GLM

General Information
Acronym of the model	GLM
Full name of the model	General Lake Model
Model components	Hydrodynamics
Supported platforms	Windows, Mac, Linux
Programming Language	C
Still maintained	Yes, by: AED research group, University of Western Australia & Broader community
Most recent version	GLM V3.1.1 2020
Model structure
Executables are available
1D
Other: Flexible grid (Lagrangian)
Model description
Model objective	Simulate water balance and vertical distribution of temperature, salinity and density in lakes and reservoirs
Specific application	– description of model structure (Hipsey et al. 2018) - stress-testing the model across lakes (Bruce et a. 2018) - cross-continental application (Read et al. 2014)
Background knowledge needed to run model	- Knowledge on physical processes in lakes - a bit of programing to handle input/output files, e.g., R, Python or Matlab
Basic procedures	1. Optional: Run one of the example simulations. 2. Prepare the input files for your lake (.csv file with meteorological data, .csv files with inflow and outflow data, a master text (.nml) file including lake morphometry and lake location, run time set up and initial conditions) 3. Check that format and units of your input data are correct 4. Split input and monitoring data into two time frames. 5. Run the model (glm.exe) for the first time frame. 6. Compare model results and observations (first water balance, then temperature profiles and (if applicable) salinity profiles) and calibrate model parameters (e.g., mixing parameters, layer thickness, extinction coefficient, wind factor). 7.Use the second time period to validate the model, i.e. compare observations and model results without further calibrating the model. 8.Optional: Use your model set-up to run scenarios.
GLM is a one-dimensional open source hydrodynamic model. It simulates temperature stratification in lakes. GLM has a broad community behind it. Thus, it is relatively easy to find support. There are R packages available to run the model and teaching modules have been developed that explain the model application. By now, GLM has been applied to many lakes and reservoirs around the world. GLM can be coupled with the biogeochemical model AED.
Link to website/manual	Website
Model characteristics
Input variables	Obligatory: – Bathymetry: depth [m] and area of the lake at that depth [m2] - Meteorological data: mean air temperature (°C), mean wind speed (m/s), mean shortwave radiation (W/m²), mean longwave radiation (W/m²) or cloud cover (fraction coverage), mean relative humidity (%), total rain fall (m/day); all in an hourly or daily resolution - Inflow data: volume [ML/day], salinity [ppt], temperature [°C]; daily resolution - Outflow data: volume [ML/day]; daily resolution - Initial profile: depths of the data points, temperature [°C], salinity [ppt] - configuration of the model, e.g. time step, mixing parameters, extinction coefficient Optional:
Input file format	.csv
Output variables	temperature, salinity, density, heat fluxes, water volume
Output file format	.netcdf or .csv
Biogeochemical model components	NA. Can be coupled to biogeochemical models through FABM
Model structure/mathematical framework	Partial differential equations
Temporal resolution	Hourly to daily
Minimal spatial resolution
Variables needing calibration	– extinction coefficient - mixing parameters - wind factor
Has successfully been used in
Climate Change Scenario	Fenocchi et al. 2018, Hansen et al. 2017, Huang et al. 2017
Shallow Lake/Reservoir	Read et al. 2014
Deep lake/Reservoir	Bruce et al. 2018, Read et al. 2014
Oligotrophic water	Bruce et al. 2018
Mesotrophic water	Bruce et al. 2018
Eutrophic water	Bruce et al. 2018
Countries in which the model has been applied	Australia, Canada, China, Denmark, France, Germany, Ireland, Israel, Italy, New Zealand, Spain, Switzerland, United Kingdom, USA
Which institutes have applied the model	Aquatic Ecodynamics Research Group, University of Western Australia
Has coding for	Ice dynamics
Accessibility
Open-Source, Prompt based, Test cases available
Available tools for pre- and post-processing	R-package (glmtools) Matlab scripts
Support	Developers website , where you can find the manual and get a very good model overview Getting started Website with the source code and manual User forum Short guide on how to use the model Teaching module
Can be coupled to the following models	AED all models that couple via FABM
How can someone get access to this model	Website Github
Miscellaneous
Comments	GLM is a community-supported open source model. There are many different source for help. If you have problems with running the model, you can check the vast information on the developer website, or you can search the user forum. It is very likely that your problem has been discussed already. If not, don’t be shy and post a question. The GLM user community is very active and helpful. Also check the links listed in this model entry.
Links	Developer’s website Website with the source code and manual User forum Short guide on how to use the model R-package to work with and run the model R-package to run the model Teaching module Distributed computing support GLM is a one-dimensional model, so that it has its limitations in applicability.
Form was updated: 2018-10-22

