Volume 67 (2019): Issue 1 (March 2019)

The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.

The contribution of snow meltwater to catchment streamflow can be quantified through hydrograph separation analyses for which stable water isotopes (18O, 2H) are used as environmental tracers. For this, the spatial and temporal variability of the isotopic composition of meltwater needs to be captured by the sampling method. This study compares an optimized snowmelt lysimeter system and an unheated precipitation collector with focus on their ability to capture snowmelt rates and the isotopic composition of snowmelt. The snowmelt lysimeter system consists of three individual unenclosed lysimeters at ground level with a surface of 0.14 m2 each. The unheated precipitation collector consists of a 30 cm-long, extended funnel with its orifice at 2.3 m above ground. Daily snowmelt samples were collected with both systems during two snowfall-snowmelt periods in 2016. The snowmelt lysimeter system provided more accurate measurements of natural melt rates and allowed for capturing the small-scale variability of snowmelt process at the plot scale, such as lateral meltwater flow from the surrounding snowpack. Because of the restricted volume of the extended funnel, daily melt rates from the unheated precipitation collector were up to 43% smaller compared to the snowmelt lysimeter system. Overall, both snowmelt collection methods captured the general temporal evolution of the isotopic signature in snowmelt.

In this paper we describe the use of modified passive capillary samplers (PCSs) to investigate the water isotope variability of snowmelt at selected sites in Slovenia during winter 2011/2012 and during winter 2012/2013. First, PCS with 3 fibreglass wicks covering approximately 1 m2 were tested to determine sample variability. We observed high variability in the amount of snowmelt water collected by individual wick (185 to 345 g) and in the isotope composition of oxygen (δ18O −10.43‰ to −9.02‰) and hydrogen (δ2H −70.5‰ to −63.6‰) of the collected water. Following the initial tests, a more detailed investigation was performed in winter 2012/2013 and the variability of snowmelt on the local scale among the different levels (i.e. within group, between the close and more distant groups of wicks) was investigated by applying 30 fibreglass wicks making use of Analysis Of Variance (ANOVA) and a balanced hierarchical sampling design. The amount of snowmelt water collected by an individual wick during the whole experiment was between 116 and 1705 g, while the isotope composition varied from −16.32‰ to −12.86‰ for δ18O and from −120.2‰ to −82.5‰ for δ2H. The main source of variance (80%) stems from the variability within the group of wicks (e.g. within group) while other sources contribute less than 20% of the variability. Amount weighted samples for the 2012-2013 season show no significant differences among groups, but significant differences for particular sampling events were observed. These investigations show that due to the variability within the group of wicks, a large number of wicks (> 5) are needed to sample snowmelt.

Nine years of seasonal δ¹⁸O values in precipitation, soilwater and groundwater were evaluated in the Uhlířská catchment between 2008 and 2016 and recharge winter/summer ratios were calculated using δ¹⁸O values. The longterm average 18O content in groundwater is lower than the mean weighted ¹⁸O content in precipitation. This is explained by more than 50% of winter- and snowmelt- induced groundwater recharge that occurs in all years except of 2010 and 2013. The recharge of the peat organic soil water is balanced between summer and winter, whereas the mineral hillslope soil is dominantly recharged by summer precipitation. The 67% portion of baseflow, dominantly generated in the winter season, is composed of groundwater and peat organic soil water, according to the hydrochemical distribution of runoff components. Isotopic mass balance of individual winters shows that precipitation in warmer winters is entirely transformed into outflow until the end of the winter season, generating no significant water storage for potential drought periods

High amounts of precipitation are temporarily stored in high-alpine snow covers and play an important role for the hydrological balance. Stable isotopes of hydrogen (δ2H) and oxygen (δ¹⁸O) in water samples have been proven to be useful for tracing transport processes in snow and meltwater since their isotopic ratio alters due to fractionation. In 18 snow profiles of two snowfall seasons, the temporal and spatial variation of isotopic composition was analysed on Mt. Zugspitze. The δ¹⁸O and δ2H ranged between -26.7‰ to -9.3‰ and -193.4‰ to -62.5‰ in 2014/2015 and between -26.5‰ to -10.5‰ and -205.0‰ to -68.0‰ in 2015/2016, respectively. Depth-integrated samples of entire 10 cm layers and point measurements in the same layers showed comparable isotopic compositions. Isotopic composition of the snowpack at the same sampling time in spatially distributed snow profiles was isotopically more similar than that analysed at the same place at different times. Melting and refreezing were clearly identified as processes causing isotope fractionation in surficial, initial base or refrozen snow layers. For the future, a higher sampling frequency with detailed isotopic composition measurements during melt periods are recommended to improve the understanding of mass transport associated with snowmelt.

