[Characteristics of chemical pollution of snow cover in Aktobe areas].

Science.gov (United States)

Iskakov, A Zh

2010-01-01

The paper gives data on the nature of snow cover pollution in the urbanized areas in relation to the remoteness from the basic sources of ambient air pollution. The total snow content of carcinogens has been estimated.

Mapping of colored-snow area on glaciers by using spectral reflectance of algae

Science.gov (United States)

Yamaga, D.; Yasumoto, A.; Hatakeyama, S.; Hasegawa, K.; Imai, M.; Bilesan, A.; Takeuchi, N.; Sugiyama, S.; Terashima, M.; Kawamata, H.; Naruse, N.; Takahashi, Y.

2017-12-01

One of the reasons for accelerating recent glacier retreat is reported that algae generated on glaciers gives color to snow; Red snow algae on the Harding icefield in Alaska, and cryoconite, a black colored substance formed by algae tangling with mineral particles. The distribution of algae on the glacier can vary widely from year to year, depending on the season. Remote sensing will play an important role to know the area of colored snow. In previous studies, however, since the satellite images of low gradation were used, the brightness in the specific area was saturated due to the high reflectance of snow. In addition, it is difficult to distinguish the colored snow area from that of water and shadows. We aim to map using Landsat8 data and quantitatively evaluate the distribution of colored snow area on glaciers by newly creating a colored-snow-sensitive index from spectral reflectance of algae. Cryoconite has low (high) reflectance in the range of 450-500nm (850-900nm) corresponding to Band2 (Band5) in Landsat8.On the other hand, the reflectance of glacier ice exhibits the opposite tendency. Focusing on the difference in reflectance between the two wavelength ranges, we can create indices sensitive to cryoconite area. The image, mapped as the cryoconite region with large difference in brightness between band 2 and 5, was different from the water and shadow areas. The cryoconite area is also consistent with the results obtained in the filed survey of qaanaaq Glacier in Greenland. Using the similar analytical method, we will also present the map of red snow observed on the glacier.

Merging a Terrain-Based Parameter and Snow Particle Counter Data for the Assessment of Snow Redistribution in the Col du Lac Blanc Area

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Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Vionnet, Vincent; Guyomarc'h, Gilbert; Heiser, Micha; Nishimura, Kouichi

2015-04-01

Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns. It does not, however, provide a quantitative estimate of changes in snow depths. The objective of our research was to introduce a new parameter to quantify changes in snow depths in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its consistently bi-modal wind directions. Our work focused on two pronounced, approximately 10 m high terrain breaks, and we worked with 1 m resolution digital snow surface models (DSM). The DSM and measured changes in snow depths were obtained with high-accuracy terrestrial laser scan (TLS) measurements. First we calculated the terrain-based parameter Sx on a digital snow surface model and correlated Sx with measured changes in snow-depths (Δ SH). Results showed that Δ SH can be approximated by Δ SHestimated = α * Sx, where α is a newly introduced parameter. The parameter α has shown to be linked to the amount of snow deposited influenced by blowing snow flux. At the Col du Lac Blanc test side, blowing snow flux is recorded with snow particle counters (SPC). Snow flux is the number of drifting snow particles per time and area. Hence, the SPC provide data about the duration and intensity of drifting snow events, two important factors not accounted for by the terrain parameter Sx. We analyse how the SPC snow flux data can be used to estimate the magnitude of the new variable parameter α . To simulate the development

SNOW AVALANCHE ACTIVITY IN PARÂNG SKI AREA REVEALED BY TREE-RINGS

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F. MESEȘAN

2014-11-01

Full Text Available Snow Avalanche Activity in Parâng Ski Area Revealed by Tree-Rings. Snow avalanches hold favorable conditions to manifest in Parâng Mountains but only one event is historically known, without destructive impact upon infrastructure or fatalities and this region wasn’t yet the object of avalanche research. The existing ski infrastructure of Parâng resort located in the west of Parâng Mountains is proposed to be extended in the steep slopes of subalpine area. Field evidence pinpoints that these steep slopes were affected by snow avalanches in the past. In this study we analyzed 11 stem discs and 31 increment cores extracted from 22 spruces (Picea abies (L. Karst impacted by avalanches, in order to obtain more information about past avalanches activity. Using the dendrogeomorphological approach we found 13 avalanche events that occurred along Scărița avalanche path, since 1935 until 2012, nine of them produced in the last 20 years. The tree-rings data inferred an intense snow avalanche activity along this avalanche path. This study not only calls for more research in the study area but also proves that snow avalanches could constitute an important restrictive factor for the tourism infrastructure and related activities in the area. It must be taken into consideration by the future extension of tourism infrastructure. Keywords: snow avalanche, Parâng Mountains, dendrogeomorphology, ski area.

Improving the Terrain-Based Parameter for the Assessment of Snow Redistribution in the Col du Lac Blanc Area and Comparisons with TLS Snow Depth Data

Science.gov (United States)

Schön, Peter; Prokop, Alexander; Naaim-Bouvet, Florence; Nishimura, Kouichi; Vionnet, Vincent; Guyomarc'h, Gilbert

2014-05-01

Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns, but has failed to quantitatively describe the snow redistribution, and correlations with measured snow heights were poor. The objective of our research was to a) identify the sources of poor correlations between predicted and measured snow re-distribution and b) improve the parameters ability to qualitatively and quantitatively describe snow redistribution in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its constant wind direction and the availability of data from a meteorological station. Our work focused on areas with terrain edges of approximately 10 m height, and we worked with 1-2 m resolution digital terrain and snow surface data. We first compared the results of the terrain-based parameter calculations to measured snow-depths, obtained by high-accuracy terrestrial laser scan measurements. The results were similar to previous studies: The parameter was able to reproduce observed patterns in snow distribution, but regression analyses showed poor correlations between terrain-based parameter and measured snow-depths. We demonstrate how the correlations between measured and calculated snow heights improve if the parameter is calculated based on a snow surface model instead of a digital terrain model. We show how changing the parameter's search distance and how raster re-sampling and raster smoothing improve the results. To improve the parameter

Variability of snow line elevation, snow cover area and depletion in the main Slovak basins in winters 2001–2014

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Krajčí Pavel

2016-03-01

Full Text Available Spatial and temporal variability of snow line (SL elevation, snow cover area (SCA and depletion (SCD in winters 2001–2014 is investigated in ten main Slovak river basins (the Western Carpathians. Daily satellite snow cover maps from MODIS Terra (MOD10A1, V005 and Aqua (MYD10A1, V005 with resolution 500 m are used.

THE SNOW DRIFT POTENTIAL IN THE PLAIN AREA OF BUZĂU COUNTY

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Romulus COSTACHE

2013-12-01

Full Text Available The aim of this study is to analyze and identify the most exposed areas to snowdrifts, in case of a blizzard phenomenon, in the plain area of Buzău County. Blizzard is the most frequently occurring climatic hazard in the study area, in the cold season, that causes the blocking of the roads and the massive snow accumulation in th built-up areas found in the open field or in negative relief forms. In order to identify areas with high potential snowdrifts, the Snow Drifts Potential Index (SDPI was defined, calculated and localized. This was achieved in the GIS by integrating three factors, namely: the use of land, river valleys and the surfaces exposure in the study area. Finally, the results showed that the most exposed areas to the the snow-drift phenomenon are the built areas found in the river beds, and also those on slopes sheltered from contact with the Carpathian. The importance of this study lies in the fact that once established and localized the areas with high potential for snow-drifts, measures can be taken in order to combat the disastrous effects generated by blizzards in the plains of Buzău County.

[Snow cover pollution monitoring in Ufa].

Science.gov (United States)

Daukaev, R A; Suleĭmanov, R A

2008-01-01

The paper presents the results of examining the snow cover polluted with heavy metals in the large industrial town of Ufa. The level of man-caused burden on the snow cover of the conventional parts of the town was estimated and compared upon exposure to a wide range of snow cover pollutants. The priority snow cover pollutants were identified among the test heavy metals.

Aerotechnogenic Monitoring of Urban Environment on Snow Cover Pollution (on the Example of Voronezh City

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Prozhorina Tatyana Ivanovna

2014-09-01

Full Text Available Snow cover is characterized by high sorption ability and represents an informative object in the process of identifying the technogenic pollution of urban environment. The article contains the results of the research on the chemical composition of the snow which fell in Voronezh in the winter period of 2013–2014. The coefficients of chemical elements concentration were calculated to provide objective characteristics of snow cover pollution. The authors analyze the connection between the presence of pollutants in snow and the level of technogenic impact. The obtained ranges of anomaly coefficients among anions reflect the composition of technogenic emissions. The mineralization of snow water reliably characterizes the intensity of anthropogenic impact on the urban environment, and the value of mineralization snow samples ranges from 62,6 (background to 183,9 mg/l. Maximum values of mineralization (more than 150 mg/l are typical for samples taken in transport area. High values of salinity (more than 120 mg/l are observed in snow samples taken in the industrial area, which confirms the high “technogenic pressure” on the urban environment in zones of industrial and transport potential of the city. The investigated functional areas can be arranged in the following series by descending level of contamination: transport area > industrial zone > residential and recreational areas > background territory. The study of the chemical composition of snow cover in the various functional areas of Voronezh allows to conclude that the pH level, mineralization and the content of suspended solids in snow waters characterize the intensity of anthropogenic pressure on the urban environment, and the composition of melt waters indicates the nature of its pollution.

Objective Characterization of Snow Microstructure for Microwave Emission Modeling

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Durand, Michael; Kim, Edward J.; Molotch, Noah P.; Margulis, Steven A.; Courville, Zoe; Malzler, Christian

2012-01-01

Passive microwave (PM) measurements are sensitive to the presence and quantity of snow, a fact that has long been used to monitor snowcover from space. In order to estimate total snow water equivalent (SWE) within PM footprints (on the order of approx 100 sq km), it is prerequisite to understand snow microwave emission at the point scale and how microwave radiation integrates spatially; the former is the topic of this paper. Snow microstructure is one of the fundamental controls on the propagation of microwave radiation through snow. Our goal in this study is to evaluate the prospects for driving the Microwave Emission Model of Layered Snowpacks with objective measurements of snow specific surface area to reproduce measured brightness temperatures when forced with objective measurements of snow specific surface area (S). This eliminates the need to treat the grain size as a free-fit parameter.

Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

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L. Dai

2017-08-01

Full Text Available Snow cover on the Qinghai–Tibetan Plateau (QTP plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow

Drones application on snow and ice surveys in alpine areas

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La Rocca, Leonardo; Bonetti, Luigi; Fioletti, Matteo; Peretti, Giovanni

2015-04-01

First results from Climate change are now clear in Europe, and in Italy in particular, with the natural disasters that damaged irreparably the territory and the habitat due to extreme meteorological events. The Directive 2007/60/EC highlight that an "effective natural hazards prevention and mitigation that requires coordination between Member States above all on natural hazards prevention" is necessary. A climate change adaptation strategy is identified on the basis of the guidelines of the European Community program 2007-2013. Following the directives provided in the financial instrument for civil protection "Union Civil Protection Mechanism" under Decision No. 1313/2013 / EU of the European Parliament and Council, a cross-cutting approach that takes into account a large number of implementation tools of EU policies is proposed as climate change adaptation strategy. In last 7 years a network of trans-Alpine area's authorities was created between Italy and Switzerland to define an adaptive strategy on climate change effects on natural enviroment based on non structural remedies. The Interreg IT - CH STRADA Project (STRategie di ADAttamento al cambiamento climatico) was born to join all the non structural remedies to climate change effects caused by snow and avalanches, on mountain sources, extreme hydrological events and to manage all transnational hydrological resources, involving all stakeholders from Italy and Switzerland. The STRADA project involved all civil protection authorities and all research centers in charge of snow, hydrology end civil protection. The Snow - meteorological center of the Regional Agency for Environment Protection (CNM of ARPA Lombardia) and the Civil Protection of Lombardy Region created a research team to develop tools for avalanche prediction and to observe and predict snow cover on Alpine area. With this aim a lot of aerial photo using Drone as been performed in unusual landscape. Results of all surveys were really interesting on a

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

Science.gov (United States)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

Monitoring of carbon isotope composition of snow cover for Tomsk region

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Akulov, P. A.; Volkov, Y. V.; Kalashnikova, D. A.; Markelova, A. N.; Melkov, V. N.; Simonova, G. V.; Tartakovskiy, V. A.

2016-11-01

This article shows the potential of using δ13C values of pollutants in snow pack to study the human impact on the environment of Tomsk and its surroundings. We believe that it is possible to use a relation between the isotope compositions of a fuel and black carbon for establishing the origin of the latter. The main object of our investigation was dust accumulated by the snow pack in the winter of 2015-2016. The study of dust samples included the following steps: determination of the total carbon content in snow pack samples of Tomsk and its surroundings, extraction of black carbon from the dust, as well as the determination of δ13C values of the total and black carbon accumulated in the snow pack. A snow survey was carried out on the 26th of January and on the 18th of March. The relative carbon content in the dust samples was determined by using an EA Flash 2000 element analyzer. It varied from 3 to 24%. The maximum carbon content was in the dust samples from areas of cottage building with individual heating systems. The δ13C values of the total and black carbon were determined by using a DELTA V Advantage isotope mass spectrometer (TomTsKP SB RAS). The isotope composition of black carbon corresponded to that of the original fuel. This fact allowed identifying the origin of black carbon in some areas of Tomsk.

Subpixel Snow-covered Area Including Differentiated Grain Size from AVIRIS Data Over the Sierra Nevada Mountain Range

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Hill, R.; Calvin, W. M.; Harpold, A. A.

2016-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Previous retrieval of subpixel snow-covered area in alpine regions used multiple snow endmembers due to the sensitivity of snow spectral reflectance to grain size. We will present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction and approximate grain size or melt state. The root mean squared error will provide a spectrum-wide assessment of the mixture model's goodness-of-fit. Analysis will compare snow-covered area and snow-cover depletion in the 2016 year, and annual variation from the 2015 year. Field data were also acquired on three days concurrent with the 2016 flights in the Sagehen Experimental Forest and will support ground validation of the airborne data set.

Combining MOD10A1 and MYD10A1 Images For Snow Cover Area Monitoring

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Tekeli, A. E.

2008-12-01

MOD10A1 and MYD10A1 daily snow cover maps at 500 m resolution are available from MODIS sensors on Terra and Aqua satellites. Aqua obtains the image of same region approximately three hours after Terra over Turkey region. MODIS is an optic sensor and cloud cover degrades the usability of derived snow cover maps. Moreover, spectral similarity between clouds and snow complicates their separability in visible imagery. Fortunately, dynamic behavior of clouds enables their discrimination from snow stationary on the surface. Combined use of MOD10A1 and MYD10A1 images mostly reduces the cloud cover present in one image alone and provides better representation of surface snow cover. Comparison of merged images with in situ data indicated higher hit ratios. The individual comparison of MOD10A1 and MYD10A1 images with ground data each yielded 31% hit ratio whereas, the merged images provided 38%. One-day shifts in comparisons increased hit ratios to 52 % and 46% whereas and two-day shifts gave 77 % and 79 % for MOD10A1 and MYD10A1 respectively. Merged maps yielded 54% and 83% for one and two day shifts. The improvement provided by the merging technique is found to be 7% for the present day, 7 % for one- day and 5% for two-day shifts for the whole season. Monthly decomposition resulted 25% improvement as the maximum. The snow cover product obtained by merging Terra and Aqua satellites provided higher hit ratios, as expected.

[MONITORING OF THE CONTENT OF HEAVY METALS AND ELEMENTS IN THE SNOW COVER IN AGRICULTURAL SOILS AT THE TERRITORY OF THE MOSCOW REGION].

Science.gov (United States)

Ermakov, A A; Karpova, E A; Malysheva, A G; Mikhaylova, R I; Ryzhova, I N

2015-01-01

The monitoring of snow cover pollution by heavy metals and elements (zinc, copper, lead, cadmium, arsenic, nickel, chromium, strontium, manganese, fluorine, lithium) was performed in 20 districts of the Moscow region in 2009, 2012 and 2013. The assessment of the levels of contamination by heavy metals and elements was given by means of comparison of them with the average values in the snow cover near Moscow in the end of the last century and in some areas of the world, that no exposed to technological environmental impact. 7 districts of Moscow region were characterized by a high content of lead and cadmium in the snow water. It requires the control of water, soil and agricultural products pollution.

Snow multivariable data assimilation for hydrological predictions in mountain areas

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Piazzi, Gaia; Campo, Lorenzo; Gabellani, Simone; Rudari, Roberto; Castelli, Fabio; Cremonese, Edoardo; Morra di Cella, Umberto; Stevenin, Hervé; Ratto, Sara Maria

2016-04-01

-based and remotely sensed data of different snow-related variables (snow albedo and surface temperature, Snow Water Equivalent from passive microwave sensors and Snow Cover Area). SMASH performance was evaluated in the period June 2012 - December 2013 at the meteorological station of Torgnon (Tellinod, 2 160 msl), located in Aosta Valley, a mountain region in northwestern Italy. The EnKF algorithm was firstly tested by assimilating several ground-based measurements: snow depth, land surface temperature, snow density and albedo. The assimilation of snow observed data revealed an overall considerable enhancement of model predictions with respect to the open loop experiments. A first attempt to integrate also remote sensed information was performed by assimilating the Land Surface Temperature (LST) from METEOSAT Second Generation (MSG), leading to good results. The analysis allowed identifying the snow depth and the snowpack surface temperature as the most impacting variables in the assimilation process. In order to pinpoint an optimal number of ensemble instances, SMASH performances were also quantitatively evaluated by varying the instances amount. Furthermore, the impact of the data assimilation frequency was analyzed by varying the assimilation time step (3h, 6h, 12h, 24h).

Proposed hybrid-classifier ensemble algorithm to map snow cover area

Science.gov (United States)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

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Chen, H.; Kelly, R. E. J.

2017-12-01

Wet snow is defined as snow with liquid water present in an ice-water mix. It is can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration having implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces and synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions ( 100m). In this study, a semi-automated workflow is developed from the change detection method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Weather station data and visible-infrared satellite observations are used to refine the wet snow area estimates. Wet snow information from National Operational Hydrologic Remote Sensing Center (NOHRSC) is used to compare with the S1A estimates. A time series of wet snow maps shows the variations in backscatter from wet snow on a pixel basis. Different land cover types in Southern Ontario are assessed with respect to their impacts on wet snow estimates. While forests and complex land surfaces can impact the ability to map wet snow, the approach taken is robust and illustrates the strong sensitivity of the approach to wet snow backscattering characteristics. The results indicate the feasibility of the change detection method on non-mountainous large areas and address the usefulness of Sentinel-1A data for wet snow mapping.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

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Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Snow Cover Maps from MODIS Images at 250 m Resolution, Part 2: Validation

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Marc Zebisch

2013-03-01

Full Text Available The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS satellite images at 250 m resolution is validated using snow cover maps (SCA based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA MODIS snow products (MOD10 and MYD10. It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition.

Snow cover volumes dynamic monitoring during melting season using high topographic accuracy approach for a Lebanese high plateau witness sinkhole

Science.gov (United States)

Abou Chakra, Charbel; Somma, Janine; Elali, Taha; Drapeau, Laurent

2017-04-01

Climate change and its negative impact on water resource is well described. For countries like Lebanon, undergoing major population's rise and already decreasing precipitations issues, effective water resources management is crucial. Their continuous and systematic monitoring overs long period of time is therefore an important activity to investigate drought risk scenarios for the Lebanese territory. Snow cover on Lebanese mountains is the most important water resources reserve. Consequently, systematic observation of snow cover dynamic plays a major role in order to support hydrologic research with accurate data on snow cover volumes over the melting season. For the last 20 years few studies have been conducted for Lebanese snow cover. They were focusing on estimating the snow cover surface using remote sensing and terrestrial measurement without obtaining accurate maps for the sampled locations. Indeed, estimations of both snow cover area and volumes are difficult due to snow accumulation very high variability and Lebanese mountains chains slopes topographic heterogeneity. Therefore, the snow cover relief measurement in its three-dimensional aspect and its Digital Elevation Model computation is essential to estimate snow cover volume. Despite the need to cover the all lebanese territory, we favored experimental terrestrial topographic site approaches due to high resolution satellite imagery cost, its limited accessibility and its acquisition restrictions. It is also most challenging to modelise snow cover at national scale. We therefore, selected a representative witness sinkhole located at Ouyoun el Siman to undertake systematic and continuous observations based on topographic approach using a total station. After four years of continuous observations, we acknowledged the relation between snow melt rate, date of total melting and neighboring springs discharges. Consequently, we are able to forecast, early in the season, dates of total snowmelt and springs low

Seasonal comparison of moss bag technique against vertical snow samples for monitoring atmospheric pollution.

Science.gov (United States)

Salo, Hanna; Berisha, Anna-Kaisa; Mäkinen, Joni

2016-03-01

This is the first study seasonally applying Sphagnum papillosum moss bags and vertical snow samples for monitoring atmospheric pollution. Moss bags, exposed in January, were collected together with snow samples by early March 2012 near the Harjavalta Industrial Park in southwest Finland. Magnetic, chemical, scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), K-means clustering, and Tomlinson pollution load index (PLI) data showed parallel spatial trends of pollution dispersal for both materials. Results strengthen previous findings that concentrate and slag handling activities were important (dust) emission sources while the impact from Cu-Ni smelter's pipe remained secondary at closer distances. Statistically significant correlations existed between the variables of snow and moss bags. As a summary, both methods work well for sampling and are efficient pollutant accumulators. Moss bags can be used also in winter conditions and they provide more homogeneous and better controlled sampling method than snow samples. Copyright © 2015. Published by Elsevier B.V.

[Evaluation of pollution of an urban area by level of heavy metals in snow cover].

Science.gov (United States)

Stepanova, N V; Khamitova, R Ia; Petrova, R S

2003-01-01

The goal of this study was to systematize various methodological approaches to evaluating the contamination of the snow cover with heavy metals (HM) by using Kazan, an industrial city with diversified industry, as an example. The findings suggest that it is necessary to characterize the contamination of the snow cover by the actual entrance of an element per area unit of the snow cover for a definite period of time rather than by the concentration of TM in the volume unit of snow water (mg/l), which minimizes the uncertainties with spatial and temporary snow cover variations. The index of the maximum allowable entrance, which is of practical value, may be used to objectively calibrate the pollution of the snow cover, by estimating the amount of a coming element and its toxicity.

A Distributed Snow Evolution Modeling System (SnowModel)

Science.gov (United States)

Liston, G. E.; Elder, K.

2004-12-01

A spatially distributed snow-evolution modeling system (SnowModel) has been specifically designed to be applicable over a wide range of snow landscapes, climates, and conditions. To reach this goal, SnowModel is composed of four sub-models: MicroMet defines the meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowMass simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. While other distributed snow models exist, SnowModel is unique in that it includes a well-tested blowing-snow sub-model (SnowTran-3D) for application in windy arctic, alpine, and prairie environments where snowdrifts are common. These environments comprise 68% of the seasonally snow-covered Northern Hemisphere land surface. SnowModel also accounts for snow processes occurring in forested environments (e.g., canopy interception related processes). SnowModel is designed to simulate snow-related physical processes occurring at spatial scales of 5-m and greater, and temporal scales of 1-hour and greater. These include: accumulation from precipitation; wind redistribution and sublimation; loading, unloading, and sublimation within forest canopies; snow-density evolution; and snowpack ripening and melt. To enhance its wide applicability, SnowModel includes the physical calculations required to simulate snow evolution within each of the global snow classes defined by Sturm et al. (1995), e.g., tundra, taiga, alpine, prairie, maritime, and ephemeral snow covers. The three, 25-km by 25-km, Cold Land Processes Experiment (CLPX) mesoscale study areas (MSAs: Fraser, North Park, and Rabbit Ears) are used as SnowModel simulation examples to highlight model strengths, weaknesses, and features in forested, semi-forested, alpine, and shrubland environments.

On the status of Snow Leopard Panthera uncial (Schreber, 1775 in Annapurna, Nepal

Directory of Open Access Journals (Sweden)

S.B. Ale

2014-03-01

Full Text Available We conducted a status-survey on Snow Leopard Panthera uncia and its main prey, the Blue Sheep Pseudois nayaur, in the Mustang District of Nepal’s Annapurna Conservation Area, in 2010 and 2011. Sign transects, covering a total linear distance of 19.4km, revealed an average density of 5.8 signs per kilometer, which compares with those from other Snow Leopard range countries. This also roughly corresponded with the minimum number of three adult Snow Leopards we obtained from nine remote cameras, deployed to monitor areas of c. 75km2 in extent. We obtained 42 pictures of Snow Leopards during nine capture events. We conclude that Mustang harbors at least three adult Snow Leopards, and probably more, along with a healthy Blue Sheep population (a total of 528 individuals, along 37.6km of Snow Leopard transect lines. We suggest that people-wildlife conflicts exist but that the local people tolerate Snow Leopards based on their Buddhist socio-religious values.

Monitoring Forsmark. Snow depth, snow water content and ice cover during the winter 2010/2011

Energy Technology Data Exchange (ETDEWEB)

Wass, Eva (Geosigma AB (Sweden))

2011-07-15

Snow depth and ice cover have been measured and observed during the winter 2010/2011. This type of measurements started in the winter 2002/2003 and has been ongoing since then. In addition to these parameters, the water content of the snow was calculated at each measurement occasion from the weight of a snow sample. Measurements and observations were conducted on a regular basis from the beginning of November 2010 until the middle of April 2011. A persistent snow cover was established in the end of November 2010 and remained until the beginning of April 2011 at the station with longest snow cover duration. The period of ice cover was 160 days in Lake Eckarfjaerden, whereas the sea bay at SFR was ice covered for 135 days

Monitoring Forsmark. Snow depth, snow water content and ice cover during the winter 2010/2011

International Nuclear Information System (INIS)

Wass, Eva

2011-07-01

Snow depth and ice cover have been measured and observed during the winter 2010/2011. This type of measurements started in the winter 2002/2003 and has been ongoing since then. In addition to these parameters, the water content of the snow was calculated at each measurement occasion from the weight of a snow sample. Measurements and observations were conducted on a regular basis from the beginning of November 2010 until the middle of April 2011. A persistent snow cover was established in the end of November 2010 and remained until the beginning of April 2011 at the station with longest snow cover duration. The period of ice cover was 160 days in Lake Eckarfjaerden, whereas the sea bay at SFR was ice covered for 135 days

A snow cover climatology for the Pyrenees from MODIS snow products

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Gascoin, S.; Hagolle, O.; Huc, M.; Jarlan, L.; Dejoux, J.-F.; Szczypta, C.; Marti, R.; Sanchez, R.

2015-05-01

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the

SWANN: The Snow Water Artificial Neural Network Modelling System

Science.gov (United States)

Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

2017-12-01

Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

Monitoring of the Liquid Water Content During Snowmelt Using C-Band SAR Data and the Snow Model CROCUS

Science.gov (United States)

Rondeau-Genesse, G.; Trudel, M.; Leconte, R.

2014-12-01

Coupling C-Band synthetic aperture radar (SAR) data to a multilayer snow model is a step in better understanding the temporal evolution of the radar backscattering coefficient during snowmelt. The watershed used for this study is the Nechako River Basin, located in the Rocky Mountains of British-Columbia (Canada). This basin has a snowpack of several meters in depth and part of its water is diverted to the Kemano hydropower system, managed by Rio-Tinto Alcan. Eighteen RADARSAT-2 ScanSAR Wide archive images were acquired in VV/VH polarization for the winter of 2011-2012, under different snow conditions. They are interpreted along with CROCUS, a multilayer physically-based snow model developed by Météo-France. This model discretizes the snowpack into 50 layers, which makes it possible to monitor various characteristics, such as liquid water content (LWC), throughout the season. CROCUS is used to model three specific locations of the Nechako River Basin. Results vary from one site to another, but in general there is a good agreement between the modeled LWC of the first layer of the snowpack and the backscattering coefficient of the RADARSAT-2 images, with a coefficient of determination (R²) of 0.80 and more. The radar images themselves were processed using an updated version of Nagler's methodology, which consists of subtracting an image in wet snow conditions to one in dry snow conditions, as wet snow can then be identified using a soft threshold centered around -3 dB. A second filter was used in order to differentiate dry snow and bare soil. That filter combines a VH/VV ratio threshold and an altitude criterion. The ensuing maps show a good agreement with the MODIS snow-covered area, which is already obtained daily over the Nechako River Basin, but with additional information on the location of wet snow and without sensibility to cloud cover. As a next step, the outputs of CROCUS will be used in Mätzler's Microwave Emission Model of Layered Snowpacks (MEMLS) to

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

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K. Aalstad

2018-01-01

Full Text Available With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD at the 1 km scale by assimilating fractional snow-covered area (fSCA satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation ensemble smoother with multiple data assimilation (ES-MDA scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e. and 0.13, respectively. The ES-MDA either

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Science.gov (United States)

Aalstad, Kristoffer; Westermann, Sebastian; Vikhamar Schuler, Thomas; Boike, Julia; Bertino, Laurent

2018-01-01

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least

Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments

Directory of Open Access Journals (Sweden)

David J. Selkowitz

2014-12-01

Full Text Available Remote sensing of snow-covered area (SCA can be binary (indicating the presence/absence of snow cover at each pixel or fractional (indicating the fraction of each pixel covered by snow. Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days

Using crowdsourced web content for informing water systems operations in snow-dominated catchments

Science.gov (United States)

Giuliani, Matteo; Castelletti, Andrea; Fedorov, Roman; Fraternali, Piero

2016-12-01

Snow is a key component of the hydrologic cycle in many regions of the world. Despite recent advances in environmental monitoring that are making a wide range of data available, continuous snow monitoring systems that can collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high-latitude or mountainous regions. The unprecedented availability of user-generated data on the web is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatiotemporally dense. In this paper, we contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow-covered area. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available. The operational value of the obtained virtual snow indexes is assessed for a real-world water-management problem, the regulation of Lake Como, where we use these indexes for informing the daily operations of the lake. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

C-Band SAR Imagery for Snow-Cover Monitoring at Treeline, Churchill, Manitoba, Canada

Directory of Open Access Journals (Sweden)

Frédérique C. Pivot

2012-07-01

Full Text Available RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest–tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5–10 mm of accumulated water equivalent are observed at 20–31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen when not attenuated by vegetation (e.g., forested and transition. With high-frequency ground-penetrating radar, we demonstrate the presence of a 10–20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (−15 dB and less observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects.

Using hacked point and shoot cameras for time-lapse snow cover monitoring in an Alpine valley

Science.gov (United States)

Weijs, S. V.; Diebold, M.; Mutzner, R.; Golay, J. R.; Parlange, M. B.

2012-04-01

In Alpine environments, monitoring snow cover is essential get insight in the hydrological processes and water balance. Although measurement techniques based on LIDAR are available, their cost is often a restricting factor. In this research, an experiment was done using a distributed array of cheap consumer cameras to get insight in the spatio-temporal evolution of snowpack. Two experiments are planned. The first involves the measurement of eolic snow transport around a hill, to validate a snow saltation model. The second monitors the snowmelt during the melting season, which can then be combined with data from a wireless network of meteorological stations and discharge measurements at the outlet of the catchment. The poster describes the hardware and software setup, based on an external timer circuit and CHDK, the Canon Hack Development Kit. This latter is a flexible and developing software package, released under a GPL license. It was developed by hackers that reverse engineered the firmware of the camera and added extra functionality such as raw image output, more full control of the camera, external trigger and motion detection, and scripting. These features make it a great tool for geosciences. Possible other applications involve aerial stereo photography, monitoring vegetation response. We are interested in sharing experiences and brainstorming about new applications. Bring your camera!

Crossing physical simulations of snow conditions and a geographic model of ski area to assess ski resorts vulnerability

Science.gov (United States)

François, Hugues; Spandre, Pierre; Morin, Samuel; George-Marcelpoil, Emmanuelle; Lafaysse, Matthieu; Lejeune, Yves

2016-04-01

In order to face climate change, meteorological variability and the recurrent lack of natural snow on the ground, ski resorts adaptation often rely on technical responses. Indeed, since the occurrence of episodes with insufficient snowfalls in the early 1990's, snowmaking has become an ordinary practice of snow management, comparable to grooming, and contributes to optimise the operation of ski resorts. It also participates to the growth of investments and is associated with significant operating costs, and thus represents a new source of vulnerability. The assessment of the actual effects of snowmaking and of snow management practices in general is a real concern for the future of the ski industry. The principal model use to simulate snow conditions in resorts, Ski Sim, has also been moving this way. Its developers introduced an artificial input of snow on ski area to complete natural snowfalls and considered different organisations of ski lifts (lower and upper zones). However the use of a degree-day model prevents them to consider the specific properties of artificial snow and the impact of grooming on the snowpack. A first proof of concept in the French Alps has shown the feasibility and the interest to cross the geographic model of ski areas and the output of the physically-based reanalysis of snow conditions SAFRAN - Crocus (François et al., CRST 2014). Since these initial developments, several ways have been explored to refine our model. A new model of ski areas has been developed. Our representation is now based on gravity derived from a DEM and ski lift localisation. A survey about snow management practices also allowed us to define criteria in order to model snowmaking areas given ski areas properties and tourism infrastructures localisation. We also suggest to revisit the assessment of ski resort viability based on the "one hundred days rule" based on natural snow depth only. Indeed, the impact of snow management must be considered so as to propose

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

Science.gov (United States)

Zaitchik, Benjamin F.; Rodell, Matthew

2008-01-01

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, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order 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 both during the snow season and, in some regions, on into the following spring.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

Science.gov (United States)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how

Modelling snow accumulation and snow melt in a continuous hydrological model for real-time flood forecasting

International Nuclear Information System (INIS)

Stanzel, Ph; Haberl, U; Nachtnebel, H P

2008-01-01

Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.

Modelling snow accumulation and snow melt in a continuous hydrological model for real-time flood forecasting

Energy Technology Data Exchange (ETDEWEB)

Stanzel, Ph; Haberl, U; Nachtnebel, H P [Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Applied Life Sciences, Muthgasse 18, 1190 Vienna (Austria)], E-mail: philipp.stanzel@boku.ac.at

2008-11-01

Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.

Robot Towed Shortwave Infrared Camera for Specific Surface Area Retrieval of Surface Snow

Science.gov (United States)

Elliott, J.; Lines, A.; Ray, L.; Albert, M. R.

2017-12-01

Optical grain size and specific surface area are key parameters for measuring the atmospheric interactions of snow, as well as tracking metamorphosis and allowing for the ground truthing of remote sensing data. We describe a device using a shortwave infrared camera with changeable optical bandpass filters (centered at 1300 nm and 1550 nm) that can be used to quickly measure the average SSA over an area of 0.25 m^2. The device and method are compared with calculations made from measurements taken with a field spectral radiometer. The instrument is designed to be towed by a small autonomous ground vehicle, and therefore rides above the snow surface on ultra high molecular weight polyethylene (UHMW) skis.

Use of gamma surveys from the aircraft for hydrological forecasts on the area with irregular snow pack

Energy Technology Data Exchange (ETDEWEB)

Vershinina, L K

1979-01-01

Gamma snow surveys from the aircraft based on the measurements of the attenuation of gamma-radiation of soils by the snow pack are discussed. Radiation rate depends on the amount of water on the soil surface and in the top layer 30 to 40 cm deep. Therefore, if measurements are made twice (without snow and with snow pack available) water equivalent of snow cover may be determined only when soil moisture content changes do not occur during the period between the dates of gamma surveys. In the areas with frequent winter thaws, standard land snow surveys do not provide snow storage evaluation with the accuracy sufficient for spring flow prediction. It is shown that when gamma-radiation of absolutely dry soils determined at the laboratory is known as well as of naturally moistened soils during the periods of gamma surveys of the snow pack from the aircraft, and when data is available on soil moisture content obtained from the measurements at the base land network, then a reliable estimation of snow storage on the watershed surfaces in the regions with irregular snow cover is quite possible. This ensures a significant accuracy increase of spring snow melt flood forecasting, particularly concerning winters with little snow.

Use of gamma surveys from the airraft for hydrological forecasts on the area with irregular snow pack

Energy Technology Data Exchange (ETDEWEB)

Vershinina, L K [State Hydrological Institute, Leningrad (USSR)

1979-01-01

Gamma snow surveys from aircraft based on the measurement of the attenuation of gamma-radiation of soils by the snow pack are discussed. Radiation rate depends on the amount of water on the soil surface and in the top layer 30 to 40 cm deep. Therefore, if measurements are made twice (without snow and with snow pack available) the water equivalent of the snow cover may be determined only in cases when soil moisture content changes do not occur during the period between the dates of gamma surveys. In areas with frequent winter thaws standard land snow surveys do not provide snow storage evaluation with accuracy sufficient for spring flow prediction. It is shown that when gamma-radiation of absolutely dry soils determined at the laboratory is known, as well as of naturally moistened soils during the periods of gamma surveys of the snow pack from aircraft, and when data is available on soil moisture content obtained from measurements at the base land network, then a reliable estimation of snow storage on the watershed surfaces in regions with irregular snow cover is quite possible. This ensures a significant accuracy increase in spring snow melt flood forecasting, in particular during winters with little snow.

Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

Directory of Open Access Journals (Sweden)

S. Naha

2016-06-01

Full Text Available The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006. Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I and Ensemble Kalman Filter (EnKF that uses observations of snow covered area (SCA to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU, rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

Science.gov (United States)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

2017-06-01

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

Application of radar polarimetry techniques for retrieval snow and rain characteristics in remote sensing

Directory of Open Access Journals (Sweden)

M. Darvishi

2013-09-01

Full Text Available The presence of snow cover has significant impacts on the both global and regional climate and water balance on earth. The accurate estimation of snow cover area can be used for forecasting runoff due to snow melt and output of hydroelectric power. With development of remote sensing techniques at different scopes in earth science, enormous algorithms for retrieval hydrometeor parameters have been developed. Some of these algorithms are used to provide snow cover map such as NLR with AVHRR/MODIS sensor for Norway, Finnish with AVHRR sensor for Finland and NASA with MODIS sensor for global maps. Monitoring snow cover at different parts of spectral electromagnetic is detectable (visible, near and thermal infrared, passive and active microwave. Recently, specific capabilities of active microwave remote sensing such as snow extent map, snow depth, snow water equivalent (SWE, snow state (wet/dry and discrimination between rain and snow region were given a strong impetus for using this technology in snow monitoring, hydrology, climatology, avalanche research and etc. This paper evaluates the potentials and feasibility of polarimetric ground microwave measurements of snow in active remote sensing field. We will consider the behavior co- and cross-polarized backscattering coefficients of snowpack response with polarimetric scatterometer in Ku and L band at the different incident angles. Then we will show how to retrieve snow cover depth, snow permittivity and density parameters at the local scale with ground-based SAR (GB-SAR. Finally, for the sake of remarkable significant the transition region between rain and snow; the variables role of horizontal reflectivity (ZHH and differential reflectivity (ZDR in delineation boundary between snow and rain and some others important variables at polarimetric weather radar are presented.

Deriving Snow-Cover Depletion Curves for Different Spatial Scales from Remote Sensing and Snow Telemetry Data

Science.gov (United States)

Fassnacht, Steven R.; Sexstone, Graham A.; Kashipazha, Amir H.; Lopez-Moreno, Juan Ignacio; Jasinski, Michael F.; Kampf, Stephanie K.; Von Thaden, Benjamin C.

2015-01-01

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectro radiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80,000 sq km domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A stations peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency.

