Measuring spatiotemporal variation in snow optical grain size under a subalpine forest canopy using contact spectroscopy

NASA Astrophysics Data System (ADS)

Molotch, Noah P.; Barnard, David M.; Burns, Sean P.; Painter, Thomas H.

2016-09-01

The distribution of forest cover exerts strong controls on the spatiotemporal distribution of snow accumulation and snowmelt. The physical processes that govern these controls are poorly understood given a lack of detailed measurements of snow states. In this study, we address one of many measurement gaps by using contact spectroscopy to measure snow optical grain size at high spatial resolution in trenches dug between tree boles in a subalpine forest. Trenches were collocated with continuous measurements of snow depth and vertical profiles of snow temperature and supplemented with manual measurements of snow temperature, geometric grain size, grain type, and density from trench walls. There was a distinct difference in snow optical grain size between winter and spring periods. In winter and early spring, when facetted snow crystal types were dominant, snow optical grain size was 6% larger in canopy gaps versus under canopy positions; a difference that was smaller than the measurement uncertainty. By midspring, the magnitude of snow optical grain size differences increased dramatically and patterns of snow optical grain size became highly directional with 34% larger snow grains in areas south versus north of trees. In winter, snow temperature gradients were up to 5-15°C m-1 greater under the canopy due to shallower snow accumulation. However, in canopy gaps, snow depths were greater in fall and early winter and therefore more significant kinetic growth metamorphism occurred relative to under canopy positions, resulting in larger snow grains in canopy gaps. Our findings illustrate the novelty of our method of measuring snow optical grain size, allowing for future studies to advance the understanding of how forest and meteorological conditions interact to impact snowpack evolution.

Trace gas and vegetation feedback responses of Alaskan tussock tundra to long-term snow depth manipulations

NASA Astrophysics Data System (ADS)

Ebbs, L. M.; Taneva, L.; Sullivan, P.; Welker, J. M.

2009-12-01

Changes in the precipitation and temperature regimes in Northern Alaska are manifesting themselves through shifts in sea ice, vegetation traits, animal migration timing and hydrologic dynamics. Changes in precipitation and soil temperature result in changes in plant mineral nutrition, soil nutrient availability, trace gas exchanges and differential nutrient acquisition strategies by arctic plants. In this study, we report on the extent to which long-term increases in snow depth, along with reductions in snow depth alter the magnitudes and pattern of CO2 exchange, soil properties and vegetation traits. A doubling of snow depth (from ~0.5 to ~1.0m) results in a delay of the growing season by ~ 2 weeks, however, by peak season, the rates of CO2 exchange are 50% higher in areas which had experienced deeper snow depth levels. To the contrary, long-term reductions in snow depth results in accelerated rates of plant phenology, however CO2 exchange rates at peak season are 30% less than those areas under ambient snow cover in the preceding winter. Reduced snow depth areas had the coldest winter soil temperatures while the deeper areas had the warmest winter soil temperatures, which may partially explain the summer CO2 fluxes indirectly via different rates of winter N mineralization and differences in leaf N properties. Our results indicate that shifting fall, winter and spring when snow is the primary form of precipitation, may have profound effects on tussock tundra systems.

Snow Climatology of Arctic Sea Ice: Comparison of Reanalysis and Climate Model Data with In Situ Measurements

NASA Astrophysics Data System (ADS)

Chevooruvalappil Chandran, B.; Pittana, M.; Haas, C.

2015-12-01

Snow on sea ice is a critical and complex factor influencing sea ice processes. Deep snow with a high albedo and low thermal conductivity inhibits ice growth in winter and minimizes ice loss in summer. Very shallow or absent snow promotes ice growth in winter and ice loss in summer. The timing of snow ablation critically impacts summer sea ice mass balance. Here we assess the accuracy of various snow on sea ice data products from reanalysis and modeling comparing them with in situ measurements. The latter are based on the Warren et al. (1999) monthly climatology derived from snow ruler measurements between 1954-1991, and on daily snow depth retrievals from few drifting ice mass balance buoys (IMB) with sufficiently long observations spanning the summer season. These were compared with snow depth data from the National Center for Environmental Prediction Department of Energy Reanalysis 2 (NCEP), the Community Climate System Model 4 (CCSM4), and the Canadian Earth System Model 2 (CanESM2). Results are quite variable in different years and regions. However, there is often good agreement between CanESM2 and IMB snow depth during the winter accumulation and spring melt periods. Regional analyses show that over the western Arctic covered primarily with multiyear ice NCEP snow depths are in good agreement with the Warren climatology while CCSM4 overestimates snow depth. However, in the Eastern Arctic which is dominated by first-year ice the opposite behavior is observed. Compared to the Warren climatology CanESM2 underestimates snow depth in all regions. Differences between different snow depth products are as large as 10 to 20 cm, with large consequences for the sea ice mass balance. However, it is also very difficult to evaluate the accuracy of reanalysis and model snow depths due to a lack of extensive, continuous in situ measurements.

Long-term snow and weather observations at Weissfluhjoch and its relation to other high-altitude observatories in the Alps

NASA Astrophysics Data System (ADS)

Marty, Christoph; Meister, Roland

2012-12-01

Snow and weather observations at Weissfluhjoch were initiated in 1936, when a research team set a snow stake and started digging snow pits on a plateau located at 2,540 m asl above Davos, Switzerland. This was the beginning of what is now the longest series of daily snow depth, new snow height and bi-monthly snow water equivalent measurements from a high-altitude research station. Our investigations reveal that the snow depth at Weissfluhjoch with regard to the evolution and inter-annual variability represents a good proxy for the entire Swiss Alps. In order to set the snow and weather observations from Weissfluhjoch in a broader context, this paper also shows some comparisons with measurements from five other high-altitude observatories in the European Alps. The results show a surprisingly uniform warming of 0.8°C during the last three decades at the six investigated mountain stations. The long-term snow measurements reveal no change in mid-winter, but decreasing trends (especially since the 1980s) for the solid precipitation ratio, snow fall, snow water equivalent and snow depth during the melt season due to a strong temperature increase of 2.5°C in the spring and summer months of the last three decades.

Remote sensing: Snow monitoring tool for today and tomorrow

NASA Technical Reports Server (NTRS)

Rango, A.

1977-01-01

Various types of remote sensing are now available or will be in the future for snowpack monitoring. Aircraft reconnaissance is now used in a conventional manner by various water resources agencies to obtain information on snowlines, depth, and melting of the snowpack for forecasting purposes. The use of earth resources satellites for mapping snowcovered area, snowlines, and changes in snowcover during the spring has increased during the last five years. Gamma ray aircraft flights, although confined to an extremely low altitude, provide a means for obtaining valuable information on snow water equivalent. The most recently developed remote sensing technology for snow, namely, microwave monitoring, has provided initial results that may eventually allow us to infer snow water equivalent or depth, snow wetness, and the hydrologic condition of the underlying soil.

Validation of A One-Dimensional Snow-Land Surface Model at the Sleepers River Watershed

NASA Astrophysics Data System (ADS)

Sun, Wen-Yih; Chern, Jiun-Dar

A one-dimensional land surface model, based on conservations of heat and water substance inside the soil and snow, is presented. To validate the model, a stand-alone experiment is carried out with five years of meteorological and hydrological observations collected from the NOAA-ARS Cooperative Snow Research Project (1966-1974) at the Sleepers River watershed in Danville, Vermont, U.S.A. The numerical results show that the model is capable of reproducing the observed soil temperature at different depths during the winter as well as a rapid increase of soil temperature after snow melts in the spring. The model also simulates the density, temperature, thickness, and equivalent water depth of snow reasonably well. The numerical results are sensitive to the fresh snow density and the soil properties used in the model, which affect the heat exchange between the snowpack and the soil.

A research on snow distribution in mountainous area using airborne laser scanning

NASA Astrophysics Data System (ADS)

Nishihara, T.; Tanise, A.

2015-12-01

In snowy cold regions, the snowmelt water stored in dams in early spring meets the water demand for the summer season. Thus, snowmelt water serves as an important water resource. However, snowmelt water also can cause snowmelt floods. Therefore, it's necessary to estimate snow water equivalent in a dam basin as accurately as possible. For this reason, the dam operation offices in Hokkaido, Japan conduct snow surveys every March to estimate snow water equivalent in the dam basin. In estimating, we generally apply a relationship between elevation and snow water equivalent. But above the forest line, snow surveys are generally conducted along ridges due to the risk of avalanches or other hazards. As a result, snow water equivalent above the forest line is significantly underestimated. In this study, we conducted airborne laser scanning to measure snow depth in the high elevation area including above the forest line twice in the same target area (in 2012 and 2015) and analyzed the relationships of snow depth above the forest line and some indicators of terrain. Our target area was the Chubetsu dam basin. It's located in central Hokkaido, a high elevation area in a mountainous region. Hokkaido is a northernmost island of Japan. Therefore it's a cold and snowy region. The target range for airborne laser scanning was 10km2. About 60% of the target range was above the forest line. First, we analyzed the relationship between elevation and snow depth. Below the forest line, the snow depth increased linearly with elevation increase. On the other hand, above the forest line, the snow depth varied greatly. Second, we analyzed the relationship between overground-openness and snow depth above the forest line. Overground-openness is an indicator quantifying how far a target point is above or below the surrounding surface. As a result, a simple relationship was clarified. Snow depth decreased linearly as overground-openness increases. This means that areas with heavy snow cover are distributed in valleys and that of light cover are on ridges. Lastly we compared the result of 2012 and that of 2015. The same characteristic of snow depth, above mentioned, was found. However, regression coefficients of linear equations were different according to the weather conditions of each year.

Spatiotemporal Variability and in Snow Phenology over Eurasian Continent druing 1966-2012

NASA Astrophysics Data System (ADS)

Zhong, X.; Zhang, T.; Wang, K.; Zheng, L.; Wang, H.

2016-12-01

Snow cover is a key part of the cryosphere, which is a critical component of the global climate system. Snow cover phenology critically effects on the surface energy budget, the surface albedo and hydrological processes. In this study, the climatology and spatiotemporal variability of snow cover phenology were investigated using the long-term (1966-2012) ground-based measurements of daily snow depth from 1103 stations across the Eurasian Continent. The results showed that the distributions of the first date, last date, snow cover duration and number of snow cover days generally represented the latitudinal zonality over the Eurasian Continent, and there were significant elevation gradient patterns in the Tibetan Plateau. The first date of snow cover delayed by about 1.2 day decade-1, the last date of snow cover advanced with the rate of -1.2 day decade-1, snow cover duration and number of snow cover days shortened by about 2.7and 0.6 day decade-1, respectively, from 1966 through 2012. Compared with precipitation, the correlation between snow cover phenology and air temperature was more significant. The changes in snow cover duration were mainly controlled by the changes of air temperature in autumn and spring. The shortened number of snow cover days was affected by rising temperature during the cold season except for the air temperature in autumn and spring.

The origin of shallow landslides in Moravia (Czech Republic) in the spring of 2006

NASA Astrophysics Data System (ADS)

Bíl, Michal; Müller, Ivo

2008-07-01

At the end of March 2006, the Czech Republic (CZ) witnessed a fast thawing of an unusually thick snow cover in conjunction with massive rainfall. Most watercourses suffered floods, and more than 90 shallow landslides occurred in the Moravian region of Eastern CZ, primarily in non-forested areas. This region, geologically part of the Outer Western Carpathians, is prone to landslides because the bedrock is highly erodible Mesozoic and Tertiary flysch. The available meteorological data (depth of snow, water equivalent of the snow, cumulative rainfall, air and soil temperatures) from five local weather stations were used to construct indices quantitatively describing the snow thaw. Among these, the Total Cumulative Precipitation ( TCP) combines the amount of water from both thawing snow and rainfall. This concurrence of rain and runoff from snow melt was the decisive factor in triggering the landslides in the spring. The TCP index was applied to data of snow thaw periods for the last 20 years, when no landslides were recorded. This was to establish the safe threshold of TCP without landslides. The calculated safe threshold value for the region is ca. 100 mm of water delivered to the soil during the spring thaw (corresponding to ca. 11 mm day - 1 ). In 2006, 10% of the landslides occurred under or at 100 mm of TCP. The upper value of 155 mm covered all of the landslides.

Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N-ICE2015) expedition

NASA Astrophysics Data System (ADS)

Gallet, Jean-Charles; Merkouriadi, Ioanna; Liston, Glen E.; Polashenski, Chris; Hudson, Stephen; Rösel, Anja; Gerland, Sebastian

2017-10-01

Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N-ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second-year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first-year ice. The average density was 350-400 kg m-3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.

Winter warming as an important co-driver for Betula nana growth in western Greenland during the past century

PubMed Central

Hollesen, Jørgen; Buchwal, Agata; Rachlewicz, Grzegorz; Hansen, Birger U; Hansen, Marc O; Stecher, Ole; Elberling, Bo

2015-01-01

Growing season conditions are widely recognized as the main driver for tundra shrub radial growth, but the effects of winter warming and snow remain an open question. Here, we present a more than 100 years long Betula nana ring-width chronology from Disko Island in western Greenland that demonstrates a highly significant and positive growth response to both summer and winter air temperatures during the past century. The importance of winter temperatures for Betula nana growth is especially pronounced during the periods from 1910–1930 to 1990–2011 that were dominated by significant winter warming. To explain the strong winter importance on growth, we assessed the importance of different environmental factors using site-specific measurements from 1991 to 2011 of soil temperatures, sea ice coverage, precipitation and snow depths. The results show a strong positive growth response to the amount of thawing and growing degree-days as well as to winter and spring soil temperatures. In addition to these direct effects, a strong negative growth response to sea ice extent was identified, indicating a possible link between local sea ice conditions, local climate variations and Betula nana growth rates. Data also reveal a clear shift within the last 20 years from a period with thick snow depths (1991–1996) and a positive effect on Betula nana radial growth, to a period (1997–2011) with generally very shallow snow depths and no significant growth response towards snow. During this period, winter and spring soil temperatures have increased significantly suggesting that the most recent increase in Betula nana radial growth is primarily triggered by warmer winter and spring air temperatures causing earlier snowmelt that allows the soils to drain and warm quicker. The presented results may help to explain the recently observed ‘greening of the Arctic’ which may further accelerate in future years due to both direct and indirect effects of winter warming. PMID:25788025

Winter warming as an important co-driver for Betula nana growth in western Greenland during the past century.

PubMed

Hollesen, Jørgen; Buchwal, Agata; Rachlewicz, Grzegorz; Hansen, Birger U; Hansen, Marc O; Stecher, Ole; Elberling, Bo

2015-06-01

Growing season conditions are widely recognized as the main driver for tundra shrub radial growth, but the effects of winter warming and snow remain an open question. Here, we present a more than 100 years long Betula nana ring-width chronology from Disko Island in western Greenland that demonstrates a highly significant and positive growth response to both summer and winter air temperatures during the past century. The importance of winter temperatures for Betula nana growth is especially pronounced during the periods from 1910-1930 to 1990-2011 that were dominated by significant winter warming. To explain the strong winter importance on growth, we assessed the importance of different environmental factors using site-specific measurements from 1991 to 2011 of soil temperatures, sea ice coverage, precipitation and snow depths. The results show a strong positive growth response to the amount of thawing and growing degree-days as well as to winter and spring soil temperatures. In addition to these direct effects, a strong negative growth response to sea ice extent was identified, indicating a possible link between local sea ice conditions, local climate variations and Betula nana growth rates. Data also reveal a clear shift within the last 20 years from a period with thick snow depths (1991-1996) and a positive effect on Betula nana radial growth, to a period (1997-2011) with generally very shallow snow depths and no significant growth response towards snow. During this period, winter and spring soil temperatures have increased significantly suggesting that the most recent increase in Betula nana radial growth is primarily triggered by warmer winter and spring air temperatures causing earlier snowmelt that allows the soils to drain and warm quicker. The presented results may help to explain the recently observed 'greening of the Arctic' which may further accelerate in future years due to both direct and indirect effects of winter warming. © 2015 John Wiley & Sons Ltd.

Do we need a dynamic snow depth threshold when comparing hydrological models with remote sensing products in mountain catchments?

NASA Astrophysics Data System (ADS)

Engel, Michael; Bertoldi, Giacomo; Notarnicola, Claudia; Comiti, Francesco

2017-04-01

To assess the performance of simulated snow cover of hydrological models, it is common practice to compare simulated data with observed ones derived from satellite images such as MODIS. However, technical and methodological limitations such as data availability of MODIS products, its spatial resolution or difficulties in finding appropriate parameterisations of the model need to be solved previously. Another important assumption usually made is the threshold of minimum simulated snow depth, generally set to 10 mm of snow depth, to respect the MODIS detection thresholds for snow cover. But is such a constant threshold appropriate for complex alpine terrain? How important is the impact of different snow depth thresholds on the spatial and temporal distribution of the pixel-based overall accuracy (OA)? To address this aspect, we compared the snow covered area (SCA) simulated by the GEOtop 2.0 snow model to the daily composite 250 m EURAC MODIS SCA in the upper Saldur basin (61 km2, Eastern Italian Alps) during the period October 2011 - October 2013. Initially, we calibrated the snow model against snow depths and snow water equivalents at point scale, taken from measurements at different meteorological stations. We applied different snow depth thresholds (0 mm, 10 mm, 50 mm, and 100 mm) to obtain the simulated snow cover and assessed the changes in OA both in time (during the entire evaluation period, accumulation and melting season) and space (entire catchment and specific areas of topographic characteristics such as elevation, slope, aspect, landcover, and roughness). Results show remarkable spatial and temporal differences in OA with respect to different snow depth thresholds. Inaccuracies of simulated and observed SCA during the accumulation season September to November 2012 were located in areas with north-west aspect, slopes of 30° or little elevation differences at sub-pixel scale (-0.25 to 0 m). We obtained best agreements with MODIS SCA for a snow depth threshold of 100 mm, leading to increased OA (> 0.8) in 13‰ of the catchment area. SCA agreement in January 2012 and 2013 was slightly limited by MODIS sensor detection due to shading effects and low illumination in areas exposed north-west to north. On the contrary, during the melting season in April 2013 and after the September 2013 snowfall event seemed to depend more on parameterisation than on snow depth thresholds. In contrast, inaccuracies during the melting season March to June 2013 could hardly be attributed to topographic characteristics and different snow depth thresholds but rather on model parameterisation. We identified specific conditions (p.e. specific snowfall events in autumn 2012 and spring 2013) when either MODIS data or the hydrological model was less accurate, thus justifying the need for improvements of precision in the snow cover detection algorithms or in the model's process description. In consequence, our study observations could support future snow cover evaluations in mountain areas, where spatially and temporally dynamic snow depth thresholds are transferred from the catchment scale to the regional scale. Keywords: snow cover, snow modelling, MODIS, snow depth sensitivity, alpine catchment

Linking Changes in Snow Cover with Nitrogen Cycling and Microbial Abundance and Functional Gene Expression in Agricultural Soils

NASA Astrophysics Data System (ADS)

Goyer, C.; Brin, L.; Zebarth, B.; Burton, D.; Wertz, S.; Chantigny, M.

2016-12-01

In eastern Canada, climate change-related warming and increased precipitation may alter winter snow cover, with potential consequences for soil conditions, microbes, and N2O fluxes. We conducted a two-year field study with snow removal, passive snow addition, and ambient treatments in a potato-barley crop system. We measured in situ greenhouse gas (N2O and CO2) fluxes and belowground gas accumulation, and quantified abundance and expression of denitrifier (nirS, nirK, nosZ) and nitrifier (ammonium oxidizing archaeal (AOA) and bacterial (AOB) amoA) genes. Soil gas accumulated throughout winter, and surface fluxes were greatest during spring thaw. Greatest mid-winter soil N2O accumulation and spring thaw N2O fluxes were associated with snow removal in winter 1 and ambient snow in winter 2. High N2O accumulation and fluxes may have been due to increased substrate availability with increased frost intensity in removal plots in winter 1, but with greatest water content in ambient plots in winter 2. In each winter, greatest abundances of nirS, nirK gene denitrifiers and/or amoA gene of AOA were observed in the treatments with the greatest N2O accumulation and fluxes. Gene expression did not vary with treatment, but highest expression of amoA gene of AOA and AOB, and nosZ gene was measured near 0ºC, indicating activity during periods of stable snow cover and spring thaw. Results suggest that the magnitude of fluxes during spring thaw were related to soil conditions and microbial communities present during the prior winter, and not solely those during thaw. Furthermore, the effects of changing snow cover on microbes and N2O fluxes were not a straightforward effect of snow depth, but were likely mediated by temperature and moisture.

The Influence of Snowmobile Trails on Coyote Movements during Winter in High-Elevation Landscapes

PubMed Central

Gese, Eric M.; Dowd, Jennifer L. B.; Aubry, Lise M.

2013-01-01

Competition between sympatric carnivores has long been of interest to ecologists. Increased understanding of these interactions can be useful for conservation planning. Increased snowmobile traffic on public lands and in habitats used by Canada lynx (Lynx canadensis) remains controversial due to the concern of coyote (Canis latrans) use of snowmobile trails and potential competition with lynx. Determining the variables influencing coyote use of snowmobile trails has been a priority for managers attempting to conserve lynx and their critical habitat. During 2 winters in northwest Wyoming, we backtracked coyotes for 265 km to determine how varying snow characteristics influenced coyote movements; 278 km of random backtracking was conducted simultaneously for comparison. Despite deep snow (>1 m deep), radio-collared coyotes persisted at high elevations (>2,500 m) year-round. All coyotes used snowmobile trails for some portion of their travel. Coyotes used snowmobile trails for 35% of their travel distance (random: 13%) for a mean distance of 149 m (random: 59 m). Coyote use of snowmobile trails increased as snow depth and penetrability off trails increased. Essentially, snow characteristics were most influential on how much time coyotes spent on snowmobile trails. In the early months of winter, snow depth was low, yet the snow column remained dry and the coyotes traveled off trails. As winter progressed and snow depth increased and snow penetrability increased, coyotes spent more travel distance on snowmobile trails. As spring approached, the snow depth remained high but penetrability decreased, hence coyotes traveled less on snowmobile trails because the snow column off trail was more supportive. Additionally, coyotes traveled closer to snowmobile trails than randomly expected and selected shallower snow when traveling off trails. Coyotes also preferred using snowmobile trails to access ungulate kills. Snow compaction from winter recreation influenced coyote movements within an area containing lynx and designated lynx habitat. PMID:24367565

The influence of snowmobile trails on coyote movements during winter in high-elevation landscapes.

PubMed

Gese, Eric M; Dowd, Jennifer L B; Aubry, Lise M

2013-01-01

Competition between sympatric carnivores has long been of interest to ecologists. Increased understanding of these interactions can be useful for conservation planning. Increased snowmobile traffic on public lands and in habitats used by Canada lynx (Lynx canadensis) remains controversial due to the concern of coyote (Canis latrans) use of snowmobile trails and potential competition with lynx. Determining the variables influencing coyote use of snowmobile trails has been a priority for managers attempting to conserve lynx and their critical habitat. During 2 winters in northwest Wyoming, we backtracked coyotes for 265 km to determine how varying snow characteristics influenced coyote movements; 278 km of random backtracking was conducted simultaneously for comparison. Despite deep snow (>1 m deep), radio-collared coyotes persisted at high elevations (>2,500 m) year-round. All coyotes used snowmobile trails for some portion of their travel. Coyotes used snowmobile trails for 35% of their travel distance (random: 13%) for a mean distance of 149 m (random: 59 m). Coyote use of snowmobile trails increased as snow depth and penetrability off trails increased. Essentially, snow characteristics were most influential on how much time coyotes spent on snowmobile trails. In the early months of winter, snow depth was low, yet the snow column remained dry and the coyotes traveled off trails. As winter progressed and snow depth increased and snow penetrability increased, coyotes spent more travel distance on snowmobile trails. As spring approached, the snow depth remained high but penetrability decreased, hence coyotes traveled less on snowmobile trails because the snow column off trail was more supportive. Additionally, coyotes traveled closer to snowmobile trails than randomly expected and selected shallower snow when traveling off trails. Coyotes also preferred using snowmobile trails to access ungulate kills. Snow compaction from winter recreation influenced coyote movements within an area containing lynx and designated lynx habitat.

Annual Soil Temperature Wave at Four Depths in Southwestern Wisconsin

Treesearch

Richard S. Sartz

1967-01-01

Soil temperature was measured for a year on a southeast-facing slope of 25 percent, latitude 43 degrees 50 minutes N. The spring-summer cover was unmowed alfalfa-bluegrass meadow, the fall-winter cover, meadow stubble. Snow cover was light or absent. The soil was Fayette silt loam, valley phase. The annual temperature wave at all depths followed the air temperature...

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

NASA Astrophysics Data System (ADS)

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.

Observational Evidence of Changes in Soil Temperatures across Eurasian Continent

NASA Astrophysics Data System (ADS)

Zhang, T.

2015-12-01

Soil temperature is one of the key climate change indicators and plays an important role in plant growth, agriculture, carbon cycle and ecosystems as a whole. In this study, variability and changes in ground surface and soil temperatures up to 3.20 m were investigated based on data and information obtained from hydrometeorological stations across Eurasian continent since the early 1950s. Ground surface and soil temperatures were measured daily by using the same standard method and by the trained professionals across Eurasian continent, which makes the dataset unique and comparable over a large study region. Using the daily soil temperature profiles, soil seasonal freeze depth was also obtained through linear interpolation. Preliminary results show that soil temperatures at various depths have increased dramatically, almost twice as much as air temperature increase over the same period. Regionally, soil temperature increase was more dramatically in high northern latitudes than mid/lower latitude regions. Air temperature changes alone may not be able to fully explain the magnitude of changes in soil temperatures. Further study indicates that snow cover establishment started later in autumn and snow cover disappearance occurred earlier in spring, while winter snow depth became thicker with a decreasing trend of snow density. Changes in snow cover conditions may play an important role in changes of soil temperatures over the Eurasian continent.

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

NASA Astrophysics Data System (ADS)

Wegmann, Martin; Dutra, Emanuel; Jacobi, Hans-Werner; Zolina, Olga

2018-06-01

This study uses daily observations and modern reanalyses in order to evaluate reanalysis products over northern Eurasia regarding the spring snow albedo feedback (SAF) during the period from 2000 to 2013. We used the state-of-the-art reanalyses from ERA-Interim/Land and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) as well as an experimental set-up of ERA-Interim/Land with prescribed short grass as land cover to enhance the comparability with the station data while underlining the caveats of comparing in situ observations with gridded data. Snow depth statistics derived from daily station data are well reproduced in all three reanalyses. However day-to-day albedo variability is notably higher at the stations than for any reanalysis product. The ERA-Interim grass set-up shows improved performance when representing albedo variability and generates comparable estimates for the snow albedo in spring. We find that modern reanalyses show a physically consistent representation of SAF, with realistic spatial patterns and area-averaged sensitivity estimates. However, station-based SAF values are significantly higher than in the reanalyses, which is mostly driven by the stronger contrast between snow and snow-free albedo. Switching to grass-only vegetation in ERA-Interim/Land increases the SAF values up to the level of station-based estimates. We found no significant trend in the examined 14-year time series of SAF, but interannual changes of about 0.5 % K-1 in both station-based and reanalysis estimates were derived. This interannual variability is primarily dominated by the variability in the snowmelt sensitivity, which is correctly captured in reanalysis products. Although modern reanalyses perform well for snow variables, efforts should be made to improve the representation of dynamic albedo changes.

