Snow Research Articles

Linking basin-scale hydrology with climatic parameters in western Himalaya: Application of satellite data, temperature index modelling and in-situ observations

Due to limited spatial and temporal in-situ runoff data availability, Himalaya-Karakoram (HK) glaciohydrology has a significant knowledge gap between large-scale and small-scale runoff modelling studies. This study reconstructs longest basin-wide runoff series in Chandra-Bhaga Basin by applying a high-resolution glaciohydrological model SPHY (Spatial Processes in Hydrology) over 1950–2022. Two-tier model calibration is done using in-situ basin-wide runoff (1973–2006) and MODIS snow cover (2003–2018). Model validation is done against in-situ Chhota Shigri Glacier catchment-wide runoff (2010–2015). The modelled mean annual basin-wide runoff is 60.21 ± 6.17 m3/s over 1950–2022, with maximum runoff in summer-monsoon months, peaking in July (182.69 m3/s). Glacier runoff (ice melt + snowmelt over glacier) contributes maximum (39%) followed by equal contributions from snowmelt runoff from non-glacierized basin area and baseflow (25%), while rainfall-runoff contributes minimum (11 %) to total runoff. There is a significant volumetric increase by ∼7% from pre- (59.17 m3/s) to post-2000 (63.47 m3/s) mainly because of early onset of snowmelt post-2000 that resulted in a hydrograph shift by ∼25 days earlier in spring. The glacier runoff is overestimated by 3% from RGI 7.0 inventory compared to different manually delineated inventories over 1950–2022, because of higher glacierized area from RGI 7.0. The precipitation shows a negative trend, but total runoff shows a positive trend due to positive trend of temperature that resulted in more glacier runoff and rainfall-runoff for basin over last 72 years. Basin-wide runoff is mainly governed by summer temperature which directly controls the amount of glacier and snowmelt runoffs and is supported by summer rainfall. This study highlights importance of basin-scale model calibration with in-situ data in large scale studies and stresses the need for in-situ observations in high-altitude Himalayan region. Basin-scale calibrated model parameters are transferable to glacier catchment scale within Chandra-Bhaga Basin, showing the model robustness at a small catchment scale.

Factors of winter moisture movement in arable soils of the Ishim steppe

Based on the materials of long-term field observations in the southern regions of the Omsk region, the influence of the main factors on the winter movement of moisture in arable soils of the Ishim steppe was traced. The penetration depth of 0 °С determines the lower boundary and vertical thickness of the moisture freezing layer. The distance between its lower edge and the penetration depth of 0 °С is 20–40 cm. The penetration depth of 0 °С is predetermined by the air temperature and the thickness of the snow cover and reaches 170–240 cm. The forms of moisture dominant in the soil are associated with the granulometric composition. Film moisture prevails in the heavy loamy, highly silty soil-ground strata of the Ishim steppe. The content of capillary-backed and free gravitational water does not exceed 6% of the soil volume at full moisture capacity, which determines the small volume of moisture freezing (up to 50 mm). Thermogradient migration of moisture is determined by field methods in highly moistened soil-ground strata. High humidity of the soils and rocks of the Ishim steppe is associated with their granulometric composition and the close occurrence of groundwater. If in the second ten days of October groundwater is recorded at a depth of less than 3 m, the layer of moisture freezing at the end of March is noted in the depth range between 40–80 and 160–200 cm, the volume of cryogenic accumulation is 30–50 mm, including in the upper meter layer – 10–25 mm. If groundwater is detected at a depth of more than 3 m in the autumn, the freezing layer is fixed at 80 to 160–200 cm, the moisture gain in it is 15–30 mm, in the 0–100 cm layer the moisture content does not change. The role of cryogenic accumulation in replenishing the post-vegetation moisture deficit in the root layer of spring grains is modest. This process is significant for the formation of conditions for anaerobiosis and the development of modern hydromorphism in the lower soil horizons and in the subsoil strata.

Spatio-Temporal Monitoring and Risk Mapping of Glacial Lake Outburst Flood in Hunza Valley, Pakistan

