calib_model() runs the model and compares it against observations provided.
It can run in parallel by using multiple cores availlable on your computer
to run quicker.
Usage
calib_aeme(
aeme,
model,
param,
path,
vars_sim = "HYD_temp",
FUN_list,
weights,
model_controls = NULL,
ctrl = NULL,
param_var_matrix = NULL,
param_df = NULL
)Arguments
- aeme
aeme; object.
- model
vector; of models to be used. Can be `dy_cd`, `glm_aed`, `gotm_wet`.
- param
dataframe; of parameters read in from a csv file. Requires the columns c("model", "file", "name", "value", "min", "max", "log")
- path
filepath; where input files are located relative to the current working directory.
- vars_sim
vector; of variables names to be used in the calculation of model fit.
- FUN_list
list of functions; named according to the variables in the
vars_sim. Funtions are of the formfunction(df)which will be used to calculate model fit. If nor provided, uses mean absolute error (MAE).- weights
a named vector; of weights for each variable in vars_sim. If not provided, defaults to 1 for each variable.
- model_controls
dataframe; of configuration loaded from "model_controls.csv".
- ctrl
list; of controls for sensitivity analysis function created using the
create_controlfunction. See create_control for more details.- param_var_matrix
list of dataframes; with parameters as rows and response variables as columns. Created using
create_param_var_matrix. This is used to specify which parameters are associated with which response variables, and therefore which parameters are updated in each generation of the calibration.- param_df
dataframe; of parameters to be used in the calibration. Requires the columns c("model", "file", "name", "value", "min", "max"). This is used to restart from a previous calibration.
Examples
aeme_file <- system.file("extdata/aeme.rds", package = "AEME")
aeme <- readRDS(aeme_file)
model_controls <- AEME::get_model_controls()
model <- c("glm_aed", "gotm_wet")
path <- "aeme"
aeme <- AEME::build_aeme(aeme = aeme, model = model, path = path,
model_controls = model_controls, ext_elev = 5) |>
AEME::run_aeme()
#> Created missing directory: D:\a\aemetools\aemetools\docs\reference\aeme
#> ℹ Using observed water level
#> ℹ No missing values in observed water level. Using observed water level
#> ℹ Correcting water balance using estimated outflows (method = 2).
#> ℹ Calculating lake level using lake depth and a sinisoidal function.
#> ℹ Building GLM-AED for lake wainamu
#> ℹ Copied in GLM nml file
#> ℹ Copied in AED nml file and supporting files
#> 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.
#> ℹ Running models... (Have you tried parallelizing?) [2026-03-04 03:01:07]
#> → GLM-AED running... [2026-03-04 03:01:07]
#> ✔ GLM-AED run successful! [2026-03-04 03:01:08]
#> → GOTM-WET running... [2026-03-04 03:01:08]
#> ✔ GOTM-WET run successful! [2026-03-04 03:01:08]
#> ✔ Model run complete! [2026-03-04 03:01:08]
#> ! The following variables are not available in model gotm_wet: RAD_extc
#> ! The following variables are not available in model gotm_wet: RAD_extc
data("aeme_parameters", package = "AEME")
param <- aeme_parameters
# Function to calculate fitness
nse <- function(df) {
# Calculate Nash-Sutcliffe Efficiency
nse <- 1 - (sum((df$obs - df$model)^2) / sum((df$obs - mean(df$obs))^2))
-1 * nse
}
FUN_list <- list(HYD_temp = nse, LKE_lvlwtr = nse)
ctrl <- create_control(method = "calib", NP = 10, itermax = 20, ncore = 2,
parallel = TRUE, file_type = "db",
file_name = "results.db")
#> Warning: `create_control()` was deprecated in aemetools 0.2.0.
#> ℹ Please use `create_calibration_control()` instead.
#> ℹ Use `create_calibration_control()` when method = 'calib'. Use
#> `create_sa_control()` when method = 'sa'.
vars_sim <- c("HYD_temp", "LKE_lvlwtr")
weights <- c("HYD_temp" = 1, "LKE_lvlwtr" = 1)
# Calibrate AEME model
sim_id <- calib_aeme(aeme = aeme, model = model, path = path,
param = param, FUN_list = FUN_list, ctrl = ctrl,
vars_sim = vars_sim, weights = weights)
#> ℹ Variables not found: `LKE_lvlwtr`.
#> Adding them to model_controls.
#> ℹ Extracting indices for "glm_aed" modelled variables [2026-03-04 03:01:10]
#> ✔ Indices extracted for "glm_aed" modelled variables [2026-03-04 03:01:11]
#> ℹ Using 2 cores for parallel calibration for "glm_aed".
