Snow cover as a source of technogenic pollution of surface water during the snow melting period

Directory of Open Access Journals (Sweden)

Labuzova Olga

2016-10-01

Full Text Available The study of pollutants in melt water of snow cover and snow disposal sites in the city of Barnaul showed that during the snow melting period the surface water is not subjected to significant technogenic impact according to a number of studied indices. The oils content is an exception: it can exceed MAC more than 20 times in river- water due to the melting of city disposal sites. Environmental damage due to an oils input into water resources during the snow melting period can be more than 300000 thousand rubles.

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.

The Potential for Snow to Supply Human Water Demand in the Present and Future

Science.gov (United States)

Mankin, Justin S.; Viviroli, Daniel; Singh, Deepti; Hoekstra, Arjen Y.; Diffenbaugh, Noah S.

2015-01-01

Runoff from snowmelt is regarded as a vital water source for people and ecosystems throughout the Northern Hemisphere (NH). Numerous studies point to the threat global warming poses to the timing and magnitude of snow accumulation and melt. But analyses focused on snow supply do not show where changes to snowmelt runoff are likely to present the most pressing adaptation challenges, given sub-annual patterns of human water consumption and water availability from rainfall. We identify the NH basins where present spring and summer snowmelt has the greatest potential to supply the human water demand that would otherwise be unmet by instantaneous rainfall runoff. Using a multi-model ensemble of climate change projections, we find that these basins - which together have a present population of approx. 2 billion people - are exposed to a 67% risk of decreased snow supply this coming century. Further, in the multi-model mean, 68 basins (with a present population of more than 300 million people) transition from having sufficient rainfall runoff to meet all present human water demand to having insufficient rainfall runoff. However, internal climate variability creates irreducible uncertainty in the projected future trends in snow resource potential, with about 90% of snow-sensitive basins showing potential for either increases or decreases over the near-term decades. Our results emphasize the importance of snow for fulfilling human water demand in many NH basins, and highlight the need to account for the full range of internal climate variability in developing robust climate risk management decisions.

Rand Corporation Mean Monthly Global Snow Depth

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — All available monthly snow depth climatologies were integrated by the Rand Corporation, in the early 1980s, into one global (excluding Africa and South America)...

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.

Global warming: Sea ice and snow cover

International Nuclear Information System (INIS)

Walsh, J.E.

1993-01-01

In spite of differences among global climate simulations under scenarios where atmospheric CO 2 is doubled, all models indicate at least some amplification of greenouse warming at the polar regions. Several decades of recent data on air temperature, sea ice, and snow cover of the high latitudes of the Northern Hemisphere are summarized to illustrate the general compatibility of recent variations in those parameters. Despite a data void over the Arctic Ocean, some noteworthy patterns emerge. Warming dominates in winter and spring, as projected by global climate models, with the warming strongest over subpolar land areas of Alaska, northwestern Canada, and northern Eurasia. A time-longitude summary of Arctic sea ice variations indicates that timescales of most anomalies range from several months to several years. Wintertime maxima of total sea ice extent contain no apparent secular trends. The statistical significance of trends in recent sea ice variations was evaluated by a Monte Carlo procedure, showing a statistically significant negative trend in the summer. Snow cover data over the 20-y period of record show a noticeable decrease of Arctic snow cover in the late 1980s. This is of potential climatic significance since the accompanying decrease of surface albedo leads to a rapid increase of solar heating. 21 refs., 3 figs., 1 tab

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

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.

7 CFR 612.2 - Snow survey and water supply forecast activities.

Science.gov (United States)

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

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.

The Airborne Snow Observatory: fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

Science.gov (United States)

Snow cover and its melt dominate regional climate and water resources in many of the world’s mountainous regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known ev...

Estimating water equivalent snow depth from related meteorological variables

International Nuclear Information System (INIS)

Steyaert, L.T.; LeDuc, S.K.; Strommen, N.D.; Nicodemus, M.L.; Guttman, N.B.

1980-05-01

Engineering design must take into consideration natural loads and stresses caused by meteorological elements, such as, wind, snow, precipitation and temperature. The purpose of this study was to determine a relationship of water equivalent snow depth measurements to meteorological variables. Several predictor models were evaluated for use in estimating water equivalent values. These models include linear regression, principal component regression, and non-linear regression models. Linear, non-linear and Scandanavian models are used to generate annual water equivalent estimates for approximately 1100 cooperative data stations where predictor variables are available, but which have no water equivalent measurements. These estimates are used to develop probability estimates of snow load for each station. Map analyses for 3 probability levels are presented

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

. The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

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

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

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?

Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy

Science.gov (United States)

Berisford, Daniel F.; Molotch, Noah P.; Painter, Thomas

2012-01-01

An ultimate goal of the climate change, snow science, and hydrology communities is to measure snow water equivalent (SWE) from satellite measurements. Seasonal SWE is highly sensitive to climate change and provides fresh water for much of the world population. Snowmelt from mountainous regions represents the dominant water source for 60 million people in the United States and over one billion people globally. Determination of snow grain sizes comprising mountain snowpack is critical for predicting snow meltwater runoff, understanding physical properties and radiation balance, and providing necessary input for interpreting satellite measurements. Both microwave emission and radar backscatter from the snow are dominated by the snow grain size stratigraphy. As a result, retrieval algorithms for measuring snow water equivalents from orbiting satellites is largely hindered by inadequate knowledge of grain size.

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.

Quantifying the Global Fresh Water Budget: Capabilities from Current and Future Satellite Sensors

Science.gov (United States)

Hildebrand, Peter; Zaitchik, Benjamin

2007-01-01

The global water cycle is complex and its components are difficult to measure, particularly at the global scales and with the precision needed for assessing climate impacts. Recent advances in satellite observational capabilities, however, are greatly improving our knowledge of the key terms in the fresh water flux budget. Many components of the of the global water budget, e.g. precipitation, atmospheric moisture profiles, soil moisture, snow cover, sea ice are now routinely measured globally using instruments on satellites such as TRMM, AQUA, TERRA, GRACE, and ICESat, as well as on operational satellites. New techniques, many using data assimilation approaches, are providing pathways toward measuring snow water equivalent, evapotranspiration, ground water, ice mass, as well as improving the measurement quality for other components of the global water budget. This paper evaluates these current and developing satellite capabilities to observe the global fresh water budget, then looks forward to evaluate the potential for improvements that may result from future space missions as detailed by the US Decadal Survey, and operational plans. Based on these analyses, and on the goal of improved knowledge of the global fresh water budget under the effects of climate change, we suggest some priorities for the future, based on new approaches that may provide the improved measurements and the analyses needed to understand and observe the potential speed-up of the global water cycle under the effects of climate change.

Effect assessment of Future Climate Change on Water Resource and Snow Quality in cold snowy regions in Japan

Science.gov (United States)

Taniguchi, Y.; Nakatsugawa, M.; Kudo, K.

2017-12-01

It is predicted that the effects of global warming on everyday life will be clearly seen in cold, snowy regions such as Hokkaido. In relation to climate change, there is the concern that the warmer climate will affect not only water resources, but also local economies, in snowy areas, when air temperature increases and snowfall decreases become more marked in the future. Communities whose economies are greatly dependent on snow as a tourism resource, such as for winter sports and snow events, will lose large numbers of visitors because of the shortened winter season. This study was done as a basic study to provide basic ideas for planning adaptation strategies against climate change based on the local characteristics of a cold, snowy region. By taking dam catchment basins in Hokkaido as the subject areas and by using the climate change prediction data that correspond to IPCCAR5, the local-level influence of future climate change on snowfall and snow quality in relation to water resources and winter sports was quantitatively assessed. The water budget was examined for a dam catchment basin in Hokkaido under the present climate (September 1984 to August 2004) and under the future climate (September 2080 to August 2100) by using rainfall, snowfall and evapotranspiration estimated by the LoHAS heat and water balance analysis model.The examination found that, under the future climate, the net annual precipitation will decrease by up to 200 mm because of decreases in precipitation and in runoff height that will result from increased evapotranspiration. The predicted decrease in annual hydro potential of snowfall was considered to greatly affect the dam reservoir operation during the snowmelt season. The snow quality analysis by SNOWPACK revealed that the future snow would become granular earlier than it does at present. Most skiers' snow preferences, from best to worst, are light dry snow (i.e., fresh snow), lightly compacted snow, compacted snow and, finally, granular

Toward an Improved Understanding of the Global Fresh Water Budget

Science.gov (United States)

Hildebrand, Peter H.

2005-01-01

The major components of the global fresh water cycle include the evaporation from the land and ocean surfaces, precipitation onto the Ocean and land surfaces, the net atmospheric transport of water from oceanic areas over land, and the return flow of water from the land back into the ocean. The additional components of oceanic water transport are few, principally, the mixing of fresh water through the oceanic boundary layer, transport by ocean currents, and sea ice processes. On land the situation is considerably more complex, and includes the deposition of rain and snow on land; water flow in runoff; infiltration of water into the soil and groundwater; storage of water in soil, lakes and streams, and groundwater; polar and glacial ice; and use of water in vegetation and human activities. Knowledge of the key terms in the fresh water flux budget is poor. Some components of the budget, e.g. precipitation, runoff, storage, are measured with variable accuracy across the globe. We are just now obtaining precise measurements of the major components of global fresh water storage in global ice and ground water. The easily accessible fresh water sources in rivers, lakes and snow runoff are only adequately measured in the more affluent portions of the world. presents proposals are suggesting methods of making global measurements of these quantities from space. At the same time, knowledge of the global fresh water resources under the effects of climate change is of increasing importance and the human population grows. This paper provides an overview of the state of knowledge of the global fresh water budget, evaluating the accuracy of various global water budget measuring and modeling techniques. We review the measurement capabilities of satellite instruments as compared with field validation studies and modeling approaches. Based on these analyses, and on the goal of improved knowledge of the global fresh water budget under the effects of climate change, we suggest

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

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.

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

Aircraft gamma-ray spectrometry in snow-water equivalent measurement

International Nuclear Information System (INIS)

Kuittinen, R.; Vironmaeki, J.

1979-01-01

During the winter periods 1976-1977 and 1977-1978 the Hydrological Office at the National Board of Waters and the Geological Survey of Finland carried out a joint study to evaluate usefuluess of gamma-ray spectrometry in snow-water equivalent measurement. A multichannel gamma-ray spectrometer was fitted in a DC-3 aircraft. Fourteen snow courses were operated using both the gravimetric method and the gamma-ray method. The snow courses were located in southern Finland in forest, swamp and agricultural land. The results shows that the gamma ray method can be considered suitable for use in Finnish conditions and the accuracy of the gamma-ray method is almost of the same magnitude as the accuracy of the gravimetric method. (Auth.)

Aircraft gamma-ray spectrometry in snow-water equivalent measurement

Energy Technology Data Exchange (ETDEWEB)

Kuittinen, R [National Board of Waters (Finland); Vironmaeki, J [Geological Survey of Finland

1979-01-01

During the winter periods 1976-1977 and 1977-1978 the Hydrological Office at the National Board of Waters and the Geological Survey of Finland carried out a joint study to evaluate usefuluess of gamma-ray spectrometry in snow-water equivalent measurement. A multichannel gamma-ray spectrometer was fitted in a DC-3 aircraft. Fourteen snow courses were operated using both the gravimetric method and the gamma-ray method. The snow courses were located in southern Finland in forest, swamp and agricultural land. The results shows that the gamma ray method can be considered suitable for use in Finnish conditions and the accuracy of the gamma-ray method is almost of the same magnitude as the accuracy of the gravimetric method.

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

Science.gov (United States)

Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We

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...

Observational evidence of changes in global snow and ice cover

International Nuclear Information System (INIS)

Barry, R.G.

1990-01-01

Sources of observational data on recent variations in the seasonal extent of snow cover and sea ice, of the terminal position and volume of alpine glaciers, and of ground temperature profiles in areas of permafrost are briefly reviewed. Recent evidence of changes in these variables is then examined. The extent of seasonal snow cover in the Northern hemisphere and of sea ice in both hemispheres has fluctuated irregularly over the last 15-20 years with a range of about 10-15% in each case. There is no clear evidence of any recent trends, despite general global warming. In contrast, most glaciers retreated and thinned from before the turn of the century until the 1960s and alaskan permafrost temperatures have risen 2-4 C per century. Recently, glacier advances have been noted, perhaps in response to increased accumulation. Problems of linking climate forcing and snow/ice responses are discussed

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

Directory of Open Access Journals (Sweden)

S. Terzago

2017-07-01

Full Text Available The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR, and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs, participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX and in the Fifth Coupled Model Intercomparison Project (CMIP5 respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around

A reassessment of North American river basin water balances in light of new estimates of mountain snow accumulation

Science.gov (United States)

Wrzesien, M.; Durand, M. T.; Pavelsky, T.

2017-12-01

The hydrologic cycle is a key component of many aspects of daily life, yet not all water cycle processes are fully understood. In particular, water storage in mountain snowpacks remains largely unknown. Previous work with a high resolution regional climate model suggests that global and continental models underestimate mountain snow accumulation, perhaps by as much as 50%. Therefore, we hypothesize that since snow water equivalent (one aspect of the water balance) is underestimated, accepted water balances for major river basins are likely wrong, particularly for mountainous river basins. Here we examine water balances for four major high latitude North American watersheds - the Columbia, Mackenzie, Nelson, and Yukon. The mountainous percentage of each basin ranges, which allows us to consider whether a bias in the water balance is affected by mountain area percentage within the watershed. For our water balance evaluation, we especially consider precipitation estimates from a variety of datasets, including models, such as WRF and MERRA, and observation-based, such as CRU and GPCP. We ask whether the precipitation datasets provide enough moisture for seasonal snow to accumulate within the basin and whether we see differences in the variability of annual and seasonal precipitation from each dataset. From our reassessment of high-latitude water balances, we aim to determine whether the current understanding is sufficient to describe all processes within the hydrologic cycle or whether datasets appear to be biased, particularly in high-elevation precipitation. Should currently-available datasets appear to be similarly biased in precipitation, as we have seen in mountain snow accumulation, we discuss the implications for the continental water budget.

Prey preferences of the snow leopard (Panthera uncia: regional diet specificity holds global significance for conservation.

Directory of Open Access Journals (Sweden)

Salvador Lyngdoh

Full Text Available The endangered snow leopard is a large felid that is distributed over 1.83 million km(2 globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica, blue sheep (Pseudois nayaur, Himalayan tahr (Hemitragus jemlahicus, argali (Ovis ammon and marmots (Marmota spp. The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36-76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation.

Prey preferences of the snow leopard (Panthera uncia): regional diet specificity holds global significance for conservation.

Science.gov (United States)

Lyngdoh, Salvador; Shrotriya, Shivam; Goyal, Surendra P; Clements, Hayley; Hayward, Matthew W; Habib, Bilal

2014-01-01

The endangered snow leopard is a large felid that is distributed over 1.83 million km(2) globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica), blue sheep (Pseudois nayaur), Himalayan tahr (Hemitragus jemlahicus), argali (Ovis ammon) and marmots (Marmota spp). The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36-76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation.

The impact of organochlorines cycling in the cryosphere on global distributions and fate – 2. Land ice and temporary snow cover

International Nuclear Information System (INIS)

Hofmann, Lorenz; Stemmler, Irene; Lammel, Gerhard

2012-01-01

Global fate and transport of γ-HCH and DDT was studied using a global multicompartment chemistry-transport model, MPI-MCTM, with and without inclusion of land ice (in Antarctica and Greenland) or snow cover (dynamic). MPI-MCTM is based on coupled ocean and atmosphere general circulation models. After a decade of simulation 4.2% γ-HCH and 2.3% DDT are stored in land ice and snow. Neglection of land ice and snow in modelling would underestimate the total environmental residence time, τ ov , of γ-HCH and overestimate τ ov for DDT, both on the order of 1% and depending on actual compartmental distribution. Volatilisation of DDT from boreal, seasonally snow covered land is enhanced throughout the year, while volatilisation of γ-HCH is only enhanced during the snow-free season. Including land ice and snow cover in modelling matters in particular for the Arctic, where higher burdens are predicted to be stored. - Highlights: ► Land ice and snow hosts 2–4% of the global environmental burden of γ-HCH and DDT. ► Inclusion of land ice and snow cover matters for global environmental residence time. ► Including of land ice and snow cover matters in particular for the Arctic. - The inclusion of cycling in temporary snow cover and land ice in the model is found relevant for predicted POPs multicompartmental distribution and fate in the Arctic and on the global scale.

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

Science.gov (United States)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate 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. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

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

Energy Technology Data Exchange (ETDEWEB)

Stuefer, Svetlana

2013-03-31

This project was funded by the U.S. Department of Energy, National Energy Technology Laboratory (NETL) to address environmental research questions specifically related to Alaska's oil and gas natural resources development. The focus of this project was on the environmental issues associated with allocation of water resources for construction of ice roads and ice pads. Earlier NETL projects showed that oil and gas exploration activities in the U.S. Arctic require large amounts of water for ice road and ice pad construction. Traditionally, lakes have been the source of freshwater for this purpose. The distinctive hydrological regime of northern lakes, caused by the presence of ice cover and permafrost, exerts influence on lake water availability in winter. Lakes are covered with ice from October to June, and there is often no water recharge of lakes until snowmelt in early June. After snowmelt, water volumes in the lakes decrease throughout the summer, when water loss due to evaporation is considerably greater than water gained from rainfall. This balance switches in August, when air temperature drops, evaporation decreases, and rain (or snow) is more likely to occur. Some of the summer surface storage deficit in the active layer and surface water bodies (lakes, ponds, wetlands) is recharged during this time. However, if the surface storage deficit is not replenished (for example, precipitation in the fall is low and near‐surface soils are dry), lake recharge is directly affected, and water availability for the following winter is reduced. In this study, we used snow fences to augment fresh water supplies in shallow arctic lakes despite unfavorable natural conditions. We implemented snow‐control practices to enhance snowdrift accumulation (greater snow water equivalent), which led to increased meltwater production and an extended melting season that resulted in lake recharge despite low precipitation during the years of the experiment. For three years (2009

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.

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.

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.

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.

Sulphur dioxide removal by turbulent transfer over grass, snow, and water surfaces

Energy Technology Data Exchange (ETDEWEB)

Whelpdale, D M; Shaw, R W

1974-01-01

Vertical gradients of sulphur dioxide concentration have been measured over grass, snow, and water surfaces in order to assess the importance of these surfaces as SO/sub 2/ sinks. Concentrations were usually found to be lower near the surface indicating that removal occurs there. Vertical concentration gradients, normalized with repect to the concentration at 8 m, were generally greatest over water and least over snow, independent of meteorological conditions, suggesting that a water surface is the strongest SO/sub 2/ sink, with grass next, and snow weakest. The turbulent transfer of SO/sub 2/ to the interface is discussed in relation to stability of the lower atmosphere and physical and chemical properties of the surfaces. Using a bulk aerodynamic transfer approach similar to that for water vapour, values of SO/sub 2/ flux averaged over periods of from one to several hours were found to be of the order of 1 microgram/M/sup 2//S to the water and grass surfaces, and an order of magnitude smaller to the snow surface. Deposition velocities were found to be of the order of 1 cm/s.

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...

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

Science.gov (United States)

Skofronick-Jackson, Gail M.; Johnson, Benjamin T.; Munchak, S. Joseph

2013-01-01

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

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.

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.

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

Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

Science.gov (United States)

Forman, Bart; Reichle, Rofl; Rodell, Matt

2011-01-01

Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

Modelling the snowmelt and the snow water equivalent by creating a simplified energy balance conceptual snow model

Science.gov (United States)

Riboust, Philippe; Thirel, Guillaume; Le Moine, Nicolas; Ribstein, Pierre

2016-04-01

A better knowledge of the accumulated snow on the watersheds will help flood forecasting centres and hydro-power companies to predict the amount of water released during spring snowmelt. Since precipitations gauges are sparse at high elevations and integrative measurements of the snow accumulated on watershed surface are hard to obtain, using snow models is an adequate way to estimate snow water equivalent (SWE) on watersheds. In addition to short term prediction, simulating accurately SWE with snow models should have many advantages. Validating the snow module on both SWE and snowmelt should give a more reliable model for climate change studies or regionalization for ungauged watersheds. The aim of this study is to create a new snow module, which has a structure that allows the use of measured snow data for calibration or assimilation. Energy balance modelling seems to be the logical choice for designing a model in which internal variables, such as SWE, could be compared to observations. Physical models are complex, needing high computational resources and many different types of inputs that are not widely measured at meteorological stations. At the opposite, simple conceptual degree-day models offer to simulate snowmelt using only temperature and precipitation as inputs with fast computing. Its major drawback is to be empirical, i.e. not taking into account all of the processes of the energy balance, which makes this kind of model more difficult to use when willing to compare SWE to observed measurements. In order to reach our objectives, we created a snow model structured by a simplified energy balance where each of the processes is empirically parameterized in order to be calculated using only temperature, precipitation and cloud cover variables. This model's structure is similar to the one created by M.T. Walter (2005), where parameterizations from the literature were used to compute all of the processes of the energy balance. The conductive fluxes into the

Snowpack snow water equivalent measurement using the attenuation of cosmic gamma radiation

International Nuclear Information System (INIS)

Osterhuber, R.; Condreva, K.

1998-01-01

Incoming, background cosmic radiation constantly fluxes through the earth's atmosphere. The high energy gamma portion of this radiation penetrates many terrestrial objects, including the winter snowpack. The attenuation of this radiation is exponentially related to the mass of the medium through which it penetrates. For the past three winters, a device measuring cosmic gamma radiation--and its attenuation through snow--has been installed at the Central Sierra Snow Laboratory, near Donner Pass, California. This gamma sensor, measuring energy levels between 5 and 15 MeV, has proved to be an accurate, reliable, non-invasive, non-mechanical instrument with which to measure the total snow water equivalent of a snowpack. This paper analyzes three winters' worth of data and discusses the physics and practical application of the sensor for the collection of snow water equivalent data from a remote location

Assessing the controls of the snow energy balance and water available for runoff in a rain-an-snow environment

Science.gov (United States)

Adam B. Mazurkiewicz; David G. Callery; Jeffrey J. McDonnell

2008-01-01

Rain-on-snow (ROS) melt production and its contribution to water available for runoff is poorly understood. In the Pacific Northwest (PNW) of the USA, ROS drives many runoff events with turbulent energy exchanges dominating the snow energy balance (EB). While previous experimental work in the PNW (most notably the H.J. Andrews Experimental Forest (HJA» has quantified...

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.

Observing the Global Water Cycle from Space

Science.gov (United States)

Hildebrand, P. H.

2004-01-01

This paper presents an approach to measuring all major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers, and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components. Using these observational requirements for a global water cycle observing system, an approach to measuring the global water cycle from space is developed. The capabilities of various active and passive microwave instruments are discussed, as is the potential of supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches and assimilation of the microwave and other data into interpretative computer models are discussed to develop the observational possibilities. The selection of orbits is then addressed, for orbit selection and antenna size/beamwidth considerations determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The results define a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

Estimating the snow water equivalent on a glacierized high elevation site (Forni Glacier, Italy)

Science.gov (United States)

Senese, Antonella; Maugeri, Maurizio; Meraldi, Eraldo; Verza, Gian Pietro; Azzoni, Roberto Sergio; Compostella, Chiara; Diolaiuti, Guglielmina

2018-04-01

We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an automatic weather station (AWS) and corroborated by data from field campaigns on the Forni Glacier in Italy. The aim of the analysis is to estimate the SWE of new snowfall and the annual SWE peak based on the average density of the new snow at the site (corresponding to the snowfall during the standard observation period of 24 h) and automated snow depth measurements. The results indicate that the daily SR50 sonic ranger measurements and the available snow pit data can be used to estimate the mean new snow density value at the site, with an error of ±6 kg m-3. Once the new snow density is known, the sonic ranger makes it possible to derive SWE values with an RMSE of 45 mm water equivalent (if compared with snow pillow measurements), which turns out to be about 8 % of the total SWE yearly average. Therefore, the methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total SWE using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.

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.

Experimental investigation of ice and snow melting process on pavement utilizing geothermal tail water

International Nuclear Information System (INIS)

Wang Huajun; Zhao Jun; Chen Zhihao

2008-01-01

Road ice and snow melting based on low temperature geothermal tail water is of significance to realize energy cascading utilization. A small scale ice and snow melting system is built in this work. Experiments of dynamic melting processes of crushed ice, solid ice, artificial snow and natural snow are conducted on concrete pavement. The results show that the melting process of ice and snow includes three phases: a starting period, a linear period and an accelerated period. The critical value of the snow free area ratio between the linear period and the accelerated period is about 0.6. The physical properties of ice and snow, linked with ambient conditions, have an obvious effect on the melting process. The difference of melting velocity and melting time between ice and snow is compared. To reduce energy consumption, the formation of ice on roads should be avoided if possible. The idling process is an effective pathway to improve the performance of melting systems. It is feasible to utilize geothermal tail water of about 40 deg. C for melting ice and snow on winter roads, and it is unnecessary to keep too high fluid temperatures during the practical design and applications. Besides, with the exception of solid ice, the density and porosity of snow and ice tend to be decreasing and increasing, respectively, as the ambient temperature decreases

Snow measurement Using P-Band Signals of Opportunity Reflectometry

Science.gov (United States)

Shah, R.; Yueh, S. H.; Xu, X.; Elder, K.

2017-12-01

Snow water storage in land is a critical parameter of the water cycle. In this study, we develop methods for estimating reflectance from bistatic scattering of digital communication Signals of Opportunity (SoOp) across the available microwave spectrum from VHF to Ka band and show results from proof-of-concept experiments at the Fraser Experimental Forest, Colorado to acquire measurements to relate the SoOp phase and reflectivity to a snow-covered soil surface. The forward modeling of this scenario will be presented and multiple sensitivities were conducted. Available SoOp receiver data along with a network of in situ sensor measurements collected since January 2016 will be used to validate theoretical modeling results. In the winter season of 2016 and 2017, we conducted a field experiment using VHF/UHF-band illuminating sources to detect SWE and surface reflectivity. The amplitude of the reflectivity showed sensitivity to the wetness of snow pack and ground reflectivity while the phase showed sensitivity to SWE. This use of this concept can be helpful to measure the snow water storage in land globally.

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

Role of Marine Snows in Microplastic Fate and Bioavailability.

