Search Results (157)

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. Full article

The relative influence of climate and Indigenous cultural burning on past forest composition in southern New England, US, remains debated. Employing varied analyses, this study compared data on Indigenous settlements from over 5000 years before present (YBP) with relative tree abundances estimated from pollen and land survey records. Results suggested that fire-tolerant vegetation, mainly oak (Quercus spp.), was more abundant near Indigenous settlements from 4955 to 205 YBP (i.e., 86–91% fire-tolerant trees), and significantly (p < 0.05) higher from 3205 to 205 YBP; fire-tolerant vegetation was less abundant away from settlements, where it also experienced greater fluctuations. Correlative models showed that warmer temperatures and distance to Indigenous settlement, which are both indicators of fire, were important predictors in the 17th–18th centuries of fire-tolerant tree abundance; soil variables were less important and their relationships with vegetation were unclear. A marked increase in oak abundance occurred above 8 °C mean annual temperature and within 16 km of major Indigenous settlements. Pyrophilic vegetation was most correlated with distance to Indigenous villages in areas with 7–9 °C mean annual temperature, typical of higher latitudes and elevations that usually supported northern hardwoods. Widespread burning in warmer areas potentially weakened relationships between distance and pyrophilic abundance. Indigenous land use imprinted upon warmer areas conducive to burning created patterns in fire-tolerant vegetation in southern New England, plausibly affecting most low-elevation areas. Results imply that restoration of fire-dependent species and of barrens, savannas, and woodlands of oak in southern New England benefit from cultural burning. Full article

In Mediterranean regions, fires are a key ecological factor, altering soil properties, biodiversity, and landscape dynamics. Post-fire recovery varies based on vegetation type, fire severity, and climate conditions. However, the specific relationship between post-fire vegetation recovery and soil temperature regimes remains poorly investigated. This study investigates this relationship in an area severely affected by a megafire. Three plots (unburned, low-severity fire, and high-severity fire) were monitored for species richness, vegetation cover and height, and soil temperature, with data from 2021 to 2024 analyzed. Vegetation surveys revealed that fire severity influenced species richness and vegetation cover and height. Particularly, burned areas showed a higher proliferation of pioneer and herbaceous species three years post-fire. Moreover, after the same period, burned areas showed consistently higher soil temperatures than the unburned ones, reflecting altered microclimatic conditions. This could be because the presence of more pioneer and herbaceous species is insufficient to mitigate the air temperatures. Our results show the impact of fires on soil and vegetation, highlighting the critical role of vegetation in modeling soil temperature. However, long-term monitoring is necessary to assess the real effect of vegetation type on soil temperature. Full article

The state of Acre, located in the Western Amazon, has been more intensely affected in recent years by extreme weather events, especially those of a hydrological nature. These are rainy seasons with major floods and record water levels and, later in the same year, severe droughts that last for more months than is normal for the dry season. In this sense, remote sensing acts as an important tool for monitoring the meteorological variables involved in this dynamic, and for predicting future climate trends. Different temporal lengths reflect the availability of reliable data for each variable, and statistical methods were applied separately to ensure robust analyses despite these differences. Our research used data on rainfall (1982–2023), air temperature (2001–2020), fire foci, vegetation, and snow cover (2001–2023) for these purposes. Snow cover data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD10CM (MODIS/Terra Snow Cover Monthly L3 Global Climate Modeling Grid). The MOD10CM product was used to quantify snow cover in an area close to the state, connected to one of the main river basins in Acre. The results showed an increase in the amount of rainfall for the month of February and a reduction in the amount for months of the dry season, as well as an extension of the same. A reduction in the percentage of snow cover was also observed in the region, which may have a direct impact on water availability for several populations, including the city of Rio Branco. The Mann–Kendall test reinforced this reduction, with a Z index of −1.98 for the month of June. Principal Component Analysis (PCA) highlighted key relationships among variables. For the first principal component (PC1), rainfall, snow cover, maximum temperature, and minimum temperature had the strongest contributions, capturing over 56% of the total variance across all datasets. A negative relationship was observed between rainfall and minimum temperature, indicating that higher minimum temperatures are associated with reduced rainfall in the region. Conversely, the second principal component (PC2), which explained approximately 29% of the variance, revealed a strong positive relationship between fire foci and maximum temperature, suggesting that higher maximum temperatures significantly increase the number of fire foci. These results reinforce the role of climatic extremes in shaping environmental dynamics in Acre. The level of statistical significance (p-value) adopted for the data was up to 0.10. Full article

This study investigates the latency of lightning-caused fires in the boreal coniferous forests of the Greater Khingan Mountains, employing advanced machine learning techniques to analyze the relationship between meteorological factors, lightning characteristics, and fire ignition and smoldering processes. Using the Random Forest Model (RFM) combined with Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP), the study identifies key factors influencing fire latency. Two methods, Min distance and Min latency, were used to determine ignition lightning, with the Min distance method proving more reliable. The results show that lightning-caused fires cluster spatially and peak temporally between May and July, aligning with lightning activity. The Fine Fuel Moisture Code (FFMC) and precipitation were identified as the most influential factors. This study underscores the importance of fuel moisture and weather conditions in determining latency of lightning-caused fire, offering valuable insights for enhancing early warning systems. Despite limitations in data resolution and the exclusion of topographic factors, this study advances our understanding of lightning-fire latency mechanisms and provides a foundation for more effective wildfire management strategies under climate change. Full article

Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency. Full article

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R² of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies. Full article

Peatlands in OKI Regency are highly flammable during extreme dry seasons and experience flooding during extreme rainy seasons. Research related to peatlands is important to be carried out in disaster mitigation efforts on peatlands. In this study, we aimed to examine the impact of climate anomalies occurring between 2019 and 2022 on the Rainfall (RF), Groundwater Level (GWL), and Soil Moisture (SM) in this region. We analyzed data collected at the CJ2 station to understand the dynamics of these parameters throughout the occurrence of the ENSO and IOD phenomena in 2019–2022. The findings of this study indicate a positive correlation between RF and GWL. Specifically, a higher amount of RF resulted in a stronger correlation. Moreover, the rate of the GWL decline was 0.24 mm/d, while the rate of the SM decline was 0.06%/day. Furthermore, the soil moisture in CJ2 OKI must be maintained at a minimum of 20% to prevent fires. In addition, an empirical equation was derived to express the relationship between GWL (m) and SM (%) as SM = 49,044 GWL + 59,142. The findings of this study provide valuable in-sights for all stakeholders involved in efforts to mitigate the impact of natural disasters on peatlands. Full article

In light of global climate change, China has set strategic goals for carbon peaking by 2030 and carbon neutrality by 2060, emphasizing the necessity of constructing a new power system with a high proportion of renewable energy sources. As coal-fired power plants are the main carbon emissions source in the power system, their low-carbon transition and morphology structure optimization is crucial. This paper explores the critical role of dynamic carbon emission factors within source–network–storage power system planning and proposes an innovative inverse dynamic carbon emission factor that effectively captures the nonlinear relationship between load rates and emissions. Comparative analyses using the HRP-38 test case demonstrate that the inverse model enhances computational efficiency, reduces solution times, and more accurately reflects the emissions characteristics of coal-fired units across varying operational conditions. Furthermore, the inverse model offers improved economic performance and broader flexibility in unit selection, highlighting its potential to balance carbon emissions control and economic optimization in future power system planning. Full article

Wildfire risk increases following non-fire disturbance events, but this relationship is not always linear or cumulative, and previous studies are not consistent in differentiating between disturbance loops versus cascades. Previous research on disturbance interactions and their influence on forest fires has primarily focused on fire-prone regions, such as North America, Australia, and Southern Europe. In contrast, less is known about these dynamics in Central Europe, where wildfire risk and hazard are increasing. In recent years, forest disturbances, particularly windthrow, insect outbreaks, and drought, have become more frequent in Central Europe. At the same time, climate change is influencing fire weather conditions that further intensify forest fire dynamics. Here, we synthesize findings from the recent literature on disturbance interactions in Central Europe with the aim to identify disturbance-driven processes that influence the regional fire regime. We propose a conceptual framework of interacting disturbances that can be used in wildfire risk assessments and beyond. In addition, we identify knowledge gaps and make suggestions for future research regarding disturbance interactions and their implications for wildfire activity. Our findings indicate that fire risk in the temperate forests of Central Europe is increasing and that non-fire disturbances and their interactions modify fuel properties that subsequently influence wildfire dynamics in multiple ways. Full article

