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Continuous megacities' development, aging infrastructure, and increasing frequency and magnitude of extreme events, the lack of flood resilience becomes a pressing issue due to inadequate planning of existing hydraulic structures to handle future threats. A more resilient urban flood risk management strategy is required to efficiently mitigate the impacts of climate change, particularly floods resulting from river and urban channel overflows. This is evident within the Aricanduva River watershed area in the east zone of So Paulo City, Brazil, a region with flood challenges arise because existent hydraulic infrastructures are ineffective in inundation control, due to extensive urbanization in the lower and middle parts of the basin. To achieve resilience in urbanized areas and reduce the risk of flash floods, the development of Early Warning Systems (EWS) is crucial. An EWS serves as a predictive tool for accurately forecasting water levels in rivers or channels in real-time, providing enough time to take action in order to reduce potential risk. Hydrologic-hydrodynamic models are increasingly employed in EWS to enhance their effectiveness. However, many urban basins lack monitoring systems, whereas products such as meteorological radar represent a feasible option since they effectively capture the spatial and temporal distribution of rainfall. In urban basins like the Aricanduva River, where the quantity and distribution of pluviometers are insufficient to spatially represent an event, the use of Quantitative Precipitation Estimation (QPE) from meteorology radar becomes essential to improve hydrological-hydrodynamic analyses. The objective of this work is to propose the presentation of a distributed hydrological-hydrodynamic model (HydroPol2D) for the Aricanduva basin, calibrated with QPEs from meteorological radar. Additionally, rainfall data from 15 gauges within and around the basin were utilized, covering a 5-year period, to generate spatial rainfall using Inverse Distance Weighted (IDW) interpolation. The results of the two rainfall databases were compared using metrics such as the Nash-Sutcliffe efficiency index, Efficiency of Kling-Gupta (KGE) index, and the percentage of bias to assess model accuracy. The findings indicate that (i) the distributed model coupled with QPEs produces favorable results and better represents the basin's dynamics, (ii) the model accurately reflects the hydraulics of existing flood control infrastructure within the basin, and (iii) the generation of an accurate and rapid rainfall-runoff model forms the initial steps in identifying risk areas, establish critical points for the early warning systems and analyzing the factors contributing to or generating the risk. The next step of this work is to assess the model with more events and to include in the model strategies to automate flow control in existing flood control infrastructures. Keywords: Urban flooding risk management, Early Warning Systems (EWS), Hydrological-hydrodynamic models, Radar Quantitative Precipitation Estimation (QPE), Climate Change.
Sánchez et al. (Sat,) studied this question.