Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.

Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.

For afforestation, it is necessary to consider habitat conditions and their impact on specific tree species, in order to enable the selection of appropriate species to improve forest productivity and stand stability. Based on the 2014 Beijing forest management inventory data, we evaluated site quality using theoretical growth equations and quantile regression; we analyzed the effects of climate, topography, and soil variables on the growth of six main tree species using random forest models optimized by a genetic algorithm; and we mapped the potential habitat of six main tree species in Beijing. The results showed that climatic factors were the most important factors affecting tree growth. The prediction models had good accuracy, with an AUC of 0.75–0.85. Among the six main tree species studied, Pinus tabulaeformis Carr. was suitable for all of Beijing’s forest land. Platycladus orientalis (Linn.) Franco, Robinia pseudoacacia Linn. and Salix matsudana Koidz. were suitable for the mountainous areas, while Sophora japonica Linn. and Populus tomentosa Carr. were suitable for planting in the plains area of southeast Beijing. The optimized random forest model applied in this study gives insight into the distribution suitability of the main tree species in Beijing, and could serve as a reference for afforestation design.

The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences on pyroCb occurrence, but the exact causal relationships are still not well understood. We used the Continuous Haines Index (C-Haines) to represent atmospheric instability and the Fuel Moisture Index (FMI) to represent fuel moisture to provide better insight into the effects of atmospheric and fuel conditions on pyroCb occurrence over southeast Australia. C-Haines and FMI were related to the probability of pyroCb occurrence by employing a logistic regression on data gathered between 1980 and 2020. Emphasis is placed on investigating the independent effects and combined effects of FMI and C-Haines, as well as assessing their potential to predict whether a pyroCb develops over a fire. The main findings of this study are: (1) high C-Haines and low FMI values are representative of favorable conditions for pyroCb occurrence, but C-Haines can offset the effect of FMI—the addition of C-Haines to the logistic model muted the significance of FMI; (2) among the components of C-Haines, air temperature lapse rate (CA) is a better predictor of pyroCb occurrence than the dryness component (CB); (3) there are important regional differences in the effect of C-Haines and FMI on pyroCb occurrence, as they have better predictive potential in New South Wales than in Victoria.

Pyrocumulonimbus (pyroCb) represents an extreme manifestation of wildfire–atmosphere interactions, capable of intensifying fire behaviour and generating severe atmospheric pollution. Although previous studies have suggested that climate change may increase environmental conditions favourable for pyroCb occurrence, a quantitative assessment of how these changes translate into future pyrocumulonimbus occurrence probabilities remains lacking. In this study, we combine statistical modelling of pyroCb occurrence with climate projections to conduct a probabilistic assessment of future pyroCb risk across temperate southeast Australia. Using output from of the NSW and ACT Regional Climate Modelling project, Phase 2 (NARCliM2.0), we examine future changes in two key environmental indices associated with pyroCb—the Continuous Haines Index (C-Haines) and the fuel moisture index (FMI)—under a low-emission scenario (SSP1-2.6) and a high-emission scenario (SSP3-7.0). Then, a previously developed statistical model is used to estimate changes in pyroCb occurrence probability driven by C-Haines and FMI. The results indicate an overall increase in pyroCb probability under future climate conditions, with larger increases projected for the near future (2025–2059) than for the far future (2065–2099). The most pronounced probability increases are found in northern New South Wales. This study provides methodological support for the quantitative assessment of long-term extreme wildfire risk.

The incidence and magnitude of extreme wildfires have increased markedly in Australia. In some extreme wildfires, violent pyroconvection can manifest as pyrocumulonimbus clouds (pyroCb), which impact not only the surface but also the atmosphere, leading to dangerous escalation of wildfire risk and severe stratospheric pollution. Climate change could amplify the conditions associated with pyroCb development, so a better understanding of pyroCb drivers and their impact mechanisms is crucial for the prediction and management of pyroCb development.Previous studies have shown that atmospheric instability, surface fire weather conditions, vegetation type, and topography are important conditions affecting pyroCb development, but the synergy and extent of the contribution of these factors have not been systematically analyzed. Also, previous studies have focused more on individual events or a certain region, lacking analysis of the drivers and occurrence patterns of pyroCb over larger spatial and temporal scales.This study therefore examined atmospheric, surface weather, topographic and vegetation conditions associated with pyroCbs and standard wildfires occurring over southeast Australia, including the mainland of New South Wales (NSW), Australian Capital Territory (ACT) and Victoria (Vic) from 1980-2020. Then we used logistic models to analyze the relative importance    and regional differences of pyroCb drivers. We found that for the whole southeast Australia, atmospheric variables are the most important pyroCb drivers, followed by topographic and surface weather variables.  Fuel moisture content was found to be the most important among the given surface weather variables. However, the importance of pyroCb drivers varied from region to region. In NSW (including ACT), atmospheric and surface weather variables were more important than topographic variables, while in Vic, topographic variables were the most important, followed by atmospheric and vegetation variables, with surface weather variables having less influence on pyroCb incidence.The results provide insights that can be drawn upon to better predict the likelihood of pyroCb occurrence and the risk of extreme wildfires over southeastern Australia.