README.md
In aemon-j/LakeEnsemblR: Run Ensemble of Lake Models
LakeEnsemblR 
Tools for running an ensemble of lake models using standardised input
data. Lake models currently incorporated are Freshwater Lake Model
(FLake), General Lake Model
(GLM), General Ocean
Turbulence Model (GOTM) (lake-branch),
Simstrat,
and MyLake.
You can find example setups here:
https://github.com/aemon-j/LER_examples.
Check out our published paper LakeEnsemblR: An R package that
facilitates ensemble modelling of
lakes.
In v1.1, some changes occurred in the configuration file. If you want to
adapt your existing setup, see:
https://github.com/aemon-j/LakeEnsemblR/wiki/From-v1.0-to-v1.1
Installation
Prior to installing the package, you will need to install the packages
which run the models and the tools for manipulating model data from
GitHub. It is recommended to do this each time before testing to ensure
all packages are up-to-date as parallel development might be ongoing.
# install.packages("remotes")
remotes::install_github("GLEON/rLakeAnalyzer")
remotes::install_github("aemon-j/GLM3r", ref = "v3.1.1")
remotes::install_github("USGS-R/glmtools", ref = "ggplot_overhaul")
remotes::install_github("aemon-j/FLakeR", ref = "inflow")
remotes::install_github("aemon-j/GOTMr")
remotes::install_github("aemon-j/gotmtools")
remotes::install_github("aemon-j/SimstratR")
remotes::install_github("aemon-j/MyLakeR")
Following this you can install LakeEnsemblR from Github with:
remotes::install_github("aemon-j/LakeEnsemblR")
Should you run into unexpected installation issues, please have a look
at this page:
https://github.com/aemon-j/LakeEnsemblR/wiki/Installation-issues
Visualize
You can download PyNcView,
a cross-platform NetCDF viewer, for viewing the NetCDF output.
Example model run
# Load LakeEnsemblR
library(LakeEnsemblR)
#>
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#> |_____\__,_|_|\_\___|_____|_| |_|___/\___|_| |_| |_|_.__/|_|_| \_\
#>
#>
#> https://github.com/aemon-j/LakeEnsemblR
# Copy template folder
template_folder <- system.file("extdata/feeagh", package= "LakeEnsemblR")
dir.create("example") # Create example folder
file.copy(from = template_folder, to = "example", recursive = TRUE)
#> [1] TRUE
setwd("example/feeagh") # Change working directory to example folder
# Set config file & models
config_file <- "LakeEnsemblR.yaml"
model <- c("FLake", "GLM", "GOTM", "Simstrat", "MyLake")
# Example run
# 1. Export settings - creates directories with all model setups and exports settings from the LER configuration file
export_config(config_file = config_file, model = model)
#> depths wtemp
#> 1 0.9 4.977
#> 2 2.5 4.965
#> 3 5.0 4.953
#> 4 8.0 4.941
#> 5 11.0 4.883
#> 6 14.0 4.887
#> 7 16.0 4.877
#> 8 18.0 4.986
#> 9 20.0 4.960
#> 10 22.0 4.944
#> 11 27.0 4.980
#> 12 32.0 4.951
#> 13 42.0 4.905
# 2. Run ensemble lake models
run_ensemble(config_file = config_file, model = model)
#> [1] "Running MyLake from 01/01/10 to 01/01/11..."
