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Real-Time Flood Forecasting Based on a High-Performance 2-D Hydrodynamic Model and Numerical Weather Predictions

Lookup NU author(s): Dr Xiaodong MingORCiD, Professor Qiuhua Liang, Dr Xilin Xia, Professor Hayley Fowler



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


©2020. The Authors. A flood forecasting system commonly consists of at least two essential components, that is, a numerical weather prediction (NWP) model to provide rainfall forecasts and a hydrological/hydraulic model to predict the hydrological response. While being widely used for flood forecasting, hydrological models only provide a simplified representation of the physical processes of flooding due to negligence of strict momentum conservation. They cannot reliably predict the highly transient flooding process from intense rainfall, in which case a fully 2-D hydrodynamic model is required. Due to high computational demand, hydrodynamic models have not been exploited to support real-time flood forecasting across a large catchment at sufficiently high resolution. To fill the current research and practical gaps, this work develops a new forecasting system by coupling a graphics processing unit (GPU) accelerated hydrodynamic model with NWP products to provide high-resolution, catchment-scale forecasting of rainfall-runoff and flooding processes induced by intense rainfall. The performance of this new forecasting system is tested and confirmed by applying it to “forecast” an extreme flood event across a 2,500-km2 catchment at 10-m resolution. Quantitative comparisons are made between the numerical predictions and field measurements in terms of water level and flood extent. To produce simulation results comparing well with the observations, the new flood forecasting system provides 34 hr of lead time when the weather forecasts are available 36 hr beforehand. Numerical experiments further confirm that uncertainties from the rainfall inputs are not amplified by the hydrodynamic model toward the final flood forecasting outputs in this case.

Publication metadata

Author(s): Ming X, Liang Q, Xia X, Li D, Fowler HJ

Publication type: Article

Publication status: Published

Journal: Water Resources Research

Year: 2020

Volume: 56

Issue: 7

Print publication date: 01/07/2020

Online publication date: 17/05/2020

Acceptance date: 04/05/2020

Date deposited: 26/07/2021

ISSN (print): 0043-1397

ISSN (electronic): 1944-7973

Publisher: Wiley-Blackwell Publishing, Inc.


DOI: 10.1029/2019WR025583


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Funder referenceFunder name
NE/P017134/1Natural Environment Research Council (NERC)
NE/K008781/1Natural Environment Research Council (NERC)
Wolfson Foundation
WM140025Royal Society