Water Engineering Research

Korea Water Resources Association (한국수자원학회)
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EVAPORATION DATA STOCHASTIC GENERATION FOR KING FAHAD DAM LAKE IN BISHAH, SAUDI ARABIA

  • Abdulmohsen A. Al-Shaikh (Associate Professor, Civil Engineering Department, College of Engineering, King Saud University)
  • Published : 2001.10.01
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Abstract

Generation of evaporation data generally assists in planning, operation, and management of reservoirs and other water works. Annual and monthly evaporation series were generated for King Fahad Dam Lake in Bishah, Saudi Arabia. Data was gathered for period of 22 years. Tests of homogeneity and normality were conducted and results showed that data was homogeneous and normally distributed. For generating annual series, an Autoregressive first order model AR(1) was used and for monthly evaporation series method of fragments was used. Fifty replicates for annual series, and fifty replicates for each month series, each with 22 values length, were generated. Performance of the models was evaluated by comparing the statistical parameters of the generated series with those of the historical data. Annual and monthly models were found to be satisfactory in preserving the statistical parameters of the historical series. About 89% of the tested values of the considered parameters were within the assigned confidence limits

Keywords

References

  1. Abraham, B. and Ledolter, J. (1983) Statistical Method for Forecasting, New York, John Wiley & Sons, Inc
  2. Al-Eid, A. (1993). Stochastic Generation of Evaporation Sequences, M.S. Thesis, King Saud University, Riyadh
  3. Al-Qahtany, S. (1998). Evaporation Analysis as Affected by Climatic Factors in Bishah, Saudi Arabia, Senior Project in Civil Engineering Department in King Saudi University, Rayadh, Saudi Arabia
  4. Al-solai, A. (1993). Stochastic Generation of Annual and Monthly Rainfall Sequences, M.S. Thesis, King Saud University, Riyadh, June
  5. Bender, M. and Simonovic, S. (1994). Time Series Modeling for Long Range Stream Flow Forecasting, Journal of Water Resources Planning and Management, Vol. 120, No. 6, Nov-Dec. p. 857-870 https://doi.org/10.1061/(ASCE)0733-9496(1994)120:6(857)
  6. D'Agostino, R.B. and Stephens, M.A. (1986). Goodness-of-Fit Techniques, Marcel Dekker, Inc., New York
  7. Elsner, J., Kara, A.., and Owens, M. (1999). Fluctuations in North Atlantic Hurricane Frequency, Journal of Climate, Vol. 12,No.2, Feb. p. 427-437 https://doi.org/10.1175/1520-0442(1999)012<0427:FINAHF>2.0.CO;2
  8. Gupta, R. (1989). Hydrology and Hydraulic Systems, Prentice Hall, Inc., Englewood, New Jersey
  9. Kontsoyiannis, D. (1992). A Non-Linear Dis- Aggregation Method With a Reduced Parameter Set for Simulation of Hydrologic Series, Journal of Water Resource Research, Vol. 28, No. 12, Dec. p. 3175-3191 https://doi.org/10.1029/92WR01299
  10. Mood, A., Graybill, F. and Boes, D., (1974). Introduction to the Theory of Statistics, McGraw-Hill International Editions, p. 564
  11. Nicks, A., and Harp, J., (1980). Stochastic Generation of Temperature and Solar Radiation Data, Journal of Hydrology, Vol. 48, Jan. p. 1-17 https://doi.org/10.1016/0022-1694(80)90062-1
  12. Salas, J., Delleur, J., Yevjevich, V. and Lane, W., (1980). Applied Modeling of Hydrologic Time Series, Water Resources Publications, Colorado, USA
  13. Srikaanthan, R. and McMahon, T. (1983). Stochastic Simulation of Evaporation Data for Australia, Nordic Hydrology, p. 207-228
  14. Tsakiris, G. (1986). Stochastic Generation of Monthly Potential Evaporation in Arid and Semi-Arid Regions, Arabian Journal for Science and Engineering, Vol. 11, No. 4, p. 371-380