Impacts and costs of Wind Storms on infrastructures: case study of the canton of Vaud (Switzerland)

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(1)Thesis. Impacts and costs of Wind Storms on infrastructures: case study of the canton of Vaud (Switzerland). ETIENNE, Christophe. Abstract Pour analyser les coûts et impacts des tempêtes de vent sur les bâtiments, une sélection de 24 tempêtes ayant durement frappé le canton de Vaud entre 1990 et 2010 est proposée. Les données à haute résolution des dommages de ces 24 tempêtes sont mises à disposition par une compagnie d'assurances. Ce travail est séparé en plusieurs parties: la distribution spatiale des dommages des 24 tempêtes est d'abord étudiée à l'aide d'outils SIG. Une carte des vents extrêmes à 50 mètre de résolution est ensuite crée à l'échelle de toute la Suisse à l'aide de Modèle Additifs Généralisés (GAMs) et de SIG; cette carte est ensuite utilisée comme input pour des modèles de dégâts qui sont appliqués au canton de Vaud. Finalement, des Modèles Climatiques Régionaux (RCMs) sont utilisés pour simuler numériquement les 24 tempêtes, et également pour estimer les possibles impacts du changement climatique sur les vitesses de vent futures en Suisse et dans le canton de Vaud. Ce travail illustre les difficultés de bien représenter les champs de vent et de prédire avec fiabilité des dommages liés à des [...]. Reference ETIENNE, Christophe. Impacts and costs of Wind Storms on infrastructures: case study of the canton of Vaud (Switzerland). Thèse de doctorat : Univ. Genève, 2012, no. Sc. 4530. URN : urn:nbn:ch:unige-274426 DOI : 10.13097/archive-ouverte/unige:27442. Available at: http://archive-ouverte.unige.ch/unige:27442 Disclaimer: layout of this document may differ from the published version..

(2) Université de Genève Section de Physique. Faculté des Sciences Professeur Martin Beniston. Impacts and Costs of Wind Storms on Infrastructures: Case Study of the Canton of Vaud (Switzerland) Thèse. présentée à la Faculté des Sciences de l’Université de Genève pour obtenir le grade de Docteur ès Sciences, mention Sciences de l’Environnement. par. Christophe Etienne de Chêne-Bougeries (GE). Thèse No 4530. Genève Institut des Sciences de l’Environnement 2012.

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(4) Remerciements. Pour commencer, je tiens à remercier le Professeur Martin Beniston et Stéphane Goyette qui furent les 2 personnes qui m’ont accompagné tout au long de ma thèse. Le premier m’a proposé ce sujet de thèse qui m’a permis de me familiariser avec le monde de la recherche, de découvrir de nouveaux outils de travail et de développer de nouvelles idées et facultés d’analyse. Il m’a également donné l’opportunité d’aller présenter mes travaux à diverses conférences. Stéphane, qui m’avait déjà accompagné pendant mon travail de master, m’a également régulièrement donné de bons conseils et était toujours disponible - même tard le soir - pour répondre à mes questions. De par sa rigueur, il m’a également appris à mieux cerner les mécanismes physiques qui régissent dans l’atmosphère et qui génèrent les tempêtes de vent. Ce travail s’inscrivait dans un cadre de collaboration entre un centre de recherche et une compagnie d’assurances: j’ai donc eu la chance de pouvoir bénéficier d’un cadre pratique pour diriger mes recherches. Je souhaite ainsi remercier les collaborateurs de l’Etablissement Cantonal d’Assurances (ECA) du canton de Vaud pour m’avoir fait confiance et m’avoir donné cette opportunité de travail. Je pense particulièrement à Jean-Marc Lance, qui fut mon correspondant tout au long de ma thèse et qui m’a bien aidé à diriger mon travail. Je remercie également Marc-Olivier Burdet et Jérôme Frachebourg pour leur présence aux réunions et pour leurs idées novatrices. Je remercie bien évidemment Olivier Latetlin, que j’ai rencontré pendant la formation au CERG en 2011 et qui a eu la gentillesse et la générosité d’accepter de faire partie du jury de ma thèse, malgré un agenda semble-t-il extrêmement chargé! Les années passées à Batelle n’auraient sans doute pas été aussi amusantes sans quelques essentiels collègues devenus amis. Ainsi, pour commencer par ceux (enfin surtout celles) qui ont partagé le même open-space, je remercie tout particulièrement Marjorie Perroud et Nicole Gallina (aka NIKIPIK) qui ont été présentes pendant tout ce temps non seulement au bureau, mais également en dehors, et avec qui j’ai pu partager mes émotions. Un grand merci à Marjorie qui m’a poussé à découvrir Matlab, outil incontournable pour toutes mes analyses durant ma thèse. Merci aussi à Margot Hill, Bastienne Uhlmann, Maura Brunetti et Ramona Maggini pour leur présence, aide et amitié. Non loin de mon bureau 313, Thierry Froidevaux, Charles-Antoine Kuszli et Ludovic Gaudard furent également très présents et sont devenus des amis de ski, de rigolade et de soirée. Merci encore à Ana Gago da Silva pour ses nombreux apports avisés en informatique et en SIG. Je n’oublie pas non plus les précieux conseils d’Anthony Lehmann en matière de statistiques et de SIG. Je remercie également les autres personnes qui font ou qui ont fait partie du Groupe Climat, notamment Enrique Moran, Walter Silverio, Ignacio Lopez-Moreno, Douglas Cripe, Denis Cohen, Roman Kanala, Gregory Giuliani, Pierre Lacroix, Kazi Rahman et Nicolas Ray. L’institut des Sciences de l’Environnement (ISE) ne se limitant pas au Groupe Climat, j’ai eu l’occasion de rencontrer des collègues et amis d’autres filières que je tiens à remercier ici pour leur amitié, aide, disponibilité, bonne humeur, etc... : Julien Forbat (aka The Gravure’s), Patrick Naef, Alain Dubois (conseils SIG à toute heure!), Aude Boni, Pauline Plagnat-Cantoreggi, Luc Tonka, Franco Romerio, Francis Bergeron et Yann Pittet, entre autres. Je pense aussi à ceux qui sont venus mettre la bonne ambiance aux weekends organisés à Zermatt, notamment Fabien Schaedler, Violeta Djambazova et Audrey Reverdin. En dehors de l’ISE, je souhaite bien évidemment remercier sincèrement Sandrine qui a fait preuve de patience et de compréhension pendant mes années de ma thèse, mais surtout sur ces derniers mois qui furent parfois longs et éprouvants, d’une part en raison de la fin de mon travail, mais aussi en raison de la naissance de Siméon au même moment. Je tiens donc à lui dire un grand MERCI! Ces années n’auraient pas non plus été pareilles sans mon entourage et mes amis proches qui m’ont permit de me changer les idées, de me confier, de rigoler, d’apprendre. Ils sont bien évidemment trop nombreux pour être tous mentionnés ici, mais je tenais à remercier tout particulièrement i.

(5) Antoine Ody (aka Le Reste), avec qui nous avons apporté une nouvelle dimension aux échanges d’emails, et Cédric Blattner (aka Le Baillis) qui a toujours été là quand j’avais besoin de me confier et avec qui j’ai partagé un nombre incalculable de moments aussi riches qu’inoubliables. Ayant déménagé 2 fois pendant mes années de thèse, j’ai eu l’occasion de vivre des moments privilégiés dans mes 2 maisons: je tiens à remercier tous les colocataires du Palais des Evaux et de Plan-lesSquats. Enfin, que seraient les hivers sans les semaines passées à Zermatt début mars? Je remercie tous ceux qui font de ces semaines des moments aussi essentiels. Et enfin, last but not least, je tiens bien évidemment à remercier mes parents qui m’ont soutenu tant financièrement que moralement depuis le début de mon cursus universitaire, et sans qui rien n’aurait été possible.. ii.

