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This briefsummarizes policy-relevant insights and recommendations for the European Commission and EU member states from UNCHAIN project publications and research. As the impacts of climate change continue to intensify, the EU is transitioning to a more climate-resilient Europe. Four key policies drive this shift: the European Green Deal, the European Commission Adaptation Strategy launched in 2021, the 2022 Mission on Adaptation to Climate Change and the EU-wide Climate Risk Assessment (EUCRA) due in 2024. Against this background, the project Unpacking Climate Impact Chains (UNCHAIN) has been well placed to offer timely and relevant insights into the EU adaptation policy process. This brief summarizes the UNCHAIN approach and results from 11 European case studies. Key Messages Policymakers can make adaptation “smarter” by using the Impact Chain approach to analyse both conventional local climate risks and lesser well-known transboundary climate risks. Policymakers should embrace uncertainties by applying a reflect-then-act rather than the predict-then-act approach; by including socio-economic scenarios for a range of possible developments, from likely to unanticipated; and by ensuring transparency in communications. International actors can invest in method innovation, data collection and decision-support tools to identify and assess transboundary climate risks. They should also foster greater cooperation in adaptation planning and implementation across jurisdictions. Policymakers and relevant stakeholders should engage throughout the climate risk assessment process, to co-develop climate information and solutions that capture their needs, knowledge and perspectives and that account for social vulnerability and equitable adaptation.

Shifting from effect-oriented toward cause-oriented and systemic approaches in sustainable climate change adaptation requires a solid understanding of the climate-related and societal causes behind climate risks. Thus, capturing, systemizing, and prioritizing factors contributing to climate risks are essential for developing cause-oriented climate risk and vulnerability assessments (CRVA). Impact chains (IC) are conceptual models used to capture hazard, vulnerability, and exposure factors that lead to a specific risk. IC modeling includes a participatory stakeholder phase and an operational quantification phase. Although ICs are widely implemented to systematically capture risk processes, they still show methodological gaps concerning, for example, the integration of dynamic feedback or balanced stakeholder involvement. Such gaps usually only become apparent in practical applications, and there is currently no systematic perspective on common challenges and methodological needs. Therefore, we reviewed 47 articles applying IC and similar CRVA methods that consider the cause–effect dynamics governing risk. We provide an overview of common challenges and opportunities as a roadmap for future improvements. We conclude that IC should move from a linear-like to an impact web–like representation of risk to integrate cause–effect dynamics. Qualitative approaches are based on significant stakeholder involvement to capture expert-, place-, and context-specific knowledge. The integration of IC into quantifiable, executable models is still highly underexplored because of a limited understanding of systems, data, evaluation options, and other uncertainties. Ultimately, using IC to capture the underlying complex processes behind risk supports effective, long-term, and sustainable climate change adaptation.

Purpose This paper aims to present the “Vulnerability Sourcebook” methodology, a standardised framework for the assessment of climate vulnerability and risk in the context of adaptation planning. The Vulnerability Sourcebook has been developed for the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) and has been applied in more than twenty countries worldwide. Design/methodology/approach It is based on a participative development of so-called climate impact chains, which are an analytical concept to better understand, systemise and prioritise the climate factors as well as environmental and socio-economic factors that drive climate related threats, vulnerabilities and risks in a specific system. Impact chains serve as the backbone for an operational climate vulnerability assessment with indicators based on quantitative approaches (data, models) combined with expert assessments. In this paper, the authors present the concept and applications of the original Vulnerability Sourcebook, published in 2015, which was based on the IPCC AR4 concept of climate vulnerability. In Section 6 of this paper, the authors report how this concept has been adapted to the current IPCC AR5 concept of climate risks. Findings The application of the Sourcebook is demonstrated in three case studies in Bolivia, Pakistan and Burundi. The results indicate that particularly the participative development of impact chains helped with generating a common picture on climate vulnerabilities and commitment for adaptation planning within a region. The mixed methods approach (considering quantitative and qualitative information) allows for a flexible application in different contexts. Challenges are mainly the availability of climate (change) and socio-economic data, as well as the transparency of value-based decisions in the process. Originality/value The Vulnerability Sourcebook offers a standardised framework for the assessment of climate vulnerability and risk in the context of adaptation planning.

