Setting up the Aquatic Ecosystem Model Ensemble is straight forward and can be done in a few steps. This vignette will guide you through the process of setting up the model for a lake in New Zealand.
library(AEME)
#>
#> Attaching package: 'AEME'
#> The following object is masked from 'package:stats':
#>
#> time
library(sf) # For spatial data
#> Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
library(tmap) # For mapping
#> Registered S3 method overwritten by 'stars':
#> method from
#> st_interpolate_aw.stars sf
tmap_mode("view") # Set tmap mode to interactive view model
#> ℹ tmap modes "plot" - "view"
#> ℹ toggle with `tmap::ttm()`
#> This message is displayed once per session.
tmap_options(basemap.server = "OpenStreetMap") # Set the basemap to OpenStreetMapLake data
The first step is to define the lake data. This includes the location of the lake, the depth and area of the lake, and the elevation of the lake.
# Define the location of the lake
lat <- -36.8898
lon <- 174.46898
# View the location of the lake in a map
coords <- data.frame(lat = lat, lon = lon) |>
st_as_sf(coords = c("lon", "lat"), crs = 4326)
tm_shape(coords) +
tm_bubbles() +
tm_view(set_view = c(lon, lat, 16))Get lake shapefile
If you have a shapefile for the lake, you can use the sf
package to read it in. However, if you do not have a shapefile you can
download most lake shapefiles from OpenStreetMap using the osmdata
package.
library(osmdata)
# Get lake shapefile
osm_data <- opq(bbox = "New Zealand") |>
add_osm_feature(key = "name", value = "Lake Wainamu") |>
osmdata_sf()
# Extract lake polygon
lake <- osm_data$osm_multipolygons |>
st_make_valid()
lakeVisualise the lake shapefile on a map.
# View the lake shapefile
tm_shape(lake) +
tm_borders(lwd = 2) As you can see it matches the location of the lake we defined earlier and that it perfectly matches the outline from the OpenStreetMap polygon.
Depth and area data
The depth and area of the lake are required for the model. These can be obtained from a variety of sources. For this example, we will use the known depth of the lake (13.07 m), however for the area we will calculate this from the shapefile.
# Set depth & area
depth <- 13.07 # Depth of the lake in metres
# Calculate the area of the lake in m2
area <- st_area(lake) |>
units::set_units("m^2") |>
as.numeric()
area
#> [1] 151938.6Elevation data
Elevation data can be acquired for New Zealand from the digital
elevation model hosted on the LINZ Data Service. There is a wrapper
function for this in the aemetools package. This requires
an API key from LINZ.
You can easily create a key on the LINZ website: https://data.linz.govt.nz/ or use the function within
the aemetools package to create one.
aemetools::create_linz_key()Then adding it to your .Renviron file.
# Add the LINZ API key to your .Renviron file
aemetools::add_linz_key(key = "your_key_here")The get_dem_value function will return the elevation of
the lake in metres above sea level.
# Get the elevation of the lake
key <- Sys.getenv("LINZ_KEY")
elevation <- aemetools::get_dem_value(lat = lat, lon = lon, key = key)
elevation # in metres above sea level
#> [1] 29
elevation
#> [1] 29We will now create a list of the lake data. This will be used to construct the AEME object.
# Define lake list
lake <- list(
name = "Wainamu",
id = 45819,
latitude = lat,
longitude = lon,
elevation = elevation,
depth = depth,
area = area
)Time data
The time data is required for the model. This includes the start and stop times for the model run.
# Define start and stop times
start <- "2020-08-01 00:00:00"
stop <- "2021-06-30 00:00:00"
time <- list(
start = start,
stop = stop
)Input data
Meteorological data
Download ERA5 data
We will use the aemetools package to download the ERA5
meteorological data for the location of our lake. This works for all
locations around the world. However, its date range is only from
1900-2021.
# Get ERA5 meteorological data
met <- aemetools::get_era5_isimip_point(lat = lat, lon = lon, years = 2020:2021)
#> INFO [2026-10-13 23:35:18] job submitted
#> INFO [2026-10-13 23:35:18] downloading
#> INFO [2026-10-13 23:35:20] extractingView the summary of the meteorological data. The units have been converted to more common units used in aquatic ecosystem modelling.
