Refine
Language
- English (17)
Is part of the Bibliography
- yes (17)
Keywords
- Kaya Identity (1)
- Sustainable cities (1)
- Taylor’s law (1)
- Urban CO2 emissions (1)
- Urban Kaya relation (1)
- Urban scaling (1)
- Zipf’s law (1)
- city clusters (1)
- percolation (1)
Institute
- Institut für Geowissenschaften (17) (remove)
Cities will play a key role in the grand challenge of nourishing a growing global population, because, due to their population density, they set the demand. To ensure that food systems are sustainable, as well as nourishing, one solution often suggested is to shorten their supply chains toward a regional rather than a global basis. While such regional systems may have a range of costs and benefits, we investigate the mitigation potential of regionalized urban food systems by examining the greenhouse gas emissions associated with food transport. Using data on food consumption for 7108 urban administrative units (UAUs), we simulate total transport emissions for both regionalized and globalized supply chains. In regionalized systems, the UAUs' demands are fulfilled by peripheral food production, whereas to simulate global supply chains, food demand is met from an international pool (where the origin can be any location globally). We estimate that regionalized systems could reduce current emissions from food transport. However, because longer supply chains benefit from maximizing comparative advantage, this emission reduction would require closing yield gaps, reducing food waste, shifting toward diversified farming, and consuming seasonal produce. Regionalization of food systems will be an essential component to limit global warming to well below 2 degrees C in the future.
Two different approaches are used to assess the impacts associated with natural hazards and climate change in cities. A bottom-up approach uses high resolution data on constituent assets within the urban area. In contrast, a top-down approach uses less detailed information but is consequently more readily transferable. Here, we compare damage curves generated by each approach for coastal flooding in London. To compare them, we fit a log-logistic regression with three parameters to the calculated damage curves. We find that the functions are remarkably similar in their shape, albeit with different inflection points and a maximum damage that differs by 13%-25%. If rescaled, the curves agree almost exactly, which enables damage assessment to be undertaken following the calculation of the three parameters.
The question of whether urbanization contributes to increasing carbon dioxide emissions has been mainly investigated via scaling relationships with population or population density. However, these approaches overlook the correlations between population and area, and ignore possible interactions between these quantities. Here, we propose a generalized framework that simultaneously considers the effects of population and area along with possible interactions between these urban metrics. Our results significantly improve the description of emissions and reveal the coupled role between population and density on emissions. These models show that variations in emissions associated with proportionate changes in population or density may not only depend on the magnitude of these changes but also on the initial values of these quantities. For US areas, the larger the city, the higher is the impact of changing its population or density on its emissions; but population changes always have a greater effect on emissions than population density.
Urban areas play an unprecedented role in potentially mitigating climate change and supporting sustainable development. In light of the rapid urbanisation in many parts on the globe, it is crucial to understand the relationship between settlement size and CO2 emission efficiency of cities. Recent literature on urban scaling properties of emissions as a function of population size has led to contradictory results and more importantly, lacked an in-depth investigation of the essential factors and causes explaining such scaling properties. Therefore, in analogy to the well-established Kaya Identity, we develop a relation combining the involved exponents. We demonstrate that application of this Urban Kaya Relation will enable a comprehensive understanding about the intrinsic factors determining emission efficiencies in large cities by applying it to a global dataset of 61 cities. Contrary to traditional urban scaling studies which use Ordinary Least Squares (OLS) regression, we show that the Reduced Major Axis (RMA) is necessary when complex relations among scaling exponents are to be investigated. RMA is given by the geometric mean of the two OLS slopes obtained by interchanging the dependent and independent variable. We discuss the potential of the Urban Kaya Relation in mainstreaming local actions for climate change mitigation.
