scholarly journals Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

2019 ◽  
Vol 11 (12) ◽  
pp. 1456 ◽  
Author(s):  
Ya-Lun S. Tsai ◽  
Andreas Dietz ◽  
Natascha Oppelt ◽  
Claudia Kuenzer
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Meteorological Data ◽  
Weather Condition ◽  
Natural Phenomenon ◽  
Snow Cover Extent ◽  
Wet Snow ◽  
Snow Conditions ◽  
Interferometric Sar ◽  
Processing Techniques

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

Links between spring snow cover and long-term alpine vegetation change

2020 ◽  
Author(s):  
Shengwei Zong ◽  
Christian Rixen
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Plant Species ◽  
Climate Warming ◽  
Vegetation Change ◽  
Alpine Vegetation ◽  
Alpine Ecosystems ◽  
Snow Cover Extent ◽  
The Relationship

<p><span>Snow is an important environmental factor determining distributions of plant species in alpine ecosystems. During the past decades, climate warming has resulted in significant reduction of snow cover extent globally, which led to remarkable alpine vegetation change. Alpine vegetation change is often caused by the combined effects of increasing air temperature and snow cover change, yet the relationship between snow cover and vegetation change is currently not fully understood. To detect changes in both snow cover and alpine vegetation, a relatively fine spatial scales over long temporal spans is necessary. In this study in alpine tundra of the Changbai Mountains, Northeast China, we (1) quantified spatiotemporal changes of spring snow cover area (SCA) during half a century by using multi-source remote sensing datasets; (2) detected long-term vegetation greening and browning trends at pixel level using Landsat archives of 30 m resolution, and (3) analyzed the relationship between spring SCA change and vegetation change. Results showed that spring SCA has decreased significantly during the last 50 years in line with climate warming. Changes in vegetation greening and browning trend were related to distributional range dynamics of a dominant indigenous evergreen shrub <em>Rhododendron aureum</em>, which extended at the leading edge and retracted at the trailing edge. Changes in <em>R. aureum</em> distribution were probably related to spring snow cover changes. Areas with decreasing <em>R. aureum</em> cover were often located in snow patches where probably herbs and grasses encroached from low elevations and adjacent communities. Our study highlights that spring SCA derived from multi-source remote sensing imagery can be used as a proxy to explore relationship between snow cover and vegetation change in alpine ecosystems. Alpine indigenous plant species may migrate upward following the reduction of snow-dominated environments in the context of climate warming and could be threatened by encroaching plants within snow bed habitats.</span></p>

scholarly journals Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout

2018 ◽  
Vol 18 (3) ◽  
pp. 869-887 ◽  
Author(s):  
Cesar Vera Valero ◽  
Nander Wever ◽  
Marc Christen ◽  
Perry Bartelt
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Meteorological Data ◽  
Liquid Water Content ◽  
Snow Avalanche ◽  
Contingency Table Analysis ◽  
Large Variability ◽  
Model Driven ◽  
Wet Snow ◽  
Table Analysis

Abstract. Snow avalanche motion is strongly dependent on the temperature and water content of the snow cover. In this paper we use a snow cover model, driven by measured meteorological data, to set the initial and boundary conditions for wet-snow avalanche calculations. The snow cover model provides estimates of snow height, density, temperature and liquid water content. This information is used to prescribe fracture heights and erosion heights for an avalanche dynamics model. We compare simulated runout distances with observed avalanche deposition fields using a contingency table analysis. Our analysis of the simulations reveals a large variability in predicted runout for tracks with flat terraces and gradual slope transitions to the runout zone. Reliable estimates of avalanche mass (height and density) in the release and erosion zones are identified to be more important than an exact specification of temperature and water content. For wet-snow avalanches, this implies that the layers where meltwater accumulates in the release zone must be identified accurately as this defines the height of the fracture slab and therefore the release mass. Advanced thermomechanical models appear to be better suited to simulate wet-snow avalanche inundation areas than existing guideline procedures if and only if accurate snow cover information is available.

