Module 8: Lake Water Quality Modelling

Module Resources

Download the GLM files for this module by clicking the download button in the tool bar .

Setting up GLM

Running GLM on MacOS

  • Open the Terminal application on your Mac
  • Use the cd (change directory) command to navigate to the Kinneret97 folder
  • In Finder, go to the macGLM folder and right-click on the glm app, select Show Package Contents
  • Enter the Contents folder then the MacOS folder, and locate the glm executable file
  • Back in terminal, enter the file path for this executable file - you can do this by dragging/dropping the file into the terminal
  • Leave a space after the file path and type --xdisp
  • Press Enter and the model will open

Running GLM on Windows

On Windows, the model can be run using a batch-file glm.bat, which you can double click from in the Kinneret97 directory. The .bat file calls the GLM model from the current working directory.

If the model is not opening make sure you’re not working in a network directory (e.g. OneDrive) and the file path to the glm.bat file has zero spaces in it.

Running GLM in RStudio

If you want to use RStudio, open the R Project, then each of the R files: Get started.R, Change parameters.R and Plot.R. Run the lines in Get started.R - it will download all the R packages from the internet, as well as an old version of GLM.

To get the latest version of GLM, paste the folder winGLM into the place where R has saved its old version of winGLM, for example, H:/My Documents/R/win-library/3.4/GLMr/extbin/winGLM. After that you can drive the model and plot everything using the other two scripts.

GLM will not run on any versions of R newer than 4.0

Configuring Model Parameters

To configure the model, open the glm3.nml file in the Kinneret97 directory with a text editor. A chunk of this .nml* file is shown below. For example, if you wanted to change the maximum number of lake layers the model simulates, you would changemax_layers = 500`.

The easiest way to edit the glm3.nml file is with the Atom text editor. You can download it here. 
!------------------------------------------------------------------------------!
!                                                                              !
!          .----------------.  .----------------.  .----------------.          !
!          | .--------------. || .--------------. || .--------------. |        !
!          | |    ______    | || |   _____      | || | ____    ____ | |        !
!          | |  .' ___  |   | || |  |_   _|     | || ||_   \  /   _|| |        !
!          | | / .'   \_|   | || |    | |       | || |  |   \/   |  | |        !
!          | | | |    ____  | || |    | |   _   | || |  | |\  /| |  | |        !
!          | | \ `.___]  _| | || |   _| |__/ |  | || | _| |_\/_| |_ | |        !
!          | |  `._____.'   | || |  |________|  | || ||_____||_____|| |        !
!          | |              | || |              | || |              | |        !
!          | '--------------' || '--------------' || '--------------' |        !
!          '----------------'  '----------------'  '----------------'          !
!                                                                              !
!-------------------------------------------------------------------------------

!-------------------------------------------------------------------------------
! general model setup
!-------------------------------------------------------------------------------
&glm_setup
   sim_name = 'Example Kinneret GLM Simulation'
   max_layers = 500
   min_layer_vol = 0.025
   min_layer_thick = 0.15
   max_layer_thick = 1.50
   density_model = 1
/

Exercises

Lake Stratification

The simulation in the Kinneret97 directory is an example 40 m deep, monomictic lake that is used to supply drinking water and support an active fishery.

Before running the model, open the file glm3.nml in a text editor and inspect the model setup etc. This file is the main configuration file that drives the model. The file glm3.nml has several key sections in it related to the model domain and parameters, the sources of boundary condition data, and details of outputs. Scan the parameters, eg number of layers, light extinction coefficient, mixing coefficient etc.

Once the model has run, you can inspect the results. There are three ways to look at output:

  • Via the time-depth contour plots that appear during the run (configured via plots.nml)
  • Further information on the daily water and energy balance is in the output file lake.csv
  • Time series values of selected state variables (e.g. temperature, salinity, etc…) can be requested for a specific depth (depth above bottom) – this is setup in &outputs (eg. WQ_5.csv)
  1. Draw a schematic picture of the lake, the relevant boundary conditions (both related to water fluxes and energy fluxes).
  2. Identify and open the boundary condition input files - these are csv files containing time-series of meteorological information, and inflow and outflow volumes. These files can be opened in Excel or a text-editor. Find where these files are called from within glm.nml.
  3. Plot the lake water balance – volume, water level and fluxes including inflows, outflows, rainfall and evaporation (refer to columns in lake.csv, and copy the appropriate data into your own excel spreadsheet).
Schematic diagram of water balance components.

