Continental-scale river hydrodynamic modeling is useful for understanding the global hydrological cycle, and model evaluation is essential for robust calibration and assessing model performance. Although many models have been robustly evaluated using several variables separately, methods for the integrated multivariable evaluation of models have yet to be established. Here, we propose an evaluation method using the overall basin skill score (OSK), based on considering the spatial distribution of different variables via a sub-basin approach. The OSK approach integrates multiple variables to overcome observation-related limitations, such as the distinct temporal and spatial dimensions and unit of measurement unique to each variable, thus judging model performance objectively at the sub-basin and basin scales. As a case study, the global river model, CaMa-Flood, was evaluated using three variables¾discharge, water surface elevation, and flooded area¾for the Amazon Basin, focusing on the impact of using different types of baseline topography data (SRTM and MERIT digital elevation models [DEMs]). CaMa-Flood with the MERIT DEM performed robustly well over a wide range of river depth parameters with a maximum OSK of 0.51 against 0.46 for the SRTM DEM. Single-variable evaluation for all three variables proved inadequate due to low sensitivity for river bathymetry, with good performance outcomes potentially arising for the wrong reasons. This study confirmed that model evaluation using this method enables a balanced evaluation of different variables and a robust estimation of the best parameter set. The proposed method proved useful for flexible, integrated multivariable model evaluation, with modifications allowed per the user’s requirements.
