scholarly journals Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

2021 ◽  
Author(s):  
Hans Lievens ◽  
Isis Brangers ◽  
Hans-Peter Marshall ◽  
Tobias Jonas ◽  
Marc Olefs ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Slight Reduction ◽  
European Alps ◽  
Mountainous Regions ◽  
Seasonal Snow ◽  
Spatio Temporal ◽  
Snow Conditions ◽  
Snow Mass

Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution in mountainous regions, where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resources management, flood forecasting and numerical weather prediction.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

scholarly journals Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
Author(s):  
Naoki Mizukami ◽  
Sanja Perica
Keyword(s):  
United States ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Western United States ◽  
Densification Rate ◽  
Snow Density ◽  
Mountainous Regions ◽  
Spatiotemporal Characteristics ◽  
Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

scholarly journals A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps

2010 ◽  
Vol 51 (54) ◽  
pp. 32-38 ◽  
Author(s):  
Luca Egli ◽  
Tobias Jonas ◽  
Jean-Marie Bettems
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Practical Importance ◽  
Cost Effective ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Virtual Network ◽  
Systematic Bias ◽  
Snow Water

AbstractDaily new snow water equivalent (HNW) and snow depth (HS) are of significant practical importance in cryospheric sciences such as snow hydrology and avalanche formation. In this study we present a virtual network (VN) for estimating HNW and HS on a regular mesh over Switzerland with a grid size of 7 km. The method is based on the HNW output data of the numerical weather prediction model COSMO-7, driving an external accumulation/melting routine. The verification of the VN shows that, on average, HNW can be estimated with a mean systematic bias close to 0 and an averaged absolute accuracy of 4.01 mm. The results are equivalent to the performance observed when comparing different automatic HNW point estimations with manual reference measurements. However, at the local scale, HS derived by the VN may significantly deviate from corresponding point measurements. We argue that the VN presented here may introduce promising cost-effective options as input for spatially distributed snow hydrological and avalanche risk management applications in the Swiss Alps.

scholarly journals The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

2019 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin
Keyword(s):  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Water Percolation ◽  
Snow Water ◽  
Snow Model ◽  
Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

scholarly journals A systematic review of ecological responses to variation in seasonal snow cover

2020 ◽  
Author(s):  
Rachel Slatyer ◽  
Pieter Andrew Arnold
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Terrestrial Ecosystems ◽  
Ecosystem Change ◽  
Future Research ◽  
Snow Layer ◽  
Spring Phenology ◽  
Ecological Responses ◽  
Seasonal Snow ◽  
Snow Conditions

Seasonal snow is among the most important factors governing the ecology of many terrestrial ecosystems, but rising global temperatures are changing snow regimes and driving widespread declines in the depth and duration of snow cover. Loss of the insulating snow layer will fundamentally change the environment. Understanding how individuals, populations, and communities respond to different snow conditions is thus essential for predicting and managing future ecosystem change. We synthesized 365 studies that have examined ecological responses to variation in winter snow conditions. This research encompasses a broad range of methods (experimental manipulations, natural snow gradients, and long-term monitoring approaches), locations (35 countries), study organisms (plants, mammals, arthropods, birds, fish, lichen, and fungi), and response measures. Earlier snowmelt was consistently associated with advanced spring phenology in plants, mammals, and arthropods. Reduced snow depth also often increased mortality and/or physical injury in plants, although there were few clear effects on animals. Neither snow depth nor snowmelt timing had clear or consistent directional effects on body size of animals or biomass of plants. With 96% of studies from the northern hemisphere, the generality of these trends across ecosystems and localities is also unclear. We identified substantial research gaps for several taxonomic groups and response types, with notably scarce research on winter-time responses. We have developed an agenda for future research to prioritize understanding of the mechanisms underlying responses to changing snow conditions and the consequences of those responses for seasonally snow-covered ecosystems.

scholarly journals Spatial and temporal variability of snow depth and SWE in a small mountain catchment

