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

AbstractLakes that are pivotal for recreation and economically relevant activities are often remote and not very 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. Then, simple relationships between the fitted parameters and lake-catchment morphometric properties are derived. The results of simulations using fitted and derived parameters are then 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 oC 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 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: LakesLake modelingoxythermal habitatsclimate change impactmodel calibrationdata-poor lakesDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsWe thank Koji Tominaga (Nanyang Technological University, Singapore) and Benjamin Laken (Cervest Inc., London, United-Kingdom) for the retrieval and preparation of the climate data. 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.