AffiliationLaboratory of Tree-Ring Research, The University of Arizona, Tucson, Arizona, 85721
KeywordsWater resources development -- Arizona.
Hydrology -- Arizona.
Hydrology -- Southwestern states.
Water resources development -- Southwestern states.
Time series analysis
Analysis of covariance
Principal components analysis
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RightsCopyright ©, where appropriate, is held by the author.
Collection InformationThis article is part of the Hydrology and Water Resources in Arizona and the Southwest collections. Digital access to this material is made possible by the Arizona-Nevada Academy of Science and the University of Arizona Libraries. For more information about items in this collection, contact firstname.lastname@example.org.
PublisherArizona-Nevada Academy of Science
AbstractStatistical analyses of existing hydrologic records suffer from the problem that such records are of relatively short duration, and therefore may not necessarily be random samples of the infinite population of events. On the hypothesis that tree-ring series and runoff series respond to a common climatic signal or signals that permit prediction of annual runoff from annual ring-width index, tree-ring data are used to extend available runoff records backwards in time to permit more accurate estimates of the 3 most common statistics used in hydrology: the mean, the variance and the 1st order correlation. It is assumed that both series are generated by the climatic parameters of precipitation, temperature, evapotranspiration, seasonal regime and spatial distribution. Of major concern in the reconstruction of annual runoff series from tree-ring records was the difference in persistence within each of the 2 series. A matrix of the tree-ring data was constructed, lagged up to 3 times and principal components were extracted. The covariation in this matrix was then decomposed by extracting the Eigen-vectors, and multiple regression was then used to weight the respective series and the differences in persistence were determined. This method was applied to watersheds of diverse characteristics and improved estimates of the mean and variance were obtained.
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