@phdthesis{SvirejevaHopkins2004,
  author    = {Svirejeva-Hopkins, Anastasia},
  title     = {Urbanised territories as a specific component of the global carbon cycle},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0001512},
  school      = {Universit{\"a}t Potsdam},
  year      = {2004},
  abstract  = {Wir betrachten folgende Teile: die zus{\"a}tzlichen Kohlenstoff(C)-emissionen, welche aus der Umwandlung von nat{\"u}rlichem Umland durch Stadtwachstum resultieren, und die {\"A}nderung des C-Flusses durch 'urbanisierte' {\"O}kosysteme, soweit atmosph{\"a}risches C durch diese in umliegende nat{\"u}rliche {\"O}kosysteme entlang der Kette \“Atmosph{\"a}re -> Vegetation -> abgestorbene organische Substanzen\” gepumpt wird: d.h. C-Export; f{\"u}r den Zeitraum von 1980 bis 2050. Als Szenario nutzen wir Prognosen der regionalen Stadtbev{\"o}lkerung, welche durch ein 'Hybridmodell' generiert werden f{\"u}r acht Regionen. Alle Sch{\"a}tzungen der C-Fl{\"u}sse basieren auf zwei Modellen: das Regression Modell und das sogenannte G-Modell. Die Siedlungsfl{\"a}che, welche mit dem Wachstum der Stadtbev{\"o}lkerung zunimmt, wird in 'Gr{\"u}nfl{\"a}chen' (Parks, usw.), Geb{\"a}udefl{\"a}chen und informell st{\"a}dtisch genutzte Fl{\"a}chen (Slums, illegale Lagerpl{\"a}tze, usw.) unterteilt. Es werden j{\"a}hrlich die regionale und globale Dynamik der C-Emissionen und des C-Exports sowie die C-Gesamtbilanz berechnet. Dabei liefern beide Modelle qualitativ {\"a}hnliche Ergebnisse, jedoch gibt es einige quantitative Unterschiede. Im ersten Modell erreicht die globale Jahresemission f{\"u}r die Dekade 2020-2030 resultierend aus der Landnutzungs{\"a}nderung ein Maximum von 205 Mt/a. Die maximalen Beitr{\"a}ge zur globalen Emission werden durch China, die asiatische und die pazifische Region erbracht. Im zweiten Modell erh{\"o}ht sich die j{\"a}hrliche globale Emission von 1.12 GtC/a f{\"u}r 1980 auf 1.25 GtC/a f{\"u}r 2005 (1Gt = 109 t). Danach beginnt eine Reduzierung. Vergleichen wir das Emissionmaximum mit der Emission durch Abholzung im Jahre 1980 (1.36 GtC/a), k{\"o}nnen wir konstatieren, daß die Urbanisierung damit in vergleichbarer Gr{\"o}sse zur Emission beitr{\"a}gt. Bezogen auf die globale Dynamik des j{\"a}hrlichen C-Exports durch Urbanisierung beobachten wir ein monotones Wachstum bis zum nahezu dreifachen Wert von 24 MtC/a f{\"u}r 1980 auf 66 MtC/a f{\"u}r 2050 im ersten Modell, bzw. im zweiten Modell von 249 MtC/a f{\"u}r 1980 auf 505 MtC/a f{\"u}r 2050. Damit ist im zweiten Fall die Transportleistung der Siedlungsgebiete mit dem C-Transport durch Fl{\"u}sse in die Ozeane (196 .. 537 MtC/a) vergleichbar. Bei der Absch{\"a}tzung der Gesamtbilanz finden wir, daß die Urbanisierung die Bilanz in Richtung zu einer 'Senke' verschiebt. Entsprechend dem zweiten Modell beginnt sich die C-Gesamtbilanz (nach ann{\"a}hernder Konstanz) ab dem Jahre 2000 mit einer fast konstanten Rate zu verringern. Wenn das Maximum im Jahre 2000 bei 905MtC/a liegt, f{\"a}llt dieser Wert anschliessend bis zum Jahre 2050 auf 118 MtC/a. Bei Extrapolation dieser Dynamik in die Zukunft k{\"o}nnen wir annehmen, daß am Ende des 21. Jahrhunderts die \“urbane\” C-Gesamtbilanz Null bzw. negative Werte erreicht.},
  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}
}