Human development has far-reaching impacts on the surface of the globe. The transformation of natural land cover occurs in different forms, and urban growth is one of the most eminent transformative processes. We analyze global land cover data and extract cities as defined by maximally connected urban clusters. The analysis of the city size distribution for all cities on the globe confirms Zipf’s law. Moreover, by investigating the percolation properties of the clustering of urban areas we assess the closeness to criticality for various countries. At the critical thresholds, the urban land cover of the countries undergoes a transition from separated clusters to a gigantic component on the country scale. We study the Zipf-exponents as a function of the closeness to percolation and find a systematic dependence, which could be the reason for deviating exponents reported in the literature. Moreover, we investigate the average size of the clusters as a function of the proximity to percolation and find country specific behavior. By relating the standard deviation and the average of cluster sizes—analogous to Taylor’s law—we suggest an alternative way to identify the percolation transition. We calculate spatial correlations of the urban land cover and find long-range correlations. Finally, by relating the areas of cities with population figures we address the global aspect of the allometry of cities, finding an exponent δ ≈ 0.85, i.e., large cities have lower densities.
Failure to consider the costs of adaptation strategies can be seen by decision makers as a barrier to implementing coastal protection measures. In order to validate adaptation strategies to sea-level rise in the form of coastal protection, a consistent and repeatable assessment of the costs is necessary. This paper significantly extends current knowledge on cost estimates by developing - and implementing using real coastal dike data - probabilistic functions of dike costs. Data from Canada and the Netherlands are analysed and related to published studies from the US, UK, and Vietnam in order to provide a reproducible estimate of typical sea dike costs and their uncertainty. We plot the costs divided by dike length as a function of height and test four different regression models. Our analysis shows that a linear function without intercept is sufficient to model the costs, i.e. fixed costs and higher-order contributions such as that due to the volume of core fill material are less significant. We also characterise the spread around the regression models which represents an uncertainty stemming from factors beyond dike length and height. Drawing an analogy with project cost overruns, we employ log-normal distributions and calculate that the range between 3x and x/3 contains 95% of the data, where x represents the corresponding regression value. We compare our estimates with previously published unit costs for other countries. We note that the unit costs depend not only on the country and land use (urban/non-urban) of the sites where the dikes are being constructed but also on characteristics included in the costs, e.g. property acquisition, utility relocation, and project management. This paper gives decision makers an order of magnitude on the protection costs, which can help to remove potential barriers to develop-ing adaptation strategies. Although the focus of this research is sea dikes, our approach is applicable and transferable to other adaptation measures.
Urban climate is determined by a variety of factors, whose knowledge can help to attenuate heat stress in the context of ongoing urbanization and climate change. We study the influence of city size and urban form on the Urban Heat Island (UHI) phenomenon in Europe and find a complex interplay between UHI intensity and city size, fractality, and anisometry. Due to correlations among these urban factors, interactions in the multi-linear regression need to be taken into account. We find that among the largest 5,000 cities, the UHI intensity increases with the logarithm of the city size and with the fractal dimension, but decreases with the logarithm of the anisometry. Typically, the size has the strongest influence, followed by the compactness, and the smallest is the influence of the degree to which the cities stretch. Accordingly, from the point of view of UHI alleviation, small, disperse, and stretched cities are preferable. However, such recommendations need to be balanced against e.g. positive agglomeration effects of large cities. Therefore, trade-offs must be made regarding local and global aims.
Sustainable development goals (SDGs) have set the 2030 agenda to transform our world by tackling multiple challenges humankind is facing to ensure well-being, economic prosperity, and environmental protection. In contrast to conventional development agendas focusing on a restricted set of dimensions, the SDGs provide a holistic and multidimensional view on development. Hence, interactions among the SDGs may cause diverging results. To analyze the SDG interactions we systematize the identification of synergies and trade-offs using official SDG indicator data for 227 countries. A significant positive correlation between a pair of SDG indicators is classified as a synergy while a significant negative correlation is classified as a trade-off. We rank synergies and trade-offs between SDGs pairs on global and country scales in order to identify the most frequent SDG interactions. For a given SDG, positive correlations between indicator pairs were found to outweigh the negative ones in most countries. Among SDGs the positive and negative correlations between indicator pairs allowed for the identification of particular global patterns. SDG 1 (No poverty) has synergetic relationship with most of the other goals, whereas SDG 12 (Responsible consumption and production) is the goal most commonly associated with trade-offs. The attainment of the SDG agenda will greatly depend on whether the identified synergies among the goals can be leveraged. In addition, the highlighted trade-offs, which constitute obstacles in achieving the SDGs, need to be negotiated and made structurally nonobstructive by deeper changes in the current strategies.
