The complex interplay between biotic and abiotic factors, believed to be responsible for several oak declines in European oak stands during the last three decades, remains poorly understood. Hence, this study aims at clarifying the temporal process of oak declines, as well as identifying individual tree and environmental variables that increase the risk of oak mortality. The study was performed in one of the few areas in northern Europe still holding high densities of old oaks (Quercus robur L.). Cross dating revealed that most trees had died during the last decade. Averaged chronologies and multiple chronological clustering suggested that the onset of the oak decline happened in 1992, when a severe drought took place. Two of the sites showed a rather short time period of heavily reduced growth prior to death, most likely caused by an insect defoliation in combination with a mildew infection of the replacement shoots. Environmental variables presented a rather weak influence on oak mortality. The results support the idea of attributing oak mortality to a combination of long- and short-term stresses, and emphasize the importance of including present as well as past factors when analysing the causes of oak declines.

abstract: Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated. Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2016

The thesis aims at corporate adaptation to climate change impacts in the logistics and transportation industry, especially for the model region Dresden. The paper employs two analyses. The first part deals with a review of the current literature within the topic. 20 references are identified and analysed with a data extraction form. More general adaptation measures are identified in the current literature which can be implemented in the corporate strategy. Crucial effects on company’s operation and its profit have flooding and sea level rise. In comparison adaptation measures, which are identified within five interviews of transportation providers in the model region Dresden, are of technological nature. The interviewed companies adapted significantly to weather extremes in the past. It is identified that heavy precipitation like rain and snow lead to crucial negative impacts to their operations which cause lost profit and customer dissatisfaction in long periods of time. On the other hand these weather conditions may have positive effects in short periods of time. Region-specific analyses in climate change impacts and the implementation of potential adaptation measures for logistics and transportation companies is still in a stage of infancy. Further research is needed on more region-specific analyses and on logistics companies in the model region Dresden as only five of them analysed in this thesis.

Der erste Teil dieser Arbeit beschäftigt sich mit der Frage, welchen Einfluss kurzzeitige Schwankungen der erneuerbaren Energiequellen auf die synchrone Netzfrequenz haben. Zu diesem Zweck wird eine lineare Antworttheorie für stochastische Störungen von dynamischen Systemen auf Netzwerken hergeleitet. Anschließend wird diese Theorie verwendet, um den Einfluss von kurzfristigen Wind- und Sonnenschwankungen auf die Netzdynamik zu analysieren. Hierbei wird gezeigt, dass die Frequenzantwort des Netzes weitestgehend homogen ist, aber die Anfälligkeit für Leistungsschwankungen aufgrund von Leitungsverlusten entlang des Leistungsflusses zunimmt. Der zweite Teil der Arbeit befasst sich mit der Modellierung von netzbildenden Wechselrichterregelungen. Bislang existiert kein universelles Modell zur Beschreibung der kollektiven Dynamik solcher Systeme. Um dies zu erreichen, wird unter Ausnutzung der inhärenten Symmetrie des synchronen Betriebszustandes eine Normalform für netzbildende Akteure abgeleitet. Anschließend wird gezeigt, dass dieses Modell eine gute Annäherung an typische Wechselrichter-Dynamiken bietet, aber auch für eine datengesteuerte Modellierung gut geeignet ist. Der letzte Teil der Arbeit befasst sich mit der Analyse des Risikos von Stromausfällen, welche durch Hurrikans verursacht werden. Hohe Windgeschwindigkeiten verursachen häufig Schäden an der Übertragungsinfrastruktur, welche wiederum zu Überlastungen anderer Komponenten führen und damit eine Kaskade von Ausfällen im gesamten Netz auslösen können. Simulationen solcher Szenarien werden durch die Kombination eines meteorologischen Windmodells sowie eines Modells für kaskadierende Leitungsausfälle durchgeführt. Durch Monte-Carlo-Simulationen in einer synthetischen Nachbildung des texanischen Übertragungsnetzes können einzelne kritische Leitungen identifiziert werden, welche zu großflächigen Stromausfällen führen. / The first part of this thesis addresses the question which impact short-term renewable fluctuations have on the synchronous grid frequency. For this purpose, a linear response theory for stochastic perturbations of networked dynamical systems is derived. This theory is then used to analyze the impact of short-term wind and solar fluctuations on the grid frequency. It is shown that while the network frequency response is mainly homogenous, the susceptibility to power fluctuations is increasing along the power flow due to transmission line losses. The second part of the thesis is concerned with modeling grid-forming inverter controls. So far there exists no universal model for studying the collective dynamics of such systems. By utilizing the inherent symmetry of the synchronous operating state, a normal form for grid-forming actors is derived. It is shown that this model provides a useful approximation of certain inverter control dynamics but is also well-suited for a data-driven modeling approach. The last part of the thesis deals with analyzing the risk of hurricane-induced power outages. High wind speeds often cause damage to transmission infrastructure which can lead to overloads of other components and thereby induce a cascade of failures spreading through the entire grid. Simulations of such scenarios are implemented by combining a meteorological wind field model with a model for cascading line failures. Using Monte Carlo simulations in a synthetic test case resembling the Texas transmission system, it is possible to identify critical lines that trigger large-scale power outages.

Seit der industriellen Revolution haben Menschen durch Verbrennung von fossilen Energieträgern die Treibhausgaskonzentration in der Atmosphäre erhöht. Die daraus folgende Erderwärmung hat weitreichende Folgen für das Klima, unter anderem häufigere und intensivere Wetterextreme. Wegen ihrer gravierenden Auswirkungen auf die Gesellschaft, ist es von allgemeinem Interesse zu verstehen, wie der menschengemachte Klimawandel diese Wetterextreme beeinflusst. In dieser kumulativen Dissertation analysiere ich erst zwei komplexe Wettereignisse, die die Nahrungsmittelproduktion in Europa beeinträchtigen: Frosttage nach dem Beginn der Apfelblüte und Feuchte Frühsommerperioden nach warmen Wintern. In einer dritten Studie untersuche ich wie dynamische Klimaveränderungen in den mittleren Breiten der Nordhalbkugel zu beständigerem Sommerwetter beitragen. Schließlich beschäftige ich mich mit tropischen Stürmen im Nordatlantik und damit, wie sie von der globalen Erwärmung beeinflusst werden. Eine zentrale methodische Herausforderung in diesem Forschungsfeld ist, dass Wetterextreme per Definition selten sind und dass es aufgrund der starken internen Klimavariabilität schwierig ist, die Veränderungen zu quantifizieren, die auf den menschgemachten Klimawandel zurück zu führen sind. In dieser Arbeit verfolge ich zweigegenläufige Ansätze um mit dieser Herausforderung um zu gehen: 1) Ich verwende große Klimasimulationsensembles um den Effekt der internen Klimavariabilität aus zu glätten und dadurch die erzwungenen Veränderungen beim Apfelfrost und in der Persistenz zu ergründen. 2) Mit Methoden, die auf Beobachtungsdaten beruhen, quantifiziere ich den Einfluss der internen Klimavariabilität auf tropische Zyklone um dann einschätzen zu können, in welchem Maß der beobachtete Anstieg der tropischen Zyklonaktivität im Atlantik der internen Klimavariabilität oder erzwungenen Veränderungen zugeschrieben werden kann. / Since the industrial revolution, humans have increased the greenhouse gas concentration of the atmosphere by burning fossil fuels. The resulting global warming has far reaching impacts on the climate system including increasingly frequent and intense weather extremes. Due to the severe impacts these weather extremes cause to societies, there is a strong interest in understanding how anthropogenic climate change affects weather extremes. In this cumulative thesis I first study two compound weather extremes that affect food production in Europe: frost days after apple blossom and wet early summers after warm winters. In a third study I quantify how dynamic changes in the climate system contribute to more persistent summer weather extremes in the northern hemispheric mid-latitudes. Finally, I analyze tropical cyclones in the Atlantic basin and changes in tropical cyclone activity as a result of global warming. One central methodological challenge in the research field is that weather extremes are rare by definition and that due to the strong internal climate variability it is difficult to quantify changes that are forced by anthropogenic climate change. In this thesis I explore two divergent approaches to this challenge: 1) Using large ensemble climate simulations I smooth out the effect of internal variability thereby exposing the forced change in apple frost and weather persistence. 2) Using observation based approaches, I quantify the contributions of internal climate variability on tropical cyclones in order to subsequently estimate to which extent the observed increase in tropical cyclone activity in the Atlantic can be attributed to internal climate variability or forced changes.

No description available.

Coastal socio-ecological systems face unprecedented challenges due to climate change, with impacts encompassing long-term, chronic changes and short-term extreme events. These events will impact society in many ways and prompt human responses that are extremely challenging to predict. This dissertation employs complex systems methods of agent-based modeling and machine learning to simulate the interactions between climatic stressors such as increased flooding and extreme weather and socio-economic aspects of coastal human systems. Escalating sea-level rise and intensified flooding has the potential to prompt relocation from flood-prone coastal areas. This can reduce flood exposure but also disconnect people from their homes and communities, sever longstanding social ties, and lower the tax base leading to difficulties in providing government services. Chapter 2 demonstrates a stochastic agent-based model to simulate human relocation influenced by flooding events, particularly focusing on the responses of rural and urban communities in coastal Virginia and Maryland. The findings indicate that a stochastic, bottom-up social system simulator is able to replicate top-down population projections and provide a baseline for assessing the impact of increasingly intense flooding. Chapter 3 leverages this model to assess how incorporating heterogeneity in relocation decisions across socio-economic groups impacts flood-induced relocation patterns. The results demonstrate how this heterogeneity leads to a decrease in low-income households, yet a rise in the proportion of elderly individuals in flood-prone regions by the end of the simulation period. Flood-prone areas also exhibit distinct income clusters at the end of simulation time horizon compared to simulations with a homogenous relocation likelihood. Lastly, Chapter 4 explores relationships between extreme weather and agricultural losses in the Delmarva Peninsula. Existing research on climatic impacts to agriculture largely focuses on changes to major crop yields, providing limited insights into impacts on diverse regional agricultural systems where human management and adaptation play a large role. By comparing various multistep modeling configurations and machine learning techniques, this work demonstrates that machine learning methods can accurately simulate and predict agricultural losses across the complex agricultural landscape that exists on the Delmarva peninsula. The multistep configurations developed in this work are able to address data imbalance and improve models' capacity to classify and estimate damage occurrence, which depends on multiple geographical, seasonal, and climatic factors. Collectively, this work demonstrates the potential for advanced modeling techniques to accurately replicate and simulate the impacts of climate on complex socio-ecological systems, providing insights that can ultimately support coastal adaptation. / Doctor of Philosophy / Coastal areas are facing increasing challenges from climate change, including rising sea levels and extreme weather conditions. This dissertation explores socio-economic consequences of these adverse environmental changes for coastal communities. Disruptive repetitive flooding due to exacerbated rise in sea levels is one of these consequences that may eventually leave some highly exposed coastal communities no alternative but migrating from their residences. Focusing on coastal Virginia and Maryland, Chapter 2 develops a data-informed model that can simulate individual relocation decisions and assess how they impact population changes and migration patterns. Chapter 3 employs this model to investigate how future changes in sea levels affect diverse socio-economic groups, their relocation decisions, and the resulting collective migration flows in flood-prone areas. We found that considering demographic differences leaves highly flood-prone areas with less low-income households, higher elderly individuals, and more economic clusters compared to simulations where these differences are not accounted for. Chapter 4 uses machine learning models to simulate the economic impact of extreme weather events as another manifestation of climate change on the agriculture in the Delmarva Peninsula. Through data-based modeling techniques, we identify the climatic conditions most responsible for agricultural losses and recognize modeling choices that enhance our predictive ability. Collectively, this dissertation demonstrates how sophisticated modeling techniques can be used to better understand the complex ways in which climate change will impact human society, with the ultimate goal of supporting adaptation strategies that can better address these impacts.