# Summary of meteorological data
summary(met)
#> Date MET_tmpair MET_pprain MET_wndspd
#> Min. :2011-01-01 Min. : 6.669 Min. : 0.0000 Min. : 1.379
#> 1st Qu.:2013-10-01 1st Qu.:13.890 1st Qu.: 0.1170 1st Qu.: 4.133
#> Median :2016-07-01 Median :16.001 Median : 0.6145 Median : 5.872
#> Mean :2016-07-01 Mean :16.246 Mean : 3.1857 Mean : 6.200
#> 3rd Qu.:2019-04-01 3rd Qu.:18.627 3rd Qu.: 3.3404 3rd Qu.: 7.938
#> Max. :2021-12-31 Max. :22.957 Max. :127.5880 Max. :15.542
#> MET_radswd MET_prsttn MET_radlwd MET_humrel
#> Min. : 13.2 Min. : 98490 Min. :260.6 Min. :51.04
#> 1st Qu.:111.8 1st Qu.:100934 1st Qu.:318.0 1st Qu.:71.57
#> Median :177.4 Median :101459 Median :335.3 Median :77.22
#> Mean :187.3 Mean :101421 Mean :335.9 Mean :77.05
#> 3rd Qu.:258.5 3rd Qu.:101927 3rd Qu.:352.8 3rd Qu.:82.67
#> Max. :383.9 Max. :103515 Max. :419.3 Max. :95.49The depth of this lake is 13.07 m, the area is 152343 m2, and the light extinction coefficient (Kw) is 1.31 m-1.
# Set Kw
Kw <- 1.31 # Light extinction coefficient in m-1Hypsograph data
If you have hypsograph data for the lake, you can use it as input for the model. This is a critical input for the model, as it defines the relationship between the lake area and the lake elevation.
However, if you do not have hypsograph data, the model will use a simple cone-shaped hypsograph based on the lake depth and area. This is not ideal, but it will work for this example.
Required column names for the hypsograph data are area,
elev, and depth.
# Generate a simple hypsograph
hypsograph_simple <- data.frame(area = c(area, 0),
elev = c(elevation, elevation - depth),
depth = c(0, -depth))
hypsograph_simple
#> area elev depth
#> 1 151938.6 29.00 0.00
#> 2 0.0 15.93 -13.07
# Plot the hypsograph
library(ggplot2)
ggplot(hypsograph_simple, aes(x = area, y = elev)) +
geom_line() +
geom_point() +
xlab("Area (m2)") +
ylab("Elevation (m)") +
theme_bw()
As you can see, the hypsograph is a simple cone shape. Ideally, you would have more detailed hypsograph data for your lake.
If you have information regarding the maximum depth of the lake, the
surface area and an estimate of volume development, you can generate a
hypsograph using the generate_hypsograph function. The
volume_development parameter is a scaling factor for the
volume development of the lake. Values below 1.5 are lakes with a
concave hypsograph, values above 1.5 are lakes with a convex hypsograph,
and values of 1.5 are lakes with a linear hypsograph.
For Wainamu Lake, we will use a volume development of 1.62 which was calculated from a bathymetry survey of the lake. You can view this on the LERNZmp platform.
# Generate a hypsograph
hypsograph <- generate_hypsograph(max_depth = depth, surface_area = area,
volume_development = 0.5, elev = elevation,
ext_elev = 1)
ggplot(hypsograph, aes(x = area, y = elev)) +
geom_line() +
geom_point() +
geom_line(data = hypsograph_simple, aes(x = area, y = elev),
linetype = "dashed") +
xlab("Area (m2)") +
ylab("Elevation (m)") +
theme_bw()
# Define input list
input <- list(
init_depth = depth,
hypsograph = hypsograph,
meteo = met,
use_lw = TRUE,
Kw = Kw
)Construct the AEME object
The aeme_constructor function will take the input data
and construct the AEME object. The minimum inputs are the
lake, time, and input data.
# Construct AEME object
aeme <- aeme_constructor(lake = lake,
time = time,
input = input)
#> Warning: ! `lake$id` was not a <character> and was coerced.
#> ℹ Supply `lake$id` as a character string to avoid this.
#> ! `time$time_step` is missing.
#> ℹ Defaulting to 3600 seconds (1 hour).
#> ! `time$spin_up` is missing.
#> ℹ Defaulting to 2 days spin-up for all models.View AEME object
The AEME object can be inspected by printing it to the console. This will show the inputs that have been used to construct the object along with default values for inputs not provided.
aeme
#> Warning: ! This <Aeme> object has no recorded AEME package version.
#> ℹ It was likely built with an older version of AEME (<0.4.0), or has never been
#> built with `build_aeme()`. Consider rebuilding with `build_aeme()` to keep it
#> in sync with the installed package (0.4.0).
#> This warning is displayed once per session.
