Melting Of Snow Cover Research Articles

Развитие модели динамики снежного покрова Гидрометцентра России

Conceptual models of runoff formation used in the operational practice of hydrological forecasting by the Hydrometeorological Center of Russia include simplified parameterization of snow cover dynamics based on the use of snow cover melting coefficient, water-retaining capacity of snow and secondary freezing of meltwater in case of return of negative air temperatures. This schematization of the process is well established for the reliable calculation of the snow water equivalent for subsequent use in the calculation of meltwater inflow to the catchment surface and the calculation of streamflow characteristics. At the same time, the lack of calculation of snow density and snow height in the used schematization restricts the use of schemes for calculating of soil freezing depth in hydrological models, which seems to be extremely important for modelling runoff on mid-latitudes rivers, i.e. for the most Russian rivers. To overcome this disadvantage, the snow density and snow height parameterization was added to the calculation scheme of the model, which was verified using Roshydromet’s snow measurement routes data and shown a good and satisfactory quality of modeling of model characteristics.

The Coupled Application of the DB-IWHR Model and the MIKE 21 Model for the Assessment of Dam Failure Risk

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Temporal Variation Analysis in Clean Ice Glacier of Annapurna Basin

Glaciers of the Himalayas are the storehouse of fresh water. The impacts of climate change, mainly the melting of snow cover have been noticed in Nepal’s Himalayas. These changes have local as well as regional implications on water resources, and at global level on the sea level rise. This study is carried out to analyze the temporal variation of glacier retreat in the Annapurna valley from 2000 to 2020 using remote sensing technology. It is a multi-step process and involves the computation of several mathematical indices for glaciers based on the spectral properties of Landsat imagery. The final outcome is the delineation of Clean Ice Glacier (CIG) area of Annapurna basin for the years 2000, 2010 and 2020. The findings from this study reveal that the glaciers of Annapurna basin are shrinking more rapidly in the recent years than before. The decrease in CIG area coupled with the increase in the CIG number clearly indicate that the glaciers are being fragmented into smaller parts creating several disconnected ice bodies. Monitoring the dynamics of glaciers along with their trend analysis is crucial for strengthening the climate change mitigation and adaptation strategies. Key Words: Clean Ice Glacier, Temporal Variation, Annapurna Basin, Remote Sensing, Climate Change

Constraining future surface air temperature change on the Tibetan Plateau

Abstract The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical intermodel warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter up to 2 °C, followed by autumn with an increase of up to about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and historical periods.

South and Southeast Asia controls black carbon characteristics of Meili Snow Mountains in southeast Tibetan Plateau

South and Southeast Asia (SSA) emitted black carbon (BC) exerts potential effects on glacier and snow melting and regional climate change in the Tibetan Plateau. In this study, online BC measurements were conducted for 1 year at a remote village located at the terminus of the Mingyong Glacier below the Meili Snow Mountains. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to investigate the contribution and potential effect of SSA-emitted BC. In addition, variations in the light absorption characteristics of BC and brown carbon (BrC) were examined. The results indicated that the annual mean concentration of BC was 415 ± 372 ngm−3, with the highest concentration observed in April (monthly mean: 930 ± 484 ngm−3). BC exhibited a similar diurnal variation throughout the year, with two peaks observed in the morning (from 8:00 to 9:00 AM) and in the afternoon (from 4:00 to 5:00 PM), with even lower values at nighttime. At a short wavelength of 370 nm, the absorption coefficient (babs) reached its maximum value, and the majority of babs values were < 20 Mm−1, indicating that the atmosphere was not overloaded with BC. At the same wavelength, BrC substantially contributed to babs, with an annual mean of 25.2 % ± 12.8 %. SSA was the largest contributor of BC (annual mean: 51.1 %) in the study area, particularly in spring (65.6 %). However, its contributions reached 20.2 % in summer, indicating non-negligible emissions from activities in other regions. In the atmosphere, the SSA BC-induced radiative forcing (RF) over the study region was positive. While at the near surface, the RF exhibited a significant seasonal variation, with the larger RF values occurring in winter and spring. Overall, our findings highlight the importance of controlling BC emissions from SSA to protect the Tibetan Plateau against pollution-related glacier and snow cover melting.

Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data

The changing climate directly affects spatial and temporal patterns of snow and ice cover globally and in the Hindu-Kush Himalayan (HKH) region. In the HKH, around 3.3 billion people across 11 countries depend on water originating from mountain glaciers and snowfields, and melting snow cover has a direct impact on their livelihood and well-being. Various studies have shown that the snow and ice cover in the HKH is declining at an alarming rate but have been limited in geographic, spatial and temporal scales. Here, we employed the Global Land Analysis and Discovery analysis ready Landsat time-series data (GLAD ARD) to map changes in perennial snow and ice between 2001 and 2021 at five-year epochs using decision tree ensemble models. These maps were used to create a stratified sampling design for reference data collection to estimate area and map accuracy. All five-year epoch maps have user's accuracies above 90% and producer's accuracies above 91%. Our sample data analysis showed that one-eighth of the extent of perennial snow and ice in the HKH, totalling 15,770 km2 (CI ± 3195 km2), disappeared over the last two decades with 105,935 km2 (CI ± 3396 km2) remaining in 2021. From map-based estimates, the largest decline of perennial snow and ice cover among the mountain ranges was found in the Himalayas with an estimated reduction of 5741 km2. Among HKH countries, China had the largest area of perennial snow and ice reduction of 10,654 km2, Nepal had the highest net perennial snow and ice reduction rate (31%). Among the river basins, the maximum net loss of perennial snow and ice cover between 2001 and 2021 was in the Indus (24.8%), followed by Brahmaputra (18.3%), and Tarim (15.7%). Our results confirm wide-spread loss of perennial snow and ice across the HKH region and provide both regionally consistent maps with derived area estimates at landform, national and basin-levels and methodology to continue monitoring snow and ice melt.

Isotopic signatures of snow, sea ice, and surface seawater in the central Arctic Ocean during the MOSAiC expedition

The Arctic Ocean is an exceptional environment where hydrosphere, cryosphere, and atmosphere are closely interconnected. Changes in sea-ice extent and thickness affect ocean currents, as well as moisture and heat exchange with the atmosphere. Energy and water fluxes impact the formation and melting of sea ice and snow cover. Here, we present a comprehensive statistical analysis of the stable water isotopes of various hydrological components in the central Arctic obtained during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020, including the understudied Arctic winter. Our dataset comprises &amp;gt;2200 water, snow, and ice samples. Snow had the most depleted and variable isotopic composition, with δ18O (–16.3‰) increasing consistently from surface (–22.5‰) to bottom (–9.7‰) of the snowpack, suggesting that snow metamorphism and wind-induced transport may overprint the original precipitation isotope values. In the Arctic Ocean, isotopes also help to distinguish between different sea-ice types, and whether there is a meteoric contribution. The isotopic composition and salinity of surface seawater indicated relative contributions from different freshwater sources: lower δ18O (approximately –3.0‰) and salinities were observed near the eastern Siberian shelves and towards the center of the Transpolar Drift due to river discharge. Higher δ18O (approximately –1.5‰) and salinities were associated with an Atlantic source when the RV Polarstern crossed the Gakkel Ridge into the Nansen Basin. These changes were driven mainly by the shifts within the Transpolar Drift that carried the Polarstern across the Arctic Ocean. Our isotopic analysis highlights the importance of investigating isotope fractionation effects, for example, during sea-ice formation and melting. A systematic full-year sampling for water isotopes from different components strengthens our understanding of the Arctic water cycle and provides crucial insights into the interaction between atmosphere, sea ice, and ocean and their spatio-temporal variations during MOSAiC.