#> user system elapsed
#> 8.68 0.65 9.40
Post-processing
# Load libraries for post-processing
library(gotmtools)
library(ggplot2)
## Plot model output using gotmtools/ggplot2
# Extract names of all the variables in netCDF
ncdf <- "output/ensemble_output.nc"
vars <- gotmtools::list_vars(ncdf)
vars # Print variables
#> [1] "temp" "ice_height" "w_level"
p1 <- plot_heatmap(ncdf)
#> [,1]
#> short_name "temp"
#> units "degC"
#> dimensions "lon lat z model member"
p1
# Change the theme and increase text size for saving
p1 <- p1 +
theme_classic(base_size = 14) +
scale_colour_gradientn(limits = c(0, 21),
colours = rev(RColorBrewer::brewer.pal(11, "Spectral")))
p1
Plot Ensemble output
# Plot ensemble mean at 0.9m
model = c("FLake", "GLM", "GOTM", "Simstrat", "MyLake")
plot_ensemble(ncdf = ncdf, model = model, var = "temp", depth = 0.9)
# Load watertemp from netCDF file as a list
wtemp <- load_var(ncdf = ncdf, var = "temp", return = "list")
#> [,1]
#> short_name "temp"
#> units "degC"
#> dimensions "lon lat z model member"
names(wtemp)
#> [1] "FLake" "GLM" "GOTM" "Simstrat" "MyLake" "Obs"
# Plot residual diagnostic plots
plots <- plot_resid(var_list = wtemp) # Plot residuals - returns a list of plots
names(plots) #
#> [1] "obs_res" "res_depth" "yday_res" "res_dist"
# Plot residuals vs. depth ("res_depth")
plots[[2]]
# Analyse the netCDF output
out <- analyse_ncdf(ncdf, model = model, spin_up = 0)
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names(out)
#> [1] "out_df" "stats" "strat" "obs_temp"
str <- out[["strat"]]
str[str$year == 2010, ]
#> model year TsMax TsMaxDay TsMin TsMinDay TbMax TbMaxDay
#> 1 obs 2010 17.67833 179 3.48499990 13 12.760635 292
#> 3 FLake 2010 17.15400 210 3.30428004 56 6.201520 331
#> 4 GLM 2010 19.04128 172 0.01679616 359 5.007843 337
#> 6 GOTM 2010 15.52526 210 2.01707268 68 12.003445 276
#> 8 Simstrat 2010 18.85400 172 2.56730008 54 11.924000 286
#> 10 MyLake 2010 17.31223 209 1.93227196 8 10.061674 305
#> TbMin TbMinDay MaxStratDur MeanStratDur TotStratDur StratStart StratEnd
#> 1 3.463672 13 184 184.00 184 105 289
#> 3 3.457870 12 216 216.00 216 107 323
#> 4 2.675814 75 201 52.00 208 115 316
#> 6 2.131356 70 133 46.00 138 136 269
#> 8 2.598400 62 170 170.00 170 104 274
#> 10 3.168770 56 205 53.75 215 100 305
#> StratFirst StratLast MeanIceDur MaxIceDur TotIceDur IceOn IceOff FirstFreeze
#> 1 105 289 NA NA NA NA NA NA
#> 3 107 323 NA NA NA NA NA NA
#> 4 103 316 NA NA NA NA NA NA
#> 6 120 269 NA NA NA NA NA NA
#> 8 104 274 NA NA NA NA NA NA
#> 10 100 327 NA NA NA NA NA NA
#> LastThaw HiceMax HiceMaxDay
#> 1 NA 0 NA
#> 3 NA 0 NA
#> 4 NA 0 NA
#> 6 NA 0 NA
#> 8 NA 0 NA
#> 10 NA 0 NA
stats <- out[["stats"]]
stats
#> model Pearson_r Variance_obs Variance_mod SD_obs SD_mod Covariance
#> 1 FLake 0.8003687 20.90594 18.49540 4.573214 4.301486 15.73826
#> 2 GLM 0.8113353 17.35083 13.11618 4.165881 3.622016 12.23952
#> 3 GOTM 0.9619836 17.35899 14.37478 4.166858 3.791814 15.19604
#> 4 Simstrat 0.9380468 17.35899 14.56246 4.166858 3.816488 14.91434
#> 5 MyLake 0.9206550 17.35899 11.86025 4.166858 3.444240 13.21010
#> Bias MAE RMSE NSE lnlikelihood
#> 1 -2.044599 2.085880 3.479253 0.4209684 -17470.67
#> 2 -2.987429 3.045955 3.861698 0.1405189 -38978.62
#> 3 -2.142192 2.163849 2.435297 0.6583516 -18127.91
#> 4 -1.798785 1.886163 2.308331 0.6930471 -16722.49
#> 5 -1.922755 1.958657 2.548731 0.6257830 -19447.17
See the vignette for further
examples.
