@phdthesis{Rikani2023,
  author    = {Rikani, Albano},
  title     = {Modeling global human migration dynamics under climate change},
  doi       = {10.25932/publishup-58321},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-583212},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 133},
  year      = {2023},
  abstract  = {International migration has been an increasing phenomenon during the past decades and has involved all the regions of the globe. Together with fertility and mortality rates, net migration rates represent the components that fully define the demographic evolution of the population in a country. Therefore, being able to capture the patterns of international migration flows and to produce projections of how they might change in the future is of relevant importance for demographic studies and for designing policies informed on the potential scenarios. Existing forecasting methods do not account explicitly for the main drivers and processes shaping international migration flows: existing migrant communities at the destination country, termed diasporas, would reduce the costs of migration and facilitate the settling for new migrants, ultimately producing a positive feedback; accounting for the heterogeneity in the type of migration flows, e.g. return and transit Ćows, becomes critical in some specific bilateral migration channels; in low- to middle- income countries economic development could relax poverty constraint and result in an increase of emigration rates. Economic conditions at both origin and destination are identified as major drivers of international migration. At the same time, climate change impacts have already appeared on natural and human-made systems such as the economic productivity. These economic impacts might have already produced a measurable effect on international migration flows. Studies that provide a quantification of the number of migration moves that might have been affected by climate change are usually specific to small regions, do not provide a mechanistic understanding of the pathway leading from climate change to migration and restrict their focus to the effective induced flows, disregarding the impact that climate change might have had in inhibiting other flows. Global climate change is likely to produce impacts on the economic development of the countries during the next decades too. Understanding how these impacts might alter future global migration patterns is relevant for preparing future societies and understanding whether the response in migration flows would reduce or increase population's exposure to climate change impacts. This doctoral research aims at investigating these questions and fill the research gaps outlined above. First, I have built a global bilateral international migration model which accounts explicitly for the diaspora feedback, distinguishes between transit and return flows, and accounts for the observed non-linear effects that link emigration rates to income levels in the country of origin. I have used this migration model within a population dynamic model where I account also for fertility and mortality rates, producing hindcasts and future projections of international migration flows, covering more than 170 countries. Results show that the model reproduces past patterns and trends well. Future projections highlight the fact that,depending on the assumptions regarding future evolution of income levels and between-country inequality, migration at the end of the century might approach net zero or be still high in many countries. The model, parsimonious in the explanatory variables that includes, represents a versatile tool for assessing the impacts of different socioeconomic scenarios on international migration. I consider then a counterfactual past without climate change impacts on the economic productivity. By prescribing these counterfactual economic conditions to the migration model I produce counterfactual migration flows for the past 30 years. I compare the counterfactual migration flows to factual ones, where historical economic conditions are used to produce migration flows. This provides an estimation of the recent international migration flows attributed to climate change impacts. Results show that a counterfactual world without climate change would have seen less migration globally. This effect becomes larger if I consider separately the increase and decrease in migration moves: a Ągure of net change in the migration flows is not representative of the effective magnitude of the climate change impact on migration. Indeed, in my results climate change produces a divergent effect on richer and poorer countries: by slowing down the economic development, climate change might have reduced international mobility from and to countries of the Global South, and increased it from and to richer countries in the Global North. I apply the same methodology to a scenario of future 3℃ global warming above pre-industrial conditions. I Ąnd that climate change impacts, acting by reorganizing the relative economic attractiveness of destination countries or by affecting the economic growth in the origin, might produce a substantial effect in international migration flows, inhibiting some moves and inducing others. Overall my results suggest that climate change might have had and might have in the future a significant effect on global patterns of international migration. It also emerges clearly that, for a comprehensive understanding of the effects of climate change on international migration, we need to go beyond net effects and consider separately induced and inhibited flows.},
  language  = {en}
}
@phdthesis{Li2024,
  author    = {Li, Yunfei},
  title     = {On the influence of density and morphology on the Urban Heat Island intensity},
  doi       = {10.25932/publishup-62150},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-621504},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xviii, 119},
  year      = {2024},
