Wind storm loss estimations in the Canton of Vaud (Western Switzerland)

A storm loss model that was first developed for Germany is applied to the much smaller geographic area of the canton of Vaud, in Western Switzerland. 24 major wind storms that struck the region during the period 1990–2010 are analysed, and outputs are compared to loss observations provided by an ins...

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Main Authors: C. Etienne, M. Beniston
Format: Article
Language:English
Published: Copernicus Publications 2012-12-01
Series:Natural Hazards and Earth System Sciences
Online Access:http://www.nat-hazards-earth-syst-sci.net/12/3789/2012/nhess-12-3789-2012.pdf
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spelling doaj-8cc5564f5d664a8698e0892ea0e946af2020-11-25T00:35:49ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812012-12-0112123789379810.5194/nhess-12-3789-2012Wind storm loss estimations in the Canton of Vaud (Western Switzerland)C. EtienneM. BenistonA storm loss model that was first developed for Germany is applied to the much smaller geographic area of the canton of Vaud, in Western Switzerland. 24 major wind storms that struck the region during the period 1990–2010 are analysed, and outputs are compared to loss observations provided by an insurance company. Model inputs include population data and daily maximum wind speeds from weather stations. These measured wind speeds are regionalised in the canton of Vaud following different methods, using either basic interpolation techniques from Geographic Information Systems (GIS), or by using an existing extreme wind speed map of Switzerland whose values are used as thresholds. A third method considers the wind power, integrating wind speeds temporally over storm duration to calculate losses. Outputs show that the model leads to similar results for all methods, with Pearson's correlation and Spearman's rank coefficients of roughly 0.7. Bootstrap techniques are applied to test the model's robustness. Impacts of population growth and possible changes in storminess under conditions of climate change shifts are also examined for this region, emphasizing high shifts in economic losses related to small increases of input wind speeds.http://www.nat-hazards-earth-syst-sci.net/12/3789/2012/nhess-12-3789-2012.pdf
collection DOAJ
language English
format Article
sources DOAJ
author C. Etienne
M. Beniston
spellingShingle C. Etienne
M. Beniston
Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
Natural Hazards and Earth System Sciences
author_facet C. Etienne
M. Beniston
author_sort C. Etienne
title Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
title_short Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
title_full Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
title_fullStr Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
title_full_unstemmed Wind storm loss estimations in the Canton of Vaud (Western Switzerland)
title_sort wind storm loss estimations in the canton of vaud (western switzerland)
publisher Copernicus Publications
series Natural Hazards and Earth System Sciences
issn 1561-8633
1684-9981
publishDate 2012-12-01
description A storm loss model that was first developed for Germany is applied to the much smaller geographic area of the canton of Vaud, in Western Switzerland. 24 major wind storms that struck the region during the period 1990–2010 are analysed, and outputs are compared to loss observations provided by an insurance company. Model inputs include population data and daily maximum wind speeds from weather stations. These measured wind speeds are regionalised in the canton of Vaud following different methods, using either basic interpolation techniques from Geographic Information Systems (GIS), or by using an existing extreme wind speed map of Switzerland whose values are used as thresholds. A third method considers the wind power, integrating wind speeds temporally over storm duration to calculate losses. Outputs show that the model leads to similar results for all methods, with Pearson's correlation and Spearman's rank coefficients of roughly 0.7. Bootstrap techniques are applied to test the model's robustness. Impacts of population growth and possible changes in storminess under conditions of climate change shifts are also examined for this region, emphasizing high shifts in economic losses related to small increases of input wind speeds.
url http://www.nat-hazards-earth-syst-sci.net/12/3789/2012/nhess-12-3789-2012.pdf
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