Impacts of Climate Change on Mountaineering Routes in the Écrins Massif (Western Alps, France)

Mountaineering is one of the most affected sports activities due to the current climate crisis and the resulting changes in the physical environment of the high mountain areas. Some routes have disappeared while others are no longer accessible in summer (hazards, technical difficulties, etc.). Research has already been conducted on the impact of climate change on mountaineering and on adaptation. The Mont-Blanc massif and the Valais Alps have been used as study areas to understand the evolution of mountaineering routes over recent decades. A mapping of the geomorphological and glaciological processes affecting mountaineering routes has been conducted for the Valais. This study aims to apply this method to Écrins massif (French Alps), which had so far remained unstudied despite its emblematic character. The famous topo-guide "Le massif des Écrins - Les 100 plus belles courses et randonnées" (1974) was used as a basis for the study with 70 routes being selected according to their notoriety and visitor numbers. All the processes affecting the routes were mapped using GIS, and a statistical analysis was carried out to gain a better understanding of the evolution of the routes, together with semi-directive interviews with experts and practitioners. A new process – the early summer melting of ice and snow cover – was identified. As is the case in the Mont-Blanc massif and the Valais, a route is affected by 9 processes on average, and almost 25% of the routes can no longer be climbed in summer. The mapping method has been successfully applied to Écrins massif, providing new knowledge of the recent evolution of mountaineering routes in the Alps.

Cryosphere changes and their impacts on regional water resources in the Chinese Altai Mountains from 2000 to 2021

The cryosphere has an important impact on regional water resources and ecosystems in the Chinese Altai Mountains and its piedmont zone. Using the latest remote-sensing datasets of cryosphere changes and combining with in-situ observation data from glacier monitoring stations and snow cover surveys, the main cryosphere elements including glaciers, snow cover, and permafrost are investigated with emphasis on their changes since 2000 and the current situation. Their water resource effects are also discussed. The results indicate that although the glaciers in the region have experienced continuous and intensive melting, mass loss has slowed because both glacier area shrinkage and thickness reduction were larger during 2000–2010 than during 2010–2021. Snow-cover water equivalent (w.e.) has increased due to obvious increases in snow depth, although snow-cover area has decreased slightly. Permafrost has been degrading. Overall, cryosphere contributions to the regional water resource are approximately 40.9% since 2000, among which snow-cover melting is the largest, contributing 37.1% to water resources in the Irtysh River Basin and significantly more in the mountainous sub-basins with increased snowfall. Glacier melting contributes 2.9%∼3.4%, lower than earlier estimations of 3.4%∼3.6% for the late 20th century. Permafrost thaw caused by active layer thickening contributes approximately 0.59%. Meteorological data shows a warming and wetting trend, but summer temperature has a much lower increase rate and a slowing increase trend after 2013. Moreover, snowfall frequency has increased. In the future, glacier water resource contribution will continue to decrease, but the water resource effects of snow-cover melting and permafrost degradation would increase.

Mechanisms of Tibetan Plateau Warming Amplification in Recent Decades and Future Projections

