GLOBAL LANDSLIDE CATALOG FOR HAZARD ASSESSMENT

It is well known that extreme or prolonged rainfall is the dominant trigger of landslides; however, there remain large uncertainties in characterizing the distribution of these hazards and meteorological triggers at the global scale. Understanding how rainfall-triggered landslides vary spatially and temporally around the world is critical for better characterizing landslide impacts as well as modeling these hazards over larger scales. The Global Landslide Catalog (GLC), available from 2007 to the present, contains information on reported rainfall-triggered landslide events around the world using online media reports, disaster databases, and other credible sources. The GLC currently has over 5,000 reported landslide events since 2007 with over 17,000 fatalities from 65 countries.

This presentation will highlight three ways in which the GLC is being applied to characterize patterns in landslide hazard frequency and distribution around the world as well as the value of comparing the GLC with satellite-based datasets. The GLC provides the first global perspective on where and when rainfall-triggered landslides occur to address questions of spatiotemporal distribution according to region, season and impact and link reported events to seasonal and interannual modulations such as the Asian monsoon and ENSO. Second, the GLC is compared with results from a 13-year satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and rainfall-triggered landslides globally. Lastly, the GLC is applied within a regional real-time landslide hazard algorithm as both a calibration and validation tool in estimating landslide triggering potential and susceptibility conditions across Mesoamerica. Through these three examples, this research provides an overview of how the GLC and other related inventories can provide critical foundations for improving landslide modeling at local to global scales and quantifying landslide triggering at daily, monthly and yearly time scales.