Noninvasive genetic population survey of snow leopards (Panthera uncia in Kangchenjunga conservation area, Shey Phoksundo National Park and surrounding buffer zones of Nepal

Directory of Open Access Journals (Sweden)

Karmacharya Dibesh B

2011-11-01

Full Text Available Abstract Background The endangered snow leopard is found throughout major mountain ranges of Central Asia, including the remote Himalayas. However, because of their elusive behavior, sparse distribution, and poor access to their habitat, there is a lack of reliable information on their population status and demography, particularly in Nepal. Therefore, we utilized noninvasive genetic techniques to conduct a preliminary snow leopard survey in two protected areas of Nepal. Results A total of 71 putative snow leopard scats were collected and analyzed from two different areas; Shey Phoksundo National Park (SPNP in the west and Kangchanjunga Conservation Area (KCA in the east. Nineteen (27% scats were genetically identified as snow leopards, and 10 (53% of these were successfully genotyped at 6 microsatellite loci. Two samples showed identical genotype profiles indicating a total of 9 individual snow leopards. Four individual snow leopards were identified in SPNP (1 male and 3 females and five (2 males and 3 females in KCA. Conclusions We were able to confirm the occurrence of snow leopards in both study areas and determine the minimum number present. This information can be used to design more in-depth population surveys that will enable estimation of snow leopard population abundance at these sites.

Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA

Science.gov (United States)

Clow, David W.; Nanus, Leora; Verdin, Kristine L.; Schmidt, Jeffrey

2012-01-01

The National Weather Service's Snow Data Assimilation (SNODAS) program provides daily, gridded estimates of snow depth, snow water equivalent (SWE), and related snow parameters at a 1-km2 resolution for the conterminous USA. In this study, SNODAS snow depth and SWE estimates were compared with independent, ground-based snow survey data in the Colorado Rocky Mountains to assess SNODAS accuracy at the 1-km2 scale. Accuracy also was evaluated at the basin scale by comparing SNODAS model output to snowmelt runoff in 31 headwater basins with US Geological Survey stream gauges. Results from the snow surveys indicated that SNODAS performed well in forested areas, explaining 72% of the variance in snow depths and 77% of the variance in SWE. However, SNODAS showed poor agreement with measurements in alpine areas, explaining 16% of the variance in snow depth and 30% of the variance in SWE. At the basin scale, snowmelt runoff was moderately correlated (R2 = 0.52) with SNODAS model estimates. A simple method for adjusting SNODAS SWE estimates in alpine areas was developed that uses relations between prevailing wind direction, terrain, and vegetation to account for wind redistribution of snow in alpine terrain. The adjustments substantially improved agreement between measurements and SNODAS estimates, with the R2 of measured SWE values against SNODAS SWE estimates increasing from 0.42 to 0.63 and the root mean square error decreasing from 12 to 6 cm. Results from this study indicate that SNODAS can provide reliable data for input to moderate-scale to large-scale hydrologic models, which are essential for creating accurate runoff forecasts. Refinement of SNODAS SWE estimates for alpine areas to account for wind redistribution of snow could further improve model performance. Published 2011. This article is a US Government work and is in the public domain in the USA.

An automated approach for mapping persistent ice and snow cover over high latitude regions

Science.gov (United States)

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly

Catchment-scale evaluation of pollution potential of urban snow at two residential catchments in southern Finland.

Science.gov (United States)

Sillanpää, Nora; Koivusalo, Harri

2013-01-01

Despite the crucial role of snow in the hydrological cycle in cold climate conditions, monitoring studies of urban snow quality often lack discussions about the relevance of snow in the catchment-scale runoff management. In this study, measurements of snow quality were conducted at two residential catchments in Espoo, Finland, simultaneously with continuous runoff measurements. The results of the snow quality were used to produce catchment-scale estimates of areal snow mass loads (SML). Based on the results, urbanization reduced areal snow water equivalent but increased pollutant accumulation in snow: SMLs in a medium-density residential catchment were two- to four-fold higher in comparison with a low-density residential catchment. The main sources of pollutants were related to vehicular traffic and road maintenance, but also pet excrement increased concentrations to a high level. Ploughed snow can contain 50% of the areal pollutant mass stored in snow despite its small surface area within a catchment.

Large-area snow-like MoSe2 monolayers: synthesis, growth mechanism, and efficient electrocatalyst application.

Science.gov (United States)

Huang, Jingwen; Liu, Huiqiang; Jin, Bo; Liu, Min; Zhang, Qingchun; Luo, Liqiong; Chu, Shijin; Chu, Sheng; Peng, Rufang

2017-07-07

This study explores the large-area synthesis of controllable morphology, uniform, and high-quality monolayer. MoSe 2 is essential for its potential application in optoelectronics, photocatalysis, and renewable energy sources. In this study, we successfully synthesized snow-like MoSe 2 monolayers using a simple chemical vapor deposition method. Results reveal that snow-like MoSe 2 is a single crystal with a hexagonal structure, a thickness of ∼0.9 nm, and a lateral dimension of up to 20 μm. The peak position of the photoluminescence spectra is ∼1.52 eV corresponding to MoSe 2 monolayer. The growth mechanism of the snow-like MoSe 2 monolayer was investigated and comprised a four-step process during growth. Finally, we demonstrate that the snow-like MoSe 2 monolayers are ideal electrocatalysts for hydrogen evolution reactions (HERs), reflected by a low Tafel slope of ∼68 mV/decade. Compared with the triangular-shaped MoSe 2 monolayer, the hexangular snow-like shape with plentiful edges is superior for perfect electrocatalysts for HERs or transmission devices of optoelectronic signals.

Spatial considerations of snow chemistry as a non-point contamination source in Alpine watersheds

International Nuclear Information System (INIS)

Elder, K.; Williams, M.; Dozier, J.

1991-01-01

Alpine watersheds act as a temporary storage basin for large volumes of precipitation as snow. Monitoring these basins for the presence and effects of acid precipitation is important because these areas are often weakly buffered and sensitive to acidification. Study of these sensitive areas may provide early detection of trends resulting form anthropogenic atmospheric inputs. In an intensive study of an alpine watershed in the Sierra Nevada in 1987 and 1988, the authors carefully monitored snow distribution and chemistry through space and time. They found that the volume-weighted mean ionic concentrations within the snowpack did not vary greatly over the basin at peak accumulation. However, the distribution of total snow water equivalence (SWE) was highly variable spatially. Coefficients of variation (CV) for SWE lead to a corresponding high spatial variance in the chemical loading of their study basin. Their results show that to obtain accurate estimates of chemical loading they must measure the chemical and physical snow parameters at a resolution proportional to their individual variances. It is therefore necessary to combine many SWE measurements with fewer carefully obtained chemistry measurements. They used a classification method based on physical parameters to partition the basin into similar zones for estimation of SWE distribution. This technique can also be used for sample design

Temporal monitoring of the soil freeze-thaw cycles over snow-cover land by using off-ground GPR

KAUST Repository

Jadoon, Khan; Lambot, Sé bastien; Dimitrov, Marin; Weihermü ller, Lutz

2013-01-01

We performed off-ground ground-penetrating radar (GPR) measurements over a bare agricultural field to monitor the freeze-thaw cycles over snow-cover. The GPR system consisted of a vector network analyzer combined with an off-ground monostatic horn

Application of the MODIS “snow cover” product for identification of the snow cover pattern in Gis-Baikal region

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E. A. Istomina

2014-01-01

Full Text Available Validation of remote sensing data MODIS «snow cover» in the period from September to May 2000/01, 2007/08, 2008/09 is realized on the base of weather stations data. Good repeatability of weather stations data and snow cover data is shown (more than 80% when snow depth is exceeds 2 cm. The minimum accuracy is in May and October for the variety of snowfall winters. Remote sensing data give possibility to extend the dot information of hydrometeorological stations network on the spatial snow distribution to the mountainous area of Predbajkalje where ground-based observations are absent. According to remote sensing earlier appearance and later melting of snow in mountain areas were identified. The plains and basins areas are characterized by later appearance and earlier melting of snow.

Effects of Planting of Calluna Vulgaris for Stable Snow Accumulation in Winter

Science.gov (United States)

Ibuki, R.; Harada, K.

2017-12-01

Recent year climate of the winter season is changing and the period of snow accumulation is reduced compared with before. It affects the management of the ski resort. Snowfall had occurred in December 2016, but the snow accumulated after January 2017 at the ski resort located in the Pacific Ocean side of the Northeast region of Japan. This situation is thought to be originated from two reasons, one is snow thawing, another is to be blown away by the strong monsoon wind. We are considering utilizing planting to stabilize snow accumulation. Currently building rock gardens with shrubs, mainly Calluna Vulgaris in the ski resort for attracting customers in the summer. These are difficult to raise in the lowlands of Japan because they are too hot, but because of their good growth in relatively low-temperature highlands, it is rare for local residents to appreciate the value of these. In addition, it is excellent in low temperature resistance, and it will not die even under the snow. We investigated the pressure resistance performance due to snowfall and the appropriateness of growth under the weather conditions of the area. Regarding Calluna Vulgaris, Firefly, the plants were not damaged even under snow more than 1 m. In addition, three years have passed since planting, relatively good growth is shown, and the stock has been growing every year. Based on these results, we plan to stabilize the snow accumulation by carrying out planting of Calluna vulgaris inside the slope. The growth of the Calluna species is gentle and the tree height grows only about 50 cm even if 15 years have passed since planting. Therefore, it is considered that the plant body is hard to put out their head on the snow surface during the ski season. Next season will monitor the snow accumulation around the planting area through the snow season.

Effects of dirty snow in nuclear winter simulations

International Nuclear Information System (INIS)

Vogelmann, A.M.; Robock, A.; Ellingson, R.G.

1988-01-01

A large-scale nuclear war would inject smoke into the atmosphere from burning forests, cities, and industries in targeted areas. This smoke could fall out onto snow and ice and would lower cryospheric albedos by as much as 50%. A global energy balance climate model is used to investigate the maximum effect these ''dirty snow'' albedos have on the surface temperature in nuclear winter simulations which span several years. These effects are investigated for different nuclear winter scenarios, snow precipitation rates, latitudinal distributions of smoke, and seasonal timings. We find that dirty snow, in general, would have a small temperature effect at mid- and low latitudes but could have a large temperature effect at polar latitudes, particularly if the soot is able to reappear significantly in later summers. Factors which limit the climatic importance of the dirty snow are (1) the dirty snow albedo is lowest when the atmosphere still contains a large amount of light-absorbing smoke; (2) even with dirty snow, sea ice areas can still increase, which helps maintain colder temperatures through the sea ice thermal inertial feedback; (3) the snow and ice areas affected by the dirty snow albedos are largest when there is little seasonal solar insolation; and (4) the area affected by the dirty snow is relatively small under all circumstances. copyright American Geophysical Union 1988

Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia

Science.gov (United States)

Zhang, Yinsheng; Ma, Ning

2018-04-01

Changes in the extent and amount of snow cover in Eurasia are of great interest because of their vital impacts on the global climate system and regional water resource management. This study investigated the spatial and temporal variability of the snow cover extent (SCE) and snow water equivalent (SWE) of the continental Eurasia using the Northern Hemisphere Equal-Area Scalable Earth Grid (EASE-Grid) Weekly SCE data for 1972-2006 and the Global Monthly EASE-Grid SWE data for 1979-2004. The results indicated that, in general, the spatial extent of snow cover significantly decreased during spring and summer, but varied little during autumn and winter over Eurasia in the study period. The date at which snow cover began to disappear in spring has significantly advanced, whereas the timing of snow cover onset in autumn did not vary significantly during 1972-2006. The snow cover persistence period declined significantly in the western Tibetan Plateau as well as partial area of Central Asia and northwestern Russia, but varied little in other parts of Eurasia. "Snow-free breaks" (SFBs) with intermittent snow cover in the cold season were principally observed in the Tibetan Plateau and Central Asia, causing a low sensitivity of snow cover persistence period to the timings of snow cover onset and disappearance over the areas with shallow snow. The averaged SFBs were 1-14 weeks during the study period and the maximum intermittence could even reach 25 weeks in certain years. At a seasonal scale, SWE usually peaked in February or March, but fell gradually since April across Eurasia. Both annual mean and annual maximum SWE decreased significantly during 1979-2004 in most parts of Eurasia except for eastern Siberia as well as northwestern and northeastern China. The possible cross-platform inconsistencies between two passive microwave radiometers may cause uncertainties in the detected trends of SWE here, suggesting an urgent need of producing a long-term, more homogeneous SWE

Investigation of snow cover dust pollution by contact and satellite observations

Science.gov (United States)

Raputa, Vladimir F.; Yaroslavtseva, Tatyana V.

2015-11-01

The problems of reconstructing the snow cover pollution fields from dusting, point, linear and area sources according to ground and satellite observations are considered. Using reconstruction models, the methods of the combined analysis of the characteristic images of snow cover pollution haloes in the vicinity of sources of dust and contact data observations have been developed. On the basis of the numerical data analysis of ground monitoring and satellite imagery, the stable quantitative regularities between the fields of dust fallouts and the intensity of a change of tones of gray in the radial directions relative to the main sources are identified.

Eco-geochemical peculiarities of mercury content in solid residue of snow in the industrial enterprises impacted areas of Tomsk

Science.gov (United States)

Filimonenko, E. A.; Lyapina, E. E.; Talovskaya, A. V.; Parygina, I. A.

2014-11-01

Snow, as short-term consignation Wednesday, has several properties that lead to its widespread use in ecologicalgeochemical and geological research. By studying the chemical composition of the dust fallout you can indirectly assess the condition of atmospheric air.1-2. Determining the content of mercury in snow cover, you can define its contribution for the longest period of the year in our region, with the most intensive use of various types of fuel (coal, gas, firewood), that puts a strain on urban ecosystems in terms of ecology.3-4. In addition, snow cleans the atmosphere of mercury, but it accumulates in the snow, and during the spring melting of snow hits the ground and rivers, polluting them. Part of the mercury back into the atmosphere. It should also be note the special nature of the circulation of air masses over the city in winter, creating a heat CAP, which contributes to air pollution of the city. 5-6-7. The high load areas of industrial impact were detected during the eco-geochemical investigations of mercury load index in the impacted areas of enterprises of Tomsk. It was found out, that aerosol particles of industrial emissions in Tomsk contain mercury. The contamination transfer character of mercury sources and occurrence modes of pollutants in snow solid residue were detected during the researches of industrial impact.

Study of critical free-area ratio during the snow-melting process on pavement using low-temperature heating fluids

Energy Technology Data Exchange (ETDEWEB)

Wang, Huajun [School of Energy and Environment Engineering, Hebei University of Technology, Tianjin 300401 (China); Chen, Zhihao [Faculty of Engineering, Yokohama National University, Hodogaya, Yokohama 240-8501 (Japan)

2009-01-15

Critical free-area ratio (CFR) is an interesting phenomenon during the snow-melting process on pavement using low-temperature heating fluids such as geothermal tail water and industrial waste water. This paper is performed to further investigate the mechanism of CFR and its influencing factors. A simplified theoretical model is presented to describe the heat and mass transfer process on pavement. Especially the variation of thermal properties and the capillary effect of snow layer are considered. Numerical computation shows that the above theoretical model is effective for the prediction of CFR during the snow-melting process. Furthermore, the mechanism of CFR is clarified in detail. CFR is independent of the layout of hydronic pipes, the fluid temperature, the idling time, and weather conditions. It is both the non-uniform temperature distribution and complicated porous structure of snow layer that lead to the occurrence of CFR. Besides, the influences of operation parameters including the fluid temperature, the idling time, the pipe spacing and buried depths on snow melting are analyzed, which are helpful for the next optimal design of snow-melting system. (author)

Study of critical free-area ratio during the snow-melting process on pavement using low-temperature heating fluids

Energy Technology Data Exchange (ETDEWEB)

Wang Huajun [School of Energy and Environment Engineering, Hebei University of Technology, Tianjin 300401 (China)], E-mail: huajunwang@126.com; Chen Zhihao [Faculty of Engineering, Yokohama National University, Hodogaya, Yokohama 240-8501 (Japan)

2009-01-15

Critical free-area ratio (CFR) is an interesting phenomenon during the snow-melting process on pavement using low-temperature heating fluids such as geothermal tail water and industrial waste water. This paper is performed to further investigate the mechanism of CFR and its influencing factors. A simplified theoretical model is presented to describe the heat and mass transfer process on pavement. Especially the variation of thermal properties and the capillary effect of snow layer are considered. Numerical computation shows that the above theoretical model is effective for the prediction of CFR during the snow-melting process. Furthermore, the mechanism of CFR is clarified in detail. CFR is independent of the layout of hydronic pipes, the fluid temperature, the idling time, and weather conditions. It is both the non-uniform temperature distribution and complicated porous structure of snow layer that lead to the occurrence of CFR. Besides, the influences of operation parameters including the fluid temperature, the idling time, the pipe spacing and buried depths on snow melting are analyzed, which are helpful for the next optimal design of snow-melting system.

Study of critical free-area ratio during the snow-melting process on pavement using low-temperature heating fluids

International Nuclear Information System (INIS)

Wang Huajun; Chen Zhihao

2009-01-01

Critical free-area ratio (CFR) is an interesting phenomenon during the snow-melting process on pavement using low-temperature heating fluids such as geothermal tail water and industrial waste water. This paper is performed to further investigate the mechanism of CFR and its influencing factors. A simplified theoretical model is presented to describe the heat and mass transfer process on pavement. Especially the variation of thermal properties and the capillary effect of snow layer are considered. Numerical computation shows that the above theoretical model is effective for the prediction of CFR during the snow-melting process. Furthermore, the mechanism of CFR is clarified in detail. CFR is independent of the layout of hydronic pipes, the fluid temperature, the idling time, and weather conditions. It is both the non-uniform temperature distribution and complicated porous structure of snow layer that lead to the occurrence of CFR. Besides, the influences of operation parameters including the fluid temperature, the idling time, the pipe spacing and buried depths on snow melting are analyzed, which are helpful for the next optimal design of snow-melting system

[Hygienic evaluation of the total mutagenic activity of snow samples from Magnitogorsk].

Science.gov (United States)

Legostaeva, T B; Ingel', F I; Antipanova, N A; Iurchenko, V V; Iuretseva, N A; Kotliar, N N

2010-01-01

The paper gives the results of 4-year monitoring of the total mutagenic activity of snow samples from different Magnitogork areas in a test for induction of dominant lethal mutations (DLM) in the gametes of Drosophila melanogaster. An association was first found between the rate of DLM and the content of some chemical compounds in the ambient air and snow samples; moreover all the substances present in the samples, which had found genotoxic effects, showed a positive correlation with the rate of DLM. Furthermore, direct correlations were first established between the rate of DLM and the air pollution index and morbidity rates in 5-7-year-old children residing in the areas under study. The findings allow the test for induction of dominant lethal mutations (DLM) in the gametes of Drosophila melanogaster to be recommended due to its unique informative and prognostic value for monitoring ambient air pollution and for extensive use in the risk assessment system.

Storing snow for the next winter: Two case studies on the application of snow farming.

Science.gov (United States)

Grünewald, Thomas; Wolfsperger, Fabian

2016-04-01

Snow farming is the conservation of snow during the warm half-year. This means that large piles of snow are formed in spring in order to be conserved over the summer season. Well-insulating materials such as chipped wood are added as surface cover to reduce melting. The aim of snow farming is to provide a "snow guaranty" for autumn or early winter - this means that a specific amount of snow will definitively be available, independent of the weather conditions. The conserved snow can then be used as basis for the preparation of winter sports grounds such as cross-country tracks or ski runs. This helps in the organization of early winter season sport events such as World Cup races or to provide appropriate training conditions for athletes. We present a study on two snow farming projects, one in Davos (Switzerland) and one in the Martell valley of South Tyrol. At both places snow farming has been used for several years. For the summer season 2015, we monitored both snow piles in order to assess the amount of snow conserved. High resolution terrestrial laser scanning was performed to measure snow volumes of the piles at the beginning and at the end of the summer period. Results showed that only 20% to 30 % of the snow mass was lost due to ablation. This mass loss was surprisingly low considering the extremely warm and dry summer. In order to identify the most relevant drivers of snow melt we also present simulations with the sophisticated snow cover models SNOWPACK and Alpine3D. The simulations are driven by meteorological input data recorded in the vicinity of the piles and enable a detailed analysis of the relevant processes controlling the energy balance. The models can be applied to optimize settings for snow farming and to examine the suitability of new locations, configurations or cover material for future snow farming projects.

Spatially explicit modeling of conflict zones between wildlife and snow sports: prioritizing areas for winter refuges.

Science.gov (United States)

Braunisch, Veronika; Patthey, Patrick; Arlettaz, Raphaël

2011-04-01

Outdoor winter recreation exerts an increasing pressure upon mountain ecosystems, with unpredictable, free-ranging activities (e.g., ski mountaineering, snowboarding, and snowshoeing) representing a major source of stress for wildlife. Mitigating anthropogenic disturbance requires the spatially explicit prediction of the interference between the activities of humans and wildlife. We applied spatial modeling to localize conflict zones between wintering Black Grouse (Tetrao tetrix), a declining species of Alpine timberline ecosystems, and two free-ranging winter sports (off-piste skiing [including snow-boarding] and snowshoeing). Track data (snow-sports and birds' traces) obtained from aerial photographs taken over a 585-km transect running along the timberline, implemented within a maximum entropy model, were used to predict the occurrence of snow sports and Black Grouse as a function of landscape characteristics. By modeling Black Grouse presence in the theoretical absence of free-ranging activities and ski infrastructure, we first estimated the amount of habitat reduction caused by these two factors. The models were then extrapolated to the altitudinal range occupied by Black Grouse, while the spatial extent and intensity of potential conflict were assessed by calculating the probability of human-wildlife co-occurrence. The two snow-sports showed different distribution patterns. Skiers' occurrence was mainly determined by ski-lift presence and a smooth terrain, while snowshoers' occurrence was linked to hiking or skiing routes and moderate slopes. Wintering Black Grouse avoided ski lifts and areas frequented by free-ranging snow sports. According to the models, Black Grouse have faced a substantial reduction of suitable wintering habitat along the timberline transect: 12% due to ski infrastructure and another 16% when adding free-ranging activities. Extrapolating the models over the whole study area results in an overall habitat loss due to ski infrastructure of

3-D image-based numerical computations of snow permeability: links to specific surface area, density, and microstructural anisotropy

Directory of Open Access Journals (Sweden)

N. Calonne

2012-09-01

Full Text Available We used three-dimensional (3-D images of snow microstructure to carry out numerical estimations of the full tensor of the intrinsic permeability of snow (K. This study was performed on 35 snow samples, spanning a wide range of seasonal snow types. For several snow samples, a significant anisotropy of permeability was detected and is consistent with that observed for the effective thermal conductivity obtained from the same samples. The anisotropy coefficient, defined as the ratio of the vertical over the horizontal components of K, ranges from 0.74 for a sample of decomposing precipitation particles collected in the field to 1.66 for a depth hoar specimen. Because the permeability is related to a characteristic length, we introduced a dimensionless tensor K*=K/r_es², where the equivalent sphere radius of ice grains (r_es is computed from the specific surface area of snow (SSA and the ice density (ρ_i as follows: r_es=3/(SSA×ρ_i. We define K and K* as the average of the diagonal components of K and K*, respectively. The 35 values of K* were fitted to snow density (ρ_s and provide the following regression: K = (3.0 ± 0.3 r_es² exp((−0.0130 ± 0.0003ρ_s. We noted that the anisotropy of permeability does not affect significantly the proposed equation. This regression curve was applied to several independent datasets from the literature and compared to other existing regression curves or analytical models. The results show that it is probably the best currently available simple relationship linking the average value of permeability, K, to snow density and specific surface area.

A GIS Software Toolkit for Monitoring Areal Snow Cover and Producing Daily Hydrologic Forecasts using NASA Satellite Imagery, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Aniuk Consulting, LLC, proposes to create a GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts. This toolkit will be...

Uptake of acetone, ethanol and benzene to snow and ice: effects of surface area and temperature

International Nuclear Information System (INIS)

Abbatt, J P D; Bartels-Rausch, T; Ullerstam, M; Ye, T J

2008-01-01

The interactions of gas-phase acetone, ethanol and benzene with smooth ice films and artificial snow have been studied. In one technique, the snow is packed into a cylindrical column and inserted into a low-pressure flow reactor coupled to a chemical-ionization mass spectrometer for gas-phase analysis. At 214 and 228 K, it is found for acetone and ethanol that the adsorbed amounts per surface area match those for adsorption to thin films of ice formed by freezing liquid water, when the specific surface area of the snow (as determined from Kr adsorption at 77 K) and the geometric surface area of the ice films are used. This indicates that freezing thin films of water leads to surfaces that are smooth at the molecular level. Experiments performed to test the effect of film growth on ethanol uptake indicate that uptake is independent of ice growth rate, up to 2.4 μm min -1 . In addition, traditional Brunauer-Emmett-Teller (BET) experiments were performed with these gases on artificial snow from 238 to 266.5 K. A transition from a BET type I isotherm indicative of monolayer formation to a BET type II isotherm indicative of multilayer uptake is observed for acetone at T≥263 K and ethanol at T≥255 K, arising from solution formation on the ice. When multilayer formation does not occur, as was the case for benzene at T≤263 K and for acetone at T≤255 K, the saturated surface coverage increased with increasing temperature, consistent with the quasi-liquid layer affecting adsorption prior to full dissolution/multilayer formation.

Sierra Nevada, California, U.S.A., Snow Algae: Snow albedo changes, algal-bacterial interrelationships and ultraviolet radiation effects

International Nuclear Information System (INIS)

Thomas, W.H.; Duval, B.

1995-01-01

In the Tioga Pass area (upper LeeVining Creek watershed) of the Sierra Nevada (California), snow algae were prevalent in the early summers of 1993 and 1994. Significant negative correlations were found between snow water content. However, red snow caused by algal blooms did not decrease mean albedos in representative snowfields. This was due to algal patchiness; mean albedos would not decrease over the whole water catchment basin; and water supplies would not be affected by the presence of algae. Albedo was also reduced by dirt on the snow, and wind-blown dirt may provide a source of allochthonous organic matter for snow bacteria. However, several observations emphasize the importance of an autochthonous source for bacterial nutrition. Bacterial abundances and production rates were higher in red snow containing algae than in noncolored snow. Bacterial production was about two orders-of-magnitude lower than photosynthetic algal production. Bacteria were also sometimes attached to algal cells. In experiments where snow algae were contained in UV-transmitting quartz tubes, ultraviolet radiation inhibited red snow (collected form open, sunlit areas) photosynthesis about 25%, while green snow (collected from forested, shady locations) photosynthesis was inhibited by 85%. Methanol extracts of red snow algae had greater absorbances in blue and UV spectral regions than did algae from green snow. These differences in UV responses and spectra may be due to habitat (sun vs shade) differences, or may be genetic, since different species were found in the two snow types. However, both habitat and genetic mechanisms may be operating together to cause these differences. 53 refs., 5 figs., 5 tabs

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

Science.gov (United States)

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37

Dynamics of actual aggregation of petroleum products in snow cover

Science.gov (United States)

Begunova, L. A.; Kuznetsova, O. V.; Begunov, D. A.; Kuznetsova, A. N.

2017-11-01

The paper presents issues of snow cover pollution by petroleum products. Petroleum products content was determined using the fluorimetric method of analysis. The samples of snow were selected on the territory of Angarsk and Irkutsk cities. According to the obtained data, the content of petroleum products in the analyzed samples exceeds the background value up to 6 times. Analysis of the reference data for similar research confirms need for creation of an environmental monitoring centralized system to monitor atmospheric precipitation and, particularly, snow cover.

Why on the snow? Winter emergence strategies of snow-active Chironomidae (Diptera) in Poland.

Science.gov (United States)

Soszyńska-Maj, Agnieszka; Paasivirta, Lauri; Giłka, Wojciech

2016-10-01

A long-term study of adult non-biting midges (Chironomidae) active in winter on the snow in mountain areas and lowlands in Poland yielded 35 species. The lowland and mountain communities differed significantly in their specific composition. The mountain assemblage was found to be more diverse and abundant, with a substantial contribution from the subfamily Diamesinae, whereas Orthocladiinae predominated in the lowlands. Orthocladius wetterensis Brundin was the most characteristic and superdominant species in the winter-active chironomid communities in both areas. Only a few specimens and species of snow-active chironomids were recorded in late autumn and early winter. The abundance of chironomids peaked in late February in the mountain and lowland areas with an additional peak in the mountain areas in early April. However, this second peak of activity consisted mainly of Orthocladiinae, as Diamesinae emerged earliest in the season. Most snow-active species emerged in mid- and late winter, but their seasonal patterns differed between the 2 regions as a result of the different species composition and the duration of snow cover in these regions. Spearman's rank correlation coefficient tests yielded positive results between each season and the number of chironomid individuals recorded in the mountain area. A positive correlation between air temperature, rising to +3.5 °C, and the number of specimens recorded on the snow in the mountain community was statistically significant. The winter emergence and mate-searching strategies of chironomids are discussed in the light of global warming, and a brief compilation of most important published data on the phenomena studied is provided. © 2015 Institute of Zoology, Chinese Academy of Sciences.

Early results from NASA's SnowEx campaign

Science.gov (United States)

Kim, Edward; Gatebe, Charles; Hall, Dorothy; Misakonis, Amy; Elder, Kelly; Marshall, Hans Peter; Hiemstra, Chris; Brucker, Ludovic; Crawford, Chris; Kang, Do Hyuk; De Marco, Eugenia; Beckley, Matt; Entin, Jared

2017-04-01

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focuses on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site is Grand Mesa and the secondary site is the Senator Beck Basin, both in western, Colorado, USA. Ten core sensors on four core aircraft will make observations using a broad suite of airborne sensors including active and passive microwave, and active and passive optical/infrared sensing techniques to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also includes an extensive range of ground truth measurements—in-situ samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some early results will be presented. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earth's surface (31 % of land areas) each year). This seasonal snow has major societal impacts in the areas of water resources, natural hazards (floods and droughts), water security, and weather and climate. The only practical way to estimate the quantity of snow on a consistent global basis is through satellites. Yet, current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly vs. estimates based on remotely-sensed observations. At peak coverage, as much as half of snow-covered terrestrial areas involve forested areas, so quantifying the challenge represented by forests is important to plan any future snow mission. Single-sensor approaches may work for certain snow types and certain conditions, but not for others

[Analysis of influencing factors of snow hyperspectral polarized reflections].

Science.gov (United States)

Sun, Zhong-Qiu; Zhao, Yun-Sheng; Yan, Guo-Qian; Ning, Yan-Ling; Zhong, Gui-Xin

2010-02-01

Due to the need of snow monitoring and the impact of the global change on the snow, on the basis of the traditional research on snow, starting from the perspective of multi-angle polarized reflectance, we analyzed the influencing factors of snow from the incidence zenith angles, the detection zenith angles, the detection azimuth angles, polarized angles, the density of snow, the degree of pollution, and the background of the undersurface. It was found that these factors affected the spectral reflectance values of the snow, and the effect of some factors on the polarization hyperspectral reflectance observation is more evident than in the vertical observation. Among these influencing factors, the pollution of snow leads to an obvious change in the snow reflectance spectrum curve, while other factors have little effect on the shape of the snow reflectance spectrum curve and mainly impact the reflection ratio of the snow. Snow reflectance polarization information has not only important theoretical significance, but also wide application prospect, and provides new ideas and methods for the quantitative research on snow using the remote sensing technology.

From the clouds to the ground - snow precipitation patterns vs. snow accumulation patterns

Science.gov (United States)

Gerber, Franziska; Besic, Nikola; Mott, Rebecca; Gabella, Marco; Germann, Urs; Bühler, Yves; Marty, Mauro; Berne, Alexis; Lehning, Michael

2017-04-01

Knowledge about snow distribution and snow accumulation patterns is important and valuable for different applications such as the prediction of seasonal water resources or avalanche forecasting. Furthermore, accumulated snow on the ground is an important ground truth for validating meteorological and climatological model predictions of precipitation in high mountains and polar regions. Snow accumulation patterns are determined by many different processes from ice crystal nucleation in clouds to snow redistribution by wind and avalanches. In between, snow precipitation undergoes different dynamical and microphysical processes, such as ice crystal growth, aggregation and riming, which determine the growth of individual particles and thereby influence the intensity and structure of the snowfall event. In alpine terrain the interaction of different processes and the topography (e.g. lifting condensation and low level cloud formation, which may result in a seeder-feeder effect) may lead to orographic enhancement of precipitation. Furthermore, the redistribution of snow particles in the air by wind results in preferential deposition of precipitation. Even though orographic enhancement is addressed in numerous studies, the relative importance of micro-physical and dynamically induced mechanisms on local snowfall amounts and especially snow accumulation patterns is hardly known. To better understand the relative importance of different processes on snow precipitation and accumulation we analyze snowfall and snow accumulation between January and March 2016 in Davos (Switzerland). We compare MeteoSwiss operational weather radar measurements on Weissfluhgipfel to a spatially continuous snow accumulation map derived from airborne digital sensing (ADS) snow height for the area of Dischma valley in the vicinity of the weather radar. Additionally, we include snow height measurements from automatic snow stations close to the weather radar. Large-scale radar snow accumulation

Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction

Science.gov (United States)

Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.

2015-12-01

Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

Science.gov (United States)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Putting humans in the loop: Using crowdsourced snow information to inform water management

Science.gov (United States)

Fedorov, Roman; Giuliani, Matteo; Castelletti, Andrea; Fraternali, Piero

2016-04-01

The unprecedented availability of user generated data on the Web due to the advent of online services, social networks, and crowdsourcing, is opening new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatio-temporally dense, possibly contributing to our ability of making better decisions. In this work, we contribute a novel crowdsourcing procedure for computing virtual snow indexes from public web images, either produced by users or generated by touristic webcams, which is based on a complex architecture designed for automatically crawling content from multiple web data sources. The procedure retains only geo-tagged images containing a mountain skyline, identifies the visible peaks in each image using a public online digital terrain model, and classifies the mountain image pixels as snow or no-snow. This operation yields a snow mask per image, from which it is possible to extract time series of virtual snow indexes representing a proxy of the snow covered area. The value of the obtained virtual snow indexes is estimated in a real world water management problem. We consider the snow-dominated catchment of Lake Como, a regulated lake in Northern Italy, where snowmelt represents the most important contribution to seasonal lake storage, and we used the virtual snow indexes for informing the daily operation of the lake's dam. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance.

Automated identification of potential snow avalanche release areas based on digital elevation models

Directory of Open Access Journals (Sweden)

Y. Bühler

2013-05-01

Full Text Available The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

Automated identification of potential snow avalanche release areas based on digital elevation models

Science.gov (United States)

Bühler, Y.; Kumar, S.; Veitinger, J.; Christen, M.; Stoffel, A.; Snehmani

2013-05-01

The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014

Directory of Open Access Journals (Sweden)

Stefan Wunderle

2016-05-01

Full Text Available In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI, has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%, a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000–2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

Hydrologic response across a snow persistence gradient on the west and east slopes of the Rocky Mountains in Colorado

Science.gov (United States)

Richard, G. A.; Hammond, J. C.; Kampf, S. K.; Moore, C. D.; Eurich, A.

2017-12-01

Snowpack trend analyses and modeling studies suggest that lower elevation snowpacks in mountain regions are most sensitive to drought and warming temperatures, however, in Colorado, most snow monitoring occurs in the high elevations where snow lasts throughout the winter and most streamflow monitoring occurs at lower elevations. The lack of combined snow and streamflow monitoring in watersheds along the transition from intermittent to persistent snow creates a gap in our understanding of snowmelt and runoff within the intermittent-persistent snow transition. Expanded hydrologic monitoring that spans the gradient of snow conditions in Colorado can help improve streamflow prediction and inform land and water managers. This study established hydrologic monitoring watersheds in intermittent, transitional, and persistent snow zones on the east slope and west slope of the Rocky Mountains in Colorado, and uses this monitoring network to improve understanding of how snow accumulation and melt affect soil moisture and streamflow generation under different snow conditions. We monitored six small watersheds (three west slope, three east slope) (0.8 to 3.9 km2) that drain intermittent, transitional, and persistent snow zones. At each site, we measured: streamflow, snow depth, soil moisture, precipitation, air temperature, and snow water equivalent (SWE). In our first season of monitoring, the west slope persistent and transitional sites had more mid-winter melt and infiltration, shorter snowpack duration, and lower peak SWE than the east slope sites. Snow cover remained at the east slope persistent site into June, whereas much of the snow at the persistent site on the west slope had already melted by early June. The difference in soil water input likely has consequences for streamflow response that we will continue to examine in future years. At the west slope intermittent site, the stream did not flow during the entire first year of monitoring, while at the east slope

Color Image of Snow White Trenches and Scraping

Science.gov (United States)

2008-01-01

This image was acquired by NASA's Phoenix Mars Lander's Surface Stereo Imager on the 31st Martian day of the mission, or Sol 31 (June 26, 2008), after the May 25, 2008 landing. This image shows the trenches informally called 'Snow White 1' (left), 'Snow White 2' (right), and within the Snow White 2 trench, the smaller scraping area called 'Snow White 3.' The Snow White 3 scraped area is about 5 centimeters (2 inches) deep. The dug and scraped areas are within the diggiing site called 'Wonderland.' The Snow White trenches and scraping prove that scientists can take surface soil samples, subsurface soil samples, and icy samples all from one unit. Scientists want to test samples to determine if some ice in the soil may have been liquid in the past during warmer climate cycles. The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is led by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver

Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout

Directory of Open Access Journals (Sweden)

C. Vera Valero

2018-03-01

Full Text Available Snow avalanche motion is strongly dependent on the temperature and water content of the snow cover. In this paper we use a snow cover model, driven by measured meteorological data, to set the initial and boundary conditions for wet-snow avalanche calculations. The snow cover model provides estimates of snow height, density, temperature and liquid water content. This information is used to prescribe fracture heights and erosion heights for an avalanche dynamics model. We compare simulated runout distances with observed avalanche deposition fields using a contingency table analysis. Our analysis of the simulations reveals a large variability in predicted runout for tracks with flat terraces and gradual slope transitions to the runout zone. Reliable estimates of avalanche mass (height and density in the release and erosion zones are identified to be more important than an exact specification of temperature and water content. For wet-snow avalanches, this implies that the layers where meltwater accumulates in the release zone must be identified accurately as this defines the height of the fracture slab and therefore the release mass. Advanced thermomechanical models appear to be better suited to simulate wet-snow avalanche inundation areas than existing guideline procedures if and only if accurate snow cover information is available.

High fidelity remote sensing of snow properties from MODIS and the Airborne Snow Observatory: Snowflakes to Terabytes

Science.gov (United States)

Painter, T.; Mattmann, C. A.; Brodzik, M.; Bryant, A. C.; Goodale, C. E.; Hart, A. F.; Ramirez, P.; Rittger, K. E.; Seidel, F. C.; Zimdars, P. A.

