MODELS TO PREDICT WATER CHEMICAL CLUSTER VARIABLES

This study is an attempt to quantify and rank variables of significanc e to predict mean values of lake pH and related variables (alkalinity, conductivity, hardness, etc.) in small glacial lakes. The work is bas ed on a new and extensive set of data from 95 Swedish lakes and their catchment areas. Several empirical models based on catchment and lake morphometric parameters have been presented. These empirical models ca n only be used to predict mean values of these variables for lakes of the same type, and these models based on ''geological'' map parameters can evidently not be used for highly time-dependent and site-typical predictions. Various hypotheses concerning the factors regulating the mean values of the cluster variables were formulated and tested. Diffe rent statistical tests were used to separate random influences from ca usal. The most important ''map parameters'' were: the percent of rocks and open (= cultivated) land in the so-called near area to the lake [ as determined with the drainage area zonation (DAZ) method], mean dept h, linked to resuspension and the form and size of lakes, relief of th e drainage area and lake area. Each of these variables only provides a limited degree of (statistical) explanation of the variability in mea n annual values of pH and the water chemical cluster variables among t he lakes. The predictability of some of the models can be markedly imp roved by accounting for the distribution of the characteristics in the drainage area. The variability in mean annual values of pH (and relat ed variables) from other parameters, such as specific anthropogenic lo ad, etc., may then be quantitatively differentiated from the impact of these ''geological'' parameters. This paper also gives a simple metho d to estimate natural, preindustrial reference values of these water c hemical variables from the presented models.