Carbon fluxes are valuable indicators of soil and ecosystem health, particularly in the context of climate change, where reducing carbon emissions from anthropogenic activities, such as forest fires, is a global priority. This study aimed to evaluate the impact of prescribed burns on soil respiration in semi-arid grasslands. Two treatments were applied: a prescribed burn on a 12.29 ha paddock of an introduced grass (Eragostis curvula) with 11.6 t ha⁻¹ of available fuel, and a simulation of three fire intensities, over 28 circular plots (80 cm in diameter) of natural grasslands (Bouteloua gracilis). Fire intensities were simulated by burning with butane gas inside an iron barrel, which represented three amounts of fuel biomass and an unburned treatment. Soil respiration was measured with a soil respiration chamber over two months, with readings collected in the morning and afternoon. Moreover, CO₂ emissions by combustion and productivity after fire treatment were quantified. The prescribed burns significantly reduced soil respiration: all fire intensities resulted in a decrease in soil respiration when compared with the unburned area. Changes in albedo increased the soil temperature; however, there was no relationship between changes in temperature and soil respiration; in contrast, precipitation highly stimulated it. These findings suggest that fire, under certain conditions, may not lead to more CO₂ being emitted into the atmosphere by stimulating soil respiration, whereas aboveground biomass was reduced by 60%. However, considering the effects of fire in the long-term on changes in nutrient deposition, aboveground and belowground biomass, and soil properties is crucial to effectively quantify its impact on the global carbon cycle. Full article

In the context of climate change, wildfires occur more frequently and significantly impact the vegetation–soil–water continuum. Soil water is a critical factor for understanding wildfire occurrence and predicting wildfire hazards. However, there is a lack of specific bibliometric analysis of the research on the mechanisms by which soil water influences wildfire occurrence. Therefore, this study conducted a bibliometric analysis of wildfire and soil water, aiming to understand their relationship, research characteristics, and future development trends. We used the Bibliometrix software package in R 4.4.0, which provides different methods for analyzing bibliometric data. A total of 1585 publications were analyzed from 1990 to 2023. The results of the study showed that the number of publications showed an overall growth trend during the period, with an average annual increase rate of 4.4%. The average annual citations per paper exhibited a pattern of rapid increase, followed by slow growth, and then rapid decrease. Ten highly productive authors in the field contributed 12.2% of the total publications during this period. Over the past 30 years, the University of Aveiro has consistently ranked first in terms of paper quantity. Most of the top ten productive institutions are in the United States, Australia, and several European countries. Fifty-eight countries engage in research related to wildfires and soil water, with close collaboration observed between the United States, Canada, and Spain. The four most frequently used keywords are “wildfire”, “fire”, “water repellency”, and “runoff” (with a total frequency of 1385). Water properties relevant to soil characteristics in the word cloud primarily include hydrophobicity, runoff, erosion, and infiltration. Erosion, wildfires, and runoff are crucial in the field but have yet to receive substantial development. The correlation of post-wildfire soil water properties with infiltration, runoff, and erosion processes is most likely to be addressed in future research. The findings will help researchers assess the post-wildfire disaster chain and its impact on the ecological environment, with clear trends, gaps, and research directions in the areas. Full article

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts. Full article

Fires affect forest dynamics in seasonally dry regions such as the Mediterranean Basin. There, fire impacts on tree growth have been widely characterized in conifers, particularly pine species, but we lack information on broadleaf tree species that sprout after fires. We investigated post-fire radial growth responses in two coexisting Mediterranean hardwood species (the evergreen Quercus ilex, the deciduous Celtis australis) using tree-ring width data. We compared growth data from burnt and unburnt stands of each species subjected to similar climatic, soil and management conditions. We also calculated climate–growth relationships to assess if burnt stands were also negatively impacted by water shortage, which could hinder growth recovery. Tree-ring data of both species allowed us to quantify post-fire growth enhancements of +39.5% and +48.9% in Q. ilex and C. australis, respectively, one year after the fire. Dry spring climate conditions reduced growth, regardless of the fire impact, but high precipitation in the previous winter enhanced growth. High June radiation was negatively related to the growth of unburnt Q. ilex and burnt C. australis stands, respectively. Post-fire growth enhancement lasted for five years after the fire and it was a transitory effect because the growth rates of burnt and unburnt stands were similar afterwards. Full article