Figure 1: Schematic diagram of water balance components.

  1. Plot the surface heat fluxes such as shortwave, longwave, sensible heat latent heat (refer to columns in lake.csv).
Schematic diagram of surface heat fluxes impacting lake water temperature. For a description of the heat flux terms refer to the [GLM paper](https://gmd.copernicus.org/articles/12/473/2019/)

Figure 2: Schematic diagram of surface heat fluxes impacting lake water temperature. For a description of the heat flux terms refer to the GLM paper

  1. Go to the &output section of glm.nml and customize the configuration to make two output files, one at 5 m from the bottom (ie. in the lake hypolimnion) and one at 35m from the bottom (ie. in the lake epilimnion). This create two .csv files (WQ_5.csv and WQ_35.csv) with conditions at these water depths. Plot the temperate of the two layers in excel. Identify the periods where the lake is stratfied and mixed.
!-------------------------------------------------------------------------------
! format for output and filename(s)
!-------------------------------------------------------------------------------
&output
   out_dir = 'output'
   out_fn  = 'output'
   nsave = 6  !This will output every 6 hours
   ! General summary file
   csv_lake_fname = 'lake'
   ! Depth specific outputs
   csv_point_nlevs = 2 
   csv_point_fname = 'WQ_'
   csv_point_at = 5.,35.
   csv_point_nvars = 4
   csv_point_vars = 'temp','salt','PHS_frp','NIT_amm'
/
  1. Assess how the degree of stratification changes as the initial water level of the lake is reduced by 20 m. To do this change the initial starting lake depth and profile details in glm.nml.
You will need to update the output depth of your surface output file

The Ecosystem Model

The Kinneret97 model simulation above has the ecological model aed2 enabled. It has been pre- configured to run the customizable AED modules. These are separate models, but have been set up to run side by side. They are configured using the text file aed2.nml.

The above simulation was only plotting temperature and salinity and these plots were configured in plots.nml. Open plots.nml to see how you can customise the graphs. There are many other variables we may be interested in plotting when we run the watter quality model such as oxygen, nutrients, and algae. A pre-configured file called plots_aed2.nml has been created. To use this file, rename it to plots.nml (you’ll first need to rename the existing plots.nml) and run the model. You should now see more outputs for the lake simulation.

Let’s also now add water quality variables to the specific depth output files (i.e. WQ_5.csv). To do so we must edit the output section of the glm3.nml file, by adding the variables as extra columns to the .csv file that were configured above.

If they are there already, make sure that the number of variables is high enough to include them all

In plots.nml the required variables are all listed, make sure the number of plots is high enough to include them all.

Table 1: Water quality variables and their descriptions.
Description Variable Name
Passive tracer TRC_tr1
Water age TRC_age
Suspended solids NCS_ss1
Dissolved oxygen OXY_oxy
Dissolved inorganic carbon CAR_dic
pH CAR_pH
Methane (dissolved) CAR_ch4
Methane (bubbles) CAR_ch4_bub
Reactive Silica SIL_rsi
Ammonium NIT_amm
Nitrate/Nitrite NIT_nit
Phosphate PHS_frp
Particulate inorganic phosphorus PHS_frp_ads
Dissolved organic carbon OGM_doc
Particualte organic carbon OGM_poc
Dissolved organic nitrogen OGM_don
Dissolved organic nitrogen OGM_pon
Particualte organic phosphorus OGM_dop
Dissolved organic phsophorus OGM_pop
Green algae PHY_green
Green algae PHY_green_IN
Green algae PHY_green_IP
Diatoms PHY_diatom
Diatoms PHY_diatom_IN
Diatoms PHY_diatom_IP
Cyrptophytes PHY_crypto
Cyrptophytes PHY_crypto_IN
Cyrptophytes PHY_crypto_IP
Zooplankton ZOO_zoo01


Now run the model as above by double clicking the glm.bat.

