Assesses the model's calibration quality with the help of the pairwise complete modelled as well as observed loads and the following metrics:

*NSE:*Nash-Sutcliffe Efficiency*mNSE:*Modified Nash-Sutcliffe Efficiency (`j = 1`

)*KGE:*Modified Kling-Gupta Efficiency*RMSE:*Root Mean Square Error*PBIAS:*Percent Bias*RSR:*Ratio of the RMSE to the standard deviation of the observations*RCV:*Ratio of the coefficients of variation*GMRAE:*Geometric Mean Relative Absolute Error*MdRAE:*Median Relative Absolute Error

In addition, a scatter plot with the observed river loads on the x- and the modelled river loads on the y-axis is displayed and provides a visual impression of the model performance. Other elements of this plot are an identity line (solid) and plus/minus 30% deviation lines (dashed).

```
# S4 method for RPhosFate
calibrationQuality(x, substance, col)
```

- x
An S4

`RPhosFate`

river catchment object.- substance
A character string specifying the substance to calculate.

- col
A character string specifying the calibration data column with the respective substance river loads.

A named numeric vector containing the assessed metrics along with the
in-channel retention ratio (one minus sum of *xxt* at catchment outlet(s)
divided by sum of *xxt_inp*).

Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I – a discussion of principles. Journal of Hydrology 10, 282–290. https://doi.org/10.1016/0022-1694(70)90255-6

Legates, D.R., McCabe Jr., G.J., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35, 233–241. https://doi.org/10.1029/1998WR900018

Kling, H., Fuchs, M., Paulin, M., 2012. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology 424–425, 264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50, 885–900.

```
# \donttest{
# temporary demonstration project copy
cv_dir <- demoProject()
#> Warning: A folder called "demoProject" already exists and is left as is.
# load temporary demonstration project
x <- RPhosFate(
cv_dir = cv_dir,
ls_ini = TRUE
)
# presupposed method calls
x <- firstRun(x, "SS")
x <- snapGauges(x)
calibrationQuality(x, "SS", "SS_load")# }
#> NSE: 0.7191828
#> mNSE: 0.5345233
#> KGE: 0.7323813
#> RMSE: 5.656556
#> PBIAS: -26.6
#> RSR: 0.4326794
#> RCV: 0.9761362
#> GMRAE: 0.3842057
#> MdRAE: 0.8072484
#>
#> In-channel retention ratio: -2.220446e-16
#>
#> NSE mNSE KGE
#> 7.191828e-01 5.345233e-01 7.323813e-01
#> RMSE PBIAS RSR
#> 5.656556e+00 -2.660000e+01 4.326794e-01
#> RCV GMRAE MdRAE
#> 9.761362e-01 3.842057e-01 8.072484e-01
#> inChannelRetentionRatio
#> -2.220446e-16
```