Prediction of forest fire occurrence in China under climate change scenarios

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).

Keywords

Climate change / Scenarios / XGBoost model / Forest fires / China

Funding

National Postdoctoral Innovative Talents Support Plan China Postdoctoral Science Foundation(BX20220038)

Key R & D Projects in Hainan Province(ZDYF2021SHFZ256)