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GENERATED MONTHLY STREAMFLOWS FOR THE MIDWESTERN AND SOUTHWESTERN UNITED STATES 1
Author(s) -
Rogers Jerry R.,
Gemmell Robert S.
Publication year - 1971
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1971.tb05791.x
Subject(s) - streamflow , skewness , environmental science , flow (mathematics) , hydrology (agriculture) , distribution (mathematics) , linear regression , stream flow , lag , climatology , mathematics , statistics , geography , geology , drainage basin , cartography , mathematical analysis , computer network , geometry , geotechnical engineering , computer science
A critical examination of single gage site, monthly streamflow statistical characteristics for two southern Illinois rivers, an Oklahoma river and a Texas river was made using a digital computer at Northwestern University. High flow variability for the rivers was evident in that, for the rivers tested, 8 to 11 months had coefficients of variation in excess of unity. The gamma distribution was not as efficient as the normal distribution for fitting power or logarithmic transforms of the historical monthly flow data (i.e., F 1‐0 , F 0‐5 , F 0‐25 , Fa 125 , F 0.085 , and log F). No single transform to a normal distribution was adequate for all twelve monthly flows, since definite seasonal grouping patterns were found for the four rivers examined. The highly variable flow in the low‐flow season(s) indicated much more skewness than was typical of the remainder of the year. For the low‐flow seasons, the higher‐root (smaller exponent) transforms were particularly useful. Flows were generated from a linear regression model of lag one utilizing two or more transforms for the twelve periods. The definite seasonal patterns found historically were reproduced quite well in the generated streamflows. The effect of a change in data transform from one season to the next was insignificant after one month. Thus the use of different transforms within the year did not bias the results from the linear regression model appreciably, but did help in reproducing the seasonal distribution pattern. The technique seems especially well suited for rivers with highly variable flows.