Stochastic modelling of the sediment load of the upper Yangtze River (China)

The temporal and spatial sediment load characteristics of Asia's longest river, the Yangtze, have been examined. Annual and monthly sediment load characteristics in the temporal domain were modelled. The annual sediment load data from 1950 to 1990 and monthly sediment load data from 1950 to 196...

Full description

Saved in:
Bibliographic Details
Published in:Hydrological sciences journal 2002-08, Vol.47 (sup1), p.S93-S105
Main Authors: GANGYAN, ZHOU, GOEL, N. K., BHATT, V. K.
Format: Article
Language:English
Subjects:
analyse harmonique
apports solides
autoregressive model
barrage des Trois Gorges
Freshwater
harmonic analysis
modèle auto-régressif
modèle de Thomas et Fiering
modélisation stochastique
sediment yield
stochastic modelling
Thomas & Fiering model
Three Gorges dam
Yangtze
Yangtze River
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The temporal and spatial sediment load characteristics of Asia's longest river, the Yangtze, have been examined. Annual and monthly sediment load characteristics in the temporal domain were modelled. The annual sediment load data from 1950 to 1990 and monthly sediment load data from 1950 to 1969 were used. Statistical tests such as the turning point test, Kendall's rank correlation test and Anderson's correlogram test were applied for randomness and trend identification. The periodicity in the sediment load data was analysed by harmonic analysis and stochastic component was modelled by auto-regressive model. The results indicate that the annual sediment load series is trend free at 5% significance level and the monthly means and standard deviations of sediment load show periodicity. The month-to-month correlation structure is nonstationary. Using the AR(1) model for the dependent stochastic component, 100 years of monthly sediment data were generated. The monthly means of observed and generated data match well.
ISSN:0262-6667
2150-3435
DOI:10.1080/02626660209493025