1. 2 Extraction of exposure and others parameters

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Articles and books

Barlow, M., H. Cullen, B. Lyon, and O. Wilhelmi, 2004: Drought Disaster in Asia. In Natural Disaster Hotspots Case Studies, International Bank for Reconstruction and Development/The World Bank and Columbia University, Washington, DC p. 1-19.

Clausen, B. and C.P. Pearson, 1995: Regional frequency analysis of annual maximum streamflow drought. Journal of Hydrology, 173, 111-130.

Dilley, M., R.S. Chen, U. Deichmann, A.L. Lerner-Lam, M. Arnold, J. Agwe, P. Buys, O. Kjekstad, B. Lyon, and G. Yetman, 2005: Global Natural Disaster Hotspots. International Bank for Reconstruction and Development/The World Bank and Columbia University, Washington, DC and New York. 132 pp.

Dracup, J.A., K.S. Lee, and E.G. Paulson, Jr., 1980: On the Definition of Droughts. Water Resources Research, 16(2), 297-302.

Fan Y., H. van den Dool (2004), Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present, J. Geophys. Res., 109, D10102, doi:10.1029/2003JD004345.

Fernández, B, and J.D. Salas, 1999: Return Period and Risk of Hydrological Events. II: Applications. Journal of Hydrologic Engineering, 4, 308-316.

Hayes. M.J., M. Svoboda, D. Le Comte, K.T. Redmond, and P. Pasteris, 2005: Drought Monitoring: New Tools for the 21st Century. In Drought and Water Crises: Science, Technology, and Management Issues, 2005, D.A. Wilhite, Ed., Taylor and Francis, Boca Raton, FL, 406 pp.

Keyantash, J. and J.A. Dracup, 2002: The Quantification of Drought: An Evaluation of Drought Indices.

Lyon, B., L. Zubair, V. Ralapanawe, and Z. Yahiya, 2008: Fine-scale evaluation of drought hazard for tropical climates: Case study in Sri Lanka. Journal of Applied Meteorology and Climatology (in press).

Mckee, T.B., N.J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, p. 179-184, Anaheim, CA.

Wilhite, D.A., and M. Buchanan-Smith, 2005: Drought as Hazard: Understanding the Natural and Social Context. In Drought and Water Crises: Science, Technology, and Management Issues, 2005, D.A. Wilhite, Ed., Taylor and Francis, Boca Raton, FL, p. 3-29.

Wilhite, D.A., 2005: Drought and Water Crises: Science, Technology, and Management Issues, Ed., Taylor and Francis, Boca Raton, FL, 406 pp.

Internet references

Early Warning Systems for Drought Preparedness and Drought Management. Workshop summary from the US Drought Mitigation Center, University of Nebraska, Lincoln: http://drought.unl.edu/monitor/EWS/EWS_WMO.html

3.4.Data sources

Precipitation Data: Monthly, gridded precipitation analyses for the globe were obtained from the Global Precipitation Climatology Center (GPCC). These analyses are based on station observations which have been spatially interpolated to generate gridded analyses at a 0.5 x 0.5 deg. lat/long resolution. The available data covered the period Jan 1951 to Dec 2004. Although gridded to a nominal 0.5 x 0.5 deg. lat/lon. grid, the precipitation analyses are likely to contain larger errors in data sparse regions. Another caveat in using these data in the context of precipitation-only drought indices such as the SPI is that other parts of the surface water balance such as infiltration, runoff, and evaporation are not considered.



