Moving beyond run-off calibration—Multivariable optimization of a surface-subsurface-atmosphere model

Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multivariable optimization of a complex coupled surface–subsurface–atmosphere model at the catchment scale in an attempt to move beyond simple run-off calibration. The model is evaluated against five independent observational data sets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM), and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions for all five observation data sets can be reduced simultaneously. Additionally, the multivariable calibration proved more robust, compared with calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio-temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization, or process description.