Climate changes are causing extreme weather conditions, which is expected to increase. Extreme precipitation is an example of this, which, through flooding, affects humans and buildings. This study aims to investigate how management of extreme precipitation takes place in two Swedish municipalities, the city of Gothenburg and Örebro municipality. Based on a thematic analysis of qualitative interviews with employees in the municipalities and document analysis of the municipalities' strategic planning documents, this study takes shape. The theoretical approach is based on what the existing research considers to be the most successful strategic, practical and organizational approaches. The result of this study shows that there is a need for better knowledge and clearer regulations regarding the management of extreme precipitation. The municipalities have come further in the work with strategic planning than with practical measures. Multifunctional facilities are a priority and, based on the study material, is the most effective way to implement practical measures.

Floods associated with extreme precipitation are one of the most serious natural hazards, which produce substantial human and socio-economic losses in central Europe. One way to reduce the impact of flooding is by increasing preparedness with better flood forecasts and warnings, which is not possible without a proper understanding of physical processes leading to a flood hazard. However, frequent research on floods in relation to causal precipitation and synoptic conditions is usually carried out regionally, although some events often affect areas of a size of entire countries or even larger. The thesis was focused exactly on these large-scale precipitation and flood events that occurred in the second half of the 20th century and then until 2013, for which the size of the affected area is as crucial in the extremity assessment as the magnitude of flood discharges or precipitation totals. The extremity indices used for the assessment of extreme precipitation and flood events connected both aspects. The larger area of interest defined within central Europe allowed examining the spatial structure of events, the differences between them, and their relation to conditions in the atmosphere. To connect the extremes of precipitation with extremes in atmospheric conditions, the causal circulation was...

Les inondations constituent le risque naturel prédominant dans le monde et les dégâts qu'elles causent sont les plus importants parmi les catastrophes naturelles. Un des principaux facteurs expliquant les inondations sont les précipitations extrêmes. En raison des changements climatiques, l'occurrence et l'intensité de ces dernières risquent fort probablement de s'accroître. Par conséquent, le risque d'inondation pourrait vraisemblablement s'intensifier. Les impacts de l'évolution des précipitations extrêmes sont désormais un enjeu important pour la sécurité du public et pour la pérennité des infrastructures. Les stratégies de gestion du risque d'inondation dans le climat futur sont essentiellement basées sur les simulations provenant des modèles numériques de climat. Un modèle numérique de climat procure notamment une série chronologique des précipitations pour chacun des points de grille composant son domaine spatial de simulation. Les séries chronologiques simulées peuvent être journalières ou infra-journalières et elles s'étendent sur toute la période de simulation, typiquement entre 1961 et 2100. La continuité spatiale des processus physiques simulés induit une cohérence spatiale parmi les séries chronologiques. Autrement dit, les séries chronologiques provenant de points de grille avoisinants partagent souvent des caractéristiques semblables. De façon générale, la théorie des valeurs extrêmes est appliquée à ces séries chronologiques simulées pour estimer les quantiles correspondants à un certain niveau de risque. La plupart du temps, la variance d'estimation est considérable en raison du nombre limité de précipitations extrêmes disponibles et celle-ci peut jouer un rôle déterminant dans l'élaboration des stratégies de gestion du risque. Par conséquent, un modèle statistique permettant d'estimer de façon précise les quantiles de précipitations extrêmes simulées par un modèle numérique de climat a été développé dans cette thèse. Le modèle développé est spécialement adapté aux données générées par un modèle de climat. En particulier, il exploite l'information contenue dans les séries journalières continues pour améliorer l'estimation des quantiles non stationnaires et ce, sans effectuer d'hypothèse contraignante sur la nature de la non-stationnarité. Le modèle exploite également l'information contenue dans la cohérence spatiale des précipitations extrêmes. Celle-ci est modélisée par un modèle hiérarchique bayésien où les lois a priori des paramètres sont des processus spatiaux, en l'occurrence des champs de Markov gaussiens. L'application du modèle développé à une simulation générée par le Modèle régional canadien du climat a permis de réduire considérablement la variance d'estimation des quantiles en Amérique du Nord. / Precipitation extremes plays a major role in flooding events and their occurrence as well as their intensity are expected to increase. It is therefore important to anticipate the impacts of such an increase to ensure the public safety and the infrastructure sustainability. Since climate models are the only tools for providing quantitative projections of precipitation, flood risk management for the future climate may be based on their simulations. Most of the time, the Extreme value theory is used to estimate the extreme precipitations from a climate simulation, such as the T-year return levels. The variance of the estimations are generally large notably because the sample size of the maxima series are short. Such variance could have a significant impact for flood risk management. It is therefore relevant to reduce the estimation variance of simulated return levels. For this purpose, the aim of this paper is to develop a non-stationary and regional statistical model especially suited for climate models that estimates precipitation extremes. At first, the non-stationarity is removed by a preprocessing approach. Thereafter, the spatial correlation is modeled by a Bayesian hierarchical model including an intrinsic Gaussian Markov random field. The model has been used to estimate the 100-year return levels over North America from a simulation by the Canadian Regional Climate Model. The results show a large estimation variance reduction when using the regional model.