Science.gov (United States)

Porter, Adam; Lyons, Brett P; Galloway, Tamara S; Lewis, Ceri

2018-06-01

Microplastics contaminate global oceans and are accumulating in sediments at levels thought sufficient to leave a permanent layer in the fossil record. Despite this, the processes that vertically transport buoyant polymers from surface waters to the benthos are poorly understood. Here we demonstrate that laboratory generated marine snows can transport microplastics of different shapes, sizes, and polymers away from the water surface and enhance their bioavailability to benthic organisms. Sinking rates of all tested microplastics increased when incorporated into snows, with large changes observed for the buoyant polymer polyethylene with an increase in sinking rate of 818 m day -1 and for denser polyamide fragments of 916 m day -1 . Incorporation into snows increased microplastic bioavailability for mussels, where uptake increased from zero to 340 microplastics individual -1 for free microplastics to up to 1.6 × 10 5 microplastics individual -1 when incorporated into snows. We therefore propose that marine snow formation and fate has the potential to play a key role in the biogeochemical processing of microplastic pollution.

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.

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.

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

Science.gov (United States)

Langlois, A.; Royer, A.; Derksen, C.; Montpetit, B.; Dupont, F.; GoïTa, K.

2012-12-01

Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Snow Monitoring Using Remote Sensing Data: Modification of Normalized Difference Snow Index

Science.gov (United States)

Kaplan, G.; Avdan, U.

2016-12-01

Snow cover is an important part of the Earth`s climate system so its continuous monitoring is necessary to map snow cover in high resolution. Satellite remote sensing can successfully fetch land cover and land cover changes. Although normalized difference snow index NDSI has quite good accuracy, topography shadow, water bodies and clouds can be easily misplaced as snow. Using Landsat TM, +ETM and TIRS/OLI satellite images, the NDSI was modified for more accurate snow mapping. In this paper, elimination of the misplaced water bodies was made using the high reflectance of the snow in the blue band. Afterwards, the modified NDSI (MNDSI) was used for estimating snow cover through the years on the highest mountains in Republic of Macedonia. The results from this study show that the MNDSI accuracy is bigger than the NDSI`s, totally eliminating the misplaced water bodies, and partly the one caused from topography and clouds. Also, it was noticed that the snow cover in the study area has been lowered through the years. For future studies, the MNDSI should be validated on different study areas with different characteristics.

Macroscopic modeling for heat and water vapor transfer in dry snow by homogenization.

Science.gov (United States)

Calonne, Neige; Geindreau, Christian; Flin, Frédéric

2014-11-26

Dry snow metamorphism, involved in several topics related to cryospheric sciences, is mainly linked to heat and water vapor transfers through snow including sublimation and deposition at the ice-pore interface. In this paper, the macroscopic equivalent modeling of heat and water vapor transfers through a snow layer was derived from the physics at the pore scale using the homogenization of multiple scale expansions. The microscopic phenomena under consideration are heat conduction, vapor diffusion, sublimation, and deposition. The obtained macroscopic equivalent model is described by two coupled transient diffusion equations including a source term arising from phase change at the pore scale. By dimensional analysis, it was shown that the influence of such source terms on the overall transfers can generally not be neglected, except typically under small temperature gradients. The precision and the robustness of the proposed macroscopic modeling were illustrated through 2D numerical simulations. Finally, the effective vapor diffusion tensor arising in the macroscopic modeling was computed on 3D images of snow. The self-consistent formula offers a good estimate of the effective diffusion coefficient with respect to the snow density, within an average relative error of 10%. Our results confirm recent work that the effective vapor diffusion is not enhanced in snow.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

Science.gov (United States)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Impact of intra- versus inter-annual snow depth variation on water relations and photosynthesis for two Great Basin Desert shrubs.

Science.gov (United States)

Loik, Michael E; Griffith, Alden B; Alpert, Holly; Concilio, Amy L; Wade, Catherine E; Martinson, Sharon J

2015-06-01

Snowfall provides the majority of soil water in certain ecosystems of North America. We tested the hypothesis that snow depth variation affects soil water content, which in turn drives water potential (Ψ) and photosynthesis, over 10 years for two widespread shrubs of the western USA. Stem Ψ (Ψ stem) and photosynthetic gas exchange [stomatal conductance to water vapor (g s), and CO2 assimilation (A)] were measured in mid-June each year from 2004 to 2013 for Artemisia tridentata var. vaseyana (Asteraceae) and Purshia tridentata (Rosaceae). Snow fences were used to create increased or decreased snow depth plots. Snow depth on +snow plots was about twice that of ambient plots in most years, and 20 % lower on -snow plots, consistent with several down-scaled climate model projections. Maximal soil water content at 40- and 100-cm depths was correlated with February snow depth. For both species, multivariate ANOVA (MANOVA) showed that Ψ stem, g s, and A were significantly affected by intra-annual variation in snow depth. Within years, MANOVA showed that only A was significantly affected by spatial snow depth treatments for A. tridentata, and Ψ stem was significantly affected by snow depth for P. tridentata. Results show that stem water relations and photosynthetic gas exchange for these two cold desert shrub species in mid-June were more affected by inter-annual variation in snow depth by comparison to within-year spatial variation in snow depth. The results highlight the potential importance of changes in inter-annual variation in snowfall for future shrub photosynthesis in the western Great Basin Desert.

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

Production of heterotrophic bacteria inhabiting macroscopic organic aggregates (marine snow) from surface waters

International Nuclear Information System (INIS)

Alldredge, A.L.; Cole, J.J.; Caron, D.A.

1986-01-01

Macroscopic detrital aggregates, known as marine snow, are a ubiquitous and abundant component of the marine pelagic zone. Descriptions of microbial communities occurring at densities 2-5 orders of magnitude higher on these particles than in the surrounding seawater have led to the suggestion that marine snow may be a site of intense heterotrophic activity. The authors tested this hypothesis using incorporation of [ 3 H]thymidine into macromolecules as a measure of bacterial growth occurring on marine snow from oceanic waters in the North Atlantic and from neritic waters off southern California. Abundances of marine snow ranged from 0.1 to 4.3 aggregates per liter. However, only 0.1-4% ration per cell on aggregates was generally equal to or lower than that of bacteria found free-living in the surrounding seawater, indicating that attached bacteria were not growing more rapidly than free-living bacteria. Bacteria inhabiting aggregates were up to 25 times larger than free-living forms

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.

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.

Arctic plant ecophysiology and water source utilization in response to altered snow: isotopic (δ18O and δ2H) evidence for meltwater subsidies to deciduous shrubs.

Science.gov (United States)

Jespersen, R Gus; Leffler, A Joshua; Oberbauer, Steven F; Welker, Jeffrey M

2018-06-28

Warming-linked woody shrub expansion in the Arctic has critical consequences for ecosystem processes and climate feedbacks. The snow-shrub interaction model has been widely implicated in observed Arctic shrub increases, yet equivocal experimental results regarding nutrient-related components of this model have highlighted the need for a consideration of the increased meltwater predicted in expanding shrub stands. We used a 22-year snow manipulation experiment to simultaneously address the unexplored role of snow meltwater in arctic plant ecophysiology and nutrient-related components of the snow-shrub hypothesis. We coupled measurements of leaf-level gas exchange and leaf tissue chemistry (%N and δ 13 C) with an analysis of stable isotopes (δ 18 O and δ 2 H) in soil water, precipitation, and stem water. In deeper snow areas photosynthesis, conductance, and leaf N increased and δ 13 C values decreased in the deciduous shrubs, Betula nana and Salix pulchra, and the graminoid, Eriophorum vaginatum, with the strongest treatment effects observed in deciduous shrubs, consistent with predictions of the snow-shrub hypothesis. We also found that deciduous shrubs, especially S. pulchra, obtained much of their water from snow melt early in the growing season (40-50%), more than either E. vaginatum or the evergreen shrub, Rhododendron tomentosum (Ledum palustre). This result provides the basis for adding a meltwater-focused feedback loop to the snow-shrub interaction model of shrub expansion in the Arctic. Our results highlight the critical role of winter snow in the ecophysiology of Arctic plants, particularly deciduous shrubs, and underline the importance of understanding how global warming will affect the Arctic winter snowpack.

Satellite Image-based Estimates of Snow Water Equivalence in Restored Ponderosa Pine Forests in Northern Arizona

Science.gov (United States)

Sankey, T.; Springer, A. E.; O'Donnell, F. C.; Donald, J.; McVay, J.; Masek Lopez, S.

2014-12-01

The U.S. Forest Service plans to conduct forest restoration treatments through the Four Forest Restoration Initiative (4FRI) on hundreds of thousands of acres of ponderosa pine forest in northern Arizona over the next 20 years with the goals of reducing wildfire hazard and improving forest health. The 4FRI's key objective is to thin and burn the forests to create within-stand openings that "promote snowpack accumulation and retention which benefit groundwater recharge and watershed processes at the fine (1 to 10 acres) scale". However, little is known about how these openings created by restoration treatments affect snow water equivalence (SWE) and soil moisture, which are key parts of the water balance that greatly influence water availability for healthy trees and for downstream water users in the Sonoran Desert. We have examined forest canopy cover by calculating a Normalized Difference Vegetation Index (NDVI), a key indicator of green vegetation cover, using Landsat satellite data. We have then compared NDVI between treatments at our study sites in northern Arizona and have found statistically significant differences in tree canopy cover between treatments. The control units have significantly greater forest canopy cover than the treated units. The thinned units also have significantly greater tree canopy cover than the thin-and-burn units. Winter season Landsat images have also been analyzed to calculate Normalized Difference Snow Index (NDSI), a key indicator of snow water equivalence and snow accumulation at the treated and untreated forests. The NDSI values from these dates are examined to determine if snow accumulation and snow water equivalence vary between treatments at our study sites. NDSI is significantly greater at the treated units than the control units. In particular, the thinned forest units have significantly greater snow cover than the control units. Our results indicate that forest restoration treatments result in increased snow pack

Snow Cover Monitoring Using MODIS Data in Liaoning Province, Northeastern China

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Yu Lu

2010-03-01

Full Text Available This paper presents the results of snow cover monitoring studies in Liaoning Province, northeastern China, using MODIS data. Snow cover plays an important role in both the regional water balance and soil moisture properties during the early spring in northeastern China. In addition, heavy snowfalls commonly trigger hazards such as flooding, caused by rapid snow melt, or crop failure, resulting from fluctuations in soil temperature associated with changes in the snow cover. The latter is a function of both regional, or global, climatic changes, as well as fluctuations in the albedo resulting from variations in the Snow Covered Area (SCA. These impacts are crucial to human activities, especially to those living in middle-latitude areas such as Liaoning Province. Thus, SCA monitoring is currently an important tool in studies of global climate change, particularly because satellite remote sensing data provide timely and efficient snow cover information for large areas. In this study, MODIS L1B data, MODIS Daily Snow Products (MOD10A1 and MODIS 8-day Snow Products (MOD10A2 were used to monitor the SCA of Liaoning Province over the winter months of November–April, 2006–2008. The effects of cloud masking and forest masking on the snow monitoring results were also assessed. The results show that the SCA percentage derived from MODIS L1B data is relatively consistent, but slightly higher than that obtained from MODIS Snow Products. In situ data from 25 snow stations were used to assess the accuracy of snow cover monitoring from the SCA compared to the results from MODIS Snow Products. The studies found that the SCA results were more reliable than MODIS Snow Products in the study area.

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

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Yurong Cui

2016-06-01

Full Text Available Snow water equivalent (SWE is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx, shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

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.

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

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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

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

Sublimation From Snow in Northern Environments

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Pomeroy, J. W.

2002-12-01

Sublimation from snow is an often neglected component of water and energy balances. Research under the Mackenzie GEWEX Study has attempted to understand the snow and atmospheric processes controlling sublimation and to estimate the magnitude of sublimation in high latitude catchments. Eddy correlation units were used to measure vertical water vapour fluxes from a high latitude boreal forest, snow-covered tundra and shrub-covered tundra in Wolf Creek Research Basin, near Whitehorse Yukon, Territory Canada. Over Jan-Apr. water vapour fluxes from the forest canopy amounted to 18.3 mm, a significant loss from winter snowfall of 54 mm. Most of this loss occurred when the canopy was snow-covered. The weight of snow measured on a suspended, weighed tree indicates that this flux is dominated by sublimation of intercepted snow. In the melt period (April), water vapour fluxes were uniformly small ranging from 0.21 mm/day on the tundra slope, 0.23 mm/day for the forest and 0.27 mm/day for the shrub-tundra. During the melt period the forest and shrub canopies was snow-free and roots were frozen, so the primary source of water vapour from all sites was the surface snow.

High-Elevation Sierra Nevada Conifers Reveal Increasing Reliance on Snow Water with Changing Climate

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Lepley, K. S.; Meko, D. M.; Touchan, R.; Shamir, E.; Graham, R.

2017-12-01

Snowpack in the Sierra Nevada Mountains accounts for around one third of California's water supply. Melting snow can provide water into dry summer months characteristic of the region's Mediterranean climate. As climate changes, understanding patterns of snowpack, snowmelt, and biological response are critical in this region of agricultural, recreational, and ecological value. Tree rings can act as proxy records to inform scientists and resource managers of past climate variability where instrumental data is unavailable. Here we investigate relationships between tree rings of high-elevation, snow-adapted conifer trees (Tsuga mertensiana, Abies magnifica) and April 1st snow-water equivalent (SWE) in the northern Sierra Nevada Mountains. The 1st principal component of 29 highly correlated regional SWE time series was modeled using multiple linear regression of four tree-ring chronologies including two lagged chronologies. Split-period verification analysis of this model revealed poor predictive skill in the early half (1929 - 1966) of the calibration period (1929 - 2003). Further analysis revealed a significant (p time. Snow water is becoming a more limiting resource to tree growth as average temperatures rise and the hydrologic regime shifts. These results highlight the need for resource managers and policy makers to consider that biological response to climate is not static.

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

Science.gov (United States)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will

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

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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.

Metagenomic and satellite analyses of red snow in the Russian Arctic

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Nao Hisakawa

2015-12-01

Full Text Available Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight, thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995. Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotroph Chlamydomonas nivalis that is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complex C. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show that C. nivalis supports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide.

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

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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.

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

Prey Preferences of the Snow Leopard (Panthera uncia): Regional Diet Specificity Holds Global Significance for Conservation

OpenAIRE

Lyngdoh, Salvador; Shrotriya, Shivam; Goyal, Surendra P.; Clements, Hayley; Hayward, Matthew W.; Habib, Bilal

2014-01-01

The endangered snow leopard is a large felid that is distributed over 1.83 million km(2) globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based o...

Effects of soil water and heat relationship under various snow cover during freezing-thawing periods in Songnen Plain, China.

Science.gov (United States)

Fu, Qiang; Hou, Renjie; Li, Tianxiao; Jiang, Ruiqi; Yan, Peiru; Ma, Ziao; Zhou, Zhaoqiang

2018-01-22

In this study, the spatial variations of soil water and heat under bare land (BL), natural snow (NS), compacted snow (CS) and thick snow (TS) treatments were analyzed. The relationship curve between soil temperature and water content conforms to the exponential filtering model, by means of the functional form of the model, it was defined as soil water and heat relation function model. On this basis, soil water and heat function models of 10, 20, 40, 60, 100, and 140 cm were established. Finally, a spatial variation law of the relationship effect was described based on analysising of the differences between the predicted and measured results. During freezing period, the effects of external factors on soil were hindered by snow cover. As the snow increased, the accuracy of the function model gradually improved. During melting period, infiltration by snowmelt affected the relationship between the soil temperature and moisture. With the increasing of snow, the accuracy of the function models gradually decreased. The relationship effects of soil water and heat increased with increasing depth within the frozen zone. In contrast, below the frozen layer, the relationship of soil water and heat was weaker, and the function models were less accurate.

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

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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

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

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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

Aircraft gamma-ray spectrometry in snow-water equivalent measurement

Energy Technology Data Exchange (ETDEWEB)

Kuittinen, R; Vironmaeki, J

1979-01-01

During the winter periods of 1976 to 1977 and 1977 to 1978, the Hydrological Office at the National Boards of Waters and the Geological Survey of Finland carried out a joint study to evaluate usefulness of gamma-ray spectrometry in snowwater equivalent measurement. A multichannel gamma-ray spectrometry was fitted out in a DC-3 aircraft. Fourteen snow courses were operated using gravimetric method and gamma-ray method. The snow courses were located in southern Finland in forest, swamp and agricultural land. The results show that the gamma ray method can be considered suitable for use in Finnish conditions and the accuracy of the gamma-ray method is almost of the same magnitude of the accuracy of the gravimetric method.

Ecosystem water availability in juniper versus sagebrush snow-dominated rangelands

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Western Juniper (J. occidentalis Hook.) now dominates over 3.6 million ha of rangeland in the Intermountain Western US. Critical ecological relationships among snow distribution, water budgets, plant community transitions, and habitat requirements for wildlife, such as sage grouse, remain poorly und...

Bioavailability of mineral-bound iron to a snow algae-bacteria co-culture and implications for albedo-altering snow algae blooms.

Science.gov (United States)

Harrold, Z R; Hausrath, E M; Garcia, A H; Murray, A E; Tschauner, O; Raymond, J; Huang, S

2018-01-26

Snow algae can form large-scale blooms across the snowpack surface and near-surface environments. These pigmented blooms can decrease snow albedo, increase local melt rates, and may impact the global heat budget and water cycle. Yet, underlying causes for the geospatial occurrence of these blooms remain unconstrained. One possible factor contributing to snow algae blooms is the presence of mineral dust as a micronutrient source. We investigated the bioavailability of iron (Fe) -bearing minerals, including forsterite (Fo 90 , Mg 1.8 Fe 0.2 SiO 4 ), goethite, smectite and pyrite as Fe sources for a Chloromonas brevispina - bacteria co-culture through laboratory-based experimentation. Fo 90 was capable of stimulating snow algal growth and increased the algal growth rate in otherwise Fe-depleted co-cultures. Fo 90 -bearing systems also exhibited a decrease in bacteria:algae ratios compared to Fe-depleted conditions, suggesting a shift in microbial community structure. The C. brevispina co-culture also increased the rate of Fo 90 dissolution relative to an abiotic control. Analysis of 16S rRNA genes in the co-culture identified Gammaproteobacteria , Betaprotoeobacteria and Sphingobacteria , all of which are commonly found in snow and ice environments. Archaea were not detected. Collimonas and Pseudomonas , which are known to enhance mineral weathering rates, comprised two of the top eight (> 1 %) OTUs. These data provide unequivocal evidence that mineral dust can support elevated snow algae growth under otherwise Fe-depleted growth conditions, and that snow algae can enhance mineral dissolution under these conditions. IMPORTANCE Fe, a key micronutrient for photosynthetic growth, is necessary to support the formation of high-density snow algae blooms. The laboratory experiments described herein allow for a systematic investigation of snow algae-bacteria-mineral interactions and their ability to mobilize and uptake mineral-bound Fe. Results provide unequivocal and

Snow precipitation on Mars driven by cloud-induced night-time convection

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Spiga, Aymeric; Hinson, David P.; Madeleine, Jean-Baptiste; Navarro, Thomas; Millour, Ehouarn; Forget, François; Montmessin, Franck

2017-09-01

Although it contains less water vapour than Earth's atmosphere, the Martian atmosphere hosts clouds. These clouds, composed of water-ice particles, influence the global transport of water vapour and the seasonal variations of ice deposits. However, the influence of water-ice clouds on local weather is unclear: it is thought that Martian clouds are devoid of moist convective motions, and snow precipitation occurs only by the slow sedimentation of individual particles. Here we present numerical simulations of the meteorology in Martian cloudy regions that demonstrate that localized convective snowstorms can occur on Mars. We show that such snowstorms--or ice microbursts--can explain deep night-time mixing layers detected from orbit and precipitation signatures detected below water-ice clouds by the Phoenix lander. In our simulations, convective snowstorms occur only during the Martian night, and result from atmospheric instability due to radiative cooling of water-ice cloud particles. This triggers strong convective plumes within and below clouds, with fast snow precipitation resulting from the vigorous descending currents. Night-time convection in Martian water-ice clouds and the associated snow precipitation lead to transport of water both above and below the mixing layers, and thus would affect Mars' water cycle past and present, especially under the high-obliquity conditions associated with a more intense water cycle.

Environmental Controls on Snow Cover Thickness and Water Equivalent in Two Sub-Arctic Mountain Catchments

OpenAIRE

Cosgrove, Christopher

2015-01-01

The spatial variability of snow cover characteristics (depth, density, and snow water equivalent [SWE]) has paramount importance for the management of water resources in mountain environments. Passive microwave (PM) inference of SWE from space-borne instrumentation is increasingly used but the reliability of this technique remains limited in mountainous areas. Complex topography and the transition between forest and alpine tundra vegetation zones create large spatial heterogeneities in the sn...

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.

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development

Science.gov (United States)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Munoz Barreto, J.; Yu, Y.

2017-12-01

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development The Field Snow Research Station (also referred to as Snow Analysis and Field Experiment, SAFE) is operated by the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport (46°52'59'' N, 68°01'07'' W) and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (provided by the Terra and Aqua Earth Observing System satellites) were validated using in situ LST (T-skin) and near-surface air temperature (T-air) observations recorded at CREST-SAFE for the winters of 2013 and 2014. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that, although all the data sets showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C). Additionally, we created a liquid water content (LWC)-profiling instrument using time-domain reflectometry (TDR) at CREST-SAFE and tested it during the snow melt period (February-April) immediately after installation in 2014. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Lastly, to improve on global snow cover mapping, a snow product capable of estimating snow depth and snow water

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

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

Science.gov (United States)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

The Global Network of Isotopes in Rivers (GNIR): Integration of Stable Water Isotopes in Riverine Research and Management

International Nuclear Information System (INIS)

Halder, J.; Terzer, S.; Wassenaar, L.; Araguas, L.; Aggarwal, P.

2015-01-01

Rivers play a crucial role in the global water cycle as watershed-integrating hydrological conduits for returning terrestrial precipitation, runoff, surface and groundwater, as well as melting snow and ice back to the world’s oceans. The IAEA Global Network of Isotopes in Rivers (GNIR) is the coherent extension of the IAEA Global Network for Isotopes in Precipitation (GNIP) and aims to fill the informational data gaps between rainfall and river discharge. Whereas the GNIP has been surveying the stable hydrogen and oxygen isotopes, and tritium composition in precipitation, the objective of GNIR is to accumulate and disseminate riverine isotope data. We introduce the new global database of riverine water isotopes and evaluate its current long-term data holdings with the objective to improve the application of water isotopes and to inform water managers and researchers. An evaluation of current GNIR database holdings confirmed that seasonal variations of the stable water isotope composition in rivers are closely coupled to precipitation and snow-melt water run-off on a global scale. Rivers could be clustered on the basis of seasonal variations in their isotope composition and latitude. Results showed furthermore, that there were periodic phases within each of these groupings and additional modelling exercises allowed a priori prediction of the seasonal variability as well as the isotopic composition of stable water isotopes in rivers. This predictive capacity will help to improve existing and new sampling strategies, help to validate and interpret riverine isotope data, and identify important catchment processes. Hence, the IAEA promulgates and supports longterm hydrological isotope observation networks and the application of isotope studies complementary with conventional hydrological, water quality, and ecological studies. (author)

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.

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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

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.

Commodifying snow, taming the waters. Socio-ecological niche construction in an Alpine village.

Science.gov (United States)

Gross, Robert; Winiwarter, Verena

White belts of snow clad mountains all over the world each winter. Even if there is no snow, the tourism industry is able to produce the white finery at the push of the button, thereby consuming large amounts of water. Studying Damüls, a well-known ski resort in Austria's westernmost province Vorarlberg, we can show that the development of a service sector within agro-pastoral landscapes was connected with novel water uses and massive interventions into Alpine landscapes. Human niche construction theory offers a unique avenue for studying the development of Alpine communities, but also highlights side effects accompanying the change from agrarian to tourism livelihoods. One aim of this paper is to broaden the scope of human niche construction theory. Inceptive, counteractive and relocational niche construction activities were coupled to the differentiation of actor groups. To incorporate social dynamics, indispensable for studies in environmental history, we propose the concept of socio-ecological niche construction. The paper investigates how villagers balanced resource limitations typical for an agrarian society with the differentiation of sub-niches, mediating selective forces on the population. When the valleys were industrialized, Damüls was almost given up as a permanent settlement. Then, tourists entered the stage, by and by turning the wheel of local development into a different direction. A tourism niche based on natural snow evolved from the 1930s onwards. While the socio-ecological niches of agriculture and tourism coexisted in the interwar years, this changed when ski lifts were built, embedded into a debt-based economy that made the tourism niche vulnerable to snow availability. Snow-dependency became a powerful selective force. It was mediated by the ski lift companies through a range of niche construction activities that turned water into an important resource of snowmaking systems.

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.

Use of AMSR-E microwave satellite data for land surface characteristics and snow cover variation

Directory of Open Access Journals (Sweden)

Mukesh Singh Boori

2016-12-01

Full Text Available This data article contains data related to the research article entitled “Global land cover classification based on microwave polarization and gradient ratio (MPGR” [1] and “Microwave polarization and gradient ratio (MPGR for global land surface phenology” [2]. This data article presents land surface characteristics and snow cover variation information from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E. This data article use the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, snow water equivalent (SWE, digital elevation model (DEM and Brightness Temperature (BT information from AMSR-E satellite data to provide land surface characteristics and snow cover variation data in all-weather condition at any time. This data information is useful to discriminate different land surface cover types and snow cover variation, which is turn, will help to improve monitoring of weather, climate and natural disasters.

Anthropogenic osmium in rain and snow reveals global-scale atmospheric contamination.

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Chen, Cynthia; Sedwick, Peter N; Sharma, Mukul

2009-05-12

Osmium is one of the rarer elements in seawater, with typical concentration of approximately 10 x 10(-15) g g(-1) (5.3 x 10(-14) mol kg(-1)). The osmium isotope composition ((187)Os/(188)Os ratio) of deep oceans is 1.05, reflecting a balance between inputs from continental crust (approximately 1.3) and mantle/cosmic dust (approximately 0.13). Here, we show that the (187)Os/(188)Os ratios measured in rain and snow collected around the world range from 0.16 to 0.48, much lower than expected (>1), but similar to the isotope composition of ores (approximately 0.2) that are processed to extract platinum and other metals to be used primarily in automobile catalytic converters. Present-day surface seawater has a lower (187)Os/(188)Os ratio (approximately 0.95) than deep waters, suggesting that human activities have altered the isotope composition of the world's oceans and impacted the global geochemical cycle of osmium. The contamination of the surface ocean is particularly remarkable given that osmium has few industrial uses. The pollution may increase with growing demand for platinum-based catalysts.

