Abstract
AbstractWe analyzed interannual and seasonal regimes of river runoff, precipitation, and air temperature for three nested regions: (1) the Upper Peninsula of Michigan, (2) the U.S. portion of the Great Lakes Basin, and (3) glaciated portions of the northeastern U.S. spanning from the Dakotas to New England. Data sources included historical records of 50 to 105 years duration from 45 USGS gauging stations and 198 U.S. National Climate Network stations, and satellite-derived estimates of global monthly precipitation gridded at 2.5 resolution for 1979-2008 (partly data about precipitations were collected and passed for analysis by Glenn Hodgkins from USGS). We examined the spatiotemporal variability of climate characteristics for these regions as a multidimensional structure obtained from empirical data using factor analysis. The structure consisted of a few (2 to 7) centers of variability for each characteristic, and reflected the diversity of landscapes within the regions examined. Trends and regime shifts for mutual time intervals of river runoff, precipitation and temperature showed different direction of changes. The results obtained at the three scales were generally in agreement.
Highlights
Boris Shmagin & Carol Johnston, South Dakota State University, Nir Y
A factor is a portion of a quantity, usually an integer or polynomial that, when multiplied by other factors, gives the entire quantity
Factor analysis allows the determination of common axes influencing sets of independent measured sets
Summary
A factor is a portion of a quantity, usually an integer or polynomial that, when multiplied by other factors, gives the entire quantity. The determination of factors is called factorization (or sometimes "factoring"). It is usually desired to break factors down into the smallest possible pieces so that no factor is itself factorable. Factor analysis allows the determination of common axes influencing sets of independent measured sets. It is "the granddaddy” of multivariate techniques The main applications of factor analytic techniques are:. (1) to reduce the number of variables and (2) to detect structure in the relationships between variables, that is to classify variables
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