The operational flash-flood forecasting system for the Kingdom of Saudi Arabia: A case study of the 24th November 2022 flash flood in Jeddah

Abstract

Predicting flash floods in the arid region of the Arabia Peninsula poses unique challenges to researchers and practitioners due to the generally limited data records and field observations. The rapid onset of these events hinders mitigation measures and limits timely decisions, resulting in fatalities and property losses. To improve our predictive capability, we deployed a flash flood forecasting system that integrates numerical weather forecasts from the Weather Research and Forecasting (WRF) model with a distributed hydrological model, the Coupled Routing and Excess STorage (CREST), and a 2D hydrodynamic model (HEC-RAS). The atmospheric component runs at cloud-resolving scales (1.6 km) to incorporate local features and strong convection. The hydrological and hydrodynamic models run at variable spatiotemporal resolution: the rainfall-runoff generation runs at 500 meter-by-hourly, routing at 30 meter-by-hourly, while the floodplain dynamics are computed at 30 meter-by-hourly. The significant differences in computational demands dictate the domain differences: CREST runs over large natural basins while HEC-RAS runs over small urban sub-basins associated with dense infrastructures and exposure.The effectiveness of the operational national scale flash flood forecasting system is evaluated in this study for the extreme precipitation event that hit Jeddah on 24 November 2022. The event was the heaviest ever recorded in the area, causing widespread flash floods across Jeddah's urban and rural areas.The atmospheric component forecast is compared to the NASA satellite precipitation product (IMERG Late) and radar-rainfall estimates that were bias-adjusted based on in situ gauge observations. Since no hydrological observations were available to the authors for this event, discharge obtained from the gauge-adjusted radar-rainfall data, which represents the benchmark precipitation, was used as a reference to assess the skill of the WRF-based flood forecasts. Finally, the effectiveness of the warning system was compared to reported localized flood incidents at the street or neighborhood level by the public ('crowd source').The results of this study reveal an excellent temporal and spatial agreement between the forecasted precipitation from WRF and the bias-adjusted radar-rainfall estimates. The same conclusions cannot be drawn for the IMERG Late data. The satellite product seems to overestimate precipitation in most cases, which is consistent with the findings of several prior satellite validation studies. Comparing the flood quantiles for the Nov. 24th flood event indicates that the WRF-driven flood peak discharge properties agree with the radar-based ones. The differences between the flood characteristics (hydrographs peak, timing, and flood volume) when using WRF-forecasted versus radar-based benchmark precipitation were also minimal. The simulated flood inundation could capture the broad patterns of inundated areas at the city level: a high degree of agreement was reached, and more than 95% of the reported incidents across the city districts fell within the forecasted high or extreme warnings provided by the operational system on Nov. 23rd, at 12.30; therefore, more than 12 hours ahead. The importance of the study comes from the fact that it provides an effective solution and a state-of-the-art methodology to forecast such types of extreme rainfall events, which can cause major flash floods in the urban areas of Saudi Arabia.