How do I setup LakeEnsemblR for my lake?
Head on over to our
website for guides
on how to set up your lake for LakeEnsemblR
How do I contribute new code back to the LakeEnsemblR project?
In order to contribute to this code, we recommend the following
workflow:
-
“fork” this repository to your own personal github account
-
clone the github repository to your computer:
\$git clone git@github.com:USER_NAME/LakeEnsemblR.git
-
modify code or add new functionality, save the code
-
add the repository master to a remote master called “upstream”
\$cd LakeEnsemblR
\$git remote add upstream git@github.com:aemon-j/LakeEnsemblR.git
-
before pushing your changes to your repository, pull in the current
version of the aemon-j master:
\$git fetch upstream
-
merge these differences with your own “master” version:
\$git merge upstream/master
-
push your changes to your github repository, in addition to changes
made by pulling in the aemon-j master:
\$git push
-
submit a pull request to aemon-j master using your account at
github.com
Information for macOS users
To ensure that all model binaries work, we advise you to install the
specific macOS packages (currently in a separated branch):
remotes::install_github("aemon-j/GLM3r", ref = "macOS")
remotes::install_github("aemon-j/FLakeR", ref = "macOS")
remotes::install_github("aemon-J/GOTMr", ref = "macOS")
remotes::install_github("aemon-j/SimstratR", ref = "macOS")
Unfortunately, this means your version of GLM will differ compared to
the one included in the executables for Windows and Linux. GLM on macOS
will be version 3.2.0a8 (most recent one on January 13, 2022),
Windows/Linux use 3.1.1.
aemon-j/LakeEnsemblR documentation built on April 11, 2025, 10:09 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
LakeEnsemblR
Tools for running an ensemble of lake models using standardised input data. Lake models currently incorporated are Freshwater Lake Model (FLake), General Lake Model (GLM), General Ocean Turbulence Model (GOTM) (lake-branch), Simstrat, and MyLake.
You can find example setups here: https://github.com/aemon-j/LER_examples.
Check out our published paper LakeEnsemblR: An R package that facilitates ensemble modelling of lakes.
In v1.1, some changes occurred in the configuration file. If you want to adapt your existing setup, see: https://github.com/aemon-j/LakeEnsemblR/wiki/From-v1.0-to-v1.1
Installation
Prior to installing the package, you will need to install the packages which run the models and the tools for manipulating model data from GitHub. It is recommended to do this each time before testing to ensure all packages are up-to-date as parallel development might be ongoing.
# install.packages("remotes")
remotes::install_github("GLEON/rLakeAnalyzer")
remotes::install_github("aemon-j/GLM3r", ref = "v3.1.1")
remotes::install_github("USGS-R/glmtools", ref = "ggplot_overhaul")
remotes::install_github("aemon-j/FLakeR", ref = "inflow")
remotes::install_github("aemon-j/GOTMr")
remotes::install_github("aemon-j/gotmtools")
remotes::install_github("aemon-j/SimstratR")
remotes::install_github("aemon-j/MyLakeR")
Following this you can install LakeEnsemblR from Github with:
remotes::install_github("aemon-j/LakeEnsemblR")
Should you run into unexpected installation issues, please have a look at this page: https://github.com/aemon-j/LakeEnsemblR/wiki/Installation-issues
Visualize
You can download PyNcView, a cross-platform NetCDF viewer, for viewing the NetCDF output.
Example model run
# Load LakeEnsemblR
library(LakeEnsemblR)
#>
#>
#> _ _ _____ _ _ ____
#> | | __ _| | _____| ____|_ __ ___ ___ _ __ ___ | |__ | | _ \
#> | | / _` | |/ / _ | _| | '_ \/ __|/ _ | '_ ` _ \| '_ \| | |_) |
#> | |__| (_| | | __| |___| | | \__ | __| | | | | | |_) | | _ <
#> |_____\__,_|_|\_\___|_____|_| |_|___/\___|_| |_| |_|_.__/|_|_| \_\
#>
#>
#> https://github.com/aemon-j/LakeEnsemblR
# Copy template folder
template_folder <- system.file("extdata/feeagh", package= "LakeEnsemblR")
dir.create("example") # Create example folder
file.copy(from = template_folder, to = "example", recursive = TRUE)
#> [1] TRUE
setwd("example/feeagh") # Change working directory to example folder
# Set config file & models
config_file <- "LakeEnsemblR.yaml"
model <- c("FLake", "GLM", "GOTM", "Simstrat", "MyLake")
# Example run
# 1. Export settings - creates directories with all model setups and exports settings from the LER configuration file
export_config(config_file = config_file, model = model)
#> depths wtemp
#> 1 0.9 4.977
#> 2 2.5 4.965
#> 3 5.0 4.953
#> 4 8.0 4.941
#> 5 11.0 4.883
#> 6 14.0 4.887
#> 7 16.0 4.877
#> 8 18.0 4.986
#> 9 20.0 4.960
#> 10 22.0 4.944
#> 11 27.0 4.980
#> 12 32.0 4.951
#> 13 42.0 4.905
# 2. Run ensemble lake models
run_ensemble(config_file = config_file, model = model)
#> [1] "Running MyLake from 01/01/10 to 01/01/11..."
#> user system elapsed
#> 8.68 0.65 9.40
Post-processing
# Load libraries for post-processing
library(gotmtools)
library(ggplot2)
## Plot model output using gotmtools/ggplot2
# Extract names of all the variables in netCDF
ncdf <- "output/ensemble_output.nc"
vars <- gotmtools::list_vars(ncdf)
vars # Print variables
#> [1] "temp" "ice_height" "w_level"
p1 <- plot_heatmap(ncdf)
#> [,1]
#> short_name "temp"
#> units "degC"
#> dimensions "lon lat z model member"
p1
# Change the theme and increase text size for saving
p1 <- p1 +
theme_classic(base_size = 14) +
scale_colour_gradientn(limits = c(0, 21),
colours = rev(RColorBrewer::brewer.pal(11, "Spectral")))
p1
Plot Ensemble output
# Plot ensemble mean at 0.9m
model = c("FLake", "GLM", "GOTM", "Simstrat", "MyLake")
plot_ensemble(ncdf = ncdf, model = model, var = "temp", depth = 0.9)
# Load watertemp from netCDF file as a list
wtemp <- load_var(ncdf = ncdf, var = "temp", return = "list")
#> [,1]
#> short_name "temp"
#> units "degC"
#> dimensions "lon lat z model member"
names(wtemp)
#> [1] "FLake" "GLM" "GOTM" "Simstrat" "MyLake" "Obs"
# Plot residual diagnostic plots
plots <- plot_resid(var_list = wtemp) # Plot residuals - returns a list of plots
names(plots) #
#> [1] "obs_res" "res_depth" "yday_res" "res_dist"
# Plot residuals vs. depth ("res_depth")
plots[[2]]
# Analyse the netCDF output
out <- analyse_ncdf(ncdf, model = model, spin_up = 0)
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names(out)
#> [1] "out_df" "stats" "strat" "obs_temp"
str <- out[["strat"]]
str[str$year == 2010, ]
#> model year TsMax TsMaxDay TsMin TsMinDay TbMax TbMaxDay
#> 1 obs 2010 17.67833 179 3.48499990 13 12.760635 292
#> 3 FLake 2010 17.15400 210 3.30428004 56 6.201520 331
#> 4 GLM 2010 19.04128 172 0.01679616 359 5.007843 337
#> 6 GOTM 2010 15.52526 210 2.01707268 68 12.003445 276
#> 8 Simstrat 2010 18.85400 172 2.56730008 54 11.924000 286
