MyLake

General Information
Acronym of the model	MyLake
Full name of the model	Multi-year simulation model for Lake thermo- and phytoplankton dynamics
Model components	Hydrodynamics, Chemistry, Biology
Supported platforms	Windows, Mac, Linux
Programming Language	MATLAB
Still maintained	Yes, by: Raoul-Marie Couture (et al.)
Most recent version	MyLake v1.2 (public); MyLake v2.0 & MyLake-C
Model structure
Needs compilation
1D
Other: Flexible grid
Model description
Model objective	Investigate, predict, or test hypotheses related to lake hydrodynamics and ice cover
Specific application	“The MyLake lake model is best suited for applications having the following characteristics: 1) the geometry of the lake basin lends itself to the 1D assumption, which neglects lateral heterogeneity, or if you can accept the limitation of working with a single 1D profile; 2) you favour short integration time over model complexity; 3) you do not need complex lake physics or ecological modelling (e.g., saline or groundwater intrusions, reservoir management, food-web, etc.); 4) you want access to the source code and would like to modify it yourself if necessary; 5) the lake experiences seasonal ice cover.” (Couture & Tominaga 2016)
Background knowledge needed to run model	Basic knowledge of MATLAB programming
Basic procedures	For one model time step(24 h):* 1) Calculate surface heat fluxes and wind stress, light attenuation, and phytoplankton growth and loss rates 2) Calculate profile of the diffusion coefficient K 3) Calculate the heat flux between water and sediment (taken as heat source/sink for each layer) 4) Solve new profile for each state variable taking into account advection, diffusion and local sources/sinks. Solving is done in following order: 1) temperature, 2) tracers, 3) chlorophyll-a, 4) particulate phosphorus, 5) dissolved phosphorus. 5) Update content of the sediment stores of chlorophyll a and particulate phosphorus A) If no ice: i) Check for (and possibly proceed) autumn/spring turnover B) If ice cover: a) If Ta<Tf (freezing) i) Calculate ice surface temperature (depending on snow cover, or ice thickness if snow is absent) ii) Calculate snow ice formation in case of isostatic imbalance iii) Calculate congelation ice growth by Stefan’s law iv) Accumulate new snow fall and subtract formed snow ice from snow cover b) If Ta≥Tf (melting) i) Melt snow or ice from top with total surface heat flux C) Melt ice from bottom with the heat diffused to the surface layer (keeping the surface layer temperature at freezing point) D) Update snow density 6) Add river inflow and update profiles of the state variables accordingly 7) Mix unstable layers until stable density profile 8) Mix water layers with the available turbulent kinetic energy TKE (TKE=0 under ice cover) 9) Check for supercooled layers and turn them into (initial) ice 10) Save results to output matrices (Return to Start) (Table 1, from Saloranta & Andersen 2004)
“MyLake (Multi-year simulation model for Lake thermo- and phytoplankton dynamics) is a one-dimensional process-based model code for simulation of daily 1) vertical distribution of lake water temperature and thus stratification, 2) evolution of seasonal lake ice and snow cover, and 3) phosphorus-phytoplankton dynamics. MyLake has a relatively simple and transparent model structure, it is easy to set up, and is suitable both for making predictions and scenarios, and to be used as an investigative tool. Short runtime allows application of comprehensive sensitivity and uncertainty analysis as well as simulation of a large number of lakes or over long periods (decades). MyLake aims to include only the most significant physical, chemical and biological processes in a well-balanced and robust way.” (Saloranta & Andersen 2004)
Link to website/manual	Model Source Code Manual
Model characteristics
Input variables	Obligatory: – Lake morphometry and initial profiles - Meteorological forcing (air temperature, air pressure, wind speed, relative humidity, precipitation, irradiance) - Inflow loadings (water volume, particulates, total nutrients) Optional:
Input file format	.xls Other, namely: MyLake v2.0 requires .txt
Output variables	– Temperature profile - Snow and ice cover (presence/absence, thickness, temperature) - Nutrient profiles - Chlorophyll-a profile - Light attenuation profile - Sediment heat flux
Output file format	Other, namely: .mat (can be exported from MATLAB as .csv or other file format)
Biogeochemical model components	- Nutrients: inorganic P, particulate P - Phytoplankton: phytoplankton biomass C, measured as chlorophyll-a and dissolved inorganic phosphorus (phosphate) P; fixed C:P composition - Zooplankton: not included in model
Model structure/mathematical framework	Partial differential equations
Temporal resolution	Daily
Minimal spatial resolution	0.5 m
Variables needing calibration	– Primary: water temperature (dissolved oxygen in v2.0) - Additional: nutrients, chlorophyll-a
Has successfully been used in
Climate Change Scenario	Couture et al. 2015 Kiuru et al. 2018
Oligotrophic water	unpublished application
Mesotrophic water	Kiuru et al. 2018
Has coding for	Ice dynamics, sediment heat flux, sediment dynamics
Accessibility
Open-Source, Open-to-use, Test cases available
Available tools for pre- and post-processing	Some exemplary tools from individual applications available at https://github.com/biogeochemistry
Support	Support
How can someone get access to this model	To gain access to the Mylake private GitHub send your github username to: raoul.couture@chm.ulaval.ca
Miscellaneous
Comments	Development of new versions and revisions often occur in parallel due to the open source nature of the model. The MyLake GitHub keeps these rather up-to-date with relevant applications, publications, and manuals for new versions.
Useful tricks and hints	Publicly-available application of MyLake in R
Links	Manual MyLake GitHub Toolbox
Form was updated: 2019-07-22

Reference list:

Saloranta & Andersen 2007 (https://doi.org/10.1016/j.ecolmodel.2007.03.018)
Gebre et al. 2014 (doi:10.5194/tc-8-1589-2014)
Couture et al. 2015 (https://doi.org/10.1002/2015JG003065)
Kiuru et al. 2018 (https://doi.org/10.1029/2018JG004585)
Couture et al. 2018 (https://doi.org/10.1016/j.scitotenv.2017.11.303)