Snow Depth Distribution Research Articles

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

MFPANet: Multi-Scale Feature Perception and Aggregation Network for High-Resolution Snow Depth Estimation

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.

Estimating snow depth based on dual polarimetric radar index from Sentinel-1 GRD data: A case study in the Scandinavian Mountains

The sensitivity of synthetic aperture radar (SAR) polarization information to snow depth changes provides new opportunities for regional snow depth retrieval in mountains with thick snow cover. However, interference from soil signals can affect the accurate quantification of snow volume scattering signals. The aim of this study was to develop a dual-polarimetric radar snow depth estimation (DpRSE) methodological framework that utilizes Sentinel-1 data for snow depth retrieval in the Scandinavian Mountains. This framework is based on the dual-polarimetric radar vegetation index (DpRVIc), which enhances the snow volume scattering signal and reduces soil interference by integrating soil purity and a conditioning factor to fully exploit the advantages of polarimetric SAR information. The results revealed a strong correlation between the DpRVIc and snow depth, and the DpRVIc significantly outperformed the other polarimetric radar indices. The multitemporal retrieval results clearly reflect the heterogeneity of the snow depth distribution. This method is applicable to areas without tree cover. Meanwhile, the shrub-filled regions also affect the snow depth retrieval accuracy. Under the same environmental conditions and using the same validation dataset, a comparison between the DpRSE method and the cross-polarization ratio method was conducted. The results indicated that the DpRSE method surpasses similar existing methods in terms of snow depth retrieval accuracy. Specifically, the coefficient of determination (R2) for the DpRSE method reached 0.66, which represents a 26.9 % improvement over that of the cross-polarization ratio method. Moreover, the mean absolute error (MAE) decreased by 20 %. This study offers a novel perspective on SAR snow depth retrieval utilizing polarization information.

From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea

Abstract. Year-round snow cover is a characteristic of the entire Antarctic sea ice cover, which has significant implications for the energy and mass budgets of sea ice, e.g., by preventing surface melt in summer and enhancing sea ice growth through extensive snow ice formation. However, substantial observational gaps in the seasonal cycle of Antarctic sea ice and its snow cover limit the understanding of important processes in the ice-covered Southern Ocean. They also introduce large uncertainties in satellite remote sensing applications and climate studies. Here we present results from 10 years of autonomous snow observations from Snow Buoys in the Weddell Sea. To distinguish between actual snow depth and potential snow ice thickness within the accumulated snowpack, a one-dimensional thermodynamic sea ice model is applied along the drift trajectories of the buoys. The results show that potential snow ice formation, with an average maximum thickness of 35 cm, was detected along 41 % of the total track length of the analyzed Snow Buoy tracks, which corresponds to about one-quarter of the snow accumulation. In addition, we simulate the evolution of internal snow properties along the drift trajectories with the more complex SNOWPACK model, which results in superimposed ice thicknesses between 0 and 14 cm on top of the snow ice layer. These estimates will provide an important reference dataset for both snow depth and meteoric ice rates in the Southern Ocean.

Arctic Freeboard and Snow Depth From Near‐Coincident CryoSat‐2 and ICESat‐2 (CRYO2ICE) Observations: A First Examination of Winter Sea Ice During 2020–2022

AbstractIn the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Spatial and temporal variability analysis of snow cover parameters according to the urbanized area profile system

