Principles of Stochastic Generation of Hydrologic Time Series for Reservoir Planning and Design: Case Study
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 11
Abstract
Simulation has been an important tool for planners in many fields of knowledge. In the field of water resources, the uncertainties because of unknown data population and the short length of the records work together to make the simulation especially important. The major utilization of water resources at the level needed in modern society makes water storage essential for satisfying the demand. Therefore, the need to reduce the uncertainty in the design of water-storage capacity is an important problem in the field of water-resources utilization. This problem can only be satisfactorily solved with the aid of simulation. The implementation of adequate exploration politics also needs to use simulations to obtain results with low uncertainty. In the simulation of water-resource systems, the use of synthetic time series is a current practice. In this study, the number of generated time series to use in the problems described are analyzed. Annual and monthly synthetic flows were generated, preserving the relevant statistics of the available historical data, and then the number of time series to generate was determined. The results of the case studies indicate that the proposed methodology is a plausible approach to solve the analyzed problem and lead to the conclusion that the number of time series to generate should be 1,200.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers wish to acknowledge the editors and associate editor of the journal and the four unknown reviewers for the insightful comments, remarks, and suggestions that improved the paper.
References
Adeloye, A. J., Montaseri, M., and Garmann, C. (2001). “Curing the misbehavior of reservoir capacity statistics by controlling shortfall during failures using the modified sequent peak algorithm.” Water Resour. Res., 37(1), 73–82.
Anderson, R. L. (1942). “Distribution of the serial correlation coefficients.” Ann. Math. Stat., 13(1), 1–13.
Burges, S. J. (1970). “Use of stochastic hydrology to determine storage requirements of reservoirs—A critical analysis.” Ph.D. thesis, Dept. of Civil Engineering, Stanford Univ., Stanford, CA.
Gumbel, E. J. (1958). Statistics of extremes, Columbia Univ. Press, New York.
Hoshi, K., and Burges, S. J. (1978). “The impact of seasonal flow characteristics and demand patterns on required reservoir storage.” J. Hydrol. (Amsterdam), 37(3–4), 241–260.
Klemes, V., Srikanthan, R., and McMahon, T. A. (1981). “Long-memory flow models in reservoir analysis: What is their practical value?” Water Resour. Res., 17(3), 737–751.
McMahon, T. A. (1978). River capacity and yield, Elsevier, Amsterdam, Netherlands.
McMahon, T. A., and Adeloye, A. J. (2005). Water resources yield, Water Resources Publications, Highlands Ranch, CO.
Phien, H. N. (1993). “Reservoir storage capacity with gamma inflows.” J. Hydrol. (Amsterdam), 146(1), 383–389.
Salas, J. D. (1993). “Analysis and modeling of hydrologic time series.” Handbook of hydrology, D. R. Maidment, ed., McGraw-Hill, New York.
Svanidze, G. G. (1980). Mathematical modeling of hydrologic series, Water Resources Publications, Fort Collins, CO.
Thomas, H. A., and Burden, R. P. (1963). “Operations research in water quality management.” Division of Applied Physics, Harvard Univ., Cambridge, MA.
Vogel, R. M., and Stedinger, J. R. (1987). “Generalized storage-reliability-yield relationships.” J. Hydrol. (Amsterdam), 89(3–4), 303–327.
Vogel, R. M., and Stedinger, J. R. (1988). “The value of stochastic streamflow models in overyear reservoir design applications.” Water Resour. Res., 24(9), 1483–1490.
Wallis, J. R., and Matalas, N. C. (1972). “Sensitivity of reservoir design to the generating mechanism of inflows.” Water Resour. Res., 8(3), 634–641.
Wilson, E. B., and Hilferty, M. M. (1931). “Distribution of chi-square.” Proc. Natl. Acad. Sci. U. S. A., 17(12), 684–688.
World Meteorological Organization (1988). “Analyzing long time series of hydrological data with respect to climate variability.” Wcap-3, WMO/TD No. 224, Geneva.
Yue, S., Pilon, P., and Cavadias, G. (2002a). “Power of the Mann-Kendall and Spearman’s rho testes for detecting monotic trends in hydrological series.” J. Hydrol. (Amsterdam), 259(1–4), 254–271.
Yue, S., Pilon, P., Phinney, B., and Cavadias, G. (2002b). “The influence of autocorrelation on the ability to detect trend in hydrological series.” Hydrol. Processes, 16, (9), 1807–1829.
Yue, S., and Wang, C. Y. (2002). “The influence of serial correlation on the Mann-Whitney test for detecting a shift in median.” Adv. Water Resour., 25(3), 325–333.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 16 • Issue 11 • November 2011
Pages: 891 - 898
Copyright
© 2011 American Society of Civil Engineers.
History
Received: May 14, 2010
Accepted: Jan 12, 2011
Published online: Jan 14, 2011
Published in print: Nov 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.