Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3261-2023
https://doi.org/10.5194/nhess-23-3261-2023
Research article
 | 
18 Oct 2023
Research article |  | 18 Oct 2023

Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus

Viewed

Total article views: 2,166 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,657 442 67 2,166 111 46 51
  • HTML: 1,657
  • PDF: 442
  • XML: 67
  • Total: 2,166
  • Supplement: 111
  • BibTeX: 46
  • EndNote: 51
Views and downloads (calculated since 13 Mar 2023)
Cumulative views and downloads (calculated since 13 Mar 2023)

Viewed (geographical distribution)

Total article views: 2,166 (including HTML, PDF, and XML) Thereof 2,096 with geography defined and 70 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Altmetrics
Final-revised paper
Preprint