(6) Résumé Les événements naturels extrêmes peuvent causer d’importants dégâts économiques en Suisse. Depuis le début des années 1990, ils ont généré des dégâts qui s’élèvent à un total d’environ CHF 4.5 milliards, soit une valeur annuelle d’environ CHF 250 millions1 . Si les inondations et les orages de grêle sont les phénomènes ayant causé le plus de dégâts en Suisse (environ 40% et 30% du total, respectivement), les tempêtes de vent arrivent en 3ème position (26%) et peuvent donc avoir un impact économique important. Des tempêtes exceptionnelles ont durement frappé la Suisse ces 20 dernières années, avec en premier lieu Lothar, souvent appelée la "tempête du siècle", qui a causé pour plus de $12 milliards de dégâts en France, en Allemagne et en Suisse (Bresch et al., 2000) en Décembre 1999. Outre les dommages matériels records, des quantités considérables d’arbres ont été déracinés par les fortes rafales (WSL and BUWAL, 2001). D’autres tempêtes de renom, telles que Vivian en 1990 (Schüepp et al., 1994) ou Klaus en 2009 (Liberato et al., 2011), ont également sévèrement touché la Suisse et ses environs lors de ces 2 décennies. Motivé par les dégâts importants occasionnés par ces tempêtes, l’Établissement d’Assurance contre l’Incendie et les Éléments Naturels du canton de Vaud (ECA) souhaite examiner le risque potentiel de dommages matériels et de pertes économiques associé à ces événements. Tandis que des efforts considérables sont fournis - dans le canton de Vaud mais également sur l’ensemble la Suisse - pour analyser les impacts d’aléas naturels gravitaires (e.g., Lateltin et al., 2005), les possibles conséquences d’événements météorologiques extrêmes et potentiellement dévastateurs tels que les tempêtes de vent sont encore méconnues. Une étude des événements passés et de la distribution spatiale des dégâts matériels vise à mieux comprendre le comportement, la fréquence d’occurrence et l’ampleur de ces phénomènes. Une sélection des 24 tempêtes les plus importantes en matière d’intensité et de dégât de la période 1990-2010 est proposée. Les dommages matériels causés par ces 24 tempêtes sont fournis par l’ECA et étudiés en détail. La distribution spatiale et l’importance des coûts économiques liés à chacune des tempêtes est analysée. Un aperçu global de la somme des montants sur ces 20 années est aussi fourni. Une discussion des éventuelles conséquences des changements climatiques sur les événements naturels extrêmes en Suisse, en particulier sur les tempêtes de vent, est proposée. Des études récentes montrent que les changements climatiques auront des impacts variés selon le type d’événement naturel considéré (Beniston, 2007). L’analyse des tempêtes futures en Europe montre que les projections varient sensiblement d’une région à l’autre (Ciscar et al., 2011). Dans l’hémisphère nord, si le nombre de tempêtes pourrait diminuer, leur intensité devrait par contre augmenter (Leckebusch et al., 2006). Une analyse annuelle et saisonnière des projections de vitesses de vent futures effectuées par un ensemble de Modèles Climatiques Régionaux (PRUDENCE, Christensen et al. 2002) est proposée pour le canton de Vaud. L’évolution des vitesses de vent extrême est étudiée en comparant les valeurs de la période 1960-1990 à celles prévues pour la période 2070-2100. Les résultats indiquent une augmentation annuelle de l’ordre de 3-4% des vitesses de vent extrême pour le canton de Vaud, avec toutefois d’importantes variations selon la saison considérée. Afin de pouvoir appliquer les modèles de dégâts en Suisse, il convient en premier lieu d’établir une carte des vents extrêmes à l’échelle du pays. Les valeurs de vitesses de vent extrême sont souvent désignées, localement, par le 98ème pourcentile des vitesses maximum de vent journalier (Klawa and Ulbrich, 2003). Ces valeurs de seuil (v98 ) ont été mesurées aux diverses stations du réseau des stations de Météosuisse et fournissent des informations sur les conditions de vents à ces stations. Le but de ce volet est de proposer une régionalisation des valeurs de v98 à travers toute la Suisse. Etant donné la topographie extrêmement complexe du paysage suisse, avec les reliefs montagneux qui occupent plus de 60% du territoire, les vitesses de vent peuvent connaitre des variations très fortes d’un endroit à l’autre en raison des nombreux obstacles orographiques présents (Barry, 1992). Ces nombreuses discontinuités rendent l’interpolation des vitesses de vent 1. AEIE: Association des établissements cantonaux d’assurance incendie. iii.

(7) dans les régions montagneuses très délicate (Tveito et al., 2008). Ainsi, pour régionaliser les valeurs de v98 mesurées aux stations météorologiques, d’autres outils doivent être utilisés. L’utilisation de Modèles Additifs Généralisés (GAMs, Hastie and Tibshirani 1990) est suggérée pour spatialiser les vitesses de vent. Ces techniques, qui sont des extensions des Modèles Linéaires Généralisés (GLM), se basent sur une description précise et détaillée des conditions topographiques autour de chaque station pour régionaliser les vitesses de vent. Le but de cette étude est donc de déterminer dans quelle mesure les vitesses de vent peuvent être expliquées par les caractéristiques topographiques locales. Des calculs de pente, d’altitude, de courbure, mais également une classification rigoureuse des différents paysages helvétiques selon un algorithme proposé par Weiss (2001) et Jenness (2006) ont été effectués à l’aide d’outils SIG. Un outil combinant les GAMs et les SIG permet de sélectionner les couches les plus pertinentes pour la détermination des champs de vent (Lehmann et al., 2002) et de créer une carte des vents sur l’ensemble du domaine. Le résultat final est présenté sous forme d’une carte des vitesses de vent extrême pour toute la Suisse à une résolution de 50 m; ces valeurs peuvent être introduites dans les modèles de dégâts pour estimer les dommages liés aux tempêtes de vent.. Pour calculer les dommages économiques dus aux tempêtes, les modèles de dégâts nécessitent une bonne estimation des vitesse de vent pendant les tempêtes. Les mesures ponctuelles effectuées par Météosuisse fournissent des valeurs aux stations, mais celles-ci sont trop espacées pour représenter efficacement les champs de vent sur l’ensemble du canton de Vaud. L’utilisation des Modèles Climatiques Régionaux (RCM), qui visent à simuler numériquement des événements climatiques à haute résolution, permet de remédier à ce manque de données en proposant des valeurs à fines échelles. Dans ce travail, les 24 tempêtes de vent ont été simulées à l’aide du Modèle Climatique Régional Canadien (CRCM, Laprise et al. 1998) avec une maille de 2 km pour la Suisse. Des techniques de downscaling avec des simulations à des échelles respectives de 60 km, 20 km et 5 km ont été utilisées pour aboutir aux simulations à 2km de résolution. L’application de la méthode de Brasseur est testée pour estimer les rafales de vent (Brasseur, 2001). La qualité des simulations de vent est évaluée en comparant les résultats avec les observations enregistrées par les stations Météosuisse. Plusieurs stations, situées dans des régions aux topographies différentes, ont été sélectionnées pour l’analyse des résultats du CRCM. Des séries temporelles sont examinées, de même que les profils verticaux effectués à la station de Payerne. Les simulations numériques de ces 24 événements permettent aussi de mieux comprendre quelques aspects de la climatologie des tempêtes de vent en Suisse.. Les modèles de dégât permettent d’estimer les dommages économiques liés au passage d’une catastrophe naturelle telle qu’une tempête de vent. Ils relient l’aspect météorologique de l’événement, essentiellement les vitesses de vent, mais aussi la distribution spatiale de la tempête, aux informations détaillant l’ensemble des valeurs économiques situées dans un domaine d’étude. Un modèle de dégât proposé par Klawa and Ulbrich (2003) est ici testé sur le canton de Vaud en étudiant les 24 tempêtes, et en calibrant les résultats avec les données de dégâts fournies par l’ECA du canton de Vaud. Ce modèle prend en compte les vitesses de vent et les données de population. Ces données de population sont généralement plus accessibles que les données de valeurs économiques et fournissent une bonne approximation des valeurs assurées. Les vitesses de vent sont fournies par les stations météorologiques de Météosuisse, sont normalisées par les valeurs de v98 de manière à tenir compte des conditions de vent locales, puis exposées au cube. Elles sont ensuite interpolées pour avoir des valeurs sur l’ensemble du canton. La combinaison des paramètres de vent et de population permet d’estimer les coûts occasionnés par un événement donné. Les valeurs de v98 sont extraites de la carte créée précédemment et détaillée dans Etienne et al. (2010). Des estimations des conséquences économiques de l’évolution démographique sont proposées, et la sensibilité du modèle est testée en modifiant les vitesses de vent initiales. iv.

