Assessing the impact of floods on vegetation worldwide from a spatiotemporal perspective

JOURNAL OF HYDROLOGY(2023)

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摘要
The impact of flooding on vegetation ecosystems worldwide has resulted in severe and widespread threats. Despite this, there is a significant knowledge gap in understanding the impact of floods on vegetation. To gain a comprehensive understanding of the response mechanism of the global vegetation ecosystem to flood disasters, it is crucial to assess the loss of vegetation caused by flood events. In this study, we analyzed 3325 flood events around the world from 2001 to 2020, including the geographic distribution, start and end dates, duration, severity and affected areas. Using kernel density estimation, we assessed the exacerbated severity and magnitude of flood occurrences, identifying spatial clustering of events. Through moving average was utilized to reveal the prolonged trends in areas impacted by floods. Additionally, by spatially matching with global land cover products, we comprehensively explored the magnitude, range, and progression of the impact of floods on vegetation. The findings indicate that (1) Extreme flood events have been increasing in number, affected area, severity, and magnitude since the 21st century, with the majority of events occurring between 10 degrees S-50 degrees N. (2) Strong spatial clustering of flood events was identified in the southeast of North America, the southeast of South America, the east of Africa, the south of Europe, and the south of Asia. (3) Flood hazards in frequent floodaffected areas pose severe threats to the terrestrial vegetation ecosystem, highlighting the necessity of further detailed estimation of flood damages to vegetation at a global scale. This research provides an extensive evaluation of the impact of floods on vegetation at the global-national-regional scale, with a focus on understanding the temporal trends and spatial distribution characteristics over the past 20 years (2001-2020).
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关键词
Flood, Vegetation, Natural disasters, Terrestrial ecosystems, Remote sensing
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