Keywords
Unpredictability, Citizens’ Risk Perception, Flood Risk Management
Start Date
15-9-2020 8:00 PM
End Date
15-9-2020 8:20 PM
Abstract
Evidence shows that cities are complex and unstable systems. In order to be reliable, flood risk models need to account for the unpredictability of the urban system changes e.g. land-use changes, socio-economic dynamics, citizen behaviours due to their risk perception. Over the last century approaches in flood risk modelling, relied on deterministic approaches, have focused exclusively on the phenomena, resulting in a distribution of results around some "best assumptions". Despite some evidences show the importance to consider the unpredictability of social system characteristics such as citizen risk perceptions and their behaviours, in flood risk modelling has instead been almost neglected. Understanding the unpredictability of social systems as another dimension of conceptualisation of uncertainty can indeed lead to a better flood risk management. The risk perception affects citizens’ decisions and could lead to failures in flood risk management process, if no balanced with traditional flood models outputs. In this work we argue that incorporating uncertainty into flood risk modelling can support decision makers to manage the extreme event in the urban context. To this aim, a multi-step methodology has been developed and implemented in a case study located in the South-Eastern part of Italy, i.e. Brindisi municipality. The first step consists in collecting and structuring citizen perceptions about the flood risk through a participative process. For this purpose, semi-structured interviews and Fuzzy Cognitive Maps were developed. Secondly, the degree of uncertainty regarding the unpredictability of social system have been detected and analysed. The work concludes that this integrated approach could be used in of exploratory modelling (e.g. Exploratory System Dynamics modelling) in flood management, in order to consider the unpredictability of system characteristics and consequently support decision-makers under uncertainty.
Dealing with uncertainty in urban flood management using participatory modelling
Evidence shows that cities are complex and unstable systems. In order to be reliable, flood risk models need to account for the unpredictability of the urban system changes e.g. land-use changes, socio-economic dynamics, citizen behaviours due to their risk perception. Over the last century approaches in flood risk modelling, relied on deterministic approaches, have focused exclusively on the phenomena, resulting in a distribution of results around some "best assumptions". Despite some evidences show the importance to consider the unpredictability of social system characteristics such as citizen risk perceptions and their behaviours, in flood risk modelling has instead been almost neglected. Understanding the unpredictability of social systems as another dimension of conceptualisation of uncertainty can indeed lead to a better flood risk management. The risk perception affects citizens’ decisions and could lead to failures in flood risk management process, if no balanced with traditional flood models outputs. In this work we argue that incorporating uncertainty into flood risk modelling can support decision makers to manage the extreme event in the urban context. To this aim, a multi-step methodology has been developed and implemented in a case study located in the South-Eastern part of Italy, i.e. Brindisi municipality. The first step consists in collecting and structuring citizen perceptions about the flood risk through a participative process. For this purpose, semi-structured interviews and Fuzzy Cognitive Maps were developed. Secondly, the degree of uncertainty regarding the unpredictability of social system have been detected and analysed. The work concludes that this integrated approach could be used in of exploratory modelling (e.g. Exploratory System Dynamics modelling) in flood management, in order to consider the unpredictability of system characteristics and consequently support decision-makers under uncertainty.
Stream and Session
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