Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing’an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0–10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.

In boreal regions, the frequency of forest fires is increasing. In this study, thermogravimetric analysis was used to analyze the pyrolysis kinetics of dead surface combustibles in different forest types within the Daxing’an Mountains, China. The results show that the combustible material load of forest types, the Larix forest (LG) is relatively high. Base on the E of kinetic parameters, the LG, and Quercus forest (QM) forest types had relatively high combustibility values and comprehensive combustibility values for 1-, 10-, and 100-h time lags. According to the obtained P values, the pyrolysis of dead surface fuels with 1-, 10-, and 100-h time lags is relatively difficult in the Larix / Betula mixed forest (L-B) and QM forest types. Therefore, mixed forests of the LG, L-B, and QM tree species can be established as fire-resistant forests to establish a fire barrier, reduce the combustibility of forest stands, and reduce the possibility of forest fires.

More and more droughts happened during the last decades, threatening natural forests in the semi-arid regions of North China. The increase in drought pressure may have an impact on stand transpiration (T) in semi-arid regions due to rising temperature and changes in precipitation. It is unclear how the transpiration of natural forest in semi-arid regions respond to drought, which is regulated by environmental factors. In this study, a relatively simple but mechanism-based forest stand T model that couples the effects of the reference T, solar radiation (Rn), vapor pressure deficit (VPD), and relative extractable water (REW) in the 0–80 cm soil layer was developed to quantify the independent impacts of Rn, VPD, and REW on T. The model was established based on the observed sap flow of four sample trees, and environmental factors were observed from May to September in different hydrological years (2015, 2017, 2018, and 2021) in a pure white birch (Betula platyphylla Sukaczev) forest stand in the southern section of the Greater Khingan Mountains, northeastern China. The sap flow data were used to calculate tree transpiration (Tt) and T to calibrate the T model. The results indicated that (1) The Tt sharply declined in the ‘dry’ year compared with that in the ‘wetter’ year. The daily Tt for small trees in the ‘dry’ year was only one-fifth of that in the ‘wetter’ year, and the daily Tt of large trees was 48% lower than that in the ‘normal’ year; (2) Large trees transpired more water than small trees, e.g., the daily Tt of small trees was 89% lower than that of the large trees in the ‘normal’ year; (3) Daily T increased with the increase in Rn, and the response conformed to a binomial function. Daily T responded to the rise of VPD and REW in an exponential function, first increasing rapidly, gradually reaching the threshold or peak value, and then stabilizing; (4) The driving factors for the T shift in different hydrological years were the REW in the ‘dry’ year, but the Rn and REW in the ‘wet’, ‘normal’, and ‘wetter’ years. The REW in the ‘wet’ and ‘wetter’ years exerted positive effects on T, but in the ‘normal’ and ‘dry’ year, exerted negative effects on T. Thus, the environmental factors affecting T were not the same in different hydrological years.

Overwintering peat fires are re-emerging in snow-covered Arctic-boreal regions, releasing unprecedented levels of carbon into the atmosphere and exacerbating climate change. Despite the critical role of fire-snow interactions in these processes, our understanding of them remains limited. Herein, we conducted small-scale outdoor experiments (20 × 20 × 20 cm³) at subzero temperatures (-5 ± 5 °C) to investigate the impact of natural snowfall and accumulated snow layers (up to 20 cm thick) on shallow smoldering peat fires. We found that even heavy natural snowfalls (a maximum water equivalent snowfall intensity of 1.1 mm/h or a 24 h accumulated snowfall water equivalent precipitation of 7.9 mm) cannot suppress a shallow smoldering peat fire. A thick snow cover on the peat surface can extract heat from the burning front underneath, and the minimum thickness of the snow layer to extinguish the peat fire was found to be 9 ± 1 cm at subzero temperatures, agreeing well with the theoretical analysis. Furthermore, larger-scale field demonstrations (1.5 × 1.5 m²) were conducted to validate the small-scale experimental phenomena. This work helps us to understand the interactions between fire and snow and reveals the persistence of smoldering wildfires under cold environments.

Fire is crucial for shaping northern forest ecosystems and can affect soil microbial community structure. However, there are few studies on the long-term effects of forest fire disturbance on soil microbial community diversity. In this study, we employed high-throughput sequencing of 16S rRNA and ITS1 to assess variations in the abundance of bacterial and fungal communities in dominant populations at 1, 6, and 11 years post-fire. Furthermore, a comprehensive analysis was conducted to examine the relationship between soil microenvironmental changes and soil microbial communities after fire disturbances, considering soil physicochemical properties, including bulk density, moisture content, pH, organic carbon, total nitrogen, ammonium nitrogen, nitrate nitrogen, available potassium, and available phosphorus. We found that fire significantly increased soil pH, NO₃⁻-N, AP, and AK contents, in which the content of NO₃⁻-N basically recovered to the pre-fire level at 11 years after fire. The soil SOC and TN contents decreased significantly 1 year after the fire. However, compared to the unfired site, the SOC content essentially recovered 11 years after the fire, while TN content was still significantly higher 11 years after fire. Furthermore, fire changed the diversity and richness of soil microbial communities to some extent. PCoA and NMDS analyses suggested that the bacterial community structures in soil samples from different burned areas with different recovery periods exhibited similarity. However, notable differences were observed in the fungal community structures between the 1-year and 6-year post-fire study sites when compared to the unburned control site. Bacterial communities predominantly comprised Proteobacteria, Actinobacteria, and Acidobacteria, while fungal communities were mainly dominated by Ascomycota and Basidiomycota. RDA confirmed the significant roles of SOC, TN, and NO₃⁻-N in affecting the diversity of soil microbial communities. Therefore, our study not only enhances our understanding of the long-term effects of forest fire disturbances on soil properties and soil microbial community structure, but also provides insights for further utilizing and controlling carbon and nitrogen content to regulate soil microbial activity and accelerate the recovery process of burned areas.

For several decades, warming-induced fires have been widespread in many forest systems. A forest fire could be a potential indicator, since the Great Xing’an Range is susceptible to global climate changes and frequent extreme events. This region has a relatively integrated forest community structure. This paper investigated 35 factors to explore how natural conditions affect fire scale. We analyzed the fire spatiotemporal distribution, by combining the Google Earth Engine (GEE) platform and historical records, and then reconstructed the fire-prone climate conditions. We used an exploratory model to minimize the climate factors and a geographically and temporally weighted regression (GTWR) model to predict regional large-scale lightning fire occurrence. The main results are (1) Lightning fire occurrence increased during the past four decades, and the regional fire season starts from the spring (May to June). (2) The time of occurrence of lightning fires had a strong correlation with the occurrence density. (3) The main natural factors affecting a fire-affected area are air moisture content, topographic slope, maximum surface air temperature, wind direction, and surface atmospheric pressure. The regional climate can be characterized that the prevailing southeastern wind bringing lots of precipitation and strong surface pressure, combined with the regional periodic lightning weather and irregular high temperatures, forming fire-prone weather. The abnormal soil water content in the spring led to vegetation growth and increased fuel storage. The low air water content and long-term water deficit made the local air dry. Lightning strikes are an influential factor in fire frequency, while climatic conditions shape the fire-affected areas. (4) The seven days of pre-fire data are more accurate for studying lightning fire occurrence. The GTWR model showed the best fitness among the four models. Fire-prone areas showed a trend of increasing from south to north. In the future, lightning fires will likely occur in this region’s north and east. Our work would promote the local forest fire policy-making process.