Reference list:
Beecham, S. “Development of an agreed set of climate projections for South Australia Final Report.” Goyder Institute for Water Research Technical Report Series 15/3 (2015).

Bueche, Thomas, et al. “Using the General Lake Model (GLM) to simulate water temperatures and ice cover of a medium-sized lake: a case study of Lake Ammersee, Germany.” Environmental Earth Sciences 76.13 (2017): 461.

Bruce, Louise C., et al. “A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network.” Environmental Modelling & Software 102 (2018): 274-291.

Carey, Cayelan C., et al. “Simulation modeling of lakes in undergraduate and graduate classrooms increases comprehension of climate change concepts and experience with computational tools.” Journal of Science Education and Technology 26.1 (2017): 1-11.

Fenocchi, Andrea, et al. “Relevance of inflows on the thermodynamic structure and on the modeling of a deep subalpine lake (Lake Maggiore, Northern Italy/Southern Switzerland).” Limnologica-Ecology and Management of Inland Waters 63 (2017): 42-56.

Fenocchi, Andrea, et al. “Forecasting the evolution in the mixing regime of a deep subalpine lake under climate change scenarios through numerical modelling (Lake Maggiore, Northern Italy/Southern Switzerland).” Climate Dynamics (2018): 1-16.

Frassl, Marieke, et al. “The General Lake Model (GLM). In Obrador, B., Jones, ID and Jennings, E.(Eds.) NETLAKE toolbox for the analysis of high-frequency data from lakes (Factsheet 3).” (2016).

Hansen, Gretchen JA, et al. “Projected shifts in fish species dominance in Wisconsin lakes under climate change.” Global change biology 23.4 (2017): 1463-1476.

Hipsey, M. R., et al. “A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)”. Geoscientific Model Development Discussions, (2017): https://www.geosci-model-dev-discuss.net/gmd-2017-257/

Huang, Lei, et al. “The Warming of Large Lakes on the Tibetan Plateau: Evidence From a Lake Model Simulation of Nam Co, China, During 1979–2012.” Journal of Geophysical Research: Atmospheres (2017).

Menció i Domingo, Anna, et al. “Groundwater dependence of coastal lagoons: the case of La Pletera salt marshes (NE Catalonia.” Journal of Hydrology, 2017, vol. 552, p. 793-806 (2017).

Read, Jordan S., et al. “Simulating 2368 temperate lakes reveals weak coherence in stratification phenology.” Ecological modelling 291 (2014): 142-150.

Read, Jordan S., et al. “Generating community-built tools for data sharing and analysis in environmental networks.” Inland Waters 6.4 (2016): 637-644.

Rose, Kevin C., et al. “Climate‐induced warming of lakes can be either amplified or suppressed by trends in water clarity.” Limnology and Oceanography Letters 1.1 (2016): 44-53.

Snortheim, Craig A., et al. “Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake.” Ecological Modelling 343 (2017): 39-53.

Subratie, Kensworth C., et al. “GRAPLEr: A distributed collaborative environment for lake ecosystem modeling that integrates overlay networks, high‐throughput computing, and WEB services.” Concurrency and Computation: Practice and Experience 29.13 (2017).

Weber, M., et al. “Optimizing withdrawal from drinking water reservoirs to reduce downstream temperature pollution and reservoir hypoxia.” Journal of environmental management 197 (2017): 96-105.

Yao, H., et al. “Comparing ice and temperature simulations by four dynamic lake models in Harp Lake: past performance and future predictions.” Hydrological processes 28.16 (2014): 4587-4601.