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Prediction of forest fire occurrence in China under climate change scenarios

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Abstract

Climate change has an impact on forest fire patterns. In the context of global warming, it is important to study the possible effects of climate change on forest fires, carbon emission reductions, carbon sink effects, forest fire management, and sustainable development of forest ecosystems. This study is based on MODIS active fire data from 2001–2020 and the influence of climate, topography, vegetation, and social factors were integrated. Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data. Under climate change scenarios of a sustainable low development path and a high conventional development path, the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Probability maps were generated and tested using ROC curves. The results show that: (1) the area under the ROC curve of training data (70%) and validation data (30%) were 0.8465 and 0.8171, respectively, indicating that the model can reasonably predict the occurrence of forest fire in the study area; (2) temperature, elevation, and precipitation were strongly correlated with fire occurrence, while land type, slope, distance from settlements and roads, and slope direction were less strongly correlated; and, (3) based on future climate change scenarios, the probability of forest fire occurrence will tend to shift from the south to the center of the country. Compared with the current climate (2001–2020), the occurrence of forest fires in 2021–2040, 2041–2060, 2061–2080, and 2081–2100 will increase significantly in Henan Province (Luoyang, Nanyang, Sanmenxia), Shaanxi Province (Shangluo, Ankang), Sichuan Province (Mianyang, Guangyuan, Ganzi), Tibet Autonomous Region (Shannan, Linzhi, Changdu), Liaoning Province (Liaoyang, Fushun, Dandong).

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Data availability

The MODIS active fire data came from NASA’s FIRMS (https://earthdata.nasa.gov); the DEM, POP, GDP data came from the Resources and Environment Data Center of CAS (https://www.resdc.cn). The datasets for roads and residential areas were downloaded from the National Geographic Information Resource Catalog System (https://www.webmap.cn). The Chinese vegetation cover map came from Big Earth Data for Three Poles (http://poles.tpdc.ac.cn/zh-hans/). The carbon emission scenarios were obtained from the World Climate website (http://worldclim.org/); We acknowledge the World Climate Research Program.

Change history

  • 04 May 2023

    The original version is updated due to funding text that has been inadvertently appeared twice.

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Acknowledgements

We would like to thank the editors and reviewers for their valuable opinions and suggestions that improved this research.

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Authors and Affiliations

  1. Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing, 100083, People’s Republic of China

    Yakui Shao, Zhongke Feng, Linhao Sun, Xuanhan Yang, Tiantian Ma & Hening Fu

  2. School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, People’s Republic of China

    Guangpeng Fan

  3. College of Forestry, Hainan University, Haikou, 570228, People’s Republic of China

    Zhongke Feng

  4. National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Re-Search Institute of Uranium Geology, Beijing, 100029, People’s Republic of China

    XuSheng Li

  5. School of Geographical Sciences, Harbin Normal University, Harbin, 150028, People’s Republic of China

    Aiai Wang

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  1. Yakui Shao
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Contributions

Conceptualization, GF, ZF and YS; data curation, YS and GF; formal analysis, YS and GF; funding acquisition, GF and ZF; investigation, YS and GF; methodology, ZF, YS and GF; project administration, GF, ZF and YS; resources, GF, ZF and YS; supervision, YS, GF and ZF; validation, GF, ZF, YS, LS, XY, TM, HF, and AW; visualization, YS and GF; writing—original draft, Y.S. and G.F.; writing—review and editing, YS, GF, ZF, LS, XY, TM, HF, and AW All authors have read and agreed to the published version of the manuscript.

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Correspondence to Guangpeng Fan.

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Project funding: This research was funded by the National Postdoctoral Innovative Talents Support Plan China Postdoctoral Science Foundation (BX20220038) and Key R & D Projects in Hainan Province (ZDYF2021SHFZ256).

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Corresponding editor: Yu Lei.

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Shao, Y., Fan, G., Feng, Z. et al. Prediction of forest fire occurrence in China under climate change scenarios. J. For. Res. 34, 1217–1228 (2023). https://doi.org/10.1007/s11676-023-01605-6

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