DOI: 10.5194/hess-21-735-2017
Scopus记录号: 2-s2.0-85011684773
论文题名: Large-watershed flood forecasting with high-resolution distributed hydrological model
作者: Chen Y ; , Li J ; , Wang H ; , Qin J ; , Dong L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 2 起始页码: 735
结束页码: 749
语种: 英语
Scopus关键词: Climate models
; Computational efficiency
; Efficiency
; Flood control
; Forecasting
; Hydrology
; Image resolution
; Land use
; Particle swarm optimization (PSO)
; Rivers
; Supercomputers
; Surveying
; Watersheds
; Weather forecasting
; Acceptable performance
; Computation efficiency
; Cross section shape
; Digital elevation model
; Distributed hydrological model
; Parameter optimization
; Parameter uncertainty
; Particle swarm optimization algorithm
; Floods
; algorithm
; computer simulation
; digital elevation model
; flood forecasting
; hydrological modeling
; land use
; optimization
; performance assessment
; river channel
; spatial resolution
; terrain
; watershed
; China
; Guangxi Zhuangzu
; Liu River
英文摘要: 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 200m × 200m 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 200m × 200m 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 500m × 500m grid cell, but the model spatial resolution with a 200m × 200m grid cell is recommended in this study to keep the model at a better performance. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79270
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou, China
Recommended Citation:
Chen Y,, Li J,, Wang H,et al. Large-watershed flood forecasting with high-resolution distributed hydrological model[J]. Hydrology and Earth System Sciences,2017-01-01,21(2)