Abstract Earth’s surface warming by external forcing depends on location. Warming amplification, or greater surface warming than the global average, occurs in the Arctic and high-elevation regions, including the Tibetan Plateau (TP). We examined the major drivers of TP warming amplification in recent decades (1979–2020) and under future climate scenarios (2061–2100) by applying local energy budget diagnosis of multiple radiative kernels based on state-of-the-art reanalysis datasets and coupled model simulations. From 1979 to 2020, both the snow–albedo feedback and cloud–radiation feedback strongly affected the seasonality of TP warming (summer vs winter). Snow cover and albedo decreased significantly in winter but showed only small changes in summer. TP total and low-level cloud cover increased in summer, causing cooling, and decreased in winter, causing warming. During winter, TP warming amplification (1.84 from 1979 to 2020) is weaker than Arctic amplification (3.64) because the positive contribution of the surface albedo feedback to TP warming is not as strong as the dual warming effects of the lapse rate feedback and surface heat flux observed in the Arctic. Our attribution analyses based on the preindustrial control (piControl) and Historical simulations of phase 6 of the Coupled Model Intercomparison Project confirmed that TP warming is caused by external forcing. Because the external forcing associated with anthropogenic activity is increasing, TP warming will continue to the end of the twenty-first century. Under likely future warming scenarios, winter TP warming amplification is still less than in the Arctic due to the effects of the lapse rate feedback and surface heat flux. Significance Statement Over the Tibetan Plateau, warming amplification, which refers to greater surface warming than the global average (∼75% more warming than the global average from 1979 to 2020), has resulted in melting permafrost, retreating glaciers, and decreasing biodiversity. This study quantified the relative effects of eight physical processes on the warming amplification over the Tibetan Plateau. In the boreal winter, the reduction in snow cover and albedo are the leading contributors (about 49%) to Tibetan Plateau warming, meaning that melting snow exposes darker ground that absorbs more sunlight than snow does. In a warmer future climate, continued melting snow cover is projected to further enhance Tibetan Plateau warming amplification.

Rise in Mid-Tropospheric Temperature Trend (MSU/AMSU 1978–2022) over the Tibet and Eastern Himalayas

The high-altitude Hindu Kush-Himalayan region (HKH, average ~5 km from msl) and the adjacent Indo-Gangetic plains (IG plains, ~0–250 m msl), due to their geographical location and complex topography, are reported to be highly sensitive to climatic changes. Recent studies show that the impacts of climate change and associated changes in water resources (glacial/snow melt water and rainfall) in this region are multifaceted, thereby affecting ecosystems, agriculture, industries, and inhabitants. In this study, 45 years of Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU)-derived mid-tropospheric temperature (TMT, 3–7 km altitude) and lower tropospheric temperature (TLT, 0–3 km altitude) data from the Remote Sensing Systems (RSS Version 4.0) were utilized to analyze the overall changes in tropospheric temperature in terms of annual/monthly trends and anomalies. The current study shows that the mid-tropospheric temperature (0–3 km altitude over the HKH region) has already alarmingly increased (statistically significant) in Tibet, the western Himalayas, and the eastern Himalayas by 1.49 °K, 1.30 °K, and 1.35 °K, respectively, over the last 45 years (1978–2022). As compared to a previous report (TMT trend for 30 years, 1979–2008), the present study of TMT trends for 45 years (1978–2022) exhibits a rise in percent change in the trend component in the high-altitude regions of Tibet, the western Himalayas, and the eastern Himalayas by approximately 310%, 80%, and 170%, respectively. In contrast, the same for adjacent plains (the western and eastern IG plains) shows a negligible or much lower percent change (0% and 40%, respectively) over the last 14 years. Similarly, dust source regions in Africa, Arabia, the Middle East, Iran, and Pakistan show only a 130% change in warming trends over the past 14 years. In the monthly breakup, the ‘November to March’ period usually shows a higher TMT trend (with peaks in December, February, and March) compared to the rest of the months, except in the western Himalayas, where the peak is observed in May, which can be attributed to the peak dust storm activity (March to May). Snow cover over the HKH region, where the growing season is known to be from September to February, is also reported to show the highest snow cover in February (with the peak in January, February, or March), which coincides with the warmest period in terms of anomaly and trend observed in the long-term mid-tropospheric temperature data (1978–2022). Thus, the current study highlights that the statistically significant and positive TMT warming trend (95% CI) and its observed acceleration over the high-altitude region (since 2008) can be attributed to being one of the major factors causing an acceleration in the rate of melting of snow cover and glaciers, particularly in Tibet and the Eastern Himalayas.