scholarly journals Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

2013 ◽  
Vol 6 (4) ◽  
pp. 1061-1078 ◽  
Author(s):  
G. Picard ◽  
L. Brucker ◽  
A. Roy ◽  
F. Dupont ◽  
M. Fily ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Microwave Emission ◽  
Alpine Regions ◽  
Wide Range ◽  
Dense Media ◽  
Wet Snow ◽  
Snow Conditions

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

scholarly journals Modelling wet snow avalanche runout to assess road safety at a high-altitude mine in the central Andes

2016 ◽  
Vol 16 (11) ◽  
pp. 2303-2323 ◽  
Author(s):  
Cesar Vera Valero ◽  
Nander Wever ◽  
Yves Bühler ◽  
Lukas Stoffel ◽  
Stefan Margreth ◽  
...  
Keyword(s):  
High Altitude ◽  
Snow Cover ◽  
Three Dimensional ◽  
Central Andes ◽  
Early Spring ◽  
Avalanche Dynamics ◽  
Wet Snow ◽  
Dynamics Modelling ◽  
Snow Conditions ◽  
Del Rio

Abstract. Mining activities in cold regions are vulnerable to snow avalanches. Unlike operational facilities, which can be constructed in secure locations outside the reach of avalanches, access roads are often susceptible to being cut, leading to mine closures and significant financial losses. In this paper we discuss the application of avalanche runout modelling to predict the operational risk to mining roads, a long-standing problem for mines in high-altitude, snowy regions. We study the 35 km long road located in the "Cajón del rio Blanco" valley in the central Andes, which is operated by the Codelco Andina copper mine. In winter and early spring, this road is threatened by over 100 avalanche paths. If the release and snow cover conditions can be accurately specified, we find that avalanche dynamics modelling is able to represent runout, and safe traffic zones can be identified. We apply a detailed, physics-based snow cover model to calculate snow temperature, density and moisture content in three-dimensional terrain. This information is used to determine the initial and boundary conditions of the avalanche dynamics model. Of particular importance is the assessment of the current snow conditions along the avalanche tracks, which define the mass and thermal energy entrainment rates and therefore the possibility of avalanche growth and long runout distances.

Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

2017 ◽  
Vol 10 (6) ◽  
pp. 2575-2588 ◽  
Author(s):  
Guangjun He ◽  
Xuezhi Feng ◽  
Pengfeng Xiao ◽  
Zhenghuan Xia ◽  
Zuo Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Mountain Areas ◽  
Sensing Data ◽  
Wet Snow

scholarly journals The Swiss snow climatology as seen by reanalyses and further gridded datasets

2021 ◽  
Author(s):  
Monika Goeldi ◽  
Stefanie Gubler ◽  
Christian Steger ◽  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Data Sets ◽  
Data Set ◽  
Climate Services ◽  
Snow Cover Extent ◽  
Snow Water

<p>Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.</p><p>In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.</p><p>The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.</p>

Seasonal to sub-seasonal hydrological forecast in Central Asia to improve water management and mitigate hydrometeorological risks

2021 ◽  
Author(s):  
Abror Gafurov ◽  
Olga Kalashnikova ◽  
Uktam Adkhamov ◽  
Akmal Gafurov ◽  
Adkham Mamaraimov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Central Asia ◽  
Snow Cover ◽  
Water Availability ◽  
Meteorological Data ◽  
River Basins ◽  
Linear Regression Method ◽  
Operational Mode ◽  
Hydrological Forecast

