What is more important for model calibration: information on the discharge dynamics or information on the discharge volume?

Previous studies have shown that information on the discharge dynamics (e.g., variation in the water level) is valuable to constrain the parameters of a lumped hydrological model for some catchments, and that information on the discharge volume further improves model performance for most catchments. It has been suggested that for some catchments an estimate of the mean discharge already leads to a good model fit but so far, there have not been any systematic studies to test this. Therefore, it remains unclear for which catchments (i.e., for which regions or for catchments with specific characteristics) information on the discharge dynamics are most valuable for model calibration, for which catchments an estimate of the mean annual discharge is already sufficient, and for which catchments both data sources are needed for model calibration. Therefore, we used a subset of the Caravan large-sample dataset and assessed the value of water level measurements, estimates of the mean discharge, and both data sources together for the calibration of a simple bucket-type hydrological model. Preliminary results suggest that mainly climatic characteristics determine the relative value of the different data types for hydrological model calibration. This type of assessment of the value of data for a wide range of catchments allows for more optimal allocation of resources when it comes to obtaining limited data for the calibration of hydrological models for ungauged catchments.