Log-transformed Kling-Gupta Efficiency fit function

kge calculated on log1p()-transformed observed and modelled values, for skewed/concentration-type variables (e.g. oxygen, chlorophyll, nutrients) where a few peak events would otherwise dominate the fit.

Usage

log_kge(obs, sim, na.rm = TRUE)

Arguments

obs

Numeric vector of observed values.

sim

Numeric vector of simulated (modelled) values.

na.rm

Logical; should missing values (NA) in either vector be removed pairwise before computation? Default is TRUE.

Value

numeric; KGE calculated on log1p(obs)/log1p(sim), with a maximum of 1 (perfect fit).

Details

$$\text{KGE}_{\text{log}} = \text{KGE}(\log_{1p}(\text{obs}), \log_{1p}(\text{sim}))$$

log1p() tolerates zeros but not negative values - not suitable for variables that can be negative. Returned in its conventional orientation where higher is better (1 = perfect fit).

calib_aeme() and run_and_fit() minimise the values returned by FUN_list entries - use log_kge_loss (which returns -1 * log_kge(obs, sim)) as a calibration objective.

See also

log_kge_loss for the minimise-oriented variant used in FUN_list.

Examples

obs <- c(1, 2, 3, 4)
sim <- c(1.1, 2.1, 2.9, 4.2)
log_kge(obs, sim)
#> [1] 0.9622493