The influence of increasing temperatures on highland barley yields and on the maximum cultivation altitude on the Tibetan Plateau

Highland barley (Hordeum vulgare Linn. cv. nudum Hook. f.) is the principal cereal crop over the Tibetan Plateau (TP). The response of highland barely to climate change in the past decades, especially in terms of yields still remains uncertain. In this study, its responses to climate change were investigated using daily weather data and agriculture data during 1961–2018. The results showed that the annual mean air temperature over the TP increased at 0.33 °C per decade during 1961–2018, and the rate of warming increased with altitude, reaching 0.41 °C per decade at altitudes of 4500–4700 m. The growing degree days (GDDs) increased by 9.6% during 2011–2018 compared with the 1960s, whereas low temperature degree days (LDDs) decreased by 40.3% over the same period, indicating that the thermal conditions for highland barley cultivation have improved. A strong relationship was observed between the yield of highland barley and LDDs (−0.76, p < 0.001) than GDDs (0.58, p < 0.001) in Xizang, where sufficient irrigation water is available from the melting of snow cover or glaciers. In Sichuan, with abundant precipitation, significant correlations were noticed between county-level barley yield and GDDs and LDDs (0.60, p < 0.001; −0.65, p < 0.001). In Qinghai, the dry regions, county-level yields were influenced significantly by temperature and precipitation. These results indicated that climate warming was beneficial to highland barley yield in most region of the TP, mainly due to decreased LDDs. The potential altitude at which highland barley cultivation is feasible increased by approximately 280–484 m during 2016–2018, compared with 1981–1983. In Xizang, highland barley could be cultivated up to an altitude of 4507 m a.s.l. between 2016 and 2018, and it increased to 4179 m a.s.l. in Qinghai. These results could help local government to take actions to adapt to global warming and improve food security.

High-Performance Forecasting of Spring Flood in Mountain River Basins with Complex Landscape Structure

We propose the methodology of building the process-driven models for medium-term forecasting of spring floods (including catastrophic ones) in the mountainous areas, the hydrological analysis of which is usually much more complicated in contrast to plains. Our methodology is based on system analytical modeling of complex hydrological processes in 34 river basins of the Altai-Sayan mountain country. Consideration of 13 types of landscapes as autonomous hydrological subsystems influencing rivers’ runoff (1951–2020) allowed us to develop the universal predictive model for the most dangerous April monthly runoff (with ice motion), which is applicable to any river basin. The input factors of the model are the average monthly air temperature and monthly precipitation for the current autumn–winter period, as well as the data on the basin landscape structure and relief calculated by GIS tools. The established universal dependences of hydrological runoffs on meteorological factors are quite complex and formed under influence of solar radiation and physical–hydrological patterns of melting snow cover, moistening, freezing, and thawing of soils. The model shows the greatest sensitivity of April floods to the landscape composition of river basins (49% of common flood variance), then to autumn precipitation (9%), winter precipitation (3%), and finally, to winter air temperature (0.7%). When it is applied to individual river basins, the forecast quality is very good, with the Nesh–Sutcliffe coefficient NSE = 0.77. In terms of the accuracy of process-driven predictive hydrological models for the mountainous areas, the designed model demonstrates high-class performance.

Water Stress-Induced Divergence Growth of Picea schrenkiana in the Western Tianshan and Its Forcing Mechanisms