The purpose of the study is to analyze the spatial and temporal variability of snow cover parameters in the urbanized area of the city of Irkutsk and its adjacent areas according to the system of profiles reflecting the features of the area microrelief. Field surveys were conducted in accordance with standard recommendations for snow and geochemical surveys. Snow sampling was carried out at the entire snow depth, with the exception of the snow depth of 0.5-1 cm. Sampling points located at least 25 meters away from the roads. Snow sampling in the residential areas of the city was carried out in the places with undisturbed snow cover and free from extraneous snow banks and landfills. Global Mapper, Golden Software Surfer, Statistica software were used to carry out statistical analysis, build the models of profile sections and obtain the diagrams of snow cover parameter distribution. A 3D relief model was obtained on the basis of radar topographic survey data, which revealed a multiple formation relief of the studied area featuring mountains, high plains with undulating flat gently sloping watersheds, valleys, hollows and depressions. The height difference is up to 230 m. The diagrams of snow depth distribution built with regard to the profiles made it possible to identify the territories with the deepest snow cover. A geochemical analysis of the melt water filtrate was carried out over a three-year period. In 2021 a high correlation with electrical conductivity was found for the contents of tungsten, sodium, bromine, calcium, molybdenum, sulfur, barium, magnesium, antimony, tantalum, cesium, titanium, chromium, and silicon. The average correlation level with electrical conductivity was recorded for arsenic, copper, and lead. The analysis data obtained indicate a different contamination level of the area as a result of atmospheric precipitation in those years due to the activity of industrial facilities. The distribution patterns of the pH snow index have been revealed. It is shown that zones with pH<6 are mostly confined to the Irkutsk aluminum smelter. The zones with pH >6 are caused by the influence of the power plants using hydrocarbon fuels: gasoline, kerosene, fuel oil, diesel fuel, coal. The snow cover was subjected to field and laboratory studies using GIS technologies and physico-chemical methods. The distribution and migration of pollutants in various spatial and temporal aspects have been revealed taking into account the terrain relief. The conducted study opens up opportunities for modeling the landscape structure, taking into account meteorological parameters, phenological processes and snow cover state for the purposes of the national economy and the location of construction of various facilities.

Estimation of Spatial Snowpack Properties in a Snow-Avalanche Release Area: An Extreme Case on Mt. Nodanishoji, Japan, in 2021

An extreme dry-slab snow avalanche occurred on January 10, 2021, at Mt. Nodanishoji, Gifu, Japan, during a heavy snowfall. The avalanche ran down a horizontal distance of approximately 2,800 m and damaged trees and infrastructures. This was estimated to be the second largest recorded avalanche in Japan. However, physical snowpack properties and their vertical profiles and spatial distribution, which caused the avalanche, were not addressed in the release area immediately following the avalanche, mainly due to unsafe and lousy weather conditions. Based on a snow depth distribution observed by an unmanned aerial vehicle and a numerical snowpack simulation in the avalanche release area, the spatial distributions of the mechanical snowpack stability and slab mass and their temporal evolution were estimated in this study. The procedure was validated by comparing the calculation results with the observed snowpit and spatial snow depth data. The results indicated that two heavy snowfall events, approximately 3 and 10 days before the avalanche onset, generated two different weak layers made of precipitation particles and associated slabs above the weak layers. The older weak layer was only generated on the northward slope due to its low temperature, whereas the newer layer was predominant over the avalanche release area. The procedure employed in this study is expected to be applied to other avalanche cases in the future.

Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques

Although passive microwave remote sensing has been widely used for snow depth (SD) retrieval, its coarse spatial resolution has led to large uncertainties in various applications, particularly in mountainous regions. Therefore, downscaling passive microwave data is an effective way to improve the accuracy of regional SD monitoring. In this study, we developed a SD downscaling algorithm based on multisource remote sensing data, generating a long-time series of daily 500 m resolution SD data from 1980 to 2020 over the Tibetan Plateau (TP). The downscaling SD product improved the spatial resolution and significantly reduced the overestimation of the SD for the TP. Based on this product, the spatial pattern and the spatiotemporal trend of the SD were analyzed by the Theil-Sen median method and the Mann–Kendall test. SD on the TP showed clear spatial heterogeneity by vertical zonal distribution, bimodal distribution features changing with longitude, and a thick SD distribution pattern at 30-32°N. The annual average SD, maximum SD, and accumulation of SD on the TP from 1980 to 2020 were 1.02 cm, 5.91 cm, and 39.45 cm, respectively, with trends of −0.06 cm/10a, −0.44 cm/10a, and 1.80 cm/10a (P > 0.05), respectively. SD on the TP increases with elevation above 5.5 km in mountainous areas and decreases in areas below 5.5 km.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

Analysis of snow load probabilistic models and calculation of reference snow pressure using maximum likelihood method for multiple cities in Liaoning Province, China