Large-scale forest dieback was reported in recent decades in many parts of the world. In Slovakia, the most endangered species is Norway spruce (Picea Abies). Spruce dieback affects also indigenous mountain forests. We analysed changes in snow cover characteristics in the disturbed spruce forest representing the tree line zone (1420 m a.s.l.) in the Western Tatra Mountains, Slovakia, in five winter seasons 2013-2017. Snow depth, density and water equivalent (SWE) were measured biweekly (10-12 times per winter) at four sites representing the living forest (Living), disturbed forest with dead trees (Dead), forest opening (Open) and large open area outside the forest (Meadow). The data confirmed statistically significant differences in snow depth between the living and disturbed forest. These differences increased since the third winter after forest dieback. The differences in snow density between the disturbed and living forest were in most cases not significant. Variability of snow density expressed by coefficient of variation was approximately half that of the snow depth. Forest dieback resulted in a significant increase (about 25%) of the water amount stored in the snow while the snowmelt characteristics (snowmelt beginning and time of snow disappearance) did not change much. Average SWE calculated for all measurements conducted during five winters increased in the sequence Living < Dead < Meadow < Open. SWE variability expressed by the coefficient of variation increased in the opposite order.

Conceptual degree-day snow models are often calibrated using runoff observations. This makes the snow models dependent on the rainfall-runoff model they are coupled with. Numerous studies have shown that using Snow Cover Area (SCA) remote sensing observation from MODIS satellites helps to better constrain parameters. The objective of this study was to calibrate the CemaNeige degree-day snow model with SCA and runoff observations. In order to calibrate the snow model with SCA observations, the original CemaNeige SCA formulation was revisited to take into account the hysteresis that exists between SCA and the snow water equivalent (SWE) during the accumulation and melt phases. Several parametrizations of the hysteresis between SWE and SCA were taken from land surface model literature. We showed that they improve the performances of SCA simulation without degrading the river runoff simulation. With this improvement, a new calibration method of the snow model was developed using jointly SCA and runoff observations. Further analysis showed that the CemaNeige calibrated parameter sets are more robust for simulating independent periods than parameter sets obtained from discharge calibration only. Calibrating the snow model using only SCA data gave mixed results, with similar performances as using median parameters from all watersheds calibration.

Predicting snow cover dynamics and relevant streamflow due to snowmelt is a challenging issue in mountainous basins. Spatio-temporal variations of snow extent can be analyzed using probabilistic snow cover maps derived from satellite images within a relatively long period. In this study, Probabilistic Snow Depletion Curves (P-SDCs) and Probabilistic Snow Lines (P-SLs) are acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-filtered daily snow cover images. Analyses of P-SDCs show a strong correlation with average daily runoff (R2 = 0.90) and temperature (R2 = 0.96). On the other hand, the challenge lies in developing noteworthy methods to use P-SDCs in streamflow estimations. Therefore, the main objective is to explore the feasibility of producing probabilistic runoff forecasts with P-SDC forcing in a snow dominated basin. Upper Euphrates Basin in Turkey has large snow extent and high snowmelt contribution during spring and summer periods. The melting characteristics are defined by P-SDCs using MODIS imagery for 2001-2012. The value of snow probability maps on ensemble runoff predictions is shown with Snowmelt Runoff Model (SRM) during 2013-2015 where the estimated runoff values indicate good consistency (NSE: 0.47-0.93) with forecasts based on the derived P-SDCs. Therefore, the probabilistic approach distinguishes the snow cover characteristics for a region and promotes a useful methodology on the application of probabilistic runoff predictions especially for snow dominated areas.

In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ΔSWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ΔSWE, showing a reasonable match with a mean accuracy of about 6 mm.

This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

The aim of the investigation was assessment of spatial variability of the characteristics of snowpack, including the snow water equivalent (SWE) as the main hydrological characteristic of a seasonal snow cover. The study was performed in Khibiny Mountains (Russia), where snow density and snow cover stratigraphy were documented with the help of the SnowMicropen measurements, allowing to determine the exact position of the snow layers’ boundaries with accuracy of 0.1 cm. The study site was located at the geomorphologically and topographically uniform area with uniform vegetation cover. The measurement was conducted at maximum seasonal SWE on 27 March 2016. Twenty vertical profiles were measured along the 10 m long transect. Vertical resolution depended on the thickness of individual layers and was not less than 10 cm. The spatial variation of the measured snowpack characteristics was substantial even within such a homogeneous landscape. Bulk snow density variability was similar to the variability in snow height. The total variation of the snowpack SWE values along the transect was about 20%, which is more than the variability in snow height or snow density, and should be taken into account in analysis of the results of normally performed in operational hydrology snow course SWE estimations by snow tubes.