Long-Term Dynamics of Snow Depth and Snow Composition in Terms of the Geochemical Landscape of Upper Reaches of the Klyazma River

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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Snowpack variability and trends at long-term stations in northern Colorado, USA

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-371-131-2015 ◽

2015 ◽

Vol 371 ◽

pp. 131-136 ◽

Cited By ~ 10

Author(s):

S. R. Fassnacht ◽

M. Hultstrand

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Entire Period ◽

Northern Colorado ◽

Snow Water ◽

Highly Correlated ◽

The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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Improvement of long-term snow depth product accuracy from passive microwave satellite observations: A case study with SNODAS data

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2016.7730272 ◽

2016 ◽

Author(s):

Xiaojing Liu ◽

Lingmei Jiang ◽

Gongxue Wang ◽

Shirui Hao

Keyword(s):

Snow Depth ◽

Satellite Observations ◽

Passive Microwave

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FEATURES OF SNOW COVER OF SEMI-DESERTS AND DRY STEPPES OF THE CASPIAN SEA ACCORDING TO SATELLITE DATA FOR THE PERIOD 2001...2020

Hydrometeorology and ecology ◽

10.54668/2789-6323-2021-101-2-80-87 ◽

2021 ◽

Vol 101 (2) ◽

pp. 80-87

Author(s):

A.G Terekhov ◽

◽

N.I. Ivkina ◽

N.N. Abayev ◽

A.V. Galayeva ◽

...

Keyword(s):

Snow Cover ◽

Caspian Sea ◽

Snow Depth ◽

Eastern Coast ◽

The Caspian Sea ◽

Dry Steppe ◽

Natural Tendency ◽

Second Mode ◽

Continental Climate

The Snow Depth FEWS NET daily product was used to analyze snowy regime of the upper part of the River Emba basin from January 1 to April 30 for the period of 2001...2020. The Emba River basin is situated in Kazakhstan at the Eastern coast of the Caspian Sea. The area is characterized by the arid and extreme continental climate with dry-steppe and semi-desert landscapes. The population is small and the anthropogenic impact on the snow cover is minimal there. These conditions give an opportunity to identify the natural tendency in long-term changes of snow covering in semidesert zone of Kazakhstan. This paper describes the characteristics of the formation and destruction of the snow cover in the last 20 years. It was indicated that snowy regime has a trigger structure including two states; low-snowy regime and others years. It was shown that the snowy conditions are triggered. There are two modes, the first, as a low-snowy regime (up to 50 % of the entire sample) and the second mode includes other years. Significant variations of snow depth in various years masked many years’ tendencies of snow cover characteristics. But low-snowy regime was observed four times during five last years that can relate with modern decreasing snow covering in semi-desert zone of Kazakhstan.

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Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

The Cryosphere ◽

10.5194/tc-12-227-2018 ◽

2018 ◽

Vol 12 (1) ◽

pp. 227-245 ◽

Cited By ~ 25

Author(s):

Xinyue Zhong ◽

Tingjun Zhang ◽

Shichang Kang ◽

Kang Wang ◽

Lei Zheng ◽

...

Keyword(s):

Land Surface ◽

Snow Depth ◽

Local Community ◽

Water Cycle ◽

Primary Objective ◽

Spatiotemporal Variability ◽

Physical Parameters ◽

Large Area ◽

Eurasian Continent

Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade−1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

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Long-term increase in snow depth leads to compositional changes in arctic ectomycorrhizal fungal communities

Global Change Biology ◽

10.1111/gcb.13294 ◽

2016 ◽

Vol 22 (9) ◽

pp. 3080-3096 ◽

Cited By ~ 21

Author(s):

Luis N. Morgado ◽

Tatiana A. Semenova ◽

Jeffrey M. Welker ◽

Marilyn D. Walker ◽

Erik Smets ◽

...

Keyword(s):

Snow Depth ◽

Fungal Communities ◽

Compositional Changes

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Ectomycorrhizal and saprotrophic fungi respond differently to long‐term experimentally increased snow depth in the High Arctic

MicrobiologyOpen ◽

10.1002/mbo3.375 ◽

2016 ◽

Vol 5 (5) ◽

pp. 856-869 ◽

Cited By ~ 17

Author(s):

Sunil Mundra ◽

Rune Halvorsen ◽

Håvard Kauserud ◽

Mohammad Bahram ◽

Leho Tedersoo ◽

...

Keyword(s):

Snow Depth ◽

High Arctic ◽

Saprotrophic Fungi

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Winter climate affects long-term trends in stream water nitrate in acid-sensitive catchments in southern Norway

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-3055-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 3055-3085 ◽

Cited By ~ 5

Author(s):

H. A. de Wit ◽

A. Hindar ◽

L. Hole

Keyword(s):

Snow Depth ◽

Stream Water ◽

N Deposition ◽

Climatic Changes ◽

N Leaching ◽

Forested Catchments ◽

Discharge Temperature ◽

Long Term Trends ◽

Southern Norway

Abstract. Controls of stream water NO3 in mountainous and forested catchments are not thoroughly understood. Long-term trends in stream water NO3 are positive, neutral and negative, often apparently independent of trends in N deposition. Here, time series of NO3 in four small acid-sensitive catchments in southern Norway were analysed in order to identify likely drivers of long-term changes in NO3. In two sites, stream water NO3 export declined ca 50% over a period of 25 years while in the other sites NO3 export increased with roughly 20%. Discharge and N deposition alone were poor predictors of these trends. The most distinct trends in NO3 were found in winter and spring. Empirical models explained between 45% and 61% of the variation in weekly concentrations of NO3, and described both upward and downward seasonal trends tolerably well. Key explaining variables were snow depth, discharge, temperature and N deposition. All catchments showed reductions in snow depth and increases in winter discharge. In two inland catchments, located in moderate N deposition areas, these climatic changes appeared to drive the distinct decreases in winter and spring concentrations and fluxes of NO3. In a coast-near mountainous catchment in a low N deposition area, these climatic changes appeared to have the opposite effect, i.e. lead to increases in especially winter NO3. This suggests that the effect of a reduced snow pack may result in both decreased and increased catchment N leaching depending on interactions with N deposition, soil temperature regime and winter discharge.

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Energy Efficient Wireless Sink Node for Monitoring of Snow Environment

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1514 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1775-1785

Author(s):

Rajesh Kumar Garg, Et. al.

Keyword(s):

Energy Efficient ◽

Snow Depth ◽

Field Performance ◽

Cost Effective ◽

Field Evaluation ◽

Sink Node ◽

Depth Sensor ◽

Power Module ◽

Term Field

Conventionally, sink node is considered to have large hardware and energy resources; however, many times sink node is working in same conditions as source nodes, especially when deployed for monitoring of the snow environment. In this paper, an effort has been made to practically realize a sink node which is energy efficient and cost effective for monitoring applications. To save energy, the Main Power Module is designed to provide controlled powers to sensors and sub-modules. The paper discusses design aspects of the sink node and its long-term field evaluation with environmental sensors, especially the Snow Depth Sensor of MaxBotix. Field performance of Snow Depth Sensor has been enhanced by Euclidean Minimum Distance filter which improved the correlation of data to 0.997. The proposed design helps to achieve energy consumption of 42.72mWh which is significantly lower than the previous work. The reliable working of the sink node in the long-term field evaluation indicates that snow environment can be monitored at less expense of energy by employing proposed sensors and the specially designed sink node.

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