Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density
<p>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 activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrom...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/24/5149/2020/hess-24-5149-2020.pdf |
| Summary: | <p>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
activities for the Prediction in Ungauged Basins (PUB): in order to do so it
is possible to estimate the model parameters based on the hydrometric
information available in the region. The informative content of the dataset (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 regionalisation approaches are
influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the
gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section.</p>
<p>The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different
regionalisation approaches.</p>
<p>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 dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position
of the closing section along the river or the percentage of shared drainage
area. Moreover, the impact of reducing station density on regionalisation
performance is analysed.</p>
<p>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, which
exploit the information of more than one donor basin and preserve the
correlation structure of the parameter, seem to be preferable for
regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to
the deterioration of the informative content of the set of donors.</p> |
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| ISSN: | 1027-5606 1607-7938 |