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Coupling heat wave and wildfire occurrence across multiple ecoregions within a Eurasia longitudinal gradient

文献类型： 外文期刊

作者： Mario, Elia 1 ; Raffaele, Lafortezza 1 ; Onofrio, Cappelluti 1 ; Maria, Costa-Saura Jose 2 ; Valentina, Bacciu 3 ; Vincenzo, Giannico 1 ; Shao, Changliang 4 ; Giovanni, Sanesi 1 ;

作者机构： 1.Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy

2.Univ Sassari, Dept Agr Sci, I-07100 Sassari, Italy

3.Natl Res Council Italy, Inst Bioecon, I-07100 Sassari, Italy

4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Natl Hulunber Grassland Ecosyst Observat & Res Stn, Beijing 10008, Peoples R China

5.Euro Mediterranean Ctr Climate Changes, Impacts Agr Forestry & Ecosyst Serv Div, Viale Italia, I-07100 Sassari, Italy

6.Natl Biodivers Future Ctr, Piazza Marina 61 C-O Palazzo Steri, I-90133 Palermo, Italy

7.Beijing Forestry Univ, Res Ctr Urban Forestry, Key Lab Silviculture & Forest Ecosyst, State Forestry & Grassland Adm, Beijing 100083, Peoples R China

关键词： Heat waves; Wildfires; Ecoregions; Euroasia; Fire regime; Risk mitigation

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：9.8； 五年影响因子：9.6 ）

ISSN： 0048-9697

年卷期： 2024 年 912 卷

页码：

收录情况： SCI

摘要： Understanding the relationship between heat wave occurrence and wildfire spread represents a key priority in global change studies due to the significant threats posed on natural ecosystems and society. Previous studies have not explored the spatial and temporal mechanism underlying the relationship between heat waves and wildfires occurrence, especially over large geographical regions. This study seeks to investigate such a relationship with a focus on 37 ecoregions within a Eurasia longitudinal gradient. The analysis is based on the wildfire dataset provided by the GlobFire Final Fire Event Detection and the meteorological dataset ERA5-land from Copernicus Climate service. In both cases we focused on the 2001-2019 timeframe. By means of a 12 km square grid, three wildfire metrics, i.e., density, seasonality, and severity of wildfires, were computed as proxy of fire regime. Heat waves were also characterized in terms of periods, duration, and intensity for the same period. Statistical tests were performed to evaluate the different patterns of heat wave and wildfire occurrence in the 37 ecoregions within the study area. By using Geographically Weighted Regression (GWR) we modeled the spatial varying relationships between heat wave characteristics and wildfire metrics. As expected, our results suggest that the 37 ecoregions identified within the Eurasia longitudinal gradient differ in terms of fire regimes. However, the occurrence of heat waves did not show significant differences among ecoregions, but a more evident variability in terms of relationship between fire regime metrics and heat waves within the study area. The outcome of the GWR analysis allowed us to identify the spatial locations (i.e., hotspot areas) where the relationship between heat waves and wildfires is positive and significant. Hence, in hotspots the presence of heat waves can be seen as a driver of wildfire occurrence in forest and steppe ecosystems. The findings from this study could contribute to a more comprehensive assessment of wildfire patterns in this geographical region, thus supporting cross-regional prevention strategies for disaster risk mitigation.

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