Seasonal Snow Cover Research Articles

Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016

Snow plays an important role in meteorological, hydrological and ecological processes, and snow phenology variation is critical for improved understanding of climate feedback on snow cover. The main purpose of the study is to explore spatial-temporal changes and variabilities of the extent, timing and duration, as well as phenology of seasonal snow cover across the large part of Eurasia from 2000 through 2016 using a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-free snow product produced in this study. The results indicate that there are no significant positive or negative interannual trends of snow cover extent (SCE) from 2000 to 2016, but there are large seasonal differences. SCE shows a significant negative trend in spring (p = 0.01) and a positive trend in winter. The stable snow cover areas accounting for 78.8% of the large part of Eurasia, are mainly located north of latitude 45° N and in the mountainous areas. In this stable area, the number of snow-covered days is significantly increasing (p < 0.05) in 6.4% of the region and decreasing in 9.1% of the region, with the decreasing areas being mainly located in high altitude mountain areas and the increasing area occurring mainly in the ephemeral snow cover areas of northeastern and southern China. In central Siberia, Pamir and the Tibetan Plateau, the snow onset date tends to be delayed while the end date is becoming earlier from 2000 to 2016. While in the relatively low altitude plain areas, such as the West Siberian Plain and the Eastern European Plain region, the snow onset date is tending to advance, the end date tends to be delayed, but the increase is not significant.

Analysis of Melt and Freeze in Shyok Sub-Basin, Indus Basin of Indian Himalaya using Ku Band Scatterometer SCATSAT-1

The spatio temporal evaluation of melting snow and ice is important, among other things for studying climatological and hydrological applications. The knowledge of melting and freezing cycles of the seasonal snow cover is essential for water resources management and flood forecasting. Microwave remote sensing is a promising tool to characterise the melt and freeze over a large spatial scale and frequent temporal basis. This study attempts to use the Ku band dual polarization satellite SCATSAT-1 launched by ISRO with a scatterometer on-board with an enhanced spatial resolution of 2.2 Km to analyse the melt and freeze conditions in the Shyok basin, which is a sub-basin of the Indus river basin. The backscatter measurements from SCATSAT are helpful in identifying melt and freeze due to its sensitivity to the water present in the illuminated region. This study aims to adopt an adaptive threshold technique to identify the melt and freeze conditions, where the winter mean and maximum standard deviation and drop in backscatter due to the water content in the snow forms the basis to identify the melt and freeze conditions of the snow covered area. Spatio-temporal variability was observed in melt onset with 18% area under melt onset in the month of June, 2018. Onset in the month of April and May was less in comparison to June. Melt incidence maps were generated for major melt months of April, May and June and were categorised into 5 categories. April observed melt for 1 – 5 days, whereas a part of the study area was in melt condition throughout the month of June. A derived snow cover map was validated with Soumi_NPP derived snow cover with correlation of 0.96, indicating that SCATSAT can be used for snow cover retrieval over Himalayas by observing melt/freeze. Availability of daily SCATSAT data makes it possible to generate daily melt incidence maps. https://doi.org/10.23953/cloud.ijarsg.467

Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015.

Xinjiang, China, is a typical arid and semi-arid region of Central Asia that significantly lacks freshwater resources, and the surface runoff in this region is mainly supplied by mountain glacier and snow cover meltwater. Based on the above background and issues of transnational water resources between Xinjiang and Central Asia along the Silk Road Economic Belt, which were highlighted in the major strategy of “The Belt and Road”, this study analysed the spatial and temporal variations in snow cover and snow cover days in the Xinjiang region from 2001 to 2015. The study area includes four subregions: Northern Xinjiang, Southern Xinjiang, Eastern Xinjiang and the Ili River Valley. Moderate-resolution Imaging Spectroradiometer (MODIS) 8-day snow cover data were used after removing clouds by combining MOD10A2 and MYD10A2. The results showed that seasonal snow cover occurred from October to April in most regions of Xinjiang and that this snow cover consisted of two processes: snow accumulation and snow ablation. The maximum snow cover occurred in January, whereas the minimum snow cover occurred from July to August. During the seasonal snow cover period, the snowfall rates in Northern Xinjiang and the Ili River Valley were higher, while the other regions had a low snowfall probability. To study the relationship between altitude and snow cover, the normalized snow elevation correlation index (NSACI) was calculated. The NSACI showed a significant correlation between snow cover and elevation in most regions of Xinjiang and was classified into five grades. Snow cover days did not fluctuate obviously from 2001 to 2015, and a decreasing trend was observed in the four subregions except for the Ili River Valley (nonsignificant decreasing trend). We also observed a correlation between snow cover and temperature and found that the correlations between monthly snow cover and monthly temperature in the four subregions were strongly related to the underlying land type and global warming background, which also suggests that the special topography of Xinjiang greatly influences both snow cover and climate change.

