Integrating WRF forecasts at different scales for pluvial flood forecasting using a rainfall threshold approach and a real-time flood model

High-resolution rainfall forecasts are essential for forecasting urban pluvial floods and providing local decision support before an event when using rainfall or model-based approaches. Regional Numerical Weather Prediction (NWP) models increase the resolution and extend forecasts by several days when initialised with global models. These models produce forecasts at higher spatial and temporal resolutions but are computationally demanding and do not necessarily result in more accurate forecasts especially in data-scarce cities. This research evaluated how rainfall forecasting model scale dependencies satisfy high-resolution hazard forecasting requirements with two different flood forecasting approaches. Rainfall forecasts of different spatial resolutions, cumulus schemes and lead times from a high-resolution Weather Research and Forecasting (WRF) model were first evaluated and then used as input in a rainfall threshold and 1D MIKE urban drainage model for flood forecasting in the datascarce city of Alexandria City, Egypt. Results indicate the flood forecast severity class and flood model simulation results vary with the neighbourhood size, forecast horizon, and chosen cumulus configuration but in general the smallest resolutions evaluated did not improve the hazard estimation for both flood forecasting approaches. Therefore, trade-offs must be made regarding model configurations, resolution, lead times and how the forecast output will be used. This study demonstrates the opportunities and limitations for better integrating highresolution meteorological for the development of a rainfall threshold-based and model-based flood forecasting in cities with similar conditions. It also highlights the need to align the selected model configuration with the goals of the flood forecasting application which is critical for effective early warning systems and anticipatory flood management.