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Harmonizing and comparing single-type natural hazard risk estimations

Authors
/persons/resource/kevin

Fleming,  K.
7.1 Centre for Early Warning Systems, 7.0 Geoservices, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/parolai

Parolai,  Stefano
7.1 Centre for Early Warning Systems, 7.0 Geoservices, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Garcia-Aristizabal,  Alexander
External Organizations;

Tyagunov,  Sergey
External Organizations;

/persons/resource/vorogus

Vorogushyn,  S.
5.4 Hydrology, 5.0 Geoarchives, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/kreib

Kreibich,  H.
5.4 Hydrology, 5.0 Geoarchives, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Mahlke,  Holger
External Organizations;

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1541892.pdf
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Citation

Fleming, K., Parolai, S., Garcia-Aristizabal, A., Tyagunov, S., Vorogushyn, S., Kreibich, H., Mahlke, H. (2016): Harmonizing and comparing single-type natural hazard risk estimations. - Annals of Geophysics, 59, 2, S0216.
https://doi.org/10.4401/ag-6987


https://gfzpublic.gfz-potsdam.de/pubman/item/item_1541892
Abstract
Single-type hazard and risk assessment is the usual framework followed by disaster risk reduction (DRR) practitioners. There is therefore a need to present and compare the results arising from different hazard and risk types. Here we describe a simple method for combining risk curves arising from different hazard types in order to gain a first impression of the total risk. We show how the resulting total (and individual) risk estimates can be examined and compared using so-called risk matrices, a format preferred by some DRR practitioners. We apply this approach to Cologne, Germany, which is subject to floods, windstorms and earthquakes. We then use a new series of risk calculations that consider epistemic uncertainty. The Mann-Whitney test is applied to determine if the losses arising from pairs of hazards are comparable for a given return period. This benefits decision makers as it allows a ranking of hazards with respect to expected damage. Such a comparison would assist planners in the allocation of resources towards the most efficient mitigation actions. However, the results are dependent upon the distribution of estimates (i.e., level of uncertainty), which is in turn a function of our state of knowledge.