A Comparison among the Performance of the Stochastic Models in Generating the Monthly Streamflow and Rainfall Data

Document Type : Original Article

Authors

1 Associate Professor, Department of Water Engineering, Urmia University, Urmia, Iran,

2 Department of Water Engineering, Urmia University, Urmia, Iran

Abstract

Synthetic data generation models have been recognized as useful tools to predict and generate alternative time series or long-term series throughout the studies conducted in the domain of water resources management. Accordingly, these models have widely been used by different researchers across the world. In the recent decades, these models have been developed to generate annual, monthly, and daily rainfall or river flow data. Among the synthetic data generated, monthly data are of great importance since they are used in the critical and important studies in the field of water resource systems, such as storage tanks and drought monitoring. Accordingly, the utilization of the monthly synthetic data models leads to more detailed analyses about the real performance of such systems. On the other hand, the theoretical basis of different stochastic models is the generation of diverse monthly data and the performance of these models can remarkably be affected by this fact. Therefore, one can argue that selecting an appropriate model is one of the major concerns of water resources experts. As such, this study made use of the Monte Carlo simulation method to compare and assess the performance of four types of non-parametric Bootstrap models as well as parametric models of Valencia-Schaake, Thomas-Fiering, and Fragment in monthly synthetic data generation. To do this, the monthly flow data of Nazluchay, Shaharchay, and Barandozchay rivers, located at the Western Azerbaijan province in the North West of Iran, were analyzed over a 47-year period. Then, 1000 synthetic time series of monthly flows for these rivers were generated and used for each of the desired seven models over a 47-year period thereof. The results indicated that, compared to other models, the Valencia-Schaake distribution model had a very high performance in terms of all well-known assessment statistical indicators.

Keywords

References

Adeloye AJ, and Montaseri M (2002) Preliminary stream flow data analyses prior to water resources planning study, Hydrological Sciences Journal, 47(5):679-692.
Barnes FB (1954) Storage required for a city water supply, Journal of the Institute of Engineers Australia, 26(9): 198-203.
Bars RL (1990) Hydrology: an introduction to hydrologic science. Addison-Wesley Publishing Co., New York, USA.
Brittan MR (1961) Probability analysis to the development of a synthetic hydrology for the Colorado River, in part IV of past and probable future variations in stream flow in the upper Colorado River, University of Colorado.
Chow VT, Maidment, DR, and Mays LW (1993) Applied hydrology, Mc Grave-Hill book Co., New York.
Debele B (2007) Accuracy evaluation of weather data generation and disaggregation methods at finer timescales, Advance water Resource, 30(5):1286-1300.
Douglas H (2010) How to measure anything: finding the value of intangibles in business. John Wiley and Sons, Inc, Canada.
Fiering MB (1967) Stream flow synthesis, Harvard Univ. Press, Cambridge, Mass.
Hazen A (1914) Storage to be provided in impounding reservoirs for municipal water supply, Trans., ASCE, 77: 1539-1540.
Kendall MG, Stuart A (1976) The advanced theory of statistics. Charles Griffin & Company, London, High Wycombe, 400,401.
Lin GF, Lee FC (1992) An aggregation-disaggregation approach for hydrologic time series modeling, Journal of Hydrology, 138:543-557.
Maheepala S, Perera BJC (1996) Monthly hydrologic data generation by disaggregation, Journal of Hydrology, 178:277-291.
McGhee JW (1985) Introductory statistics, West Publishing Co., New York, USA.
McMahon TA, Mein RG (1986) Water and reservoir yield. Water Resources Publication, Littleton, Colorado.
Montaseri M, Adeloye AJ (1999) Critical period of reservoir systems for planning purpose, Journal of Hydrology, 224(4-3):136-115.
Montaseri M, Adeloye AJ (2002) Effects of integrated planning on capacity-yield-performance functions, Journal of Water Resources Planning and Management, 128(6):456–461.
Montaseri M, Adeloye AJ (2004) A graphical rule for volumetric evaporation loss correction in reservoir capacity-yield-performance planning in Urmia region, Iran, Water Resources Management, 18:55-74.
Nurul AI (2004) Synthetic simulation of stream flow and rainfall data using disaggregation and aggregation models, Jurnal Kejuruteraan Awam 16(2):56-65.
Porter JW, and Pink BJ, 1991. A method of synthetic fragments for disaggregation in stochastic data generation, Pp. 187-191. Proceedings In Hydrology and Water Resources Symposium. Australia.
Salas JD (1993) Analysis and modeling of hydrologic time series, In Handbook of Hydrology, Edited by Maidment, McGrow – Hill book CO., New York.
Salas JD, Deller JW, Yevjevich V, and Lane WL (1980) Applied modeling of hydrologic time series, Water Resources Publications, Littleton,  Colorado.
Savic DA, Burn DH, and Zrinji Z (1989) A comparison of streamflow generation models for reservoir capacity-yield analysis, Water Resources Bulletin, 25(5): 977-983.
Srikanthan R, and McMahon TA (1982) Stochastic generation of monthly streamflows, Journal of the Hydraulics Division, ASCE, 108(HY3), 419-441.
Srikanthan R, McMahon TA (1985) Stochastic generation of rainfall and evaporation data AWRC Technical Paper No.84, 301pp.
Srikanthan R, McMahon TA (2001) Stochastic generation of annual, monthly and daily climate data. Hydrology and Earth System Sciences, 5(4):653–670.
Srinivas VV, Srinivasan K (2005) Hybrid moving block bootstrap stochastic simulation of multi-site multi-season for stream flows. Journal of Hydrology, 302:307-330.
Thomas HA, Fiering MP (1962) Mathematical synthesis of stream flow sequences for the analysis of river basins by simulation. Chapter 12 in: Design of water resources systems A. Mass, S. Marglin and G. Fair (Eds.), Harvard University Press, Cambridge, Massachusetts USA.
Vogel RM, Kroll CN (1989) Low flow frequency analysis using probability plot correlation coefficients. Journal of Water Resources Planning and Management, 115(3):338-357.
Vogel RM, Shallcros AL (1996) The moving blocks bootstrap versus parametric time series models, Water Resources Research, 32(6):1875-1882.
Yevjevich V (1972) Probability and statistics in hydrology. Water Resources Publications Fort Collins, Colorado, philosophy.

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History

  • Receive Date: 13 January 2015
  • Accept Date: 05 August 2015

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