#>
#> ── AEME not yet built ──────────────────────────────────────────────────────────
#>
#> ── Lake ──
#>
#> Wainamu (ID: 45819)
#> • Lat: -36.89; Lon: 174.47
#> • Elev: 29m; Depth: 13.07m; Area: 151938.62 m2
#>
#> ── Time ──
#>
#> • Start: 2020-08-01 00:00:00; Stop: 2021-06-30 00:00:00; Time step: 3600 s;
#> Output step: 86400 s
#> • Timezone: UTC (timestamps stored UTC)
#> • Spin up (days): GLM: 2; GOTM: 2; DYRESM: 2; Simstrat: 2
#>
#> ── Configuration ──
#>
#> • Model: glm_aed
#> • Path: D:/a/AEME/AEME/vignettes
#> • Model controls: Present
#> • Use biogeochemical model: No
#> ┌ Model Configuration ─────────────────────────────────────────┐
#> │ Model Physical Biogeochemical │
#> │ --- │
#> │ DY-CD Absent Absent │
#> │ GLM-AED Absent Absent │
#> │ GOTM-WET Absent Absent │
#> │ SIMSTRAT-AED2 Absent Absent │
#> │ SIMSTRAT-AED Absent Absent │
#> └──────────────────────────────────────────────────────────────┘
#>
#> ── Observations ──
#>
#> • Lake: Absent; Level: Absent
#>
#> ── Input ──
#>
#> • Initial profile: Absent; Initial depth: 13.07m
#> • Hypsograph: Present (n=44)
#> • Meteo: Present; Use longwave: TRUE; Kw: 1.31
#>
#> ── Inflows ──
#>
#> • Number of inflows: 0; Names: None
#> • Scaling factors: DY-CD: 1; GLM-AED: 1; GOTM-WET: 1; Simstrat-AED2: 1
#>
#> ── Outflows ──
#>
#> • Number of outflows: 0; Names: None; Elevations: N/A
#> • Scaling factors: DY-CD: 1; GLM-AED: 1; GOTM-WET: 1; Simstrat-AED2: 1
#>
#> ── Water Balance ──
#>
#> • Method: 2; Use: obs
#> • Modelled: Absent; Water balance: Absent
#>
#> ── Parameters ──
#>
#> • Number of parameters: 0
#>
#> ── Output ──
#>
#> • DY-CD: 0
#> • GLM-AED: 0
#> • GOTM-WET: 0
#> • SIMSTRAT-AED2: 0
#> • SIMSTRAT-AED: 0
#> • Variables: 0
#> NoneIn the configuration section of the output, under “Physical” and “Biogeochemical” for each model are labelled “Absent”. This is because the model configurations have not been built. This is done in the next step.
Building the AEME ensemble
Model controls
The model controls are the settings for the AEME ensemble. These are
read in from a CSV file. The default CSV file is stored within the
package and can be accessed using the get_model_controls
function. It has the argument use_bgc which is a logical
value to indicate whether to simulate the default biogeochemical
variables with the hydrodynamic variables or just the hydrodynamic
variables.
The model controls has the following columns:
-
var_aeme: The variable name in the AEME object -
simulate: Whether to simulate the variable -
inf_default: The default inflow value -
initial_wc: The initial water column value -
initial_sed: The initial sediment value
# Get model controls
model_controls <- get_model_controls()
model_controls
#> <model_controls> 7/85 variables simulated
#> (78 not-simulated variable(s) hidden -- print(x, all = TRUE) to show all)
#> var_aeme simulate inf_default initial_wc initial_sed conversion_aed
#> CHM_salt yes 0 0 0 1
#> HYD_dens yes - - - 1
#> HYD_strat yes - - - 1
#> HYD_temp yes 15 11 - 1
#> HYD_thmcln yes - - - 1
#> RAD_extc yes - - - 1
#> RAD_par yes - - - 1Build the ensemble
The build_aeme function will take the AEME object and
the model controls and build the ensemble. The model
argument is a character vector of the models to include in the ensemble.
The models available are dy_cd, glm_aed, and
gotm_wet.
# Select models
model <- c("dy_cd", "glm_aed", "gotm_wet")
# Path for model directory
path <- "aeme"
# Build ensemble
aeme <- build_aeme(aeme = aeme, model = model, model_controls = model_controls,
path = path)
#> ✔ `MET_wnduvv`: converted from km/h to m/s.
#>
#>
#> ── Calculating water balance ──
#>
#>
#>
#> Resolving water level
#>
#> ℹ No water level present. Using constant water level.
#> ℹ Estimating surface water temperature
#>
#> ℹ Insufficient lake temperature observations (<10).
#> ℹ Using Stefan & Preud'homme (2007) method to estimate surface temperature.
#> ✔ Estimating surface water temperature [55ms]
#>
#>
#>
#> Estimating lake water levels for dy_cd
#>
#> ℹ Optimizing parameters for water balance
#>
#> ✔ Optimization Complete: C = 0.001, h_inv = 29, Final RMSE = 0.0675
#>
#> Reusing water level fit for glm_aed (shares evaporation physics with an
#> already-fitted model)
#>
#> Estimating lake water levels for gotm_wet
#>
#> ℹ Optimizing parameters for water balance
#>
#> ✔ Optimization Complete: C = 0.001, h_inv = 29, Final RMSE = 0.0551
#>
#> ℹ Correcting water balance using estimated outflows (method = 2).
#> ℹ Building DYRESM-CAEDYM for lake wainamu
#> ℹ Copied in DYRESM .par file
#> ℹ Writing DYRESM configuration file
#> ℹ Writing DYRESM-CAEDYM control file
#> Warning in get_obs(aeme, var_sim = "HYD_temp"): No observations found for the
#> selected variable.
