Snow Cover Conditions Research Articles

Response of shallow soil temperature to climate change on the Qinghai–Tibetan Plateau

AbstractUnder climate change, soil temperature change remarkably influences regional landscapes, ecosystems, hydrological processes, and other parameters. Based on daily soil temperature data from 56 stations from 1965 to 2014, the spatiotemporal variations in shallow soil temperature (0, 5, 10, 15, and 20 cm) on the Qinghai–Tibetan Plateau (QTP) were investigated. The mean shallow soil temperature decreased obviously with altitude both annually and seasonally, except in winter. Primarily dependent on station latitude, the spatial distribution of winter soil temperature was significantly affected by the snow cover conditions. As the soil depth increased, the soil temperature showed a cooling trend in spring and summer but a warming trend in autumn and winter. In the past 50 years, the shallow soil temperature increased considerably on the QTP both annually and seasonally. The warming rate of soil temperature was greatly dependent on station altitude in winter and dependent on latitude in summer and longitude in autumn. Unlike the increasing air temperature that was contributed mainly by winter, the warming rate of soil temperature in winter at depths of 5, 10, 15, and 20 cm was obviously lower than that in other seasons and air temperature in winter. This occurrence may be caused by relatively less snow cover in winter, which produces a weak insulation effect and further makes the soils more vulnerable to cooling from cold air. For the entire region, the soil temperature variability was strongly correlated with the change in air temperature but weakly correlated with precipitation. Although the warming rate in shallow soil tended to be low (high) at stations with a high amount of precipitation (snow cover), the influence of precipitation (including snow) on shallow soil temperature was limited on the QTP in the past few decades.

Adaptability and productivity of new varieties of strawberry breeding of All-Russian Horticultural Institute for Breeding, Agrotechnology and Nursery in the conditions of the Orenburg Urals

The article presents the results of the collection study of new varieties of strawberry breeding in All-Russian Horticultural Institute for Breeding, Agrotechnology and Nursery in the conditions of the Ural region (on the example of the Orenburg region). Unfavorable climate features for strawberries in the Orenburg Urals are low negative temperatures at the beginning of winter in the absence of snow cover and dry conditions during the growing season. According to the results of the study, Bereginya, Tsaritsa, Kokinskaya, Rosinka, Studencheskaya varieties were distinguished by good adaptation, high productivity (more than 0.2-0.3 kg/bush), and large — fruiting (average weight-more than 9.0 g). According to the indicated parameters, these varieties surpassed the zoned variety Orlets.

Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture

Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.

Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

October snow cover and winter atmospheric conditions in Siberia

In this study, the relationship between autumn Siberian snow cover and atmospheric conditions of the following winter is evaluated in time and space. NOAA observational data, NCEP2, and ERA-Interim reanalysis data, and results of a climate model INMCM4 are used. The study is carried out for a territory of Western Siberia of 55° – 74° N and 60° – 90° E. The results obtained show that the relationship does not have a constant manifestation in time. It is hypothesized that the abnormal amount of snow in autumn in Siberia and the anomalies of meteorological parameters in the following winter are initiated by some third process which occurred earlier, possibly at the beginning of autumn or at the end of summer.

Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data

Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where in situ time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with in situ spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture in situ-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

Spatio-temporal variability and seasonal dynamics of snow cover regime in Estonia

Climate warming influences highly on snow cover regime in the midlatitudes. Snow cover conditions, in turn, affect human activity very much. The aim of this study was (a) to analyse spatial and temporal variability of snow cover duration, (b) to analyse spatial and temporal variability of the start and end dates of the period with the permanent snow cover, (c) to describe spatial, temporal and seasonal variability of median and maximum snow depth in Estonia and (d) to determine the presence of long-term changes and trends in these parameters during the period 1950/51–2015/16. Time series of daily snow depth at 22 stations for that period were processed in order to obtain reliable estimates of changes in the snow regime. Snow cover data are non-normally distributed, therefore, median and quartile range were used to describe the mean state and variability of snow cover. Only these dates were included into the analysis when snow cover was observed at least on 50% of days in the time series. Trend analysis was made using the Mann-Kendall test and trend values were found using the Theil-Sen’s method. A large spatio-temporal variability of snow cover duration was found. The median number of days with snow cover at the 22 stations was 112, varying between 61 and 130 days. In the coastal regions of Estonia and especially on the western coast of Saaremaa Island snow cover duration has been much lower than in the continental part. The longest snow cover period is observed on uplands in south-eastern and north-eastern Estonia. It was found that, in the average, the period with the permanent snow cover in the continental Estonia begins on 19 December and ends on 18 March. There was a negative trend in snow cover duration due to the earlier snow melting in spring at the majority of stations. The end date of the permanent snow cover has shifted earlier by 10–30 days in 66 years and its duration has decreased accordingly. The maximum snow depth has been recorded on uplands of south-eastern Estonia with the median value 38 cm. There was a decreasing multiannual trend in snow depth from January to the end of March. Changes in snow depth were largest in the end of February and in March when the trend was statistically significant. In the average, snow depth has decreased by 0.5–1.5 cm per decade, i.e. by 2–9 cm throughout the whole study period.

Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

Characterising spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: the Clutha Catchment, New Zealand

Abstract. A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.

High resistance to climatic variability in a dominant tundra shrub species.

Climate change is modifying temperature and precipitation regimes across all seasons in northern ecosystems. Summer temperatures are higher, growing seasons extend into spring and fall and snow cover conditions are more variable during winter. The resistance of dominant tundra species to these season-specific changes, with each season potentially having contrasting effects on their growth and survival, can determine the future of tundra plant communities under climate change. In our study, we evaluated the effects of several spring/summer and winter climatic variables (i.e., summer temperature, growing season length, growing degree days, and number of winter freezing days) on the resistance of the dwarf shrub Empetrum nigrum. We measured over six years the ability of E. nigrum to keep a stable shoot growth, berry production, and vegetative cover in five E. nigrum dominated tundra heathlands, in a total of 144 plots covering a 200-km gradient from oceanic to continental climate. Overall, E. nigrum displayed high resistance to climatic variation along the gradient, with positive growth and reproductive output during all years and sites. Climatic conditions varied sharply among sites, especially during the winter months, finding that exposure to freezing temperatures during winter was correlated with reduced shoot length and berry production. These negative effects however, could be compensated if the following growing season was warm and long. Our study demonstrates that E. nigrum is a species resistant to fluctuating climatic conditions during the growing season and winter months in both oceanic and continental areas. Overall, E. nigrum appeared frost hardy and its resistance was determined by interactions among different season-specific climatic conditions with contrasting effects.

Changing snow depth in the Great Lakes basin (USA): Implications and trends

Abstract In the Great Lakes basin of North America, snow plays a critical role in the regional hydroclimate, where snow ablation events can serve both as a resource and a hazard. The frequency and magnitude of an ablation event is governed by the availability of meteorological conditions to ablate snow, and the physical presence of snow to be ablated. While the meteorological conditions leading to ablation have been well documented, examining changes in atmospheric conditions alone have been unable to completely explain observed ablation trends. As such, this study applies a gridded snow depth dataset to evaluate snow depth variability, while speaking to the implications of such variability on ablation frequency and water resources. Snow cover is present in the basin from November – April, with the more northerly regions (Lake Superior and Lake Huron basins) exhibiting a deeper and more seasonally-persistent snowpack. Seasonal basin-wide snow depth decreased by approximately 25% from 1960 to 2009, with some of the most significant decreases occurring north of Lake Superior. Surface air temperatures are negatively associated snow depth, and warming temperatures are likely contributing to snow depth declines. These regional decreases in snow depth spatially corroborate previously observed decreases in the frequency of ablation events in the basin, and highlight the importance of examining both snow cover and meteorological conditions when seeking to explain snow ablation variability. The results from this study can be applied to inform water resource management decisions in the region.

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry.

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios

Global warming has considerably challenged the natural environment and livelihood conditions. Understanding potential future changes in critical climatic variables, such as temperature and precipitation, is important for regional agricultural and water resource management. This study proposes a new approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Intercomparison Project Phase 5 (CMIP5) emission scenarios and aims to test its applicability in cold areas and to evaluate the response of temperature and precipitation, in amount and form, under future warmer climate trends. Three stations in northeastern China are set as case sites, and 50 years of daily weather observations are used for model calibration and validation. Future synthetic time-series of daily precipitation and daily maximum and minimum temperatures is generated by the calibrated LARS-WG based on three Representative Concentration Pathway (RCP) scenarios with various radiative forcing levels of 14 general circulation models (GCMs) outputs for the periods 2041–2060 (2050s) and 2061–2080 (2070s). The results show that the CMIP5 scenarios can be successfully used in a LARS-WG model and that the model performs well in cold weather conditions to repeat the current status of the case sites; the model is able to provide downscaling analysis for future daily weather generation via updating calibrated model parameters based on various GCM outputs. A generally warming and wetting conversion would last into the future for the study sites, but there is great inconsistency among different GCMs. An ensemble approach is adopted with mean values of multi-GCMs to avoid the uncertainty associated with using a single GCM, based on which the changes in the form of precipitation are further estimated. As a result of the decrease in freezing conditions, although annual precipitation will continue to increase in the future, there will be relatively less annual snowfall, which will be primarily focused in deep winter. Such changes in snow cover conditions will potentially disturb the original rules of local overwintering agriculture. In addition, more intense and earlier snowmelt discharge and more rainfall in summer will latently impact the watershed hydrologic process. The influences of climate change are significant, and related projects for agricultural and water resource management should be of great concern in local decision-making.

Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

Abstract This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

Effects of a snow-compaction treatment on soil freezing, snowmelt runoff, and soil nitrate movement: A field-scale paired-plot experiment

A frozen soil layer may impede snowmelt infiltration, resulting in a large amount of runoff that influences the soil water balance and anion movement in the soil profile. To examine the relationships among soil frost depth, snowmelt runoff, and nitrate leaching in agricultural fields, we measured both the snowmelt runoff for three winters and other environmental factors including the soil frost depth and nitrate content in a ∼10,000 m2 field. We divided the field into two subplots: one was maintained in a natural snow cover condition (the control plot), and the snow cover was compacted on the other plot (the treated plot) to enhance the development of the soil frost depth. In all three winters, soil frost depths in the control plot were <0.2 m and very little runoff was observed during the snowmelt period. In contrast, the soil frost depth became >0.4 m and a large amount of snowmelt runoff was observed in the treated plot. The depth of the peak nitrate concentration after the snowmelt period was shallower in the treated plot compared to the control plot. Moreover, a significant linear relationship was observed between (1) the amount of nitrate in the 0–0.3 m depth after the snowmelt period and (2) the total amount of snowmelt infiltration calculated by subtracting the amount of snowmelt runoff from the amount of snowmelt water. These results suggest that snow compaction can be a promising technique to develop a uniform soil frost depth in large-scale fields, which consequently controls the soil water and nutrient movement in the soil layer.

Simulation of Past Changes in the Austrian Snow Cover 1948–2009

Abstract A distributed snow model is applied to simulate the spatiotemporal evolution of the Austrian snow cover at 1 km × 1 km spatial and daily temporal resolution for the period 1948–2009. After a comprehensive model validation, changes in snow cover conditions are analyzed for all of Austria as well as for different Austrian subregions and elevation belts focusing on the change in snow cover days (SCDs). The comparison of SCDs for the period 1950–79 to those achieved for 1980–2009 for all of Austria shows a decrease in SCDs with a maximum of &amp;gt;35 SCDs near Villach (Carinthia). The analysis of SCD changes in different subregions of Austria reveals mean changes between −11 and −15 days with highest absolute change in SCDs for southern Austria. Two decrease maxima could be identified in elevations of 500–2000 m MSL (between −13 and −18 SCDs depending on the subregion considered) and above 2500 m MSL (over −20 SCDs in the case of central Austria). The temporal distribution of SCD change in the Austrian subregions is characterized by a reduction of SCDs in midwinter and at the end of winter rather than by fewer SCDs in early winter. With respect to the temporal distribution of SCD change in different elevation belts, changes in elevations below 1000 m MSL are characterized by a distinct reduction of SCDs in January. With increasing elevation the maximum change in SCDs shifts toward the summer season, reaching a maximum decrease in the months of June–August above 2500 m MSL.

Effect of snow-straw collocation on the complexity of soil water and heat variation in the Songnen Plain, China

The goal of this study was to explore the effect of the collocation coverage of snow and straw on the complexity variation of soil water and heat and its appropriate evaluation method during freeze-thaw periods in the Songnen Plain of China. Based on field trials, the characteristics of the spatial variability in soil water and heat and the correlation between the water/heat in snow and in soil were analyzed under conditions of bare land (BL), natural snow (FB0), natural snow plus 5-cm-thick straw (FB5) and natural snow plus 10-cm-thick straw (FB10). Additionally, a spectral entropy algorithm based on Fourier transform was used to calculate the spectral entropy of the soil water and heat sequences, and the spatial variation law of soil water and heat complexity in vertical section was explored; furthermore, the positive effect of straw on the soil water and heat environment was revealed. (1) During the freezing period (i.e., the rapid freezing period and stable freezing period), the collocation coverage of straw and snow weakened the differences between the surface soil and the deeper soil; furthermore, the presence of straw hindered the energy exchange between snow and soil, and as the amount of straw coverage increased, the difference between snow water/heat and that in soil increased. During the melting period, the infiltration of snowmelt water affected the variation of soil water and heat, whereas the straw, due to its water storage characteristics, absorbed large amounts of snowmelt water and provided a stable water supply to the soil. With the increase of straw coverage, the correlation between snow water/heat and that in soil was increased under the FB5 and FB10 treatments. (2) The spectral entropy algorithm evaluation values were in good agreement with the classical statistical analysis, demonstrating that the algorithm can be used to efficiently and accurately evaluate the complexity of soil water and heat time series. The spectral entropy of soil water and heat in the 10-cm soil layer under the treatments of FB0, FB5 and FB10 was less than the corresponding entropy under the BL treatment during rapid freezing and stable freezing periods. As the soil depth increased, the spectral entropy decreased under the different treatments. Affected by the maximum freezing depth, the soil water and heat spectral entropy values peaked in the vertical section during the stable freezing period; as the amount of straw coverage increased, the position of the peak value gradually increased. However, during the melting period, the complexity of soil water and heat under the three snow cover conditions increased significantly relative to that under the BL treatment. As the straw coverage increased, the complexity of soil water and heat decreased. The complexity evaluation method of the spectral entropy algorithm is reliable, and the measurement accuracy meets the evaluation requirements. The algorithm has application value for the evaluation of soil water and heat variation in cold regions.