(8) Resume Extreme natural events can lead to significant economic losses in Switzerland. Since the early 1990s, they have generated losses of approximately CHF 4.5 billion, which corresponds to an annual CHF 250 million2 . If floods and hailstorms are the phenomena that caused the highest losses in Switzerland (about 40% and 30% of the total, respectively), wind storms arrive next (26%) and can therefore have strong economic impacts. Exceptional storms have hit Switzerland in the past 20 years: firstly Lothar, which is often referred to as the "storm of the century", has caused economic losses exceeding $ 12 billion in France, Germany and Switzerland in December 1999 (Bresch et al., 2000). In addition to these exceptional losses, considerable amounts of trees were uprooted by the strong gusts (WSL and BUWAL, 2001). Other renowned storms such as Vivian in 1990 (Schüepp et al., 1994) or Klaus in 2009 (Liberato et al., 2011) have also severely affected Switzerland and the neighboring countries during these two decades. Prompted by the extensive damage caused by these storms, the Insurance Company of the Canton of Vaud (ECA: Établissement Cantonal d’Assurance) is willing to examine the economic losses associated to severe storm events and the potential risk of property damage. While important efforts are made - in the Canton of Vaud but also throughout Switzerland - to analyze impacts of natural hazards such as floods or landslides (e.g., Lateltin et al., 2005), investigations still need to be done regarding extreme weather events such as potentially devastating windstorms. An analysis of past events and the spatial distribution of their associated damage aims at a better understanding of the behavior, the frequency of occurrence and the magnitude of these phenomena. A selection of the 24 largest storms in terms of intensity and damage from the 1990-2010 period is proposed. Loss data of these 24 storms is provided by the ECA, and the spatial distribution and the magnitude of these economic costs are studied in detail. An overview of the sum of the losses of these 20 years is also provided. A discussion of the possible climate change consequences on extreme natural events in Switzerland, particularly for windstorms, is proposed. Recent studies show that the impact of climate change might differ depending on the considered natural hazard (Beniston, 2007). Investigations of future storminess in Europe indicate that projections vary significantly from one region to another (Ciscar et al., 2011). In the northern hemisphere, if the number of storms could decrease, their intensity might however increase (Leckebusch et al., 2006). To evaluate the future storminess in the Canton of Vaud, an analysis of annual and seasonal projections of future wind speeds performed by a set of Regional Climate Models (PRUDENCE, Christensen et al. 2002). The evolution of extreme wind speeds is studied by comparing the wind velocities of the 1960-1990 period to those projected for the 2070-2100 period. Results indicate an annual increase of extreme wind speeds of approximately 3-4 % for the Canton of Vaud, with nevertheless significant seasonal variations. In order to apply damage models to Switzerland, it is first necessary to produce a map of extreme winds across the country. The values of extreme wind speeds are often associated to the 98th percentile of maximum daily wind speeds (Klawa and Ulbrich, 2003). These threshold values (v98 ) were measured at various stations of the MeteoSwiss weather stations network, providing information on local wind conditions in the surrounding of these stations. The purpose of this preliminary analysis is to investigate a method to regionalize the v98 values throughout Switzerland. Given the extremely complex topography of Switzerland, with mountainous terrain occupying more than 60% of the land, the wind speeds may exhibit some large discrepancies from one place to another because of the numerous orographic obstacles (Barry, 1992). These discontinuities make interpolation of wind speeds in the mountainous regions very delicate (Tveito et al., 2008). Thus, to regionalize v98 values measured at weather stations, other tools must be used. The use of Generalized Additive Models (GAMs, Hastie and Tibshirani 1990) is suggested to spatialize the wind velocities. These techniques, which are extensions of Generalized Linear Models (GLMs), are based on an accurate and 2. AEIE: Association des établissements cantonaux d’assurance incendie. v.

(9) detailed description of the topographic conditions around each station to regionalize wind speeds. The purpose of this study is to determine to what extent wind speeds can be explained by local topography. Calculations of the slope, elevation and curvature were performed using GIS tools, together with a rigorous classification of the different Swiss landscapes according to an algorithm proposed by Weiss (2001) and Jenness (2006). A statistical tool combining the GAMs and GIS layers (Lehmann et al., 2002) is used for the selection of the most relevant layers to assess wind fields. The final result is presented in the form of a map of extreme wind speeds for all of Switzerland at a resolution of 50 m, which values can be implemented in storm loss models to estimate damage related to windstorms. To calculate the economic losses associated to storms, damage models require a good estimation of wind speeds during storm events. The MeteoSwiss weather stations provide excellent and detailed wind statistics, but these stations are too far apart to effectively represent the wind fields throughout the Canton of Vaud. The use of Regional Climate Models (RCM), which aim to numerically simulate weather events, high-resolution overcomes this lack of data by providing values to fine scales. In this work, the 24 wind storms were simulated using the Canadian Regional Climate Model (CRCM, Laprise et al. 1998) with a spatial resolution of 2 km for Switzerland. Downscaling techniques with numerical simulations at respective spatial scales of 60 km, 20 km and 5 km were carried out to drive final simulations at a 2km resolution. The Brasseur method is tested to estimate wind gusts (Brasseur, 2001). The quality of wind simulations is assessed by comparing the results to the observations recorded by the MeteoSwiss stations. A few stations that are located in regions with different topographical conditions were selected for the analysis of results from the CRCM at 2 km. Time series are examined, together with vertical profiles performed at station Payerne. Numerical simulations of these 24 events can furthermore help understand some characteristics of the climatology of wind storms of Switzerland. Damage models help estimate the economic losses associated to natural disasters such as windstorms. They link the meteorological aspects of the storm event, mainly the wind speeds, but also the spatial distribution of the hazard, with information detailing the exposure, i.e. all the economic values located in an area of study. A storm loss model proposed by Klawa and Ulbrich (2003) is tested here in the Canton of Vaud by studying the 24 storms. The model is calibrated with the detailed economic loss records provided by the ECA. This storm loss model takes into account the wind speeds and population data. The population data is generally more available than the distribution of economic values, and thus providing a good approximation of insured values. Wind speeds are provided by the MeteoSwiss weather stations, are normalized by the v98 values to take into consideration the local wind conditions, then exposed to the cube. They are then interpolated for the entire Canton of Vaud. The combination of wind parameters and population allows the estimation of the costs related to a given event. The v98 values are extracted from the map created previously and detailed in Etienne et al. (2010). Estimation of economic consequences of demographic forcing are proposed, and the sensitivity of the storm loss model is tested by changing the initial wind speeds.. vi.

