Snow Depth Measurements Research Articles

Expansion of Tree Vegetation in the Forest–Mountain Steppe Ecotone on the Southern Urals in Relation to Changes in Climate and Habitat Moisture

As found by comparing historical topographic maps and present-day satellite images, steppificated areas in the southern part of the eastern macroslope of the Southern Urals have decreased in size by 17.6% between 1986 and 2015 due to forest expansion. Analysis of the age structure of tree stands in the forest–mountain steppe ecotone provided evidence for intense pine and larch regeneration in this region from 1915 to the 1960s, which has led to significant increase in stand density and upward shift of treelines along slopes and ravines by the end of the 20th century. Intense pine regeneration during the past 35–40 years has been observed at the boundary between closed and open forests and in sparse woodlands. Forest expansion to the mountain steppes throughout the ecotone proceeded against the background of increase in temperature and precipitation during the cold period of the year. The rate of forest expansion to particular areas varied depending on differences in moisture supply, which, in turn, depended on local microclimatic and edaphic conditions. This is confirmed by our measurements of snow depth and density and soil moisture and thickness, which show that forest has expanded at the highest rate in more snowy and moisture-abundant slope areas.

Decadal Variations of Sea Ice Thickness in the Amundsen‐Bellingshausen and Weddell Seas Retrieved From ICESat and IceBridge Laser Altimetry, 2003–2017

AbstractThe changing distributions of sea ice thickness in different regions around Antarctica are important metrics of climate variability, but estimates have large uncertainties. Here, we analyze laser altimetry data from ICESat and IceBridge to generate a record of total freeboard and sea ice thickness in the Amundsen‐Bellingshausen and Weddell seas during austral springs from 2003 to 2017. We improve the buoyancy approach (BOC) to estimate sea ice thickness from total freeboard without altimetry‐coincident measurements of snow depth. The sea ice thickness estimated by the improved BOC agrees well with field measurements and is slightly larger than those derived by previous approaches of zero ice freeboard and empirical relationship. The average sea ice thickness had large interannual variations with no significant trend in both seas from 2003 to 2017. Although sea ice thickness usually did not covary with sea ice extent in these regions, sea ice thickness showed a similar rapid decline with ice extents in the Weddell Sea during 2014–2017. The nearly continuous long‐term record of sea ice thickness is of great significance for the sea ice and global climatic model communities, especially given the future extension of the record using measurements from ICESat‐2 launched in late 2018.

Estimating snow depth Inversion Model Assisted Vector Analysis based on temperature brightness for North Xinjiang region of China

ABSTRACT The measurement of snow depth based on temperature brightness with passive microwave sensing is still achallenging problem. Snow depth for the snow cover hydrological model and climate model is asignificant input parameter. Hence, this study concentrates on Inversion Model Assisted Vector Analysis (IMAV) for estimating snow depth in north Xinjiang based on the brightness of temperature. Further, the estimated set of IMAV has been hybridized to address the problem. The results suggested that for both horizontal and vertically polarized PMW radiation the IMAV outperforms SVM at 11.05, 19.6, and 38.4 GHz. If the root mean square error (RMSE) in the IMAV performance is 8 K or below, compared with anormal SVM calculation, then the average over the nine-year study period across the North Xinjiang region of China, the failure correlation coefficient is 7 or greater. Compared with SVM tests, the RMSE was decreased by more than 17% for any of the six frequencies and polarization combinations evaluated, while the anomaly coefficient was raised by more than 50%.Such results suggest that the IMAV is asuperior alternative to the SVM for subsequent use in adata assimilation system as acalculating operator.

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.

