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ABSTRACT This study explores the combined effects of autumn snow cover anomalies on the Tibetan Plateau (TP) and Northeast Asia (NEA) on winter Eurasian temperature using observational data for the period 1972–2021 and a linear baroclinic model (LBM). Distinctive wintertime temperature patterns are found across the Eurasian continent corresponding to increased autumn snow cover in NEA when TP experiences normal, above‐normal, or below‐normal snow cover condition. In the scenario with an anomalous increase in autumn snow cover in NEA in combination with normal snow cover condition in TP, the overall winter temperature anomalies tend to be generally weak across the Eurasian continent. In years when autumn TP snow cover is above normal, the spatial distribution of winter temperature anomalies over the Eurasian continent associated with more NEA snow cover exhibits a ‘cold Arctic‐warm Eurasia’ (CAWE) pattern. The emergence of this CAWE pattern can be attributed to the low‐pressure system induced by the intensified NEA snow cover, which is further reinforced by the atmospheric wave train generated by negative North Atlantic sea surface temperature anomalies (SSTAs) in winter. This low‐pressure system amplifies the polar vortex and causes cooling in polar regions. Simultaneously, southeasterly winds along the southwestern flank of the North Pacific high‐pressure system contribute to warming in the mid‐latitude regions of Eurasia. While in years when autumn snow cover in TP is less than normal, more snow cover over NEA is accompanied by a ‘warm Arctic‐cold Eurasia’ (WACE) temperature anomaly pattern prevalent during the winter season. The decrease in autumn Barents‐Kara Sea ice is accompanied by a circulation pattern akin to the negative phase of the Arctic Oscillation during winter, favouring the southward intrusion of cold air, thus contributing to this WACE temperature anomaly pattern. Further analysis reveals that the impact of snow cover on the WACE temperature pattern is, for the most part, independent of the Arctic sea ice. [ABSTRACT FROM AUTHOR]

Methane, a type of greenhouse gas, poses considerable concern for humans. This study uses field experiments and satellite measurements to explore methane emission mechanisms during the freezing of seasonal permafrost and the contributing factors. In the transitional seasons of autumn and winter, as soil begins to freeze, methane emissions surge dramatically in a brief period. During this phase, the emissions peak, enabling the soil to accumulate over 9000 mg/m3 of methane rapidly. Snow cover also plays a crucial role in mitigating methane emissions. The porous nature of a sufficiently thick snow cover aids in temporarily trapping methane through a stratified blocking process, effectively matching the inhibitory capability of unfrozen soil. In comparison to unfrozen soil (54–237 mg/m3), snow cover can suppress methane emissions up to 20 times more, reducing emissions by as much as 3399 mg/m3. [ABSTRACT FROM AUTHOR]

Premise: Reduced snow cover and increasing temperature variability can increase freezing stress for herbaceous plants in northern temperate regions. Legumes have emerged as a plant functional group that is highly sensitive to these changes relative to other herbaceous species in these regions. We explored root‐nodulating bacterial associations and cyanogenesis as potential mechanisms explaining this relatively low freezing tolerance of legumes. Methods: To examine the influence of bacterial associations, we grew four legume species with or without crushed‐nodule inoculum at three severities of freezing, and three concentrations of nitrogen to disambiguate the direct benefits of increased nitrogen from the total bacterial effect. We quantified cyanogenesis via hydrogen cyanide production in both true leaves and cotyledons for nine legume species. Results: Root nodulation generally only affected legume survival under low nitrogen, when freezing severity was moderate or low. However, for the frost‐surviving plants, the growth advantage provided by nodulation decreased (it was often no longer significant with increasing freezing severity), and greater freezing severity reduced total nodule mass. In contrast, cyanogenesis was only detected in two of the nine species. Conclusions: The diminished performance of nodulated plants in response to freezing could place legumes at a competitive disadvantage and potentially explain their high sensitivity to freezing relative to other herbaceous species in northern temperate regions. Overall, this result has important implications for changes in soil fertility, community composition, and plant productivity in these ecosystems in the context of a changing winter climate. [ABSTRACT FROM AUTHOR]

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R2 ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. [ABSTRACT FROM AUTHOR]

Snow cover is a relevant component of the Earth's climate system, influencing water supply, ecosystem health, and natural hazard management. This study aims to monitor daily snow cover in the Cantabrian Mountains using Sentinel-2, Landsat (5–8), and MODIS data processed in Google Earth Engine (GEE). The main purpose is to extract metrics on snow cover extent, duration, frequency, and trends. Key findings reveal significant spatial and temporal variability in Snow-Cover Days (SCDs) across the region. Over the past 23 years, there has been a notable overall decrease in snow-cover days (−0.26 days per year, and −0.92 days per year in areas with a significant trend). Altitudes between 1000–2000 m a.s.l. showed marked decreases. The analysis of Snow-Cover Fraction (SCF) indicates high interannual variability and records the highest values at the end of January and the beginning of February. The effectiveness of satellite data and GEE is highlighted in providing detailed, long-term snow cover analysis, despite some limitations in steep slopes, forests, and prolonged cloud-cover areas. These results underscore the capacity for continuous monitoring with satellite imagery, especially in areas with sparse snow observation networks, where studies could be enhanced with more localized studies or additional ground-based observations. [ABSTRACT FROM AUTHOR]

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP's impact on the TP. [ABSTRACT FROM AUTHOR]

Climate change has resulted in a myriad of stressors to wild organisms. Phenotypic plasticity, including behavioral plasticity, is hypothesized to play a key role in allowing animals to cope with rapid climate change and mitigate its negative fitness consequences. Camouflage mismatch resulting from decreasing duration of snow cover presents a stressor to species that undergo coat color molts to maintain camouflage against seasonally changing backgrounds. Winter white animals appear highly conspicuous against dark, snowless background and experience increased predation‐induced mortality. Here, we evaluate the potential of behavioral plasticity to buffer against camouflage mismatch in mountain hares Lepus timidus in Scotland. We carried out field surveys in three populations over two years and found no evidence that hares modify their behaviors in response to increasing camouflage mismatch. Hares did not prefer to rest closer to light‐colored rocks or farther from conspecifics with increasing color contrast. Furthermore, whiter hares did not seek to rest closer to snowy backgrounds; rather, hares preferred to sit farther from snow. These results suggest that behavioral plasticity might not be a universal, rapid mechanism facilitating adaptation to climate change. Keywords: behavioral plasticity, camouflage, climate change, mountain hares, phenological mismatch [ABSTRACT FROM AUTHOR]

