The Residual Snow Cover in the Canadian Arctic in July: A Means to Evaluate the Regional Maximum Snow Depth in Winter

ARCTIC ◽  
1986 ◽  
Vol 39 (4) ◽  
Author(s):  
Bernard Lauriol ◽  
Yvan Carrier ◽  
Hector Beaudet ◽  
Gilles Binda
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Canadian Arctic ◽  
Maximum Snow Depth

scholarly journals Tree-Ring Response to Snow Cover and Reconstruction of Century annual Maximum Snow Depth for Northern Tianshan Mountains, China

2016 ◽  
Vol 43 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Li Qin ◽  
Yujiang Yuan ◽  
Ruibo Zhang ◽  
Wenshou Wei ◽  
Shulong Yu ◽  
...  
Keyword(s):  
Snow Cover ◽  
Tree Ring ◽  
Snow Depth ◽  
Tianshan Mountains ◽  
Severe Drought ◽  
Annual Maximum ◽  
Climate History ◽  
Important Indicator ◽  
Maximum Snow Depth ◽  
Northern Tianshan

Abstract Heavy snowfall and extreme snow depth cause serious losses of human life and property in the northern Tianshan Mountains almost every winter. Snow cover is an important indicator of climate change. In this study, we developed five tree-ring-width chronologies of Schrenk spruce (Picea schrenkiana Fisch. et Mey) from the northern Tianshan Mountains using standard dendrochronological methods. Correlation analyses indicated that radial growth of trees in the northern Tianshan Mountains is positively affected by annual maximum snow depth. This relationship was validated and models of annual maximum snow depth back to the 18th century were developed. The reconstruction explains 48.3% of the variance in the instrumental temperature records during the 1958/59–2003/04 calibration periods. It indicates that quasi-periodic changes exist on 2.0–4.0-yr, 5.3-yr, 14.0-yr, and 36.0-yr scales. The reconstructed series shows that maximum snow depth exhibits obvious stages change, the periods characterized by lower maximum snow depth were 1809/10–1840/41, 1873/74–1893/94, 1909/10–1929/30, 1964/65–1981/82, and the periods characterized by higher maximum snow depth were 1841/42–1872/73, 1894/95–1908/09, 1930/31–1963/64, and 1982/83–present. The lower period of annual maximum snow depth during the 1920s–1930s is consistent with the severe drought that occurred at this time in northern China. From the 1970s to the present, the maximum snow depth has increased clearly with the change to a warmer and wetter climate in Xinjiang. The reconstruction sheds new light on snow cover variability and change in a region where the climate history for the past several centuries is poorly understood.

scholarly journals Snow cover variations across China from 1951–2018

2020 ◽  
Author(s):  
Xiaodong Huang ◽  
Changyu Liu ◽  
Zhaojun Zheng ◽  
Yunlong Wang ◽  
Xubing Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regional Differences ◽  
Meteorological Factors ◽  
Onset Date ◽  
The Tibetan Plateau ◽  
Maximum Snow Depth ◽  
The Postponement

Abstract. Based on a snow depth dataset retrieved from meteorological stations, this experiment explored snow indices, including snow depth (SD), snow covered days (SCDs), and snow phenology variations, across China from 1951 to 2018. The results indicated that the snow cover in China exhibits regional differences. The annual mean SD tended to increase, and the increases in mean and maximum snow depth were 0.04 cm and 0.1 cm per decade, respectively. SCDs tended to increase by approximately 0.5 days per decade. The significant increases were concentrated at latitudes higher than 40° N, especially in Northeast China. However, in the Tibetan Plateau, the SD and SCDs tended to decrease but not significantly. Regarding the snow phenology variations, the snow duration days in China decreased, and 25.2 % of the meteorological stations showed significant decreasing trends. This result was mainly caused by the postponement of the snow onset date and the advancement of the snow end date. Geographical and meteorological factors are closely related to snow cover, especially the change in temperature, which will lead to significant changes in snow depth and phenology.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

scholarly journals GIS spatial analysis of the distribution of snow depth: A study of western Rhodopes, Bulgaria

2016 ◽  
Vol 96 (1) ◽  
pp. 46-55
Author(s):  
Valentina Nikolova ◽  
Aleksandar Penkov
Keyword(s):  
Spatial Analysis ◽  
Snow Cover ◽  
Snow Depth ◽  
Kriging Interpolation ◽  
Map Algebra ◽  
Explanatory Variables ◽  
Distance Weighted ◽  
Point Data ◽  
Gis Environment ◽  
Elevation Model

The aim of the present research is to show the advantages of information technology in investigating the snow cover. The snow data is usually taken from the measurement in meteorological stations which are often sparsely and insufficient. The problem in the analysis of the snow cover is how to present point data spatially and what is the most appropriate model. The area of the present research is the western part of Rhodopes mountain (Southern Bulgaria). The relief is variable from low to high mountainous and the climate is influenced by the high altitude and Mediterranean air advections. The spatial analysis of the distribution of snow depth is done in ArcGIS by application of Spatial Statistics Tools and Geostatistical Analyst. We considered altitude, aspect and slope as explanatory variables that could be used for determination of the territorial distribution of the snow depth. These factors are determined on the base of digital elevation model and the relationship between variables is evaluated by application of regression analysis, ordinary less squares (OLS) analysis and geographically weighted regression (GWR). The high values of R2 (above 0.7) show the representativeness of the model. A map of spatial distribution of snow depth is created by Map algebra in GIS environment, applying the regression equation of the relation snow depth - altitude. Inverse distance weighted and ordinary kriging interpolation are also carried out. The research shows that spatial presentation of point snow data and its interpretation should be done taking into account the relief and the exposition of the territory.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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