Analysis of stochastic characteristics of the Benue River flow process

Martins Y. Otache; Mohammad Bakir; Zhijia Li

doi:10.1007/s00343-008-0142-0

Your Location：

Home >

Browse articles >

Analysis of stochastic characteristics of the Benue River flow process

Updated：2023-05-16

- Analysis of stochastic characteristics of the Benue River flow process
- Journal of Oceanology and Limnology Vol. 26, Issue 2, Pages: 142-151(2008)
- Affiliations：
  
  State Key Laboratory of Hydrology-Water Resources & Hydraulic Engineering Hohai University
- Author bio：
- Funds：
- DOI：10.1007/s00343-008-0142-0
  CLC：
- Online First：28 May 2008，
  
  Published：2008
- Accepted：
Scan QR Code
Otache, M.Y., Bakir, M. & Li, Z. Analysis of stochastic characteristics of the Benue River flow process., Chin. J. Ocean. Limnol. 26, 142–151 (2008). https://doi.org/10.1007/s00343-008-0142-0
DOI：

Otache, M.Y., Bakir, M. & Li, Z. Analysis of stochastic characteristics of the Benue River flow process., Chin. J. Ocean. Limnol. 26, 142–151 (2008). https://doi.org/10.1007/s00343-008-0142-0 DOI：

摘要

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%

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

...

365 estimated “over realisations” have their coefficients significantly different from other coefficients.

关键词

Keywords

references

Burn, D. H. and M. A. Hag Elnur, 2002, Detection of hydrologic trends and variability. Hydrology 255: 107–122.

Dickey, D. A. and W. A. Fuller, 1979. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74: 423–431.

Gimeno, R., B. Manchado and R. Minguez, 1999. Stationarity tests for financial time Series. Physica A 269(1): 72–78.

Hirsch, R. M., J. R. Slack and R. A. Smith, 1982. Techniques of trend analysis for monthly water quality data. Water Resources Research 18(1): 107–121.

Hirsch, R. M. and J. R. Slack, 1984. A non-parametric trend test for seasonal data with serial dependence. Water Resources Research 20(6): 727–732.

Kendall, M. G, 1939. A new measure of rank correlation. Biometrika, 30: 81–93.

Kottegoda, N. T., 1980. Stochastic Water Resources Technology. The Macmillan Press Ltd, London and Basingstoke. Kurt Annen, Unit root DLL (BETA); www.web-reg.de

Lins, H. F. and J. R. Slack, 1999. Streamflow trends in Sweden 1807-2002. Hydrol. Sci. J. 49(1): 69–83.

Mann, H. B, 1945. Non-parametric test against trend. Econometrica, 13: 245–259.

Mitosek, H. T, 2000. On stochastic properties of daily river flow processes. J. Hydrology 228: 188–205.

Papoulis, A., 1965. Probability, Random Variables, and Stochastic Processes. McGraw-Hill. New York.

Salas, J. D., D. C. Boes and R. A. Smith, 1982. Estimation of ARMA Models with Seasonal Parameters. Water Resources Research 18(4): 1 006–1 010.

Said, S. E. and D. Dickey, 1984. Testing for unit roots in autoregressive moving-average models with unknown order. Biometrika, 71: 599–607.

Toro, S. M, 1997. Post construction effects of the Cameroonian Lagdao Dam on the River Benue. J. Chartered Institute, Water Environmental Management,11(2): 109–113.

Von Storch, H., 1995. Misuses of statistical analysis in climate research: In: H. V. Storch and A. Navarra (eds). Analysis of climate: Applications of Statistical Techniques. Springer-Verlag, New York. pp. 11–26.

Wang, W., P. H. A. J. M. van Gelder and J. K. Vrijing, 2005. Detection of changes in Streamflow series in Western Europe over 1901-2000. Water Sci. and Technology, Water Supply 5(6): 289–299.

Zhang, X., K. D. Harvey and R. Uzyk, 2001. Trends in Canadian streamflow. Water Resources Research 37(4): 987–998.

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Exploring seasonal fluctuations in the zooplankton communities from the WPWP epipelagic and mesopelagic zones by means of eDNA metabarcoding

Reproductive strategy of Antarctic krill (Euphausia superba) in the south Scotia Sea: revisiting the oocyte development and morphological changes with multiple seasonal analysis

Persistent mixing bursts in the equatorial Pacific thermocline induced by persistent equatorial waves

Decadal variation and trend of the upper layer salinity in the South China Sea from 1960 to 2010

Seasonal response of surface wind to SST perturbation in the Northern Hemisphere

Related Author

Chunsheng WANG

Chen FANG

Qianwen SHAO

Dong SUN

Yunzhi FENG

Guoping ZHU

Yu LIU

Xiang GONG

Related Institution

Ningbo Institute of Oceanography

Southern Marine Science and Engineering Guangdong Laboratory

School of Oceanography, Shanghai Jiao Tong University

Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources

Polar Marine Ecosystem Group, The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education

Address：Editorial Office of JOL, 88 Haijun Rd. Qingdao, 266000, China
Tel：+86-532-82898754 Email：jol@qdio.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