Abstract
In this paper we demonstrate a framework for urban flood modelling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modelling on a level of detail that is currently inaccessible in many data-scarce environments. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.
Highlights
The global trend of expanding settlements in flood prone areas exposes an increasing share of the world’s population to floods (United Nations, 2018; Winsemius et al, 2018)
Community Mapping for Flood Modeling community mapping contributes to improved assessments of urban flood risks in resource-strained environments, by filling essential data gaps on drainage networks and urban topography using a community mapping approach
It can be considered as a form of Volunteered Geographic Information (VGI) which arose in the early 2000’s through platforms such as OpenStreetMap (OSM),1 an online world map which can be edited by anyone (Goodchild, 2007; Zook et al, 2010; Ramm et al, 2011)
Summary
The global trend of expanding settlements in flood prone areas exposes an increasing share of the world’s population to floods (United Nations, 2018; Winsemius et al, 2018). State-ofthe-art urban flood models combine detailed geometric data on sewer pipes and drainage channels with digital terrain data to model flood events, combining both overland flow and channel flow through channels and pipes (Bach et al, 2014) These models simulate the impact of storm events in urban environments, which allow for flood risk assessments at a high level of detail. The internet has allowed for open source platforms where geographical information can be produced and stored, which has remarkably lowered the costs to collect the data needed to build urban resilience This has accommodated the rise of community mapping projects to collect geographical data for pre-disaster, in-disaster, and post-disaster management (Paul et al, 2018). VGI combined with smartphones has put mapping, a task that for centuries has been reserved for official agencies, in the hands of anyone who wants to contribute to online maps (Flanagin and Metzger, 2008). Davids et al (2018) suggest that smartphone-based data collection activities should be a part of science and engineering curricula, aiming for standardized data collection methods and open access
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