Displays

There is ample evidence about the added-value of anticipatory financing mechanism to mitigate the impact of extreme droughts on the livelihoods of vulnerable communities. Various projects have tried to optimize parametric insurance via different methods, resulting in useful lessons learnt for both macro- and micro-level insurance. In parallel, novel satellite-derived sources of information, such as soil moisture or evaporative stress, have become available to monitor key variables of the hydrological cycle and strengthen the drought narrative via cross-validation.  The Next Generation Drought Index project was funded by the World Bank to develop a generic framework and related technical toolbox that allows decision-makers to understand every step of index design, calibration and validation. An interactive dashboard is linked directly to different data sources, the outputs of financial risk models and socioeconomic information to link climate hazard and impact information. Collaboration partners range from African Risk Capacity to the United Nations World Food Programme, the START Network, the World Bank’s Global Index Insurance Facility and the European Space Agency. The overall goal is to reduce basis risk without creating an analytical black box as well as to identify and use ‘low hanging fruits’, such as the detection of early season moisture deficits via remote sensing. The finding from Senegal suggest that the effectiveness of insurance might be improved through client centered design through participatory/crowdsourced processes, a suite of advanced satellite data and models, available government/institutional data and  structured decision tree processes based on key performance indicators.

How to cite: Osgood, D. and Enenkel, M. and the Daniel E Osgood: The Next Generation Drought Index Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12023, https://doi.org/10.5194/egusphere-egu2020-12023, 2020.

How to cite: Weiss, R., Cummins, V., Kelsey, H., Ferse, S., Scheffers, A., Forbes, D., and Glavovic, B.: Our Coastal Futures: pathways to sustainable development, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20976, https://doi.org/10.5194/egusphere-egu2020-20976, 2020.

Given the increased attention put on strengthening disaster resilience, there is a growing need to invest in its measurement and the overall accountability of resilience strengthening initiatives. There is a major gap in evidence about what actually makes communities more resilient when an event occurs, because there are no empirically validated measures of disaster resilience. Similarly, an effort to identify operational indicators has gained some traction only more recently. The Flood Resilience Measurement for Communities (FRMC) framework and associated, fully operational, integrated tool takes a systems-thinking, holistic approach to serve the dual goals of generating data on the determinants of community flood resilience, and providing decision-support for on-the-ground investment. The FRMC framework measures “sources of resilience” before a flood happens and looks at the post-flood impacts afterwards. It is built around the notion of five types of capital (the 5Cs: human, social, physical, natural, and financial) and the 4Rs of a resilient system (robustness, redundancy, resourcefulness, and rapidity). The sources of resilience are graded based on Zurich’s Risk Engineering Technical Grading Standard. Results are displayed according to the 5Cs and 4Rs, the disaster risk management (DRM) cycle, themes and context level, to give the approach further flexibility and accessibility.

The Zurich Flood Resilience Alliance (ZFRA) has identified the measurement of resilience as a valuable ingredient in building community flood resilience. In the first application phase (2013-2018), we measured flood resilience in 118 communities across nine countries, building on responses at household and community levels. Continuing this endeavor in the second phase (2018 – 2023) will allow us to enrich the understanding of community flood resilience and to extend this unique data set.

We find that at the community level, the FRMC enables users to track community progress on resilience over time in a standardized way. It thus provides vital information for the decision-making process in terms of prioritizing the resilience-building measures most needed by the community. At community and higher decision-making levels, measuring resilience also provides a basis for improving the design of innovative investment programs to strengthen disaster resilience.

By exploring data across multiple communities (facing different flood types and with very different socioeconomic and political contexts), we can generate evidence with respect to which characteristics contribute most to community disaster resilience before an event strikes. This contributes to meeting the challenge of demonstrating that the work we do has the desired impact – that it actually builds resilience. Our findings suggest that stronger interactions between community functions induce co-benefits for community development.

How to cite: Szoenyi, M., Laurien, F., and Keating, A.: Flood resilience measurement for communities: data for science and practice, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3934, https://doi.org/10.5194/egusphere-egu2020-3934, 2020.

We present a methodology to include qualitative aspects of flood resilience such as emotion, social connections and experience into urban planning using qualitative GIS. The geographic information system (GIS) has become ubiquitous in urban planning and disaster risk reduction, but often results in resilience being conceptualised and deployed in highly technocratic and quantitative ways. Yet in the urban Global South, where the rate of informal growth often outstrips our ability to collect spatial data, the knowledge infrastructures used for resilience planning leave little room for participation and consideration of local experience. This presentation outlines two interlinked projects (‘Why we Disagree about Resilience’ and the follow-on ‘Expressive Mapping of Resilient Futures’) experimenting with qualitative GIS methodologies to map resilience as defined by informal settlement residents. We show examples from two case study cities, Nairobi (Kenya) and Cape Town (South Africa), with applicability across the urban Global South. Four map layers were generated: (i) flood footprints showing the detailed spatial knowledge of floods generated by locals; (ii) georeferenced, narrated 360° StorySpheres capturing differing perspectives about a space; (iii) spatial social network maps showing residents connections to formal and informal actors before and during floods; (iv) multimedia pop-ups communicating contextual details missing from traditional GIS maps. We show that for informal settlements, many locations and aspects of resilience have vague or imprecise spatial locations, and that placing markers on a map makes them visible in ways that planners can begin to engage with. We discuss challenges such as privacy, legacy and participation. Although challenges remain, we found openness by city-level actors to use qualitative forms of evidence, and that the contextual detail aided their retention and understanding of resilience. The ‘messy’ maps we present here illustrate that in the era of big data and metrics, there is a space for qualitative understanding of resilience, and that existing knowledge and spatial data infrastructures have potential to be more inclusive and holistic.

How to cite: Taylor, F., Millington, J., Jacob, E., Malamud, B., and Pelling, M.: Messy Maps: Qualitative GIS for Urban Flood Resilience , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1807, https://doi.org/10.5194/egusphere-egu2020-1807, 2020.

The purpose of the “Flood Resilience Dashboard” is to put geo-spatial flood resilience data into the hands of practitioners. The idea is to provide an intuitive platform that combines as much open, peer-reviewed flood resilience related spatial data as possible with available related spatial data from the Flood Resilience Alliance, which in turn can be used to inform decisions. This data will include among others the Zurich Flood Resilience Measurement for Communities (FRMC) data, Vulnerability Capacity Assessment (VCA) maps, remote sensing derived information on flooding and other biophysical datasets (e.g. forest cover, water extent), modelled risk information, satellite imagery (e.g. night-time lights), crowdsourced data and more. 

The Dashboard will, as much as possible, lower the entry barrier for non-technical users, providing a simple login experience for the users. Users should be able to explore the Dashboard using standard web map navigation tools. The various charts and tables on the Dashboard dynamically refresh as features on the map are selected or the map extent is changed. No previous experience or understanding of geo-spatial data is required, beyond basic web-map navigation.

How to cite: McCallum, I., Velev, S., Laurien, F., Mechler, R., Keating, A., Hochrainer-Stigler, S., and Szoenyi, M.: The Flood Resilience Dashboard, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21602, https://doi.org/10.5194/egusphere-egu2020-21602, 2020.

      Adaptation is an indispensable part of climate change impact, and risk assessment plays an important role between data arrangement and strategy planning. This study aims at developing a framework from risk assessment to information presentation, then applying to risk communication. This framework refers to Climate Risk Template, defining risk as to the integration of hazard, exposure and sensitivity; simultaneously, Climate Risk Template is an auxiliary tool basing on Climate Change Adaptation Six Steps(CCA6Steps), which is the systematic procedure to analyze risk and plan adaptation pathway. This study emphasized on landslide disaster as the key issue and selected community residents, roads as the protected targets. First of all, collate stimulated results of landslide potential evaluation and literature, cases, questionnaires which were probed into exposure and sensitivity. Next, establish a factors list of climate risk and giving weights to correlation factors by Entropy Method. Finally, use risk matrix to evaluate the risk value and present the results of risk assessment by infographic. For essentially helping on risk communication, this study proposes a framework to make the general public understand the causes of regional disaster risk and assists executive units to implement climate risk assessment and adaptation pathway planning. Eventually, the study will innovate a prototype of using this framework; therefore, users just have to write down the key issue, protected target and choose the composition factors of risk, then they can accomplish climate risk assessment and generate climate risk infographic by themselves.

Keywords: Climate risk template, Climate risk assessment, Risk communication, infographic

How to cite: Liu, C. C. and Tung, C. P.: The Framework for Generating Climate Risk Infographic and Applying to Risk Communication, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6355, https://doi.org/10.5194/egusphere-egu2020-6355, 2020.

In the context of proactive drought management plans, a key element consists of analyzing, selecting and allocating measures aimed at increasing resistance to droughts and reducing its potential impacts on the society, environment and economy. Currently, these measures, known as preventive drought management measures (Fatulová et al., 2015), are embedded within measures for flood management, catchment management plans, rural development plans, among others. This situation raises two issues. Firstly, information about potential preventive drought management measures (PDMM) is commonly fragmented and it is not a trivial task find or select measures that could be implemented as PDDM. Secondly, even though the same measure can be implemented from different management perspectives (Flood management, land degradation management, catchment management, rural development plans,) its applicability, advantages and limitations, may change according to the management perspective.

