EGU General Assembly 2020
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the Creative Commons Attribution 4.0 License.

Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans

Dalia Kirschbaum1, Thomas Stanley1,2, Robert Emberson1,2, Pukar Amatya1,2, Sana Khan1,3, and Hakan Tanyas1,2
Dalia Kirschbaum et al.
  • 1NASA Goddard Space Flight Center, 617 - Hydrological Sciences Branch, Greenbelt, Maryland United States of America
  • 2Universities Space Research Association, Columbia, Maryland, United States of America
  • 3Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, United States of America

A remote sensing-based system has been developed to characterize the potential for rainfall-triggered landslides across the globe in near real-time. The Landslide Hazard Assessment for Situational Awareness (LHASA) model uses a decision tree framework to combine a static susceptibility map derived from information on slope, rock characteristics, forest loss, distance to fault zones and distance to road networks with satellite precipitation estimates from the Global Precipitation Measurement (GPM) mission. Since 2016, the LHASA model has been providing near real-time and retrospective estimates of potential landslide activity. Results of this work are available at

In order to advance LHASA’s capabilities to characterize landslide hazards and impacts dynamically, we have implemented a new approach that leverages machine learning, new parameters, and new inventories. LHASA 2.0 uses the XGBoost machine learning model to bring in dynamic variables as well as additional static variables to better represent landslide hazard globally. Global rainfall forecasts are also being evaluated to provide a 1-3 day forecast of potential landslide activity. Additional factors such as recent seismicity and burned areas are also being considered to represent the preconditioning or changing interactions with subsequent rainfall over affected areas. A series of parameters are being tested within this structure using NASA’s Global Landslide Catalog as well as many other event-based and multi-temporal inventories mapped by the project team or provided by project partners.

In addition to estimates of landslide hazard, LHASA Version 2 will incorporate dynamic estimates of exposure including population, roads and infrastructure to highlight the potential impacts that rainfall-triggered landslides. The ultimate goal of LHASA Version 2.0 is to approximate the relative probabilities of landslide hazard and exposure across different space and time scales to inform hazard assessment retrospectively over the past 20 years, in near real-time, and in the future. In addition to the hazard. This presentation will outline the new activities for LHASA Version 2.0 and present some next steps for this system.

How to cite: Kirschbaum, D., Stanley, T., Emberson, R., Amatya, P., Khan, S., and Tanyas, H.: Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11012,, 2020

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