Global warming in the context of 2000 years of Australian alpine temperature and snow cover.

Science.gov (United States)

McGowan, Hamish; Callow, John Nikolaus; Soderholm, Joshua; McGrath, Gavan; Campbell, Micheline; Zhao, Jian-Xin

2018-03-13

Annual resolution reconstructions of alpine temperatures are rare, particularly for the Southern Hemisphere, while no snow cover reconstructions exist. These records are essential to place in context the impact of anthropogenic global warming against historical major natural climate events such as the Roman Warm Period (RWP), Medieval Climate Anomaly (MCA) and Little Ice Age (LIA). Here we show for a marginal alpine region of Australia using a carbon isotope speleothem reconstruction, warming over the past five decades has experienced equivalent magnitude of temperature change and snow cover decline to the RWP and MCA. The current rate of warming is unmatched for the past 2000 years and seasonal snow cover is at a minimum. On scales of several decades, mean maximum temperatures have undergone considerable change ≈ ± 0.8 °C highlighting local scale susceptibility to rapid temperature change, evidence of which is often masked in regional to hemisphere scale temperature reconstructions.

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.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

Science.gov (United States)

Bennett, Katrina E.; Urrego Blanco, Jorge R.; Jonko, Alexandra; Bohn, Theodore J.; Atchley, Adam L.; Urban, Nathan M.; Middleton, Richard S.

2018-01-01

The Colorado River Basin is a fundamentally important river for society, ecology, and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent, and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space-filling Latin Hypercube Sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach. We find that snow-dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

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

Evaluation of an assimilation scheme to estimate snow water equivalent in the High Atlas of Morocco.

Science.gov (United States)

Baba, W. M.; Baldo, E.; Gascoin, S.; Margulis, S. A.; Cortés, G.; Hanich, L.

2017-12-01

The snow melt from the Atlas mountains represents a crucial water resource for crop irrigation in Morocco. Due to the paucity of in situ measurements, and the high spatial variability of the snow cover in this semi-arid region, assimilation of snow cover area (SCA) from high resolution optical remote sensing into a snowpack energy-balance model is considered as a promising method to estimate the snow water equivalent (SWE) and snow melt at catchment scales. Here we present a preliminary evaluation of an uncalibrated particle batch smoother data assimilation scheme (Margulis et al., 2015, J. Hydrometeor., 16, 1752-1772) in the High-Atlas Rheraya pilot catchment (225 km2) over a snow season. This approach does not require in situ data since it is based on MERRA-2 reanalyses data and satellite fractional snow cover area data. We compared the output of this prior/posterior ensemble data assimilation system to output from the distributed snowpack evolution model SnowModel (Liston and Elder, 2006, J. Hydrometeor. 7, 1259-1276). SnowModel was forced with in situ meteorological data from five automatic weather stations (AWS) and some key parameters (precipitation correction factor and rain-snow phase transition parameters) were calibrated using a time series of 8-m resolution SCA maps from Formosat-2. The SnowModel simulation was validated using a continuous snow height record at one high elevation AWS. The results indicate that the open loop simulation was reasonably accurate (compared to SnowModel results) in spite of the coarse resolution of the MERRA-2 forcing. The assimilation of Formosat-2 SCA further improved the simulation in terms of the peak SWE and SWE evolution over the melt season. During the accumulation season, the differences between the modeled and estimated (posterior) SWE were more substantial. The differences appear to be due to some observed precipitation events not being captured in MERRA-2. Further investigation will determine whether additional

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.

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

Insects, Fires, and Climate Change: Implications for Snow Cover, Water Resources and Ecosystem Recovery in Western North America

Science.gov (United States)

Brooks, P. D.; Harpold, A. A.; Biederman, J. A.; Litvak, M. E.; Broxton, P. D.; Gochis, D.; Molotch, N. P.; Troch, P. A.; Ewers, B. E.

2012-12-01

Unprecedented levels of insect induced tree mortality and massive wildfires both have spread through the forests of Western North America over the last decade. Warming temperatures and increased drought stress have been implicated as major factors in the increasing spatial extent and frequency of these forest disturbances, but it is unclear how simultaneous changes in forest structure and climate will interact to affect either downstream water resources or the regeneration and recovery of forested ecosystems. Because both streamflow and ecosystem productivity depend on seasonal snowmelt, a critical knowledge gap exists in how these disturbances will interact with a changing climate to control to the amount, timing, and the partitioning of seasonal snow cover This presentation will address this knowledge gap by synthesizing recent work on snowpack dynamics and ecosystem productivity from seasonally snow-covered forests along a gradient of snow depth and duration from Arizona to Montana. These include undisturbed sites, recently burned forests, and areas of extensive insect-induced forest mortality. Both before-after and control-impacted studies of forest disturbance on snow accumulation and ablation suggest that the spatial scale of snow distribution increases following disturbance, but net snow water input likely will not increase under a warming climate. While forest disturbance changes spatial scale of snowpack partitioning, the amount and especially the timing of snow cover accumulation and ablation are strongly related to interannual variability in ecosystem productivity with both earlier snowmelt and later snow accumulation associated with decreased carbon uptake. These observations suggest that the ecosystem services of water provision and carbon storage may be very different in the forests that regenerate after disturbance.

A New Estimate of North American Mountain Snow Accumulation From Regional Climate Model Simulations

Science.gov (United States)

Wrzesien, Melissa L.; Durand, Michael T.; Pavelsky, Tamlin M.; Kapnick, Sarah B.; Zhang, Yu; Guo, Junyi; Shum, C. K.

2018-02-01

Despite the importance of mountain snowpack to understanding the water and energy cycles in North America's montane regions, no reliable mountain snow climatology exists for the entire continent. We present a new estimate of mountain snow water equivalent (SWE) for North America from regional climate model simulations. Climatological peak SWE in North America mountains is 1,006 km3, 2.94 times larger than previous estimates from reanalyses. By combining this mountain SWE value with the best available global product in nonmountain areas, we estimate peak North America SWE of 1,684 km3, 55% greater than previous estimates. In our simulations, the date of maximum SWE varies widely by mountain range, from early March to mid-April. Though mountains comprise 24% of the continent's land area, we estimate that they contain 60% of North American SWE. This new estimate is a suitable benchmark for continental- and global-scale water and energy budget studies.

A coupled melt-freeze temperature index approach in a one-layer model to predict bulk volumetric liquid water content dynamics in snow

Science.gov (United States)

Avanzi, Francesco; Yamaguchi, Satoru; Hirashima, Hiroyuki; De Michele, Carlo

2016-04-01

Liquid water in snow rules runoff dynamics and wet snow avalanches release. Moreover, it affects snow viscosity and snow albedo. As a result, measuring and modeling liquid water dynamics in snow have important implications for many scientific applications. However, measurements are usually challenging, while modeling is difficult due to an overlap of mechanical, thermal and hydraulic processes. Here, we evaluate the use of a simple one-layer one-dimensional model to predict hourly time-series of bulk volumetric liquid water content in seasonal snow. The model considers both a simple temperature-index approach (melt only) and a coupled melt-freeze temperature-index approach that is able to reconstruct melt-freeze dynamics. Performance of this approach is evaluated at three sites in Japan. These sites (Nagaoka, Shinjo and Sapporo) present multi-year time-series of snow and meteorological data, vertical profiles of snow physical properties and snow melt lysimeters data. These data-sets are an interesting opportunity to test this application in different climatic conditions, as sites span a wide latitudinal range and are subjected to different snow conditions during the season. When melt-freeze dynamics are included in the model, results show that median absolute differences between observations and predictions of bulk volumetric liquid water content are consistently lower than 1 vol%. Moreover, the model is able to predict an observed dry condition of the snowpack in 80% of observed cases at a non-calibration site, where parameters from calibration sites are transferred. Overall, the analysis show that a coupled melt-freeze temperature-index approach may be a valid solution to predict average wetness conditions of a snow cover at local scale.

Water, ice and mud: Lahars and lahar hazards at ice- and snow-clad volcanoes

Science.gov (United States)

Waythomas, Christopher F.

2014-01-01

Large-volume lahars are significant hazards at ice and snow covered volcanoes. Hot eruptive products produced during explosive eruptions can generate a substantial volume of melt water that quickly evolves into highly mobile flows of ice, sediment and water. At present it is difficult to predict the size of lahars that can form at ice and snow covered volcanoes due to their complex flow character and behaviour. However, advances in experiments and numerical approaches are producing new conceptual models and new methods for hazard assessment. Eruption triggered lahars that are ice-dominated leave behind thin, almost unrecognizable sedimentary deposits, making them likely to be under-represented in the geological record.

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

Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall–Runoff model

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P. Ala-aho

2017-10-01

Full Text Available Tracer-aided hydrological models are increasingly used to reveal fundamentals of runoff generation processes and water travel times in catchments. Modelling studies integrating stable water isotopes as tracers are mostly based in temperate and warm climates, leaving catchments with strong snow influences underrepresented in the literature. Such catchments are challenging, as the isotopic tracer signals in water entering the catchments as snowmelt are typically distorted from incoming precipitation due to fractionation processes in seasonal snowpack. We used the Spatially distributed Tracer-Aided Rainfall–Runoff (STARR model to simulate fluxes, storage, and mixing of water and tracers, as well as estimating water ages in three long-term experimental catchments with varying degrees of snow influence and contrasting landscape characteristics. In the context of northern catchments the sites have exceptionally long and rich data sets of hydrometric data and – most importantly – stable water isotopes for both rain and snow conditions. To adapt the STARR model for sites with strong snow influence, we used a novel parsimonious calculation scheme that takes into account the isotopic fractionation through snow sublimation and snowmelt. The modified STARR setup simulated the streamflows, isotope ratios, and snow pack dynamics quite well in all three catchments. From this, our simulations indicated contrasting median water ages and water age distributions between catchments brought about mainly by differences in topography and soil characteristics. However, the variable degree of snow influence in catchments also had a major influence on the stream hydrograph, storage dynamics, and water age distributions, which was captured by the model. Our study suggested that snow sublimation fractionation processes can be important to include in tracer-aided modelling for catchments with seasonal snowpack, while the influence of fractionation during snowmelt

Laser-filamentation-induced water condensation and snow formation in a cloud chamber filled with different ambient gases.

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Liu, Yonghong; Sun, Haiyi; Liu, Jiansheng; Liang, Hong; Ju, Jingjing; Wang, Tiejun; Tian, Ye; Wang, Cheng; Liu, Yi; Chin, See Leang; Li, Ruxin

2016-04-04

We investigated femtosecond laser-filamentation-induced airflow, water condensation and snow formation in a cloud chamber filled respectively with air, argon and helium. The mass of snow induced by laser filaments was found being the maximum when the chamber was filled with argon, followed by air and being the minimum with helium. We also discussed the mechanisms of water condensation in different gases. The results show that filaments with higher laser absorption efficiency, which result in higher plasma density, are beneficial for triggering intense airflow and thus more water condensation and precipitation.

Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

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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.

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

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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...

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

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Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In

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

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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.

The assessment of EUMETSAT HSAF Snow Products for mountainuos areas in the eastern part of Turkey

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Akyurek, Z.; Surer, S.; Beser, O.; Bolat, K.; Erturk, A. G.

2012-04-01

Monitoring the snow parameters (e.g. snow cover area, snow water equivalent) is a challenging work. Because of its natural physical properties, snow highly affects the evolution of weather from daily basis to climate on a longer time scale. The derivation of snow products over mountainous regions has been considered very challenging. This can be done by periodic and precise mapping of the snow cover. However inaccessibility and scarcity of the ground observations limit the snow cover mapping in the mountainous areas. Today, it is carried out operationally by means of optical satellite imagery and microwave radiometry. In retrieving the snow cover area from satellite images bring the problem of topographical variations within the footprint of satellite sensors and spatial and temporal variation of snow characteristics in the mountainous areas. Most of the global and regional operational snow products use generic algorithms for flat and mountainous areas. However the non-uniformity of the snow characteristics can only be modeled with different algorithms for mountain and flat areas. In this study the early findings of Satellite Application Facilities on Hydrology (H-SAF) project, which is financially supported by EUMETSAT, will be presented. Turkey is a part of the H-SAF project, both in product generation (eg. snow recognition, fractional snow cover and snow water equivalent) for mountainous regions for whole Europe, cal/val of satellite-derived snow products with ground observations and cal/val studies with hydrological modeling in the mountainous terrain of Europe. All the snow products are operational on a daily basis. For the snow recognition product (H10) for mountainous areas, spectral thresholding methods were applied on sub pixel scale of MSG-SEVIRI images. The different spectral characteristics of cloud, snow and land determined the structure of the algorithm and these characteristics were obtained from subjective classification of known snow cover features

Snow and ice: Chapter 3

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Littell, Jeremy; McAfee, Stephanie A.; O'Neel, Shad; Sass, Louis; Burgess, Evan; Colt, Steve; Clark, Paul; Hayward, Gregory D.; Colt, Steve; McTeague, Monica L.; Hollingsworth, Teresa N.

2017-01-01

Temperature and precipitation are key determinants of snowpack levels. Therefore, climate change is likely to affect the role of snow and ice in the landscapes and hydrology of the Chugach National Forest region.Downscaled climate projections developed by Scenarios Network for Alaska and Arctic Planning (SNAP) are useful for examining projected changes in snow at relatively fine resolution using a variable called “snowday fraction (SDF),” the percentage of days with precipitation falling as snow.We summarized SNAP monthly SDF from five different global climate models for the Chugach region by 500 m elevation bands, and compared historical (1971–2000) and future (2030–2059) SDF. We found that:Snow-day fraction and snow-water equivalent (SWE) are projected to decline most in late autumn (October to November) and at lower elevations.Snow-day fraction is projected to decrease 23 percent (averaged across five climate models) from October to March, between sea level and 500 m. Between sea level and 1000 m, SDF is projected to decrease by 17 percent between October and March.Snow-water equivalent is projected to decrease most in autumn (October and November) and at lower elevations (below 1500 m), an average of -26 percent for the 2030–2059 period compared to 1971– 2000. Averaged across the cool season and the entire domain, SWE is projected to decrease at elevations below 1000 m because of increased temperature, but increase at higher elevations because of increased precipitation.Compared to 1971–2000, the percentage of the landscape that is snowdominant in 2030–2059 is projected to decrease, and the percentage in which rain and snow are co-dominant (transient hydrology) is projected to increase from 27 to 37 percent. Most of this change is at lower elevations.Glaciers on the Chugach National Forest are currently losing about 6 km3 of ice per year; half of this loss comes from Columbia Glacier (Berthier et al. 2010).Over the past decade, almost all

Snow and Water Imaging Spectrometer (SWIS): first alignment and characterization results

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Bender, Holly A.; Mouroulis, Pantazis; Haag, Justin; Smith, Christopher D.; Van Gorp, Byron E.

2017-09-01

The Snow and Water Imaging Spectrometer (SWIS) is a fast, high-uniformity, low-polarization sensitivity imaging spectrometer and telescope system designed for integration on a 6U CubeSat platform. Operating in the 350-1700 nm spectral region with 5.7 nm sampling, SWIS is capable of simultaneously addressing the demanding needs of coastal ocean science and snow and ice monitoring. New key technologies that facilitate the development of this instrument include a linear variable anti-reflection (LVAR) detector coating for stray light management, and a single drive on-board calibration mechanism utilizing a transmissive diffuser for solar calibration. We provide an overview of the SWIS instrument design and potential science applications and describe the instrument assembly and alignment, supported by laboratory measurements.

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

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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.

A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

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Schellekens, Jaap; Dutra, Emanuel; Martínez-de la Torre, Alberto; Balsamo, Gianpaolo; van Dijk, Albert; Sperna Weiland, Frederiek; Minvielle, Marie; Calvet, Jean-Christophe; Decharme, Bertrand; Eisner, Stephanie; Fink, Gabriel; Flörke, Martina; Peßenteiner, Stefanie; van Beek, Rens; Polcher, Jan; Beck, Hylke; Orth, René; Calton, Ben; Burke, Sophia; Dorigo, Wouter; Weedon, Graham P.

2017-07-01

The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979-2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr-1 (334 kg m-2 yr-1), while the ensemble mean of total evaporation was 537 kg m-2 yr-1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

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J. Schellekens

2017-07-01

Full Text Available The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution. The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i snow-dominated regions and (ii tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1, while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu, and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

Fungal spores as potential ice nuclei in fog/cloud water and snow

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Bauer, Heidi; Goncalves, Fabio L. T.; Schueller, Elisabeth; Puxbaum, Hans

2010-05-01

INTRODUCTION: In discussions about climate change and precipitation frequency biological ice nucleation has become an issue. While bacterial ice nucleation (IN) is already well characterized and even utilized in industrial processes such as the production of artificial snow or to improve freezing processes in food industry, less is known about the IN potential of fungal spores which are also ubiquitous in the atmosphere. A recent study performed at a mountain top in the Rocky Mountains suggests that fungal spores and/or pollen might play a role in increased IN abundance during periods of cloud cover (Bowers et al. 2009). In the present work concentrations of fungal spores in fog/cloud water and snow were determined. EXPERIMENTAL: Fog samples were taken with an active fog sampler in 2008 in a traffic dominated area and in a national park in São Paulo, Brazil. The number concentrations of fungal spores were determined by microscopic by direct enumeration by epifluorescence microscopy after staining with SYBR Gold nucleic acid gel stain (Bauer et al. 2008). RESULTS: In the fog water collected in the polluted area at a junction of two highly frequented highways around 22,000 fungal spores mL-1 were counted. Fog in the national park contained 35,000 spores mL-1. These results were compared with cloud water and snow samples from Mt. Rax, situated at the eastern rim of the Austrian Alps. Clouds contained on average 5,900 fungal spores mL-1 cloud water (1,300 - 11,000) or 2,200 spores m-3 (304 - 5,000). In freshly fallen snow spore concentrations were lower than in cloud water, around 1,000 fungal spores mL-1 were counted (Bauer et al. 2002). In both sets of samples representatives of the ice nucleating genus Fusarium could be observed. REFERENCES: Bauer, H., Kasper-Giebl, A., Löflund, M., Giebl, H., Hitzenberger, R., Zibuschka, F., Puxbaum, H. (2002). The contribution of bacteria and fungal spores to the organic carbon content of cloud water, precipitation and aerosols

Retrospective Snow Analysis Across the Continental United States for the National Water Model

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Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2016-12-01

For large portions of the United States, snow plays a vital role in hydrologic prediction. This is particularly true in the mountain west where snowmelt contributes up to 80% of total streamflow runoff. The Office of Water Prediction (OWP) will begin running the National Water Model (NWM) during the second half of 2016, which is a continental-scale implementation of the WRF-Hydro community hydrologic modeling framework. Assessing and benchmarking the performance of the snow component of the NWM is important for future research-to-operations activities and for forecasters to better understand NWM output. For this study, WRF-Hydro was ran using the same domain and physics options as the NWM (1 km LSM, 250m overland routing, and NHDPlus Version 2.1 channel network). The land surface component chosen is Noah-MP land surface model. Forcing from the National Land Data Assimilation System (NLDAS-2) was downscaled from the native 0.125 degree resolution to the 1 km modeling domain to drive the model. The model was ran over a 5-year retrospective period to gauge multi-year performance of the snow states. Output was analyzed against both in-situ observations, such as SNOTEL, and the Snow Data Assimilation System (SNODAS). In addition, gridded snow states and SNODAS grids were aggregated to Omernik-derived ecological regions. This was done in order to break up snow analysis by regions that share similar ecological and physiographic characteristics. Results show WRF-Hydro is able to capture peak timing across most of the mountain west fairly well. In terms of magnitudes, the model struggles across portions of the west with a low bias. This is especially true in the Cascades, which could be traced back to precipitation partitioning issues in the model. Across the central Rockies, the model exhibits a lower dry bias showing improved performance there. Previous literature suggests a dry bias in the precipitation out west may be contributing to model performance. East of the

INTRODUCTION: Anticipated changes in the global atmospheric water cycle

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Allan, Richard P.; Liepert, Beate G.

2010-06-01

The atmospheric branch of the water cycle, although containing just a tiny fraction of the Earth's total water reserves, presents a crucial interface between the physical climate (such as large-scale rainfall patterns) and the ecosystems upon which human societies ultimately depend. Because of the central importance of water in the Earth system, the question of how the water cycle is changing, and how it may alter in future as a result of anthropogenic changes, present one of the greatest challenges of this century. The recent Intergovernmental Panel on Climate Change report on Climate Change and Water (Bates et al 2008) highlighted the increasingly strong evidence of change in the global water cycle and associated environmental consequences. It is of critical importance to climate prediction and adaptation strategies that key processes in the atmospheric water cycle are precisely understood and determined, from evaporation at the surface of the ocean, transport by the atmosphere, condensation as cloud and eventual precipitation, and run-off through rivers following interaction with the land surface, sub-surface, ice, snow and vegetation. The purpose of this special focus issue of Environmental Research Letters on anticipated changes in the global atmospheric water cycle is to consolidate the recent substantial advances in understanding past, present and future changes in the global water cycle through evidence built upon theoretical understanding, backed up by observations and borne out by climate model simulations. Thermodynamic rises in water vapour provide a central constraint, as discussed in a guest editorial by Bengtsson (2010). Theoretical implications of the Clausius-Clapeyron equation are presented by O'Gorman and Muller (2010) and with reference to a simple model (Sherwood 2010) while observed humidity changes confirm these anticipated responses at the land and ocean surface (Willett et al 2008). Rises in low-level moisture are thought to fuel an

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

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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

Snow observations in Mount Lebanon (2011-2016)

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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

Snow hydrology in Mediterranean mountain regions: A review

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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

Collaborative Research: Snow Accumulation and Snow Melt in a Mixed Northern Hardwood-Conifer Forest, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — This data set contains snow depth, Snow Water Equivalent (SWE), and forest cover characteristics for sites at the Hubbard Brook Experimental Forest in northern New...

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

Numerical modeling of coupled water flow and heat transport in soil and snow

Science.gov (United States)

Thijs J. Kelleners; Jeremy Koonce; Rose Shillito; Jelle Dijkema; Markus Berli; Michael H. Young; John M. Frank; William Massman

2016-01-01

A one-dimensional vertical numerical model for coupled water flow and heat transport in soil and snow was modified to include all three phases of water: vapor, liquid, and ice. The top boundary condition in the model is driven by incoming precipitation and the surface energy balance. The model was applied to three different terrestrial systems: A warm desert bare...

Cloud-based Computing and Applications of New Snow Metrics for Societal Benefit

Science.gov (United States)

Nolin, A. W.; Sproles, E. A.; Crumley, R. L.; Wilson, A.; Mar, E.; van de Kerk, M.; Prugh, L.

2017-12-01

Seasonal and interannual variability in snow cover affects socio-environmental systems including water resources, forest ecology, freshwater and terrestrial habitat, and winter recreation. We have developed two new seasonal snow metrics: snow cover frequency (SCF) and snow disappearance date (SDD). These metrics are calculated at 500-m resolution using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data (MOD10A1). SCF is the number of times snow is observed in a pixel over the user-defined observation period. SDD is the last date of observed snow in a water year. These pixel-level metrics are calculated rapidly and globally in the Google Earth Engine cloud-based environment. SCF and SDD can be interactively visualized in a map-based interface, allowing users to explore spatial and temporal snowcover patterns from 2000-present. These metrics are especially valuable in regions where snow data are sparse or non-existent. We have used these metrics in several ongoing projects. When SCF was linked with a simple hydrologic model in the La Laguna watershed in northern Chile, it successfully predicted summer low flows with a Nash-Sutcliffe value of 0.86. SCF has also been used to help explain changes in Dall sheep populations in Alaska where sheep populations are negatively impacted by late snow cover and low snowline elevation during the spring lambing season. In forest management, SCF and SDD appear to be valuable predictors of post-wildfire vegetation growth. We see a positive relationship between winter SCF and subsequent summer greening for several years post-fire. For western US winter recreation, we are exploring trends in SDD and SCF for regions where snow sports are economically important. In a world with declining snowpacks and increasing uncertainty, these metrics extend across elevations and fill data gaps to provide valuable information for decision-making. SCF and SDD are being produced so that anyone with Internet access and a Google

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.

Processes that generate and deplete liquid water and snow in thin midlevel mixed-phase clouds

Science.gov (United States)

Smith, Adam J.; Larson, Vincent E.; Niu, Jianguo; Kankiewicz, J. Adam; Carey, Lawrence D.

2009-06-01

This paper uses a numerical model to investigate microphysical, radiative, and dynamical processes in mixed-phase altostratocumulus clouds. Three cloud cases are chosen for study, each of which was observed by aircraft during the fifth or ninth Complex Layered Cloud Experiment (CLEX). These three clouds are numerically modeled using large-eddy simulation (LES). The observed and modeled clouds consist of a mixed-phase layer with a quasi-adiabatic profile of liquid, and a virga layer below that consists of snow. A budget of cloud (liquid) water mixing ratio is constructed from the simulations. It shows that large-scale ascent/descent, radiative cooling/heating, turbulent transport, and microphysical processes are all significant. Liquid is depleted indirectly via depositional growth of snow (the Bergeron-Findeisen process). This process is more influential than depletion of liquid via accretional growth of snow. Also constructed is a budget of snow mixing ratio, which turns out to be somewhat simpler. It shows that snow grows by deposition in and below the liquid (mixed-phase) layer, and sublimates in the remainder of the virga region below. The deposition and sublimation are balanced primarily by sedimentation, which transports the snow from the growth region to the sublimation region below. In our three clouds, the vertical extent of the virga layer is influenced more by the profile of saturation ratio below the liquid (mixed-phase) layer than by the mixing ratio of snow at the top of the virga layer.

Anoxia in the snow

Science.gov (United States)

Bristow, Laura A.

2018-04-01

Substantial amounts of denitrification and other anaerobic metabolisms can occur in anoxic microenvironments within marine snow particles, according to model simulations. This microbial activity may have a global impact on nitrogen cycling.

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.

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.

New nitrogen uptake strategy: specialized snow roots.

