Large-watershed flood forecasting with high-resolution distributed hydrological model

Yangbo Chen,Liming Dong,Huanyu Wang,Ji Li,Jianming Qin

doi:10.5194/hess-21-735-2017

Abstract

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m × 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m × 200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m × 500 m grid cell, but the model spatial resolution with a 200 m × 200 m grid cell is recommended in this study to keep the model at a better performance.

Highlights

Flooding is one of the most devastating natural disasters in the world, and huge damages have been caused (Krzmm, 1992; Kuniyoshi, 1992; Chen, 1995; EEA, 2010)
The whole watershed is first divided into 1 469 900 cells by the digital elevation model (DEM) horizontally, which were further categorized into hillslope cells and river cells
By employing Liuxihe model, a physically based distributed hydrological model, this study sets up a distributed hydrological model for the flood forecasting of the Liujiang River basin in southern China that could be regarded as a large watershed

Summary

Introduction

Flooding is one of the most devastating natural disasters in the world, and huge damages have been caused (Krzmm, 1992; Kuniyoshi, 1992; Chen, 1995; EEA, 2010). The most popular hydrological model for watershed flood forecasting is still the lumped model (Refsgaard et al, 1997), which averages the terrain property and precipitation over the watershed, as well as the model parameters. Due to the inhomogeneity of terrain property over the watershed, which is true even in very small watershed, the watershed flood forecasting could not be forecasted accurately if the model parameters are averaged over the watershed. For this reasons, new models are needed to improve the watershed flood forecasting capability, for large-watershed flood forecasting

Methods

Results

Conclusion