Model Library

General Lake Model (GLM)

Model name: General Lake Model (GLM)

Developed by: AED research group at the University of Western Australia with numerous collaborators from GLEON and AEMON (Last update: 2020)

Model type: 1D, hydrodynamic, lake water balance and stratification model

History: The core layer structure and mixing algorithms are founded on principles and experience from model platforms including the DYnamic REservoir Simulation Model (DYRESM) and the Dynamic Lake Model (DLM)

Computational requirements: Windows, Linux and Macintosh

Software requirements: GIS: optional

Link to download model

  • Source code
  • GLM+ model coupled with AED2 and AED2+ can be requested.

Capabilities and Limitations:

Capabilities

  • GLM is a simple, modernized model designed for ease of use and efficiency, featuring multiple customization options for surface heating, mixing, and inflow/outflow simulations (Bruce et al., 2018; Hipsey et al., 2019);
  • The latest version, GLM V3.0, includes the water quality model AED2 as a packaged binary (AED-UWA, 2020);
  • GLM can integrate with biogeochemical and ecological models to study temperature, stratification, and vertical mixing impacts on lake ecology dynamics (Bruce et al., 2018);
  • A teaching module was developed for undergraduates and graduates to explore lake responses to climate change (Carey and Gougis, 2017, Hipsey et al., 2019).

Limitations

  • The model is based on assumptions that are common to previous model applications (Bruce et al., 2018);
  • Its 1-D structure assumes minimal horizontal variation impacts on vertical transport (Bruce et al., 2018);
  • Wind effects, such as increased magnitude across a lake, are not captured using bulk drag coefficients. Accurate wind speed measurements and parameterization based on lake characteristics are crucial for improving model performance across diverse lake properties (Bruce et al., 2018);
  • GLM's prediction of ice-on and break-up dates requires refinement, as ice formation is sometimes simulated before surface temperatures reach 0 °C (Bueche et al., 2017).
  • The model is unable to capture spatial gradients in nutrient limitation (Wang and Boegman, 2021).

Model Inputs and Outputs:

Inputs

Morphometry, Meteorological data, Inflow data (flow, temperature, salinity & WQ), Outflow data (flow)

Outputs

The model conducts a lake mass and energy balance to compute vertical profiles of temperature, salinity and density while accounting for the effect of inflows and outflows, surface heating and cooling, mixing and ice cover on the lake.

Examples:

References

Wang, Q., & Boegman, L. (2021). Multi-Year Simulation of Western Lake Erie Hydrodynamics and Biogeochemistry to Evaluate Nutrient Management Scenarios. Sustainability, 13(14), 7516. https://doi.org/10.3390/su13147516

Bruce, L. C., Frassl, M. A., Arhonditsis, G. B., Gal, G., Hamilton, D. P., Hanson, P. C., et al. (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

Objectives

The aim of this study was to (1) calibrate AED–GLM using model independent parameter estimation; (2) apply the model to simulate the interannual variation in the development of nuisance algae blooms in Lake Erie since 1979; (3) evaluate the long-term effectiveness of nutrient load reductions and BMPs on reducing bloom severity.

The aims of the study were to: (1) Ascertain levels of model performance and relate it to model input uncertainty; (2) Identify lake attributes (e.g. depth, inflows, and climate) that correspond with high (or low) prediction accuracy; (3) Relate sensitivity of model output variables to changes in surface exchange, heating and mixing parameters that characterize 1D lake models; (4) Document the transferability of the model without recalibration of individual parameters among lakes, even where these lakes may strongly differ in their properties; (5) Provide guidance to lake modelers as to how to focus data collation and model application efforts to improve predictions for lake ecosystems.