Glacial lake outburst flood (GLOF) disasters are serious and potentially increase huge risks to livelihoods and infrastructure in the mountain regions of the world. The northern highland regions of Pakistan are home to some of the biggest alpine glaciers. In this investigation, the Hunza Valley of Pakistan has undergone remote sensing-based risk assessment for Glacial Lake Outburst Floods. Borith and Passu Lakes were chosen to identify flood risk in the downstream areas. For such, Landsat images were used from 1990-2020. Different spectral indices such as the Normalized Difference Snow Index (NDSI), Normalized Difference Glacier Index (NDGI) and Normalized Difference Water Index (NDWI) were applied to evaluate snow cover changes. Furthermore, these Lakes were digitized to evaluate and check the total increase in Lake Areas. Also, built-up areas close to lakes were digitized to identify total risk. The Land Surface Temperature (LST), NDSI, NDGI, NDWI, Digital Elevation Model (DEM) and Slope variables were given weights using the Analytic Hierarchy Process (AHP) method. In the analysis of flood risk mapping, maximum weight was assigned to Land Surface Temperature, and minimum weight was assigned to the slope. The result revealed that the settlements located in the Ghulkin, Gulmit, Husseini, Passu, Zarabad, and Khorramabad are at moderate risk while settlements located near Hunza River such as Karimabad, Khanna Abad, and Aliabad are at high risk. The outcome also showed that Borith Lake's area expanded, going from 0.059 km2 in 1985 to 0.074 km2 in 2020 and Passu Lake's area also grew, going from 0.074 km2 in 2005 to 0.077 km2 in 2020. In last, Buffer analysis was performed to identify areas that are likely to be affected by the flood. The result of the study can help carry out a downstream risk assessment and better preparedness for future flood hazards.

Genetic and environmental drivers of legume cover crop performance: Hairy vetch

AbstractAmong 50 environments in the United States, we screened 35 hairy vetch (Vicia villosa Roth.) lines for traits of interest to cover cropping. We analyzed the influence of genotype, environment, and the genotype‐by‐environment interaction (G × E) on biomass, vigor, winter survival, emergence, flowering time, and nitrogen fixation. To explore how environments and G × E impacted each trait, we associated environment predictions and G × E loadings with weather and soil parameters. Environment had the largest influence on all traits, representing more than half of the variance. Environment predictions were significantly associated with weather and/or soil parameters for each trait. Biomass was associated with growing degree days, winter survival with freezing degree days without snow cover, growth stage with shortwave radiation, and emergence with soil texture. The G × E interaction was larger than genotypic variance for all traits except for winter survival and flowering time. The G × E interaction loadings were associated with soil sand content for biomass, air temperature for fall vigor and emergence, and snow cover for winter survival. Although it represented the smallest proportion of total variance, genetic effects were significant for all traits except for emergence, Ndfa, %N, and C:N. New hairy vetch breeding lines were superior to all commercially available lines for biomass and winter survival. Biomass harvest timing did not significantly change line rank, indicating that top‐performing lines can be used in diverse management systems. To select for high nitrogen contribution to subsequent crops, breeding programs can indirectly select for biomass rather than expensively evaluating symbiotic nitrogen fixation.

Technological advances in high mountains: Development of an augmented reality and IoT Application for snow cover monitoring on Huascaran, Peru

Climate change has had a negative impact on Andean glaciers, especially on the snow-capped Huascaran in Peru, which has experienced a loss of 42 % of its glacier mass. Lack of access to accurate snowpack data complicates understanding and informed decision-making in mountainous environments. To address this problem, an application has been developed based on an AR-IoT architecture that integrates augmented reality (AR) and the Internet of Things (IoT) for the periodic monitoring of snow cover on the Huascaran snow-capped mountain. This application uses data collected from meteorological repositories and simulates IoT interactions, while a mobile app offers an interactive and understandable visualization of the data. The Scrum methodology was applied iteratively, from the creation of the 3D model to the connection with the Ubidots platform to store and share the data. The analysis of 58 readings, obtained between April and May 2023, showed correlations between variables such as snow depth, temperature, and wind speed, which served to validate the operation of the AR-IoT technology implemented. These variables offer an initial understanding of atmospheric conditions in high mountains. In addition, the technology developed has the potential to expand to include more relevant data in future analyses. The tool demonstrates the effectiveness of combining these technologies for monitoring high mountain snow cover and making informed decisions about water resources management.

Susceptibility assessment of glacier-related debris flow on the southeastern Tibetan Plateau using different hybrid machine learning models

The southeastern Tibetan Plateau (SETP) is a construction area of several key infrastructure projects in China, such as the Sichuan-Tibet Railway and hydropower developments, which has historically faced the threat of glacier-related debris flows. However, a robust assessment of such debris flow susceptibility is a challenge due to the complex and variable climate, terrain and glacial environment. In this study, we used the hybrid models that combine statistical techniques (certainty factors, CF) with machine learning methods (logistic regression, LR; random forest, RF; extreme gradient boosting, XGBoost) to more accurately identify debris flow susceptible (DFS) areas. Topography, geology, and hydrological factors including glaciers and snow cover were used in these models to assess the DFS. Results show that 21 % to 42 % of the study area is very high susceptible to debris flows, particularly from Ranwu to Bomi and around Namcha Barwa. The hybrid models effectively enhance the accuracy of the DFS assessments. The CF-RF model showed the greatest improvement, with an 8.4 % increase in accuracy compared to the single model, the DFS spatial distribution of which aligns closely with field survey results. The glacial area ratio and annual snowmelt positively impact DFS accuracy, ranking 2nd and 9th in the factor importance, respectively. The results of this research could provide valuable assistance and guidance in mitigating glacier-related debris flow hazards.