2012-12-01

The response of the cryosphere to climate forcings largely determines Earth's climate sensitivity. However, our understanding of the strength of the simulated snow albedo feedback varies by a factor of three in the GCMs used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, mainly caused by uncertainties in snow extent and the albedo of snow-covered areas from imprecise remote sensing retrievals. Additionally, the Western US and other regions of the globe depend predominantly on snowmelt for their water supply to agriculture, industry and cities, hydroelectric power, and recreation, against rising demand from increasing population. In the mountains of the Upper Colorado River Basin, dust radiative forcing in snow shortens snow cover duration by 3-7 weeks. Extended to the entire upper basin, the 5-fold increase in dust load since the late-1800s results in a 3-week earlier peak runoff and a 5% annual loss of total runoff. The remotely sensed dynamics of snow cover duration and melt however have not been factored into hydrological modeling, operational forecasting, and policymaking. To address these deficiencies in our understanding of snow properties, we have developed and validated a suite of MODIS snow products that provide accurate fractional snow covered area and radiative forcing of dust and carbonaceous aerosols in snow. The MODIS Snow Covered Area and Grain size (MODSCAG) and MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithms, developed and transferred from imaging spectroscopy techniques, leverage the complete MODIS surface reflectance spectrum. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. We have created the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties, and provide complete

Snow observations in Mount Lebanon (2011-2016)

Science.gov (United States)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

2017-08-01

We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011-2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m-3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing

Monitoring and forecasting local landslide hazard in the area of Longyearbyen, Svalbard - early progress and experiences from the Autumn 2016 events

Science.gov (United States)

Wang, Thea; Krøgli, Ingeborg; Boje, Søren; Colleuille, Hervé

2017-04-01

Since 2013 the Norwegian Water Resources and Energy Directorate (NVE) has operated a landslide early warning system (LEWS) for mainland Norway. The Svalbard islands, situated 800 km north of the Norwegian mainland, and 1200 km from the North Pole, are not part of the conventional early warning service. However, following the fatal snow avalanche event 19 Dec. 2015 in the settlement of Longyearbyen (78° north latitude), local authorities and the NVE have initiated monitoring of the hydro-meteorological conditions for the area of Longyearbyen, as an extraordinary precaution. Two operational forecasting teams from the NVE; the snow avalanche and the landslide hazard forecasters, perform hazard assessment related to snow avalanches, slush flows, debris flows, shallow slides and local flooding. This abstract will focus on recent experiences made by the landslide hazard team during the autumn 2016 landslide events, caused by a record setting wet and warm summer and autumn of 2016. The general concept of the Norwegian LEWS is based on frequency intervals of extreme hydro-meteorological conditions. This general concept has been transposed to the Longyearbyen area. Although the climate is considerably colder and drier than mainland Norway, experiences so far are positive and seem useful to the local authorities. Initially, the landslide hazard evaluation was intended to consider only slush flow hazard during the snow covered season. However, due to the extraordinary warm and wet summer and autumn 2016, the landslide hazard forecasters unexpectedly had to issue warnings for the local authorities due to increased risk of shallow landslides and debris flows. This was done in close cooperation with the Norwegian Meteorological Institute, who provided weather forecasts from the recently developed weather prediction model, AROME-Arctic. Two examples, from 14-15 Oct and 8-9 Nov 2016, will be given to demonstrate how the landslide hazard assessment for the Longyearbyen area is

A spatially distributed isotope sampling network in a snow-dominated catchment for the quantification of snow meltwater

Science.gov (United States)

Rücker, Andrea; Boss, Stefan; Von Freyberg, Jana; Zappa, Massimiliano; Kirchner, James

2017-04-01

In mountainous catchments with seasonal snowpacks, river discharge in downstream valleys is largely sustained by snowmelt in spring and summer. Future climate warming will likely reduce snow volumes and lead to earlier and faster snowmelt in such catchments. This, in turn, may increase the risk of summer low flows and hydrological droughts. Improved runoff predictions are thus required in order to adapt water management to future climatic conditions and to assure the availability of fresh water throughout the year. However, a detailed understanding of the hydrological processes is crucial to obtain robust predictions of river streamflow. This in turn requires fingerprinting source areas of streamflow, tracing water flow pathways, and measuring timescales of catchment storage, using tracers such as stable water isotopes (18O, 2H). For this reason, we have established an isotope sampling network in the Alptal, a snowmelt-dominated catchment (46.4 km2) in Central-Switzerland, as part of the SREP-Drought project (Snow Resources and the Early Prediction of hydrological DROUGHT in mountainous streams). Precipitation and snow cores are analyzed for their isotopic signature at daily or weekly intervals. Three-week bulk samples of precipitation are also collected on a transect along the Alptal valley bottom, and along an elevational transect perpendicular to the Alptal valley axis. Streamwater samples are taken at the catchment outlet as well as in two small nested sub-catchments (automatic snow lysimeter system was developed, which also facilitates real-time monitoring of snowmelt events, system status and environmental conditions (air and soil temperature). Three lysimeter systems were installed within the catchment, in one forested site and two open field sites at different elevations, and have been operational since November 2016. We will present the isotope time series from our regular sampling network, as well as initial results from our snowmelt lysimeter sites. Our

On the impact of snow cover on daytime pollution dispersion

Science.gov (United States)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Hildebrand, P.; Rogers, F. A.; Cramer, J.; Schanot, A.

A preliminary evaluation of the impact of snow cover on daytime pollutant dispersion conditions is made by using conceptual, scaling, and observational analyses. For uniform snow cover and synoptically unperturbed sunny conditions, observations indicate a considerate suppression of the surface sensible heat flux, the turbulence, and the development of the daytime atmospheric boundary layer (ABL) when compared to snow-free conditions. However, under conditions of non-uniform snow cover, as in urban areas, or associated with vegetated areas or bare ground patches, a milder effect on pollutant dispersion conditions would be expected. Observed concentrations of atmospheric particles within the ABL, and surface pollutant concentrations in urban areas, reflect the impact of snow cover on the modification of ABL characteristics.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques. [geological mapping of New England test area

Science.gov (United States)

Wobber, F. J.; Martin, K. R. (Principal Investigator); Amato, R. V.; Leshendok, T.

1974-01-01

The author has identified the following significant results. The procedure for conducting a regional geological mapping program utilizing snow-enhanced ERTS-1 imagery has been summarized. While it is recognized that mapping procedures in geological programs will vary from area to area and from geologist to geologist, it is believed that the procedure tested in this project is applicable over a wide range of mapping programs. The procedure is designed to maximize the utility and value of ERTS-1 imagery and aerial photography within the initial phase of geological mapping programs. Sample products which represent interim steps in the mapping formula (e.g. the ERTS Fracture-Lineament Map) have been prepared. A full account of these procedures and products will be included within the Snow Enhancement Users Manual.

A distributed snow-evolution modeling system (SnowModel)

Science.gov (United States)

Glen E. Liston; Kelly. Elder

2006-01-01

SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D...

Radiation area monitoring by wireless-communicating area monitor with surveillance camera

International Nuclear Information System (INIS)

Shimura, Mitsuo; Kobayashi, Hiromitsu; Kitahara, Hideki; Kobayashi, Hironobu; Okamoto, Shinji

2004-01-01

Aiming at a dose reduction and a work efficiency improvement for nuclear power plants that have high dose regions, we have developed our system of wireless-communicating Area Monitor with Surveillance Camera, and have performed an on-site test. Now we are implementing this Area Monitor with Surveillance Camera for a use as a TV camera in the controlled-area, which enables a personal computer to simultaneously display two or more dose values and site live images on the screen. For the radiation detector of this Area Monitor System, our wireless-communicating dosimeter is utilized. Image data are transmitted via a wireless Local Area Network (LAN). As a test result, image transmission of a maximum of 20 frames per second has been realized, which shows that this concept is a practical application. Remote-site monitoring also has been realized from an office desk located within the non-controlled area, adopting a Japan's wireless phone system, PHS (Personal Handy Phone) for the transmission interface. (author)

Extraordinary blowing snow transport events in East Antarctica

Energy Technology Data Exchange (ETDEWEB)

Scarchilli, Claudio; Agnoletto, Lucia [ENEA, Rome (Italy); Universita di Siena, Dipartimento di Scienze della Terra, Siena (Italy); Frezzotti, Massimo; Grigioni, Paolo; Silvestri, Lorenzo de [ENEA, Rome (Italy); Dolci, Stefano [CNR, Rome (Italy); Consorzio P.N.R.A. S.C.r.l., Rome (Italy)

2010-06-15

In the convergence slope/coastal areas of Antarctica, a large fraction of snow is continuously eroded and exported by wind to the atmosphere and into the ocean. Snow transport observations from instruments and satellite images were acquired at the wind convergence zone of Terra Nova Bay (East Antarctica) throughout 2006 and 2007. Snow transport features are well-distinguished in satellite images and can extend vertically up to 200 m as first-order quantitatively estimated by driftometer sensor FlowCapt trademark. Maximum snow transportation occurs in the fall and winter seasons. Snow transportation (drift/blowing) was recorded for {proportional_to}80% of the time, and 20% of time recorded, the flux is >10{sup -2} kg m{sup -2} s{sup -1} with particle density increasing with height. Cumulative snow transportation is {proportional_to}4 orders of magnitude higher than snow precipitation at the site. An increase in wind speed and transportation ({proportional_to}30%) was observed in 2007, which is in agreement with a reduction in observed snow accumulation. Extensive presence of ablation surface (blue ice and wind crust) upwind and downwind of the measurement site suggest that the combine processes of blowing snow sublimation and snow transport remove up to 50% of the precipitation in the coastal and slope convergence area. These phenomena represent a major negative effect on the snow accumulation, and they are not sufficiently taken into account in studies of surface mass balance. The observed wind-driven ablation explains the inconsistency between atmospheric model precipitation and measured snow accumulation value. (orig.)

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

Science.gov (United States)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

Combining low-cost GPS receivers with upGPR to derive continuously liquid water content, snow height and snow water equivalent in Alpine snow covers

Science.gov (United States)

Koch, Franziska; Schmid, Lino; Prasch, Monika; Heilig, Achim; Eisen, Olaf; Schweizer, Jürg; Mauser, Wolfram

2015-04-01

The temporal evolution of Alpine snowpacks is important for assessing water supply, hydropower generation, flood predictions and avalanche forecasts. Especially in high mountain regions with an extremely varying topography, it is until now often difficult to derive continuous and non-destructive information on snow parameters. Since autumn 2012, we are running a new low-cost GPS (Global Positioning System) snow measurement experiment at the high alpine study site Weissfluhjoch (2450 m a.s.l.) in Switzerland. The globally and freely broadcasted GPS L1-band (1.57542 GHz) was continuously recorded with GPS antennas, which are installed at the ground surface underneath the snowpack. GPS raw data, containing carrier-to-noise power density ratio (C/N0) as well as elevation and azimuth angle information for each time step of 1 s, was stored and analyzed for all 32 GPS satellites. Since the dielectric permittivity of an overlying wet snowpack influences microwave radiation, the bulk volumetric liquid water content as well as daily melt-freeze cycles can be derived non-destructively from GPS signal strength losses and external snow height information. This liquid water content information is qualitatively in good accordance with meteorological and snow-hydrological data and quantitatively highly agrees with continuous data derived from an upward-looking ground-penetrating radar (upGPR) working in a similar frequency range. As a promising novelty, we combined the GPS signal strength data with upGPR travel-time information of active impulse radar rays to the snow surface and back from underneath the snow cover. This combination allows determining liquid water content, snow height and snow water equivalent from beneath the snow cover without using any other external information. The snow parameters derived by combining upGPR and GPS data are in good agreement with conventional sensors as e.g. laser distance gauges or snow pillows. As the GPS sensors are cheap, they can easily

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Science.gov (United States)

Wang, Kun; Zhang, Li; Qiu, Yubao; Ji, Lei; Tian, Feng; Wang, Cuizhen; Wang, Zhiyong

2013-01-01

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.

Dust radiative forcing in snow of the Upper Colorado River Basin: 1. A 6 year record of energy balance, radiation, and dust concentrations

Science.gov (United States)

Painter, Thomas H.; Skiles, S. Mckenzie; Deems, Jeffrey S.; Bryant, Ann C.; Landry, Christopher C.

2012-07-01

Dust in snow accelerates snowmelt through its direct reduction of snow albedo and its further indirect reduction of albedo by accelerating the growth of snow grains. Since the westward expansion of the United States that began in the mid-19th century, the mountain snow cover of the Colorado River Basin has been subject to five-fold greater dust loading, largely from the Colorado Plateau and Great Basin. Radiative forcing of snowmelt by dust is not captured by conventional micrometeorological measurements, and must be monitored by a more comprehensive suite of radiation instruments. Here we present a 6 year record of energy balance and detailed radiation measurements in the Senator Beck Basin Study Area, San Juan Mountains, Colorado, USA. Data include broadband irradiance, filtered irradiance, broadband reflected flux, filtered reflected flux, broadband and visible albedo, longwave irradiance, wind speed, relative humidity, and air temperatures. The gradient of the snow surface is monitored weekly and used to correct albedo measurements for geometric effects. The snow is sampled weekly for dust concentrations in plots immediately adjacent to each tower over the melt season. Broadband albedo in the last weeks of snow cover ranged from 0.33 to 0.55 across the 6 years and two sites. Total end of year dust concentration in the top 3 cm of the snow column ranged from 0.23 mg g-1 to 4.16 mg g-1. These measurements enable monitoring and modeling of dust and climate-driven snowmelt forcings in the Upper Colorado River Basin.

Distribution of Snow and Maximum Snow Water Equivalent Obtained by LANDSAT Data and Degree Day Method

Science.gov (United States)

Takeda, K.; Ochiai, H.; Takeuchi, S.

1985-01-01

Maximum snow water equivalence and snowcover distribution are estimated using several LANDSAT data taken in snowmelting season over a four year period. The test site is Okutadami-gawa Basin located in the central position of Tohoku-Kanto-Chubu District. The year to year normalization for snowmelt volume computation on the snow line is conducted by year to year correction of degree days using the snowcover percentage within the test basin obtained from LANDSAT data. The maximum snow water equivalent map in the test basin is generated based on the normalized snowmelt volume on the snow line extracted from four LANDSAT data taken in a different year. The snowcover distribution on an arbitrary day in snowmelting of 1982 is estimated from the maximum snow water equivalent map. The estimated snowcover is compared with the snowcover area extracted from NOAA-AVHRR data taken on the same day. The applicability of the snow estimation using LANDSAT data is discussed.

Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming

Science.gov (United States)

Sexstone, Graham A.; Clow, David W.; Fassnacht, Steven R.; Liston, Glen E.; Hiemstra, Christopher A.; Knowles, John F.; Penn, Colin A.

2018-01-01

Snow sublimation is an important component of the snow mass balance, but the spatial and temporal variability of this process is not well understood in mountain environments. This study combines a process‐based snow model (SnowModel) with eddy covariance (EC) measurements to investigate (1) the spatio‐temporal variability of simulated snow sublimation with respect to station observations, (2) the contribution of snow sublimation to the ablation of the snowpack, and (3) the sensitivity and response of snow sublimation to bark beetle‐induced forest mortality and climate warming across the north‐central Colorado Rocky Mountains. EC‐based observations of snow sublimation compared well with simulated snow sublimation at stations dominated by surface and canopy sublimation, but blowing snow sublimation in alpine areas was not well captured by the EC instrumentation. Water balance calculations provided an important validation of simulated sublimation at the watershed scale. Simulated snow sublimation across the study area was equivalent to 28% of winter precipitation on average, and the highest relative snow sublimation fluxes occurred during the lowest snow years. Snow sublimation from forested areas accounted for the majority of sublimation fluxes, highlighting the importance of canopy and sub‐canopy surface sublimation in this region. Simulations incorporating the effects of tree mortality due to bark‐beetle disturbance resulted in a 4% reduction in snow sublimation from forested areas. Snow sublimation rates corresponding to climate warming simulations remained unchanged or slightly increased, but total sublimation losses decreased by up to 6% because of a reduction in snow covered area and duration.

Snow model design for operational purposes

Science.gov (United States)

Kolberg, Sjur

2017-04-01

A parsimonious distributed energy balance snow model intended for operational use is evaluated using discharge, snow covered area and grain size; the latter two as observed from the MODIS sensor. The snow model is an improvement of the existing GamSnow model, which is a part of the Enki modelling framework. Core requirements for the new version have been: 1. Reduction of calibration freedom, motivated by previous experience of non-identifiable parameters in the existing version 2. Improvement of process representation based on recent advances in physically based snow modelling 3. Limiting the sensitivity to forcing data which are poorly known over the spatial domain of interest (often in mountainous areas) 4. Preference for observable states, and the ability to improve from updates. The albedo calculation is completely revised, now based on grain size through an emulation of the SNICAR model (Flanner and Zender, 2006; Gardener and Sharp, 2010). The number of calibration parameters in the albedo model is reduced from 6 to 2. The wind function governing turbulent energy fluxes has been reduced from 2 to 1 parameter. Following Raleigh et al (2011), snow surface radiant temperature is split from the top layer thermodynamic temperature, using bias-corrected wet-bulb temperature to model the former. Analyses are ongoing, and the poster will bring evaluation results from 16 years of MODIS observations and more than 25 catchments in southern Norway.

Development of web monitoring radiation area monitor

International Nuclear Information System (INIS)

Jung, Hoon Jin; Lee, Jun Hee; Namkoong, Phil; Lee, Dong Hoon; Lee, Su Hong; Lee, Gun Bae

2005-01-01

Recently the increasing number of radioisotope industry and nuclear facility have ever raised the possibility of radiation safety accident. As such a result, radioisotope companies and nuclear facility operators have become to be much interested in radiation area monitoring for efficient radiation protection. At present, almost of the radiation area monitors which are imported products are outdated in aspect of their functions. Diversification of the monitoring work is urgently demanding additional functions to be added. Thus we have developed new-type digital area monitor which enables remote web monitoring with image and radiation dose rate value at distant places through using internet, the latest IT technology, and radiation measurement technology

Twenty-four year record of Northern Hemisphere snow cover derived from passive microwave remote sensing

Science.gov (United States)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm

Improving Surface Mass Balance Over Ice Sheets and Snow Depth on Sea Ice

Science.gov (United States)

Koenig, Lora Suzanne; Box, Jason; Kurtz, Nathan

2013-01-01

Surface mass balance (SMB) over ice sheets and snow on sea ice (SOSI) are important components of the cryosphere. Large knowledge gaps remain in scientists' abilities to monitor SMB and SOSI, including insufficient measurements and difficulties with satellite retrievals. On ice sheets, snow accumulation is the sole mass gain to SMB, and meltwater runoff can be the dominant single loss factor in extremely warm years such as 2012. SOSI affects the growth and melt cycle of the Earth's polar sea ice cover. The summer of 2012 saw the largest satellite-recorded melt area over the Greenland ice sheet and the smallest satellite-recorded Arctic sea ice extent, making this meeting both timely and relevant.

The Snow Data System at NASA JPL

Science.gov (United States)

Laidlaw, R.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Bormann, K.; Brodzik, M. J.; Burgess, A. B.; Rittger, K.; Goodale, C. E.; Joyce, M.; McGibbney, L. J.; Zimdars, P.

2014-12-01

NASA JPL's Snow Data System has a data-processing pipeline powered by Apache OODT, an open source software tool. The pipeline has been running for several years and has successfully generated a significant amount of cryosphere data, including MODIS-based products such as MODSCAG, MODDRFS and MODICE, with historical and near-real time windows and covering regions such as the Artic, Western US, Alaska, Central Europe, Asia, South America, Australia and New Zealand. The team continues to improve the pipeline, using monitoring tools such as Ganglia to give an overview of operations, and improving fault-tolerance with automated recovery scripts. Several alternative adaptations of the Snow Covered Area and Grain size (SCAG) algorithm are being investigated. These include using VIIRS and Landsat TM/ETM+ satellite data as inputs. Parallel computing techniques are being considered for core SCAG processing, such as using the PyCUDA Python API to utilize multi-core GPU architectures. An experimental version of MODSCAG is also being developed for the Google Earth Engine platform, a cloud-based service.

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Science.gov (United States)

Griessinger, Nena; Seibert, Jan; Magnusson, Jan; Jonas, Tobias

2016-09-01

In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation

Changes in Andes snow cover from MODIS data, 2000-2016

Science.gov (United States)

Saavedra, Freddy A.; Kampf, Stephanie K.; Fassnacht, Steven R.; Sibold, Jason S.

2018-03-01

The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36° S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann-Kendall and Theil-Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño-Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29° S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2) with persistent snow cover between 29 and 36° S experienced a significant loss of snow cover (2-5 fewer days of snow year-1). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10-30 m year-1 south of 29-30° S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31° S, whereas the SAM better predicted SP south of 31° S.

Features of Duration and Borders of the Bedding of Snow Cover in the Conditions of Climatic Changes in the Territory of Northern Kazakhstan According to Land and Space Monitoring

Science.gov (United States)

Salnikov, Vitaliy; Turulina, Galina; Polyakova, Svetlana; Muratova, Nadiya; Kauazov, Azamat; Abugalieva, Aigul; Tazhibayeva, Tamara

2014-05-01

Precipitation and air temperature datasets from 34 meteorological stations were analyzed to reveal the regional climate changes at the territory in North Kazakhstan over the last 58 years (i.e., 1950-2008). Peculiarities and conditions of snow cover formation and melting have been analyzed at territory of Northern Kazakhstan using surface and space monitoring data. Methods of both the geo-informational processing of remote probing data and statistical processing of databases on snow cover, air temperature and precipitations have been used. Analysis of snow cover observations data for territory of Northern Kazakhstan has shown that the stable snow cover might be observed since the middle of November till the beginning of April. In a few last decades the tendency is observed for longevity decrease of snow cover bedding that appears to be on the background air temperature increase and insignificant increase of cold period precipitations due to the later bedding of the snow cover and its earlier destruction. Peculiarities of atmospheric circulation in Atlantic-Eurasian sector of Northern Semi sphere and their influence of formation of snow cover at territory of Northern Kazakhstan. The higher longevity of the snow cover bedding is defined by the predominance of E form circulation and lower longevity - by the predominance of W+C circulation form. Analysis conducted of the highest height of snow cover bedding has shown that for period of 1936-2012 in the most cases the statistically reliable decreasing trends are observed with the linear trend coefficients of 0,50 - 0,60 cm/year. The method is offered for determination of probable characteristics of the snow cover decade height. Using data of space monitoring are allocated the frontiers of snow cover bedding for the period of snow melting 1982-2008 and the snow cover melting maps are developed. The results further confirm the proposition that snow cover availability is an important and limiting factor in the generation

Monitoring the Impacts of Forest Management on Snowpack Duration

Science.gov (United States)

O'Halloran, T.; Tyler, S.; Gaffney, R.; Pai, H.

2017-12-01

Seasonal snowpack constitutes a significant portion of the hydrologic budget in mountain watersheds and influences dynamic (e.g., runoff magnitude and timing, soil moisture availability) and energetic processes (e.g., surface-atmosphere energy fluxes, ground temperature). Altered forest structure can affect snow accumulation and ablation. As part of a long-term monitoring project, this work examines the impact of forest management practices on snow cover in Lassen National Forest, California. We deployed a fiber optic distributed temperature sensing (DTS) cable and multiple meteorological stations in thinned, clear-cut, and untreated areas of forest. The DTS data was collected at 1 meter spatial intervals every 4 hours from February to May 2017. To determine snow cover, daily temperature variations were examined along locations of the DTS cable associated with our areas of interest. Between the various treatments, snow duration was greater in both clear-cut and untreated forest compared to the thinned area. However, snow duration varied by only six days. We also investigated other meteorological forcings, such as average winter temperature and precipitation, which coupled with forest modifications could explain snow duration in our study.

Snow Roads at McMurdo Station, Antarctica

Science.gov (United States)

2010-05-01

is minimized distribution of low strength areas (also called “ holidays ”). Holidays are actually areas of pulverized snow that are missed or inade...quately processed. Though holidays usually occur sporadically, any single flaw may extend through the entire thickness of an unelevated road. The...Pegasus Road and the LDB pad. A few additional sites were chosen for comparison (Williams Field, virgin snow, etc.). Williams Field Road densities

Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

DEFF Research Database (Denmark)

Cimoli, Emiliano; Marcer, Marco; Vandecrux, Baptiste Robert Marcel

2017-01-01

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternati...... areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond....... methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from......-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger...

Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2

Science.gov (United States)

Saha, Subodh Kumar; Sujith, K.; Pokhrel, Samir; Chaudhari, Hemantkumar S.; Hazra, Anupam

2017-03-01

The Noah version 2.7.1 is a moderately complex land surface model (LSM), with a single layer snowpack, combined with vegetation and underlying soil layer. Many previous studies have pointed out biases in the simulation of snow, which may hinder the skill of a forecasting system coupled with the Noah. In order to improve the simulation of snow by the Noah, a multilayer snow scheme (up to a maximum of six layers) is introduced. As Noah is the land surface component of the Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP), the modified Noah is also coupled with the CFSv2. The offline LSM shows large improvements in the simulation of snow depth, snow water equivalent (SWE), and snow cover area during snow season (October to June). CFSv2 with the modified Noah reveals a dramatic improvements in the simulation of snow depth and 2 m air temperature and moderate improvements in SWE. As suggested in the previous diagnostic and sensitivity study, improvements in the simulation of snow by CFSv2 have lead to the reduction in dry bias over the Indian subcontinent (by a maximum of 2 mm d-1). The multilayer snow scheme shows promising results in the simulation of snow as well as Indian summer monsoon rainfall and hence this development may be the part of the future version of the CFS.

Temporal monitoring of the soil freeze-thaw cycles over snow-cover land by using off-ground GPR

KAUST Repository

Jadoon, Khan

2013-07-01

We performed off-ground ground-penetrating radar (GPR) measurements over a bare agricultural field to monitor the freeze-thaw cycles over snow-cover. The GPR system consisted of a vector network analyzer combined with an off-ground monostatic horn antenna, thereby setting up an ultra-wideband stepped-frequency continuous-wave radar. Measurements were performed during nine days and the surface of the bare soil was exposed to snow fall, evaporation and precipitation as the GPR antenna was mounted 110 cm above the ground. Soil surface dielectric permittivity was retrieved using an inversion of time-domain GPR data focused on the surface reflection. The GPR forward model used combines a full-waveform solution of Maxwell\\'s equations for three-dimensional wave propagation in planar layered media together with global reflection and transmission functions to account for the antenna and its interactions with the medium. Temperature and permittivity sensors were installed at six depths to monitor the soil dynamics in the top 8 cm depth. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and permittivity data and in particular freeze and thaw events were clearly visible. A good agreement of the trend was observed between the temperature, permittivity and GPR time-lapse data with respect to five freeze-thaw cycles. The GPR-derived permittivity was in good agreement with sensor observations. The proposed method appears to be promising for the real-time mapping and monitoring of the frozen layer at the field scale. © 2013 IEEE.

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

Directory of Open Access Journals (Sweden)

E. E. Stigter

2017-07-01

Full Text Available 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.

Composition of microbial communities in aerosol, snow and ice samples from remote glaciated areas (Antarctica, Alps, Andes)

Science.gov (United States)

Elster, J.; Delmas, R. J.; Petit, J.-R.; Řeháková, K.

2007-06-01

Taxonomical and ecological analyses were performed on micro-autotrophs (cyanobacteria and algae together with remnants of diatom valves), micro-fungi (hyphae and spores), bacteria (rod, cocci and red clusters), yeast, and plant pollen extracted from various samples: Alps snow (Mt. Blank area), Andean snow (Illimani, Bolivia), Antarctic aerosol filters (Dumont d'Urville, Terre Adélie), and Antarctic inland ice (Terre Adélie). Three methods for ice and snow sample's pre-concentration were tested (filtration, centrifugation and lyophilisation). Afterwards, cultivation methods for terrestrial, freshwater and marine microorganisms (micro-autotrophs and micro-fungi) were used in combination with liquid and solid media. The main goal of the study was to find out if micro-autotrophs are commonly transported by air masses, and later stored in snow and icecaps around the world. The most striking result of this study was the absence of culturable micro-autotrophs in all studied samples. However, an unusual culturable pigmented prokaryote was found in both alpine snow and aerosol samples. Analyses of many samples and proper statistical analyses (PCA, RDA- Monte Carlo permutation tests) showed that studied treatments highly significantly differ in both microbial community and biotic remnants composition F=9.33, p=0.001. In addition, GLM showed that studied treatments highly significantly differ in numbers of categories of microorganisms and remnants of biological material F=11.45, p=0.00005. The Antarctic aerosol samples were characterised by having red clusters of bacteria, the unusual prokaryote and yeasts. The high mountain snow from the Alps and Andes contained much more culturable heterotrophs. The unusual prokaryote was very abundant, as were coccoid bacteria, red clusters of bacteria, as well as yeasts. The Antarctic ice samples were quite different. These samples had higher numbers of rod bacteria and fungal hyphae. The microbial communities and biological remnants of

Photochemical degradation of PCBs in snow.

Science.gov (United States)

Matykiewiczová, Nina; Klánová, Jana; Klán, Petr

2007-12-15

This work represents the first laboratory study known to the authors describing photochemical behavior of persistent organic pollutants in snow at environmentally relevant concentrations. The snow samples were prepared by shock freezing of the corresponding aqueous solutions in liquid nitrogen and were UV-irradiated in a photochemical cold chamber reactor at -25 degrees C, in which simultaneous monitoring of snow-air exchange processeswas also possible. The main photodegradation pathway of two model snow contaminants, PCB-7 and PCB-153 (c approximately 100 ng kg(-1)), was found to be reductive dehalogenation. Possible involvement of the water molecules of snow in this reaction has been excluded by performing the photolyses in D2O snow. Instead, trace amounts of volatile organic compounds have been proposed to be the major source of hydrogen atom in the reduction, and this hypothesis was confirmed by the experiments with deuterated organic cocontaminants, such as d6-ethanol or d8-tetrahydrofuran. It is argued that bimolecular photoreduction of PCBs was more efficient or feasible than any other phototransformations under the experimental conditions used, including the coupling reactions. The photodegradation of PCBs, however, competed with a desorption process responsible for the pollutant loss from the snow samples, especially in case of lower molecular-mass congeners. Organic compounds, apparently largely located or photoproduced on the surface of snow crystals, had a predisposition to be released to the air but, at the same time, to react with other species in the gas phase. It is concluded that physicochemical properties of the contaminants and trace co-contaminants, their location and local concentrations in the matrix, and the wavelength and intensity of radiation are the most important factors in the evaluation of organic contaminants' lifetime in snow. Based on the results, it has been estimated that the average lifetime of PCBs in surface snow, connected

Combining snow depth and innovative skier flow measurements in order to improve snow grooming techniques

Science.gov (United States)

Carmagnola, Carlo Maria; Albrecht, Stéphane; Hargoaa, Olivier

2017-04-01

In the last decades, ski resort managers have massively improved their snow management practices, in order to adapt their strategies to the inter-annual variability in snow conditions and to the effects of climate change. New real-time informations, such as snow depth measurements carried out on the ski slopes by grooming machines during their daily operations, have become available, allowing high saving, efficiency and optimization gains (reducing for instance the groomer fuel consumption and operation time and the need for machine-made snow production). In order to take a step forward in improving the grooming techniques, it would be necessary to keep into account also the snow erosion by skiers, which depends mostly on the snow surface properties and on the skier attendance. Today, however, most ski resort managers have only a vague idea of the evolution of the skier flows on each slope during the winter season. In this context, we have developed a new sensor (named Skiflux) able to measure the skier attendance using an infrared beam crossing the slopes. Ten Skiflux sensors have been deployed during the 2016/17 winter season at Val Thorens ski area (French Alps), covering a whole sector of the resort. A dedicated software showing the number of skier passages in real time as been developed as well. Combining this new Skiflux dataset with the snow depth measurements from grooming machines (Snowsat System) and the snow and meteorological conditions measured in-situ (Liberty System from Technoalpin), we were able to create a "real-time skiability index" accounting for the quality of the surface snow and its evolution during the day. Moreover, this new framework allowed us to improve the preparation of ski slopes, suggesting new strategies for adapting the grooming working schedule to the snow quality and the skier attendance. In the near future, this work will benefit from the advances made within the H2020 PROSNOW project ("Provision of a prediction system allowing

Food, energy, and water in an era of disappearing snow

Science.gov (United States)

Mote, P.; Lettenmaier, D. P.; Li, S.; Xiao, M.

2017-12-01

Mountain snowpack stores a significant quantity of water in the western US, accumulating during the wet season and melting during the dry summers and supplying more than 65% of the water used for irrigated agriculture, energy production (both hydropower and thermal), and municipal and industrial uses. The importance of snow to western agriculture is demonstrated by the fact that most snow monitoring is performed by the US Department of Agriculture. In a paper published in 2005, we showed that roughly 70% of monitoring sites showed decreasing trends through 2002. Now, with 14 additional years of data, over 90% of snow monitoring sites with long records across the western US show declines through 2016, of which 33% are significant (vs 5% expected by chance) and 2% are significant and positive (vs 5% expected by chance). Declining trends are observed across all months, states, and climates, but are largest in spring, in the Pacific states, and in locations with mild winter climate. We corroborate and extend these observations using a gridded hydrology model, which also allows a robust estimate of total western snowpack and its decline. Averaged across the western US, the decline in total April 1 snow water equivalent since mid-century is roughly 15-30% or 25-50 km3, comparable in volume to the West's largest man-made reservoir, Lake Mead. In the absence of rapid reductions in emissions of greenhouse gases, these losses will accelerate; snow losses on this scale demonstrate the necessity of rethinking water storage, policy, and usage.

High-resolution LIDAR and ground observations of snow cover in a complex forested terrain in the Sierra Nevada - implications for optical remote sensing of seasonal snow.

Science.gov (United States)

Kostadinov, T. S.; Harpold, A.; Hill, R.; McGwire, K.

2017-12-01

Seasonal snow cover is a key component of the hydrologic regime in many regions of the world, especially those in temperate latitudes with mountainous terrain and dry summers. Such regions support large human populations which depend on the mountain snowpack for their water supplies. It is thus important to quantify snow cover accurately and continuously in these regions. Optical remote-sensing methods are able to detect snow and leverage space-borne spectroradiometers with global coverage such as MODIS to produce global snow cover maps. However, snow is harder to detect accurately in mountainous forested terrain, where topography influences retrieval algorithms, and importantly - forest canopies complicate radiative transfer and obfuscate the snow. Current satellite snow cover algorithms assume that fractional snow-covered area (fSCA) under the canopy is the same as the fSCA in the visible portion of the pixel. In-situ observations and first principles considerations indicate otherwise, therefore there is a need for improvement of the under-canopy correction of snow cover. Here, we leverage multiple LIDAR overflights and in-situ observations with a distributed fiber-optic temperature sensor (DTS) to quantify snow cover under canopy as opposed to gap areas at the Sagehen Experimental Forest in the Northern Sierra Nevada, California, USA. Snow-off LIDAR overflights from 2014 are used to create a baseline high-resolution digital elevation model and classify pixels at 1 m resolution as canopy-covered or gap. Low canopy pixels are excluded from the analysis. Snow-on LIDAR overflights conducted by the Airborne Snow Observatory in 2016 are then used to classify all pixels as snow-covered or not and quantify fSCA under canopies vs. in gap areas over the Sagehen watershed. DTS observations are classified as snow-covered or not based on diel temperature fluctuations and used as validation for the LIDAR observations. LIDAR- and DTS-derived fSCA is also compared with

Improvement of distributed snowmelt energy balance modeling with MODIS-based NDSI-derived fractional snow-covered area data

Science.gov (United States)

Joel W. Homan; Charles H. Luce; James P. McNamara; Nancy F. Glenn

2011-01-01

Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain-front scale is important for improvements in large-scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snowcovered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale-up snowmelt models....

Snow farming: conserving snow over the summer season

Science.gov (United States)

Grünewald, Thomas; Wolfsperger, Fabian; Lehning, Michael

2018-01-01

Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63 % of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9 % of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters thickness of the sawdust layer, air temperature, precipitation and wind speed were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for mid

Snow measurement by cosmic radiation; Mesure de la neige par rayonnement cosmique

Energy Technology Data Exchange (ETDEWEB)

NONE

2004-02-01

The knowledge of the water content equivalence of the snow cover is an important element for the improvement of the water resource management. It allows in particular to evaluate and foresee the filling up supplies of big seasonal reservoirs. Electricite de France (EdF), in collaboration with the national center of scientific research (CNRS) and Meteo France, has developed a new generation of sensors, the cosmic radiation snow gauge, allowing the automatic monitoring of the status of snow stocks by the measurement of the water value of the snow cover. (J.S.)

Sentinels for snow science

Science.gov (United States)

Gascoin, S.; Grizonnet, M.; Baba, W. M.; Hagolle, O.; Fayad, A.; Mermoz, S.; Kinnard, C.; Fatima, K.; Jarlan, L.; Hanich, L.

2017-12-01

Current spaceborne sensors do not allow retrieving the snow water equivalent in mountain regions, "the most important unsolved problem in snow hydrology" (Dozier, 2016). While the NASA is operating an airborne mission to survey the SWE in the western USA, elsewhere, however, snow scientists and water managers do not have access to routine SWE measurements at the scale of a mountain range. In this presentation we suggest that the advent of the Copernicus Earth Observation programme opens new perspectives to address this issue in mountain regions worldwide. The Sentinel-2 mission will provide global-scale multispectral observations at 20 m resolution every 5-days (cloud permitting). The Sentinel-1 mission is already imaging the global land surface with a C-band radar at 10 m resolution every 6 days. These observations are unprecedented in terms of spatial and temporal resolution. However, the nature of the observation (radiometry, wavelength) is in the continuity of previous and ongoing missions. As a result, it is relatively straightforward to re-use algorithms that were developed by the remote sensing community over the last decades. For instance, Sentinel-2 data can be used to derive maps of the snow cover extent from the normalized difference snow index, which was initially proposed for Landsat. In addition, the 5-days repeat cycle allows the application of gap-filling algorithms, which were developed for MODIS based on the temporal dimension. The Sentinel-1 data can be used to detect the wet snow cover and track melting areas as proposed for ERS in the early 1990's. Eventually, we show an example where Sentinel-2-like data improved the simulation of the SWE in the data-scarce region of the High Atlas in Morocco through assimilation in a distributed snowpack model. We encourage snow scientists to embrace Sentinel-1 and Sentinel-2 data to enhance our knowledge on the snow cover dynamics in mountain regions.

CellDyM: A room temperature operating cryogenic cell for the dynamic monitoring of snow metamorphism by time-lapse X-ray microtomography

Science.gov (United States)

Calonne, N.; Flin, F.; Lesaffre, B.; Dufour, A.; Roulle, J.; Puglièse, P.; Philip, A.; Lahoucine, F.; Geindreau, C.; Panel, J.-M.; Roscoat, S. Rolland; Charrier, P.

2015-05-01

Monitoring the time evolution of snow microstructure in 3-D is crucial for a better understanding of snow metamorphism. We, therefore, designed a cryogenic cell that precisely controls the experimental conditions of a sample while it is scanned by X-ray tomography. Based on a thermoelectrical regulation and a vacuum insulation, the cell operates at room temperature. It is, thus, adaptable to diverse scanners, offering advantages in terms of imaging techniques, resolution, and speed. Three-dimensional time-lapse series were obtained under equitemperature and temperature gradient conditions at a 7.8 μm precision. The typical features of each metamorphism and the anisotropic faceting behavior between the basal and prismatic planes, known to occur close to -2°C, were observed in less than 30 h. These results are consistent with the temperature fields expected from heat conduction simulations through the cell. They confirm the cell's accuracy and the interest of relatively short periods to study snow metamorphism.

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

NARCIS (Netherlands)

Stigter, Emmy E.; Wanders, Niko; Saloranta, Tuomo M.; Shea, Joseph M.; Bierkens, M.F.P.; Immerzeel, W.W.

2017-01-01

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

The Snowcloud System: Architecture and Algorithms for Snow Hydrology Studies

Science.gov (United States)

Skalka, C.; Brown, I.; Frolik, J.

2013-12-01

Snowcloud is an embedded data collection system for snow hydrology field research campaigns conducted in harsh climates and remote areas. The system combines distributed wireless sensor network technology and computational techniques to provide data at lower cost and higher spatio-temporal resolution than ground-based systems using traditional methods. Snowcloud has seen multiple Winter deployments in settings ranging from high desert to arctic, resulting in over a dozen node-years of practical experience. The Snowcloud system architecture consists of multiple TinyOS mesh-networked sensor stations collecting environmental data above and, in some deployments, below the snowpack. Monitored data modalities include snow depth, ground and air temperature, PAR and leaf-area index (LAI), and soil moisture. To enable power cycling and control of multiple sensors a custom power and sensor conditioning board was developed. The electronics and structural systems for individual stations have been designed and tested (in the lab and in situ) for ease of assembly and robustness to harsh winter conditions. Battery systems and solar chargers enable seasonal operation even under low/no light arctic conditions. Station costs range between 500 and 1000 depending on the instrumentation suite. For remote field locations, a custom designed hand-held device and data retrieval protocol serves as the primary data collection method. We are also developing and testing a Gateway device that will report data in near-real-time (NRT) over a cellular connection. Data is made available to users via web interfaces that also provide basic data analysis and visualization tools. For applications to snow hydrology studies, the better spatiotemporal resolution of snowpack data provided by Snowcloud is beneficial in several aspects. It provides insight into snowpack evolution, and allows us to investigate differences across different spatial and temporal scales in deployment areas. It enables the

Changes in Snow Albedo Resulting from Snow Darkening Caused by Black Carbon

Science.gov (United States)

Engels, J.; Kloster, S.; Bourgeois, Q.

2014-12-01

We investigate the potential impact of snow darkening caused by pre-industrial and present-day black carbon (BC) emissions on snow albedo and subsequently climate. To assess this impact, we implemented the effect of snow darkening caused by BC emitted from natural as well as anthropogenic sources into the Max Planck Institute for Meteorology Earth System Model (MPI-M ESM). Considerable amounts of BC are emitted e.g. from fires and are transported through the atmosphere for several days before being removed by rain or snow precipitation in snow covered regions. Already very small quantities of BC reduce the snow reflectance significantly, with consequences for snow melting and snow spatial coverage. We implemented the snow albedo reduction caused by BC contamination and snow aging in the one layer land surface component (JSBACH) of the atmospheric general circulation model ECHAM6, developed at MPI-M. For this we used the single-layer simulator of the SNow, Ice, and Aerosol Radiation (SNICAR-Online (Flanner et al., 2007); http://snow.engin.umich.edu) model to derive snow albedo values for BC in snow concentrations ranging between 0 and 1500 ng(BC)/g(snow) for different snow grain sizes for the visible (0.3 - 0.7 μm) and near infrared range (0.7 - 1.5 μm). As snow grains grow over time, we assign different snow ages to different snow grain sizes (50, 150, 500, and 1000 μm). Here, a radius of 50 μm corresponds to new snow, whereas a radius of 1000 μm corresponds to old snow. The deposition rates of BC on snow are prescribed from previous ECHAM6-HAM simulations for two time periods, pre-industrial (1880-1889) and present-day (2000-2009), respectively. We perform a sensitivity study regarding the scavenging of BC by snow melt. To evaluate the newly implemented albedo scheme we will compare the modeled black carbon in snow concentrations to observed ones. Moreover, we will show the impact of the BC contamination and snow aging on the simulated snow albedo. The

Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

Directory of Open Access Journals (Sweden)

Emiliano Cimoli

2017-11-01

Full Text Available The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively. Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond.

Understanding snow-transport processes shaping the mountain snow-cover

Directory of Open Access Journals (Sweden)

R. Mott

2010-12-01

Full Text Available Mountain snow-cover is normally heterogeneously distributed due to wind and precipitation interacting with the snow cover on various scales. The aim of this study was to investigate snow deposition and wind-induced snow-transport processes on different scales and to analyze some major drift events caused by north-west storms during two consecutive accumulation periods. In particular, we distinguish between the individual processes that cause specific drifts using a physically based model approach. Very high resolution wind fields (5 m were computed with the atmospheric model Advanced Regional Prediction System (ARPS and used as input for a model of snow-surface processes (Alpine3D to calculate saltation, suspension and preferential deposition of precipitation. Several flow features during north-west storms were identified with input from a high-density network of permanent and mobile weather stations and indirect estimations of wind directions from snow-surface structures, such as snow dunes and sastrugis. We also used Terrestrial and Airborne Laser Scanning measurements to investigate snow-deposition patterns and to validate the model. The model results suggest that the in-slope deposition patterns, particularly two huge cross-slope cornice-like drifts, developed only when the prevailing wind direction was northwesterly and were formed mainly due to snow redistribution processes (saltation-driven. In contrast, more homogeneous deposition patterns on a ridge scale were formed during the same periods mainly due to preferential deposition of precipitation. The numerical analysis showed that snow-transport processes were sensitive to the changing topography due to the smoothing effect of the snow cover.

Deriving Snow Cover Metrics for Alaska from MODIS

Directory of Open Access Journals (Sweden)

Chuck Lindsay

2015-09-01

Full Text Available Moderate Resolution Imaging Spectroradiometer (MODIS daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1 and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel’s position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179–311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/.

Quantifying forest mortality with the remote sensing of snow

Science.gov (United States)

Baker, Emily Hewitt

Greenhouse gas emissions have altered global climate significantly, increasing the frequency of drought, fire, and pest-related mortality in forests across the western United States, with increasing area affected each year. Associated changes in forests are of great concern for the public, land managers, and the broader scientific community. These increased stresses have resulted in a widespread, spatially heterogeneous decline of forest canopies, which in turn exerts strong controls on the accumulation and melt of the snowpack, and changes forest-atmosphere exchanges of carbon, water, and energy. Most satellite-based retrievals of summer-season forest data are insufficient to quantify canopy, as opposed to the combination of canopy and undergrowth, since the signals of the two types of vegetation greenness have proven persistently difficult to distinguish. To overcome this issue, this research develops a method to quantify forest canopy cover using winter-season fractional snow covered area (FSCA) data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. In areas where the ground surface and undergrowth are completely snow-covered, a pixel comprises only forest canopy and snow. Following a snowfall event, FSCA initially rises, as snow is intercepted in the canopy, and then falls, as snow unloads. A select set of local minima in a winter F SCA timeseries form a threshold where canopy is snow-free, but forest understory is snow-covered. This serves as a spatially-explicit measurement of forest canopy, and viewable gap fraction (VGF) on a yearly basis. Using this method, we determine that MODIS-observed VGF is significantly correlated with an independent product of yearly crown mortality derived from spectral analysis of Landsat imagery at 25 high-mortality sites in northern Colorado. (r =0.96 +/-0.03, p =0.03). Additionally, we determine the lag timing between green-stage tree mortality and

Airborne gamma-radiation snow water-equivalent and soil-moisture measurements and satellite areal extent of snow-cover measurements. A user's guide. Version 3.0

International Nuclear Information System (INIS)

Carroll, T.; Allen, M.

1988-01-01

The National Remote Sensing Hydrology Program is managed by the Office of Hydrology and consists of the Airborne Snow Survey Section and the Satellite Hydrology Section. The Airborne Snow Survey Section makes airborne snow water-equivalent and soil-moisture measurements over large areas of the country subject to a severe and chronic snowmelt flooding threat. The User's Guide is intended primarily to provide field hydrologists with some background on the technical and administrative aspects of the National Remote Sensing Hydrology Program. The guide summarizes the techniques and procedures used to make and distribute real-time, operational airborne snow water-equivalent measurements and satellite areal extent of snow-cover measurements made over large areas of the country. The current airborne and satellite databases are summarized, and procedures to access the real-time observations through both AFOS and through a commercial, electronic bulletin board system are given in the appendices

Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

KAUST Repository

Jadoon, Khan

2015-09-18

We tested an off-ground ground-penetrating radar (GPR) system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

KAUST Repository

Jadoon, Khan; Weihermller, Lutz; McCabe, Matthew; Moghadas, Davood; Vereecken, Harry; Lambot, Sbastien

2015-01-01

We tested an off-ground ground-penetrating radar (GPR) system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar

Directory of Open Access Journals (Sweden)

Khan Zaib Jadoon

2015-09-01

Full Text Available We tested an off-ground ground-penetrating radar (GPR system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

The stepwise discriminant algorithm for snow cover mapping based on FY-3/MERSI data

Science.gov (United States)

Han, Tao; Wang, Dawei; Jiang, Youyan; Wang, Xiaowei

2013-10-01

Medium Resolution Spectral Imager (MERSI) on board China's new generation polar orbit meteorological satellite FY- 3A provides a new data source for snow monitoring in large area. As a case study, the typical snow cover of Qilian Mountains in northwest China was selected in this paper to develop the algorithm to map snow cover using FY- 3A/MERSI. By analyzing the spectral response characteristics of snow and other surface elements, as well as each channel image quality on FY-3A/MERSI, the widely used Normalized Difference Snow Index (NDSI) was defined to be computed from channel 2 and channel 7 for this satellite data. Basing on NDSI, a tree-structure prototype version of snow identification model was proposed, including five newly-built multi-spectral indexes to remove those pixels such as forest, cloud shadow, water, lake ice, sand (salty land), or cloud that are usually confused with snow step by step, especially, a snow/cloud discrimination index was proposed to eliminate cloud, apart from use of cloud mask product in advance. Furthermore, land cover land use (LULC) image has been adopted as auxiliary dataset to adjust the corresponding LULC NDSI threshold constraints for snow final determination and optimization. This model is composed as the core of FY-3A/MERSI snow cover mapping flowchart, to produce daily snow map at 250m spatial resolution, and statistics can be generated on the extent and persistence of snow cover in each pixel for time series maps. Preliminary validation activities of our snow identification model have been undertaken. Comparisons of the 104 FY- 3A/MERSI snow cover maps in 2010-2011 snow season with snow depth records from 16 meteorological stations in Qilian Mountains region, the sunny snow cover had an absolute accuracy of 92.8%. Results of the comparison with the snow cover identified from 6 Terra/MODIS scenes showed that they had consistent pixels about 85%. When the two satellite resultant snow cover maps compared with the 6

Improving snow water equivalent simulations in an alpine basin using blended gage precipitation and snow pillow measurements

Science.gov (United States)

Sohrabi, M.; Safeeq, M.; Conklin, M. H.

2017-12-01

Snowpack is a critical freshwater reservoir that sustains ecosystem, natural habitat, hydropower, agriculture, and urban water supply in many areas around the world. Accurate estimation of basin scale snow water equivalent (SWE), through both measurement and modeling, has been significantly recognized to improve regional water resource management. Recent advances in remote data acquisition techniques have improved snow measurements but our ability to model snowpack evolution is largely hampered by poor knowledge of inherently variable high-elevation precipitation patterns. For a variety of reasons, majority of the precipitation gages are located in low and mid-elevation range and function as drivers for basin scale hydrologic modeling. Here, we blend observed gage precipitation from low and mid-elevation with point observations of SWE from high-elevation snow pillow into a physically based snow evolution model (SnowModel) to better represent the basin-scale precipitation field and improve snow simulations. To do this, we constructed two scenarios that differed in only precipitation. In WTH scenario, we forced the SnowModel using spatially distributed gage precipitation data. In WTH+SP scenario, the model was forced with spatially distributed precipitation data derived from gage precipitation along with observed precipitation from snow pillows. Since snow pillows do not directly measure precipitation, we uses positive change in SWE as a proxy for precipitation. The SnowModel was implemented at daily time step and 100 m resolution for the Kings River Basin, USA over 2000-2014. Our results show an improvement in snow simulation under WTH+SP as compared to WTH scenario, which can be attributed to better representation in high-elevation precipitation patterns under WTH+SP. The average Nash Sutcliffe efficiency over all snow pillow and course sites was substantially higher for WTH+SP (0.77) than for WTH scenario (0.47). The maximum difference in observed and simulated

Long-term analyses of snow dynamics within the french Alps on the 1900-2100 period. Analyses of historical snow water equivalent observations, modelisations and projections of a hundred of snow courses.

Science.gov (United States)

Mathevet, T.; Joel, G.; Gottardi, F.; Nemoz, B.

2017-12-01

The aim of this communication is to present analyses of climate variability and change on snow water equivalent (SWE) observations, reconstructions (1900-2016) and scenarii (2020-2100) of a hundred of snow courses dissiminated within the french Alps. This issue became particularly important since a decade, in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production. As a water resources manager in french mountainuous regions, EDF (french hydropower company) has developed and managed a hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurments of a hundred of snow courses within the french Alps. EDF have been operating an automatic SWE sensors network, complementary to the snow course network. Based on numerous SWE observations time-series and snow accumulation and melt model (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2016 period. These reconstructions have been extented to 1900 using 20 CR reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii. Considering various mountainous areas within the french Alps, this communication focuses on : (1) long term (1900-2016) analyses of variability and trend of total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length , (2) long term variability of hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii. Comparing historical period (1950-1984) to recent period (1984-2016), quantitative results within a region in the north Alps (Maurienne) shows an increase of air temperature by 1.2 °C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season length by 15 days. These analyses will be extended from north to south

Improving snow cover mapping in forests through the use of a canopy reflectance model

International Nuclear Information System (INIS)

Klein, A.G.; Hall, D.K.; Riggs, G.A.

1998-01-01

MODIS, the moderate resolution imaging spectro radiometer, will be launched in 1998 as part of the first earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy. A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem–Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow reflectance model was used in conjunction with a canopy reflectance model (GeoSAIL) to model the reflectance of a snow-covered forest stand. Using these coupled models, the effects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated. Using both the TM images and the reflectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithm's classification accuracy in forested areas. The improvements include using the normalized difference snow index and normalized difference vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classified incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identified as snow on non-snow-covered TM scenes. (author)

Android Based Area Web Monitoring

Science.gov (United States)

Kanigoro, Bayu; Galih Salman, Afan; Moniaga, Jurike V.; Chandra, Eric; Rezky Chandra, Zein

2014-03-01

The research objective is to develop an application that can be used in the monitoring of an area by using a webcam. It aims to create a sense of security on the user's application because it can monitor an area using mobile phone anywhere. The results obtained in this study is to create an area with a webcam monitoring application that can be accessed anywhere as long as the monitoring results have internet access and can also be accessed through Android Based Mobile Phone.

Android Based Area Web Monitoring

Directory of Open Access Journals (Sweden)

Kanigoro Bayu

2014-03-01

Full Text Available The research objective is to develop an application that can be used in the monitoring of an area by using a webcam. It aims to create a sense of security on the user's application because it can monitor an area using mobile phone anywhere. The results obtained in this study is to create an area with a webcam monitoring application that can be accessed anywhere as long as the monitoring results have internet access and can also be accessed through Android Based Mobile Phone.

Calculation of new snow densities from sub-daily automated snow measurements

Science.gov (United States)

Helfricht, Kay; Hartl, Lea; Koch, Roland; Marty, Christoph; Lehning, Michael; Olefs, Marc

2017-04-01

In mountain regions there is an increasing demand for high-quality analysis, nowcasting and short-range forecasts of the spatial distribution of snowfall. Operational services, such as for avalanche warning, road maintenance and hydrology, as well as hydropower companies and ski resorts need reliable information on the depth of new snow (HN) and the corresponding water equivalent (HNW). However, the ratio of HNW to HN can vary from 1:3 to 1:30 because of the high variability of new snow density with respect to meteorological conditions. In the past, attempts were made to calculate new snow densities from meteorological parameters mainly using daily values of temperature and wind. Further complex statistical relationships have been used to calculate new snow densities on hourly to sub-hourly time intervals to drive multi-layer snow cover models. However, only a few long-term in-situ measurements of new snow density exist for sub-daily time intervals. Settling processes within the new snow due to loading and metamorphism need to be considered when computing new snow density. As the effect of these processes is more pronounced for long time intervals, a high temporal resolution of measurements is desirable. Within the pluSnow project data of several automatic weather stations with simultaneous measurements of precipitation (pluviometers), snow water equivalent (SWE) using snow pillows and snow depth (HS) measurements using ultrasonic rangers were analysed. New snow densities were calculated for a set of data filtered on the basis of meteorological thresholds. The calculated new snow densities were compared to results from existing new snow density parameterizations. To account for effects of settling of the snow cover, a case study based on a multi-year data set using the snow cover model SNOWPACK at Weissfluhjoch was performed. Measured median values of hourly new snow densities at the different stations range from 54 to 83 kgm-3. This is considerably lower than a 1

An Inexpensive, Implantable Electronic Sensor for Autonomous Measurement of Snow Pack Parameters

Science.gov (United States)

De Roo, R. D.; Haengel, E.; Rogacki, S.

2015-12-01

Snow accumulations on the ground are an important source of water in many parts of the world. Mapping the accumulation, usually represented as the snow water equivalent (SWE), is valuable for water resource management. The longest record of regional and global maps of SWE are from orbiting microwave radiometers, which do not directly measure SWE but rather measure the scatter darkening from the snow pack. Robustly linking the scatter darkening to SWE eludes us to this day, in part because the snow pack is highly variable in both time and space. The data needed is currently collected by hand in "snow pits," and the labor-intensive process limits the size of the data sets that can be obtained. In particular, time series measurements are only a one or two samples per day at best, and come at the expense of spatial sampling. We report on the development of a low-power wireless device that can be embedded within a snow pack to report on some of the critical parameters needed to understand scatter darkening. The device autonomously logs temperature, the microwave dielectric constant and infrared backscatter local to the device. The microwave dielectric constant reveals the snow density and the presence of liquid water, while the infrared backscatter measurement, together with the density measurement, reveals a characteristic grain size of the snow pack. The devices are made to be inexpensive (less than $200 in parts each) and easily replicated, so that many can be deployed to monitor variations vertically and horizontally in the snow pack. The low-power operation is important both for longevity of observations as well as insuring minimal anomalous metamorphism of the snow pack. The hardware required for the microwave measurement is intended for wireless communications, and this feature will soon be implemented for near real-time monitoring of snow conditions. We will report on the design, construction and initial deployment of about 30 of these devices in northern lower

SNOW CLEARING SERVICE WINTER 2001-2002

CERN Multimedia

ST-HM Group; Tel. 72202

2001-01-01

As usual at this time of the year, the snowing clearing service, which comes under the control of the Transport Group (ST-HM), is preparing for the start of snow-clearing operations (timetable, stand-by service, personnel responsible for driving vehicles and machines, preparation of useful and necessary equipment, work instructions, etc.) in collaboration with the Cleaning Service (ST-TFM) and the Fire Brigade (TIS-FB). The main difficulty for the snow-clearing service is the car parks, which cannot be properly cleared because of the presence of CERN and private vehicles parked there overnight in different parts of the parking areas. The ST-HM Transport Group would therefore like to invite you to park vehicles together in order to facilitate the access of the snow ploughs, thus allowing the car parks to be cleared more efficiently before the personnel arrives for work in the mornings.

Winter survival of Scots pine seedlings under different snow conditions.

Science.gov (United States)

Domisch, Timo; Martz, Françoise; Repo, Tapani; Rautio, Pasi

2018-04-01

Future climate scenarios predict increased air temperatures and precipitation, particularly at high latitudes, and especially so during winter. Soil temperatures, however, are more difficult to predict, since they depend strongly on the fate of the insulating snow cover. 'Rain-on-snow' events and warm spells during winter can lead to thaw-freeze cycles, compacted snow and ice encasement, as well as local flooding. These adverse conditions could counteract the otherwise positive effects of climatic changes on forest seedling growth. In order to study the effects of different winter and snow conditions on young Scots pine (Pinus sylvestris L.) seedlings, we conducted a laboratory experiment in which 80 1-year-old Scots pine seedlings were distributed between four winter treatments in dasotrons: ambient snow cover (SNOW), compressed snow and ice encasement (ICE), flooded and frozen soil (FLOOD) and no snow (NO SNOW). During the winter treatment period and a 1.5-month simulated spring/early summer phase, we monitored the needle, stem and root biomass of the seedlings, and determined their starch and soluble sugar concentrations. In addition, we assessed the stress experienced by the seedlings by measuring chlorophyll fluorescence, electric impedance and photosynthesis of the previous-year needles. Compared with the SNOW treatment, carbohydrate concentrations were lower in the FLOOD and NO SNOW treatments where the seedlings had almost died before the end of the experiment, presumably due to frost desiccation of aboveground parts during the winter treatments. The seedlings of the ICE treatment showed dead needles and stems only above the snow and ice cover. The results emphasize the importance of an insulating and protecting snow cover for small forest tree seedlings, and that future winters with changed snow patterns might affect the survival of tree seedlings and thus forest productivity.

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

Directory of Open Access Journals (Sweden)

S. Kolberg

2006-01-01

Full Text Available A method for assimilating remotely sensed snow covered area (SCA into the snow subroutine of a grid distributed precipitation-runoff model (PRM is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC, which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E, based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started

J-SEx : The Jollie Snow Experiment, New Zealand

Science.gov (United States)

Kerr, T.; Singh, S.; Kees, L.; Webster, C.; Clark, M.; Hendrikx, J.; Woods, R.

2012-04-01

Intensive snow observations have been collected in a steep alpine catchment (the Jollie Valley) in the Southern Alps of New Zealand for four years at the time of maximum snow storage. The campaign, called the Jollie Snow Experiment (J-SEx), was undertaken to improve understanding of snow variability in a steep alpine landscape. Observation methods included manual depth-probing at hundreds of locations with associated snowpit-digging, and surface and air-borne ground penetrating radar. In addition, repeat (daily) oblique photography was carried out on a subcatchment for two of the years. Analysis of the observations, in conjunction with similar observations from around the world has enabled direction to be given for selecting optimal modelling scales, and an indication of what processes need to be resolved at the different scales. For instance, if models are to operate at sub-100 m horizontal scales, they need to resolve drifting, sloughing and avalanching processes. Binary regression tree methods have been applied to identify terrain variables which explain the observed snow mass variability. This has enabled an assessment of total catchment snow storage to be established for each year. This assessment showed that the controlling variables change from year to year, so that no single terrain-based interpolation method may be generally applied. The change in the terrain relationships each year has been taken as an indication that the different frequencies of snowfall-related climate types from one year to the next affects which terrain characteristics have the greatest impact on snow variability. Assessment of terrain effects at the slope scale indicates that slope angle has the potential to be a strong influence on snow variability in that steep slopes do not build up large accumulations, and that low-angled regions below steep areas become areas of large snow build-up. This "slope" effect is clearly evident from the repeat photography, with the last areas to melt

Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011-2017

Science.gov (United States)

Bair, Edward H.; Davis, Robert E.; Dozier, Jeff

2018-03-01

The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California - Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011-2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011-2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q.

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Science.gov (United States)

Alonso-González, Esteban; López-Moreno, J. Ignacio; Gascoin, Simon; García-Valdecasas Ojeda, Matilde; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Revuelto, Jesús; Ceballos, Antonio; Jesús Esteban-Parra, María; Essery, Richard

2018-02-01

We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

Science.gov (United States)

Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

Snow Matters

DEFF Research Database (Denmark)

Gyimóthy, Szilvia; Jensen, Martin Trandberg

2018-01-01

attribute of high altitude mountain destinations. Hitherto, researchers mostly engaged with snowclad landscapes as a backstage; trying to deconstruct the complex symbolism and representational qualities of this elusive substance. Despite snow being a strategically crucial condition for tourism in the Alps......This chapter explores the performative potential of snow for Alpine tourism, by drawing attention to its material and nonrepresentational significance for tourism practices. European imagination has been preoccupied with snow since medieval times and even today, snow features as the sine que non...

Constraining snowmelt in a temperature-index model using simulated snow densities

KAUST Repository

Bormann, Kathryn J.

2014-09-01

Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from -0.027gcm-3 to -0.004gcm-3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150-200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the

Constraining snowmelt in a temperature-index model using simulated snow densities

KAUST Repository

Bormann, Kathryn J.; Evans, Jason P.; McCabe, Matthew

2014-01-01

Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from -0.027gcm-3 to -0.004gcm-3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150-200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the

Geology and Mineral Deposits of the Snow Camp-Saxapahaw Area, Central North Carolina

Science.gov (United States)

Schmidt, Robert G.; Gumiel, Pablo; Payas, Alba

2006-01-01

The Snow Camp-Saxapahaw study area, in the Carolina slate belt in the Southeastern United States, is notable for large zones of high-sulfidation alteration in arc-related metavolcanic rocks. The area has potential for additional significant pyrophyllite and related aluminosilicate refractory mineral deposits and may have potential for small- to medium-size gold deposits also associated with the high-sulfidation hydrothermal systems. The Carolina slate belt is an elongate zone of mostly low-grade metamorphic rocks of Neoproterozoic to early Paleozoic age that extends from northeastern Georgia to southern Virginia. It is dominated by volcanic rocks but locally consists of fine-grained epiclastic sedimentary rocks. Plutons and subvolcanic bodies have intruded the rocks of the Carolina slate belt in many places and have been important in controlling the metamorphism and in localizing hydrothermal alteration. The Snow Camp-Saxapahaw area is mostly underlain by volcanic and volcaniclastic rocks and lesser amounts of intrusive shallow plutons. The volcanic rocks range in composition from basalt to rhyolite; however andesites, dacites, and rhyodacites are the most abundant. The intrusive bodies are largely granite and quartz monzonite; gabbroic bodies also are common. It was possible to establish the relative ages of only part of these rocks. Two northeast-trending fault zones and fractures divide the map area into three structural blocks; the central block was tilted down to the southwest to form a grabenlike structure. Most of the hydrothermally altered rocks and all of the intensely altered zones are confined to the downdropped block, which we think may have been calderalike in origin. A major volcanic unit, the Reedy Branch Tuff, is limited to the southwestern part of the graben and may be the youngest volcanic rock in the area. Layered rocks record one or more strong folding events, but the diversity of rock types, lack of recognizable stratigraphic markers, and

Monitoring and evaluation of seasonal snow cover in Kashmir valley ...

Indian Academy of Sciences (India)

89 to 2007–08) climatic conditions prevailed in both ranges of Kashmir valley. Region-wise ... effective use of snowmelt runoff models (Rango and Martinec ... J. Earth Syst. Sci. 118, No. ... of cloud cover can affect delineation of snow cover,.

Changes in Andes snow cover from MODIS data, 2000–2016

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F. A. Saavedra

2018-03-01

Full Text Available The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS satellite sensors (500 m, 8-day maximum snow cover extent. This analysis is conducted between 8 and 36° S due to high frequency of cloud (>  30 % of the time south and north of this range. We ran Mann–Kendall and Theil–Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP  =  20 %. We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2 and University of Delaware datasets and climate indices as El Niño–Southern Oscillation (ENSO, Southern Annular Mode (SAM, and Pacific Decadal Oscillation (PDO. Areas north of 29° S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2 with persistent snow cover between 29 and 36° S experienced a significant loss of snow cover (2–5 fewer days of snow year−1. Snow loss was more pronounced (62 % of the area with significant trends on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10–30 m year−1 south of 29–30° S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31° S, whereas the SAM better predicted SP south of 31° S.

Shifting mountain snow patterns in a changing climate from remote sensing retrieval.

Science.gov (United States)

Dedieu, J P; Lessard-Fontaine, A; Ravazzani, G; Cremonese, E; Shalpykova, G; Beniston, M

2014-09-15

Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000-2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover

An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data

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Théo Masson

2018-04-01

Full Text Available The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI and spectral unmixing (SU. These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates, the Pyrenees (30 dates, and the Moroccan Atlas (24 dates. This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version

Area monitoring intelligent system - SIMA

International Nuclear Information System (INIS)

Bhoem, P.; Hisas, F.; Gelardi, G.

1990-01-01

The area monitoring intelligent system (SIMA) is an equipment to be used in radioprotection. SIMA has the function of monitoring the radiation levels of determined areas of the installations where radioactive materials are handled. (Author) [es

Assessment of Snow Status Changes Using L-HH Temporal-Coherence Components at Mt. Dagu, China

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Yong Wang

2015-09-01

Full Text Available Multitemporal Phased Array type L-band Synthetic Aperture Radar (PALSAR horizontally transmitted and horizontally received (HH coherence data was decomposed into temporal-coherence, spatial-coherence, and thermal noise components. The multitemporal data spanned between February and May of 2008, and consisted of two pairs of interferometric SAR (InSAR images formed by consecutive repeat passes. With the analysis of ancillary data, a snow increase process and a snow decrease process were determined. Then, the multiple temporal-coherence components were used to study the variation of thawing and freezing statuses of snow because the components can mostly reflect the temporal change of the snow that occurred between two data acquisitions. Compared with snow mapping results derived from optical images, the outcomes from the snow increase process and the snow decrease process reached an overall accuracy of 71.3% and 79.5%, respectively. Being capable of delineating not only the areas with or without snow cover but also status changes among no-snow, wet snow, and dry snow, we have developed a critical means to assess the water resource in alpine areas.

Remote detection of oil spilled under ice and snow using nuclear magnetic resonance

International Nuclear Information System (INIS)

Nedwed, T.; Srnka, L.; Thomann, H.

2008-01-01

The technical challenge of detecting oil that has been accidentally spilled under ice and snow was discussed with particular reference to the tools used to characterize the molecular composition of liquids and solids. One such tool is nuclear magnetic resonance (NMR) which works by releasing electromagnetic energy. The NMR signals from oil and water can be differentiated based on the inherent differences in the NMR signal responses from different fluid types. The method can also use the Earth's magnetic field as the static magnetic field and thereby eliminate the complexity and cost of generating an independent magnetic field for remotely detecting fluids below a surface. This study examined the feasibility of altering existing surface-based instruments and placing them in a helicopter for aerial monitoring. The goal of this research was to develop a tool for remote detection of oil under ice in a marine environment, or for detection of oil under snow on land using an inexpensive tool that can quickly inspect large areas. The proposed tool and technique produces a direct hydrocarbon signal that may not have interference from ice and snow. 9 refs., 6 figs

Variability in snow depth time series in the Adige catchment

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Giorgia Marcolini

2017-10-01

New hydrological insights for the region: Stations located above and below 1650 m a.s.l. show different dynamics, with the latter experiencing in the last decades a larger reduction of average snow depth and snow cover duration, than the former. Wavelet analyses show that snow dynamics change with elevation and correlate differently with climatic indices at multiple temporal scales. We also observe that starting from the late 1980s snow cover duration and mean seasonal snow depth are below the average in the study area. We also identify an elevation dependent correlation with the temperature. Moreover, correlation with the Mediterranean Oscillation Index and with the North Atlantic Oscillation Index is identified.

Snow hydrology in Mediterranean mountain regions: A review

Science.gov (United States)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; López-Moreno, Juan Ignacio; Drapeau, Laurent; Page, Michel Le; Escadafal, Richard

2017-08-01

Water resources in Mediterranean regions are under increasing pressure due to climate change, economic development, and population growth. Many Mediterranean rivers have their headwaters in mountainous regions where hydrological processes are driven by snowpack dynamics and the specific variability of the Mediterranean climate. A good knowledge of the snow processes in the Mediterranean mountains is therefore a key element of water management strategies in such regions. The objective of this paper is to review the literature on snow hydrology in Mediterranean mountains to identify the existing knowledge, key research questions, and promising technologies. We collected 620 peer-reviewed papers, published between 1913 and 2016, that deal with the Mediterranean-like mountain regions in the western United States, the central Chilean Andes, and the Mediterranean basin. A large amount of studies in the western United States form a strong scientific basis for other Mediterranean mountain regions. We found that: (1) the persistence of snow cover is highly variable in space and time but mainly controlled by elevation and precipitation; (2) the snowmelt is driven by radiative fluxes, but the contribution of heat fluxes is stronger at the end of the snow season and during heat waves and rain-on-snow events; (3) the snow densification rates are higher in these regions when compared to other climate regions; and (4) the snow sublimation is an important component of snow ablation, especially in high-elevation regions. Among the pressing issues is the lack of continuous ground observation in high-elevation regions. However, a few years of snow depth (HS) and snow water equivalent (SWE) data can provide realistic information on snowpack variability. A better spatial characterization of snow cover can be achieved by combining ground observations with remotely sensed snow data. SWE reconstruction using satellite snow cover area and a melt model provides reasonable information that

Canadian snow and sea ice: historical trends and projections

Science.gov (United States)

Mudryk, Lawrence R.; Derksen, Chris; Howell, Stephen; Laliberté, Fred; Thackeray, Chad; Sospedra-Alfonso, Reinel; Vionnet, Vincent; Kushner, Paul J.; Brown, Ross

2018-04-01

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020-2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5-10 % per decade (or 15-30 % in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10 % per decade (30 % in total) are projected across southern Canada.

Snow conditions and usability value of pastureland for semi-domesticated reindeer (Rangifer tarandus tarandus in northern boreal forest area

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Jouko Kumpula

2009-01-01

Full Text Available We studied variation in snow conditions and selection of pasture area according to altitude by semi-domesticated reindeer (Rangifer tarandus tarandus during 1999 - 2002 in a pine forest area utilised by forest industry in the Ivalo reindeer herding district, northern Finland. Snow conditions were measured over the course of three winters along equilateral triangles (side 3.5 km for three times per winter. The altitudinal selection of pasture area by reindeer was studied using GPS tracking data (10 977 locations from 29 female reindeer. We observed that interannual weather variation mostly affected the depth, density and hardness of snow in the study area. At the forest landscape level, snow depth and density increased with altitude. Thinnest and deepest snow cover occurred on western and northern slopes, respectively. In contrast, forest harvesting did not seem to affect snow conditions. From spring to autumn, reindeer mainly used higher altitudes in pastures. In early and mid-winter, when snow conditions were easy or moderate reindeer still preferred higher altitudes, but in late winter when snow conditions and food accession were at their most difficult, they preferred lower altitudes. We conclude that especially the use of high elevation forestland pastures may become more difficult for reindeer if the global climatic change causes higher winter precipitation to the northern boreal forest area. In general, the low-elevation forestland areas have primary winter grazing value for reindeer but these areas are also intensively used by forest industry.Abstract in Finnish / Tiivistelmä:Lumiolosuhteet ja laidunten käyttöarvo poronhoidossa pohjoisella havumetsäaluella Lumiolosuhteiden vaihtelua ja porojen (Rangifer tarandus tarandus laidunalueen valintaa maaston korkeuden perusteella tutkittiin vuosina 1999–2002 metsätalouden hyödyntämällä mäntymetsäalueella Ivalon paliskunnassa, Pohjois-Suomessa. Lumiolosuhteet mitattiin kolme kertaa

Simulating Snow in Canadian Boreal Environments with CLASS for ESM-SnowMIP

Science.gov (United States)

Wang, L.; Bartlett, P. A.; Derksen, C.; Ireson, A. M.; Essery, R.

2017-12-01

The ability of land surface schemes to provide realistic simulations of snow cover is necessary for accurate representation of energy and water balances in climate models. Historically, this has been particularly challenging in boreal forests, where poor treatment of both snow masking by forests and vegetation-snow interaction has resulted in biases in simulated albedo and snowpack properties, with subsequent effects on both regional temperatures and the snow albedo feedback in coupled simulations. The SnowMIP (Snow Model Intercomparison Project) series of experiments or `MIPs' was initiated in order to provide assessments of the performance of various snow- and land-surface-models at selected locations, in order to understand the primary factors affecting model performance. Here we present preliminary results of simulations conducted for the third such MIP, ESM-SnowMIP (Earth System Model - Snow Model Intercomparison Project), using the Canadian Land Surface Scheme (CLASS) at boreal forest sites in central Saskatchewan. We assess the ability of our latest model version (CLASS 3.6.2) to simulate observed snowpack properties (snow water equivalent, density and depth) and above-canopy albedo over 13 winters. We also examine the sensitivity of these simulations to climate forcing at local and regional scales.

Brightness temperature simulation of snow cover based on snow grain size evolution using in situ data

Science.gov (United States)

Wu, Lili; Li, Xiaofeng; Zhao, Kai; Zheng, Xingming; Jiang, Tao

2016-07-01

Snow depth parameter inversion from passive microwave remote sensing is of great significance to hydrological process and climate systems. The Helsinki University of Technology (HUT) model is a commonly used snow emission model. Snow grain size (SGS) is one of the important input parameters, but SGS is difficult to obtain in broad areas. The time series of SGS are first evolved by an SGS evolution model (Jordan 91) using in situ data. A good linear relationship between the effective SGS in HUT and the evolution SGS was found. Then brightness temperature simulations are performed based on the effective SGS and evolution SGS. The results showed that the biases of the simulated brightness temperatures based on the effective SGS and evolution SGS were -6.5 and -3.6 K, respectively, for 18.7 GHz and -4.2 and -4.0 K for 36.5 GHz. Furthermore, the model is performed in six pixels with different land use/cover type in other areas. The results showed that the simulated brightness temperatures based on the evolution SGS were consistent with those from the satellite. Consequently, evolution SGS appears to be a simple method to obtain an appropriate SGS for the HUT model.

A Method for Snow Reanalysis: The Sierra Nevada (USA) Example

Science.gov (United States)

Girotto, Manuela; Margulis, Steven; Cortes, Gonzalo; Durand, Michael

2017-01-01

This work presents a state-of-the art methodology for constructing snow water equivalent (SWE) reanalysis. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data from the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. This presentation illustrates how the reanalysis dataset was used to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years and ultimately improve real-time streamflow predictions.

Improvement of a snow albedo parameterization in the Snow-Atmosphere-Soil Transfer model: evaluation of impacts of aerosol on seasonal snow cover

Science.gov (United States)

Zhong, Efang; Li, Qian; Sun, Shufen; Chen, Wen; Chen, Shangfeng; Nath, Debashis

2017-11-01

The presence of light-absorbing aerosols (LAA) in snow profoundly influence the surface energy balance and water budget. However, most snow-process schemes in land-surface and climate models currently do not take this into consideration. To better represent the snow process and to evaluate the impacts of LAA on snow, this study presents an improved snow albedo parameterization in the Snow-Atmosphere-Soil Transfer (SAST) model, which includes the impacts of LAA on snow. Specifically, the Snow, Ice and Aerosol Radiation (SNICAR) model is incorporated into the SAST model with an LAA mass stratigraphy scheme. The new coupled model is validated against in-situ measurements at the Swamp Angel Study Plot (SASP), Colorado, USA. Results show that the snow albedo and snow depth are better reproduced than those in the original SAST, particularly during the period of snow ablation. Furthermore, the impacts of LAA on snow are estimated in the coupled model through case comparisons of the snowpack, with or without LAA. The LAA particles directly absorb extra solar radiation, which accelerates the growth rate of the snow grain size. Meanwhile, these larger snow particles favor more radiative absorption. The average total radiative forcing of the LAA at the SASP is 47.5 W m-2. This extra radiative absorption enhances the snowmelt rate. As a result, the peak runoff time and "snow all gone" day have shifted 18 and 19.5 days earlier, respectively, which could further impose substantial impacts on the hydrologic cycle and atmospheric processes.

Climate change impacts on snow water availability in the Euphrates-Tigris basin

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M. Özdoğan

2011-09-01

Full Text Available This study investigates the effects of projected climate change on snow water availability in the Euphrates-Tigris basin using the Variable Infiltration Capacity (VIC macro scale hydrologic model and a set of regional climate-change outputs from 13 global circulation models (GCMs forced with two greenhouse gas emission scenarios for two time periods in the 21st century (2050 and 2090. The hydrologic model produces a reasonable simulation of seasonal and spatial variation in snow cover and associated snow water equivalent (SWE in the mountainous areas of the basin, although its performance is poorer at marginal snow cover sites. While there is great variation across GCM outputs influencing snow water availability, the majority of models and scenarios suggest a significant decline (between 10 and 60 percent in available snow water, particularly under the high-impact A2 climate change scenario and later in the 21st century. The changes in SWE are more stable when multi-model ensemble GCM outputs are used to minimize inter-model variability, suggesting a consistent and significant decrease in snow-covered areas and associated water availability in the headwaters of the Euphrates-Tigris basin. Detailed analysis of future climatic conditions point to the combined effects of reduced precipitation and increased temperatures as primary drivers of reduced snowpack. Results also indicate a more rapid decline in snow cover in the lower elevation zones than the higher areas in a changing climate but these findings also contain a larger uncertainty. The simulated changes in snow water availability have important implications for the future of water resources and associated hydropower generation and land-use management and planning in a region already ripe for interstate water conflict. While the changes in the frequency and intensity of snow-bearing circulation systems or the interannual variability related to climate were not considered, the simulated

[Investigation of PFOS and PFOA pollution in snow in Shenyang, China].

Science.gov (United States)

Liu, Wei; Jin, Yi-he; Quan, Xie; Dong, Guang-hui; Liu, Bing; Wang, Jing; Wang, Ke; Yu, Qi-lin; Norimitsu, Saito

2007-09-01

The purpose of this study was to investigate the atmospheric perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) pollution in Shenyang by analyzing the concentration of the two compounds in snow. Snow samples were collected from both urban and suburban areas on Feb. 6 (n=36) and Feb. 25 (n=5) in 2006 in Shenyang, China. PFOS and PFOA were extracted from the snowmelt by solid phase extraction and then were analyzed by LC-MS-SIM. PFOS and PFOA were discernable in all the samples. In the snow samples collected on Feb. 6, 2006, the geometric average concentrations of PFOS and PFOA were 2.0 ng x L(-1) (range: 0.4-46.2 ng x L(-1)) and 3.6 ng x L(-1) (range: 1.6-22.4 ng x L(-1)), respectively. The 95% confidence intervals of PFOS and PFOA were 1.5-2.8 ng x L(-1) and 3.1-4.2 ng x L(-1), respectively. The concentration of PFOS and PFOA showed significant correlation at the center of Shenyang City (n=9), while the highest concentrations of PFOS and PFOA were detected at the same site located in the rural area. Geometric average concentrations of PFOS and PFOA were both 2.2 ng x L(-1) in the Feb. 25 snow samples. Concentrations of PFOS in two snow events did not show significant difference. Whereas PFOA concentration in Feb. 25 snow was higher than that in Feb. 6 snow. Results suggested the widespread PFOS and PFOA pollution in snow in Shenyang. There may be similar resources for the two compounds in regional area. Moreover, there may be continual import of PFOS to the atmosphere in Shenyang from specific resources, while PFOS and PFOA may exhibit different environmental behavior.

Characteristics of snow cover duration across the northeast United States of America

Science.gov (United States)

Leathers, Daniel J.; Luff, Barbara L.

1997-11-01

The presence or absence of a snow cover affects a myriad of environmental and societal systems through its modification of the surface radiation balance and its ultimate impact on near-surface air temperatures. Daily snow cover data were collected for a network of 91 stations covering the northeast USA from Maine, south through to West Virginia. The snow cover data along with ancillary temperature, snowfall and precipitation data were used to investigate the characteristics of snow cover duration in this region and the effects of the snow cover on boundary layer climate variables for the snow cover seasons 1948-1949 through to 1987-1988.Results indicate that snow cover duration is variable in both space and time. The duration of a snow cover of 2.5 cm or greater varies from greater than 100 days in northern New England to less than 20 days across areas of Delaware, Maryland and West Virginia. Temporally, snow cover duration for the region as a whole was very short from the late 1940s through to the mid-1950s. From the late 1950s to the end of the period snow cover duration has varied around a consistent mean value. No long-term trends in snow cover duration are apparent in the record for the northeast USA.Principal components analysis and clustering techniques were utilized to isolate spatially coherent regions in which snow cover duration has varied similarly over the period of record. This analysis resulted in the identification of four snow-cover-duration regions across the northeast USA: including (i) the West Virginia area, (ii) the mid-Atlantic from southern New England through to western Pennsylvania, (iii) western and central New York and (iv) northern New England. Snow cover duration is shown to be highly associated with snowfall and temperature but not strongly related to total liquid precipitation. The intra-annual variability of snow cover duration is also investigated for each region.

Snow, ice… and other reasons to be cheerful!

CERN Document Server

2013-01-01

In total, CERN’s roads, car parks, pavements and pedestrian areas cover an area of 60 hectares. The EN-HE group is responsible for clearing snow from, and gritting and salting, the roads and car parks. The GS-IS group, through its cleaning and green-area maintenance contracts, is responsible for clearing snow from, and gritting and salting, pavements, pathways and the entrances to buildings, as well as keeping salt bins stocked.   In the event of heavy snowfall, general snow clearance operations are initiated by the CERN Control Centre, starting at 3 a.m. for roads and car parks, and 4 a.m. for pavements and the entrances to buildings. One-off operations during the day may be initiated by the Fire Brigade, site managers, security guards or the CMS control room. Salt bins are also available for you to salt the approaches to entrances. Make use of them! At weekends and on public holidays, the procedure is the same, with the exception of the times: general operations are ini...

Integration of snow management practices into a detailed snow pack model

Science.gov (United States)

Spandre, Pierre; Morin, Samuel; Lafaysse, Matthieu; Lejeune, Yves; François, Hugues; George-Marcelpoil, Emmanuelle

2016-04-01

The management of snow on ski slopes is a key socio-economic and environmental issue in mountain regions. Indeed the winter sports industry has become a very competitive global market although this economy remains particularly sensitive to weather and snow conditions. The understanding and implementation of snow management in detailed snowpack models is a major step towards a more realistic assessment of the evolution of snow conditions in ski resorts concerning past, present and future climate conditions. Here we describe in a detailed manner the integration of snow management processes (grooming, snowmaking) into the snowpack model Crocus (Spandre et al., Cold Reg. Sci. Technol., in press). The effect of the tiller is explicitly taken into account and its effects on snow properties (density, snow microstructure) are simulated in addition to the compaction induced by the weight of the grooming machine. The production of snow in Crocus is carried out with respect to specific rules and current meteorological conditions. Model configurations and results are described in detail through sensitivity tests of the model of all parameters related to snow management processes. In-situ observations were carried out in four resorts in the French Alps during the 2014-2015 winter season considering for each resort natural, groomed only and groomed plus snowmaking conditions. The model provides realistic simulations of the snowpack properties with respect to these observations. The main uncertainty pertains to the efficiency of the snowmaking process. The observed ratio between the mass of machine-made snow on ski slopes and the water mass used for production was found to be lower than was expected from the literature, in every resort. The model now referred to as "Crocus-Resort" has been proven to provide realistic simulations of snow conditions on ski slopes and may be used for further investigations. Spandre, P., S. Morin, M. Lafaysse, Y. Lejeune, H. François and E. George

The development of web monitoring digital area monitor

International Nuclear Information System (INIS)

Jung, Hoon-Jin; Lee, Jun-Hee; Namkoong, Phil; Lee, Dong-Hoon; Lee, Su-Hong; Kim, We-Su

2005-01-01

As CCTV and radiation area monitor have been used separately to date, there have existed inconveniences in managing the view images and radiation dose rates from them. Thus we became to develop the web monitoring digital area monitor which incorporated the existing two factors, CCTV and radiation area monitor, into one. As incorporated with digitalisation, this device will usually become linked with PC, so that the view image and radiation dose rate can be concurrently identified in convenience. These measured figures automatically become data-based on PC by SW program, and become displayed in various format. Moreover, they can be monitored in remote and real time basis in the internet environment. Its local unit uses the cost-effective GM tube and CMOS image sensor, and has the small LCD which directly indicates the measured dose rate. The image sensor is designed to be operated with pan and tilt motion, thus can eliminate the dead view zone. It is thought that the developed device at this time could make the radiation safety management of each work field be done with low cost-high efficiency manner, making role of the CCTV inspection system

The development of web monitoring digital area monitor

Energy Technology Data Exchange (ETDEWEB)

Jung, Hoon-Jin; Lee, Jun-Hee; Namkoong, Phil; Lee, Dong-Hoon; Lee, Su-Hong; Kim, We-Su [Iljin Radiation Engineering Co., Seoul (Korea, Republic of)

2005-11-15

As CCTV and radiation area monitor have been used separately to date, there have existed inconveniences in managing the view images and radiation dose rates from them. Thus we became to develop the web monitoring digital area monitor which incorporated the existing two factors, CCTV and radiation area monitor, into one. As incorporated with digitalisation, this device will usually become linked with PC, so that the view image and radiation dose rate can be concurrently identified in convenience. These measured figures automatically become data-based on PC by SW program, and become displayed in various format. Moreover, they can be monitored in remote and real time basis in the internet environment. Its local unit uses the cost-effective GM tube and CMOS image sensor, and has the small LCD which directly indicates the measured dose rate. The image sensor is designed to be operated with pan and tilt motion, thus can eliminate the dead view zone. It is thought that the developed device at this time could make the radiation safety management of each work field be done with low cost-high efficiency manner, making role of the CCTV inspection system.

What controls the isotopic composition of Greenland surface snow?

Directory of Open Access Journals (Sweden)

H. C. Steen-Larsen

2014-02-01

Full Text Available Water stable isotopes in Greenland ice core data provide key paleoclimatic information, and have been compared with precipitation isotopic composition simulated by isotopically enabled atmospheric models. However, post-depositional processes linked with snow metamorphism remain poorly documented. For this purpose, monitoring of the isotopic composition (δ18O, δD of near-surface water vapor, precipitation and samples of the top (0.5 cm snow surface has been conducted during two summers (2011–2012 at NEEM, NW Greenland. The samples also include a subset of 17O-excess measurements over 4 days, and the measurements span the 2012 Greenland heat wave. Our observations are consistent with calculations assuming isotopic equilibrium between surface snow and water vapor. We observe a strong correlation between near-surface vapor δ18O and air temperature (0.85 ± 0.11‰ °C−1 (R = 0.76 for 2012. The correlation with air temperature is not observed in precipitation data or surface snow data. Deuterium excess (d-excess is strongly anti-correlated with δ18O with a stronger slope for vapor than for precipitation and snow surface data. During nine 1–5-day periods between precipitation events, our data demonstrate parallel changes of δ18O and d-excess in surface snow and near-surface vapor. The changes in δ18O of the vapor are similar or larger than those of the snow δ18O. It is estimated using the CROCUS snow model that 6 to 20% of the surface snow mass is exchanged with the atmosphere. In our data, the sign of surface snow isotopic changes is not related to the sign or magnitude of sublimation or deposition. Comparisons with atmospheric models show that day-to-day variations in near-surface vapor isotopic composition are driven by synoptic variations and changes in air mass trajectories and distillation histories. We suggest that, in between precipitation events, changes in the surface snow isotopic composition are driven by these changes in near

Enhanced hemispheric-scale snow mapping through the blending of optical and microwave satellite data

Science.gov (United States)

Armstrong, R. L.; Brodzik, M. J.; Savoie, M.; Knowles, K.

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. Global snow cover fluctuation can now be monitored over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere weekly snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Decadal trends and their significance are compared for the two data types. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as throughout the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the

NOAA's National Snow Analyses

Science.gov (United States)

Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

2005-12-01

NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

Directory of Open Access Journals (Sweden)

Simonetta Paloscia

2017-01-01

Full Text Available In this work, X band images acquired by COSMO-SkyMed (CSK on alpine environment have been analyzed for investigating snow characteristics and their effect on backscattering variations. Preliminary results confirmed the capability of simultaneous optical and Synthetic Aperture Radar (SAR images (Landsat-8 and CSK in separating snow/no-snow areas and in detecting wet snow. The sensitivity of backscattering to snow depth has not always been confirmed, depending on snow characteristics related to the season. A model based on Dense Media Radiative Transfer theory (DMRT-QMS was applied for simulating the backscattering response on the X band from snow cover in different conditions of grain size, snow density and depth. By using DMRT-QMS and snow in-situ data collected on Cordevole basin in Italian Alps, the effect of grain size and snow density, beside snow depth and snow water equivalent, was pointed out, showing that the snow features affect the backscatter in different and sometimes opposite ways. Experimental values of backscattering were correctly simulated by using this model and selected intervals of ground parameters. The relationship between simulated and measured backscattering for the entire dataset shows slope >0.9, determination coefficient, R2 = 0.77, and root mean square error, RMSE = 1.1 dB, with p-value <0.05.

The Electrical Self-Potential Method as a Non-Intrusive Snow-Hydrological Sensor

Science.gov (United States)

Kulessa, B.; Thompson, S. S.; Luethi, M. P.; Essery, R.

2015-12-01

Building on growing momentum in the application of geophysical techniques to snow problems and, specifically, on new theory and an electrical geophysical snow hydrological model published recently; we demonstrate for the first time that the electrical self-potential geophysical technique can sense in-situ bulk meltwater fluxes. This has broad and immediate implications for snow measurement practice, modelling and operational snow forecasting. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Chemical elements contamination of snow cover in region of coal production 'Karazhyra'

International Nuclear Information System (INIS)

Evlampieva, E.P.; Panin, M.S.

2008-01-01

Peculiarities of space distribution of chemical elements in hardphase and water-soluble falls of snow cover in region of coal deposit 'Karazhyra' are investigated. The maximal, minimal and background areas of elements accumulation in the snow of this region and distribution of their cumulative rates are determined. The main pollutants of snow cover unto background level are revealed.

Experimental measurement and modeling of snow accumulation and snowmelt in a mountain microcatchment

Science.gov (United States)

Danko, Michal; Krajčí, Pavel; Hlavčo, Jozef; Kostka, Zdeněk; Holko, Ladislav

2016-04-01

Fieldwork is a very useful source of data in all geosciences. This naturally applies also to the snow hydrology. Snow accumulation and snowmelt are spatially very heterogeneous especially in non-forested, mountain environments. Direct field measurements provide the most accurate information about it. Quantification and understanding of processes, that cause these spatial differences are crucial in prediction and modelling of runoff volumes in spring snowmelt period. This study presents possibilities of detailed measurement and modeling of snow cover characteristics in a mountain experimental microcatchment located in northern part of Slovakia in Western Tatra mountains. Catchment area is 0.059 km2 and mean altitude is 1500 m a.s.l. Measurement network consists of 27 snow poles, 3 small snow lysimeters, discharge measurement device and standard automatic weather station. Snow depth and snow water equivalent (SWE) were measured twice a month near the snow poles. These measurements were used to estimate spatial differences in accumulation of SWE. Snowmelt outflow was measured by small snow lysimeters. Measurements were performed in winter 2014/2015. Snow water equivalent variability was very high in such a small area. Differences between particular measuring points reached 600 mm in time of maximum SWE. The results indicated good performance of a snow lysimeter in case of snowmelt timing identification. Increase of snowmelt measured by the snow lysimeter had the same timing as increase in discharge at catchment's outlet and the same timing as the increase in air temperature above the freezing point. Measured data were afterwards used in distributed rainfall-runoff model MIKE-SHE. Several methods were used for spatial distribution of precipitation and snow water equivalent. The model was able to simulate snow water equivalent and snowmelt timing in daily step reasonably well. Simulated discharges were slightly overestimated in later spring.

Obtaining sub-daily new snow density from automated measurements in high mountain regions

Science.gov (United States)

Helfricht, Kay; Hartl, Lea; Koch, Roland; Marty, Christoph; Olefs, Marc

2018-05-01

The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m-3, with a standard deviation of 9 kg m-3. Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships (r2 > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow

Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

Science.gov (United States)

Prugh, L.; Verbyla, D.; van de Kerk, M.; Mahoney, P.; Sivy, K. J.; Liston, G. E.; Nolin, A. W.

2017-12-01

Snow enshrouds up to one third of the global land mass annually and exerts a major influence on animals that reside in these "snowscapes," (landscapes covered in snow). Dynamic snowscapes may have especially strong effects in arctic and boreal regions where dry snow persists for much of the year. Changes in temperature and hydrology are transforming northern regions, with profound implications for wildlife that are not well understood. We report initial findings from a NASA ABoVE project examining effects of snow properties on Dall sheep (Ovis dalli dalli). We used the MODSCAG snow fraction product to map spring snowline elevations and snow-off dates from 2000-2015 throughout the global range of Dall sheep in Alaska and northwestern Canada. We found a negative effect of spring snow cover on Dall sheep recruitment that increased with latitude. Using meteorological data and a daily freeze/thaw status product derived from passive microwave remote sensing from 1983-2012, we found that sheep survival rates increased in years with higher temperatures, less winter precipitation, fewer spring freeze-thaw events, and more winter freeze-thaw events. To examine the effects of snow depth and density on sheep movements, we used location data from GPS-collared sheep and a snowpack evolution model (SnowModel). We found that sheep selected for shallow, fluffy snow at high elevations, but they selected for denser snow as depth increased. Our field measurements identified a critical snow density threshold of 329 (± 18 SE) kg/m3 to support the weight of Dall sheep. Thus, sheep may require areas of shallow, fluffy snow for foraging, while relying on hard-packed snow for winter travel. These findings highlight the importance of multiple snowscape properties on wildlife movements and demography. The integrated study of snow properties and ecological processes, which we call snowscape ecology, will greatly improve global change forecasting.

Monitoring Food Security Indicators from Remote Sensing and Predicting Cereal Production in Afghanistan

Science.gov (United States)

Pervez, M. S.; Budde, M. E.; Rowland, J.

2015-12-01

We extract percent of basin snow covered areas above 2500m elevation from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-meter 8-day snow cover composites to monitor accumulation and depletion of snow in the basin. While the accumulation and depletion of snow cover extent provides an indication of the temporal progression of the snow pack, it does not provide insight into available water for irrigation. Therefore, we use snow model results from the National Operational Hydrologic Remote Sensing Center to quantify snow water equivalent and volume of water available within the snowpack for irrigation. In an effort to understand how water availability, along with its inter-annual variability, relates to the food security of the country, we develop a simple, effective, and easy-to-implement model to identify irrigated areas across the country on both annual and mid-season basis. The model is based on applying thresholds to peak growing season vegetation indices—derived from 250-meter MODIS images—in a decision-tree classifier to separate irrigated crops from non-irrigated vegetation. The spatial distribution and areal estimates of irrigated areas from these maps compare well with irrigated areas classified from multiple snap shots of the landscape from Landsat 5 optical and thermal images over selected locations. We observed that the extents of irrigated areas varied depending on the availability of snowmelt and can be between 1.35 million hectares in a year with significant water deficit and 2.4 million hectares in a year with significant water surplus. The changes in the amount of available water generally can contribute up to a 30% change in irrigated areas. We also observed that the strong correlation between inter-annual variability of irrigated areas and the variability in the country's cereal production could be utilized to predict an annual estimate of cereal production, providing early indication of food security scenarios for the country.

Surface energy balance of seasonal snow cover for snow-melt ...

Indian Academy of Sciences (India)

This study describes time series analysis of snow-melt, radiation data and energy balance for a seasonal snow cover at Dhundi field station of SASE, which lies in Pir Panjal range of the. N–W Himalaya, for a winter season from 13 January to 12 April 2005. The analysis shows that mean snow surface temperature remains ...

Small scale variability of snow density on Antarctic sea ice

Science.gov (United States)

Wever, N.; Leonard, K. C.; Paul, S.; Jacobi, H. W.; Proksch, M.; Lehning, M.

2016-12-01

Snow on sea ice plays an important role in air-ice-sea interactions. For example, snow may smooth the ice surface when snow drift is occurring, while at the same time it may also generate roughness elements by interactions with the wind. Snow density is a key property in many processes, for example by influencing the thermal conductivity of the snow layer, radiative transfer inside the snow as well as the effects of aerodynamic forcing on the snowpack. We present data from an in-situ measurement campaign in the Weddell Sea during two subsequent cruises of RV Polarstern. By comparing snow density from snow pits and snow micro penetrometer (SMP) measurements, augmented by terrestrial laser scanning (TLS) on an area of 50x50 m2, highly resolved density profiles and surface topology were acquired at a horizontal resolution of approximately 30 cm. Average snow densities are about 280 kg/m3, but the analysis also reveals a high spatial variability in snow density on sea ice in both horizontal and vertical direction, ranging from roughly 170 to 360 kg/m3. This variability is expressed by coherent snow structures over several meters, which disappear over larger distances. A comparison with TLS data indicates that the spatial variability is related to deviations in surface topology. This suggests a strong influence from surface processes, for example wind, on the temporal development of density profiles. The fundamental relationship between density variations, surface roughness and changes therein as investigated in this study are interpreted with respect to larger-scale ice-movement and the ice mass balance.

The electrical self-potential method is a non-intrusive snow-hydrological sensor

Science.gov (United States)

Thompson, S. S.; Kulessa, B.; Essery, R. L. H.; Lüthi, M. P.

2015-08-01

Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Digging of 'Snow White' Begins

Science.gov (United States)

2008-01-01

NASA's Phoenix Mars Lander began excavating a new trench, dubbed 'Snow White,' in a patch of Martian soil located near the center of a polygonal surface feature, nicknamed 'Cheshire Cat.' The trench is about 2 centimeters (.8 inches) deep and 30 centimeters (about 12 inches) long. The 'dump pile' is located at the top of the trench, the side farthest away from the lander, and has been dubbed 'Croquet Ground.' The digging site has been named 'Wonderland.' At this early stage of digging, the Phoenix team did not expect to find any of the white material seen in the first trench, now called 'Dodo-Goldilocks.' That trench showed white material at a depth of about 5 centimeters (2 inches). More digging of Snow White is planned for coming sols, or Martian days. The dark portion of this image is the shadow of the lander's solar panel; the bright areas within this region are not in shadow. Snow White was dug on Sol 22 (June 17, 2008) with Phoenix's Robotic Arm. This picture was acquired on the same day by the lander's Surface Stereo Imager. This image has been enhanced to brighten shaded areas. The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

Quantifying spatial distribution of snow depth errors from LiDAR using Random Forests

Science.gov (United States)

Tinkham, W.; Smith, A. M.; Marshall, H.; Link, T. E.; Falkowski, M. J.; Winstral, A. H.

2013-12-01

There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. Comparison of LiDAR-derived snow depths to manual snow depth surveys revealed that LiDAR based estimates were more accurate in areas of low lying vegetation such as shrubs (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34 m across the different cover types. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 × 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy. Future work could explore the utility of Terrestrial LiDAR Scanners to produce validation of snow-on surface

Research of Snow-Melt Process on a Heated Platform

Directory of Open Access Journals (Sweden)

Vasilyev Gregory P.

2016-01-01

Full Text Available The article has shown the results of experimental researches of the snow-melt on a heated platform-near building heat-pump snow-melt platform. The near-building (yard heat pump platforms for snow melt with the area up to 10-15 m2 are a basis of the new ideology of organization of the street cleaning of Moscow from snow in the winter period which supposes the creation in the megalopolis of the «distributed snow-melt system» (DSMS using non-traditional energy sources. The results of natural experimental researches are presented for the estimation of efficiency of application in the climatic conditions of Moscow of heat pumps in the snow-melt systems. The researches were conducted on a model sample of the near-building heat-pump platform which uses the low-potential thermal energy of atmospheric air. The conducted researches have confirmed experimentally in the natural conditions the possibility and efficiency of using of atmospheric air as a source of low-potential thermal energy for evaporation of the snow-melt heat pump systems in the climatic conditions of Moscow. The results of laboratory researches of snow-melt process on a heated horizontal platform are presented. The researches have revealed a considerable dependence of efficiency of the snow-melt process on its piling mode (form-building and the organization of the process of its piling mode (form-building and the organization of the process of its (snow mass heat exchange with the surface of the heated platform. In the process of researches the effect of formation of an «ice dome» under the melting snow mass called by the fact that in case of the thickness of snow loaded on the platform more than 10 cm the water formed from the melting snow while the contact with the heating surface don’t spread on it, but soaks into the snow, wets it due to capillary effect and freezes. The formation of «ice dome» leads to a sharp increase of snow-melt period and decreases the operating

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

Science.gov (United States)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real

Potential genotoxic effects of melted snow from an urban area revealed by the Allium cepa test.

Science.gov (United States)

Blagojević, Jelena; Stamenković, Gorana; Vujosević, Mladen

2009-09-01

The presence of well-known atmospheric pollutants is regularly screened for in large towns but knowledge about the effects of mixtures of different pollutants and especially their genotoxic potential is largely missing. Since falling snow collects pollutants from the air, melted snow samples could be suitable for evaluating potential genotoxicity. For this purpose the Allium cepa anaphase-telophase test was used to analyse melted snow samples from Belgrade, the capital city of Serbia. Samples of snow were taken at two sites, characterized by differences in pollution intensity, in three successive years. At the more polluted site the analyses showed a very high degree of both toxicity and genotoxicity in the first year of the study corresponding to the effects of the known mutagen used as the positive control. At the other site the situation was much better but not without warning signals. The results showed that standard analyses for the presence of certain contaminants in the air do not give an accurate picture of the possible consequences of urban air pollution because the genotoxic potential remains hidden. The A. cepa test has been demonstrated to be very convenient for evaluation of air pollution through analyses of melted snow samples.

Snow in a very steep rock face: accumulation and redistribution during and after a snowfall event

Directory of Open Access Journals (Sweden)

Christian Gabriel Sommer

2015-12-01

Full Text Available Terrestrial laser scanning was used to measure snow thickness changes (perpendicular to the surface in a rock face. The aim was to investigate the accumulation and redistribution of snow in extremely steep terrain (>60°. The north-east face of the Chlein Schiahorn in the region of Davos in eastern Switzerland was scanned before and several times after a snowfall event. A summer scan without snow was acquired to calculate the total snow thickness. An improved postprocessing procedure is introduced. The data quality could be increased by using snow thickness instead of snow depth (measured vertically and by consistently applying Multi Station Adjustment to improve the registration.More snow was deposited in the flatter, smoother areas of the rock face. The spatial variability of the snow thickness change was high. The spatial patterns of the total snow thickness were similar to those of the snow thickness change. The correlation coefficient between them was 0.86. The fresh snow was partly redistributed from extremely steep to flatter terrain, presumably mostly through avalanching. The redistribution started during the snowfall and ended several days later. Snow was able to accumulate permanently at every slope angle. The amount of snow in extremely steep terrain was limited but not negligible. Areas steeper than 60° received 15% of the snowfall and contained 10% of the total amount of snow.

Missing (in-situ) snow cover data hampers climate change and runoff studies in the Greater Himalayas.

Science.gov (United States)

Rohrer, Mario; Salzmann, Nadine; Stoffel, Markus; Kulkarni, Anil V

2013-12-01

The Himalayas are presently holding the largest ice masses outside the polar regions and thus (temporarily) store important freshwater resources. In contrast to the contemplation of glaciers, the role of runoff from snow cover has received comparably little attention in the past, although (i) its contribution is thought to be at least equally or even more important than that of ice melt in many Himalayan catchments and (ii) climate change is expected to have widespread and significant consequences on snowmelt runoff. Here, we show that change assessment of snowmelt runoff and its timing is not as straightforward as often postulated, mainly as larger partial pressure of H2O, CO2, CH4, and other greenhouse gases might increase net long-wave input for snowmelt quite significantly in a future atmosphere. In addition, changes in the short-wave energy balance - such as the pollution of the snow cover through black carbon - or the sensible or latent heat contribution to snowmelt are likely to alter future snowmelt and runoff characteristics as well. For the assessment of snow cover extent and depletion, but also for its monitoring over the extremely large areas of the Himalayas, remote sensing has been used in the past and is likely to become even more important in the future. However, for the calibration and validation of remotely-sensed data, and even more so in light of possible changes in snow-cover energy balance, we strongly call for more in-situ measurements across the Himalayas, in particular for daily data on new snow and snow cover water equivalent, or the respective energy balance components. Moreover, data should be made accessible to the scientific community, so that the latter can more accurately estimate climate change impacts on Himalayan snow cover and possible consequences thereof on runoff. © 2013 Elsevier B.V. All rights reserved.

A unique radiation area monitoring system

International Nuclear Information System (INIS)

Murphy, P.C.; Allen, G.C.

1978-01-01

The Remote Area Monitoring Systems (RAMS) monitors four radiation areas with two independent systems in each area. Each system consists of power supplies, four ionization chambers, and four analog and digital circuits. The first system controls the warning beacons, horns, annunciation panel and interlocks. The second system presents a quantitative dose rate indication at the console and in the radiation area

Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing

International Nuclear Information System (INIS)

Xiong, Chuan; Shi, Jiancheng

2014-01-01

To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing. -- Highlights: • Bicontinuous random medium were used for real snow microstructure modeling. • Photon tracing technique with polarization status tracking ability was applied. • SSA–albedo relationship of snow is close to that of sphere based medium. • Validation of albedo and BRDF showed good results. • Validation of polarized reflectance showed good agreement with experiment data

Environmental control by means of studying the natural areas with nuclear physics methods

International Nuclear Information System (INIS)

Boyarkina, A.P.; Glukhov, G.G.; Rezchikov, V.I.; Shelud'ko, S.I.

1988-01-01

Element composition of precipitation accumulated in natural areas (snow, peat) is investigated by neutron activation technique using nuclear reactor. Content of microelements (Fe, Co, Sc, Au, Hg, Sb, Br, Na) is determined in both maximal contamination industrial area and at a distance more, than 40 km from the area. Snow cover and peat occurrence are shown to be convenient substrates to investigate contamination of the industrial centre vicinities in space and time. Standard development of MPC (maximum permissible concentration) contents of industrial dust ingredients, especially toxic ones, in these substrates allows to solve quickly many problems raised by environmental monitoring. 10 refs.; 2 tabs

Gender and snow crab occupational asthma in Newfoundland and Labrador, Canada

International Nuclear Information System (INIS)

Howse, Dana; Gautrin, Denyse; Neis, Barbara; Cartier, Andre; Horth-Susin, Lise; Jong, Michael; Swanson, Mark C.

2006-01-01

Fish and shellfish processing employs many thousands of people globally, with shellfish processing becoming more important in recent years. Shellfish processing is associated with multiple occupational health and safety (OHS) risks. Snow crab occupational asthma (OA) is work-related asthma associated with processing snow crab. We present a gender analysis of findings from a 3-year multifaceted study of snow crab OA in Newfoundland and Labrador, Canada. The study was carried out in four snow crab processing communities between 2001 and 2004. An anonymous survey questionnaire on knowledge, beliefs, and concerns related to processing snow crab administered to 158 workers attending community meetings at the start of the research found that women were significantly more likely than men to associate certain health problems, especially chest tightness, difficulty breathing, and cough, with crab processing (P<0.001). Worker health assessments carried out with 215 processing workers (187 current/28 former; 120 female/95 male) found that female participants were more likely to be diagnosed as almost certain/highly probable snow crab OA and allergy (P=0.001) and to be sensitized to snow crab (P=0.01) than male participants. Work histories from the health assessments were used to classify processing jobs as male or female. Allergen sampling (211 allergen samples: 115 area, 96 personal breathing zone) indicated that the plant areas where these male jobs were concentrated were associated with lower levels of aerosolized crab allergens (the agents responsible for OA to snow crab) than areas associated with female jobs. This difference was statistically significant in the two plants with poor ventilation (p<0.001 and P=0.017 for these plants). A gender analysis of work history data showed that female health assessment participants were likely to have worked longer processing snow crab than males (5 years versus 3.5 years, respectively). Cross-referencing of work history results

Snow chemistry of high altitude glaciers in the French Alps

OpenAIRE

MAUPETIT, FRANÇOIS; DELMAS, ROBERT J.

2011-01-01

Snow samples were collected as snowcores in the accumulation zone of four high altitude glaciers (2980–3540 m.a.s.l.) from each of the 4 highest mountain areas of the French Alps, during 3 consecutive years: 1989, 1990 and 1991. Sampling was performed in spring (∼ May), before the onset of late spring–summer percolation. The accumulated snow therefore reflects winter and spring conditions. A complementary sampling of fresh-snow was performed on an event basis, on one of the studied glaciers, ...

Snow and ice blocking of tunnels

Energy Technology Data Exchange (ETDEWEB)

Lia, Leif

1998-12-31

Hydroelectric power development in cold regions causes much concern about operational reliability and dam safety. This thesis studies the temperature distribution in tunnels by means of air temperature measurements in six tunnel spillways and five diversion tunnels. The measurements lasted for two consecutive winters. The air through flow tunnel is used as it causes cooling of both rock and water. In open spillway tunnels, frost reaches the entire tunnel. In spillway tunnels with walls, the frost zones reach about 100 m from the downstream end. In mildly-inclined diversion tunnels, a frost free zone is located in the middle of the tunnel and snow and ice problems were only observed in the inlet and outlet. Severe aufeis is accumulation is observed in the frost zones. The heat transfer from rock to air, water and ice is calculated and used in a prediction model for the calculation of aufeis build-up together with local field observation data. The water penetration of snow plugs is also calculated, based on the heat balance. It takes 20 to 50 days for water to enter the blocked tunnel. The empirical values are 30 to 60 days, but only 1 day if the temperature of the snow pack is 0{sup o}C. Sensitivity analyses are carried out for temperature variations in rock, snow, water and ice. Systematic field observation shows that it is important for hydropower companies to know about the effects of snow and ice blocking in an area. A risk analysis of dam safety is presented for a real case. Finally, the thesis proposes solutions which can reduce the snow and ice problems. 79 refs., 63 figs., 11 tabs.

Scales of snow depth variability in high elevation rangeland sagebrush

Science.gov (United States)

Tedesche, Molly E.; Fassnacht, Steven R.; Meiman, Paul J.

2017-09-01

In high elevation semi-arid rangelands, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush ( Artemisia tridentata Nutt.) at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions (0.05 to 25 m) and extents (2 to 1000 m). Finer scale data were collected specifically for this study to examine the correlation between snow depth, sagebrush microtopography, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Variograms were used to identify the spatial structure and the Moran's I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

Retrieval of snow albedo and grain size using reflectance measurements in Himalayan basin

Directory of Open Access Journals (Sweden)

H. S. Negi

2011-03-01

Full Text Available In the present paper, spectral reflectance measurements of Himalayan seasonal snow were carried out and analysed to retrieve the snow albedo and effective grain size. The asymptotic radiative transfer (ART theory was applied to retrieve the plane and spherical albedo. The retrieved plane albedo was compared with the measured spectral albedo and a good agreement was observed with ±10% differences. Retrieved integrated albedo was found within ±6% difference with ground observed broadband albedo. The retrieved snow grain sizes using different models based on the ART theory were compared for various snow types and it was observed that the grain size model using two channel method (one in visible and another in NIR region can work well for the Himalayan seasonal snow and it was found consistent with temporal changes in grain size. This method can work very well for clean, dry snow as in the upper Himalaya, but sometimes, due to the low reflectances (<20% using wavelength 1.24 μm, the ART theory cannot be applied, which is common in lower and middle Himalayan old snow. This study is important for monitoring the Himalayan cryosphere using air-borne or space-borne sensors.

Response of snow-dependent hydrologic extremes to continued global warming

Energy Technology Data Exchange (ETDEWEB)

Diffenbaugh, Noah [Stanford University; Scherer, Martin [Stanford University; Ashfaq, Moetasim [ORNL

2012-01-01

Snow accumulation is critical for water availability in the Northern Hemisphere1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions1,3. Although regional hydrologic changes have been observed (for example, refs 1,3 5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate- change impacts3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the Northern Hemisphere, with areas of western North America, northeastern Europe and the Greater Himalaya showing the strongest emergence during the near- termdecadesandat2 Cglobalwarming.Theoccurrenceof extremely low snow years becomes widespread by the late twenty-first century, as do the occurrences of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the Northern Hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2 C above the pre-industrial baseline.

Energy expenditure and clearing snow: a comparison of shovel and snow pusher.

Science.gov (United States)

Smolander, J; Louhevaara, V; Ahonen, E; Polari, J; Klen, T

1995-04-01

In order to assess the energy demands of manual clearing of snow, nine men did snow clearing work for 15 min with a shovel and a snow pusher. The depth of the snowcover was 400-600 mm representing a very heavy snowfall. Heart rate (HR), oxygen consumption (VO2), pulmonary ventilation (VE), respiratory exchange ratio (R), and rating of perceived exertion (RPE) were determined during the work tasks. HR, VE, R, and RPE were not significantly different between the shovel and snow pusher. HR averaged (+/- SD) 141 +/- 20 b min-1 with the shovel, and 142 +/- 19 beats.min-1 with the snow pusher. VO2 was 2.1 +/- 0.41.min-1 (63 +/- 12%VO2 max) in shovelling and 2.6 +/- 0.51.min-1 (75 +/- 14%VO2max) in snow pushing (p < 0.001). In conclusion manual clearing of snow in conditions representing heavy snowfalls was found to be strenuous physical work, not suitable for persons with cardiac risk factors, but which may serve as a mode of physical training in healthy adults.

Missing (in-situ) snow cover data hampers climate change and runoff studies in the Greater Himalayas

International Nuclear Information System (INIS)

Rohrer, Mario; Salzmann, Nadine; Stoffel, Markus; Kulkarni, Anil V.

2013-01-01

The Himalayas are presently holding the largest ice masses outside the polar regions and thus (temporarily) store important freshwater resources. In contrast to the contemplation of glaciers, the role of runoff from snow cover has received comparably little attention in the past, although (i) its contribution is thought to be at least equally or even more important than that of ice melt in many Himalayan catchments and (ii) climate change is expected to have widespread and significant consequences on snowmelt runoff. Here, we show that change assessment of snowmelt runoff and its timing is not as straightforward as often postulated, mainly as larger partial pressure of H 2 O, CO 2 , CH 4 , and other greenhouse gases might increase net long-wave input for snowmelt quite significantly in a future atmosphere. In addition, changes in the short-wave energy balance – such as the pollution of the snow cover through black carbon – or the sensible or latent heat contribution to snowmelt are likely to alter future snowmelt and runoff characteristics as well. For the assessment of snow cover extent and depletion, but also for its monitoring over the extremely large areas of the Himalayas, remote sensing has been used in the past and is likely to become even more important in the future. However, for the calibration and validation of remotely-sensed data, and even more so in light of possible changes in snow-cover energy balance, we strongly call for more in-situ measurements across the Himalayas, in particular for daily data on new snow and snow cover water equivalent, or the respective energy balance components. Moreover, data should be made accessible to the scientific community, so that the latter can more accurately estimate climate change impacts on Himalayan snow cover and possible consequences thereof on runoff. - Highlights: • Remotely sensed snow-cover data need to be validated by in-situ measurements. • More in-situ snow measurement programs are needed along

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Science.gov (United States)

Roth, Travis R.; Nolin, Anne W.

