Regional features of present winter snow accumulation variability in the North Eurasia from data of observations, reanalysis and satellites

Popova, V. V.; Morozova, Polina; Титкова, Т. Б.; Семенов, В. А.; Черенкова, Е. А.; Shiryaeva, A. V.; Китаев, Л.М.

doi:10.15356/2076-6734-2015-4-73-86

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…С утолщением снежного покрова соглашение между двумя ти пами данных улучшается [14]. В качестве порого вого значения наличия снежного покрова выбра но превышение 50% покрытия ячейки снегом, чтобы соответствовать определению [6], соглас но которому день со снежным покровом на ГМС отмечается, когда более половины видимой ок рестности ГМС покрыто снегом. Наша задача -рассмотреть характеристики снежного покрова для всей территории России.…”

Section: данные и методыunclassified

“…Климатические тренды, в том числе хо лодного периода, за последние десятилетия представлены в ряде работ, исследующих изме нение характеристик снежного покрова [1][2][3][4][5][6][7]. В Северном полушарии с 1980х годов изза эф фекта глобального потепления площадь снежно го покрова уменьшилась, особенно в переходные сезоны [1,8,9].…”

Section: Introductionunclassified

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Snow occurrence time on the Russia’s territory in the early 21st century (from satellite data)

Титкова

Vinogradova

2017

Lëd i sneg

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SummaryTime of the snow cover appearance, existence and disappearance on the Russia's territory in the early 21 st century (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) was corrected using the MODIS/Terra satellite data (the 8-day discreteness, and the 0.5×0.5° resolution). The satellite data errors were estimated from data of the ground stations observations. The errors were found to be maximal in autumn and minimal in spring. The relationship between the snow cover characteristics and the climate ones was investigated using data obtained at the ground-based stations together with correlation between dates of snow appearance and loss and the climate parameters. The dependences obtained were tested by means of correlation and regression analysis over the longitudinal sectors. Significant coefficients of correlation (the Student criterion of probability was equal to 0.95) were found between time of the snow cover presence and dates of the temperature drop below 0 °С and the amount of days with negative temperatures. Changes in the climate characteristics result in that due to decreasing of the solid precipitation in winter time the snow presence duration becomes shorter over the European part of Russia and in the Western Siberia. The shortening in the Middle Siberia is caused by the spring warming. Durations of the snow occurrence in the Far East area are different. On the Chukotka peninsula the duration is longer because of the autumn fall in temperature while in the Kamchatka region the snow occurrence time is shorter due to significant decrease of a period with negative temperatures in both the autumn and spring seasons. Ключевые слова: климатические показатели, MODIS, продолжительность залегания снега, снежный покров. Оценена погрешность определения параметров снежного покрова в осенний и весенний периоды по спутниковым данным MODIS путём сравнения с материалами наземных наблюдений. По данным MODIS уточнены сроки установления и схода снежного покрова, а также продолжительность его залегания в 2000-2015 гг.; приведены карты этих характеристик. Отслежены тренды характеристик залегания снежного покрова на территории России в разных долготных секторах.

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Section: данные и методыunclassified

Section: Introductionunclassified

Snow occurrence time on the Russia’s territory in the early 21st century (from satellite data)

Титкова

Vinogradova

2017

Lëd i sneg

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“…An important influence on natural and artificial systems may result from the effect of global warming on snow cover (early snowmelt, shorter period with stable snow cover). Since the snow cover in the mountainous and lowland areas of the temperate climate zone is more subject to temperature fluctuations, snowmelt is most likely to increase in these areas in the near future (Popova et al 2015). The water resources (changes in the intensity of spring floods, greater capacity for evaporation) and the agriculture sectors that depend on them are impacted by variations in the regime of snow accumulation and snow melting (Barnett et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Snow Height and Snow Water Equivalent Estimation from Snow Cover Fraction Using Sentinel-2 Satellite Images in North Kazakhstan

Teleubay¹,

Yermekov²,

Toleubekova³

et al. 2023

AJS

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Climate change's influence on snowpack can significantly affect natural and anthropogenic processes. Water resources and agri-business, which depend on winter precipitation, are highly affected by variations in a snowbank and melting regimes. This paper demonstrates the comparison results of the snowpack thickness estimation in the LLP "North Kazakhstan AES" adopting distinctive techniques (quadratic, exponential, and linear functions) for assessing Fractional Snow Cover (SCF) and demonstrating Snow Water Equivalent (SWE) on the one hand, and in-situ perspective on the other. Between the 26–29 of February 2020, a field measurement was managed on the territory of 25,000 hectares. Accordingly, the thickness of the snowpack was surveyed at 560 points, and its density was measured at 70 points. Applying existing methodologies of SCF computation, it became apparent that the quadratic equation provides more reliable results at RMSE of 0.01 m, followed by linear -0.12 m and exponential -0.13 m methods. This work showed a strong correlation between snow height and SCF, namely the quadratic function in Northern Kazakhstan. Thus, we strongly suggest using Sentinel-2 MSI and the quadratic SCF estimation function for snow cover estimation, further spring flood forecasting, and other hydrological studies. Keywords: snow height, snow cover fraction, normalized-difference snow index, snow water equivalent, Sentinel-2 MSI, North Kazakhstan

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“…The effect of climate warming on snow cover (earlier snowmelt, shorter period with stable snow cover) can lead to a serious impact on natural and anthropogenic systems. Snow cover in mountainous regions and on plains, in regions with a temperate climate, is very sensitive to temperature fluctuations, therefore such regions are most likely to suffer from a possible increase in snow melting (Popova et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Calculation of Snow Height (Hs) and Snow Water Equivalent (Swe) at Key Points of the Northern Kazakhstan Region From Sentinel-2 Satellite Images

Teleubay¹,

Mazhrenova²

2021

KAZATU

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The article presents a comparative assessment of the snow height (HS) in the Limited Liability Partnership "North Kazakhstan Agricultural Experimental Station" using three different approaches of calculation of Snow Cover Fraction (SCF) with further determination of Snow Water Equivalent (SWE) from the one hand and in-situ prospective from the other. It was clear that the quadratic formula of SCF calculation provide better and reliable outcomes with an RMSE 1.36 cm which is followed by linear (12.06 cm) and exponential approaches (12.86). The lowest water level was 9 mm, the average level was about 50 mm, and the highest level was up to 62 mm, according to the SWE map produced by the quadratic equation while the highest, average and the lowest snow height (HS) have reached up to 28 cm, 13 cm and 5 cm respectively. Based on the results and the accuracy obtained, we strongly recommend to use the given methodology in the whole northern and central regions of Kazakhstan to estimate the amount of snow for the further hydrological plans and decisions.

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Regional features of present winter snow accumulation variability in the North Eurasia from data of observations, reanalysis and satellites

Cited by 13 publications

References 15 publications

Snow occurrence time on the Russia’s territory in the early 21st century (from satellite data)

Snow occurrence time on the Russia’s territory in the early 21st century (from satellite data)

Snow Height and Snow Water Equivalent Estimation from Snow Cover Fraction Using Sentinel-2 Satellite Images in North Kazakhstan

Calculation of Snow Height (Hs) and Snow Water Equivalent (Swe) at Key Points of the Northern Kazakhstan Region From Sentinel-2 Satellite Images

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