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.. e elevation of 13804 feet (4207m). Mean elevation is 7416 feet (2260m). Sixty percent of the drainage is underlain by Tertiary formations. and extensive areas of Green River shale. Though poor water quality has not been a problem in the upper reaches of the basin. the lower reach of the Green River shows a large increase in salinity load as dissolved solids (DeLong 1977). Flaming Gorge Reservoir. immediately downstream of our study area shows sporadic, though increasingly severe. summer eutrophication which has affected adversely both fishing and body- contact recreation (U.S. Environmental Protection Agency 1977, Southwestern Wyoming Water Quality Planning Association 1978, Fannin 1983, Parker, et ale 1984). The low human population density, few industries or facilities requiring surface water discharge permits (Wagner 1984), and relatively high proportion of agricultural land use support the observation that non-point sources are respon- sible for 88% of the phosphorus input to Flaming Gorge Reservoir (Southwestern Wyoming Water Quality Planning Association 1978). e No systematic basin-wide investigation of the origin of dissolved and suspended substances in the Green River has yet been done. Such a study would be quite useful as a baseline study, both in the accumulation and organization of existing data about water quality and its sources, and in relat- ing present associations of water quality to basin characteristics. Practical applications of such knowledge would be apportioning loadings to a spec- ific source area of the drainage, predicting changes in water quality from changes in basin characteristics such as land use. or investigating if associations of water quality with basin char- acteristics change with time. Perhaps one of the reasons such a basin-wide investigation has not been done is the sheer size of the area. However, Lystrom, et ale (1978) pro- posed and useq a multiple regression modeling approach to associate various 2asin parameters with water quality in the 27,510 mi Susquehanna River watershed. We report here the results of an invest- igation of the association of watershed character- istics with water quality in the Green River basin of Wyoming and Utah. using a similar multiple reg- ression technique. The objectives of this project are to: 1) associate attributes of the Green River watershed with water quality in the Green River system using multiple regression. A prerequisite to this objective is the collec- tion and organization of water quality data and information about the basin which could conceivably affect water quality. 2) estimate water quality changes in Flaming Gorge Reservoir which may be associated with upstream basin characteristics. e 3) achieve these objectives by analyzing data .xi.tins in publi.hed record.. report., paper., and maps. No field work is required. In this paper, we will: 1) demonstrate and document I)ur application of multiple regression to associate water quality with attributes of the Green River basin. 2) Propose possible causes of such significant water quality/basin attribute-associations. 3) Discuss the general advantages and disad- vantages of using multiple regression tech- niques to model water quality in the basin. In conducting this research we assumed that water quality is indeed a function of physical, chemical and biological characteristics of the drainage, that multiple regression is suited for associating such characteristics with water qual- ity, and that non-point sources are paramount in determining water quality in the watershed. MATERIALS AND METHODS Regression Models Multiple linear regression describes variation of a single dependent variable as a function of variations in several independent variables. In this case, a single water quality parameter is the dependent variable, and its variation is accounted for by the variation in two or more independent variables of physical. chemical. or biological basin characteristics. The general equation (from Edwards 1979) is: Y' = a+blXl+b2X2+...bkXk where Y' is the dependent variable. X's are the independent variables, k the number of independent variables in the equation, and a is the regression constant. By choosing appropriate independent vari- ables (basin parameters), we seek to maximize the correlation between the predicted value of our water quality variable and the actual value of the variable. The basis of our choice of independent variables derives from an interpretation of results from an SPSS (Nie, et ale 1975) multiple regression program, as detailed in Regression, below. Independent Variables In this paper. we've defined an independent variables as the unique numerical measure of some feature of the drainage basin. The five major types of independent variables (also referred to as "basin attributes"), detailed in table 2, roughly correspond to those of Lystrom, et al., but the individual attributes within each of our categories were dictated by the data available for the Green River basin. Much of the data from which we derived basin attributes had to be transformed from maps. charts, or lists. We used a COMPAQ microcomputer with a Houston Instruments ll"xll" digitizer to measure areas from maps, and the LOTUS 123 software (Lotus, Dlvelopment Corporation 1983) to Itore and m.nip~ ., ulate collected information. Sources of info~n and a description of their transformation into in- dependent variables follov. 202