Climate-related sudden-onset events (e.g., floods, cyclones) and slow-onset processes (e.g., sea level rise and heat waves) pose a major risk to communities all over the world. With intensifying climate change in combination with unequal socioeconomic development, climate-related risks are expected to escalate in the future, potentially leading to critical losses and damages. This calls for efficient and achievable risk management strategies. Climate Risk Management (CRM) is a leading approach to identify, assess and reduce risks, through an integration of Disaster Risk Reduction, Climate Change Adaptation, and sustainable development. CRM aims at comprehensively managing risks and increasing resilience and adaptive capacity. To date, several conceptual CRM frameworks have been developed, which have, however, rarely been applied to real-world cases.Based on this conceptual literature, we further develop a comprehensive CRM framework, comprising both the risk assessment as well as the implementation and monitoring domains of CRM, and test it on three real-world risk cases in Peru, India and Austria. The cases have distinct spatial scales, from local level in Peru, to district level in India, to nationwide in Austria. The risks covered in these cases are linked to different hazards, ranging from glacier lake outburst floods (Peru), sea level rise, salinization and cyclones (India), to riverine flooding and agricultural droughts (Austria).The aim of this complementary case study approach is to validate the overall structure and individual steps of the CRM framework against actual risk management practices in the three case studies. Based on the specific results and common insights from the three cases, we are able to (1) evaluate the applicability of the proposed conceptual CRM framework in real world circumstances, (2) present evidence on the extent to which comprehensive management of climate-related risks has been achieved in the three cases, and (3) synthesize policy recommendations towards an achievable comprehensive CRM in practice, acknowledging specific local contexts and characteristics.

Die Zunahme von Wetter- und Klimaextremen durch den voranschreitenden Klimawandel ist zunehmend mit gesellschaftlichen Auswirkungen und ökonomischen Kosten verbunden. Eine umfassende Quantifizierung und nutzerspezifische Kommunikation dieser sozioökonomischen Auswirkungen an politische und privatwirtschaftliche Entscheider ist für die Vermeidung möglicher Folgen und eine adäquate Anpassung unerlässlich. Gleichzeitig gewinnt die Frage nach dem sozioökonomischen Nutzen von Wetterdiensten und deren Leistungen eine immer größere Relevanz. Der sozioökonomische Nutzen beschränkt sich dabei nicht nur auf monetäre Aspekte, denn Wetterdienste versetzen die Gesellschaft durch die Bereitstellung entsprechender Informationen in die Lage qua Verhalten besser, sicherer und nachhaltiger auf Wetter- und Klimaereignisse zu reagieren. Dieser Vortrag erörtert bestehende Aktivitäten der Wetterdienste aus Deutschland, Österreich und der Schweiz (DACH) im Bereich Risiko, Impacts und sozioökonomische Nutzenbetrachtungen, u.a. mit Einblicken in die Wetter- und Klimakommunikation aus sozial- und verhaltenswissenschaftlicher Perspektive, die Integration von Daten über Auswirkungen, Verluste und Schäden in einer einheitlichen Ereignisdatenbank (CESARE), Ziele und Nutzen eines Risiko- und Auswirkungs-orientierten Ansatzes für Wetterdienste, am Beispiel des RiskLabs der ZAMG die Entwicklung und Anwendung der open-source Python Plattform CLIMADA [1] im Bereich impact-based warnings (MeteoSchweiz) und Abschätzung sozioökonomischer Klimafolgen (DWD), über 20 weltweite Klimaanpassungsstudien (Economics of Climate Adaptation, ECA [2]) mit Fokus auf Extremwetter, abgeschlossene und laufende Themenschwerpunkte des National Centre for Climate Services (NCCS) bisherige und anstehende sozioökonomische Nutzenbetrachtungen. Basierend auf einer Synthese der bisherigen Arbeiten erfolgt eine Identifikation zukünftiger, gemeinsamer Ziele im Rahmen der D-A-CH Kooperation. Dies reicht von einem gemeinsamen, konzeptionellen und methodischen Verständnis der Bewertung von Auswirkungen und Risiken im Kontext Wetter und Klima bis hin zur Etablierung gemeinsamer Anwendungen und Plattformen zur Durchführung tri-nationaler Projekte. Referenzen: [1] CLIMADA Python plattform, https://wcr.ethz.ch/research/climada.html [2] Economics of Climate Adaptation (ECA), https://eca-network.org/

