Application of the RAGLAD geolocated landslide dam dataset to explore
landslide dam formation
Abstract
Landslide dams can result in substantial flood hazards caused by dam
formation, overtopping, and dam failure. Previous studies have
established datasets on a regional or global scale and identified
indices to estimate the probability of landslide dam formation. These
datasets are collections of landslide dam records from multiple data
sources. However, the precision and accuracy of the landslide dam
record’s spatial information hinder the completeness of data and
prevents the possibility of linking with other relevant datasets, and
thus hinders the exploration of factors affecting landslide dam
formation. We established a new global-scale landslide dam dataset,
named River Augmented Global Landslide Dams (RAGLAD), which geolocates
those records whose location was vaguely known or completely unknown and
combined this with additional data from global fluvial datasets to make
the data record more comprehensive. We use RAGLAD to study the processes
of landslide dam formation. The spatial distribution of landslide dam
records, data distribution, triggering processes and preconditions, and
the relationships between geomorphological parameters directly derived
from RAGLAD help understanding areas prone to landslide dam conditions,
and delineate potential thresholds for landslide dam formation. The
results are compared with relationships achieved from general landslides
studies to find the specific conditions of landslide dam formation.
These conditions can be further applied for filtering the potential
hazard occurrence area and calculating the landslide dam formation
susceptibility.