#>
#> ── Building GLM-AED for lake wainamu ──
#>
#> ℹ Copied in GLM nml file (glm4.nml)ℹ Copied in AED nml file and supporting filesℹ Copied in GLM plots nml file! Forcing sed_heat_model from 2 to 1: sed_heat_model = 2 needs an active WQ
#> module and `use_bgc` is FALSE.
#> Warning: GLM ignores the AirPres met column in daily mode and uses the default 1013.25 hPa instead.
#> This warning is displayed once per session.
#> ℹ Building GOTM-WET model for lake wainamu
#> ℹ Copied in GOTM configuration files
#> ✔ GOTM YAML validation completed - no issues detected.
#> ✔ GLM nml validation completed - no issues detected.
print(aeme)
#>
#> ── AEME v0.4.0 ─────────────────────────────────────────────────────────────────
#>
#> ── Lake ──
#>
#> Wainamu (ID: 45819)
#> • Lat: -36.89; Lon: 174.47
#> • Elev: 29m; Depth: 13.07m; Area: 151938.62 m2
#>
#> ── Time ──
#>
#> • Start: 2020-08-01 00:00:00; Stop: 2021-06-30 00:00:00; Time step: 3600 s;
#> Output step: 86400 s
#> • Timezone: UTC (timestamps stored UTC)
#> • Spin up (days): GLM: 2; GOTM: 2; DYRESM: 2; Simstrat: 2
#>
#> ── Configuration ──
#>
#> • Model: dy_cd, glm_aed, and gotm_wet
#> • Path: D:\a\AEME\AEME\vignettes\aeme
#> • Model controls: Present
#> • Use biogeochemical model: No
#> ┌ Model Configuration ─────────────────────────────────────────┐
#> │ Model Physical Biogeochemical │
#> │ --- │
#> │ DY-CD Present Present │
#> │ GLM-AED Present Present │
#> │ GOTM-WET Present Present │
#> │ SIMSTRAT-AED2 Absent Absent │
#> │ SIMSTRAT-AED Absent Absent │
#> └──────────────────────────────────────────────────────────────┘
#>
#> ── Observations ──
#>
#> • Lake: Absent; Level: Absent
#>
#> ── Input ──
#>
#> • Initial profile: Present; Initial depth: 13.07m
#> • Hypsograph: Present (n=44)
#> • Meteo: Present; Use longwave: TRUE; Kw: 1.31
#>
#> ── Inflows ──
#>
#> • Number of inflows: 0; Names: None
#> • Scaling factors: DY-CD: 1; GLM-AED: 1; GOTM-WET: 1; Simstrat-AED2: 1
#>
#> ── Outflows ──
#>
#> • Number of outflows: 1; Names: wbal; Elevations: -1, -1
#> • Scaling factors: DY-CD: 1; GLM-AED: 1; GOTM-WET: 1; Simstrat-AED2: 1
#>
#> ── Water Balance ──
#>
#> • Method: 2; Use: obs
#> • Modelled: Absent; Water balance: Present
#>
#> ── Parameters ──
#>
#> • Number of parameters: 0
#>
#> ── Output ──
#>
#> • DY-CD: 0
#> • GLM-AED: 0
#> • GOTM-WET: 0
#> • SIMSTRAT-AED2: 0
#> • SIMSTRAT-AED: 0
#> • Variables: 0
#> NoneBy default, the build_aeme function will build the file
configuration for each model. This will create the necessary files for
each model to run. The files are also stored in the aeme
object in the configuration slot with a list for
hydrodynamic and ecosystem model configurations.
# View the files
cfg <- configuration(aeme)
names(cfg[["glm_aed"]])
#> [1] "hydrodynamic" "bgc" "hydrodynamic_file"All of the information and data needed to run an ensemble of models
is now contained within the aeme object. This allows for
easy storage of all the data and also for easy sharing of the data with
others. Sharing the aeme object with others allows them to
run the ensemble of models without needing to reconstruct the
object.
# Run the ensemble
aeme <- run_aeme(aeme = aeme)
#> ℹ Running models... (Have you tried parallelizing?) [2026-10-13 23:35:44]
#> ℹ DYRESM-CAEDYM running... [2026-10-13 23:35:44]
#> ======= createDYref V2.5.0-5 =======
#> netCDF library version: VERSION of Sep 1 2004 16:00:21 $
#>
#> ----- Successfully created the reference file DYref.nc -----
#>
#> ======= createDYsim V3.0.0-02 =======
#> netCDF library version: VERSION of Sep 1 2004 16:00:21 $
#>
#> Creating netCDF file for DYRESM-CAEDYM run.