Human development has far-reaching impacts on the surface of the globe. The transformation of natural land cover occurs in different forms, and urban growth is one of the most eminent transformative processes. We analyze global land cover data and extract cities as defined by maximally connected urban clusters. The analysis of the city size distribution for all cities on the globe confirms Zipf’s law. Moreover, by investigating the percolation properties of the clustering of urban areas we assess the closeness to criticality for various countries. At the critical thresholds, the urban land cover of the countries undergoes a transition from separated clusters to a gigantic component on the country scale. We study the Zipf-exponents as a function of the closeness to percolation and find a systematic dependence, which could be the reason for deviating exponents reported in the literature. Moreover, we investigate the average size of the clusters as a function of the proximity to percolation and find country specific behavior. By relating the standard deviation and the average of cluster sizes—analogous to Taylor’s law—we suggest an alternative way to identify the percolation transition. We calculate spatial correlations of the urban land cover and find long-range correlations. Finally, by relating the areas of cities with population figures we address the global aspect of the allometry of cities, finding an exponent δ ≈ 0.85, i.e., large cities have lower densities.
This paper assesses the seasonality of the urban heat island (UHI) effect in the Greater London area (United Kingdom). Combining satellite-based observations and urban boundary layer climate modeling with the UrbClim model, the authors are able to address the seasonality of UHI intensity, on the basis of both land surface temperature (LST) and 2-m air temperature, for four individual times of the day (0130, 1030, 1330, and 2230 local time) and the daily means derived from them. An objective of this paper is to investigate whether the UHI intensities that are based on both quantities exhibit a similar hysteresis-like trajectory that is observed for LST when plotting the UHI intensity against the background temperature. The results show that the UrbClim model can satisfactorily reproduce both the observed urban rural LSTs and 2-m air temperatures as well as their differences and the hysteresis in the surface UHI. The hysteresis-like seasonality is largely absent in both the observed and modeled 2-m air temperatures, however. A sensitivity simulation of the UHI intensity to incoming solar radiation suggests that the hysteresis of the LST can mainly be attributed to the seasonal variation in incoming solar radiation.
In contrast to recent advances in projecting sea levels, estimations about the economic impact of sea level rise are vague. Nonetheless, they are of great importance for policy making with regard to adaptation and greenhouse-gas mitigation. Since the damage is mainly caused by extreme events, we propose a stochastic framework to estimate the monetary losses from coastal floods in a confined region. For this purpose, we follow a Peak-over-Threshold approach employing a Poisson point process and the Generalised Pareto Distribution. By considering the effect of sea level rise as well as potential adaptation scenarios on the involved parameters, we are able to study the development of the annual damage. An application to the city of Copenhagen shows that a doubling of losses can be expected from a mean sea level increase of only 11 cm. In general, we find that for varying parameters the expected losses can be well approximated by one of three analytical expressions depending on the extreme value parameters. These findings reveal the complex interplay of the involved parameters and allow conclusions of fundamental relevance. For instance, we show that the damage typically increases faster than the sea level rise itself. This in turn can be of great importance for the assessment of sea level rise impacts on the global scale. Our results are accompanied by an assessment of uncertainty, which reflects the stochastic nature of extreme events. While the absolute value of uncertainty about the flood damage increases with rising mean sea levels, we find that it decreases in relation to the expected damage.
