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Abstract

While global hydrological models (GHMs) are affected by large uncertainties regarding model structure, forcing and calibration data, and parameters, observations of model output variables are rarely used to calibrate the model. Pareto-dominance-based multi-objective calibration, often referred to as Pareto-optimal calibration (POC), may serve to estimate model parameter sets and analyse trade-offs among different objectives during calibration. Within a POC framework, we determined optimal parameter sets for the WaterGAP global hydrological model (WGHM) in the two largest basins of the Indian subcontinent – the Ganges and the Brahmaputra, collectively supporting nearly 580 million inhabitants. The selected model parameters, determined through a multi-variable, multi-signature sensitivity analysis, were estimated using up to four types of observations: in situ streamflow (Q), GRACE and GRACE Follow-On terrestrial water storage anomaly (TWSA), LandFlux evapotranspiration (ET), and surface water storage anomaly (SWSA) derived from multi-satellite observations. While our sensitivity analysis ensured that the model parameters that are most influential for the four variables were identified in a transparent and comprehensive way, the rather large number of calibration parameters, 10 for the Ganges and 16 for the Brahmaputra, had a negative impact on parameter identifiability during the calibration process. Calibration against observed Q was crucial for reasonable streamflow simulations, while additional calibration against TWSA was crucial for the Ganges basin and helpful for the Brahmaputra basin to obtain a reasonable simulation of both Q and TWSA. Additionally calibrating against ET and SWSA enhanced the overall model performance slightly. We identified several trade-offs among the calibration objectives, with the nature of these trade-offs closely tied to the physiographic and hydrologic characteristics of the study basins. The trade-offs were particularly pronounced in the Ganges basin, in particular between Q and SWSA, as well as between Q and ET. When considering the observational uncertainty of the calibration data, model performance decreases in most cases. This indicates an overfitting to the singular observation time series by the calibration algorithm. We therefore propose a transparent algorithm to identify high-performing Pareto solutions under consideration of observational uncertainties of the calibration data.