Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing

Martins Y. Otache; Zhijia Li; Mohammad Bakir

doi:10.1007/s00343-008-0313-z

Your Location：

Home >

Browse articles >

Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing

OriginalPaper | Updated：2023-05-16

- Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing
- Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing
- 海洋湖沼学报（英文） 2008年26卷第3期页码：313-322
- Affiliations：
  
  State Key Laboratory of Hydrology-Water Resources & Hydraulic Engineering Hohai University
- Author bio：
- Funds：
  
  Supported by the Post Graduate Research Fund from Federal Government of Nigeria under BEA Scholarship Program (No. CSC2001566010)
- DOI：10.1007/s00343-008-0313-z
  中图分类号：
- 网络首发：2008-08-20，
  
  纸质出版：2008
- Accepted：
Scan QR Code
Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing[J]. 海洋湖沼学报（英文）, 2008,26(3):313-322.

Otache, M.Y., Li, Z. & Bakir, M. Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing., Chin. J. Ocean. Limnol. 26, 313–322 (2008). https://doi.org/10.1007/s00343-008-0313-z
Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing[J]. 海洋湖沼学报（英文）, 2008,26(3):313-322. DOI： 10.1007/s00343-008-0313-z.

Otache, M.Y., Li, Z. & Bakir, M. Analysis of long-term dependence phenomenon in Benue River flow process and its hypothesis testing., Chin. J. Ocean. Limnol. 26, 313–322 (2008). https://doi.org/10.1007/s00343-008-0313-z DOI：

摘要

Abstract

In this paper

the long-term dependence phenomenon (the Hurst Effect) which characterizes hydrological and other geophysical times series is studied. The long-term memory is analysed for both daily and monthly streamflow series of the Benue River at Makurdi

Nigeria by using heuristic methods and testing specifically the null hypothesis of short-term memory in the monthly flow series. Results obtained by applying heuristic procedures indicated that there may be the presence of long-term memory component in mean daily flow series but there is no discernible reason to suspect the presence in both average monthly and maximum monthly flow series (extreme event). Hypothesis testing was conducted by using original and modified versions of rescaled range statistic. When the modified rescaled range

which accounts for short-term memory in the series

is used

the null hypothesis is accepted for both the average monthly and maximum monthly flow series

indicating little or no probable presence of long-term memory in the series. An identical conclusion is also arrived at when second null hypothesis for independence of the monthly flow series is tested. Therefore

apart from the mean daily flow series

there is little evidence of long-term dependence in the Benue River streamflow series at Makurdi. However

considering the limited length of data used

the results are inconclusive.

关键词

Keywords

references

Beran, J., 1989. A test of location for data with slow decaying serial correlations. Biometrika 76: 261–269

Hipel, K. W. and A. I. McLeod, 1994. Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam.

Hurst, H., 1951. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116: 770–799.

Kashyap, R. L. and A. R. Rao, 1976. Dynamic Stochastic Models from Empirical Data. Academic Press, New York.

Kottegoda, N. T., 1980. Stochastic Water Resources Technology. Macmillan, New York. p. 34–41.

Lo, A. L., 1991. Long-term memory in stock market prices. Econometrica 59(5): 1 279–1 313.

Lye, L. M. and Y. Lin, 1994. Long-term dependence in annual peak flows of Canadian rivers. J. Hydrology 160:89–103.

Mesa, O. J. and G. Poveda, 1993. The Hurst effect: the scale of fluctuations approach. Water Resources Research, 29(12): 3 995–4 002.

Montanari. A., R. Renzo, and S. M. Taqqu, 2000. A seasonal fractional ARIMA Model applied to the Nile River monthly flows at Aswan. Water Resources Research 36(5): 1 249–1 259.

Patra, K. C., 2001. Hydrology and Water Resources Engineering. Alpha Science International Ltd, UK. p. 68–89.

Rao, A. R. and D. Bhattacharya, 1999. Hypothesis testing for long-term memory in hydrologic series. J. Hydrology 216: 183–196.

Rosenblatt, M., 1956. A central limit theorem and a strong mixing condition. Proceedings of the National Academy of Sciences 42: 27–60.

Salas, J. D., 1993. Analysis and Modelling of Hydrologic Time series. In: D. R. Maidement (Ed.), Handbook of Hydrology, McGraw-Hill, New York, Chapter 19.

Wallis, J. R. and N. C. Matalas, 1970. Small sample properties of H and K-estimates of Hurst’s coefficients h. Water Resources Research 6(6): 1 583–1 594

Wang, W., 2006. Stochasticity, non-linearity and forecasting of streamflow processes. Unpublished Ph.D Dissertation, Technische Universiteit Defts, Netherlands. p. 45–54.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI, ABA and NT2

双转子轴间滑油颗粒运动轨迹参数化模型

增广预测模型的航空发动机多变量约束预测控制

基于LMI极点配置的高空台飞行环境模拟系统PI增益调度控制研究

一种涡轮基组合动力的整机低速风洞试验研究