Table 9.3.1. gives a matrix of correlations between ionic component concentrations, fluvial parameters of the upper Parsêta, and the independent variables. The analysis of part A of the matrix leads to the conclusion that the discharge remains the most important factor responsible for changes in the concentration of the listed components (except Mg^{2+}). The small number of significant coefficients of correlation for precipitation indices may be taken to suggest a substantial transformation of precipitation water in the catchment system. Part B of the correlation matrix shows that the dependences are inversely proportional for dissolved substances, and directly proportional for solid particles. After the preliminary analysis of the independent variables and selection of those that turned out to be significant, possible predictive models of the dependent variables under study were determined (Table 9.3.2) using the procedure of multiple regression with a stepwise elimination of variables. Most of the models thus derived are weakly predictive. In the case of sodium and potassium ions, the models are more sensitive than those based solely on variations in water discharge. There is a notable lack of hydrometeorological indices in predictive models for calcium and sulphate ions (Table 9.3.2a) and a total lack of significant dependences for magnesium ions. Better fits of predictive models were obtained for AL and SiO_{2}, and poorer for Cd, Cs and Cb (Figs. 9.3.1a, 9.3.2a, 9.3.3a, 9.3.4a, 9.3.5a, Table 9.3.2b).

Precipitation is the main factor responsible for the dynamics of supply, transformation and carrying away of material in a river catchment system. The reason why a group of precipitation indices forms a pattern of time intervals of up to one week, and usually up to four days, seems to lie in the catchment's retaining capacity which defines the duration of rainwater circulation in the form of overland flow and throughflow. The lengthening of the 4-day impact of retention in the upper Parsêta catchment is the result of overlap of such factors as the intensity of rain, dryness or moisture of the soil covers, hydrogeological conditions of the substratum, and the distance of the particular subcatchments from the Storkowo measurement profile. The influence of 20-day precipitation indices on the transport of chemical substances is especially well marked in the predictive model for potassium ions. The appearance in the predictive models of precipitation indices of varying time intervals makes it possible to draw conclusions about the productivity of flood discharges.

A verification procedure (e.g. residual variable analysis - Figs. 9.3.1b, 9.3.ba, 9.3.3b, 9.3.4b, 9.3.5b, 9.3.6, 9.3.7, Table 9.3.3) has shown that for five dependent variables (AL, SiO_{2}, HCO_{3}^{-}, K^{+} and SO_{4}^{2-}) the predictive models constructed can be used in forecasting (Table 9.5.1).