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIFrelative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. [ABSTRACT FROM AUTHOR]

More than half of Japan's land area experiences significant snowfall during winter, and the damage caused by various snow and ice disasters remains a dire issue, which also leads to decreased living standards. Simultaneously, the nature of snow and ice disasters has been transformed due to climate change and the increasing occurrence of extreme weather conditions. The National Research Institute for Earth Science and Disaster Resilience (NIED) has been continuously conducting research to address these problems in relation to snow and ice disasters. This study presents the results of the project "Research on Combining Risk Monitoring and Forecasting Technologies for Mitigation of Increasingly Diverse Snow Disaster" conducted by the NIED over a seven-year period from April 2016 to March 2023. This project developed technology for conducting accurate observations of snowfall and snow cover conditions over wide areas as well as technology for areal prediction of snow and ice disasters through simulations. Based on collaboration with stakeholders, such as local governments, our study investigated how to optimize the use of our information products for snow and ice disaster mitigation. Through these insights, the NIED provides information for prompt and appropriate responses to snow and ice disasters, thus supporting safe and comfortable living in both snowy and non-snowy areas. [ABSTRACT FROM AUTHOR]

Monitoring vulnerable species inhabiting mountain environments is crucial to track population trends and prioritize conservation efforts. However, the challenging nature of these remote areas poses difficulties in implementing effective and consistent monitoring programmes. To address these challenges, we examined the potential of passive acoustic monitoring of a cryptic high mountain bird species, the Rock Ptarmigan Lagopus muta. For 5 months in each of two consecutive years, we deployed 38 autonomous recording units in 10 areas of the Swiss Alps where the species is monitored by a national count monitoring programme. Once the recordings were collected, we built a machine‐learning algorithm to automate call recognition. We focused on studying the species' daily and seasonal calling phenology and relating these to meteorological and climatic data. Rock Ptarmigans were vocally active from March to July, with a peak of activity occurring between mid‐March and late April, 1 or 2 months earlier than the second half of May when the counts of the monitoring programme take place. The calling rate peaked at dawn before dropping rapidly until sunrise. Daily vocal activity demonstrated a consistent association with weather conditions and moon phase, whereas the timing of seasonal vocal activity varied with temperature and snow conditions. We found that the peak of vocal activity occurred when the snowpack was still thick and snow cover was close to 100% but with a local peak of high temperatures. Between our two study years, the peak of vocal activity occurred 30 days later in the colder year, suggesting phenological plasticity in relation to environmental conditions. Passive acoustic monitoring has the potential to complement conventional acoustic counts of cryptic birds by highlighting periods of higher detectability of individuals, and to survey small populations that often remain undetected during single visits. Moreover, our study supports the idea that passive acoustic monitoring can provide valuable data over large spatial and temporal scales, allowing decryption of hidden ecological patterns and assisting in conservation efforts. [ABSTRACT FROM AUTHOR]

The Western Himalayas receive significant snowfall, and it's essential to monitor the snow cover for various purposes like managing water resources, conducting hydrological research, and predicting avalanches using remote sensing techniques. With the pan-sharpening of different satellite sensor images, the essential features can be obtained to understand the snow cover dynamics. The resampling algorithms play a very important role in the pan-sharpening procedure to highlight the important features of the pan-sharpened dataset. However, the performance of various resampling algorithms for pan-sharpening is very rarely validated with optical and microwave datasets. In this article, the spatial and spectral analysis of different resampling algorithms, i.e., Nearest Neighbour (NN), Bilinear (BI), and Cubic Convolution (CC) resampling, have been performed using the fusion of optical and microwave satellite images. In this study, two datasets, i.e., MODIS (optical) and SCATSAT-1 (microwave) were used over the western Himalayas (i.e., Ladakh, Jammu and Kashmir, Himachal Pradesh, and Uttrakhand). To evaluate the performance of each resampled pan-sharpened dataset, the output is classified using a Support Vector Machine (SVM) classifier and a Spectral Angle Mapper (SAM) classifier. Root Mean Square Error values were computed to quantify the level of agreement between the pan-sharpened datasets and the ground truth data. The result of statistical analysis showed that NN-based pan-sharpened performed better than BI-based and CC-based pan-sharpened classified images with both classifiers i.e., SVM and SAM. This study is important in terms of the effective utilization of the resampling techniques along with pan-sharpening algorithms. [ABSTRACT FROM AUTHOR]

Among 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal “negative–positive” dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered. [ABSTRACT FROM AUTHOR]

The impact of a slightly modified broadband snow surface albedo parameterization, which explicitly considers the cloud dependence of the snow albedo, is evaluated in simulations of a coupled regional climate model of the Arctic. The cloud dependence of the snow albedo leads to a more realistic simulation of the variability of the surface albedo during the snowmelt period in late May and June. In particular, the reproduction of lower albedo values under cloud-free/broken-cloud conditions during the snowmelt period represents an improvement and results in an earlier disappearance of the snow cover and an earlier onset of sea-ice melt. In this way, the consideration of the cloud dependence of the snow albedo results in an amplification of the two-stage snow/ice albedo feedback in the model. This finds expression in considerably increased sea-ice melt during the summer months and ends up in a new quasi-stationary equilibrium in sea ice with statistically significant lower sea-ice volume and statistically significant lower summer sea-ice area. [ABSTRACT FROM AUTHOR]

