Gong et al. (2025) Spatiotemporal Dynamics of Forest Fire Risk in Southeastern China Under Climate Change: Hydrothermal Drivers and Future Projections

Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-10-15
• Authors: Dapeng Gong, Min Jing
• DOI: 10.3390/atmos16101189

Research Groups

• Forest and Grassland Fire Prevention and Control Research Center, China Fire and Rescue Institute, Beijing, China
• Key Laboratory of Forest and Grassland Fire Risk Prevention, Ministry of Emergency Management, Beijing, China
• Department of Fire Engineering, China Fire and Rescue Institute, Beijing, China

Short Summary

This study developed a meteorology-driven machine learning model to assess and project forest fire risk in Southeastern China under climate change scenarios, revealing a significant northward and inland migration and aggregation of high-risk zones by the end of the 21st century, despite a historical decline in fire frequency.

Objective

• To characterize the spatiotemporal distribution characteristics of forest fires in Southeastern China.
• To project how hydrothermal conditions will evolve under climate change scenarios.
• To determine the future migration trends of high-risk forest fire areas.

Study Configuration

• Spatial Scale: Southeastern China (South Central China and East China, excluding Shanghai, Hong Kong, Macau, and Taiwan), analyzed on a 0.1° × 0.1° grid (8372 cells).
• Temporal Scale:
  • Observational period for fire data: 2008–2024.
  • Historical baseline for climate data: 1985–2014.
  • Future projections: Mid-21st century (2021–2050) and End-21st century (2071–2100).

Methodology and Data

• Models used:
  • Random Forest (RF) regression model for predicting forest fire probability based on meteorological drivers.
  • Multi-model ensemble mean (MME) of CMIP6 climate models (CanESM5, CMCC-CM2-SR5, EC-Earth3, EC-Earth3-Veg-LR) for future climate projections under SSP1-2.6 and SSP5-8.5 scenarios.
  • Kernel Density Estimation (KDE) for quantifying the fire risk index.
  • Standard Deviational Ellipse (SDE) method for characterizing directional trends and spatial evolution of high-risk zones.
• Data sources:
  • Satellite monitoring data of forest fires (2008–2024) from the National Forest and Grassland Fire Prevention and Suppression Information Sharing Platform.
  • Meteorological data (mean temperature, relative humidity, mean wind speed, and precipitation) from national ground observation stations (China Meteorological Administration).
  • CMIP6 climate model data (SSP1-2.6 and SSP5-8.5 scenarios) from the IPSL node.

Main Results

• Forest fire frequency in Southeastern China exhibited a highly significant declining trend from 2008 to 2024, decreasing at an average rate of 467.3 fires per year (10.8% annual reduction, p < 0.001).
• Despite the overall decline, Guangdong and Fujian Provinces showed a significant increase in fire frequency after 2016 (p < 0.01), with their combined contribution to total fires rising from 31.9% (2008–2016) to 52.8% (2016–2024).
• Fires were highly seasonal, with 74.0% occurring in the dry season (December–March), peaking sharply in January.
• The meteorologically driven random forest model demonstrated excellent performance (R² = 0.885, RMSE = 0.0564, MAE = 0.0316, CCC = 0.936).
• By the end of the 21st century under the SSP5-8.5 scenario, mean temperatures are projected to increase by 5.5 °C (reaching 23.9 °C), precipitation is projected to increase from 120.6 mm to 152.5 mm with 23% higher variability, relative humidity is projected to decrease by 0.5–1.1%, and wind speed is projected to slightly decrease from 6.62 m/s to 6.49 m/s.
• High-risk areas, which historically covered 12.9% of the total area and were concentrated in southern coastal provinces (Guangxi, Fujian), are projected to expand to 19.2% coverage under SSP5-8.5 by the end of the century.
• The centroid of high-risk areas is projected to migrate northward by 564.22 km and eastward by 234 km (SSP5-8.5, end-21st century vs. historical baseline), shifting from near the Guangdong–Hunan border to the Hubei–Anhui border.
• This migration is accompanied by a shift from diffuse to more spatially aggregated high-risk patterns, with dramatic increases in high-risk area share in provinces like Jiangsu (from <1% to 79.5%) and Zhejiang (from <1% to 59.6%).

Contributions

• Developed a robust meteorology-driven machine learning model for forest fire risk prediction in Southeastern China, addressing limitations of traditional indices (e.g., FWI) in this specific subtropical monsoon region.
• Provided a systematic "data–model–scenario" framework to assess and project the spatiotemporal dynamics of forest fire risk under contrasting climate change scenarios (SSP1-2.6 and SSP5-8.5).
• Quantified the significant northward and inland migration and spatial aggregation of high-risk forest fire zones, attributing these shifts to the reconfiguration of regional hydrothermal regimes.
• Offered a scientific foundation for developing dynamic, region-specific forest fire management and prevention strategies, particularly highlighting emerging high-risk areas in the Yangtze River Delta and Mid-Yangtze River regions.

Funding

• China Fire and Rescue Institute Scientific Research Project [XFKZD202502]
• Key Technologies for Preventing Safety Risks in the Construction and Operation of Photovoltaic Projects [HZ202509-02]

Citation

@article{Gong2025Spatiotemporal,
  author = {Gong, Dapeng and Jing, Min},
  title = {Spatiotemporal Dynamics of Forest Fire Risk in Southeastern China Under Climate Change: Hydrothermal Drivers and Future Projections},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16101189},
  url = {https://doi.org/10.3390/atmos16101189}
}