Millions of people fall ill with malaria every year&mdash, most of them are located in sub-Saharan Africa. The weight of the burden of malaria on a society is determined by a complex interplay of environmental and social factors, including poverty, awareness and education, among others. A substantial share of the affected population is characterized by a general lack of anticipation and coping capacities, which renders them particularly vulnerable to the disease and its adverse side effects. This work aims at identifying interdependencies and feedback mechanisms in the malaria social vulnerability system and their variations in space by combining concepts, methods and tools from Climate Change Adaptation, Spatial Analysis, and Statistics and System Dynamics. The developed workflow is applied to a selected set of social, economic and biological vulnerability indicators covering five East-African Nations. As the study areas&rsquo, local conditions vary in a multitude of aspects, the social vulnerability system is assumed to vary accordingly throughout space. The study areas&rsquo, spatial entities were therefore aggregated into three system-regions using correlation-based clustering. Their respective correlation structures are displayed as Causal Loop Diagrams (CLDs). While the three resulting CLDs do not necessarily display causal relations (as the set of social vulnerability indicators are likely linked through third variables and parts of the data are proxies), they give a good overview of the data, can be used as basis for discussions in participatory settings and can potentially enhance the understanding the malaria vulnerability system.

Climate risk, and related impacts, are determined by a variety of natural, climatological and socio-economic factors. In its fifth Assessment Report, the Intergovernmental Panel on Climate Change has adapted the concept and terminology in this respect. The challenge is: How can relevant influencing factors be identified and integrated? And, how can these factors be represented spatially and integratively in order to provide decision makers with a sound basis for adaptation measures? The central starting question is: Where do I do what (and when)? Within the Austrian ACRP project 'RESPECT', a novel climate change risk analysis for the natural hazard 'flooding' was developed. Special attention is paid to the modelling of socio-economic and physical vulnerability and its integration into a spatially explicit climate risk analysis. As a result, spatial and thematic hotspots of social and physical vulnerability and climate risk for Austria are identified, which serve as a basis for the identification of adaptation measures.As a result, climate risk maps are available for Austria, which show risk and vulnerability hotspots as homogeneous spatial regions, independent from administrative boundaries and traditional raster-based approaches. These hotspots are quantitatively evaluated by an index value as a measure of climate risk. In addition to the purely quantitative evaluation, it is also possible to characterise and present the spatial units qualitatively, in terms of 'problem areas' and contributing factors. This is a significant development compared to 'traditional' spatial units (grid cell based; based on administrative units). Thus the question mentioned at the beginning can be answered - where are which intervention measures necessary. The results are available for socio-economic and physical climate risk, which are flanked by corresponding hazard and vulnerability maps. Results for the present and the future have been produced using proxy indicators from the high-resolution Austrian climate change scenario data (ÖKS15). This makes it possible to identify future hot spots under the assumption of different climate scenarios. The presentations presents the adapted risk concept and methodological approach, respectively, and reflects critically on the opportunities and challenges of climate risk analysis in Austria and in general for the planning of climate change adaptation measures.

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step,...

