Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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Probabilistic Forecasting of Seasonal Inflow to Reservoir

계절별 저수지 유입량의 확률예측

  • Kang, Jaewon (International Water Resources Research Institute, Chungnam National University)
  • 강재원 (충남대학교 국제수자원연구소)
  • Received : 2012.12.28
  • Accepted : 2013.04.17
  • Published : 2013.08.31
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Abstract

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

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References

  1. Kim, H. S., Park, J. U., Kim, J. H., 1998, Hurst Phenomenon in Hydrologic Time Series, Journal of The Korean Society of Civil Engineers, 18(II-6), pp. 571-582.
  2. Allison, P.D., 1999, Logistic Regression Using the SAS System: Theory and Application, SAS Institute Inc.
  3. Awadallah, A.G., Rousselle, J., 2000, Improving Forecasts of Nile Flood Using SST Inputs in TFN Model, Journal of Hydrologic Engineering, 5(4), pp. 371-379. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:4(371)
  4. Bender, M., Simonovic, S., 1994, Time-Series Modeling for Long-Range Streamflow Forecasting, Journal of Water Resources Planning and Management, 120(6), pp. 857-870. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:6(857)
  5. Beran, J., 1994, Statistics for Long-memory Processes, Chapman and Hall.
  6. Dalezios, N.R., Tyraskis, P.A., 1989, Maximum Entropy Spectra for Regional Precipitation Analysis and Forecasting, Journal of Hydrology, 109, pp. 25-42. https://doi.org/10.1016/0022-1694(89)90004-8
  7. Davis, J.C., 1986, Statistics and Data Analysis in Geology, John Wiley & Sons.
  8. Efron, B., Tibshirani, R.J., 1993, An Introduction to the Bootstrap, Chapman & Hall.
  9. Garen, D.C., 1993, Improved Techniques in Regressionbased Streamflow Volume Forecasting, Journal of Water Resources Planning and Management, 118(6), pp. 654-670.
  10. Montanari, A., Rosso, R., Taqqu, M.S., 1997, Fractionally Differenced ARIMA Models Applied to Hydrologic Time Series: Identification, Estimation, and Simulation, Water Resources Research, 33(5), pp. 1035-1044. https://doi.org/10.1029/97WR00043
  11. Panofsky, H.A., Brier, G.W., 1968, Some Applications of Statistics to Meteorology, The Pennsylvania State University.
  12. Pelletier, J.D., Turcotte, D.L., 1997, Long-range Persistence in Climatological and Hydrological Time Series: Analysis, Modeling and Application to Drought Hazard Assessment, Journal of Hydrology, 203, pp. 198-208. https://doi.org/10.1016/S0022-1694(97)00102-9
  13. Piechota, T.C., Chiew, F.H.S., Dracup, J.A., McMahon, T.A., 1998, Seasonal Streamflow Forecasting in Eastern Australia and the El Nino-Southern Oscillation, Water Resources Research, 34(11), pp. 3035-3044. https://doi.org/10.1029/98WR02406
  14. Sharma, A., 2000a, Seasonal to Interannual Rainfall Probabilistic Forecasts for Improved Water Supply Management: Part 1 - A Strategy for System Predictor Identification, Journal of Hydrology, 239, pp. 232-239. https://doi.org/10.1016/S0022-1694(00)00346-2
  15. Sharma, A., 2000b, Seasonal to Interannual Rainfall Probabilistic Forecasts for Improved Water Supply Management: Part 3 - A Nonparametric Probabilistic Forecast Model, Journal of Hydrology, 239, pp. 249-258. https://doi.org/10.1016/S0022-1694(00)00348-6
  16. Sharma, A. Luk, K.C., Cordery, I., Lall, U., 2000, Seasonal to Interannual Rainfall Probabilistic Forecasts for Improved Water Supply Management: Part 2 - Predictor Identification of Quarterly Rainfall Using Ocean-Atmosphere Information, Journal of Hydrology, 239, pp. 232-239. https://doi.org/10.1016/S0022-1694(00)00346-2
  17. Simpson, H.J., Cane, M.A., Herczeg, A.L., Zebiak, S.E., Simpson, J.H., 1993, Annual River Discharge in Southeastern Australia Related to El Nino-Southern Oscillation Forecasts of Sea Surface Temperatures, Water Resources Research, 34(11), pp. 3035-3044.
  18. Strang, G., 1986, Introduction to Applied Mathematics, Wellesley-Cambridge Press.
  19. Teverovsky, V., Taqqu, M.S., 1997, Testing for Longrange Dependence in the Presence of Shifting Means or a Slowly Declining Trend Using a Variance Type Estimator, Journal of Time Series Analysis, 18(3), pp. 279-304. https://doi.org/10.1111/1467-9892.00050
  20. Wilks, D.S., 1995, Statistical Methods in the Atmospheric Sciences: An Introduction, Academic Press.