Potential of Balloon Photogrammetry for Spatially Continuous Snow Depth Measurements

We carried out two aerial surveys using a weather balloon as the platform to measure the snow depth in the Wolverton watershed, CA, USA: one when the site was snow covered and the other one after the snow melted out. We reconstructed the 3-D surfaces of the site using the structure-from-motion (SfM) photogrammetry of the photographs taken in the surveys and differenced the heights of the two surfaces to obtain the snow depth. The snow depth estimates corresponded well with 32 manual measurements of snow depth, with R = 0.87 (p <; 0.05) and a root-mean-square error (RMSE) of 7.6 cm, the majority of which is a 6-cm systematic bias due to the vegetation rebound in the snow-off measurements. The relative depth error is 17% in the extremely dry year of sampling (i.e., 2015) and is expected to decrease for deeper snow because the absolute error of SfM is relatively static. The processed snow depth is able to capture the snow spatial variability at submeter scale. This study suggests that balloon photogrammetry is a repeatable, flexible, economical, and safe method for continuous snow depth measurement at small scales and could complement existing remote sensing platforms (e.g., aircrafts, satellites, and drones) for snow observations in open areas by providing spatial continuity, long observation time, and customizable resolution.

Information content of snow hydrological signatures based on streamflow, precipitation and air temperature

AbstractHydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter‐annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter‐annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter‐catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.

Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain

Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

Snow Load Calculations for Alaska Using GHCN Data (1950–2017)

Abstract This paper presents the results of snow load calculations in Alaska based on measurements of snow depth and snow water equivalent from 1950 to 2017 acquired from the Global Historical Clim...

In-Situ LED-Based Observation of Snow Surface and Depth Transects.

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).

Spatial Representativeness Analysis for Snow Depth Measurements of Meteorological Stations in Northeast China

AbstractTriple collocation (TC) is a popular technique for determining the data quality of three products that estimate the same geophysical variable using mutually independent methods. When TC is applied to a triplet of one point-scale in situ and two coarse-scale datasets that have the similar spatial resolution, the TC-derived performance metric for the point-scale dataset can be used to assess its spatial representativeness. In this study, the spatial representativeness of in situ snow depth measurements from the meteorological stations in northeast China was assessed using an unbiased correlation metric estimated with TC. Stations are considered representative if ; that is, in situ measurements explain no less than 50% of the variations in the “ground truth” of the snow depth averaged at the coarse scale (0.25°). The results confirmed that TC can be used to reliably exploit existing sparse snow depth networks. The main findings are as follows. 1) Among all the 98 stations in the study region, 86 stations have valid values, of which 57 stations are representative for the entire snow season (October–December, January–April). 2) Seasonal variations in are large: 63 stations are representative during the snow accumulation period (December–February), whereas only 25 stations are representative during the snow ablation period (October–November, March–April). 3) The is positively correlated with mean snow depth, which largely determines the global decreasing trend in from north to south. After removing this trend, residuals in can be explained by heterogeneity features concerning elevation and conditional probability of snow presence near the stations.

On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model

To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, where the variability of topography and meteorology over short distances strongly influences the distribution of snow cover, the dominant component in the alpine water cycle. For the high alpine Research Catchment Zugspitze (RCZ) a new method for objective delineation of hydrological response units (HRUs) using a time series of high resolution LIDAR derived snow depth maps and the physiographic properties of the RCZ is introduced. Via principle component analysis (PCA) of these maps, a dominant snow depth pattern, that turned out to be largely defined during the (winter) accumulation period was identified. This dominant pattern serves as a reference for HRU delineations on the basis of cluster analyses of the catchment’s physiographic properties. The method guarantees for an appropriate, objective, spatial discretization scheme, which allows for a reliable and meaningful reproduction of snow cover variability with the Cold Regions Hydrological Model—at the same time avoiding significant increase of computational demands. Different HRU schemes were evaluated with measured snow depth and the comparison of their model results identified significant differences in model output and best performance of the scheme which best represents measured snow depth distribution.

Assimilating snow observations to snow interception process simulations

AbstractSnow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post‐storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time‐lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule‐based direct insertion method. Interception determined using density estimates from the Hedstrom‐Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree‐measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time‐lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.

Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation

Snow is an important water resource and plays a critical role in the hydrologic cycle. Accurate measurements of snow depth are needed by scientists to set up a more refined meteorology–hydrology model. Recently, the Global Navigation Satellite System Reflectometry (GNSS-R) has been developed and applied for snow depth monitoring, with low cost and high resolution. We propose an improved snow depth retrieval method using a combination of GNSS triple-frequency carrier phase. The topographic feature of the reflecting surface is considered for estimating the snow depth by using the density-based spatial clustering of applications with noise algorithm and normalization method. Observables from the GNSS station in Alaska, USA, are used to monitor snow depth and compared with the ground-truth measurements. Compared with the traditional triple-frequency snow depth retrieval method, the new approach has better performance for Galileo and BDS. The RMSE of the snow depth estimate reduces by nearly 40%, and the correlation coefficient increases from 0.93 to 0.97 for Galileo and from 0.91 to 0.95 for BDS, respectively. The research findings show no notable deviations on snow depth average estimation between Galileo and BDS observations compared to the GPS ones. Moreover, the solution with the proposed method results in improving spatial resolution due to the increasing number of satellites and better azimuth coverage.

Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

AbstractCalifornia depends on snow accumulation in the Sierra Nevada for its water supply. Snowfall is measured by a combination of snow pillows, snow courses, and rain gauges. However, the paucity of locations of these measurements, particularly at high elevations, can introduce artifacts into precipitation estimates that are detrimental for hydrologic forecasting. To reduce errors, we need high‐resolution, spatially complete measurements of precipitation. Remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve storms, could help mitigate this problem in snow‐dominated watersheds. Since 2013, National Aeronautics and Space Administration's Airborne Snow Observatory (ASO) has measured snow depth in the Tuolumne basin of California's Sierra Nevada to advance streamflow forecasting through improved estimates of SWE. In early April 2015, two flights 6 days apart bracketed a single storm. The work herein documents a new use for ASO and presents a methodology to directly measure the spatial variability of frozen precipitation. In an end‐to‐end analysis, we also compare gauge‐interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO change in SWE. The work shows that the extension of ASO operations to additional storms could benefit our understanding of mountain hydrometeorology by delivering observations that can truly evaluate the spatial distribution of snowfall for both statistical and numerical models.

Impact of Surface Albedo Assimilation on Snow Estimation

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

Ski piste snow ablation versus potential infiltration (Veporic Unit, Western Carpathians)

Abstract Snow production results in high volume of snow that is remaining on the low-elevation ski pistes after snowmelt of natural snow on the off-piste sites. The aim of this study was to identify snow/ice depth, snow density, and snow water equivalent of remaining ski piste snowpack to calculate and to compare snow ablation water volume with potential infiltration on the ski piste area at South-Central Slovak ski center Košútka (Inner Western Carpathians; temperate zone). Snow ablation water volume was calculated from manual snow depth and density measurements, which were performed at the end of five winter seasons 2010–2011 to 2015–2016, except for season 2013–2014. The laser diffraction analyzes were carried out to identify soil grain size and subsequently the hydraulic conductivity of soil to calculate the infiltration. The average rate of water movement through soil was seven times as high as five seasons’ average ablation rate of ski piste snowpack; nevertheless, the ski piste area was potentially able to infiltrate only 47% of snow ablation water volume on average. Limitation for infiltration was frozen soil and ice layers below the ski piste snowpack and low snow-free area at the beginning of the studied ablation period.

Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

Abstract. Ice and snowmelt feed the Indus River and Amu Darya in western High Mountain Asia, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem. However, recently we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). To validate SWE reconstruction, at each station, accumulated precipitation and SWE were derived from snow depth using the numerical snow cover model SNOWPACK. High-resolution (500 m) reconstructed SWE estimates from the Parallel Energy Balance Model (ParBal) were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km resolution to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provides stability information. The median number of critical layers and percentage of faceted layers across all of the pixels containing the AKAH stations were computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at peak SWE. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. A heavily faceted snowpack was observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

14 years of extended snow depth and snow water equivalent measurements in eastern parts of Finland

14 years of extended snow depth and snow water equivalent measurements in eastern parts of Finland

Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.