  1. Create a well formatted, interesting graph of variables of your choice (for example, you may like to consider all of the phytoplankton groups, all of the nitrogen variables, or create sums of these to generate a NPZD model) to see how they interact and change over time. If any variables are much bigger or smaller than the others, then use two y axes or multiple plots.
  2. How would these concentrations change if the air temperature increased by 2°C? (You will need to edit the met_hourly.csv file). Make sure to save with correct date format, taking note these met measurements are recorded hourly, so YYYY-MM-DD hh:mm. A pre-made version of this file can be found in bcs which is called met_1997_2004_airT2.csv. To use this file, update meteo_fl = 'bcs/met_1997_2004.csv' in the &meteorology section of glm3.nml.
!-------------------------------------------------------------------------------
! meteorology
!-------------------------------------------------------------------------------
&meteorology
   met_sw      = .true.
   lw_type     = 'LW_IN'
   rain_sw     = .false.
   atm_stab    = 0
   fetch_mode  = 0
  !rad_mode    = 1
   albedo_mode = 1
   cloud_mode  = 4
   !-- BC file details
   subdaily    = .true.
   meteo_fl    = 'bcs/met_1997_2004.csv' <------- Update this!
   wind_factor = 0.9
   lw_factor   = 1.0
   lw_offset   = 0.0
   !-- Parameters
   ce = 0.0013
   ch = 0.0013
   cd  = 0.0013
  !catchrain      = .true.
  !rain_threshold = 0.001
  !runoff_coef    = 0.0
/
  1. What happens if we change the water clarity? Increase and decrease from the original value to see the response
You must change the light extinction coefficient Kw (range 0-1) in the glm.nml file.

Assessing Error & Sensitivity

  1. Calculate the mean absolute error in the simulation using the observed temperature data (ObservedTempData.xlsx from the data folder in Kinneret97) and the simulated temperature data (the simulated temperature data is from the surface layer in lake.csv).
\[\begin{equation} MAE = \frac{\sum_{i}\left | T_{i}^{sim}-T_{i}^{obs} \right |}{n} \tag{1} \end{equation}\]
  1. Calculate the sensitivity of the modelled temperature to changes in water clarity (the light extinction coefficient, \(K_w\)) and wind speed (wind_factor). These can be found in glm.nml. Try increasing and decreasing the default parameter value by 0.2 and see how much the output changes.
\[\begin{equation} SI = \frac{(Output_{new}-Output_{original})/Output_{original}}{(Parameter_{new}-Parameter_{original})/Parameter_{original}} \tag{2} \end{equation}\]
  1. Assess how sensitive the phytoplankton biomass is to water clarity:
GLM results Water clarity Decrease 0.37 Water clarity Original 0.57 Water clarity Increase 0.77
Average WQ_35 temperature
Average WQ_35 phytoplankton (green, crypto, diatom) biomass


  1. Assess how sensitive the phytoplankton biomass is to wind speed:
GLM results Wind Speed Decrease 0.37 Wind Speed Original 0.57 Wind Speed Increase 0.77
Average WQ_35 temperature
Average WQ_35 phytoplankton (green, crypto, diatom) biomass

Submission

Submit properly formatted graphs and tables of the following sections of the lab:

Lake Stratification:

  • Hand drawn diagram of the lake and any variables you used
  • Plots of the water balance
  • Plots of the heat fluxes
  • Plots of the temperature at different layers
  • Plots of the shallower lake

The Ecosystem Model:

  • Hand drawn diagram of the lake with any variables you used
  • Interesting plot of the selected variables
  • Any results of the higher air temperature and changed water clarity

Assessing Error & Sensitivity:

  • Temperature error assessment
  • Temperature and phytoplankton sensitivity assessment
  • Tables for submission

These are to be uploaded in a word doc or PDF format. No screenshots of figures from Excel/Excel spreadsheets to be uploaded

General professional formatting guidelines:

  • All figures are to have adequate captions explaining them
  • For graphs, figure captions go below the plot
  • For tables, the caption goes above the table
  • Make sure figures and their text size is readable

Excel hints:

  • When there is a caption for a plot, you remove the title
  • Remove the plot border and gridlines
  • Make sure both axes have visible lines and tick marks
  • Units need to be noted properly with the axis label - ‘Temperature (°C)’
  • Round numbers to be reasonable