Science.gov (United States)

Onipchenko, Vladimir G; Makarov, Mikhail I; van Logtestijn, Richard S P; Ivanov, Viktor B; Akhmetzhanova, Assem A; Tekeev, Dzhamal K; Ermak, Anton A; Salpagarova, Fatima S; Kozhevnikova, Anna D; Cornelissen, Johannes H C

2009-08-01

The evolution of plants has yielded a wealth of adaptations for the acquisition of key mineral nutrients. These include the structure, physiology and positioning of root systems. We report the discovery of specialized snow roots as a plant strategy to cope with the very short season for nutrient uptake and growth in alpine snow-beds, i.e. patches in the landscape that remain snow-covered well into the summer. We provide anatomical, chemical and experimental (15)N isotope tracking evidence that the Caucasian snow-bed plant Corydalis conorhiza forms extensive networks of specialized above-ground roots, which grow against gravity to acquire nitrogen directly from within snow packs. Snow roots capture nitrogen that would otherwise partly run off down-slope over a frozen surface, thereby helping to nourish these alpine ecosystems. Climate warming is changing and will change mountain snow regimes, while large-scale anthropogenic N deposition has increased snow N contents. These global changes are likely to impact on the distribution, abundance and functional significance of snow roots.

Light-absorbing Particles in Snow and Ice: Measurement and Modeling of Climatic and Hydrological Impact

Energy Technology Data Exchange (ETDEWEB)

Qian, Yun; Yasunari, Teppei J.; Doherty, Sarah J.; Flanner, M. G.; Lau, William K.; Ming, J.; Wang, Hailong; Wang, Mo; Warren, Stephen G.; Zhang, Rudong

2015-01-01

Light absorbing particles (LAP, e.g., black carbon, brown carbon, and dust) influence water and energy budgets of the atmosphere and snowpack in multiple ways. In addition to their effects associated with atmospheric heating by absorption of solar radiation and interactions with clouds, LAP in snow on land and ice can reduce the surface reflectance (a.k.a., surface darkening), which is likely to accelerate the snow aging process and further reduces snow albedo and increases the speed of snowpack melt. LAP in snow and ice (LAPSI) has been identified as one of major forcings affecting climate change, e.g. in the fourth and fifth assessment reports of IPCC. However, the uncertainty level in quantifying this effect remains very high. In this review paper, we document various technical methods of measuring LAPSI and review the progress made in measuring the LAPSI in Arctic, Tibetan Plateau and other mid-latitude regions. We also report the progress in modeling the mass concentrations, albedo reduction, radiative forcing, andclimatic and hydrological impact of LAPSI at global and regional scales. Finally we identify some research needs for reducing the uncertainties in the impact of LAPSI on global and regional climate and the hydrological cycle.

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.

Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions

Directory of Open Access Journals (Sweden)

Fassnacht, S. R.

2012-05-01

Full Text Available The relation between snow water equivalent (SWE and 28 variables (27 topographically-based topographic variables and canopy density for the Colorado River Basin, USA was explored through a multi-variate regression. These variables include location, slope and aspect at different scales, derived variables to indicate the distance to sources of moisture and proximity to and characteristics of obstacles between these moisture sources and areas of snow accumulation, and canopy density. A weekly time step of snow telemetry (SNOTEL SWE data from 1990 through 1999 was used. The most important variables were elevation and regional scale (81 km² slope. Since the seasonal and inter-annual variability is high, a regression relationship should be formulated for each time step. The inter-annual variation in the relation between SWE and topographic variables partially corresponded with the amount of snow accumulated over the season and the El Niño Southern Oscillation cycle.Se analiza la relación entre el equivalente de agua en la nieve (SWE y 28 variables (27 variables topográficas y otra basada en la densidad del dosel para la Cuenca del Río Colorado, EE.UU. mediante regresión multivariante. Estas variables incluyen la localización, pendiente y orientación a diferentes escalas, además de variables derivadas para indicar la distancia a las fuentes de humedad y la proximidad a las barreras topográficas, además de las características de las barreras topográficas entre las fuentes de humedad, las áreas de acumulación de nieve y la densidad del dosel. Se utilizaron telemetrías semanales de nieve (SNOTEL desde 1990 hasta 1999. Las variables más importantes fueron la elevación y la pendiente a escala regional (81 km². Dada la alta variabilidad estacional e interanual, fue necesario establecer regresiones específicas para cada intervalo disponible de datos. La variación interanual en la relación entre variables topográficas y el SWE se

Evaluation of NVE's snow station network; Subreport in R et D project 302H15 Good snow data; Evaluering av NVE sitt snoestasjonsnettverk

Energy Technology Data Exchange (ETDEWEB)

Ree, Bjoerg Lirhus; Landroe, Hilde; Trondsen, Elise; Moeen, Knut M.

2011-03-15

NVE has measured snow water equivalent of snow pillow in forty years. Our snow station network has risen since 1997 from 6 to 25 stations. It was therefore absolutely necessary to do a review and quality assurance of NVE's snow data. This report discusses the snow data measured continuously - snow water equivalent and snow depth. Each station and the parameters it measures are described and evaluated. It is concluded in relation to whether stations should be continued or not. Stations technical solutions are well described, both of NVE's standard stations and the two test stations, Filefjell and Svarttjoernbekken. It has been o importance to bring out what problems the instruments have or may have and provide suggestions for solutions to them. Problems related to measure the water equivalent under Norwegian conditions, with the challenges and winter rain and re-freezing provides, is also reviewed. Alternatives to water equivalent measurements with a snow pillow, which is the traditional way in this country, are presented. Some of the alternative methods NVE tests out, for the others only description and our opinion is given. (Author)

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Directory of Open Access Journals (Sweden)

Juha Lemmetyinen

2018-01-01

Full Text Available Current methods for retrieving SWE (snow water equivalent from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm. The use of SAR (Synthetic Aperture Radar at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar and passive (radiometer microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer, which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer. Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar

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

Physically-based global downscaling climate change projections for a full century

International Nuclear Information System (INIS)

Ghan, S J; Shippert, T

2005-01-01

A global atmosphere/land model with an embedded subgrid orography scheme is used to simulate the period 1977-2100 using ocean surface conditions and radiative constituent concentrations for a climate change scenario. Climate variables simulated for multiple elevation classes are mapping according to a high-resolution elevation dataset in ten regions with complex terrain. Analysis of changes in the simulated climate leads to the following conclusions. Changes in precipitation vary widely, with precipitation increasing more with increasing altitude in some region, decreasing more with altitude in others, and changing little in still others. In some regions the sign of the precipitation change depends on surface elevation. Changes in surface air temperature are rather uniform, with at most a two-fold difference between the largest and smallest changes within a region; in most cases the warming increases with altitude. Changes in snow water are highly dependent on altitude. Absolute changes usually increase with altitude, while relative changes decrease. In places where snow accumulates, an artificial upper bound on snow water limits the sensitivity of snow water to climate change considerably. The simulated impact of climate change on regional mean snow water varies widely, with little impact in regions in which the upper bound on snow water is the dominant snow water sink, moderate impact in regions with a mixture of seasonal and permanent snow, and profound impacts on regions with little permanent snow

Analyses of newly digitised and reconstructed snow series over the last 100+ years in Switzerland

Science.gov (United States)

Scherrer, S. C.; Wüthrich, C.; Croci-Maspoli, M.; Appenzeller, C.

2010-09-01

Snow is an important socio-economic factor in the Swiss Alpine region (tourism, hydro-electricity, drinking water) and responsible for considerable natural hazards such as avalanches. In addition, high-quality long-term snow series can be used as an excellent indicator of climate change. The objectives of this study are threefold. First, suitable long-term snow series from different altitudes and regions in Switzerland have been selected, missing data digitized and the entire series quality checked. Second, the long-term snow series have been used for trend analyses over a time period >100 years. Third, snow depth series have been reconstructed using daily new snow, temperature and precipitation as input variables. This made it possible to analyse snow depth related variables such as days with snow pack. Results show that the snow cover is varying substantially on seasonal and decadal time scales. The analyses of the decadal new snow trends during the last 100 years shows unprecedented low new snow sums in the winter seasons (DJF) of the 1990s. The 100 year trend of days with snow pack reveals a significant decrease for stations below 800 m asl in the winter season (DJF) and for stations around 1800 m asl in spring (MAM). Similar results were found for seasonal new snow sums. The results of the trend analyses are also discussed with respect to temperature and precipitation trends. Finally we will also shortly discuss how especially "precious" snow measurements have been identified and incorporated in a National Basic Climatological Network (NBCN) as well as in the Global Climate Observing System (GCOS).

Resilience to Changing Snow Depth in a Shrubland Ecosystem.

Science.gov (United States)

Loik, M. E.

2008-12-01

Snowfall is the dominant hydrologic input for high elevations and latitudes of the arid- and semi-arid western United States. Sierra Nevada snowpack provides numerous important services for California, but is vulnerable to anthropogenic forcing of the coupled ocean-atmosphere system. GCM and RCM scenarios envision reduced snowpack and earlier melt under a warmer climate, but how will these changes affect soil and plant water relations and ecosystem processes? And, how resilient will this ecosystem be to short- and long-term forcing of snow depth and melt timing? To address these questions, our experiments utilize large- scale, long-term roadside snow fences to manipulate snow depth and melt timing in eastern California, USA. Interannual snow depth averages 1344 mm with a CV of 48% (April 1, 1928-2008). Snow fences altered snow melt timing by up to 18 days in high-snowfall years, and affected short-term soil moisture pulses less in low- than medium- or high-snowfall years. Sublimation in this arid location accounted for about 2 mol m- 2 of water loss from the snowpack in 2005. Plant water potential increased after the ENSO winter of 2005 and stayed relatively constant for the following three years, even after the low snowfall of winter 2007. Over the long-term, changes in snow depth and melt timing have impacted cover or biomass of Achnatherum thurberianum, Elymus elemoides, and Purshia tridentata. Growth of adult conifers (Pinus jeffreyi and Pi. contorta) was not equally sensitive to snow depth. Thus, complex interactions between snow depth, soil water inputs, physiological processes, and population patterns help drive the resilience of this ecosystem to changes in snow depth and melt timing.

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

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.

Snow Water Equivalent SAR and Radiometer

Data.gov (United States)

National Aeronautics and Space Administration — After nearly four decades of international effort developing remote sensing techniques, measurement of land surface snow remains a significant challenge. Developing...

Merging datasets from NASA SnowEx to better understand how snow falls and moves.

Science.gov (United States)

Gongora, J. A.; Marshall, H. P.; Glenn, N. F.

2017-12-01

A modelers ability to estimated snow depth and snow water equivalent (SWE) for mountain snow depends strongly on their understanding of snowpack spatial distribution and the soundness of the models initial conditions. This work focuses on the application of data science and efficient algorithms to find optimal locations in mountainous terrain to act as initital conditions for machine driven interpolation methods. By using graphs, collections of pairwise relationships between objects, to describe our data we were able to efficiently search, partition, and understand snowpack from the persepetive of this data structure.

Pavement Snow Melting

Energy Technology Data Exchange (ETDEWEB)

Lund, John W.

2005-01-01

The design of pavement snow melting systems is presented based on criteria established by ASHRAE. The heating requirements depends on rate of snow fall, air temperature, relative humidity and wind velocity. Piping materials are either metal or plastic, however, due to corrosion problems, cross-linked polyethylene pipe is now generally used instead of iron. Geothermal energy is supplied to systems through the use of heat pipes, directly from circulating pipes, through a heat exchanger or by allowing water to flow directly over the pavement, by using solar thermal storage. Examples of systems in New Jersey, Wyoming, Virginia, Japan, Argentina, Switzerland and Oregon are presented. Key words: pavement snow melting, geothermal heating, heat pipes, solar storage, Wyoming, Virginia, Japan, Argentina, Klamath Falls.

Geophysical investigations of underplating at the Middle American Trench, weathering in the critical zone, and snow water equivalent in seasonal snow

Science.gov (United States)

St. Clair, James

indicate that to a first order, the permeability structure of the CZ can be predicted with knowledge of the regional tectonic stress field and local topography. In landscapes characterized by strongly compressive tectonic stresses or closely space ridges and valleys, deep zones of permeable bedrock are found beneath ridges, while the depth to impermeable bedrock beneath drainages is comparatively shallow. In landscapes characterized by weakly compressive tectonic stresses or widely spaced ridges and valleys, the depth to impermeable bedrock is approximately uniform throughout the landscape. In Chapter 3, a semi-automated method of estimating snow water equivalent (SWE) in seasonal snow packs from common offset Ground Penetrating Radar (GPR) data is presented. Many mountainous regions of the world depend on seasonal snow for fresh water resources. Water forecasting relies principally on historical records that relate SWE observations at a limited number of locations to stream discharge. As climate change contributes to a wider range of variability in seasonal snow fall, water forecasts are likely to become less reliable, thus there is a need to find new methods of estimating how much water is stored in seasonal snow. GPR has been shown to be an effective tool for measuring SWE if the radar velocity can be measured. In this chapter, a method that was originally developed to measure seismic velocities from zero-offset seismic reflection data is applied to common-offset GPR data collected over seasonal snow. The method involves suppressing continuous reflections in the image so that the velocity information contained in diffracted energy can be exploited. The filtered images are migrated through a suite of velocities and the velocity that best focus the diffracted energy is chosen on the basis of the varimax norm, which measures how peaked the energy distribution is. GPR derived SWE estimates agree with manual measurements within the uncertainty bounds of both methods. In

Rainwater propagation through snowpack during rain-on-snow sprinkling experiments under different snow conditions

Directory of Open Access Journals (Sweden)

R. Juras

2017-09-01

Full Text Available The mechanisms of rainwater propagation and runoff generation during rain-on-snow (ROS events are still insufficiently known. Understanding storage and transport of liquid water in natural snowpacks is crucial, especially for forecasting of natural hazards such as floods and wet snow avalanches. In this study, propagation of rainwater through snow was investigated by sprinkling experiments with deuterium-enriched water and applying an alternative hydrograph separation technique on samples collected from the snowpack runoff. This allowed us to quantify the contribution of rainwater, snowmelt and initial liquid water released from the snowpack. Four field experiments were carried out during winter 2015 in the vicinity of Davos, Switzerland. Blocks of natural snow were isolated from the surrounding snowpack to inhibit lateral exchange of water and were exposed to artificial rainfall using deuterium-enriched water. The experiments were composed of four 30 min periods of sprinkling, separated by three 30 min breaks. The snowpack runoff was continuously gauged and sampled periodically for the deuterium signature. At the onset of each experiment antecedent liquid water was first pushed out by the sprinkling water. Hydrographs showed four pronounced peaks corresponding to the four sprinkling bursts. The contribution of rainwater to snowpack runoff consistently increased over the course of the experiment but never exceeded 86 %. An experiment conducted on a non-ripe snowpack suggested the development of preferential flow paths that allowed rainwater to efficiently propagate through the snowpack limiting the time for mass exchange processes to take effect. In contrast, experiments conducted on ripe isothermal snowpack showed a slower response behaviour and resulted in a total runoff volume which consisted of less than 50 % of the rain input.

Measurement of the Spectral Absorption of Liquid Water in Melting Snow With an Imaging Spectrometer

Science.gov (United States)

Green, Robert O.; Dozier, Jeff

1995-01-01

Melting of the snowpack is a critical parameter that drives aspects of the hydrology in regions of the Earth where snow accumulates seasonally. New techniques for measurement of snow melt over regional scales offer the potential to improve monitoring and modeling of snow-driven hydrological processes. In this paper we present the results of measuring the spectral absorption of liquid water in a melting snowpack with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data were acquired over Mammoth Mountain, in east central California on 21 May 1994 at 18:35 UTC. The air temperature at 2926 m on Mammoth Mountain at site A was measured at 15-minute intervals during the day preceding the AVIRIS data acquisition. At this elevation. the air temperature did not drop below freezing the night of the May 20 and had risen to 6 degrees Celsius by the time of the overflight on May 21. These temperature conditions support the presence of melting snow at the surface as the AVIRIS data were acquired.

[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.

SnowCloud - a Framework to Predict Streamflow in Snowmelt-dominated Watersheds Using Cloud-based Computing

Science.gov (United States)

Sproles, E. A.; Crumley, R. L.; Nolin, A. W.; Mar, E.; Lopez-Moreno, J. J.

2017-12-01

Streamflow in snowy mountain regions is extraordinarily challenging to forecast, and prediction efforts are hampered by the lack of timely snow data—particularly in data sparse regions. SnowCloud is a prototype web-based framework that integrates remote sensing, cloud computing, interactive mapping tools, and a hydrologic model to offer a new paradigm for delivering key data to water resource managers. We tested the skill of SnowCloud to forecast monthly streamflow with one month lead time in three snow-dominated headwaters. These watersheds represent a range of precipitation/runoff schemes: the Río Elqui in northern Chile (200 mm/yr, entirely snowmelt); the John Day River, Oregon, USA (635 mm/yr, primarily snowmelt); and the Río Aragon in the northern Spain (850 mm/yr, snowmelt dominated). Model skill corresponded to snowpack contribution with Nash-Sutcliffe Efficiencies of 0.86, 0.52, and 0.21 respectively. SnowCloud does not require the user to possess advanced programming skills or proprietary software. We access NASA's MOD10A1 snow cover product to calculate the snow metrics globally using Google Earth Engine's geospatial analysis and cloud computing service. The analytics and forecast tools are provided through a web-based portal that requires only internet access and minimal training. To test the efficacy of SnowCloud we provided the tools and a series of tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience and provided feedback, and the results of our multi-cultural assessment are also presented. While our results focus on SnowCloud, they outline methods to develop cloud-based tools that function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in developing

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)

Use of In-Situ and Remotely Sensed Snow Observations for the National Water Model in Both an Analysis and Calibration Framework.

Science.gov (United States)

Karsten, L. R.; Gochis, D.; Dugger, A. L.; McCreight, J. L.; Barlage, M. J.; Fall, G. M.; Olheiser, C.

2017-12-01

Since version 1.0 of the National Water Model (NWM) has gone operational in Summer 2016, several upgrades to the model have occurred to improve hydrologic prediction for the continental United States. Version 1.1 of the NWM (Spring 2017) includes upgrades to parameter datasets impacting land surface hydrologic processes. These parameter datasets were upgraded using an automated calibration workflow that utilizes the Dynamic Data Search (DDS) algorithm to adjust parameter values using observed streamflow. As such, these upgrades to parameter values took advantage of various observations collected for snow analysis. In particular, in-situ SNOTEL observations in the Western US, volunteer in-situ observations across the entire US, gamma-derived snow water equivalent (SWE) observations courtesy of the NWS NOAA Corps program, gridded snow depth and SWE products from the Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO), gridded remotely sensed satellite-based snow products (MODIS,AMSR2,VIIRS,ATMS), and gridded SWE from the NWS Snow Data Assimilation System (SNODAS). This study explores the use of these observations to quantify NWM error and improvements from version 1.0 to version 1.1, along with subsequent work since then. In addition, this study explores the use of snow observations for use within the automated calibration workflow. Gridded parameter fields impacting the accumulation and ablation of snow states in the NWM were adjusted and calibrated using gridded remotely sensed snow states, SNODAS products, and in-situ snow observations. This calibration adjustment took place over various ecological regions in snow-dominated parts of the US for a retrospective period of time to capture a variety of climatological conditions. Specifically, the latest calibrated parameters impacting streamflow were held constant and only parameters impacting snow physics were tuned using snow observations and analysis. The adjusted parameter datasets were then used to

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Science.gov (United States)

Ehrlich, André; Bierwirth, Eike; Istomina, Larysa; Wendisch, Manfred

2017-09-01

The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff

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

Estimation of the condition of snow cover in Voronezh according to the chemical analysis of water from melted snow

OpenAIRE

Prozhorina Tatyana Ivanovna; Bespalova Elena Vladimirovna; Yakunina Nadezhda

2014-01-01

Snow cover possesses high sorption ability and represents informative object to identify technogenic pollution of an urban environment. In this article the investigation data of a chemical composition of snow fallen in Voronezh during the winter period of 2014 are given. Relationships between existence of pollutants in snow and the level of technogenic effect are analyzed.

Hydrological Implications of Covering Wind-Blown Snow Accumulations with Geotextiles on Mount Aragats, Armenia

Directory of Open Access Journals (Sweden)

Alexander Nestler

2014-07-01

Full Text Available Snow is an excellent water reservoir, naturally storing large quantities of water at time scales from a few days to several months. In summer-dry countries, like Armenia, runoff due to snow melt from mountain regions is highly important for a sustained water supply (irrigation, hydropower. Snow fields on Mount Aragats, Armenia’s highest peak, often persist until July, providing vital amounts of melt water. Artificially managing these wind-driven snow accumulations as a natural water reservoir might have considerable potential. In the context of the Swiss-Armenian joint venture, Freezwater, snow fields are covered with geotextiles in order to delay snow melt long enough to provide additional melt water in the dry season of the year. In this study, we analyze the hydrological effectiveness of the artificial management of the natural snow cover on Mount Aragats based on various field measurements acquired over a three-year period and numerical modeling. Over the winter season, partly more than five meter-thick snow deposits are formed supported by snow redistribution by strong wind. Repeated mappings of snow fields indicate that snow cover patterns remain highly consistent over time. Measurements of ablation below manually applied geotextiles show a considerable reduction of melt rates by more than 50%. Simulations with an energy-balance model and a distributed temperature-index model allow assessing the hydrological effect of artificial snow management for different initial snow depths and elevations and suggest that coverage is needed at a large scale in order to generate a significant impact on discharge.

Global water cycle

Science.gov (United States)

Robertson, Franklin; Goodman, Steven J.; Christy, John R.; Fitzjarrald, Daniel E.; Chou, Shi-Hung; Crosson, William; Wang, Shouping; Ramirez, Jorge

1993-01-01

This research is the MSFC component of a joint MSFC/Pennsylvania State University Eos Interdisciplinary Investigation on the global water cycle extension across the earth sciences. The primary long-term objective of this investigation is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates change on both global and regional scales. Significant accomplishments in the past year are presented and include the following: (1) water vapor variability; (2) multi-phase water analysis; (3) global modeling; and (4) optimal precipitation and stream flow analysis and hydrologic processes.

Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

OpenAIRE

A. N. Gelfan; V. M. Moreido

2014-01-01

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good ...

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.

High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

Science.gov (United States)

Henn, Brian; Painter, Thomas H.; Bormann, Kat J.; McGurk, Bruce; Flint, Alan L.; Flint, Lorraine E.; White, Vince; Lundquist, Jessica D.

2018-02-01

Hydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar-derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidar-derived snow data set over 3 years (2013-2015) during a prolonged drought in California, to estimate basin-scale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short-term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin-mean ET. Warm-season ET estimated from this approach is 168 (85-252 at 95% confidence), 162 (0-326) and 191 (48-334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full-year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part-year time period. Using streamflow and ASO snow observations, we quantify spatially-distributed hydrologic processes otherwise difficult to observe.

Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

Science.gov (United States)

Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

2017-06-01

Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

Iron snow in the Martian core?

Science.gov (United States)

Davies, Christopher J.; Pommier, Anne

2018-01-01

The decline of Mars' global magnetic field some 3.8-4.1 billion years ago is thought to reflect the demise of the dynamo that operated in its liquid core. The dynamo was probably powered by planetary cooling and so its termination is intimately tied to the thermochemical evolution and present-day physical state of the Martian core. Bottom-up growth of a solid inner core, the crystallization regime for Earth's core, has been found to produce a long-lived dynamo leading to the suggestion that the Martian core remains entirely liquid to this day. Motivated by the experimentally-determined increase in the Fe-S liquidus temperature with decreasing pressure at Martian core conditions, we investigate whether Mars' core could crystallize from the top down. We focus on the "iron snow" regime, where newly-formed solid consists of pure Fe and is therefore heavier than the liquid. We derive global energy and entropy equations that describe the long-timescale thermal and magnetic history of the core from a general theory for two-phase, two-component liquid mixtures, assuming that the snow zone is in phase equilibrium and that all solid falls out of the layer and remelts at each timestep. Formation of snow zones occurs for a wide range of interior and thermal properties and depends critically on the initial sulfur concentration, ξ0. Release of gravitational energy and latent heat during growth of the snow zone do not generate sufficient entropy to restart the dynamo unless the snow zone occupies at least 400 km of the core. Snow zones can be 1.5-2 Gyrs old, though thermal stratification of the uppermost core, not included in our model, likely delays onset. Models that match the available magnetic and geodetic constraints have ξ0 ≈ 10% and snow zones that occupy approximately the top 100 km of the present-day Martian core.

Evaluating the performance of coupled snow-soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

Science.gov (United States)

Barrere, Mathieu; Domine, Florent; Decharme, Bertrand; Morin, Samuel; Vionnet, Vincent; Lafaysse, Matthieu

2017-09-01

Climate change projections still suffer from a limited representation of the permafrost-carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

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

Probing the water and CO snow lines in the young protostar NGC 1333-IRAS4B

Science.gov (United States)

Anderl, Sibylle; Maret, Sébastien; André, Philippe; Maury, Anaëlle; Belloche, Arnaud; Cabrit, Sylvie; Codella, Claudio; Lefloch, Bertrand

2015-08-01

Today, we believe that the onset of life requires free energy, water, and complex, probably carbon-based chemistry. In the interstellar medium, complex organic molecules seem to mostly form in reactions happening on the icy surface of dust grains, such that they are released into the gas phase when the dust is heated. The resulting “snow lines”, marking regions where ices start to sublimate, play an important role for planet growth and bulk composition in protoplanetary disks. However, they can already be observed in the envelopes of the much younger, low-mass Class 0 protostars that are still in their early phase of heavy accretion. The information on the sublimation regions of different kinds of ices can be used to understand the chemistry of the envelope, its temperature and density structure, and may even hint at the history of the accretion process. Accordingly, it is a crucial piece of information in order to get the full picture of how organic chemistry evolves already at the earliest stages of the formation of sun-like stars. As part of the CALYPSO Large Program (http://irfu.cea.fr/Projets/Calypso/), we have obtained observations of C18O, N2H+ and CH3OH towards the Class 0 protostar NGC 1333-IRAS4B with the IRAM Plateau de Bure interferometer at sub-arcsecond resolution. Of these we use the methanol observations as a proxy for the water snow line, assuming methanol is trapped in water ice. The observed anti-correlation of C18O and N2H+, with N2H+ forming a ring around the centrally peaked C18O emission, reveals for the first time the CO snow line in this protostellar envelope, with a radius of ~300 AU. The methanol emission is much more compact than that of C18O, and traces the water snow line with a radius of ~40 AU. We have modeled the emission using a chemical model coupled with a radiative transfer module. We find that the CO snow line appears further inwards than expected from the binding energy of pure CO ices. This may hint at CO being frozen out

Can GRACE detect winter snows in Japan?