#> readCaedymConfig: geochemFile = 'caedym3p1.chm'
#> readCaedymConfig: sedimentFile = 'caedym3p1.sed'
#> LIGHT
#> PHYTOPLANKTON
#> JELLYFISH
#> ZOOPLANKTON
#> FISH
#> BENTHIC
#> PATHOGEN
#>
#> TRACER
#> OXYGEN
#> BACTERIA
#> CNP
#> IRON
#> MANGANESE
#> SULFUR
#> ALUMINIUM
#> ZINC
#> ORGANIC
#> Zn
#> 0
#> 2
#> Group
#> 2
#> 4
#> 5
#> 7
#> 2
#> 0.0200
#> 1.0005
#> 050.00
#> 0.0000
#> 0.0000
#> 4
#> 0.0200
#> 5.0005
#> 050.00
#> 0.0000
#> 0.0000
#> 5
#> 0.0200
#> 1.0005
#> 050.00
#> 0.0000
#> 0.0000
#> 7
#> 0.0200
#> 5.0005
#> 100.00
#> 0.0000
#> 0.0000
#> GEOCHEMISTRY
#>
#> INORGANIC
#> STATIC
#> DYNAMIC
#>
#>
#> ----- Successfully created the simulation file DYsim.nc -----
#>
#> ======= extractDYinfo V1.4.0-3 =======
#> netCDF library version: VERSION of Sep 1 2004 16:00:21 $
#>
#> ----- Successfully configured the simulation file DYsim.nc -----
#>
#> InitDySimIO_OK: ncid = 3
#> WordSymbols_OK: the output attributes:-
#> TEMPTURE
#> DENSITY
#> SALINITY
#> totNumOfProfiles = 336
#> Attributes selected for profile output:
#> i = 1 paramName = TEMPTURE
#> i = 2 paramName = DENSITY
#> i = 3 paramName = SALINITY
#> numOutputSelections = 3 numOutflowAttribs = 3
#> maxNumOfWetLayers = 77
#> netCDFVarName = dyresmLAYER_HTS_Var
#> netCDFVarName = dyresmTime
#> netCDFVarName = dyresmLayerCount
#> i = 1 netCDFVarName = dyresmTEMPTURE_Var
#> i = 2 netCDFVarName = dyresmDENSITY_Var
#> i = 3 netCDFVarName = dyresmSALINITY_Var
#> ========== SetSizesForOutflows_OK ENTRY POINT
#>
#> numOutflowDevices = 2 numOutletDevices = 1
#> numOutflowAttribs = 3
#> numOutflowsPerDevice = 336
#> ========== SetSizesForMetAndAtmos_OK ENTRY POINT
#>
#> totNumOfTimeSteps = 8064
#> numberStr : 1
#> variableName: stream_1_VOL
#> numberStr : 1
#> variableName: stream_1_TEMPTURE
#> numberStr : 1
#> variableName: stream_1_SALINITY
#> numberStr : 1
#> variableName: stream_1_DO
#> numberStr : 1
#> variableName: stream_1_PO4
#> numberStr : 1
#> variableName: stream_1_DOPL
#> numberStr : 1
#> variableName: stream_1_POPL
#> numberStr : 1
#> variableName: stream_1_PIP
#> numberStr : 1
#> variableName: stream_1_NH4
#> numberStr : 1
#> variableName: stream_1_NO3
#> numberStr : 1
#> variableName: stream_1_DONL
#> numberStr : 1
#> variableName: stream_1_PONL
#> numberStr : 1
#> variableName: stream_1_DOCL
#> numberStr : 1
#> variableName: stream_1_POCL
#> numberStr : 1
#> variableName: stream_1_SiO2
#> numberStr : 1
#> variableName: stream_1_CYANO
#> numberStr : 1
#> variableName: stream_1_CHLOR
#> numberStr : 1
#> variableName: stream_1_FDIAT
#> numberStr : 1
#> variableName: stream_1_SSOL1
#> withdrawal_outflow
#> ======= DYRESM-CAEDYM simulation completed =======
#> Stop - Program terminated.
#> ✔ DYRESM-CAEDYM running... [2026-10-13 23:36:12] [27.2s]
#>
#> ℹ GLM-AED running... [2026-10-13 23:36:12]
#>
#> Reading configuration from D:\a\AEME\AEME\vignettes\aeme/45819_wainamu/glm_aed/glm4.nml
#> No 'particles' config, assuming no particles
#> NOTE: value for base_elev is no longer used; H[1] is assumed.