2017-11-01

Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in sub-canopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4-year study of snow-forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that relative to nearby open areas, the dense, relatively warm forests at Low and Mid sites impede snow accumulation via canopy snow interception and increase sub-canopy snowpack energy inputs via longwave radiation. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4-26 and 11-33 days, respectively). However, we see the opposite relationship at the relatively colder High sites, with the Forest site maintaining snow longer into the spring by 15-29 days relative to the nearby Open site. Canopy interception efficiency (CIE) values at the Low and Mid Forest sites averaged 79 and 76 % of the total event snowfall, whereas CIE was 31 % at the lower density High Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 93, 92, and 47 % of total energy inputs to the snowpack at the Low, Mid, and High Forest sites, respectively. Higher wind speeds in the High Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High Forest site create preferential snowfall deposition. These results show the importance of

Missing (in-situ) snow cover data hampers climate change and runoff studies in the Greater Himalayas

Energy Technology Data Exchange (ETDEWEB)

Rohrer, Mario [Meteodat GmbH, Technoparkstrasse 1, CH-8005 Zurich (Switzerland); Salzmann, Nadine [Department of Geosciences, Geography, University of Fribourg, Chemin du Musée 4, CH-1700 Fribourg (Switzerland); Stoffel, Markus, E-mail: markus.stoffel@unige.ch [Institute for Environmental Sciences, University of Geneva, Chemin de Drize 7, CH-1227 Carouge, Geneva (Switzerland); Dendrolab.ch, Institute of Geological Sciences, University of Bern, Baltzerstrasse 1+3, CH-3012 Bern (Switzerland); Kulkarni, Anil V. [Divecha Center for Climate Change, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore 560 012 (India)

2013-12-01

The Himalayas are presently holding the largest ice masses outside the polar regions and thus (temporarily) store important freshwater resources. In contrast to the contemplation of glaciers, the role of runoff from snow cover has received comparably little attention in the past, although (i) its contribution is thought to be at least equally or even more important than that of ice melt in many Himalayan catchments and (ii) climate change is expected to have widespread and significant consequences on snowmelt runoff. Here, we show that change assessment of snowmelt runoff and its timing is not as straightforward as often postulated, mainly as larger partial pressure of H{sub 2}O, CO{sub 2}, CH{sub 4}, and other greenhouse gases might increase net long-wave input for snowmelt quite significantly in a future atmosphere. In addition, changes in the short-wave energy balance – such as the pollution of the snow cover through black carbon – or the sensible or latent heat contribution to snowmelt are likely to alter future snowmelt and runoff characteristics as well. For the assessment of snow cover extent and depletion, but also for its monitoring over the extremely large areas of the Himalayas, remote sensing has been used in the past and is likely to become even more important in the future. However, for the calibration and validation of remotely-sensed data, and even more so in light of possible changes in snow-cover energy balance, we strongly call for more in-situ measurements across the Himalayas, in particular for daily data on new snow and snow cover water equivalent, or the respective energy balance components. Moreover, data should be made accessible to the scientific community, so that the latter can more accurately estimate climate change impacts on Himalayan snow cover and possible consequences thereof on runoff. - Highlights: • Remotely sensed snow-cover data need to be validated by in-situ measurements. • More in-situ snow measurement programs are

Modeling snow accumulation and ablation processes in forested environments

Science.gov (United States)

Andreadis, Konstantinos M.; Storck, Pascal; Lettenmaier, Dennis P.

2009-05-01

The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

Science.gov (United States)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

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

Science.gov (United States)

Zhong, Xinyue; Zhang, Tingjun; Kang, Shichang; Wang, Kang; Zheng, Lei; Hu, Yuantao; Wang, Huijuan

2018-01-01

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.

Fukushima Nuclear Accident Recorded in Tibetan Plateau Snow Pits

Science.gov (United States)

Wang, Ninglian; Wu, Xiaobo; Kehrwald, Natalie; Li, Zhen; Li, Quanlian; Jiang, Xi; Pu, Jianchen

2015-01-01

The β radioactivity of snow-pit samples collected in the spring of 2011 on four Tibetan Plateau glaciers demonstrate a remarkable peak in each snow pit profile, with peaks about ten to tens of times higher than background levels. The timing of these peaks suggests that the high radioactivity resulted from the Fukushima nuclear accident that occurred on March 11, 2011 in eastern Japan. Fallout monitoring studies demonstrate that this radioactive material was transported by the westerlies across the middle latitudes of the Northern Hemisphere. The depth of the peak β radioactivity in each snow pit compared with observational precipitation records, suggests that the radioactive fallout reached the Tibetan Plateau and was deposited on glacier surfaces in late March 2011, or approximately 20 days after the nuclear accident. The radioactive fallout existed in the atmosphere over the Tibetan Plateau for about one month. PMID:25658094

Genetic differentiation between sympatric and allopatric wintering populations of Snow Geese

Science.gov (United States)

Humphries, E.M.; Peters, J.L.; Jonsson, J.E.; Stone, R.; Afton, A.D.; Omland, K.E.

2009-01-01

Blackwater National Wildlife Refuge on the Delmarva Peninsula, Maryland, USA has been the wintering area of a small population of Lesser Snow Geese (Chen caerulescens caerulescens; LSGO) since the 1930s. Snow Geese primarily pair in wintering areas and gene flow could be restricted between this and other LSGO wintering populations. Winter pair formation also could facilitate interbreeding with sympatric but morphologically differentiated Greater Snow Geese (C. c. atlantica; GSGO).We sequenced 658 bp of the mitochondrial DNA control region for 68 Snow Geese from East Coast and Louisiana wintering populations to examine the level of genetic differentiation among populations and subspecies. We found no evidence for genetic differentiation between LSGO populations but, consistent with morphological differences, LSGO and GSGO were significantly differentiated. We also found a lack of genetic differentiation between different LSGO morphotypes from Louisiana. We examined available banding data and found the breeding range of Delmarva LSGO overlaps extensively with LSGO that winter in Louisiana, and documented movements between wintering populations. Our results suggest the Delmarva population of LSGO is not a unique population unit apart from Mid-Continent Snow Geese. ?? 2009 by the Wilson Ornithological Society.

Scaling and Numerical Model Evaluation of Snow-Cover Effects on the Generation and Modification of Daytime Mesoscale Circulations.

Science.gov (United States)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Ye, Z.

1991-04-01

Consideration of the sensible heat flux characteristics over a snow surface suggests a significant diminution in the magnitude of the flux, compared to that over a snow-free surface under the same environmental conditions. Consequently, the existence of snow-covered mesoscale areas adjacent to snow-free areas produces horizontal thermal gradients in the lower atmosphere during the daytime, possibly resulting in a `snow breeze.' In addition, suppression of the daytime thermally induced upslope flow over snow-covered slopes is likely to occur. The present paper provides scaling and modeling evaluations of these situations, with quantification of the generated and modified circulations. These evaluations suggest that under ideal situations involved with uniform snow cover over large areas, particularly in late winter and early spring, a noticeable `snow breeze' is likely to develop. Additionally: suppression of the daytime thermally induced upslope flow is significant and may even result in a daytime drainage flow. The effects of bare ground patchiness in the snow cover on these circulations are also explored, both for flat terrain and slope-flow situations. A patchiness fraction greater than 0.5 is found to result in a noticeably reduced snow-breeze circulation, while a patchiness fraction of only 0.1 caused the simulated daytime drainage flow over slopes to he reversed.

Concentrations and source regions of light-absorbing particles in snow/ice in northern Pakistan and their impact on snow albedo

Science.gov (United States)

Gul, Chaman; Praveen Puppala, Siva; Kang, Shichang; Adhikary, Bhupesh; Zhang, Yulan; Ali, Shaukat; Li, Yang; Li, Xiaofei

2018-04-01

Black carbon (BC), water-insoluble organic carbon (OC), and mineral dust are important particles in snow and ice which significantly reduce albedo and accelerate melting. Surface snow and ice samples were collected from the Karakoram-Himalayan region of northern Pakistan during 2015 and 2016 in summer (six glaciers), autumn (two glaciers), and winter (six mountain valleys). The average BC concentration overall was 2130 ± 1560 ng g-1 in summer samples, 2883 ± 3439 ng g-1 in autumn samples, and 992 ± 883 ng g-1 in winter samples. The average water-insoluble OC concentration overall was 1839 ± 1108 ng g-1 in summer samples, 1423 ± 208 ng g-1 in autumn samples, and 1342 ± 672 ng g-1 in winter samples. The overall concentration of BC, OC, and dust in aged snow samples collected during the summer campaign was higher than the concentration in ice samples. The values are relatively high compared to reports by others for the Himalayas and the Tibetan Plateau. This is probably the result of taking more representative samples at lower elevation where deposition is higher and the effects of ageing and enrichment are more marked. A reduction in snow albedo of 0.1-8.3 % for fresh snow and 0.9-32.5 % for aged snow was calculated for selected solar zenith angles during daytime using the Snow, Ice, and Aerosol Radiation (SNICAR) model. The daily mean albedo was reduced by 0.07-12.0 %. The calculated radiative forcing ranged from 0.16 to 43.45 W m-2 depending on snow type, solar zenith angle, and location. The potential source regions of the deposited pollutants were identified using spatial variance in wind vector maps, emission inventories coupled with backward air trajectories, and simple region-tagged chemical transport modeling. Central, south, and west Asia were the major sources of pollutants during the sampling months, with only a small contribution from east Asia. Analysis based on the Weather Research and Forecasting (WRF-STEM) chemical transport model identified a

An Australian Feeling for Snow: Towards Understanding Cultural and Emotional Dimensions of Climate Change

Directory of Open Access Journals (Sweden)

Andrew Gorman-Murray

2010-03-01

Full Text Available In Australia, snow is associated with alpine and subalpine regions in rural areas; snow is a component of ‘natural’ rather than urban environments. But the range, depth and duration of Australia’s regional snow cover is imperilled by climate change. While researchers have considered the impacts of snow retreat on the natural environment and responses from the mainland ski industry, this paper explores associated cultural and emotional dimensions of climate change. This responds to calls to account for local meanings of climate, and thus localised perceptions of and responses to climate change. Accordingly, this paper presents a case study of reactions to the affect of climate change on Tasmania’s snow country. Data is drawn from a nationwide survey of responses to the impact of climate change on Australia’s snow country, and a Tasmanian focus group. Survey respondents suggested the uneven distribution of Australia’s snow country means snow cover loss may matter more in certain areas: Tasmania was a key example cited by residents of both that state and others. Focus group respondents affirmed a connection between snow and Tasmanian cultural identity, displaying sensitivity to recent changing snow patterns. Moreover, they expressed concerns about the changes using emotive descriptions of local examples: the loss of snow cover mattered culturally and emotionally, compromising local cultural activities and meanings, and invoking affective responses. Simultaneously, respondents were ‘realistic’ about how important snow loss was, especially juxtaposed with sea level rise. Nevertheless, the impact of climate change on cultural and emotional attachments can contribute to urgent ethical, practical and political arguments about arresting global warming.

How much of stream and groundwater comes from snow? A stable isotope perspective in the Swiss Alps

Science.gov (United States)

Beria, H.; Schaefli, B.; Ceperley, N. C.; Michelon, A.; Larsen, J.

2017-12-01

Precipitation which once fell as snow is predicted to fall more often as liquid rain now that climate is, and continues, warming. Within snow dominated areas, preferential winter groundwater recharge has been observed, however a shorter winter season and smaller snow fraction results in earlier snowmelt and thinner snowpacks. This has the potential to change the supply of snow water sources to both streams and groundwater, which has important implications for flow regimes and water resources. Stable isotopes of water (2H and 18O) allow us to discriminate rain vs snow signatures within water flowing in the stream or the subsurface. Using one year of isotope data collected in a Swiss Alpine catchment (Vallon de Nant, Vaud), we developed novel forward Bayesian mixing models, based on statistical and empirical likelihoods, to quantify source contributions and uncertainty estimates. To account for the spatial heterogeneity in precipitation isotopes, we parameterized the model accounting for elevation effects on isotopes, calculated using the network of GNIP stations in Switzerland. Instead of sampling meltwater, we sampled snowpack throughout the season and across a steep elevation gradient (1241m to 2455m) to infer the snowmelt transformation factor. Due to continuous mixing within the snowpack, the snowmelt water shows much lower variability in its isotopic range which is reflected in the snow transformation factor. Snowmelt yield to groundwater recharge per unit amount of precipitation was found to be greater than rainfall in Vallon de Nant, suggesting strongly preferential winter recharge. Seasonal dynamics of stream responses to rain-on-snow events, fog deposition, snowmelt and summer rain were also explored. Innovative monitoring and sampling with tools such as stable isotopes and forward Bayesian mixing models are key to improved comprehension of global recharge mechanisms.

Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

Science.gov (United States)

Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

2017-12-01

We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

Past and future of the Austrian snow cover - results from the CC-Snow project

Science.gov (United States)

Strasser, Ulrich; Marke, Thomas; Hanzer, Florian; Ragg, Hansjörg; Kleindienst, Hannes; Wilcke, Renate; Gobiet, Andreas

2013-04-01

This study has the goal to simulate the evolution of the Austrian snow cover from 1971 to 2050 by means of a coupled modelling scheme, and to estimate the effect of climate change on the evolution of the natural snow cover. The model outcomes are interepreted with focus on both the future natural snow conditions, and the effects on winter skiing tourism. Therefore the regional temperature-index snow model SNOWREG is applied, providing snow maps with a spatial resolution of 250 m. The model is trained by means of assimilating local measurements and observed natural snow cover patterns. Meteorological forcing consists of the output of four realizations of the ENSEMBLES project for the A1B emission scenario. The meteorological variables are downscaled and error corrected with a quantile based empirical-statistical method on a daily time basis. The control simulation is 1971-2000, and the scenario simulation 2021-2050. Spatial interpolation is performed on the basis of parameter-elevation relations. We compare the four different global/regional climate model combinations and their effect on the snow modelling, and we explain the patterns of the resulting snow cover by means of regional climatological characteristics. The provinces Tirol and Styria serve as test regions, being typical examples for the two climatic subregions of Austria. To support the interpretation of the simulation results we apply indicators which enable to define meaningful measures for the comparison of the different periods and regions. Results show that the mean duration of the snow cover will decrease by 15 to 30 days per winter season, mostly in elevations between 2000 and 2500 m. Above 3000 m the higher winter precipitation can compensate this effect, and mean snow cover duration may even slightly increase. We also investigate the local scale by application of the physically based mountain snow model AMUNDSEN. This model is capable of producing 50 m resolution output maps for indicators

Snow mechanics and avalanche formation: field experiments on the dynamic response of the snow cover

Science.gov (United States)

Schweizer, Jürg; Schneebeli, Martin; Fierz, Charles; Föhn, Paul M. B.

1995-11-01

Knowledge about snow mechanics and snow avalanche formation forms the basis of any hazard mitigation measures. The crucial point is the snow stability. The most relevant mechanical properties - the compressive, tensile and shear strength of the individual snow layers within the snow cover - vary substantially in space and time. Among other things the strength of the snow layers depends strongly on the state of stress and the strain rate. The evaluation of the stability of the snow cover is hence a difficult task involving many extrapolations. To gain insight in the release mechanism of slab avalanches triggered by skiers, the skier's impact is measured with a load cell at different depths within the snow cover and for different snow conditions. The study focused on the effects of the dynamic loading and of the damping by snow compaction. In accordance with earlier finite-element (FE) calculations the results show the importance of the depth of the weak layer or interface and the snow conditions, especially the sublayering. In order to directly measure the impact force and to study the snow properties in more detail, a new instrument, called rammrutsch was developed. It combines the properties of the rutschblock with the defined impact properties of the rammsonde. The mechanical properties are determined using (i) the impact energy of the rammrutsch and (ii) the deformations of the snow cover measured with accelerometers and digital image processing of video sequences. The new method is well suited to detect and to measure the mechanical processes and properties of the fracturing layers. The duration of one test is around 10 minutes and the method seems appropriate for determining the spatial variability of the snow cover. A series of experiments in a forest opening showed a clear difference in the snow stability between sites below trees and ones in the free field of the opening.

Experimental and model based investigation of the links between snow bidirectional reflectance and snow microstructure

Science.gov (United States)

Dumont, M.; Flin, F.; Malinka, A.; Brissaud, O.; Hagenmuller, P.; Dufour, A.; Lapalus, P.; Lesaffre, B.; Calonne, N.; Rolland du Roscoat, S.; Ando, E.

2017-12-01

Snow optical properties are unique among Earth surface and crucial for a wide range of applications. The bi-directional reflectance, hereafter BRDF, of snow is sensible to snow microstructure. However the complex interplays between different parameters of snow microstructure namely size parameters and shape parameters on reflectance are challenging to disentangle both theoretically and experimentally. An accurate understanding and modelling of snow BRDF is required to correctly process satellite data. BRDF measurements might also provide means of characterizing snow morphology. This study presents one of the very few dataset that combined bi-directional reflectance measurements over 500-2500 nm and X-ray tomography of the snow microstructure for three different snow samples and two snow types. The dataset is used to evaluate the approach from Malinka, 2014 that relates snow optical properties to the chord length distribution in the snow microstructure. For low and medium absorption, the model accurately reproduces the measurements but tends to slightly overestimate the anisotropy of the reflectance. The model indicates that the deviation of the ice chord length distribution from an exponential distribution, that can be understood as a characterization of snow types, does not impact the reflectance for such absorptions. The simulations are also impacted by the uncertainties in the ice refractive index values. At high absorption and high viewing/incident zenith angle, the simulations and the measurements disagree indicating that some of the assumptions made in the model are not met anymore. The study also indicates that crystal habits might play a significant role for the reflectance under such geometries and wavelengths. However quantitative relationship between crystal habits and reflectance alongside with potential optical methodologies to classify snow morphology would require an extended dataset over more snow types. This extended dataset can likely be obtained

Seasonal snow of arctic Alaska R4D investigations. Final report

Energy Technology Data Exchange (ETDEWEB)

Benson, C.S.

1993-02-01

Seasonal snow is present on the Arctic Slope of Alaska for nine months each year. Its presence or absence determines whether 80% of the solar radiation is reflected or absorbed, respectively. Although life on the Arctic Slope is adapted to, and in some cases dependent upon seasonal snow, little is known about it from a scientific point of view. Its quantity has been grossly underestimated, and knowledge of its distribution and the extent of wind transport and redistribution is very limited. This research project dealt with the amount, regional distribution and physical properties of wind blown snow and its biological role in the R4D area of the Arctic Slope. Physical processes which operate within the snow that were studied included the flux of heat and vapor and the fractionation of stable isotopes through it during fall and winter, and the complex heat and mass transfer within the snow and between snow, its substrate and the overlying atmosphere during the melt period.

Seasonal snow cover regime and historical change in Central Asia from 1986 to 2008

Science.gov (United States)

Zhou, Hang; Aizen, Elena; Aizen, Vladimir

2017-01-01

A series of statistics describing seasonal Snow Cover Extent and timing in Central Asia (CA) have been derived from AVHRR satellite images for the time period from 1986 to 2008. Analysis of long term mean snow cover statistics shows that the area weighted mean of long term Snow Covering Days (SCD) for the whole CA is 95.2 ± 65.7 days. High elevation mountainous areas above 3000 m in Altai, Tien Shan and Pamir, which account for about 2.8% of total area in CA, have SCD > 240 days. Deserts (Karakorum Desert, Taklamakan Desert, Kumtag Desert) and rain shadow areas of major mountains, accounting for 27.0% of total area in CA, have SCD in the range of 0-30 days. Factors affecting snow cover distribution have been analyzed using simple linear regression and segmented regression. For plain regions and windward regions, the SCD rate is + 5.9 days/100 m, while for leeward regions, the rate jumps from + 0.7 days/100 m to + 10.0 days/100 m at about 2335 m. Latitude affects the SCD, especially in plain regions with insignificant change of elevation, with rates of 9-10 days/degree from south to north. The Mann-Kendal test and the Theil-Sen regression methods have been applied to analyze the spatial heterogeneous trends of change of SCD, Snow Cover Onset Date (SCOD), and Snow Cover Melt Date (SCMD). Area weighed mean SCD in the whole CA does not exhibit significant trend of change from 1986 to 2008. Increase of SCD was observed in the northeastern Kazakh Steppe. Low elevation areas below 2000 m in Central Tien Shan and Eastern Tien Shan, as well as mid-elevation areas from 1000 m to 3000 m in Western Tien Shan, Pamiro-Alai and Western Pamir, also experienced increase of SCD, associated with both earlier SCOD and later SCMD. Decrease of SCD was observed in mountainous areas of Altai, Tien Shan and Pamir, and vast areas in plains surrounding the Aral Sea.

Inducing Water Productivity from Snow Cover for Sustainable Water Management in Ibrahim River Basin, Lebanon

OpenAIRE

Darwish , Talal; SHABAN , Amin; Portoghese , Ivan; Vurro , Michele; Khadra , Roula; Saqallah , Sagedah; Drapeau , Laurent; Gascoin , Simon; Amacha , Nabil

2015-01-01

International audience; The aim of this paper is to explore the effects and linkages between snow cover areas, distribution, probability and measured water discharge along east Mediterranean coastal watershed using moderate-resolution satellite images (MODIS-Terra). The Nahr Ibrahim River is a typical Lebanese watershed with an area of 326 km2 stretching between the sea and mountainous terrain to the east. The largest snow cover often exists in January-February with snow-free conditions betwe...

Role of nitrite in the photochemical formation of radicals in the snow.

Science.gov (United States)

Jacobi, Hans-Werner; Kleffmann, Jörg; Villena, Guillermo; Wiesen, Peter; King, Martin; France, James; Anastasio, Cort; Staebler, Ralf

2014-01-01

Photochemical reactions in snow can have an important impact on the composition of the atmosphere over snow-covered areas as well as on the composition of the snow itself. One of the major photochemical processes is the photolysis of nitrate leading to the formation of volatile nitrogen compounds. We report nitrite concentrations determined together with nitrate and hydrogen peroxide in surface snow collected at the coastal site of Barrow, Alaska. The results demonstrate that nitrite likely plays a significant role as a precursor for reactive hydroxyl radicals as well as volatile nitrogen oxides in the snow. Pollution events leading to high concentrations of nitrous acid in the atmosphere contributed to an observed increase in nitrite in the surface snow layer during nighttime. Observed daytime nitrite concentrations are much higher than values predicted from steady-state concentrations based on photolysis of nitrate and nitrite indicating that we do not fully understand the production of nitrite and nitrous acid in snow. The discrepancy between observed and expected nitrite concentrations is probably due to a combination of factors, including an incomplete understanding of the reactive environment and chemical processes in snow, and a lack of consideration of the vertical structure of snow.

Snow, ice… and other reasons to be cheerful!

CERN Multimedia

2013-01-01

CERN’s roads, car parks, pavements and pedestrian areas cover an area of 60 hectares in total. The EN-HE Group is responsible for clearing snow from and salting the roads and car parks. The GS-IS Group, through its contracts for the cleaning and maintenance of green spaces, is responsible for clearing the snow from, salting and sanding the pavements, pedestrian routes and entrances to buildings, and for replenishing the bins of silica sand*, which will be used in place of salt in the bins from now on.   In the event of heavy snowfall, general snow-clearing operations are initiated by the CERN Control Centre (CCC), from 3 a.m. for roads and car parks and from 4 a.m. for pavements, pedestrian routes and building entrances in preparation for clearing to begin at 3.45 a.m. and 4.30 a.m. respectively. One-off operations during the day may be initiated by the Fire Brigade, site managers, the security guards or the CMS control room. Bins of silica sand are also provided so that individuals...

Snow, ice… and other reasons to be cheerful!

CERN Multimedia

2014-01-01

CERN’s roads, car parks, pavements and pedestrian areas cover an area of 60 hectares in total. The EN-HE Group is responsible for clearing snow from and salting the roads and car parks. The GS-IS Group, through its contracts for the cleaning and maintenance of green spaces, is responsible for clearing the snow from, salting and sanding the pavements, pedestrian routes and entrances to buildings, and for replenishing the bins of silica sand*, which will be used in place of salt in the bins from now on.   In the event of heavy snowfall, general snow-clearing operations are initiated by the CERN Control Centre (CCC), from 3 a.m. for roads and car parks and from 4 a.m. for pavements, pedestrian routes and building entrances in preparation for clearing to begin at 3.45 a.m. and 4.30 a.m. respectively. One-off operations during the day may be initiated by the Fire Brigade, site managers, the security guards or the CMS control room. Bins of silica sand are also provided so that individuals...

Development and testing of a snow interceptometer to quantify canopy water storage and interception processes in the rain/snow transition zone of the North Cascades, Washington, USA

Science.gov (United States)

Martin, Kael A.; Van Stan, John T.; Dickerson-Lange, Susan E.; Lutz, James A.; Berman, Jeffrey W.; Gersonde, Rolf; Lundquist, Jessica D.

2013-06-01

Tree canopy snow interception is a significant hydrological process, capable of removing up to 60% of snow from the ground snowpack. Our understanding of canopy interception has been limited by our ability to measure whole canopy water storage in an undisturbed forest setting. This study presents a relatively inexpensive technique for directly measuring snow canopy water storage using an interceptometer, adapted from Friesen et al. (2008). The interceptometer is composed of four linear motion position sensors distributed evenly around the tree trunk. We incorporate a trunk laser-mapping installation method for precise sensor placement to reduce signal error due to sensor misalignment. Through calibration techniques, the amount of canopy snow required to produce the measured displacements can be calculated. We demonstrate instrument performance on a western hemlock (Tsuga heterophylla) for a snow interception event in November 2011. We find a snow capture efficiency of 83 ± 15% of accumulated ground snowfall with a maximum storage capacity of 50 ± 8 mm snow water equivalent (SWE). The observed interception event is compared to simulated interception, represented by the variable infiltration capacity (VIC) hydrologic model. The model generally underreported interception magnitude by 33% using a leaf area index (LAI) of 5 and 16% using an LAI of 10. The interceptometer captured intrastorm accumulation and melt rates up to 3 and 0.75 mm SWE h-1, respectively, which the model failed to represent. While further implementation and validation is necessary, our preliminary results indicate that forest interception magnitude may be underestimated in maritime areas.

Effects of Snow/ Soil Interface on Microwave Backscatter of Terrestrial Snowpack at X- and Ku- Band

Science.gov (United States)

Kang, D. H.; Tan, S.; Zhu, J.; Gu, W.; Tsang, L.; Kim, E. J.

2017-12-01

Recent advances in monitoring and modeling capabilities to support remote sensing of terrestrial snow is encouraging to develop satellite mission concept in monitoring cold-region hydrological processes on global scales. However, it is still challenging to link back the active microwave backscattering signals to physical snowpack parameters. One of the limitations resides in the ignorance of the vegetation and soil conditions beneath the snowpack in the microwave scattering/ emission modeling and the snow water equivalent (SWE) retrieval algorithm. During the SnowEx 2017 winter campaign in Grand Mesa, CO, a particular effort has been made on comprehensive measurements of the underlying vegetation and soil characteristics from the snowpit measurements. Besides conducting standard snow core sampling, we have made additional protocols to record the background information beneath the snowpack. Recent works on active SWE retrieval algorithm using backscatters at X- (9.6 GHz) and Ku- (17.2 GHz) band suggest the significant signals from the background scattering characterization. The background scattering arising from the rough snow/ soil interface and the buried vegetation inside and beneath the snowpack modifies the sensitivity of the total backscatter to SWE. In this paper, we summarize the snow/ soil interface conditions as observed in the SnowEx campaign. We also develop standards for future in-situ snowpit measurements to include regular snow/ soil interface observations to accommodate the interpretation of microwave backscatter both for modeling and observation of microwave signatures. These observations first provide inputs to the microwave scattering models to predict the backscattering contribution from background, which is one of the key factors to be included to improve the SWE retrieval performance.

Measuring snow water equivalent from common-offset GPR records through migration velocity analysis

Science.gov (United States)

St. Clair, James; Holbrook, W. Steven

2017-12-01

Many mountainous regions depend on seasonal snowfall for their water resources. Current methods of predicting the availability of water resources rely on long-term relationships between stream discharge and snowpack monitoring at isolated locations, which are less reliable during abnormal snow years. Ground-penetrating radar (GPR) has been shown to be an effective tool for measuring snow water equivalent (SWE) because of the close relationship between snow density and radar velocity. However, the standard methods of measuring radar velocity can be time-consuming. Here we apply a migration focusing method originally developed for extracting velocity information from diffracted energy observed in zero-offset seismic sections to the problem of estimating radar velocities in seasonal snow from common-offset GPR data. Diffractions are isolated by plane-wave-destruction (PWD) filtering and the optimal migration velocity is chosen based on the varimax norm of the migrated image. We then use the radar velocity to estimate snow density, depth, and SWE. The GPR-derived SWE estimates are within 6 % of manual SWE measurements when the GPR antenna is coupled to the snow surface and 3-21 % of the manual measurements when the antenna is mounted on the front of a snowmobile ˜ 0.5 m above the snow surface.

Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

Directory of Open Access Journals (Sweden)

T. M. Saloranta

2012-11-01

Full Text Available Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE, snow depth (SD, and the snow bulk density (ρ. In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R²-values for model fit are 0.60 for (log-transformed SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

Science.gov (United States)

Crumley, R. L.; Hill, D. F.; Arendt, A. A.; Wikstrom Jones, K.; Wolken, G. J.; Setiawan, L.

2017-12-01

Modeling seasonal snow pack in alpine environments includes a multiplicity of challenges caused by a lack of spatially extensive and temporally continuous observational datasets. This is partially due to the difficulty of collecting measurements in harsh, remote environments where extreme gradients in topography exist, accompanied by large model domains and inclement weather. Engaging snow enthusiasts, snow professionals, and community members to participate in the process of data collection may address some of these challenges. In this study, we use SnowModel to estimate seasonal snow water equivalence (SWE) in the Thompson Pass region of Alaska while incorporating snow depth measurements collected by citizen scientists. We develop a modeling approach to assimilate hundreds of snow depth measurements from participants in the Community Snow Observations (CSO) project (www.communitysnowobs.org). The CSO project includes a mobile application where participants record and submit geo-located snow depth measurements while working and recreating in the study area. These snow depth measurements are randomly located within the model grid at irregular time intervals over the span of four months in the 2017 water year. This snow depth observation dataset is converted into a SWE dataset by employing an empirically-based, bulk density and SWE estimation method. We then assimilate this data using SnowAssim, a sub-model within SnowModel, to constrain the SWE output by the observed data. Multiple model runs are designed to represent an array of output scenarios during the assimilation process. An effort to present model output uncertainties is included, as well as quantification of the pre- and post-assimilation divergence in modeled SWE. Early results reveal pre-assimilation SWE estimations are consistently greater than the post-assimilation estimations, and the magnitude of divergence increases throughout the snow pack evolution period. This research has implications beyond the

A new web-based system to improve the monitoring of snow avalanche hazard in France

Science.gov (United States)

Bourova, Ekaterina; Maldonado, Eric; Leroy, Jean-Baptiste; Alouani, Rachid; Eckert, Nicolas; Bonnefoy-Demongeot, Mylene; Deschatres, Michael

2016-05-01

Snow avalanche data in the French Alps and Pyrenees have been recorded for more than 100 years in several databases. The increasing amount of observed data required a more integrative and automated service. Here we report the comprehensive web-based Snow Avalanche Information System newly developed to this end for three important data sets: an avalanche chronicle (Enquête Permanente sur les Avalanches, EPA), an avalanche map (Carte de Localisation des Phénomènes d'Avalanche, CLPA) and a compilation of hazard and vulnerability data recorded on selected paths endangering human settlements (Sites Habités Sensibles aux Avalanches, SSA). These data sets are now integrated into a common database, enabling full interoperability between all different types of snow avalanche records: digitized geographic data, avalanche descriptive parameters, eyewitness reports, photographs, hazard and risk levels, etc. The new information system is implemented through modular components using Java-based web technologies with Spring and Hibernate frameworks. It automates the manual data entry and improves the process of information collection and sharing, enhancing user experience and data quality, and offering new outlooks to explore and exploit the huge amount of snow avalanche data available for fundamental research and more applied risk assessment.

Improving automated disturbance maps using snow-covered landsat time series stacks

Science.gov (United States)

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2012-01-01

Snow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation...

Pollution from the 2014-15 Bárðarbunga eruption monitored by snow cores from the Vatnajökull glacier, Iceland

Science.gov (United States)

Galeczka, Iwona; Eiriksdottir, Eydis Salome; Pálsson, Finnur; Oelkers, Eric; Lutz, Stefanie; Benning, Liane G.; Stefánsson, Andri; Kjartansdóttir, Ríkey; Gunnarsson-Robin, Jóhann; Ono, Shuhei; Ólafsdóttir, Rósa; Jónasdóttir, Elín Björk; Gislason, Sigurdur R.

2017-11-01

The chemical composition of Icelandic rain and snow is dominated by marine aerosols, however human and volcanic activity can also affect these compositions. The six month long 2014-15 Bárðarbunga volcanic eruption was the largest in Iceland for more than 200 years and it released into the atmosphere an average of 60 kt/day SO2, 30 kt/day CO2, 500 t/day HCl and 280 t/day HF. To study the effect of this eruption on the winter precipitation, snow cores were collected from the Vatnajökull glacier and the highlands northeast of the glacier. In addition to 29 bulk snow cores from that precipitated from September 2014 until March 2015, two cores were sampled in 21 and 44 increments to quantify the spatial and time evolution of the chemical composition of the snow. The pH and chemical compositions of melted snow samples indicate that snow has been affected by the volcanic gases emitted during the Bárðarbunga eruption. The pH of the melted bulk snow cores ranged from 4.41 to 5.64 with an average value of 5.01. This is four times greater H+ activity than pure water saturated with the atmospheric CO2. The highest concentrations of volatiles in the snow cores were found close to the eruption site as predicted from CALPUFF SO2 gas dispersion quality model. The anion concentrations (SO4, Cl, and F) were higher and the pH was lower compared to equivalent snow samples collected during 1997-2006 from the unpolluted Icelandic Langjökull glacier. Higher SO4 and Cl concentrations in the snow compared with the unpolluted rainwater of marine origin confirm the addition of a non-seawater SO4 and Cl. The δ34S isotopic composition confirms that the sulphur addition is of volcanic aerosol origin. The chemical evolution of the snow with depth reflects changes in the lava effusion and gas emission rates. Those rates were the highest at the early stage of the eruption. Snow that fell during that time, represented by samples from the deepest part of the snow cores, had the lowest pH and

Small scale variability of snow properties on Antarctic sea ice

Science.gov (United States)

Wever, Nander; Leonard, Katherine; Paul, Stephan; Jacobi, Hans-Werner; Proksch, Martin; Lehning, Michael

2016-04-01

Snow on sea ice plays an important role in air-ice-sea interactions, as snow accumulation may for example increase the albedo. Snow is also able to smooth the ice surface, thereby reducing the surface roughness, while at the same time it may generate new roughness elements by interactions with the wind. Snow density is a key property in many processes, for example by influencing the thermal conductivity of the snow layer, radiative transfer inside the snow as well as the effects of aerodynamic forcing on the snowpack. By comparing snow density and grain size from snow pits and snow micro penetrometer (SMP) measurements, highly resolved density and grain size profiles were acquired during two subsequent cruises of the RV Polarstern in the Weddell Sea, Antarctica, between June and October 2013. During the first cruise, SMP measurements were done along two approximately 40 m transects with a horizontal resolution of approximately 30 cm. During the second cruise, one transect was made with approximately 7.5 m resolution over a distance of 500 m. Average snow densities are about 300 kg/m3, but the analysis also reveals a high spatial variability in snow density on sea ice in both horizontal and vertical direction, ranging from roughly 180 to 360 kg/m3. This variability is expressed by coherent snow structures over several meters. On the first cruise, the measurements were accompanied by terrestrial laser scanning (TLS) on an area of 50x50 m2. The comparison with the TLS data indicates that the spatial variability is exhibiting similar spatial patterns as deviations in surface topology. This suggests a strong influence from surface processes, for example wind, on the temporal development of density or grain size profiles. The fundamental relationship between variations in snow properties, surface roughness and changes therein as investigated in this study is interpreted with respect to large-scale ice movement and the mass balance.

An indirect study of air pollution by neutron activation analysis of snow

International Nuclear Information System (INIS)

Zikovsky, L.; Badillo, M.

1987-01-01

Al, As, Br, Cd, Ce, Cl, Co, Cs, Cu, Fe, Hg, K, La, Mg, Mn, Mo, Na, Sb, Sc, Sr, Ti, V and Zn were determined in the soluble and insoluble fractions of snow which was collected at 6 different sites in the Montreal area. From these data the variability coefficients and the enrichment factors were calculated and the probable sources of each element are discussed. The overall concentrations found in the snow were compared with the concentrations of these elements in the air sampled in the same area by high volume samplers. From these, the snow-out ratios for Cr, Cu, Fe, Mn, V and Zn and their mean were calculated. (author) 8 refs.; 6 tabs

Snow Precipitation and Snow Cover Climatic Variability for the Period 1971–2009 in the Southwestern Italian Alps: The 2008–2009 Snow Season Case Study

Directory of Open Access Journals (Sweden)

Simona Fratianni

2010-10-01

Full Text Available Snow cover greatly influences the climate in the Alpine region and is one of the most relevant parameters for the climate change analysis. Nevertheless, snow precipitation variability is a relatively underexplored field of research because of the lack of long-term, continuous and homogeneous time series. After a historical research aiming to recover continuous records, three high quality time series of snow precipitation and snow depth recorded in the southwestern Italian Alps were analyzed. The comparison between the climatological indices over the 30 years reference period 1971–2000 and the decade 2000–2009 outlined a general decrease in the amount of snow precipitation, and a shift in the seasonal distribution of the snow precipitation in the most recent period. In the analysis of the last decade snow seasons characteristics, the attention was focused on the heavy snowfalls that occurred in Piedmont during the 2008–2009 snow season: MODerate resolution Imager Spectroradiometer (MODIS snow cover products were used to evaluate snow cover extension at different times during the snow season, and the results were set in relation to the temperatures.

Realism versus simplicity in the snow routine of the HBV model

Science.gov (United States)

Girons Lopez, Marc; Vis, Marc; Seibert, Jan

2017-04-01

The HBV model still enjoys great popularity, in part because of its simplicity. Depicting the hydrological processes in a catchment in a simple way reduces data requirements and minimises parameter uncertainty. However, the representation of some processes might benefit from an increased model complexity. This is, for instance, the case of snow routine in the HBV-light version of the HBV model. HBV-light uses a degree-day method with a single threshold parameter to distinguish between rain and snow and simulate snow melt. Recent research has shown that hydrological models with a more realistic representation of snow processes might be more successful in estimating runoff. In this study we explore and test different improvements to the HBV-light snow routine design by considering different threshold temperature values for rain and snow distinction as well as for the beginning of snow melt, and introducing gradual transitions instead of the current sharp threshold. The use of radiation data, which recently became available as gridded data product in Switzerland, is an additional possibility. These modifications would allow for a more realistic depiction of important hydrological processes in alpine and other snow-covered areas while preserving the characteristic simplicity of HBV. Furthermore, in this contribution we evaluate the balance between introducing more realism into the snow routine of the HBV model and keeping the number of parameters as low as possible.

Snow accretion on overhead wires

Energy Technology Data Exchange (ETDEWEB)

Sakamoto, Y. [Meteorological Research Inst. for Technology Co. Ltd., Tokyo (Japan); Tachizaki, S.; Sudo, N. [Tohoku Electric Power Co. Ltd., Miyagi (Japan)

2005-07-01

Wet snow accretion can cause extensive damage to transmission systems. This paper reviewed some of the difficulties faced by researchers in the study of wet snow accretion on overhead lines in Japan. The study of snow accretion phenomena is complicated by the range of phase changes in water. Snowflakes produced in an upper atmospheric layer with a temperature below freezing do not melt when they go through a lower atmospheric layer with a temperature above freezing, but are in a mixed state of solid and liquid due to the latent heat of melting. The complicated properties of water make studies of snow accretion difficult, as well as the fact that snow changes its physical properties rapidly, due to the effects of ambient temperature, rainfall, and solar radiation. The adhesive forces that cause snow accretion include freezing; bonding through freezing; sintering; condensation and freezing of vapor in the air; mechanical intertwining of snowflakes; capillary action due to liquids; coherent forces between ice particles and water formed through the metamorphosis of snowflakes. In addition to these complexities, differences in laboratory room environments and natural snow environments can also pose difficulties for researchers. Equations describing the relationship between the density of accreted snow and the meteorological parameters involved were presented, as well as empirical equations which suggested that snow accretion efficiency has a dependency on air temperature. An empirical model for estimating snow loads in Japan was outlined, as well as various experiments observing show shedding. Correlations for wet snow accretion included precipitation intensity; duration of precipitation; air temperature; wind speed and wind direction in relation to the overhead line. Issues concerning topography and wet snow accretion were reviewed. It was concluded that studies of snow accretion will benefit by the collection of data in each matrix of the relevant parameters. 12 refs

Sensitivity of Alpine Snow and Streamflow Regimes to Climate Changes

Science.gov (United States)

Rasouli, K.; Pomeroy, J. W.; Marks, D. G.; Bernhardt, M.

