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| DOI | 10.1029/2023EF004088 |
| Current and Future Patterns of Global Wildfire Based on Deep Neural Networks | |
| 发表日期 | 2024 |
| EISSN | 2328-4277 |
| 起始页码 | 12 |
| 结束页码 | 2 |
| 卷号 | 12期号:2 |
| 英文摘要 | Global climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on convolutional neural networks (CNNs) was constructed in the reference period (1997-2014) by using wildfire driving factors and historical burned areas derived from the Global Fire Emissions Database (GFED4s). The shifting spatial patterns of global burned areas in future scenarios for the twenty-first century was investigated by using shared socioeconomic pathways (SSPs) published by CMIP6. Projected burned areas are analyzed by using nine climate models from CMIP6 under four SSPs (SSP126, SSP245, SSP370 and SSP585) for four defined periods. The evolution of the spatial pattern of global wildfires was further described based on terrestrial ecoregions and GFED regions. The results showed that for the reference period (1997-2014), burned areas were generally distributed in tropical and subtropical regions. The projection results exhibited a systematic increasing trend under the four SSPs from a global perspective in response to climate warming. The increasing trend for the burned area in the SSP370 and SSP585 scenarios was more obvious than that for the SSP126 and SSP245 scenarios. As the severity of the emission scenarios increases, severe wildfires will gradually shift to higher latitudes in the mid-to-long term (2061-2080) and long term (2081-2100). Understanding how wildfire patterns might change under climate change is critical for developing fire management strategies. In this study, the convolutional neural networks (CNNs) regression model with a deep architecture for spatial prediction was constructed by establishing the relationship between wildfire explanatory variables and the historical burned area in the reference period. The spatial evolution of the global wildfire patterns under current and future climate change scenarios employing the proposed CNN model were investigated and were further analyzed based on the terrestrial ecoregion and GFED region. The projection results demonstrated a systematic increasing trend in burned area under the four SSPs relative to that in the reference period from a global perspective in response to climate warming. The increase in the burned area under the SSP370 and SSP585 scenarios is more obvious than that for the SSP126 and SSP245 scenarios. The most recent CMIP6 climate models and four SSPs are used to analyze the possible changes in global wildfire occurrence Spatial burned area patterns under current and future climate conditions are presented and analyzed Increased emissions lead to larger burned areas under the four SSPs |
| 英文关键词 | wildfire; deep neural networks; convolutional neural network; CMIP6; burned area |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001163893100001 |
| 来源期刊 | EARTHS FUTURE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/287283 |
| 作者单位 | Beijing Normal University; Beijing Normal University |
| 推荐引用方式 GB/T 7714 | . Current and Future Patterns of Global Wildfire Based on Deep Neural Networks[J],2024,12(2). |
| APA | (2024).Current and Future Patterns of Global Wildfire Based on Deep Neural Networks.EARTHS FUTURE,12(2). |
| MLA | "Current and Future Patterns of Global Wildfire Based on Deep Neural Networks".EARTHS FUTURE 12.2(2024). |
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