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

Abstract

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.

A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom

“…For typical hydrological and risk assessment applications, any coupling of models and data has been achieved by defining linear model chains whereby outputs from one system (e.g., point or distributed observation, numerical weather prediction or climate simulation based data) are fed into a hydrological and/or hydraulic model in order to simulate the land surface response and risk of flood hazard (e.g., Coxon et al, 2019a;Flack et al, 2019;Ming et al, 2020). For coastal flood hazards, for example, Couasnon et al (2020) recently illustrated the need to consider both fluvial and coastal flood drivers in the estimation of compound flood risk at coastal locations at a global scale, with river and coastal surge data obtained from two independent sources, although with both driven by the same ERA-Interim reanalysis of the meteorological forcing.…”

An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan

“…The most fundamental requirement for a successful flash flood simulation or predic tion is a large amount of high-resolution topographic data [10][11][12][13][14][15][16][17][18][19][20][21][22][23]; hence, the light detec tion and ranging (LiDAR) technique has been widely applied in creating a three-dimen sional set of points for high-resolution DEMs with an accuracy of up to 0.1 m in the vertica direction. LiDAR technology is based on a scanning laser combined with both a globa positioning system (GPS) and inertial technology.…”

“…However, the employment of shallow water equations (continuity and momentum equations for the x and y directions) is still important for expressing the details of flooding dynamics and for diminishing the uncertainty of flooding forecasts due to the simplifications of the model and prefixed flood propagation scenarios. Owing to the high computational demand, two-dimensional, fully hydrodynamic models have not been exploited to support flood forecasts across a large catchment at a sufficiently high spatial resolution [16]. Therefore, there is a strong need to develop a physically-based two-dimensional, unstructured-grid, flood inundation model and to accelerate its computing efficiency with enough lead time for emergence response.…”

Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat

“…Traditional flood forecast systems exploit flood-related hydrologic processes simulated by physical models, which rely on quantified descriptions of catchment and river physical characteristics, and are driven by rainfall outputs from a numerical weather prediction (NWP) model [32][33][34]. For example, a flood forecasting system utilizing graphics processing unit (GPU) computation showed potential in predicting water level and flood extent with 34 hours of lead time for a selected catchment [35]. Considering the highly nonlinear correspondence between rainfall and flood inundation; and the lack of accurate descriptions of hydrologic parameters at sub-kilometer levels, data-driven approaches represent an alternative to physically-based forecast systems by leveraging the flexibility of machine-learning methods in linking rainfall inputs and inundation outputs [36][37][38].…”