Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.

Towards flash flood prediction in the dry Dead Sea region utilizing radar rainfall information

Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

• Research in urban flood modelling has received a major attention in last 4 years. • The topics cover flow process understanding and innovation in numerical modelling. • Many new experimental set-ups and numerical models were developed. • Innovative calculations aim at improving prediction quality or efficiency. • Challenges remain for an efficient and precise urban flood calculations. This review provides a synthesis of advances in our understanding of urban flood processes and their modelling over the last four years (2018–2021). Four aspects are covered: knowledge of urban flood flow and transport processes, stability of humans and objects within flooded streets, reliability of computational modelling and approaches for speeding-up computations of urban flood event. New laboratory setups have shed light on previously unexplored processes such as flow intrusion into buildings or contaminant exchanges between surface and underground drainage. The stability of a single pedestrians or object (e.g., vehicles, waste containers) under urban flooding was analysed, but not group effects such as clogging. Improvements in computations were achieved by new strategies for merging and processing various sources of high quality topographic and forcing data (e.g., precipitation), the incorporation of more and more details on the drainage systems (e.g., effect of gullies), and 3D instead of 2D simulations. Computational efficiency was enhanced based on massive parallelization, adaptive mesh, porosity models, surrogate models as well as machine learning. Finally crowd-sourced data are shown to offer an avenue for next generation model validation methods. Remaining knowledge gaps and guidance for future research are proposed and predict that additional research work will be performed in following years. 

Hydraulic modelling of inland urban flooding: recent advances

Influence of urban forms on long-duration urban flooding: Laboratory experiments and computational analysis

This paper presents an integrated modeling framework aiming at accurate predictions of flood hazard from heavy rainfalls. The accuracy of such predictions generally depends on the complexity and resolution of the employed model components. We propose an integration of complementary models in one framework that facilitates GPUs to improve accuracy and simulation time. The spatially distributed runoff model integrates surface flow routing based on the full shallow water equations, infiltration based on the Green–Ampt equation, and interception. In urban areas, the runoff model is coupled with the Storm Water Management Model (SWMM). The integrated model is validated and tested on laboratory, rural and urban scenarios with regards to accuracy and computational efficiency. The GPU acceleration yields speedups of 1000 times compared to a CPU implementation and enables the coupled simulation of flash floods at 1 m resolution for an urban area of 200 km2 in realtime. 

https://doi.org/10.1016/j.envsoft.2022.105480

An integrated GPU-accelerated modeling framework for high-resolution simulations of rural and urban flash floods

This material is based upon work supported by the National Science Foundation under Award Numbers SES 1444755 and 1934933. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors draw on discussions held during a virtual workshop of the National Science Foundation funded Urban Resilience to Extremes Sustainability Research Network on April 2, 2020. This workshop was attended by UREx researchers and practitioners representing 10 United States and Latin American cities.

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K. Grove

Papers

The Value of Urban Flood Modeling