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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-326
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-326
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Aug 2020

13 Aug 2020

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This preprint is currently under review for the journal HESS.

The evaluation of the potential of global data products for snow hydrological modelling in ungauged high alpine catchments

Michael Weber1, Franziska Koch2, Matthias Bernhardt2, and Karsten Schulz2 Michael Weber et al.
  • 1Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
  • 2Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria

Abstract. Worldwide, there is a strong discrepancy between the importance of high alpine catchments for the water cycle and the availability of meteorological and snow hydrological in situ measurements. Good knowledge on the timing and quantity of snow meltwater is crucial for numerous hydrological applications, also far way downstream. For several decades, the number of global data sets of different meteorological and land surface parameters has been increasing, but their applicability in modelling high alpine regions has been insufficiently investigated so far. We tested such data for a 10-year period with the physically-based Cold Regions Hydrological Model (CRHM). Our study site is the gauged high alpine Research Catchment Zugspitze (RCZ) of 12 km2 in the European Alps. We used a selection of nine different meteorological driver data setups including data transferred from another alpine station, data from an atmospheric model and hybrid data, whereof we investigated data for all meteorological parameters and substituting precipitation only. For one product, we applied an advanced downscaling approach to test the advantage of such methods. The range between all setups is high at 3.5 °C for the mean decadal temperature and at 1510 mm for the mean decadal precipitation sum. The comparison of all model results with measured snow depth and reference simulations driven with in situ meteorological data demonstrates that the setup with the transferred data performs best, followed by the substitution of precipitation only with hybrid data. All other setups were unrealistic or showed plausible results only for some parts of the RCZ. As a second goal, we investigated potential differences in model performance resulting from topographic parameterization according to three globally available digital elevation models (DEMs); two with 30 m and one with 1 km resolution. As reference, we used a 2.5 m resolution DEM. The simulations with all DEM setups performed well at the snow depth measurement sites and on catchment scale, even if they indicate considerable differences. Differences are mainly caused by product specific topography induced differences in solar radiation. Surprisingly, the setup with the coarsest DEM performed best in describing the catchment mean due to averaged out topographic differences. However, this was not the case for a finer resolution. For the two plausible meteorological setups and all DEM setups, we additionally investigated the maximum quantity and the temporal development of the snowpack as well as the runoff regime. Even those quite plausible setups revealed differences of up to 20 % in snowpack volume and duration, which consequently lead to considerable shifts in runoff. Overall, we could demonstrate that global data are a valuable source to substitute single missing meteorological variables or topographic information, but the exclusive use of such driver data does not provide sufficiently accurate results for the RCZ. For the future, however, we expect an increasing role of global data in modelling ungauged high alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

Michael Weber et al.

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Michael Weber et al.

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