Global detection of rainfall-triggered landslide clusters

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.

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

“…Rapid response to landslide events (and other natural hazards) is necessary to assess damage and save lives. This response effort includes ground-based teams of local residents, government officials and logistics coordinators, scientists, engineers, and more, all working together to identify critically damaged areas (e.g., Benz and Blum, 2019;Inter-Agency Standing Committee, 2015). Yet, many response efforts are impeded by a lack of detailed information on the condition or location of damaged areas following large and widespread landslide events (Lacroix et al, 2018;Robinson et al, 2019).…”

Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters

“…Thus, landslide deaths are common, such as the three deaths at Morro do Borel, Tijuca, and the ten deaths and ten missing at Morro dos Prazeres, Santa Tereza, on April 2010. Benz and Blum (2019) showed the most intense cluster, happened in Rio de Janeiro, Brazil, as well as neighboring cities Niterói and São Gonçalo in 2010. In an area of approximately 2800 km 2 , 111 landslide events were recorded within only 3 day, predominantly on 6 April 2010.…”

Rainfall‐induced landslide inventories for Lower Mekong based on Planet imagery and a semi‐automatic mapping method

“…These inventories can also contribute to the global data set used to investigate the potential of a global precipitation forecast (Khan et al, 2021) for use in a landslide forecasting system. These data will also be a valuable addition to the GLC (Kirschbaum et al, 2010, 2015), which has been used for various purposes globally (Benz & Blum, 2019; Jia et al, 2020, 2021; Stanley & Kirschbaum, 2017; Stanley et al, 2021).…”