Importance of the information content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density
Authors: Neri Mattia , Parajka Juraj , Toth Elena 
Published: February 2020

Abstract. The set up of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activity for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to regionalise the model parameters based on the information available in gauged sections in the study region. The information content in the data set of gauged river stations plays an essential role in the assessment of the best regionalisation method: this study analyses how the performances of different model regionalisation approaches are influenced by the information richness of the available regional data set, and in particular by its gauging density and by the presence of nested catchments, that are expected to be hydrologically very similar. The research is carried out over a densely gauged dataset covering the Austrian country, applying two different rainfall-runoff models: a semi-distributed version of the HBV model (TUW model), and the Cemaneige-GR6J model. The regionalisation approaches include both methods which transfer the entire set of model parameters from donor catchments, thus maintaining correlation among parameters (output averaging techniques), and methods which derive each target parameter independently, as a function of the calibrated donors’ ones (parameter averaging techniques). The regionalisation techniques are first implemented using all the basins in the dataset as potential donors, showing that the output-averaging methods outperform the parameter-averaging kriging method, highlighting the importance of maintaining the correlation between the parameter values. The regionalisation is then repeated decreasing the information content of the data set, by excluding the nested basins, identified taking into account either the position of the closing section along the river or the percentage of shared drainage area. The parameter-averaging kriging is the method that is less impacted by the exclusion of the nested donors, whereas the methods transferring the entire parameter set from only one donor suffer the highest deterioration, since the single most similar or closest donor is often a nested one. On the other hand, the output-averaging methods degrade more gracefully, showing that exploiting the information resulting from more than one donor increases the robustness of the approach also in regions that do not have so many nested catchments as the Austrian one. Finally, the deterioration resulting from decreasing the station density on the regionalisation was analysed, showing that the output averaging methods using as similarity measure a set of catchment descriptors, rather than the geographical distance, are more capable to adapt to less dense datasets. The study confirms how the predictive accuracy of parameter regionalisation techniques strongly depends on the information content of the dataset of available donor catchments and indicates that the output-averaging approaches, using more than one donor basin but preserving the correlation structure of the parameter set, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions.