Articles | Volume 22, issue 1
https://doi.org/10.5194/hess-22-373-2018
https://doi.org/10.5194/hess-22-373-2018
Research article
 | 
17 Jan 2018
Research article |  | 17 Jan 2018

Participatory flood vulnerability assessment: a multi-criteria approach

Mariana Madruga de Brito, Mariele Evers, and Adrian Delos Santos Almoradie

Abstract. This paper presents a participatory multi-criteria decision-making (MCDM) approach for flood vulnerability assessment while considering the relationships between vulnerability criteria. The applicability of the proposed framework is demonstrated in the municipalities of Lajeado and Estrela, Brazil. The model was co-constructed by 101 experts from governmental organizations, universities, research institutes, NGOs, and private companies. Participatory methods such as the Delphi survey, focus groups, and workshops were applied. A participatory problem structuration, in which the modellers work closely with end users, was used to establish the structure of the vulnerability index. The preferences of each participant regarding the criteria importance were spatially modelled through the analytical hierarchy process (AHP) and analytical network process (ANP) multi-criteria methods. Experts were also involved at the end of the modelling exercise for validation. The final product is a set of individual and group flood vulnerability maps. Both AHP and ANP proved to be effective for flood vulnerability assessment; however, ANP is preferred as it considers the dependences among criteria. The participatory approach enabled experts to learn from each other and acknowledge different perspectives towards social learning. The findings highlight that to enhance the credibility and deployment of model results, multiple viewpoints should be integrated without forcing consensus.

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Short summary
This paper sheds light on the integration of interdisciplinary knowledge in the assessment of flood vulnerability in Taquari-Antas river basin, Brazil. It shows how stakeholder participation is crucial for increasing not only the acceptance of model results but also its quality.