Models to predict organic content of lake sediments

The aim of this study is to quantify and rank variables of significance to predict mean organic content (IG, loss on ignition) of surficial (0–1 cm) lake sediments in small glacial lakes. Various hypotheses concerning the factors regulating IG in lakes were formulated and tested. Different statistical tests were used to separate random influences from causal influences. The best model provides an r2-value (r = the correlation coefficient) of 0.83 when model data are compared to empirical data. This model is based on five readily available, standard map parameters: the percent of till and mires in the drainage area, the size and relief of the catchment, and the volume development (= the form factor), linked to resuspension and form and size of the lake. Each of these variables only provides a limited degree of (statistical) explanation of the variability in IG among the lakes. The predictability of the model can not be improved by accounting for the zonation problem, (i.e., the distribution of the characteristics in the drainage area, as given by the Drainage Area Zonation-method, DAZ), or for water chemical variables (pH, alkalinity, conductivity, total-P, colour, etc.). The model allows mean values of IG to be estimated from readily available data of “geological” characteristics of the lake and its drainage area. The variability in IG from other factors/variables, such as specific anthropogenic sources, etc. may then be quantitatively differentiated from the impact of these “geological” factors. These predictive models are, to the best of the authorʹs knowledge, the first ever to be presented for IG. These empirical models can only, however, be used to predict IG for lakes of the same lake type, and for lakes with model variables within the range of the given model variables.