Les événements de pluies extrêmes et les inondations qui en résultent constituent une préoccupation majeure en France comme dans le monde. Dans le domaine de l'ingénierie, les méthodes d'analyse probabiliste sont pratiquement utilisées pour prédire les risques, dimensionner des ouvrages hydrauliques et préparer l'atténuation. Ces méthodes sont classiquement basées sur l'hypothèse que les observations sont identiquement distribuées. Il y a aujourd'hui de plus en plus d'éléments montrant que des variabilités climatiques à grande échelle (par exemple les oscillations El Niño – La Niña, cf. indice ENSO) ont une influence significative sur les précipitations dans le monde. Par ailleurs, les effets attendus du changement climatique sur le cycle de l'eau remettent en question l'hypothèse de variables aléatoires "identiquement distribuées" dans le temps. Il est ainsi important de comprendre et de prédire l'impact de la variabilité et du changement climatique sur l'intensité et la fréquence des événements hydrologiques, surtout les extrêmes. Cette thèse propose une étape importante vers cet objectif, en développant un cadre spatio-temporel d'analyse probabiliste régionale qui prend en compte les effets de la variabilité climatique sur les événements hydrologiques. Les données sont supposées suivre une distribution, dont les paramètres sont liés à des variables temporelles et/ou spatiales à l'aide de modèles de régression. Les paramètres sont estimés avec une méthode de Monte-Carlo par Chaînes de Markov dans un cadre Bayésien. La dépendance spatiale des données est modélisée par des copules. Les outils de comparaison de modèles sont aussi intégrés. L'élaboration de ce cadre général de modélisation est complétée par des simulations Monte-Carlo pour évaluer sa fiabilité. Deux études de cas sont effectuées pour confirmer la généralité, la flexibilité et l'utilité du cadre de modélisation pour comprendre et prédire l'impact de la variabilité climatique sur les événements hydrologiques. Ces cas d'études sont réalisés à deux échelles spatiales distinctes: • Echelle régionale: les pluies d'été dans le sud-est du Queensland (Australie). Ce cas d'étude analyse l'impact de l'oscillation ENSO sur la pluie totale et la pluie maximale d'été. En utilisant un modèle régional, l'impact asymétrique de l'ENSO est souligné: une phase La Niña induit une augmentation significative sur la pluie totale et maximale, alors qu'une phase El Niño n'a pas d'influence significative. • Echelle mondiale: une nouvelle base de données mondiale des précipitations extrêmes composée de 11588 stations pluviométriques est utilisée pour analyser l'impact des oscillations ENSO sur les précipitations extrêmes mondiales. Cette analyse permet d'apprécier les secteurs où ENSO a un impact sur les précipitations à l'échelle mondiale et de quantifier son impact sur les estimations de quantiles extrêmes. Par ailleurs, l'asymétrie de l'impact ENSO et son caractère saisonnier sont également évalués. / Extreme precipitations and their consequences (floods) are one of the most threatening natural disasters for human beings. In engineering design, Frequency Analysis (FA) techniques are an integral part of risk assessment and mitigation. FA uses statistical models to estimate the probability of extreme hydrological events which provides information for designing hydraulic structures. However, standard FA methods commonly rely on the assumption that the distribution of observations is identically distributed. However, there is now a substantial body of evidence that large-scale modes of climate variability (e.g. El-Niño Southern Oscillation, ENSO; Indian Ocean Dipole, IOD; etc.) exert a significant influence on precipitation in various regions worldwide. Furthermore, climate change is likely to have an influence on hydrology, thus further challenging the “identically distributed” assumption. Therefore, FA techniques need to move beyond this assumption. In order to provide a more accurate risk assessment, it is important to understand and predict the impact of climate variability/change on the severity and frequency of hydrological events (especially extremes). This thesis provides an important step towards this goal, by developing a rigorous general climate-informed spatio-temporal regional frequency analysis (RFA) framework for incorporating the effects of climate variability on hydrological events. This framework brings together several components (in particular spatio-temporal regression models, copula-based modeling of spatial dependence, Bayesian inference, model comparison tools) to derive a general and flexible modeling platform. In this framework, data are assumed to follow a distribution, whose parameters are linked to temporal or/and spatial covariates using regression models. Parameters are estimated with a Monte Carlo Markov Chain method under the Bayesian framework. Spatial dependency of data is considered with copulas. Model comparison tools are integrated. The development of this general modeling framework is complemented with various Monte-Carlo experiments aimed at assessing its reliability, along with real data case studies. Two case studies are performed to confirm the generality, flexibility and usefulness of the framework for understanding and predicting the impact of climate variability on hydrological events. These case studies are carried out at two distinct spatial scales: • Regional scale: Summer rainfall in Southeast Queensland (Australia): this case study analyzes the impact of ENSO on the summer rainfall totals and summer rainfall maxima. A regional model allows highlighting the asymmetric impact of ENSO: while La Niña episodes induce a significant increase in both the summer rainfall totals and maxima, the impact of El Niño episodes is found to be not significant. • Global scale: a new global dataset of extreme precipitation including 11588 rainfall stations worldwide is used to describe the impact of ENSO on extreme precipitations in the world. This is achieved by applying the regional modeling framework to 5x5 degrees cells covering all continental areas. This analysis allows describing the pattern of ENSO impact at the global scale and quantifying its impact on extreme quantiles estimates. Moreover, the asymmetry of ENSO impact and its seasonal pattern are also evaluated.