#> → Starting generation 1/2, 10 members. [2026-03-04 03:01:11]
#> Parameter summary for generation 1:
#> light/Kw MET_wndspd MET_radswd mixing/coef_mix_conv
#> mean 2.744 1.0070 1.0050 0.15040
#> median 2.707 0.9956 1.0190 0.15260
#> sd 1.646 0.1818 0.1966 0.02886
#> mixing/coef_wind_stir mixing/coef_mix_shear mixing/coef_mix_turb
#> mean 0.24870 0.1510 0.4499
#> median 0.24740 0.1534 0.4631
#> sd 0.03003 0.0300 0.1528
#> mixing/coef_mix_hyp outflow inflow
#> mean 0.5977 1.5150 1.4910
#> median 0.6030 1.4940 1.4430
#> sd 0.1238 0.6027 0.5759
#> ✔ Completed generation 1/2
#> for "glm_aed". [2026-03-04 03:01:26]
#> Best fit: 61.4 (sd: 2660.8) Parameters: [ 1.58, 1.14, 1.17, 0.12, 0.274, 0.185,
#> 0.215, 0.411, 0.629, and 0.679 ]
#> Writing output for generation 1 to results.db with sim ID: "45819_glmaed_C_001"
#> [2026-03-04 03:01:26]
#> ℹ Survival rate: 0.8
#> → Starting generation 2/2, 10 members. [2026-03-04 03:01:26]
#> Parameter summary for generation 2:
#> light/Kw MET_wndspd MET_radswd mixing/coef_mix_conv
#> mean 2.604 1.0040 1.118 0.14740
#> median 2.937 0.9956 1.092 0.15920
#> sd 1.473 0.2387 0.117 0.03781
#> mixing/coef_wind_stir mixing/coef_mix_shear mixing/coef_mix_turb
#> mean 0.25290 0.190000 0.3296
#> median 0.24650 0.192300 0.3033
#> sd 0.02539 0.007898 0.1356
#> mixing/coef_mix_hyp outflow inflow
#> mean 0.4602 0.9744 1.001
#> median 0.4330 1.0910 1.048
#> sd 0.1118 0.4034 0.350
#> Writing output for generation 2 to results.db with sim ID: "45819_glmaed_C_001"
#> [2026-03-04 03:01:33]
#> ✔ Completed generation 2/2
#> for "glm_aed". [2026-03-04 03:01:33]
#> Best fit: 4.62 (sd: 4233.2)
#> ℹ Survival rate: 1
#> ℹ Extracting indices for "gotm_wet" modelled variables [2026-03-04 03:01:33]
#> ✔ Indices extracted for "gotm_wet" modelled variables [2026-03-04 03:01:34]
#> ℹ Using 2 cores for parallel calibration for "gotm_wet".
#> → Starting generation 1/2, 10 members. [2026-03-04 03:01:35]
#> Parameter summary for generation 1:
#> turbulence/turb_param/k_min light_extinction/A/constant_value
#> mean 5.090e-06 0.52800
#> median 5.019e-06 0.52890
#> sd 3.147e-06 0.07763
#> light_extinction/g1/constant_value light_extinction/g2/constant_value
#> mean 0.59160 1.329
#> median 0.59210 1.301
#> sd 0.09194 0.851
#> MET_wndspd MET_radswd outflow inflow
#> mean 1.0030 1.0090 1.4860 1.4910
#> median 1.0190 1.0020 1.4740 1.4890
#> sd 0.1873 0.1836 0.5946 0.6027
#> ✔ Completed generation 1/2
#> for "gotm_wet". [2026-03-04 03:01:52]
#> Best fit: 15.1 (sd: 10676) Parameters: [ 4.27e-06, 0.493, 0.601, 0.644, 0.711,
#> 1.23, 1.21, and 1.24 ]
#> Writing output for generation 1 to results.db with sim ID:
#> "45819_gotmwet_C_001" [2026-03-04 03:01:52]
#> ℹ Survival rate: 0.8
#> → Starting generation 2/2, 10 members. [2026-03-04 03:01:53]
#> Parameter summary for generation 2:
#> turbulence/turb_param/k_min light_extinction/A/constant_value
#> mean 2.313e-06 0.5309
#> median 2.338e-06 0.5352
#> sd 2.025e-06 0.0619
#> light_extinction/g1/constant_value light_extinction/g2/constant_value
#> mean 0.58710 1.2480
#> median 0.58820 1.1920
#> sd 0.01494 0.6213
#> MET_wndspd MET_radswd outflow inflow
#> mean 0.9246 1.0130 0.9679 0.9958
#> median 0.9552 1.0490 0.9201 0.9602
#> sd 0.1575 0.1882 0.4447 0.4133
#> Writing output for generation 2 to results.db with sim ID:
#> "45819_gotmwet_C_001" [2026-03-04 03:02:02]
#> ✔ Completed generation 2/2
#> for "gotm_wet". [2026-03-04 03:02:02]
#> Best fit: 15.1 (sd: 4452.2)
#> ℹ Survival rate: 0.9