and 0.997 for data from the Platte River. Analysis of the state error covariance matrix for both rivers
indicates that some reduction in the number of parameters associated with the difference equation
formulated for mode 1 dynamics is possible.
The extended Kalman filter developed in this report provides a basis for projecting ice-affected
streamflow at other gaging stations by adjusting filter parameters to site-specific conditions. The
filter can project daily mean flow during periods of ice effects by use of real-time climatological and
hydrological data.
LITERATURE CITED
Bar-Shalom, Y., and Li Xiao-Rong (1993) Estimation and Tracking: Principles, Techniques, and Software.
Boston, Massachusetts: Artech House.
Bozic, S.M. (1994) Digital and Kalman Filtering. New York: Halsted Press, 2nd edition.
Brown, R.G., and P.Y.C., Hwang (1997) Introduction to Random Signals and Applied Kalman Filtering.
New York: John Wiley, 3rd edition.
Condes de la Torre, A. (1994) Operation of hydrologic data-collection stations by the U.S. Geological
Survey in 1993. U.S. Geological Survey, Open-File Report 94-84.
Grewal, M. S., and A.P. Andrews (1993) Kalman Filtering Theory and Practice. Englewood Cliffs, New
Jersey: Prentice Hall Information and System Sciences Series.
Holtschlag, D.J. (1996) A dynamical-systems approach for computing ice-affected streamflow. U.S.
Geological Survey, Water-Supply Paper 2473.
Melcher, N.B., and J.F. Walker (1992) Evaluation of selected methods for determining streamflow
during periods of ice effects. U.S. Geological Survey, Water-Supply Paper 2378.
Mendel, J.M. (1995) Lessons in Estimation Theory for Signal Processing, Communications, and Control.
Englewood Cliffs, New Jersey: Prentice Hall PTR.
Novak, C.E. (1985) WRD data reports preparation guide. U.S. Geological Survey, Open-File Report
85480.
Weiss, M.A. (1996) Algorithms, Data Structures, and Problem Solving with C++. Menlo Park, California:
Addison-Wesley Publishing.
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