Improved Techniques in Regression‐Based Streamflow Volume Forecasting
Publication: Journal of Water Resources Planning and Management
Volume 118, Issue 6
Abstract
Although multiple linear regression has been used for many years to predict seasonal streamflow volumes, typical practice has not realized the maximum accuracy obtainable from regression. Several techniques can help provide superior forecast accuracy using regression models: (1) Using only data known at forecast time; (2) principal components regression; (3) cross validation; and (4) systematic searching for optimal or near‐optimal combinations of variables. Using no future data requires that a separate equation be used each month that forecasts are made rather than using a single equation throughout the forecast season. Consistency of month‐to‐month forecasts can be obtained by judicious selection of variables to maintain a high degree of similarity in the monthly equations. Results for the South Fork Boise River at Anderson Ranch Dam and other basins in the West indicate that these new regression procedures can give substantial improvements in forecast accuracy over existing procedures without sacrificing month‐to‐month forecast consistency.
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References
1.
Barton, M., and Burke, M. (1977). “SNOTEL: An operational data acquisition system using meteor burst technology.” Proc., Western Snow Conference, 82–87.
2.
Crook, A. G. (1984). “The SNOTEL data acquisition system: A tool in runoff forecasting.” A critical assessment of forecasting in western water resources management; Proc., AWRA Symp., J. J. Cassidy and D. P. Lettenmaier, eds., Seattle, Wash., 25–30.
3.
Day, G. N. (1985). “Extended streamflow forecasting using NWSRFS.” J. Water Resour. Planning and Mgmt., ASCE, 111(2), 157–170.
4.
Druce, D. J. (1984). “Seasonal inflow forecasts by a conceptual hydrologic model for Mica Dam, British Columbia.” A critical assessment of forecasting in western water resources management; Proc. of AWRA Symp., J. J. Cassidy and D. P. Lettenmaier, eds., Seattle, Wash., 85–91.
5.
Haan, C. T. (1977). Statistical methods in hydrology. Iowa State University Press, Ames, Iowa.
6.
Haan, C. T., and Allen, D. M. (1972). “Comparison of multiple regression and principal component regression for predicting water yields in Kentucky.” Water Resour. Res., 8(6), 1593–1596.
7.
Kleinbaum, D. G., Kupper, L. L., and Muller, K. E. (1988). Applied regression analysis and other multivariable methods, 2nd Ed., PWS‐KENT Publishing Co., Boston, Mass.
8.
Koch, R. W. (1990). “Influences of climate variability on streamflow variability: Implications in streamflow prediction and forecasting.” Final report for grant award 14‐08‐0001‐G1316, U.S. Geological Survey, Washington, D.C.
9.
Krzysztofowicz, R. (1986a). “Expected utility, benefit, and loss criteria for seasonal water supply planning.” Water Resour. Res., 22(3), 303–312.
10.
Krzysztofowicz, R. (1986b). “Optimal water supply planning based on seasonal runoff forecasts.” Water Resour. Res., 22(3), 313–321.
11.
Kuehl, D. W. (1979). “Volume forecasts using the SSARR model in a zone mode.” Proc., Western Snow Conference, 38–47.
12.
Lins, H. F. (1985). “Interannual streamflow variability in the United States based on principal components.” Water Resour. Res., 21(5), 691–701.
13.
Marsden, M. A., and Davis, R. T. (1968). “Regression on principal components as a tool in water supply forecasting.” Proc., Western Snow Conference, 33–40.
14.
McCuen, R. H. (1985). Statistical methods for engineers. Prentice‐Hall, Englewood Cliffs, N.J.
15.
McCuen, R. H., Rawls, W. J., and Whaley, B. L. (1979). “Comparative evaluation of statistical methods for water supply forecasting.” Water Resour. Bulletin, 15(4), 935–947.
16.
McCuen, R. H., and Snyder, W. M. (1986). Hydrologic modeling: Statistical methods and applications. Prentice‐Hall, Englewood Cliffs, N.J.
17.
Pearson, T. (1974). “Simulating runoff to the Hungry Horse reservoir of western Montana.” Proc., Western Snow Conference, 96–102.
18.
Rallison, R. E. (1981). “Automated system for collecting snow and related hydro‐logical data in mountains of the western United States.” Hydrological Sci. Bulletin, 26(1), 83–89.
19.
Schermerhorn, V., and Barton, M. (1968). “A method for integrating snow survey and precipitation data.” Proc., Western Snow Conference, 27–32.
20.
“Snow survey and water supply forecasting.” (1972). National engineering handbook, section 22. Soil Conservation Service (SCS), U.S. Department of Agriculture, Washington, D.C.
21.
Speers, D. D., and Versteeg, J. D. (1982). “Runoff forecasting for reservoir operations—the past and the future.” Proc., Western Snow Conference, 149–156.
22.
Stedinger, J. R., Grygier, J., and Yin, H. (1988). “Seasonal streamflow forecasts based upon regression.” Computerized decision support systems for water managers; Proc. 3rd Water Resour. Operations and Mgmt. Workshop, ASCE, New York, N.Y., 266–279.
23.
Twedt, T. M., Schaake, J. C., Jr., and Peck, E. L. (1977). “National Weather Service extended streamflow prediction.” Proc., Western Snow Conference, 52–57.
24.
Wortman, R. T. (1989). “Statistical forecast model for Libby basin, Montana.” Proc., Western Snow Conference, 100–107.
25.
Wu, C. F. J. (1986). “Jackknife, bootstrap and other resampling methods in regression analysis.” The Annals of Statistics, 14(4), 1261–1295.
26.
Zuzel, J. F., and Cox, L. M. (1978). “A review of operational water supply forecasting techniques in areas of seasonal snowcover.” Proc., Western Snow Conference, 69–77.
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Copyright © 1992 ASCE.
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Published online: Nov 1, 1992
Published in print: Nov 1992
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