Influence of the density and of the “nestedness” of the gauged donors when regionalising a rainfall-runoff-model

Abstract:

The setup 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). The informative content of the data set (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method: this study analyses how the performances of model regionalisation approaches are influenced by the “information richness” of the available regional data set and in particular by the gauging density and by the presence of nested donor catchments, that are expected to be hydrologically very similar to the target section.

The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches.

The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors, and then re-applied decreasing the informative content of the data set. The first analysis consists in excluding the basins that are nested with the target one and the status of “nestedness” is identified taking into account either the position of the closing section along the river or the percentage of shared drainage area. Secondly, the impact of reducing station density on regionalisation performance is analysed.

The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, exploiting the information of more than one donor basin but preserving the correlation structure of the parameter set, and using, as similarity measure, a set of catchment descriptors, rather than the geographical distance, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions.