Uncovering the Spatiotemporal Evolution and Driving Factors of Flash Flood in the Qinghai–Tibet Plateau

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals.

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Publication Details

Journal
Remote Sensing
Published
2026-03-26
DOI
https://doi.org/10.3390/rs18070996
Primary Topic
Flood Risk Assessment and Management
Type
article
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article

Uncovering the Spatiotemporal Evolution and Driving Factors of Flash Flood in the Qinghai–Tibet Plateau

Wen Jin, Xun Feng, Guotao Zhang, C. Li et al.
Remote Sensing
Flood Risk Assessment and Management
article

Uncovering the Spatiotemporal Evolution and Driving Factors of Flash Flood in the Qinghai–Tibet Plateau

Wen Jin, Xun Feng, Guotao Zhang, C. Li, Zhonggen Wang, Chaoyue Li
article en

Abstract

Frequent flash floods threaten human well-being, hydropower infrastructure, and ecosystems. However, the long-term evolution of flash flood patterns over recent decades remains insufficiently understood, particularly in data-scarce high-altitude regions. Using multi-source remote sensing data integrated with historical disaster records and field investigations, this study examined the spatiotemporal evolution and driving factors of flash floods across the Qinghai–Tibet Plateau (QTP). The results indicate that flash floods have increased exponentially, which may be influenced by disaster management policies, with peaks in July–August and frequent occurrences from April to September. The seasonal trajectory of the center of gravity of flash floods from April to September exhibited a clear directional pattern. Regions with the highest disaster density were concentrated in the headwaters of five major rivers, including the Yarlung Zangbo, Jinsha, Nu, Lancang, and Yellow Rivers. Shapley Additive Explanation (SHAP) and Random Forest analyses reveal that soil moisture, anthropogenic intensity, and seasonal runoff variability are the dominant driving factors. With ongoing socioeconomic development, intensified human activities have become a key contributor to the increasing frequency of flash floods. These findings highlight the value of remote sensing-based assessments for flash flood monitoring and early warning and provide scientific support for risk mitigation, loss reduction, and the advancement of water-related targets under the United Nations’ Sustainable Development Goals.

Remote SensingVol. 18(7)
National Institute of Disaster Management (IN), Shandong Normal University (CN), Northwest University (CN), China National Commission for Disaster Reduction (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
Openalex Percentile: Top 3%
Flood Risk Assessment and Management
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