Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/nhess-2017-326
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).
Modelling Vulnerability to Severe Weather
Tobias Pardowitz 1Hans Ertel Centre for Weather Research, Optimal Use of Weather Forecast Branch
2Freie Universität Berlin, Institute of Meteorology, Carl-Heinrich-Becker Weg 6–10, 12165 Berlin, Germany
Abstract. We present a spatial analysis of weather related fire brigade operations in Berlin. By comparing operation occurrences to insured losses for a set of severe weather events we demonstrate the representativeness and usefulness of such data in the analysis of weather impacts on local scales. We investigate factors influencing the local rate of operation occurrence. While depending on multiple factors – which are often not available – we focus on publicly available quantities. These include 10 topographic features, land use information based on satellite data and information on urban structure based on data from the open street map project. After identifying suitable predictors such as housing density or local density of the road network we set-up a statistical model to be able to predict local operation densities. Such model can be used to determine potential hotspots for weather impacts even in areas or cities where no systematic records are available and can thus serve as a basis for a broad range of tools or applications in emergency management and planning.

Citation: Pardowitz, T.: Modelling Vulnerability to Severe Weather, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-326, in review, 2017.
Tobias Pardowitz
Tobias Pardowitz
Tobias Pardowitz

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Short summary
The paper presents a statistical analysis of socio-economic factors influencing vulnerability and exposure to severe weather. By means of statistical modelling, the risks of weather impacts can be predicted on very high spatial resolutions. Such model can serve as a basis for a broad range of tools or applications in emergency management and planning and thus might help to enhance resilience to severe weather.
The paper presents a statistical analysis of socio-economic factors influencing vulnerability...
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