A comparison of the use of local legacy soil data and global datasets for hydrological modelling a small-scale watersheds: Implications for nitrate loading estimation

Appropriate soil property data are important inputs for the development of hydrological models, and as the scale of the study decreases, the relevance of these data increases. Watershed soil surveys require time and money, so readily available data are often used. This study compares the hydrological and nitrate loading prediction performance of the globally available dataset SoilGrids and a dataset prepared from local legacy data by a random forest approach. The study was carried out using the SWAT model for a 33 km(2) watershed in the Czech Republic. Model performance was tested by applying a Latin hypercube-sampled parameter set to both a model using the SoilGrids data as input, and another model based on the local soil dataset. The SoilGrids dataset generally shows finer soil texture and higher organic carbon content than the dataset based on local legacy data. These differences are also reflected in the hydrological process predictions, where the SoilGrids-based model produced less lateral flow than that of the local dataset, with the compensation of higher evapotranspiration. This difference in water balance led to worse nitrate loading estimation by the SoilGrids-based model. The results of this case study suggest that the use of a local dataset is still more appropriate, despite the availability of global data with mid resolution.