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Liao, ZH; Hong, Y; Adler, RF; Bach, D (2011). A physically based SLIDE model for landslide hazard assessments using remotely sensed data sets. GEOMECHANICS AND GEOTECHNICS: FROM MICRO TO MACRO, VOLS 1 AND 2, 807-813.

Abstract
Landslides rank among the most devastating natural disasters, causing property damages and deaths around the world. However, predicting landslide occurrences is very difficult and expensive in terms of time and money. By integrating with recent advances of satellite remote sensing technology, a physically-based SLIDE (SLope-Infiltration-Distributed Equilibrium) model has been developed to identify the times and locations for landslides induced by heavy rainfall, the primary trigger. We evaluated this system with a widespread landslide event triggered by Hurricanes Ivan at the Blue Ridge Mountains of North Carolina in September 2004. The inputs include 6-meter LiDAR DEM, NASA's TRMM precipitation, MODIS land cover/use data, and USGS STATSGO soils information. The results are positively validated with landslide inventory documentation from NCGS and other resources. Extensive validation is underway through comparison with various inventory databases and news reports of landslide disasters in other regions including Indonesia, Central America, and China. Success of this prototype system bears promise as an early warning system for landslide disaster preparedness given the fact that landslides usually occur after a period of heavy rainfall. Additionally, the warning lead-time can be extended by using medium-range rainfall forecasts (7 days) from operational numerical weather prediction models. Currently, this SLIDE is integrated into an existing landslide prediction system operated at NASA Goddard TRMM website (http://trmm.gsfc.nasa.gov/publications_dir/potential_landslide.html) to advance its empirical rainfall Intensity-Duration Threshold to a more physically-based landslide modeling component. Ultimate goal of this work is to provide landslide decision support tools that rapidly disseminate real-time rainfall-triggered landslide alerts for disaster preparation and mitigation activities on a global basis.

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