Figure 6 . Boxplots of dispersion of three model parameters before (Initial) and after the single-variable calibration (Q – discharge; h – water level; A – flood extent; TWS – total water storage anomalies; ET - vegetation ET; W – soil moisture), and multi-variable calibration (All – variables except discharge; h+W – water level and soil moisture). The spread of the values in the boxplots stems from 300 model runs (100 for each calibration experiment). Description of parameters is presented in Supporting Information Table S2. A complete figure with boxplots for all parameters is presented in Supporting Information Figure S2.

Spatial Evaluation

For model calibration, we used one streamflow gauge for discharge, one virtual station for water level, and averaged RS data for the whole basin for TWS, ET and soil moisture. However, many recent studies investigated the potential for using RS spatially distributed information in model calibration, for instance with bias-insensitive metrics (Demirel et al., 2018; Zink et al., 2018; Dembele et al., 2020). Here we further analyze how the lumped calibration affected the simulated spatial patterns (Figure 7; Figure S3 in Supporting Information).
For discharge, water level, flood extent and TWS, spatial patterns are well reproduced even when running the model with the initial parameter set, because the spatial patterns of these variables are determined by intrinsic characteristics of the basin. Nonetheless, for ET, the spatial patterns are completely different between the initial parameter set and the calibrated setup. In this case, the calibration with spatially aggregated ET was able to recover the spatial representation of MOD16. A similar result was found for soil moisture spatial representation by Demirel et al. (2019), that calibrated a model with spatially aggregated soil moisture and TWS data.
In summary, these results highlight the overall model capability to retrieve the ET spatial pattern even by using a lumped calibration approach. However, for other variables, the spatial pattern was not considerably affected by the differing model calibration strategies.