Urban Flood Resilience

Abstract

Resilience to disasters in times of climate change highlights the importance of reflexive governance, facilitation of socio-technical co-evolution, co-creation of knowledge, and innovative and collaborative learning processes by considering the critical role of governance, people, and technology in addressing challenges and creating solutions in a place-based, integrated, inclusive, risk-aware, and future-oriented manner. Therefore, this thesis aims to develop, test, and improve methods for assessing and strengthening resilience to climate change through new approaches and data. To achieve this goal, three main contributions are made: <br /> From an idiographic or top-down perspective, in order to operationalize the concept of disaster resilience as an important milestone for a better understanding of resilience characteristics, the first paper presents a multi-criteria approach (composite indicator building) to develop a resilience index for benchmarking flood resilience, and a hybrid multi-criteria decision-making method (AHP-TOPSIS) to comparatively assess the level of flood resilience of 22 urban districts in Tehran, Iran. <br /> To provide opportunities for linking top-down and bottom-up approaches to enabling change in disaster resilience, a framework for scaling transformative urban resilience through the use of crowdsourcing and Volunteered Geographic Information (VGI) was developed to provide a comprehensive understanding of the complexity and capacity of using these new data sources for transformative disaster resilience. Based on a qualitative content analysis of available resources, the second paper explores key aspects of using VGI for transformative disaster resilience and proposes a comprehensive framework structured around 18 key concepts under five identified legal, institutional, social, economic, and technical aspects to formalize the process of adopting VGI in transformative resilience initiatives. Crowdsourcing and VGI-based models can be considered either as stand-alone or complementary mechanisms when traditional approaches are less suitable for promoting collective community resilience and institutional collaboration, and when administrative data are less appropriate for providing open, accessible, and timely geospatial data to both the community and decision-makers. <br /> From a nomothetic or bottom-up perspective and using the framework proposed, in the third contribution, a near-real-time analysis of social media crowdsourcing and an online survey on the July 2021 flood in Germany was conducted to understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, the study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people's experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to the traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives.