8258 - Laserfiche WebLink

Home Browse Search

e has a lo~er measure of error (in this case, a lower residual mean square) than would an equation with a poorer fit. Our second criterion was that the equa- tion minimize combinations of strongly interacting inde~endent variables, as defined by a correlation of r >0.60. Given these criteria, we tempered their strict application by the philosophy that "a relationship may be statistically significant without being sub- stantively il"portant" (Milliken and Johnson 1984). Lystrom, et al. also chose their best models based on other-than-statistical criteria; that is, "con- ceptual knowledge of the water-quality process." In other words, if a regression was best statistic- ally, but we could find no conceptual reason for the association of its basin attributes with water quality, we chose a statistically less good but conceptually more sensible model. RESULTS From IS subbasin values for each of a selected set of basin attributes, and eighteen values of three water quality parameters taken one at a time, we obtained three regression models with significant and conceptually acceptable relations between the attributes and the parameter (table 3). Table 3.--Multiple-regression models of basin attributes associated with water quality in the Green River basin. e ADJUSTED R2 RESIDUAL MEAN SQUARE II ATTRIBUTES CONSIDERED PHOSPHORUS CONCENTRATION (mg/l) = -0.144 + 0.563(K FACTOR) +0.0393(FLOOD RATIO) REGRESSION EQUATION 0.97S/0.000/24 NITRATE CONCENTRATION(mg/l) a -2.30 + 2.7l(FLOOD RATIO) + 0.0043(CRETACEOU~ ROCK [mi )) 0.893/0.442/5 TDS LOAD (tons/year) . 9730 + 36.5(TOTAL LENGTH OF CHANNiLS [mil) + 493(IRRIGATED CROPLAND !mi )) + 135(MlXED RANGELAND [mi ]) 0.993/2.21xlOS/27 In table 3, FLOOD RATIO is the quotient of the average la-year flood divided by the maximUIII dis- charge recorded for the study period (water years 1965 to 1979). The 5 attributes considered for the nitrate concentration model are a nonintercorrelated subset of an original set of 16 attributes2contain- ing some highly intercorrelated members (r >0.60). The results of the regression analyses illus- trate the application of our criteria for accept- ~ ance of a regression model. PHOSPHORUS CONCENTRA- ,.., TIOH had a relatively low intercorrelacion ratio (0,323). and the variables first selected by the regression analysis made sense conceptually. (The intercorrelation ratio is t2e number of attribute significant correlations [r >.60] divided by the number of interactions in the correlation matrix.) TDS LOAD, on the other hand, had a high inter- correlation ratio (0.567), but since the variables first selected by the analysis, which implies that they were statistically best, also were concept- ually related to TDS, we accepted this model as best. TOTAL LENGTH OF CWl\~ELS is~ however, cor- related with IRRIGATED CROPLAND (R =0.74), so some caution should be used when applying this model. NITRATE C6NC~~TION illustrates conceptual acceptability over statistical significanc~. The intercorrelation ratio was comparable to that of PHOSPHORUS CONCENTRATION, but the initial, or best statistical, regression analysis yielded MEAN JULY MAXIMUM TEMPERATURE as the only significant asso- ciated attribute. Because we could think of no process associating temperature with NITRATE CON- CENTRATION in the basin, we sequentially removed intercorrelated variables and continued regression analyses after each deletion. The best regression we found then, was the one in a set of conceptuall~ acceptabl~ models which had the highest adjusted R , and lowest, residual mean square. DISCUSSION Green River Regression Models The PHOSPHORUS CONCENTRATION model is concept- ually acceptable because phosphorus (as total phos- phorus, measured by the U.S. Geological Survey) is associated with particulate matter in streams. Since K FACTOR is a measure of soil erodability, and FLOOD'RATIO and estimate of flooding intensity, we may expect that an increase of either or them could be associated with an increase in partic- ulates and therefore total phosphorus in streams. For NITRATE CONCENTRATION, positive assoc- iation of a geologic variable (CRETACEOUS ROCK) and an estimate of flood intensity (FLOOD RATIO) with dissolved nitrate in a river would be expected if the Cretaceous rock bears minerals high in nitrate or perhaps other nitrogenous compounds. The pred- ictors and their relationship to nitrate COncentra- tion are therefore conceptually acceptable, although we have not ye~ investigated whether the mineral components of the Cretaceous rock formations in the subbasins are in fact nitrogeneous. The TDS LOAD model was the only model incor- porating land use parameters as predictors of water quality. A positive association of TDS with irri- gated cropland is not unexpected, since TDS in- creases from 223 mg/l (3359 tons/year) above an irrigated area on the Big Sandy River to 2630 mg/l (147,000 tons/year) below it. A disturbed MIXED RANGELAND also could increase TDS LOAD if infil- tration increased as a result of reduced plant cover. Increases in TOTAL LENGTH OF CHANNELS may imply an increased chance of infiltrating precipitation being captured by a stream and measured in a sample, rather than beins "lost" to deeper sroundwater. 4, ., "" ,"" Application of the Techniques There are several advantages in applying mult- 204