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

NASA Astrophysics Data System (ADS)

Kwok, Ron; Kurtz, Nathan T.; Brucker, Ludovic; Ivanoff, Alvaro; Newman, Thomas; Farrell, Sinead L.; King, Joshua; Howell, Stephen; Webster, Melinda A.; Paden, John; Leuschen, Carl; MacGregor, Joseph A.; Richter-Menge, Jacqueline; Harbeck, Jeremy; Tschudi, Mark

2017-11-01

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Snowpack Variation and Hydrologic Impacts across the Middle East and North Africa

NASA Astrophysics Data System (ADS)

Robinson, D. A.; Ward, M. N.

2017-12-01

The Middle East is a region historically sensitive to climate variability and change, and contains snowpacks that have been shown to be important inputs to key regional water resources, including the Tigris-Euphrates river system. Focusing on the Middle East (and the smaller snowpacks of northwestern Africa), this presentation aims to (i) quantify each year's snowpack development and recession over recent decades, highlighting interannual to decadal variability, and (ii) advance understanding on the connection between the snowpack variations and aspects of regional hydrology. The presentation draws on satellite-based products, station data, and model reanalyses. Variation is summarized using space-time statistical techniques, as well as simpler regional indices: Northwestern Iran / Southern Caucasus (NWIC, includes Zagros Mountains); Eastern Turkey (ETKY, includes Taurus Mountains); and smaller scale indices for Lebanon and the Atlas Mountains. The Interactive Multisensor Snow and Ice Mapping System archives daily snow cover extent at 24 km resolution for 1999-present (primarily from visible satellite imagery). These data show that for both NWIC and ETKY, the mean snow extent peaks in late January with substantial coverage ( 300,000 km2 in each region), contracting to near zero by late June. A very large mid-winter interannual variance is also shown, implying substantial variation in hydrologic impacts during spring melt. Variability and decadal trends are compared with station snow depth reports (Global Historical Climatology Network - Daily). Strong agreement gives confidence in data quality, as well as, indicating high covariation of depth and extent. The connection with hydrologic impacts is investigated using reanalysis products, including the Global Land Data Assimilation System V2, which for the Middle East, shows broad agreement with observed maximum snow extent and spring retreat. The connections internal to the reanalysis between snow cover, melt and runoff are quantified, delivering insights into the mechanisms of variability and change in the regional water resources and their dependence on snowpack. Specific case study melt seasons highlight the ways in which snowpack and seasonal climate combine to produce changes in magnitude and timing of runoff during late winter and spring.

Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western U.S. based on WRF chemistry and regional climate simulations

NASA Astrophysics Data System (ADS)

Qian, Y.; Gustafson, W. I.; Leung, R.; Ghan, S. J.

2008-12-01

Radiative forcing induced by soot on snow is an important anthropogenic forcing affecting the global climate. In this study we simulated the deposition of soot aerosol on snow and the resulting impact on snowpack and the hydrological cycle in the western United States. A yearlong simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine the soot deposition, followed by three simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the surface net solar radiation flux during late winter to early spring, increase the surface air temperature, and reduce the snow accumulation and spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow covered regions. For a doubled snow albedo perturbation, the change to surface energy and temperature is around 50-80%, however, snowpack reduction is nonlinearly accelerated.

Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western United States based on Weather Research and Forecasting chemistry and regional climate simulations

NASA Astrophysics Data System (ADS)

Qian, Yun; Gustafson, William I.; Leung, L. Ruby; Ghan, Steven J.

2009-02-01

Radiative forcing induced by soot on snow is an important anthropogenic forcing affecting the global climate. In this study we simulated the deposition of soot aerosol on snow and the resulting impact on snowpack and the hydrological cycle in the western United States. A year-long simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine the soot deposition, followed by three simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the surface net solar radiation flux during late winter to early spring, increase the surface air temperature, and reduce the snow accumulation and spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow-covered regions. For a doubled snow albedo perturbation, the change to surface energy and temperature is around 50-80%; however, snowpack reduction is nonlinearly accelerated.

Historical Late-Winter and Spring Snowpack Depth and Equivalent Water-Content Data for Maine

DTIC Science & Technology

2005-01-01

span at least 50 years up to the present (2004), and are at least 50-percent complete for the first and for the second half of their record. The oldest measurements at the 37 snow-survey sites in this report are from 1906.

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

USGS Publications Warehouse

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.

Spatiotemporal Variability of Great Lakes Basin Snow Cover Ablation Events

NASA Astrophysics Data System (ADS)

Suriano, Z. J.; Leathers, D. J.

2017-12-01

In the Great Lakes basin of North America, annual runoff is dominated by snowmelt. This snowmelt-induced runoff plays an important role within the hydrologic cycle of the basin, influencing soil moisture availability and driving the seasonal cycle of spring and summer Lake levels. Despite this, relatively little is understood about the patterns and trends of snow ablation event frequency and magnitude within the Great Lakes basin. This study uses a gridded dataset of Canadian and United States surface snow depth observations to develop a regional climatology of snow ablation events from 1960-2009. An ablation event is defined as an inter-diurnal snow depth decrease within an individual grid cell. A clear seasonal cycle in ablation event frequency exists within the basin and peak ablation event frequency is latitudinally dependent. Most of the basin experiences peak ablation frequency in March, while the northern and southern regions of the basin experience respective peaks in April and February. An investigation into the inter-annual frequency of ablation events reveals ablation events significantly decrease within the northeastern and northwestern Lake Superior drainage basins and significantly increase within the eastern Lake Huron and Georgian Bay drainage basins. In the eastern Lake Huron and Georgian Bay drainage basins, larger ablation events are occurring more frequently, and a larger impact to the hydrology can be expected. Trends in ablation events are attributed primarily to changes in snowfall and snow depth across the region.

Optimizing Observations of Sea Ice Thickness and Snow Depth in the Arctic

DTIC Science & Technology

2014-09-30

changes in the thickness of sea ice, glaciers , and ice sheets. These observations are critical for predicting the response of Earth’s polar ice to...Arctic Sea Ice Conditions in Spring 2009 - 2013 Prior to Melt , Geophys. Res. Lett., 40, 5888-5893, doi: 10.1002/2013GL058011. [published, refereed

Export of Algal Communities from Land Fast Arctic Sea Ice Influenced by Overlying Snow Depth and Episodic Rain Events

NASA Astrophysics Data System (ADS)

Neuer, S.; Juhl, A. R.; Aumack, C.; McHugh, C.; Wolverton, M. A.; Kinzler, K.

2016-02-01

Sea ice algal communities dominate primary production of the coastal Arctic Ocean in spring. As the sea ice bloom terminates, algae are released from the ice into the underlying, nutrient-rich waters, potentially seeding blooms and feeding higher trophic levels in the water column and benthos. We studied the sea ice community including export events over four consecutive field seasons (2011-2014) during the spring ice algae bloom in land-fast ice near Barrow, Alaska, allowing us to investigate both seasonal and interannual differences. Within each year, we observed a delay in algal export from ice in areas covered by thicker snow compared to areas with thinner snow coverage. Variability in snow cover therefore resulted in a prolonged supply of organic matter to the underlying water column. Earlier export in 2012 was followed by a shift in the diatom community within the ice from pennates to centrics. During an unusual warm period in early May 2014, precipitation falling as rain substantially decreased the snow cover thickness (from snow depth > 20 cm down to 0-2 cm). After the early snowmelt, algae were rapidly lost from the sea ice, and a subsequent bloom of taxonomically-distinct, under-ice phytoplankton developed a few days later. The typical immured sea ice diatoms never recovered in terms of biomass, though pennate diatoms (predominantly Nitzschia frigida) did regrow to some extent near the ice bottom. Sinking rates of the under-ice phytoplankton were much more variable than those of ice algae particles, which would potentially impact residence time in the water column, and fluxes to the benthos. Thus, the early melt episode, triggered by rain, transitioned directly into the seasonal melt and the release of biomass from the ice, shifting production from sea ice to the water column, with as-of-yet unknown consequences for the springtime Arctic food web.

Idiosyncratic Responses of High Arctic Plants to Changing Snow Regimes

PubMed Central

Rumpf, Sabine B.; Semenchuk, Philipp R.; Dullinger, Stefan; Cooper, Elisabeth J.

2014-01-01

The Arctic is one of the ecosystems most affected by climate change; in particular, winter temperatures and precipitation are predicted to increase with consequent changes to snow cover depth and duration. Whether the snow-free period will be shortened or prolonged depends on the extent and temporal patterns of the temperature and precipitation rise; resulting changes will likely affect plant growth with cascading effects throughout the ecosystem. We experimentally manipulated snow regimes using snow fences and shoveling and assessed aboveground size of eight common high arctic plant species weekly throughout the summer. We demonstrated that plant growth responded to snow regime, and that air temperature sum during the snow free period was the best predictor for plant size. The majority of our studied species showed periodic growth; increases in plant size stopped after certain cumulative temperatures were obtained. Plants in early snow-free treatments without additional spring warming were smaller than controls. Response to deeper snow with later melt-out varied between species and categorizing responses by growth forms or habitat associations did not reveal generic trends. We therefore stress the importance of examining responses at the species level, since generalized predictions of aboveground growth responses to changing snow regimes cannot be made. PMID:24523859

Idiosyncratic responses of high Arctic plants to changing snow regimes.

PubMed

Rumpf, Sabine B; Semenchuk, Philipp R; Dullinger, Stefan; Cooper, Elisabeth J

2014-01-01

The Arctic is one of the ecosystems most affected by climate change; in particular, winter temperatures and precipitation are predicted to increase with consequent changes to snow cover depth and duration. Whether the snow-free period will be shortened or prolonged depends on the extent and temporal patterns of the temperature and precipitation rise; resulting changes will likely affect plant growth with cascading effects throughout the ecosystem. We experimentally manipulated snow regimes using snow fences and shoveling and assessed aboveground size of eight common high arctic plant species weekly throughout the summer. We demonstrated that plant growth responded to snow regime, and that air temperature sum during the snow free period was the best predictor for plant size. The majority of our studied species showed periodic growth; increases in plant size stopped after certain cumulative temperatures were obtained. Plants in early snow-free treatments without additional spring warming were smaller than controls. Response to deeper snow with later melt-out varied between species and categorizing responses by growth forms or habitat associations did not reveal generic trends. We therefore stress the importance of examining responses at the species level, since generalized predictions of aboveground growth responses to changing snow regimes cannot be made.

Snow hydrology in a general circulation model

NASA Technical Reports Server (NTRS)

Marshall, Susan; Roads, John O.; Glatzmaier, Gary

1994-01-01

A snow hydrology has been implemented in an atmospheric general circulation model (GCM). The snow hydrology consists of parameterizations of snowfall and snow cover fraction, a prognostic calculation of snow temperature, and a model of the snow mass and hydrologic budgets. Previously, only snow albedo had been included by a specified snow line. A 3-year GCM simulation with this now more complete surface hydrology is compared to a previous GCM control run with the specified snow line, as well as with observations. In particular, the authors discuss comparisons of the atmospheric and surface hydrologic budgets and the surface energy budget for U.S. and Canadian areas. The new snow hydrology changes the annual cycle of the surface moisture and energy budgets in the model. There is a noticeable shift in the runoff maximum from winter in the control run to spring in the snow hydrology run. A substantial amount of GCM winter precipitation is now stored in the seasonal snowpack. Snow cover also acts as an important insulating layer between the atmosphere and the ground. Wintertime soil temperatures are much higher in the snow hydrology experiment than in the control experiment. Seasonal snow cover is important for dampening large fluctuations in GCM continental skin temperature during the Northern Hemisphere winter. Snow depths and snow extent show good agreement with observations over North America. The geographic distribution of maximum depths is not as well simulated by the model due, in part, to the coarse resolution of the model. The patterns of runoff are qualitatively and quantitatively similar to observed patterns of streamflow averaged over the continental United States. The seasonal cycles of precipitation and evaporation are also reasonably well simulated by the model, although their magnitudes are larger than is observed. This is due, in part, to a cold bias in this model, which results in a dry model atmosphere and enhances the hydrologic cycle everywhere.

Inter-annual and spatial variability in hillslope runoff and mercury flux during spring snowmelt.

PubMed

Haynes, Kristine M; Mitchell, Carl P J

2012-08-01

Spring snowmelt is an important period of mercury (Hg) export from watersheds. Limited research has investigated the potential effects of climate variability on hydrologic and Hg fluxes during spring snowmelt. The purpose of this research was to assess the potential impacts of inter-annual climate variability on Hg mobility in forested uplands, as well as spatial variability in hillslope hydrology and Hg fluxes. We compared hydrological flows, Hg and solute mobility from three adjacent hillslopes in the S7 watershed of the Marcell Experimental Forest, Minnesota during two very different spring snowmelt periods: one following a winter (2009-2010) with severely diminished snow accumulation (snow water equivalent (SWE) = 48 mm) with an early melt, and a second (2010-2011) with significantly greater winter snow accumulation (SWE = 98 mm) with average to late melt timing. Observed inter-annual differences in total Hg (THg) and dissolved organic carbon (DOC) yields were predominantly flow-driven, as the proportion by which solute yields increased was the same as the increase in runoff. Accounting for inter-annual differences in flow, there was no significant difference in THg and DOC export between the two snowmelt periods. The spring 2010 snowmelt highlighted the important contribution of melting soil frost in the timing of a considerable portion of THg exported from the hillslope, accounting for nearly 30% of the THg mobilized. Differences in slope morphology and soil depths to the confining till layer were important in controlling the large observed spatial variability in hydrological flowpaths, transmissivity feedback responses, and Hg flux trends across the adjacent hillslopes.

Soil moisture ground truth: Steamboat Springs, Colorado, site and Walden, Colorado, site

NASA Technical Reports Server (NTRS)

Jones, E. B.

1976-01-01

Ground-truth data taken at Steamboat Springs and Walden, Colorado in support of the NASA missions in these areas during the period March 8, 1976 through March 11, 1976 was presented. This includes the following information: snow course data for Steamboat Springs and Walden, snow pit and snow quality data for Steamboat Springs, and soil moisture report.

Estimation de la superficie du couvert nival a partir d'une combinaison des donnees de teledetection MODIS et AMSR-E dans un contexte de prevision des crues printanieres au Quebec

NASA Astrophysics Data System (ADS)

Bergeron, Jean

Snow cover estimation is a principal source of error for spring streamflow simulations in Québec, Canada. Optical and near infrared remote sensing can improve snow cover area (SCA) estimation due to high spatial resolution but is limited by cloud cover and incoming solar radiation. Passive microwave remote sensing is complementary by its near-transparence to cloud cover and independence to incoming solar radiation, but is limited by its coarse spatial resolution. The study aims to create an improved SCA product from blended passive microwave (AMSR-E daily L3 Brightness Temperature) and optical (MODIS Terra and Aqua daily snow cover L3) remote sensing data in order to improve estimation of river streamflow caused by snowmelt with Québec's operational MOHYSE hydrological model through direct-insertion of the blended SCA product in a coupled snowmelt module (SPH-AV). SCA estimated from AMSR-E data is first compared with SCA estimated with MODIS, as well as with in situ snow depth measurements. Results show good agreement (+95%) between AMSR-E-derived and MODIS-derived SCA products in spring but comparisons with Environment Canada ground stations and SCA derived from Advanced Very High Resolution Radiometer (AVHRR) data show lesser agreements (83 % and 74% respectively). Results also show that AMSR-E generally underestimates SCA. Assimilating the blended snow product in SPH-AV coupled with MOHYSE yields significant improvement of simulated streamflow for the aux Écorces et au Saumon rivers overall when compared with simulations with no update during thaw events, These improvements are similar to results driven by biweekly ground data. Assimilation of remotely-sensed passive microwave data was also found to have little positive impact on springflood forecast due to the difficulty in differentiating melting snow from snow-free surfaces. Considering the direct-insertion and Newtonian nudging assimilation methods, the study also shows the latter method to be superior to the former, notably when assimilating noisy data. Keywords: Snow cover, spring streamflow, MODIS, AMSR-E, hydrological model.

Ground-Truthing a Next Generation Snow Radar

NASA Astrophysics Data System (ADS)

Yan, S.; Brozena, J. M.; Gogineni, P. S.; Abelev, A.; Gardner, J. M.; Ball, D.; Liang, R.; Newman, T.

2016-12-01

During the early spring of 2016 the Naval Research Laboratory (NRL) performed a test of a next generation airborne snow radar over ground truth data collected on several areas of fast ice near Barrow, AK. The radar was developed by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas, and includes several improvements compared to their previous snow radar. The new unit combines the earlier Ku-band and snow radars into a single unit with an operating frequency spanning the entire 2-18 GHz, an enormous bandwidth which provides the possibility of snow depth measurements with 1.5 cm range resolution. Additionally, the radar transmits on dual polarizations (H and V), and receives the signal through two orthogonally polarized Vivaldi arrays, each with 128 phase centers. The 8 sets of along-track phase centers are combined in hardware to improve SNR and narrow the beamwidth in the along-track, resulting in 8 cross-track effective phase centers which are separately digitized to allow for beam sharpening and forming in post-processing. Tilting the receive arrays 30 degrees from the horizontal also allows the formation of SAR images and the potential for estimating snow-water equivalent (SWE). Ground truth data (snow depth, density, salinity and SWE) were collected over several 60 m wide swaths that were subsequently overflown with the snow radar mounted on a Twin Otter. The radar could be operated in nadir (by beam steering the receive antennas to point beneath the aircraft) or side-looking modes. Results from the comparisons will be shown.

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

PubMed

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.

Fukushima Nuclear Accident Recorded in Tibetan Plateau Snow Pits

PubMed Central

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

2015-01-01

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

Seasonality of soil CO2 efflux in a temperate forest: Biophysical effects of snowpack and spring freeze–thaw cycles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chuankuan; Han, Yi; Chen, Jiquan

2013-08-15

Changes in characteristics of snowfall and spring freeze–thaw-cycle (FTC) events under the warming climate make it critical to understand biophysical controls on soil CO2 efflux (RS) in seasonally snow-covered ecosystems. We conducted a snow removal experiment and took year-round continuous automated measurements of RS, soil temperature (T5) and soil volumetric water content at the 5 cm depth (W5) with a half-hour interval in a Chinese temperate forest in 2010–2011. Our objectives were to: (1) develop statistical models to describe the seasonality of RS in this forest; (2) quantify the contribution of seasonal RS to the annual budget; (3) examine biophysicalmore » effects of snowpack on RS; and (4) test the hypothesis that an FTC-induced enhancement of RS is jointly driven by biological and physical processes.« less

Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain

NASA Astrophysics Data System (ADS)

Webb, Ryan W.; Fassnacht, Steven R.; Gooseff, Michael N.

2018-01-01

In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow-soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4 % of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170 % at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together.

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

NASA Astrophysics Data System (ADS)

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?

Incorporating Cold-Air Pooling into Downscaled Climate Models Increases Potential Refugia for Snow-Dependent Species within the Sierra Nevada Ecoregion, CA

PubMed Central

Curtis, Jennifer A.; Flint, Lorraine E.; Flint, Alan L.; Lundquist, Jessica D.; Hudgens, Brian; Boydston, Erin E.; Young, Julie K.

2014-01-01

We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist. PMID:25188379

Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA

USGS Publications Warehouse

Curtis, Jennifer A.; Flint, Lorraine E.; Flint, Alan L.; Lundquist, Jessica D.; Hudgens, Brian; Boydston, Erin E.; Young, Julie K.

2014-01-01

We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist.

Comparing different snow products to assess spatio-temporal snow cover patterns in the Central Taurus Mountains, Turkey

NASA Astrophysics Data System (ADS)

Sturm, K.; Helmschrot, J.

2013-12-01

Snow and its spatial and temporal patterns are important for catchment hydrology in the semi-arid eastern Mediterranean. Since most of the annual rainfall is stored as snow during winter and released during drier conditions in spring and summer, downstream regions of the Taurus Mountains relying on snow water temporarily stored in reservoirs for agricultural use are heavily dependent on the timing of snowmelt discharge. Runoff is controlled by the amount of accumulated snow, its distribution, and the climatic conditions controlling spring snowmelt. Thus, knowledge about spatial and temporal snow cover dynamics is essential for sustainable water resources management. The lack of observations in high-altitude regions reinforces the application of different snow products for a better assessment of spatio-temporal snow cover patterns. To better assess the quality of such products, simulated daily snow cover and EO-based snow cover products were compared for the Egribuk subcatchment, in the Central Taurus Mountains, Turkey. Daily information on snow cover, depths, and snow water equivalent was derived from distributed hydrological modeling using the J2000 model. Furthermore, 8-day MODIS snow cover data from Terra (MOD10A2) and Aqua (MYD10A2) satellites at a spatial resolution of 500 m were synchronized to receive cloud-free images. From this effort, 253 images covering the period between 07/04/2002 and 12/27/2007 were used for further analyses. The products were analyzed individually to determine the number of snow-covered days in relation to freezing days, spring snowmelt onsets, and temporal patterns, reflecting the effect of altitude on the percentage snow-covered area (SCA) along a topographic gradient at various time-steps. Monthly and 8-day spatial patterns of a single snow season were also examined. When SCA peaks at all altitudes, in February and March, the results of both products show a good agreement regarding SCA extent. In contrast, the extent of SCA differs notably during snow accumulation and ablation periods, the highest deviations occurring in December, April, and May. The highest SCA inconsistencies are observed in the low and mid altitudes, whereas the higher elevations are snow-covered very early in the snow season as modeled by J2000. During these periods, J2000 simulates a significantly larger SCA than MODIS. The analysis of individual time steps suggests that the J2000 daily model does capture individual snow events, whereas the MODIS products fail to do so due to their temporal resolution. Furthermore, acquisition time and inner-daily melt and re-freezing effects may affect SCA estimates from MODIS data. In other cases, differences can clearly be associated to insufficient model input data, primarily due to limited spatial precipitation and temperature data. Our study indicates that individual products might provide inconsistent information on temporal and spatial snow cover. We recommend considering a combined analysis of different snow products in order to provide reliable information on snow cover dynamics, in particular in eastern Mediterranean high-altitude environments.

Global Snow-Cover Evolution from Twenty Years of Satellite Passive Microwave Data

USGS Publications Warehouse

Mognard, N.M.; Kouraev, A.V.; Josberger, E.G.

2003-01-01

Starting in 1979 with the SMMR (Scanning Multichannel Microwave Radiometer) instrument onboard the satellite NIMBUS-7 and continuing since 1987 with the SSMI (Special Sensor Microwave Imager) instrument on board the DMSP (Defence Meteorological Satellite Program) series, more then twenty years of satellite passive microwave data are now available. This dataset has been processed to analyse the evolution of the global snow cover. This work is part of the AICSEX project from the 5th Framework Programme of the European Community. The spatio-temporal evolution of the satellite-derived yearly snow maximum extent and the timing of the spring snow melt were estimated and analysed over the Northern Hemisphere. Significant differences between the evolution of the yearly maximum snow extent in Eurasia and in North America were found. A positive correlation between the maximum yearly snow cover extent and the ENSO index was obtained. High interannual spatio-temporal variability characterises the timing of snow melt in the spring. Twenty-year trends in the timing of spring snow melt have been computed and compared with spring air temperature trends for the same period and the same area. In most parts of Eurasia and in the central and western parts of North America the tendency has been for earlier snow melt. In northeastern Canada, a large area of positive trends, where snow melt timing starts later than in the early 1980s, corresponds to a region of positive trends of spring air temperature observed over the same period.

Changes in the relation between snow station observations and basin scale snow water resources

NASA Astrophysics Data System (ADS)

Sexstone, G. A.; Penn, C. A.; Clow, D. W.; Moeser, D.; Liston, G. E.

2017-12-01

Snow monitoring stations that measure snow water equivalent or snow depth provide fundamental observations used for predicting water availability and flood risk in mountainous regions. In the western United States, snow station observations provided by the Natural Resources Conservation Service Snow Telemetry (SNOTEL) network are relied upon for forecasting spring and summer streamflow volume. Streamflow forecast accuracy has declined for many regions over the last several decades. Changes in snow accumulation and melt related to climate, land use, and forest cover are not accounted for in current forecasts, and are likely sources of error. Therefore, understanding and updating relations between snow station observations and basin scale snow water resources is crucial to improve accuracy of streamflow prediction. In this study, we investigated the representativeness of snow station observations when compared to simulated basin-wide snow water resources within the Rio Grande headwaters of Colorado. We used the combination of a process-based snow model (SnowModel), field-based measurements, and remote sensing observations to compare the spatiotemporal variability of simulated basin-wide snow accumulation and melt with that of SNOTEL station observations. Results indicated that observations are comparable to simulated basin-average winter precipitation but overestimate both the simulated basin-average snow water equivalent and snowmelt rate. Changes in the representation of snow station observations over time in the Rio Grande headwaters were also investigated and compared to observed streamflow and streamflow forecasting errors. Results from this study provide important insight in the context of non-stationarity for future water availability assessments and streamflow predictions.

Impact of absorbing aerosol deposition on snow albedo reduction over the southern Tibetan plateau based on satellite observations

NASA Astrophysics Data System (ADS)

Lee, Wei-Liang; Liou, K. N.; He, Cenlin; Liang, Hsin-Chien; Wang, Tai-Chi; Li, Qinbin; Liu, Zhenxin; Yue, Qing

2017-08-01

We investigate the snow albedo variation in spring over the southern Tibetan Plateau induced by the deposition of light-absorbing aerosols using remote sensing data from moderate resolution imaging spectroradiometer (MODIS) aboard Terra satellite during 2001-2012. We have selected pixels with 100 % snow cover for the entire period in March and April to avoid albedo contamination by other types of land surfaces. A model simulation using GEOS-Chem shows that aerosol optical depth (AOD) is a good indicator for black carbon and dust deposition on snow over the southern Tibetan Plateau. The monthly means of satellite-retrieved land surface temperature (LST) and AOD over 100 % snow-covered pixels during the 12 years are used in multiple linear regression analysis to derive the empirical relationship between snow albedo and these variables. Along with the LST effect, AOD is shown to be an important factor contributing to snow albedo reduction. We illustrate through statistical analysis that a 1-K increase in LST and a 0.1 increase in AOD indicate decreases in snow albedo by 0.75 and 2.1 % in the southern Tibetan Plateau, corresponding to local shortwave radiative forcing of 1.5 and 4.2 W m-2, respectively.