Most climate change impacts manifest in the form of natural hazards. Damage assessment typically relies on damage functions that translate the magnitude of extreme events to a quantifiable damage. In practice, the availability of damage functions is limited due to a lack of data sources and a lack of understanding of damage processes. The study of the characteristics of damage functions for different hazards could strengthen the theoretical foundation of damage functions and support their development and validation. Accordingly, we investigate analogies of damage functions for coastal flooding and for wind storms and identify a unified approach. This approach has general applicability for granular portfolios and may also be applied, for example, to heat-related mortality. Moreover, the unification enables the transfer of methodology between hazards and a consistent treatment of uncertainty. This is demonstrated by a sensitivity analysis on the basis of two simple case studies (for coastal flood and storm damage). The analysis reveals the relevance of the various uncertainty sources at varying hazard magnitude and on both the microscale and the macroscale level. Main findings are the dominance of uncertainty from the hazard magnitude and the persistent behaviour of intrinsic uncertainties on both scale levels. Our results shed light on the general role of uncertainties and provide useful insight for the application of the unified approach.
Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties for both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic wind-damage relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two sources of wind gust data, direct observations by the German Weather Service and ERA-Interim reanalysis data. While the choice of gust data has little impact on the evaluation of German storm loss, spatially resolved coefficients of variation reveal dependence between model and data choice. The comparison shows that the probabilistic models by Heneka et al. (2006) and Prahl et al. (2012) both provide accurate loss predictions for moderate to extreme losses, with generally small coefficients of variation. We favour the latter model in terms of model applicability. Application of the versatile deterministic model by Klawa and Ulbrich (2003) should be restricted to extreme loss, for which it shows the least bias and errors comparable to the probabilistic model by Prahl et al. (2012).
We perform a systematic study of all cities in Europe to assess the Urban Heat Island (UHI) intensity by means of remotely sensed land surface temperature data. Defining cities as spatial clusters of urban land cover, we investigate the relationships of the UHI intensity, with the cluster size and the temperature of the surroundings. Our results show that in Europe, the UHI intensity in summer has a strong correlation with the cluster size, which can be well fitted by an empirical sigmoid model. Furthermore, we find a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves. We characterize the shape and identify apparent regional patterns.
While sea level rise is one of the most likely consequences of climate change, the provoked costs remain highly uncertain. Based on a block-maxima approach, we provide a stochastic framework to estimate the increase of expected damages with sea level rise as well as with meteorological changes and demonstrate the application to two case studies. In addition, the uncertainty of the damage estimations due to the stochastic nature of extreme events is studied. Starting with the probability distribution of extreme flood levels, we calculate the distribution of implied damages in a specific region employing stage-damage functions. Universal relations of the expected damages and their standard deviation, which demonstrate the importance of the shape of the damage function, are provided. We also calculate how flood protection reduces the damages leading to a more complex picture, where the extreme value behavior plays a fundamental role. Citation: Boettle, M., D. Rybski, and J. P. Kropp (2013), How changing sea level extremes and protection measures alter coastal flood damages, Water Resour. Res., 49, 1199-1210, doi: 10.1002/wrcr.20108.
Analyzing insurance-loss data we derive stochastic storm-damage functions for residential buildings. On district level we fit power-law relations between daily loss and maximum wind speed, typically spanning more than 4 orders of magnitude. The estimated exponents for 439 German districts roughly range from 8 to 12. In addition, we find correlations among the parameters and socio-demographic data, which we employ in a simplified parametrization of the damage function with just 3 independent parameters for each district. A Monte Carlo method is used to generate loss estimates and confidence bounds of daily and annual storm damages in Germany. Our approach reproduces the annual progression of winter storm losses and enables to estimate daily losses over a wide range of magnitudes. Citation: Prahl, B. F., D. Rybski, J. P. Kropp, O. Burghoff, and H. Held (2012), Applying stochastic small-scale damage functions to German winter storms, Geophys. Res. Lett., 39, L06806, doi: 10.1029/2012GL050961.