Abstract
Abstract. An increasing awareness of the cost of landslides on the global economy and of the associated loss of human life has led to the development of various global landslide databases. However, these databases typically report landslide events instead of individual landslides, i.e., a group of landslides with a common trigger and reported by media, citizens and/or government officials as a single unit. The latter results in significant cataloging and reporting biases. To counteract these biases, this study aims to identify clusters of landslide events that were triggered by the same rainfall event. An algorithm is developed that finds a series of landslide events that (a) is continuous with no more than 2 d between individual events and where (b) precipitation at the location of an individual event correlates with precipitation of at least one other event. The developed algorithm is applied to the Global Landslide Catalog (GLC) maintained by NASA. The results show that more than 40 % of all landslide events are connected to at least one other event and that 14 % of all studied landslide events are actually part of a landslide cluster consisting of at least 10 events and up to 108 events in 1 d. Duration of the detected clusters also varies greatly from 1 to 24 d. Our study intends to enhance our understanding of landslide clustering and thus will assist in the development of improved, internationally streamlined mitigation strategies for rainfall-related landslide clusters.
Highlights
The fatal and catastrophic nature of landslides has led to the development and maintenance of various global databases, such as the NASA Global Landslide Catalog (GLC; e.g. Kirschbaum et al, 2015) and recently the Global Fatal Landslide Database (GFLD) by Froude and Petley (2018)
In the GLC only 3 % of the analyzed landslide events are linked to triggers unrelated to rainfall such as construction, volcanos, or earthquakes
Due to the low number of events in this category, future research is necessary to test and thoroughly validate these findings as well as to assess possible reasons and implications of this phenomenon. We assume that this is mainly caused by biased reporting and cataloging of landslide events, where events linked to larger disasters such as earthquakes might be reported as one large landslide event, whereas landslides linked to rainfall might be individually reported
Summary
The fatal and catastrophic nature of landslides has led to the development and maintenance of various global databases, such as the NASA Global Landslide Catalog (GLC; e.g. Kirschbaum et al, 2015) and recently the Global Fatal Landslide Database (GFLD) by Froude and Petley (2018). Through these databases we are able to provide first estimates on the number of recorded fatalities, which were more than 55 000 between 2004 and 2016 (Froude and Petley, 2018) and map near real-time risk for landslides almost on a global scale (Kirschbaum and Stanley, 2018) Still, while they play a key role in understanding the effects of landslides on our society, it is important to note that they are primarily based on news and government reports. For large databases, this is merely qualitative and describes the number of individual landslides and impacts such as economic or human losses This classification is commonly based on press releases and is heavily biased depending on the news outlet reporting each event This classification is commonly based on press releases and is heavily biased depending on the news outlet reporting each event (e.g. Carrara et al, 2003)
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