Maps synthesizing climate, biophysical and socioeconomic data have become part ofthe standard tool-kit for communicating the risks of climate change to society. Vulnerability maps are used to direct attention to geographic areas where impacts on society are expected to be greatest and that may therefore require adaptation interventions. Under the Green Climate Fund and other bilateral climate adaptation funding mechanisms, donors are investing billions of dollars of adaptation funds, often with guidance from modeling results, visualized and communicated through maps and spatial decision sup- port tools. This paper presents the results of a systematic review of 84 studies that map social vulnerability to climate impacts. These assessments are compiled by interdisciplin- ary teams ofresearchers, span many regions, range in scale from local to global, and vary in terms of frameworks, data, methods, and thematic foci. The goal is to identify com- mon approaches to mapping, evaluate their strengths and limitations, and offer recom- mendations and future directions for the field. The systematic review finds some convergence around common frameworks developed by the Intergovernmental Panel on Climate Change, frequent use of linear index aggregation, and common approaches to the selection and use of climate and socioeconomic data. Further, it identifies limitations such as a lack of future climate and socioeconomic projections in many studies, insuffi- cient characterization of uncertainty, challenges in map validation, and insufficient engagement with policy audiences for those studies that purport to be policy relevant. Finally, it provides recommendations for addressing the identified shortcomings.

The debate on "Loss and Damage" (L&D) has gained traction over the last few years. Supported by growing scientific evidence of anthropogenic climate change amplifying frequency, intensity and duration of climate-related hazards as well as observed increases in climate-related impacts and risks in many regions, the "Warsaw International Mechanism for Loss and Damage" was established in 2013 and further supported through the Paris Agreement in 2015. Despite advances, the debate currently is broad, diffuse and somewhat confusing, while concepts, meth ods and tools, as well as directions for policy remain vague and often contested. This book, a joint effort of the Loss and Damage Network - a partnership effort by scientists and practitioners from around the globe - provides evidence-based insight into the L&D discourse by highlighting state-of-the-art research conducted across multiple disciplines, by showcasing applications in practice and by providing insight into policy contexts and salient policy options. This introductory chapter summarises key findings of the twenty-two book chapters in terms of five propositions. These propositions, each building on relevant findings linked to forward-looking sugges tions for research, policy and practice, reflect the architecture of the book, whose sections proceed from setting the stage to critical issues, followed by a section on methods and tools, to chapters that provide geographic perspectives, and finally to a section that identifies potential policy options. The propositions comprise (1) Risk management can be an effective entry point for aligning perspectives and debates, if framed comprehensively, coupled with climate justice considerations and linked to established risk management and adaptation practice; (2) Attribution science is advancing rapidly and fundamental to informing actions to minimise, avert, and address losses and damages; (3) Climate change research, in addition to identifying physical/hard limits to adaptation, needs to more systematically examine soft limits to adaptation, for which we find some evidence across several geographies globally; (4) Climate risk insurance mechanisms can serve the prevention and cure aspects emphasised in the L&D debate but solidarity and accountability aspects need further attention, for which we find tentative indication in applications around the world; (5) Policy deliberations may need to overcome the perception that L&D constitutes a win-lose negotiation "game" by developing a more inclusive narrative that highlights collective ambition for tackling risks, mutual benefits and the role of Transformation.

Accessibility and satisfaction related to healthcare services are conceived as multidimensional concepts. These concepts can be studied using objective and subjective measures. In this study, we created two indices: a composite healthcare accessibility index (CHCA) and a composite healthcare satisfaction index (CHCS). To calculate the CHCA index we used three indicators based on three components of multidimensional healthcare accessibility: availability, acceptability and accessibility. In the indicator based on the component of accessibility, we included an innovative perceived time-decay parameter. The three indicators of the CHCA index were weighted through the application of a principal components analysis. To calculate the CHCS index, we used three indicators: the waiting time after the patient arrives at the healthcare service, the quality of the healthcare, and the healthcare service supply. These three indicators making up the CHCA index were weighted by applying an analytical hierarchy process. Three kinds of regressions were subsequently applied in order to explain the CHCA and CHCS indices: namely the Linear Least Squares, Ordinal Logistic, and Random Forests regressions. In these regressions, we used different independent social and health-related variables. These variables represented the predisposing, enabling, and need factors of people´s behaviors related to healthcare. All the calculations were applied to a study area: the city of Quito, Ecuador. Results showed that there are health-related inequalities in regard to healthcare accessibility and healthcare satisfaction in our study area. We also identified specific social factors that explained the indices developed. The present work is a mixed-methods approach to evaluate multidimensional healthcare accessibility and healthcare satisfaction, incorporating a pluralistic perspective, as well as a multidisciplinary framework. The results obtained can also be considered as tools for healthcare and urban planners, for more integrative social analyses that can improve the quality of life in urban residents.