Spatio-Temporal Assessment of Areal Fragmentation and Volume of Snow Cover in the Central Himalaya

Spatio-Temporal Assessment of Areal Fragmentation and Volume of Snow Cover in the Central Himalaya

The influence of atmospheric circulation on the seasonal dynamics of the chemical composition of snow cover in the Pechora-Ilychsky reserve

The influence of atmospheric circulation on the seasonal dynamics of the chemical composition of snow cover in the Pechora-Ilychsky reserve

Comparison of hunting site strategies of the common buzzard Buteo buteo in open landscapes and along expressways.

The expansion of human activities in their many forms increases the frequency, diversity, and scale of human-wildlife interactions. One such negative form is the expansion of road infrastructure, causing road kill and traffic-related noise as well as habitat loss and fragmentation. Even so, habitats around road infrastructure are attractive foraging areas that attract certain bird species. We assessed the impact of road infrastructure on the foraging strategies of the common buzzard Buteo buteo. Birds were observed during two winter seasons in two land-use types, along an expressway and an open agricultural landscape. Individual birds were tracked for a 10-min sequence as a separate sample was analysed. The material, covering 1,220 min along the expressway, and 1,100 min in the agricultural landscape, was collected. Time spent by buzzards on medium-height sites was higher along the expressway than in farmland. Buzzards changed their hunting sites following the mean wind speed. Also, they more often changed their sites along the expressway than in farmland. The land-use types, snow cover, and the mean wind speed mediated the number of attacks on prey. These results illustrate the high plasticity of the buzzards' behaviour, which can adapt their hunting strategies to both foraging locations (expressway and farmland) and weather conditions. Roadsides along expressways are attractive foraging areas for this diurnal raptor, so reducing the risk of vehicle collisions with this and other birds of prey may require targeted planning efforts.

Variation in Glacier Albedo on the Tibetan Plateau between 2001 and 2022 Based on MODIS Data

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10−2 yr−1 over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10−2 yr−1, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt.

Severe winter conditions in the Citlaltepetl-Cofre de Perote mountain range, 2021-2022

The central border that divides the states of Puebla and Veracruz is characterized by being the highest territorial limit in Mexico; it is composed of three of the highest mountains in the country. This border reaches a length of 100 km, forming a watershed that continuously exceeds 3000 m in altitude. Due to its elevation, winter freezing conditions are frequent; however, due to changing climate patterns, in recent winter seasons, severe frosts and snowfalls have occurred at elevations well below the main peaks, affecting the health of thousands of vulnerable residents of both states surrounding this mountain range. Because the 2021-2022 winter season was one of the most intense in all North America, with severe impacts in Mexico as a result of freezing temperatures and snowfall, this work analyzes the winter conditions in 2021-2022 through the monitoring of frontal systems and the analysis of data from automatic meteorological stations near the study area. From the records, the lower limit of surface freezing was estimated for each arrival of cold air masses; the occurrence of snowfall was determined by remote sensing, and the area of snow cover was also calculated. At the same time, the population affected by freezing weather conditions was estimated. The surface freezing starts at elevations as low as 2259 m, covering an area of up to 2949 km2, which corresponds to the entire study region, affecting 139 towns in Veracruz and 163 in Puebla.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Internal Variability Dominated the Extreme Cold Wave Over North America in December 2022

AbstractIn December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas.

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

PSXI-2 Impact of flexible prepartum supplementation strategies on cow-calf and subsequent progeny feedlot performance

