City models

The main questions to be answered by urban modeling are about the endogenous dynamics of the urban system and about options to steer the urban development towards sustainable/desirable paths. In the realm of adaptation to future climate change this means that the potentially adverse effects of the various climate change stimuli on the manifold of changing urban functions have to be understood. From that a multitude of specific adaptation options naturally emerges - which then has to be evaluated against its compliance with basic urban development rules. A similar procedure holds for the reduction of urban CO2 emissions - from the multitude of mitigation options the "urban" ones have to be identified which are in accordance with the fabric of urban functions. The three models described below contribute to these tasks.


The UrbGrav model explores the concept that growth is more likely to take place close to urban space, as formulated more generally by W.R. Tobler 1970 "Everything is related to everything else, but near things are more related than distant things". The probability that a site becomes urban is solely given by the distance to urban areas (Rybski et al. 2013). The simplistic model involves two parameters - an exponent, determining how strongly the attraction decays with the distance, and a proportionality constant, determining the overall growth rate. The approach is capable of reproducing power-law size distribution and fractality of urban structures. Calibrating to real-world growth of cities, the model is used to extrapolate growth. Since it is statistical, it provides ensembles of trajectories.


D-CEP describes the relation between the structure of urban agglomerations, their local climate and the larger scale climatic regime. Presently it comprises two pillars, an empirical approach towards the relation between the Urban Heat Island effect (UHI, based on remotely sensed infrared data) as well as aggregated structural properties of the urban agglomerations and a mechanistic approach which introduces a sophisticated, fine scale urban submodel into an existing mesoscale weather model, resulting in, e.g., a respective intra-urban temperature field. While the first approach gives, e.g., insight in the relation of city size and averaged UHI (Zhou et al. 2013), the second allows for the assessment of different intra-urban measures to reduce the UHI (Schubert & Grossman-Clarke 2013), resulting in, e.g., statements on the optimum amount and distribution of urban green areas under a given population density.


City-Options is a (growing) collection of modules which can be combined to quantitatively assess adaptation options regarding future Climate Change in urban areas. Furthermore, first steps are made towards the assessment of mitigation options of CO2-emissions from urban areas and the possibility of peri-urban food supply depending on the inter-urban structure. The modules related to the development of effective urban adaptation measures cover the whole cause-effect chain for specific Climate Change impacts, including the projection of local climate change stimuli and their uncertainties (Lüdeke et al. 2012), the identification of physically exposed locations within the urban area (Kit et al. 2011), the localization of sensitive social groups within the urban population (Kit & Lüdeke, 2013) and damage functions for flood (Böttle et al. 2013) and storm events (Prahl et al. 2012). Regarding mitigation options a module for the GHG-emissions from urban heating- and cooling under Climate Change is available (Olonscheck et al. 2011). A further module allows assessing regional options of urban food supply for given spatial distributions of urban agglomerations (Kriewald et al. 2014).