The Suitability of Snow and Meteorological Conditions of South-Central Slovakia for Ski Slope Operation at Low Elevation—A Case Study of the Košútka Ski Centre

In this study, the snow conditions of South-Central Slovakia (Inner Western Carpathians; temperate zone) were analyzed to assess the suitability for ski slope operations without snow production under 1000 m a.s.l. For the study site of the Košútka Ski Centre, meteorological conditions for snowmaking, snowpack characteristics, and snow water equivalent (SWE) compared with seasonal precipitation were identified. To identify the months suitable for snowmaking, the number of potential snowmaking days (PSD) and the required number of snowmaking days (RNSD) were calculated for six winter seasons from 2010–2011 to 2015–2016. The results showed that the conditions of natural snow cover were not appropriate for ski slope operation because of a low natural snow depth. For the Košútka Ski Centre, it was concluded that the essential base layer snowmaking for ski slope operation is possible only for a few days in the winter season because of the increasing mean value of the mean average daily temperature and the consequently higher occurrence of liquid precipitation in the winter season. Essential high snow production results in the heterogeneous distribution of snow on the ski slope, and in high snow depth, density, and SWE of the ski slope snowpack, and in prolonged melting.

WRF simulation of surface wind in high latitudes

High-resolution Weather Research and Forecasting (WRF) simulations of surface wind, pressure, temperature, humidity and snow depth in Arctic coastal regions are performed in this study. We compare simulation results for different elevation resolutions, surface roughness lengths, PBL schemes, initialization data and snow cover conditions with meteorological observations and validate WRF's usage in high latitudes. WRF performs well in simulations of high-latitude strong wind if the roughness length is carefully modified considering site's real vegetation type, though WRF produces insufficient wind fluctuations. Noah LSM is effective in updating surface roughness length according to snow depth. Higher elevation resolution increases surface pressure accuracy. MYNN3/YSU PBL scheme is recommended for wind speed/temperature simulation. In addition, the vertical structure of the Arctic atmospheric boundary layer is discussed, and a statically stable and turbulent surface layer is the Arctic winter is reported.

Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data

Snow cover condition across the Tibetan Plateau (TP) is not only a significant indicator of climate change but also a vital variable in water availability because of its water storage function in high-mountain regions of Southwest China and the surrounding Asian countries. Limited by low spatial resolution, incomplete spatial coverage, and short time span of the current snow cover products, the long-term snow cover change across the TP under the climate change background remains unclear. To resolve this issue, a composite long-term gap-filled TP daily 5-km snow cover extent (SCE) record (TPSCE) is generated by integrating SCE from the Advanced Very High-Resolution Radiometer (AVHRR) surface reflectance climate data record (CDR) and several existing snow cover data sets, with the help of a decision tree snow cover mapping algorithm, for the period 1981–2016. A snow discrimination process was used to classify the land surface into snow (pre-TPSCE) and non-snow using AVHRR surface reflectance CDR. To fill gaps caused by invalid observations and cloud contamination in pre-TPSCE, several existing daily SCE products, including MOD10C1, MYD10C1, IMS, JASMES, and a passive microwave snow depth data set are employed in the composition process. The daily snow discrimination accuracy, tested by ground snow-depth observations during 2000–2014, shows that the TPSCE captures the distribution of snow duration days (R2 = 0.80, bias = 3.93 days) effectively. The comparison between the TPSCE and fine-resolution snow cover maps (MCD10A1-TP) indicates high comparability between the TPSCE and MCD10A1-TP. In addition, cross-comparisons with changes in temperature, precipitation, and land surface albedo indicate that the TPSCE is reliable in climate change studies. In summary, the TPSCE is spatially complete and covers the longest period among all current snow cover products from satellite observations. The TPSCE seamlessly records changes in snow cover across the TP over the past 36 years, thereby providing valuable snow information for climate change and hydrological studies.