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REGIONAL ANALYSES OF ST'REAMFLOW CHARACTERISTICS 9 . frequency curves that requires less work. , More commonly the short records in a re- gion are not closely correlated with longer ones; in practice few short records will be found that meet the criterion for extension. Thus a decision on whether to extend or not to extend may be required only infrequently. Frequency data for use in a regional analy- sis are often based on records for a selected period of years, called a base period. Adjust- ment of all records to a base period requires that parts of long records be discarded and that short records be extended. The objective of using a base period is to obtain a group of records all affected by the same weather oc- currences so that the differences among the frequency characteristics are largely due to differences in basin characteristics. This ob- jective mayor may not be met depending on the particular streamflow characteristic being studied and on the size of the region consid- ered. The records for a base period should pro- duce a regional regression relation with a smaller standard error than would records for periods of various lengths. However, the purpose of a regionalization is to average the variability due to random weather occur- rences. The more samples (in time) used the more likely will the average represent long- term conditions. But use of a base period minimizes the number of independent events and thus may produce a biased result. Ordinarily the flood-frequency characteris- tics should be defined by all the record avail- able at each site. If an extension of a record is made to improve the definition of the fre- quency curve, the extension should cover the entire length of the longer record, not just a part of it. Lack of independence of flood occurrences at the various sites used in a regional analy- sis has two effects: (1) The variability of the slope of the regression line is reduced, and (2) the variability of the intercept is in- creased; that is, the slope of the regional relation is better defined because of a depend- ence among stations but its position is less well defined (Matalas and Benson, 1961). For example, suppose that all the stations in ,a . . region are affected by the same storms, that the 20-year flood is defined at each station, and that these 20-year floods are related to basin characteristics. The resulting regres- sion equation may describe very well the rela- tive effects of the various basin characteris- tics on flood magnitude, but we do not know whether the magnitude is that of a 20-year flood or of one having a very different recur- rence interval because we have essentially only one sample of flood experience. In most parts of the United States, the longer flood records can not be considered homogeneous because of man-made changes in the flow regimen. It has been proposed that a hydrologic basin model be used to ad- just the annual floods of record to undevel- oped basin conditions. This would add con- siderable information for use in a regional analysis. Of course the results of the regional analysis would not apply to that particular stream under its existing pattern of regula- tion. Model and parameters The regression model used in regional flood- frequency analyses is of the form Qn = a A 'B'G" . . . . the log transform of which is linear. Selection of suitable independent variables is often made on a statistical basis; that is, many variables are used in preliminary regressions and those that lack statistical significance are discarded. This practice occasionally results in the retention of a variable whose effect in the regression does not conform to known hydrologic principles. Usually the effect of such a variable on the result is trivial (a few percent reduction in standard error). The fact that the particular variable does not ap- pear in regressions for other areas may indi- cate that it does not exert an effect of prac- tical significance. It seems desirable to select in advance those variables which are expected, on the basis of previous work, to have practical significance. However, some commonly used and widely accepted variable may not prove significant in a particular regression if the range in that variable is small. For example, channel slope