#> VolAtCrest= 1477586.28035; MaxVol= 1477586.28035 (m3)
#> No 'snowice' section, setting defaults & assuming no snowfall
#> 'sediment' section present, simulating sediment heating
#> DBG calling initialise_lake
#> DBG entered initialise_lake
#> DBG init_profiles: num_heights=0 num_depths=2 the_heights=0000000000000000 the_depths=000002019C0D1E50 lake_depth=13.070000
#> WARNING: Initial profiles problem - expected 0 wd_init_vals entries but got 1
#> DBG initialise_lake returned
#>
#> -------------------------------------------------------
#> | General Lake Model (GLM) Version 4.0.0 |
#> -------------------------------------------------------
#>
#> glm built using gcc version 16.1.0
#>
#> nDays= 200; timestep= 3600.000000 (s)
#> Maximum lake depth is 14.070000
#> Depth where flow will occur over the crest is 14.070000
#> *sed_temp_mean[0] = 10.00000
#> Heat pump disabled (heat_pump_switch = 0)
#> Oxygenation disabled
#>
#> Wall clock start time : Tue Oct 13 23:36:12 2026
#>
#> Simulation begins...
#> Running day 2459061, 0.30% of days complete Running day 2459062, 0.60% of days complete Running day 2459063, 0.89% of days complete Running day 2459064, 1.19% of days complete Running day 2459065, 1.49% of days complete Running day 2459066, 1.79% of days complete Running day 2459067, 2.08% of days complete Running day 2459068, 2.38% of days complete Running day 2459069, 2.68% of days complete Running day 2459070, 2.98% of days complete Running day 2459071, 3.27% of days complete Running day 2459072, 3.57% of days complete Running day 2459073, 3.87% of days complete Running day 2459074, 4.17% of days complete Running day 2459075, 4.46% of days complete Running day 2459076, 4.76% of days complete Running day 2459077, 5.06% of days complete Running day 2459078, 5.36% of days complete Running day 2459079, 5.65% of days complete Running day 2459080, 5.95% of days complete Running day 2459081, 6.25% of days complete Running day 2459082, 6.55% of days complete Running day 2459083, 6.85% of 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#>
#> Wall clock finish time : Tue Oct 13 23:36:12 2026
#> Wall clock runtime was 0 seconds : 00:00:00 [hh:mm:ss]
#>
#> Model Run Complete
#> -------------------------------------------------------
#> ✔ GLM-AED running... [2026-10-13 23:36:12] [485ms]
#>
#> ℹ GOTM-WET running... [2026-10-13 23:36:12]
#> ------------------------------------------------------------------------
#> GOTM started on 2026/10/13 at 23:36:12
#> ------------------------------------------------------------------------
#> init_gotm
#> ------------------------------------------------------------------------
#> Reading yaml configuration from: gotm.yaml
#> configuring modules ....
#> init_airsea_yaml
#> done
#> init_stim_yaml
#> done.
#> init_observations_yaml
#> configure_streams_yaml
#> done
#> init_turbulence_yaml
#> done.
#> configure_gotm_fabm
#> done.
#> init_meanflow_yaml
#> done
#> init_eqstate_yaml
#> done.
#> GOTM simulation
#> Using 41 layers to resolve a depth of 13.0700000000000
#> The station wainamu is situated at (lat,long) -36.8898000000000
#> 174.468980000000
#> wainamu
#> initializing modules....
#> init_input
#> done
#> init_time
#> Time step: 3600.00000000000 seconds
#> Time format: 2
#> Start: 2020-07-30 00:00:00
#> Stop: 2021-06-30 00:00:00
#> ==> 335 day(s) and 0 seconds ==>
#> 8040 micro time steps
#> post_init_meanflow
#> allocation meanflow memory..
#> done.
#> init_hypsograph
#> reading hypsograph from:
#> inputs/hypsograph.dat
#> # of lines 44 read order 2
#> init_tridiagonal
#> external specified sigma coordinates
#> post_init_observations
#> allocation observation memory..
#> Reading /salinity from:
#> inputs/s_prof_file.dat
#> Reading /temperature from:
#> inputs/t_prof_file.dat
#> Using constant /mimic_3d/ext_pressure/h= 0.000000000000000E+000
#> Using constant /mimic_3d/ext_pressure/dpdx= 0.000000000000000E+000
#> Using constant /mimic_3d/ext_pressure/dpdy= 0.000000000000000E+000
#> extinction method
#> Using constant /light_extinction/A= 0.580000000000000
#> Using constant /light_extinction/g1= 0.350000000000000
#> Using constant /light_extinction/g2= 0.760000000000000
#> Using constant /mimic_3d/w/height= 0.000000000000000E+000
#> Using constant /surface/wave/Hs= 0.000000000000000E+000
#> Using constant /surface/wave/Tz= 0.000000000000000E+000
#> Using constant /surface/wave/phiw= 0.000000000000000E+000
#> done.
#> post_init_streams
#> Reading /streams/wbal/flow from:
#> inputs/outf_wbal.dat
#> Using constant /streams/wbal/temp= -1.00000000000000
#> Using constant /streams/wbal/salt= -1.00000000000000
#>
#> Only one set of profiles is present in inputs/s_prof_file.dat. These will be us
#> ed throughout the simulation
#>
#> Only one set of profiles is present in inputs/t_prof_file.dat. These will be us
#> ed throughout the simulation
#> post_init_turbulence
#> allocation turbulence memory..
