Towards an integrated assessment of future flooding in dynamic and data-scarce urban environments by linking Urban Structure Types with Bayesian Network modelling

Abstract

Limited knowledge on current and future processes as well as data scarcity pose a major challenge when it comes to the evaluation of adaptation strategies towards flooding. Current simulation approaches often lack the flexibility do deal with the inherit dynamics of future development (land use, urban growth, re-development of slum areas, infrastructure construction, etc.) in coastal cities and the resulting changes in flood hazard, exposure and vulnerability whilst facing a lack of sufficient data. Therefore, we developed a modelling approach which is able to integrate future dynamics in the three risk components, hazard, exposure and vulnerability under the uncertainties arising from lacking data as well as limited knowledge. We used Mumbai, India as a first case study to combine Urban Structure Types with Bayesian Networks (BN) and to assess pluvial flooding. BN structures are defined by process understanding supported by existing models, literature and expert evaluations. The quantification of the BNs is done by using urban structure types as proxies for relevant parameters/nodes where data is not available, like the distribution and capacity drainage infrastructure and its condition or the degree of imperviousness of certain areas. This is justified by the assumption that the appearance and the processes in urban structure types are similar. However, the probabilistic definition of nodes in a BN allows to account for the variability within an urban structure type class. As a first step, the approach was set up for the hazard component of risk. Here first results of the simulation of pluvial flooding are shown and validated against flood hotspots reported by the government of Mumbai. The simulation approach reproduced the flooding hotspots, however it has a great sensitivity towards certain parameters, especially towards the digital elevation model and the condition of the drainage infrastructure. In a next step BNs for multi-hazard evaluation and vulnerability assessment will be developed and linked, i.e. fluvial and coastal flooding as well as social vulnerability. The integration of different risk components and the flexibility of the approach help to assess the effect of individual and combinations of soft and hard adaptation measures on future flood risk.