Large river water basins play a critical role in the economic, health, and biodiversity conditions of a region. In some basins, such as the Zambezi River Basin, extreme weather events introduce cycles of drought and heavy rainfall that can have extreme impacts on local communities vulnerable to environmental shifts. Annual flood pulse dynamics drive ecological dynamics in the system. In the dry season, water dependent wildlife in northern Botswana concentrates along the Chobe River- Floodplains. Elephant concentration, in particular, is matched to surface water quality declines. These flood pulse events have been linked to diarrheal disease outbreaks in the local population, the magnitude of which is associated positively with flood height. Modeling these interactions can advance our ability to predict events and develop mitigation and prevention actions. However, many challenges hinder this development including availability of data in regions that lack resources and the difficulties in create models for such large basins that account for overland water movement. This thesis presents work focused on addressing these challenges. Chapter 2 reports the development of a freely available Large Basin Data Portal (LBDP) that can be used to identify and create critical inputs for hydrodynamic models. This portal was used to create a hydrological model of the Upper Zambezi River Basin model (Chapter 3), a hydrodynamic model of the one of the three subbasins of the Zambezi River. The model was used to calculate downstream river discharges entering the Chobe-Zambezi Floodplains based on upstream rain events. The Upper Zambezi River Basin model was integrated with another more detailed model of the Chobe- Zambezi Floodplains (Chapter 4) that is designed to model the Chobe River and flood water movement in the floodplains. The models were created using the set of MIKE modeling software. The models were used to study various scenarios including water reductions that might occur due to climate change or drought and water increase that might be associated with extreme weather events. / Doctor of Philosophy / River water plays a key role in the livelihood of people and wildlife especially in region of the world suffering chronic economic challenges. The areas surrounding the Zambezi River in Africa is home to one of the most diverse ecological systems in the world. Extreme weather conditions bring cycles of drought and flooding especially in the Upper Zambezi region where wildlife, including the largest population of African elephants in the world, move closer to the Chobe River, a tributary of the Zambezi River, seeking water in the dry seasons. This research is focused on building a set of tools and models to enable studying the linkage between these events and aid in predicting the extent of the floods in the Chobe River Floodplain system based on rainfall in the Angolan high lands and other landscape features. Understanding how these dynamics are linked and the outcome in the downstream system provides a lead time for potential action.

Extreme weather events and effects of climate change are threats to the transport sector’s functionality and safety. Risk management in this context implies a necessity to focus on the connection between near-term experiences and coping strategies on one hand, and long-term adaptation analyses on the other. How learning from past events and subsequent knowledge sharing can be adopted is a question that needs to be explored, discussed and tested. A systematic approach to lessons learned calls for measures of investigation, reporting, planning, implementation and evaluation. A qualitative case study approach was used in this thesis. In the first paper the practices of accident investigation in operation and maintenance were inventoried within the Swedish Transport Administration (STA). Three accident investigation methods were applied and tested on a cloudburst event, causing flooding in a railway tunnel in Sweden. In the second paper, semi-structured interviews, documents, and archival records were used as means for penetrating deeper into the attitudes and understanding of lessons learned concerning extreme weather events within a procured public-private partnership. The results of the two studies showed weak signals of feedback on lessons learned. Partly, these weak signals could be traced back to weak steering signals. Various obstacles impeded learning curves from lessons learned. The obstacles were of both hard and soft values, e.g. resources in time and equipment, systematic investigation methods, incentives for lessons learned, education and knowledge, values, norms and attitudes towards how and why identified problems should be solved. Successful knowledge sharing requires that close attention is paid to such obstacles and that an adaptive approach is adopted. / Den pågående och framtida klimatförändringen sätter press på aktörer att möta risker som associeras med klimatförändring. Syftet med denna avhandling är att bidra med kunskap om lärande och kunskapsöverföring inom offentlig förvaltning av landtransportinfrastruktur. Eftersom lärande och kunskapsöverföring är grundläggande för planering och beslutsfattande om strategier och åtgärder som främjar ett robust transportsystem. Målet för detta arbete är att belysa lärande och kunskapsöverföring inom och mellan olika organisationer i det svenska samhället. Det socio-tekniska systemperspektivet - som används som analysram i båda studierna ger en djupare förståelse för bakomliggande faktorer. En kvalitativ ansats, som omfattar intervjuer, deltagande observationer och dokumentanalys, har används i detta licentiatarbete. I den första studien belyses möjligheten att använda sig av industriella utredningsmetoder på naturolyckor i en svensk kontext av en översvämning i en järnvägstunnel. De industriella utredningsmetoderna visade sig vara användbara för utredning av konsekvenser efter skyfall, med olika metodologiska fördelar och nackdelar. Den andra studien utforskar hur Trafikverkets verksamhetsområde Underhåll arbetar med lärande kopplat till väderextremer; hur de fångar upp erfarenheter och kunskap som finns hos kontrakterade entreprenörer, samt hur entreprenörerna uppfattar att lärande och erfarenhetsåterföring sker. Resultatet från båda studierna visar på både svaga styrsignaler och svaga återkopplingssignaler, vilket medför svaga lärandekurvor. Olika hinder sågs ligga bakom med svaga styr- och återkopplingssignaler bl.a. resurser i tid och processer, systematik i utredning av naturolyckor, incitament att lära av varandra, utbildning och kunskap, värderingar normer och attityder till hur och varför identifierade problem ska lösas. Ett adaptivt förhållningssätt innebär att man behöver ta hänsyn till dessa hinder på ett systematiskt sätt. / The agreement in Paris in 2015 was an historic manifestation that society has to work with both mitigation and adaptation to achieve a reduction of the adverse effects of climate change. One way to achieve adaptation is through the integration of present coping strategies. A first step is to study the existing processes and routines that support short-term coping. This licentiate thesis targets different aspects of learning as a strategy for coping and building adaptive capacity. Road infrastructure and maintenance in relation to extreme weather are used as the physical context and the Swedish Transport Administration as a case to study. Paper I shows the possibility to apply industrial accident investigation methods to an extreme weather event and get useful insights into underlying root causes. Paper II shows the intra- and interrelated patterns that exist in public-private partnerships (PPP) in Sweden. The paper describes a parallel of systems with infrequent overlaps regarding lessons learned.  In both papers the socio-technical perspective approach was used to highlight aspects of learning from and investigating damage due to extreme weather at different tiers in society. The socio-technical perspective provides an understanding of how decisions and legislation that affect our actions and behavior today may have been taken in different time and space settings. This thesis contributes to concept and theory building regarding the socio-technical system approach. / <p>Paper 2 ingick i licentiatuppsatsen som manuskript, nu publicerat.</p>