The simultaneous occurrences of several extreme events, known as compound extremes, are often associated with greater impact than univariate extremes. Flooding and windstorms are widespread hazards in Europe which can lead to severe property damage and fatalities. During winter, extreme wind and precipitation often co-occur, since they are associated with a common driver, namely extratropical cyclones. In this project, the occurrences of compound wind and precipitation events in Europe are investigated using the ERA5 reanalysis dataset. The analysis covers the years 1979-2019 with a focus on boreal winter. Areas that experience the highest occurrence of compound wet-windy extremes are the west coast of Norway, the Iberian peninsula, parts of central Europe, and southeast of the Black Sea. A few case studies are discussed with the purpose to give an idea of the magnitude of possible impacts. Further, the relationship between extreme wind and precipitation events and the North Atlantic Oscillation (NAO) is presented. During days with positive NAO, extreme precipitation and wind events occur in the central and northern parts of Europe while the negative phase brings extreme wind and precipitation to the southern parts of Europe. Lastly, a short analysis to discover changes in the occurrences of compound precipitation and wind events for the two periods 1979-1999 and 2000-2019 is performed. The result showed no clear changes. The number of compound extremes does not seem to vary for the two periods. / Olika extrema väderhändelser som sammanfaller orsakar ofta större skada än enskilda extrema händelser på många håll i samhället. Översvämningar och vindstormar är vanligt förekommande i Europa och kan leda till kostsamma skador och dödsfall. Extrema vind-och nederbördshändelser sammanfaller vanligen under vintern eftersom de båda ofta orsakas av Nordatlantiska cykloner, som är vanligast under den årstiden. I detta projekt kartläggs sammanfallande vind- och nederbördshändelser i Europa under vintermånaderna december-februari, med hjälp av ERA5 reanalysdata för åren 1979-2019. Områden med relativt hög förekomst av sammanfallande vind- och nederbördshändelser är Norges västkust, Iberiska halvön, delar av Centraleuropa och östra Turkiet. Några fallstudier kopplade till dessa områden är diskuterade för att ge en uppfattning om konsekvenserna av dessa sammanfallande händelser. Eventuella kopplingar mellan sammanfallande vind- och nederbördshändelser och den Nordatlantiska Oscillationen (NAO) är också undersökt. Under den positiva fasen av NAO sker extrema nederbörd- och vindhändelser i norra och centrala delar av Europa medan den negativa fasen ger extrem vind och nederbörd i de södra delarna av Europa.En kort analys för undersöka om förekomsten av extrema sammanfallande händelser har ändrats genomfördes genom att jämföra andelen sammanfallande händelser under de två perioderna 1979-1999 och 2000-2019. Ingen betydande förändring i andelen sammanfallande händelser mellan dessa perioder hittades.