Science.gov (United States)

Heki, Kosuke

2010-05-01

Current spatial resolution of the GRACE (Gravity Recovery and Climate Experiment) satellites is 300-400 km, and so its hydrological applications have been limited to continents and large islands. The Japanese Islands have width slightly smaller than this spatial resolution, but are known to show large amplitude seasonal changes in surface masses due mainly to winter snow. Such loads are responsible for seasonal crustal deformation observed with GEONET, a dense array of GPS (Global Positioning System) receivers in Japan (Heki, 2001). There is also a dense network of surface meteorological sensors for, e.g. snow depths, atmospheric pressures, etc. Heki (2004) showed that combined effects of surface loads, i.e. snow (predominant), atmosphere, soil moisture, dam impoundment, can explain seasonal crustal deformation observed by GPS to a large extent. The total weight of the winter snow in the Japanese Islands in its peak season may reach ~50 Gt. This is comparable to the annual loss of mountain glaciers in the Asian high mountains (Matsuo & Heki, 2010), and is above the detection level of GRACE. In this study, I use GRACE Level-2 Release-4 data from CSR, Univ. Texas, up to 2009 November, and evaluated seasonal changes in surface loads in and around the Japanese Islands. After applying a 350 km Gaussian filter and a de-striping filter, the peak-to-peak change of the water depth becomes ~4 cm in northern Japan. The maximum value is achieved in February-March. The region of large winter load spans from Hokkaido, Japan, to northeastern Honshu, which roughly coincides with the region of deep snow in Japan. Next I compiled snow depth data from surface meteorological observations, and converted them to loads using time-dependent snow density due to compaction. By applying the same spatial filter as the GRACE data, its spatial pattern becomes similar to the GRACE results. The present study suggests that GRACE is capable of detecting seasonal mass changes in an island arc not

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

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.

The Snow Must Go On: Ground Ice Encasement, Snow Compaction and Absence of Snow Differently Cause Soil Hypoxia, CO2 Accumulation and Tree Seedling Damage in Boreal Forest.

Science.gov (United States)

Martz, Françoise; Vuosku, Jaana; Ovaskainen, Anu; Stark, Sari; Rautio, Pasi

2016-01-01

At high latitudes, the climate has warmed at twice the rate of the global average with most changes observed in autumn, winter and spring. Increasing winter temperatures and wide temperature fluctuations are leading to more frequent rain-on-snow events and freeze-thaw cycles causing snow compaction and formation of ice layers in the snowpack, thus creating ice encasement (IE). By decreasing the snowpack insulation capacity and restricting soil-atmosphere gas exchange, modification of the snow properties may lead to colder soil but also to hypoxia and accumulation of trace gases in the subnivean environment. To test the effects of these overwintering conditions changes on plant winter survival and growth, we established a snow manipulation experiment in a coniferous forest in Northern Finland with Norway spruce and Scots pine seedlings. In addition to ambient conditions and prevention of IE, we applied three snow manipulation levels: IE created by artificial rain-on-snow events, snow compaction and complete snow removal. Snow removal led to deeper soil frost during winter, but no clear effect of IE or snow compaction done in early winter was observed on soil temperature. Hypoxia and accumulation of CO2 were highest in the IE plots but, more importantly, the duration of CO2 concentration above 5% was 17 days in IE plots compared to 0 days in ambient plots. IE was the most damaging winter condition for both species, decreasing the proportion of healthy seedlings by 47% for spruce and 76% for pine compared to ambient conditions. Seedlings in all three treatments tended to grow less than seedlings in ambient conditions but only IE had a significant effect on spruce growth. Our results demonstrate a negative impact of winter climate change on boreal forest regeneration and productivity. Changing snow conditions may thus partially mitigate the positive effect of increasing growing season temperatures on boreal forest productivity.

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.)

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Directory of Open Access Journals (Sweden)

A. Ehrlich

2017-09-01

Full Text Available The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow. Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C.In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S, λ2 = 1650 nm (sensitive to τ, and λ3 = 2100 nm (sensitive to reff, C are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012 were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice

Determination of total arsenic and arsenic species in drinking water, surface water, wastewater, and snow from Wielkopolska, Kujawy-Pomerania, and Lower Silesia provinces, Poland.

Science.gov (United States)

Komorowicz, Izabela; Barałkiewicz, Danuta

2016-09-01

Arsenic is a ubiquitous element which may be found in surface water, groundwater, and drinking water. In higher concentrations, this element is considered genotoxic and carcinogenic; thus, its level must be strictly controlled. We investigated the concentration of total arsenic and arsenic species: As(III), As(V), MMA, DMA, and AsB in drinking water, surface water, wastewater, and snow collected from the provinces of Wielkopolska, Kujawy-Pomerania, and Lower Silesia (Poland). The total arsenic was analyzed by inductively coupled plasma mass spectrometry (ICP-MS), and arsenic species were analyzed with use of high-performance liquid chromatography inductively coupled plasma mass spectrometry (HPLC/ICP-MS). Obtained results revealed that maximum total arsenic concentration determined in drinking water samples was equal to 1.01 μg L(-1). The highest concentration of total arsenic in surface water, equal to 3778 μg L(-1) was determined in Trująca Stream situated in the area affected by geogenic arsenic contamination. Total arsenic concentration in wastewater samples was comparable to those determined in drinking water samples. However, significantly higher arsenic concentration, equal to 83.1 ± 5.9 μg L(-1), was found in a snow sample collected in Legnica. As(V) was present in all of the investigated samples, and in most of them, it was the sole species observed. However, in snow sample collected in Legnica, more than 97 % of the determined concentration, amounting to 81 ± 11 μg L(-1), was in the form of As(III), the most toxic arsenic species.

Using machine learning to predict snow water equivalent in the Sierra Nevada USA and Afghanistan

Science.gov (United States)

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

2017-12-01

In many mountain regions, snowmelt provides most of the runoff. Ranges such as the Sierra Nevada USA benefit from hundreds of manual and automated snow measurement stations as well as basin-wide snow water equavalent (SWE) estimates from new platforms like the Airborne Snow Observatory. Thus, we have been able to use the Sierra Nevada as a testbed to validate an approach called SWE reconstruction, where the snowpack is built-up in reverse using downscaled energy balance forcings. Our past work has shown that SWE reconstruction produces some of the most accurate basin-wide SWE estimates, comparable in accuracy to a snow pillow/course interpolation, but requires no in situ measurements, which is its main advantage. The disadvantages are that reconstruction cannot be used for a forecast and is only valid during the ablation period. To address these shortcomings, we have used machine learning trained on reconstructed SWE in the Sierra Nevada and Afghanistan, where there are no accessible snowpack measurements. Predictors are physiographic and remotely-sensed variables, including brightness temperatures from a new enhanced resolution passive microwave dataset. Two machine learning techniques—bagged regression trees and feed-forward neural networks—were used. Results show little bias on average and training period, i.e. the ablation period.

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

Ice haze, snow, and the Mars water cycle

International Nuclear Information System (INIS)

Kahn, R.

1990-01-01

Images of the limb of Mars reveal discrete cloud layers between 20 and 80 km above the surface. They appear to be composed of water ice and have a number of characteristics similar to hazes that produce diamond dust precipitation in the continental Antarctic of Earth. Temperatures from 170 K to 190 K are deduced at the condensation levels. Eddy diffusion coefficients around 10 5 cm 2 s -1 , typical of a nonconvecting atmosphere, are also derived in the haze regions at times when the atmosphere is relatively clear of dust. This parameter apparently changes by more than 3 orders of magnitude with season and local conditions, with important implications for vertical transport of water and dust and for models of photochemistry and middle atmosphere dynamics. For the cases studied, particle sizes vary systematically by more than an order of magnitude with condensation level, in such a way that the characteristic fall time for particles is always about 1 Mars day, which is the dominant thermal forcing time. The hazes may play a key role in the seasonal water cycle of Mars. They provide a mechanism for growing particles large enough to move atmospheric water closer to the surface, thereby improving the efficiency of adsorption and ice deposit formation in the regolith. This is particularly likely in late northern summer, when the rapid hemispheric decrease in atmospheric water vapor may reflect the precipitation of snow. This rapid decrease in late summer involves atmospheric water vapor in about the quantities needed to supply the mid-latitude regolith with the water that appears in the atmosphere early in the following spring

Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

Directory of Open Access Journals (Sweden)

A. N. Gelfan

2014-01-01

Full Text Available A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity. The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique.

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

Directory of Open Access Journals (Sweden)

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

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.

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

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.

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

Quasi-steady temperature gradient metamorphism in idealized, dry snow

International Nuclear Information System (INIS)

Christon, M.

1994-01-01

A three-dimensional model for heat and mass transport in microscale ice lattices of dry snow is formulated consistent with conservation laws and solid-vapor interface constraints. A finite element model that employs continuous mesh deformation is developed, and calculation of the effective diffusion rates in snow, metamorphosing under a temperature gradient, is performed. Results of the research provide basic insight into the movement of heat and water vapor in seasonal snowcovers. Agreement between the numerical results and measured data of effective thermal conductivity is excellent. The enhancement to the water vapor diffusion rate in snow is bracketed in the range of 1.05--2.0 times that of water vapor in dry air

Non-linear Feedbacks Between Forest Mortality and Climate Change: Implications for Snow Cover, Water Resources, and Ecosystem Recovery in Western North America (Invited)

Science.gov (United States)

Brooks, P. D.; Harpold, A. A.; Biederman, J. A.; Gochis, D. J.; Litvak, M. E.; Ewers, B. E.; Broxton, P. D.; Reed, D. E.

2013-12-01

Unprecedented levels of tree mortality from insect infestation and wildfire are dramatically altering forest structure and composition in Western North America. Warming temperatures and increased drought stress have been implicated as major factors in the increasing spatial extent and frequency of these forest disturbances, but it is unclear how these changes in forest structure will interact with ongoing climate change to affect snowmelt water resources either for society or for ecosystem recovery following mortality. Because surface discharge, groundwater recharge, and ecosystem productivity all depend on seasonal snowmelt, a critical knowledge gap exists not only in predicting discharge, but in quantifying spatial and temporal variability in the partitioning of snowfall into abiotic vapor loss, plant available water, recharge, and streamflow within the complex mosaic of forest disturbance and topography that characterizes western mountain catchments. This presentation will address this knowledge gap by synthesizing recent work on snowpack dynamics and ecosystem productivity from seasonally snow-covered forests along a climate gradient from Arizona to Wyoming; including undisturbed sites, recently burned forests, and areas of extensive insect-induced forest mortality. Both before-after and control-impacted studies of forest disturbance on snow accumulation and ablation suggest that the spatial scale of snow distribution increases following disturbance, but net snow water input in a warming climate will increase only in topographically sheltered areas. While forest disturbance changes spatial scale of snowpack partitioning, the amount and especially the timing of snow cover accumulation and ablation are strongly related to interannual variability in ecosystem productivity with both earlier snowmelt and later snow accumulation associated with decreased carbon uptake. Empirical analyses and modeling are being developed to identify landscapes most sensitive to

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

The impact of atmospheric mineral aerosol deposition on the albedo of snow and sea ice: are snow and sea ice optical properties more important than mineral aerosol optical properties?

OpenAIRE

M. L. Lamare; J. Lee-Taylor; M. D. King

2015-01-01

Knowledge of the albedo of polar regions is crucial for understanding a range of climatic processes that have an impact on a global scale. Light absorbing impurities in atmospheric aerosols deposited on snow and sea ice by aeolian transport absorb solar radiation, reducing albedo. Here, the effects of five mineral aerosol deposits reducing the albedo of polar snow and sea ice are considered. Calculations employing a coupled atmospheric and snow/sea ice radiative-transfer model (TUV-snow) show...

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

The Critical Depth of Freeze-Thaw Soil under Different Types of Snow Cover

Directory of Open Access Journals (Sweden)

Qiang Fu

2017-05-01

Full Text Available Snow cover is the most common upper boundary condition influencing the soil freeze-thaw process in the black soil farming area of northern China. Snow is a porous dielectric cover, and its unique physical properties affect the soil moisture diffusion, heat conduction, freezing rate and other variables. To understand the spatial distribution of the soil water-heat and the variable characteristics of the critical depth of the soil water and heat, we used field data to analyze the freezing rate of soil and the extent of variation in soil water-heat in a unit soil layer under bare land (BL, natural snow (NS, compacted snow (CS and thick snow (TS treatments. The critical depth of the soil water and heat activity under different snow covers were determined based on the results of the analysis, and the variation fitting curve of the difference sequences on the soil temperature and water content between different soil layers and the surface 5-cm soil layer were used to verify the critical depth. The results were as follows: snow cover slowed the rate of soil freezing, and the soil freezing rate under the NS, CS and TS treatments decreased by 0.099 cm/day, 0.147 cm/day and 0.307 cm/day, respectively, compared with that under BL. In addition, the soil thawing time was delayed, and the effect was more significant with increased snow cover. During freeze-thaw cycles, the extent of variation in the water and heat time series in the shallow soil was relatively large, while there was less variation in the deep layer. There was a critical stratum in the vertical surface during hydrothermal migration, wherein the critical depth of soil water and heat change gradually increased with increasing snow cover. The variance in differences between the surface layer and both the soil water and heat in the different layers exhibited “steady-rising-steady” behavior, and the inflection point of the curve is the critical depth of soil freezing and thawing. This critical

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.

Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps

Science.gov (United States)

Verfaillie, Deborah; Lafaysse, Matthieu; Déqué, Michel; Eckert, Nicolas; Lejeune, Yves; Morin, Samuel

2018-04-01

This article investigates the climatic response of a series of indicators for characterizing annual snow conditions and corresponding meteorological drivers at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future changes were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5-EURO-CORDEX GCM-RCM pairs spanning historical (1950-2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006-2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20 % typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in average snow conditions, and sustained interannual variability. The impact of the RCPs becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Changes in local meteorological and snow conditions show significant correlation with global temperature changes. Global temperature levels 1.5 and 2 °C above preindustrial levels correspond to a 25 and 32 % reduction, respectively, of winter mean snow depth with respect to the reference period 1986-2005. Larger reduction rates are expected for global temperature levels exceeding 2 °C. The method can address other geographical areas and sectorial indicators, in the field of water resources, mountain tourism or natural hazards.

Effects of snow grain non-sphericity on climate simulations: Sensitivity tests with the NorESM model

Science.gov (United States)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

2017-04-01

Snow grains are non-spherical and generally irregular in shape. Still, in radiative transfer calculations, they are often treated as spheres. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this work, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (≈ 0.78 in the visible region) than in the spherical case (≈ 0.89). Therefore, for a given snow grain size, the use of non-spherical snow grains yields a higher snow broadband albedo, typically by ≈0.03. Consequently, considering the spherical case as the baseline, the use of non-spherical snow grains results in a negative radiative forcing (RF), with a global-mean top-of-the-model value of ≈ -0.22 W m-2. Although this global-mean RF is modest, it has a rather substantial impact on the climate simulated by NoRESM. In particular, the global annual-mean 2-m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further found that the difference between NONSPH and SPH could be largely "tuned away" by adjusting the snow grain size in the NONSPH experiment by ≈ 70%. The impact of snow grain shape on the radiative effect (RE) of absorbing aerosols in snow (black carbon and mineral dust) is also discussed. For an

Snow and SMOW

International Nuclear Information System (INIS)

1967-01-01

In the midst of Vienna's hottest weather spell this year, members of the Agency's headquarters laboratory staff found themselves unpacking a consignment of fresh snow from the Antarctic. It had been sent by air through Los Angeles, not for cooling purposes, but to assist in making more accurate measurements as part of the study of the world's water distribution and movement. The same research also involves SMOW (Standard Mean Ocean Water) and samples of water from the mid-Pacific will also soon arrive for scientific examination

Planetesimal formation starts at the snow line

Science.gov (United States)

Drążkowska, J.; Alibert, Y.

2017-12-01

Context. The formation stage of planetesimals represents a major gap in our understanding of the planet formation process. Late-stage planet accretion models typically make arbitrary assumptions about planetesimal and pebble distribution, while dust evolution models predict that planetesimal formation is only possible at some orbital distances. Aims: We wish to test the importance of the water snow line in triggering the formation of the first planetesimals during the gas-rich phase of a protoplanetary disk, when cores of giant planets have to form. Methods: We connected prescriptions for gas disk evolution, dust growth and fragmentation, water ice evaporation and recondensation, the transport of both solids and water vapor, and planetesimal formation via streaming instability into a single one-dimensional model for protoplanetary disk evolution. Results: We find that processes taking place around the snow line facilitate planetesimal formation in two ways. First, because the sticking properties between wet and dry aggregates change, a "traffic jam" inside of the snow line slows the fall of solids onto the star. Second, ice evaporation and outward diffusion of water followed by its recondensation increases the abundance of icy pebbles that trigger planetesimal formation via streaming instability just outside of the snow line. Conclusions: Planetesimal formation is hindered by growth barriers and radial drift and thus requires particular conditions to take place. The snow line is a favorable location where planetesimal formation is possible for a wide range of conditions, but not in every protoplanetary disk model, however. This process is particularly promoted in large cool disks with low intrinsic turbulence and an increased initial dust-to-gas ratio. The movie attached to Fig. 3 is only available at http://www.aanda.org

Snow cover distribution over elevation zones in a mountainous catchment

Science.gov (United States)

Panagoulia, D.; Panagopoulos, Y.

2009-04-01

A good understanding of the elevetional distribution of snow cover is necessary to predict the timing and volume of runoff. In a complex mountainous terrain the snow cover distribution within a watershed is highly variable in time and space and is dependent on elevation, slope, aspect, vegetation type, surface roughness, radiation load, and energy exchange at the snow-air interface. Decreases in snowpack due to climate change could disrupt the downstream urban and agricultural water supplies, while increases could lead to seasonal flooding. Solar and longwave radiation are dominant energy inputs driving the ablation process. Turbulent energy exchange at the snow cover surface is important during the snow season. The evaporation of blowing and drifting snow is strongly dependent upon wind speed. Much of the spatial heterogeneity of snow cover is the result of snow redistribution by wind. Elevation is important in determining temperature and precipitation gradients along hillslopes, while the temperature gradients determine where precipitation falls as rain and snow and contribute to variable melt rates within the hillslope. Under these premises, the snow accumulation and ablation (SAA) model of the US National Weather Service (US NWS) was applied to implement the snow cover extent over elevation zones of a mountainous catchment (the Mesochora catchment in Western-Central Greece), taking also into account the indirectly included processes of sublimation, interception, and snow redistribution. The catchment hydrology is controlled by snowfall and snowmelt and the simulated discharge was computed from the soil moisture accounting (SMA) model of the US NWS and compared to the measured discharge. The elevationally distributed snow cover extent presented different patterns with different time of maximization, extinction and return during the year, producing different timing of discharge that is a crucial factor for the control and management of water resources systems.

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

Science.gov (United States)

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.

Monitoring snow cover and its effect on runoff regime in the Jizera Mountains

Science.gov (United States)

Kulasova, Alena

2015-04-01

The Jizera Mountains in the northern Bohemia are known by its rich snow cover. Winter precipitation represents usually a half of the precipitation in the hydrological year. Gradual snow accumulation and melt depends on the course of the particular winter period, the topography of the catchments and the type of vegetation. During winter the snow depth, and especially the snow water equivalent, are affected by the changing character of the falling precipitation, air and soil temperatures and the wind. More rapid snowmelt occurs more on the slopes without forest oriented to the South, while a gradual snowmelt occurs on the locations turned to the North and in forest. Melting snow recharges groundwater and affects water quality in an important way. In case of extreme situation the snowmelt monitoring is important from the point of view of flood protection of communities and property. Therefore the immediate information on the amount of water in snow is necessary. The way to get this information is the continuous monitoring of the snow depth and snow water equivalent. In the Jizera Mountains a regular monitoring of snow cover has been going on since the end of the 19th century. In the 80s of the last century the Jizera Mountains were affected by the increased fallout of pollutants in the air. There followed a gradual dieback of the forest cover and cutting down the upper part of the ridges. In order to get data for the quantification of runoff regime changes in the changing natural environment, the Czech Hydrometeorological Institute (CHMI) founded in the upper part of the Mountains several experimental catchments. One of the activities of the employees of the experimental basis is the regular measurement of snow cover at selected sites from 1982 up to now. At the same time snow cover is being observed using snow pillows, where its mass is monitored with the help of pressure sensors. In order to improve the reliability of the continuous measurement of the snow water

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

Science.gov (United States)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf; Debernard, Jens B.

2017-12-01

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. -0.22 W m-2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the

[Measurement and estimation methods and research progress of snow evaporation in forests].

Science.gov (United States)

Li, Hui-Dong; Guan, De-Xin; Jin, Chang-Jie; Wang, An-Zhi; Yuan, Feng-Hui; Wu, Jia-Bing

2013-12-01

Accurate measurement and estimation of snow evaporation (sublimation) in forests is one of the important issues to the understanding of snow surface energy and water balance, and it is also an essential part of regional hydrological and climate models. This paper summarized the measurement and estimation methods of snow evaporation in forests, and made a comprehensive applicability evaluation, including mass-balance methods (snow water equivalent method, comparative measurements of snowfall and through-snowfall, snow evaporation pan, lysimeter, weighing of cut tree, weighing interception on crown, and gamma-ray attenuation technique) and micrometeorological methods (Bowen-ratio energy-balance method, Penman combination equation, aerodynamics method, surface temperature technique and eddy covariance method). Also this paper reviewed the progress of snow evaporation in different forests and its influencal factors. At last, combining the deficiency of past research, an outlook for snow evaporation rearch in forests was presented, hoping to provide a reference for related research in the future.

The New Global Gapless GLASS Albedo Product from 1981 to 2014

Science.gov (United States)

Dou, B.; Liu, Q.; Qu, Y.; Wang, L.; Feng, Y.; Nie, A.; Li, X.; Zhang, J.; Niu, H.; Cai, E.; Zhao, L.

2016-12-01

Long-time series and various spatial resolution albedo products are needed for climate change and environmental studies at both global and regional scale. To meet these requirements, GLASS (Global LAnd Surface Satellites) gapless albedo product from 1981 to 2010 was firstly released in 2012 and widely used in long-term earth change researches. However, only shortwave albedo product in spatial resolution of 0.05 degree and 1 km were provided, which limits extensive applications for visible and near-infrared bands. Thus, new GLASS albedo product are produced and comprehensively enhanced in time series, algorithm and product content. Five major updates are conducted: 1) Time region is expanded from 1981-2010 to 1981-2014; 2) Physically ART (radiative transfer theory) and TCOWA (Three-Component Ocean Water Albedo) models rather than previous RTLSR (Rose-Thick Li-Sparse Reciprocal kernel combination) model are adopted for snow and inland water albedo estimation, respectively; 3) global shortwave, visible, and near-infrared albedos in spatial resolution of 0.05 degree and 1 km are released; 4) Clear-sky albedo is provided beyond the traditional black-sky albedo and white sky-albedo for amateurish user; 5) 250 m albedo product is provided in part of global for regional application. In this study, we firstly detail the updates of this inspiring product. Then the product is compared with the previous GLASS albedo product and preliminary assessed against field measurements under various land covers. Significant improvements are reported for snow and water albedo. The results demonstrate that the new GLASS albedo product is a gapless, long-term continuous, and self-consistent data-set. Comparing to previous GLASS albedo product, lower black-sky albedo and higher white-sky albedo are proved for permanent snow-cover region. Moreover, higher albedo of inland water and seasonal snow-cover mountain are captured. This product brings new chance and view to understanding long

Are water markets globally applicable?

Science.gov (United States)

Endo, Takahiro; Kakinuma, Kaoru; Yoshikawa, Sayaka; Kanae, Shinjiro

2018-03-01

Water scarcity is a global concern that necessitates a global perspective, but it is also the product of multiple regional issues that require regional solutions. Water markets constitute a regionally applicable non-structural measure to counter water scarcity that has received the attention of academics and policy-makers, but there is no global view on their applicability. We present the global distribution of potential nations and states where water markets could be instituted in a legal sense, by investigating 296 water laws internationally, with special reference to a minimum set of key rules: legalization of water reallocation, the separation of water rights and landownership, and the modification of the cancellation rule for non-use. We also suggest two additional globally distributed prerequisites and policy implications: the predictability of the available water before irrigation periods and public control of groundwater pumping throughout its jurisdiction.

Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan Plateau, China.

Science.gov (United States)

Wang, Siyuan; Wang, Xiaoyue; Chen, Guangsheng; Yang, Qichun; Wang, Bin; Ma, Yuanxu; Shen, Ming

2017-09-01

Snow cover dynamics are considered to play a key role on spring phenological shifts in the high-latitude, so investigating responses of spring phenology to snow cover dynamics is becoming an increasingly important way to identify and predict global ecosystem dynamics. In this study, we quantified the temporal trends and spatial variations of spring phenology and snow cover across the Tibetan Plateau by calibrating and analyzing time series of the NOAA AVHRR-derived normalized difference vegetation index (NDVI) during 1983-2012. We also examined how snow cover dynamics affect the spatio-temporal pattern of spring alpine vegetation phenology over the plateau. Our results indicated that 52.21% of the plateau experienced a significant advancing trend in the beginning of vegetation growing season (BGS) and 34.30% exhibited a delaying trend. Accordingly, the snow cover duration days (SCD) and snow cover melt date (SCM) showed similar patterns with a decreasing trend in the west and an increasing trend in the southeast, but the start date of snow cover (SCS) showed an opposite pattern. Meanwhile, the spatial patterns of the BGS, SCD, SCS and SCM varied in accordance with the gradients of temperature, precipitation and topography across the plateau. The response relationship of spring phenology to snow cover dynamics varied within different climate, terrain and alpine plant community zones, and the spatio-temporal response patterns were primarily controlled by the long-term local heat-water conditions and topographic conditions. Moreover, temperature and precipitation played a profound impact on diverse responses of spring phenology to snow cover dynamics. Copyright © 2017 Elsevier B.V. All rights reserved.

Role of Tibetan Buddhist monasteries in snow leopard conservation.

