Snow Accumulation Research Articles

Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System

AbstractSeasonal predictions of spring‐summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA‐enabled accuracy gains are most consistent and explainable for short‐lead, late‐season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring‐summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late‐season snowpack. fSCA also improved accuracy for long‐lead, early‐season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring‐summer snowmelt. The AI‐based hydrologic model generally outperformed the statistical model and, in some cases, better‐capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data‐driven hydrologic models using genetic algorithm‐based predictor selection could help guide new SNOTEL site selection.

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Bacterial and fungal communities in sub-Arctic tundra heaths are shaped by contrasting snow accumulation and nutrient availability.

Climate change is affecting winter snow conditions significantly in northern ecosystems but the effects of the changing conditions for soil microbial communities are not well-understood. We utilized naturally occurring differences in snow accumulation to understand how the wintertime subnivean conditions shape bacterial and fungal communities in dwarf shrub-dominated sub-Arctic Fennoscandian tundra sampled in mid-winter, early, and late growing season. Phospholipid fatty acid (PLFA) and quantitative PCR analyses indicated that fungal abundance was higher in windswept tundra heaths with low snow accumulation and lower nutrient availability. This was associated with clear differences in the microbial community structure throughout the season. Members of Clavaria spp. and Sebacinales were especially dominant in the windswept heaths. Bacterial biomass proxies were higher in the snow-accumulating tundra heaths in the late growing season but there were only minor differences in the biomass or community structure in winter. Bacterial communities were dominated by members of Alphaproteobacteria, Actinomycetota, and Acidobacteriota and were less affected by the snow conditions than the fungal communities. The results suggest that small-scale spatial patterns in snow accumulation leading to a mosaic of differing tundra heath vegetation shapes bacterial and fungal communities as well as soil carbon and nutrient availability.

Hydrological dynamics of snowmelt induced streamflow in a high mountain catchment of the Pyrenees under contrasting snow accumulation and duration years

AbstractSnowmelt drives a large portion of streamflow in many mountain areas of the world. However, the water paths from snowmelt to the arrival of the water in the streams are still largely unknown. This work analyzes for first time the influence of snowmelt on spring streamflow with different snow accumulation and duration, in an alpine catchment of the central Spanish Pyrenees. This study presents the water balance of the main melting months (May and June). Piezometric values, water temperature, electrical conductivity and isotope data (δ18O) allow a better understanding of the hydrological functioning of the basin during these months. Results of the water balance calculations showed that snow represented on average 73% of the water available for streamflow in May and June while precipitation during these months accounted for only 27%. However, rainfall during the melting period was important to determine the shape of the spring hydrographs. On average, 78% of the sum of both the snow water equivalent (SWE) accumulated at the beginning of May and the precipitation in May and June converted into runoff during the May–June melting period. The average evaporation‐sublimation during the 2 months corresponded to 8.4% of the accumulated SWE and rainfall, so that only a small part of the water input was ultimately available for soil and groundwater storage. When snow cover disappeared from the catchment, soil water storage and streamflow showed a sharp decline. Consequently, streamflow electrical conductivity, temperature and δ18O showed a marked tipping point towards higher values. The fast hydrological response of the catchment to snow and meteorological fluctuations, as well as the marked diel fluctuations of streamflow δ18O during the melting period, strongly suggests short meltwater transit times. As a consequence of this hydrological behaviour, independently of the amount of snow accumulated and of melting date, summer streamflow remained always low, with only small runoff peaks driven by rainfall events.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

AbstractAccurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive.

Numerical study of snow accumulation on bogies for long marshalling high-speed trains

Current numerical research on snow accumulation on bogies often simplifies train models as 1.5 cars or 3 cars, and there is less research on long marshalling trains with real bogies. In this article, based on the Euler–Lagrange method, the airflow structure and snow accumulation characteristics in the bogie area of an 8-car high-speed train (HST) are studied by numerical simulation. The results show that the airflow velocity and snow distribution concentration inside the bogie cabin vary along the flow direction. The downstream bogie cabin has a lower airflow speed, spatial snow particle concentration, and components snow accumulation. Because of the bogie cabin cover structure, the main airflow direction above the first and second bogie cabin is different, resulting in higher snow concentration and range on the windward side of the first bogie. There is a significant difference in snow accumulation characteristics between powered and nonpowered bogies. The powered bogie gearbox is directly affected by the airflow carrying snow particles, leading to severe snow accumulation on the windward side. This study provides a research foundation and engineering guidance for the snow protection strategy of HST bogies and is important for improving the comfort and safety of HSTs on snow-covered rails.