Study of aerosol effect on accelerated snow melting over the Tibetan Plateau during boreal spring

NASA Astrophysics Data System (ADS)

Lee, Woo-Seop; Bhawar, Rohini L.; Kim, Maeng-Ki; Sang, Jeong

2013-08-01

In the present study, a coupled atmosphere-ocean global climate model (CSIRO-Mk3.6) is used to investigate the role of aerosol forcing agents as drivers of snow melting trends in the Tibetan Plateau (TP) region. Anthropogenic aerosol-induced snow cover changes in a warming climate are calculated from the difference between historical run (HIST) and all forcing except anthropogenic aerosol (NoAA). Absorbing aerosols can influence snow cover by warming the atmosphere, reducing snow reflectance after deposition. The warming the rate of snow melt, exposing darker surfaces below to short-wave radiation sooner, and allowing them to heat up even faster in the Himalayas and TP. The results show a strong spring snow cover decrease over TP when absorbing anthropogenic aerosol forcing is considered, whereas snow cover fraction (SCF) trends in NoAA are weakly negative (but insignificant) during 1951-2005. The enhanced spring snow cover trends in HIST are due to overall effects of different forcing agents: When aerosol forcing (AERO) is considered, a significant reduction of SCF than average can be found over the western TP and Himalayas. The large decreasing trends in SCF over the TP, with the maximum reduction of SCF around 12-15% over the western TP and Himalayas slope. Also accelerated snow melting during spring is due to effects of aerosol on snow albedo, where aerosol deposition cause decreases snow albedo. However, the SCF change in the “NoAA” simulations was observed to be less.

Chapter 7: Precipitation Change in the United States

NASA Technical Reports Server (NTRS)

Easterling, D. R.; Kunkel, K. E.; Arnold, J. R.; Knutson, T.; LeGrande, A. N.; Leung, L. R.; Vose, R. S.; Waliser, D. E.; Wehner, M. F.

2017-01-01

Annual precipitation has decreased in much of the West, Southwest, and Southeast and increased in most of the Northern and Southern Plains, Midwest, and Northeast. A national average increase of 4% in annual precipitation since 1901 is mostly a result of large increases in the fall season. Heavy precipitation events in most parts of the United States have increased in both intensity and frequency since 1901. There are important regional differences in trends, with the largest increases occurring in the northeastern United States. In particular, mesoscale convective systems (organized clusters of thunderstorms)-the main mechanism for warm season precipitation in the central part of the United States-have increased in occurrence and precipitation amounts since 1979. The frequency and intensity of heavy precipitation events are projected to continue to increase over the 21st century (high confidence). Mesoscale convective systems in the central United States are expected to continue to increase in number and intensity in the future. There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern United States, including Alaska, is projected to receive more precipitation in the winter and spring, and parts of the southwestern United States are projected to receive less precipitation in the winter and spring. Northern Hemisphere spring snow cover extent, North America maximum snow depth, snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of the northern United States have increased. Projections indicate large declines in snowpack in the western United States and shifts to more precipitation falling as rain than snow in the cold season in many parts of the central and eastern United States.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

Synchronous flowering despite differences in snowmelt timing among habitats of Empetrum hermaphroditum

NASA Astrophysics Data System (ADS)

Bienau, Miriam J.; Kröncke, Michael; Eiserhardt, Wolf L.; Otte, Annette; Graae, Bente J.; Hagen, Dagmar; Milbau, Ann; Durka, Walter; Eckstein, R. Lutz

2015-11-01

The topography within arctic-alpine landscapes is very heterogeneous, resulting in diverse snow distribution patterns, with different snowmelt timing in spring. This may influence the phenological development of arctic and alpine plant species and asynchronous flowering may promote adaptation of plants to their local environments. We studied how flowering phenology of the dominant dwarf shrub Empetrum hermaphroditum varied among three habitats (exposed ridges, sheltered depressions and birch forest) differing in winter snow depth and thus snowmelt timing in spring, and whether the observed patterns were consistent across three different study areas. Despite significant differences in snowmelt timing between habitats, full flowering of E. hermaphroditum was nearly synchronous between the habitats, and implies a high flowering overlap. Our data show that exposed ridges, which had a long lag phase between snowmelt and flowering, experienced different temperature and light conditions than the two late melting habitats between snowmelt and flowering. Our study demonstrates that small scale variation seems matter less to flowering of Empetrum than interannual differences in snowmelt timing.

Coupling of a Simple 3-Layer Snow Model to GISS GCM

NASA Astrophysics Data System (ADS)

Aleinov, I.

2001-12-01

Appropriate simulation of the snow cover dynamics is an important issue for the General Circulation Models (GCMs). The presence of snow has a significant impact on ground albedo and on heat and moisture balance. A 3-layer snow model similar to the one proposed by Lynch-Stieglitz was developed with the purpose of using it inside the GCM developed in the NASA Goddard Institute for Space Studies (GISS). The water transport between the layers is modeled explicitly while the heat balance is computed implicitly between the snow layers and semi-implicitly on the surface. The processes of melting and refreezing and compactification of layers under the gravitational force are modeled appropriately. It was noticed that implicit computation of the heat transport can cause a significant under- or over-estimation of the incoming heat flux when the temperature of the upper snow layer is equal to 0 C. This may lead in particular to delayed snow melting in spring. To remedy this problem a special flux-control algorithm was added to the model, which checks computed flux for possible errors and if such are detected the heat transport is recomputed again with the appropriate corrections. The model was tested off-line with Sleepers River forcing data and exhibited a good agreement between simulated and observed quantities for snow depth, snow density and snow temperature. The model was then incorporated into the GISS GCM. Inside the GCM the model is driven completely by the data simulated by other parts of the GCM. The screening effect of the vegetation is introduced by means of masking depth. For a thin snowpack a fractional cover is implemented so that the total thickness of the the snow is never less then 10 cm (rather, the areal fraction of the snow cover decreases when it melts). The model was tested with 6 year long GCM speed-up runs. It proved to be stable and produced reasonable results for the global snow cover. In comparison to the old GISS GCM snow model (which was incorporating the snow into the first soil layer) the new snow model has better insulating properties, thus preventing the ground from overcooling in winter. It also provides better simulation for water retention and release by the snow which results in more physical ground water runoff.

Spring snow albedo feedback in daily data over Russia: Comparing in-situ measurements with reanalysis products.

NASA Astrophysics Data System (ADS)

Wegmann, M.; Zolina, O.; Jacobi, H. W.

2016-12-01

Global warming is enhanced at high northern latitudes where the Arctic surface air temperature has risen at twice the rate of the global average in recent decades - a feature called Arctic amplification. This recent Arctic warming signal likely results from several factors such as the albedo feedback due to a diminishing cryosphere, enhanced poleward atmospheric and oceanic heat transport, and changes in humidity. Surface albedo feedback is stating that the additional amount of shortwave radiation at the top of the atmosphere decreases with decreasing surface albedo whereas surface air temperature increases with decreasing surface albedo. It is considered a positive feedback in that an initial warming perturbation than kicks off a strengthening warming. Looking at the Northern Hemisphere with its large landmasses, snow albedo feedback is especially strong since most of these landmasses experience snow cover during boreal wintertime. Unfortunately, so far there remains a lack of reliable observational data over large parts of the cryosphere. Satellite products cover large parts of the NH, however lack high temporal resolution and have problems with large solar zenith angles as well as over complex terrain (eg. Wang et al. 2014). Our analysis focuses at the Russian territory where we utilize in-situ radiation and snow depth measurements. We found 50 stations which measure both variables on a daily basis for the period 2000-2013. Since Hall (2004) found that 50% of the notal NH snow albedo feedback caused by global warming occurs during NH spring, we focus on the transition period of March to June (MAMJ). Thackeray & Fletcher 2006 compared albedo feedback processes CMIP3 and CMIP5 model families and found while the models represent the feedback process accurately, there are still inherent biases and outdated parameterizations. Therefore we use the daily observations and state of the art reanalysis products to 1) evaluate reanalysis and model products in respect to radiation properties, 2) investigate snow albedo feedbacks on a daily scale during spring and 3) to suggest climate change signals over Russia in albedo feedback between 2000 - 2013 based on in-situ measurements.

Spring snow goose hunting influences body composition of waterfowl staging in Nebraska

USGS Publications Warehouse

Pearse, Aaron T.; Krapu, Gary L.; Cox, Robert R.

2012-01-01

A spring hunt was instituted in North America to reduce abundance of snow geese (Chen caerulescens) by increasing mortality of adults directly, yet disturbance from hunting activities can indirectly influence body condition and ultimately, reproductive success. We estimated effects of hunting disturbance by comparing body composition of snow geese and non-target species, greater white-fronted geese (Anser albifrons) and northern pintails (Anas acuta) collected in portions of south-central Nebraska that were open (eastern Rainwater Basin, ERB) and closed (western Rainwater Basin, WRB; and central Platte River Valley, CPRV) to snow goose hunting during springs 1998 and 1999. Lipid content of 170 snow geese was 25% (57 g) less in areas open to hunting compared to areas closed during hunting season but similar in all areas after hunting was concluded in the ERB. Protein content of snow geese was 3% (14 g) less in the region open to hunting. Greater white-fronted geese had 24% (76 g; n = 129) less lipids in the hunted portion of the study area during hunting season, and this difference persisted after conclusion of hunting season. We found little difference in lipid or protein content of northern pintails in relation to spring hunting. Indirect effects of spring hunting may be considered a collateral benefit regarding efforts to reduce overabundant snow goose populations. Disrupted nutrient storage observed in greater white-fronted geese represents an unintended consequence of spring hunting that has potential to adversely affect reproduction for this and other species of waterbirds staging in the region.

On the extraordinary snow on the sea ice off East Antarctica in late winter, 2012

NASA Astrophysics Data System (ADS)

Toyota, Takenobu; Massom, Robert; Lecomte, Olivier; Nomura, Daiki; Heil, Petra; Tamura, Takeshi; Fraser, Alexander D.

2016-09-01

In late winter-early spring 2012, the second Sea Ice Physics and Ecosystems Experiment (SIPEX II) was conducted off Wilkes Land, East Antarctica, onboard R/V Aurora Australis. The sea-ice conditions were characterized by significantly thick first-year ice and snow, trapping the ship for about 10 days in the near coastal region. The deep snow cover was particularly remarkable, in that its average value of 0.45 m was almost three times that observed between 1992 and 2007 in the region. To reveal factors responsible, we used in situ observations and ERA-Interim reanalysis (1990-2012) to examine the relative contribution of the different components of the local-regional snow mass balance equation i.e., snow accumulation on sea ice, precipitation minus evaporation (P-E), and loss by (i) snow-ice formation and (ii) entering into leads due to drifting snow. Results show no evidence for significantly high P-E in the winter of 2012. Ice core analysis has shown that although the snow-ice layer was relatively thin, indicating less transformation from snow to snow-ice in 2012 as compared to measurements from 2007, the difference was not enough to explain the extraordinarily deep snow. Based on these results, we deduce that lower loss of snow into leads was probably responsible for the extraordinary snow in 2012. Statistical analysis and satellite images suggest that the reduction in loss of snow into leads is attributed to rough ice surface associated with active deformation processes and larger floe size due to sea-ice expansion. This highlights the importance of snow-sea ice interaction in determining the mean snow depth on Antarctic sea ice.

Modeling and measuring snow for assessing climate change impacts in Glacier National Park, Montana

USGS Publications Warehouse

Fagre, Daniel B.; Selkowitz, David J.; Reardon, Blase; Holzer, Karen; Mckeon, Lisa L.

2002-01-01

A 12-year program of global change research at Glacier National Park by the U.S. Geological Survey and numerous collaborators has made progress in quantifying the role of snow as a driver of mountain ecosystem processes. Spatially extensive snow surveys during the annual accumulation/ablation cycle covered two mountain watersheds and approximately 1,000 km2 . Over 7,000 snow depth and snow water equivalent (SWE) measurements have been made through spring 2002. These augment two SNOTEL sites, 9 NRCS snow courses, and approximately 150 snow pit analyses. Snow data were used to establish spatially-explicit interannual variability in snowpack SWE. East of the Continental Divide, snowpack SWE was lower but also less variable than west of the Divide. Analysis of snowpacks suggest downward trends in SWE, a reduction in snow cover duration, and earlier melt-out dates during the past 52 years. Concurrently, high elevation forests and treelines have responded with increased growth. However, the 80 year record of snow from 3 NRCS snow courses reflects a strong influence from the Pacific Decadal Oscillation, resulting in 20-30 year phases of greater or lesser mean SWE. Coupled with the fine-resolution spatial snow data from the two watersheds, the ecological consequences of changes in snowpack can be empirically assessed at a habitat patch scale. This will be required because snow distribution models have had varied success in simulating snowpack accumulation/ablation dynamics in these mountain watersheds, ranging from R2=0.38 for individual south-facing forested snow survey routes to R2=0.95 when aggregated to the watershed scale. Key ecological responses to snowpack changes occur below the watershed scale, such as snow-mediated expansion of forest into subalpine meadows, making continued spatially-explicit snow surveys a necessity. 

Catchment-scale snow depth monitoring with balloon photogrammetry

NASA Astrophysics Data System (ADS)

Durand, M. T.; Li, D.; Wigmore, O.; Vanderjagt, B. J.; Molotch, N. P.; Bales, R. C.

2016-12-01

Field campaigns and permanent in-situ facilities provide extensive measurements of snowpack properties at catchment (or smaller) scales, and have consistently improved our understanding of snow processes and the estimation of snow water resources. However, snow depth, one of the most important snow states, has been measured almost entirely with discrete point-scale samplings in field measurements; spatiotemporally continuous snow depth measurements are nearly nonexistent, mainly due to the high cost of airborne flights and the ban of Unmanned Aerial Systems in many areas (e.g. in all the national parks). In this study, we estimate spatially continuous snow depth from photogrammetric reconstruction of aerial photos taken from a weather balloon. The study was conducted in a 0.2 km2 watershed in Wolverton, Sequoia National Park, California. We tied a point-and-shoot camera on a helium-inflated weather balloon to take aerial images; the camera was scripted to automatically capture images every 3 seconds and to record the camera position and orientation at the imaging times using a built-in GPS. With the 2D images of the snow-covered ground and the camera position and orientation data, the 3D coordinates of the snow surface were reconstructed at 10 cm resolution using photogrammetry software PhotoScan. Similar measurements were taken for the snow-free ground after snowmelt, and the snow depth was estimated from the difference between the snow-on and snow-off measurements. Comparing the photogrammetric-estimated snow depths with the 32 manually measured depths, taken at the same time as the snow-on balloon flight, we find the RMSE of the photogrammetric snow depth is 7 cm, which is 2% of the long-term peak snow depth in the study area. This study suggests that the balloon photogrammetry is a repeatable, economical, simple, and environmental-friendly method to continuously monitor snow at small-scales. Spatiotemporally continuous snow depth could be regularly measured in future field measurements to supplement traditional snow property observations. In addition, since the process of collecting and processing balloon photogrammetry data is straightforward, the photogrammetric snow depth could be shared with the public in real time using our cloud platform that is currently under development.

When Models and Observations Collide: Journeying towards an Integrated Snow Depth Product

NASA Astrophysics Data System (ADS)

Webster, M.; Petty, A.; Boisvert, L.; Markus, T.; Kurtz, N. T.; Kwok, R.; Perovich, D. K.

2017-12-01

Knowledge of snow depth is essential for assessing changes in sea ice mass balance due to snow's insulating and reflective properties. In remote sensing applications, the accuracy of sea ice thickness retrievals from altimetry crucially depends on snow depth. Despite the need for snow depth data, we currently lack continuous observations that capture the basin-scale snow depth distribution and its seasonal evolution. Recent in situ and remote sensing observations are sparse in space and time, and contain uncertainties, caveats, and/or biases that often require careful interpretation. Likewise, using model output for remote sensing applications is limited due to uncertainties in atmospheric forcing and different treatments of snow processes. Here, we summarize our efforts in bringing observational and model data together to develop an approach for an integrated snow depth product. We start with a snow budget model and incrementally incorporate snow processes to determine the effects on snow depth and to assess model sensitivity. We discuss lessons learned in model-observation integration and ideas for potential improvements to the treatment of snow in models.

Developing a Model for Predicting Snowpack Parameters Affecting Vehicle Mobility,

DTIC Science & Technology

1983-05-01

Service River Forecast System -Snow accumulation and JO ablation model. NOAA Technical Memorandum NWS HYDRO-17, National Weather Service, JS Silver Spring... Forecast System . This model indexes each phys- ical process that occurs in the snowpack to the air temperature. Although this results in a signifi...pressure P Probability Q Energy Q Specific humidity R Precipitation s Snowfall depth T Air temperature t Time U Wind speed V Water vapor

Snow depth evolution on sea ice from Snow Buoy measurement

NASA Astrophysics Data System (ADS)

Nicolaus, M.; Arndt, S.; Hendricks, S.; Hoppmann, M.; Katlein, C.; König-Langlo, G.; Nicolaus, A.; Rossmann, H. L.; Schiller, M.; Schwegmann, S.; Langevin, D.

2016-12-01

Snow cover is an Essential Climate Variable. On sea ice, snow dominates the energy and momentum exchanges across the atmosphere-ice-ocean interfaces, and actively contributes to sea ice mass balance. Yet, snow depth on sea ice is one of the least known and most difficult to observe parameters of the Arctic and Antarctic; mainly due to its exceptionally high spatial and temporal variability. In this study; we present a unique time series dataset of snow depth and air temperature evolution on Arctic and Antarctic sea ice recorded by autonomous instruments. Snow Buoys record snow depth with four independent ultrasonic sensors, increasing the reliability of the measurements and allowing for additional analyses. Auxiliary measurements include surface and air temperature, barometric pressure and GPS position. 39 deployments of such Snow Buoys were achieved over the last three years either on drifting pack ice, on landfast sea ice or on an ice shelf. Here we highlight results from two pairs of Snow Buoys installed on drifting pack ice in the Weddell Sea. The data reveals large regional differences in the annual cycle of snow depth. Almost no reduction in snow depth (snow melt) was observed in the inner and southern part of the Weddell Sea, allowing a net snow accumulation of 0.2 to 0.9 m per year. In contrast, summer snow melt close to the ice edge resulted in a decrease of about 0.5 m during the summer 2015/16. Another array of eight Snow Buoys was installed on central Arctic sea ice in September 2015. Their air temperature record revealed exceptionally high air temperatures in the subsequent winter, even exceeding the melting point but with almost no impact on snow depth at that time. Future applications of Snow Buoys on Arctic and Antarctic sea ice will allow additional inter-annual studies of snow depth and snow processes, e.g. to support the development of snow depth data products from airborne and satellite data or though assimilation in numerical models.

Snow cover and snow goose Anser caerulescens caerulescens distribution during spring migration

USGS Publications Warehouse

Hupp, Jerry W.; Zacheis, Amy B.; Anthony, R. Michael; Robertson, Donna G.; Erickson, Wallace P.; Palacios, Kelly C.

2001-01-01

Arctic geese often use spring migration stopover areas when feeding habitats are partially snow covered. Melting of snow during the stopover period causes spatial and temporal variability in distribution and abundance of feeding habitat. We recorded changes in snow cover and lesser snow goose Anser caerulescens caerulescens distribution on a spring migration stopover area in south-central Alaska during aerial surveys in 1993-1994. Our objectives were to determine whether geese selected among areas with different amounts of snow cover and to assess how temporal changes in snow cover affected goose distribution. We also measured temporal changes in chemical composition of forage species after snow melt. We divided an Arc/Info coverage of the approximately 210 km2 coastal stopover area into 2-km2 cells, and measured snow cover and snow goose use of cells. Cells that had 10-49.9% snow cover were selected by snow geese, whereas cells that lacked snow cover were avoided. In both years, snow cover diminished along the coast between mid-April and early May. Flock distribution changed as snow geese abandoned snow-free areas in favour of cells where snow patches were interspersed with bare ground. Snow-free areas may have been less attractive to geese because available forage had been quickly exploited as bare ground was exposed, and because soils became drier making extraction of underground forage more difficult. Fiber content of two forage species increased whereas non-structural carbohydrate concentrations of forage plants appeared to diminish after snow melt, but changes in nutrient concentrations likely occurred too slowly to account for abandonment of snow-free areas by snow geese.

[A snow depth inversion method for the HJ-1B satellite data].

PubMed

Dong, Ting-Xu; Jiang, Hong-Bo; Chen, Chao; Qin, Qi-Ming

2011-10-01

The importance of the snow is self-evident, while the harms caused by the snow have also received more and more attention. At present, the retrieval of snow depth mainly focused on the use of microwave remote sensing data or a small amount of optical remote sensing data, such as the meteorological data or the MODIS data. The small satellites for environment and disaster monitoring of China are quite different form the meteorological data and MODIS data, both in the spectral resolution or spatial resolution. In this paper, aimed at the HJ-1B data, snow spectral of different underlying surfaces and depths were surveyed. The correlation between snow cover index and snow depth was also analyzed to establish the model for the snow depth retrieval using the HJ-1B data. The validation results showed that it can meet the requirements of real-time monitoring the snow depth on the condition of conventional snow depth.

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

Snow Depth from Lidar: Challenges and New Technology for Measurements in Extreme Terrain

NASA Astrophysics Data System (ADS)

Berisford, D. F.; Kadatskiy, V.; Boardman, J. W.; Bormann, K.; Deems, J. S.; Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Richardson, M.; Painter, T. H.

2014-12-01

The Airborne Snow Observatory (ASO) uses an airborne LiDAR system to measure basin-wide snow depth with cm-scale accuracy at ~1m spatial resolution. This is accomplished by creating a Digital Elevation Model (DEM) over snow-free terrain in the summer, then repeating the flights again when the terrain is snow-covered and subtracting the elevations. Snow Water Equivalent (SWE) is then calculated by incorporating modeled snow density estimates, and when combined with coincident spectrometer albedo measurements, informs distributed hydrologic modeling and runoff prediction. This method provides SWE estimates of unprecedented accuracy and extent compared to traditional snow surveys and towers, and 24hr latency data products through the ASO processing pipeline using Apache Tika and OODT software. The timely ASO outputs support operational decision making by water/dam operators for optimal water management. The water-resource snowpack in the western US lies in remote mountainous terrain, spanning large areas containing steep faces at all aspects, often amongst tree canopy. This extreme terrain presents unusual challenges for LiDAR, and requires high altitude flights to achieve wide area coverage, high point density to capture small terrain features, and the ability to capture all slope aspects without shadowing. These challenges were met by the new state-of-the-art Riegl LMS-Q1560 LiDAR system, which incorporates two independent laser channels and a single rotating mirror. Both lasers and mirror are designed to provide forward, backward, and nadir look capability, which minimizes shadowing and ensures data capture even on very steep slopes. The system is capable of logging more than 10 simultaneous pulses in the air, which allows data collection at extremely high resolution while maintaining very high altitude which reduces complete region acquisition time significantly, and allows data collection over terrain with extreme elevation variation. Our experience to-date includes acquisition of data over terrain relief of more than 3500m, and ranges of up to 6000m in a single swath. We present data acquired during spring of 2013 and 2014 in western Colorado and the central Sierra Nevada, which demonstrates the capability of the new LiDAR technology and shows basin-wide measured snow depth and SWE results.

Landscape and hydrologic changes in the permafrost regions of the Western Canadian Arctic

NASA Astrophysics Data System (ADS)

Marsh, P.

2012-12-01

The Western Canadian Arctic, in the vicinity of the Mackenzie River Delta, is characterized by long cold winters, short summers, low precipitation, thin organic soils, and ice-rich continuous permafrost. Over the last few decades, this region has undergone dramatic changes in climate, with warming air temperature and decreasing winter and summer precipitation. This has resulted in various landscape changes, including the warming of the upper layers of the permafrost, deepening of the active layer, drainage of permafrost affected lakes, an ongoing change from tundra to shrub tundra, and earlier spring breakup of streams, rivers and lakes. However, interactions between climate, hydrology, snow, and vegetation greatly affect both the spatial and temporal changes to the permafrost and hydrology of this region. Knowledge of these changes is important to the understanding of methane dynamics in this permafrost landscape, and for predicting future changes. Two examples of observed landscape change will be discussed. First, ground based observations and analysis of air photo images has demonstrated that shrub expansion is not uniform across the landscape, but instead is characterized by shrub patches of varying size. This patchiness is likely related to existing variations in soil temperature and moisture, active layer depth, snowcover, and tundra fires. As shrub patches further develop, they impact soil temperature and active layer depth. For example, small patches of shrubs typically have snow depths that are deeper than surrounding tundra areas due to the accumulation of blowing snow, and as a result have much warmer soil temperatures and deeper active layers. In contrast to these small shrub patches, large shrub patches have snow depths only slightly larger than found in the surrounding tundra and therefore only slightly warmer winter soil temperatures. However, shading of the surface during the summer may result in cooler summer soil temperatures. The overall effect of large shrub patches may be either deeper or shallower active layer depths than the surrounding tundra areas, depending on the leaf area index, the degree of shrub bending during the winter, and snow accumulation. Second, in contrast to many areas in Alaska and Siberia where increased rates of lake drainage have been reported, the rate of lake drainage in the Western Canadian Arctic has been decreasing over the past 50 years. The primary factors causing lake drainage in this region are high lake levels and winter cracking of ice wedges in the area immediately around the lake. Hydrologic modelling has suggested that summer lake levels have not changed significantly over the last 50 years, and therefore are not responsible for the decrease in drainage. However, the role of factors such as snow dams at lake outlets that result in high spring water levels, or the offsetting factors of warmer, but less snowy winters on ice wedge cracking are not well understood. As a result, further research is required to better understand how these lakes will respond to future changes in climate. Given the potential changes to methane dynamics in areas of changing permafrost, there is an urgent need to better understand ongoing, and future, changes in the landscape of these permafrost regions.

[Effect of different snow depth and area on the snow cover retrieval using remote sensing data].

PubMed

Jiang, Hong-bo; Qin, Qi-ming; Zhang, Ning; Dong, Heng; Chen, Chao

2011-12-01

For the needs of snow cover monitoring using multi-source remote sensing data, in the present article, based on the spectrum analysis of different depth and area of snow, the effect of snow depth on the results of snow cover retrieval using normalized difference snow index (NDSI) is discussed. Meanwhile, taking the HJ-1B and MODIS remote sensing data as an example, the snow area effect on the snow cover monitoring is also studied. The results show that: the difference of snow depth does not contribute to the retrieval results, while the snow area affects the results of retrieval to some extents because of the constraints of spatial resolution.