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures. [ABSTRACT FROM AUTHOR]

openAMUNDSEN (the open source version of the Alpine MUltiscale Numerical Distributed Simulation ENgine) is a fully distributed snow-hydrological model, designed primarily for calculating the seasonal evolution of snow cover and melt rates in mountain regions. It resolves the mass and energy balance of snow-covered surfaces and layers of the snowpack, thereby including the most important processes that are relevant in complex mountain topography. The potential model applications are very versatile; typically, it is applied in areas ranging from the point scale to the regional scale (i.e., up to some thousands of square kilometers) using a spatial resolution of 10–1000 m and a temporal resolution of 1–3 h or daily. Temporal horizons may vary between single events and climate change scenarios. The openAMUNDSEN model has already been used for many applications, which are referenced herein. It features a spatial interpolation of meteorological observations, several layers of snow with different density and liquid-water contents, wind-induced lateral redistributions, snow–canopy interactions, glacier ice responses to climate, and more. The model can be configured according to each specific application case. A basic consideration for its development was to include a variety of process descriptions of different complexity to set up individual model runs which best match a compromise between physical detail, transferability, simplicity, and computational performance for a certain region in the European Alps, typically a (preferably gauged) hydrological catchment. The Python model code and example data are available as an open-source project on GitHub (https://github.com/openamundsen/openamundsen , last access: 1 June 2024). [ABSTRACT FROM AUTHOR]

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change. [ABSTRACT FROM AUTHOR]

Snow cover is an important source of data for the study of environmental pollution due to its ability to accumulate atmospheric dust particles. A total reflection x‐ray fluorescence method (TXRF) was applied to assess the concentration levels of potentially toxic elements (As, Pb, Zn, Ni, Cu, Cr, V, Mn, and Sr) in samples of snow cover solid phase collected in suburban areas of the Irkutsk region with industrial activity. Suspension was chosen as an optimal sample preparation procedure for solid phases of snow samples obtained after melting and filtration. Results were compared with those obtained by wavelength‐dispersive x‐ray fluorescence spectrometry and atomic absorption spectrometry. Precision of TXRF results expressed by repeatability values was not exceeded 15% (n = 3) for most of the elements, and the trueness of the TXRF method expressed by recovery values was in the range of 87%–115% for concentration levels of 5.0–350 μg/g. Forty samples of snow cover solid phase were analyzed by TXRF method and results were compared with the background values. The investigation showed low contamination of the studied area by Pb, Cu, V, Mn, and Sr, medium contamination by Ni, As, and Cr, and high contamination by Zn. TXRF is advantageous in being rapid, cost‐efficient, and simple and can be used as a promising method for an assessment of environmental pollution. [ABSTRACT FROM AUTHOR]

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth's energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring of dam reservoirs is crucial for developing effective management strategies especially in regions with slope instabilities. Recent developments in satellite synthetic aperture radar (SAR) sensors and their processing techniques enabled frequent observations, allowing monitoring of landslides and revealing the triggering factors. In this study, we analyzed the slope movements in Kalekoy dam reservoir built in 2018 in Bingol Province, Türkiye using weekly Sentinel-1 SAR time series data. We used LiCSAR (Looking into Continents from Space with SAR) products freely served by the COMET-LiCS (Center for the Observation and Modeling of Earthquake Volcanoes and Tectonics) web portal to perform interferometric SAR (InSAR) time-series analysis. Using land use land cover, snow and vegetation indices derived from the Sentinel-2 optical imagery, and combining with precipitation, lake level measurements, and seismic and anthropogenic activities between 2015 and 2022, our findings revealed that the use of SAR data with regular global coverage is essential for deformation monitoring in dam reservoirs and ensure a comprehensive understanding of landslide phenomena in regions prone to such events. The slope movements were clearly observable in the InSAR time series with seasonal cycles especially in winter, and changes of snow cover were linked to them. Vegetation cover decreased over time in the region, likely influenced by the dam reservoir. Sudden changes in slope deformations were related to lake water levels and spring snowmelt as the saturation of landslide toe zones increased pore water pressure. Despite varying characteristics, changes in lake water levels and snowmelt were common triggers for all landslides analyzed. [ABSTRACT FROM AUTHOR]

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow's albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model's performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production. [ABSTRACT FROM AUTHOR]

The growing population and expansion of rural activities, along with changing climatic patterns and the need for water during drought periods, have led to a rise in the water demand worldwide. As a result, the construction of water storage structures such as dams has increased in recent years to meet the water needs. However, dam construction can bring significant alterations to the natural flow regime of rivers, and it is therefore essential to understand the potential effects of human structures on the hydrological regime of rivers to reduce their destructive impacts. This study analyzes the hydrological changes in the Shahrchai River in response to the Shahrchai Dam construction in Urmia, Iran. The study period was from 1950 to 2017 at the Urmia Band station. The Indicators of Hydrological Alteration (IHA) were used to analyze the hydrological changes before and after regulating, accounting for land use changes and climatic factors. The results revealed the adverse effects of the Shahrchai Dam on the hydrological indices. The analysis showed an increase in the average flow rate during the summer season and a decrease in other seasons. However, the combined effects of water transferring for drinking purposes, a decrease in permanent snow cover upstream of the dam, and an increase in water use for irrigation and agricultural purposes resulted in a decrease in the released river flow. Furthermore, the minimum and maximum daily flow rates decreased by approximately 85% and 65%, respectively, after the construction of the Shahrchai Dam. Additionally, the number of days with maximum flow rates increased from 117 days in the pre-dam period to 181 days in the post-dam period. As a concluding remark, the construction of the Shahrchai Dam, land use/cover changes, and a decrease in permanent snow cover had unfavorable effects on the hydrological regime of the river. Therefore, the hydrological indicators should be adjusted to an acceptable level compared to the natural state to preserve the river ecosystem. The findings of this study are expected to guide water resource managers in regulating the sustainable flow regime of permanent rivers. [ABSTRACT FROM AUTHOR]

The polymer-facilitated flux of ice algae on Arctic shelves can initiate benthic activity and growth after the nutritionally constrained winter period. Lipid-rich ice algae are readily consumed by benthos and those entering the sediment can benefit deposit feeders. Ice algae assimilated by benthic organisms cascade up multiple trophic levels within the benthic sub-web, re-entering the pelagic sub web through habitat coupling species. Pelagic predators can have significant ice-algal carbon signals obtained from the benthic compartment. Sympagic-pelagic-benthic coupling on Arctic shelves is expected to weaken with ongoing sea-ice change. This review discusses the phenology, quantity, and quality of ice-algal contributions to coupling, linked to thinning snow and ice cover including multi-year ice replacement. Predicting future coupling between marine sub-webs requires focused research that considers trophic markers of multiple carbon sources. [ABSTRACT FROM AUTHOR]