(10) Contents Remerciements . Résumé . . . . . Resume . . . . . Table of Contents. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 1 Overall Introduction 1.1 Extreme events and Economic Losses . . . . . . . . . . . . . . . . . 1.1.1 Risk assessment . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 PhD goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Introduction outline . . . . . . . . . . . . . . . . . . . . . . 1.2 Swiss Insurance System and Natural Hazards . . . . . . . . . . . . 1.2.1 Historical Losses related to Natural Hazards in Switzerland 1.2.2 Public Insurance System of Switzerland . . . . . . . . . . . 1.2.3 Natural Hazards - Prevention . . . . . . . . . . . . . . . . . 1.2.4 Danger Matrix . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.5 Wind damage and wind load legislation (SIA 261 Norm) . . 1.3 Climate of Switzerland . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Swiss Climate Conditions . . . . . . . . . . . . . . . . . . . 1.3.2 Dominant winds in Switzerland . . . . . . . . . . . . . . . . 1.3.3 Historical Swiss Storms . . . . . . . . . . . . . . . . . . . . 1.4 Canton of Vaud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Wind storm detection for the Canton of Vaud . . . . . . . . 1.4.2 Insurance data of ECA Vaud . . . . . . . . . . . . . . . . . 1.4.3 Synthesis of the 24 storm losses . . . . . . . . . . . . . . . . 1.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Loss Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Storm Loss Models . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Variables other than wind speeds . . . . . . . . . . . . . . . 1.5.4 Vulnerability . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Numerical Modeling and Climate Change . . . . . . . . . . . . . . 1.6.1 Global Circulation Models . . . . . . . . . . . . . . . . . . . 1.6.2 Regional Climate Models . . . . . . . . . . . . . . . . . . . 1.6.3 Climate Change and extreme events . . . . . . . . . . . . . 1.6.4 The PRUDENCE Project . . . . . . . . . . . . . . . . . . . 1.6.5 Wind speed projections for Switzerland . . . . . . . . . . . 1.6.6 Wind speed projections for the Canton of Vaud . . . . . . . 1.7 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . vii. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . .. i iii iv vi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 1 1 2 2 3 3 3 3 4 5 5 7 7 8 9 11 11 12 13 14 15 15 16 16 17 17 17 18 19 20 21 22.

(11) Contents. 2 Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Data and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Weather stations network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Topographic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Sampling physiographical conditions at the meteorological stations . . . . . . 2.4.4 Generalized regression analysis and spatial predictions . . . . . . . . . . . . . 2.4.5 Model selection steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Spatial predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Model accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusions and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 23 24 24 26 27 27 27 28 29 30 31 31 33 34 36 37 38. 3 Numerical investigations of extreme winds over Switzerland during 1990–2010 winter storms with the Canadian Regional Climate Model 3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Data and numerical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Meteoswiss data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Lake temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Storm events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.5 The atmospheric model: the Canadian RCM . . . . . . . . . . . . . . . . . . 3.3.6 Wind gust parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Model configuration and setup . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Synoptic conditions during the occurrence of the storms . . . . . . . . . . . . 3.5.2 Statistics for all stations applied to 3 typical storms . . . . . . . . . . . . . . 3.5.3 Time series at weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Vertical profiles analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 45 46 46 48 48 48 49 50 51 51 52 53 53 54 54 54 60 65 66 67. 4 Wind Storm Loss estimations in 4.1 Abstract . . . . . . . . . . . . . 4.2 Introduction . . . . . . . . . . . 4.3 Study area and data . . . . . . 4.3.1 Study area . . . . . . . 4.3.2 Storm loss model . . . . 4.3.3 Insurance data . . . . . 4.4 Methods . . . . . . . . . . . . . 4.4.1 Interpol . . . . . . . . . 4.4.2 Map98 . . . . . . . . . .. 73 74 74 75 75 76 77 77 77 78. the Canton of Vaud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii. (Western Switzerland) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .. . . . . . . . . ..

(12) Contents. . . . . . . . .. 78 79 79 81 81 82 84 85. 5 Conclusions 5.1 Main Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Outlook and suggestions for future research . . . . . . . . . . . . . . . . . . . . . . .. 89 89 91. References. 92. 4.5. 4.6 4.7. 4.4.3 Wind power . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Results for the Canton of Vaud . . . . . 4.5.2 Results for the confined area . . . . . . 4.5.3 Discussion . . . . . . . . . . . . . . . . . 4.5.4 Demographic growth and climate change Conclusions . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. Appendix A Districts of the Canton of Vaud. 103. Appendix B Economic losses for each storm event. 105. Appendix C ECA and Population data. 107. ix.

(13) Contents. x.

(14) Chapter 1. Overall Introduction 1.1. Extreme events and Economic Losses. Extreme natural hazards have always affected the planet. Recently, the March 2011 Japan earthquake has highlighted the numerous socio-economic impacts such events can have. In addition to the loss of many human lives, the economic consequences of the japanese earthquake and the following tsunami are unprecedented, as entire cities and large regions were devastated by the big wave. The international nuclear crises that followed showed that a local catastrophe can have global repercussions (Weiss, 2012). There are many types of extreme natural hazards, their characteristics and scale depending on the local geographic, geologic and climatic conditions. Coastal zones are particularly exposed to devastating events, as they are often struck by hurricanes, which are frequently accompanied by intense floods, as it was the case for hurricane Katrina in 2005 (Comfort, 2006). Loss analysis in the United States East and Gulf coasts suggests that the average annual financial loss related to hurricanes could be of approximately $5 billion (Pielke and Landsea, 1998). Inner lands or mountain regions can also experience important and destructive natural events (landslides, debris flows or avalanches) that are difficult to predict and can cause important damage to infrastructures and communication routes. These events are generally triggered by heavy rainfalls, as it is often the case in Brazil for example (Lima and Satyamurty, 2010). The Swiss Alps, due to their complex hydrological system and highly heterogenous terrain, also commonly suffer from landslides events (e.g., Lateltin et al., 2005; Hilker et al., 2009). Floods can also have major consequences and have been widely studied, in Europe notably, where the major flood events of the second part of the 20th century have been investigated (e.g., Christensen and Christensen, 2003; Barredo, 2007). Heat waves can moreover also have severe socio-economic impacts (Della-Marta and Beniston, 2008). Often, casualties are greatest when disaster strikes developing countries, whereas economic losses are highest in developed countries. Over the past 40 years, there are numerous examples of natural disasters that caused severe damage and led to important fatalities in various parts of the globe. Damage related to a disaster can be subdivided into direct damages (to buildings, infrastructure, forests, automobiles, . . . ) and indirect losses (due to cleanup efforts, loss of production, . . . ). Not all damages are insured, however, and thus the total economic losses are invariably higher than insured losses. To provide an idea of the potential economic consequences natural disasters can have, historical losses at a global level related to both man-made and natural events of the past 40 years were compiled by SwissRe (Figure 1.1, left). This figure allows the comparison of losses of man-made events such as the September 2001 attacks to natural disasters. Hurricanes over the United-States seem to lead to the most severe damage, along with earthquakes; the European storm Lothar is also among the costliest events of the 1970-2009 period. Figure 1.1 (left) indicates that if economic losses related to man-made disasters show no particular change over this 40-year period, losses 1.

