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Photograph of a landslide on a mountain.


Landslides are one of the most pervasive hazards in the world, resulting in more fatalities and economic damage than is generally recognized. Every year they block roads, damage infrastructure, and cause thousands of fatalities. Intense and prolonged rainfall is the most frequent landslide trigger around the world, but earthquakes and human influence can also cause significant and widespread landsliding. Using satellite data, we can identify the conditions under which landslides typically occur, helping to improve monitoring and modeling of these hazards


Rainfall Triggered Landslides

Researchers need accurate and timely rainfall information to better understand and model where and when severe floods, frequent landslides and devastating droughts may occur. GPM's global rainfall data will help to better prepare and respond to a wide range of natural disasters.

This video is public domain and can be downloaded at: 

Modeling and Reporting Landslides

The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to provide situational awareness of landslide hazards for a wide range of users. The model combines TRMM and GPM near real-time precipitation data with a global landslide susceptibility map to generate estimates of where and when rainfall-triggered landslides are likely to occur around the world. Information on landslide reports is available on the Cooperative Open Online Landslide Repository (COOLR), which combines data from NASA's Global Landslide Catalog, other landslide inventories and contributions from citizen scientists via the Landslide Reporter Application.

For more information on global landslide inventories and NASA’s landslide modeling activities please visit

Further reading on the model structure and evaluation is available at:

Please cite the following publications when using this information:

For the susceptibility map:

Stanley, T., and D. B. Kirschbaum (2017), A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 1–20, doi:10.1007/s11069-017-2757-y

For the global model:

Kirschbaum, D. and Stanley, T. (2018), Satellite‐Based Assessment of Rainfall‐Triggered Landslide Hazard for Situational Awareness. Earth's Future. . doi:10.1002/2017EF000715

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