Since the 1950s, divergence problems have reduced the temporal stability of tree rings in response to climate, shaken the foundations of dendroclimatology, and affected the reliability of reconstructed models based on tree rings and the accuracy of historical climate series. Therefore, it is of great importance to investigate divergence problems, which will help us to better understand the growth strategies of trees in response to climate warming and provide a scientific basis for accurate climate reconstruction and simulation of forest dynamics. This paper aims to elucidate the mechanism of divergent growth of Picea schrenkiana at high altitudes in the western Tianshan from three aspects: variations in atmospheric circulation, changes in climatic factors, and the coping strategy of trees with climate change. High spring temperatures accelerate the melting of snow cover. Large amounts of snowmelt initially replenish soil water, leading to the rapid growth of trees, but later, the continuous consumption of snowmelt reduces the available water capacity of soil, resulting in water stress on trees. The pattern of trees’ response to changes in climate ranges from a pure temperature limitation to a collaborative temperature–moisture limitation. Since the 1990s, the weakening of the westerly circulation and the North Atlantic Oscillation has reduced their impacts on the hydroclimate in the Tianshan Mountains of Central Asia. The combination of heat-induced water deficit and a long-term weakening trend in atmospheric circulation has slowed tree growth over the past decade.

Особенности распределения снежного покрова в насаждениях Рудного Алтая

The article presents the results of examining the formation and peculiarities of snow cover in fir plantations compared to open shrubland in the conditions of Southwestern Altai obtained during 2020-2021 snow period. The forest stand affects snow distribution and characteristics in fir woods by capturing snow with tree crowns, snow accumulation around crown perimeter and, as a result, leads to the formation of near-trunk snowdrifts, in their turn facilitating higher snow accumulation among trees and at forest edges. The maximum snow cover thickness in fir woods ranged from 80 cm (under crowns) to 150 cm (among trees and along forest edges). In the open (control) zone, snow distribution appeared more homogenous – from 126 to 135 cm. During the period of maximum snow accumulation, snow density in fir plantations ranged from 0.19 to 0.26 g/cm3; and, from 0.24 to 0.29 g/cm3 in the control zone. Snow cover humidity likewise demonstrated significant differences between fir plantations and control site, and amounted to 258.3±13.47 mm and 279.7±12.80 mm, respectively. Whereas in the control (overgrown) zone the complete snow cover melting ended in the second 10-day period of April, in fir woods it finished in the second 10-day period of May. The research findings suggest that fir plantations in river catchment areas boost winter precipitation retention and slow down its loss resulting in more uniform water transition into subsurface runoff and longer period of river water replenishment.

Climate and elevation control snow depth and snow phenology on the Tibetan Plateau

Snowmelt from the Tibetan Plateau (TP) is the water source of many major Asian rivers, with significant importance for regional water supply and ecosystem services. The spatiotemporal variations of snow cover on the TP and its response to climate change at different altitudes are still unclear because of the lack of in situ observations on the western TP, and limitations of optical remote sensing due to clouds. Using a daily passive microwave remote sensing snow depth dataset from 1979 to 2020 across the TP, this study investigates the spatiotemporal variations of snow depth and snow phenology as well as their responses to climate change. The results showed that during the past 40 years, the maximum snow depth (SDmax) and the snow cover duration days (SCD) on the TP decreased significantly at a rate of 0.6 cm/decade and 2.9 d/decade, respectively. The snow cover starting date (SCS) was delayed at a rate of 1.1 d/decade, whereas the snow cover melt date (SCM) advanced at a rate of 1.1 d/decade. The decrease of SCD was driven by the advance of SCM, which was caused by the decrease of SDmax, whereas the decrease of SDmax was driven by the increase in snow accumulation season temperature (Ta). The TP showed significant elevation-dependent warming (EDW) (0.04 ℃ decade-1 km−1) and elevation-dependent decrease of SDmax (0.12 cm decade-1 km−1) and SCD (4.1 d decade-1 km−1). The EDW explains the elevation-dependent decrease of SDmax (r = −0.72, P < 0.05) and SCD (r = −0.83, P < 0.05). It is expected that the negative feedback effect of SDmax and SCD on EDW may further exacerbate the EDW of the TP, and the earlier snowmelt due to the continuous decrease of snow depth may increase the frequency of droughts and floods. Our findings support the enhancement of EDW on the TP, depletion of solid reservoirs, and intensification of the regional water crisis in a warmer future.

