Lin, Yude (1993) Modeling long term serial correlation in flood series. Masters thesis, Memorial University of Newfoundland.
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The standard assumption in flood frequency analysis is that annual peak flows are independent events. Hydrologists pay little attention to the serial correlation of flood peak series in flood frequency analysis because standard statistical tests for independence usually do not reject the null hypothesis of serial independence at the 90% or 95% confidence level for most rivers. This study was undertaken to investigate the validity of this assumption with regard to Canadian rivers by statistically analyzing the short-term and long-term dependence of annual peak flows. Ninety stationary flood series of Canadian rivers were tested by eleven tests for short-term dependence and one test for long-term dependence. The results show that about 18%(at 5% level) - 28%(at 10% level) flood series passed the short-term dependence tests but failed the long-term dependence test. The presence of long term serial correlation in flood series is evident and can not be disregarded in flood risk analysis. -- Modeling annual flow series by considering the effect of both short-term and long-term dependence was then discussed. This study considered two newly developed models: harmonic analysis of cumulative departures, and the mixed-noise model. These two models were further developed and a comparison between them was made. Finally, the effect of long-term dependence on flood risk analysis is evaluated using Monte Carlo simulations. It was found that neglecting serial correlation could cause considerable uncertainty in the estimated flood risk.
|Item Type:||Thesis (Masters)|
|Additional Information:||Bibliography: leaves 93-96.|
|Department(s):||Engineering and Applied Science, Faculty of|
|Library of Congress Subject Heading:||Floods--Canada--Mathematical models|
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