| Analysis of stochastic characteristics of the Benue River flow process |
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| 作者姓名： | 李致家 |
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| 作者单位： | State Key Laboratory of Hydrology-Water Resources ＆ Hydraulic Engineering, Hohai University, Nanjing 210098, China |
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| 基金项目： | Supported by the Post Graduate Research Fund from Federal Government of Nigeria under the BEA Scholarship Program (No.CSC2005566002) |
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| 摘 要： | Stochastic characteristics of the Benue River streamflow process are examined under conditions of data austerity. The streamflow process is investigated for trend, non-stationarity and seasonality for a time period of 26 years. Results of trend analyses with Mann-Kendall test show that there is no trend in the annual mean discharges. Monthly flow series examined with seasonal Kendall test indicate the presence of positive change in the trend for some months, especially the months of August, January, and February. For the stationarity test, daily and monthly flow series appear to be stationary whereas at 1%, 5%, and 10% significant levels, the stationarity alternative hypothesis is rejected for the annual flow series. Though monthly flow appears to be stationary going by this test, because of high seasonality, it could be said to exhibit periodic stationarity based on the seasonality analysis. The following conclusions are drawn： （1） There is seasonality in both the mean and variance with unimodal distribution. （2） Days with high mean also have high variance. （3） Skewness coefficients for the months within the dry season period are greater than those of the wet season period, and seasonal autocorrelations for streamflow during dry season are generally larger than those of the wet season. Precisely, they are significantly different for most of the months. （4） The autocorrelation functions estimated ＂over time＂ are greater in the absolute value for data that have not been deseasonalised but were initially normalised by logarithmic transformation only, while autocorrelation functions for i = 1, 2 365 estimated ＂over realisations＂ have their coefficients significantly different from other coefficients.
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| 关 键 词： | 本尼河 流程 发展趋势 平稳性 |
Analysis of stochastic characteristics of the Benue River flow process |
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| Martins?Y.?Otache,Mohammad?Bakir,Zhijia?Li.Analysis of stochastic characteristics of the Benue River flow process[J].Chinese Journal of Oceanology and Limnology,2008,26(2):142-151. |
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| Authors: | Martins Y Otache Mohammad Bakir Zhijia Li |
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| Institution: | (1) State Key Laboratory of Hydrology-Water Resources & Hydraulic Engineering, Hohai University, Nanjing, 210098, China |
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| Abstract: | Stochastic characteristics of the Benue River streamflow process are examined under conditions of data austerity.The streamflow process is investigated for trend,non-stationarity and seasonality for a time period of 26 years.Results of trend analyses with Mann-Kendall test show that there is no trend in the annual mean discharges.Monthly flow series examined with seasonal Kendall test indicate the presence of positive change in the trend for some months,especially the months of August,January,and February.For the stationarity test,daily and monthly flow series appear to be stationary whereas at 1%,5%,and 10% significant levels,the stationarity alternative hypothesis is rejected for the annual flow series.Though monthly flow appears to be stationary going by this test,because of high seasonality,it could be said to exhibit periodic stationarity based on the seasonality analysis.The following conclusions are drawn:(1) There is seasonality in both the mean and variance with unimodal distribution.(2) Days with high mean also have high variance.(3) Skewness coefficients for the months within the dry season period are greater than those of the wet season period,and seasonal autocorrelations for streamflow during dry season are generally larger than those of the wet season.Precisely,they are significantly different for most of the months.(4) The autocorrelation functions estimated "over time" are greater in the absolute value for data that have not been deseasonalised but were initially normalised by logarithmic transformation only,while autocorrelation functions for i=1,2,…,365 estimated "over realisations" have their coefficients significantly different from other coefficients. |
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| Keywords: | trend stationarity seasonality over time over realisation stochastic skewness |
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