This research addresses the need for proactive climate risk management (CRM) by developing and applying a spatial climate risk and vulnerability assessment (CRVA) to flooding under consideration of the socio-economic dimension in Austria. Our research builds on a consolidated risk and vulnerability framework targeting both disaster risk reduction (DRR) and climate change adaptation (CCA) while integrating the consolidated risk approach of the Intergovernmental Panel on Climate Change (IPCC). Furthermore, our research advances current methodologies by applying a spatially explicit and indicator-based approach, which allows the targeted and place-specific identification of intervention options&mdash, independent from the spatial bias of administrative units. The flooding CRVA is based on a comprehensive list of 14 primary indicators and 35 socio-economic sub-indicators. Our results indicate that high levels of socio-economic vulnerability related to flooding are concentrated in the northern and eastern regions of Austria. When integrating a climate hazard proxy, statistically significant risk hotspots (&gt, 90% confidence) can be identified in central-northern Austria and towards the east. Furthermore, we established a typology of regions following a spatially enabled clustering approach. Finally, our research provides a successful operationalization of the IPCC Fifth Assessment Report (AR5) risk framework in combination with enhanced spatial analysis methods.

Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB). This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe. Different climate change scenarios were downscaled for the time period 1960–2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks.

Human settlements are often at risk from multiple hydro-meteorological hazards, which include fluvial floods, short-time extreme precipitation (leading to ‘pluvial’ floods) or coastal floods. In th ...

The European (EC FP7-funded) HEALTHY FUTURES project aims at improving anticipation of climate change effects on three water related vector-borne diseases (malaria, schistosomiasis, and Rift Valley fever) in eastern Africa. The intention of the HEALTHY FUTURES Atlas is to present key project results, and to support decision makers in identifying context and place-specific interventions. The Atlas provides information on past, present, and future conditions of risk and its components (hazard, vulnerability) to the three diseases, and allows the exploration of results through interactive tools.

This research aims to employ a novel methodology for modelling uncertainty in the GIS environment. The spatially explicit sensitivity and uncertainty analysis was applied on Multicriteria Decision Analysis (MCDA) for an economic vulnerability assessment within the Salzach Basin. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimize the associated uncertainty within an economic vulnerability assessment. In order to achieve this objective, the following methodology, composed four steps, was applied: (1) computation of criteria weights using Analytic Hierarchy Process (AHP); (2) Monte Carlo Simulation was applied for computing the uncertainties associated with AHP weights; (3) the Global Sensitivity Analysis (GSA) was employed in the form of the model-independent method of output variance decomposition, in which the variability of vulnerability maps is apportioned to every criterion weight, generating one first-order (S) and one total-effect (ST) sensitivity index map per criterion weight; and (4) an Ordered Weighted Averaging method was applied for producing vulnerability maps. The results of this research demonstrated the robustness of spatially explicit GSA for minimizing the uncertainty associated with GISMCDA models. According to the achieved results, we conclude that applying the variance based GSA leads to a spatial and statistical assessment of the importance of each input factor on the outcome of the GIS-MCDA method, which allows us to introduce and recommend GIS based GSA as a useful methodology for minimizing uncertainty of GISMCDA.