#> done.
#>
#> --------------------------------------------------------
#> You are using the k-epsilon model
#> with the following properties:
#>
#> ce1 = 1.44000000000000
#> ce2 = 1.92000000000000
#> ce3minus = -0.620912126249001
#> ce3plus = 1.00000000000000
#> sig_k = 1.00000000000000
#> sig_e = 1.20265265449834
#>
#> Value of the stability function
#> in the log-law, cm0 = 0.526464710835324
#> in shear-free turbulence, cmsf = 0.731006013490010
#>
#> von Karman constant, kappa = 0.400000000000000
#> homogeneous decay rate, d = -1.08695652173913
#> spatial decay rate (no shear), alpha = -7.82461519721493
#> length-scale slope (no shear), L = 4.662134571526204E-002
#> steady-state Richardson-number, Ri_st= 0.250000000000000
#> --------------------------------------------------------
#>
#> post_init_airsea
#> Reading /surface/meteo/swr from:
#> inputs/meteo_swr.dat
#> Reading /surface/meteo/u10 from:
#> inputs/meteo.dat
#> Reading /surface/meteo/v10 from:
#> inputs/meteo.dat
#> Reading /surface/meteo/airp from:
#> inputs/meteo.dat
#> Reading /surface/meteo/airt from:
#> inputs/meteo.dat
#> Reading /surface/meteo/hum from:
#> inputs/meteo.dat
#> Reading /surface/meteo/cloud from:
#> inputs/meteo.dat
#> Air-sea exchanges will be calculated
#> heat- and momentum-fluxes:
#> using Fairall et. all formulation
#> net longwave radiation:
#> using Clark formulation
#> Reading /surface/meteo/precip from:
#> inputs/meteo.dat
#> rain_impact= T
#> calc_evaporation= T
#> Using constant /surface/sst= 11.0000000000000
#> Using constant /surface/sss= 0.000000000000000E+000
#> done
#> register_all_variables()
#> register_coordinate_variables()
#> register_meanflow_variables()
#> register_airsea_variables()
#> register_stim_variables()
#> register_observation_variables()
#> register_stream_variables()
#> register_turbulence_variables()
#> register_diagnostic_variables()
#> post_init_stim
#> done.
#> done.
#> ------------------------------------------------------------------------
#> saving initial conditions
#> Processing output category /state:
#> - temp
#> - salt
#> - u
#> - uo
#> - v
#> - vo
#> - xP
#> - h
#> - ho
#> - tke
#> - tkeo
#> - eps
#> - num
#> - nuh
#> - nus
#> Processing output category /:
#> - lon
#> - lat
#> - temp
#> - salt
#> - rho
#> - temp_obs
#> - salt_obs
#> - u
#> - v
#> - xP
#> - h
#> - tke
#> - eps
#> - num
#> - nuh
#> - nus
#> - u_obs
#> - v_obs
#> - fric
#> - drag
#> - SS
#> - P
#> - uu
#> - vv
#> - ww
#> - NN
#> - NNT
#> - NNS
#> - buoy
#> - kb
#> - epsb
#> - G
#> - Pb
#> - avh
#> - L
#> - gamu
#> - gamv
#> - gamb
#> - gamh
#> - gams
#> - cmue1
#> - cmue2
#> - gam
#> - an
#> - as
#> - at
#> - r
#> - Rig
#> - xRf
#> - taux
#> - tauy
#> - u_taus
#> - u10
#> - v10
#> - airt
#> - airp
#> - hum
#> - es
#> - ea
#> - qs
#> - qa
#> - rhoa
#> - cloud
#> - albedo
#> - precip
#> - evap
#> - int_precip
#> - int_evap
#> - int_swr
#> - int_heat
#> - int_total
#> - I_0
#> - qh
#> - qe
#> - ql
#> - heat
#> - tx
#> - ty
#> - sst
#> - sst_obs
#> - sss
#> - zeta
#> - mld_surf
#> - rad
#> - bioshade
#> - ga
#> - Af
#> - z
#> - zi
#> - int_flow
#> - int_water_balance
#> - Tice_surface
#> - Tf
#> - Hice
#> - surface_ice_energy
#> - bottom_ice_energy
#> - ocean_ice_flux
#> - Hfrazil
#> - dHis
#> - dHib
#> - Qlayer
#> - Qs
#> - Qt
#> - wq
#> - FQ
#> - Qres
#> - Q_wbal
#> - int_inflow
#> - int_outflow
#> - mld_bott
#> - Ekin
#> - Epot
#> - Eturb
#> - secchi
#> Processing output category /:
#> - lon
#> - lat
#> - temp
#> - salt
#> - rho
#> - temp_obs
#> - salt_obs
#> - u
#> - v
#> - xP
#> - h
#> - tke
#> - eps
#> - num
#> - nuh
#> - nus
#> - u_obs
#> - v_obs
#> - fric
#> - drag
#> - SS
#> - P
#> - uu
#> - vv
#> - ww
#> - NN
#> - NNT
#> - NNS
#> - buoy
#> - kb
#> - epsb
#> - G
#> - Pb
#> - avh
#> - L
#> - gamu
#> - gamv
#> - gamb
#> - gamh
#> - gams
#> - cmue1