Snow depth on Arctic sea ice from historical in situ data

NASA Astrophysics Data System (ADS)

Shalina, Elena V.; Sandven, Stein

2018-06-01

The snow data from the Soviet airborne expeditions Sever in the Arctic collected over several decades in March, April and May have been analyzed in this study. The Sever data included more measurements and covered a much wider area, particularly in the Eurasian marginal seas (Kara Sea, Laptev Sea, East Siberian Sea and Chukchi Sea), compared to the Soviet North Pole drifting stations. The latter collected data mainly in the central part of the Arctic Basin. The following snow parameters have been analyzed: average snow depth on the level ice (undisturbed snow) height and area of sastrugi, depth of snow dunes attached to ice ridges and depth of snow on hummocks. In the 1970s-1980s, in the central Arctic, the average depth of undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 30 % of the ice surface) was 36.2 cm and the average depth of snow near hummocks and ridges was about 65 cm. For the marginal seas, the average depth of undisturbed snow on the level ice varied from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, which had a larger fraction of multiyear ice. In the marginal seas the spatial variability of snow depth was characterized by standard deviation varying between 66 and 100 %. The average height of sastrugi varied from 23 cm to about 32 cm with standard deviation between 50 and 56 %. The average area covered by sastrugi in the marginal seas was estimated to be 36.5 % of the total ice area where sastrugi were observed. The main result of the study is a new snow depth climatology for the late winter using data from both the Sever expeditions and the North Pole drifting stations. The snow load on the ice observed by Sever expeditions has been described as a combination of the depth of undisturbed snow on the level ice and snow depth of sastrugi weighted in proportion to the sastrugi area. The height of snow accumulated near the ice ridges was not included in the calculations because there are no estimates of the area covered by those features from the Sever expeditions. The effect of not including that data can lead to some underestimation of the average snow depth. The new climatology refines the description of snow depth in the central Arctic compared to the results by Warren et al. (1999) and provides additional detailed data in the marginal seas. The snow depth climatology is based on 94 % Sever data and 6 % North Pole data. The new climatology shows lower snow depth in the central Arctic comparing to Warren climatology and more detailed data in the Eurasian seas.

Interannual consistency in fractal snow depth patterns at two Colorado mountain sites

Treesearch

Jeffrey S. Deems; Steven R. Fassnacht; Kelly J. Elder

2008-01-01

Fractal dimensions derived from log-log variograms are useful for characterizing spatial structure and scaling behavior in snow depth distributions. This study examines the temporal consistency of snow depth scaling features at two sites using snow depth distributions derived from lidar datasets collected in 2003 and 2005. The temporal snow accumulation patterns in...

Snow Depth Mapping at a Basin-Wide Scale in the Western Arctic Using UAS Technology

NASA Astrophysics Data System (ADS)

de Jong, T.; Marsh, P.; Mann, P.; Walker, B.

2015-12-01

Assessing snow depths across the Arctic has proven to be extremely difficult due to the variability of snow depths at scales from metres to 100's of metres. New Unmanned Aerial Systems (UAS) technology provides the possibility to obtain centimeter level resolution imagery (~3cm), and to create Digital Surface Models (DSM) based on the Structure from Motion method. However, there is an ongoing need to quantify the accuracy of this method over different terrain and vegetation types across the Arctic. In this study, we used a small UAS equipped with a high resolution RGB camera to create DSMs over a 1 km2 watershed in the western Canadian Arctic during snow (end of winter) and snow-free periods. To improve the image georeferencing, 15 Ground Control Points were marked across the watershed and incorporated into the DSM processing. The summer DSM was subtracted from the snowcovered DSM to deliver snow depth measurements across the entire watershed. These snow depth measurements were validated by over 2000 snow depth measurements. This technique has the potential to improve larger scale snow depth mapping across watersheds by providing snow depth measurements at a ~3 cm . The ability of mapping both shallow snow (less than 75cm) covering much of the basin and snow patches (up to 5 m in depth) that cover less than 10% of the basin, but contain a significant portion of total basin snowcover, is important for both water resource applications, as well as for testing snow models.

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

DOE PAGES

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste; ...

2017-04-03

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wainwright, Haruko M.; Liljedahl, Anna K.; Dafflon, Baptiste

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE=2.9cm), with a spatial sampling of 10cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE=6.0cm) andmore » a fine spatial sampling (4cm×4cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE=6.0cm), at 0.5m resolution and over the lidar domain (750m×700m).« less

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

PubMed

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.

The Fate of Aspen in a World with Diminishing Snowpacks

NASA Astrophysics Data System (ADS)

Kavanagh, K.; Link, T. E.; Seyfried, M. S.; Kemp, K. B.

2010-12-01

Aspen (Populus tremuloides) productivity is tightly coupled with soil moisture. In the mountainous regions of the western USA, annual replenishment of soil moisture commonly occurs during snowmelt. Therefore, snow pack depth and duration can play an important role in sustaining aspen productivity. The presence of almost 50 years of detailed climate data across an elevational transect in the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho offers a novel opportunity to better understand the role of shifting precipitation patterns on aspen productivity. Over the past 50 years, the proportion of the precipitation falling in the form of snow decreased by almost a factor of 2 at mid to low elevations in the RCEW, coupled with a roughly four week advance of snow ablation, and decline of large snow drifts that release moisture into the early summer. Results from growth ring increment, stable isotope analysis, sapflux and a process model (Biome BGC), will be used to determine the impact of shifting precipitation patterns on tree productivity along this transect over the past 50 years. Aspen trees located on moist microsites continue to transpire water and maintain high stomatal conductance 21 days later in the growing season relative to individuals on drier microsites. Predictions of net primary productivity (NPP) in aspen are very sensitive to precipitation patterns. NPP becomes negative as early as day 183 (90 days post budbreak) for years with little winter and spring precipitation whereas, in years with ample winter and spring precipitation, NPP remains positive until day 260 when leaf fall occurs. These results give unique insight into the conditions that deciduous tree species will encounter in a warming climate where snow water equivalent continues to diminish and soil moisture declines soon after budbreak occurs.

A longer vernal window: the role of winter coldness and snowpack in driving spring transitions and lags.

PubMed

Contosta, Alexandra R; Adolph, Alden; Burchsted, Denise; Burakowski, Elizabeth; Green, Mark; Guerra, David; Albert, Mary; Dibb, Jack; Martin, Mary; McDowell, William H; Routhier, Michael; Wake, Cameron; Whitaker, Rachel; Wollheim, Wilfred

2017-04-01

Climate change is altering the timing and duration of the vernal window, a period that marks the end of winter and the start of the growing season when rapid transitions in ecosystem energy, water, nutrient, and carbon dynamics take place. Research on this period typically captures only a portion of the ecosystem in transition and focuses largely on the dates by which the system wakes up. Previous work has not addressed lags between transitions that represent delays in energy, water, nutrient, and carbon flows. The objectives of this study were to establish the sequence of physical and biogeochemical transitions and lags during the vernal window period and to understand how climate change may alter them. We synthesized observations from a statewide sensor network in New Hampshire, USA, that concurrently monitored climate, snow, soils, and streams over a three-year period and supplemented these observations with climate reanalysis data, snow data assimilation model output, and satellite spectral data. We found that some of the transitions that occurred within the vernal window were sequential, with air temperatures warming prior to snow melt, which preceded forest canopy closure. Other transitions were simultaneous with one another and had zero-length lags, such as snowpack disappearance, rapid soil warming, and peak stream discharge. We modeled lags as a function of both winter coldness and snow depth, both of which are expected to decline with climate change. Warmer winters with less snow resulted in longer lags and a more protracted vernal window. This lengthening of individual lags and of the entire vernal window carries important consequences for the thermodynamics and biogeochemistry of ecosystems, both during the winter-to-spring transition and throughout the rest of the year. © 2016 The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Influence of tundra snow layer thickness on measured and modelled radar backscatter

NASA Astrophysics Data System (ADS)

Rutter, N.; Sandells, M. J.; Derksen, C.; King, J. M.; Toose, P.; Wake, L. M.; Watts, T.

2017-12-01

Microwave radar backscatter within a tundra snowpack is strongly influenced by spatial variability of the thickness of internal layering. Arctic tundra snowpacks often comprise layers consisting of two dominant snow microstructures; a basal depth hoar layer overlain by a layer of wind slab. Occasionally there is also a surface layer of decomposing fresh snow. The two main layers have strongly different microwave scattering properties. Depth hoar has a greater capacity for scattering electromagnetic energy than wind slab, however, wind slab usually has a larger snow water equivalent (SWE) than depth hoar per unit volume due to having a higher density. So, determining the relative proportions of depth hoar and wind slab from a snowpack of a known depth may help our future capacity to invert forward models of electromagnetic backscatter within a data assimilation scheme to improve modelled estimates of SWE. Extensive snow measurements were made within Trail Valley Creek, NWT, Canada in April 2013. Snow microstructure was measured at 18 pit and 9 trench locations throughout the catchment (trench extent ranged between 5 to 50 m). Ground microstructure measurements included traditional stratigraphy, near infrared stratigraphy, Specific Surface Area (SSA), and density. Coincident airborne Lidar measurements were made to estimate distributed snow depth across the catchment, in addition to airborne radar snow backscatter using a dual polarized (VV/VH) X- and Ku-band Synthetic Aperture Radar (SnowSAR). Ground measurements showed the mean proportion of depth hoar was just under 30% of total snow depth and was largely unresponsive to increasing snow depth. The mean proportion of wind slab is consistently greater than 50% and showed an increasing trend with increasing total snow depth. A decreasing trend in the mean proportion of surface snow (approximately 25% to 10%) with increasing total depth accounted for this increase in wind slab. This new knowledge of variability in stratigraphic thickness, relative to respective proportions of total snow depth, was used to investigate the representativeness of point measurements of density and microstructure for forward simulations of the SMRT microwave scattering model, using Lidar derived snow depths.

Measurement of snow depth distribution in the Kamikochi-Azusa river basin using an airborne laser scanning

NASA Astrophysics Data System (ADS)

Suzuki, K.; Sasaki, A.

2013-12-01

In the Japanese Alps region, large amounts of precipitation in the form of snow constitute a more important water resource than rain. During the winter, precipitation that is deposited as snowfall accumulates in the river basins, and it forms natural dams known as 'white dams.' A quantitative understanding of snow depth distribution in these mountainous areas is important not only for evaluating water resource volume, but also for understanding the effects of snow in terms of its impact on landforms and its effect on the distribution of vegetation. However, it is not easy to perform a quantitative evaluation of snow depth distribution in mountainous areas. Several methods have been proposed for clarifying snow depth distribution. The most widely used of these is a method of inserting a sounding rod into the snow to measure its depth at each geographic position. Another method is to dig a trench in the snow and then perform an observational measurement of the side of the trench. These methods enable accurate measurement of the snow depth; however, when the snow is several meters deep, the methods may be limited by the measuring capacity of the equipment, or by the time restrictions of the survey. For these reasons, wide area measurement of the spatial distribution of snow is very difficult, and it is not suitable for investigating snow depth distribution in river basins. There is a method of using ultrasonics or radar to measure the depth of snow and to make observations of snow depth at certain positions. This method offers high measurement precision and high time resolution at the observation points. However, for observations in areas of very deep snow, it becomes technically difficult to install the equipment, and it is difficult to make a large number of installations to cover a wide area. There are also methods of indirectly measuring snow depth. One of these is to use aerial photographs taken when there is no snow cover and when there is snow cover, draw contour lines, and then use the difference between them to clarify the snow depth. This method allows researchers to grasp the snow depth over a wide area, but it needs to be made more precise if it is to incorporate high-precision information on equivalent elevation points on the snow surface. In recent years, a measurement technology has been developed that uses laser scanners mounted on aircraft. This method enables researchers to obtain ground surface coordinate data with high precision over a wide area from the air. Using such a scanner to measure the ground surface during snow coverage and during no snow coverage, and then finding the differences between the surface elevations, has made it possible to ascertain snow depth with high precision. Airborne laser measurement enables high-precision measurements over a wide area and in a short amount of time, and measurements can be made regardless of geographical factors such as sloping ground. As such, it enables measurement of snow depth distribution over a wide area without having to worry about the undulations of the land. In this study, airborne laser scanning was carried out on the snow surface in the upstream region of the Kamikochi-Azusa River in Japan on March 29, 2012, in order to clarify the snow depth distribution.

NOHRSC Interactive Snow Information

Science.gov Websites

-present) RFC Basin Other (non-RFC) Basin State NSA region (Discussion) NSA subregion (Disc.) Basins by None Snow Water Equivalent Snow Depth Shallow SWE Shallow Snow Depth Snow Temperature Snow Density Snow Melt Snow Precipitation Non-Snow Precipitation Air Temperature Solar Radiation Relative Humidity

Earth observations taken during the STS-59 mission

NASA Image and Video Library

1994-04-18

STS059-228-094 (9-20 April 1994) --- The spring thaw along the coast of the Gulf of Alaska has not touched the St. Elias Mountains, southeast of Yakutat Bay and Malaspina Glacier. A prominent glacier flows from Mt. Fairweather (15,300 feet) at right center, to form Cape Fairweather. Another glacier to the northwest almost reaches the sea; the valley of the Alsek River forms a broad, braided plan at upper left. The low sun elevation and oblique angle of this photograph provide a striking 3-dimensional appearance to the black-and-white landscape. SRL investigators will study microwave response to varying depths and conditions of ice and snow along this coast, in Spring and Summer. Hasselblad photograph.

Influence of Western Tibetan Plateau Summer Snow Cover on East Asian Summer Rainfall

NASA Astrophysics Data System (ADS)

Wang, Zhibiao; Wu, Renguang; Chen, Shangfeng; Huang, Gang; Liu, Ge; Zhu, Lihua

2018-03-01

The influence of boreal winter-spring eastern Tibetan Plateau snow anomalies on the East Asian summer rainfall variability has been the focus of previous studies. The present study documents the impacts of boreal summer western and southern Tibetan Plateau snow cover anomalies on summer rainfall over East Asia. Analysis shows that more snow cover in the western and southern Tibetan Plateau induces anomalous cooling in the overlying atmospheric column. The induced atmospheric circulation changes are different corresponding to more snow cover in the western and southern Tibetan Plateau. The atmospheric circulation changes accompanying the western Plateau snow cover anomalies are more obvious over the midlatitude Asia, whereas those corresponding to the southern Plateau snow cover anomalies are more prominent over the tropics. As such, the western and southern Tibetan Plateau snow cover anomalies influence the East Asian summer circulation and precipitation through different pathways. Nevertheless, the East Asian summer circulation and precipitation anomalies induced by the western and southern Plateau snow cover anomalies tend to display similar distribution so that they are more pronounced when the western and southern Plateau snow cover anomalies work in coherence. Analysis indicates that the summer snow cover anomalies over the Tibetan Plateau may be related to late spring snow anomalies due to the persistence. The late spring snow anomalies are related to an obvious wave train originating from the western North Atlantic that may be partly associated with sea surface temperature anomalies in the North Atlantic Ocean.

Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

NASA Astrophysics Data System (ADS)

Qiao, D.; Li, Z.; Wang, N.; Zhou, J.; Zhang, P.; Gao, S.

2018-04-01

Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002-2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20 cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20 cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs.

Estimating the impacts of reservoir elevation changes on kokanee emergence in flaming Gorge Reservoir, Wyoming-Utah

USGS Publications Warehouse

Modde, T.; Jeric, R.J.; Hubert, W.A.; Gipson, R.D.

1997-01-01

Flaming Gorge Reservoir, like many western North American reservoirs, is managed to release water during the winter months to allow for water storage associated with melting snow and rain during spring. Decreases in reservoir elevation during winter can cause mortalities of kokanee Oncorhynchus nerka spawned along the shoreline the previous fall. This study compared data on depth distribution of embryos and depth-adjusted survival to estimate the relative survival of emergent kokanee at different depths and the effect of winter drawdown on the proportion of deposited eggs that survive to emergence. Estimates of decreases in kokanee survival to emergence were 8.3% and 38.1% for reservoir elevation reductions of 1.0 m and 5.0 m, respectively.

Under-Ice Phytoplankton Blooms Inhibited by Spring Convective Mixing in Refreezing Leads

NASA Astrophysics Data System (ADS)

Lowry, Kate E.; Pickart, Robert S.; Selz, Virginia; Mills, Matthew M.; Pacini, Astrid; Lewis, Kate M.; Joy-Warren, Hannah L.; Nobre, Carolina; van Dijken, Gert L.; Grondin, Pierre-Luc; Ferland, Joannie; Arrigo, Kevin R.

2018-01-01

Spring phytoplankton growth in polar marine ecosystems is limited by light availability beneath ice-covered waters, particularly early in the season prior to snowmelt and melt pond formation. Leads of open water increase light transmission to the ice-covered ocean and are sites of air-sea exchange. We explore the role of leads in controlling phytoplankton bloom dynamics within the sea ice zone of the Arctic Ocean. Data are presented from spring measurements in the Chukchi Sea during the Study of Under-ice Blooms In the Chukchi Ecosystem (SUBICE) program in May and June 2014. We observed that fully consolidated sea ice supported modest under-ice blooms, while waters beneath sea ice with leads had significantly lower phytoplankton biomass, despite high nutrient availability. Through an analysis of hydrographic and biological properties, we attribute this counterintuitive finding to springtime convective mixing in refreezing leads of open water. Our results demonstrate that waters beneath loosely consolidated sea ice (84-95% ice concentration) had weak stratification and were frequently mixed below the critical depth (the depth at which depth-integrated production balances depth-integrated respiration). These findings are supported by theoretical model calculations of under-ice light, primary production, and critical depth at varied lead fractions. The model demonstrates that under-ice blooms can form even beneath snow-covered sea ice in the absence of mixing but not in more deeply mixed waters beneath sea ice with refreezing leads. Future estimates of primary production should account for these phytoplankton dynamics in ice-covered waters.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Spring floods prediction with the use of optical satellite data in Québec

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Turcotte, R.

2009-04-01

The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model, which integrates a snow model, for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty and induces generally to an important error in stream flow simulation. Therefore, the National snow and ice center (NSIDC) produces, from MODIS (Moderate Resolution Imaging Spectroradiometer) data, continuous and homogeneous spatial snow cover (snow/swow-free) data on the whole territory, but with a cloud contamination. This research aims to improve the prediction of spring floods and the estimation of the rate of discharge by integrating snow cover data in the CEHQ's snow model. The study is done on two watersheds: du Nord river watershed (45,8°N) and Aux Écorces river watershed (47,9°N). The snow model used in the study (SPH-AV) is an implementation developed by the CEHQ of the snowmelt model of HYDROLTEL in is hydrological forecast system to simulate the melted water. The melted water estimated is then used as input in the empirical hydrological model MOHYSE to simulate stream flow. MODIS data are considered valid only when the cloud cover on each pixel of the watersheds is less then 30%. A pixel by pixel correction is applied to the snow pack when there is a difference between satellite snow cover and modeled snow cover. In the case of model shows to much snow, a factor is applied on temperatures by iterative process (starting from the last valid MODIS data) to melt the snow. In the opposite case, the snow quantity added to the last valid MODIS data is found by iterative process so that the pixel's snow water equivalent is equal to the nonzero neighbor minimum value. The study shows, through the simulations done on the two watersheds, the interest of the use of snow/snow-free product for the operational update of snow water equivalent with the objective to improve spring snowmelt stream flow simulations. The binary aspect (snow/snowfree) of the data is however a limit. Alternatives are discussed (passive microwave data). Keywords : satellite snow cover data, MODIS, satellite data integration, snow model, hydrological model, stream flow simulation, flood.

Use of MODIS Snow-Cover Maps for Detecting Snowmelt Trends in North America

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Robinson, David A.; Hoon-Starr, Jody A.

2012-01-01

Research has shown that the snow season in the Northern Hemisphere has been getting shorter in recent decades, consistent with documented global temperature increases. Specifically, the snow is melting earlier in the spring allowing for a longer growing season and associated land-cover changes. Here we focus on North America. Using the Moderate-Resolution Imaging Radiometer (MODIS) cloud-gap-filled standard snow-cover data product we can detect a trend toward earlier spring snowmelt in the approx 12 years since the MODIS launch. However, not all areas in North America show earlier spring snowmelt over the study period. We show examples of springtime snowmelt over North America, beginning in March 2000 and extending through the winter of 2012 for all of North America, and for various specific areas such as the Wind River Range in Wyoming and in the Catskill Mountains in New York. We also compare our approx 12-year trends with trends derived from the Rutgers Global Snow Lab snow cover climate-data record.

Snow Radar Derived Surface Elevations and Snow Depths Multi-Year Time Series over Greenland Sea-Ice During IceBridge Campaigns

NASA Astrophysics Data System (ADS)

Perkovic-Martin, D.; Johnson, M. P.; Holt, B.; Panzer, B.; Leuschen, C.

2012-12-01

This paper presents estimates of snow depth over sea ice from the 2009 through 2011 NASA Operation IceBridge [1] spring campaigns over Greenland and the Arctic Ocean, derived from Kansas University's wideband Snow Radar [2] over annually repeated sea-ice transects. We compare the estimates of the top surface interface heights between NASA's Atmospheric Topographic Mapper (ATM) [3] and the Snow Radar. We follow this by comparison of multi-year snow depth records over repeated sea-ice transects to derive snow depth changes over the area. For the purpose of this paper our analysis will concentrate on flights over North/South basin transects off Greenland, which are the closest overlapping tracks over this time period. The Snow Radar backscatter returns allow for surface and interface layer types to be differentiated between snow, ice, land and water using a tracking and classification algorithm developed and discussed in the paper. The classification is possible due to different scattering properties of surfaces and volumes at the radar's operating frequencies (2-6.5 GHz), as well as the geometries in which they are viewed by the radar. These properties allow the returns to be classified by a set of features that can be used to identify the type of the surface or interfaces preset in each vertical profile. We applied a Support Vector Machine (SVM) learning algorithm [4] to the Snow Radar data to classify each detected interface into one of four types. The SVM algorithm was trained on radar echograms whose interfaces were visually classified and verified against coincident aircraft data obtained by CAMBOT [5] and DMS [6] imaging sensors as well as the scanning ATM lidar. Once the interface locations were detected for each vertical profile we derived a range to each interface that was used to estimate the heights above the WGS84 ellipsoid for direct comparisons with ATM. Snow Radar measurements were calibrated against ATM data over areas free of snow cover and over GPS land surveyed areas of Thule and Sondrestrom air bases. The radar measurements were compared against the ATM and the GPS measurements that were located in the estimated radar footprints, which resulted in an overall error of ~ 0.3 m between the radar and ATM. The agreement between ATM and GPS survey is within +/- 0.1 m. References: [1] http://www.nasa.gov/mission_pages/icebridge/ [2] Panzer, B. et. al, "An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn," Submitted to J. of Glaciology Instr. and Tech., July 23, 2012. [3] Krabill, William B. 2009 and 2011, updated current year. IceBridge ATM L1B Qfit Elevation and Return Strength. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [4] Chih-Chung Chang and Chih-Jen Lin. "Libsvm: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:2:27:1-27:27, 2011. [5] Krabill, William B. 2009 and 2011, updated current year. IceBridge CAMBOT L1B Geolocated Images, [2009-04-25, 2011-04-15]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [6] Dominguez, Roseanne. 2011, updated current year. IceBridge DMS L1B Geolocated and Orthorectified Images. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media

Drought and Snow: Analysis of Drivers, Processes and Impacts of Streamflow Droughts in Snow-Dominated Regions

NASA Astrophysics Data System (ADS)

Van Loon, Anne; Laaha, Gregor; Van Lanen, Henny; Parajka, Juraj; Fleig, Anne; Ploum, Stefan

2016-04-01

Around the world, drought events with severe socio-economic impacts seem to have a link with winter snowpack. That is the case for the current California drought, but analysing historical archives and drought impact databases for the US and Europe we found many impacts that can be attributed to snowpack anomalies. Agriculture and electricity production (hydropower) were found to be the sectors that are most affected by drought related to snow. In this study, we investigated the processes underlying hydrological drought in snow-dominated regions. We found that drought drivers are different in different regions. In Norway, more than 90% of spring streamflow droughts were preceded by below-average winter precipitation, while both winter air temperature and spring weather were indifferent. In Austria, however, spring streamflow droughts could only be explained by a combination of factors. For most events, winter and spring air temperatures were above average (70% and 65% of events, respectively), and winter and spring precipitation was below average (75% and 80%). Because snow storage results from complex interactions between precipitation and temperature and these variables vary strongly with altitude, snow-related drought drivers have a large spatial variability. The weather input is subsequently modified by land properties. Multiple linear regression between drought severity variables and a large number of catchment characteristics for 44 catchments in Austria showed that storage influences both drought duration and deficit volume. The seasonal storage of water in snow and glaciers was found to be a statistically important variable explaining streamflow drought deficit. Our drought impact analysis in Europe also showed that 40% of the selected drought impacts was caused by a combination of snow-related and other drought types. For example, the combination of a winter drought with a preceding or subsequent summer drought was reported to have a large effect on reservoir levels and, consequently, on drinking water and electricity production. Snow storage therefore, is an important factor to consider in drought monitoring, prediction and management.

Drought and Snow: Analysis of Drivers, Processes and Impacts of Streamflow Droughts in Snow-Dominated Regions

NASA Astrophysics Data System (ADS)

Van Loon, A.; Laaha, G.; Van Lanen, H.; Parajka, J.; Fleig, A. K.; Ploum, S.

2015-12-01

Around the world, drought events with severe socio-economic impacts seem to have a link with winter snowpack. That is the case for the current California drought, but analysing historical archives and drought impact databases for the US and Europe we found many impacts that can be attributed to snowpack anomalies. Agriculture and electricity production (hydropower) were found to be the sectors that are most affected by drought related to snow. In this study, we investigated the processes underlying hydrological drought in snow-dominated regions. We found that drought drivers are different in different regions. In Norway, more than 90% of spring streamflow droughts were preceded by below-average winter precipitation, while both winter air temperature and spring weather were indifferent. In Austria, however, spring streamflow droughts could only be explained by a combination of factors. For most events, winter and spring air temperatures were above average (70% and 65% of events, respectively), and winter and spring precipitation was below average (75% and 80%). Because snow storage results from complex interactions between precipitation and temperature and these variables vary strongly with altitude, snow-related drought drivers have a large spatial variability. The weather input is subsequently modified by land properties. Multiple linear regression between drought severity variables and a large number of catchment characteristics for 44 catchments in Austria showed that storage influences both drought duration and deficit volume. The seasonal storage of water in snow and glaciers was found to be a statistically important variable explaining streamflow drought deficit. Our drought impact analysis in Europe also showed that 40% of the selected drought impacts was caused by a combination of snow-related and other drought types. For example, the combination of a winter drought with a preceding or subsequent summer drought was reported to have a large effect on reservoir levels and, consequently, on drinking water and electricity production. Snow storage therefore, is an important factor to consider in drought monitoring, prediction and management.

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

NASA Astrophysics Data System (ADS)

Dai, Liyun; Che, Tao; Ding, Yongjian; Hao, Xiaohua

2017-08-01

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 depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

Snowmelt in a High Latitude Mountain Catchment: Effect of Vegetation Cover and Elevation

NASA Astrophysics Data System (ADS)

Pomeroy, J. W.; Essery, R. L.; Ellis, C. R.; Hedstrom, N. R.; Janowicz, R.; Granger, R. J.

2004-12-01

The energetics and mass balance of snowpacks in the premelt and melt period were compared from three elevation bands in a high latitude mountain catchment, Wolf Creek Research Basin, Yukon. Elevation is strongly correlated with vegetation cover and in this case the three elevation bands (low, middle, high) correspond to mature spruce forest, dense shrub tundra and sparse tundra (alpine). Measurements of radiation, ground heat flux, snow depth, snowfall, air temperature, wind speed were made on a half-hourly basis at the three elevations for a 10 year period. Sondes provided vertical gradients of air temperature, humidity, wind speed and air pressure. Snow depth and density surveys were conducted monthly. Comparisons of wind speed, air temperature and humidity at three elevations show that the expected elevational gradients in the free atmosphere were slightly enhanced just above the surface canopies, but that the climate at the snow surface was further influenced by complex canopy effects. Premelt snow accumulation was strongly affected by intercepted snow in the forest and blowing snow sublimation in the sparse tundra but not by the small elevational gradients in snowfall. As a result the maximum premelt SWE was found in the mid-elevation shrub tundra and was roughly double that of the sparse tundra or forest. Minimum variability of SWE was observed in the forest and shrub tundra (CV=0.25) while in the sparse tundra variability doubled (CV=0.5). Snowmelt was influenced by differences in premelt accumulation as well as differences in the net energy fluxes to snow. Elevation had a strong effect on the initiation of melt with the forest melt starting on average 16 days before the shrub tundra and 19 days before the sparse tundra. Mean melt rates showed a maximum in middle elevations and increased from 860 kJ/day in the forest to 1460 kJ/day in the sparse tundra and 2730 kJ/day in the shrub tundra. The forest canopy reduced melt while the shrub canopy enhanced it relative to the sparsely vegetated tundra. Duration of melt was similar in the forest and shrub tundra at 20 days while the sparse tundra was shorter at 13 days; the differences due to differing snow accumulation and melt rates. The greatest variability in the timing and rate of melt was found in the shrub tundra, where the effect of the shrub canopy over snow depends on snow depth and insolation and is reduced in years with high snow accumulation or extensive cloudy periods in spring. The results show that it is necessary to consider the combination of elevation and vegetation effects on snow microclimate and melt processes in high latitude mountain catchments, but that weather patterns induce substantial variability on the effect these factors.

Snow depth on Arctic and Antarctic sea ice derived from autonomous (Snow Buoy) measurements

NASA Astrophysics Data System (ADS)

Nicolaus, Marcel; Arndt, Stefanie; Hendricks, Stefan; Heygster, Georg; Huntemann, Marcus; Katlein, Christian; Langevin, Danielle; Rossmann, Leonard; Schwegmann, Sandra

2016-04-01

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic and Arctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS) and the International Arctic Buoy Programme (IABP). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences. Finally, results from seven snow buoys co-deployed on Arctic sea ice throughout the winter season 2015/16 suggest the great importance of local effects, weather events, and potential influences of dynamic sea ice processes on snow accumulation.

Comparison of the Snow Simulations in Community Land Model Using Two Snow Cover Fraction Parameterizations

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong

2016-04-01

Snow cover is an important component of local- and regional-scale energy and water budgets, especially in mountainous areas. This paper evaluates the snow simulations by using two snow cover fraction schemes in CLM4.5 (NY07 is the original snow-covered area parameterization used in CLM4, and SL12 is the default scheme in CLM4.5). Off-line simulations are carried out forced by the China Meteorological forcing dataset from January 1, 2001 to December 31, 2010 over the Tibetan Plateau. Simulated snow cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product, the daily snow depth dataset of China, and China Meteorological Administration (CMA) in-situ snow depth and SWE observations. The comparison results indicate significant differences existing between those two SCF parameterizations simulations. Overall, the SL12 formulation shows a certain improvement compared to the NY07 scheme used in CLM4, with the percentage of correctly modeled snow/no snow being 75.8% and 81.8% when compared with the IMS snow product, respectively. Yet, this improvement varies both temporally and spatially. Both these two snow cover schemes overestimated the snow depth, in comparison with the daily snow depth dataset of China, the average biases of simulated snow depth are 7.38cm (8.77cm), 6.97cm (8.2cm) and 5.49cm (5.76cm) NY07 (and SL12) in the snow accumulation period (September through next February), snowmelt period (March through May) and snow-free period (June through August), respectively. When compared with the CMA in-situ snow depth observations, averaged biases are 3.18cm (4.38cm), 2.85cm (4.34cm) and 0.34cm (0.34cm) for NY07 (SL12), respectively. Though SL12 does worse snow depth simulation than NY07, the simulated SWE by SL12 is better than that by NY07, with average biases being 2.64mm, 6.22mm, 1.33mm for NY07, and 1.47mm, 2.63mm, 0.31mm for SL12, respectively. This study demonstrates that future improvements on snow simulation over the Tibetan Plateau are in urgent need for better representing the variability of snow in CLM. Furthermore, these findings lay a foundation for follow-up studies on the modification of snow cover parameterization in the land surface model. Keywords: snow cover, CLM, Tibetan Plateau, simulation.

Effects of seasonal snow and climatic controls on spring and autumn phenology in Alpine forest regions

NASA Astrophysics Data System (ADS)

Xie, J.; Kneubühler, M.; Garonna, I.; Jong, R. D.; Schaepman, M. E.

2017-12-01

Seasonal accumulation and melt of snow in mountainous regions varies with meteorological factors and affects forest phenology in various ways. However, our knowledge about the relationship between seasonal snow and forest phenology - and particularly its topographical variation - is still limited and needs further investigation. We tested the relationship between a number of snow, meteorological and land surface phenology metrics (satellite-derived and gridded) in the forested regions of the Swiss Alps for the period of 2003-2014. Satellite-derived start of season and end of season metrics (SOS and EOS, respectively), in combination with snow accumulation (SA), snow cover melt date (SCMD), monthly maximum, mean and minimum temperature, monthly mean relative sunshine duration and precipitation were considered in our analysis. We calculated Spearman's rank correlation of interannual differences (Δ) of SOS and EOS with snow and meteorological metrics and examined the variation of these correlations with elevation (from 200 up to 2400 meter above sea level (m a.s.l.)). We found SOS to have a significant (p < 0.05) positive correlation with both SCMD (mean R=0.71, over 34.2% of all pixels) and SA (mean R=0.62, over 19.0% of all pixels). On the other hand, SOS showed a significant negative correlation with spring temperature and relative sunshine duration. EOS showed significant positive correlation with autumn temperature (mean R=0.70, over 30.4% of all pixels). Moreover, we found the forest phenology of the northern and eastern Swiss Alps to be more sensitive to seasonal snow but less sensitive to meteorological factors than in the southern and western Swiss Alps. The areas which are sensitive to seasonal snow and meteorological factors are more pronounced at higher elevations. We conclude that the effect of snow melt on spring phenology is of equal magnitude as spring temperature and relative sunshine duration. Autumn forest phenology is mainly influenced by autumn temperature. The effects of seasonal snow and climatic controls on spring and autumn phenology are more pronounced at higher than at lower elevations. We suggest that alpine forest ecosystems above 1500 m a.s.l. will therefore be particularly sensitive to future changes of seasonal snow and climate warming scenarios in the Swiss Alps.

Scales of snow depth variability in high elevation rangeland sagebrush

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

A passive microwave snow depth algorithm with a proxy for snow metamorphism

USGS Publications Warehouse

Josberger, E.G.; Mognard, N.M.

2002-01-01

Passive microwave brightness temperatures of snowpacks depend not only on the snow depth, but also on the internal snowpack properties, particularly the grain size, which changes through the winter. Algorithms that assume a constant grain size can yield erroneous estimates of snow depth or water equivalent. For snowpacks that are subject to temperatures well below freezing, the bulk temperature gradient through the snowpack controls the metamorphosis of the snow grains. This study used National Weather Service (NWS) station measurements of snow depth and air temperature from the Northern US Great Plains to determine temporal and spatial variability of the snow depth and bulk snowpack temperature gradient. This region is well suited for this study because it consists primarily of open farmland or prairie, has little relief, is subject to very cold temperatures, and has more than 280 reporting stations. A geostatistical technique called Kriging was used to grid the randomly spaced snow depth measurements. The resulting snow depth maps were then compared with the passive microwave observations from the Special Sensor Microwave Imager (SSM/I). Two snow seasons were examined: 1988-89, a typical snow year, and 1996-97, a record year for snow that was responsible for extensive flooding in the Red River Basin. Inspection of the time series of snow depth and microwave spectral gradient (the difference between the 19 and 37 GHz bands) showed that while the snowpack was constant, the spectral gradient continued to increase. However, there was a strong correlation (0.6 < R2 < 0.9) between the spectral gradient and the cumulative bulk temperature gradient through the snowpack (TGI). Hence, TGI is an index of grain size metamorphism that has occurred within the snowpack. TGI time series from 21 representative sites across the region and the corresponding SSM/I observations were used to develop an algorithm for snow depth that requires daily air temperatures. Copyright ?? 2002 John Wiley & Sons, Ltd.

Snow depth of the Weddell and Bellingshausen sea ice covers from IceBridge surveys in 2010 and 2011: An examination

NASA Astrophysics Data System (ADS)

Kwok, R.; Maksym, T.

2014-07-01

We examine the snow radar data from the Weddell and Bellingshausen Seas acquired by eight IceBridge (OIB) flightlines in October of 2010 and 2011. In snow depth retrieval, the sidelobes from the stronger scattering snow-ice (s-i) interfaces could be misidentified as returns from the weaker air-snow (a-s) interfaces. In this paper, we first introduce a retrieval procedure that accounts for the structure of the radar system impulse response followed by a survey of the snow depths in the Weddell and Bellingshausen Seas. Limitations and potential biases in our approach are discussed. Differences between snow depth estimates from a repeat survey of one Weddell Sea track separated by 12 days, without accounting for variability due to ice motion, is -0.7 ± 13.6 cm. Average snow depth is thicker in coastal northwestern Weddell and thins toward Cape Norvegia, a decrease of >30 cm. In the Bellingshausen, the thickest snow is found nearshore in both Octobers and is thickest next to the Abbot Ice Shelf. Snow depth is linearly related to freeboard when freeboards are low but diverge as the freeboard increases especially in the thicker/rougher ice of the western Weddell. We find correlations of 0.71-0.84 between snow depth and surface roughness suggesting preferential accumulation over deformed ice. Retrievals also seem to be related to radar backscatter through surface roughness. Snow depths reported here, generally higher than those from in situ records, suggest dissimilarities in sample populations. Implications of these differences on Antarctic sea ice thickness are discussed.

Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Maksym, Ted

2007-01-01

Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-200 1 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow- ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.

Using high resolution Lidar data from SnowEx to characterize the sensitivity of snow depth retrievals to point-cloud density and vegetation

NASA Astrophysics Data System (ADS)

Patterson, V. M.; Bormann, K.; Deems, J. S.; Painter, T. H.

2017-12-01

The NASA SnowEx campaign conducted in 2016 and 2017 provides a rich source of high-resolution Lidar data from JPL's Airborne Snow Observatory (ASO - http://aso.jpl.nasa.gov) combined with extensive in-situ measurements in two key areas in Colorado: Grand Mesa and Senator Beck. While the uncertainty in the 50m snow depth retrievals from NASA's ASO been estimated at 1-2cm in non-vegetated exposed areas (Painter et al., 2016), the impact of forest cover and point-cloud density on ASO snow lidar depth retrievals is relatively unknown. Dense forest canopies are known to reduce lidar penetration and ground strikes thus affecting the elevation surface retrieved from in the forest. Using high-resolution lidar point cloud data from the ASO SnowEx campaigns (26pt/m2) we applied a series of data decimations (up to 90% point reduction) to the point cloud data to quantify the relationship between vegetation, ground point density, resulting snow-off and snow-on surface elevations and finally snow depth. We observed non-linear reductions in lidar ground point density in forested areas that were strongly correlated to structural forest cover metrics. Previously, the impacts of these data decimations on a small study area in Grand Mesa showed a sharp increase in under-canopy surface elevation errors of -0.18m when ground point densities were reduced to 1.5pt/m2. In this study, we expanded the evaluation to the more topographically challenging Senator Beck basin, have conducted analysis along a vegetation gradient and are considering snow the impacts of snow depth rather than snow-off surface elevation. Preliminary analysis suggest that snow depth retrievals inferred from airborne lidar elevation differentials may systematically underestimate snow depth in forests where canopy density exceeds 1.75 and where tree heights exceed 5m. These results provide a basis from which to identify areas that may suffer from vegetation-induced biases in surface elevation models and snow depths derived from airborne lidar data, and help quantify expected spatial distributions of errors in the snow depth that can be used to improve the accuracy of ASO basin-scale depth and water equivalent products.

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harder, Phillip; Schirmer, Michael; Pomeroy, John; Helgason, Warren

2016-11-01

Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s-1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

USGS Publications Warehouse

Balk, B.; Elder, K.; Baron, Jill S.

1998-01-01

Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

Atmospheric mercury depletion event study in Ny-Alesund (Svalbard) in spring 2005. Deposition and transformation of Hg in surface snow during springtime.

PubMed

Ferrari, Christophe P; Padova, Cyril; Faïn, Xavier; Gauchard, Pierre-Alexis; Dommergue, Aurélien; Aspmo, Katrine; Berg, Torunn; Cairns, Warren; Barbante, Carlo; Cescon, Paolo; Kaleschke, Lars; Richter, Andreas; Wittrock, Folkard; Boutron, Claude

2008-07-01

A field campaign was conducted in Ny-Alesund (78 degrees 54'N, 11 degrees 53'E), Svalbard (Norway) during April and May 2005. An Atmospheric Mercury (Hg) Depletion Event (AMDE) was observed from the morning of April 24 until the evening of April 27. Transport of already Hg and ozone (O3) depleted air masses could explain this observed depletion. Due to a snowfall event during the AMDE, surface snow Hg concentrations increased two fold. Hg deposition took place over a short period of time corresponding to 3-4 days. More than 80% of the deposited Hg was estimated to be reemitted back to the atmosphere in the days following the event. During the campaign, we observed night and day variations in surface snow Hg concentrations, which may be the result of gaseous elemental mercury (GEM) oxidation to divalent Hg at the snow/air interface by daylight surface snow chemistry. Finally, a decrease in the reactive Hg (HgR) fraction of total Hg (HgT) in the surface snow was observed during spring. We postulate that the transformation of HgR to a more stable form may occur in Arctic snow during spring.

Forecasting for natural avalanches during spring opening of Going-to-the-Sun Road, Glacier National Park, Montana, USA

USGS Publications Warehouse

Reardon, Blase; Lundy, Chris

2004-01-01

The annual spring opening of the Going-to-the-Sun Road in Glacier National Park presents a unique avalanche forecasting challenge. The highway traverses dozens of avalanche paths mid-track in a 23-kilometer section that crosses the Continental Divide. Workers removing seasonal snow and avalanche debris are exposed to paths that can produce avalanches of destructive class 4. The starting zones for most slide paths are within proposed Wilderness, and explosive testing or control are not currently used. Spring weather along the Divide is highly variable; rain-on-snow events are common, storms can bring several feet of new snow as late as June, and temperature swings can be dramatic. Natural avalanches - dry and wet slab, dry and wet loose, and glide avalanches - present a wide range of hazards and forecasting issues. This paper summarizes the forecasting program instituted in 2002 for the annual snow removal operations. It focuses on tools and techniques for forecasting natural wet snow avalanches by incorporating two case studies, including a widespread climax wet slab cycle in 2003. We examine weather and snowpack conditions conducive to wet snow avalanches, indicators for instability, and suggest a conceptual model for wet snow stability in a northern intermountain snow climate.

Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

USGS Publications Warehouse

Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

2018-01-01

Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate risks posed by mobile disease hosts. More broadly, we demonstrate how mechanistic movement models can provide predictions of ecological conditions that are consistent with climate change but may be more extreme than has been observed historically.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

PubMed

Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

2015-01-01

Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of which is almost comparable to that due to CO2 (2.83 W m-2) increases since 1750. Our results thus highlight the necessity of realistic representation of snow albedo in the model and demonstrate the use of satellite-based snow albedo to improve model behaviors, which opens new avenues for constraining snow albedo feedback in earth system models.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations

PubMed Central

Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

2015-01-01

Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of which is almost comparable to that due to CO2 (2.83 W m-2) increases since 1750. Our results thus highlight the necessity of realistic representation of snow albedo in the model and demonstrate the use of satellite-based snow albedo to improve model behaviors, which opens new avenues for constraining snow albedo feedback in earth system models. PMID:26366564

Impacts of Snow Darkening by Absorbing Aerosols on Eurasian Climate

NASA Technical Reports Server (NTRS)

Kim, Kyu-Myong; Lau, William K M.; Yasunari, Teppei J.; Kim, Maeng-Ki; Koster, Randal D.

2016-01-01

The deposition of absorbing aerosols on snow surfaces reduces snow-albedo and allows snowpack to absorb more sunlight. This so-called snow darkening effect (SDE) accelerates snow melting and leads to surface warming in spring. To examine the impact of SDE on weather and climate during late spring and early summer, two sets of NASA GEOS-5 model simulations with and without SDE are conducted. Results show that SDE-induced surface heating is particularly pronounced in Eurasian regions where significant depositions of dust transported from the North African deserts, and black carbon from biomass burning from Asia and Europe occur. In these regions, the surface heating due to SDE increases surface skin temperature by 3-6 degrees Kelvin near the snowline in spring. Surface energy budget analysis indicates that SDE-induced excess heating is associated with a large increase in surface evaporation, subsequently leading to a significant reduction in soil moisture, and increased risks of drought and heat waves in late spring to early summer. Overall, we find that rainfall deficit combined with SDE-induced dry soil in spring provide favorable condition for summertime heat waves over large regions of Eurasia. Increased frequency of summer heat waves with SDE and the region of maximum increase in heat-wave frequency are found along the snow line, providing evidence that early snowmelt by SDE may increase the risks of extreme summer heat wave. Our results suggest that climate models that do not include SDE may significantly underestimate the effect of global warming over extra-tropical continental regions.

Estimating terrestrial snow depth with the Topex-Poseidon altimeter and radiometer

USGS Publications Warehouse

Papa, F.; Legresy, B.; Mognard, N.M.; Josberger, E.G.; Remy, F.

2002-01-01

Active and passive microwave measurements obtained by the dual-frequency Topex-Poseidon radar altimeter from the Northern Great Plains of the United States are used to develop a snow pack radar backscatter model. The model results are compared with daily time series of surface snow observations made by the U.S. National Weather Service. The model results show that Ku-band provides more accurate snow depth determinations than does C-band. Comparing the snow depth determinations derived from the Topex-Poseidon nadir-looking passive microwave radiometers with the oblique-looking Satellite Sensor Microwave Imager (SSM/I) passive microwave observations and surface observations shows that both instruments accurately portray the temporal characteristics of the snow depth time series. While both retrievals consistently underestimate the actual snow depths, the Topex-Poseidon results are more accurate.

Near-Record Early Snowmelt and Signs of Environmental Change in Barrow, Alaska

NASA Astrophysics Data System (ADS)

Stanitski, D.; Cox, C.; Sweeney, C.; Divoky, G.; George, C.; Stone, R.

2015-12-01

The 2015 spring transition in Barrow, AK, was notable with the second earliest date of snow melt on record (JD148, May 28) and earliest ice free conditions on a local lagoon (JD178, June 27). The 73-year time series from the NOAA Global Monitoring Division's Barrow Observatory (BRW) has shown a trend toward earlier spring snowmelt, reinforced in 2015. Anomalous early snowmelt was also observed at nearby Cooper Island where a colony of sea birds, the Black Guillemot, nests each year once snow disappears. The appearance of "first egg" is well correlated with the date of snowmelt at BRW (Fig. 1), as is the ice-out date at the Isaktoak Lagoon (ISK). In 2015, the first egg was observed on JD159 (June 8), the earliest in the 40-year record (source: Friends of Cooper Island, http://cooperisland.org/). Each day of advance in the melt date at BRW results in an annual net radiation increase at the surface of about 1%. The documented changes can influence biogeochemical cycles, permafrost temperatures, and potentially the release of stored carbon. By mid July 2015, a 1°C increase in soil temperature at 0.5-m depth was measured compared to prior years; therefore, the active layer is expected to be unusually deep by autumn. The anomalous warmth that prevailed during spring 2015 can be attributed, in part, to atmospheric circulation, influenced by two typhoons in the North Pacific and the onset of El Niño. Warming was likely amplified locally as the early melting of snow increased absorption of solar radiation. Key factors influencing the trend toward earlier spring snowmelt will be presented as well as those contributing to the anomalous 2015 spring at BRW (e.g., winter snowfall, cloud cover, advection, local sea ice extent), and the impact early melt had on the 2015 summer surface radiation budget. Analysis of interactions underlying this anomaly will aid in developing strategies for improving predictability of interannual variability of the melt season and long-term change.

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

USGS Publications Warehouse

Grippa, M.; Mognard, N.; Le, Toan T.; Josberger, E.G.

2004-01-01

One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.

A Comprehensive Snow Density Model for Integrating Lidar-Derived Snow Depth Data into Spatial Snow Modeling

NASA Astrophysics Data System (ADS)

Marks, D. G.; Kormos, P.; Johnson, M.; Bormann, K. J.; Hedrick, A. R.; Havens, S.; Robertson, M.; Painter, T. H.

2017-12-01

Lidar-derived snow depths when combined with modeled or estimated snow density can provide reliable estimates of the distribution of SWE over large mountain areas. Application of this approach is transforming western snow hydrology. We present a comprehensive approach toward modeling bulk snow density that is reliable over a vast range of weather and snow conditions. The method is applied and evaluated over mountainous regions of California, Idaho, Oregon and Colorado in the western US. Simulated and measured snow density are compared at fourteen validation sites across the western US where measurements of snow mass (SWE) and depth are co-located. Fitting statistics for ten sites from three mountain catchments (two in Idaho, one in California) show an average Nash-Sutcliff model efficiency coefficient of 0.83, and mean bias of 4 kg m-3. Results illustrate issues associated with monitoring snow depth and SWE and show the effectiveness of the model, with a small mean bias across a range of snow and climate conditions in the west.

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

USGS Publications Warehouse

Biancamaria, S.; Mognard, N.M.; Boone, A.; Grippa, M.; Josberger, E.G.

2008-01-01

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987-1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is - 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient - 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29). ?? 2007 Elsevier Inc. All rights reserved.

Factors Impacting Spatial Patterns of Snow Distribution in a Small Catchment near Nome, AK

NASA Astrophysics Data System (ADS)

Chen, M.; Wilson, C. J.; Charsley-Groffman, L.; Busey, R.; Bolton, W. R.

2017-12-01

Snow cover plays an important role in the climate, hydrology and ecological systems of the Arctic due to its influence on the water balance, thermal regimes, vegetation and carbon flux. Thus, snow depth and coverage have been key components in all the earth system models but are often poorly represented for arctic regions, where fine scale snow distribution data is sparse. The snow data currently used in the models is at coarse resolution, which in turn leads to high uncertainty in model predictions. Through the DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic, high resolution snow distribution data is being developed and applied in catchment scale models to ultimately improve representation of snow and its interactions with other model components in the earth system models . To improve these models, it is important to identify key factors that control snow distribution and quantify the impacts of those factors on snow distribution. In this study, two intensive snow depth surveys (1 to 10 meters scale) were conducted for a 2.3 km2 catchment on the Teller road, near Nome, AK in the winter of 2016 and 2017. We used a statistical model to quantify the impacts of vegetation types, macro-topography, micro-topography, and meteorological parameters on measured snow depth. The results show that snow spatial distribution was similar between 2016 and 2017, snow depth was spatially auto correlated over small distance (2-5 meters), but not spatially auto correlated over larger distance (more than 2-5 meters). The coefficients of variation of snow depth was above 0.3 for all the snow survey transects (500-800 meters long). Variation of snow depth is governed by vegetation height, aspect, slope, surface curvature, elevation and wind speed and direction. We expect that this empirical statistical model can be used to estimate end of winter snow depth for the whole watershed and will further develop the model using data from other arctic regions to estimate seasonally dynamic snow coverage and properties for use in catchment scale to pan-Arctic models.

Resilience to Changing Snow Depth in a Shrubland Ecosystem.

NASA Astrophysics Data System (ADS)

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.

Quantifying the effects of mountain pine beetle infestation on water and biogeochemical cycles at multiple spatial and temporal scales

NASA Astrophysics Data System (ADS)

Brooks, P. D.; Harpold, A. A.; Somor, A. J.; Troch, P. A.; Gochis, D. J.; Ewers, B. E.; Pendall, E.; Biederman, J. A.; Reed, D.; Barnard, H. R.; Whitehouse, F.; Aston, T.; Borkhuu, B.

2010-12-01

Unprecedented levels of bark beetle infestation over the last decade have radically altered forest structure across millions of hectares of Western U.S. montane environments. The widespread extent of this disturbance presents a major challenge for governments and resource managers who lack a predictive understanding of how water and biogeochemical cycles will respond to this disturbance over various temporal and spatial scales. There is a widespread perception, largely based on hydrological responses to fire or logging, that a reduction in both transpiration and interception following tree death will increase soil water availability and catchment water yield. However, few studies have directly addressed the effects of insect-induced forest decline on water and biogeochemical cycling. We address this knowledge gap using observations and modeling at scales from 100 to 109 m2 across study sites in CO and WY that vary in the intensity and timing of beetle infestation and tree death. Our focus on multiple sites with different levels of impact allows us to address two broad, organizing questions: How do changes in vegetation structure associated with MPB alter the partitioning of energy and water? And How do these changes in energy and water availability affect local to regional scale water and biogeochemical cycles? This presentation will focus primarily on energy balance and water partitioning, providing context for ongoing biogeochemical work. During the growing season, stand-scale transpiration declines rapidly and soil moisture increases following infestation, consistent with streamflow data from regional catchments that shows an increase in baseflow following widespread attack. During the winter and spring, stand scale snow surveys and continuous snow depth sensors suggested that the variability in snow cover decreased as the severity of beetle impact increases, but there were no significant stand-scale differences in snow depth among levels of impact. This is due both to an increase in snow under the canopies of dead trees and a decrease in snow cover in canopy gaps. For example, mean snow depth under the canopy was 86cm (CV 0.02) in unimpacted sites and 95cm (CV 0.05) in heavily impacted sites. In canopy gaps however, mean snow depth was 117cm (CV 0.11) in unimpacted sites but only 93cm (CV 0.07) in heavily impacted sites. At the watershed scale, bark beetle infestation was more likely to decrease the amount of both snowmelt and annual runoff, suggesting that the opening of the canopy increases sublimation and evaporation of the snow cover. These data suggest that the disturbance due to bark beetle infestation is both quantitatively and qualitatively different than either fire or logging. Using these observations, we develop a conceptual model for evaluating how biotic and abiotic processes couple water and biogeochemical cycles in forest ecosystems.

Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback

NASA Astrophysics Data System (ADS)

Déry, Stephen J.; Brown, Ross D.

2007-11-01

Monotonic trend analysis of Northern Hemisphere snow cover extent (SCE) over the period 1972-2006 with the Mann-Kendall test reveals significant declines in SCE during spring over North America and Eurasia, with lesser declines during winter and some increases in fall SCE. The weekly mean trend attains -1.28, -0.78, and -0.48 × 106 km2 (35 years)-1 over the Northern Hemisphere, North America, and Eurasia, respectively. The standardized SCE time series vary and trend coherently over Eurasia and North America, with evidence of a poleward amplification of decreasing SCE trends during spring. Multiple linear regression analyses reveal a significant dependence of the retreat of the spring continental SCE on latitude and elevation. The poleward amplification is consistent with an enhanced snow-albedo feedback over northern latitudes that acts to reinforce an initial anomaly in the cryospheric system.

Using Commercial Digital Cameras and Structure-for-Motion Software to Map Snow Cover Depth from Small Aircraft

NASA Astrophysics Data System (ADS)

Sturm, M.; Nolan, M.; Larsen, C. F.

2014-12-01

A long-standing goal in snow hydrology has been to map snow cover in detail, either mapping snow depth or snow water equivalent (SWE) with sub-meter resolution. Airborne LiDAR and air photogrammetry have been used successfully for this purpose, but both require significant investments in equipment and substantial processing effort. Here we detail a relatively inexpensive and simple airborne photogrammetric technique that can be used to measure snow depth. The main airborne hardware consists of a consumer-grade digital camera attached to a survey-quality, dual-frequency GPS. Photogrammetric processing is done using commercially available Structure from Motion (SfM) software that does not require ground control points. Digital elevation models (DEMs) are made from snow-free acquisitions in the summer and snow-covered acquisitions in winter, and the maps are then differenced to arrive at snow thickness. We tested the accuracy and precision of snow depths measured using this system through 1) a comparison with airborne scanning LiDAR, 2) a comparison of results from two independent and slightly different photogrameteric systems, and 3) comparison to extensive on-the-ground measured snow depths. Vertical accuracy and precision are on the order of +/-30 cm and +/- 8 cm, respectively. The accuracy can be made to approach that of the precision if suitable snow-free ground control points exists and are used to co-register summer to winter DEM maps. Final snow depth accuracy from our series of tests was on the order of ±15 cm. This photogrammetric method substantially lowers the economic and expertise barriers to entry for mapping snow.

The intraannual variability of land-atmosphere coupling over North America in the Canadian Regional Climate Model (CRCM5)

NASA Astrophysics Data System (ADS)

Yang Kam Wing, G.; Sushama, L.; Diro, G. T.

2016-12-01

This study investigates the intraannual variability of soil moisture-temperature coupling over North America. To this effect, coupled and uncoupled simulations are performed with the fifth-generation Canadian Regional Climate Model (CRCM5), driven by ERA-Interim. In coupled simulations, land and atmosphere interact freely; in uncoupled simulations, the interannual variability of soil moisture is suppressed by prescribing climatological values for soil liquid and frozen water contents. The study also explores projected changes to coupling by comparing coupled and uncoupled CRCM5 simulations for current (1981-2010) and future (2071-2100) periods, driven by the Canadian Earth System Model. Coupling differs for the northern and southern parts of North America. Over the southern half, it is persistent throughout the year while for the northern half, strongly coupled regions generally follow the freezing line during the cold months. Detailed analysis of the southern Canadian Prairies reveals seasonal differences in the underlying coupling mechanism. During spring and fall, as opposed to summer, the interactive soil moisture phase impacts the snow depth and surface albedo, which further impacts the surface energy budget and thus the surface air temperature; the air temperature then influences the snow depth in a feedback loop. Projected changes to coupling are also season specific: relatively drier soil conditions strengthen coupling during summer, while changes in soil moisture phase, snow depth, and cloud cover impact coupling during colder months. Furthermore, results demonstrate that soil moisture variability amplifies the frequency of temperature extremes over regions of strong coupling in current and future climates.

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

NASA Technical Reports Server (NTRS)

Toure, Ally M.; Rodell, Matthew; Yang, Zong-Liang; Beaudoing, Hiroko; Kim, Edward; Zhang, Yongfei; Kwon, Yonghwan

2015-01-01

This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Jeyaratnam, Jeyavinoth

2016-01-01

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

Snowpack displacement measured by terrestrial radar interferometry as precursor for wet snow avalanches

NASA Astrophysics Data System (ADS)

Caduff, Rafael; Wiesmann, Andreas; Bühler, Yves

2016-04-01

Wet snow and full depth gliding avalanches commonly occur on slopes during springtime when air temperatures rise above 0°C for longer time. The increase in the liquid water content changes the mechanical properties of the snow pack. Until now, forecasts of wet snow avalanches are mainly done using weather data such as air and snow temperatures and incoming solar radiation. Even tough some wet snow avalanche events are indicated before the release by the formation of visible signs such as extension cracks or compressional bulges in the snow pack, a large number of wet snow avalanches are released without any previously visible signs. Continuous monitoring of critical slopes by terrestrial radar interferometry improves the scale of reception of differential movement into the range of millimetres per hour. Therefore, from a terrestrial and remote observation location, information on the mechanical state of the snow pack can be gathered on a slope wide scale. Recent campaigns in the Swiss Alps showed the potential of snow deformation measurements with a portable, interferometric real aperture radar operating at 17.2 GHz (1.76 cm wavelength). Common error sources for the radar interferometric measurement of snow pack displacements are decorrelation of the snow pack at different conditions, the influence of atmospheric disturbances on the interferometric phase and transition effects from cold/dry snow to warm/wet snow. Therefore, a critical assessment of those parameters has to be considered in order to reduce phase noise effects and retrieve accurate displacement measurements. The most recent campaign in spring 2015 took place in Davos Dorf/GR, Switzerland and its objective was to observe snow glide activity on the Dorfberg slope. A validation campaign using total station measurements showed good agreement to the radar interferometric line of sight displacement measurements in the range of 0.5 mm/h. The refinement of the method led to the detection of numerous gliding patches distributed over the entire slope. Typically, patches showing (full depth) snow gliding reach extensions from 5x10 metres up to 40x60 metres. Using a sampling interval of 1-3 minutes, the temporal displacement of such snow glide-hot spots can be tracked and thus revealing the individual signature of deformation. Nearly linear behaviour over several days, peaking in a final acceleration releasing an avalanche was observed as well characteristic acceleration and deceleration cycles during day and night could be captured. These cycles sometimes trigger an avalanche and sometimes reach a full stop of the differential snow glide movement. Findings of the different campaigns will be presented. We put them in the context for possible future campaigns that could be used to solve scientific questions regarding the mechanical properties of the snow pack. We evaluate the possibilities for the use of terrestrial radar interferometry for hazard management and avalanche forecast.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Snow Water Equivalent Retrieval By Markov Chain Monte Carlo Based on Memls and Hut Snow Emission Model

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Vanderjagt, B. J.

2014-12-01

The Markov chain Monte Carlo (MCMC) method had been proved to be successful in snow water equivalent retrieval based on synthetic point-scale passive microwave brightness temperature (TB) observations. This method needs only general prior information about distribution of snow parameters, and could estimate layered snow properties, including the thickness, temperature, density and snow grain size (or exponential correlation length) of each layer. In this study, the multi-layer HUT (Helsinki University of Technology) model and the MEMLS (Microwave Emission Model of Layered Snowpacks) will be used as observation models to assimilate the observed TB into snow parameter prediction. Previous studies had shown that the multi-layer HUT model tends to underestimate TB at 37 GHz for deep snow, while the MEMLS does not show sensitivity of model bias to snow depth. Therefore, results using HUT model and MEMLS will be compared to see how the observation model will influence the retrieval of snow parameters. The radiometric measurements at 10.65, 18.7, 36.5 and 90 GHz at Sodankyla, Finland will be used as MCMC input, and the statistics of all snow property measurement will be used to calculate the prior information. 43 dry snowpits with complete measurements of all snow parameters will be used for validation. The entire dataset are from NorSREx (Nordic Snow Radar Experiment) experiments carried out by Juha Lemmetyinen, Anna Kontu and Jouni Pulliainen in FMI in 2009-2011 winters, and continued two more winters from 2011 to Spring of 2013. Besides the snow thickness and snow density that are directly related to snow water equivalent, other parameters will be compared with observations, too. For thin snow, the previous studies showed that influence of underlying soil is considerable, especially when the soil is half frozen with part of unfrozen liquid water and part of ice. Therefore, this study will also try to employ a simple frozen soil permittivity model to improve the performance of retrieval. The behavior of the Markov chain in soil parameters will be studied.

CO2 emissions from permafrost regions in Alaska during the nongrowing seasons

NASA Astrophysics Data System (ADS)

Natali, S.; Risk, D. A.; Minions, C.; Ludwig, S.; Watts, J. D.; Rogers, B. M.; Goetz, S. J.; Jastrow, J. D.; Jorgenson, T.; Schade, J. D.

2017-12-01

Surface air temperatures in the Arctic have been increasing twice as fast as the global average, and climate models project that this rate of warming will continue through the century, with the greatest warming occurring during the winter months. An increase in wintertime temperature may reduce belowground carbon storage due to enhanced microbial respiration during the snow-covered period when plant carbon uptake has predominantly ceased. Carbon emissions during the nongrowing season (NGS: i.e., autumn, winter and spring) are an important component of annual respiratory loss, yet there are large uncertainties in local and regional estimates of NGS CO2 fluxes. To address these uncertainties, we established a network of automated soil respiration sensors that run throughout the year at 10 locations across AK, including several paired burned and unburned sites in tundra and boreal regions. We measured soil CO2 flux, soil temperature (15, 50, 100 cm), soil moisture, and snow depth throughout the NGS, and plant cover, stand density, organic layer depth and thaw depth, and we analyzed active layer soils for total C and N, and organic matter composition. During spring thaw, all sites exhibited a strong pulse of CO2, a result of physical release of CO2 produced during the NGS. CO2 flux rates during the spring thaw were 1-2 orders of magnitude higher than winter CO2 fluxes and twice as high as fluxes during the early growing season. While temperature was a key driver of NGS fluxes across sites, our results suggest that soil organic matter content and composition were also important for NGS CO2 production. Despite warmer soils in burned spruce forests (Nome Creek, 2004 burn and Hess Creek, 2003 burn; 1-2 C warmer at 50-100 cm) compared to mature forests, NGS fluxes were either not significantly different or were higher in the mature stands than in burned stands, which may be a result of substrate limitation to NGS fluxes following fire. Quantifying the magnitude and drivers of NGS CO2 flux is critical for determining whether the Arctic is currently a source or sink for carbon and how this is likely to change as warming continues.

Snow depth retrieval from L-band satellite measurements on Arctic and Antarctic sea ice

NASA Astrophysics Data System (ADS)

Maaß, N.; Kaleschke, L.; Wever, N.; Lehning, M.; Nicolaus, M.; Rossmann, H. L.

2017-12-01

The passive microwave mission SMOS provides daily coverage of the polar regions and measures at a low frequency of 1.4 GHz (L-band). SMOS observations have been used to operationally retrieve sea ice thickness up to 1 m and to estimate snow depth in the Arctic for thicker ice. Here, we present how SMOS-retrieved snow depths compare with airborne measurements from NASA's Operation IceBridge mission (OIB) and with AMSR-2 satellite retrievals at higher frequencies, and we show first applications to Antarctic sea ice. In previous studies, SMOS and OIB snow depths showed good agreement on spatial scales from 50 to 1000 km for some days and disagreement for other days. Here, we present a more comprehensive comparison of OIB and SMOS snow depths in the Arctic for 2011 to 2015. We find that the SMOS retrieval works best for cold conditions and depends on auxiliary information on ice surface temperature, here provided by MODIS thermal imagery satellite data. However, comparing SMOS and OIB snow depths is difficult because of the different spatial resolutions (SMOS: 40 km, OIB: 40 m). Spatial variability within the SMOS footprint can lead to different snow conditions as seen from SMOS and OIB. Ideally the comparison is made for uniform conditions: Low lead and open water fraction, low spatial and temporal variability of ice surface temperature, no mixture of multi- and first-year ice. Under these conditions and cold temperatures (surface temperatures below -25°C), correlation coefficients between SMOS and OIB snow depths increase from 0.3 to 0.6. A finding from the comparison with AMSR-2 snow depths is that the SMOS-based maps depend less on the age of the sea ice than the maps derived from higher frequencies. Additionally, we show first results of SMOS snow depths for Antarctic sea ice. SMOS observations are compared to measurements of autonomous snow buoys drifting in the Weddell Sea since 2014. For a better comparability of these point measurements with SMOS data, we use model simulations along these trajectories made with a sea ice version of SNOWPACK, a 1D multi-layer thermodynamic snow model driven by reanalysis data. These simulations are especially helpful for indicating the occurrence of snow-ice-transformation, which cannot be identified in the buoy data and contributes to the measured snow height.

The prelaying interval of emperor geese on the Yukon-Kuskokwim Delta, Alaska

USGS Publications Warehouse

Hupp, Jerry W.; Schmutz, J.A.; Ely, Craig R.

2006-01-01

We marked 136 female Emperor Geese (Chen canagica) in western Alaska with VHF or satellite (PTT) transmitters from 1999 to 2003 to monitor their spring arrival and nest initiation dates on the Yukon Delta, and to estimate prelaying interval lengths once at the nesting area. Ninety-two females with functional transmitters returned to the Yukon Delta in the spring after they were marked, and we located the nests of 35 of these individuals. Prelaying intervals were influenced by when snow melted in the spring and individual arrival dates on the Yukon Delta. The median prelaying interval was 15 days (range = 12-19 days) in a year when snow melted relatively late, and 11 days (range = 4-16 days) in two warmer years when snow melted earlier. In years when snow melted earlier, prelaying intervals of <12 days for 11 of 15 females suggested they initiated rapid follicle development on spring staging areas. The prelaying interval declined by approximately 0.4 days and nest initiation date increased approximately 0.5 days for each day a female delayed her arrival. Thus, females that arrived first on the Yukon Delta had prelaying intervals up to four days longer, yet they nested up to five days earlier, than females that arrived last. The proximity of spring staging areas on the Alaska Peninsula to nesting areas on the Yukon Delta may enable Emperor Geese to alter timing of follicle development depending on annual conditions, and to invest nutrients acquired from both areas in eggs during their formation. Plasticity in timing of follicle development is likely advantageous in a variable environment where melting of snow cover in the spring can vary by 2-3 weeks annually. ?? The Cooper Ornithological Society 2006.

National Snow Analyses - NOHRSC - The ultimate source for snow information

Science.gov Websites

Equivalent Thumbnail image of Modeled Snow Water Equivalent Animate: Season --- Two weeks --- One Day Snow Depth Thumbnail image of Modeled Snow Depth Animate: Season --- Two weeks --- One Day Average Snowpack Temp Thumbnail image of Modeled Average Snowpack Temp Animate: Season --- Two weeks --- One Day SWE

Effect of snow cover on soil frost penetration

NASA Astrophysics Data System (ADS)

Rožnovský, Jaroslav; Brzezina, Jáchym

2017-12-01

Snow cover occurrence affects wintering and lives of organisms because it has a significant effect on soil frost penetration. An analysis of the dependence of soil frost penetration and snow depth between November and March was performed using data from 12 automated climatological stations located in Southern Moravia, with a minimum period of measurement of 5 years since 2001, which belong to the Czech Hydrometeorological institute. The soil temperatures at 5 cm depth fluctuate much less in the presence of snow cover. In contrast, the effect of snow cover on the air temperature at 2 m height is only very small. During clear sky conditions and no snow cover, soil can warm up substantially and the soil temperature range can be even higher than the range of air temperature at 2 m height. The actual height of snow is also important - increased snow depth means lower soil temperature range. However, even just 1 cm snow depth substantially lowers the soil temperature range and it can therefore be clearly seen that snow acts as an insulator and has a major effect on soil frost penetration and soil temperature range.

Estimation of Sea Ice Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data

NASA Technical Reports Server (NTRS)

Kurtz, Nathan T.; Markus, Thorsten; Cavalieri, Donald J.; Sparling, Lynn C.; Krabill, William B.; Gasiewski, Albin J.; Sonntag, John G.

2009-01-01

Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the non-linear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean ice thickness values.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Marksu, T.; Ivanoff, A.; Miller, J. A.; Brucker, L.; Sturm, M.; Maslanik, J. A.; Heinrichs, J. F.; Gasiewski, A.; Leuschen, C.;

Airborne radar surveys of snow depth over Antarctic sea ice during Operation IceBridge

NASA Astrophysics Data System (ADS)

Panzer, B.; Gomez-Garcia, D.; Leuschen, C.; Paden, J. D.; Gogineni, P. S.

2012-12-01

Over the last decade, multiple satellite-based laser and radar altimeters, optimized for polar observations, have been launched with one of the major objectives being the determination of global sea ice thickness and distribution [5, 6]. Estimation of sea-ice thickness from these altimeters relies on freeboard measurements and the presence of snow cover on sea ice affects this estimate. Current means of estimating the snow depth rely on daily precipitation products and/or data from passive microwave sensors [2, 7]. Even a small uncertainty in the snow depth leads to a large uncertainty in the sea-ice thickness estimate. To improve the accuracy of the sea-ice thickness estimates and provide validation for measurements from satellite-based sensors, the Center for Remote Sensing of Ice Sheets deploys the Snow Radar as a part of NASA Operation IceBridge. The Snow Radar is an ultra-wideband, frequency-modulated, continuous-wave radar capable of resolving snow depth on sea ice from 5 cm to more than 2 meters from long-range, airborne platforms [4]. This paper will discuss the algorithm used to directly extract snow depth estimates exclusively using the Snow Radar data set by tracking both the air-snow and snow-ice interfaces. Prior work in this regard used data from a laser altimeter for tracking the air-snow interface or worked under the assumption that the return from the snow-ice interface was greater than that from the air-snow interface due to a larger dielectric contrast, which is not true for thick or higher loss snow cover [1, 3]. This paper will also present snow depth estimates from Snow Radar data during the NASA Operation IceBridge 2010-2011 Antarctic campaigns. In 2010, three sea ice flights were flown, two in the Weddell Sea and one in the Amundsen and Bellingshausen Seas. All three flight lines were repeated in 2011, allowing an annual comparison of snow depth. In 2011, a repeat pass of an earlier flight in the Weddell Sea was flown, allowing for a comparison of snow depths with two weeks elapsed between passes. [1] Farrell, S.L., et al., "A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice," IEEE Tran. Geoscience and Remote Sensing, Vol. 50, No. 6, pp. 2098-2111, June 2012. [2] Kwok, R., and G. F. Cunningham, "ICESat over Arctic sea ice: Estimation of snow depth and ice thickness," J. Geophys. Res., 113, C08010, 2008. [3] Kwok, R., et al., "Airborne surveys of snow depth over Arctic sea ice," J. Geophys. Res., 116, C11018, 2011. [4] Panzer, B., et al., "An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn," Submitted to J. Glaciology, July 23, 2012. [5] Wingham, D.J., et al., "CryoSat: A Mission to Determine the Fluctuations in Earth's Land and Marine Ice Fields," Advances in Space Research, Vol. 37, No. 4, pp. 841-871, 2006. [6] Zwally, H. J., et al., "ICESat's laser measurements of polar ice, atmosphere, ocean, and land," J. Geodynamics, Vol. 34, No. 3-4, pp. 405-445, Oct-Nov 2002. [7] Zwally, H. J., et al., "ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea," J. Geophys. Res., 113, C02S15, 2008.

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

NASA Astrophysics Data System (ADS)

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 force the model over an independent period for analysis against both snow and streamflow observations to see if improvements took place. The goal of this work is to further improve snow physics in the NWM, along with identifying areas where further work will take place in the future, such as data assimilation or further forcing improvements.

Arctic tundra shrub invasion and soot deposition: Consequences for spring snowmelt and near-surface air temperatures

NASA Astrophysics Data System (ADS)

Strack, John E.; Pielke, Roger A.; Liston, Glen E.

2007-12-01

Invasive shrubs and soot pollution both have the potential to alter the surface energy balance and timing of snow melt in the Arctic. Shrubs reduce the amount of snow lost to sublimation on the tundra during the winter leading to a deeper end-of-winter snowpack. The shrubs also enhance the absorption of energy by the snowpack during the melt season by converting incoming solar radiation to longwave radiation and sensible heat. Soot deposition lowers the albedo of the snow, allowing it to more effectively absorb incoming solar radiation and thus melt faster. This study uses the Colorado State University Regional Atmospheric Modeling System version 4.4 (CSU-RAMS 4.4), equipped with an enhanced snow model, to investigate the effects of shrub encroachment and soot deposition on the atmosphere and snowpack in the Kuparuk Basin of Alaska during the May-June melt period. The results of the simulations suggest that a complete invasion of the tundra by shrubs leads to a 2.2°C warming of 3 m air temperatures and a 108 m increase in boundary layer depth during the melt period. The snow-free date also occurred 11 d earlier despite having a larger initial snowpack. The results also show that a decrease in the snow albedo of 0.1, owing to soot pollution, caused the snow-free date to occur 5 d earlier. The soot pollution caused a 1.0°C warming of 3 m air temperatures and a 25 m average deepening of the boundary layer.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Spatial properties of snow cover in the Upper Merced River Basin: implications for a distributed snow measurement network

NASA Astrophysics Data System (ADS)

Bouffon, T.; Rice, R.; Bales, R.

2006-12-01

The spatial distributions of snow water equivalent (SWE) and snow depth within a 1, 4, and 16 km2 grid element around two automated snow pillows in a forested and open- forested region of the Upper Merced River Basin (2,800 km2) of Yosemite National Park were characterized using field observations and analyzed using binary regression trees. Snow surveys occurred at the forested site during the accumulation and ablation seasons, while at the open-forest site a survey was performed only during the accumulation season. An average of 130 snow depth and 7 snow density measurements were made on each survey, within the 4 km2 grid. Snow depth was distributed using binary regression trees and geostatistical methods using the physiographic parameters (e.g. elevation, slope, vegetation, aspect). Results in the forest region indicate that the snow pillow overestimated average SWE within the 1, 4, and 16 km2 areas by 34 percent during ablation, but during accumulation the snow pillow provides a good estimate of the modeled mean SWE grid value, however it is suspected that the snow pillow was underestimating SWE. However, at the open forest site, during accumulation, the snow pillow was 28 percent greater than the mean modeled grid element. In addition, the binary regression trees indicate that the independent variables of vegetation, slope, and aspect are the most influential parameters of snow depth distribution. The binary regression tree and multivariate linear regression models explain about 60 percent of the initial variance for snow depth and 80 percent for density, respectively. This short-term study provides motivation and direction for the installation of a distributed snow measurement network to fill the information gap in basin-wide SWE and snow depth measurements. Guided by these results, a distributed snow measurement network was installed in the Fall 2006 at Gin Flat in the Upper Merced River Basin with the specific objective of measuring accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs.

Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado

NASA Astrophysics Data System (ADS)

Webb, Ryan W.

2017-09-01

Snow is an important environmental variable in headwater systems that controls hydrological processes such as streamflow, groundwater recharge, and evapotranspiration. These processes will be affected by both the amount of snow available for melt and the rate at which it melts. Snow water equivalent (SWE) and snowmelt are known to vary within complex subalpine terrain due to terrain and canopy influences. This study assesses this variability during the melt season using ground penetrating radar to survey multiple plots in northwestern Colorado near a snow telemetry (SNOTEL) station. The plots include south aspect and flat aspect slopes with open, coniferous (subalpine fir, Abies lasiocarpa and engelman spruce, Picea engelmanii), and deciduous (aspen, populous tremuooides) canopy cover. Results show the high variability for both SWE and loss of SWE during spring snowmelt in 2014. The coefficient of variation for SWE tended to increase with time during snowmelt whereas loss of SWE remained similar. Correlation lengths for SWE were between two and five meters with melt having correlation lengths between two and four meters. The SNOTEL station regularly measured higher SWE values relative to the survey plots but was able to reasonably capture the overall mean loss of SWE during melt. Ground Penetrating Radar methods can improve future investigations with the advantage of non-destructive sampling and the ability to estimate depth, density, and SWE.

Snow Depth Depicted on Mt. Lyell by NASA Airborne Snow Observatory

NASA Image and Video Library

2013-05-02

A natural color image of Mt. Lyell, the highest point in the Tuolumne River Basin top image is compared with a three-dimensional color composite image of Mt. Lyell from NASA Airborne Snow Observatory depicting snow depth bottom image.

The impact of the snow cover on sea-ice thickness products retrieved by Ku-band radar altimeters

NASA Astrophysics Data System (ADS)

Ricker, R.; Hendricks, S.; Helm, V.; Perovich, D. K.

2015-12-01

Snow on sea ice is a relevant polar climate parameter related to ocean-atmospheric interactions and surface albedo. It also remains an important factor for sea-ice thickness products retrieved from Ku-band satellite radar altimeters like Envisat or CryoSat-2, which is currently on its mission and the subject of many recent studies. Such satellites sense the height of the sea-ice surface above the sea level, which is called sea-ice freeboard. By assuming hydrostatic equilibrium and that the main scattering horizon is given by the snow-ice interface, the freeboard can be transformed into sea-ice thickness. Therefore, information about the snow load on hemispherical scale is crucial. Due to the lack of sufficient satellite products, only climatological values are used in current studies. Since such values do not represent the high variability of snow distribution in the Arctic, they can be a substantial contributor to the total sea-ice thickness uncertainty budget. Secondly, recent studies suggest that the snow layer cannot be considered as homogenous, but possibly rather featuring a complex stratigraphy due to wind compaction and/or ice lenses. Therefore, the Ku-band radar signal can be scattered at internal layers, causing a shift of the main scattering horizon towards the snow surface. This alters the freeboard and thickness retrieval as the assumption that the main scattering horizon is given by the snow-ice interface is no longer valid and introduces a bias. Here, we present estimates for the impact of snow depth uncertainties and snow properties on CryoSat-2 sea-ice thickness retrievals. We therefore compare CryoSat-2 freeboard measurements with field data from ice mass-balance buoys and aircraft campaigns from the CryoSat Validation Experiment. This unique validation dataset includes airborne laser scanner and radar altimeter measurements in spring coincident to CryoSat-2 overflights, and allows us to evaluate how the main scattering horizon is altered by the presence of a complex snow stratigraphy.

Does seasonal snowpacks enhance or decrease mercury contamination of high elevation ecosystems?

NASA Astrophysics Data System (ADS)

Pierce, A.; Fain, X.; Obrist, D.; Helmig, D.; Barth, C.; Jacques, H.; Chowanski, K.; Boyle, D.; William, M.

