4.4 Article

Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical Antecedent Daily Rainfall Model

期刊

PURE AND APPLIED GEOPHYSICS
卷 157, 期 6-8, 页码 1059-1079

出版社

BIRKHAUSER VERLAG AG
DOI: 10.1007/s000240050017

关键词

landslides; probabilistic threshold determination; rainfall threshold; critical water content

向作者/读者索取更多资源

Rainfall-triggered landslides constitute a serious hazard and an important geomorphic process in many parts of the world. Attempts have been made at various scales in a number of countries to investigate triggering conditions in order to identify patterns in behaviour and, ultimately, to define or calculate landslide-triggering rainfall thresholds. This study was carried out in three landslide-prone regions in the North Island of New Zealand. Regional landslide-triggering rainfall thresholds were calculated using an empirical Antecedent Daily Rainfall Model. In this model, first introduced by CROZIER and EYLES (1980), triggering rainfall conditions are represented by a combination of rainfall occurring in a period before the event (antecedent rainfall) and rainfall on the day of the event. A physically-based decay coefficient is derived for each region from the recessional behaviour of storm hydrographs and is used to produce an index for antecedent rainfall. Statistical techniques are employed to obtain the thresholds which best separate the rainfall conditions associated with landslide occurrence from those of non-occurrence or a given probability of occurrence. The resultant regional models are able to represent the probability of occurrence of landsliding events on the basis of rainfall conditions. The calculated thresholds show regional differences in susceptibility of a given landscape to landslide-triggering rainfall. These differences relate to both the landslide database and the difference of existing physical conditions between the regions.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据