2024/12/19 by Kushanav Bhuyan, Kamal Rana, Uğur Öztürk +7 · 1 voice
Environmental Science · Engineering · #Landslides and related hazards #Flood Risk Assessment and Management #Geotechnical Engineering and Analysis
paper · doi:10.1016/j.enggeo.2024.107866
openalex publication_date 2024/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mapping landslide-depleted source areas is pivotal for refining predictive models and volume estimations, yet these critical regions are often conflated with landslide runouts, leading to sub-optimal assessments. The source (or scarp) areas are typically the regions where the actual failure occurs, providing crucial information on the initiation mechanisms and the nature of landslide propagation. Catering to this objective, we built a method based on a landslide’s topology and morphological information to delineate the source and runout margins. We develop and test this method in geomorphologically distinct regions such as Dominica, Turkey, Italy, Nepal, and Japan (Niigata) to showcase the model’s robust adaptive capacity. The model can demarcate the source and runout zones from landslide planforms found in inventories with accuracy deviations under 15%–20%. While distinguishing landslide source and runout areas, the model also considers triggering information and movement types. We also deploy the model in Chile, Japan (Hokkaido), Colombia, Papua New Guinea, and China. In these new regions, we found the mean area of the scarp to be consistently under 30% of the total landslide area. We additionally showcased the application of our model to the area–volume scaling of the coseismic landslides triggered by the 2018 Hokkaido Eastern Iburi Earthquake ( M W 6.6) in Japan. Our analysis revealed that area–volume fitting using the landslide source areas instead of the total landslide planforms or polygons improves the linear fit from R 2 =0.49 to R 2 =0.81. Our work could improve diverse landslide analysis, such as hazard and runout models, and facilitate a deeper understanding of landslide behaviour.