A Decade of the Global Landslide Catalog: Spatial and Temporal Analysis, Applications, and Limitations
Abstract
Landslide events result in casualties and property damage that negatively impact livelihoods and economies across the globe. NASAs Global Landslide Catalog (GLC) compiles a record of rainfall-triggered landslide events from media reports, academic articles, and existing databases at global scale. The database consists of all types of mass movement events that are triggered by rainfall and represents a minimum number of events occurring between 2007 2017. Each recorded event is described by nominal location information, date and time of occurrence, triggering factor, event type, relative size of event, casualties, economic impact, and the geographic location in latitude and longitude with a measure of location accuracy in km. The research presented here evaluates the database and discusses its potential applications and limitations. The evaluation includes an analysis of the spatial and temporal distribution of global landslide events in the GLC. This database has been cited in several research studies where it has been used to estimate landslide hotspots, evaluate geographic patterns in landslides, and train and validate landslide models from local to global scale. Additionally, the relationship between the country GDP and landslide occurrence and impacts is assessed to determine the correlation between economic status and landslide hazard. This work also explores anthropogenic impacts that may cause or exacerbate the conditions for landslide activity such as road construction, mining, and undercutting of slopes to influence slope stability and lead to landslides. This research also assesses the patterns in human activities and other contributing factors besides rainfall that cause landslide events in the GLC. The general goal of this research is to show how the GLC can be used for landslide prediction and modeling and to address the biases and limitations of this database.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMNH35E0501D