Madruga de Brito, Mariana: A participatory multi-criteria approach for flood vulnerability assessment. - Bonn, 2018. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc:
author = {{Mariana Madruga de Brito}},
title = {A participatory multi-criteria approach for flood vulnerability assessment},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2018,
month = sep,

note = {The management of flood risk calls for a better understanding of vulnerability, as hazards only become disasters if they impact a system that is vulnerable to their effects. Although different frameworks have been proposed to assess vulnerability, they often focus on the physical vulnerability of structures, assuming a homogeneous social vulnerability and coping capacity for the entire population. Furthermore, the multiple relationships between input criteria are often neglected and the role of stakeholder participation in the modeling process has received little attention.
To tackle these issues and increase the model transparency, this thesis addresses the design and deployment of a participatory approach for flood vulnerability assessment. More specifically, it focuses on how multi-criteria tools can be combined with participatory methods to overcome common issues in the development of indexes and to open up the "black-box" nature of vulnerability models. The main argument which is pursued throughout the thesis is that participation and collaboration are key aspects for bridging the gap between modelers and end users.
The applicability of the proposed transdisciplinary framework is demonstrated in the municipalities of Lajeado and Estrela, Brazil. The model was co-constructed by 101 expert stakeholders from governmental organizations, universities, research institutes, NGOs, and private companies. Participatory methods such as the Delphi survey, focus groups, questionnaires and workshops were applied. A participatory problem structuration, in which the modelers work closely with stakeholders, was used to establish the structure of the vulnerability index. The preferences of each participant regarding the criteria importance were spatially modeled through the analytic hierarchy process (AHP) and analytic network process (ANP) multi-criteria methods. Experts were also involved at the end of the modeling exercise for validation. The robustness of the model was investigated by employing a one-at-a-time sensitivity and uncertainty analysis.
Both AHP and ANP proved to be effective for flood vulnerability assessment; however, ANP is preferred by participants as it leads to more robust results. The results of the spatially-explicit sensitivity analysis helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. The validation questionnaire indicated that the participants found the results clear, trustworthy, and valuable, suggesting that participatory modeling exercises like the one proposed here are worthwhile. These findings highlight that the use of a transdisciplinary approach to acknowledge and integrate multiple viewpoints without forcing consensus improved the results acceptance. In summary, the combination of qualitative and quantitative methods for flood vulnerability assessment led to an increased, shared understanding of the problem by avoiding the limited perspective of a single expert.
The approach proposed herein is particularly novel in the context of vulnerability assessment in the respect that stakeholders were actively involved in all steps of the vulnerability modeling process and that the relationship between criteria was considered. The use of participatory tools in combination with multi-criteria methods can support social learning processes and enhance the credibility and deployment of vulnerability indicators, as stakeholders' opinion, expert judgment, and local knowledge are taken into consideration throughout the entire modeling process. From a practical standpoint, the outcomes of this Ph.D. thesis can support local authorities to understand the vulnerability patterns in the region, its associated uncertainty, and the criteria contributing to this uncertainty.},

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