Seasonal controls on snow distribution and aerial ablation at the snow-patch and landscape scales, McMurdo Dry Valleys, Antarctica

Abstract. Accumulated snow in the McMurdo Dry Valleys, while limited, has great ecological significance to subnivian soil environments. Though sublimation dominates the ablation process in this region, measurable increases in soil moisture and insulation from temperature extremes provide more favorable conditions with respect to subnivian soil communities. While precipitation is not substantial, significant amounts of snow can accumulate, via aeolian redistribution, in topographic lees along the valley bottoms, forming thousands of discontinuous snow patches. These patches have the potential to act as significant sources of local melt water, controlling biogeochemical cycling and the landscape distribution of microbial communities. Therefore, determining the spatial and temporal dynamics of snow at multiple scales is imperative to understanding the broader ecological role of snow in this region. High-resolution satellite imagery acquired during the 2009–2010 and 2010–2011 austral summers was used to quantify the distribution of snow across Taylor and Wright Valleys. Extracted snow-covered area from the imagery was used as the basis for assessing seasonal variability and seasonal controls on accumulation and ablation of snow at multiple scales. In addition, fifteen 1 km2 plots (3 in each of 5 study regions) were selected to assess the prevalence of snow cover at finer spatial scales. Results confirm that snow patches tend to form in the same locations each year with some minor deviations observed. At the snow-patch scale, neighboring patches often exhibit considerable differences in aerial ablation rates, and particular snow patches do not reflect trends for snow-covered area observed at the landscape scale. These differences are presumably related to microtopographic influences over snow depth and exposure. This highlights the importance of both the landscape and snow-patch scales in assessing the effects of snow cover on biogeochemical cycling and microbial communities.

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Seasonal controls on snow distribution and aerial ablation at the snow-patch and landscape scales, McMurdo Dry Valleys, Antarctica

The Cryosphere ◽

10.5194/tc-7-917-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 917-931 ◽

Cited By ~ 7

Author(s):

J. W. Eveland ◽

M. N. Gooseff ◽

D. J. Lampkin ◽

J. E. Barrett ◽

C. D. Takacs-Vesbach

Keyword(s):

Microbial Communities ◽

Snow Cover ◽

Temporal Dynamics ◽

Multiple Scales ◽

Biogeochemical Cycling ◽

Mcmurdo Dry Valleys ◽

Dry Valleys ◽

Snow Covered Area ◽

Covered Area ◽

Patch Scale

Abstract. Accumulated snow in the McMurdo Dry Valleys, while limited, has great ecological significance to subnivian soil environments. Though sublimation dominates the ablation process in this region, measurable increases in soil moisture and insulation from temperature extremes provide more favorable conditions with respect to subnivian soil communities. While precipitation is not substantial, significant amounts of snow can accumulate, via wind transport, in topographic lees along the valley bottoms, forming thousands of discontinuous snow patches. These patches have the potential to act as significant sources of local meltwater, controlling biogeochemical cycling and the landscape distribution of microbial communities. Therefore, determining the spatial and temporal dynamics of snow at multiple scales is imperative to understanding the broader ecological role of snow in this region. High-resolution satellite imagery acquired during the 2009–2010 and 2010–2011 austral summers was used to quantify the distribution of snow across Taylor and Wright valleys. Extracted snow-covered area from the imagery was used as the basis for assessing inter-annual variability and seasonal controls on accumulation and ablation of snow at multiple scales. In addition to landscape analyses, fifteen 1 km2 plots (3 in each of 5 study regions) were selected to assess the prevalence of snow cover at finer spatial scales, referred to herein as the snow-patch scale. Results confirm that snow patches tend to form in the same locations each year with some minor deviations observed. At the snow-patch scale, neighboring patches often exhibit considerable differences in aerial ablation rates, and particular snow patches do not reflect trends for snow-covered area observed at the landscape scale. These differences are presumably related to microtopographic influences acting on individual snow patches, such as wind sheltering and differences in snow depth due to the underlying topography. This highlights the importance of both the landscape and snow-patch scales in assessing the effects of snow cover on biogeochemical cycling and microbial communities.

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Spatio-temporal dynamics of snow cover based on multi-source remote sensing data in China

10.5194/tc-2016-124 ◽

2016 ◽

Author(s):

Xiaodong Huang ◽

Jie Deng ◽

Xiaofang Ma ◽

Yunlong Wang ◽

Qisheng Feng ◽

...

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Depth ◽

Temporal Dynamics ◽

Microwave Remote Sensing ◽

Annual Number ◽

The Tibetan Plateau ◽

Annual Average ◽

Snow Covered Area ◽

Covered Area

Abstract. Through combining optical remote sensing snow cover products with passive microwave remote-sensing snow depth data, we produced a MODIS cloudless binary snow cover product and a 500-m spatial resolution snow depth product for December 2000 to November 2014. We used the synthesized products to analyze the temporal and spatial variation of the snow cover in China. The results indicated that in the past 14 years, the overall annual number of snow-covered days and average snow depth in China increased. The annual average snow-covered area did not change significantly, and the number of snow-covered days in summer in China decreased. The number of snow-covered days in the winter, spring, and fall seasons all increased. The average snow-covered area in the summer and winter seasons decreased, whereas the average snow-covered area in the spring and fall seasons increased. The average snow depth in the winter, summer, and fall seasons decreased. Only the average snow depth in spring increased. The spatial distribution of the increase and decrease in the annual average snow depth was highly consistent with that of the annual number of snow-covered days. The spatial distributions of the variation of the number of snow-covered days and the average snow depth of each season were also highly consistent. The regional differences in the snow cover variation in China were significant. The snow cover increased significantly in South and Northeast China, decreased significantly in Xinjiang, increased in the southwest edge and southeast of the Tibetan Plateau, and mainly decreased in the north and northwest regions of the plateau.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.1017/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 200

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.3189/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 85

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Utilization of Satellite Snow-Cover Observations for Seasonal Streamflow Estimates in the Western Himalayas

Hydrology Research ◽

10.2166/nh.1983.0020 ◽

1983 ◽

Vol 14 (5) ◽

pp. 257-266 ◽

Cited By ~ 14

Author(s):

B. Dey ◽

D. C. Goswami ◽

A. Rango

Keyword(s):

Regression Model ◽

Snow Cover ◽

Multiple Regression ◽

Snowmelt Runoff ◽

Indus River ◽

Snow Covered Area ◽

Covered Area ◽

Measured Flow ◽

Seasonal Runoff ◽

Kabul River

The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo-interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correlation between seasonal runoff and snow cover is significant at the 97% level for the Indus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent runoff data are then used to estimate the snowmelt runoff for that particular year.The difference between the estimated and observed dircharge values averaged over the 11 year study period is 10%. Satellite derived snow-covered area is the best available input for snowmelt-runoff estimation in remote, data sparse basins like the Indus and Kabul Rivers. The study has operational relevance to water resource planning and management in the Himalayan region.

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Fractional snow-covered area: scale-independent peak of winter parameterization

The Cryosphere ◽

10.5194/tc-15-615-2021 ◽

2021 ◽

Vol 15 (2) ◽

pp. 615-632

Author(s):

Nora Helbig ◽

Yves Bühler ◽

Lucie Eberhard ◽

César Deschamps-Berger ◽

Simon Gascoin ◽

...

Keyword(s):

Snow Depth ◽

Hydrological Modeling ◽

Spatial Scales ◽

Empirical Relationship ◽

Ground Surface ◽

Model Parameters ◽

Data Sets ◽

Snow Covered Area ◽

Covered Area ◽

Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

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A Spectral Analysis of Snow in Mt. Rainier

Journal of Geography and Geology ◽

10.5539/jgg.v10n3p20 ◽

2018 ◽

Vol 10 (3) ◽

pp. 20

Author(s):

Shrinidhi Ambinakudige ◽

Pushkar Inamdar ◽

Aynaz Lotfata

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Thermal Emission ◽

Major Influence ◽

Landsat 8 ◽

Run Off ◽

Snow Covered Area ◽

Covered Area ◽

Local Water ◽

Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

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Genetic signatures indicate widespread antibiotic resistance and phage infection in microbial communities of the McMurdo Dry Valleys, East Antarctica

Polar Biology ◽

10.1007/s00300-015-1649-4 ◽

2015 ◽

Vol 38 (6) ◽

pp. 919-925 ◽

Cited By ~ 16

Author(s):

Sean T. S. Wei ◽

Colleen M. Higgins ◽

Evelien M. Adriaenssens ◽

Don A. Cowan ◽

Stephen B. Pointing

Keyword(s):

Antibiotic Resistance ◽

Microbial Communities ◽

East Antarctica ◽

Phage Infection ◽

Mcmurdo Dry Valleys ◽

Dry Valleys ◽

Genetic Signatures

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Towards a scale-independent fractional snow-covered area parameterization for complex terrain

10.5194/egusphere-egu2020-14575 ◽

2020 ◽

Author(s):

Nora Helbig ◽

Yves Bühler ◽

Lucie Eberhard ◽

César Deschamps-Berger ◽

Simon Gascoin ◽

...

Keyword(s):

Snow Depth ◽

Hydrological Modeling ◽

Spatial Scales ◽

Ground Surface ◽

Longwave Radiation ◽

Data Sets ◽

Depth Data ◽

Snow Covered Area ◽

Covered Area ◽

Wide Range

<p>Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.</p><p>We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.</p>

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Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/2008jhm1042.1 ◽

2009 ◽

Vol 10 (1) ◽

pp. 130-148 ◽

Cited By ~ 79

Author(s):

Benjamin F. Zaitchik ◽

Matthew Rodell

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Water Equivalent ◽

Land Surface Model ◽

Radiation Budget ◽

Surface Model ◽

Energy Fluxes ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.

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