Abstract
Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.
Highlights
Landslides cause thousands of casualties and substantial socioeconomic impacts around the world every year (Kirschbaum et al, 2015a; Froude and Petley, 2018)
We present the methodology for Landslide Hazard Assessment for Situational Awareness (LHASA) version 2 here and compare the performance of both versions over the period 2015–2020 with new landslide inventories
Some important false negatives, such as the February 6, 2019 landslides in Rio de Janeiro (Figure 4) and the Regent landslide in Sierra Leone, were not predicted due to the absence of heavy rainfall shown by Integrated Multi-satellitE Retrievals for GPM (IMERG)
Summary
Landslides cause thousands of casualties and substantial socioeconomic impacts around the world every year (Kirschbaum et al, 2015a; Froude and Petley, 2018). Near realtime information on the spatiotemporal distribution of potential landslide hazards may mitigate loss and improve the speed and effectiveness of disaster response and recovery. While physically based models can be difficult to apply over large areas, some practitioners have reported success with this approach (Raia et al, 2014; Hsu et al, 2018; Thomas et al, 2018). Based models typically require a significant amount of information about subsurface conditions and can be sensitive to small errors in these values (Iverson et al, 2015). Because accurate descriptions of the ground are rarely available for large areas, most regional landslide hazard assessment systems instead rely on an empirical approach (Guzzetti et al, 2020)
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