Science.gov (United States)

Li, Juan; Wang, Dajun; Yin, Hang; Zhaxi, Duojie; Jiagong, Zhala; Schaller, George B; Mishra, Charudutt; McCarthy, Thomas M; Wang, Hao; Wu, Lan; Xiao, Lingyun; Basang, Lamao; Zhang, Yuguang; Zhou, Yunyun; Lu, Zhi

2014-02-01

The snow leopard (Panthera uncia) inhabits the rugged mountains in 12 countries of Central Asia, including the Tibetan Plateau. Due to poaching, decreased abundance of prey, and habitat degradation, it was listed as endangered by the International Union for Conservation of Nature in 1972. Current conservation strategies, including nature reserves and incentive programs, have limited capacities to protect snow leopards. We investigated the role of Tibetan Buddhist monasteries in snow leopard conservation in the Sanjiangyuan region in China's Qinghai Province on the Tibetan Plateau. From 2009 to 2011, we systematically surveyed snow leopards in the Sanjiangyuan region. We used the MaxEnt model to determine the relation of their presence to environmental variables (e.g., elevation, ruggedness) and to predict snow leopard distribution. Model results showed 89,602 km(2) of snow leopard habitat in the Sanjiangyuan region, of which 7674 km(2) lay within Sanjiangyuan Nature Reserve's core zones. We analyzed the spatial relation between snow leopard habitat and Buddhist monasteries and found that 46% of monasteries were located in snow leopard habitat and 90% were within 5 km of snow leopard habitat. The 336 monasteries in the Sanjiangyuan region could protect more snow leopard habitat (8342 km(2) ) through social norms and active patrols than the nature reserve's core zones. We conducted 144 household interviews to identify local herders' attitudes and behavior toward snow leopards and other wildlife. Most local herders claimed that they did not kill wildlife, and 42% said they did not kill wildlife because it was a sin in Buddhism. Our results indicate monasteries play an important role in snow leopard conservation. Monastery-based snow leopard conservation could be extended to other Tibetan Buddhist regions that in total would encompass about 80% of the global range of snow leopards. © 2013 Society for Conservation Biology.

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.

Snowmelt runoff in the Green River basin derived from MODIS snow extent

Science.gov (United States)

Barton, J. S.; Hall, D. K.

2011-12-01

The Green River represents a vital water supply for southwestern Wyoming, northern Colorado, eastern Utah, and the Lower Colorado River Compact states (Arizona, Nevada, and California). Rapid development in the southwestern United States combined with the recent drought has greatly stressed the water supply of the Colorado River system, and concurrently increased the interest in long-term variations in stream flow. Modeling of snowmelt runoff represents a means to predict flows and reservoir storage, which is useful for water resource planning. An investigation is made into the accuracy of the Snowmelt Runoff Model of Martinec and Rango, driven by Moderate Resolution Imaging Spectroradiometer (MODIS) snow maps for predicting stream flow within the Green River basin. While the moderate resolution of the MODIS snow maps limits the spatial detail that can be captured, the daily coverage is an important advantage of the MODIS imagery. The daily MODIS snow extent is measured using the most recent clear observation for each 500-meter pixel. Auxiliary data used include temperature and precipitation time series from the Snow Telemetry (SNOTEL) and Remote Automated Weather Station (RAWS) networks as well as from National Weather Service records. Also from the SNOTEL network, snow-water equivalence data are obtained to calibrate the conversion between snow extent and runoff potential.

Snow fracture: From micro-cracking to global failure

Science.gov (United States)

Capelli, Achille; Reiweger, Ingrid; Schweizer, Jürg

2017-04-01

Slab avalanches are caused by a crack forming and propagating in a weak layer within the snow cover, which eventually causes the detachment of the overlying cohesive slab. The gradual damage process leading to the nucleation of the initial failure is still not entirely understood. Therefore, we studied the damage process preceding snow failure by analyzing the acoustic emissions (AE) generated by bond failure or micro-cracking. The AE allow studying the ongoing progressive failure in a non-destructive way. We performed fully load-controlled failure experiments on snow samples presenting a weak layer and recorded the generated AE. The size and frequency of the generated AE increased before failure revealing an acceleration of the damage process with increased size and frequency of damage and/or microscopic cracks. The AE energy was power-law distributed and the exponent (b-value) decreased approaching failure. The waiting time followed an exponential distribution with increasing exponential coefficient λ before failure. The decrease of the b-value and the increase of λ correspond to a change in the event distribution statistics indicating a transition from homogeneously distributed uncorrelated damage producing mostly small AE to localized damage, which cause larger correlated events which leads to brittle failure. We observed brittle failure for the fast experiment and a more ductile behavior for the slow experiments. This rate dependence was reflected also in the AE signature. In the slow experiments the b value and λ were almost constant, and the energy rate increase was moderate indicating that the damage process was in a stable state - suggesting the damage and healing processes to be balanced. On a shorter time scale, however, the AE parameters varied indicating that the damage process was not steady but consisted of a sum of small bursts. We assume that the bursts may have been generated by cascades of correlated micro-cracks caused by localization of

Objective Characterization of Snow Microstructure for Microwave Emission Modeling

Science.gov (United States)

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.

First Gridded Spatial Field Reconstructions of Snow from Tree Rings

Science.gov (United States)

Coulthard, B. L.; Anchukaitis, K. J.; Pederson, G. T.; Alder, J. R.; Hostetler, S. W.; Gray, S. T.

2017-12-01

Western North America's mountain snowpacks provide critical water resources for human populations and ecosystems. Warmer temperatures and changing precipitation patterns will increasingly alter the quantity, extent, and persistence of snow in coming decades. A comprehensive understanding of the causes and range of long-term variability in this system is required for forecasting future anomalies, but snowpack observations are limited and sparse. While individual tree ring-based annual snowpack reconstructions have been developed for specific regions and mountain ranges, we present here the first collection of spatially-explicit gridded field reconstructions of seasonal snowpack within the American Rocky Mountains. Capitalizing on a new western North American snow-sensitive network of over 700 tree-ring chronologies, as well as recent advances in PRISM-based snow modeling, our gridded reconstructions offer a full space-time characterization of snow and associated water resource fluctuations over several centuries. The quality of reconstructions is evaluated against existing observations, proxy-records, and an independently-developed first-order monthly snow model.

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.

A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

Science.gov (United States)

Varade, D. M.; Dikshit, O.

2017-12-01

Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction

Directory of Open Access Journals (Sweden)

C. Mitterer

2013-02-01

Full Text Available Wet-snow avalanches are notoriously difficult to predict; their formation mechanism is poorly understood since in situ measurements representing the thermal and mechanical evolution are difficult to perform. Instead, air temperature is commonly used as a predictor variable for days with high wet-snow avalanche danger – often with limited success. As melt water is a major driver of wet-snow instability and snow melt depends on the energy input into the snow cover, we computed the energy balance for predicting periods with high wet-snow avalanche activity. The energy balance was partly measured and partly modelled for virtual slopes at different elevations for the aspects south and north using the 1-D snow cover model SNOWPACK. We used measured meteorological variables and computed energy balance and its components to compare wet-snow avalanche days to non-avalanche days for four consecutive winter seasons in the surroundings of Davos, Switzerland. Air temperature, the net shortwave radiation and the energy input integrated over 3 or 5 days showed best results in discriminating event from non-event days. Multivariate statistics, however, revealed that for better predicting avalanche days, information on the cold content of the snowpack is necessary. Wet-snow avalanche activity was closely related to periods when large parts of the snowpack reached an isothermal state (0 °C and energy input exceeded a maximum value of 200 kJ m⁻² in one day, or the 3-day sum of positive energy input was larger than 1.2 MJ m⁻². Prediction accuracy with measured meteorological variables was as good as with computed energy balance parameters, but simulated energy balance variables accounted better for different aspects, slopes and elevations than meteorological data.

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

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Bair, Edward H.; Abreu Calfa, Andre; Rittger, Karl; Dozier, Jeff

2018-05-01

In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques - bagged regression trees and feed-forward neural networks - produced similar mean results, with 0-14 % bias and 46-48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

Suppression of the water ice and snow albedo feedback on planets orbiting red dwarf stars and the subsequent widening of the habitable zone.

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Joshi, Manoj M; Haberle, Robert M

2012-01-01

M stars comprise 80% of main sequence stars, so their planetary systems provide the best chance for finding habitable planets, that is, those with surface liquid water. We have modeled the broadband albedo or reflectivity of water ice and snow for simulated planetary surfaces orbiting two observed red dwarf stars (or M stars), using spectrally resolved data of Earth's cryosphere. The gradual reduction of the albedos of snow and ice at wavelengths greater than 1 μm, combined with M stars emitting a significant fraction of their radiation at these same longer wavelengths, means that the albedos of ice and snow on planets orbiting M stars are much lower than their values on Earth. Our results imply that the ice/snow albedo climate feedback is significantly weaker for planets orbiting M stars than for planets orbiting G-type stars such as the Sun. In addition, planets with significant ice and snow cover will have significantly higher surface temperatures for a given stellar flux if the spectral variation of cryospheric albedo is considered, which in turn implies that the outer edge of the habitable zone around M stars may be 10-30% farther away from the parent star than previously thought.

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

Acoustic Wave Propagation in Snow Based on a Biot-Type Porous Model

Science.gov (United States)

Sidler, R.

2014-12-01

Despite the fact that acoustic methods are inexpensive, robust and simple, the application of seismic waves to snow has been sparse. This might be due to the strong attenuation inherent to snow that prevents large scale seismic applications or due to the somewhat counterintuitive acoustic behavior of snow as a porous material. Such materials support a second kind of compressional wave that can be measured in fresh snow and which has a decreasing wave velocity with increasing density of snow. To investigate wave propagation in snow we construct a Biot-type porous model of snow as a function of porosity based on the assumptions that the solid frame is build of ice, the pore space is filled with a mix of air, or air and water, and empirical relationships for the tortuosity, the permeability, the bulk, and the shear modulus.We use this reduced model to investigate compressional and shear wave velocities of snow as a function of porosity and to asses the consequences of liquid water in the snowpack on acoustic wave propagation by solving Biot's differential equations with plain wave solutions. We find that the fast compressional wave velocity increases significantly with increasing density, but also that the fast compressional wave velocity might be even lower than the slow compressional wave velocity for very light snow. By using compressional and shear strength criteria and solving Biot's differential equations with a pseudo-spectral approach we evaluate snow failure due to acoustic waves in a heterogeneous snowpack, which we think is an important mechanism in triggering avalanches by explosives as well as by skiers. Finally, we developed a low cost seismic acquisition device to assess the theoretically obtained wave velocities in the field and to explore the possibility of an inexpensive tool to remotely gather snow water equivalent.

Simulating the Dependence of Aspen on Redistributed Snow

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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

Seasonal snow accumulation in the mid-latitude forested catchment

Czech Academy of Sciences Publication Activity Database

Šípek, Václav; Tesař, Miroslav

2014-01-01

Roč. 69, č. 11 (2014), s. 1562-1569 ISSN 0006-3088 R&D Projects: GA TA ČR TA02021451 Institutional support: RVO:67985874 Keywords : snow depth * snow water equivalent * forested catchment Subject RIV: DA - Hydrology ; Limnology Impact factor: 0.827, year: 2014

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.

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

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Science.gov (United States)

Riggs, George A.; Hall, Dorothy K.; Román, Miguel O.

2017-10-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Science.gov (United States)

Riggs, George A.; Hall, Dorothy K.; Roman, Miguel O.

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data

Correlated declines in Pacific arctic snow and sea ice cover

Science.gov (United States)

Stone, Robert P.; Douglas, David C.; Belchansky, Gennady I.; Drobot, Sheldon

2005-01-01

Simulations of future climate suggest that global warming will reduce Arctic snow and ice cover, resulting in decreased surface albedo (reflectivity). Lowering of the surface albedo leads to further warming by increasing solar absorption at the surface. This phenomenon is referred to as “temperature–albedo feedback.” Anticipation of such a feedback is one reason why scientists look to the Arctic for early indications of global warming. Much of the Arctic has warmed significantly. Northern Hemisphere snow cover has decreased, and sea ice has diminished in area and thickness. As reported in the Arctic Climate Impact Assessment in 2004, the trends are considered to be outside the range of natural variability, implicating global warming as an underlying cause. Changing climatic conditions in the high northern latitudes have influenced biogeochemical cycles on a broad scale. Warming has already affected the sea ice, the tundra, the plants, the animals, and the indigenous populations that depend on them. Changing annual cycles of snow and sea ice also affect sources and sinks of important greenhouse gases (such as carbon dioxide and methane), further complicating feedbacks involving the global budgets of these important constituents. For instance, thawing permafrost increases the extent of tundra wetlands and lakes, releasing greater amounts of methane into the atmosphere. Variable sea ice cover may affect the hemispheric carbon budget by altering the ocean–atmosphere exchange of carbon dioxide. There is growing concern that amplification of global warming in the Arctic will have far-reaching effects on lower latitude climate through these feedback mechanisms. Despite the diverse and convincing observational evidence that the Arctic environment is changing, it remains unclear whether these changes are anthropogenically forced or result from natural variations of the climate system. A better understanding of what controls the seasonal distributions of snow and ice

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

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.

Global monthly water stress: II. Water demand and severity of water

NARCIS (Netherlands)

Wada, Y.; Beek, L.P.H. van; Viviroli, D.; Dürr, H.H.; Weingartner, R.; Bierkens, M.F.P.

2011-01-01

This paper assesses global water stress at a finer temporal scale compared to conventional assessments. To calculate time series of global water stress at a monthly time scale, global water availability, as obtained from simulations of monthly river discharge from the companion paper, is confronted

Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model

Directory of Open Access Journals (Sweden)

P. Räisänen

2017-12-01

Full Text Available Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR model and in the Los Alamos sea ice model, version 4 (CICE4, both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM. In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH is compared with another (NONSPH in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region than in the spherical case ( ≈  0.89. Therefore, for the same effective snow grain size (or equivalently, the same specific projected area, the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain

Modelling technical snow production for skiing areas in the Austrian Alps with the physically based snow model AMUNDSEN

Science.gov (United States)

Hanzer, F.; Marke, T.; Steiger, R.; Strasser, U.

2012-04-01

Tourism and particularly winter tourism is a key factor for the Austrian economy. Judging from currently available climate simulations, the Austrian Alps show a particularly high vulnerability to climatic changes. To reduce the exposure of ski areas towards changes in natural snow conditions as well as to generally enhance snow conditions at skiing sites, technical snowmaking is widely utilized across Austrian ski areas. While such measures result in better snow conditions at the skiing sites and are important for the local skiing industry, its economic efficiency has also to be taken into account. The current work emerges from the project CC-Snow II, where improved future climate scenario simulations are used to determine future natural and artificial snow conditions and their effects on tourism and economy in the Austrian Alps. In a first step, a simple technical snowmaking approach is incorporated into the process based snow model AMUNDSEN, which operates at a spatial resolution of 10-50 m and a temporal resolution of 1-3 hours. Locations of skiing slopes within a ski area in Styria, Austria, were digitized and imported into the model environment. During a predefined time frame in the beginning of the ski season, the model produces a maximum possible amount of technical snow and distributes the associated snow on the slopes, whereas afterwards, until to the end of the ski season, the model tries to maintain a certain snow depth threshold value on the slopes. Due to only few required input parameters, this approach is easily transferable to other ski areas. In our poster contribution, we present first results of this snowmaking approach and give an overview of the data and methodology applied. In a further step in CC-Snow, this simple bulk approach will be extended to consider actual snow cannon locations and technical specifications, which will allow a more detailed description of technical snow production as well as cannon-based recordings of water and energy

Investigating the Relationships between Canopy Characteristics and Snow Depth Distribution at Fine Scales: Preliminary Results from the SnowEX TLS Campaign

Science.gov (United States)

Glenn, N. F.; Uhlmann, Z.; Spaete, L.; Tennant, C.; Hiemstra, C. A.; McNamara, J.

2017-12-01

Predicting changes in forested seasonal snowpacks under altered climate scenarios is one of the most pressing hydrologic challenges facing today's society. Airborne- and satellite-based remote sensing methods hold the potential to transform measurements of terrestrial water stores in snowpack, improve process representations of snowpack accumulation and ablation, and to generate high quality predictions that inform potential strategies to better manage water resources. While the effects of forest on snowpack are well documented, many of the fine-scale processes influenced by the forest-canopy are not directly accounted for because most snow models don't explicitly represent canopy structure and canopy heterogeneity. This study investigates the influence of forest canopy on snowpack distribution at fine scales and quantifies the influence of canopy heterogeneity on snowpack accumulation and ablation processes. We use terrestrial laser scanning (TLS) data collected during the SnowEX campaign to discover how the relationships between canopy and snow distributions change across scales. Our sample scales range from individual trees to patches of trees across the Grand Mesa, CO, SnowEx site.

Snow darkening caused by black carbon emitted from fires

Science.gov (United States)

Engels, Jessica; Kloster, Silvia; Bourgeois, Quentin

2014-05-01

We implemented the effect of snow darkening caused by black carbon (BC) emitted from forest fires into the Max Planck Institute for Meteorology Earth System Model (MPI-M ESM) to estimate its potential climate impact of present day fire occurrence. Considerable amounts of black carbon emitted from fires are transported into snow covered regions. Already very small quantities of black carbon reduce the snow reflectance, with consequences for snow melting and snow spatial coverage. Therefore, the SNICAR (SNow And Ice Radiation) model (Flanner and Zender (2005)) is implemented in the land surface component (JSBACH) of the atmospheric general circulation model ECHAM6, developed at the MPI-M. The SNICAR model includes amongst other processes a complex calculation of the snow albedo depending on black carbon in snow and snow grain growth depending on water vapor fluxes for a five layer snow scheme. For the implementation of the SNICAR model into the one layer scheme of ECHAM6-JSBACH, we used the SNICAR-online version (http://snow.engin.umich.edu). This single-layer simulator provides the albedo of snow for selectable combinations of impurity content (e.g. black carbon), snow grain size, and incident solar flux characteristics. From this scheme we derived snow albedo values for black carbon in snow concentrations ranging between 0 and 1500 ng(BC)/g(snow) and 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 required snow age is taken from the BATS (Biosphere Atmosphere Transfer Scheme, Dickinson et al. (1986)) snow albedo implementation in ECHAM6-JSBACH. Here, we will present an extended evaluation of the model including a comparison of modeled black carbon in snow concentrations to observed

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.

Assessment of dynamic probabilistic methods for mapping snow cover in Québec Canada

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De Seve, D.; Perreault, L.; Vachon, F.; Guay, F.; choquette, Y.

2012-04-01

Hydro-Quebec is the leader in electricity production in North America and uses hydraulic resources to generate 97% of its overall production where snow represents 30% of its annual energy reserve. Information on snow cover extent (SC) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in Nordic regions where a majority of total precipitations falls as snow. Accurate estimation of the spatial distribution of snow cover variables is required to measure the extent of this resource but snow surveys are expensive due to inaccessibility factors and to the large extent nature of the Quebec geography. Consequently, the follow-up of snowmelt is particularly challenging for operational forecasting resulting in the need to develop a new approach to assist forecasters. For improved understanding of the dynamics of snow melting over watersheds and to generate optimized power production, Hydro-Québec's Research Institute (IREQ) has developed expertise in in-situ, remote sensing monitoring and statistical treatment of such data. The main goal of this Hydro-Quebec project is to develop an automatic and dynamic snow mapping system providing a daily snow map by merging remote sensing (AVHRR and SSMI) and in situ data. This paper focuses on the work accomplished on passive microwave SSM/I data to follow up snow cover. In our problematic, it is highly useful to classify snow, more specifically during the snowmelt period. The challenge is to be able to discriminate ground from wet snow as it will react as a black body, therefore, adding noise to global brightness temperature. Two dynamic snow classifiers were developed and tested. For this purpose, channels at 19 and 37 GHz in vertical polarization have been used to feed each model. SWE values from gamma ray in situ stations (GMON) and data snow depth from ultrasonic sensor (SR50) were used to validate the output models. The first algorithm is based on a standard K-mean clustering approach, combined

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

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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.

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

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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.

Snow reliability in ski resorts considering artificial snowmaking

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Hofstätter, M.; Formayer, H.; Haas, P.

2009-04-01

Snow reliability is the key factor to make skiing on slopes possible and to ensure added value in winter tourism. In this context snow reliability is defined by the duration of a snowpack on the ski runs of at least 50 mm snow water equivalent (SWE), within the main season (Dec-Mar). Furthermore the snowpack should form every winter and be existent early enough in season. In our work we investigate the snow reliability of six Austrian ski resorts. Because nearly all Austrian resorts rely on artificial snowmaking it is of big importance to consider man made snow in the snowpack accumulation and ablation in addition to natural snow. For each study region observed weather data including temperature, precipitation and snow height are used. In addition we differentiate up to three elevations on each site (valley, intermediate, mountain top), being aware of the typical local winter inversion height. Time periods suitable for artificial snow production, for several temperature threshold (-6,-4 or -1 degree Celsius) are calculated on an hourly base. Depending on the actual snowpack height, man made snow can be added in the model with different defined capacities, considering different technologies or the usage of additives. To simulate natural snowpack accumulation and ablation we a simple snow model, based on daily precipitation and temperature. This snow model is optimized at each site separately through certain parameterization factors. Based on the local observations and the monthly climate change signals from the climate model REMO-UBA, we generate long term time series of temperature and precipitation, using the weather generator LARS. Thereby we are not only able to simulate the snow reliability under current, but also under future climate conditions. Our results show significant changes in snow reliability, like an increase of days with insufficient snow heights, especially at mid and low altitudes under natural snow conditions. Artificial snowmaking can partly

Application of snow models to snow removal operations on the Going-to-the-Sun Road, Glacier National Park

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Fagre, Daniel B.; Klasner, Frederick L.

2000-01-01

Snow removal, and the attendant avalanche risk for road crews, is a major issue on mountain highways worldwide. The Going-to-the-Sun Road is the only road that crosses Glacier National Park, Montana. This 80-km highway ascends over 1200m along the wall of a glaciated basin and crosses the continental divide. The annual opening of the road is critical to the regional economy and there is public pressure to open the road as early as possible. Despite the 67-year history of snow removal activities, few stat on snow conditions at upper elevations were available to guide annual planning for the raod opening. We examined statistical relationships between the opening date and nearby SNOTEL data on snow water equivalence (WE) for 30 years. Early spring SWE (first Monday in April) accounted for only 33% of the variance in road opening dates. Because avalanche spotters, used to warn heavy equipment operators of danger, are ineffective during spring storms or low-visibility conditions, we incorporated the percentage of days with precipitation during plowing as a proxy for visibility. This improved the model's predictive power to 69%/ A mountain snow simulator (MTSNOW) was used to calculate the depth and density of snow at various points along the road and field data were collected for comparison. MTSNOW underestimated the observed snow conditions, in part because it does not yet account for wind redistribution of snow. The severe topography of the upper reaches of the road are subjected to extensive wind redistribution of snow as evidence by the formation of "The Big Drift" on the lee side of Logan Pass.

Snow as a habitat for microorganisms

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Hoham, Ronald W.

1989-01-01

There are three major habitats involving ice and snow, and the microorganisms studied from these habitats are most eukaryotic. Sea ice is inhabited by algae called diatoms, glacial ice has sparse populations of green algai cal desmids, and the temporary and permanent snows in mountainous regions and high latitudes are inhabited mostly by green algal flagellates. The life cycle of green algal flagellates is summarized by discussing the effects of light, temperature, nutrients, and snow melts. Specific examples of optimal conditions and environmental effects for various snow algae are given. It is not likely that the eukaryotic snow algae presented are candidated for life on the planet Mars. Evolutionally, eukaryotic cells as know on Earth may not have had the opportunity to develop on Mars (if life evolved at all on Mars) since eukaryotes did not appear on Earth until almost two billion years after the first prokaryotic organisms. However, the snow/ice ecosystems on Earth present themselves as extreme habitats were there is evidence of prokaryotic life (eubacteria and cyanbacteria) of which literally nothing is known. Any future surveillances of extant and/or extinct life on Mars should include probes (if not landing sites) to investigate sites of concentrations of ice water. The possibility of signs of life in Martian polar regions should not be overlooked.

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

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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

Ultratrace analysis for organolead compounds in Greenland snow

International Nuclear Information System (INIS)

Lobinski, R.; Szpunar-Lobinska, J.; Adams, F.C.

1994-01-01

The degradation products of tetraalkyllead compounds used as antiknock additives are unique indicators of automotive environmental pollution by lead. Recent dramatic improvements in species-specific ultrasensitive analytical procedures enabled the identification and quantification of organolead compounds in ancient Greenland snow which is considered as the archives of northern hemispheric pollution records. Organolead species determined in fresh and ancient polar snow demonstrate unambiguously the global range of petrol-related pollution not only with ionic Pb 2+ but also with more toxic metalloorganic compounds. (authors). 9 refs., 5 figs

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

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

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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

Organic compounds and suspended matter in the White Sea snow-ice cover

International Nuclear Information System (INIS)

Nemirovskaya, I.; Shevchenko, V.

2008-01-01

The pollution of the White Sea snow-ice cover was estimated by examining the distribution of organic compounds, including oil and pyrogenic hydrocarbons. Ice and snow cores were taken from Chupa Bay and the Kandalaksha Gulf in the Cape Kartesh area in the spring of 2004 and from the mouth of the Severnaya Dvina River in the spring of 2005, 2006, and 2007. This paper presented data on the lipid content, aliphatic hydrocarbons (AHC), polycyclic aromatic hydrocarbons (PAH) and suspended particulate matter in snow, ice and under-ice water. This paper focused on organic compounds and suspended matter (SM) concentrations in the sea snow-ice cover and described the ice forming conditions and interactions of the substances with ice, snow and sub-ice water. The amount of particulate matter and organic compounds in the snow increased sharply near industrial centres. The concentration of compounds decreased further away from these centres, suggesting that most pollutants are deposited locally. The study revealed that organic compounds concentrate in barrier zones, such as snow-ice and water-ice, depending on the source of pollution. There was no obvious evidence of petrogenic sources of PAHs in particulate matter from the White Sea snow-ice cover. The SM and organic compounds accumulated in layers characterized by local depositional processes. The zones remained biogeochemically active even under low temperature conditions, but the accumulation of both SM and organic compounds was at its highest during the initial stage of ice formation. 16 refs., 2 tabs., 4 figs

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.

[Research on hyperspectral remote sensing in monitoring snow contamination concentration].

Science.gov (United States)

Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan

2011-05-01

Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.