The accurate determination of snow loads is crucial for structural design, and determining the reference snow pressure is particularly important for structures vulnerable to snow. The Load Code for the Design of Building Structures (GB50009–2012) provides a standard probabilistic model and fitting method to facilitate the calculation of reference snow pressure during the design reference period. However, using a unified load probabilistic distribution model across different provinces in China may result in overestimation or underestimation of reference snow pressure due to variations in geographic locations. Therefore, this study takes Liaoning Province, including all its cities as an example, which is the only province in northeast China that is both coastal and inland. Measured data from the Global Land Data Assimilation System (GLDAS) were used to establish the extreme value type I distribution, extreme value type III distribution, lognormal distribution, and generalized extreme value distribution models for snow depth using maximum likelihood and moment methods. The Kolmogorov–Smirnov test was used to evaluate the goodness-of-fit of the established models, and the maximum likelihood method was compared with the fitting method (moment method) recommended by the Load Code for the Design of Building Structures. The results showed that the lognormal distribution model and the maximum likelihood method achieved the best goodness-of-fit for the probabilistic distribution of snow depth. Additionally, the influence of snow density uncertainty on reference snow pressure was analyzed using measured data for snow pressure and snow depth.

Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas

Abstract. Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km2), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.

Performance of the WRF Model at the Convection‐Permitting Scale in Simulating Snowfall and Lake‐Effect Snow Over the Tibetan Plateau

AbstractThis study investigated the performance of the Weather Research Forecasting (WRF) model at 4‐km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor–Yamada–Janjic), no cumulus and multi‐scale Kain‐Fritsch, two land surface models (Noah and Noah‐MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%–53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3°C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37–0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (∼0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow‐related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection‐permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions.

Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar

Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.

Adaptability analysis of snow in the Zhangjiakou competition zone of the Beijing Olympic Winter Games for the next 30 years

Study regionZhangjiakou competition zone (ZJK-CZ) of the Beijing Olympic Winter Games. Study focusUnder the extreme uncertainty of future climate change scenarios, a system solution capable of snow depth forecasting is urgently needed for the sustainable use of Olympic Winter Games venues and the regular organization of winter sports in the post-Winter-Olympic era. This study predicts the snow depth distribution and changes in the ZJK-CZ of the Beijing Olympic Winter Games for the next 30 years utilizing the GBEHM (Geomorphology-Based Ecohydrological Model) model, together with CMIP6 climate forecast model data, and analyzes the operation and profitability of the ZJK-CZ in the post-Winter-Olympic era. New hydrological insights for the regionThis study developed a new integrated simulation methodology for the climate risk assessment of Olympic Winter Games venues in the context of future climate change. The percent bias of the snow depth simulated based on the new integrated model was found to be only 3 %. In the next 30 years, the ZJK-CZ will need to be replenished with at least 190 mm of artificial snow annually to meet the minimum standards for suitable winter sports days and requires an average of at least 49 days of artificially assisted snowmaking per year to ensure proper operation and profitability. This study is expected to provide a climate risk assessment for the construction and operation of winter sports venues.

Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR Measurements

Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> ) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow-ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat-2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> , derived from empirical and waveform-fitting retracker algorithms. The retrieved thickness-related parameters based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> from the lognormal waveform-fitting retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which was used in the previous density parameterization, showing a higher agreement with values from in-situ measurements. The spatial and interannual variabilities of SIT increased when the results from this study were compared with those based on previous parameterizations.

Combination of Snow Process Model Priors and Site Representativeness Evaluation to Improve the Global Snow Depth Retrieval Based on Passive Microwaves

The spatiotemporal distribution of snow depth (SD) has a significant impact on the energy and water balances of the Earth’s system. However, passive microwave remote sensing widely used for SD estimation has large uncertainties due to the variations in snow physical properties. In this study, we demonstrate a new method to minimize these uncertainties and to increase the accuracy of SD estimation. Our method is based on the synergy between the passive microwave AMSR-2 brightness temperature (TB) and a physical snow process model (SNTHERM) to estimate snow grain size, snow density and first-guess SD as priors. On one hand, we used TB from three frequencies and removed non-representative ground measurements at the stations to improve deep snow estimation. Then, we applied a machine learning (ML) algorithm based on both the AMSR-2 TB and the SNTHERM simulations to retrieve the global SD. The results showed that the root-mean-squared error (RMSE) of the retrieved SD was 12.4 cm at the meteorological stations. Independent validations showed that our method significantly reduced the SD and snow water equivalent (SWE) underestimation in the mountains compared to the current satellite products.

Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.

Intermediate snowpack melt-out dates guarantee the highest seasonal grasslands greening in the Pyrenees

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019–2020 and 2020–2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.