Investigating regionalization techniques for large-scale hydrological modelling

This work investigates regionalization techniques for large-scale model applications in the frame of a pan-European assessment of water resources covering approx. 740,000 km² in Western Europe. Using the SWAT platform, four variants of the similarity based regionalization approach were compared. The first two involved unsupervised clustering to define hydrological regions before performing hydrological model calibration, whereas the last two involved supervised clustering after performing calibration. Similarity is defined using Partial Least Squares Regression (PLSR) analysis that identifies watershed physiographic characteristics that are most relevant for the selected hydrological response indices. The PLSR results indicate that typically available watershed characteristics such as geomorphology, land-use, climate, and soil properties describe reasonably well the average hydrological conditions but poorly the extreme events. Regionalization variants considering unsupervised clustering and supervised clustering performed similarly well when using all available information. However, results indicate that supervised clustering uses data more efficiently and may be more suitable when data are scarce. It is demonstrated that parsimonious use of available data can be achieved using both regionalization techniques. Finally, model performance consistently becomes acceptable by calibrating watersheds covering only 10% of the model domain, thus, making the calibration task affordable in terms of time and computational resources required.