Towards modeling data-poor lakes at the regional scale using parameters from data-rich lakes and relationships to lake characteristics

Lakes pivotal for recreation and economically relevant activities are often remote and not well studied, which hinders the application of predictive lake models for their management. Here, we provide an approach to simulate-by means of the process-oriented model MyLake-water temperature, ice cover duration, dissolved oxygen, and light attenuation in 198 data-poor lakes based on parameters obtained for a subgroup of 12 data-rich lakes and morphometric data. Specifically, the model is first calibrated using a genetic algorithm on well-studied lakes. Simple relationships between the fitted parameters and lake-catchment morphometric properties are then derived, and the results of simulations using fitted and derived parameters are compared. The loss in goodness-of-fit, expressed as root mean square error (RMSE) incurred by using estimated rather than calibrated parameters, is 0.17 C-degrees for water temperature and 0.82 mg L-1 for dissolved oxygen. These general relationships are then used to provide the model parameters for 198 data-poor lakes distributed throughout Sweden and to model these lakes. Overall, this proof of concept allows simulating lakes selected based on their relevance for lake management rather than based on the availability of extensive field datasets.