Biogeosciences Research Spotlight

A Near-Real-Time Tool to Characterize Global Landslide Hazards

By fusing susceptibility information with precipitation data, a new model generates “nowcasts” to predict the potential for rainfall-triggered landslides in steep terrain between 50°N and 50°S.

Source: Earth's Future


Unlike earthquakes, cyclones, volcanic eruptions, and other natural disasters that are observed in real time by worldwide networks of satellites and sensors, landslides and other types of mass movement are not consistently monitored on a global scale, despite their widespread occurrence and strong likelihood of causing fatalities and disrupting critical infrastructure. This is due, in part, to the challenges of monitoring events that occur across a wide range of settings, lithologies, and climatic zones; that are triggered by both anthropogenic and natural factors; and whose volumes can span 10 orders of magnitude.

To help fill this gap, Kirschbaum and Stanley have developed a system that generates near-real-time estimates of potential rainfall-triggered landslide activity. Their Landslide Hazard Assessment for Situational Awareness (LHASA) model melds information regarding slope, lithology, deforested areas, and proximity to fault zones and roads to derive a map of landslide susceptibility, which is then combined with satellite-derived estimates of precipitation from the past week to develop “nowcasts” of areas that are susceptible to landslides.

A new model combines susceptibility information with precipitation data to predict the potential for rainfall-triggered landslides.
A view of the potential landslide activity during July in Southeast Asia as evaluated by NASA’s LHASA model. Overlaid are reported landslide fatalities dating back to 2007. Credit: NASA

When compared with a previously compiled landslide inventory, retrospective moderate- and high-hazard nowcasts coincided with a cataloged landslide up to 60% of the time. The team found that increasing the window between the issuance of a nowcast and the occurrence of a landslide from 1 to 3 days and also increasing the area involved raised the likelihood of detection by more than 10%. The authors attribute this improvement to multiple factors, including time zone differences and difficulties in pinpointing exactly where a landslide started.

The authors caution that the current version of the model has several inherent limitations, including an inability to detect landslides at high latitudes, due to shortcomings in the precipitation data, with the model considering only information between 50°N and 50°S. The nowcasts are also unlikely to predict landslides caused by factors not attributable to rainfall, such as earthquakes, freeze-thaw processes, extreme temperatures, and anthropogenic activities. Despite these limitations, the LHASA model represents an important step forward in our ability to routinely estimate potential landslide activity around the globe, the results of which may be used to support hazard assessments and study long-term trends in landslide distribution. (Earth’s Future,, 2018)

—Terri Cook, Freelance Writer

Citation: Cook, T. (2018), A near-real-time tool to characterize global landslide hazards, Eos, 99, Published on 10 May 2018.
Text © 2018. The authors. CC BY-NC-ND 3.0
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