Abstract
Abstract. For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. 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.
Highlights
For the annual mean temperature, it is obvious that no dataset except some years of DWD_Wendelstein as well as the GLDAS2 and GLDAS2_repro is within the range that was measured at DWD during the last 30 years
We present the effect of different meteorological and digital elevation model (DEM) model input datasets on specific snow hydrological indices calculated for the entire Research Catchment Zugspitze (RCZ) and single
We evaluated the applicability of global data products regarding their application for snow hydrological modelling in a high-alpine region for potential use in ungauged basins
Summary
A broad range of mountain catchments, which contribute snow melt as major runoff component to the rivers, are affected by the lack of sufficient instrumentation and data (Hrachowitz et al, 2013; Wortmann et al, 2018; Zhang et al, 2015; Dussaillant et al, 2012). This is problematic, as mountain catchments are highly relevant for local water supply and that of the adjacent lowlands (Bandyopadhyay et al, 1997; Viviroli et al, 2011; Meybeck et al, 2001; Wesemann et al, 2018; Mauser and Prasch, 2016; Zhang et al, 2015; Koch et al, 2011; Huggel et al, 2015). In situ SWE measurements are increasingly facilitated by continuous improvements of existing methods like snow pillows or scales and developments of new methods like snow sensing with Global Navigation Satellite System (GNSS) signals and cosmic rays (Koch et al, 2019; Schattan et al, 2017)
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