Considering the above, this study attempts to provide a review of PDMM that includes: measure description, applicability, limitations, mathematical representation (For further implementation in modelling systems) and classification, from a drought management perspective. It is worth to mention that this study is focused on hydrologically based measures, applicable for agricultural and hydrological drought management.

The research methodology is divided in three phases. The first phase consists of identifying drivers that trigger and/or enhance agricultural and hydrological droughts. This analysis is carried out from a hydrological angle, where land surface processes and human activities are potential drivers agricultural and hydrological droughts (Van Loon et al., 2016). The second phase examines an extensive list of technical documents, books, books sections, journal articles and case studies in order to identify those measures that could manage or mitigate the impact of potential drivers of agricultural and hydrological droughts. In this phase, PDMM are described in terms of applicability, advantages, limitations and mathematical representation for further implementation in modelling systems. Based on the analysis of the PDMM, the third phase of the study focusses on their classification, into three categories: nature-based solutions, grey infrastructure and changes in human water consumption

How to cite: paez, A., Corzo, G., and Solomatine, D.: Review, mathematical representation and classification of preventive drought management measures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5868, https://doi.org/10.5194/egusphere-egu2020-5868, 2020.

Despite solid evidence regarding the large benefits of reducing disaster risk, it has remained difficult to motivate sustained investment into disaster risk reduction and resilience. Recently, international policy debate has started to emphasize the need for focusing DRR investment toward actions that generate multiple dividends, including reducing loss of lives and livelihoods, unlocking development, and creating development co-benefits. We examine whether available and innovative decision support tools are fit-for-purpose. Focusing on the Asia region, we identify evidence of multiple dividends crafted using expert-based methods, such as cost–benefit analysis for selecting and evaluating “hard resilience-type” interventions. Given a rising demand for “softer” and systemic DRR investments in projects and programs, participatory decision support tools have become increasingly relevant. As one set of tools, resilience capacity (capital) measurement approaches may be used to support actions and decisions throughout all stages of the project cycle. Measuring capacity for resilience dividends, not outcome, such tools can serve as participatory decision support for organizations working at community and other levels for scoping out how development and disaster risk interact, as well as for supporting the co-generation of multiple resilience dividend-type solutions with those at risk.

How to cite: Mechler, R. and Hochrainer-Stigler, S.: Generating multiple resilience dividends from managing unnatural disasters. Opportunities for measurement and policy., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19990, https://doi.org/10.5194/egusphere-egu2020-19990, 2020.

In recent years, the impact of climate change has caused critical risks to urban and rural systems, how to mitigate the damage caused by extreme climate events has become a topic of considerable concern in various countries in recent years. The United Nations International Strategy for Disaster Reduction (UNISDR) mentioned in the Hyogo Framework for Action (HFA) and the Sendai Framework for Disaster Risk Reduction 2015-2030 (Sendai Framework) that improving community resilience will help to deal with the harm caused by climate change. However, most of the previous research on resilience have only focused solely on urban or rural only, and have failed to clearly identify the differences in resilience between urban and rural areas. In fact, if we can understand the difference in resilience between urban and rural in the face of climate change, it will provide planners with better planning strategies or resource allocation. Based on this, the study first developed the resilience index through literature review, and then filtered and screened the index through Principle Component Analysis (PCA). After that, the resilience index was applied to empirical areas, and the spatial correlation of resilience was explored through Local Indicators of Spatial Autocorrelation (LISA). Finally, the binary logistic regression is used to analyze the difference in resilience of urban or rural under climate change.

How to cite: Pai, S.-E. and Chang, H.-S.: A Study on the Difference of Urban and Rural Resilience under Climate Change- A Case Study of Chiayi County, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2488, https://doi.org/10.5194/egusphere-egu2020-2488, 2020.

Resilience is commonly defined as the ability to recover from a shock and quickly restore antecedent conditions. Although it is widely recognized as crucial to reduce adverse impacts and it is gaining importance at global level, resilience to most natural hazards is difficult to measure and predict, as both direct and indirect impacts matter. In this work the mutual connection between flood resilience and indirect flood impacts is investigated through a mathematical model which describes the temporal evolution of the state of the system after an urban inundation event. The inputs to the resilience model are i) a hydraulic model simulating the flood hazard; ii) a vulnerability and recovery model estimating the physical damage to cultural heritage and the temporal persistence of direct and indirect consequences. The method is applied to the historic district of Florence (Italy) affected by a severe flood in 1966. The variables selected as proxies of the state of the system are the number of monuments open to the public after the flood and the number of visitors, which represent a measure of indirect social and economic impacts on the city. The model results show that the resilience model helps the quantification of indirect impacts due to the loss of accessibility of cultural heritage and allows evaluating the effectiveness of prevention measures.

