Mean Snow Cover Research Articles

Quantifying the influencing factors of the thermal state of permafrost in Northeast China

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Snow depth and the associated offset in ground temperatures in a landscape manipulated with snow-fences

Seasonal snow cover has an important impact on the difference between soil- and air temperature because of the insulation effect, and is therefore a key parameter in ecosystem models. However, it is still uncertain how specific variations in soil moisture, vegetation composition, and surface air warming, combined with snow dynamics such as compaction affect the difference between soil- and air temperature. Here, we present an analysis of 8 years (2012–2020) of snow dynamics in an Arctic ecosystem manipulation experiment (using snow fences) on Disko Island, West Greenland. We explore the snow insulation effect under different treatments (mesic tundra heath as a dry site and fen area as a wet site, snow addition from snow fences, warming using open top chambers, and shrub removal) on a plot-level scale. The snow fences significantly changed the inter-annual variation in snow depths and -phenology. The maximum annual mean snow depths were 90 cm on the control side and 122 cm on the snow addition side during all study years. Annual mean snow cover duration across 8 years was 234 days on the control side and 239 days on the snow addition side. The difference between soil- and air temperature was significantly higher on the snow addition side than on the control side of the snow fences. Based on a linear mixed-effects model, we conclude that the snow depth was the decisive factor affecting the difference between soil- and air temperature in the snow cover season (p < 0.0001). The change rate of the difference between soil- and air temperature, as a function of snow depth, was slower during the period before maximum snow depth than during the period between the day with maximum snow depth until snow ending day. During the snow-free season, the effects of the open top chambers were stronger than the effects of the shrub removal, and the combination of both contributed to the highest soil temperature in the dry site, but the warming effect of open top chambers was limited and shrub removal warmed soil temperature in the wet site. The warming effects of open top chambers and shrub removal were weakened on the snow addition side, which indicates a lagged effect of snow on soil temperature. This study quantifies important dynamics in soil-air temperature offsets linked to both snow and ecosystem changes mimicking climate change and provides a reference for future surface process simulations.

Uncertainty Assessment for Mean Snow Cover Depth Derived from Direct Measurements on Aldegondabreen Glacier (Svalbard)

Received March 27, 2022; revised May 5, 2023; accepted June 27, 2023This study introduces an empirical equation allowing to estimate an uncertainty of area-averaged snow depth on the Aldegondabreen Glacier, computed from standard snow surveys and made by an avalanche probe or by similar equipment. The two-decade history of the ongoing mass-balance monitoring program on this glacier shows that the methodology of field work on snow-measuring survey varies somewhat from year to year: the number and location of measurement points change. To identify and quantify long-term trends and variations in snow cover, it is crucial to assess the inter-comparability of the data in the obtained measurement series. The proposed equation was intended to solve this task basing on the collected data only, allowing to estimate the uncertainty even retrospectively. To build this equation, we applied a bootstrap statistical approach to the results of snow surveys carried out in Svalbard in 2015–2021. After interpolating the field measurements, obtained rasters were sampled sequentially with different numbers of points, simulating the real snow survey. The points were initially located in a form of a quasiregular grid and then randomly shifted between the iterations. After a thousand simulations for each number of points, the standard deviations were calculated relative to the “true” values, derived from corresponding rasters. These standard deviations, which we admit to be a random error of the area-averaged snow depth value, expectedly decrease with the number of sampling points and increase with the coefficient of variation (\({{{\text{C}}}_{\user1{v}}}\)). The well-known \({{{\text{C}}}_{\user1{v}}}\) index indirectly characterizes the irregularity of snow cover. After approximating the bootstrap results, the authors derived an equation that yields a relative error. The equation includes only two predictors which are the probing density per area unit and the \({{{\text{C}}}_{\user1{v}}}\), which potentially allows using it for the other glaciers. However, the universality of the empirically obtained coefficients is debatable, since they may vary due toa glacier size, its morphology and other parameters.