Rain‐on‐snow (ROS) events occur when rain falls on snowpack and can have substantial ecological and social impacts. During ROS events, liquid water in the snowpack can decrease the surface albedo, which contributes to the positive snow‐albedo feedback and further accelerates snowmelt. In a warming climate, the frequency and spatial coverage of ROS events are projected to increase in the high‐latitude regions, especially in northern Alaska. Multi‐year ground observations at two northern Alaska sites are utilized to evaluate 59 ROS events from 2012 to 2022. Results show that ROS events lead to dramatic snow albedo changes with a mean decline of −0.04 per day, which is considerably larger than the multi‐year mean of −0.005 in May and −0.008 in June. A snow albedo model is used to simulate the daily snow albedo changes due to snowpack liquid water content. The simulated impact of liquid water content accounts for only 10% of the observed snow albedo changes. In addition, composite synoptic conditions from reanalysis products reveal different moisture sources for ROS events. ROS events in May are associated with anomalous high pressure systems over the site and meridional transport of warm and moist air from lower latitudes. While the June synoptic conditions for ROS events show little deviation from the climatological mean and suggest local moisture contributions. ROS events in June show comparable snow albedo changes as in May despite the difference in moisture sources, which implies a prolonged impact of ROS events on rapid snow deterioration during late spring. Plain Language Summary: Rain‐on‐snow (ROS) events occur when rain falls on a snow‐covered surface, and can decrease surface reflectivity, accelerate snowmelt, exacerbate flooding, and deeply affect the local ecology and society. As global temperature increases, ROS events will become more frequent and intense at high latitudes, especially in northern Alaska. This study leverages a long record of ground observations in northern Alaska from 2012 to 2022 to detect ROS events and evaluate their impacts on snow cover. Results show that ROS events can cause distinct declines in snow reflectivity. The influence of liquid water in the surface snow has been simulated by a model and compared with observations. Additionally, analyses of composite environmental conditions reveal different moisture sources for ROS events. ROS events in May rely on the remote transport of moisture from lower latitudes, while those in June are associated with local moisture sources. Our results have indicated that ROS events will play a more influential role in accelerating surface snow melting at high latitudes in a warming climate. Key Points: Rain‐on‐snow events accelerate albedo decline during May and June at the North Slope of AlaskaSimulated influence of liquid water content explains 10% of the observed snow albedo changesRain‐on‐snow events in May rely on anomalous remote moisture transport, while those in June are associated with local moisture sources [ABSTRACT FROM AUTHOR]

Snow cover on the Qinghai-Tibet Plateau significantly impacts the climate, hydrology, and ecology of China and East Asia. Current studies mainly use snow cover days to describe its duration, overlooking the snow's discontinuous nature. This study analyzes snow phenology and the spatiotemporal distribution of continuous snow cover events on the Qinghai-Tibet Plateau from 1961 to 2019. The findings indicate that continuous snow cover days better capture the temporal discontinuity of snow cover compared to snow cover days. The contribution and continuity are lower than regions like North America, Europe, Northeast and Xinjiang in China, indicating poorer snow cover continuity on the Qinghai-Tibet Plateau. Additionally, we found that temperature and precipitation, especially autumn temperatures and spring and winter precipitation, significantly impact various snow indices. Wind speed also significantly impacts snow cover, particularly in autumn. Atmospheric circulation indirectly affects the snow cover discontinuity by influencing temperature and precipitation. [ABSTRACT FROM AUTHOR]

Though climate change and its adverse ecological and geohydrological impacts are being experienced across the world in all types of ecosystems but as far as the Himalaya mountain ecosystem is concerned, the rate of climate change and subsequent impacts have reached an alarming stage due to anthropogenic and technogenic intervention on natural process and now need most effective and less time taking management strategy. Addressing this burning environmental problem, a geospatial artificial intelligence (GeoAI) technique–based case study is presented here from one of the most densely populated and urbanized regions of Himalaya mountain, viz Uttarakhand Himalaya, which is also called central Himalaya. The results of the study suggest that due to quite a high rate of climate change, the climatic zones shifting towards higher altitudes at the average rate of 5.6 2 m/year, causing several adverse ecological impacts in terms of decreasing quality dense temperate forest cover (0.05%/year), snow cover (0.02%/year), water bodies (0.01%/year), agricultural land (0.31%/year), and horticultural land (0.01%/year). Conversion of these eco-friendly land use land cover into barren land, fallow land, and built-up land causes geohydrological consequences of climate change in terms of decreasing rainy days (1%/year), drying perennial springs (0.20%/year), perennial streams (0.11%/year), decreasing spring and stream discharge during non-monsoon season, increased extreme rainfall events (6–8%/year), and subsequent surface runoff during monsoon season. Further, the study advocates that the degraded geohydrological process has resulted in an increased frequency of disaster events (floods, cloudbursts, landslides. etc.) with a 3% (12 events) annual rate, causing great loss of environment, infrastructure, lives, and economy each year. Therefore, it has been very urgent to mitigate climate change and increase geohydrological disaster events through an integrated approach. Keep in view this, the present study proposed an integrated watershed management plan which is equally useful to be implemented across the Himalaya region and other similar ecosystems across the world. [ABSTRACT FROM AUTHOR]

