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

ABSTRACT

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 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.

KEYWORDS:

• climate change impact
• data-poor lakes; lake modeling
• lakes
• model calibration
• oxythermal habitats

Acknowledgements

We thank Koji Tominaga (Nanyang Technological University, Singapore) and Benjamin Laken (Cervest Inc., London, United-Kingdom) for the retrieval and preparation of the climate data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

RMC acknowledges funding from the Sentinel North program of Université Laval, made possible in part thanks to funding from the Canada First Research Excellence program. Support from the Natural Sciences and Engineering Research Council of Canada, through the Discovery Grant program, from the Advancing climate science in Canada project “Changing carbon sinks in subarctic Canada” and from the Institut nordique du Québec (INQ) is also acknowledged. GE, DOH, TA and AGF acknowledge support from the Research Council of Norway projects #224779 and #221410.