Dynamics of soil freezing and thawing in the agroforestry landscape

The article presents the dynamics of soil freezing and thawing in the agroforestry landscape of the dry-steppe zone. These processes in winters with unstable snow cover have their own characteristics. The purpose of this study was to investigate the character of soil freezing and thawing in the agroforestry landscape under conditions of low snowfall winters. Observations were carried out in the winter period of 2020–2021 at the existing runoff-erosion research station in Volgograd. The dynamics of soil freezing and thawing was studied using Danilin freeze-thaw meters installed in the field and in the center of a four-row forest belt. Simultaneously, snow cover height was measured in triplicate with a snow measuring rod. The weather conditions were characterized by alternating thaws, which promoted snow cover melt, and frosts, which increased the depth of soil freezing. The formation of a 10-15 cm snow cover did not affect soil freezing. In the absence of snow on the background of a brief thaw, the lower boundary of the frozen layer in the field decreased by 11 cm compared to the forest belt, which even in the leafless state affected the inflow of solar thermal energy. Average freezing at the end of winter was 85 cm in the field and 67 cm in the forest belt. Thawing in the field was faster. During the first two weeks, the rate of soil thawing averaged 2.3 cm/day in the field and 1.3 cm/day in the forest belt. After that, the depth of freezing in the whole agroforestry landscape became equal. During the next two weeks, the thawing rate doubled. After complete soil thawing in the field in the forest belt, the thickness of the frozen layer averaged 32 cm.

Understanding wind-driven melt of patchy snow cover

Abstract. The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.

Snow Exclusion Does Not Affect Soil Ammonia-Oxidizing Bacteria and Archaea Communities

Soil ammonia-oxidizing microorganisms play important roles in nitrogen (N) cycling in cold ecosystems, but how changes in snow cover will affect their distribution and associated functional characteristics remains unclear. A snow manipulation experiment was conducted to explore the effects of snow exclusion on soil ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) communities and functional characteristics in a spruce forest in the eastern Tibet Plateau. Results showed that the amoA gene abundance and community composition of AOA and AOB did not differ between snow regimes but varied among winter periods. AOA and AOB gene abundances showed a decreasing trend during the snow cover melting period. During the deep snow cover period, Thaumarchaeota and Crenarchaeota in the AOA community decreased significantly, while Proteobacteria and Nitrosospira in the AOB community increased significantly. The main factors affecting the changes in AOA and AOB community diversity and composition were soil MBN, nitrate nitrogen, and temperature, while AOA and AOB community diversity and composition were also significantly correlated with soil enzyme activities related to N cycling. These results recommend that the season-driven variations strongly affected soil ammonia-oxidizing community and functional characteristics more than momentary snow cover change. Such findings offer new insights into how soil N-cycling processes would respond to reduced snowfall in high-altitude regions.

Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia.

The present study is focused on the flood inundation in Brahmaputra Basin, which is one of the most recurrent and destructive natural disasters of the region. The flood inundation was assessed using C-Band Sentinel 1A synthetic aperture radar (SAR) during 2015–2020 with precipitation patterns, runoff discharge, and their impacts on land cover in the basin. The study exhibited a very high precipitation during monsoon in the upper catchment resulting in severe flood inundation in downslopes of Brahmaputra Basin. A very high (900–2000 mm) to extremely high (>2000 mm) monthly cumulative precipitation in the south and south-eastern parts of basin led to high discharge (16,000 to 18,000 m3s-1) during July-August months. The river discharge increases with cumulative effects of precipitation and melting of snow cover during late summer and monsoon season, and induced flood inundation in lower parts of basin. This flood has largely affected agricultural land (>77% of total basin), forests (~3%), and settlement (426 to 1758 km2) affecting large wildlife and livelihood during 2015–2020. The study highlights the regions affected with recurrent flood and necessitates adopting an integrated, multi-hazard, multi-stakeholder approach with an emphasis on self-reliance of the community for sustenance with local resources and practices.