Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

López‐Moreno, Juan I.; Leppänen, Leena; Luks, Bartłomiej; Holko, Ladislav; Picard, Ghislain; Sanmiguel‐Vallelado, Alba; Alonso‐González, Esteban; Finger, David C.; Arslan, Ali; Gillemot, Katalin; Şensoy, Aynur; Sorman, Arda; Ertaş, M. Cansaran; Fassnacht, S. R.; Fierz, Charles; Marty, Christoph

doi:10.1002/hyp.13785

Cited by 32 publications

(37 citation statements)

References 31 publications

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“…The GNSS-derived SWE values were accurate compared to the reference data and no particular dependence on the elevation of the sites or their differing local snow conditions were found, which implies that the algorithm is in addition to high-alpine sites also suitable for lower laying sites. Regarding all sites and the two winter seasons overall, the RMSE was 34 mm and the RMSRE 11 % compared to manual reference measurements, which also have an uncertainty of at least 5 % (López-Moreno et al, 2020). Previously reported findings on GNSS-based SWE measurements (Henkel et al, 2018;Koch et al, 2019;Steiner et al, 2019a) at Weissfluhjoch are in agreement with our results.…”

Section: Gnss-derived Snow Cover Properties and Reference Datasupporting

confidence: 92%

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Capelli

Koch

Henkel³

et al. 2021

Preprint

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Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

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Section: Gnss-derived Snow Cover Properties and Reference Datasupporting

confidence: 92%

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Capelli

Koch

Henkel³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Therefore, SWE records might be biased towards early and midwinter new-snow densities, which are lower (e.g., Jonas et al, 2009). Still, there are also some indications that using, for example, 100 kg m −3 as constant for new-snow density when modeling SWE results in an overestimation of precipitation (up to 30 % according to Mair et al, 2016). The calibrated value for ρ 0 can be regarded as a reasonable result, even more when only considering it as a model parameter but not as a physical constant.…”

Section: New-snow Density ρmentioning

confidence: 99%

Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Winkler

Schellander

Gruber

2021

Hydrol. Earth Syst. Sci.

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Abstract. Reliable historical manual measurements of snow depths are available for many years, sometimes decades, across the globe, and increasingly snow depth data are also available from automatic stations and remote sensing platforms. In contrast, records of snow water equivalent (SWE) are sparse, which is significant as SWE is commonly the most important snowpack feature for hydrology, climatology, agriculture, natural hazards, and other fields. Existing methods of modeling SWE either rely on detailed meteorological forcing being available or are not intended to simulate individual SWE values, such as seasonal “peak SWE”. Here we present a new semiempirical multilayer model, Δsnow, for simulating SWE and bulk snow density solely from a regular time series of snow depths. The model, which is freely available as an R package, treats snow compaction following the rules of Newtonian viscosity, considers errors in measured snow depth, and treats overburden loads due to new snow as additional unsteady compaction; if snow is melted, the water mass is stepwise distributed from top to bottom in the snowpack. Seven model parameters are subject to calibration. Snow observations of 67 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps, are used to find an optimal parameter setting. Data from another 71 independent winters from 15 stations are used for validation. Results are very promising: median bias and root mean square error for SWE are only −3.0 and 30.8 kg m−2, and +0.3 and 36.3 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, and even sophisticated thermodynamic snow models do not necessarily perform better. As such, the new model offers a means to derive robust SWE estimates from historical snow depth data and, with some modification, to generate distributed SWE from remotely sensed estimates of spatial snow depth distribution.

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“…An overall uncertainty of about 5% is estimated for the bulk snow density estimation (cf. López-Moreno et al, 2020). The areal SWE is then determined by multiplying these mean density estimates with the spatially scattered SD measurements (Huss et al, 2015).…”

Section: Manual In-situ Measurementsmentioning

confidence: 99%

Continuous Spatio-Temporal High-Resolution Estimates of SWE Across the Swiss Alps – A Statistical Two-Step Approach for High-Mountain Topography

et al. 2021

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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.

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Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Abstract: Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: instrumental bias and variability induced by observers. Hydrological Processes.

Cited by 32 publications

References 31 publications

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Continuous Spatio-Temporal High-Resolution Estimates of SWE Across the Swiss Alps – A Statistical Two-Step Approach for High-Mountain Topography

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