The impacts of falling ice radiative effects (FIREs) on land‐atmosphere feedback processes were examined, with a focus on the fidelity of land surface properties and their variability as inferred by global climate models (GCMs). We conducted a pair of sensitivity experiments using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 1 (CESM1) in fully coupled modes with FIREs turned on and off. This allowed us to investigate the seasonal response of land surface properties to changes in radiation fluxes and land surface temperature (LST) associated with FIREs across global land areas. Our findings indicate that during boreal winter, excluding FIREs results in less surface downward longwave and net flux (∼5–15 Wm−2), leading to a colder land surface (∼2–4 K) and air temperatures (∼1–4 K) at mid‐ and high latitudes. Consequently, the surface frozen soil layer and snow cover persist through spring, delaying snowmelt and thawing until summer. This delay reduces liquid soil moisture, thereby suppressing vegetation productivity in subsequent seasons. Conversely, tropical regions, exhibit contrasting responses, with a warmer land surface (∼0.5 K) and warmer air temperatures (∼0.1–0.5 K) due to increased surface downward shortwave and net flux (∼2–10 Wm−2). This enhancement in radiation fosters increased vegetation productivity throughout the seasonal cycle. These findings illustrate a local response of land surface properties to changes in the surface energy balance and LST, highlighting the significant role that FIREs play in land surface modeling within GCMs. Plain Language Summary: We explored the seasonal response of land surface properties to changes in radiation fluxes and land surface temperature across global land areas using the CESM1‐CAM5 coupled model with falling ice radiative effects (FIREs) both turned on and off. During boreal winter, excluding FIREs reduces surface downward longwave and net flux, resulting in a colder land surface and colder air temperatures at mid‐ and high latitudes. These cold conditions lead to the persistence of the surface frozen soil layer and snow cover through spring, delaying snowmelt and thawing until summer, which in turn reduces liquid soil moisture and suppresses vegetation growth in subsequent seasons. In contrast, tropical regions exhibit a warmer land surface and air temperatures due to increased downward shortwave and net flux, promoting vegetation growth throughout the year. Key Points: Most Coupled Model Intercomparison Project Phase 5 models did not consider falling ice radiative effects (FIREs)Excluding FIREs results in cold air, prolonging frozen soil and snow cover, which suppress vegetation growth at mid‐ to high latitudesExcluding FIREs leads to warmer air temperatures and promotes enhanced vegetation growth in tropical latitudes [ABSTRACT FROM AUTHOR]

A nearly continuous daily, global Environmental Science Data Record of NASA Standard MODerate-resolution Imaging Spectroradiometer (MODIS) snow-cover extent (SCE) data products has been available since 2000. When the MODIS record ends, the 'moderate resolution' SCE record will continue with NASA Standard Visible Infrared Imaging Radiometer Suite (VIIRS) SCE data products. The objective of this work is to evaluate and quantify the continuity between the MODIS and VIIRS SCE data products to enable the merging of the data product records. A climate data record (CDR) could be developed when 30 years of daily global moderate-resolution SCE become available if the continuity of the MODIS and VIIRS records can be established. Here, we focus on the daily cloud-gap-filled MODIS and VIIRS SCE NASA standard data products, MOD10A1F and VNP10A1F, respectively, for a case study in the Great Basin of the western United States during a period of sensor overlap. Using the methodologies described herein (daily percent of snow cover, duration of snow cover, average monthly number of days (Ndays) of snow cover, and trends in Ndays of snow cover, we show that the snow maps display excellent agreement. For example, the average monthly number of days of snow cover in the Great Basin calculated using MOD10A1F and VNP10A1F agrees with a Pearson's correlation coefficient of r = 0.99 for our 11-year study period from WY 2013 to 2023. Additionally, the SCE derived from each data product agrees very well with meteorological station data, with a Pearson's correlation coefficient of r = 0.91 and r = 0.92 for MOD10A1F and VNP10A1F, respectively. Our results support the eventual creation of a CDR. [ABSTRACT FROM AUTHOR]

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region. [ABSTRACT FROM AUTHOR]

Hydrological droughts occur as a result of various hydrometeorological conditions, such as precipitation deficits, reduced snow cover, and high evapotranspiration. Droughts caused by precipitation deficits and occurring during warm seasons are usually longer in duration. This important observation raises the question that climate change associated with global warming may increase drought conditions. Consequently, it is important to understand changes in the processes leading to dry periods in order to predict potential changes in the future. This study is a scientific analysis of the impact of climate change on drought conditions in the Zhaiyk–Caspian, Tobyl–Torgai, Yesil, and Nura–Sarysu water management basins using the standardized precipitation index (SPI) and streamflow drought index (SDI). The analysis methods include the collection of hydrometeorological data for the entire observation period up to and including 2021 and the calculation of drought indices to assess their intensity and duration. The results of this study indicate an increase in the intensity and frequency of drought periods in the areas under consideration, which is associated with changes in climatic conditions. The identified trends have serious implications for agriculture, ecological balance, and water resources. The conclusions of this scientific study can be useful for the development of climate change adaptation strategies and the sustainable management of natural resources in the regions under consideration. [ABSTRACT FROM AUTHOR]

The atmospheric circulation significantly influences the snowpack over mid-high-latitude Eurasia. This study examines the characteristics of the leading subseasonal variability mode of boreal winter sea level pressure (SLP) with 20-80-day period and its relationship with snowpack over mid-high-latitude Eurasia, using the fifth generation of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data and different snowpack datasets. The SLP leading mode, characterized by a monopole pattern with a strong surface anomalous high centered near the Ural Mountains, exhibits a barotropic structure and extends from the surface to the tropopause. Above SLP and geopotential height anomalies propagate southeastward from the Barents-Kara Sea to East Asia. This leading SLP mode contributes to surface air temperature (SAT) and snowfall circulation anomalies over mid-high-latitude Eurasia. The latter two both directly influence on snowpack anomalies in situ. Over high latitude region, snowfall circulation anomaly is the dominant factor to control the snow depth anomaly. Over middle latitude region, both SAT and snowfall circulation anomalies lead to the snowpack anomaly. Furthermore, the response of snow depth to the leading subseaonal SLP mode occurs 2–5 days earlier than the response of snow cover to the same mode over middle latitude region. In addition, it is suggested that the Arctic Oscillation (AO), East Atlantic/West Russia (EAWR) and Polar/Eurasia (PEU) pattern may contribute to the development of the leading SLP mode and subsequently influence snowpack anomalies. [ABSTRACT FROM AUTHOR]

