On the contribution of remote sensing-based calibration to model hydrological and hydraulic processes in tropical regions

Abstract

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. While calibration is usually performed with discharge estimates, the internal model processes might be misrepresented, and the model might be getting the “right results for the wrong reasons”, thus compromising model reliability. An alternative is to calibrate model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted the potential of RS-based calibration to improve discharge estimates, focusing less on other variables of the water cycle. In this study, we analyzed in detail the contribution of five RS-based variables (water level (h), flood extent (A), terrestrial water storage (TWS), evapotranspiration (ET) and soil moisture (W)) to calibrate a coupled hydrologic-hydrodynamic model for a large Amazon sub-basin with extensive floodplains. Single-variable calibration experiments with all variables were able to improve discharge KGE from around 6.1% to 52.9% when compared to a priori parameter sets. Water cycle representation was improved with multi-variable calibration: KGE for all variables were improved in the evaluation period. By analyzing different calibration setups, a consistent selection of complementary variables for model calibration resulted in a better performance than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, inconsistencies in model structure and parameterization were found, which would remain unknown if only discharge observations were considered.