#> - cmue2
#> - gam
#> - an
#> - as
#> - at
#> - r
#> - Rig
#> - xRf
#> - taux
#> - tauy
#> - u_taus
#> - u10
#> - v10
#> - airt
#> - airp
#> - hum
#> - es
#> - ea
#> - qs
#> - qa
#> - rhoa
#> - cloud
#> - albedo
#> - precip
#> - evap
#> - int_precip
#> - int_evap
#> - int_swr
#> - int_heat
#> - int_total
#> - I_0
#> - qh
#> - qe
#> - ql
#> - heat
#> - tx
#> - ty
#> - sst
#> - sst_obs
#> - sss
#> - zeta
#> - mld_surf
#> - rad
#> - bioshade
#> - ga
#> - Af
#> - z
#> - zi
#> - int_flow
#> - int_water_balance
#> - Tice_surface
#> - Tf
#> - Hice
#> - surface_ice_energy
#> - bottom_ice_energy
#> - ocean_ice_flux
#> - Hfrazil
#> - dHis
#> - dHib
#> - Qlayer
#> - Qs
#> - Qt
#> - wq
#> - FQ
#> - Qres
#> - Q_wbal
#> - int_inflow
#> - int_outflow
#> - mld_bott
#> - Ekin
#> - Epot
#> - Eturb
#> - secchi
#> ------------------------------------------------------------------------
#> time_loop
#> 0 %
#> 10 %
#> 20 %
#> 30 %
#> 40 %
#> 50 %
#> 60 %
#> 70 %
#> 80 %
#> 90 %
#> 100 %
#> ------------------------------------------------------------------------
#> clean_up
#> clean_ice
#> de-allocation ice memory ...
#> done.
#> clean_hypsograph
#> de-allocation hypsograph memory ...
#> done
#> clean_meanflow
#> de-allocation meanflow memory ...
#> done.
#> clean_turbulence
#> de-allocating turbulence memory ...
#> done.
#> clean_observations
#> de-allocate observation memory ...
#> done.
#> clean_tridiagonal
#> ------------------------------------------------------------------------
#> GOTM finished on 2026/10/13 at 23:36:13
#> ------------------------------------------------------------------------
#> CPU time: 0.5156250 seconds
#> Simulated time/CPU time: 56133818.1818182
#> ------------------------------------------------------------------------
#> GOTM: v5.3-685-g2895ac94-dirty (au branch)
#> YAML: 0.1.0 (unknown branch)
#> flexout: 0.1.0 (unknown branch)
#> STIM: (unknown branch)
#> FABM: v0.95.3-300-g3910177 (au branch)
#> WET: 6cd445e-dirty (master branch)
#> NetCDF: 3.6.1-beta1 of Mar 7 2018 17:17:53 $
#> ------------------------------------------------------------------------
#> Compiler: Intel 2021.8.0.20221119
#>
#> ✔ GOTM-WET running... [2026-10-13 23:36:13] [656ms]
#>
#> ✔ Model run complete! [2026-10-13 23:36:13]! The following variables are not available in model gotm_wet: RAD_extcView the output
The output from the model run is stored in the output
slot of the aeme object. This is a list with a list for
each model. The list contains the output data from the model run.
# View the output
plot_output(aeme = aeme)
#> Warning: Removed 246 rows containing missing values or values outside the scale range
#> (`geom_col()`).
Saving the AEME object
Saving the aeme object to a file can be done using the
saveRDS function. This will save the object to a file with
the .rds.
# Save the AEME object
saveRDS(aeme, "aeme.rds")Using AEME in pipes
The AEME functions can be used in pipes to make the workflow more
efficient. For example, the build_aeme function can be used
in a pipe to build the ensemble and then the run_aeme
function can be used in the same pipe to run the ensemble.
This allows for a more streamlined workflow and reduces the amount of code needed to build and run the ensemble. This is especially useful when building and running multiple ensembles for different lakes or different scenarios.
This approach has been used with the {targets} package to build and run multiple ensembles for different lakes and scenarios in a reproducible workflow.
# Build and run the ensemble in a pipe
aeme <- aeme |>
add_obs(lake = lake_obs, level = level_obs) |>
add_hypsograph(hypsograph = hypsograph) |>
add_met(met = met) |>
add_inflows(data = inf_data) |>
add_outflows(data = outf_data) |>
add_param(param = param) |>
build_aeme(model = model, use_bgc = TRUE, path = path) |>
run_aeme()