The study presents a simulation of climate change across Northern Eurasia during the 20th and 21st centuries using two different versions of the Earth system model developed by the Marchuk Institute of Numerical Mathematics at the Russian Academy of Sciences (INMCM). Model version INMCM5 participates in Coupled Model Intercomparison Project Phase 6 (CMIP6) and has the lowest equilibrium climate sensitivity (ECS) among the CMIP6 models. In the next model version, INMCM6, changes in the physical parameterisations lead to an increase in ECS by a factor of 2. Changes in near-surface temperature, precipitation, snow cover area and sea ice extent simulated by both model versions are compared with available observational and reanalysis data. Climate change predictions for the middle and end of the twenty-first century are provided by two model versions. Both model versions simulate similar climate changes for the upcoming two decades. After the middle of twenty-first century, the model version with high equilibrium climate sensitivity simulates stronger climate changes over Northern Eurasia than the model version with low sensitivity. But, in general, the ratio of predicted warming is much less than the ratio of ECS. [ABSTRACT FROM AUTHOR]

The Siberian High (SH) is one of the most prominent wintertime circulation systems in the Northern Hemisphere. The variability of the intensity of the SH during the boreal winter has been extensively studied, while the reasons behind its formation remain largely elusive. This study investigates the interannual variation and underlying mechanisms of the SH formation, especially the three-dimensional circulation and associated surface boundary conditions. Early SH formation is usually accompanied by a Eurasian teleconnection (EU)-like pattern and a strengthened subtropical westerly jet, and opposite conditions are true for a delayed SH formation. Boundary conditions, including North Atlantic sea surface temperature (NASST) anomalies and the high-latitude Central Eurasia snow-cover extent (HCESCE) anomalies could modulate SH formation by affecting the overlying atmospheric circulations. Specifically, anomalously cold NASSTs can result in an anomalous equivalent barotropic anticyclone via diabatic heating and transient eddy forcing. Such an anomalous anticyclone and related divergence anomalies in the upper troposphere act as effective atmospheric Rossby sources, triggering a downstream propagating atmospheric teleconnection with a pattern similar to that of a positive EU-like wave train, favoring the early formation of the SH. In addition, the reduced HCESCE warms the overlying atmosphere, thus weakening the atmospheric baroclinicity and transient eddy activities over the SH region. These weakened eddy activities can intensify the EU-like pattern via vorticity forcing and strengthen the subtropical westerly jet via the eddy–mean flow interaction. The processes for the impacts of the NASST and HCESCE anomalies on the formation of the SH can be verified by a series of numerical simulations. [ABSTRACT FROM AUTHOR]

Accurate forecast of daily streamflow of the major Himalayan rivers is crucial for understanding changes in their hydrological regimes caused by precipitation variability, as well as their potential implications for hydropower generation and flood management in the downstream region. However, the study pertaining to daily streamflow forecasting in the western Himalayas is lacking, primarily due to the limited availability and accessibility of long-term daily gauged data. In this study, four machine learning (ML) models, namely support vector machine, random forest, deep neural networks, and bi-directional long-short-term memory (BLSTM), were employed to forecast daily streamflow in the Sutlej river basin. The models were trained (11 years) and tested (2 years) using publicly accessible earth observation and reanalysis datasets for a period of 13 hydrological years, grouped into four distinct scenarios, denoted as M1, M2, M3, and M4. In each scenario, feature parameters were generated from the pool of six hydro-meteorological variables namely- precipitation (P), air temperature ( T a ), evapotranspiration (ET), snowfall, snow cover area (SCA), and discharge. The optimal feature combinations (e.g., scenarios) were identified using the time lag cross correlation technique. All of the ML models demonstrated reasonable performance (NSE > 0.82, R > 0.91, and RMSE < 101.2 m3/s) in simulating streamflow during testing period, the BLSTM model showed a slightly superior level of accuracy across all scenarios (NSE = 0.97, R = 0.99 and RMSE = 42.7 m3/s for M4). The BLSTM was further used to reconstruct daily timeseries discharge for the period 2012–2021. The variability in T a , ET, and P exerts a greater influence on streamflow generation in the glacierized catchment in comparison to snowfall. The observed phenomenon can be ascribed to the dampening effect of snowfall on discharge, as their effects become apparent within a timeframe of five to seven days. [ABSTRACT FROM AUTHOR]

The number of Arctic glaciers with direct, long-term measurements of mass balance is limited. Here we used satellite-based observations of the glacier snowline altitude (SLA), the location of the transition between snow cover and ice late in the summer, to approximate the position of the equilibrium-line altitude (ELA), a variable important for mass balance assessment and for understanding the response of glaciers to climate change. We mapped the snowline (SL) on a subset of 269 land-terminating glaciers above 60° N latitude in the latest available summer, clear-sky Landsat satellite image between 1984 and 2022. The mean SLA was extracted using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). We compared the remotely observed SLA observations with available long-term field-based measurements of ELA and with ERA5-Land reanalysis climate data. Over the last 4 decades, Arctic glacier SLAs have risen an average of ∼152 m (3.9±0.4 m yr -1 ; R2=0.74 , p<0.001), with a corresponding summer (June, July, August) temperature shift of +1.2 °C at the glacier locations. This equates to a 127±5 m shift per 1 °C of summer warming. However, we note that the effect of glacier surface thinning could bias our estimates of SLA rise by up to ∼1 m yr -1 , a significant fraction (∼25 %) of the overall rate of change, and thus should be interpreted as a maximum constraint. Along with warming, we observe an overall decrease in snowfall, an increase in rainfall, and a decrease in the total number of days in which the mean daily temperature is less than or equal to 0 °C. Glacier SLA is most strongly correlated with the number of freezing days, emphasizing the dual effect of multi-decadal trends in mean annual temperature on both ablation (increasing melt) and accumulation processes (reducing the number of days in which snow can fall). Although we find evidence for a negative morpho-topographic feedback that occurs as glaciers retreat to higher elevations, we show that more than 50 % of the glaciers studied here could be entirely below the late-summer SLA by 2100, assuming the pace of global warming and the mean rate of SLA rise are maintained. [ABSTRACT FROM AUTHOR]

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake's lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape. [ABSTRACT FROM AUTHOR]