(15) Chapter 1. Overall Introduction. Figure 1.1: Left: Man-made disasters versus Natural catastrophes for the 1970-2009 period as recorded by SwissRe. Right: Natural catastrophes losses split into primary and secondary perils (see text for details). All values are indexed to 2009 prices.. related to extreme natural events exhibit a significative positive trend. This positive trend may be explained by an increasing number of severe hurricanes (Emanuel, 2005), but also because population and population density have been growing over time. Moreover, important urbanisation processes were observed during the last three decades, and some major cities are located in risk-prone areas. Therefore the exposure has dramatically increased, thus leading to strong probabilities of destructive events leading to major financial losses and important casualties. Insurance statistics also point out that although "secondary perils" - that are generally more localized such as landslides or floods have less individual financial loss burdens, they nevertheless overall have a constant and significant impact on total economic losses (Figure 1.1, right). Starting from the mid-80s, the impact of these "secondary perils" are slightly increasing, costing approximately $10 billion per year.. 1.1.1. Risk assessment. Countries or regions that are in risk-prone areas need to anticipate potential future destructive extreme natural events. Insurance and re-insurance companies provide good solutions to avoid major economic losses and to transfer the economic risk, but they cannot prevent the loss of human lives. Adaptation and prevention strategies can be designed, but as the scale of events is very variable, a unique method cannot be applied to find helpful solutions to reduce the risk for all extreme events (Beniston, 2007). A better comprehension of the complex mechanisms that generate the various types of climate extremes are necessary to develop efficient risk mitigation plans. Apart form the substantial rise in the number of inhabitants and increase of infrastructure in risk-prone areas, one needs to deal with the uncertainty linked to future behavior of catastrophic events. Cyclone tracks could for example be slightly modified under future climate conditions (Bengtsson et al., 2006), therefore regions that are now reasonably safe from extremes could become more vulnerable in the future. There is thus a very real need to link climate research to economic models, in order to better assess the potential for damage that may trigger a particular region to enable appropriate response strategies.. 1.1.2. PhD goals. This PhD work aims at analyzing the impacts and costs of strong winter storms on infrastructures in the canton of Vaud, in Western Switzerland. This vast canton has suffered severe damage due to heavy storms in the past 20 years, notably related to the major events detailed in section 1.3.3. Motivated by these historical losses, the state Insurance company of the Canton of Vaud (Établissement Cantonal d’Assurances, ECA) is willing to fully analyze these past events in order to prevent major losses and reduce the economic risk related to such events. As similar investigations have already been considered for other natural hazards such as flooding or seismicity, an approach 2.

(16) Chapter 1. Overall Introduction. to prevent major losses related to extreme wind speeds is needed. An analysis on possible zones that are at higher risk or that have encountered greatest losses is suggested in order to adopt strategical prevention plans. Currently, no specific legislation exists on construction permits, therefore specific resistant construction materials would be suggested for buildings located in estimated critical zones that need to be pointed out.. 1.1.3. Introduction outline. In this PhD introduction chapter, an overview of the main natural hazards that occurred in Switzerland is given, along with a explanation of how the Swiss Insurance system deals with these costly events. A section that details the climate conditions of Switzerland is then proposed, including a comprehensive description of three of the most severe wind storms that hit Switzerland and moreover the canton of Vaud since 1990. Apart from these 3 storms, a list of 24 storms of major relevance is provided, and the spatial distribution of their related economic losses is analyzed in detail. Finally, an overview of the existing storm loss models is given, followed by investigations of future climate conditions and future storminess in Switzerland.. 1.2. Swiss Insurance System and Natural Hazards. In this section, an overview of the economic impacts related to natural hazards in Switzerland is given for the past 20 years. A description of the public insurance system of Switzerland is provided, along with a explanation of how the country and the Swiss cantons try to cope with, prevent major human and economic losses and protect themselves against these various natural hazards, notably against strong winds.. 1.2.1. Historical Losses related to Natural Hazards in Switzerland. The amount of economic losses related to natural hazards in Switzerland and their distribution across the various hazard types during 1991-2010 are plotted in Figure 1.2. Losses can show important variations from year to year, as they for example exceeded CHF 880 million in 2005 and dropped to below CHF 130 million the following year (Figure 1.2, left). The high expenses in 2005 are related to the severe flooding that occurred in the central Alps, in particular in Les Diablerets (Canton of Vaud). In 1999, due to Lothar in particular, economic losses exceeded a billion CHF1 . Overall losses for these 20 years exceeded CHF 4.5 billion, which corresponds to an average CHF 250 million per year. Apart from meteorological hazards, different types of natural events have had important economic impacts in Switzerland since 1991 (Figure 1.2, right). With total losses of CHF 1.76 billion, floods are the most damaging events for the 1990-2010 period, followed by hail (CHF 1.42 billion) and wind storms (CHF 1.22 billion).. 1.2.2. Public Insurance System of Switzerland. The type of natural hazards that occur in Switzerland can differ significantly from one canton to another, due to the highly heterogenous terrain and the numerous local climate conditions of the country (as discussed further in section 1.3.1). As a consequence, no federal law regarding insurance against fires and natural hazards exists in Switzerland: laws related to insurance and prevention against fires and natural hazards are established individually by each canton. A system of public cantonal insurance companies (ECAs) exists and is implemented in 19 of the 26 swiss cantons. These state insurance companies are competent to apply the cantonal laws in terms of prevention and protection related to fires or natural hazards. In the canton of Vaud, the state political authorities gave 3 main public service assignments to the ECA-Vaud, which are to prevent 1. Source: Damage Statistics of the AEAI. 3.

