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Landslide trigger factors on populated, unstable slopes, Tusion, Tajikistan

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soil creep
soil creep
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2800 m
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North-West
... Second, the global inter-comparison between IMERG Early and GEOS-Forecast is carried out to evaluate the forecast precipitation (GEOS-Forecast) against the satellite precipitation (IMERG Early), which is currently used within the LHASA framework. In the context of landslide monitoring, this global evaluation is further enhanced by focusing on three landslide hotspots: the Mekong region (Thailand, Vietnam, Laos, Cambodia, and Myanmar), Colombia (South America), and Gorno-Badakhshan Autonomous Oblast (GBAO) province in Tajikistan (Domej 2015; https://thinkhazard.org/en/report/239-tajiki stan/LS). More details on the precipitation and climatology of the regions are presented in Section 3. ...
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Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.
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