Background and Aims Reduced snow cover and increased air temperature variability are predicted to expose overwintering herbaceous plants to more severe freezing in some northern temperate regions. Legumes are a key functional group that may exhibit lower freezing tolerance than other species in these regions, but this trend has been observed only for non-native legumes. Our aim was to confirm if this trend is restricted to non-native legumes or whether native legumes in these regions also exhibit low freezing tolerance. Methods First, we transplanted legumes (five non-native species and four native species) into either an old field (non-native) or a prairie (native) and used snow removal to expose the plots to increased soil freezing. Second, we grew plants in mesocosms (old field) and pots (prairie species) and exposed them in controlled environment chambers to a range of freezing treatments (control, 0, −5 or −10 °C) in winter or spring. We assessed freezing responses by comparing differences in biomass, cover and nodulation between freezing (or snow removal) treatments and controls. Key Results Among legume species, lower freezing tolerance was positively correlated with a lower proportion of nodulated plants and active nodules, and under controlled conditions, freezing-induced reductions in above-ground biomass were lower on average in native legumes than in non-native legumes. Nevertheless, both non-native and native legumes (except Desmodium canadense) exhibited greater reductions in biomass in response to increased freezing than their non-leguminous neighbours, both in controlled environments and in the field. Conclusions These results demonstrate that both native and non-native legumes exhibit low freezing tolerance relative to other herbaceous species in northern temperate plant communities. By reducing legume biomass and nodulation, increased soil freezing could reduce nitrogen inputs into these systems. [ABSTRACT FROM AUTHOR]

It has been proposed that the level of air pollution in a city should be estimated based on the accumulation of pollutants in the snow cover of urban forests. This study presents a bioluminescence method for estimating the extent of snow cover pollution in the urbanised areas of boreal forests in Krasnoyarsk city. A bioluminescent assay involving NAD(P)H:FMN oxidoreductase (Red) and luciferase with luminous bacteria (BLuc) was employed to measure the concentrations of six heavy metals (As, Cd, Zn, Co, Hg, and Pb) in the snow cover. The tested snow samples demonstrated a correlation between the reduced activity of the enzyme system and variations in Cd concentration. Furthermore, the research indicated that the period of unfavourable meteorological conditions in Krasnoyarsk city resulted in a notable decline in the activity of the BLuc–Red enzyme system, which may be associated with elevated air pollution levels. This study underscores the potential of the bioluminescence method for monitoring environmental pollution in urban forested areas. [ABSTRACT FROM AUTHOR]

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the "Third Pole" of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km2 to around 9.01 thousand km2; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

The melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift‐permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift‐permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land‐surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow‐covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods. Plain Language Summary: The spring melting of snowpacks in mountainous regions is crucial for providing freshwater to downstream river basins. Accurate simulation of mountain snowpacks requires accounting for factors like energy input, redistribution of snow, and forest canopies. However, including all these factors can be computationally challenging for large areas. In this study, the Canadian Hydrological Model (CHM) was used to simulate snowpacks at fine scales (about 50 m) across the Canadian Cordillera and nearby regions. By using a multiscale approach, the computational requirements were reduced substantially while maintaining the range of landscape features. The CHM accounts for various factors such as windflow, mountain shadowing, temperature, humidity, and precipitation changes with elevation, as well as snow redistribution through avalanching and wind transport. The model was validated against multiscale observations including satellite imagery, lidar data, and point observations. By incorporating snow redistribution and sublimation processes in the model, the accuracy of snow cover predictions improved over spring and summer. At larger scales, considering snow redistribution delayed the complete melting of snowpacks by an average of 33 days, showcasing the importance of scale‐dependent redistribution and ablation processes. These simulations demonstrate how multiscale modeling enhances snowpack predictions, aiding in forecasts of water supply, droughts, and floods. Key Points: A novel, large extent, snowdrift permitting scale simulation of ≈1.4 M km2 was performedThe inclusion of snow redistribution was scale emergent and delayed full snowcover ablation by 33 days on averageThe inclusion of snow redistribution processes improved the summer simulated versus observed snow‐covered area r2 from 0.7 to 0.9 [ABSTRACT FROM AUTHOR]

SWAP land surface model, developed by the authors of this article, was used to carry out many-year calculations of snow cover characteristics for forest and field areas in the main regions of Asian Russia: West Siberia, East Siberia, and Far East for a historic period (1967−2019). The comparison of simulation results with the appropriate data of route snow surveys at meteorological stations in the Asian part of RF showed that SWAP adequately reproduces the dynamics of snow water equivalent, snow depth and density in open areas and under forest canopy at the chosen sites. For all sites located in snow survey areas, climatic values of the characteristics of snow cover formation regime were obtained for two types of the land surface (forest and field) and two climatic periods (1967−1992 and 1993−2019) and used to identify trends in changes in these characteristics in the historic period. It was shown that the direction of changes in the climatic values of snow cover characteristics in forest and field areas in West Siberia, East Siberia, and Far East is the same and corresponds to projections for the XXI century derived from climate models. [ABSTRACT FROM AUTHOR]

High Mountain Asia (HMA), recognized as a third pole, needs regular and intense studies as it is susceptible to climate change. An accurate and high-resolution Digital Elevation Model (DEM) for this region enables us to analyze it in a 3D environment and understand its intricate role as the Water Tower of Asia. The science teams of NASA realized an 8-m DEM using satellite stereo imagery for HMA, termed HMA 8-m DEM. In this research, we assessed the vertical accuracy of HMA 8-m DEM using reference elevations from ICESat-2 geolocated photons at three test sites of varied topography and land covers. Inferences were made from statistical quantifiers and elevation profiles. For the world's highest mountain, Mount Everest, and its surroundings, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) resulted in 1.94 m and 1.66 m, respectively; however, a uniform positive bias observed in the elevation profiles indicates the seasonal snow cover change will dent the accurate estimation of the elevation in this sort of test sites. The second test site containing gentle slopes with forest patches has exhibited the Digital Surface Model (DSM) features with RMSE and MAE of 0.58 m and 0.52 m, respectively. The third test site, situated in the Zanda County of the Qinghai-Tibet, is a relatively flat terrain bed, mostly bare earth with sudden river cuts, and has minimal errors with RMSE and MAE of 0.32 m and 0.29 m, respectively, and with a negligible bias. Additionally, in one more test site, the feasibility of detecting the glacial lakes was tested, which resulted in exhibiting a flat surface over the surface of the lakes, indicating the potential of HMA 8-m DEM for deriving the hydrological parameters. The results accrued in this investigation confirm that the HMA 8-m DEM has the best vertical accuracy and should be of high use for analyzing natural hazards and monitoring glacier surfaces. [ABSTRACT FROM AUTHOR]