#> 10 MyLake 2010 17.31223 209 1.93227196 8 10.061674 305
#> TbMin TbMinDay MaxStratDur MeanStratDur TotStratDur StratStart StratEnd
#> 1 3.463672 13 184 184.00 184 105 289
#> 3 3.457870 12 216 216.00 216 107 323
#> 4 2.675814 75 201 52.00 208 115 316
#> 6 2.131356 70 133 46.00 138 136 269
#> 8 2.598400 62 170 170.00 170 104 274
#> 10 3.168770 56 205 53.75 215 100 305
#> StratFirst StratLast MeanIceDur MaxIceDur TotIceDur IceOn IceOff FirstFreeze
#> 1 105 289 NA NA NA NA NA NA
#> 3 107 323 NA NA NA NA NA NA
#> 4 103 316 NA NA NA NA NA NA
#> 6 120 269 NA NA NA NA NA NA
#> 8 104 274 NA NA NA NA NA NA
#> 10 100 327 NA NA NA NA NA NA
#> LastThaw HiceMax HiceMaxDay
#> 1 NA 0 NA
#> 3 NA 0 NA
#> 4 NA 0 NA
#> 6 NA 0 NA
#> 8 NA 0 NA
#> 10 NA 0 NA
stats <- out[["stats"]]
stats
#> model Pearson_r Variance_obs Variance_mod SD_obs SD_mod Covariance
#> 1 FLake 0.8003687 20.90594 18.49540 4.573214 4.301486 15.73826
#> 2 GLM 0.8113353 17.35083 13.11618 4.165881 3.622016 12.23952
#> 3 GOTM 0.9619836 17.35899 14.37478 4.166858 3.791814 15.19604
#> 4 Simstrat 0.9380468 17.35899 14.56246 4.166858 3.816488 14.91434
#> 5 MyLake 0.9206550 17.35899 11.86025 4.166858 3.444240 13.21010
#> Bias MAE RMSE NSE lnlikelihood
#> 1 -2.044599 2.085880 3.479253 0.4209684 -17470.67
#> 2 -2.987429 3.045955 3.861698 0.1405189 -38978.62
#> 3 -2.142192 2.163849 2.435297 0.6583516 -18127.91
#> 4 -1.798785 1.886163 2.308331 0.6930471 -16722.49
#> 5 -1.922755 1.958657 2.548731 0.6257830 -19447.17
See the vignette for further examples.
How do I setup LakeEnsemblR for my lake?
Head on over to our website for guides on how to set up your lake for LakeEnsemblR
How do I contribute new code back to the LakeEnsemblR project?
In order to contribute to this code, we recommend the following workflow:
-
“fork” this repository to your own personal github account
-
clone the github repository to your computer:
\$git clone git@github.com:USER_NAME/LakeEnsemblR.git
-
modify code or add new functionality, save the code
-
add the repository master to a remote master called “upstream”
\$cd LakeEnsemblR
\$git remote add upstream git@github.com:aemon-j/LakeEnsemblR.git
-
before pushing your changes to your repository, pull in the current version of the aemon-j master:
\$git fetch upstream
-
merge these differences with your own “master” version:
\$git merge upstream/master
-
push your changes to your github repository, in addition to changes made by pulling in the aemon-j master:
\$git push
-
submit a pull request to aemon-j master using your account at github.com
Information for macOS users
To ensure that all model binaries work, we advise you to install the specific macOS packages (currently in a separated branch):
remotes::install_github("aemon-j/GLM3r", ref = "macOS")
remotes::install_github("aemon-j/FLakeR", ref = "macOS")
remotes::install_github("aemon-J/GOTMr", ref = "macOS")
remotes::install_github("aemon-j/SimstratR", ref = "macOS")
Unfortunately, this means your version of GLM will differ compared to the one included in the executables for Windows and Linux. GLM on macOS will be version 3.2.0a8 (most recent one on January 13, 2022), Windows/Linux use 3.1.1.
R Package Documentation
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