This study quantifies and ranks variables of significance to predict mean values of Secchi depth in small glacial lakes. The work is based on a new, extensive set of data from 88 Swedish lakes and their catchments. Several empirical models based on catchment and lake morphometric parameters are presented. These empirical models can only be used to predict Secchi depth for lakes of the same type, and the models based on “geological map” parameters can evidently not be used for time-dependent and site typical predictions of Secchi depth. However, many of the principles behind the results ought to be valid for lakes in general. Various hypotheses concerning the factors regulating the variability in mean Secchi depth among lakes are formulated and tested. The most important variables are: Lake colour (expressing allogenic input of different types of humic materials), total-P and lake temperature (measures of production of autogenic materials). The most important “map” parameters are: The mean depth (linked to resuspension and lake morphometry) and the ratio between the drainage area and lake area (expressing the linkage between catchment and lake). The predictability of some of the models cannot be markedly improved by accounting for the distribution of the characteristics in the drainage area (using the drainage area zonation technique). The variability in mean Secchi depth from other factors, such as precipitation and anthropogenic load, may then be quantitatively differentiated from the impact of these “geological” factors, which can statistically explain 68% of the variability in Secchi depth among these lakes. The model based on map parameters can also be used to estimate natural, preindustrial reference values of Secchi depth.
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Håkanson, L. Models to predict Secchi depth in small glacial lakes. Aquatic Science 57, 31–53 (1995). https://doi.org/10.1007/BF00878025
- Secchi depth
- predictive models
- water chemistry
- catchment characteristics
- natural Secchi depth