Abstract. The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.

a b s t r a c tChanges in water temperatures resulting from climate warming can alter the structure and function of aquatic ecosystems. Lake-specific physical characteristics may play a role in mediating individual lake responses to climate. Past mechanistic studies of lake-climate interactions have simulated generic lake classes at large spatial scales or performed detailed analyses of small numbers of real lakes. Understanding the diversity of lake responses to climate change across landscapes requires a hybrid approach that couples site-specific lake characteristics with broad-scale environmental drivers. This study provides a substantial advancement in lake ecosystem modeling by combining open-source tools with freely available continental-scale data to mechanistically model daily temperatures for 2368 Wisconsin lakes over three decades . The model accurately predicted observed surface layer temperatures (RMSE: 1.74 • C) and the presence/absence of stratification (81.1% agreement). Among-lake coherence was strong for surface temperatures and weak for the timing of stratification, suggesting individual lake characteristics mediate some -but not all -ecologically relevant lake responses to climate.Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

Highlights  The General Lake Model (GLM) is stress tested against 32 globally distributed lakes.  There was low correlation between input data uncertainty and model performance.  Model performance related to lake-morphometry, light extinction and flow regime; deep, clear lakes with high residence times had the lowest model error.

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The physical dynamics of lake temperature and ice phenology are important in the modelling and management of temperate aquatic ecosystems. One-dimensional hydrothermal lake models have not been well evaluated in terms of how they simulate ice dynamics in particular. We chose four models (Hostetler, Minlake, Simple Ice Model or SIM and General Lake Model) to test and compare their performance modelling water temperature and ice dynamics using 16 years of field data from Harp Lake, an extensively studied inland lake in south-central Ontario. Each model produced satisfactory water temperature profiles over the simulated period, with small differences in the model performance. Model fits for ice phenology and ice thickness were, however, considerably lower than those for water temperature, with Minlake generating the best agreement with observed ice-on and ice-off dates as well as ice thickness, followed by SIM. The responses of lake ice dynamics to future climate scenarios were simulated by running each of the four models for 91 years, from 2010 to 2100. The predicted decrease in ice season length was significantly different among models, varying between 30 and 81 days, with an average of 48 days. Corresponding decreases in ice thickness varied between 0.11 and 0.20 m, averaging 0.17 m. This study demonstrates that uncertainty due to model performance and selection is considerable, and further testing and refinement of hydrothermal lake dynamic models are needed to improve predictive abilities for ice dynamics. Figure 7. Ice phenology (modelled versus observed) for four models: Hostetler (a), Minlake (b), GLM (c) and SIM (d), in terms of three ice features: ice thickness (i), ice-on date (ii) and ice-off date (iii) 4597 COMPARING ICE AND TEMPERATURE SIMULATIONS BY LAKE MODELS