The critical period of reservoir systems considering performance indices on Malaysia rivers

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The behavior of reservoir systems can be investigated using Critical Period (CP) which determines the aggregation level of the data (monthly or annual) that are required to be utilized in the reservoir analysis. Currently there are a number of methods that could approximate the behavior of reservoir systems, however the efficiency of these approaches have not been studied and verified for the Malaysia Rivers. In this study two different hypothetical reservoirs on Malaysia Rivers are selected. The stream flow data are subjected to preliminary analysis and evaluation of the fittest probability distribution function. Afterwards, the CP is estimated by applying a Monte Carlo simulation technique and considering performance indices. The CP from this study is used to determine the within-year or over-year behavior and these results are compared with those of the previous well-known equations in this area. It is observed that existing equations are incomplete and other parameters such as reliability and vulnerability should be considered to predict the behavior of reservoir systems. Consequently two separate regression equations are proposed to estimate the CP of these reservoir systems in Malaysia and some suggestions are made to generalize and extend this study.

    Keywords: Critical Period, Monte Carlo Simulation, Over-Year Behavior, Performance Indices, Reliability, Vulnerability, Within-Year Behavior.

  • References

      D. P. Loucks, J. R. Stedinger & D. A. Haith, Water Resources System Planning and Analysis, Prentice-Hill, Englewood Cliffs, New Jersey, USA, 1981.
    1. J. R. Stedinger & M. R. Taylor, Synthetic stream flows generation. 2. Effect of parameter uncertainty, Water Resources Research, 18 (4), 919924, 1982.
    2. H. N. Phien, Reservoir storage capacity with gamma inflows, Journal of Hydrology, 146, 383389, 1993.
    3. A. T. Silva & M. M. Portela, Disaggregation modeling of monthly stream flows using a new approach of the method of fragments, Hydrological Sciences Journal, 57(5), 2012.
    4. S. M. Lele, Improved algorithms for reservoir capacity calculation incorporating storagedependent losses and reliability norm, Water Resour. Res. 23 (10), 18191823, 1987.
    5. D. P. Loucks, Quantifying trends in system sustainability, Hydrol. Sci. J. 42 (4), 513530, 1998.
    6. A. J. Adeloye & N.R. Nawaz, The inherent time-based reliability of storage-yield estimates for some reservoir sites in Yorkshire, Yorkshire Water Services Ltd., Halifax Road, Bradford, England, 1997.
    7. T. Hashimoto, J. R. Stedinger & D. P. Louck, Reliability, resiliency and vulnerability criteria for water resource system performance evaluation, Water Resources Research, 18(1), 14-20, 1982.
    8. A. J. Adeloye, M. Montaseri & C. Garmann, Curing the misbehavior of reservoir capacity statistics by controlling shortfall during failures using the modified sequent peak algorithm, Water Resources Research, 37(1), 73-82, 2001.
    9. B. W. Gould, Statistical methods for estimating the design capacity of dams, J. Institut. Eng., Australia, 33(12), 405-416, 1916.
    10. P. A. P. Moran, A probability theory of dams and storage systems, Australian Jour. Applied Science, 5, 116-124, 1954.
    11. R. A. Wurbs, Reservoir system simulation and optimisation models, Journal of Water Resources Planning and Management, American Society of Civil Engineers, 119(4), 455-472, 1993.
    12. T. A. McMahon & A. J. Adeloye, Water Resources Yield, Water Resources Publications, LLC, USA, 2005.
    13. United States Corps of Engineers, Hydrologic engineering methods for water resources development, Vol. 8 Reservoir Yield. Hydrologic Engineering Center, Davis, California, 1975.
    14. T. A. McMahon & R. G. Mein, River and Reservoir Yield, Water Resources Publications, Col., USA, 1986.
    15. M. Montaseri & A. J. Adeloye, Critical period of reservoir systems for planning purposes, Journal of Hydrology, 224,115-136, 1999.
    16. R. M. Vogel & J.R. Stedinger, Generalised storage-reliability-yield relationships, Journal of Hydrology, 89, 303-327, 1987.
    17. R. M. Vogel, M. Lane, R.S. Ravindrian and P. Kirshen, Storage reservoir behavior in the United States, Journal of Water Resources Planning and Management, American Society of Civil Engineers, 125(5), 245-254, 1999.
    18. N. T. Kottegoda, Stochastic water resources technology, The Macmillan Press Ltd, 1980.
    19. J. D. Salas, Analysis and Modeling of hydrologic time series in Hand book of hydrology, Edited by D. R. Maidment, McGraw Hill book Co., New York, 1993.
    20. R. M. Vogel, The Probability Plot Correlation Coefficient Test for the Normal, Lognormal, and Gumbel Distributional Hypotheses, Water Resources Research, 22(4), 587-590, 1986.
    21. D. Valencia & J.C. Schaake, Disaggregation process in stochastic hydrology, Water Resources Research, 9(3), 580-585, 1973.
    22. H. A. Thomas & M. B. Fiering, Mathematical synthesis of stream flows sequences for the analysis of river basins by simulation, In: Maass (Ed.). Design of Water Resource Systems, Harvard University Press, Cambridge, MA, chap. 12, 1962.
    23. A. J. Adeloye & M. Montaseri, Adaptation of a single reservoir technique for multiple reservoir storageyieldreliability analysis, In: Zebidi, H. (Ed.). Water: A Looming Crisis, Proceedings of the International Conference on World Water Resources at the Beginning of the 21st Century, UNESCO, Paris, pp. 349355, 1998.
    24. X. Zxongue, K. Jinno, A. Kawanura, S. Takesaki & K. Ito, Performance risk analysis for Fukuoka water supply system, Water Resources Management, 12, 13-30, 1998.




Article ID: 2250
DOI: 10.14419/ijet.v3i2.2250

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.