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JOURNAL OF FORESTRY RESEARCH ›› 2026, Vol. 37 ›› Issue (1): 1-.DOI: 10.1007/s11676-026-02001-6

• Original Paper •    

Evolution law of ignition driving factors for seasonal wildfires on the Mongolian Plateau and their future prediction

Heng Zhang1,2, Jianan Yu1,2, Yongchun Hua1,2   

  1. 1College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, People’s Republic of China

    2National Field Scientific Observation and Research Station of Forest Ecosystem in Greater Khingan Mountains, Genhe 022350, People’s Republic of China

  • Received:2025-09-28 Accepted:2025-12-01 Online:2026-02-09 Published:2026-01-01
  • Supported by:
    This research was supported by the National Natural Science Foundation of China (32460393 and 32060344); the Youth Science and Technology Talent Development Program of Colleges and Universities in Inner Mongolia Autonomous Region (NJYT24042); Self-established Scientific Research Fund Project of the First-level Discipline of Forestry, Inner Mongolia Agricultural University of Inner Mongolia (LXQB2025-1 and LX20250625-8); the Science and Technology Program of Inner Mongolia Autonomous Region—“2024 National Field Scientific Observation and Research Station for Forest Ecosystems in the Greater Khingan Mountains of Inner Mongolia” (2024KYPT0003); and Basic Research Funding Projects of Universities Directly Under the Inner Mongolia Autonomous Region– “Capacity Building Project for the National Field Scientific Observation and Research Station for Forest Ecosystems in the Greater Khingan Mountains of Inner Mongolia” (BR251002).

Abstract: Under the dual pressures of climate change and human activities, the frequency and intensity of global wildfires have significantly increased. While seasonal differences profoundly affect the intensity and spatial patterns of wildfire driving factors, past research has largely focused on annual scales, with insufficient attention paid to the dynamic changes and deeper impacts of driving factors in the seasonal dimension. Taking seasonal variations as the core entry point, this study integrated cross-border resources in the Sino-Mongolian border area, adopted satellite fire point data from 2001 to 2022, fused multi-source data including meteorological, topographic, vegetation, socioeconomic and anthropogenic activity data, incorporated meteorological data under three future climate scenarios, and compared the applicability of six models (Logistic Regression (LR), Gompit Regression (GR), Random Forest (RF), Boosted Regression Trees (BRT), XGBoost, and Support Vector Machine (SVM)) in wildfire prediction on the Mongolian Plateau. The results indicate that the Boosted Regression Trees model is the optimal model. Daily average relative humidity (Hum) and yearly average wind speed (Ywin) are the primary driving factors. The eastern provinces of Mongolia, Khovd Province, Selenge Province, and Hulunbuir City in China are identified as extremely high-risk areas for wildfires, with an increasing trend in wildfire incidents on the Mongolian Plateau in the future. This study improves the analysis of fire risk level zoning to accurately identify the spatial characteristics of high-risk areas and clarifies critical thresholds through the marginal benefit analysis of driving factors. Based on this, differentiated early warning systems can be initiated in conjunction with the specific conditions of high-risk areas, supported by targeted prevention and control measures, enhancing the foresight and effectiveness of wildfire risk management in cross-border regions.

Key words: Mongolian Plateau, Seasonal classification, Driving factors, Model comparison, Fire risk zoning, Future projection

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