Seasonal Modulation of Crustal Seismicity in Northeastern Japan Driven by Snow Load

AbstractNumerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface‐load‐induced stress perturbation relative to the long‐term‐averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

What Controls the December Precipitation Deficit over the Southern Tibetan Plateau and the Northern Indian Continent?

Abstract Winter precipitation is critical for glacier growth, snow accumulation, and terrestrial storage on the Tibetan Plateau (TP). However, less attention has been paid to the changes in winter precipitation on the TP. In this study, based on the newly developed high-accuracy precipitation dataset, our diagnosis illustrated a distinct precipitation deficit over the southern TP and northern Indian continent (STNI) in December. Together with the ERA5 reanalysis, CESM1.1 large ensemble experiments, and pacemaker experiments, the dynamic diagnosis revealed that the precipitation deficit was attributed to the decrease in dynamic effects. In particular, the enhanced descending motion, accompanied by the strengthened anticyclone and regional meridional circulation over the STNI, resulted in the precipitation deficit. The changes in circulation system related to the precipitation deficit could be attributed to the Pacific and Indian sea surface temperature (SST) variability. With the negative interdecadal Pacific oscillation (IPO), there were circumglobal alternative geopotential height anomalies and an anomalous anticyclone over the southern TP, and descending motion around the STNI associated with the enhanced Walker circulation over the Indo-Pacific, which exhibited a similar pattern to the precipitation deficit over the STNI in the observations. In addition, the synergistic effect of Pacific and Indian SST variability can improve the simulated precipitation deficit over the STNI relative to the Pacific SST variability alone. These results imply a contribution of the tropical SST variability to the precipitation trends over the STNI in early winter and provide an insight into the understanding of climate warming on the winter climate over the TP.

Disentangling the drivers of future Antarctic ice loss with a historically calibrated ice-sheet model

Abstract. We use an observationally calibrated ice-sheet model to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge, on the one hand, and the surface mass balance, on the other. Our ensemble of simulations, forced by a panel of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), suggests that the ocean will be the primary driver of short-term Antarctic mass loss, initiating ice loss in West Antarctica already during this century. The atmosphere initially plays a mitigating role through increased snowfall, leading to an Antarctic contribution to global mean sea-level rise by 2100 of 6 (−8 to 15) cm under a low-emission scenario and 5.5 (−10 to 16) cm under a very high-emission scenario. However, under the very high-emission pathway, the influence of the atmosphere shifts beyond the end of the century, becoming an amplifying driver of mass loss as the ice sheet's surface mass balance decreases. We show that this transition occurs when Antarctic near-surface warming exceeds a critical threshold of +7.5 ∘C, at which the increase in surface runoff outweighs the increase in snow accumulation, a signal that is amplified by the melt–elevation feedback. Therefore, under the very high-emission scenario, oceanic and atmospheric drivers are projected to result in a complete collapse of the West Antarctic ice sheet along with significant grounding-line retreat in the marine basins of the East Antarctic ice sheet, leading to a median global mean sea-level rise of 2.75 (6.95) m by 2300 (3000). Under a more sustainable socio-economic pathway, we find that the Antarctic ice sheet may still contribute to a median global mean sea-level rise of 0.62 (1.85) m by 2300 (3000). However, the rate of sea-level rise is significantly reduced as mass loss is likely to remain confined to the Amundsen Sea Embayment, where present-day climate conditions seem sufficient to commit to a continuous retreat of Thwaites Glacier.