  abstract  = {The urban heat island (UHI) effect, describing an elevated temperature of urban areas compared with their natural surroundings, can expose urban dwellers to additional heat stress, especially during hot summer days. A comprehensive understanding of the UHI dynamics along with urbanization is of great importance to efficient heat stress mitigation strategies towards sustainable urban development. This is, however, still challenging due to the difficulties of isolating the influences of various contributing factors that interact with each other. In this work, I present a systematical and quantitative analysis of how urban intrinsic properties (e.g., urban size, density, and morphology) influence UHI intensity. To this end, we innovatively combine urban growth modelling and urban climate simulation to separate the influence of urban intrinsic factors from that of background climate, so as to focus on the impact of urbanization on the UHI effect. The urban climate model can create a laboratory environment which makes it possible to conduct controlled experiments to separate the influences from different driving factors, while the urban growth model provides detailed 3D structures that can be then parameterized into different urban development scenarios tailored for these experiments. The novelty in the methodology and experiment design leads to the following achievements of our work. First, we develop a stochastic gravitational urban growth model that can generate 3D structures varying in size, morphology, compactness, and density gradient. We compare various characteristics, like fractal dimensions (box-counting, area-perimeter scaling, area-population scaling, etc.), and radial gradient profiles of land use share and population density, against those of real-world cities from empirical studies. The model shows the capability of creating 3D structures resembling real-world cities. This model can generate 3D structure samples for controlled experiments to assess the influence of some urban intrinsic properties in question. [Chapter 2] With the generated 3D structures, we run several series of simulations with urban structures varying in properties like size, density and morphology, under the same weather conditions. Analyzing how the 2m air temperature based canopy layer urban heat island (CUHI) intensity varies in response to the changes of the considered urban factors, we find the CUHI intensity of a city is directly related to the built-up density and an amplifying effect that urban sites have on each other. We propose a Gravitational Urban Morphology (GUM) indicator to capture the neighbourhood warming effect. We build a regression model to estimate the CUHI intensity based on urban size, urban gross building volume, and the GUM indicator. Taking the Berlin area as an example, we show the regression model capable of predicting the CUHI intensity under various urban development scenarios. [Chapter 3] Based on the multi-annual average summer surface urban heat island (SUHI) intensity derived from Land surface temperature, we further study how urban intrinsic factors influence the SUHI effect of the 5,000 largest urban clusters in Europe. We find a similar 3D GUM indicator to be an effective predictor of the SUHI intensity of these European cities. Together with other urban factors (vegetation condition, elevation, water coverage), we build different multivariate linear regression models and a climate space based Geographically Weighted Regression (GWR) model that can better predict SUHI intensity. By investigating the roles background climate factors play in modulating the coefficients of the GWR model, we extend the multivariate linear model to a nonlinear one by integrating some climate parameters, such as the average of daily maximal temperature and latitude. This makes it applicable across a range of background climates. The nonlinear model outperforms linear models in SUHI assessment as it captures the interaction of urban factors and the background climate. [Chapter 4] Our work reiterates the essential roles of urban density and morphology in shaping the urban thermal environment. In contrast to many previous studies that link bigger cities with higher UHI intensity, we show that cities larger in the area do not necessarily experience a stronger UHI effect. In addition, the results extend our knowledge by demonstrating the influence of urban 3D morphology on the UHI effect. This underlines the importance of inspecting cities as a whole from the 3D perspective. While urban 3D morphology is an aggregated feature of small-scale urban elements, the influence it has on the city-scale UHI intensity cannot simply be scaled up from that of its neighbourhood-scale components. The spatial composition and configuration of urban elements both need to be captured when quantifying urban 3D morphology as nearby neighbourhoods also cast influences on each other. Our model serves as a useful UHI assessment tool for the quantitative comparison of urban intervention/development scenarios. It can support harnessing the capacity of UHI mitigation through optimizing urban morphology, with the potential of integrating climate change into heat mitigation strategies.},
  language  = {en}
}
@phdthesis{Middelanis2023,
  author    = {Middelanis, Robin},
  title     = {Global response to local extremes—a storyline approach on economic loss propagation from weather extremes},
  doi       = {10.25932/publishup-61112},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-611127},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 237},
  year      = {2023},
  abstract  = {Due to anthropogenic greenhouse gas emissions, Earth's average surface temperature is steadily increasing. As a consequence, many weather extremes are likely to become more frequent and intense. This poses a threat to natural and human systems, with local impacts capable of destroying exposed assets and infrastructure, and disrupting economic and societal activity. Yet, these effects are not locally confined to the directly affected regions, as they can trigger indirect economic repercussions through loss propagation along supply chains. As a result, local extremes yield a potentially global economic response. To build economic resilience and design effective adaptation measures that mitigate adverse socio-economic impacts of ongoing climate change, it is crucial to gain a comprehensive understanding of indirect impacts and the underlying economic mechanisms. Presenting six articles in this thesis, I contribute towards this understanding. To this end, I expand on local impacts under current and future climate, the resulting global economic response, as well as the methods and tools to analyze this response. Starting with a traditional assessment of weather extremes under climate change, the first article investigates extreme snowfall in the Northern