A 20-Year MODIS-Based Snow Cover Dataset for Svalbard and Its Link to Phenological Timing and Sea Ice Variability

The climate in Svalbard has been warming dramatically compared with the global average for the last few decades. Seasonal snow cover, which is sensitive to temperature and precipitation changes, is therefore expected to undergo both spatial and temporal changes in response to the changing climate in Svalbard. This will in turn have implications for timing of terrestrial productivity, which is closely linked to the disappearance of seasonal snow. We have produced a 20-year snow cover fraction time series for the Svalbard archipelago, derived from MODIS (Moderate Resolution Imaging Spectroradiometer) Terra data to map and identify changes in the timing of the first snow-free day (FSFD) for the period 2000–2019. Moreover, we investigate the influence of sea ice concentration (SIC) variations on FSFD and how FSFD is related to the start of the phenological growing season in Svalbard. Our results revealed clear patterns of earlier FSFD in the southern and central parts of the archipelago, while the northernmost parts exhibit little change or trend toward later FSFD, resulting in weaker trends in summer and winter duration. We found that FSFD preceded the onset of the phenological growing season with an average difference of 12.4 days for the entire archipelago, but with large regional variations that are indicative of temperature dependence. Lastly, we found a significant correlation between variations of time-integrated SIC and variations in FSFD, which maximizes when correlating SIC northeast of Svalbard with FSFD averaged over Nordaustlandet. Prolonged sea ice cover in the spring was correlated with late snow disappearance, while lower-than-average sea ice cover correlated with early snow disappearance, indicating that proximity to sea ice plays an important role in regulating the timing of snow disappearance on land through influencing the regional air temperature and therefore rate of spring snowmelt.

Snow as a parameter of bryophyte niche partitioning in snow-beds of the Tatra Mountains (Western Carpathians)

Abstract Understanding relationships between microclimate and bryophyte species diversity in alpine conditions creates a background for investigation into the response of vegetation to potential climate changes and anthropogenic pressure. This study presents the reaction of spore-bearing plants (liverworts and mosses) to the main thermal gradients in the snow-beds of the Tatra Mountains (Western Carpathians) and the mechanisms behind these response patterns. The results were based on direct, year-long temperature measurements in 47 permanent plots located at sites where the plant cover is formed largely by bryophytes. Data were gathered in 2010–2013 using Onset Hobo Pro v.2 temperature data loggers. Habitat variables i.e. seasonal snow cover duration (SCD), zero curtain period (ZC) and July mean ground surface temperature (MGST_VII) were determined using the ground surface temperature (GST) method. Additionally, the sum of potential incoming solar radiation in July (PISR_VII) was analysed on the basis of the localisation of permanent plots. Using detrended correspondence analysis (DCA) and redundancy analysis (RDA), we analysed the relationships between the species composition and the microclimatic variables. The generalised additive model (GAM) was employed to investigate the response curves of bryophyte species to snow parameters (SCD and ZC). Humidity (expressed as ZC temperature parameter) proved to have the highest impact on the distribution of bryophytes in snow-beds. Moreover, ground temperature in the vegetative season (in July, MGST_VII) and altitude had statistically significant effects. The responses of liverworts to microclimate gradients were more varied in comparison with those of the mosses. Humidity significantly affects the occurrence of the species of both groups in snow-beds. However, mosses respond to ground temperature (MGST_VII), whereas liverworts respond to SCD, and this distinguishes the niches of these groups. The ZC is a significant parameter affecting plant functional group composition, demonstrating a high relationship between cryptogamic plants and alpine climate. Pohlia ludwigii, Nardia breidleri and Moerckia blyttii prefer the high humidity of the snow-beds, while Marsupella condensata, M. apiculata and Pohlia drummondii are associated with dry habitats.

Soil temperature survey in a mountain basin

Shallow soil temperature (Tss) was monitored at many clusters of sites in valley bottom areas, bordering mountain slopes, and subalpine upland areas within the Tahoe Basin of the Sierra Nevada, USA. Objectives of this survey were to: (i) accurately log Tss for an entire year across the panoply of ground surface environments that cover the southern Tahoe Basin area, and (ii) identify the primary factors responsible for inter-site variations in seasonal and annual mean Tss. The Tss data for snow-free and annual mean periods exhibit wider inter-site ranges than published observations for other regions, attributable to the great diversity of ground surface environments sampled. Two energy balance based models of ground surface temperature are introduced, each applicable to a wide variety of surface environments and formulated with regional calibration coefficients that can incorporate effects of seasonal snow-cover on annual mean Tss. The models closely fit summer monthly and annual mean Tss data from most sites, and confirm that the large inter-site range of shading (by vegetation and terrain) is responsible for most of the large inter-site range of observed Tss. Additionally, data and model results strongly suggest spatially heterogeneous soil warming rates in response to regional climate warming, and support the value of resolving spatial variations in Tss for groundwater flow tracing techniques.

