality is measured using two novel regional air quality indicators (AQIs) for PM2.5 and PM10 estimated from weather observations and NASA satellite data on Aerosol Optical Depth (AOD). Results from an Environmental Kuznets Curve (EKC) analysis, based on a dynamic panel data model, show that forest fires and volcanic activity have significantly worsened regional air quality in Indonesia. The model also shows that the EKC hypothesis is rejected for Indonesia as cities have followed a monotonically increasing trend in air pollution with respect to economic growth. An additional 1% of economic growth increased long run pollution as measured by AQI PM2.5 and PM10 by 0.35% and 0.46%, and forest fire events by 0.11% and 0.06%. A partial Benefit-Cost Analysis (BCA) of forest fires shows that particulate matter pollution is excessive. Decreasing the frequency of forest fires by 1% would generate a net benefit of between US$17 million to 145 million. The lower estimate of the health benefits exceeds the agricultural benefits of forest fire without accounting for other non-market costs due to forest fires. Our results imply that, as Indonesia grows economically, air quality is likely to continue deteriorating. Further, regions affected by regular volcanic activity and wildfires should apply tighter emission standards to controllable point source emissions from industry and non-point source emissions such as agricultural forest fires.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.