All urban drainage networks are designed to manage a maximum rainfall. This situation implies an accepted flood risk for any greater rainfall event. This risk is often underestimated as factors such as city growth and climate change are ignored. But even major structural changes cannot guarantee that urban drainage networks would cope with all future rain events. Thus, being able to forecast urban flooding in real time is one of the main issues of integrated flood risk management. Runoff and hydraulic models can be essential elements of flood forecast systems, as an active part of the system or as studying tools. This paper gives an overview of current available options for pluvial flood modelling in urban areas, from basic estimations with a onedimensional urban drainage model to detailed flood process representation with one dimensional-two dimensional hydrodynamic coupled models. Each type of modelling solution is described with pros and cons regarding urban flood analysis. The paper then elaborates on real-time flood forecast systems and the influence of their main components. A classification of real-time urban flood systems is given based on the use of urban models, i.e. empirical scenarios, pre-simulated scenarios and real-time simulations. A review of existing operational systems is done using this classification.
In the last decade, real-time flood forecasting has become a more feasible approach to reducing the impacts of flooding in urban areas. Two key tools in this context are high resolution hydrodynamic modelling in combination with accurate hydrological forcing. In some cases, when it is not possible to produce such accurate flood forecasts based on high resolution models and data, it may nevertheless be possible to use the resources currently available, accepting that there is a greater degree of uncertainty involved. This paper demonstrates the feasibility of a remotely controlled, real-time, pluvial flood forecasting system for Castries, St. Lucia that utilises the limited data available locally. The results from the study suggest that although Global Forecast System (GFS) rainfall data may be considered coarse for urban applications, there is still a significant amount of skill and usability after it is postprocessed and used in combination with observed rainfall data. Evidence from the study also suggests that the use of images from different sources is invaluable for 2D overland model calibration and validation in urban areas. Conclusions from the study are potentially transferable to other sites in similar data-scare and resource-limited locations.
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