State-dependent impact of major volcanic eruptions observed in ice-core records of the last glacial period

Abstract. Recently, a record of large, mostly unknown volcanic eruptions occurring during the younger half of the last glacial period (12–60 ka) has been compiled from ice-core records. In both Greenland and Antarctica these eruptions led to significant deposition of sulfate aerosols, which were likely transported in the stratosphere, thereby inducing a climate response. Here we report the first attempt to identify the climatic impact of volcanic eruptions in the last glacial period from ice cores. Average negative anomalies in high-resolution Greenland and Antarctic oxygen isotope records suggest a multi-annual volcanic cooling. Due to internal climate variability, glaciological noise, and uncertainties in the eruption age, the high-frequency noise level often exceeds the cooling induced by individual eruptions. Thus, cooling estimates for individual eruptions cannot be determined reliably. The average isotopic anomaly at the time of deposition also remains uncertain, since the signal degrades over time as a result of layer thinning and diffusion, which act to lower the resolution of both the oxygen isotope and sulfur records. Regardless of these quantitative uncertainties, there is a clear relationship of the magnitude of isotopic anomaly and sulfur deposition. Further, the isotopic signal during the cold stadial periods is larger in Greenland and smaller in Antarctica than during the milder interstadial periods for eruptions of equal sulfur deposition magnitude. In contrast, the largest reductions in snow accumulation associated with the eruptions occur during the interstadial periods. This may be the result of a state-dependent climate sensitivity, but we cannot rule out the possibility that changes in the sensitivity of the isotope thermometer or in the radiative forcing of eruptions of a given sulfur ejection may play a role as well.

Development of snow removal system using embedded pipes inside road with solar thermal energy collector and packed bed latent heat thermal energy storage

To enhance winter safety for drivers and pedestrians, this study developed and assessed an efficient snow removal system. Utilizing a packed bed latent heat thermal energy storage system with a solar thermal energy collector and phase change material (PCM), the research demonstrated performance over sensible thermal energy storage, offering increased storage capacity, isothermal characteristics, and efficient charging and discharging processes. In this study, a packed bed PCM container was proposed for the improved heat transfer performance of latent heat thermal energy storage (LHTES). The heat and fluid flow inside LHTES was numerically and experimentally analyzed for the optimization. The snow removal system comprised solar thermal energy collectors, a packed bed LHTES, and concrete pavement. As a results, the phase change material temperature increases from 30 °C to 75 °C during a 27-hour charging process, accumulating 58 MJ of thermal energy. The system's snow removal effectiveness was validated in an experiment, affirming the efficient transfer of stored heat to melt 10 cm-thick snow at −5 to 13 °C. Notably, no snow accumulation occurred on the concrete pavement surface.

GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers

Abstract. Snow modelling is often hampered by the availability of input and calibration data, which can affect the choice of models, their complexity, and transferability. To address the trade-off between model parsimony and transferability, we present the Generalizable Empirical Model of Snow Accumulation and Melt (GEMS), a machine-learning-based model, which requires only daily precipitation, temperature or its daily diurnal cycle, and basic topographic features to simulate snow water equivalent (SWE). The model embeds a support vector regression pretrained on a large dataset of daily observations from a diverse set of the SNOwpack TELemetry Network (SNOTEL) stations in the United States. GEMS does not require any user calibration, except for the option to adjust the temperature threshold for rain–snow partitioning, though the model achieves robust simulation results with the default value. We validated the model with long-term daily observations from numerous independent SNOTEL stations not included in the training and with data from reference stations of the Earth System Model–Snow Model Intercomparison Project. We demonstrate how the model advances large-scale SWE modelling in regions with complex terrain that lack in situ snow mass observations for calibration, such as the Pamir and Andes mountains, by assessing the model's ability to reproduce daily snow cover dynamics. Future model improvements should consider the effects of vegetation, improve simulation accuracy for shallow snow in warm locations at lower elevations, and possibly address wind-induced snow redistribution. Overall, GEMS provides a new approach for snow modelling that can be useful for hydroclimatic research and operational monitoring in regions where in situ snow observations are scarce.

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

AbstractAccuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed‐forest basin in the Great Lakes region of central Ontario, Canada. Sub‐basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi‐objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi‐objective calibration or data assimilation, as demonstrated in this study.

