Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets
Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate t...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/6/1038 |
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| author | Mengxin Bai Peng Zhang Pei Xing Wupeng Du Zhixin Hao Hui Zhang Yifan Shi Lulu Liu |
| author_facet | Mengxin Bai Peng Zhang Pei Xing Wupeng Du Zhixin Hao Hui Zhang Yifan Shi Lulu Liu |
| author_sort | Mengxin Bai |
| collection | DOAJ |
| description | Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a<sup>−1</sup> (<i>p</i> < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. |
| format | Article |
| id | doaj-art-c1454a6e1f814138babc269fec9eea86 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c1454a6e1f814138babc269fec9eea862025-08-20T02:43:02ZengMDPI AGRemote Sensing2072-42922025-03-01176103810.3390/rs17061038Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model DatasetsMengxin Bai0Peng Zhang1Pei Xing2Wupeng Du3Zhixin Hao4Hui Zhang5Yifan Shi6Lulu Liu7Beijing Municipal Climate Center, Beijing Meteorological Service, Beijing 100089, ChinaChina Institute of Water Resources and Hydropower Research, Beijing 100048, ChinaBeijing Municipal Climate Center, Beijing Meteorological Service, Beijing 100089, ChinaBeijing Municipal Climate Center, Beijing Meteorological Service, Beijing 100089, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaBeijing Academy of Emergency Management Science and Technology, Beijing 101101, ChinaBeijing Academy of Emergency Management Science and Technology, Beijing 101101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaUnderstanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a<sup>−1</sup> (<i>p</i> < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China.https://www.mdpi.com/2072-4292/17/6/1038North Chinaburned areaSOM modesfine fuel moisture codeatmospheric circulation |
| spellingShingle | Mengxin Bai Peng Zhang Pei Xing Wupeng Du Zhixin Hao Hui Zhang Yifan Shi Lulu Liu Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets Remote Sensing North China burned area SOM modes fine fuel moisture code atmospheric circulation |
| title | Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets |
| title_full | Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets |
| title_fullStr | Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets |
| title_full_unstemmed | Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets |
| title_short | Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets |
| title_sort | spatiotemporal characteristics causes and prediction of wildfires in north china a study using satellite reanalysis and climate model datasets |
| topic | North China burned area SOM modes fine fuel moisture code atmospheric circulation |
| url | https://www.mdpi.com/2072-4292/17/6/1038 |
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