Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Abstract

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system called the Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to 3 days in advance at 1 km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is used to test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system. The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1- and 2-days lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions

“…More methods for the prediction of landslides should also be considered, such as the probabilistic threshold propose by Zhao, B. et al [54]. Also, Khan et al [55] introduced the global LHASA-Forecast (LHASA-F) framework, which incorporated the forecasted precipitation data. It demonstrates the feasibility of using rainfall prediction data to predict future landslide hazard.…”

Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica

“…have become essential in deciphering how these hazards evolve and affect people via modeling the risk of floods and landslides. Over the past few decades, a plethora of research has focused on utilizing these techniques alone or in combination with in situ retrieved data to understand landslides and flooding, both at different scales [30,[45][46][47][48][49][50][51][52][53][54][55][56][57][58]. Here in Table 1, we list satellite-based tools and datasets applicable for understanding landslides and floods at effective spatial resolutions for SIDS like Jamaica.…”

Landslides Triggered by Medicane Ianos in Greece, September 2020: Rapid Satellite Mapping and Field Survey

“…Satellite-derived precipitation (e.g., Global Precipitation Measurement-GPM) enables monitoring of rainfall amount and patterns in near real-time (Figure 1b), and precipitation maps can be derived for forecasting or study of landslide events such as hurricanes and Medicanes [18,19]. However, results from Medicane Ianos satellite-derived precipitation showed an inconsistency with ground station measurements, with significantly lower values measured from the satellite sensors [12].…”