Aerial river management by smart cross-border reforestation

In the face of increasing socio-economic and climatic pressures in growing cities, it is rational for managers to consider multiple approaches for securing water availability. One often disregarded option is the promotion of reforestation in source regions supplying important quantities of atmospheric moisture transported over long distances through aerial rivers, affecting water resources of a city via precipitation and runoff (‘smart reforestation’). Here we present a case demonstrating smart reforestation's potential as a water management option. Using numerical moisture back-tracking models, we identify important upwind regions contributing to the aerial river of Santa Cruz de la Sierra (Bolivia). Simulating the effect of reforestation in the identified regions, annual precipitation and runoff reception in the city was found to increase by 1.25% and 2.30% respectively, while runoff gain during the dry season reached 26.93%. Given the city's population growth scenarios, the increase of the renewable water resource by smart reforestation could cover 22–59% of the additional demand by 2030. Building on the findings, we argue for a more systematic consideration of aerial river connections between regions in reforestation and land planning for future challenges. © 2019 The Author

Landscape matters: Insights from the impact of mega-droughts on Colombia's energy transition

Mega-droughts can cause disruption to the affected society sparking a transition. We explore the causes and effects of the 2015−2016 mega-drought in Colombia. Using the multi-level perspective as a framework, we found that the mega-drought sparked an energy transition in Colombia whose dynamics were impacted both by the institutionalization of niches as well as the ability to predict the next drought. We were able to trace, using the current understanding of anthropogenic forces, the cause of the mega-drought to socio-technical landscape development influenced by human-induced warming and land use change. We found that the regimes in Bolivia and Brazil were able to influence the landscape through deforestation, and hence contribute to the intensity of a mega-drought in Colombia. The knowledge that a regime can cause disruption in regimes in other geographies and sectors has implications for transition research as well as decision-making for coping with droughts and other disasters. © 202

A Gini approach to spatial CO2 emissions

Combining global gridded population and fossil fuel based CO2 emission data at 1 km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between these two datasets by a quasi-Lorenz curve which enables us to discern the individual contributions of densely and sparsely populated regions to the national CO2 emissions. We observe pronounced country-specific characteristics and quantify them using an indicator resembling the Gini-index. As demonstrated by a robustness test, the Gini-index for each country arise from a compound distribution between the population and emissions which differs among countries. Relating these indices with the degree of socio-economic development measured by per capita Gross Domestic Product (GDP) at purchase power parity, we find a strong negative correlation between the two quantities with a Pearson correlation coefficient of -0.71. More specifically, this implies that in developing countries locations with large population tend to emit relatively more CO2, and in developed countries the opposite tends to be the case. Based on the relation to urban scaling, we discuss the implications for CO2 emissions from cities. Our results show that general statements with regard to the (in)efficiency of large cities should be avoided as it is subject to the socio-economic development of respective countries. Concerning the political relevance, our results suggest a differentiated spatial prioritization in deploying climate change mitigation measures in cities for developed and developing countries

Typology of coastal urban vulnerability under rapid urbanization

Coastal areas are urbanizing at unprecedented rates, particularly in low- and middle-income countries. Combinations of long-standing and emerging problems in these urban areas generate vulnerability for human well-being and ecosystems alike. This baseline study provides a spatially explicit global systematization of these problems into typical urban vulnerability profiles for the year 2000 using largely sub-national data. Using 11 indicator datasets for urban expansion, urban population growth, marginalization of poor populations, government effectiveness, exposures and damages to climate-related extreme events, low-lying settlement, and wetlands prevalence, a cluster analysis reveals a global typology of seven clearly distinguishable clusters, or urban profiles of vulnerability. Each profile is characterized by a specific data-value combination of indicators representing mechanisms that generate vulnerability. Using 21 studies for testing the plausibility, we identify seven key profile-based vulnerabilities for urban populations, which are relevant in the context of global urbanization, expansion, and climate change. We show which urban coasts are similar in this regard. Sensitivity and exposure to extreme climate-related events, and government effectiveness, are the most important factors for the huge asymmetries of vulnerability between profiles. Against the background of underlying global trends we propose entry points for profile-based vulnerability reduction. The study provides a baseline for further pattern analysis in the rapidly urbanizing coastal fringe as data availability increases. We propose to explore socio-ecologically similar coastal urban areas as a basis for sharing experience and vulnerability-reducing measures among them