2014-12-01

Understanding the sensitivity of hydrological processes to climate change in alpine areas with snow dominated regimes is of paramount importance as alpine basins show both high runoff efficiency associated with the melt of the seasonal snowpack and great sensitivity of snow processes to temperature change. In this study, meteorological data measured in a selection of alpine headwaters basins including Reynolds Mountain East, Idaho, USA, Wolf Creek, Yukon in Canada, and Zugspitze Mountain, Germany with climates ranging from arctic to continental temperate were used to study the snow and streamflow sensitivity to climate change. All research sites have detailed multi-decadal meteorological and snow measurements. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a model representing a typical alpine headwater basin discretized into hydrological response units with physically based representations of snow redistribution by wind, complex terrain snowmelt energetics and runoff processes in alpine tundra. The sensitivity of snow hydrology to climate change was investigated by changing air temperature and precipitation using weather generating methods based on the change factors obtained from different climate model projections for future and current periods. The basin mean and spatial variability of peak snow water equivalent, sublimation loss, duration of snow season, snowmelt rates, streamflow peak, and basin discharge were assessed under varying climate scenarios and the most sensitive hydrological mechanisms to the changes in the different alpine climates were detected. The results show that snow hydrology in colder alpine climates is more resilient to warming than that in warmer climates, but that compensatory factors to warming such as reduced blowing snow sublimation loss and reduced melt rate should also be assessed when considering climate change impacts on alpine hydrology.

UAS applications in high alpine, snow-covered terrain

Science.gov (United States)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

Access to snow-covered, alpine terrain is often difficult and dangerous. Hence parameters such as snow depth or snow avalanche release and deposition zones are hard to map in situ with adequate spatial and temporal resolution and with spatial continuous coverage. These parameters are currently operationally measured at automated weather stations and by observer networks. However such isolated point measurements are not able to capture the information spatial continuous and to describe the high spatial variability present in complex mountain topography. Unmanned Aerial Systems (UAS) have the potential to fill this gap by frequently covering selected high alpine areas with high spatial resolution down to ground resolutions of even few millimeters. At the WSL Institute for Snow and Avalanche Research SLF we test different photogrammetric UAS with visual and near infrared bands. During the last three years we were able to gather experience in more than 100 flight missions in extreme terrain. By processing the imagery applying state-of-the-art structure from motion (SfM) software, we were able to accurately document several avalanche events and to photogrammetrically map snow depth with accuracies from 1 to 20 cm (dependent on the flight height above ground) compare to manual snow probe measurements. This was even possible on homogenous snow surfaces with very little texture. A key issue in alpine terrain is flight planning. We need to cover regions at high elevations with large altitude differences (up to 1 km) with high wind speeds (up to 20 m/s) and cold temperatures (down to - 25°C). Only a few UAS are able to cope with these environmental conditions. We will give an overview on our applications of UAS in high alpine terrain that demonstrate the big potential of such systems to acquire frequent, accurate and high spatial resolution geodata in high alpine, snow covered terrain that could be essential to answer longstanding questions in avalanche and snow hydrology

Relationship between snow depth and gray wolf predation on white-tailed deer

Science.gov (United States)

Nelson, M.E.; Mech, L.D.

1986-01-01

Survival of 203 yearling and adult white-tailed deer (Odocoileus virginianus) was monitored for 23,441 deer days from January through April 1975-85 in northeastern Minnesota. Gray wolf (Canis lupus) predation was the primary mortality cause, and from year to year during this period, the mean predation rate ranged from 0.00 to 0.29. The sum of weekly snow depths/month explained 51% of the variation in annual wolf predation rate, with the highest predation during the deepest snow.

Long-term monitoring of fecal steroid hormones in female snow leopards (Panthera uncia during pregnancy or pseudopregnancy.

Directory of Open Access Journals (Sweden)

Kodzue Kinoshita

Full Text Available Knowledge of the basic reproductive physiology of snow leopards is required urgently in order to develop a suitable management conditions under captivity. In this study, the long-term monitoring of concentrations of three steroid hormones in fecal matter of three female snow leopards was performed using enzyme immunoassays: (1 estradiol-17β, (2 progesterone and (3 cortisol metabolite. Two of the female animals were housed with a male during the winter breeding season, and copulated around the day the estradiol-17β metabolite peaked subsequently becoming pregnant. The other female was treated in two different ways: (1 first housed with a male in all year round and then (2 in the winter season only. She did not mate with him on the first occasion, but did so latter around when estradiol-17β metabolite peaked, and became pseudopregnant. During pregnancy, progesterone metabolite concentrations increased for 92 or 94 days, with this period being approximately twice as long as in the pseudopregnant case (31, 42, 49 and 53 days. The levels of cortisol metabolite in the pseudopregnant female (1.35 µg/g were significantly higher than in the pregnant females (0.33 and 0.24 µg/g (P<0.05. Similarly, during the breeding season, the levels of estradiol-17β metabolite in the pseudopregnant female (2.18 µg/g were significantly higher than those in the pregnant females (0.81 and 0.85 µg/g (P<0.05. Unlike cortisol the average levels of estradiol-17β during the breeding season were independent of reproductive success.The hormone levels may also be related to housing conditions and the resulting reproductive success in female leopards. The female housed with a male during the non-breeding season had high levels of cortisol metabolites and low levels of estradiol-17β in the breeding season, and failed to become pregnant. This indicates that housing conditions in snow leopards may be an important factor for normal endocrine secretion and resulting breeding

Estimation of Snow Particle Model Suitable for a Complex and Forested Terrain: Lessons from SnowEx

Science.gov (United States)

Gatebe, C. K.; Li, W.; Stamnes, K. H.; Poudyal, R.; Fan, Y.; Chen, N.

2017-12-01

SnowEx 2017 obtained consistent and coordinated ground and airborne remote sensing measurements over Grand Mesa in Colorado, which feature sufficient forested stands to have a range of density and height (and other forest conditions); a range of snow depth/snow water equivalent (SWE) conditions; sufficiently flat snow-covered terrain of a size comparable to airborne instrument swath widths. The Cloud Absorption Radiometer (CAR) data from SnowEx are unique and can be used to assess the accuracy of Bidirectional Reflectance-Distribution Functions (BRDFs) calculated by different snow models. These measurements provide multiple angle and multiple wavelength data needed for accurate surface BRDF characterization. Such data cannot easily be obtained by current satellite remote sensors. Compared to ground-based snow field measurements, CAR measurements minimize the effect of self-shading, and are adaptable to a wide variety of field conditions. We plan to use the CAR measurements as the validation data source for our snow modeling effort. By comparing calculated BRDF results from different snow models to CAR measurements, we can determine which model best explains the snow BRDFs, and is therefore most suitable for application to satellite remote sensing of snow parameters and surface energy budget calculations.

An electrostatic charge measurement of blowing snow particles focusing on collision frequency to the snow surface

Science.gov (United States)

Omiya, S.; Sato, A.

2010-12-01

Blowing snow particles are known to have an electrostatic charge. This charge may be a contributing factor in the formation of snow drifts and snow cornices and changing of the trajectory of blowing snow particles. These formations and phenomena can cause natural disaster such as an avalanche and a visibility deterioration, and obstruct transportation during winter season. Therefore, charging phenomenon of the blowing snow particles is an important issue in terms of not only precise understanding of the particle motion but disaster prevention. The primary factor of charge accumulation to the blowing snow particles is thought to be due to “saltation” of them. The “saltation” is one of movement forms of blowing snow: when the snow particles are transported by the wind, they repeat frictional collisions with the snow surface. In previous studies, charge-to-mass ratios measured in the field were approximately -50 to -10 μC/kg, and in the wind tunnel were approximately -0.8 to -0.1 μC/kg. While there were qualitatively consistent in sign, negative, there were huge gaps quantitatively between them. One reason of those gaps is speculated to be due to differences in fetch. In other words, the difference of the collision frequency of snow particles to the snow surface has caused the gaps. But it is merely a suggestion and that has not been confirmed. The purpose of this experiment is to measure the charge of blowing snow particles focusing on the collision frequency and clarify the relationship between them. Experiments were carried out in the cryogenic wind tunnel of Snow and Ice Research Center (NIED, JAPAN). A Faraday cage and an electrometer were used to measure the charge of snow particles. These experiments were conducted over the hard snow surface condition to prevent the erosion of the snow surface and the generation of new snow particles from the surface. The collision frequency of particle was controlled by changing the wind velocity (4.5 to 7 m/s) under

Forest Fires Darken Snow for Years following Disturbance: Magnitude, Duration, and Composition of Light Absorbing Impurities in Seasonal Snow across a Chronosequence of Burned Forests in the Colorado River Headwaters

Science.gov (United States)

Gleason, K. E.; Arienzo, M. M.; Chellman, N.; McConnell, J.

2017-12-01

Charred forests shed black carbon and burned debris, which accumulates and concentrates on winter snowpack, reducing snow surface albedo, and subsequently increasing snowmelt rates, and advancing the date of snow disappearance. Forest fires have occurred across vast areas of the seasonal snow zone in recent decades, however we do not understand the long-term implications of burned forests in montane headwaters to snow hydrology and downstream water resources. Across a chronosequence of nine burned forests in the Colorado River Headwaters, we sampled snow throughout the complete snowpack profile to conserve the composition, properties, and vertical stratigraphy of impurities in the snowpack during maximum snow accumulation. Using state-of-the-art geochemical analyses, we determined the magnitude, composition, and particle size distribution of black carbon, dust, and other impurities in the snowpack relative to years-since fire. Forest fires continue to darken snow for many years following fire, however the magnitude, composition, and particle size distribution of impurities change through time, altering the post-fire radiative forcing on snow as a burned forest ages.

Application of Snowfall and Wind Statistics to Snow Transport Modeling for Snowdrift Control in Minnesota.

Science.gov (United States)

Shulski, Martha D.; Seeley, Mark W.

2004-11-01

Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s-1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4 0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40% 90% of the total potential in south-central and western areas of the state.

Snow cover dynamics and water balance in complex high alpine terrain

Science.gov (United States)

Warscher, Michael; Kraller, Gabriele; Kunstmann, Harald; Strasser, Ulrich; Franz, Helmut

2010-05-01

The water balance in high alpine regions in its full complexity is so far insufficiently understood. High altitudinal gradients, a strong variability of meteorological variables in time and space, complex hydrogeological situations, unquantified lateral snow transport processes and heterogenous snow cover dynamics result in high uncertainties in the quantification of the water balance. To achieve interpretable modeling results we have complemented the deterministic hydrological model WaSiM-ETH with the high-alpine specific snow model AMUNDSEN. The integration of the new snow module was done to improve the modeling of water fluxes influenced by the dynamics of the snow cover, which greatly affect the water cycle in high alpine regions. To enhance the reproduction of snow deposition and ablation processes, the new approach calculates the energy balance of the snow cover considering the terrain-dependent radiation fluxes, the interaction between tree canopy and snow cover as well as lateral snow transport processes. The test site for our study is the Berchtesgaden National Park which is characterized by an extreme topography with mountain ranges covering an altitude from 607 to 2713 m.a.s.l. About one quarter of the investigated catchment area, which comprises 433 km² in total, is terrain steeper than 35°. Due to water soluble limestone being predominant in the region, a high number of subsurface water pathways (karst) exist. The results of several tracer experiments and extensive data of spring observations provide additional information to meet the challenge of modeling the unknown subsurface pathways and the complex groundwater system of the region. The validation of the new snow module is based on a dense network of meteorological stations which have been adapted to measure physical properties of the snow cover like snow water equivalent and liquid water content. We will present first results which show that the integration of the new snow module generates a

Snow clearance

CERN Multimedia

Mauro Nonis

2005-01-01

In reply to the numerous questions received, we should like to inform you of the actions and measures taken in an effort to maintain the movements of vehicles and pedestrians since the heavy snow fall on Sunday 23 January. Our contractor's employees began clearing the snow during the morning of Sunday 23 January on the main CERN sites (Meyrin, Prévessin), but an accident prevented them from continuing. The vehicle in question was repaired by Monday morning when two other vehicles joined it to resume snow clearing; priority was given to access points to the main sites and the LHC sites, as well as to the main roads inside the sites. The salt sprinklers were also brought into action that same day; the very low temperature during the night from Monday to Tuesday prevented the snow from melting and compacted the ice; the continuing cold during the day on Tuesday (-6°C at 10:00 on the Meyrin site) meant that all efforts to remove the ice were doomed to failure. In order to ensure more efficie...

Removal of snow cover inhibits spring growth of Arctic ice algae through physiological and behavioral effects

DEFF Research Database (Denmark)

Lund-Hansen, L.C.; Hawes, Ian; Sorrell, Brian Keith

2014-01-01

The snow cover of Arctic sea ice has recently decreased, and climate models forecast that this will continue and even increase in future. We therefore tested the effect of snow cover on the optical properties of sea ice and the biomass, photobiology, and species composition of sea ice algae at Ka...... of the spring bloom is predominantly due to temperature effects on brine channel volume, and that the algal decline after snow removal was primarily due to emigration rather than photodamage.......The snow cover of Arctic sea ice has recently decreased, and climate models forecast that this will continue and even increase in future. We therefore tested the effect of snow cover on the optical properties of sea ice and the biomass, photobiology, and species composition of sea ice algae...... at Kangerlussuaq, West Greenland, during March 2011, using a snow-clearance experiment. Sea ice algae in areas cleared of snow was compared with control areas, using imaging variable fluorescence of photosystem II in intact, unthawed ice sections. The study coincided with the onset of spring growth of ice algae...

Distributed snow modeling suitable for use with operational data for the American River watershed.

Science.gov (United States)

Shamir, E.; Georgakakos, K. P.

2004-12-01

The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

Science.gov (United States)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

Science.gov (United States)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential

Snow cover dynamics in Andean watersheds of Chile (32.0-39.5° S) during the years 2000-2016

Science.gov (United States)

Stehr, Alejandra; Aguayo, Mauricio

2017-10-01

Andean watersheds present important snowfall accumulation mainly during the winter, which melts during the spring and part of the summer. The effect of snowmelt on the water balance can be critical to sustain agriculture activities, hydropower generation, urban water supplies and wildlife. In Chile, 25 % of the territory between the region of Valparaiso and Araucanía comprises areas where snow precipitation occurs. As in many other difficult-to-access regions of the world, there is a lack of hydrological data of the Chilean Andes related to discharge, snow courses, and snow depths, which complicates the analysis of important hydrological processes (e.g. water availability). Remote sensing provides a promising opportunity to enhance the assessment and monitoring of the spatial and temporal variability of snow characteristics, such as the snow cover area (SCA) and snow cover dynamic (SCD). With regards to the foregoing questions, the objective of the study is to evaluate the spatiotemporal dynamics of the SCA at five watersheds (Aconcagua, Rapel, Maule, Biobío and Toltén) located in the Chilean Andes, between latitude 32.0 and 39.5° S, and to analyse its relationship with the precipitation regime/pattern and El Niño-Southern Oscillation (ENSO) events. Those watersheds were chosen because of their importance in terms of their number of inhabitants, and economic activities depending on water resources. The SCA area was obtained from MOD10A2 for the period 2000-2016, and the SCD was analysed through a number of statistical tests to explore observed trends. In order to verify the SCA for trend analysis, a validation of the MOD10A2 product was done, consisting of the comparison of snow presence predicted by MODIS with ground observations. Results indicate that there is an overall agreement of 81 to 98 % between SCA determined from ground observations and MOD10A2, showing that the MODIS snow product can be taken as a feasible remote sensing tool for SCA estimation in

Annual Greenland Accumulation Rates (2009-2012) from Airborne Snow Radar

Science.gov (United States)

Koenig, Lora S.; Ivanoff, Alvaro; Alexander, Patrick M.; MacGregor, Joseph A.; Fettweis, Xavier; Panzer, Ben; Paden, John D.; Forster, Richard R.; Das, Indrani; McConnell, Joseph R.;

A Citizen Science Campaign to Validate Snow Remote-Sensing Products

Science.gov (United States)

Wikstrom Jones, K.; Wolken, G. J.; Arendt, A. A.; Hill, D. F.; Crumley, R. L.; Setiawan, L.; Markle, B.

2017-12-01

The ability to quantify seasonal water retention and storage in mountain snow packs has implications for an array of important topics, including ecosystem function, water resources, hazard mitigation, validation of remote sensing products, climate modeling, and the economy. Runoff simulation models, which typically rely on gridded climate data and snow remote sensing products, would be greatly improved if uncertainties in estimates of snow depth distribution in high-elevation complex terrain could be reduced. This requires an increase in the spatial and temporal coverage of observational snow data in high-elevation data-poor regions. To this end, we launched Community Snow Observations (CSO). Participating citizen scientists use Mountain Hub, a multi-platform mobile and web-based crowdsourcing application that allows users to record, submit, and instantly share geo-located snow depth, snow water equivalence (SWE) measurements, measurement location photos, and snow grain information with project scientists and other citizen scientists. The snow observations are used to validate remote sensing products and modeled snow depth distribution. The project's prototype phase focused on Thompson Pass in south-central Alaska, an important infrastructure corridor that includes avalanche terrain and the Lowe River drainage and is essential to the City of Valdez and the fisheries of Prince William Sound. This year's efforts included website development, expansion of the Mountain Hub tool, and recruitment of citizen scientists through a combination of social media outreach, community presentations, and targeted recruitment of local avalanche professionals. We also conducted two intensive field data collection campaigns that coincided with an aerial photogrammetric survey. With more than 400 snow depth observations, we have generated a new snow remote-sensing product that better matches actual SWE quantities for Thompson Pass. In the next phase of the citizen science portion of

Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed

Directory of Open Access Journals (Sweden)

H.-Y. Li

2011-07-01

Full Text Available An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE between the measured and simulated discharges is 0.673, and the volume difference (Dv is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

SNOW THICKNESS ON AUSTRE GRØNFJORDBREEN, SVALBARD, FROM RADAR MEASUREMENTS AND STANDARD SNOW SURVEYS

Directory of Open Access Journals (Sweden)

I. I. Lavrentiev

2018-01-01

Full Text Available Summary Comparison of two methods of measurements of snow cover thickness on the glacier Austre Grønfjordbreen, Svalbard was performed in the spring of 2014. These methods were the radar (500 MHz observations and standard snow surveys. Measurements were conducted in 77 different points on the surface of the glacier. A good correlation (R2 = 0.98 was revealed. In comparison with the data of snow surveys, the radar measurements show a similar but more detailed pattern of the distribution of the snow cover depth. The discrepancy between the depths of snow cover on maps plotted from data of both methods did not exceed 30 cm in most parts of the glacier. The standard error of interpolation of the radar data onto the entire glacier surface amounts, on average, to 18 cm. This corresponds to the error of radar measurements of 18.8% when an average snow depth is about 160 cm and 9.4% at its maximum thickness of 320 cm. The distance between the measurement points at which the spatial covariance of the snow depth disappears falls between 236 and 283 m along the glacier, and between 117 and 165 m across its position. We compared the results of radar measurements of the pulse-delay time of reflections from the base of the snow cover with the data of manual probe measurements at 10 points and direct measurements of snow depth and average density in 12 snow pits. The average speed of radio waves propagation in the snow was determined as Vcr = 23.4±0.2 cm ns−1. This magnitude and the Looyenga and Kovacs formulas allowed estimating the average density of snow cover ρL = 353.1±13.1 kg m−3 and ρK = 337.4±12.9 kg m−3. The difference from average density measured in 12 pits ρav.meas = 387.4±12.9 kg m−3 amounts to −10.8% and −14.8%. In 2014, according to snow and radar measurements, altitudinal gradient of snow accumulation on the glacier Austre Grønfjordbreen was equal to 0.21 m/100 m, which is smaller than the

New Perspectives on Blowing Snow Transport, Sublimation, and Layer Thermodynamic Structure over Antarctica

Science.gov (United States)

Palm, Steve; Kayetha, Vinay; Yang, Yuekui; Pauly, Rebecca M.

2017-01-01

Blowing snow over Antarctica is a widespread and frequent event. Satellite remote sensing using lidar has shown that blowing snow occurs over 70% of the time over large areas of Antarctica in winter. The transport and sublimation of blowing snow are important terms in the ice sheet mass balance equation and the latter is also an important part of the hydrological cycle. Until now the only way to estimate the magnitude of these processes was through model parameterization. We present a technique that uses direct satellite observations of blowing snow and model (MERRA-2) temperature and humidity fields to compute both transport and sublimation of blowing snow over Antarctica for the period 2006 to 2016. The results show a larger annual continent-wide integrated sublimation than current published estimates and a significant transport of snow from continent to ocean. The talk will also include the lidar backscatter structure of blowing snow layers that often reach heights of 200 to 300 m as well as the first dropsonde measurements of temperature, moisture and wind through blowing snow layers.

Machine Learning on Images: Combining Passive Microwave and Optical Data to Estimate Snow Water Equivalent

Science.gov (United States)

Dozier, J.; Tolle, K.; Bair, N.

2014-12-01

We have a problem that may be a specific example of a generic one. The task is to estimate spatiotemporally distributed estimates of snow water equivalent (SWE) in snow-dominated mountain environments, including those that lack on-the-ground measurements. Several independent methods exist, but all are problematic. The remotely sensed date of disappearance of snow from each pixel can be combined with a calculation of melt to reconstruct the accumulated SWE for each day back to the last significant snowfall. Comparison with streamflow measurements in mountain ranges where such data are available shows this method to be accurate, but the big disadvantage is that SWE can only be calculated retroactively after snow disappears, and even then only for areas with little accumulation during the melt season. Passive microwave sensors offer real-time global SWE estimates but suffer from several issues, notably signal loss in wet snow or in forests, saturation in deep snow, subpixel variability in the mountains owing to the large (~25 km) pixel size, and SWE overestimation in the presence of large grains such as depth and surface hoar. Throughout the winter and spring, snow-covered area can be measured at sub-km spatial resolution with optical sensors, with accuracy and timeliness improved by interpolating and smoothing across multiple days. So the question is, how can we establish the relationship between Reconstruction—available only after the snow goes away—and passive microwave and optical data to accurately estimate SWE during the snow season, when the information can help forecast spring runoff? Linear regression provides one answer, but can modern machine learning techniques (used to persuade people to click on web advertisements) adapt to improve forecasts of floods and droughts in areas where more than one billion people depend on snowmelt for their water resources?

Wide-area, real-time monitoring and visualization system

Science.gov (United States)

Budhraja, Vikram S.; Dyer, James D.; Martinez Morales, Carlos A.

2013-03-19

A real-time performance monitoring system for monitoring an electric power grid. The electric power grid has a plurality of grid portions, each grid portion corresponding to one of a plurality of control areas. The real-time performance monitoring system includes a monitor computer for monitoring at least one of reliability metrics, generation metrics, transmission metrics, suppliers metrics, grid infrastructure security metrics, and markets metrics for the electric power grid. The data for metrics being monitored by the monitor computer are stored in a data base, and a visualization of the metrics is displayed on at least one display computer having a monitor. The at least one display computer in one said control area enables an operator to monitor the grid portion corresponding to a different said control area.

Snow accumulation and ablation in different canopy structures at a plot scale: using degree-day approach and measured shortwave radiation

Directory of Open Access Journals (Sweden)

Michal Jeníček

2017-02-01

The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. The model simulated best in open area and slightly worse in healthy forest. The model showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest.

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Science.gov (United States)

Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

2015-01-01

Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

Directory of Open Access Journals (Sweden)

T. R. Roth

2017-11-01

Full Text Available Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in sub-canopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4-year study of snow–forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that relative to nearby open areas, the dense, relatively warm forests at Low and Mid sites impede snow accumulation via canopy snow interception and increase sub-canopy snowpack energy inputs via longwave radiation. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4–26 and 11–33 days, respectively. However, we see the opposite relationship at the relatively colder High sites, with the Forest site maintaining snow longer into the spring by 15–29 days relative to the nearby Open site. Canopy interception efficiency (CIE values at the Low and Mid Forest sites averaged 79 and 76 % of the total event snowfall, whereas CIE was 31 % at the lower density High Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 93, 92, and 47 % of total energy inputs to the snowpack at the Low, Mid, and High Forest sites, respectively. Higher wind speeds in the High Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High Forest site create preferential snowfall deposition. These

A webgis supported snow information system with long time satellite data for Turkey

Science.gov (United States)

Surer, S.; Bolat, K.; Akyurek, Z.

2012-04-01

KARBILSIS is an online platform which is developed in order to provide end-users with daily remote sensing snow products for Turkey (www.karbilsis.com). The project has been started as a research activity after an award by Ministry of Science and Technology has been granted to our company. At the first stage of our project MODIS atmospherically corrected reflectance data has been downloaded covering the period of 2000-2011 which makes more than ten years of satellite imagery for Turkey. The archived MODIS data that have been obtained from National Snow and Ice Data Center (NSIDC) is mainly MOD09GA product that includes seven spectral bands. Only the tiles which are covering Turkey have been archived namely 19&20 horizontal and 4&5 vertical ones. In order to provide scientists with a website giving the availability of analysis of snow covered area for long terms based on their area of interests, a fractional snow extent (FSE) product has been generated. For FSE product a normalized difference snow index (NDSI) based algorithm has been developed using daily land surface reflectance values (MOD09GA). In addition to MODIS data, four different Landsat images belonging to different days of snowy period (January, March, and May) have been used during algorithm development taking into account a better representation of different reflectance values of snow which highly varies depending on the accumulation and melting periods. Landsat images were used as reference images. First the Landsat images were orthorectified and mapped to a cartographic projection. Then image segmentation was applied to obtain homogeneous tiles, where the homogeneity is defined as similarity in pixel values. The mean-shift segmentation approach, where each pixel was associated with a significant mode of the joint domain density located in its neighborhood, was applied. After segmentation, the image was classified into snow and no-snow classes with Maximum Likelihood Classification Method. FSE

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

Science.gov (United States)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by

Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

Science.gov (United States)

Revuelto, Jesús; Azorin-Molina, Cesar; Alonso-González, Esteban; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Rico, Ibai; López-Moreno, Juan Ignacio

2017-12-01

This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a

Measuring snow water equivalent from common-offset GPR records through migration velocity analysis

Directory of Open Access Journals (Sweden)

J. St. Clair

2017-12-01

Full Text Available Many mountainous regions depend on seasonal snowfall for their water resources. Current methods of predicting the availability of water resources rely on long-term relationships between stream discharge and snowpack monitoring at isolated locations, which are less reliable during abnormal snow years. Ground-penetrating radar (GPR has been shown to be an effective tool for measuring snow water equivalent (SWE because of the close relationship between snow density and radar velocity. However, the standard methods of measuring radar velocity can be time-consuming. Here we apply a migration focusing method originally developed for extracting velocity information from diffracted energy observed in zero-offset seismic sections to the problem of estimating radar velocities in seasonal snow from common-offset GPR data. Diffractions are isolated by plane-wave-destruction (PWD filtering and the optimal migration velocity is chosen based on the varimax norm of the migrated image. We then use the radar velocity to estimate snow density, depth, and SWE. The GPR-derived SWE estimates are within 6 % of manual SWE measurements when the GPR antenna is coupled to the snow surface and 3–21 % of the manual measurements when the antenna is mounted on the front of a snowmobile  ∼  0.5 m above the snow surface.

Quantifying small-scale spatio-temporal variability of snow stratigraphy in forests based on high-resolution snow penetrometry

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Teich, M.; Hagenmuller, P.; Bebi, P.; Jenkins, M. J.; Giunta, A. D.; Schneebeli, M.

2017-12-01

Snow stratigraphy, the characteristic layering within a seasonal snowpack, has important implications for snow remote sensing, hydrology and avalanches. Forests modify snowpack properties through interception, wind speed reduction, and changes to the energy balance. The lack of snowpack observations in forests limits our ability to understand the evolution of snow stratigraphy and its spatio-temporal variability as a function of forest structure and to observe snowpack response to changes in forest cover. We examined the snowpack under canopies of a spruce forest in the central Rocky Mountains, USA, using the SnowMicroPen (SMP), a high resolution digital penetrometer. Weekly-repeated penetration force measurements were recorded along 10 m transects every 0.3 m in winter 2015 and bi-weekly along 20 m transects every 0.5 m in 2016 in three study plots beneath canopies of undisturbed, bark beetle-disturbed and harvested forest stands, and an open meadow. To disentangle information about layer hardness and depth variabilities, and to quantitatively compare the different SMP profiles, we applied a matching algorithm to our dataset, which combines several profiles by automatically adjusting their layer thicknesses. We linked spatial and temporal variabilities of penetration force and depth, and thus snow stratigraphy to forest and meteorological conditions. Throughout the season, snow stratigraphy was more heterogeneous in undisturbed but also beneath bark beetle-disturbed forests. In contrast, and despite remaining small diameter trees and woody debris, snow stratigraphy was rather homogenous at the harvested plot. As expected, layering at the non-forested plot varied only slightly over the small spatial extent sampled. At the open and harvested plots, persistent crusts and ice lenses were clearly present in the snowpack, while such hard layers barely occurred beneath undisturbed and disturbed canopies. Due to settling, hardness significantly increased with depth at

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

Science.gov (United States)

Kwon, Yonghwan; Yang, Zong-Liang; Zhao, Long; Hoar, Timothy J.; Toure, Ally M.; Rodell, Matthew

2016-01-01

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T(sub B) at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T(sub B) based on their correlations with the prior T(sub B) (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177mRMSE). Significant improvement of the snow depth estimates in the rule-based RA as observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

Science.gov (United States)

Margulis, Steven A.; Girotto, Manuela; Cortes, Gonzalo; Durand, Michael

2015-01-01

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%-82% and 60%-68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBSRMSE was approx.54%of that seen in the EnBS, while for snow courses the PBSRMSE was approx.79%of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

Unexpected Patterns in Snow and Dirt

Science.gov (United States)

Ackerson, Bruce J.

2018-01-01

For more than 30 years, Albert A. Bartlett published "Thermal patterns in the snow" in this journal. These are patterns produced by heat sources underneath the snow. Bartlett's articles encouraged me to pay attention to patterns in snow and to understanding them. At winter's end the last snow becomes dirty and is heaped into piles. This snow comes from the final clearing of sidewalks and driveways. The patterns observed in these piles defied my intuition. This melting snow develops edges where dirt accumulates, in contrast to ice cubes, which lose sharp edges and become more spherical upon melting. Furthermore, dirt absorbs more radiation than snow and yet doesn't melt and round the sharp edges of snow, where dirt accumulates.

Impact of the snow cover scheme on snow distribution and energy budget modeling over the Tibetan Plateau

Science.gov (United States)

Xie, Zhipeng; Hu, Zeyong; Xie, Zhenghui; Jia, Binghao; Sun, Genhou; Du, Yizhen; Song, Haiqing

2018-02-01

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.

Estimation of snow albedo reduction by light absorbing impurities using Monte Carlo radiative transfer model

Science.gov (United States)

Sengupta, D.; Gao, L.; Wilcox, E. M.; Beres, N. D.; Moosmüller, H.; Khlystov, A.

2017-12-01

Radiative forcing and climate change greatly depends on earth's surface albedo and its temporal and spatial variation. The surface albedo varies greatly depending on the surface characteristics ranging from 5-10% for calm ocean waters to 80% for some snow-covered areas. Clean and fresh snow surfaces have the highest albedo and are most sensitive to contamination with light absorbing impurities that can greatly reduce surface albedo and change overall radiative forcing estimates. Accurate estimation of snow albedo as well as understanding of feedbacks on climate from changes in snow-covered areas is important for radiative forcing, snow energy balance, predicting seasonal snowmelt, and run off rates. Such information is essential to inform timely decision making of stakeholders and policy makers. Light absorbing particles deposited onto the snow surface can greatly alter snow albedo and have been identified as a major contributor to regional climate forcing if seasonal snow cover is involved. However, uncertainty associated with quantification of albedo reduction by these light absorbing particles is high. Here, we use Mie theory (under the assumption of spherical snow grains) to reconstruct the single scattering parameters of snow (i.e., single scattering albedo ῶ and asymmetry parameter g) from observation-based size distribution information and retrieved refractive index values. The single scattering parameters of impurities are extracted with the same approach from datasets obtained during laboratory combustion of biomass samples. Instead of using plane-parallel approximation methods to account for multiple scattering, we have used the simple "Monte Carlo ray/photon tracing approach" to calculate the snow albedo. This simple approach considers multiple scattering to be the "collection" of single scattering events. Using this approach, we vary the effective snow grain size and impurity concentrations to explore the evolution of snow albedo over a wide

Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

Science.gov (United States)

Marchetti, E.; Ripepe, M.; Ulivieri, G.; Kogelnig, A.

2015-11-01

Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool to identify clear signals related to avalanches. We present here a method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria), which provides a significant improvement to overcome this limit. The method is based on array-derived wave parameters, such as back azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification by considering avalanches as a moving source of infrasound. We validate the efficiency of the automatic infrasound detection with continuous observations with Doppler radar and we show how the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that is able to provide the number and the time of occurrence of snow avalanches occurring all around the array, which represent key information for a proper validation of avalanche forecast models and risk management in a given area.

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Science.gov (United States)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Brief communication "Application of mobile laser scanning in snow cover profiling"

Directory of Open Access Journals (Sweden)

S. Kaasalainen

2011-03-01

Full Text Available We present a snowmobile-based mobile mapping system and its first application to snow cover roughness and change detection measurement. The ROAMER mobile mapping system, constructed at the Finnish Geodetic Institute, consists of the positioning and navigating systems, a terrestrial laser scanner, and the carrying platform (a snowmobile sledge in this application. We demonstrate the applicability of the instrument to snow cover roughness profiling and change detection by presenting preliminary results from a mobile laser scanning (MLS campaign. The results show the potential of MLS for fast and efficient snow profiling from large areas in a millimetre scale.

Water availability forecasting for Naryn River using ground-based and satellite snow cover data

Directory of Open Access Journals (Sweden)

O. Y. Kalashnikova

2017-01-01

Full Text Available The main source of river nourishment in arid regions of Central Asia is the melting of seasonal snow accu‑ mulated in mountains during the cold period. In this study, we analyzed data on seasonal snow cover by ground‑based observations from Kyrgyzhydromet network, as well as from MODIS satellite imagery for the period of 2000–2015. This information was used to compile the forecast methods of water availability of snow‑ice and ice‑snow fed rivers for the vegetation period. The Naryn river basin was chosen as a study area which is the main tributary of Syrdarya River and belongs to the Aral Sea basin. The representative mete‑ orological stations with ground‑based observations of snow cover were identified and regression analysis between mean discharge for the vegetation period and number of snow covered days, maximum snow depth based on in‑situ data as well as snow cover area based on MODIS images was conducted. Based on this infor‑ mation, equations are derived for seasonal water availability forecasting using multiple linear regression anal‑ ysis. Proposed equations have high correlation coefficients (R = 0.89÷0.92 and  and fore‑ casting accuracy. The methodology was implemented in Kyrgyzhydromet and is used for forecasting of water availability in Naryn basin and water inflow into Toktogul Reservoir.

In Situ Observations of Snow Metamorphosis Acceleration Induced by Dust and Black Carbon

Science.gov (United States)

Schneider, A. M.; Flanner, M.

2017-12-01

Previous studies demonstrate the dependence of shortwave infrared (SWIR) reflectance on snow specific surface area (SSA) and others examine the direct darkening effect dust and black carbon (BC) deposition has on snow and ice-covered surfaces. The extent to which these light absorbing aerosols (LAAs) accelerate snow metamorphosis, however, is challenging to assess in situ as measurement techniques easily disturb snowpack. Here, we use two Near-Infrared Emitting Reflectance Domes (NERDs) to measure 1300 and 1550nm bidirectional reflectance factors (BRFs) of natural snow and experimental plots with added dust and BC. We obtain NERD measurements and subsequently collect and transport snow samples to the nearby U.S. Army Corps of Engineers' Cold Regions Research and Engineering Lab for micro computed tomography (micro-CT) analysis. Snow 1300 (1550) nm BRFs evolve from 0.6 (0.15) in fresh snow to 0.2 (0.03) after metamorphosis. Hourly-scale time evolving snow surface BRFs and SSA estimates from micro-CT reveal more rapid SWIR darkening and snow metamorphosis in contaminated versus natural plots. Cloudiness and high wind speeds can completely obscure these results if LAAs mobilize before absorbing enough radiant energy. These findings verify experimentally that dust and BC deposition can accelerate snow metamorphosis and enhance snow albedo feedback in sunny, calm weather conditions. Although quantifying the enhancement of snow albedo feedback induced by LAAs requires further surface temperature, solar irradiance, and impurity concentration measurements, this study provides experimental verification of positive feedback occurring where dust and BC accelerate snow metamorphosis.

Applications of moisture monitoring using TAUPE-sensors

International Nuclear Information System (INIS)

Koeniger, F.

2007-01-01

TAUPE as a moisture sensor has been developed in cooperation with Technology Transfer Division in Forschungszentrum Karlsruhe since 1996. These sensors can be used to monitor moisture in a variety of materials, using time domain or frequency domain techniques. Major applications are large area supervision of landfill sealings, determination of the snow water equivalent, e.g. in the project SNOWPOWER for the forecasting of the amount of water for energy production in storage lakes and, as a new project, continuous monitoring of groundwater level in the flood plane of river Rhine. (orig.)

The value of snow cover

Science.gov (United States)

Sokratov, S. A.

2009-04-01

Snow is the natural resource, like soil and water. It has specific properties which allow its use not just for skiing but also for houses cooling in summer (Swedish experience), for air fields construction (Arctic and Antarctic), for dams (north of Russia), for buildings (not only snow-houses of some Polar peoples but artistic hotel attracting tourists in Sweden), and as art material (Sapporo snow festival, Finnish events), etc. "Adjustment" of snow distribution and amount is not only rather common practice (avalanche-protection constructions keeping snow on slopes) but also the practice with long history. So-called "snow irrigation" was used in Russia since XIX century to protect winter crop. What is now named "artificial snow production", is part of much larger pattern. What makes it special—it is unavoidable in present climate and economy situation. 5% of national income in Austria is winter tourism. 50% of the economy in Savoy relay on winter tourism. In terms of money this can be less, but in terms of jobs and income involved this would be even more considerable in Switzerland. As an example—the population of Davos is 14000 in Summer and 50000 in Winter. Skiing is growing business. In present time you can find ski slopes in Turkey and Lebanon. To keep a cite suitable for attracting tourists you need certain amount of sunny days and certain amount of snow. The snow cannons are often the only way to keep a place running. On the other hand, more artificial snow does not necessary attract more tourists, while heavy natural snowfall does attract them. Artificial snow making is costly and requires infrastructure (ponds and electric lines) with very narrow range of weather conditions. Related companies are searching for alternatives and one of them can be "weather regulation" by distribution of some chemical components in clouds. It did not happen yet, but can happen soon. The consequences of such interference in Nature is hardly known. The ski tourism is not the

Central Asian supra-glacier snow melt enhanced by anthropogenic black carbon

Science.gov (United States)

Schmale, Julia; Flanner, Mark; Kang, Shichang; Sprenger, Michael; Farinotti, Daniel; Zhang, Qianggong; Guo, Junming; Li, Yang; Lawrence, Mark; Schwikowski, Margit

2016-04-01

In Central Asia, more than 60 % of the population depends on water stored in glaciers and mountain snow. Densely populated areas near lower-lying mountain ranges are particularly vulnerable and a recent study showed that the region might lose 50 % of its glacier mass by 2050. While temperature, precipitation and dynamic processes are key drivers of glacial change, deposition of light absorbing impurities such as mineral dust and black carbon can lead to accelerated melting through surface albedo reduction. Here, we discuss the origin of deposited mineral dust and black carbon and their impacts on albedo change and snow melt. 218 snow samples were taken on 4 glaciers, Abramov (Pamir), Suek, Glacier No. 354 and Golubin (Tien Shan), representing deposition between summer 2012 and 2014. They were analyzed for elemental carbon, mineral dust and iron among other parameters. We find the elemental carbon concentration to be at the higher end of the range reported for neighboring mountain ranges between 70 and 502 ng g-1 (interquartile range). To investigate the origin of the snow impurities, we used a Lagrangian particle dispersion model, LAGRANTO. Back trajectory ensembles of 40 members with varied starting points to capture the meteorological spread were released every 6 hours for the covered period at all sites. "Footprints" were calculated and combined with emission inventories to estimate the relative contribution of anthropogenic and natural BC to deposited aerosol on the glaciers. We find that more than 94 % of BC is of anthropogenic origin and the major source region is Central Asia followed by the Middle East. Further exploring the implications of mineral dust and BC deposition, we calculate the snow albedo reduction with the Snow-Ice-Aerosol-Radiative model (SNICAR). Even though mineral dust concentrations were up to a factor of 50 higher than BC concentrations, BC dominates the albedo reduction. Using these results we calculate the snow melt induced by

The Snow Darkening Effect and the Simulation of Extremes over Eurasia

Science.gov (United States)

Yasunari, T. J.; Lau, W. K. M.; Kim, K. M.; Koster, R. D.

