A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network
Bruce, Louise C.; Frassl, Marieke A.; Arhonditsis, George B.; Gal, Gideon; Hamilton, David P.; Hanson, Paul C.; Hetherington, Amy L.; Melack, John M.; Read, Jordan S.; Rinke, Karsten; Rigosi, Anna; Trolle, Dennis; Winslow, Luke; Adrian, Rita; Ayala, Ana I.; Bocaniov, Serghei A.; Boehrer, Bertram; Boon, Casper; Brookes, Justin D.; Bueche, Thomas; Busch, Brendan D.; Copetti, Diego; Cortes, Alicia; de Eyto, Elvira; Elliott, J. Alex; Gallina, Nicole; Gilboa, Yael; Guyennon, Nicolas; Huang, Lei; Kerimoglu, Onur; Lenters, John D.; MacIntyre, Sally; Makler-Pick, Vardit; McBride, Chris G.; Moreira, Santiago; Özkundakci, Deniz; Pilotti, Marco; Rueda, Francisco J.; Rusak, James A.; Samal, Nihar R.; Schmid, Martin; Shatwell, Tom; Snorthheim, Craig; Soulignac, Frédéric; Valerio, Giulia; van der Linden, Leon; Vetter, Mark; Vincon-Leite, Brigitte; Wang, Junbo; Weber, Michael; Wickramaratne, Chaturangi; Woolway, R. Iestyn; Yao, Huaxia; Hipsey, Matthew R.
Accepted version, 1.769Mb
Bruce, L. C., Frassl, M. A., Arhonditsis, G. B., Gal, G., Hamilton, D. P., Hanson, P. C., … Rinke, K. (2018). A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network. Environmental Modelling & Software, 102, 274–291. https://doi.org/10.1016/j.envsoft.2017.11.016
Permanent Research Commons link: https://hdl.handle.net/10289/13165
The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required.
This is an author’s accepted version of an article published in the journal: Environmental Modelling & Software. © 2018 Elsevier.