Emergency Response Resilience to Floods Operationalised with Applied Geoinformatics

A. Tzavella

Research output: PhD ThesisPhD-Thesis – Research and graduation external

Abstract

In cities, timely emergency response (ER) presupposes timely citywide accessibility enabled by the road transport system’s uninterrupted functioning. However, in this era of increasing frequency and intensity of extreme weather events and hydrometeorological hazards, delays or blockages challenge timely accessibility. Therefore, the thesis aims to contribute to saving lives by reducing losses in critical infrastructure (CI) functioning for adaptive emergency response (ER) provision towards the population’s and the emergency responders’ safety. For this purpose, the urban ER system is presented as a complex adaptive system of systems (SoS) that, under the stressor of floods, can adapt and transform so to retain its critical functionality considering safety and security aspects. For a deepened understanding of flood risks, their cascading impacts and interrelation with the resilience of a complex adaptive SoS, the thesis introduces an operational resilience framework that adopts an interdependent resiliencies concept and combines a top-down and a bottom-up spatial scaling approach. The SoS resilience concept, as applied to an urban ER system, introduces an operational framework for the urban emergency response resilience (ERR) that follows the 4R model (4Resilience characteristics: robustness, resourcefulness, redundancy, rapidity of response) in an interdependent form. The usefulness and intent of adopting the urban ERR concept from European stakeholders and researchers and emergency response and civil protection officials are analysed with semi-structured interviews. The CAS theory applied to the urban ER system enables its division to the agent, system and network level and identifies the hierarchy between its constituent systems. The road transport system is higher in the hierarchy due to its pivotal role in the urban ER system’s behaviour and, therefore, is the ‘zero-point’ for further flood risk assessments. The graph theory and the complex network theory assist with graphical representations and compartmentalisation of the urban ER system to its systems, networks, and components and digitisation using geographic information systems (GIS) for ERR assessments. The ERR to regular and extreme scenarios of riverine floods and flash floods is assessed with a multi-criteria risk-based time-dependent accessibility indicator (RITAI) for Cologne’s fire brigade system in Germany. The RITAI utilises applied geoinformatics with geographic information systems (GIS) to identify first-, second and third-order flood risks in various scales and levels of this urban ER system, with a top-down and an eight-step GIS-based spatial upscaling approach. Safety and security aspects are considered with the RITAI’s benchmarking according to the fire trucks’ safe driving capacity through flooded waters, the flood depths and the road types. After defining analyses’ units on a road network level, a developed semi-automated GIS-Toolkit integrates flood depth and flood-impacted road type-dependent speeds in the road network database for each of the selected flood scenarios. The resulting flood-risk informative road networks are utilised for large-scale road network resilience capacities, assessed with changes in transport characteristics. Later and after the definition of city units, citywide connectivity and accessibility assessments are conducted with network analyses. For a pattern identification of the fire brigade system’s ERR to floods, the RITAI is assessed and visualised in each city unit, after classification according to Cologne's fire brigades' official ER time thresholds - eight minutes. Geovisualisation and fuzzification techniques are utilised for simplification and aggregation of the information. Flood-impact statistical curves are also generated for aggregation of information and preparedness of response to escalating or compound flood events. The data utilised were retrieved from open sources and fire brigade and flood management local officials in raster, vector, Excel files and official reports and were visualised in maps. The data undertook cleaning and transformation for interoperability purposes and further handling. The RITAI’s general application and handling of data can be time-consuming, with the processing costs depending highly on the selected units of analyses and the computer’s memory capacity. The results, i.e., large-scale road network exposure, redundancy and resourcefulness, citywide accessibility route plans and spatial hexagonal urban ER system connectivity and ERR matrixes, are visualised in maps. They indicate that the citywide ER efficiency in cities depends highly on large-scale geolocated flood extent and flood depth information and the road type and the rescue vehicles’ capacity for safe drivability through flooded waters. It is identified that the regular and extreme flash floods scenarios follow a similar geographical locality of occurrence. However, the extreme flash flood scenario causes a higher ERR decrease, which indicates its dependence on the road type exposed to floods and the geolocation of flood intensities. Moreover, in cities, the local enhancement of the road network’s resilience (absorption, adaptation and transformation) capacities, considering the emergency responders’ safety, enhances the fire brigade system’s ERR to floods. The local extension of CI functioning is achieved by enhancing resourcefulness (transformation capacity) with an extension of the road transport system’s endogenous redundancy (adaptation capacity). This extension further extends its exogenous redundancy of alternative accessibility route paths, enhancing the fire brigade system’s response capacity. Additionally, statistical analyses of the road transport system’s resilience capacities in case of escalating floods revealed that its resilience capacity for ER provision is highly decreased. Finally, ERR assessments indicate that the ER provision will potentially be highly incapacitated in case of an extreme riverine flood scenario and highly delayed with an extreme flash flood scenario. It is also identified that east Cologne needs further attention in the preparedness phase for timely ER under flooded conditions. Nevertheless, the results depend on the correctness of data used, their resolution and unit of analyses, which can cause biases in the calculation processes. Biases in interpreting the results are reduced by simplifying the system’s connectivity and ERR information in hexagonal spatial matrixes. With the concept of ERR and its operationalisation approach, current silo-thinking disaster risk management (DRM) approaches are enriched with CAS, resilience, security and spatial thinking, enabling holistic and collaborative risk mitigation strategies. For this purpose, an identified lacking connection between the application fields of emergency rescue systems, civil protection and critical infrastructure protection (CIP) is now established with the suggested urban ER system. Additionally, the enhancement of the ERR and the communities’ resilience through timely ER provision is achieved with enhanced geospatial preparedness for adaptive management. Applied geoinformatics and GIS provide the means for identifying, assessing, visualising and timely exchanging a range of systemic and cascading first-, second-and third-order flood impacts for adaptive management. Adaptation is attained with approaches that consider safety and security aspects and enable accurate assessments of, for example, operational costs associated with the transfer of heavy rescue equipment, emergency humanitarian logistics, community and CI resilience. The concept’s flexible and interdisciplinary character is valuable for further applications to various SoS and scenario- and place-based multi-criteria risk analyses and interdependency analyses valuable for training purposes in different countries, urban districts, and counties where floods are not typical. The thesis also discusses in detail further methodological improvements, enrichments and potential use cases.
Original languageEnglish
QualificationDr.
Awarding Institution
  • Bergische Universität Wuppertal
Award date11 Mar 2021
DOIs
Publication statusPublished - 2021

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