(17) Chapter 1. Overall Introduction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igure 1.2: Left: the evolution of economic losses related to natural hazards in Switzerland for the 1991-2010 period, expressed in CHF millions (prices not indexed to inflation rates). Right: the distribution of economic losses related to the various types of natural hazards in Switzerland (source: Switzerland Association of Cantonal Insurance Companies (AEAI)).. damages, to secure and insure the infrastructures located in the canton. These duties rely on 3 existing laws created in the canton of Vaud related to natural disasters2 , which notably ensure that insurance for buildings and movable properties is mandatory in Vaud (LAIEN) and state that efficient efforts should be made to protect buildings and movable properties from fires and natural hazards (LPIEN). Public insurance establishments of the 19 cantons are in close cooperation, providing financial supports to particular cantons in case of severe localised damages. Each cantonal ECA contributes to a common fund - called the CIREN - which was created by the association of the ECAs (AEAI: Association des Etablissements Cantonaux d’Assurance Incendie) to provide sufficient financial assistance in case of extremely costly natural disasters. Moreover, an additional reinsurance system was fulfilled by the cantonal insurance companies: the Intercantonal Reinsurance Union (UIR). The latter works similarly to global reinsurance companies, but at lower prices for the Swiss cantons. This unique solution of a public solidarity insurance system allows to face the potential high economic losses due to weather extremes; it also allows to cope with possible strong interannual variability in terms of monetary losses related to natural hazards.. 1.2.3. Natural Hazards - Prevention. As prevention plans associated with fires have already been implemented in Switzerland, risk assessment related to natural hazards and the adoption of prevention strategies are more delicate to estimate since the analysis of such hazards, in terms of their occurrence and localisation, is far more complex. Until recently, damage related to natural events were rather scarce and only a few isolated buildings were concerned. In the last decades, losses have increased, mainly due to the increase of insured values, but also linked to an possible rise in extreme events, justifying a more complete analysis of these phenomenons and their socio-economic impacts. Risk prevention strategies against natural hazards within the ECAs is divided in 3 sections, which are prevention against land surface hazards (landslides, rockfalls, avalanches and floods), geological hazards (earthquakes) and meteorological hazards (storms, hail or snow). A better understanding of meteorological hazards such as storms would help assess the economic risk and furthermore the zones at high risk that would be likely to benefit from specific building constraints. The devastating and costly wind storms of 1999 helped to realize the importance and the geographical extent of damage meteorological extremes can have. Investigation of potential economic losses can also provide 2. The three laws are the LAIEN, LPIEN and LSDIS.. 4.

(18) Chapter 1. Overall Introduction. information of the sufficient financial provisions that insurance companies should put aside, as well as the reinsurance conditions, with the intercantonal companies (19 cantonal ECAs) or with external reinsurance companies.. 1.2.4. Danger Matrix. Regarding the land surface hazards (landslides, rockfalls, avalanches and floods), substantial efforts have been made in the past 10 years to assess the risk related to these phenomena. The frequency of occurrence of these events was assessed for the entire country, using statistical methods and modeling tools that were based on historical records of past events. Matrix diagrams combining intensity and probability of a given event were used, classifying the risk according to 4 different levels: high - moderate - low - very low (Figure 1.3). Based on the results of the modeling analysis and with the matrix diagram, it is then possible to determine the expected hazard level for any point on a map. A detailed explanation of these hazard assessment procedures applied for the landslides can be found in Lateltin et al. (2005). Hazard maps for the 4 land surfaces hazards covering the entire surface of the country are expected to be completed for Switzerland in 2012.. Figure 1.3: Matrix diagram that combines the probability and intensity of a given hazard, with the 4 hazard levels: red (high), blue (moderate), yellow (low) and yellow-white hatched (very low).. For a given hazard (land surface hazard), these maps at a national scale thereby classify the country into high (red), blue (moderate), yellow (low) and yellow-white hatched (very low) hazard levels (Figure 1.3). The results of these investigations should be used in land-use planning strategies in order to efficiently reduce the socio-economic risk. Buildings are not allowed to be constructed in the red zones, whereas they would need to match specific building requirements in the blue zones. In the yellow zone, building codes should also be considered, and information to land-owners should be provided. Existing structures in high hazard level areas should be protected by special prevention measures, such as dikes for floods.. 1.2.5. Wind damage and wind load legislation (SIA 261 Norm). Similar detailed analysis have not been investigated for meteorological extremes such as wind storms or hail events. Unlike land surface hazards, which occur under specific topographic conditions and therefore threaten limited regions of the country, storms or hail can prevail in very large areas. And as discussed further in section 1.3.2, wind storms are very delicate to estimate, both in terms of their occurrence and localization. It is consequently difficult to accurately perform detailed hazard 5.

(19) Chapter 1. Overall Introduction. maps at the scale of Switzerland. Nevertheless, investigations on past storm events can provide important information on the amount and the spatial distribution of historical economic losses. A analysis of old storms, combined with an overview of the insured values, can contribute to assess the risk related to high winds. An estimation of the impacts of demographic growth and the increase of insured values on future economic losses is required in order to evaluate the financial needs to face even higher losses. Additionally, the eventual consequences of changing climate conditions can be analyzed. To prevent wind speed damage on infrastructures in Switzerland, engineers include building codes that are given by the SIA 261 Norm. This norm, that was established by the Swiss Society of Engineers and Architects (SIA) in 2003 for Switzerland, indicates the local 50-year return period wind gusts across the country at a 10 meter height. These wind speed calculations are based on historical wind data and numerical simulations. In the SIA 261 Norm, wind speeds values are expressed in terms of their related dynamic pressure exerted on surface structures. The dynamic pressure map of Switzerland is shown in Figure 1.4. The resulting map divides the country into six different zones that exhibit specific wind characteristics according to their topographical features (such as Alpine ridges/peaks, foehn valleys or the Swiss Plateau). In the Alps, wind speeds - and consequently their related dynamic pressure - are generally higher (280 km/h, or 3.3 kN/m2 ) than those measured in the Swiss Plateau (140 km/h, or 0.9 kN/m2 ) or in foehn valleys (1.3 kN/m2 , or 170 km/h).. Dynamic Pressure map of Switzerland (SIA 261 Norm). 0.9 kN/m2 1.1 kN/m2 1.1-3.3 kN/m2 1.3 kN/m2 1.4-3.3 kN/m2 2.4 kN/m2 3.3 kN/m2. 0. 50. 100 km. Figure 1.4: Dynamic Pressure map of Switzerland provided by the Swiss Society of Engineers and Architects (SIA, 2003) that is used for the 261 Norm. The extent of the canton of Vaud is also shown.. All recent buildings in Switzerland should be built following the indications provided by the SIA Norm 261, and therefore resist to dynamic pressures related to 50-year return period wind gusts. To assess the wind loads on infrastructures following this norm, engineers furthermore take into account altitude, landuse and specific building characteristics (such as building height and shape). Damage related to strong winds can nevertheless be observed in Switzerland. Nowadays, the ECAs refund building damage when sufficiently high wind speeds are recorded (on the basis of official records), and if more than one building is damaged within a specific area. 6.

(20) Chapter 1. Overall Introduction. 1.3. Climate of Switzerland. This section describes the main climate conditions that prevail in Switzerland. The dominant wind conditions are also discussed, followed by a detailed description of three of the past most severe historical wind storms in Switzerland.. 1.3.1. Swiss Climate Conditions. Switzerland is a small country of approximately 41’000 km2 , located in central Europe. The landscape is extremely diversified and irregular, as flat areas are mixed with important mountain chains, big lakes with large glaciers and open pastures with partly or completely forested zones. The climate is mainly driven by synoptic conditions (Kirchhofer, 1982), with a combination of continental, polar, Atlantic, Mediterranean and even Saharan air masses influencing the swiss weather conditions. The important presence of mountains in Switzerland plays a large role in the Swiss climate, as the two main mountain chains - namely the Alps and the Jura - occupy more than 60% of the territory (Figure 1.5, left). The Jura is a relatively long, narrow but rather low mountain range, it has therefore a limited influence on the climate conditions of Switzerland compared to the Alps. With peaks often exceeding 4000 m above sea level and an extremely high altitudinal gradient, the Alps act as a climate barrier between northern Switzerland, which is under the influence of both Atlantic and continental air masses, and southern Switzerland, where the Mediterranean Sea exerts a substantial influence (Beniston, 2006). They therefore significantly affect large-scale atmospheric flows simply by their sheer physical presence. Many local specific climate conditions can be found in the Alps, in particular due to the topographic characteristics and the exposure of the surface to climatic elements. This can lead to extremely large variabilities in the annual values of temperatures for example; their inter-annual variability is indeed stronger than the global values (Beniston, 2005). Although mountains can generate their own local weather conditions, the climate of Switzerland is still mainly under the influence of the general atmospheric circulation patterns. Climate in Switzerland will certainly be modified under future climate conditions. Projections towards the 2050s indicate temperature rises of 2◦ C for winters and of up to 2.5◦ C for the summer seasons, whereas values for 2100 are estimated to increase from 3.5◦ C to 7◦ C compared to the 19601990 period (OcCC, 2008). In the Alps, for the end of the 21rst century, climate model simulations. Figure 1.5: Left: Map of Switzerland, highlighting the important presence of mountains, and the Swiss Plateau stretching from east to west. To the west, the extent of the Canton of Vaud is also shown. Right: Map of the Canton of Vaud and its 368 municipalities, boarded to the south/south-east by the Alps and to the north by the Jura mountains. The main cities and the weather stations are also shown.. 7.

(21) Chapter 1. Overall Introduction. suggest a temperature increase of roughly 4◦ C and 6◦ C for the winter and summer seasons, respectively, as reported by Beniston (2009). Hence, studies indicate that the risk of extreme cold events might be reduced due to these increasing mean temperatures in winter, whereas droughts and heat waves might become more common during future summers (Schar et al., 2004; Christensen et al., 2007). Shifts in precipitations patterns are also expected, with probable increases in winter and decreases of up to 30% for summer seasons (Schmidli et al., 2007). Although there are important uncertainties linked to future climate scenarios, studies generally agree that Switzerland will shift towards a Mediterranean-type climate in the future (Marinucci et al., 1995; Rotach et al., 1997). A complete analysis of the possible effects of climate change for Switzerland, notably regarding extreme natural events, is discussed in section 1.6.3.. 1.3.2. Dominant winds in Switzerland. Strong winds in Switzerland usually originate either over the Atlantic ocean or are associated with foehn-type flows over the Alpine region; they are the consequences of large-scale atmospheric processes, and have very little to do with local climatic conditions in the Alps (Beniston, 2007). However, this complex and mountainous terrain has an evident effect on synoptic processes, and near-surface winds are widely formed from the interactions of the incoming large-scale winds and the locally forced wind systems. Among the notable local winds, in Western-Switzerland for example, the “Joran” is generated from the North-West along the Jura mountains following the passage of cold fronts (Bohle-Carbonell, 1991). Other examples in Western Switzerland are the Vaudaire, which is driven by a strong Venturi effect and pulls out form the Rhone valley into the Geneva lake, or the Bornan. Moreover, wind gravity wave breaking processes can also be generated in mountain regions and lead to strong winds (Qingfang and Doyle, 2004). A detailed description and classification of the numerous local wind regimes in Switzerland can be found in Schüepp (1978) or Weber and Furger (2001). Many large-scale factors are likely to have an influence on the winds over Switzerland. Among them, the North Atlantic Oscillation index (NAO), which generates typical temperature and precipitation patterns of the European continent (Casty et al., 2007), has a obvious influence on the climate variability, and notably on storms. The NAO index plays an important role in determining the synoptic conditions over Europe, and therefore has an influence on the continental cyclone tracks. Studies have investigated the relationship between the NAO and the storms in Europe, pointing out that extreme cyclones are more frequent in positive NAO phases (Raible, 2007; Pinto et al., 2009b). However, other factors need to be taken into account as the NAO alone is not sufficient for explaining the variability of cyclone counts over the North Atlantic and Western Europe regions (Mailier et al., 2006). The following section details the most common wind regimes of Switzerland and their associated synoptic conditions. Westerly flows Westerly winds originate over the Atlantic Ocean and bring humid air masses towards Switzerland. These winds coincide with the presence of a low-pressure center over Scandinavia combined with secondary low-pressure waves over the United Kingdom and and high pressures in the region of the Azores. As tropical cyclones (or hurricanes) take their energy from very warm oceanic surface temperatures and homogeneous wind directions, extratropical cyclones - also called mid-latitude cyclones, since they generally occur outside of the tropics - notably prevail in Europe and are characterized by synoptic scale low pressure weather systems and meteorological zonal fronts. They are generated by an atmospheric horizontal temperature contrast between a warm and a cold air mass, and develop when a wave forms on a frontal surface separating these two air masses of different temperatures. As the amplitude of the forming wave rises, the pressure at the centre of disturbance 8.

(22) Chapter 1. Overall Introduction. decreases, eventually intensifying to the point at which a cyclonic circulation begins. These lowpressure systems are commonly associated with strong winds where the pressure gradients are steep, precipitation, and temperature changes. When the cold air mass sweeps under the warm air of the system (occlusion), the cyclone decays, as it is subsequently uniquely composed of a cold air mass. A complete description, analysis and classification of these extra-tropical cyclones can be found in Deveson et al. (2002) or Gray and Dacre (2006). The strongest extra-tropical cyclones mainly occur during winter, since the north-south pressure gradients in mid-latitude zones are strongest during this period. Foehn (South Flows) On the contrary, Foehn winds are southerly warm and dry winds that were originated below the Alpine domain. They are usually associated with low-pressures over the British Isles combined with a high pressure zone located roughly over the Mediterranean Sea. Humid air masses are directed northwards before being physically blocked by the alpine arc, where severe precipitations are often observed, and are then warmed (due to important latent heat release) and dried by descent by adiabatic compression on the lee side of the mountains (Hoinka, 1985). Such phenomenons can lead to very strong gusts and cause important damage, as it was the case for example in 1982 (Fallot and Roten, 1985). Because they lead to a significant and sudden rise of temperatures, foehn winds have as well some climatic consequences as they are also notably responsible for the snow melting and local droughts on the North side, and possible landslides and flooding due to heavy precipitations on the South. Pressure differences between the north and the south of the Alps can be very important during foehn events (Drechsel and Mayr, 2008). Similar foehn winds can also come from the North, when the anticyclone is located West of Switzerland: the inverse conditions are then observed, with precipitation to the North and warm winds to the South of the Alps; the foehn intensity are however smaller in this case. Due to the imposing presence of mountains, apart from the foehn, numerous small-scale and generally localized winds that are generated by specific topographic conditions occur throughout Switzerland (e.g., the Joran or the Vaudaire). Bise (Easterly Flows) In Switzerland, the "Bise" wind occurs when high-pressure cells are located to the North (or North-West) of Europe. This North-Easterly wind generally brings cold and dry air from the baltic regions. Due to channelling effects, the Bise is often the strongest in the confluent region of the Jura and the Alps, in the so-called "Swiss-Plateau" (Wanner and Furger, 1990).. 1.3.3. Historical Swiss Storms. Since 1500, most of the important damaging historical storms have occurred during the winter period (October to March) (Pfister, 1999; Alexandersson et al., 2000). In the past 20 years, wind storms of major relevance for the insurance industry occurred in Switzerland, causing severe damage to infrastructures, forests and other important economic sectors. Three of these events are detailed here. Vivian The Vivian storm was characterized by a deep cyclone in the North Atlantic and by strong geopotential and baroclinic north-south gradients in the troposphere over Western Europe, which induced a series of intense wind storms with extremely high wind speeds through Europe in January and February 1990, for example in the United Kingdom (Hammond, 1990; McCallum and Norris, 1990) and Switzerland (Schüepp et al., 1994). Together, these windstorms caused economic losses of more than US $10 billion. Over 50 million m3 of wood were destroyed in Germany, Austria, 9.