Quantifying seasonal cornice dynamics using a terrestrial laser scanner in Svalbard, Norway

Abstract. Snow cornices develop along mountain ridges, edges of plateaus, and marked inflections in topography throughout regions with seasonal and permanent snow cover. Despite the recognized hazard posed by cornices in mountainous locations, limited modern research on cornice dynamics exists and accurately forecasting cornice failure continues to be problematic. Cornice failures and associated cornice fall avalanches comprise a majority of observed avalanche activity and endanger human life and infrastructure annually near Longyearbyen in central Svalbard, Norway. In this work, we monitored the seasonal development of the cornices along the plateaus near Longyearbyen with a terrestrial laser scanner (TLS) during the 2016–2017 and 2017–2018 winter seasons. The spatial resolution at which we acquired snow surface data with TLS enabled us to observe and quantify changes to the cornice systems in detail not previously achieved. We focused primarily on the evolution and failure of the lower cornice surfaces where accessibility has precluded previous research. We measured cornice accretion rates in excess of 10 mm h−1 during several accretion events coinciding with winter storms. We observed five cornice fall avalanche events following periods of cornice accretion and one event following a warm period with midwinter rain. The results of our investigation provide quantitative reinforcement to existing conceptual models of cornice dynamics and illustrate cornice response to specific meteorological events. Our results demonstrate the utility of TLS for monitoring cornice processes and as a viable method for quantitative cornice studies in this and other locations where cornices are of scientific or operational interest.

Topographic and climatic influence on seasonal snow cover: Implications for the hydrology of ungauged Himalayan basins, India

Himalayan glaciers exert considerable influence on basin hydrology and its response to climate change. Melt-runoff generated from ungauged Himalayan basins (UHB) requires an understanding of snow and ice cover extent along with prevailing meteorological conditions. Therefore, an estimation of seasonal snow cover distribution, topographic (elevation, aspect, and slope) and climatic variability was carried out using satellite data and meteorological observations from three automatic weather stations (AWSs) located at different elevations in an UHB (Chorabari Glacier). Results suggest that the topography and the meteorological conditions of the basin influence the dynamics of snow cover and the corresponding processes responsible for the melt-runoff generation. The snow cover area (SCA) has high variability in the elevation range of 3799–5000 m, indicating that as glacier ablation begins, SCA below this elevation primarily contributes to the melt-runoff. Likewise, the eastern aspect and the slopes (0–10° and 70–80°) show higher variability. Further, the annual distribution of air temperature gradients (dT/dZ) or temperature lapse rates (TLRs) exhibits a bimodal pattern. The mean annual TLR for the basin is 6.0 °C km−1, which is lower than the traditionally used adiabatic or environmental lapse rate (6.5 °C km−1). We also established the role of TLRs in the dynamics of SCA, which is an important parameter used for the computation of melt-runoff. The 0 °C isotherm established indicates that the elevation zone above 5000–5500 m has persistent snow cover throughout the year and snow cover below this zone contributes to the melt-runoff during the ablation season. Therefore, validating the equilibrium line altitude (ELA) of Chorabari Glacier lies within this zone. Since the TLR and SCA vary with space and time, our study in an ungauged glacierized basin of river Ganga could be useful for policymakers as well as other researchers working on the regional hydrology.

Defining frigid winter illuminates its loss across seasonally snow-covered areas of eastern North America

Winter is often understudied in ecosystem sciences and viewed as a burden for human systems and infrastructure. However, the importance of winter in regulating ecological processes and shaping human communities has emerged as a topic of great interest, particularly in areas that experience seasonal snow cover. Traditional seasonal definitions may not fully represent below freezing winters and snow accumulation that have historically characterized these areas. Here we: (1) propose the concept of ‘frigid winter’ to address longstanding problems with traditional delineations of winter; and (2) define frigid winter as a period of sustained temperatures below freezing and snow accumulation that together regulate ecological processes and their services. We explore this definition and the changes occurring within it using 100 years of meteorological data from northeastern North America. Trend analysis demonstrates that frigid winters have shortened by ∼3 weeks over the last century, that cold, snowy conditions have become more intermittent, and that the choice of winter delineation (astronomical, meteorological, hibernal, or frigid) influences the apparent rate at which winter conditions disappear.