Surface mass balance and climate of the Last Glacial Maximum Northern Hemisphere ice sheets: simulations with CESM2.1

Abstract. The Last Glacial Maximum (LGM, from ∼26 to 20 ka BP) was the most recent period with large ice sheets in Eurasia and North America. At that time, global temperatures were 5–7 ∘C lower than today, and sea level ∼125 m lower. LGM simulations are useful to understand earth system dynamics, including climate–ice sheet interactions, and to evaluate and improve the models representing those dynamics. Here, we present two simulations of the Northern Hemisphere ice sheet climate and surface mass balance (SMB) with the Community Earth System Model v2.1 (CESM2.1) using the Community Atmosphere Model v5 (CAM5) with prescribed ice sheets for two time periods that bracket the LGM period: 26 and 21 ka BP. CESM2.1 includes an explicit simulation of snow/firn compaction, albedo, refreezing, and direct coupling of the ice sheet surface energy fluxes with the atmosphere. The simulated mean snow accumulation is lowest for the Greenland and Barents–Kara Sea ice sheets (GrIS, BKIS) and highest for British and Irish (BIIS) and Icelandic (IcIS) ice sheets. Melt rates are negligible for the dry BKIS and GrIS, and relatively large for the BIIS, North American ice sheet complex (NAISC; i.e. Laurentide, Cordilleran, and Innuitian), Scandinavian ice sheet (SIS), and IcIS, and are reduced by almost a third in the colder (lower temperature) 26 ka BP climate compared with 21 ka BP. The SMB is positive for the GrIS, BKIS, SIS, and IcIS during the LGM (26 and 21 ka BP) and negative for the NAISC and BIIS. Relatively wide ablation areas are simulated along the southern (terrestrial), Pacific and Atlantic margins of the NAISC, across the majority of the BIIS, and along the terrestrial southern margin of the SIS. The integrated SMB substantially increases for the NAISC and BIIS in the 26 ka BP climate, but it does not reverse the negative sign. Summer incoming surface solar radiation is largest over the high interior of the NAISC and GrIS, and minimum over the BIIS and southern margin of NAISC. Summer net radiation is maximum over the ablation areas and minimum where the albedo is highest, namely in the interior of the GrIS, northern NAISC, and all of the BKIS. Summer sensible and latent heat fluxes are highest over the ablation areas, positively contributing to melt energy. Refreezing is largest along the equilibrium line altitude for all ice sheets and prevents 40 %–50 % of meltwater entering the ocean. The large simulated melt for the NAISC suggests potential biases in the climate simulation, ice sheet reconstruction, and/or highly non-equilibrated climate and ice sheet at the LGM time.

Aesthetic aspect of integration of devices for prevention of snow accumulation on roofs of buildings into modern urban landscape

In the context of the dynamics of urban processes, the problem of time requirements - the need to create engineering projects that ensure sustainable urban development, remain relevant and mobile for the long term, combining innovation and aesthetics - is touched upon. As an example, the urbanistic principles of development and design of a device for preventing snow accumulation on the roofs of buildings are highlighted. Within the framework of the principle of combining aesthetics and functionality, the design features of a specially designed universal canopy are revealed, which provides not only protection of the aerodynamic route of the device, but also allows to preserve the aesthetics of the building, and even – to organically fit outdated buildings that have no historical value into the urban landscape.

What recent global atmospheric reanalyses and regional climate models can represent observed snow accumulation on Antarctica?

Recent compiled surface mass balance observations on the Antarctic ice sheet are used to examine the performance of four global reanalysis products and two regional climate models (RCMs), i.e., the European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI), the fifth generation ECMWF reanalysis (ERA5), the Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), Modèle Atmosphérique Régional (MAR), and the Regional Atmospheric Climate Model (RACMO). All these models perform similarly well in reproducing spatial patterns of snow accumulation from the coast to inland plateau. But they differ in their magnitude with respective to in-situ observations with either evident overestimation (MAR) or underestimation (ERAI and CFSR) in the interior of the Antarctica. By comparison, MERRA2 and ERA5 at each elevation bin of 200 m match best with observations. Among the models, ERAI shows the smallest amount of snow accumulation averaged over the entire ice sheet. All models present some skills for reproducing the inter-annual variability in observed snow accumulation. By comparison, ERA5 probably provides the most realistic representation of variability in Antarctic snow accumulation since 1979.