(23) Chapter 1. Overall Introduction. Czechoslovakia, Switzerland, and France (Flavin, 1994). Along with Vivian, storms Daria and Wiebke were the most severe european storms of the winter of 1990. Wind storm Vivian crossed Switzerland on February 27th (Schraft et al., 1993), particularly hitting the alpine region, with a historical record gust of 74.5 m/s recorded at the station of Col du Grand St-Bernard, and causing major destruction to the transportation sector and devastating important tracts of forests, with over 52 million m3 of wood blown to the ground (Flavin, 1994; Schüepp et al., 1994). The Vivian storm was studied in numerous papers and analyzed with regional climate models (e.g., Goyette et al., 2001). Lothar The synoptic characteristics (Ulbrich et al., 2001; Wernli et al., 2002) as well as statistical analysis of possible return periods (Goyette et al., 2003; Ceppi et al., 2008) of the December 26th , 1999 Lothar storm have been widely studied in the literature. The storm originated over the Atlantic ocean, and first came along Europe as a low intensity cyclone. By approaching the zonal Atlantic jet-stream axis, its intensity increased rapidly in a strongly baroclinic environment over the central and eastern Atlantic. Lothar’s centre then travelled through France and Germany with a minimum pressure value of 974 hPa. Extremely large pressure gradients generated the highest wind velocities and most severe damage in northern Switzerland. In the Jura mountains and the Alps maximum speeds between 180 and 250 km/h were observed. For many stations in the North of Switzerland, it was the highest wind speed ever recorded; the southern part of the Alpine region was less affected. Highest damage due to the Lothar storm were observed in France, Germany, and Switzerland (Bresch et al., 2000; MunichRe, 2001), with total economic losses estimated at about US $12 billion. In addition, more than 110 fatalities across Europe were attributed to this storm, which was ranked among the 15 most costliest events of the 1970-2009 period3 . Storm Lothar also led to important damages to forests in Switzerland, as nearly 13 millions m3 of wood have fallen in Switzerland (WSL and BUWAL, 2001), which roughly corresponds to three times the annual harvest. These damages to forests exceed by far those previously experienced in Switzerland, as the second and third most damaging storms in 130 years occurred in 1990 (storm Vivian - 4.9 million m3 ) and in February 1967 (2.4 million m3 ). These two storms were already classified as ‘extreme’ (Pfister, 1999), a classification used only for four events in total between 1500 and 1995 (Braun et al., 2003). The record damage related to Lothar may as well have been promoted by a preceding storm that occurred two weeks earlier. Klaus Ten years after the "storm of the century", another major winter storm (storm Klaus) struck countries of south-west Europe, particularly hitting northern Spain and southern France on January 24th , 2009. At first perceived as a small perturbation over the Atlantic Ocean, it was then characterized by extremely rapid extratropical cyclogenesis as the developing storm wave reached the westerly flow and experienced a sudden and sharp intensification on January 23rd as it moved rapidly towards the Bay of Biscay, where it deepened further (Liberato et al., 2011). When reaching the coasts, core pressures fell very rapidly from 1001 to 973 hPa and finally even to 963 hPa (Knippertz and Wernli, 2010). Although such heavy storms appear less often over southern Europe (Pinto et al., 2009b; Trigo, 2006), it still triggered important damages in these countries and in Switzerland, with gusts above 150 km/h; it came along with heavy rains and intense flooding. Nearly 1 million m3 of wood were timbered in the Landes forest, which corresponds to more than 60% of the forest. Winter storm Klaus, which led to insured losses of US $3.5 billion, was ranked as the costliest event of 2009 in the world by insurance companies4 . 3 4. http://media.swissre.com/documents/sigma1_2010_en.pdf http://media.swissre.com/documents/pr_20091130_sigma_fr.pdf. 10.

(24) Chapter 1. Overall Introduction. 1.4. Canton of Vaud. The Canton of Vaud is one of the largest of the 26 Swiss cantons with a surface area greater than 3’000 km2 (Figure 1.5, right). It is divided into 10 districts, which themselves are subdivided into communes (municipalities) which numbered 326 in January 2012. In the manner of the entire country, the canton’s landscape is very varied: it is bordered to the south by the Lake of Geneva one of the biggest in Europe - , to the west by the Jura, to the north by the Lake of Neuchâtel and to the east by the alpine and pre-alpine mountains. In the centre, the rather flat but still irregular Swiss Plateau stretches from the west to the northeast. The lowest altitude (370 m) is located on the surface of the Lake of Geneva, whereas the highest point exceeds 3’200 m and is situated in the Alps. South of the Lake of Geneva is France, with its steep alpine faces falling abruptly in the water. Numerous local topographically or thermally induced winds can be observed on the two major lakes, either induced by the alpine or the Jura mountain chains. The climatic conditions and more particularly the main wind regimes of Switzerland detailed in section 1.3.2 also prevail in the Canton of Vaud. The population of the Canton of Vaud was of 713’281 at the end of 20105 , which ranks it 3rd among the 26 cantons. This corresponds to a population density of approximately 222 hab/km2 . The 3 major cities are Lausanne, Yverdon and Montreux, and are all located on the shore of the lakes. Therefore, the insured values of Vaud are mostly concentrated on these lake shores.. 1.4.1. Wind storm detection for the Canton of Vaud. The major wind storm events that occurred in the Canton of Vaud during the past 20 years are analyzed in this PhD study. These events were detected by investigating both the wind statistics provided by the swiss weather stations network and historical economic loss data provided by the ECA-Vaud. By combining these two sources, the major events in terms of wind speeds and monetary losses were identified for the Canton of Vaud. To detect major wind storms, wind observations were gathered as a first step. The weather station network of Switzerland consists of more than 100 weather stations that have been recording wind speeds every 10 minutes since 1981, providing reliable and quality-checked data in digital form (Bantle, 1989; Begert et al., 2005). Meteorological stations located either in or in the close neighborhood of the Canton of Vaud were chosen to detect the past storms that occurred in the Canton of Vaud; 22 stations were hereby selected. In storm loss models, damage is expected to occur when wind speeds exceed the local 98th percentiles of the daily maximum wind speeds (Klawa and Ulbrich, 2003). Following this idea, similar threshold values were calculated for each of the 22 selected weather stations. Then statistics of daily maximum wind speeds from these 22 stations were examined, and values that exceeded the local 98th percentile thresholds at each station were identified. In order to avoid selecting events characterized by only local and isolated gusts, a storm event was detected and the corresponding dates were extracted when a sufficient number of daily thresholds were simultaneously exceeded in different locations. Finally, these extracted storm dates were matched to corresponding available historical loss data records to extract the dates of major wind storms, resulting in a list of 24 events (Table 1.1). These 24 storms are the most damaging events of the past 20 years for the Canton of Vaud. The first identified wind event was winter storm Vivian, in 1990 (see section 1.3.3), and the last storm was the Xynthia storm that hit the Canton of Vaud on February 28th , 2010. All these storms occurred during the extended winter period (November - April). The majority of these storms were extratropical cyclones (such as Vivian and Lothar), and the remaining were intense south foehn events (for example storm Xynthia) or Bise events. This storm detection methodology is based on extracting only the major wind storms, in terms of their intensity and damage; this implies that a number of 5. Swiss Federal Statistical Office.. 11.