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\, e Table 2.--Major categories of basin attributes for the Green River drainage, the number of variables originally within each category, and some examples of independent variables from each category. Basin attribute cate~orv GEOLOGY Number of variables 51 Examples Glacial area Tipton shale area Area of Precamb- rian rock Soil pH K factor Hean minimum-temp Area juniper % area of juniper Total range area Bifurcation ratio Total stream length 10 year flood cfs SOILS 19 CLIMATE ) LAND COVER/LAND USE 75 HYDROLOGY 16 Number of variables 164 Geology We calculated areas of all geological forma- tions shown on three hydrologic investigations maps (Welder and HcGreevy 1966, Whitcomb and Lowry 1968, and Welder 1968). The area of each formation in each of 18 subbasins (see Dependent Variables) were recorded and areas of geologically similar forma- tions summed as independent variables. Soils - From Young and Singleton (1977) we found which soil series were represented in soil associations in the watershed and the area of each association in each subbasin. From corresponding soil series data sheets supplied by Hunn (1984), we calculated and weighted the characteristics of all soil series within each association by area to obtain the sub- basin values. Climate Haps from Lowers (1960) were enlarged and minimum-maximum temperatures, weighted by area, calculated for each subbasin. Land Cover/Land Use Anderson et al. (1984) complied a land cover map of Wyoming from which we obtained values of cover, weighted by area, for each subbasin. Hydrology Hydrological variables were estimated using data taken from U.S" Geological Survey 1:250,000 scale topographic maps of the basin. Areas were obtained with the digitizer, and linear measures . with a map measuring wheel. Transformations and calculations were performed within Lotus spread- sheet f nes. e Reduction of Number of Independent Variables We reduced the number of independent variables form the original 164 by first eliminating vari- ables which were percentages or sums of other vari- ables (except for Geological variables, where we kept the sums and eliminated their co~ponents). We make a further reduction in the number of variables by dropping variables which were not significantly related to a water quality variable (p=0.05) in a simple bivariate regression. Thus, for every de- pendent water .quality variable, we ha~ a unique set of independent basin attributes for the multiple regression analysis. Dependent Variables The Wyoming Water Research Center maintains a copy of the U.S." Geological Survey's surface water quality and discharge data for Wyoming. From this we extracted all water quality data for all sampling stations in the watershed. We selected stations with the greatest number of acceptable water quality parameters. A water quality parameeters was accept- able if it had at least seven years of data between water years 1965 (when Flaming Gorge Reservoir's dam closed) and 1979, with at least one year of data comprised of ten or more samples. Using,these cri- teria, we found only eight water quality variables for at each of eighteen stations. The areas above these eighteen stations also defined the subbasins for which we complied basin attribute values. The concentration of many water quality para- meters depends upon discharge (Lystrom, et al.). For these parameters, mean loads should be calc- ulated as the sum of instantaneous loads derived from the concentration/discharge relationship. For the parameters considered in this report [phos- phorus (P), nitrate nitrogen (NO)), and total dis- solved solids (TOS)], only TOS concentration showed such a significant relation. We therefore used TDS loads, and phosphorus and nitrate concentrations as our dependent variables in the multiple regression analyses. Regression Each of the three water quality parameters had a unique set of associated independent variables. A Pearson correlation analysis (Nie, et al. 1975) was used to investigate intercorrelations among the independent variables prior to the regression anal- ysis. For the regression method we chose Hull and Nie's (1981) stepwise NEW REGRESSION, with prob- abilities of F-to-enter and I-to-remove at default values of 0.05 and 0.10 respectively. All SPSS analyses were conducted on a Control Data Corpora- tion Cyber 760 computer. Our interpretation of regression results to find the "best" association of water quality with basin attributes hinged on two objective criteria and one somewhat philosophical principle. Our first criterion was that a good regression equation explains the most of the variance about the 2epen- dint vuillbl. (i.... 1'1.. . hlahu aclju.hd R ). .~~. ., , '--: 203