2009-12-01

Mercury (Hg) is an extremely toxic pollutant globally dispersed in the environment. Natural and anthropogenic sources emit Hg to the atmosphere, either as gaseous elemental mercury (GEM; Hg0) or as divalent mercury species. Due to the long lifetime of GEM mercury contamination is not limited to industrialized sites, but also a concern in remote areas such as high elevation mountain environments. During winter and spring 2009, we investigated the fate of atmospheric mercury deposited to mountain ecosystems in the Sierra Nevada (Sagehen station, California, USA) and the Rocky Mountains (Niwot Ridge station, Colorado, USA). At Sagehen, we monitored mercury in snow (surface snow sampling and snow pits), wet deposition, and stream water during the snow-dominated season. Comparison of Hg stream discharge to snow Hg wet deposition showed that only a small fraction of Hg wet deposition reached stream in the melt water. Furthermore, Hg concentration in soil transects (25 different locations) showed no correlations to wet deposition Hg loads due to pronounced altitudinal precipitation gradient suggesting that Hg deposited to the snowpack was not transferred to ecosystems. At Niwot Ridge, further characterization of the chemical transformation involving mercury species within snowpacks was achieved by 3-months of continuous monitoring of GEM and ozone concentrations in the snow air at eight depths from the soil-snow interface to the top of the up to 2 meter deep snowpack. Divalent mercury concentrations were monitored as well (surface snow sampling and snow pits). GEM levels in snow air exhibited strong diurnal pattern indicative of both oxidation and reduction processes. Low levels of divalent mercury concentrations in snow pack suggest that large fractions of Hg originally deposited as wet deposition was reemitted back to the atmosphere after reduction. Hence, these results suggest that the presence of a seasonal snowpack may decrease effective wet deposition of mercury and transfer to the underlying ground due to significant evasion losses of Hg from the snowpack to the atmosphere.

Increased spring freezing vulnerability for alpine shrubs under early snowmelt.

PubMed

Wheeler, J A; Hoch, G; Cortés, A J; Sedlacek, J; Wipf, S; Rixen, C

2014-05-01

Alpine dwarf shrub communities are phenologically linked with snowmelt timing, so early spring exposure may increase risk of freezing damage during early development, and consequently reduce seasonal growth. We examined whether environmental factors (duration of snow cover, elevation) influenced size and the vulnerability of shrubs to spring freezing along elevational gradients and snow microhabitats by modelling the past frequency of spring freezing events. We sampled biomass and measured the size of Salix herbacea, Vaccinium myrtillus, Vaccinium uliginosum and Loiseleuria procumbens in late spring. Leaves were exposed to freezing temperatures to determine the temperature at which 50% of specimens are killed for each species and sampling site. By linking site snowmelt and temperatures to long-term climate measurements, we extrapolated the frequency of spring freezing events at each elevation, snow microhabitat and per species over 37 years. Snowmelt timing was significantly driven by microhabitat effects, but was independent of elevation. Shrub growth was neither enhanced nor reduced by earlier snowmelt, but decreased with elevation. Freezing resistance was strongly species dependent, and did not differ along the elevation or snowmelt gradient. Microclimate extrapolation suggested that potentially lethal freezing events (in May and June) occurred for three of the four species examined. Freezing events never occurred on late snow beds, and increased in frequency with earlier snowmelt and higher elevation. Extrapolated freezing events showed a slight, non-significant increase over the 37-year record. We suggest that earlier snowmelt does not enhance growth in four dominant alpine shrubs, but increases the risk of lethal spring freezing exposure for less freezing-resistant species.

Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects

NASA Astrophysics Data System (ADS)

Jeong, Dae Il; Sushama, Laxmi; Naveed Khaliq, M.

2017-06-01

Snow is an important component of the cryosphere and it has a direct and important influence on water storage and supply in snowmelt-dominated regions. This study evaluates the temporal evolution of snow water equivalent (SWE) for the February-April spring period using the GlobSnow observation dataset for the 1980-2012 period. The analysis is performed for different regions of hemispherical to sub-continental scales for the Northern Hemisphere. The detection-attribution analysis is then performed to demonstrate anthropogenic and natural effects on spring SWE changes for different regions, by comparing observations with six CMIP5 model simulations for three different external forcings: all major anthropogenic and natural (ALL) forcings, greenhouse gas (GHG) forcing only, and natural forcing only. The observed spring SWE generally displays a decreasing trend, due to increasing spring temperatures. However, it exhibits a remarkable increasing trend for the southern parts of East Eurasia. The six CMIP5 models with ALL forcings reproduce well the observed spring SWE decreases at the hemispherical scale and continental scales, whereas important differences are noted for smaller regions such as southern and northern parts of East Eurasia and northern part of North America. The effects of ALL and GHG forcings are clearly detected for the spring SWE decline at the hemispherical scale, based on multi-model ensemble signals. The effects of ALL and GHG forcings, however, are less clear for the smaller regions or with single-model signals, indicating the large uncertainty in regional SWE changes, possibly due to stronger influence of natural climate variability.

Projected changes of snow conditions and avalanche activity in a warming climate: a case study in the French Alps over the 2020-2050 and 2070-2100 periods

NASA Astrophysics Data System (ADS)

Castebrunet, H.; Eckert, N.; Giraud, G.; Durand, Y.; Morin, S.

2014-01-01

Projecting changes in snow cover due to climate warming is important for many societal issues, including adaptation of avalanche risk mitigation strategies. Efficient modeling of future snow cover requires high resolution to properly resolve the topography. Here, we detail results obtained through statistical downscaling techniques allowing simulations of future snowpack conditions for the mid- and late 21st century in the French Alps under three climate change scenarios. Refined statistical descriptions of snowpack characteristics are provided with regards to a 1960-1990 reference period, including latitudinal, altitudinal and seasonal gradients. These results are then used to feed a statistical model of avalanche activity-snow conditions-meteorological conditions relationships, so as to produce the first prognoses at annual/seasonal time scales of future natural avalanche activity eventually based on past observations. The resulting statistical indicators are fundamental for the mountain economy in terms of changes anticipation. At all considered spatio-temporal scales, whereas precipitations are expected to remain quite stationary, temperature increase interacting with topography will control snow-related variables, for instance the rate of decrease of total and dry snow depths, and the successive increase/decrease of the wet snow pack. Overall, with regards to the reference period, changes are strong for the end of the 21st century, but already significant for the mid-century. Changes in winter are somewhat less important than in spring, but wet snow conditions will appear at high elevations earlier in the season. For a given altitude, the Southern French Alps will not be significantly more affected than the Northern French Alps, so that the snowpack characteristics will be preserved more lately in the southern massifs of higher mean altitude. Regarding avalanche activity, a general -20-30% decrease and interannual variability is forecasted, relatively strong compared to snow and meteorological parameters changes. This decrease is amplified in spring and at low altitude. In contrast, an increase of avalanche activity is expected in winter at high altitude because of earlier wet snow avalanches triggers, at least as long as a minimal snow cover will be present. Comparison with the outputs of the deterministic avalanche hazard model MEPRA shows generally consistent results but suggests that, even if the frequency of winters with high avalanche activity will clearly decrease, the decreasing trend may be less strong and smooth than suggested by the changes in snowpack characteristics. This important point for risk assessment pleads for further work focusing on shorter time scales. Finally, small differences between different climate change scenarios show the robustness of the predicted avalanche activity changes.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Measurements of seasonal frost depth by frost tube in Japan

NASA Astrophysics Data System (ADS)

Harada, K.; Yoshikawa, K.; Iwahana, G.; Stanilovskaya, J. V.; Sawada, Y.; Sone, T.

2017-12-01

Since 2011 winter season, frost depths have been measured as an outreach program in Hokkaido, northern part of Japan, where seasonal ground freezing occurs in winter. Frost depths were measured in elementary, junior high and high schools in order to emphasis their interest for earth sciences. At schools, using simple frost tube, measurements were conducted directly once a week by students or teacher during ground freezing under no snow-removal condition. A lecture was made in class and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. In 2011 winter season, we started measurements at three schools, and the number of school extended to 32 in 2016 season, 26 elementary schools, 5 junior high schools and one high school. We visited schools in summer time or just before frost season to talk about the method of measurement, and measurements by students started just after ground freezing. After the end of frozen period, we visited schools again to explain results of each school or another schools in Japan, Alaska, Canada or Russia. The measured frost depths in Hokkaido ranged widely, from only a few centimeter to more than 50 cm. However, some schools had no frost depth due to heavy snow. We confirmed that the frost depth strongly depends on air temperature and snow depth. The lecture was made to student why the frost depth ranged widely, and the effect of snow was explained by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at the same elementary school. At the end of December, depths had no significant difference between these conditions, and the difference went to 14 cm after one month, with about 30 cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth.

CO2 flux over young and snow-covered Arctic pack ice in winter and spring

NASA Astrophysics Data System (ADS)

Nomura, Daiki; Granskog, Mats A.; Fransson, Agneta; Chierici, Melissa; Silyakova, Anna; Ohshima, Kay I.; Cohen, Lana; Delille, Bruno; Hudson, Stephen R.; Dieckmann, Gerhard S.

2018-06-01

Rare CO2 flux measurements from Arctic pack ice show that two types of ice contribute to the release of CO2 from the ice to the atmosphere during winter and spring: young, thin ice with a thin layer of snow and older (several weeks), thicker ice with thick snow cover. Young, thin sea ice is characterized by high salinity and high porosity, and snow-covered thick ice remains relatively warm ( > -7.5 °C) due to the insulating snow cover despite air temperatures as low as -40 °C. Therefore, brine volume fractions of these two ice types are high enough to provide favorable conditions for gas exchange between sea ice and the atmosphere even in mid-winter. Although the potential CO2 flux from sea ice decreased due to the presence of the snow, the snow surface is still a CO2 source to the atmosphere for low snow density and thin snow conditions. We found that young sea ice that is formed in leads without snow cover produces CO2 fluxes an order of magnitude higher than those in snow-covered older ice (+1.0 ± 0.6 mmol C m-2 day-1 for young ice and +0.2 ± 0.2 mmol C m-2 day-1 for older ice).

Mapping Snow Depth with Automated Terrestrial Laser Scanning - Investigating Potential Applications

NASA Astrophysics Data System (ADS)

Adams, M. S.; Gigele, T.; Fromm, R.

2017-11-01

This contribution presents an automated terrestrial laser scanning (ATLS) setup, which was used during the winter 2016/17 to monitor the snow depth distribution on a NW-facing slope at a high-alpine study site. We collected data at high temporal [(sub-)daily] and spatial resolution (decimetre-range) over 0.8 km² with a Riegl LPM-321, set in a weather-proof glass fibre enclosure. Two potential ATLS-applications are investigated here: monitoring medium-sized snow avalanche events, and tracking snow depth change caused by snow drift. The results show the ATLS data's high explanatory power and versatility for different snow research questions.

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

DOE PAGES

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang; ...

2017-08-18

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

Towards a well-founded and reproducible snow load map for Austria

NASA Astrophysics Data System (ADS)

Winkler, Michael; Schellander, Harald

2017-04-01

"EN 1991-1-3 Eurocode 1: Part 1-3: Snow Loads" provides standard for the determination of the snow load to be used for the structural design of buildings etc. Since 2006 national specifications for Austria define a snow load map with four "load zones", allowing the calculation of the characteristic ground snow load sk for locations below 1500 m asl. A quadratic regression between altitude and sk is used, as suggested by EN 1991-1-3. The actual snow load map is based on best meteorological practice, but still it is somewhat subjective and non-reproducible. Underlying snow data series often end in the 1980s; in the best case data until about 2005 is used. Moreover, extreme value statistics only rely on the Gumbel distribution and the way in which snow depths are converted to snow loads is generally unknown. This might be enough reasons to rethink the snow load standard for Austria, all the more since today's situation is different to what it was some 15 years ago: Firstly, Austria is rich of multi-decadal, high quality snow depth measurements. These data are not well represented in the actual standard. Secondly, semi-empirical snow models allow sufficiently precise calculations of snow water equivalents and snow loads from snow depth measurements without the need of other parameters like temperature etc. which often are not available at the snow measurement sites. With the help of these tools, modelling of daily snow load series from daily snow depth measurements is possible. Finally, extreme value statistics nowadays offers convincing methods to calculate snow depths and loads with a return period of 50 years, which is the base of sk, and allows reproducible spatial extrapolation. The project introduced here will investigate these issues in order to update the Austrian snow load standard by providing a well-founded and reproducible snow load map for Austria. Not least, we seek for contact with standards bodies of neighboring countries to find intersections as well as to avoid inconsistencies and duplications of effort.

Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

NASA Astrophysics Data System (ADS)

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 Alaskan context because it increases our ability to constrain snow modeling outputs by making use of snow measurements collected by non-expert, citizen scientists.

Influence of the long-range transport of dust over central Himalayan region - study using MERRA-2 and GLDAS Ver.2.1

NASA Astrophysics Data System (ADS)

Kumar, A.; Singh, N.; A.

2017-12-01

To elucidate upon the effect of dust loading on the central Himalayan glaciers and snow cover, a study is carried out over the geographical boundary between 28-34° N and 78-98° E, for the period 2011-2015. Only spring and summer seasons are investigated, as the long range transport over the region are usually more prominent during these seasons. To ascertain the dust sources, data obtained from the level-2 of Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) ver. 4.10, Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) trajectory model, Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) ver. 5.12.4 are utilized. The snow depth and snow fall data are taken from MERRA-2, while, for the surface Albedo, data from Global land data assimilation system (GLDAS) ver. 2.1 Noah land surface model L4 is used. ERA-Interim wind products are also used to understand the prevailing wind pattern over the site during the period of study. To show the impact of aerosols on glaciated surface and the snow fall, a regression analysis is performed between these parameters and the dust column mass density for the period of 1980-2016 using MERRA-2 reanalysis data.

Under-canopy snow accumulation and ablation measured with airborne scanning LiDAR altimetry and in-situ instrumental measurements, southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Kirchner, P. B.; Bales, R. C.; Musselman, K. N.; Molotch, N. P.

2012-12-01

We investigated the influence of canopy on snow accumulation and melt in a mountain forest using paired snow on and snow off scanning LiDAR altimetry, synoptic measurement campaigns and in-situ time series data of snow depth, SWE, and radiation collected from the Kaweah River watershed, Sierra Nevada, California. Our analysis of forest cover classified by dominant species and 1 m2 grided mean under canopy snow accumulation calculated from airborne scanning LiDAR, demonstrate distinct relationships between forest class and under-canopy snow depth. The five forest types were selected from carefully prepared 1 m vegetation classifications and named for their dominant tree species, Giant Sequoia, Jeffrey Pine, White Fir, Red Fir, Sierra Lodgepole, Western White Pine, and Foxtail Pine. Sufficient LiDAR returns for calculating mean snow depth per m2 were available for 31 - 44% of the canopy covered area and demonstrate a reduction in snow depth of 12 - 24% from adjacent open areas. The coefficient of variation in snow depth under canopies ranged from 0.2 - 0.42 and generally decreased as elevation increased. Our analysis of snow density snows no statistical significance between snow under canopies and in the open at higher elevations with a weak significance for snow under canopies at lower elevations. Incident radiation measurements made at 15 minute intervals under forest canopies show an input of up to 150 w/m2 of thermal radiation from vegetation to the snow surface on forest plots. Snow accumulated on the mid to high elevation forested slopes of the Sierra Nevada represents the majority of winter snow storage. However snow estimates in forested environments demonstrate a high level of uncertainty due to the limited number of in-situ observations and the inability of most remote sensing platforms to retrieve reflectance under dense vegetation. Snow under forest canopies is strongly mediated by forest cover and decoupled from the processes that dictate accumulation and ablation of snow in open locations, where almost all precipitation and meteorlogic measurements concerning snow are made. Snow accumulation is intercepted by vegetation until it accumulates to a depth equal to or greater than the height of the vegetation, is reduced by the amount of sublimation or evaporation occurring while on the canopy and is redistributed beneath the canopy at a different density or as liquid water. Ablation processes are dictated by the energy environment surrounding vegetation where sensible heat is mediated by shading of short wave radiation.

Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

NASA Astrophysics Data System (ADS)

Gu, Lingjia; Ren, Ruizhi; Zhao, Kai; Li, Xiaofeng

2014-01-01

The precision of snow parameter retrieval is unsatisfactory for current practical demands. The primary reason is because of the problem of mixed pixels that are caused by low spatial resolution of satellite passive microwave data. A snow passive microwave unmixing method is proposed in this paper, based on land cover type data and the antenna gain function of passive microwaves. The land cover type of Northeast China is partitioned into grass, farmland, bare soil, forest, and water body types. The component brightness temperatures (CBT), namely unmixed data, with 1 km data resolution are obtained using the proposed unmixing method. The snow depth determined by the CBT and three snow depth retrieval algorithms are validated through field measurements taken in forest and farmland areas of Northeast China in January 2012 and 2013. The results show that the overall of the retrieval precision of the snow depth is improved by 17% in farmland areas and 10% in forest areas when using the CBT in comparison with the mixed pixels. The snow cover results based on the CBT are compared with existing MODIS snow cover products. The results demonstrate that more snow cover information can be obtained with up to 86% accuracy.

The cumulative effect of consecutive winters' snow depth on moose and deer populations: a defence

USGS Publications Warehouse

McRoberts, R.E.; Mech, L.D.; Peterson, R.O.

1995-01-01

1. L. D. Mech et al. presented evidence that moose Alces alces and deer Odocoileus virginianus population parameters re influenced by a cumulative effect of three winters' snow depth. They postulated that snow depth affects adult ungulates cumulatively from winter to winter and results in measurable offspring effects after the third winter. 2. F. Messier challenged those findings and claimed that the population parameters studied were instead affected by ungulate density and wolf indexes. 3. This paper refutes Messier's claims by demonstrating that his results were an artifact of two methodological errors. The first was that, in his main analyses, Messier used only the first previous winter's snow depth rather than the sum of the previous three winters' snow depth, which was the primary point of Mech et al. Secondly, Messier smoothed the ungulate population data, which removed 22-51% of the variability from the raw data. 4. When we repeated Messier's analyses on the raw data and using the sum of the previous three winter's snow depth, his findings did not hold up.

Winter snow conditions on Arctic sea ice north of Svalbard during the Norwegian young sea ICE (N-ICE2015) expedition

NASA Astrophysics Data System (ADS)

Merkouriadi, Ioanna; Gallet, Jean-Charles; Graham, Robert M.; Liston, Glen E.; Polashenski, Chris; Rösel, Anja; Gerland, Sebastian

2017-10-01

Snow is a crucial component of the Arctic sea ice system. Its thickness and thermal properties control heat conduction and radiative fluxes across the ocean, ice, and atmosphere interfaces. Hence, observations of the evolution of snow depth, density, thermal conductivity, and stratigraphy are crucial for the development of detailed snow numerical models predicting energy transfer through the snow pack. Snow depth is also a major uncertainty in predicting ice thickness using remote sensing algorithms. Here we examine the winter spatial and temporal evolution of snow physical properties on first-year (FYI) and second-year ice (SYI) in the Atlantic sector of the Arctic Ocean, during the Norwegian young sea ICE (N-ICE2015) expedition (January to March 2015). During N-ICE2015, the snow pack consisted of faceted grains (47%), depth hoar (28%), and wind slab (13%), indicating very different snow stratigraphy compared to what was observed in the Pacific sector of the Arctic Ocean during the SHEBA campaign (1997-1998). Average snow bulk density was 345 kg m-3 and it varied with ice type. Snow depth was 41 ± 19 cm in January and 56 ± 17 cm in February, which is significantly greater than earlier suggestions for this region. The snow water equivalent was 14.5 ± 5.3 cm over first-year ice and 19 ± 5.4 cm over second-year ice.

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

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

2010-01-01

MODIS-derived snow cover measured on 30 April in any given year explains approximately 89 % of the variance in stream discharge for maximum monthly streamflow in that year. Observed changes in streamflow appear to be related to increasing maximum air temperatures over the last four decades causing lower spring snow-cover extent. The majority (>70%) of the water supply in the western United States comes from snowmelt, thus analysis of the declining spring snowpack (and resulting declining stream discharge) has important implications for streamflow management in the drought-prone western U.S.

The Influence of Weather and Lemmings on Spatiotemporal Variation in the Abundance of Multiple Avian Guilds in the Arctic

PubMed Central

Robinson, Barry G.; Franke, Alastair; Derocher, Andrew E.

2014-01-01

Climate change is occurring more rapidly in the Arctic than other places in the world, which is likely to alter the distribution and abundance of migratory birds breeding there. A warming climate can provide benefits to birds by decreasing spring snow cover, but increases in the frequency of summer rainstorms, another product of climate change, may reduce foraging opportunities for insectivorous birds. Cyclic lemming populations in the Arctic also influence bird abundance because Arctic foxes begin consuming bird eggs when lemmings decline. The complex interaction between summer temperature, precipitation, and the lemming cycle hinder our ability to predict how Arctic-breeding birds will respond to climate change. The main objective of this study was to investigate the relationship between annual variation in weather, spring snow cover, lemming abundance and spatiotemporal variation in the abundance of multiple avian guilds in a tundra ecosystem in central Nunavut, Canada: songbirds, shorebirds, gulls, loons, and geese. We spatially stratified our study area based on vegetation productivity, terrain ruggedness, and freshwater abundance, and conducted distance sampling to estimate strata-specific densities of each guild during the summers of 2010–2012. We also monitored temperature, rainfall, spring snow cover, and lemming abundance each year. Spatial variation in bird abundance matched what was expected based on previous ecological knowledge, but weather and lemming abundance also significantly influenced the abundance of some guilds. In particular, songbirds were less abundant during the cool, wet summer with moderate snow cover, and shorebirds and gulls declined with lemming abundance. The abundance of geese did not vary over time, possibly because benefits created by moderate spring snow cover were offset by increased fox predation when lemmings were scarce. Our study provides an example of a simple way to monitor the correlation between weather, spring snow cover, lemming abundance, and spatiotemporal variations in Arctic-breeding birds. PMID:24983471

Influence of spring snowpack melting on thunderstorm activity in the Catalan Pyrenees

NASA Astrophysics Data System (ADS)

Pascual, R.; Callado, A.; Terradelles, E.; Téllez, B.

2009-09-01

Catalan Pyrenees, the eastern half of the Pyrenees range, is a very popular area for tourism, hiking and climbing. This sector of the range is 200 km long and, on average, 80 km wide. Its highest peaks reach 3000 m ASL and there are many summits above 2500 m ASL. Two of the main climatic characteristics of the region are the very frequent summer convective storms and the late autumn, winter and spring snow-cover. Both characteristics have normally been studied from different points of view, and weather forecasts in late spring have not normally considered the plausible relationship between them. The snowpack melting from April to June, especially rapid in May, leads to important changes on the surface energy balance since the evolution from snow-covered ground to bare soil or canopy, significantly alters the surface albedo and the turbulent, latent and sensible, heat fluxes. These modifications have a noticeable influence in developing or inhibiting thermally-induced mesoscale circulations such as upslope winds, valley breezes or plane-mountain breezes, and could condition the triggering of convection, showers and storm activity. In order to gain insight into the relationship between the spring snowpack melting and the location of thunderstorm activity, a comparison between seasonal snow-cover and thunderstorm frequency evolution (using lightning network data) for a period of 5 years has been carried out, showing a progressive transition from a non-convective to a convective precipitation regime in areas where the snowpack has melted recently Furthermore, a meso-beta scale non-hydrostatic numerical weather prediction model at a 2.5-km horizontal resolution is used to study the sensitivity of snowpack extension on the thunderstorms development over the complex orography of the Catalan Pyrenees. A spring case with thunderstorm activity restricted to snow-free areas has been selected and accurately simulated. A number of sensitivity runs with different initial snow fields has been performed, so allowing evaluation of the influence of snow-cover on the triggering of convection.

Early Spring Post-Fire Snow Albedo Dynamics in High Latitude Boreal Forests Using Landsat-8 OLI Data

NASA Technical Reports Server (NTRS)

Wang, Zhuosen; Erb, Angela M.; Schaaf, Crystal B.; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A.; Roman, Miguel O.

2016-01-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (less than 100 m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high-burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500 m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

The influence of weather and lemmings on spatiotemporal variation in the abundance of multiple avian guilds in the arctic.

PubMed

Robinson, Barry G; Franke, Alastair; Derocher, Andrew E

2014-01-01

Climate change is occurring more rapidly in the Arctic than other places in the world, which is likely to alter the distribution and abundance of migratory birds breeding there. A warming climate can provide benefits to birds by decreasing spring snow cover, but increases in the frequency of summer rainstorms, another product of climate change, may reduce foraging opportunities for insectivorous birds. Cyclic lemming populations in the Arctic also influence bird abundance because Arctic foxes begin consuming bird eggs when lemmings decline. The complex interaction between summer temperature, precipitation, and the lemming cycle hinder our ability to predict how Arctic-breeding birds will respond to climate change. The main objective of this study was to investigate the relationship between annual variation in weather, spring snow cover, lemming abundance and spatiotemporal variation in the abundance of multiple avian guilds in a tundra ecosystem in central Nunavut, Canada: songbirds, shorebirds, gulls, loons, and geese. We spatially stratified our study area based on vegetation productivity, terrain ruggedness, and freshwater abundance, and conducted distance sampling to estimate strata-specific densities of each guild during the summers of 2010-2012. We also monitored temperature, rainfall, spring snow cover, and lemming abundance each year. Spatial variation in bird abundance matched what was expected based on previous ecological knowledge, but weather and lemming abundance also significantly influenced the abundance of some guilds. In particular, songbirds were less abundant during the cool, wet summer with moderate snow cover, and shorebirds and gulls declined with lemming abundance. The abundance of geese did not vary over time, possibly because benefits created by moderate spring snow cover were offset by increased fox predation when lemmings were scarce. Our study provides an example of a simple way to monitor the correlation between weather, spring snow cover, lemming abundance, and spatiotemporal variations in Arctic-breeding birds.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data

PubMed Central

Wang, Zhuosen; Erb, Angela M.; Schaaf, Crystal B.; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A.; Román, Miguel O.

2018-01-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems. PMID:29769751

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data.

PubMed

Wang, Zhuosen; Erb, Angela M; Schaaf, Crystal B; Sun, Qingsong; Liu, Yan; Yang, Yun; Shuai, Yanmin; Casey, Kimberly A; Román, Miguel O

2016-11-01

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Seasonal and inter-annual snowmelt patterns in the southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Musselman, K. N.; Molotch, N. P.; Margulis, S. A.