Abstract Implementation of minimal supplemental protein strategies during late gestation may have the potential to minimize any negative postweaning progeny performance while decreasing feed costs. A 3-yr study was conducted to evaluate the effects of mid- to late-gestation supplementation strategies on reproduction, cow body weight (BW), and calf performance in May-calving mature cows grazing upland native range. Cows received one of 3 supplementation strategies: 1) 0.227 kg/d of a 30% CP distillers-based supplement (HalfSupp), 2) 0.454 kg/d of a 30% CP distillers-based supplement (Supp), or 3) brief and intermittent supplementation of Supp at 0.454 kg/cow based on periods of acute environmental stress, such as snow cover, wet winter events, and is best described as flexible supplementation (Flex). Supplementation was initiated in December each year and terminated approximately 30-d prior to the start of the calving season in April. Across all 3 yr, average supplement feeding rate was 25.5, 50.9, and 5.4 kg/cow during the 112-d supplemental period, which resulted in average yearly prepartum supplemental costs of $27.30, 54.60, and 1.99 for HalfSupp, Supp, and Flex; respectively. After weaning, steers grazed sub-irrigated meadow with a dried distiller grain supplement until May. In May, all steers grazed sub-irrigated meadow without additional supplement until August/September when steers were shipped to West Central Research and Extension Center (WCREC). Steers were placed in a GrowSafe feeding system for the rest of the finishing feeding period. Each year, steers were slaughtered at a commercial facility (Tyson Fresh Meats, Lexington, NE) when estimated visually to have 1.3 cm fat thickness over the 12th rib as a group. Cow BW was not different (P = 0.86) at the initiation of supplementation in Jan. However, cows fed Half had a lighter (P < 0.01) BW at pre-calving and tended (P = 0.08) to have a lighter BW at pre-breeding than their counterparts. After a 45-d breeding season, pregnancy rates were not influenced (P = 0.91) by prepartum supplementation strategies. Prepartum supplementation strategies did not influence (P > 0.56) preweaning calf BW at birth, pre-breeding, and weaning. In addition, steer backgrounding BW was also not influenced (P > 0.63) by prepartum supplementation strategy of their dam. Upon entry into the feedlot, steer BW was not impacted (P > 0.84) by prepartum supplementation strategy of their dam. Feedlot performance (BW, average daily gain, and feed efficiency) was not influenced (P > 0.59) by prepartum supplementation strategy of their dam. After harvest, all carcass characteristics were not impacted (P > 0.11) by the prepartum supplementation strategy of the dam. Considering the cost for prepartum supplementation, feeding a protein supplement just during environmentally stressful periods during gestation appears to be a viable alternative to more conventional methods and reduces winter feed costs.

Winter atmospheric deposition of trace elements in the Arkhangelsk region (NW Russia): Insights into environmental effects

Atmospheric precipitation is an important indicator of ecological status. Snow cover accumulates all atmospheric precipitation that occurs during the cold season and releases large amounts of matter into surface water streams during spring snowmelt. We measured chemical composition of suspended solids (>0.45 μm) and dissolved matter (<0.45 μm) of atmospheric precipitation sampled on a monthly basis in 4 stations of Arkhangelsk region in cold seasons of 2018–2021. The fluxes of insoluble particles increase consistently in the spring months. In winter, the largest fluxes of suspended solids from the atmosphere were observed in the urbanized areas (in Severodvinsk and Onega). Snow in Onega showed a significant enrichment in Mn as well as Rb, Sr, Cs, Ba and Pb, which is related to boiler house emissions. Severodvinsk snow is enriched in V, Ni in dissolved fraction and Al, V, Cu, Sr, Pb in suspended solids. The environmental risk assessment shows that Onega station is affected to high and extremely polluted conditions for Mn concentration and Severodvinsk station is affected to multi-element pollution from moderate to significant levels. Comparison with the composition of the river runoff of the Northern Dvina showed that the concentrations of Cu, Zn, Cd and Pb in the snow are equal to or even higher than the river concentrations, indicating that atmospheric precipitation accumulated during winter can significantly affect the geochemistry of river runoff with respect to these elements.

Climate change and the climate reliability of hosts in the second century of the Winter Olympic Games

ABSTRACT In the year the world first surpassed the 1.5°C dangerous global warming threshold set out in the Paris Climate Agreement, the international community celebrates the 100th anniversary of the Olympic Winter Games and considers its future in a warmer world. In the largest study to date, the climate reliability of 93 locations to host Olympic and Paralympic Winter Games (OWG and PWG) snow sports is examined. Under a more probable mid-range emission scenario (RCP4.5, SSP2-45), 52 locations remain climate-reliable for the OWG in the 2050s and 46 in the 2080s. The scheduling of the PWG in March put it at higher risk, with only 22 climate-reliable locations in the 2050s and 16 in the 2080s. When a more stringent minimum snow depth requirement was applied, the number of reliable locations declined slightly for both OWG and PWG, signifying the importance of advanced sustainable snowmaking as an adaptation strategy. While it is inevitable that climate change will impact the geography and development of winter sports to some degree, a reassuring finding is that even with a diminished pool of potential host locations, with continued adaptation, the OWG-PWG can endure as a genuinely global celebration of sport.

Snow depth retrieval method for PolSAR data using multi-parameters snow backscattering model

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method.