<p>Central Asia is facing a water shortage due to the negative impacts of climate change and demographic development. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. However, a limited observation network is available in these mountain systems and many are malfunctioning. Thus, the region needs new innovative methods to forecast seasonal and sub-seasonal water availability to ensure better water resources management and mitigate hydro-meteorological risks.</p><p>In this study, we present the results of our efforts for many years to develop a forecasting tool and implementation in the region. Since the region has limited observed meteorological data, we use primarily remote sensing data on snow cover for this purpose. We apply the MODIS snow cover data that is processed, including cloud removal, using the MODSNOW-Tool. We have applied this tool, which can be used to monitor snow cover in an operational mode and forecast water availability for the vegetation period but also for the monthly scale using the multiple linear regression method.</p><p>Our results show that snow is important in most of the river basins and can also be used as a single predictor to forecast seasonal water availability. Especially, in remote areas with limited observations, this approach gives a possibility of forecasting water availability for different time period. Besides seasonal hydrological forecast, the MODSNOW-Tool was also used to forecast water availability for upcoming months. The validity of forecasts were tested against observed discharge for the last 20 years and mostly above 70 % verification was achieved. Additionally to remote sensing based snow cover data, observed meteorological information was also used as predictors and improved the validity of forecast models in some river basins.</p><p>The implementation of the MODSNOW-Tool to improve the hydrological forecast was done for 28 river basins in Central Asia that are located in the territories of five post-Soviet countries Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.  The MODSNOW-Tool was also implemented at the National Hydrometeorological Services (NHMS) of each post-Soviet country.</p>

scholarly journals Projected changes of snow conditions and avalanche activity in a warming climate: the French Alps over the 2020–2050 and 2070–2100 periods

2014 ◽  
Vol 8 (5) ◽  
pp. 1673-1697 ◽  
Author(s):  
H. Castebrunet ◽  
N. Eckert ◽  
G. Giraud ◽  
Y. Durand ◽  
S. Morin
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
21St Century ◽  
Mitigation Strategies ◽  
Climate Change Scenarios ◽  
Stability Estimates ◽  
Reference Period ◽  
French Alps ◽  
Wet Snow ◽  
Snow Conditions

Abstract. Projecting changes in snow cover due to climate warming is important for many societal issues, including the adaptation of avalanche risk mitigation strategies. Efficient modelling of future snow cover requires high resolution to properly resolve the topography. Here, we introduce results obtained through statistical downscaling techniques allowing simulations of future snowpack conditions including mechanical stability estimates for the mid and late 21st century in the French Alps under three climate change scenarios. Refined statistical descriptions of snowpack characteristics are provided in comparison to a 1960–1990 reference period, including latitudinal, altitudinal and seasonal gradients. These results are then used to feed a statistical model relating avalanche activity to snow and meteorological conditions, so as to produce the first projection on annual/seasonal timescales of future natural avalanche activity based on past observations. The resulting statistical indicators are fundamental for the mountain economy in terms of anticipation of changes. Whereas precipitation is expected to remain quite stationary, temperature increase interacting with topography will constrain the evolution of snow-related variables on all considered spatio-temporal scales and will, in particular, lead to a reduction of the dry snowpack and an increase of the wet snowpack. Overall, compared to the reference period, changes are strong for the end of the 21st century, but already significant for the mid century. Changes in winter are less important than in spring, but wet-snow conditions are projected to appear at high elevations earlier in the season. At the same altitude, the southern French Alps will not be significantly more affected than the northern French Alps, which means that the snowpack will be preserved for longer in the southern massifs which are higher on average. Regarding avalanche activity, a general decrease in mean (20–30%) and interannual variability is projected. These changes are relatively strong compared to changes in snow and meteorological variables. The decrease is amplified in spring and at low altitude. In contrast, an increase in avalanche activity is expected in winter at high altitude because of conditions favourable to wet-snow avalanches earlier in the season. Comparison with the outputs of the deterministic avalanche hazard model MEPRA (Modèle Expert d'aide à la Prévision du Risque d'Avalanche) shows generally consistent results but suggests that, even if the frequency of winters with high avalanche activity is clearly projected to decrease, the decreasing trend may be less strong and smooth than suggested by the statistical analysis based on changes in snowpack characteristics and their links to avalanches observations in the past. This important point for risk assessment pleads for further work focusing on shorter timescales. Finally, the small differences between different climate change scenarios show the robustness of the predicted avalanche activity changes.

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