A model for the estimation of storm losses and the identification of severe winter storms in Germany
A storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2003-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/3/725/2003/nhess-3-725-2003.pdf |
| _version_ | 1828725370738704384 |
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| author | M. Klawa U. Ulbrich |
| author_facet | M. Klawa U. Ulbrich |
| author_sort | M. Klawa |
| collection | DOAJ |
| description | A storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to take local conditions at the stations into account, and thus permits a simple spatial interpolation of the storm field. The next step is the computation of a loss index for each storm. It is based on the excess of (scaled) wind speed over the upper 2% quantile, and on population numbers in the individual districts within Germany, with the latter serving as a proxy for the spatial distribution of values that could be affected by a storm. Using wind speeds in excess of the percentile value also serves to take spatial heterogeneity of vulnerability against storms into account. The aggregated storm index gives an estimate of the severity of an individual storm.</p> <p style='line-height: 20px;'>Finally, the relation between actual loss produced by a storm and the index is estimated using published annual insurance loss due to windstorm in Germany. Index values are accumulated for each year, and the relation to actual loss is computed. The average ratio for the whole reference period is eventually used. It is shown that the interannual variability of storm-related losses can be reproduced with a correlation coefficient of<i> r </i>= 0.96, and even individual storm damages can be estimated. Based on these evaluations we found that only 50 storms account for about 80% of insured storm losses between 1970 and 1997. |
| first_indexed | 2024-04-12T13:26:12Z |
| format | Article |
| id | doaj.art-43f85ff04524435d900657698f64931c |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-04-12T13:26:12Z |
| publishDate | 2003-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-43f85ff04524435d900657698f64931c2022-12-22T03:31:20ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812003-01-0136725732A model for the estimation of storm losses and the identification of severe winter storms in GermanyM. KlawaU. UlbrichA storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to take local conditions at the stations into account, and thus permits a simple spatial interpolation of the storm field. The next step is the computation of a loss index for each storm. It is based on the excess of (scaled) wind speed over the upper 2% quantile, and on population numbers in the individual districts within Germany, with the latter serving as a proxy for the spatial distribution of values that could be affected by a storm. Using wind speeds in excess of the percentile value also serves to take spatial heterogeneity of vulnerability against storms into account. The aggregated storm index gives an estimate of the severity of an individual storm.</p> <p style='line-height: 20px;'>Finally, the relation between actual loss produced by a storm and the index is estimated using published annual insurance loss due to windstorm in Germany. Index values are accumulated for each year, and the relation to actual loss is computed. The average ratio for the whole reference period is eventually used. It is shown that the interannual variability of storm-related losses can be reproduced with a correlation coefficient of<i> r </i>= 0.96, and even individual storm damages can be estimated. Based on these evaluations we found that only 50 storms account for about 80% of insured storm losses between 1970 and 1997.http://www.nat-hazards-earth-syst-sci.net/3/725/2003/nhess-3-725-2003.pdf |
| spellingShingle | M. Klawa U. Ulbrich A model for the estimation of storm losses and the identification of severe winter storms in Germany Natural Hazards and Earth System Sciences |
| title | A model for the estimation of storm losses and the identification of severe winter storms in Germany |
| title_full | A model for the estimation of storm losses and the identification of severe winter storms in Germany |
| title_fullStr | A model for the estimation of storm losses and the identification of severe winter storms in Germany |
| title_full_unstemmed | A model for the estimation of storm losses and the identification of severe winter storms in Germany |
| title_short | A model for the estimation of storm losses and the identification of severe winter storms in Germany |
| title_sort | model for the estimation of storm losses and the identification of severe winter storms in germany |
| url | http://www.nat-hazards-earth-syst-sci.net/3/725/2003/nhess-3-725-2003.pdf |
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