A Citizen Science Approach to the Characterisation and Modelling of Urban Pluvial Flooding

Urban pluvial flooding (UPF), a growing challenge across cities worldwide that is expected to worsen due to climate change and urbanisation, requires comprehensive response strategies. However, the characterisation and simulation of UPF is more complex than traditional catchment hydrological modelling because UPF is driven by a complex set of interconnected factors and modelling constraints. Different integrated approaches have attempted to address UPF by coupling humans and environmental systems and reflecting on the possible outcomes from the interactions among varied disciplines. Nonetheless, it is argued that current integrated approaches are insufficient. To further improve the characterisation and modelling of UPF, this study advances a citizen science approach that integrates local knowledge with the understanding and interpretation of UPF. The proposed framework provides an avenue to couple quantitative and qualitative community-based observations with traditional sources of hydro-information. This approach allows researchers and practitioners to fill spatial and temporal data gaps in urban catchments and hydrologic/hydrodynamic models, thus yielding a more accurate characterisation of local catchment response and improving rainfall-runoff modelling of UPF. The results of applying this framework indicate how community-based practices provide a bi-directional learning context between experts and residents, which can contribute to resilience building by providing UPF knowledge necessary for risk reduction and response to extreme flooding events.

Automated summary

Urban pluvial flooding is a global challenge that is expected to worsen due to climate change and urbanisation. Current integrated approaches to address this issue are deemed insufficient. This study proposes a citizen science approach that integrates local knowledge with the understanding and interpretation of urban pluvial flooding, filling data gaps in hydrologic models and improving rainfall-runoff modelling. The application of this framework highlights the importance of community-based practices in fostering bi-directional learning and contributing to resilience building.