Enhanced solar energy absorption by internally-mixed black carbon in snow grains

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M. G. Flanner

2012-05-01

Full Text Available Here we explore light absorption by snowpack containing black carbon (BC particles residing within ice grains. Basic considerations of particle volumes and BC/snow mass concentrations show that there are generally 0.05–10⁹ BC particles for each ice grain. This suggests that internal BC is likely distributed as multiple inclusions within ice grains, and thus the dynamic effective medium approximation (DEMA (Chýlek and Srivastava, 1983 is a more appropriate optical representation for BC/ice composites than coated-sphere or standard mixing approximations. DEMA calculations show that the 460 nm absorption cross-section of BC/ice composites, normalized to the mass of BC, is typically enhanced by factors of 1.8–2.1 relative to interstitial BC. BC effective radius is the dominant cause of variation in this enhancement, compared with ice grain size and BC volume fraction. We apply two atmospheric aerosol models that simulate interstitial and within-hydrometeor BC lifecycles. Although only ~2% of the atmospheric BC burden is cloud-borne, 71–83% of the BC deposited to global snow and sea-ice surfaces occurs within hydrometeors. Key processes responsible for within-snow BC deposition are development of hydrophilic coatings on BC, activation of liquid droplets, and subsequent snow formation through riming or ice nucleation by other species and aggregation/accretion of ice particles. Applying deposition fields from these aerosol models in offline snow and sea-ice simulations, we calculate that 32–73% of BC in global surface snow resides within ice grains. This fraction is smaller than the within-hydrometeor deposition fraction because meltwater flux preferentially removes internal BC, while sublimation and freezing within snowpack expose internal BC. Incorporating the DEMA into a global climate model, we simulate increases in BC/snow radiative forcing of 43–86%, relative to scenarios that apply external optical properties to all BC. We

Enhanced Solar Energy Absorption by Internally-mixed Black Carbon in Snow Grains

Energy Technology Data Exchange (ETDEWEB)

Flanner, M. G.; Liu, Xiaohong; Zhou, Cheng; Penner, Joyce E.; Jiao, C.

2012-05-30

Here we explore light absorption by snowpack containing black carbon (BC) particles residing within ice grains. Basic considerations of particle volumes and BC/snow mass concentrations show that there are generally 0:05-109 BC particles for each ice grain. This suggests that internal BC is likely distributed as multiple inclusions within ice grains, and thus the dynamic effective medium approximation (DEMA) (Chylek and Srivastava, 1983) is a more appropriate optical representation for BC/ice composites than coated-sphere or standard mixing approximations. DEMA calculations show that the 460 nm absorption cross-section of BC/ice composites, normalized to the mass of BC, is typically enhanced by factors of 1.8-2.1 relative to interstitial BC. BC effective radius is the dominant cause of variation in this enhancement, compared with ice grain size and BC volume fraction. We apply two atmospheric aerosol models that simulate interstitial and within-hydrometeor BC lifecycles. Although only {approx}2% of the atmospheric BC burden is cloud-borne, 71-83% of the BC deposited to global snow and sea-ice surfaces occurs within hydrometeors. Key processes responsible for within-snow BC deposition are development of hydrophilic coatings on BC, activation of liquid droplets, and subsequent snow formation through riming or ice nucleation by other species and aggregation/accretion of ice particles. Applying deposition fields from these aerosol models in offline snow and sea-ice simulations, we calculate that 32-73% of BC in global surface snow resides within ice grains. This fraction is smaller than the within-hydrometeor deposition fraction because meltwater flux preferentially removes internal BC, while sublimation and freezing within snowpack expose internal BC. Incorporating the DEMA into a global climate model, we simulate increases in BC/snow radiative forcing of 43-86%, relative to scenarios that apply external optical properties to all BC. We show that snow metamorphism

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

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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

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

Snow water equivalent monitoring retrieved by assimilating passive microwave observations in a coupled snowpack evolution and microwave emission models over North-Eastern Canada

Science.gov (United States)

Royer, A.; Larue, F.; De Sève, D.; Roy, A.; Vionnet, V.; Picard, G.; Cosme, E.

2017-12-01

Over northern snow-dominated basins, the snow water equivalent (SWE) is of primary interest for spring streamflow forecasting. SWE retrievals from satellite data are still not well resolved, in particular from microwave (MW) measurements, the only type of data sensible to snow mass. Also, the use of snowpack models is challenging due to the large uncertainties in meteorological input forcings. This project aims to improve SWE prediction by assimilation of satellite brightness temperature (TB), without any ground-based observations. The proposed approach is the coupling of a detailed multilayer snowpack model (Crocus) with a MW snow emission model (DMRT-ML). The assimilation scheme is a Sequential Importance Resampling Particle filter, through ensembles of perturbed meteorological forcings according to their respective uncertainties. Crocus simulations driven by operational meteorological forecasts from the Canadian Global Environmental Multiscale model at 10 km spatial resolution were compared to continuous daily SWE measurements over Québec, North-Eastern Canada (56° - 45°N). The results show a mean bias of the maximum SWE overestimated by 16% with variations up to +32%. This observed large variability could lead to dramatic consequences on spring flood forecasts. Results of Crocus-DMRT-ML coupling compared to surface-based TB measurements (at 11, 19 and 37 GHz) show that the Crocus snowpack microstructure described by sticky hard spheres within DMRT has to be scaled by a snow stickiness of 0.18, significantly reducing the overall RMSE of simulated TBs. The ability of assimilation of daily TBs to correct the simulated SWE is first presented through twin experiments with synthetic data, and then with AMSR-2 satellite time series of TBs along the winter taking into account atmospheric and forest canopy interferences (absorption and emission). The differences between TBs at 19-37 GHz and at 11-19 GHz, in vertical polarization, were assimilated. This assimilation

Inorganic carbon addition stimulates snow algae primary productivity

Science.gov (United States)

Hamilton, T. L.; Havig, J. R.

2017-12-01

Earth has experienced glacial/interglacial oscillations throughout its history. Today over 15 million square kilometers (5.8 million square miles) of Earth's land surface is covered in ice including glaciers, ice caps, and the ice sheets of Greenland and Antarctica, most of which are retreating as a consequence of increased atmospheric CO2. Glaciers are teeming with life and supraglacial snow and ice surfaces are often red due to blooms of photoautotrophic algae. Recent evidence suggests the red pigmentation, secondary carotenoids produced in part to thrive under high irradiation, lowers albedo and accelerates melt. However, there are relatively few studies that report the productivity of snow algae communities and the parameters that constrain their growth on snow and ice surfaces. Here, we demonstrate that snow algae primary productivity can be stimulated by the addition of inorganic carbon. We found an increase in light-dependent carbon assimilation in snow algae microcosms amended with increasing amounts of inorganic carbon. Our snow algae communities were dominated by typical cosmopolitan snow algae species recovered from Alpine and Arctic environments. The climate feedbacks necessary to enter and exit glacial/interglacial oscillations are poorly understood. Evidence and models agree that global Snowball events are accompanied by changes in atmospheric CO2 with increasing CO2 necessary for entering periods of interglacial time. Our results demonstrate a positive feedback between increased CO2 and snow algal productivity and presumably growth. With the recent call for bio-albedo effects to be considered in climate models, our results underscore the need for robust climate models to include feedbacks between supraglacial primary productivity, albedo, and atmospheric CO2.

Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

Science.gov (United States)

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

2017-12-01

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. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we 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. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

Impacts of 1, 1.5, and 2 Degree Warming on Arctic Terrestrial Snow and Sea Ice

Science.gov (United States)

Derksen, C.; Mudryk, L.; Howell, S.; Flato, G. M.; Fyfe, J. C.; Gillett, N. P.; Sigmond, M.; Kushner, P. J.; Dawson, J.; Zwiers, F. W.; Lemmen, D.; Duguay, C. R.; Zhang, X.; Fletcher, C. G.; Dery, S. J.

2017-12-01

The 2015 Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) established the global temperature goal of "holding the increase in the global average temperature to below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels." In this study, we utilize multiple gridded snow and sea ice products (satellite retrievals; assimilation systems; physical models driven by reanalyses) and ensembles of climate model simulations to determine the impacts of observed warming, and project the relative impacts of the UNFCC future warming targets on Arctic seasonal terrestrial snow and sea ice cover. Observed changes during the satellite era represent the response to approximately 1°C of global warming. Consistent with other studies, analysis of the observational record (1970's to present) identifies changes including a shorter snow cover duration (due to later snow onset and earlier snow melt), significant reductions in spring snow cover and summer sea ice extent, and the loss of a large proportion of multi-year sea ice. The spatial patterns of observed snow and sea ice loss are coherent across adjacent terrestrial/marine regions. There are strong pattern correlations between snow and temperature trends, with weaker association between sea ice and temperature due to the additional influence of dynamical effects such wind-driven redistribution of sea ice. Climate model simulations from the Coupled Model Inter-comparison Project Phase 5(CMIP-5) multi-model ensemble, large initial condition ensembles of the Community Earth System Model (CESM) and Canadian Earth System Model (CanESM2) , and warming stabilization simulations from CESM were used to identify changes in snow and ice under further increases to 1.5°C and 2°C warming. The model projections indicate these levels of warming will be reached over the coming 2-4 decades. Warming to 1.5°C results in an increase in the

Snow multivariable data assimilation for hydrological predictions in mountain areas

Science.gov (United States)

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

2016-04-01

The seasonal presence of snow on alpine catchments strongly impacts both surface energy balance and water resource. Thus, the knowledge of the snowpack dynamics is of critical importance for several applications, such as water resource management, floods prediction and hydroelectric power production. Several independent data sources provide information about snowpack state: ground-based measurements, satellite data and physical models. Although all these data types are reliable, each of them is affected by specific flaws and errors (respectively dependency on local conditions, sensor biases and limitations, initialization and poor quality forcing data). Moreover, there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine observational and modeled information to obtain the most likely estimate of snowpack state. Indeed, by combining all the available sources of information, the implementation of DA schemes can quantify and reduce the uncertainties of the estimations. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model, strengthened by a robust multivariable data assimilation algorithm. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of an Ensemble Kalman Filter (EnKF) scheme enables to assimilate simultaneously ground

Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping

Science.gov (United States)

Kadlec, Jiri

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed

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.

Global 30m 2000-2014 Surface Water Dynamics Map Derived from All Landsat 5, 7, and 8

Science.gov (United States)

Hudson, A.; Hansen, M.

2015-12-01

Water is critical for human life, agriculture, and ecosystems. A better understanding of where it is and how it is changing will enable better management of this valuable resource and guide protection of sensitive ecological areas. Global water maps have typically been representations of surface water at one given time. However, there is both seasonal and interannual variability: rivers meander, lakes disappear, floods arise. To address this ephemeral nature of water, in this study University of Maryland has developed a method that analyzes every Landsat 5, 7, and 8 scene from 1999-2015 to produce global seasonal maps (Winter, Spring, Summer, Fall) of surface water dynamics from 2000-2014. Each Landsat scene is automatically classified into land, water, cloud, haze, shadow, and snow via a decision tree algorithm. The land and water observations are aggregated per pixel into percent occurrence of water in a 3 year moving window for each meteorological season. These annual water percentages form a curve for each season that is discretized into a continuous 3 band RGB map. Frequency of water observation and type of surface water change (loss, gain, peak, or dip) is clearly seen through brightness and hue respectively. Additional data layers include: the year the change began, peak year, minimum year, and the year the change process ended. Currently these maps have been created for 18 1°x1° test tiles scattered around the world, and a portion of the September-November map over Bangladesh is shown below. The entire Landsat archive from 1999-2015 will be processed through a partnership with Google Earth Engine to complete the global product in the coming months. In areas where there is sufficient satellite data density (e.g. the United States), this project could be expanded to 1984-2015. This study provides both scientific researchers and the public an understandable, temporally rich, and globally consistent map showing surface water changes over time.

Water equivalent of snow survey of the Red River Basin and Heart/Cannonball River Basin, March 1978

International Nuclear Information System (INIS)

Feimster, E.L.

1979-10-01

The water equivalent of accumulated snow was estimated in the Red River and Heart/Cannonball River basins and surrounding areas in North Dakota during the period 8 to 17 March 1978. A total of 570 km were flown, covering a 274 km section of the Red River Basin watershed. These lines had been surveyed in March 1974. Twelve flight lines were flown over the North Dakota side of the Red River from a point 23 km south of the Canadian border southward to the city of Fargo, North Dakota. The eight flight lines flown over the Minnesota side of the Red River extended from 23 km south of the Canadian border southward to Breckenridge, Minnesota. Using six flight lines, a total of 120 km were flown in the Heart/Cannonball River Basin, an area southwest of the city of Bismark, North Dakota. This was the first such flight in the Heart/Cannonball River Basin area. Computed weighted average water equivalents on each flight line in the Red River Basin ranged from 4.8 cm to 12.7 cm of water, averaging 7.6 cm for all lines. In the Heart/Cannonball River Basin, the weighted water equivalent ranged from 8.9 cm to 19.1 cm of water, averaging 12.7 cm for all lines. The method used employs the measurement of the natural gamma rays both before and after snow covers the ground

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...

Impact of snow deposition on major and trace element concentrations and elementary fluxes in surface waters of the Western Siberian Lowland across a 1700 km latitudinal gradient

Science.gov (United States)

Shevchenko, Vladimir P.; Pokrovsky, Oleg S.; Vorobyev, Sergey N.; Krickov, Ivan V.; Manasypov, Rinat M.; Politova, Nadezhda V.; Kopysov, Sergey G.; Dara, Olga M.; Auda, Yves; Shirokova, Liudmila S.; Kolesnichenko, Larisa G.; Zemtsov, Valery A.; Kirpotin, Sergey N.

2017-11-01

In order to better understand the chemical composition of snow and its impact on surface water hydrochemistry in the poorly studied Western Siberia Lowland (WSL), the surface layer of snow was sampled in February 2014 across a 1700 km latitudinal gradient (ca. 56.5 to 68° N). We aimed at assessing the latitudinal effect on both dissolved and particulate forms of elements in snow and quantifying the impact of atmospheric input to element storage and export fluxes in inland waters of the WSL. The concentration of dissolved+colloidal (metalloids (As, Sb), Mo and U in the discontinuous to continuous permafrost zone (64-68° N) can be explained solely by melting of accumulated snow. The impact of snow deposition on riverine fluxes of elements strongly increased northward, in discontinuous and continuous permafrost zones of frozen peat bogs. This was consistent with the decrease in the impact of rock lithology on river chemical composition in the permafrost zone of the WSL, relative to the permafrost-free regions. Therefore, the present study demonstrates significant and previously underestimated atmospheric input of many major and trace elements to their riverine fluxes during spring floods. A broader impact of this result is that current estimations of river water fluxes response to climate warming in high latitudes may be unwarranted without detailed analysis of winter precipitation.

Gridded Snow Water Equivalent Reconstruction for Utah Using Forest Inventory and Analysis Tree-Ring Data

Directory of Open Access Journals (Sweden)

Daniel Barandiaran

2017-06-01

Full Text Available Snowpack observations in the Intermountain West are sparse and short, making them difficult for use in depicting past variability and extremes. This study presents a reconstruction of April 1 snow water equivalent (SWE for the period of 1850–1989 using increment cores collected by the U.S. Forest Service, Interior West Forest Inventory and Analysis program (FIA. In the state of Utah, SWE was reconstructed for 38 snow course locations using a combination of standardized tree-ring indices derived from both FIA increment cores and publicly available tree-ring chronologies. These individual reconstructions were then interpolated to a 4-km grid using an objective analysis with elevation correction to create an SWE product. The results showed a significant correlation with observed SWE as well as good correspondence to regional tree-ring-based drought reconstructions. Diagnostic analysis showed statewide coherent climate variability on inter-annual and inter-decadal time-scales, with added geographical details that would not be possible using courser pre-instrumental proxy datasets. This SWE reconstruction provides water resource managers and forecasters with better spatial resolution to examine past variability in snowpack, which will be important as future hydroclimatic variability is amplified by climate change.

Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR

Science.gov (United States)

Singh, D.; Flanner, M.; Millour, E.

2017-12-01

The current version of Laboratoire de Météorologie Dynamique (LMD) Mars GCM (original-MGCM) uses annually repeating (prescribed) albedo values from the Thermal Emission Spectrometer observations. We integrate the Snow, Ice, and Aerosol Radiation (SNICAR) model with MGCM (SNICAR-MGCM) to prognostically determine H2O and CO2 ice cap albedos interactively in the model. Over snow-covered regions mean SNICAR-MGCM albedo is higher by about 0.034 than original-MGCM. Changes in albedo and surface dust content also impact the shortwave energy flux at the surface. SNICAR-MGCM model simulates a change of -1.26 W/m2 shortwave flux on a global scale. Globally, net CO2 ice deposition increases by about 4% over one Martian annual cycle as compared to original-MGCM simulations. SNICAR integration reduces the net mean global surface temperature, and the global surface pressure of Mars by about 0.87% and 2.5% respectively. Changes in albedo also show a similar distribution as dust deposition over the globe. The SNICAR-MGCM model generates albedos with higher sensitivity to surface dust content as compared to original-MGCM. For snow-covered regions, we improve the correlation between albedo and optical depth of dust from -0.91 to -0.97 with SNICAR-MGCM as compared to original-MGCM. Using new diagnostic capabilities with this model, we find that cryospheric surfaces (with dust) increase the global surface albedo of Mars by 0.022. The cryospheric effect is severely muted by dust in snow, however, which acts to decrease the planet-mean surface albedo by 0.06.

Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions

Directory of Open Access Journals (Sweden)

P. P. Ebner

2017-07-01

Full Text Available Stable water isotopes (δ18O obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect of airflow on the snow isotopic composition through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapour is elucidated. The observed disequilibrium between snow and vapour isotopes led to the exchange of isotopes between snow and vapour under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process. These findings are pertinent to the interpretation of the records of stable isotopes of water from ice cores. These laboratory measurements suggest that a highly resolved climate history is relevant for the interpretation of the snow isotopic composition in the field.

Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

Science.gov (United States)

Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

2016-04-01

Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to

Needs assessment to strengthen capacity in water and sanitation research in Africa: experiences of the African SNOWS consortium.

Science.gov (United States)

Hunter, Paul R; Abdelrahman, Samira H; Antwi-Agyei, Prince; Awuah, Esi; Cairncross, Sandy; Chappell, Eileen; Dalsgaard, Anders; Ensink, Jeroen H J; Potgieter, Natasha; Mokgobu, Ingrid; Muchiri, Edward W; Mulogo, Edgar; van der Es, Mike; Odai, Samuel N

2014-12-15

Despite its contribution to global disease burden, diarrhoeal disease is still a relatively neglected area for research funding, especially in low-income country settings. The SNOWS consortium (Scientists Networked for Outcomes from Water and Sanitation) is funded by the Wellcome Trust under an initiative to build the necessary research skills in Africa. This paper focuses on the research training needs of the consortium as identified during the first three years of the project. We reviewed the reports of two needs assessments. The first was a detailed needs assessment led by one northern partner, with follow-up visits which included reciprocal representation from the African universities. The second assessment, led by another northern partner, focused primarily on training needs. The reports from both needs assessments were read and stated needs were extracted and summarised. Key common issues identified in both assessments were supervisory skills, applications for external research funding, research management, and writing for publication in the peer-reviewed scientific literature. The bureaucratisation of university processes and inconsistencies through administration processes also caused problems. The lack of specialist laboratory equipment presented difficulties, particularly of inaccessibility through a lack of skilled staff for operation and maintenance, and of a budget provision for repairs and running costs. The lack of taught PhD modules and of research training methods also caused problems. Institutionally, there were often no mechanisms for identifying funding opportunities. On the other hand, grantees were often unable to understand or comply with the funders' financial and reporting requirements and were not supported by their institution. Skills in staff recruitment, retention, and performance were poor, as were performance in proposal and paper writing. The requirements for ethical clearance were often not known and governance issues not understood

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

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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.

Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States

Science.gov (United States)

Storck, Pascal; Lettenmaier, Dennis P.; Bolton, Susan M.

2002-11-01

The results of a 3 year field study to observe the processes controlling snow interception by forest canopies and under canopy snow accumulation and ablation in mountain maritime climates are reported. The field study was further intended to provide data to develop and test models of forest canopy effects on beneath-canopy snowpack accumulation and melt and the plot and stand scales. Weighing lysimeters, cut-tree experiments, and manual snow surveys were deployed at a site in the Umpqua National Forest, Oregon (elevation 1200 m). A unique design for a weighing lysimeter was employed that allowed continuous measurements of snowpack evolution beneath a forest canopy to be taken at a scale unaffected by variability in canopy throughfall. Continuous observations of snowpack evolution in large clearings were made coincidentally with the canopy measurements. Large differences in snow accumulation and ablation were observed at sites beneath the forest canopy and in large clearings. These differences were not well described by simple relationships between the sites. Over the study period, approximately 60% of snowfall was intercepted by the canopy (up to a maximum of about 40 mm water equivalent). Instantaneous sublimation rates exceeded 0.5 mm per hour for short periods. However, apparent average sublimation from the intercepted snow was less than 1 mm per day and totaled approximately 100 mm per winter season. Approximately 72 and 28% of the remaining intercepted snow was removed as meltwater drip and large snow masses, respectively. Observed differences in snow interception rate and maximum snow interception capacity between Douglas fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), and lodgepole pine (Pinus contorta) were minimal.

Analysis of Extreme Snow Water Equivalent Data in Central New Hampshire

Science.gov (United States)

Vuyovich, C.; Skahill, B. E.; Kanney, J. F.; Carr, M.

2017-12-01

Heavy snowfall and snowmelt-related events have been linked to widespread flooding and damages in many regions of the U.S. Design of critical infrastructure in these regions requires spatial estimates of extreme snow water equivalent (SWE). In this study, we develop station specific and spatially explicit estimates of extreme SWE using data from fifteen snow sampling stations maintained by the New Hampshire Department of Environmental Services. The stations are located in the Mascoma, Pemigewasset, Winnipesaukee, Ossipee, Salmon Falls, Lamprey, Sugar, and Isinglass basins in New Hampshire. The average record length for the fifteen stations is approximately fifty-nine years. The spatial analysis of extreme SWE involves application of two Bayesian Hierarchical Modeling methods, one that assumes conditional independence, and another which uses the Smith max-stable process model to account for spatial dependence. We also apply additional max-stable process models, albeit not in a Bayesian framework, that better model the observed dependence among the extreme SWE data. The spatial process modeling leverages readily available and relevant spatially explicit covariate data. The noted additional max-stable process models also used the nonstationary winter North Atlantic Oscillation index, which has been observed to influence snowy weather along the east coast of the United States. We find that, for this data set, SWE return level estimates are consistently higher when derived using methods which account for the observed spatial dependence among the extreme data. This is particularly significant for design scenarios of relevance for critical infrastructure evaluation.

Trace analysis of iron in environmental water and snow samples from Poland

International Nuclear Information System (INIS)

Golimowski, J.

1989-01-01

A voltammetric method for the determination of iron at detection limit of 4 μg/l is described, using the catalytic current of the reduction of the Fe(III)-triethanolamine (TEA) complex in the presence of bromate ions. The determination was performed at a mercury hanging drop electrode without preconcentration, using the TEA alkaline solution as a supporting electrolyte and the differential pulse technique. A peak current for the Fe-(III)-TEA catalytic reduction was observed at a potential of -1.0 V (Ag/AgCl saturated electrode). The influence of TEA, BrO 3 and NaOH concentrations on the peak height was studied. It was found that a 100-fold excess of Mn, a 50-fold excess of Cr(VI) and Zn did not interfere in the determination. This method was applied to the determination of iron in water, snow and waste water samples

Remote sensing techniques and their urgency for snow and glacier mapping in Himalayas

Energy Technology Data Exchange (ETDEWEB)

Das, M C; Chattopadhyay, S N; Murty, A S

1979-01-01

The mighty Himalayas are great repositories of snow and ice. The river system of Indus, the Ganges and Brahmaputra owe their perennial flow to these large snow and ice masses. The demand for systematic exploitation of water resources of these great mountain ranges calls for a thorough inventory of these water-holding bodies. Rough and difficult terrain, inclement weather and very inaccessible altitudes stood in the way for better understanding of these vast sources of life giving water. In this paper, the urgency for snow and glacier mapping of this Himalayan region is highlighted in the light of the fast evolving techniques of remote sensing. Aerospace photography, use of radars and infrared sensing methods microwave sensing, and application of gamma radiation with the help of satellites, are examined for their present status and future potential for application in this ice and snow capped, top of the world.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

Science.gov (United States)

Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

2018-06-01

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

Springtime warming and reduced snow cover from carbonaceous particles

Directory of Open Access Journals (Sweden)

M. G. Flanner

2009-04-01

Full Text Available Boreal spring climate is uniquely susceptible to solar warming mechanisms because it has expansive snow cover and receives relatively strong insolation. Carbonaceous particles can influence snow coverage by warming the atmosphere, reducing surface-incident solar energy (dimming, and reducing snow reflectance after deposition (darkening. We apply a range of models and observations to explore impacts of these processes on springtime climate, drawing several conclusions: 1 Nearly all atmospheric particles (those with visible-band single-scatter albedo less than 0.999, including all mixtures of black carbon (BC and organic matter (OM, increase net solar heating of the atmosphere-snow column. 2 Darkening caused by small concentrations of particles within snow exceeds the loss of absorbed energy from concurrent dimming, thus increasing solar heating of snowpack as well (positive net surface forcing. Over global snow, we estimate 6-fold greater surface forcing from darkening than dimming, caused by BC+OM. 3 Equilibrium climate experiments suggest that fossil fuel and biofuel emissions of BC+OM induce 95% as much springtime snow cover loss over Eurasia as anthropogenic carbon dioxide, a consequence of strong snow-albedo feedback and large BC+OM emissions from Asia. 4 Of 22 climate models contributing to the IPCC Fourth Assessment Report, 21 underpredict the rapid warming (0.64°C decade⁻¹ observed over springtime Eurasia since 1979. Darkening from natural and anthropogenic sources of BC and mineral dust exerts 3-fold greater forcing on springtime snow over Eurasia (3.9 W m⁻² than North America (1.2 W m⁻². Inclusion of this forcing significantly improves simulated continental warming trends, but does not reconcile the low bias in rate of Eurasian spring snow cover decline exhibited by all models, likely because BC deposition trends are negative or near-neutral over much of Eurasia. Improved Eurasian

Black carbon in the atmosphere and snow, from pre-industrial times until present

Directory of Open Access Journals (Sweden)

R. B. Skeie

2011-07-01

Full Text Available The distribution of black carbon (BC in the atmosphere and the deposition of BC on snow surfaces since pre-industrial time until present are modelled with the Oslo CTM2 model. The model results are compared with observations including recent measurements of BC in snow in the Arctic. The global mean burden of BC from fossil fuel and biofuel sources increased during two periods. The first period, until 1920, is related to increases in emissions in North America and Europe, and the last period after 1970 are related mainly to increasing emissions in East Asia. Although the global burden of BC from fossil fuel and biofuel increases, in the Arctic the maximum atmospheric BC burden as well as in the snow was reached in 1960s, with a slight reduction thereafter. The global mean burden of BC from open biomass burning sources has not changed significantly since 1900. With current inventories of emissions from open biomass sources, the modelled burden of BC in snow and in the atmosphere north of 65° N is small compared to the BC burden of fossil fuel and biofuel origin. From the concentration changes radiative forcing time series due to the direct aerosol effect as well as the snow-albedo effect is calculated for BC from fossil fuel and biofuel. The calculated radiative forcing in 2000 for the direct aerosol effect is 0.35 W m⁻² and for the snow-albedo effect 0.016 W m⁻² in this study. Due to a southward shift in the emissions there is an increase in the lifetime of BC as well as an increase in normalized radiative forcing, giving a change in forcing per unit of emissions of 26 % since 1950.