International audience; This case study proposes an alternative spatial sampling design when there is no enumeration of the target population. Empirical studies and spatial quantitative assessments at a local scale, in the global South or in a sensitive context often face the limitations of baseline knowledge and secondary data, resulting in the challenge of choosing methods or models appropriate to the situation. Yet, to understand the vulnerability of population to disasters and improve their resilience require a clear picture of the situation. Various methods have already been proposed for sampling when data on apopulation are limited. While taking these methods and their limits into account, we presenta new approachto designa population sample by using Global Positioning Systems (GPS) loggers. GPS systems are now widely available and easy to use and are useful when it comes to collect accurate and consistent data. Astratified survey,combined with 4,095 questionnaires,was carried out in 2015 in Malda(West Bengal). The survey allowed us to capture the spatial pattern of the erosion-affected population and to better understand their vulnerability. The method therefore shows promise for use in case studies in environments where detailed geographic and human census data have eitherlimited availability orlittle relevance.

International audience; Human displacement is liable to occur in riverside regions where people are vulnerable to riverbank erosion. This vulnerability depends on factors such as population density and the economic conditions of the region’s population. Short-term socioeconomic impacts on the displaced population include loss of home, agricultural land, jobs and assets. There will also be long-term socioeconomic impacts on the displaced population, including direct impacts on their living conditions and indirect impacts on human health and development, such as schooling for children and the health of mothers and children. Generally, short-term impacts are assessed in terms of needs for ex post assistance. However, the long-term socioeconomic impacts of riverbank erosion are rarely assessed from a policy perspective. The present study assesses these long-term impacts of bank erosion along the River Ganges. Analysis of survey data showed that the long-term socioeconomic impacts are severe, including increased income, or expenditure, poverty and also human poverty, in terms of poor living conditions, health and education. This paper concludes with a discussion of rehabilitation policy based on the survey results.

The design of methods and tools to build adequate representations of complex geographical phenomena in a way that spatial patterns are emphasized is one of the core objectives of GIScience. In this...

The paper deals with the development of a general as well as integrative and holistic framework to systematize and assess vulnerability, risk and adaptation. The framework is a thinking tool meant as a heuristic that outlines key factors and different dimensions that need to be addressed when assessing vulnerability in the context of natural hazards and climate change. The approach underlines that the key factors of such a common framework are related to the exposure of a society or system to a hazard or stressor, the susceptibility of the system or community exposed, and its resilience and adaptive capacity. Additionally, it underlines the necessity to consider key factors and multiple thematic dimensions when assessing vulnerability in the context of natural and socio-natural hazards. In this regard, it shows key linkages between the different concepts used within the disaster risk management (DRM) and climate change adaptation (CCA) research. Further, it helps to illustrate the strong relationships between different concepts used in DRM and CCA. The framework is also a tool for communicating complexity and stresses the need for societal change in order to reduce risk and to promote adaptation. With regard to this, the policy relevance of the framework and first results of its application are outlined. Overall, the framework presented enhances the discussion on how to frame and link vulnerability, disaster risk, risk management and adaptation concepts.

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is - with the exception of arid environments -comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.

The concept of vulnerability is increasingly used in the fields of disaster risk reduction and climate change adaptation, as well as socioeconomic studies. This paper reviews research inputs into the concept of vulnerability and highlights the challenges of resolving its spatial and temporal variability with building resilience and adaptation. We hypothesise that a clear understanding of scale is key to integrating these related issues, by differentiating three dimensions of scale when analysing relationships between the observed and the intrinsic scale of a given phenomenon, namely space, time and dimensional level. The paper analyses 20 vulnerability assessment approaches, ranging from the global down to the local scale, and positions them with regard to their integration of the spatial component. We then develop a vulnerability cube as a framework to position existing approaches and to map them in a three-dimensional space. The three axes represent space, time and dimension and provide a structure for the different notions of scales and ultimately for a spatial analysis workflow. The vulnerability cube framework helps us to position different vulnerability assessments and to identify overlaps, differences and specific characteristics. Additionally, this three-dimensional conceptualisation allows the identification and discussion of appropriate scaling issues.