Although numerous recent studies have explored the relationship between permafrost degradation and rock slope failure, there is still a need for in‐depth investigations to develop relevant hazard assessment approaches. We investigate the predisposing, triggering and propagation processes of a rock avalanche (c. 225,000 m3) that occurred in Vallon d'Étache (France) on 18 June 2020, whose scar was coated by ice and water. Weather records and energy balance models show that the rock avalanche occurred right after the warmest spring and winter since at least 1985, but also right after the spring with the highest water supply anomaly (snowmelt and rainfall). Measured ground surface temperature and geoelectrical surveys reveal that relatively ice‐rich permafrost could exist in the NW face (release area) while it is inexistent below the SE face, contradicting certain permafrost maps. Heat transfer simulations suggest that the rock avalanche occurred during a transition from cold to warm permafrost conditions at failure depth (30 m), with a temperature increase of up to 0.6°C per decade since 2012 (when considering potential snow cover effect), and current temperature ranging between −3 and −1°C, depending on the applied model forcing. This warming certainly contributed to predispose slope to failure. In addition, the shift towards warm permafrost and water infiltration potentially enhancing permafrost degradation along fractures through heat advection or favouring the development of high hydrostatic pressures may have played as triggering factors. Finally, propagation simulations show that the rock avalanche involved several phases with different rheological properties due to the incorporation of snow and material segregation within the deposit. These new insights at various scales highlight the complexity of the triggering and propagation processes of rock slope failure occurring in high mountains, a significant part of which can be linked to snow effects on ground temperature, water supply and rheological properties. [ABSTRACT FROM AUTHOR]

Phenological changes are of great significance to the study of climate response and ecological environment.Based on the MODIS V6 snow product and reflectivity product in the past 20 years， the snow and lake ice phenology in the Qinghai Lake Basin were obtained， and the spatial distribution characteristics of the two were analyzed.On this basis， Theil-Sen Median method and linear regression method were used to analyze the variation trend of snow phenology and lake ice phenology， as well as the correlation between them in low altitude areas.The results show that： （1） Freeze-up start， Break-up start and Exist Duration of lake ice in Qinghai Lake are in the range of 321~389 d， 425~464 d and 0~174 d， respectively.On the whole， Freeze-up start and Break-up start of lake ice were delayed， and the delay rates were 0.3 d·a-1 and 0.2 d·a-1， respectively.Exist Duration of lake ice showed a shortening trend， with a shortening rate of 0.6 d·a-1.There is a significant correlation between lake ice phenology and longitude.From east to west， Freeze-up start is postponed， Break-up start is advanced， and Exist Duration of lake ice is shortened.（2） Start of snow cover days， End of snow cover days and Snow cover days in Qinghai Lake Basin are distributed in the range of 275~404 d， 353~484 d and 3~209 d， respectively.Among them， start of snow cover days and End of snow cover days showed an early trend and a delayed trend respectively， and the change rates were 0.8 d·a-1 and 0.11 d·a-1 respectively.Snow cover days showed an increasing trend， with a growth rate of 0.6 d·a-1.Snow phenology is closely related to altitude.With the increase of altitude， start of snow cover days is advanced， End of snow cover days is delayed， and Snow cover days increases.（3） Air temperature and negative accumulated temperature in winter are important factors affecting lake ice phenology.With the increase of temperature and negative accumulated temperature in winter， Freeze-up start will be delayed， Break-up start will be advanced， and Exist Duration of lake ice will be shortened.For snow phenology， there is a significant negative correlation between Snow cover days and the temperature.The temperature decreases and Snow cover days increases.（4） There is a potential relationship between some snow cover and lake ice phenology parameters in low-altitude watersheds.There is a significant negative correlation between the beginning date of snow cover and the beginning date of lake ice freezing， and the correlation coefficient is -0.404.As the lake surface insulation layer， the increase of snow cover days will also greatly slow down the speed of lake ice melting， resulting in the delay of lake ice melting date.Therefore， there is a positive correlation between the two， and the correlation coefficient is 0.349.The change law of ecosystem in the basin revealed by this study is of positive significance to the local ecosystem， and can provide theoretical basis and technical support for the environmental monitoring of Qinghai Lake Basin.

The increasing frequency of extreme weather events linked to climate change has made flood forecasting an important issue, particularly in mountainous regions where snowmelt is a major driver of seasonal flooding. This study explores the application of snow cover estimation techniques to assess snowmelt dynamics and their potential impact on flood risks in the Ulba and Uba basins in East Kazakhstan. To achieve this, high-resolution multispectral satellite imagery from the Sentinel-2 Surface Reflectance dataset is used, focusing on images collected between March and October for the years 2021 to 2024. The images are processed in Google Earth engine platform with strict filtering based on spatial intersection with the basins and cloud cover pixels percentage, ensuring high-quality data for snow cover analysis. The study utilizes multiple remote sensing indices for snow cover estimation. The normalized difference snow index is calculated using the green and shortwave infrared bands to detect snow-covered pixels. Fractional snow-covered area is derived from the NDSI using the 'FRA6T' relationship, offering a more nuanced estimate of snow distribution across the basins. Additionally, a near-infrared to shortwave infrared ratio threshold is employed to minimize confusion between snow and water, improving the detection of snow cover, particularly in regions near water bodies or during melt periods. The resulting snow cover maps and fSCA estimates provide a detailed picture of snow distribution and melt dynamics, contributing to the assessment of snowmelt’s role in flood risk development. The obtained insights can assist in refining flood forecasting models, improving early warning systems, and supporting informed water resource management in vulnerable regions.