Hazard model (International Centre for Geohazards / NGI)

Helge Smebye

Bjørn Kalsnes

Hazard methodology reviewed by:

Kyoji Sassa (University of Kyoto, Japan; President of ICL)

Nicola Casagli (University of Firenze, Italy; Director of ICL-Europe)

Lynn Highland (United States Geological Survey, USA)

Dwikorita Karnawati (Associate Professor, Gadjah Mada University, Indonesia)

Alexander Strom (Institute of the Geospheres Dynamics, Russia, Assistant to the ICL President)


The term landslide in this report refers to slides with rapid mass movement, like rockslides, debris flows, induced by both rainfall and earthquake; which pose a threat to human life. Slow moving slides have significant economic consequences for constructions and infrastructure, but rarely cause any fatalities. Rapid mass movement also includes snow avalanche, this later is not covered in this report, as well as rock avalanches and submarine slides.

Landslides represent a major threat to human life, property and constructed facilities, infrastructure and natural environment in most mountainous and hilly regions of the world. Statistics from the Centre for Research on the Epidemiology of Disasters (CRED) show that, on average, landslides are responsible for a small fraction of all fatalities from natural hazards worldwide. The socio-economic impact of landslides is, however, greatly underestimated because landslides are usually not separated from other natural hazard triggers, such as extreme precipitation, earthquakes or floods in natural catastrophe databases. This underestimation contributes to reducing the awareness and concern of both authorities and general public about landslide risk.

Only the population exposure to precipitation-induced landslides was considered in the event-per-event analysis. The fatalities caused by earthquake-induced landslides are attributed to “earthquakes” in the EM-DAT database, and including them again under “landslides” would lead to an overestimation of risk.

Modelling landslides

The landslide hazard, defined as the annual probability of occurrence of a potentially destructive landslide event, depends on the combination of trigger and susceptibility (Figure 12). In the analyses performed in this study, a landslide hazard index was defined using six parameters: slope factor, lithological (or geological) conditions, soil moisture condition, vegetation cover, precipitation and seismic conditions.

Figure 12. Schematic approach for landslide hazard and risk evaluation.

For each factor, an index of influence was determined and a relative landslide hazard indicator was obtained by multiplying and summing the indices.

The obtained landslide hazard indices were calibrated against the databases of landslide events in selected (mostly European) countries to obtain the frequency of the events.

Table 2 Landslide hazards frequencies and severity

Figure 13 Map of Precipitation triggered Landslides Hazard distribution

Figure 14 Map of Earthquakes triggered Landslides Hazard distribution

Figure 13 and show the global distribution of Landslides around the world. The trigger influence is clearly visible by comparing both maps in areas like, Brasil, Africa, parts of Southeast Asia, etc.

Figure 15 Zoom over central America for landslides triggered by precipitations

Difficulties and limitations

Human impact is a very important triggering factor for landslides, which is ignored in the model. On a global scale analysis, one could introduce an index that is related to population density and/or infrastructure density.

The lithology factor is probably the weakest link of the model, mainly because of the coarse resolution of the Geological Map of the World that has been used. We are not aware of any other similar database for global geology with a finer resolution. An index that could better describe the soil conditions would have made the model much better.

A universally accepted measure of landslide severity is not available at present. Some researchers define landslide intensity qualitatively as “a set of spatially distributed parameters describing the destructiveness of a landslide”. In this context, landslide intensity has been addressed and defined quantitatively using a variety of parameters, such as maximum landslide velocity, total displacement, differential displacement (relative to points adjacent to the point under consideration), depth of the moving mass, depth of deposits after the movement ceases, depth of erosion, unit discharge, kinetic energy per unit area, maximum thrust, impact pressure, maximum normal or shear strain at or below ground surface, and so on.

In the present study, all landslides capable of causing injury or fatality are considered as “events”. Beyond that, no attempt was made at considering the severity of different landslide events.

Next steps

Some of the indices used in the present model for landslide hazard, for example the vegetation cover index or the lithology index, were only available at a crude resolution. It is expected that these data will be available at much better resolution in the coming years. Furthermore, several important factors that are known to influence the susceptibility to landslides, for instance the impact of human activities and effects of deforestation, were not directly dealt with because of lack of data and/or availability of well-tested models. These factors could be included in future studies.

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