Modelling of early winter snow density using fully polarimetric C-band SAR data in the Indian Himalayas

The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas, and snow density is one of the vital parameters in the determination of the hydrological potential of snow. The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles, as compared to fresh dry snow. The surface component of backscatter is significant in case of melt and frost. In the conventional decomposition based methods, the surface component is not considered for the inversion of permittivity, as proposed in this study. In this paper, we also propose a methodology for the estimation of snow density using bi-temporal fully polarimetric C-band RADARSAT-2 synthetic aperture radar (SAR) data. We utilize the relation between the differential modified Mueller matrix components, the attenuation constants, and the Fresnel transmission coefficients. The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation. The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in-situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India. The snow density estimates using the proposed method are observed to correlate with the in-situ measurements and were also found to be better than the decomposition based methods.

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography

The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit: the operational land imager (OLI) on-board Landsat 8, the multispectral instrument (MSI) on-board the Sentinel-2 satellites, and the moderate resolution imaging spectroradiometer (MODIS) on-board Terra and Aqua. We consider several retrieval algorithms that fall into three classes: thresholding of the normalized difference snow index (NDSI), regression on the NDSI, and spectral unmixing. We conduct the evaluation at a high-Arctic site in Svalbard, Norway, by comparing satellite retrieved fSCA to coincident high-resolution snow-cover maps obtained from a terrestrial automatic camera system. For the lower resolution MODIS retrievals, the regression-based retrievals outperformed the unmixing-based retrievals for all metrics but the bias. For the higher resolution sensors (OLI and MSI), retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board. We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI.

A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016

Snowoff (SO) date—defined as the last day of observed seasonal snow cover—is an important governor of ecologic and hydrologic processes across Alaska and Arctic-Boreal landscapes; however, our understanding and capacity for the monitoring of spatial and temporal variability in the SO date is still lacking. In this study, we present a 6.25 km spatially gridded passive microwave (PMW) SO data record, complimenting current Alaskan SO records from Moderate Resolution Imaging Spectrometer (MODIS) and Landsat, but extending the SO record an additional 13 years. The PMW SO record was validated against in situ snow depth observations and showed favorable accuracy (0.66–0.92 mean correlations; 2–10 day mean absolute errors) for the major climate regions of Alaska. The PMW SO results were also within 10 days of finer spatial scale SO observational records, including Interactive Multisensor Snow and Ice Mapping System (IMS), MODIS, and Landsat, for a majority (75%) of Alaska. However, the PMW record showed a general SO delay at higher elevations and across the Alaska North Slope, and earlier SO in the Alaska interior and southwest regions relative to the other SO records. Overall, we assign an uncertainty +/−11 days to the PMW SO. The PMW SO record benefits from the near-daily temporal fidelity of underlying brightness temperature (Tb) observations and reveals a mean regional trend in earlier SO timing (−0.39 days yr−1), while significant (p < 0.1) SO trend areas encompassed 11% of the Alaska domain and ranged from −0.11 days yr−1 to −1.31 days yr−1 over the 29-year satellite record. The observed SO dates also showed anomalous early SO dates during markedly warm years. Our results clarify the pattern and rate of SO changes across Alaska, which are interactive with global warming and contributing to widespread permafrost degradation, changes in regional hydrology, ecosystems, and associated services. Our results also provide a robust means for SO monitoring from satellite PMW observations with similar precision as more traditional and finer scale observations.

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

Ground surface temperature variability and permafrost distribution over mountainous terrain in northern Mongolia

ABSTRACT Permafrost plays an important role in numerous environmental processes at high latitudes and in high mountain areas. The identification of mountain permafrost, particularly in the discontinuous permafrost regions, is challenging due to limited data availability and the high spatial variability of controlling factors. This study focuses on mountain permafrost in a data-scarce environment of northern Mongolia, at the interface between the boreal forest and the dry steppe mid-latitudes. In this region, the ground temperature has been increasing continuously since 2011 and has a high spatial variability due to the distribution of incoming solar radiation, as well as seasonal snow and vegetation cover. We analyzed the effect of these controlling factors to understand the climate–permafrost relationship based on in situ observations. Furthermore, mean ground surface temperature (MGST) was calculated at 30-m resolution to predict permafrost distribution. The calculated MGST, with a root mean square error of ±1.4°C, shows permafrost occurrence on north-facing slopes and at higher elevations and absence on south-facing slopes. Borehole temperature data indicate a serious wildfire-induced permafrost degradation in the region; therefore, special attention should be paid to further investigations on ecosystem resilience and climate change mitigation in this region.

Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

Adaptation to climate change in Kazakhstan using data from space monitoring of snowmelt

The present article is concerned with the applied aspects of applying the results of space monitoring of snow cover, in particular, it is proposed to present the results of the analysis in the form of specialized bulletins. The purpose of this publication is to present the available results of space monitoring of snow cover in Kazakhstan as an element of adaptation to climate change. A three-level system of space monitoring of snow cover is proposed, which includes three technological complexes: operational mapping of snow cover boundaries; monitoring of seasonal snow cover dynamics; analysis of long-term snow cover dynamics. A map of snow melting in Kazakhstan in 2020, the dynamics of long-term changes of snow covered area, as well as methods for analyzing the spatial- temporal distribution of snow cover and formats of special bulletins are presented. It is most appropriate to present the results of space monitoring of snow cover in a complex, maximally generalized form (product). The results of the work can be applied in the scientific, industrial and educational spheres to adapt and increase resistance.

Snow depth in Dhundi: an estimate based on weighted bias corrected differential phase observations of dual polarimetric bi-temporal Sentinel-1 data

ABSTRACTThe studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method.

Climate and topographic controls on snow phenology dynamics in the Tienshan Mountains, Central Asia

Abstract Although previous studies have characterized changes in seasonal snow cover or made predictions about snow cover in a changing climate, no comprehensive spatiotemporal analysis of snow phenology has been presented for the Tienshan Mountains, Central Asia. Relying on daily cloud-free snow cover fraction products originating from Moderate Resolution Imaging Spectroradiometer (MODIS) for 2002/03–2017/18, the snow phenology parameters (i.e., snow cover duration [SCD], snow onset date [SOD] and snow end date [SED]) are derived for each hydrological year within the study period and the characteristics analyzed for the Tienshan Mountains. The spatiotemporal changes of snow phenology have strong altitude dependence. The mean gradients of SCD, SOD, and SED with elevation are 6.0, −2.55, and 3.44 d/100 m, respectively. Because of differences in solar radiation and water vapor sources, the north-facing areas generally have a higher SCD, earlier SOD, and later SED than south-facing areas. Also, the trends of the snow phenology parameters at high and low altitudes show opposite changes. Consistent with the increase in snow cover area in recent years, SCD for the entire Tienshan region showed a clear uptick, especially in Northern Tienshan. This prolonged SCD was more related to advanced SOD than to SED, as decreased temperature and increased precipitation in autumn are conducive to snow accumulation.

Lake water and glacier mass gains in the northwestern Tibetan Plateau observed from multi-sensor remote sensing data: Implication of an enhanced hydrological cycle

Lakes and glaciers are widely distributed in the northwestern Tibetan Plateau. A synchronized examination of lake and glacier mass variations there has not previously been conducted, which has limited the understanding of the lake water balance and the hydrologic cycle in the region. In this study, we comprehensively examine lake area and volume, and glacier mass changes using multi-sensor satellite data. We find that lake expansion in the northwestern Tibetan Plateau was more robust from 1976 to 2018 when compared to other regions of the plateau, especially for the glacier-fed and endorheic lakes. Lake volume changes show that most of the lakes had an increase in water volume, particularly in 2000–2018 with a total water storage gain of ~28.6 Gt. By using high-resolution KH-9 and TanDEM-X data, we observe that the glacier surface elevation of the western Kunlun Mountains had a slight thinning rate of −0.07 ± 0.16 m/yr in 1973–2000, but a positive rate of 0.002 ± 0.003 m/yr in 2000–2018. The heterogeneous pattern of glacier elevation changes between the north (N) and south (S) slopes are revealed, i.e. -0.02 ± 0.01 m/yr (N) against −0.12 ± 0.03 m/yr (S) in 1973–2000 and − 0.05 ± 0.02 m/yr (N) against 0.06 ± 0.02 m/yr (S) in 2000–2018. Overall, the glaciers trend to a stable state (~0.05 m/yr) in both the south slope of the western Kunlun Mountains and Aru Co regions between 2000 and 2018. Similar patterns are also found for basin-wide examinations of lake storage changes (mass gains) and glacier mass budgets (positive or close to stable). The seasonal snow cover area changes, derived from cloud-free MODIS snow cover products, present a variable and insignificant trend between 2003 and 2017. Snow depth derived from passive microwave remote-sensing data, exhibits a decreasing trend between 1979 and 2015, but the water equivalent could contribute only an insignificant amount to the observed lake changes. The lake water gains, and almost positive glacier mass balance imply that the hydrological cycle in the northwestern Tibetan Plateau has become enhanced.