Time‐Series Modeling for Long‐Range Stream‐Flow Forecasting
Publication: Journal of Water Resources Planning and Management
Volume 120, Issue 6
Abstract
Currently used methods for long‐range water‐supply forecasting are compared with statistical time‐series tools, such as seasonal auto‐regressive integrated moving‐average modeling. Evaluation of several theoretical models under a range of flow conditions provided insight into development of a technique using engineering knowledge and experience to improve the quality of forecasts. Rules governing model selection are developed from analysis of forecast residuals within a sensitivity analysis. Context‐sensitive model selection provided a means of improving forecast accuracy. Increased confidence in the optimal forecasted operating and planning policies are consequences of improved forecasts. The modeling tools provide the means of evaluating the performance of long‐range monthly probabilistic stream‐flow forecasts at Manitoba Hydro. Manitoba Hydro is a large utility that operates a multireservoir electric‐power generation system. The needs and priorities of the system demand forecasts up to 1 year in advance for planning budgets and release policies.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Abraham, B., and Ledolter, J. (1983). Statistical methods for forecasting. John Wiley and Sons, New York, N.Y.
2.
Bowerman, B. L., and O'Connell, R. T. (1987). Time series forecasting. Duxbury Press, Boston, Mass.
3.
Box, G. L. P., and Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden‐Days, Inc., San Francisco, Calif.
4.
Fast, B. (1990). Analysis of seasonal inflow volume forecasts produced using regression equations. Operations Control Department, British Columbia Hydro.
5.
Georgakakos, A. P. (1989). “The value of streamflow forecasting in reservoir operation.” Water Resour. Bull., 25(4), 789–800.
6.
Georgakakos, K. P., and Smith, G. F. (1990). “On improved hydrologic forecasting—results from a WMO real‐time forecasting experiment.” J. Hydrol., 114, 17–45.
7.
McLeod, A. I., Noakes, D. J., Hipel, K. W., and Thompstone, R. M. (1987). “Combining hydrologic forecasts.” J. Water Resour. Plng. and Mgmt., ASCE, 113(1), 29–41.
8.
Mishalani, N. R., and Palmer, R. N. (1988). “Forecast uncertainty in water supply reservoir operation.” Water Resour. Bull., 24(6), 1237–1245.
9.
Newbold, P., and Granger, C. W. J. (1974). “Experience with forecasting univariate time series and the combination of forecasts.” J. Royal Statistics Soc., London, England, A(137), 131–165.
10.
Oron, G., Mehrez, A., and Rabinowitz, G. (1991). “Forecasting in optimizing dual system for energy generation and irrigation.” J. Water Resour. Plng. and Mgmt., ASCE, 117(3), 287–300.
11.
Puente, C. E., and Bras, R. L. (1987). “Application of nonlinear filtering in the real time forecasting of river flows.” Water Resour. Res., 23(4), 675–682.
12.
Salas, J. D., et al. (1980). Applied modelling of hydrologic time series. Water Resources Publications, Littleton, Colo.
13.
Shafer, B. A., and Huddleston, J. M. (1984). “Analysis of seasonal volume stream‐flow forecast errors in the western U.S.” Water Resour. Bull., Jun, 117–126.
14.
Tao, T., and Lai, L. M. S. (1990). “Design Specifications of Short Term Inflow Forecaster.” Internal Report DWP‐OP‐24, Operations Plng. Facilities Dept., Ontario Hydro, Toronto, Ontario.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 120 • Issue 6 • November 1994
Pages: 857 - 870
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: Mar 29, 1993
Published online: Nov 1, 1994
Published in print: Nov 1994
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.