Hemisphere until the end of the century. Analyzing an ensemble of global climate model projections reveals an increase of the most extreme snowfall, while mean snowfall decreases. Assessing repercussions beyond local impacts, I employ numerical simulations to compute indirect economic effects from weather extremes with the numerical agent-based shock propagation model Acclimate. This model is used in conjunction with the recently emerged storyline framework, which involves analyzing the impacts of a particular reference extreme event and comparing them to impacts in plausible counterfactual scenarios under various climate or socio-economic conditions. Using this approach, I introduce three primary storylines that shed light on the complex mechanisms underlying economic loss propagation. In the second and third articles of this thesis, I analyze storylines for the historical Hurricanes Sandy (2012) and Harvey (2017) in the USA. For this, I first estimate local economic output losses and then simulate the resulting global economic response with Acclimate. The storyline for Hurricane Sandy thereby focuses on global consumption price anomalies and the resulting changes in consumption. I find that the local economic disruption leads to a global wave-like economic price ripple, with upstream effects propagating in the supplier direction and downstream effects in the buyer direction. Initially, an upstream demand reduction causes consumption price decreases, followed by a downstream supply shortage and increasing prices, before the anomalies decay in a normalization phase. A dominant upstream or downstream effect leads to net consumption gains or losses of a region, respectively. Moreover, I demonstrate that a longer direct economic shock intensifies the downstream effect for many regions, leading to an overall consumption loss. The third article of my thesis builds upon the developed loss estimation method by incorporating projections to future global warming levels. I use these projections to explore how the global production response to Hurricane Harvey would change under further increased global warming. The results show that, while the USA is able to nationally offset direct losses in the reference configuration, other countries have to compensate for increasing shares of counterfactual future losses. This compensation is mainly achieved by large exporting countries, but gradually shifts towards smaller regions. These findings not only highlight the economy's ability to flexibly mitigate disaster losses to a certain extent, but also reveal the vulnerability and economic disadvantage of regions that are exposed to extreme weather events. The storyline in the fourth article of my thesis investigates the interaction between global economic stress and the propagation of losses from weather extremes. I examine indirect impacts of weather extremes — tropical cyclones, heat stress, and river floods — worldwide under two different economic conditions: an unstressed economy and a globally stressed economy, as seen during the Covid-19 pandemic. I demonstrate that the adverse effects of weather extremes on global consumption are strongly amplified when the economy is under stress. Specifically, consumption losses in the USA and China double and triple, respectively, due to the global economy's decreased capacity for disaster loss compensation. An aggravated scarcity intensifies the price response, causing consumption losses to increase. Advancing on the methods and tools used here, the final two articles in my thesis extend the agent-based model Acclimate and formalize the storyline approach. With the model extension described in the fifth article, regional consumers make rational choices on the goods bought such that their utility is maximized under a constrained budget. In an out-of-equilibrium economy, these rational consumers are shown to temporarily increase consumption of certain goods in spite of rising prices. The sixth article of my thesis proposes a formalization of the storyline framework, drawing on multiple studies including storylines presented in this thesis. The proposed guideline defines eight central elements that can be used to construct a storyline. Overall, this thesis contributes towards a better understanding of economic repercussions of weather extremes. It achieves this by providing assessments of local direct impacts, highlighting mechanisms and impacts of loss propagation, and advancing on methods and tools used.},
  language  = {en}
}
@phdthesis{Kuhla2022,
  author    = {Kuhla, Kilian},
  title     = {Impact, distribution, and adaptation},
  doi       = {10.25932/publishup-55266},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-552668},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 309},
  year      = {2022},
  abstract  = {Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises - caused by anthropogenic greenhouse gas emissions - the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions. First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble. Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 - 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises. Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions. Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses. As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.},
  language  = {en}
}
@phdthesis{Schlemm2022,
  author    = {Schlemm, Tanja},
  title     = {The marine ice cliff instability of the Antarctic ice sheet},
  doi       = {10.25932/publishup-58633},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586333},
  school      = {Universit{\"a}t Potsdam},
  pages     = {107},
  year      = {2022},
  abstract  = {The Antarctic ice sheet is the largest freshwater reservoir worldwide. If it were to melt completely, global sea levels would rise by about 58 m. Calculation of projections of the Antarctic contribution to sea level rise under global warming conditions is an ongoing effort which yields large ranges in predictions. Among the reasons for this are uncertainties related to the physics of ice sheet modeling. These uncertainties include two processes that could lead to runaway ice retreat: the Marine Ice Sheet Instability (MISI), which causes rapid grounding line retreat on retrograde bedrock, and the Marine Ice Cliff Instability (MICI), in which tall ice cliffs become unstable and calve off, exposing even taller ice cliffs. In my thesis, I investigated both marine instabilities (MISI and MICI) using the Parallel Ice Sheet Model (PISM), with a focus on MICI.},
