浅层滑坡启动的概率降雨阈值——以印度西高止山脉Nilgiris地区为例

Edison Thennavan, Ganapathy Ganapathy Pattukandan, S. S. Chandrasekaran, A. S. Rajawat
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引用次数: 10

摘要

降雨是造成全球各地山体滑坡的主要原因之一。最近几天,降雨引发的山体滑坡造成的死亡和破坏有所增加。位于泰米尔纳德邦西高止山脉的尼尔吉里斯地区是印度山体滑坡高发到严重的地区之一。本研究的重点是在Nilgiris地区部分地区不同滑坡易发斜坡上估算降雨阈值和滑坡的时间概率。本研究采用早期研究中确定的滑坡易发区域。收集1824 ~ 2018年滑坡位置数据,建立滑坡盘存空间数据库。对2009年滑坡的详细清单进行了分析,并用于计算降雨阈值。二零零零年至二零一一年的月及年雨量资料是由不同政府机构的37个雨量站收集而成。根据数据的质量和数量,估计了14个不同地点的降雨阈值,即Aderly, Coonoor, Coonoor铁路,省长Sola, Ooty(靠近植物园),Runnymedu, Burliar,这些地点发生滑坡的可能性很高。计算了4年(1年、3年、5年和10年)滑坡发生的时间概率。本研究可作为在Nilgiris地区建立预警系统的关键。
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Probabilistic Rainfall Thresholds for Shallow Landslides Initiation – A Case Study from the Nilgiris District, Western Ghats, India
Rainfall is one of the major causes of landslides/landslips across the globe. The fatalities and damage caused by rainfall induced landslides increased in recent days. The Nilgiris district in Western Ghats part of Tamil Nadu state is one of the very high to severe landslide hazard-prone areas of India.  The present study is focused on estimation of rainfall thresholds and the temporal probability of landslides in different landslide-prone slopes in part of The Nilgiris district. The landslide prone areas identified in earlier research are used for the present study.  The landslide locations data for the years 1824 to 2018 were collected and a spatial database on landslide inventory was created. A detailed inventory carried out on the 2009 landslides were analysed and used for the calculation of rainfall thresholds. Monthly and Yearly Rainfall data for the years 2000 to 2011 were collected for 37 rainguage stations from various government agencies. Based on the quality and quantity of data, the rainfall thresholds for 14 different locations were estimated viz., Aderly, Coonoor, Coonoor Railway, Governor Sola, Ooty (Near Botanical Garden), Runnymedu, Burliar where the probability of landslide occurrences is high.  The temporal probability of landslide was calculated for four years viz., 1, 3, 5 and 10 years. The present study can be used as a key to develop an early warning system in The Nilgiris District.
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