IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
Log On
About IAHRDirectoryCommitteesMy IAHRNews & JournalseLibraryeShopEventsJoin IAHRWorld CongressDonate
spacer.gif
spacer.gif eLibrary
spacer.gif eLibrary
You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Flood risk management and adaptation : Real-time channel flood forecasting model with multi-segmental roughness coefficients updating dynam...
Real-time channel flood forecasting model with multi-segmental roughness coefficients updating dynamically based on ensemble kalman filter
Author : XINGYA XU(1), HONGWEI FANG(1) ,RUIXUN LAI(1)(2), YUEFENG ZHANG(1),LEI HUANG(1),XIAOBO LIU(3)
ABSTRACT
Reasonable estimation of roughness coefficient is one of the important and effective ways to improve the accuracy of
channel flood forecasting model. The value of the roughness coefficient is controlled by the channel physical condition,
like bed geology and cross-section geometry, and affected by flow condition, like submerged extent of vegetation and
flow turbulence intensity. Given the longitude variation of channel physical condition and unsteady state of flood flow,
roughness coefficient in the flood forecasting model should have temporal-spatial character. A new method is developed
to consider both spatial distribution and time-varying process of roughness coefficient in the channel flood forecasting
model by using simultaneous stage observations of multiple gauge stations. The river channel is spatially divided into
several river segments which are assigned independent roughness coefficients, taking the locations of the gauge
stations as the interior boundaries in the channel. At the time step of real-time stage observations available, the optimal
stages at the gauge stations will be estimated based on ensemble Kalman filter, which is a Monte Carlo sequential data
assimilation algorithm considering both the errors of observation and model. Taking the estimated optimal stages as the
targets, roughness coefficients of each segment can be updated to match the current flow state respectively. The
updated roughness coefficients are used in the flood forecasting model for prediction of the coming flood flow. An
application case of real-time forecasting of a real flood event in the channel from Cuntan to Wanxian in Three Gorges of
the Yangtze River shows that, the accuracy of model predictions can be improved effectively with the technique of multisegmental
roughness coefficients updating.
File Size : 670,298 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Flood risk management and adaptation
Date Published : 28/08/2015
Download Now