Recent Mass Balance Anomalies on the Djankuat Glacier, Northern Caucasus

A 54-year-long series of continuous instrumental measurements of mass balance and its main components has already been accumulated at the Djankuat Glacier, which is representative of the Caucasus and the most studied glacier in Russia. The anomalies of these indicators in 2017/2018–2020/2021 were evaluated against an analysis of meteorological reasons that predetermined them. Each of the four balance years under consideration represents a particular anomaly of varying severity. As for conditions of mass income, three years saw accumulation higher than average, and in one year (2018/2019) it approached the norm. As for summer ablation conditions, similarly, in one season (2019) the melting differed from the average only slightly, but in the other three it was much higher. Consequently, in one year (2020/2021) the state of the glacier was close to normal, in another (2017/2018) the budget situation was much more favorable for Djankuat, and in the other two the final losses significantly exceeded the average annual mass loss rate. At the same time, in 2019/2020, an absolute record of ablation since the beginning of monitoring in 1967/1968 was recorded (4360 mm w.e.). Nevertheless, although negative mass balance values continue to be recorded annually, signs of an inevitable slowdown in the rate of glacier degradation in the Caucasus have appeared in the last 4-year-long period: the continued growth of winter snow accumulation overlaps the ongoing intensification of summer melting. The growth of debris cover in terms of area and thickness also affects this mass loss slowdown to some extent. This inhibits ablation, exerting a heat-insulating effect. Because of this, the congruence of mass balance parameters vs. altitude curves is distorted. Also, a tendency toward increasing annual glacier mass turnover was revealed for the last half-century. This fact gradually increases the energy of glaciation and indirectly indicates a weakening of continentality in the climate of the Caucasian highlands.

High-resolution repeat topography of drifting ice floes in the Arctic Ocean from terrestrial laser scanning

Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km2 drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.

Numerical Simulation of Falling‐Snow Deposition Pattern Over 3D‐Hill

AbstractSnow distribution over complex terrain is an important input quantity for hydrological modeling. Earlier numerical studies on falling‐snow deposition pattern mostly focused on two‐dimensional (2D) hills. This is simulated over a three‐dimensional (3D) hill using a large‐eddy simulation model. It is shown that for both 2D and 3D cases, the hill blocking effect leads to the formation of a snow accumulation zone on the windward slope. For the 3D case, the maximum snow deposition occurs on the leeward toe, but not for the 2D case under the same background conditions. The main reason for these differences is that the spanwise terrain change in the 3D case weakens this hill blocking effect and concurrently generates terrain‐scale vortices which cause spatially variable snow depositions. It is found that an additional snow accumulation zone forms on the leeward slope of the 3D‐hill due to the presence of a horseshoe vortex there. The low wind velocity regime bracketed by the vorticity streamers forms another large snow accumulation zone on the leeward toe. While the results here are only applicable to small hills in a mountain chain, they provide the necessary information for the parameterization of large‐scale snow patterns for hydrological modeling.

Feasibility assessment of implementing energy pile-based snowmelt system on a practical bridge deck in diverse climate conditions across China

The accumulation of snow on roads poses substantial challenges to traffic flow, incurring profound human safety concerns and considerable financial losses. To address this issue, a cost-effective and environmentally friendly solution is presented in the form of an energy pile-based snowmelt system. This research provides a comprehensive framework, which integrates the principle of bridge deck energy balance and numerical models of energy pile and practical bridge deck, aiming to evaluate the feasibility of the system in diverse climatic zones across China. First, the long-term climate and geography data pertaining to the snowmelt heat flux demand for the investigated climate regions are gathered and correlated. The Pearson Correlation Coefficients (PCCs) of each parameter (99 % confidence interval) elucidate the strongest correlation between ambient temperature and heat flux demand. Employing the proposed framework, the subsequent feasibility assessment demonstrates that the system's snow-melting effect varies with regional climate and snowmelt targets, and when real-time snow-melting is nonessential (snow-free area ratio, Ar, with values of 0 and 0.5), the system is feasible in most cities except Mohe, where the ambient temperature (Ta) reaches −28.74 °C and the snowfall rate water equivalent (s) is 1.56 mm/h. However, the system fails to meet the real-time snow-melting goal (Ar = 1) when it is located in extreme climate (Ta < −17 °C or s > 1 mm/h). The findings also reveal that snowmelt systems exhibit consistent behavior within the same climate zone. The study provides valuable insights into the practical application of energy pile-based snowmelt systems in different climate conditions, and highlights the critical role of climate considerations in the successful implementation of such innovative technologies.