Multi-decadal observations in the Alps reveal less and wetter snow, with increasing variability

Snowpack is an important temporal water storage for downstream areas, a potential source of natural hazards (avalanches or floods) and a prerequisite for winter tourism. Here, we use thousands of manual measurements of the water equivalent of the snow cover (SWE) from almost 30 stations between 1,200 and 2,900 m a.s.l. from four long-term monitoring programs (earliest start in 1937) in the center of the European Alps to derive daily SWE based on snow depth data for each station. The inferred long-term daily SWE time series were analyzed regarding spatial differences, as well as potential temporal changes in variability and seasonal averages during the last 7 decades (1957–2022). The investigation based on important hydro-climatological SWE indicators demonstrates significant decreasing trends for mean SWE (Nov-Apr) and for maximum SWE, as well as a significantly earlier occurrence of the maximum SWE and earlier disappearance of the continuous snow cover. The anomalies of mean SWE revealed that the series of low-snow winters since the 1990s is unprecedented since the beginning of measurements. Increased melting during the accumulation period below 2000 m a.s.l is also observed–especially in the most recent years–as well as slower melt rates in spring, and higher day-to-day variability. For these trends no regional differences were found despite the climatological variability of the investigated stations. This indicates that the results are transferable to other regions of the Alps.

Potential impacts of climate change on the spatial distribution of Chinese ski resorts

Chinese skiing tourism is rapidly developing, but it will be potentially affected by snow resources and climate conditions. This study systematically analysed the spatial characteristics of snow resources, climatic conditions and ski resorts in China; revealed the potential impacts of climatic environment on current ski resorts by using Regional Climate Model (RegCM4.4); and proposed suggestions for climate resource assessment and spatial planning of ski resorts. The mean snow depth and snow cover days are more than 4.50 cm and 150 d between the years 2000 and 2014, respectively, in high-latitude areas, where fewer ski resorts are located. In the middle-low latitude areas, the mean snow depth and snow cover days are less than 3.00 cm and 100 d, respectively, with ski resorts being intensively distributed. In most of these areas, the pronounced climate warming exceeded 0.50 °C during the ski period of 2009–2015 relative to 1961–2015, and mean annual precipitation was less than 800 mm, with some even less than 400 mm. In the 2030s and 2050s, the regions with drastic temperature increases are mainly in the Qinghai–Tibetan Plateau, Northeast China, and the Yangtze River Basin. The upper and middle reaches of the Yangtze River are expected to experience reduced snowfalland temperature increases with an almost 1–1.6 °C, respectively. In this context, some ski resorts will face major threats because of shortened skiing durations and rising management costs. Skiing tourism should not pursue a one-sided growth rate in the number of ski resorts in areas with short snowpack periods. Instead, it should rely highly on snow resources, climatic conditions, geographical environment, population and economic basis, and rationalise its spatial layout to promote the industry towards sustainable development.

Determining recharge distribution in fractured carbonate aquifers in central Italy using environmental isotopes: snowpack cover as an indicator for future availability of groundwater resources

Environmental isotopes were used to determine the source and to understand the physical–chemical processes involved in groundwater movement along a flowpath. This study applies groundwater stable isotopes to assess snow-cover influence on the recharge processes of some regional carbonate aquifers of central Italy. Starting with extensively investigated aquifers, 17 springs were selected and sampled (June–October 2016) for isotope analyses. The δ18O–δD results follow the local meteoric water line; the low mismatch between the 2016 sampling surveys suggests that those springs are not influenced by seasonal variability. Nevertheless, the average elevations of recharge areas calculated using the vertical isotope gradient were higher compared to those obtained with hypsographic profiles. This means that the relevant contribution to recharge comes from higher elevation areas; hence, snowpack coverage and snowpack persistence over time on recharge areas were analysed using satellite images. Four different relationships between the snowpack characteristics and the elevation of recharge areas have been identified. These offer relevant information about the different degrees of dependence of the regional aquifers of central Italy on the recharge due to high-elevation subbasins where the snowpack cover is significant. A possible correlation emerges between computed isotope recharge elevation and mean snow cover elevation, revealing how snowmelt is a primary source for aquifer recharge. Consequently, to evaluate the risk of groundwater resource depletion in a climate-change scenario, there is discussion on how a potential snow-cover reduction would affect the recharge rate of mountainous aquifers.