2014-12-01

We have recently completed an updated ensemble of NASA GEOS-5 simulations with a snow-darkening module (now officially named GOddard SnoW Impurity Module, or GOSWIM, and summarized in the published paper by Yasunari et al., SOLA, 2014; see at: https://www.jstage.jst.go.jp/article/sola/10/0/10_2014-011/_article). This ensemble ("snow-darkening case (SDC)"), consisting of ten parallel simulations (differing only in their initial conditions) spanning 2002-2011, is compared here to a corresponding ensemble with all snow-darkening effects disabled ("non-SDC"). We focus particularly on the production of extremes associated with snow darkening. To identify regions of interest over Eurasia, we first rank the 100 separate spring (MAM) or summer (JJA) values of a given quantity in each combined 100-yr data (i.e., 10-yr x 10-ensemble), and then compute the differences of the 90th percentile values between SDC and non-SDC. For spring, large differences are seen in a specific area of Europe and Central Asia (ECA), and for summer, they are seen for an area in the Russian Arctic (RA). The next step in our analysis addresses the month-by-month variation of the percentile differences within these identified regions - for each month, and for a given meteorological or hydrological variable, we determined the SDC percentile that corresponds to the 90th percentile value found for the non-SDC ensemble. For example, in the RA domain, the surface air temperature corresponding to the 90th percentile in the non-SDC ensemble has a consistently lower percentile in the SDC data - not only during spring and summer through the increased absorption of radiation by snow polluted with dust, black carbon, and organic carbon, but also in the post-snow season through some form of memory in the system. The temperature extremes in the SDC ensemble thus exceed those of the non-SDC ensemble throughout the year. This analysis supports the idea that the consideration of snow darkening effect in global

Modelling of snow exceedances

Science.gov (United States)

Jordanova, Pavlina K.; Sadovský, Zoltán; Stehlík, Milan

2017-07-01

Modelling of snow exceedances is of great importance and interest for ecology, civil engineering and general public. We suggest the favorable fit for exceedances related to the exceptional snow loads from Slovakia, assuming that the data is driven by Generalised Pareto Distribution or Generalized Extreme Value Distribution. Further, the statistical dependence between the maximal snow loads and the corresponding altitudes is studied.

Rapid determination of radioactive general β in the rain and snow

International Nuclear Information System (INIS)

Li Shuqing; Jiao Shufeng

1992-01-01

Deposit filtration has been successfully applied to the determination of radioactive general β in the rain and snow. This method is more direct and rapid than evaporation, and more efficient. The method has been widely used in the emergency monitoring of nuclear pollution

Patterns of Snow Leopard Site Use in an Increasingly Human-Dominated Landscape.

Directory of Open Access Journals (Sweden)

Justine Shanti Alexander

Full Text Available Human population growth and concomitant increases in demand for natural resources pose threats to many wildlife populations. The landscapes used by the endangered snow leopard (Panthera uncia and their prey is increasingly subject to major changes in land use. We aimed to assess the influence of 1 key human activities, as indicated by the presence of mining and livestock herding, and 2 the presence of a key prey species, the blue sheep (Pseudois nayaur, on probability of snow leopard site use across the landscape. In Gansu Province, China, we conducted sign surveys in 49 grid cells, each of 16 km2 in size, within a larger area of 3392 km2. We analysed the data using likelihood-based habitat occupancy models that explicitly account for imperfect detection and spatial auto-correlation between survey transect segments. The model-averaged estimate of snow leopard occupancy was high [0.75 (SE 0.10], but only marginally higher than the naïve estimate (0.67. Snow leopard segment-level probability of detection, given occupancy on a 500 m spatial replicate, was also high [0.68 (SE 0.08]. Prey presence was the main determinant of snow leopard site use, while human disturbances, in the form of mining and herding, had low predictive power. These findings suggest that snow leopards continue to use areas very close to such disturbances, as long as there is sufficient prey. Improved knowledge about the effect of human activity on large carnivores, which require large areas and intact prey populations, is urgently needed for conservation planning at the local and global levels. We highlight a number of methodological considerations that should guide the design of such research.

An airborne gamma ray snow survey of a forest covered area with a deep snowpack

International Nuclear Information System (INIS)

Glynn, J.E.; Carroll, T.R.; Holman, P.B.; Grasty, R.L.

1988-01-01

Problems arising from the airborne gamma ray measurement of snow water equivalent over a forest covered deep snowpack are examined. The principal sources of error are believed to be due to the radioactivity in the biomass and to variability in the snow cover. A theoretical model is developed to correct the airborne measurements for these sources of error. The application of the theory to data collected over the St. John River Basin, located in the eastern part of Canada and the United States, is found to significantly improve the airborne results

An indirect study of air pollution by neutron activation analysis of snow

International Nuclear Information System (INIS)

Zikovsky, L.; Badillo, M.

1986-01-01

Al, As, Br, Cd, Ce, Cl, Co, Cs, Cn, Fe, Hg, K, La, Mg, Mn, Mo, Na, Sb, Sc, Sr, Ti, V and Zn were determined in the soluble and insoluble fractions of snow which was collected at 6 different sites in the Montreal area. From these data the variability coeffecients and the enrichment factors were calculated and the probable sources of each element are discussed. The overall concentrations found in the snow were compared with the concentrations of these elements in the air sampled in the same area by high volume samplers. From these, the snow-out ratios for Cr, Cu, Fe, Mn, V and Zn and their mean were calculated. They are equal to 3338, 3389, 2322, 526, 563, 2603 and 2913 m 3 /kg respectively. (author)

Performances of the snow accumulation melting model SAMM: results in the Northern Apennines test area

Science.gov (United States)

Lagomarsino, Daniela; Martelloni, Gianluca; Segoni, Samuele; Catani, Filippo; Fanti, Riccardo

2013-04-01

In this work we propose a snow accumulation-melting model (SAMM) to forecast the snowpack height and we compare the results with a simple temperature index model and an improved version of the latter.For this purpose we used rainfall, temperature and snowpack thickness 5-years data series from 7 weather stations in the Northern Apennines (Emilia Romagna Region, Italy). SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a mass conservation equation is solved to model snowpack thickness and an empirical equation is used for the snow density. The processes linked to the accumulation/depletion of the snowpack (e.g. compression of the snowpack due to newly fallen snow and effects of rainfall) are modelled identifying limiting and inhibitory factors according to a kinetic approach. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. In order to verify the improvement of SAMM with respect to a temperature-index model, the latter was applied considering, for the amount of snow melt, the following equation: M = fm(T-T0), where M is hourly melt, fm is the melting factor and T0 is a threshold temperature. In this case the calculation of the depth of the snowpack requires the use of 3 parameters: fm, T0 and ?0 (the mean density of the snowpack). We also performed a simulation by replacing the SAMM melting module with the above equation and leaving unchanged the accumulation module: in this way we obtained a model with 9 parameters. The simulations results suggest that any further extension of the simple temperature index model brings some improvements with a consequent decrease of the mean error

Antarctic snow and global climate

International Nuclear Information System (INIS)

Granberg, H.B.

2001-01-01

Global circulation models (GCM) indicate that global warming will be most pronounced at polar regions and high latitudes, causing concern about the stability of the Antarctic ice cap. A project entitled the Seasonal Snow in Antarctica examined the properties of the near surface snow to determine the current conditions that influence snow cover development. The goal was to assess the response of the snow cover in Queen Maud Land (QML) to an increased atmospheric carbon dioxide content. The Antarctic snow cover in QML was examined as part of the FINNARP expeditions in 1999 and 2000 which examined the processes that influence the snow cover. Its energy and mass balance were also assessed by examining the near surface snow strata in shallow (1-2 m) pits and by taking measurements of environmental variables. This made it possible to determine if the glacier is in danger of melting at this northerly location in the Antarctic. The study also made it possible to determine which variables need to change and by how much, for significant melting to occur. It was shown that the Antarctic anticyclone creates particular conditions that protect the snow cover from melting. The anticyclone brings dry air from the stratosphere during most of the year and is exempt from the water vapour feedback. It was concluded that even a doubling of atmospheric carbon dioxide will not produce major snow melt runoff. 8 refs

Role of Snow Deposition of Perfluoroalkylated Substances at Coastal Livingston Island (Maritime Antarctica).

Science.gov (United States)

Casal, Paulo; Zhang, Yifeng; Martin, Jonathan W; Pizarro, Mariana; Jiménez, Begoña; Dachs, Jordi

2017-08-01

Perfluoroalkyl substances (PFAS) are ubiquitous in the environment, including remote polar regions. To evaluate the role of snow deposition as an input of PFAS to Maritime Antarctica, fresh snow deposition, surface snow, streams from melted snow, coastal seawater, and plankton samples were collected over a three-month period (December 2014-February 2015) at Livingston Island. Local sources of PFASs were significant for perfluoroalkyl sulfonates (PFSAs) and C7-14 perfluoroalkyl carboxylates (PFCAs) in snow but limited to the transited areas of the research station. The concentrations of 14 ionizable PFAS (∑PFAS) in freshly deposited snow (760-3600 pg L -1 ) were 1 order of magnitude higher than those in background surface snow (82-430 pg L -1 ). ∑PFAS ranged from 94 to 420 pg L -1 in seawater and from 3.1 to 16 ng g dw -1 in plankton. Ratios of individual PFAS concentrations in freshly deposited snow relative to surface snow (C SD /C Snow ), snowmelt (C SD /C SM ), and seawater (C SD /C SW ) were close to 1 (from 0.44 to 1.4) for all perfluorooctanesulfonate (PFOS) isomers, suggesting that snowfall does not contribute significantly to PFOS in seawater. Conversely, these ratios for PFCAs ranged from 1 to 33 and were positively correlated with the number of carbons in the PFCA alkylated chain. These trends suggest that snow deposition, scavenging sea-salt aerosol bound PFAS, plays a role as a significant input of PFCAs to the Maritime Antarctica.

Independent evaluation of the SNODAS snow depth product using regional scale LiDAR-derived measurements

Science.gov (United States)

Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

2014-06-01

Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically-based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500 m × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled LiDAR snow depths were then compared to the SNODAS-estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, suggesting natural processes specific to these regions as causal influences on model uncertainty.

Occurrence of blowing snow events at an alpine site over a 10-year period: Observations and modelling

Science.gov (United States)

Vionnet, V.; Guyomarc'h, G.; Naaim Bouvet, F.; Martin, E.; Durand, Y.; Bellot, H.; Bel, C.; Puglièse, P.

2013-05-01

Blowing snow events control the evolution of the snow pack in mountainous areas and cause inhomogeneous snow distribution. The goal of this study is to identify the main features of blowing snow events at an alpine site and assess the ability of the detailed snowpack model Crocus to reproduce the occurrence of these events in a 1D configuration. We created a database of blowing snow events observed over 10 years at our experimental site. Occurrences of blowing snow events were divided into cases with and without concurrent falling snow. Overall, snow transport is observed during 10.5% of the time in winter and occurs with concurrent falling snow 37.3% of the time. Wind speed and snow age control the frequency of occurrence. Model results illustrate the necessity of taking the wind-dependence of falling snow grain characteristics into account to simulate periods of snow transport and mass fluxes satisfactorily during those periods. The high rate of false alarms produced by the model is investigated in detail for winter 2010/2011 using measurements from snow particle counters.

MODIS Snow and Sea Ice Products

Science.gov (United States)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.

2004-01-01

In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

Sensitivity of the French Alps snow cover to the variation of climatic variables

Directory of Open Access Journals (Sweden)

E. Martin

Full Text Available In order to study the sensitivity of snow cover to changes in meteorological variables at a regional scale, a numerical snow model and an analysis system of the meteorological conditions adapted to relief were used. This approach has been successfully tested by comparing simulated and measured snow depth at 37 sites in the French Alps during a ten year data period. Then, the sensitivity of the snow cover to a variation in climatic conditions was tested by two different methods, which led to very similar results. To assess the impact of a particular "doubled CO₂" scenario, coherent perturbations were introduced in the input data of the snow model. It was found that although the impact would be very pronounced, it would also be extremely differentiated, dependent on the internal state of the snow cover. The most sensitive areas are the elevations below 2400 m, especially in the southern part of the French Alps.

[APPROACHES TO URBAN AREA RANKING ACCORDINGLY TO THE LEVEL OF HEAVY METAL POLLUTION].

Science.gov (United States)

Stepanova, N V; Valeeva, E R; Fomina, S F

2015-01-01

Urban area ranking was performed according to the level of the heavy metal pollution based on the data of the snow and soil chemical characteristics. With reference to cumulative rates of the snow cover and soil pollution by heavy metals in the territory of the city of Kazan there were selected four areas: I--Derbyshki; II--Teplocontrol; III--Gorki; IV--Kirovsky district. The pollution level of snow cover in the territory of the city was determined by pollution level indices calculated with the application of Maximum Permissible Concentration (MPC) of chemical substances in ambient waters for household and recreational and service facilities use. The assessment of the pollution level in soils in the city showed the total territory of Kazan to be mildly polluted by manganese, concerning other heavy metals the categories of the soil pollution vary on areas. Results of hair biological monitoring in children are an informative auxiliary tool for the evaluation of the present ecological situation concerning heavy metals in certain territories of the city.

The Impact Of Snow Melt On Surface Runoff Of Sava River In Slovenia

Science.gov (United States)

Horvat, A.; Brilly, M.; Vidmar, A.; Kobold, M.

2009-04-01

Snow is a type of precipitation in the form of crystalline water ice, consisting of a multitude of snowflakes that fall from clouds. Snow remains on the ground until it melts or sublimates. Spring snow melt is a major source of water supply to areas in temperate zones near mountains that catch and hold winter snow, especially those with a prolonged dry summer. In such places, water equivalent is of great interest to water managers wishing to predict spring runoff and the water supply of cities downstream. In temperate zone like in Slovenia the snow melts in the spring and contributes certain amount of water to surface flow. This amount of water can be great and can cause serious floods in case of fast snow melt. For this reason we tried to determine the influence of snow melt on the largest river basin in Slovenia - Sava River basin, on surface runoff. We would like to find out if snow melt in Slovenian Alps can cause spring floods and how serious it can be. First of all we studied the caracteristics of Sava River basin - geology, hydrology, clima, relief and snow conditions in details for each subbasin. Furtermore we focused on snow and described the snow phenomenom in Slovenia, detailed on Sava River basin. We collected all available data on snow - snow water equivalent and snow depth. Snow water equivalent is a much more useful measurement to hydrologists than snow depth, as the density of cool freshly fallen snow widely varies. New snow commonly has a density of between 5% and 15% of water. But unfortunately there is not a lot of available data of SWE available for Slovenia. Later on we compared the data of snow depth and river runoff for some of the 40 winter seasons. Finally we analyzed the use of satellite images for Slovenia to determine the snow cover for hydrology reason. We concluded that snow melt in Slovenia does not have a greater influence on Sava River flow. The snow cover in Alps can melt fast due to higher temperatures but the water distributes

Using Snow Fences to Augument Fresh Water Supplies in Shallow Arctic Lakes

Energy Technology Data Exchange (ETDEWEB)

Stuefer, Svetlana

2013-03-31

, 2010, and 2011), we selected and monitored two lakes with similar hydrological regimes. Both lakes are located 30 miles south of Prudhoe Bay, Alaska, near Franklin Bluffs. One is an experimental lake, where we installed a snow fence; the other is a control lake, where the natural regime was preserved. The general approach was to compare the hydrologic response of the lake to the snowdrift during the summers of 2010 and 2011 against the baseline conditions in 2009. Highlights of the project included new data on snow transport rates on the Alaska North Slope, an evaluation of the experimental lake's hydrological response to snowdrift melt, and cost assessment of snowdrift‐generated water. High snow transport rates (0.49 kg/s/m) ensured that the snowdrift reached its equilibrium profile by winter's end. Generally, natural snowpack disappeared by the beginning of June in this area. In contrast, snow in the drift lasted through early July, supplying the experimental lake with snowmelt when water in other tundra lakes was decreasing. The experimental lake retained elevated water levels during the entire open‐water season. Comparison of lake water volumes during the experiment against the baseline year showed that, by the end of summer, the drift generated by the snow fence had increased lake water volume by at least 21-29%. We estimated water cost at 1.9 cents per gallon during the first year and 0.8 cents per gallon during the second year. This estimate depends on the cost of snow fence construction in remote arctic locations, which we assumed to be at $7.66 per square foot of snow fence frontal area. The snow fence technique was effective in augmenting the supply of lake water during summers 2010 and 2011 despite low rainfall during both summers. Snow fences are a simple, yet an effective, way to replenish tundra lakes with freshwater and increase water availability in winter. This research project was synergetic with the NETL project, "North Slope Decision

Observations of distributed snow depth and snow duration within diverse forest structures in a maritime mountain watershed

Science.gov (United States)

Dickerson-Lange, Susan E.; Lutz, James A.; Gersonde, Rolf; Martin, Kael A.; Forsyth, Jenna E.; Lundquist, Jessica D.

2015-11-01

Spatially distributed snow depth and snow duration data were collected over two to four snow seasons during water years 2011-2014 in experimental forest plots within the Cedar River Municipal Watershed, 50 km east of Seattle, Washington, USA. These 40 × 40 m forest plots, situated on the western slope of the Cascade Range, include unthinned second-growth coniferous forests, variable density thinned forests, forest gaps in which a 20 m diameter (approximately equivalent to one tree height) gap was cut in the middle of each plot, and old-growth forest. Together, this publicly available data set includes snow depth and density observations from manual snow surveys, distributed snow duration observations from ground temperature sensors and time-lapse cameras, meteorological data collected at two open locations and three forested locations, and forest canopy data from airborne light detection and ranging (LiDAR) data and hemispherical photographs. These colocated snow, meteorological, and forest data have the potential to improve understanding of forest influences on snow processes, and provide a unique model-testing data set for hydrological analyses in a forested, maritime watershed. We present empirical snow depletion curves within forests to illustrate an application of these data to improve subgrid representation of snow cover in distributed modeling.

Indicative properties on snow cover based on the results of experimental studies in the winter 2011/12 in the central part of the East European Plain

Directory of Open Access Journals (Sweden)

L. M. Kitaev

2013-01-01

Full Text Available Local and regional differences in the snow formation were studied in different landscapes of the central part of the East European Plain – within reserves in the Moscow and Tver’ regions (south-north direction; the study period is the winter 2011/12. The observed increase of snow storage in 1.3–1.5 times in the direction south-north is connected, apparently. The difference in the five-day appearance of snow cover maximum is related to differences in regional winter air temperature. Throughout the snow depth and snow storage in spruce are smaller than in deciduous forest – in the ratio of 0.81 in south area and 0.93 in north area; in spruce the large part of solid precipitation is intercepted by the crowns pine trees. Snow stratigraphy at south areas has four layers, six layers at the north area are more variable in snow density and snow storage. Perhaps, gravitational conversion is more noticeable due to larger snow depth. Snow density and snow storage at the open areas are more heterogeneous than in the forest. This is due to sharp fluctuations in air temperature, wind transport and compaction of snow, evaporation from the snow surface. The stratigraphy of snow also reflects the history of winter changes of air temperature and snow accumulation. Common feature for reserves at south and north is the availability of layers with maximum snow storage in the middle of the snow thickness, which were formed during the air temperature drops to the lowest seasonal values in period with increase of snow depth to maximum. Formation of depth hoar in snow thickness are touched everywhere the bottom and middle layers, respectively, it was formed both before and during the period with minimal air temperature. Thus, the results of experimental studies confirm the significance of the differences of individual components of the landscape setting. Analytical conclusions are largely qualitative in nature due to the lack to date of initial information, and

Monitoring System for ALICE Surface Areas

CERN Document Server

Demirbasci, Oguz

2016-01-01

I have been at CERN for 12 weeks within the scope of Summer Student Programme working on a monitoring system project for surface areas of the ALICE experiment during this period of time. The development and implementation of a monitoring system for environmental parameters in the accessible areas where a cheap hardware setup can be deployed were aim of this project. This report explains how it was developed by using Arduino, Raspberry PI, WinCC OA and DIM protocol.

Snow and Ice Climatology of the Western United States and Alaska from MODIS

Science.gov (United States)

Rittger, K. E.; Painter, T. H.; Mattmann, C. A.; Seidel, F. C.; Burgess, A.; Brodzik, M.

2013-12-01

The climate and hydroclimate of the Western US and Alaska are tightly coupled to their snow and ice cover. The Western US depends on mountain snowmelt for the majority of its water supply to agriculture, industrial and urban use, hydroelectric generation, and recreation, all driven by increasing population and demand. Alaskan snow and glacier cover modulate regional climate and, as with the Western US, dominate water supply and hydroelectric generation in much of the state. Projections of climate change in the Western US and Alaska suggest that the most pronounced impacts will include reductions of mountain snow and ice cover, earlier runoff, and a greater fraction of rain instead of snow. We establish a snow and ice climatology of the Western US and Alaska using physically based MODIS Snow Covered Area and Grain size model (MODSCAG) for fractional snow cover, the MODIS Dust Radiative Forcing in Snow model (MODDRFS) for radiative forcing by light absorbing impurities in snow, and the MODIS Permanent Ice model (MODICE) for annual minimum exposed snow. MODSCAG and MODDRFS use EOS MOD09GA historical reflectance data (2000-2012) to provide daily and 8-day composites and near real time products since the beginning of 2013, themselves ultimately composited to 8-day products. The compositing method considers sensor-viewing geometry, solar illumination, clouds, cloud shadows, aerosols and noisy detectors in order to select the best pixel for an 8-day period. The MODICE annual minimum exposed snow and ice product uses the daily time series of fractional snow and ice from MODSCAG to generate annual maps. With this project we have established an ongoing, national-scale, consistent and replicable approach to assessing current and projected climate impacts and climate-related risk in the context of other stressors. We analyze the products in the Northwest, Southwest, and Alaska/Arctic regions of the National Climate Assessment for the last decade, the nation's hottest on record

Diversity of bacteria producing pigmented colonies in aerosol, snow and soil samples from remote glacial areas (Antarctica, Alps and Andes)

OpenAIRE

González-Toril , E.; Amils , R.; Delmas , R. J.; Petit , J.-R.; Komárek , J.; Elster , J.

2008-01-01

Four different communities and one culture of pigmented microbial assemblages were obtained by incubation in mineral medium of samples collected from high elevation snow in the Alps (Mt. Blanc area) and the Andes (Nevado Illimani summit, Bolivia), from Antarctic aerosol (French station Dumont d'Urville) and a maritime Antarctic soil (King George Island, South Shetlands, Uruguay Station Artigas). Molecular analysis of more than 200 16S rRNA gene sequences showed that all cultured cells be...

Comparison on the heat requirements of a four-span greenhouse with a melting snow system and a single-span greenhouse

International Nuclear Information System (INIS)

Furuno, S.; Sase, S.; Ishii, M.

2004-01-01

The heat requirements were measured and compared between a four-span greenhouse with a melting snow system and a typical single-span greenhouse with no melting snow system. Generally, single-span greenhouses require no melting snow system because snow drops off naturally from the roofs by gravity. The results for the four-span greenhouse showed that the provided heat by a heater for melting snow increased with an increase in snowfall, and there was a high correlation between them. The heat requirement per unit floor area of the four-span greenhouse was slightly less than that of the single-span greenhouse. This suggests that the decrease in heat requirement for internal air because of the larger floor/surface area ratio of the four-span greenhouse was more than the increase in heat requirement for melting snow. The measured heat requirement of the four-span greenhouse with the melting snow system was equal to the estimated heat load based on a common calculation procedure. On the other hand, that of the single-span greenhouse was slightly smaller than the estimated heat load. These suggest that the estimated heat load based on the common calculation procedure was slightly overestimated and larger than the actual heat requirement excluding the heat for the melting snow in snowy area. This is likely due to the fact that the parameters in the common calculation procedure were determined under the condition of larger net radiation than that in snowy area

Everywhere and nowhere: snow and its linkages

Science.gov (United States)

Hiemstra, C. A.

2017-12-01

Interest has grown in quantifying higher latitude precipitation change and snow-related ecosystem and economic impacts. There is a high demand for creating and using snow-related datasets, yet available datasets contain limitations, aren't scale appropriate, or lack thorough validation. Much of the uncertainty in snow estimates relates to ongoing snow measurement problems that are chronic and pervasive in windy, Arctic environments. This, coupled with diminishing support for long-term snow field observations, creates formidable hydrologic gaps in snow dominated landscapes. Snow touches most aspects of high latitude landscapes and spans albedo, ecosystems, soils, permafrost, and sea ice. In turn, snow can be impacted by disturbances, landscape change, ecosystem, structure, and later arrival of sea or lake ice. Snow, and its changes touch infrastructure, housing, and transportation. Advances in snow measurements, modeling, and data assimilation are under way, but more attention and a concerted effort is needed in a time of dwindling resources to make required advances during a time of rapid change.

[Characteristics of mercury exchange flux between soil and atmosphere under the snow retention and snow melting control].

Science.gov (United States)

Zhang, Gang; Wang, Ning; Ai, Jian-Chao; Zhang, Lei; Yang, Jing; Liu, Zi-Qi

2013-02-01

Jiapigou gold mine, located in the upper Songhua River, was once the largest mine in China due to gold output, where gold extraction with algamation was widely applied to extract gold resulting in severe mercury pollution to ambient environmental medium. In order to study the characteristics of mercury exchange flux between soil (snow) and atmosphere under the snow retention and snow melting control, sampling sites were selected in equal distances along the slope which is situated in the typical hill-valley terrain unit. Mercury exchange flux between soil (snow) and atmosphere was determined with the method of dynamic flux chamber and in all sampling sites the atmosphere concentration from 0 to 150 cm near to the earth in the vertical direction was measured. Furthermore, the impact factors including synchronous meteorology, the surface characteristics under the snow retention and snow melting control and the mercury concentration in vertical direction were also investigated. The results are as follows: During the period of snow retention and melting the air mercury tends to gather towards valley bottom along the slope and an obvious deposit tendency process was found from air to the earth's surface under the control of thermal inversion due to the underlying surface of cold source (snow surface). However, during the period of snow melting, mercury exchange flux between the soil and atmosphere on the surface of the earth with the snow being melted demonstrates alternative deposit and release processes. As for the earth with snow covered, the deposit level of mercury exchange flux between soil and atmosphere is lower than that during the period of snow retention. The relationship between mercury exchange flux and impact factors shows that in snow retention there is a remarkable negative linear correlation between mercury exchange flux and air mercury concentration as well as between the former and the air temperature. In addition, in snow melting mercury exchange

Measured Black Carbon Deposition on the Sierra Nevada Snow Pack and Implication for Snow Pack Retreat

Energy Technology Data Exchange (ETDEWEB)

Hadley, O.L.; Corrigan, C.E.; Kirchstetter, T.W.; Cliff, S.S.; Ramanathan, V.

2010-01-12

Modeling studies show that the darkening of snow and ice by black carbon deposition is a major factor for the rapid disappearance of arctic sea ice, mountain glaciers and snow packs. This study provides one of the first direct measurements for the efficient removal of black carbon from the atmosphere by snow and its subsequent deposition to the snow packs of California. The early melting of the snow packs in the Sierras is one of the contributing factors to the severe water problems in California. BC concentrations in falling snow were measured at two mountain locations and in rain at a coastal site. All three stations reveal large BC concentrations in precipitation, ranging from 1.7 ng/g to 12.9 ng/g. The BC concentrations in the air after the snow fall were negligible suggesting an extremely efficient removal of BC by snow. The data suggest that below cloud scavenging, rather than ice nuclei, was the dominant source of BC in the snow. A five-year comparison of BC, dust, and total fine aerosol mass concentrations at multiple sites reveals that the measurements made at the sampling sites were representative of large scale deposition in the Sierra Nevada. The relative concentration of iron and calcium in the mountain aerosol indicates that one-quarter to one-third of the BC may have been transported from Asia.

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

Directory of Open Access Journals (Sweden)

Kyeong-Sang Lee

2017-01-01

Full Text Available Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS data using dynamic wavelength warping (DWW, which is based on dynamic time warping (DTW. DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2. Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

Response of alpine vegetation growth dynamics to snow cover phenology on the Tibetan Plateau

Science.gov (United States)

Wang, X.; Wu, C.

2017-12-01

Alpine vegetation plays a crucial role in global energy cycles with snow cover, an essential component of alpine land cover showing high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000-2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among plant functional types, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidences between vegetation growth and snow cover phenology, and changes in

Estimating snow water equivalent (SWE) using interferometric synthetic aperture radar (InSAR)

Science.gov (United States)

Deeb, Elias J.

Since the early 1990s, radar interferometry and interferometric synthetic aperture radar (InSAR) have been used extensively to measure changes in the Earth's surface. Previous research has presented theory for estimating snow properties, including potential for snow water equivalent (SWE) retrieval, using InSAR. The motivation behind using remote sensing to estimate SWE is to provide a more complete, continuous set of "observations" to assist in water management operations, climate change studies, and flood hazard forecasting. The research presented here primarily investigates the feasibility of using the InSAR technique at two different wavelengths (C-Band and L-Band) for SWE retrieval of dry snow within the Kuparuk watershed, North Slope, Alaska. Estimating snow distribution around meteorological towers on the coastal plain using a three-day repeat orbit of C-Band InSAR data was successful (Chapter 2). A longer wavelength L-band SAR is evaluated for SWE retrievals (Chapter 3) showing the ability to resolve larger snow accumulation events over a longer period of time. Comparisons of InSAR estimates and late spring manual sampling of SWE show a R2 = 0.61 when a coherence threshold is used to eliminate noisy SAR data. Qualitative comparisons with a high resolution digital elevation model (DEM) highlight areas of scour on windward slopes and areas of deposition on leeward slopes. When compared to a mid-winter transect of manually sampled snow depths, the InSAR SWE estimates yield a RMSE of 2.21cm when a bulk snow density is used and corrections for bracketing the satellite acquisition timing is performed. In an effort to validate the interaction of radar waves with a snowpack, the importance of the "dry snow" assumption for the estimation of SWE using InSAR is tested with an experiment in Little Cottonwood Canyon, Alta, Utah (Chapter 5). Snow wetness is shown to have a significant effect on the velocity of propagation within the snowpack. Despite the radar

Snow, ice and solar radiation

NARCIS (Netherlands)

Kuipers Munneke, P.

2009-01-01

The snow-covered ice sheets of Antarctica and Greenland reflect most of the incoming solar radiation. The reflectivity, commonly called the albedo, of snow on these ice sheets has been observed to vary in space and time. In this thesis, temporal and spatial changes in snow albedo is found to depend

Spatial and temporal variability in seasonal snow density

KAUST Repository

Bormann, Kathryn J.

2013-03-01

Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

Spatial and temporal variability in seasonal snow density

KAUST Repository

Bormann, Kathryn J.; Westra, Seth; Evans, Jason P.; McCabe, Matthew

2013-01-01

Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

A comprehensive analysis of the content of heavy rare-earth elements and platinum in snow samples to assess the ecological hazard of air pollution in urban areas

Science.gov (United States)

Vinokurov, S. F.; Tarasova, N. P.; Trunova, A. N.; Sychkova, V. A.

2017-07-01

Snow samples from the territory of the Setun River Valley Wildlife Sanctuary are analyzed for the content of rare-earth elements, heavy metals, and other hazardous elements by the inductively coupled plasma mass-spectrometry method. The changes in the concentrations of rare-earth elements, Pt, Pd, and indicator ratios of elements in the solid fractions of snow are revealed. A trend toward a decrease in the content of several elements northeastward of the Moscow Ring Road (MRR) is established. The level of seasonal atmospheric contamination of the area under study is assessed, and a possible source is identified.

Moving sidewalk for snow board gelande; Snow board gerendemuke ugoku hodo

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-04-20

This is a moving sidewalk installed on the indoor type artificial snow board gelande at Shigenobu-cho, Ehime prefecture, constructed for the first time in Shikoku. It carries snow boarders in gelande. The main specifications are as follows. Type: 800 type. Sidewalk width: 600mm. Length: 76.0m. Speed: 30m/min. Inclination angle: 13 degrees (inclination type). The features are as follows. (1) The tread is rubber-belt made and skid-resistant if it gets wet. (2) It is equipped with the each-part antifreezer, considering the snow quality and the environment where it is used at low temperature. (translated by NEDO)

Unexpected Patterns in Snow and Dirt

Science.gov (United States)

Ackerson, Bruce J.

2018-01-01

For more than 30 years, Albert A. Bartlett published "Thermal patterns in the snow" in this journal. These are patterns produced by heat sources underneath the snow. Bartlett's articles encouraged me to pay attention to patterns in snow and to understanding them. At winter's end the last snow becomes dirty and is heaped into piles. This…

Simulating the Dependence of Aspen on Redistributed Snow

Science.gov (United States)

Soderquist, B.; Kavanagh, K.; Link, T. E.; Seyfried, M. S.; Winstral, A. H.

2013-12-01

In mountainous regions across the western USA, the distribution of aspen (Populus tremuloides) is often directly related to heterogeneous soil moisture subsidies resulting from redistributed snow. With decades of climate and precipitation data across elevational and precipitation gradients, the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho provides a unique opportunity to study the relationship between aspen and redistributed snow. Within the RCEW, the total amount of precipitation has not changed in the past 50 years, but there are sharp declines in the percentage of the precipitation falling as snow. As shifts in the distribution of available moisture continue, future trends in aspen net primary productivity (NPP) remain uncertain. In order to assess the importance of snowdrift subsidies, NPP of three aspen stands was simulated at sites spanning elevational and precipitation gradients using the biogeochemical process model BIOME-BGC. At the aspen site experiencing the driest climate and lowest amount of precipitation from snow, approximately 400 mm of total precipitation was measured from November to March of 2008. However, peak measured snow water equivalent (SWE) held in drifts directly upslope of this stand was approximately 2100 mm, 5 times more moisture than the uniform winter precipitation layer initially assumed by BIOME-BGC. BIOME-BGC simulations in dry years forced by adjusted precipitation data resulted in NPP values approximately 30% higher than simulations assuming a uniform precipitation layer. Using BIOME-BGC and climate data from 1985-2011, the relationship between simulated NPP and measured basal area increments (BAI) improved after accounting for redistributed snow, indicating increased simulation representation. In addition to improved simulation capabilities, soil moisture data, diurnal branch water potential, and stomatal conductance observations at each site detail the use of soil moisture in the rooting zone and the onset

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

Science.gov (United States)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

Testing a blowing snow model against distributed snow measurements at Upper Sheep Creek, Idaho, United States of America

Science.gov (United States)

Rajiv Prasad; David G. Tarboton; Glen E. Liston; Charles H. Luce; Mark S. Seyfried

2001-01-01

In this paper a physically based snow transport model (SnowTran-3D) was used to simulate snow drifting over a 30 m grid and was compared to detailed snow water equivalence (SWE) surveys on three dates within a small 0.25 km2 subwatershed, Upper Sheep Creek. Two precipitation scenarios and two vegetation scenarios were used to carry out four snow transport model runs in...

Water losses during technical snow production

Science.gov (United States)

Grünewald, Thomas; Wolfsperger, Fabian

2017-04-01

These days, the production of technical snow can be seen as a prerequisite for winter tourism. Huge amounts of water are used for technical snow production by ski resorts, especially in the beginning of the winter season. The aim is to guarantee an appropriate amount of snow to reliably provide optimal ski runs until the date of season opening in early December. Technical snow is generated by pumping pressurized water through the nozzles of a snow machine and dispersing the resulting spray of small water droplets which freeze during their travel to the ground. Cooling and freezing of the droplets can only happen if energy is emitted to the air mass surrounding the droplets. This heat transfer is happening through convective cooling and though evaporation and sublimation of water droplets and ice particles. This means that also mass is lost from the droplets and added in form of vapor to the air. It is important to note that not all water that is pumped through the snow machine is converted to snow distributed on the ground. Significant amounts of water are lost due to wind drift, sublimation and evaporation while droplets are traveling through the air or to draining of water which is not fully frozen when arriving at the ground. Studies addressing this question are sparse and the quantity of the water losses is still unclear. In order to assess this question in more detail, we obtained several systematic field observations at a test site near Davos, Switzerland. About a dozen of snow making tests had been performed during the last winter seasons. We compare the amount of water measured at the intake of the snow machine with the amount of snow accumulating at the ground during a night of snow production. The snow mass was calculated from highly detailed repeated terrestrial laser scanning measurements in combination with manually gathered snow densities. In addition a meteorological station had been set up in the vicinity observing all relevant meteorological

Establishing Winter Origins of Migrating Lesser Snow Geese Using Stable Isotopes

Directory of Open Access Journals (Sweden)

Viviane Hénaux

2012-06-01

Full Text Available Increases in Snow Goose (Chen caerulescens populations and large-scale habitat changes in North America have contributed to the concentration of migratory waterfowl on fewer wetlands, reducing resource availability, and enhancing risks of disease transmission. Predicting wintering locations of migratory individuals is critical to guide wildlife population management and habitat restoration. We used stable carbon (δ13C, nitrogen (δ15N, and hydrogen (δ2H isotope ratios in muscle tissue of wintering Snow Geese to discriminate four major wintering areas, the Playa Lake Region, Texas Gulf Coast, Louisiana Gulf Coast, and Arkansas, and infer the wintering locations of individuals collected later during the 2007 and 2008 spring migrations in the Rainwater Basin (RWB of Nebraska. We predicted the wintering ground derivation of migrating Snow Geese using a likelihood-based approach. Our three-isotope analysis provided an efficient discrimination of the four wintering areas. The assignment model predicted that 53% [95% CI: 37-69] of our sample of Snow Geese from the RWB in 2007 had most likely originated in Louisiana, 38% [23-54] had wintered on Texas Gulf Coast, and 9% [0-20] in Arkansas; the assessment suggested that 89% [73-100] of our 2008 sample had most likely come from Texas Gulf Coast, 9% [0-27] from Louisiana Gulf Coast, and 2% [0-9] from Arkansas. Further segregation of wintering grounds and additional sampling of spring migrating Snow Geese would refine overall assignment and help explain interannual variations in migratory connectivity. The ability to distinguish origins of northbound geese can support the development of spatially-adaptive management strategies for the midcontinent Snow Goose population. Establishing migratory connectivity using isotope assignment techniques can be extended to other waterfowl species to determine critical habitat, evaluate population energy requirements, and inform waterfowl conservation and management