High-Resolution Urban Flood Forecasting by Using a Coupled Atmospheric and Hydrodynamic Flood Models

Guangzhao Chen,Lei Huang,Shaoxiong Yang,Feng Su,Xu Bi,Jingming Hou,Nie Zhou,Yu Tong

doi:10.3389/feart.2020.545612

Abstract

Flood forecasting is one of the most significant tools for reducing flood risk and avoiding losses. To solve the problem of low resolution and short lead time of the traditional urban flood forecasting method, this work develops a novel high-accuracy and long leading time model through coupling the atmospheric and hydrological-hydrodynamic models. The GRAPE_MESO model is applied as atmospheric model predicting rainstorms. To improve the reliability, a reconstructed method is put forward to correct predicted rainstorm data. The reconstructed predicted rainstorm is then used as input data for the hydrodynamic flood model. Finally, the urban flood inundation process is forecasted by the coupled atmospheric and flood model. Though applying the coupled model at Fengxi New Town (China), the performance is evaluated for realistic urban flood forecasting. The results show that the coupled modeling system can predict satisfactory urban flood inundation process with high-resolution and long lead time.

Highlights

Urban flood disasters are becoming more and more frequent with the increasing growth of urbanization and the effects of climate change
Aiming to extend the lead time, ground-based rain gauge measured rainfall should be replaced by highresolution predicted rainstorm products from the meso scale atmospheric model as input data of flood inundation forecasting system (Siccardi et al, Urban Flood Forecasting 2005; Li et al, 2017)
The measured inundation area of four points compares with the simulated results, it can be seen from Table 3 that the simulated inundation point has good agreement with the measurement, and the average relative error of inundation area is only 3.95%, showing that the model is reliable, and it can effectively simulate urban flood inundation process

Summary

Introduction

Urban flood disasters are becoming more and more frequent with the increasing growth of urbanization and the effects of climate change. Research from the Intergovernmental Panel on Climate Change (IPCC) estimates that the annual probability of 500 mm extreme rainfall was around 1% between 1981 and 2000, and that this probability is likely to increase to 18% by 2100 (Emanuel, 2017). Aiming to extend the lead time, ground-based rain gauge measured rainfall should be replaced by highresolution predicted rainstorm products from the meso scale atmospheric model (numerical weather prediction, NWP) as input data of flood inundation forecasting system

Methods

Results

Conclusion