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

In the context of global warming, relevant studies have shown that China will experience the largest temperature rise in the Qinghai–Tibet Plateau and northwestern regions in the future. Based on MOD10A2 and MYD10A2 snow products and snow depth data, this study analyzes the temporal and spatial evolution characteristics of the snow cover fraction, snow depth, and snow cover days in the three stable snow cover areas in China, and combines 15 modes in CMIP6 snow cover data in four different scenarios with three kinds of variables, predicting the spatiotemporal evolution pattern of snow cover in China’s three stable snow cover areas in the future. The results show that (1) the mean snow cover fraction, snow depth, and snow cover days in the snow cover area of Northern Xinjiang are all the highest. Seasonal changes in the snow cover areas of the Qinghai–Tibet Plateau are the most stable. The snow cover fraction, snow depth, and snow cover days of the three stable snow cover areas are consistent in spatial distribution. The high values are mainly distributed in the southeast and west of the Qinghai–Tibet Plateau, the south and northeast of Northern Xinjiang, and the north of the snow cover area of Northeast China. (2) The future snow changes in the three stable snow cover areas will continue to decline with the increase in development imbalance. Snow cover fraction and snow depth decrease most significantly in the Qinghai–Tibet Plateau and the snow cover days in Northern Xinjiang decrease most significantly under the SSPs585 scenario. In the future, the southeast of the Qinghai–Tibet Plateau, the northwest of Northern Xinjiang, and the north of Northeast China will be the center of snow cover reduction. (3) Under the four different scenarios, the snow cover changes in the Qinghai–Tibet Plateau and Northern Xinjiang are the most significant. Under the SSPs126 and SSPs245 scenarios, the Qinghai–Tibet Plateau snow cover has the most significant change in response. Under the SSPs370 and SSPs585 scenarios, the snow cover in Northern Xinjiang has the most significant change.

Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Modelling runoff generation in high-elevation Alpine catchments requires detailed knowledge on the spatio-temporal distribution of snow storage. With Sentinel-2 MultiSpectral Instrument (MSI), it is possible to map snow cover with a high temporal and spatial resolution. In contrast to the coarse MODIS data, Sentinel-2 MSI enables the investigation of small-scale differences in snow cover duration in complex terrains due to gravitational redistribution (slope), energy balance and wind-driven redistribution (aspect). In this study, we describe the generation of high-resolution spatial and temporal snow cover data sets from Sentinel-2 images for a high-elevation Alpine catchment and discuss how the data contribute to our understanding of the spatio-temporal snow cover distribution. The quality of snow and cloud detection is evaluated against in-situ snow observations and against other snow and cloud products. The main problem was in the false detection of snow in the presence of clouds and in topographically shaded areas. We then seek to explore the potential of the generated high-resolution snow cover maps in calibrating the gravitational snow redistribution module of a physically based snow model, especially for an area with a very data-scarce point snow observation network. Generally, the calibrated snow model is able to simulate both the mean snow cover duration with a high F1 accuracy score of > 0.9 and the fractional snow-covered area with a correlation coefficient of 0.98. The snow model is also able to reproduce spatio-temporal variability in snow cover duration due to surface energy balance dynamics, wind and gravitational redistribution.

Snow cover variability and trend over the Hindu Kush Himalayan region using MODIS and SRTM data

Abstract. Snow cover changes have a direct bearing on the regional and global energy and water cycles and the change in the Earth's climate conditions. We studied the relatively long-term (2000–2017) altitudinal spatiotemporal changes in the coverage of snow and glaciers in one of the world's largest mountainous regions, the Hindu Kush Himalayan (HKH) region, including Tibet, using remote sensing data (5 km grid resolution) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. This dataset provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (accumulating season and melting season) and interannual variations in snow and glacial cover over the HKH region. The zonal and altitudinal (hypsographic) analyses were carried out for the melting season and accumulating season. The altitude-wise linear trend analysis (Pearson's) of snow cover, shown as a hypsographic curve, clearly indicates a major decline in snow cover (average of 5 % or more at 100 m interval aggregates) between 4000–4500 and 5500–6000 m altitudes, which is consistent with the median trend (Theil–Sen – TS) and the monotonic trend (Mann–Kendall – MK; statistics) analysis. This analysis also revealed the regions and altitudes where major and statistically significant increases (10 % to 30 %) or decreases (−10 % to −30 %) in snow cover are identified. The extrapolation of the altitude-wise linear trend shows that it may take between ∼ 74 and 7900 years, for 3001–6000 and 6000–7000 m altitude zones respectively, for mean snow cover to decline approximately 25 % in the HKH. More detailed analysis based on longer observational records and model simulations is warranted to better understand the underlying factors, processes, and feedbacks that affect the dynamic of snow cover in HKH. These preliminary results suggest a need for continued monitoring of this highly sensitive region to climate variability and change that depends on snow as a major source of freshwater for all human activities.

Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation

AbstractMountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated and compared four snow depth datasets over the Tianshan Mountains, including snow depth datasets from ERA5, ERA5‐Land, passive microwave, as well as a dynamically downscaled simulation by Weather Research and Forecasting (WRF) model. The snow depth was evaluated against in situ observations while the snow cover extent was evaluated by interactive multisensor snow and ice mapping system (IMS) snow cover. Furthermore, the snow‐related metrics, such as annual mean snow depth and snow cover days, were compared among the four datasets. The results showed that the spatial patterns of snow‐related metrics were relatively consistent among datasets, although discrepancies existed in magnitude. Additionally, the temporal variations in snow‐related metrics depended on the dataset employed. The simulated snow depth from WRF got the lowest bias in daily snow depth value against the in situ observations (mean bias = 0.12 cm, root mean square error = 2.08 cm), additionally, it had the best performance in classifying correct snow grids compared with IMS (probability of detection = 0.766). It also outperformed in annual mean snow depth and snow cover days by 47.2 and 13.3% compared with ERA5‐Land snow depth. This study highlights the confidence in characterizing both the spatial pattern and temporal variations of snow depth products based on WRF model simulation.

Changes in snow cover occurrence and the atmospheric circulation impact in Pozna\u0144 (Poland)

The variability of occurrence of snow cover and the impact of atmospheric circulation on the snow cover occurrence in the period 1966/1967–2019/2020 in Poznań (Poland) have been examined. The implementation of the primary study objective covers the comprehensive analysis of the winter snow and thermal conditions using various indicators. This paper is based on daily data from the years 1966–2020 concerning the winter period. Winters in Poznań are highly variable and differentiated, considering the duration of particular seasons, number of days with snow cover, mean snow cover thickness, winter snowiness coefficient, or winter severity index. Negative trends concerning days with snow cover total snow cover depth winter snowiness coefficient and winter severity index in Poznań prove statistically significant. A higher probability of occurrence of snow cover was determined during cyclonic than anticyclonic circulation. The westerly and northerly types especially favoured the occurrence of days with snow cover. The increase of snow cover was associated with the northerly inflow mainly. Westerly types of circulation caused the decrease of snow cover predominantly.

Arctic Amplification Weakens the Variability of Daily Temperatures over Northern Middle-High Latitudes

AbstractArctic amplification (AA) reduces meridional temperature gradients (dT/dy) over the northern mid-high latitudes, which may weaken westerly winds. It is suggested that this may lead to wavier and more extreme weather in the midlatitudes. However, temperature variability is shown to decrease over the northern mid-high latitudes under increasing greenhouse gases due to reduced dT/dy. Here, through analyses of coupled model simulations and ERA5 reanalysis, it is shown that consistent with previous studies, cold-season surface and lower-mid troposphere temperature (T) variability decreases over northern mid-high latitudes even in simulations with suppressed AA and sea ice loss under increasing CO2; however, AA and sea ice loss further reduce the T variability greatly, leading to a narrower probability distribution and weaker cold or warm extreme events relative to future mean climate. Increased CO2 strengthens meridional wind (υ) with a wavenumber-4 pattern but weakens meridional thermal advection [−υ(dT/dy)] over most northern mid-high latitudes, and AA weakens the climatological υ and −υ(dT/dy). The weakened thermal advection and its decreased variance are the primary causes of the T variability decrease, which is enlarged by a positive feedback between the variability of T and −υ(dT/dy). AA not only reduces dT/dy, but also its variance, which further decreases T variability through −υ(dT/dy). While the mean snow and ice cover decreases, its variability increases over many northern latitudes, and these changes do not weaken the T variability. Thus, AA’s influence on midlatitude temperature variability comes mainly from its impact on thermal advection, rather than on winds as previously thought.

Climatological conditions of the Black Sea‐effect snowfall events in Istanbul, Turkey

AbstractA climatological analysis and overlying synoptic conditions of Black Sea‐effect snowfall events were investigated for Istanbul, Turkey, during the 1971–2006 winter (DJF) periods. Using the synoptic climatological approach, the Lamb Weather Type (LWT) method was applied to NCEP/NCAR daily mean sea level pressure data. Basically, northwesterly (NW), northerly (N), and northeasterly (NE) circulation types (CTs), which blow from the Black Sea (BS), were thought to be important for sea‐effect snowfall events to occur. Wind speeds and flows at 850‐hPa, directional shear, and temperature difference between sea surface and 850‐hPa level (SST‐T850) thresholds were applied to these three CTs in order to find suitable snowfall cases originating from the Black‐Sea. The results showed that 4, 14, and 111 snowfall episodes occurred during NW, N, and NE circulation types over Istanbul with the 2.8, 4.1, and 3.5 cm daily mean snow cover depths (DMSCD), respectively. In particular, it was found that interaction between a surface high located over continental Europe and a low pressure located over the central Black Sea, and a relatively warm sea surface temperature (SST), and cold temperature anomaly at the low level of the atmosphere (SST‐T850 &gt; 17°C) are a favourable environment for the development of intense Black Sea‐effect snowstorms (DMSCD &gt; 10 cm) sourced by NE cases. A statistically significant positive relation between snow cover depths and SST‐Tmax (daily maximum temperature) under NE cases (r = 0.28, p &lt; .05) indicated that we observe intense daily snow accumulation when land‐sea temperature contrast increases (&gt;7°C) in the region.

