TY  - JOUR
A1  - Francke, Till
A1  - Baroni, Gabriele
A1  - Brosinsky, Arlena
A1  - Foerster, Saskia
A1  - Lopez-Tarazon, José Andrés
A1  - Sommerer, Erik
A1  - Bronstert, Axel
T1  - What Did Really Improve Our Mesoscale Hydrological Model?
BT  - a Multidimensional Analysis Based on Real Observations
JF  - Water resources research
N2  - Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
KW  - modeling
KW  - sensitivity analyses
KW  - model enhancement
KW  - sediment
Y1  - 2018
U6  - https://doi.org/10.1029/2018WR022813
SN  - 0043-1397
SN  - 1944-7973
VL  - 54
IS  - 11
SP  - 8594
EP  - 8612
PB  - American Geophysical Union
CY  - Washington
ER  -