Schrumpfung und Urban Sprawl - analytische und planerische Problemstellungen

Das vorliegende UFZ-Diskussionspapier ist die Dokumentation des Workshops Schrumpfung und Urban Sprawl, der am 3. November 2003 am UFZ stattfand. Es führt damit eine Diskussions- und Forschungslinie fort, die in den 1990er Jahren durch Forscher und Praktiker aus unterschiedlichen Einrichtungen der Region Halle-Leipzig begründet wurde. Im Arbeitskreis Suburbanisierung wurden unter Koordination des UFZ disziplinäre Zugänge und praktische Erfahrungen zusammengeführt und daraus Handlungsempfehlungen abgeleitet. Zwischenzeitlich hat der Suburbanisierungsdruck, der noch Ende der 1990er Jahre konstatiert wurde, deutlich abgenommen nicht nur in der Region, sondern in ganz Ostdeutschland. Nichtsdestoweniger ist Suburbanisierung ein zentraler Gegenstand von raumbezogener Politik und räumlicher Planung geblieben und hat im Zusammenhang mit dem Thema Stadtumbau neue Relevanz gewonnen. So ist davon auszugehen, dass auch in der Region Halle-Leipzig die intensive Beschäftigung mit dem Problem der Suburbanisierung anhalten wird allerdings unter veränderten Vorzeichen. Im Mittelpunkt steht nunmehr die Frage, welche Anforderungen sich aus der Situation von demographischer und städtischer Schrumpfung für die wissenschaftliche und praktische Auseinandersetzung mit Suburbanisierung ergeben. So gilt es, unter anderem, zu klären, ob sich die Richtung von Sprawl unter Schrumpfungsbedingungen umkehrt, ob das Zusammenspiel von Schrumpfung und Sprawl zu einer neuen Stadtstruktur führt oder ob sich durch diese spezifische Situation die Segregationsmuster verändern. Auf dem Workshop selbst wurden vor allem die Möglichkeiten der Steuerung von Suburbanisierung bzw. Sprawl unter Schrumpfungsbedingungen behandelt. Dazu wurden Überlegungen und Ergebnisse, die im EU-Projekt URBS PANDENS am Fallbeispiel Leipzig gewonnen wurden, vorgestellt, diskutiert und mit Erfahrungen aus der Praxis bzw. aus einer anderen Region konfrontiert. Neben der empirischen Analyse spielt dabei ein im Rahmen von URBS PANDENS entwickeltes qualitatives Modell des Urban Sprawl eine zentrale Rolle. --

Integration of case studies on Global Change by means of qualitative differential equations

We present a novel methodology to integrate qualitative knowledge from different case studies on Global Change related issues into a single framework. The method is based on the concept of qualitative differential equations (QDEs) which represents a mathematically well-defined approach to investigate classes of ordinary differential equations (ODEs) used in conventional modeling exercises. These classes are defined by common qualitative features, e.g., monotonicity, signs, etc. Using the QSIM-algorithm it is possible to derive the set of possible solutions of all ODEs in the class. By this one can formulate a common, qualitatively specified cause–effect scheme valid for all case studies. The scheme is validated by testing it against the actually observed histories in the study regions with respect to their reconstruction by the corresponding QDE. The method is outlined theoretically and exemplary applied to the problem of land-use changes due to smallholder agriculture in developing countries. It is shown that the seven case-studies used can be described by a single cause–effect scheme which thus constitutes a pattern of Global Change. As a generally valid prerequisite for sustainability of this kind of land-use the presence of wage labor is shown to represent a decisive factor

Evaluating the use of uncertainty visualization for exploratory analysis of land cover change: a qualitative expert user study

Extensive research on geodata uncertainty has been conducted in the past decades, mostly related to modeling, quantifying, and communicating uncertainty. But findings on if and how users can incorporate this information into spatial analyses are still rare. In this paper we address these questions with a focus on land cover change analysis. We conducted semi-structured interviews with three expert groups dealing with change analysis in the fields of climate research, urban development, and vegetation monitoring. During the interviews we used a software prototype to show change scenarios that the experts had analyzed before, extended by visual depiction of uncertainty related to land cover change.This paper describes the study, summarizes results, and discusses findings as well as the study method. Participants came up with several ideas for applications that could be supported by uncertainty, for example, identification of erroneous change, description of change detection algorithm characteristics, or optimization of change detection parameters. Regarding the aspect of reasoning with uncertainty in land cover change data the interviewees saw potential in better-informed hypotheses and insights about change. Communication of uncertainty information to users was seen as critical, depending on the users’ role and expertize. We judge semi-structured interviews to be suitable for the purpose of this study and emphasize the potential of qualitative methods (workshops, focus groups etc.) for future uncertainty visualization studies

Texture-based identification of urban slums in Hyderabad, India using remote sensing data

This paper outlines a methodology to identify informal settlements out of high resolution satellite imagery using the concept of lacunarity. Principal component analysis and line detection algorithms were applied alternatively to obtain a high resolution binary representation of the city of Hyderabad, India and used to calculate lacunarity values over a 60 × 60 m grid. A number of ground truthing areas were used to classify the resulting datasets and to identify lacunarity ranges which are typical for settlement types that combine high density housing and small dwelling size – features characteristic for urban slums in India. It was discovered that the line detection algorithm is advantageous over principal component analysis in providing suitable binary datasets for lacunarity analysis as it is less sensitive to spectral variability within mosaicked imagery. The resulting slum location map constitutes an efficient tool in identifying particularly overcrowded areas of the city and can be used as a reliable source in vulnerability and resilience assessments at a later stage. The proposed methodology allows for rapid analysis and comparison of multi-temporal data and can be applied on many developing urban agglomerations around the world