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. Cortés Cortés, Alicia Rueda Valdivia, Francisco José Hipsey, Matthew R. Lake physics General Lake Model (GLM) Global Lake Ecological Observatory Network (GLEON) Model portability Stress-testing GLM development and funding support for LCB, BDB, CB and MRH was provided by the Australian Research Council (ARC) (grants DP130104078 & LP130100756). 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. 2026-02-18T07:36:48Z 2026-02-18T07:36:48Z 2018-02-14 journal article Published version: Bruce, L. C., M. A. Frassl, G. B. Arhonditsis, G. Gal, D. P. Hamilton, P. C. Hanson, A. L. Hetherington, J. M. Melack, A. Cortés, and others. 2018. A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network. Environ. Model. Softw. 302:274-291, doi: 10.1016/j.envsoft.2017.11.016 https://hdl.handle.net/10481/111129 10.1016/j.envsoft.2017.11.016 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier