Predicting future forest fire occurrence probability based on drought characteristics at various temporal scales in P. R. China

Xianzhuang Shao, Chunlin Li, Yu Chang, Zaiping Xiong, Hongwei Chen and Rongping Li

PLOS ONE, 2025, vol. 20, issue 12, 1-28

Abstract: Future climate change will lead to extreme weather events, such as droughts, which may exacerbate forest fire regimes. However, the impact of future drought characteristics on forest fire regimes has rarely been reported in China. Here, we employed principal component analysis to reduce the dimensionality of drought characteristics, and then used geographically weighted logistic regression models to develop predictive models. These models were applied to future climate simulations under different scenarios to provide projections for different periods, which were then compared with the historical period (2000−2019) to assess the relative changes. We found that the model performed well in its predictions (AUC > 0.75). By comparing the Brier scores, it was found that the models with better predictive performance were those using the SPEI-1 and SPEI-12 timescales. We also found that in the near and medium term of the future, with climate change, the forest fire occurrence probability in most forest land of northern China (NWC, NC, and NEC), especially in Northeast China (NEC), shows an increasing trend, but a decreasing trend in most forest land of southern China (SC, SWC, and EC). Our research can provide a scientific basis for the development of future forest fire management practices that mitigate drought stress according to local conditions.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0337473

DOI: 10.1371/journal.pone.0337473

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