The main goal of this research is to quantitatively assess the resilience and vulnerability of geotechnical infrastructure to extreme events under a changing climate. In the first part, pertinent facts and statistics regarding California’s extreme drought and current status of its levees are presented. Weakening processes such as soil strength reduction, soil desiccation cracking, land subsidence and surface erosion, and oxidation of soil organic carbon are comprehensively evaluated to illustrate the devastating impacts that the California drought can have on earthen structures. In the second part, rainfall-triggered slope instabilities are analyzed using extreme precipitation estimates, derived using the historical stationary and a proposed future nonstationary approach. The extremes are integrated into a series of fully coupled 2D finite element simulations. The final part of this study investigates the impact of simultaneous variations in soil moisture and temperature changes in the California region on soil strength through a proposed thermo-hydro-mechanical framework.

During intensive rainfall, the ground is at risk of flooding if the water has no opportunity to infiltrate into the ground or drain. Cities are most heavily affected by such pluvial floods due to their predominantly solid surfaces. An example of such a city is Umeå, which experienced extensive floods causing up to 40 million Swedish kronor in damages. During the period 1970 to 2020, the population of Umeå has increased from 70,000 to 130,000 inhabitants, which means that both housing demand and thus the proportion of hardened surfaces in the municipality increased rapidly. To avoid future flooding problems, studying intensive precipitation trends as well as factors that affect the risk of pluvial floods is of the utmost importance. The purpose of this research is thus to investigate heavy precipitation trends as well as study how climate change and hardened surfaces affect the risk of flooding in Umeå. This study shows that the frequency of intense rainfall in Umeå has increased compared to the mean of the period 1963-1987 and that climate change could lead to an even higher frequency. Increased frequency of intensive precipitation in combination with an increased proportion of hardened surfaces increases the risk of flooding problems. Furthermore, heavy rainfalls, defined as precipitation above ten millilitres a day, occurred eighteen times a year during the period 1996-2020, which is four days more than the 1963-1987 average. By the end of the twenty-first century, climate change is expected to increase these number of days by a further 20-30% (equivalent to 7-12 days) according to SMHI predictions. Intense rainfall is likely to become more common in the future and population growth in Umeå will likely lead to an increase in the number of paved areas. These changes, in combination, place high demands on Umeå municipality to work efficiently with urban planning and climate adaptation.

Basierend auf der Theorie von Netzwerken wird ein allgemeines Rahmenwerk entwickelt, um kollektive Synchronisationsphänome von Extremereignissen in komplexen Systemen zu studieren. Die Methode vergleicht die Variabilität der einzelnen Teile des Systems auf Grundlage von Beobachtungszeitreihen mit dem Ziel, emergente Synchronisationsmuster von Extremereignissen auf makroskopischer Ebene aufzudecken. Zu diesem Zweck werden die einzelnen Zeitreihen eines interaktiven Systems mit den Knoten eines Netzwerks identifiziert und die Abhängigkeiten zwischen diesen durch die Kanten des Netzwerks dargestellt. Die komplexe interne Synchronisationsstruktur des Systems wird so in Form der Netzwerktopologie mathematisch zugänglich gemacht und kann durch die Einführung geeigneter Netzwerkmaße analysiert werden. Die Methode wird im Folgenden auf räumlich und zeitlich hochaufgelöste Regendaten aus Satellitenmessungen angewendet, um die kollektive Dynamik extremer Regenereignisse in Südamerika zu untersuchen. Diese Anwendung verfolgt drei Ziele: Erstens wird gezeigt, wie die hier entwickelte Methode zur klimatologischen Analyse verwendet werden kann. Zweitens können Quellen und Senken von Extremereignissen durch die Einführung des Konzeptes der Netzwerkdivergenz identifiziert werden. Dies erlaubt es, die gerichteten Netzwerkpfade, entlang derer Extremereignisse synchronisieren, nachzuverfolgen. Auf dieser Grundlage wird eine statistische Regel gewonnen, die beträchtliche Anteile der extremen Regenereignisse in den Zentralanden vorhersagt. Drittens werden die bis dahin entwickelten Methoden und gewonnenen Einsichten dazu verwendet, die Darstellung extre- mer Regenereignisse in verschiedenen Datensätzen zu vergleichen. Insbesondere wird in diesem Kontext die Implementierung solcher Ereignisse in drei gängigen Klimamodellen evaluiert. / Based on the theory of networks, a general framework is developed to study collective synchronization phenomena of extreme events in complex systems. The method relies on observational time series encoding the variability of the single parts of the system, and is intended to reveal emerging patterns of extreme event synchronization on the macroscopic level. For this purpose, the time series obtained from an interactive system under consideration are identified with network nodes, and the possibly delayed and non-linear interdependence of extreme events in different time series is represented by network links connecting the nodes. In this way, the complex internal synchronization structure of the system becomes accessible in terms of the topology of the network, which can be analyzed by introducing suitable network measures. The methodology is applied to satellite-derived rainfall time series of high spatiotemporal resolution in order to investigate the collective dynamics of extreme rainfall events in South America. The purpose of this application is threefold: First, it is shown how the methodology can be used for climatic analysis by revealing climatological mechanism from the spatial patterns exhibited by different network measures. Second, by introducing the concept of network divergence, sink and source regions of extreme events can be identified, allowing to track their directed synchronization pathways through the network. A simple statistical forecast rule is derived on this basis, predicting substantial fractions of extreme rainfall events in the Central Andes. Third, the methodology and the insights developed in the first two steps are used to evaluate the dynamical representation of extreme events in different datasets, and in particular their dynamical implementation in three state of the art climate models.

One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena.

Anthropogenic activities including urbanization, rapid industrialization, deforestation and burning of fossil fuels are broadly agreed on as primary causes for ongoing climate change. Scientists agree that climate change over the next century will continue to impact water resources with serious implications including storm surge flooding and a sea level rise projected for North America. To date, the majority of climate change studies conducted across the globe have been for large-sized watersheds; more attention is required to assess the impact of climate change on smaller watersheds, which can help to better frame sustainable water management strategies. In the first of three studies described in this dissertation, trends in annual precipitation and air-temperature across the Commonwealth of Kentucky were evaluated using the non-parametric Mann-Kendall test considering meteorological time series data from 84 weather stations. Results indicated that while annual precipitation and mean annual temperature have been stable for most of Kentucky over the period 1950-2010, there is evidence of increases (averages of 4.1 mm/year increase in annual precipitation and 0.01 °C/year in mean annual temperature) along the borders of the Kentucky. Considered in its totality, available information indicates that climate change will occur – indeed, it is occurring – and while much of the state might not clearly indicate it at present, Kentucky will almost certainly not be exempt from its effects. Spatial analysis of the trend results indicated that eastern part of the state, which is characterized by relatively high elevations, has been experiencing decreasing trends in precipitation. In the second study, trends and variability of seven extreme precipitation indices (total precipitation on wet days, PRCPTOT; maximum length of dry and wet periods, CDD and CWD, respectively; number of days with precipitation depth ≥20 mm, R20mm; maximum five-day precipitation depth, RX5day; simple daily precipitation intensity, SDII; and standardized precipitation index, SPI were analyzed for the Kentucky River Basin for both baseline period of 1986-2015 and the late-century time frame of 2070-2099. For the baseline period, the majority of the indices demonstrated increasing trends; however, statistically significant trends were found for only ~11% of station-index combinations of the 16 weather stations considered. Projected magnitudes for PRCPTOT, CDD, CWD, RX5day and SPI, indices associated with the macroweather regime, demonstrated general consistency with trends previously identified and indicated modest increases in PRCPTOT and CWD, slight decreases in CDD, mixed results for RX5day, and increased non-drought years in the late century relative to the baseline period. The study’s findings indicate that future conditions might be characterized by more rainy days but fewer large rainfall events; this might lead to a scenario of increased average annual rainfall but, at the same time, increased water scarcity during times of maximum demand. In the third and final study, the potential impact of climate change on hydrologic processes and droughts over the Kentucky River basin was studied using the watershed model Soil and Water Assessment Tool (SWAT). The SWAT model was successfully calibrated and validated and then forced with forecasted precipitation and temperature outputs from a suite of CMIP5 global climate model (GCMs) corresponding to two different representative concentration pathways (RCP 4.5 and 8.5) for two time periods: 2036-2065 and 2070-2099, referred to as mid-century and late-century, respectively. Climate projections indicate that there will be modest increases in average annual precipitation and temperature in the future compared to the baseline (1976-2005) period. Monthly variations of water yield and surface runoff demonstrated an increasing trend in spring and autumn, while winter months are projected as having decreasing trends. In general, maximum drought length is expected to increase, while drought intensity might decrease under future climatic conditions. Hydrological droughts (reflective of water availability), however, are predicted to be less intense but more persistent than meteorological droughts (which are more reflective of only meteorological variables). Results of this study could be helpful for preparing any climate change adaptation plan to ensure sustainable water resources in the Kentucky River Basin.