Characteristics and Drivers of Clustered Landslides Induced by Extreme Rainstorm on June 16 in Fujian-Guangdong-Jiangxi Junction Area
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摘要: 2024年6月16日，闽粤赣边区发生极端强降雨事件诱发数以万计的滑坡灾害，造成大量人员伤亡和财产损失.快速查明滑坡特征与驱动因素可为灾害预报预警和风险防控提供数据支撑.利用灾后光学影像进行智能识别，结合现场抽检复核，分析滑坡的空间分布和发育特征，并结合气候生态因素、地质条件和地形地貌探讨驱动因素.结果显示，共解译滑坡35 407处，总面积约41.27 km2；以小规模为主，集中分布在雨量超240 mm的山区；自然滑坡形状较规则、流动性强，工程滑坡形态复杂、流动性较弱.滑坡分布受气候生态因素、地质条件和地形地貌特征显著影响；自然滑坡受地形主导，而工程滑坡则随机性更强.本研究深化了对群发滑坡特征和驱动机制的理解，为防灾减灾救灾提供科学依据.Abstract: On June 16, 2024, an extreme rainfall event occurred in the Fujian-Guangdong-Jiangxi junction area, triggering tens of thousands of landslides and causing significant casualties and property losses. This study aims to rapidly identify the characteristics and driving factors of landslides to provide data support for disaster forecasting, early warning, and risk management. Post-disaster optical imagery was used for intelligent landslide identification, supplemented by on-site validation, to analyze the spatial distribution and developmental characteristics of the landslides. The study further investigated the driving factors by integrating the meteorological, ecological factors, geological conditions topographic features. The results reveal a total of 35 407 landslides, covering an area of approximately 41.27 km2, predominantly small-scale and concentrated in mountainous areas where rainfall exceeded 250 mm. Natural landslides exhibited relatively regular shapes and higher mobility, while landslides induced by engineering activities showed more complex shapes and lower mobility. The distribution of landslides was significantly influenced by the meteorological, ecological factors, geological conditions and topographic characteristics. Natural landslides were primarily controlled by topography, whereas engineering-induced landslides displayed greater randomness. This study deepens the understanding of the characteristics and driving mechanisms of clustered landslides, providing valuable scientific guidance for disaster prevention, mitigation, and relief efforts.
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图 1 极端降雨诱发群发灾害现场照片
Fig. 1. Field photos of clustered disasters induced by extreme rainfall-induced disasters
图 3 “6·16”事件2024年6月15日20∶00时至16日20∶00时降雨情况
Fig. 3. Spatial distribution of rainfall of the "6·16" event from 20∶00 on June 15 to 20∶00 on June 16, 2024
图 6 光学遥感分布与滑坡解译情况
Fig. 6. Optical remote sensing and landslide interpretation in the study area
图 7 “6·16”事件群发滑坡点密度
Fig. 7. Density of rainfall-induced clustered landslides on June 16, 2024
图 12 群发滑坡降雨及生态分布特征
Fig. 12. Rainfall and ecological distribution characteristics of landslides
图 14 群发滑坡在不同岩性区的分布
Fig. 14. Distribution of clustered landslides across different lithology
图 15 群发滑坡在不同土壤类型的分布
Fig. 15. Distribution of clustered landslides across different soil types
图 16 群发浅层滑坡失稳过程示意图
Fig. 16. Schematic diagram of failure process of clustered landslides
表 1 不同变量的描述与公式定义
Table 1. Descriptive statistics for selected explanatory variables
变量 定义 公式 地形粗糙度指数 地形粗糙度指数是地形表面粗糙度的指标，用于量化局部地形单元内高程变化的幅度.具有较高地形粗糙度指数值的区域通常表现出更复杂的地形 $ TRI=\frac{1}{\mathrm{c}\mathrm{o}\mathrm{s}\beta } $，其中$ \beta $表示坡度(弧度制) 地形位置指数 地形位置指数通过计算某点与其邻域平均海拔之间的高程差异，表征该点在地貌中的相对位置.地形位置指数计算可用于区分山谷、坡地和山脊，从而揭示滑坡易发区的地形特征 $ TPI={z}_{0}-\frac{1}{n}\sum\nolimits_{i=1}^{n}{z}_{i} $，其中z0表示中心点高程，zi表示邻域第i个点高程、n表示邻域中点数量 地形湿度指数 地形湿度指数是衡量地貌积水能力的指标，结合了汇水面积和坡度对水分积聚的影响.较高的地形湿度指数值表明该区域更容易积聚水分，从而增加滑坡的潜在风险 $ TWI=\mathrm{l}\mathrm{n}\left({A}_{s}/\mathrm{t}\mathrm{a}\mathrm{n}\beta \right) $，其中$ {A}_{\mathrm{s}} $表示汇水面积(m2)、β表示坡度(弧度制) 溪流动力指数 溪流动力指数评估水流的侵蚀能力.较高的溪流动力指数值表明该区域的水流侵蚀能力更强，可能加剧地表的不稳定性 $ SPI={A}_{\mathrm{s}}· \mathrm{t}\mathrm{a}\mathrm{n}\beta $，其中$ {A}_{\mathrm{s}} $表示汇水面积(m2)、$ \beta $表示坡度(弧度制) 
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