By G. F. Koltun
Prepared in cooperation with the
Ohio Department of Transportation, and the
U. S. Department of Transportation, Federal Highway Administration
Water-Resources Investigations Report 03-4164
Regional equations for estimating 2-, 5-, 10-, 25-, 50-, 100-, and 500-year
flood-peak discharges at ungaged sites on rural, unregulated streams in Ohio
were developed by means of ordinary and generalized least-squares (GLS) regression
techniques. One-variable, simple equations and three-variable, full-model equations
were developed on the basis of selected basin characteristics and flood-frequency
estimates determined for 305 streamflow-gaging stations in Ohio and adjacent
states. The average standard errors of prediction ranged from about 39 to 49
percent for the simple equations, and from about 34 to 41 percent for the full-model
equations. Flood-frequency estimates determined by means of log-Pearson Type
III analyses are reported along with weighted flood-frequency estimates, computed
as a function of the log-Pearson Type III estimates and the regression estimates.
Values of explanatory variables used in the regression models were determined from digital spatial data sets by means of a geographic information system (GIS), with the exception of drainage area, which was determined by digitizing the area within basin boundaries manually delineated on topographic maps. Use of GIS-based explanatory variables represents a major departure in methodology from that described in previous reports on estimating flood-frequency characteristics of Ohio streams.
Examples are presented illustrating application of the regression equations to ungaged sites on ungaged and gaged streams. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site on the same stream.
A region-of-influence method, which employs a computer program to estimate flood-frequency characteristics for ungaged sites based on data from gaged sites with similar characteristics, was also tested and compared to the GLS full-model equations. For all recurrence intervals, the GLS full-model equations had superior prediction accuracy relative to the simple equations and therefore are recommended for use.
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