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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.
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.
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.
Many agriculture-based economies are increasingly under stress from climate change and socio-economic pressures. The excessive exploitation of natural resources still represents the standard procedure to achieve socio-economic development. In the area of the Moulouya river basin, Morocco, natural water availability represents a key resource for all economic activities. Agriculture represents the most important sector, and frequently occurring water deficits are aggravated by climate change. On the basis of historical trends taken from CRU TS 2.1, this paper analyses the impact of climate change on the per capita water availability under inclusion of population trends. The Climatic Water Balance (CWB) shows a significant decrease for the winter period, causing adverse effects for the main agricultural season. Further, moisture losses due to increasing evapotranspiration rates indicate problems for the annual water budget and groundwater recharge. The per capita blue water availability falls below a minimum threshold of 500 m(3) per year, denoting a high regional vulnerability to increasing water scarcity assuming a no-response scenario. Regional development focusing on the water-intense sectors of agriculture and tourism appears to be at risk. Institutional capacities and policies need to address the problem, and the prompt implementation of innovative water production and efficiency measures is recommended.
The coastal stretch of north-eastern Mediterranean Morocco holds vitally important ecological, social, and economic functions. The implementation of large-scale luxury tourism resorts shall push socio-economic development and facilitate the shift from a mainly agrarian to a service economy. Sufficient water availability and intact beaches are among the key requirements for the successful realization of regional development plans. The water situation is already critical, additional water-intense sectors could overstrain the capacity of water resources. Further, coastal erosion caused by sea-level rise is projected. Regional climate change is observable, and must be included in regional water management. Long-term climate trends are assessed for the larger region (Moulouya basin) and for the near-coastal zone at Saidia. The amount of additional water demand is assessed for the large-dimensioned Saidia resort; including the monthly, seasonal and annual tourist per capita water need under inclusion of irrigated golf courses and garden areas. A shift of climate patterns is observed, a lengthening of the dry summer season, and as well a significant decline of annual precipitation. Thus, current water scarcity is mainly human-induced; however, climate change will aggravate the situation. As a consequence, severe environmental damage due to water scarcity is likely and could impinge on the quality of local tourism. The re-adjustment of current management routines is therefore essential. Possible adjustments are discussed and the analysis concludes with management recommendations for innovative regional water management of tourism facilities.
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.
Most South Asian countries have challenges in ensuring water, energy, and food (WEF) security, which are often interacting positively or negatively. To address these challenges, the nexus approach provides a framework to identify the interactions of the WEF sectors as an integrated system. However, most nexus studies only qualitatively discuss the interactions between these sectors. This study conducts a systematic analysis of the WEF security nexus in South Asia by using open data sources at the country scale. We analyze interactions between the WEF sectors statistically, defining positive and negative correlations between the WEF security indicators as synergies and trade-offs, respectively. By creating networks of the synergies and trade-offs, we further identify most positively and negatively influencing indicators in the WEF security nexus. We observe a larger share of trade-offs than synergies within the water and energy sectors and a larger share of synergies than trade-offs among the WEF sectors for South Asia. However, these observations vary across the South Asian countries. Our analysis highlights that strategies on promoting sustainable energy and discouraging fossil fuel use could have overall positive effects on the WEF security nexus in the countries. This study provides evidence for considering the WEF security nexus as an integrated system rather than just a combination of three different sectors or securities.
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.
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).
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.
Changing food consumption patterns and associated greenhouse gas (GHG) emissions have been a matter of scientific debate for decades. The agricultural sector is one of the major GHG emitters and thus holds a large potential for climate change mitigation through optimal management and dietary changes. We assess this potential, project emissions, and investigate dietary patterns and their changes globally on a per country basis between 1961 and 2007. Sixteen representative and spatially differentiated patterns with a per capita calorie intake ranging from 1,870 to >3,400 kcal/day were derived. Detailed analyses show that low calorie diets are decreasing worldwide, while in parallel diet composition is changing as well: a discernable shift towards more balanced diets in developing countries can be observed and steps towards more meat rich diets as a typical characteristics in developed countries. Low calorie diets which are mainly observable in developing countries show a similar emission burden than moderate and high calorie diets. This can be explained by a less efficient calorie production per unit of GHG emissions in developing countries. Very high calorie diets are common in the developed world and exhibit high total per capita emissions of 3.7-6.1 kg CO2eq./day due to high carbon intensity and high intake of animal products. In case of an unbridled demographic growth and changing dietary patterns the projected emissions from agriculture will approach 20 Gt CO2eq./yr by 2050.
