Abstract
Snow and precipitation estimates in high-mountain regions typically suffer from low temporal and spatial resolution and large uncertainties. Here, we present a two-step statistically based model to derive spatio-temporal highly resolved estimates of snow water equivalent (SWE) across the Swiss Alps. A multiple linear regression model (Step-1 MLR) was first used to combine the CombiPrecip radar-gauge product with the precipitation and wind speed (10 m from the ground) of the numerical weather prediction model COSMO-1 in order to adjust the precipitation estimates. Step-1 MLR was trained with SWE data from a cosmic ray sensor (CRS) installed on the Plaine Morte glacier and tested with SWE data from a CRS on the Findel glacier. Step-1 MLR was then applied to the entire area of eight Swiss glaciers and evaluated with scattered end-of-season in-situ manual SWE measurements. The cumulative estimates of Step-1 MLR were found to agree well with the end-of-season measurements. The observed differences can partially be explained by considering the radar visibility, melting processes and preferential snow deposition, which are dictated by the local topography and local weather conditions. To address these limitations of Step-1 MLR, several high-resolution topographical parameters and a solar radiation parameter were included in the subsequent MLR version (Step-2 MLR). Step-2 MLR was evaluated by means of cross-validation, and it showed an overall correlation of 0.78 and a mean bias error of 4 mm with respect to end-of-season in-situ measurements. Step-2 MLR was also evaluated for non-glacierized regions by evaluating it against twice-monthly manual SWE measurements at 44 sites in the Swiss Alps. In such a setting, the Step-2 model showed an overall weaker correlation (0.53) and a higher mean bias error (31 mm). On the other hand, negative variations of the measured SWE were removed because of the lower altitude of the sites, thereby leading to more pronounced melting periods, which again increased the correlation values to 0.63 and reduced the mean bias error to 12 mm. Such results confirm the high potential of the model for applications to other mountainous regions.
Highlights
Knowledge of the spatio-temporal distribution of snow (snow depth (SD) and snow water equivalent (SWE)) during winter in high-mountain regions is essential to understand key processes of hydrology (e.g., Kobold and Suselj, 2005), glaciology (e.g., Zhang, 2005; Fujita, 2008), climatology (e.g., Salzmann et al, 2014), climate-cryospheric interactions (e.g., Hock et al, 2017) and of the related applied fields, such as natural hazard studies (e.g., Wood et al, 2016) or water resource studies
The performance is assessed by evaluating the results against independent spatially scattered end-of-season in-situ SWE measurements (Section 4.1) and temporally continuous cosmic ray sensor (CRS) observations (Section 4.2)
The minima of the adjusted CombiPrecip is clearly located at a higher altitude, and exactly where the largest SWE amounts are found for the in-situ measurements
Summary
Knowledge of the spatio-temporal distribution of snow (snow depth (SD) and snow water equivalent (SWE)) during winter in high-mountain regions is essential to understand key processes of hydrology (e.g., Kobold and Suselj, 2005), glaciology (e.g., Zhang, 2005; Fujita, 2008), climatology (e.g., Salzmann et al, 2014), climate-cryospheric interactions (e.g., Hock et al, 2017) and of the related applied fields, such as natural hazard studies (e.g., Wood et al, 2016) or water resource studies. SD and SWE measurements are obtained annually in-situ on many mountain glaciers during winter mass balance monitoring (e.g., GLAMOS, 2018). These measurements are often the only ones available in remote high-mountain regions, making them an important source of data. These data usually only provide measurements for single points once a year, that is, at the end of the accumulation period (e.g., Huss et al, 2015). SD and SWE measurements are obtained in-situ in non-glacierized areas for the purpose of long-term climate monitoring (e.g., Seiz et al, 2010), avalanche warning (e.g., Lehning et al, 1999) and/or hydrological run off prediction. Unlike the measurements conducted on glaciers, these measurements are taken more frequently in time (twice a month in Switzerland), albeit at lower altitudes (from 1,059m.a.s.l. to 2,626m.a.s.l. in the Swiss Alps (cf. Jonas et al, 2009; Marty, 2017))
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