The UK’s decarbonisation strategy to increasingly electrify heating and transport will change the demand requirement on the electricity system. Additionally, under a climate change future, it is projected that the decarbonised grid will need to be able to operate under higher average temperatures in the UK, increasing the need for comfort cooling during summer and leading to additional electricity demand. These new demands will result in greater variation between minimum and peak demand as well as a significant increase in overall demand. Concurrently, supply-side decarbonisation programmes may lead to more intermittent renewables such as wind, PV, tidal and wave, elevating variability in electricity generation. Coupled with the anticipated higher variation in demand this brings on several challenges in operating the electricity grid. In order to characterise these challenges this research develops a bespoke electricity dispatch model which builds on hourly models of demand and generation. The hourly demand profiles are based on a high electrification of heating, transport and cooling coupled with future temperatures premised on the UKCP09 high emission scenario climate projections. The demand profiles show a significant increase in peak demand by 2050 reaching 194 GW, mainly due to summer cooling loads which contribute 70% of the demand. The cumulative CO2 emissions budgets of the GB power sector that are consistent with avoiding global climate change to 2°C are used to develop two low carbon generation scenarios distinguished by the amount of intermittent renewable generation technologies. The dispatch model tests the capability of generation scenarios with the use of hourly generation models in meeting future demand profiles out to 2050.The outputs from dispatch model indicate that there are shortages and excesses of generation relative to demand from 2030 onwards. The variability analysis outlines low and high generation periods from intermittent technologies along with the pace at which intermittent generation increases or decreases within an hour. The characterisation of variability analysis reveals the type of reserve capacity or smart solutions that are required to maintain the security of electricity supply. The solutions that could address the challenges quantified from the model outputs in operating a decarbonised GB electricity grid are explored using expert interviews. The analysis of the stakeholder interviews suggests smart grid solutions that include technologies as well as changes in operational procedures in order to enhance the operational resilience of the grid. Active Network Management through monitoring and control, demand management, storage systems and interconnectors are proposed to address challenges arising from varying demand and generation variability.