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.

This paper explores the potential for real-time urban flood forecasting based on literature and the results from an online worldwide survey with 176 participants. The survey investigated the use of data in urban flood management as well as the perceived challenges in data acquisition and its principal constraints in urban flood modelling. It was originally assumed that the lack of real-time urban flood forecasting systems is related to the lack of relevant data. Contrary to this assumption, the study found that a significant number of the participants have used some kind of data and that a possible explanation for so few cases is that urban flood managers or modellers (practitioners)may not be aware they have the means to make a pluvial flood forecast. This paper highlights that urban flood practitioners can make a flood forecast with the resources currently available.

The phenomenon of urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and can have significant economic and social consequences. The complex nature of quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models has facilitated a need to model and manage uncertainty. This paper presents a probabilistic approach for modelling uncertainty from single-valued QPFs at different forecast lead times. The uncertainty models in the form of probability distributions of rainfall forecasts combined with a sewer model is an important advancement in real-time forecasting at the urban scale. The methodological approach utilized in this paper involves a retrospective comparison between historical forecasted rainfall from a NWP model and observed rainfall from rain gauges from which conditional probability distributions of rainfall forecasts are derived. Two different sampling methods, respectively, a direct rainfall quantile approach and the Latin hypercube sampling based method were used to determine the uncertainty in forecasted variables (water level, volume) for a test urban area, the city of Aarhus. The results show the potential for applying probabilistic rainfall forecasts and their subsequent use in urban drainage forecasting for estimation of prediction uncertainty.