Increases in animal products consumption and the associated environmental consequences have been a matter of scientific debate for decades. Consequences of such increases include rises in greenhouse gas emissions, growth of consumptive water use, and perturbation of global nutrients cycles. These consequences vary spatially depending on livestock types, their densities and their production system. In this letter, we investigate the spatial distribution of embodied crop calories in animal products. On a global scale, about 40% of the global crop calories are used as livestock feed (we refer to this ratio as crop balance for livestock) and about 4 kcal of crop products are used to generate 1 kcal of animal products (embodied crop calories of around 4). However, these values vary greatly around the world. In some regions, more than 100% of the crops produced is required to feed livestock requiring national or international trade to meet the deficit in livestock feed. Embodied crop calories vary between less than 1 for 20% of the livestock raising areas worldwide and greater than 10 for another 20% of the regions. Low values of embodied crop calories are related to production systems for ruminants based on fodder and forage, while large values are usually associated with production systems for non-ruminants fed on crop products. Additionally, we project the future feed demand considering three scenarios: (a) population growth, (b) population growth and changes in human dietary patterns and (c) changes in population, dietary patterns and feed conversion efficiency. When considering dietary changes, we project the global feed demand to be almost doubled (1.8-2.3 times) by 2050 compared to 2000, which would force us to produce almost equal or even more crops to raise our livestock than to directly nourish ourselves in the future. Feed demand is expected to increase over proportionally in Africa, South-Eastern Asia and Southern Asia, putting additional stress on these regions.
This study explores the potential for regions to shift to a local food supply using food self-sufficiency (FSS) as an indicator. We considered a region food self-sufficient when its total calorie production is enough to meet its demand. For future scenarios, we considered population growth, dietary changes, improved feed conversion efficiency, climate change, and crop yield increments. Starting at the 5' resolution, we investigated FSS from the lowest administrative levels to continents. Globally, about 1.9 billion people are self-sufficient within their 5' grid, while about 1 billion people from Asia and Africa require cross-continental agricultural trade in 2000. By closing yield gaps, these regions can achieve FSS, which also reduces international trade and increases a self-sufficient population in a 5' grid to 2.9 billion. The number of people depending on international trade will vary between 1.5 and 6 billion by 2050. Climate change may increase the need for international agricultural trade by 4% to 16%.
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.
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.
In order to achieve meaningful climate protection targets at the global scale, each country is called to set national energy policies aimed at reducing energy consumption and carbon emissions. By calculating the monthly heating energy demand of dwellings in the Netherlands, our case study country, we contrast the results with the corresponding aspired national targets. Considering different future population scenarios, renovation measures and temperature variations, we show that a near zero energy demand in 2050 could only be reached with very ambitious renovation measures. While the goal of reducing the energy demand of the building sector by 50% until 2030 compared to 1990 seems feasible for most provinces and months in the minimum scenario, it is impossible in our scenario with more pessimistic yet still realistic assumptions regarding future developments. Compared to the current value, the annual renovation rate per province would need to be at least doubled in order to reach the 2030 target independent of reasonable climatic and population changes in the future. Our findings also underline the importance of policy measures as the annual renovation rate is a key influencing factor regarding the reduction of the heating energy demand in dwellings. (C) 2015 Elsevier Ltd. All rights reserved.
Water is an essential input to the majority of human activities. Often, access to sufficient water resources is limited by quality and infrastructure aspects, rather than by resource availability alone, and each activity has different requirements regarding the nature of these aspects. This paper develops an integrated approach to assess the adequacy of water resources for the three major water users: the domestic, agricultural and industrial sectors. Additionally, we include environmental water requirements. We first outline the main determinants of water adequacy for each sector. Subsequently, we present an integrated approach using fuzzy logic, which allows assessing sector-specific as well as overall water adequacy. We implement the approach in two case study settings to exemplify the main features of the approach. Using results from two climate models and two forcing RCPs (representative concentration pathways), as well as population projections, we further assess the impacts of climate change in combination with population growth on the adequacy of water resources. The results provide an important step forward in determining the most relevant factors, impeding adequate access to water, which remains an important challenge in many regions of the world. The methodology allows one to directly identify the factors that are most decisive in determining the adequacy of water in each region, pointing towards the most efficient intervention points to improve conditions. Our findings underline the fact that, in addition to water volumes, water quality is a limitation for all sectors and, especially for the environmental sector, high levels of pollution are a threat to water adequacy.
Climate impacts on human livelihoods: where uncertainty matters in projections of water availability
(2014)
Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions - and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions.
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.