4.7 Review

Hydraulic modelling of inland urban flooding: Recent advances

Journal

JOURNAL OF HYDROLOGY
Volume 609, Issue -, Pages -

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ELSEVIER
DOI: 10.1016/j.jhydrol.2022.127763

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This review summarizes the recent advances in understanding urban flood processes and their modeling from 2018 to 2021. It covers four aspects: knowledge of urban flood flow and transport processes, stability of humans and objects within flooded streets, reliability of computational modeling, and approaches for speeding up computations of urban flood events. New laboratory setups have revealed previously unexplored processes, and improvements in computations have been achieved through strategies such as merging and processing various data sources and incorporating more details on drainage systems.
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.

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