2010 ◽  
Vol 4 (1) ◽  
pp. 1-30 ◽  
Author(s):  
T. Grünewald ◽  
M. Schirmer ◽  
R. Mott ◽  
M. Lehning
Keyword(s):  
Temporal Variability ◽  
Snow Depth ◽  
Energy Transport ◽  
Snow Water Equivalent ◽  
Laser Scanner ◽  
Ablation Rate ◽  
Spatial And Temporal Variability ◽  
M Cell ◽  
Snow Water ◽  
Spatio Temporal

Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner (TLS), which is particularly suited for measurements of snow covered surfaces, snow depth, snow water equivalent (SWE) and melt rates have been monitored in a high alpine catchment during an ablation period. This allowed for the first time to get a high resolution (2.5 m cell size) picture of spatial variability and its temporal development. A very high variability in which maximum snow depths between 0–9 m at the end of the accumulation season was found. This variability decreased during the ablation phase, although the dominant snow deposition features remained intact. The spatial patterns of calculated SWE were found to be similar to snow depth. Average daily melt rate was between 15 mm/d at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of melt rates increased during the ablation rate and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It could be qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway

2018 ◽  
Vol 49 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Thomas Skaugen ◽  
Hanneke Luijting ◽  
Tuomo Saloranta ◽  
Dagrun Vikhamar-Schuler ◽  
Karsten Müller
Keyword(s):  
Energy Balance ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Threshold Temperature ◽  
Temperature Index ◽  
Ungauged Sites ◽  
Temperature And Precipitation ◽  
Snow Conditions ◽  
Model Structures

Abstract In order to use the best suited snow models to investigate snow conditions at ungauged sites and for a changed climate, we have tested four snow models for 17 catchments in Norway. The Crocus and seNorge models are gridded whereas the Distance Distribution Dynamics (DDD) model with its two versions, DDD_CX and DDD_EB, is catchment based. Crocus and DDD_EB use energy balance for estimating snowmelt and SeNorge and DDD_CX use temperature-index methods. SeNorge has calibrated the temperature-index against observed snowmelt, whereas DDD_CX has calibrated the temperature-index against runoff. The models use gridded temperature and precipitation at 1 h resolution for the period 2013–2016. Crocus needs additional forcing from a numerical weather prediction model, whereas DDD_EB calculates the energy-balance elements by using proxy models forced by temperature and precipitation. The threshold temperature for solid and liquid precipitation is common for all the models and equal to 0.5 °C. No corrections of precipitation or temperature are allowed. The snow simulations are validated against observed snow water equivalent (SWE) and against satellite derived snow covered area (SCA). SeNorge and DDD_EB perform best with respect to both SWE and SCA suggesting model structures suited for describing snow conditions at ungauged sites and for a changed climate.

scholarly journals Recent advances in remote sensing of seasonal snow

2010 ◽  
Vol 56 (200) ◽  
pp. 1141-1150 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Analysis Techniques ◽  
Seasonal Snow ◽  
The Past ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Instruments And Techniques

AbstractRemote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified.

scholarly journals Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps

2017 ◽  
Vol 18 (4) ◽  
pp. 1021-1031 ◽  
Author(s):  
Christoph Marty ◽  
Anna-Maria Tilg ◽  
Tobias Jonas
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Critical Role ◽  
European Alps ◽  
Measurement Series ◽  
Snow Water ◽  
Snow Mass

Abstract Snow plays a critical role in the water cycle of many mountain regions and heavily populated areas downstream. In this study, changes of snow water equivalent (SWE) time series from long-term stations in five Alpine countries are analyzed. The sites are located between 500 and 3000 m above mean sea level, and the analysis is mainly based on measurement series from 1 February (winter) and 1 April (spring). The investigation was performed over different time periods, including the last six decades. The large majority of the SWE time series demonstrate a reduction in snow mass, which is more pronounced for spring than for winter. The observed SWE decrease is independent of latitude or longitude, despite the different climate regions in the Alpine domain. In contrast to measurement series from other mountain ranges, even the highest sites revealed a decline in spring SWE. A comparison with a 100-yr mass balance series from a glacier in the central Alps demonstrates that the peak SWEs have been on a record-low level since around the beginning of the twenty-first century at high Alpine sites. In the long term, clearly increasing temperatures and a coincident weak reduction in precipitation are the main drivers for the pronounced snow mass loss in the past.

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