Acknowledgments

Authors were beneficiary of funding by Italian Ministry of Education, University and Research (MIUR) under the PRIN 2015 programme with the Project MICHe “Mitigating the impacts of natural hazards on cultural heritage sites, structures and artefacts”

How to cite: Arrighi, C. and Castelli, F.: Flood resilience and indirect impacts in art cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4852, https://doi.org/10.5194/egusphere-egu2020-4852, 2020.

How to cite: Koopman, J.: From Ashes to Fire: The Possibilities of the Phoenix Effect in Post-Disaster Lombok, Indonesia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10315, https://doi.org/10.5194/egusphere-egu2020-10315, 2020.

Natural disasters lead to many victims and major damage in France and around the world. In 2017, Hurricane Irma hit the French islands of Saint-Martin and Saint-Barthélemy (West Indies), killing 11 people and causing more than €2 billion in insured damage. Ranked 5 in category on the Saffir-Simpson scale, with average winds of 287 km/h, this hurricane highlighted the vulnerability of our society to this type of phenomenon.

One can question the inability of society to face up to and recover from the consequences of these events. In this sense, this work questions the adaptation of the island of Saint-Martin to hurricanes and its entire environment. We have chosen to focus on the evolution of this island over 65 years: from 1954 to 2017 (before Hurricane Irma). We mainly used aerial images of IGN (Institut National de l’Information Géographique et Forestière) available regularly since 1947. Among the elements that have served us to characterize this evolution, we have focused on land use (buildings, infrastructure and anthropization) and demographics.

We show, in this study, that between 1954 and 2017 (before Hurricane Irma), Saint Martin had to adapt to numerous constraints, some of which were far more important than hurricanes. In 65 years, the population density of the French part of Saint Martin increased from 75 to 668 inhab/km². The majority of this increase occurred in a five year period following the Pons law of 1986 which favoured tax breaks for real estate investment. More than 12 000 buildings have been built in Saint Martin to welcome the new inhabitants of the island as well as tourists. Many neighbourhoods experienced significant growth which started in the late 80's. However we observe differences in urban planning, a result of social and territorial segregation which exists on the island. On the one hand, there are private residences in affluent neighbourhoods, on the other hand working-class neighbourhoods with vulnerable dwellings. The effect of hurricanes on this society, which has been highly unequal since the 1960's up to the 1980's, is to reinforce inequalities. The fragile habitats of the poorest populations have been more deeply affected than the richest parts of the population which have been financially supported for reconstruction.

How to cite: Pasquon, K., Jouannic, G., Gargani, J., Tran Duc Minh, C., and Crozier, D.: The impact of hurricanes on the island of Saint-Martin (French West Indies) from 1954 to 2017: how are our society changes?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14406, https://doi.org/10.5194/egusphere-egu2020-14406, 2020.

Africa’s population is growing fast and is expected to double by 2050, meaning the food production must follow the cadence in order to meet the demand. However, one of the major challenges of agriculture in Africa is productivity (World Bank, 2009; IFRI, 2016). For instance, more than 40 million hectares of farmland were dedicated to maize in Africa in 2017 (approx. 20% of world total maize farms), but only 7.4% of the total world maize production came from the African continent (FAO, 2017). This shows the poor productivity which has its causes rooted in lack of good climate and weather information, slow technology uptake and financial support for farmers. In West Africa, where more than 70% of crop production is rain-fed, millions of farmers depend on rainfall, yet the region is one of the most vulnerable and least monitored in terms of climate change and rainfall variability. With a high uncertainty of future climate conditions in the region, one must foresee the big challenges ahead: farmers will be exposed to a lot of damages and losses leading to food insecurity resulting in famine and poverty if measures are not put in place to improve productivity. This study aims at addressing low productivity in agriculture by providing farmers with the right moment to start farming in order to improve efficiency and productivity of crop water use. By analyzing yield response to water availability of specific crops using AquaCrop, the Food and Agriculture Organization crop growth model, we investigate the crop water productivity variability throughout the rainy season and come up with recommendations that help optimize rainfall water use and maximize crop yield.

How to cite: Agoungbome, S. M. D., van de Giesen, N., Annor, F. O., and ten Veldhuis, M.-C.: Improving crop water use in West Africa in the context of climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11885, https://doi.org/10.5194/egusphere-egu2020-11885, 2020.