Outbreaks of Rift Valley fever (RVF), a relatively recently emerged zoonosis endemic to large parts of sub-Saharan Africa that has the potential to spread beyond the continent, have profound health and socio-economic impacts, particularly in communities where resilience is already low. Here output from a new, dynamic disease model [the Liverpool RVF (LRVF) model], driven by downscaled, bias-corrected climate change data from an ensemble of global circulation models from the Inter-Sectoral Impact Model Intercomparison Project run according to two radiative forcing scenarios [representative concentration pathway (RCP)4.5 and RCP8.5], is combined with results of a spatial assessment of social vulnerability to the disease in eastern Africa. The combined approach allowed for analyses of spatial and temporal variations in the risk of RVF to the end of the current century. Results for both scenarios highlight the high-risk of future RVF outbreaks, including in parts of eastern Africa to date unaffected by the disease. The results also highlight the risk of spread from/to countries adjacent to the study area, and possibly farther afield, and the value of considering the geography of future projections of disease risk. Based on the results, there is a clear need to remain vigilant and to invest not only in surveillance and early warning systems, but also in addressing the socio-economic factors that underpin social vulnerability in order to mitigate, effectively, future impacts.

Despite the decline in malaria incidence due to intense interventions, potentials for malaria transmission persist in Rwanda. To eradicate malaria in Rwanda, strategies need to expand beyond approaches that focus solely on malaria epidemiology and also consider the socioeconomic, demographic and biological/disease-related factors that determine the vulnerability of potentially exposed populations. This paper analyses current levels of social vulnerability to malaria in Rwanda by integrating a set of weighted vulnerability indicators. The paper uses regionalisation techniques as a spatially explicit approach for delineating homogeneous regions of social vulnerability to malaria. This overcomes the limitations of administrative boundaries for modelling the trans-boundary social vulnerability to malaria. The utilised approach revealed high levels of social vulnerability to malaria in the highland areas of Rwanda, as well as in remote areas where populations are more susceptible. Susceptibility may be due to the populations’ lacking the capacity to anticipate mosquito bites, or lacking resilience to cope with or recover from malaria infection. By highlighting the most influential indicators of social vulnerability to malaria, the applied approach indicates which vulnerability domains need to be addressed, and where appropriate interventions are most required. Interventions to improve the socioeconomic development in highly vulnerable areas could prove highly effective, and provide sustainable outcomes against malaria in Rwanda. This would ultimately increase the resilience of the population and their capacity to better anticipate, cope with, and recover from possible infection.

Dengue fever has gradually re-emerged across the global South, particularly affecting urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions, as well as local demographic and socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we evaluate the role of socioeconomic and environmental factors associated with this outbreak at the neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic factors include population density and socioeconomic stratum, whereas environmental factors are proximity to both tire shops and plant nurseries and the presence of a sewage system (R2=0.64). The strength of the association between these factors and the incidence of dengue fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to support public health strategies in allocating resources locally, which will enable a better detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of the population.

Not available.

Recently the cultivation of opium poppy in Afghanistan reached unprecedented levels. It is agreed that the complex and coupled interactions of social, economic and environmental drivers are crucial for understanding the spatial and temporal dynamics of opium poppy cultivation in Afghanistan. In this context, we present an integrated risk concept, which considers environmental and socio-economic drivers of opium poppy cultivation. A set of spatially explicit indicators for the environmental suitability and socio-economic vulnerability was established and populated from a variety of databases. Subsequently, novel methods of modelling homogeneous and spatially explicit regions of opium poppy cultivation suitability, socio-economic vulnerability and risk are developed and applied. The risk assessment results demonstrate the complex nature of the illicit crops production in Afghanistan and prompt a more profound examination of the drivers of opium poppy cultivation in a spatial context. The study also confirms what has already been widely discussed in literature: that reasons for cultivation are spatially diverse and often distinct, meaning that any formulation of generalized explanations cannot be drawn without ignoring a more complex reality. Thus, an integrative spatial view of risk, which integrates the social dimension as well as environmental parameters, is required to better identify context-specific intervention measures.