Snow-avalanche impact craters in southern Norway: Their morphology and dynamics compared with small terrestrial meteorite craters

Science.gov (United States)

Matthews, John A.; Owen, Geraint; McEwen, Lindsey J.; Shakesby, Richard A.; Hill, Jennifer L.; Vater, Amber E.; Ratcliffe, Anna C.

2017-11-01

This regional inventory and study of a globally uncommon landform type reveals similarities in form and process between craters produced by snow-avalanche and meteorite impacts. Fifty-two snow-avalanche impact craters (mean diameter 85 m, range 10-185 m) were investigated through field research, aerial photographic interpretation and analysis of topographic maps. The craters are sited on valley bottoms or lake margins at the foot of steep avalanche paths (α = 28-59°), generally with an easterly aspect, where the slope of the final 200 m of the avalanche path (β) typically exceeds 15°. Crater diameter correlates with the area of the avalanche start zone, which points to snow-avalanche volume as the main control on crater size. Proximal erosional scars ('blast zones') up to 40 m high indicate up-range ejection of material from the crater, assisted by air-launch of the avalanches and impulse waves generated by their impact into water-filled craters. Formation of distal mounds up to 12 m high of variable shape is favoured by more dispersed down-range deposition of ejecta. Key to the development of snow-avalanche impact craters is the repeated occurrence of topographically-focused snow avalanches that impact with a steep angle on unconsolidated sediment. Secondary craters or pits, a few metres in diameter, are attributed to the impact of individual boulders or smaller bodies of snow ejected from the main avalanche. The process of crater formation by low-density, low-velocity, large-volume snow flows occurring as multiple events is broadly comparable with cratering by single-event, high-density, high-velocity, small-volume projectiles such as small meteorites. Simple comparative modelling of snow-avalanche events associated with a crater of average size (diameter 85 m) indicates that the kinetic energy of a single snow-avalanche impact event is two orders of magnitude less than that of a single meteorite-impact event capable of producing a crater of similar size

A Vision for an International Multi-Sensor Snow Observing Mission

Science.gov (United States)

Kim, Edward

2015-01-01

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

What color should snow algae be and what does it mean for glacier melt?

Science.gov (United States)

Dial, R. J.; Ganey, G. Q.; Loso, M.; Burgess, A. B.; Skiles, M.

2017-12-01

Specialized microbes colonize glaciers and ice sheets worldwide and, like all organisms, they are unable to metabolize water in its solid form. It is well understood that net solar radiation controls melt in almost all snow and ice covered environments, and theoretical and empirical studies have documented the substantial reduction of albedo by these microbes both on ice and on snow, implicating a microbial role in glacier melt. If glacial microbiomes are limited by liquid water, and the albedo-reducing properties of individual cells enhance melt rates, then natural selection should favor those microbes that melt ice and snow crystals most efficiently. Here we: (1) argue that natural selection favors a red color on snow and a near-black color on ice based on instantaneous radiative forcing. (2) Review results of the first replicated, controlled field experiment to both quantify the impact of microbes on snowmelt in "red-snow" communities and demonstrate their water-limitation and (3) show the extent of snow-algae's spatial distribution and estimate their contribution to snowmelt across a large Alaskan icefield using remote sensing. On the 700 km2 of a 2,000 km2 maritime icefield in Alaska where red-snow was present, microbes increased snowmelt over 20% by volume, a percentage likely to increase as the climate warms and particulate pollution intensifies with important implications for models of sea level rise.

Agricultural Water Use under Global Change

Science.gov (United States)

Zhu, T.; Ringler, C.; Rosegrant, M. W.

2008-12-01

Irrigation is by far the single largest user of water in the world and is projected to remain so in the foreseeable future. Globally, irrigated agricultural land comprises less than twenty percent of total cropland but produces about forty percent of the world's food. Increasing world population will require more food and this will lead to more irrigation in many areas. As demands increase and water becomes an increasingly scarce resource, agriculture's competition for water with other economic sectors will be intensified. This water picture is expected to become even more complex as climate change will impose substantial impacts on water availability and demand, in particular for agriculture. To better understand future water demand and supply under global change, including changes in demographic, economic and technological dimensions, the water simulation module of IMPACT, a global water and food projection model developed at the International Food Policy Research Institute, is used to analyze future water demand and supply in agricultural and several non-agricultural sectors using downscaled GCM scenarios, based on water availability simulation done with a recently developed semi-distributed global hydrological model. Risk analysis is conducted to identify countries and regions where future water supply reliability for irrigation is low, and food security may be threatened in the presence of climate change. Gridded shadow values of irrigation water are derived for global cropland based on an optimization framework, and they are used to illustrate potential irrigation development by incorporating gridded water availability and existing global map of irrigation areas.

Technical snow production in skiing areas: conditions, practice, monitoring and modelling. A case study in Mayrhofen/Austria

Science.gov (United States)

Strasser, Ulrich; Hanzer, Florian; Marke, Thomas; Rothleitner, Michael

2017-04-01

The production of technical snow today is a self-evident feature of modern alpine skiing resort management. Millions of Euros are invested every year for the technical infrastructure and its operation to produce a homogeneous and continuing snow cover on the skiing slopes for the winter season in almost every larger destination in the Alps. In Austria, skiing tourism is a significant factor of the national economic structure. We present the framing conditions of technical snow production in the mid-size skiing resort of Mayrhofen (Zillertal Alps/Austria, 136 km slopes, elevation range 630 - 2.500 m a.s.l.). Production conditions are defined by the availability of water, the planned date for the season opening, and the climatic conditions in the weeks before. By means of an adapted snow production strategy an attempt is made to ecologically and economically optimize the use of water and energy resources. Monitoring of the snow cover is supported by a network of low-cost sensors and mobile snow depth recordings. Finally, technical snow production is simulated with the spatially distributed, physically based hydroclimatological model AMUNDSEN. The model explicitly considers individual snow guns and distributes the produced snow along the slopes. The amount of simulated snow produced by each device is a function of its type, of actual wet-bulb temperature at the location, of ski area infrastructure (in terms of water supply and pumping capacity), and of snow demand.

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

Science.gov (United States)

Galin, Natalia; Worby, Anthony; Markus, Thorsten; Leuschen, Carl; Gogineni, Prasad

2012-01-01

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

Validation of A Global Hydrological Model

Science.gov (United States)

Doell, P.; Lehner, B.; Kaspar, F.; Vassolo, S.

Freshwater availability has been recognized as a global issue, and its consistent quan- tification not only in individual river basins but also at the global scale is required to support the sustainable use of water. The Global Hydrology Model WGHM, which is a submodel of the global water use and availability model WaterGAP 2, computes sur- face runoff, groundwater recharge and river discharge at a spatial resolution of 0.5. WGHM is based on the best global data sets currently available, including a newly developed drainage direction map and a data set of wetlands, lakes and reservoirs. It calculates both natural and actual discharge by simulating the reduction of river discharge by human water consumption (as computed by the water use submodel of WaterGAP 2). WGHM is calibrated against observed discharge at 724 gauging sta- tions (representing about 50% of the global land area) by adjusting a parameter of the soil water balance. It not only computes the long-term average water resources but also water availability indicators that take into account the interannual and seasonal variability of runoff and discharge. The reliability of the model results is assessed by comparing observed and simulated discharges at the calibration stations and at se- lected other stations. We conclude that reliable results can be obtained for basins of more than 20,000 km2. In particular, the 90% reliable monthly discharge is simu- lated well. However, there is the tendency that semi-arid and arid basins are modeled less satisfactorily than humid ones, which is partially due to neglecting river channel losses and evaporation of runoff from small ephemeral ponds in the model. Also, the hydrology of highly developed basins with large artificial storages, basin transfers and irrigation schemes cannot be simulated well. The seasonality of discharge in snow- dominated basins is overestimated by WGHM, and if the snow-dominated basin is uncalibrated, discharge is likely to be underestimated

Through the Looking Glass: Droughtorama to Snowpocalypse in the Sierra Nevada as studied with the NASA Airborne Snow Observatory

Science.gov (United States)

Painter, T. H.; Bormann, K.; Deems, J. S.; Hedrick, A. R.; Marks, D. G.; Skiles, M.; Stock, G. M.

2017-12-01

Across the last five years, the Sierra Nevada has seen increasing drought and then an abrupt return to a top five snowpack. Fortunately, the NASA Airborne Snow Observatory has been flying the Central Sierra Nevada since the spring of 2013, quantifying critical mountain basins' snow water equivalent and snow albedo. The huge variation of snowpack years captured by the NASA ASO is of enormous benefit to water cycle science, ecosystem science, and water management utilization of ASO data and its modeling. It allows a much broader understanding of mountain basin snow season cases for understanding snowmelt runoff, snow/rain mixes, snowfall distribution, evapotranspiration, soil moisture, and glacier mass balance. For water management, trust in empirical and physically-based modeling from the ASO data for application anywhere in the range of snow years is greatly improved by having consistency in that modeling with the span of years ASO has characterized. The NASA ASO was designed to characterize mountain snowpack and fill this void in water cycle science. Our original conversations with partner California Department of Water Resources in 2011 focused on the utility of ASO for flood risk mitigation, given the large snowfall of that year. However, from 2012 through 2016, California snowpacks expressed horrible drought, reaching the nadir in 2015 with the lowest snowpack on record. The 2016 snowpack was nearly normal according to snow pillows and snow courses (ASO's record is too short to define a `normal' year). However, 2017 had enormous snowfall in January and February, keeping snow pillows on track with the largest year on record, 1982-83. However, March backed off and the record year was lost. Still, accumulation was enormous. In parts of the San Joaquin basin, snow depths were > 30 m. The sum of near April 1 ASO total basin SWE for 2013 through 2016 in the Tuolumne Basin was only 92% of the near April 1, 2017 acquisition. In addition to the large accumulation of

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

Making up for lost snow: lessons from a warming Sierra Nevada

Science.gov (United States)

Bales, R. C.

2017-12-01

Snowpack- and glacier-dependent river basins are home to over 1.2 billion people, one-sixth of the world's current population. These areas face severe challenges in a warmer climate, as declines in snow resources put more pressure on dams and groundwater. Closer to home, the seasonal snowpacks in California's Sierra Nevada provide water storage to both sustain productive forests and support the world's 6th largest economy. Rivers draining the Sierra supply the state's large cities, plus agricultural areas that provide nearly half of the nation's fruits and vegetables. Water storage is central to water security, especially given California's hot dry summers and high interannual variability in precipitation. On average seasonal snowpacks store about half as much water as do dams on Sierra rivers; and both the magnitude and duration of snowpack storage are decreasing. Precipitation amount and snow accumulation across the mountains in any given day, month or year remain uncertain. As historical index-statistical methods for hydrologic forecasts give way to tools based on mass and energy balances distributed across the landscape, opportunities are arising to broadly implement spatial measurements of snowpack storage and the equally important regolith-water storage. Advances in applying satellite and aircraft remote sensing, plus spatially distributed wireless-sensor networks, are filling this need. These same unprecedented data are driving process understanding to improve knowledge of snow-energy-forest interactions, snowmelt estimates, and hydrologic forecasts for hydropower, water supply, and flood control. Estimating the value of snowpacks and how they are changing provides a baseline for evaluating investments in restoration of headwater forests that will affect snowmelt runoff, and in providing replacement storage as snow declines. With California facing billions of dollars of green and grey infrastructure improvements, which must be compatible with the state

Experimental Insights on Natural Lava-Ice/Snow Interactions and Their Implications for Glaciovolcanic and Submarine Eruptions

Science.gov (United States)

Edwards, B. R.; Karson, J.; Wysocki, R.; Lev, E.; Bindeman, I. N.; Kueppers, U.

2012-12-01

Lava-ice-snow interactions have recently gained global attention through the eruptions of ice-covered volcanoes, particularly from Eyjafjallajokull in south-central Iceland, with dramatic effects on local communities and global air travel. However, as with most submarine eruptions, direct observations of lava-ice/snow interactions are rare. Only a few hundred potentially active volcanoes are presently ice-covered, these volcanoes are generally in remote places, and their associated hazards make close observation and measurements dangerous. Here we report the results of the first large-scale experiments designed to provide new constraints on natural interactions between lava and ice/snow. The experiments comprised controlled effusion of tens of kilograms of melted basalt on top of ice/snow, and provide insights about observations from natural lava-ice-snow interactions including new constraints for: 1) rapid lava advance along the ice-lava interface; 2) rapid downwards melting of lava flows through ice; 3) lava flow exploitation of pre-existing discontinuities to travel laterally beneath and within ice; and 4) formation of abundant limu o Pele and non-explosive vapor transport from the base to the top of the lava flow with minor O isotope exchange. The experiments are consistent with observations from eruptions showing that lava is more efficient at melting ice when emplaced on top of the ice as opposed to beneath the ice, as well as the efficacy of tephra cover for slowing melting. The experimental extrusion rates are as within the range of those for submarine eruptions as well, and reproduce some features seen in submarine eruptions including voluminous production of gas rich cavities within initially anhydrous lavas and limu on lava surfaces. Our initial results raise questions about the possibility of secondary ingestion of water by submarine and glaciovolcanic lava flows, and the origins of apparent primary gas cavities in those flows. Basaltic melt moving down

The Modification of Orographic Snow Growth Processes by Cloud Nucleating Aerosols

Science.gov (United States)

Cotton, W. R.; Saleeby, S.

2011-12-01

Cloud nucleating aerosols have been found to modify the amount and spatial distribution of snowfall in mountainous areas where riming growth of snow crystals is known to contribute substantially to the total snow water equivalent precipitation. In the Park Range of Colorado, a 2km deep supercooled liquid water orographic cloud frequently enshrouds the mountaintop during snowfall events. This leads to a seeder-feeder growth regime in which snow falls through the orographic cloud and collects cloud water prior to surface deposition. The addition of higher concentrations of cloud condensation nuclei (CCN) modifies the cloud droplet spectrum toward smaller size droplets and suppresses riming growth. Without rime growth, the density of snow crystals remains low and horizontal trajectories carry them further downwind due to slower vertical fall speeds. This leads to a downwind shift in snowfall accumulation at high CCN concentrations. Cloud resolving model simulations were performed (at 600m horizontal grid spacing) for six snowfall events over the Park Range. The chosen events were well simulated and occurred during intensive observations periods as part of two winter field campaigns in 2007 and 2010 based at Storm Peak Laboratory in Steamboat Springs, CO. For each event, sensitivity simulations were run with various initial CCN concentration vertical profiles that represent clean to polluted aerosol environments. Microphysical budget analyses were performed for these simulations in order to determine the relative importance of the various cloud properties and growth processes that contribute to precipitation production. Observations and modeling results indicate that initial vapor depositional growth of snow tends to be maximized within about 1km of mountaintop above the windward slope while the majority of riming growth occurs within 500m of mountaintop. This suggests that precipitation production is predominantly driven by locally enhanced orography. The large scale

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.

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.

Development of snow water equivalent survey methods using airborne gamma measurements. Research progress, January 1975--September 1975 and suggested directions for future work

International Nuclear Information System (INIS)

Fritzsche, A.; Jupiter, C.

1975-01-01

This is a summary of the progress made during the period March 1975 through September 1975 on EG and G's support of the National Oceanic and Atmospheric Administration for development of airborne techniques for measurement of the water equivalent of snow and soil moisture. The work included a series of snow and soil moisture surveys and development of a new detector and data acquisition system. The status of this work is summarized here together with a review of plans for the immediate future

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

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

Science.gov (United States)

Snauffer, Andrew M.; Hsieh, William W.; Cannon, Alex J.; Schnorbus, Markus A.

2018-03-01

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.

The Global Politics of Water Grabbing

NARCIS (Netherlands)

Franco, J.; Mehta, L.; Veldwisch, G.J.A.

2013-01-01

The contestation and appropriation of water is not new, but it has been highlighted by recent global debates on land grabbing. Water grabbing takes place in a field that is locally and globally plural-legal. Formal law has been fostering both land and water grabs but formal water and land management

The global politics of water grabbing

NARCIS (Netherlands)

Franco, Jennifer; Mehta, Lyla; Veldwisch, Gert Jan

2016-01-01

The contestation and appropriation of water is not new, but it has been highlighted by recent global debates on land grabbing. Water grabbing takes place in a field that is locally and globally plural-legal. Formal law has been fostering both land and water grabs but formal water and land

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.

Changing Snow Cover and Stream Discharge in the Western United States - Wind River Range, Wyoming

Science.gov (United States)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Barton, Jonathan S.; Riggs, George A.

2011-01-01

Earlier onset of springtime weather has been documented in the western United States over at least the last 50 years. Because the majority (>70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack has important implications for the management of water resources. We studied ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover products, 40 years of stream discharge and meteorological station data and 30 years of snow-water equivalent (SWE) SNOw Telemetry (SNOTEL) data in the Wind River Range (WRR), Wyoming. Results show increasing air temperatures for.the 40-year study period. Discharge from streams in WRR drainage basins show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades. Changes in streamflow may be related to increasing air temperatures which are probably contributing to a reduction in snow cover, although no trend of either increasingly lower streamflow or earlier snowmelt was observed within the decade of the 2000s. And SWE on 1 April does not show an expected downward trend from 1980 to 2009. The extent of snow cover derived from the lowest-elevation zone of the WRR study area is strongly correlated (r=0.91) with stream discharge on 1 May during the decade of the 2000s. The strong relationship between snow cover and streamflow indicates that MODIS snow-cover maps can be used to improve management of water resources in the drought-prone western U.S.

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

The PCR-GLOBWB global hydrological reanalysis product

Science.gov (United States)

Wanders, Niko; Bierkens, Marc; Sutanudjaja, Edwin; van Beek, Rens

2014-05-01

Accurate and long time series of hydrological data are important for understanding land surface water and energy budgets in many parts of the world, as well as for improving real-time hydrological monitoring and climate change anticipation. The ultimate goal of the present work is to produce a multi-decadal "land surface hydrological reanalysis" dataset with retrospective and updated hydrological states and fluxes that are constrained to available in-situ river discharge measurements. Here we use PCR-GLOBWB (van Beek et al., 2011), which is a large-scale hydrological model intended for global to regional studies. PCR-GLOBWB provides a grid-based representation of terrestrial hydrology with a typical spatial resolution of approximately 50×50 km (currently 0.5° globally) on a daily basis. For each grid cell, PCR-GLOBWB simulates moisture storage in two vertically stacked soil layers as well as the water exchange between the soil and the atmosphere and the underlying groundwater reservoir. Exchange to the atmosphere comprises precipitation, evaporation and transpiration, as well as snow accumulation and melt, which are all simulated by considering vegetation phenology and sub-grid variations of elevation, land cover and soil saturation distribution. The model includes improved schemes for runoff-infiltration partitioning, interflow, groundwater recharge and baseflow, as well as river routing of discharge. It also dynamically simulates water storage in reservoirs, water demand and the withdrawal, allocation and consumptive use of surface water and groundwater resources. By embedding the PCR-GLOBWB model in an Ensemble Kalman Filter framework, we calibrate the model parameters based on the discharge observations from the Global Runoff Data Centre. The parameters calibrated are related to snow accumulation and melt, runoff-infiltration partitioning, groundwater recharge, channel discharge and baseflow processes, as well as pre-factors to correct forcing precipitation

Influence of beech and spruce sub-montane forests on snow cover in Poľana Biosphere Reserve

Czech Academy of Sciences Publication Activity Database

Šatala, T.; Tesař, Miroslav; Hanzelová, M.; Bartík, M.; Šípek, Václav; Škvarenina, J.; Minďáš, J.; Waldhauserová, P.D.

2017-01-01

Roč. 72, č. 8 (2017), s. 854-861 ISSN 0006-3088 R&D Projects: GA ČR GA16-05665S Institutional support: RVO:67985874 Keywords : snow water equivalent * snow depth * snow accumulation * snow melting Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 0.759, year: 2016

Organic micropollutants in snow. Distribution and behaviour during snowmelt

Energy Technology Data Exchange (ETDEWEB)

Herrmann, R.; Schoendorf, T.; Schrimpff, E.; Simmleit, N.

1988-03-01

The regional patterns of the concentrations of organic micropollutants can be explained by local emissions and enhanced wet and dry deposition of pollutants from atmospheric long-range transport especially in exposed elevations. During the melting process in deep snow covers, particle-bound pollutants with a high distribution coefficient will not be released until the final stage of snow melting in terms of a surge of high concentrations. Organic micropollutants with a small distribution coefficient, by contrast, are also dissolved and flushed out with the first melt water. As a result, this pollutant group features two concentration peaks: one in the first melt water and another in the last melt water. The melt water seeping into the soil and the underlying aquifers (e.g. in karst areas) very rapidly loses its load of pollutants by adsorption in the soil zone.

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 ...

Regional pattern of snow characteristics around Antarctic Lake Vostok

Science.gov (United States)

Vladimirova, Diana; Ekaykin, Alexey; Popov, Sergey; Shibaev, Yuriy; Kozachek, Anna; Lipenkov, Vladimir

2015-04-01

Since 1998 Russian Antarctic Expedition has organized several scientific traverses in the region of subglacial Lake Vostok mainly devoted to the radar echo and seismic sounding of the glacier and water (the results have been published elsewhere). Along with the geophysical studies, a number of glaciological investigations have been carried out: snow pit digging, installation of accumulation stakes, snow sampling to study the stable water isotope content. Here we for the first time present a synthesis of these works and demonstrate a series of maps that characterize the snow density, isotope content and accumulation rate the studied region. A general tendency of the snow accumulation rate and isotope content is a significant increase from south (south-west) to north (north-east) from 35 to 23 mm w.e. per year and from -53,3 ‰ to -57,3 ‰ for delta oxygen-18 respectively, which likely reflects the continental-scale pattern, i.e., increase from inland to the coast. Deuterium excess varies from 11,7 ‰ to 16,3 ‰ is negatively correlated with the isotope content, which is typical for central Antarctica. The snow density demonstrate different pattern: higher values offshore the lake (up to 0,356 g/cm^3), and lower values within the lake's shoreline (lower limit is 0,328 g/cm^3). We suggest that this is related to the katabatic wind activity: very flat nearly horizontal surface of the glacier above the lake is not favorable for the strong winds, which leads to lower surface snow density. Superimposed on the main trend is the regional pattern, namely, curved contour lines in the middle part of the lake. We suggest that it may be related to the local anomalies of the snow drift by wind. Indeed, on the satellite images of the lake one can easily see a snowdrift stretching from the lake's western shore downwind in the middle part of the lake. The isolines of delta oxygen-18 and deuterium excess become perpendicular to each other in the north part of the lake which also

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

Directory of Open Access Journals (Sweden)

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.

Snow and ice perturbation during historical volcanic eruptions and the formation of lahars and floods

Science.gov (United States)

Major, Jon J.; Newhall, Christopher G.

1989-10-01

Historical eruptions have produced lahars and floods by perturbing snow and ice at more than 40 volcanoes worldwide. Most of these volcanoes are located at latitudes higher than 35°; those at lower latitudes reach altitudes generally above 4000 m. Volcanic events can perturb mantles of snow and ice in at least five ways: (1) scouring and melting by flowing pyroclastic debris or blasts of hot gases and pyroclastic debris, (2) surficial melting by lava flows, (3) basal melting of glacial ice or snow by subglacial eruptions or geothermal activity, (4) ejection of water by eruptions through a crater lake, and (5) deposition of tephra fall. Historical records of volcanic eruptions at snow-clad volcanoes show the following: (1) Flowing pyroclastic debris (pyroclastic flows and surges) and blasts of hot gases and pyroclastic debris are the most common volcanic events that generate lahars and floods; (2) Surficial lava flows generally cannot melt snow and ice rapidly enough to form large lahars or floods; (3) Heating the base of a glacier or snowpack by subglacial eruptions or by geothermal activity can induce basal melting that may result in ponding of water and lead to sudden outpourings of water or sediment-rich debris flows; (4) Tephra falls usually alter ablation rates of snow and ice but generally produce little meltwater that results in the formation of lahars and floods; (5) Lahars and floods generated by flowing pyroclastic debris, blasts of hot gases and pyroclastic debris, or basal melting of snow and ice commonly have volumes that exceed 105 m3. The glowing lava (pyroclastic flow) which flowed with force over ravines and ridges...gathered in the basin quickly and then forced downwards. As a result, tremendously wide and deep pathways in the ice and snow were made and produced great streams of water (Wolf 1878).

Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — This data set consists of a Northern Hemisphere subset of the Canadian Meteorological Centre (CMC) operational global daily snow depth analysis. Data include daily...

Predicting Clear-Sky Reflectance Over Snow/Ice in Polar Regions

Science.gov (United States)

Chen, Yan; Sun-Mack, Sunny; Arduini, Robert F.; Hong, Gang; Minnis, Patrick

2015-01-01

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the nearinfrared (NIR; 1.24, 1.6 or 2.13 micrometers), visible (VIS; 0.63 micrometers) and vegetation (VEG; 0.86 micrometers) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) to help identify clouds and retrieve their properties in both snow-free and snow-covered conditions. Thus, it is critical to have reliable distributions of clear-sky albedo for all of these channels. In CERES Edition 4 (Ed4), the 1.24-micrometer channel is used to retrieve cloud optical depth over snow/ice-covered surfaces. Thus, it is especially critical to accurately predict the 1.24-micrometer clear-sky albedo alpha and reflectance rho for a given location and time. Snow albedo and reflectance patterns are very complex due to surface texture, particle shapes and sizes, melt water, and vegetation protrusions from the snow surface. To minimize those effects, this study focuses on the permanent snow cover of Antarctica where vegetation is absent and melt water is minimal. Clear-sky albedos are determined as a function of solar zenith angle (SZA) from observations over all scenes determined to be cloud-free to produce a normalized directional albedo model (DRM). The DRM is used to develop alpha(SZA=0 degrees) on 10 foot grid for each season. These values provide the basis for predicting r at any location and set of viewing & illumination conditions. This paper examines the accuracy of this approach for two theoretical snow surface reflectance models.