Predicting organic carbon in lakes from climate drivers and catchment properties

We combine a synoptic data set on organic carbon (C) concentrations in similar to 1000 Norwegian lakes with detailed information on catchment properties derived from digital maps of elevation, climate, vegetation density, and land use. The resulting regression model explains 83% of the variance in total organic C from eight predictor variables: mean air temperature, area specific runoff, terrain slope, vegetation density, atmospheric nitrogen deposition, water residence time, and catchment area fractions of bogs and arable land. Vegetation density, expressed as the normalized difference vegetation index (NDVI), was by far the strongest predictor, while area fraction of bogs in the catchment also gave a strong positive effect on organic C concentrations and export fluxes. The unbiased sampling of lakes relative to whole population of catchments in the region and the general robustness of the model, as witnessed by imputation and resampling tests, allowed confident prediction of organic C concentrations and export fluxes for all 20,000 catchments of the Norwegian mainland. The method used in this study can, with some modifications, be extended to larger geographical regions and holds promise for predicting organic C fluxes under changed climatic conditions.