  language  = {en}
}
@phdthesis{Kotz2022,
  author    = {Kotz, Maximilian},
  title     = {The economic costs of climate change},
  school      = {Universit{\"a}t Potsdam},
  pages     = {279},
  year      = {2022},
  language  = {en}
}
@phdthesis{Mester2023,
  author    = {Mester, Benedikt},
  title     = {Modeling flood-induced human displacement risk under global change},
  doi       = {10.25932/publishup-60929},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-609293},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XVI, 143},
  year      = {2023},
  abstract  = {Extreme flooding displaces an average of 12 million people every year. Marginalized populations in low-income countries are in particular at high risk, but also industrialized countries are susceptible to displacement and its inherent societal impacts. The risk of being displaced results from a complex interaction of flood hazard, population exposed in the floodplains, and socio-economic vulnerability. Ongoing global warming changes the intensity, frequency, and duration of flood hazards, undermining existing protection measures. Meanwhile, settlements in attractive yet hazardous flood-prone areas have led to a higher degree of population exposure. Finally, the vulnerability to displacement is altered by demographic and social change, shifting economic power, urbanization, and technological development. These risk components have been investigated intensively in the context of loss of life and economic damage, however, only little is known about the risk of displacement under global change. This thesis aims to improve our understanding of flood-induced displacement risk under global climate change and socio-economic change. This objective is tackled by addressing the following three research questions. First, by focusing on the choice of input data, how well can a global flood modeling chain reproduce flood hazards of historic events that lead to displacement? Second, what are the socio-economic characteristics that shape the vulnerability to displacement? Finally, to what degree has climate change potentially contributed to recent flood-induced displacement events? To answer the first question, a global flood modeling chain is evaluated by comparing simulated flood extent with satellite-derived inundation information for eight major flood events. A focus is set on the sensitivity to different combinations of the underlying climate reanalysis datasets and global hydrological models which serve as an input for the global hydraulic model. An evaluation scheme of performance scores shows that simulated flood extent is mostly overestimated without the consideration of flood protection and only for a few events dependent on the choice of global hydrological models. Results are more sensitive to the underlying climate forcing, with two datasets differing substantially from a third one. In contrast, the incorporation of flood protection standards results in an underestimation of flood extent, pointing to potential deficiencies in the protection level estimates or the flood frequency distribution within the modeling chain. Following the analysis of a physical flood hazard model, the socio-economic drivers of vulnerability to displacement are investigated in the next step. For this purpose, a satellite- based, global collection of flood footprints is linked with two disaster inventories to match societal impacts with the corresponding flood hazard. For each event the number of affected population, assets, and critical infrastructure, as well as socio-economic indicators are computed. The resulting datasets are made publicly available and contain 335 displacement events and 695 mortality/damage events. Based on this new data product, event-specific displacement vulnerabilities are determined and multiple (national) dependencies with the socio-economic predictors are derived. The results suggest that economic prosperity only partially shapes vulnerability to displacement; urbanization, infant mortality rate, the share of elderly, population density and critical infrastructure exhibit a stronger functional relationship, suggesting that higher levels of development are generally associated with lower vulnerability. Besides examining the contextual drivers of vulnerability, the role of climate change in the context of human displacement is also being explored. An impact attribution approach is applied on the example of Cyclone Idai and associated extreme coastal flooding in Mozambique. A combination of coastal flood modeling and satellite imagery is used to construct factual and counterfactual flood events. This storyline-type attribution method allows investigating the isolated or combined effects of sea level rise and the intensification of cyclone wind speeds on coastal flooding. The results suggest that displacement risk has increased by 3.1 to 3.5\% due to the total effects of climate change on coastal flooding, with the effects of increasing wind speed being the dominant factor. In conclusion, this thesis highlights the potentials and challenges of modeling flood- induced displacement risk. While this work explores the sensitivity of global flood modeling to the choice of input data, new questions arise on how to effectively improve the reproduction of flood return periods and the representation of protection levels. It is also demonstrated that disentangling displacement vulnerabilities is feasible, with the results providing useful information for risk assessments, effective humanitarian aid, and disaster relief. The impact attribution study is a first step in assessing the effects of global warming on displacement risk, leading to new research challenges, e.g., coupling fluvial and coastal flood models or the attribution of other hazard types and displacement events. This thesis is one of the first to address flood-induced displacement risk from a global perspective. The findings motivate for further development of the global flood modeling chain to improve our understanding of displacement vulnerability and the effects of global warming.},
  language  = {en}
}
@phdthesis{Stechemesser2023,
  author    = {Stechemesser, Annika},
  title     = {Human behaviour in a warming world},
  school      = {Universit{\"a}t Potsdam},
  pages     = {339},
  year      = {2023},
  language  = {en}
}