Modeling Blowing Snow Over the Tibetan Plateau With the Community Land Model: Method and Preliminary Evaluation

AbstractSnow cover in mountainous terrain plays an important role in regional and global water and energy balances, climate change, and ecosystems. Blowing snow is a frequent and important weather phenomenon over the Tibetan Plateau (TP); however, this process is neglected in most current land surface models, despite the consequential role it plays in the land surface and atmospheric water and energy budgets. In this paper, we present a blowing snow model PIEKTUK coupled with the Community Land Model (CLM4.5) to provide a better estimate of the snow dynamics for the consideration of snow redistribution induced by wind. Two simulations with a 0.065° spatial resolution were performed in 2010 over the TP, namely, a sensitivity experiment with the inclusion of blowing snow effects (CLM_BS) and a control run with the original model (CLM). A specific objective of this study was to evaluate the improvements in the simulations of snow dynamics and other key variables in surface energy partitioning provided by the coupled model, such as the surface albedo and land surface temperature (LST). Compared with a variety of remote‐sensing observations, the results show that the surface snow cover, snow depth, and surface albedo can be better reproduced in most of the TP region by CLM_BS than by the original CLM, particularly in the Kunlun Mountains, Hoh Xil area, and the southwestern TP. In areas with reduced bias, variations in the monthly mean snow cover fraction can be reflected particularly well by CLM_BS. For LST, however, a significant decrease in the nighttime LST bias was detected in CLM_BS, while the bias in the daytime LST increases. The results show considerable potential for the inclusion of the blowing snow process to promote the modeling of snow dynamics and land‐atmosphere interactions on the TP.

Using Multisource Satellite Data to Assess Recent Snow-Cover Variability and Uncertainty in the Qinghai–Tibet Plateau

Abstract Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. Fractional snow cover (FSC) derived from five satellite sources [the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)] were intercompared over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. Compared to in situ snow-depth observations, the cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC. Contrary to MODIS, IMS shows increasing variability in snow cover and snow duration over the last decade (2006–17). Differences in variabilities of FSC and snow duration between products are greater at 5–6 km than lower elevations, with seasonal snow-cover change showing the largest uncertainty in snowmelt date.

Developing Daily Cloud‐Free Snow Composite Products From MODIS and IMS for the Tienshan Mountains

AbstractDaily cloud‐free snow cover products are important premise and foundation for hydrological simulation, climate system research, and snow disaster monitoring in the Tienshan Mountains, Central Asia. In this study, partial clouds appearing in the Moderate Resolution Imaging Spectroradiometer (MODIS) were removed by temporal and spatial filtering, and the remaining cloud pixels were replaced by Interactive Multisensor Snow and Ice Mapping System (IMS). The results show that the retrieved annual mean snow cover obtained from the improved MODIS product data is 31.5% higher than the original MODIS product. Through validation performed via in situ observations, the overall accuracy, land accuracy, and snow accuracy also increased to 88.2%, 91.9%, and 81.9%, respectively. However, the overall accuracy shows a lower accuracy in transitional months compared to other months, which derives mainly from the difference in spatial scales between IMS and in situ observations.

Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks

Remote sensing fractional snow cover area (fSCA) has been increasingly used to get an improved estimate of the spatiotemporal distribution of snow water equivalent (SWE) through reanalysis using different data assimilation (DA) schemes. Although the effective assimilation period of fSCA is well recognized in previous studies, little attention has been given to explicitly account for the relative significance of measurements in constraining model parameters and states. Timing of the more informative period varies both spatially and temporally in response to various climatic and physiographic factors. Here we use an automatic detection approach to locate the critical points in the time axis where the mean snow cover changes and where the melt-out period starts. The assimilation period was partitioned into three timing windows based on these critical points. A fuzzy coefficient was introduced in two ensemble-based DA schemes to take into account for the variability in informational value of fSCA observations with time. One of the DA schemes used in this study was the particle batch smoother (Pbs). The main challenge in Pbs and other Bayesian-based DA schemes is, that most of the weights are carried by few ensemble members. Thus, we also considered an alternative DA scheme based on the limits of acceptability concept (LoA) and certain hydrologic signatures and it has yielded an encouraging result. An improved estimate of SWE was also obtained in most of the analysis years as a result of introducing the fuzzy coefficients in both DA schemes. The most significant improvement was obtained in the correlation coefficient between the predicted and observed SWE values (site-averaged); with an increase by 8% and 16% after introducing the fuzzy coefficient in Pbs and LoA, respectively.

