Application of remotely sensed data for landslide hazard assessment and forecasting
Abstract:Over the past five years, rainfall-triggered landslides have caused over 16,000 fatalities in 65 countries and have resulted in higher annual property losses than any other natural disaster. Yet while hurricanes and earthquakes have global monitoring systems in place to alert disaster response agencies, governments and regional humanitarian groups of potential disasters and related impacts, no such real-time monitoring system exists for rainfall-triggered landslides. This work introduces a new regionally-based system to evaluate landslide hazards in near-real-time through the application of remotely sensed and in situ data. Build upon existing modeling efforts, the landslide hazard assessment and nowcasting system couples satellite precipitation data, a modeled and satellite-based soil moisture product and susceptibility information to improve the characterization of areas that may experience landslide activity. The goal of this system is to better inform decision-making and disaster response agencies on landslide hazards at the regional scale. This system outputs a straightforward, easily-interpreted set of landslide hazard assessment products available in near real-time for the Mesoamerica region that can be used to both identify landslide-prone areas and forecast the potential location and timing of landslide initiation in the future.
This research presents the prototype regional model tested over Central America and the Caribbean region using satellite-based information including Tropical Rainfall Measurement Mission (TRMM) near real-time rainfall, modeled soil moisture, topography, soils, road networks and distance to fault zones. These variables are integrated within a simple algorithm framework and model outputs provide a probabilistic representation of potential landslide activity over the region. This presentation summarizes the preliminary results of this modeling framework, discusses the utility of these products for landslide hazard characterization, and outlines the path forward for this modeling approach.