Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics

Abstract

Abstract. For large-sample hydrological studies over large spatial domains, large-scale meteorological forcing data are often desired. For Europe, the EStreams dataset and catalogue satisfies this demand. In EStreams, the meteorological time series are obtained from the Ensemble Observation (E-OBS) product which is available for all of Europe. Due to the large spatial extent of this dataset, limitations and regional variations of data quality have to be expected when the dataset is compared to smaller-scale datasets, e.g., at national level. In this study, we compare the meteorological time series included for 2682 catchments in EStreams to eight smaller datasets (mostly CAMELS datasets). We assess how the different meteorological data impact the performance of a bucket-type hydrological model. For most catchments, the precipitation amounts derived from E-OBS are lower than the ones from the CAMELS data, while the temperature and the potential evapotranspiration values are higher. Model performances tend to be lower when the E-OBS data are used than when the CAMELS datasets are used for calibration. Exceptions arise when the station density in the E-OBS data is high. This study provides the first assessment of the E-OBS data at a continental scale for hydrological applications and shows that, despite some limitations, the dataset offers a reasonable basis for large-sample hydrological modelling across Europe.