Environmental Earth Sciences

, Volume 66, Issue 6, pp 1683–1696 | Cite as

Advances in landslide nowcasting: evaluation of a global and regional modeling approach

  • Dalia Bach KirschbaumEmail author
  • Robert Adler
  • Yang Hong
  • Sujay Kumar
  • Christa Peters-Lidard
  • Arthur Lerner-Lam
Special Issue


The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of ‘nowcasts’ that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work’s recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity–duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.


Landslide forecasting Hazard inventory Algorithm development Central America Hurricane Mitch 



This research was supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center, administered by Oak Ridge Associated Universities through a contract with NASA. The authors thank Yudong Tian for his computing help and support. Thanks also go to Chiara Lepore for her help in evaluating susceptibility methodologies. Thank you to those at the U.S. Geological Survey for providing the landslide inventory data used in this analysis and to Graziella Devoli, Giovanni Molina, Estuardo Lira and Gerald Wieczorek for providing lithology information within the evaluated areas. The global landslide algorithm studies are supported by NASA’s Applied Sciences program. The contributions of Dr. Peters-Lidard and Dr. Kumar were supported by a NASA Advanced Information System Technology program project (AIST-08-0077, PI: Christa D Peters-Lidard). This support is gratefully acknowledged.


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Copyright information

© US Government 2011

Authors and Affiliations

  • Dalia Bach Kirschbaum
    • 1
    Email author
  • Robert Adler
    • 2
  • Yang Hong
    • 3
    • 4
  • Sujay Kumar
    • 5
  • Christa Peters-Lidard
    • 1
  • Arthur Lerner-Lam
    • 6
  1. 1.Hydrological Sciences Branch, Code 614.3, NASA Goddard Space Flight CenterGreenbeltUSA
  2. 2.Earth System Science Interdisciplinary Center (ESSIC)University of Maryland College ParkCollege ParkUSA
  3. 3.School of Civil Engineering and Environmental SciencesUniversity of OklahomaNormanUSA
  4. 4.ARRC Atmospheric Radar Research Center, The National Weather Center, University of OklahomaNormanUSA
  5. 5.Science Applications International Corporation (SAIC)BeltsvilleUSA
  6. 6.Lamont-Doherty Earth ObservatoryColumbia UniversityPalisadesUSA

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