Spatiotemporal analysis of snow cover in Iran based on topographic characteristics

This study investigates the distribution of snow cover in Iran using the 8-day moderate-resolution imaging spectroradiometer (MODIS) snow product (MOD10A2) during 2001–2015. The year 2008 was found to have the most extensive snow cover and was used to determine the snow-covered areas into five regions based on topographic characteristics (elevation, slope, and aspect). Our results show that the mean snow cover extent (SCE) on the slopes higher than 20° is greater (62.94% on average) than on the slopes lower than 20° (55.28% on average). The highest and lowest SCEs are related to the north-facing aspect and flat-surface regions, respectively. In addition, a direct relationship between SCE and elevation was revealed, with snow cover reaching 100% in the highlands (above 3000 m). The highest values of the mean (171,905.1 km2 in slope1 from region 3), standard deviation (SD) (70,856.53 km2 in slope 1 from region 3), and coefficient of variation (CV) (214.65% in region 1) of SCE were obtained from 17 to 24 January, 9 to 16 January, and 10 to 17 February, respectively. The SD peaked in the early 8 days and then decreased in the middle and ending 8 days. The highest CV was obtained in the Caspian coastal region and decreased with increasing elevation.

Variability in snow depth time series in the Adige catchment

Study regionThe Upper and Middle Adige catchment, Trentino-South Tyrol, Italy. Study focusWe provide evidence of changes in mean seasonal snow depth and snow cover duration in the region occurred in the period from 1980 to 2009. New hydrological insights for the regionStations located above and below 1650 m a.s.l. show different dynamics, with the latter experiencing in the last decades a larger reduction of average snow depth and snow cover duration, than the former. Wavelet analyses show that snow dynamics change with elevation and correlate differently with climatic indices at multiple temporal scales. We also observe that starting from the late 1980s snow cover duration and mean seasonal snow depth are below the average in the study area. We also identify an elevation dependent correlation with the temperature. Moreover, correlation with the Mediterranean Oscillation Index and with the North Atlantic Oscillation Index is identified.

Influences of forest on MODIS snow cover mapping and snow variations in the Amur River basin in Northeast Asia during 2000–2014

AbstractThe study applies the improved cloud‐free moderate resolution imaging spectral radiometer daily snow cover product (MODMYD_MC) to investigate the snow cover variations from snow hydrologic year (HY) HY2000 to HY2013 in the Amur River basin (ARB), Northeast Asia. The fractions of forest cover were 38%, 63%, and 47% in 2009 in China (the southern ARB), Russia (the northern ARB), and ARB, respectively. Validation results show that MODMYD_MC has a snow agreement of 88% against in situ snow depth (SD) observations (SD ≥ 4 cm). The agreement is about 10% lower at the forested stations than at the nonforested stations. Snow cover durations (SCDs) from MODMYD_MC are 20 days shorter than ground observations (SD ≥ 1 cm) at the forested stations, whereas they are just 8 days shorter than ground observations (SD ≥ 1 cm) at the nonforested stations. Annual mean SCDs from MODMYD_MC in the forested areas are 21 days shorter than those in the nearby farmland in the Sanjiang Plain. This indicates forest has a complex influence on the snow accumulation and melting processes and even on optical satellite snow cover mapping. Meanwhile, SCD and mean snow cover are negatively correlated with air temperature in ARB, especially in the snow melting season, when mean air temperature in March and April can explain 86% and 74% of the mean snow cover variations in China ARB and Russia ARB, respectively. From 1961 to 2015, the annual mean air temperature presented an increased trend by 0.33 °C/decade in both China ARB and Russia ARB, whereas it had a decrease trend from HY2000 to HY2013. The decrease of air temperature led to an increase of snow cover, which is different from the global decrease trend of snow cover variations. SCD and snow cover had larger increase rates in China ARB than in Russia ARB, and they were larger in the forested areas than in the nearby farmland in the Sanjiang Plain.