2012-12-01

In the Sierra Nevada, seasonal snow represents a critical component of California's water resource infrastructure in that it affords reliable water during otherwise arid summers. Complex spatial, seasonal and inter-annual snowmelt patterns determine when and where that meltwater is available. Our knowledge of snowmelt dynamics is typically limited to what we can infer from sparse, point-scale snow measurement stations. Limitations such as these motivate the use of numerical snowmelt models. We evaluate the ability of the Alpine3D model system to represent three years of snow dynamics over an 1800 km2 area of Sequoia National Park. The domain spans a 3600 m elevation gradient and ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. The model results were evaluated against data from a multi-scale measurement campaign that included airborne LiDAR, clusters of snow depth sensors, repeated manual snow surveys, and automated SWE stations. Compared to these measurements, Alpine3D consistently performed well in middle elevation conifer forests; compared to LiDAR data, the mean snow depth error in forested regions was < 2%. The model also simulated the snow disappearance date within two days of that measured by regional automated sensors. At upper elevations, however, the model tended to overestimate SWE by 50% to as much as 100% in some areas and the errors were linearly correlated (R2 > 0.80, p<0.01) with the distance of the SWE measurements from the nearest precipitation gauge used to derive the model forcing. The results suggest that Alpine3D is highly accurate during the melt season and that precipitation uncertainty may be a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover was episodic and seasonal mean melt rates computed on days when melt was simulated were generally low (< 3 mm day-1). At upper elevations, melt occurred during less than 65% of the snow-covered period, occurred later in the season and mean melt rates were the highest of the region (> 6 mm day-1). Middle elevations remained continuously snow covered throughout the winter and early spring, were prone to frequent but intermittent melt, and provided the most sustained period of seasonal mean snowmelt (~ 5 mm day-1). The melt dynamics (e.g. timing and melt rate) unique to these middle elevations may be critical to the local forest ecosystem. Furthermore, the three years evaluated in this study indicate a marked sensitivity of this elevation range to seasonal meteorology, suggesting that it could be highly sensitive to future changes in climate.

Past and future changes in climate and hydrological indicators in the US Northeast

USGS Publications Warehouse

Hayhoe, K.; Wake, C.P.; Huntington, T.G.; Luo, L.; Schwartz, M.D.; Sheffield, J.; Wood, E.; Anderson, B.; Bradbury, J.; DeGaetano, A.; Troy, T.J.; Wolfe, D.

2007-01-01

To assess the influence of global climate change at the regional scale, we examine past and future changes in key climate, hydrological, and biophysical indicators across the US Northeast (NE). We first consider the extent to which simulations of twentieth century climate from nine atmosphere-ocean general circulation models (AOGCMs) are able to reproduce observed changes in these indicators. We then evaluate projected future trends in primary climate characteristics and indicators of change, including seasonal temperatures, rainfall and drought, snow cover, soil moisture, streamflow, and changes in biometeorological indicators that depend on threshold or accumulated temperatures such as growing season, frost days, and Spring Indices (SI). Changes in indicators for which temperature-related signals have already been observed (seasonal warming patterns, advances in high-spring streamflow, decreases in snow depth, extended growing seasons, earlier bloom dates) are generally reproduced by past model simulations and are projected to continue in the future. Other indicators for which trends have not yet been observed also show projected future changes consistent with a warmer climate (shrinking snow cover, more frequent droughts, and extended low-flow periods in summer). The magnitude of temperature-driven trends in the future are generally projected to be higher under the Special Report on Emission Scenarios (SRES) mid-high (A2) and higher (A1FI) emissions scenarios than under the lower (B1) scenario. These results provide confidence regarding the direction of many regional climate trends, and highlight the fundamental role of future emissions in determining the potential magnitude of changes we can expect over the coming century. ?? Springer-Verlag 2006.

From drones to ASO: Using 'Structure-From-Motion' photogrammetry to quantify variations in snow depth at multiple scales

NASA Astrophysics Data System (ADS)

Skiles, M.

2017-12-01

The ability to accurately measure and manage the natural snow water reservoir in mountainous regions has its challenges, namely mapping of snowpack depth and snow water equivalent (SWE). Presented here is a scalable method that differentially maps snow depth using Structure from Motion (SfM); a photogrammetric technique that uses 2d images to create a 3D model/Digital Surface Model (DSM). There are challenges with applying SfM to snow, namely, relatively uniform snow brightness can make it difficult to produce quality images needed for processing, and vegetation can limit the ability to `see' through the canopy to map both the ground and snow beneath. New techniques implemented in the method to adapt to these challenges will be demonstrated. Results include a time series at (1) the plot scale, imaged with an unmanned areal vehicle (DJI Phantom 2 adapted with Sony A5100) over the Utah Department of Transportation Atwater Study Plot in Little Cottonwood Canyon, UT, and at (2) the mountain watershed scale, imaged from the RGB camera aboard the Airborne Snow Observatory (ASO), over the headwaters of the Uncompahgre River in the San Juan Mountains, CO. At the plot scale we present comparisons to measured snow depth, and at the watershed scale we present comparisons to the ASO lidar DSM. This method is of interest due to its low cost relative to lidar, making it an accessible tool for snow research and the management of water resources. With advancing unmanned aerial vehicle technology there are implications for scalability to map snow depth, and SWE, across large basins.

Variability in snow-depth time series within the Adige catchment

NASA Astrophysics Data System (ADS)

Marcolini, Giorgia; Bellin, Alberto; Disse, Markus; Gabriele, Chiogna

2017-04-01

Snow cover extension and duration is particularly sensitive to climate change because strongly influenced by changes in temperature and precipitation. It affects the hydrological cycle of Alpine catchments as well as many other aspects of life in mountainous regions, such as ecosystem functioning and economy. Despite its relevance, variability in snow related parameters has not been investigated in the Southern side of the Alps as extensively as in the Northern side of the Alps. In this work, we investigate the temporal variability of mean seasonal snow depth (computed by averaging the daily snow depth in the period 1 November-30 April between two following years) and of snow cover duration (defined, similarly, as the number of days in the period 1 November-30 April with snow depth higher than 30 cm) for the homogeneous stations within the Adige catchment (North-East Italy) by using wavelets transform. We focus our analysis on the period 1980-2010, which with 37 time series is the richest of data and we group the stations in four elevation classes (below 1350 m a.s.l., between 1350 m a.s.l. and 1650 m a.s.l., between 1650 m a.s.l. and 2000 m a.s.l. and above 2000 m a.s.l.). Stations located above and below 1650 m a.s.l. show different behaviors, with the latter showing in the last decades a larger reduction of mean seasonal snow depth and snow cover duration, than the former. We also observe that starting from the late '80s snow cover duration and mean seasonal snow depth display values below the average in the study area, confirming the observations performed in other regions of the Alps. We also find an elevation-dependent correlation between the increase in winter teperature and snow cover extension and duration.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

USGS Publications Warehouse

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

2014-01-01

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

Daily Snow Depth Measurements from 195 Stations in the United States (1997) (NDP-059)

DOE Data Explorer

Easterling, D. R. [NOAA, National Climatic Data Center; Jamason, P. [NOAA, National Climatic Data Center; Bowman, D. P. [NOAA, National Climatic Data Center; Hughes, P. Y. [NOAA, National Climatic Data Center; Mason, E. H. [NOAA, National Climatic Data Center; Allison, L. J. [ORNL, Carbon Dioxide Information Analysis Center (CDIAC)

1997-02-01

This data package provides daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893-1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station's daily data values for a period of one month, including data source, measurement, and quality flags. The snow depth data have undergone extensive manual and automated quality assurance checks by NCDC and the Carbon Dioxide Information Analysis Center (CDIAC). These reviews involved examining the data for completeness, reasonableness, and accuracy, and included comparison of some data records with records in NCDC's Summary of the Day First Order online database. Since the snow depth measurements have been taken at NWS first-order stations that have long periods of record, they should prove useful in monitoring climate change.

Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM

NASA Astrophysics Data System (ADS)

Abe, Manabu; Takata, Kumiko; Kawamiya, Michio; Watanabe, Shingo

2017-09-01

The Earth system model, Model for Interdisciplinary Research on Climate-Earth system model (MIROC-ESM), in which the leaf area index (LAI) is calculated interactively with an ecological land model, simulated future changes in the snow water equivalent under the scenario of global warming. Using MIROC-ESM, the effects of the snow albedo feedback (SAF) in a boreal forest region of northern Eurasia were examined under the possible climate future scenario RCP8.5. The simulated surface air temperature (SAT) in spring greatly increases across Siberia and the boreal forest region, whereas the snow cover decreases remarkably only in western Eurasia. The large increase in SAT across Siberia is attributed to strong SAF, which is caused by both the reduced snow-covered fraction and the reduced surface albedo of the snow-covered portion due to the vegetation masking effect in those grid cells. A comparison of the future changes with and without interactive LAI changes shows that in Siberia, the vegetation masking effect increases the spring SAF by about two or three times and enhances the spring warming by approximately 1.5 times. This implies that increases in vegetation biomass in the future are a potential contributing factor to warming trends and that further research on the vegetation masking effect is needed for reliable future projection.

The Snowtweets Project: communicating snow depth measurements from specialists and non-specialists via mobile communication technologies and social networks

NASA Astrophysics Data System (ADS)

King, J. M.; Cabrera, A. R.; Kelly, R. E.

2009-12-01

With the global decline of in situ snow measurements for hydrometeorological applications, there is an evolving need to find alternative ways to collect localized measurements of snow. The Snowtweets Project is an experiment aimed at providing a way for people interested in making snow measurements to quickly broadcast their measurements to the internet. The goal of the project is to encourage specialists and non-specialists alike to share simple snow depth measurements through widely available social networking sites. We are currently using the rapidly growing microblogging site Twitter (www.twitter.com) as a broadcasting vehicle to collect the snow depth measurements. Using 140 characters or less, users "tweet" their snow depth from their location through the Twitter website. This can be done from a computer or smartphone with internet access or through SMS messaging. The project has developed a Snowtweets web application that interrogates Twitter by parsing the 140 character string to obtain a geographic position and snow depth. GeoRSS and KML feeds are available to visualize the tweets in GoogleEarth or they can be viewed in our own visualiser, Snowbird. The emphasis is on achieving wide coverage to increase the number of microblogs. Furthermore, after some quality control filters, the project is able to combine the broadcast snow depths with traditional and objective satellite remote sensing-based observations or hydrologic model estimates. Our site, snowcore.uwaterloo.ca, was launched in July 2009 and is ready for the 2009-2010 northern hemisphere winter. We invite comments from experienced community participation projects to help improve our product.

Soil Biogeochemistry in a Changing Climate: Effect of Snow Removal

NASA Astrophysics Data System (ADS)

Patel, K.; Tatariw, C.; Fernandez, I. J.; Macrae, J. D.; Ohno, T.

2016-12-01

Winter snowpack plays an important role in ecosystem functioning, thermally insulating the subnivean soil from freezing temperatures. Wintertime microbial mineralization of organic material results in accumulation of nutrients under the snowpack, which are available post-melt for plant root uptake. The northeastern United States has experienced declining snow accumulation, and climate models project this trend will continue in the future. Intermittent and reduced snow cover increases soil freezing and frost damage, which can have implications on spring nutrient availability and forest productivity. We conducted a 2-year snow removal experiment in the Dwight B. DeMeritt Forest at the University of Maine to study subnivean winter processes, and to examine the effect of a decreased snowpack on soil winter and spring biogeochemistry. Surface organic soils were collected during winter and spring of 2015 and 2016, years with sharply contrasting snow accumulation, to track temporal changes in nutrient dynamics as the system evolved from under the snowpack. Laboratory extractions and incubations were performed to quantify the inorganic available nitrogen, dissolved organic carbon (DOC), and potential net N-mineralization (PNNM) in field moist soils. Snow removal resulted in decreased winter soil temperatures (2-8°C colder than the reference plots). There was an increased incidence of rain-on-soil events in the winter, forming concrete frost. Freeze-thaw cycles in the treatment plots resulted in higher NH4-N and DOC concentrations, but lower PNNM, compared to the reference plots. Treatment effects on DOC and NH4-N concentrations were not seen in the spring, although the effects on PNNM persisted. Our findings demonstrated that freeze-thaw cycles play an important role in the timing and magnitude of soil nutrient availability during the vernal transition. Understanding these processes becomes increasingly important when defining forest ecosystem response to a changing climate.

Time lapse photography as an approach to understanding glide avalanche activity

USGS Publications Warehouse

Hendrikx, Jordy; Peitzsch, Erich H.; Fagre, Daniel B.

2012-01-01

Avalanches resulting from glide cracks are notoriously difficult to forecast, but are a recurring problem for numerous avalanche forecasting programs. In some cases glide cracks are observed to open and then melt away in situ. In other cases, they open and then fail catastrophically as large, full-depth avalanches. Our understanding and management of these phenomena are currently limited. It is thought that an increase in the rate of snow gliding occurs prior to full-depth avalanche activity so frequent observation of glide crack movement can provide an index of instability. During spring 2011 in Glacier National Park, Montana, USA, we began an approach to track glide crack avalanche activity using a time-lapse camera focused on a southwest facing glide crack. This crack melted in-situ without failing as a glide avalanche, while other nearby glide cracks on north through southeast aspects failed. In spring 2012, a camera was aimed at a large and productive glide crack adjacent to the Going to the Sun Road. We captured three unique glide events in the field of view. Unfortunately, all of them either failed very quickly, or during periods of obscured view, so measurements of glide rate could not be obtained. However, we compared the hourly meteorological variables during the period of glide activity to the same variables prior to glide activity. The variables air temperature, relative humidity, air pressure, incoming and reflected long wave radiation, SWE, total precipitation, and snow depth were found to be statistically different for our cases examined. We propose that these are some of the potential precursors for glide avalanche activity, but do urge caution in their use, due to the simple approach and small data set size. It is hoped that by introducing a workable method to easily record glide crack movement, combined with ongoing analysis of the associated meteorological data, we will improve our understanding of when, or if, glide avalanche activity will ensue.

Effects of fire disturbance on soil respiration in the non-growing season in a Larix gmelinii forest in the Daxing'an Mountains, China.

PubMed

Hu, Tongxin; Sun, Long; Hu, Haiqing; Guo, Futao

2017-01-01

In boreal forests, fire is an important part of the ecosystem that greatly influences soil respiration, which in turn affects the carbon balance. Wildfire can have a significant effect on soil respiration and it depends on the fire severity and environmental factors (soil temperature and snow water equivalent) after fire disturbance. In this study, we quantified post-fire soil respiration during the non-growing season (from November to April) in a Larix gmelinii forest in Daxing'an Mountains of China. Soil respiration was measured in the snow-covered and snow-free conditions with varying degrees of natural burn severity forests. We found that soil respiration decreases as burn severity increases. The estimated annual C efflux also decreased with increased burn severity. Soil respiration during the non-growing season approximately accounted for 4%-5% of the annual C efflux in all site types. Soil temperature (at 5 cm depth) was the predominant determinant of non-growing season soil respiration change in this area. Soil temperature and snow water equivalent could explain 73%-79% of the soil respiration variability in winter snow-covering period (November to March). Mean spring freeze-thaw cycle (FTC) period (April) soil respiration contributed 63% of the non-growing season C efflux. Our finding is key for understanding and predicting the potential change in the response of boreal forest ecosystems to fire disturbance under future climate change.

Effects of fire disturbance on soil respiration in the non-growing season in a Larix gmelinii forest in the Daxing'an Mountains, China

PubMed Central

Hu, Tongxin; Guo, Futao

2017-01-01

In boreal forests, fire is an important part of the ecosystem that greatly influences soil respiration, which in turn affects the carbon balance. Wildfire can have a significant effect on soil respiration and it depends on the fire severity and environmental factors (soil temperature and snow water equivalent) after fire disturbance. In this study, we quantified post-fire soil respiration during the non-growing season (from November to April) in a Larix gmelinii forest in Daxing'an Mountains of China. Soil respiration was measured in the snow-covered and snow-free conditions with varying degrees of natural burn severity forests. We found that soil respiration decreases as burn severity increases. The estimated annual C efflux also decreased with increased burn severity. Soil respiration during the non-growing season approximately accounted for 4%–5% of the annual C efflux in all site types. Soil temperature (at 5 cm depth) was the predominant determinant of non-growing season soil respiration change in this area. Soil temperature and snow water equivalent could explain 73%–79% of the soil respiration variability in winter snow-covering period (November to March). Mean spring freeze–thaw cycle (FTC) period (April) soil respiration contributed 63% of the non-growing season C efflux. Our finding is key for understanding and predicting the potential change in the response of boreal forest ecosystems to fire disturbance under future climate change. PMID:28665958

On the influence of recrystallization on snow fabric and microstructure: study of a snow profile in Central East Antarctica

NASA Astrophysics Data System (ADS)

Calonne, Neige; Schneebeli, Martin; Montagnat, Maurine; Matzl, Margret

2016-04-01

Temperature gradient metamorphism affects the Antarctic snowpack up to 5 meters depth, which lead to a recrystallization of the ice grains by sublimation of ice and deposition of water vapor. By this way, it is well known that the snow microstructure evolves (geometrical changes). Also, a recent study shows an evolution of the snow fabric, based on a cold laboratory experiment. Both fabric and microstructure are required to better understand mechanical behavior and densification of snow, firn and ice, given polar climatology. The fabric of firn and ice has been extensively investigated, but the publications by Stephenson (1967, 1968) are to our knowledge the only ones describing the snow fabric in Antarctica. In this context, our work focuses on snow microstructure and fabric in the first meters depth of the Antarctic ice sheet, where temperature gradients driven recrystallization occurs. Accurate details of the snow microstructure are observed using micro-computed tomography. Snow fabrics were measured at various depths from thin sections of impregnated snow with an Automatic Ice Texture Analyzer (AITA). A definite relationship between microstructure and fabric is found and highlights the influence of metamorphism on both properties. Our results also show that the metamorphism enhances the differences between the snow layers properties. Our work stresses the significant and complex evolution of snow properties in the upper meters of the ice sheet and opens the question of how these layer properties will evolve at depth and may influence the densification.

Climate Effects and Efficacy of Dust and Soot in Snow

NASA Astrophysics Data System (ADS)

Zender, C. S.; Flanner, M. G.; Randerson, J. T.; Mahowald, N. M.; Rasch, P. J.; Yoshioka, M.; Painter, T.

2006-12-01

Dust and industrial and biomass burning emissions from low and mid-latitudes dominate the absorbing impurities trapped in snow at mid- and high-latitudes. We study the effects of dust and smoke on global and regional climate using a general circulation model driven by observed and predicted aerosol emissions determined from satellite and in situ observations. The model has sophisticated treatments of aerosol and snowpack radiative and thermodynamic processes that compare well with observations of snow albedo evolution and impurity concentration. This presentation focuses on the individual and combined contributions of present day dust and soot to snow-albedo forcing and on the global temperature and snowpack responses. Results are emphasized near India and East Asia, where the anthropogenic aerosol forcing of surface albedo and hydrology is greatest. We find that dust and black carbon (BC) aerosols have climate change efficacies (surface temperature change per unit forcing) about 3--4 times greater than CO2, making them the most efficacious forcing agents known. We estimate present day dust and soot snowpack-forcing of ~ 0.050 W m-2 warms global climate by ~ 0.16 °K. Anthropogenic soot from fossil fuel sources causes more than 50% of this warming, and biomass burning can account for up to 30% in strong tropical or boreal burn years. The greatest forcings occur in the Tarim/Mongol region (due to dust), northeastern China (due to soot), and the Tibetan Plateau (both). Dirty springtime snow in these regions can darken albedo by more than 0.1 and increase surface absorption by more than 20 W m-2. These results have implications for the strength of the Asian Monsoon, which is negatively correlated with antecedent snow cover in non-ENSO years. Dust and soot have such strong efficacies because they increase spring melt rates thus reduce summer snow cover. In some regions and seasons, dirty snow reduces snowpack depth and cover by 50%, triggering strong snow and sea-ice albedo feedbacks.

Arctic tundra shrub invasion and soot deposition: Consequences for spring snowmelt and near-surface air temperatures

NASA Astrophysics Data System (ADS)

Strack, John E.

Invasive shrubs and soot pollution both have the potential to alter the surface energy balance and timing of snow melt in the Arctic. Shrubs reduce the amount of snow lost to sublimation on the tundra during the winter leading to a deeper end-of-winter snowpack. The shrubs also enhance the absorption of energy by the snowpack during the melt season, by converting incoming solar radiation to longwave radiation and sensible heat. This results in a faster rate of snow melt, warmer near-surface air temperatures, and a deeper boundary layer. Soot deposition lowers the albedo of the snow allowing it to more effectively absorb incoming solar radiation and thus melt faster. This study uses the Colorado State University Regional Atmospheric Modeling System version 4.4 (CSU-RAMS 4.4), equipped with an enhanced snow model, to investigate the effects of shrub encroachment and soot deposition on the atmosphere and snowpack in the Kuparuk Basin of Alaska during the May-June melt period. The results of the simulations suggest that a complete invasion of the tundra by shrubs leads to a 1.5 degree C warming of 2-m air temperatures, 17 watts per meter square increase in surface sensible heat flux, and a 108 m increase in boundary layer depth during the melt period. The snow free-date also occurred 11 days earlier despite having a larger initial snowpack. The results also show that a decrease in the snow albedo of 0.1, due to soot pollution, caused the snow-free date to occur five days earlier. The soot pollution caused a 0.5 degree C warming of 2-m air temperatures and a 2 watts per meter square increase in surface sensible heat flux. In addition, the boundary layer averaged 25 m deeper in the polluted snow simulation.

COSMO-SkyMed Image Investigation of Snow Features in Alpine Environment

PubMed Central

Paloscia, Simonetta; Pettinato, Simone; Santi, Emanuele; Valt, Mauro

2017-01-01

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. PMID:28054962

Outreach program by measurements of frost depth in Japan

NASA Astrophysics Data System (ADS)

Harada, K.; Yoshikawa, K.; Iwahana, G.; Stanilovskaya, J. V.; Sawada, Y.

2015-12-01

In order to emphasis their interest for earth sciences, an outreach program through measurements of frost depth is conducting in Japan since 2011. This program is made at elementary, junior high and high schools in Hokkaido, northern part of Japan where seasonal ground freezing occurs in winter. At schools, a lecture was made and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. Frost depth was measured directly once a week at each school by students during ground freezing under no snow-removal condition. In 2011 season, we started this program at three schools, and the number of participated school is extended to 29 schools in 2014 winter season, 23 elementary schools, 5 junior high schools and one high school. We visited schools summer time and just before frost season to talk about the method of measurement. After the end of measured period, we also visited schools to explain measured results by each school and the other schools in Japan, Alaska, Canada and Russia. The measured values of frost depth in Hokkaido were ranged between 0cm and more than 50cm. We found that the frost depth depends on air temperature and snow depth. We discussed with student why the frost depth ranged widely and explained the effect of snow by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at one elementary school. At the end of December, depths had no significant difference between these conditions, 11cm and 10cm, and the difference went to 14cm, 27cm and 13cm after one month, with about 30cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth. The network of this program will be expected to expand, finally more than a hundred schools.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

Climate Variations and Alaska Tundra Vegetation Productivity Declines in Spring

NASA Astrophysics Data System (ADS)

Bhatt, U. S.; Walker, D. A.; Bieniek, P.; Raynolds, M. K.; Epstein, H. E.; Comiso, J. C.; Pinzon, J. E.; Tucker, C. J.

2015-12-01

While sea ice has continued to decline, vegetation productivity increases have declined particularly during spring in Alaska as well as many parts of the Arctic tundra. To understand the processes behind these features we investigate spring climate variations that includes temperature, circulation patterns, and snow cover to determine how these may be contributing to spring browning. This study employs remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2014. Maximum NDVI (MaxNDVI, Maximum Normalized Difference Vegetation Index), Time Integrated NDVI (TI-NDVI), Summer Warmth Index (SWI, sum of degree months above freezing during May-August), atmospheric reanalysis data, dynamically downscaled climate data, meteorological station data, and snow water equivalent (GlobSnow, assimilated snow data set). We analyzed the data for the full period (1982-2014) and for two sub-periods (1982-1998 and 1999-2014), which were chosen based on the declining Alaska SWI since 1998. MaxNDVI has increased from 1982-2014 over most of the Arctic but has declined from 1999 to 2014 southwest Alaska. TI-NDVI has trends that are similar to those for MaxNDVI for the full period but display widespread declines over the 1999-2014 period. Therefore, as the MaxNDVI has continued to increase overall for the Arctic, TI-NDVI has been declining since 1999 and these declines are particularly noteworthy during spring in Alaska. Spring declines in Alaska have been linked to increased spring snow cover that can delay greenup (Bieniek et al. 2015) but recent ground observations suggest that after an initial warming and greening, late season freezing temperature are damaging the plants. The late season freezing temperature hypothesis will be explored with meteorological climate/weather data sets for Alaska tundra regions. References P.A. Bieniek, US Bhatt, DA Walker, MK Raynolds, JC Comiso, HE Epstein, JE Pinzon, CJ Tucker, RL Thoman, H Tran, N Mölders, M Steele, J Zhang, and W Ermold, 2015: Climate drivers of changing seasonality of Alaska coastal tundra vegetation productivity, (conditionally accepted) Earth Interactions.

Spring Snow Melt Timing and Changes over Arctic Lands

NASA Technical Reports Server (NTRS)

Foster, J. L.; Robinson, D. A.; Hall, D. K.; Estilow, T. W.

2006-01-01

Spring snow cover over Arctic lands has, on average, melted approximately 4-7 days earlier since the late 1980s compared to the previous 20 years. The earlier disappearance of snow has been identified in non-mountainous regions at the 60 deg and 70 deg N parallels over Eurasia and North America using visible satellite observations of continental snow cover extent (SCE) mapped by the National Oceanic and Atmospheric Administration. The change was greater in the farthest north continental locations. Northern hemisphere SCE declined by almost 10% (May) to 20% (June) between the two intervals. At latitude 70 deg N, eight segments of longitude (each 10 deg in width) show significant (negative) trends. However, only two longitudinal segments at 60 deg N show significant trends, (one positive and one negative). SCE changes coincide with increasing spring warmth and the earlier diminution of sea ice in the last several decades. However, while sea ice has continued to decrease during this recent interval, snowmelt dates in the Arctic changed in a step-like fashion during the mid to late 1980s and have remained much the same since that time.

Do Peatlands Hibernate?

NASA Astrophysics Data System (ADS)

Dorrepaal, E.; Signarbieux, C.; Jassey, V.; Mills, R.; Buttler, A.; Robroek, B.

2014-12-01

Winter seasonality with extensive frost, snow cover and low incoming radiation characterise large areas at mid- and high latitudes, especially in mountain ranges and in the arctic. Given these adverse conditions, it is often assumed that ecosystem processes, such as plant photosynthesis, nutrient uptake and microbial activities, cease, or at best diminish to marginal rates compared to summer. However, snow is a good thermal insulator and a sufficiently thick snow cover might enable temperature-limited processes to continue in winter, especially belowground. Changes in winter precipitation may alter these conditions, yet, relative to the growing season, winter ecosystem processes remain poorly understood. We performed a snow-removal experiment on an ombrotrophic bog in the Swiss Jura mountains (1036 m.a.s.l.) to compare above- and belowground ecosystem processes with and without snow cover during mid- and late-winter (February and April) with the subsequent spring (June) and summer (July). The presence of 1m snow in mid-winter and 0.4m snow in late-winter strongly reduced the photosynthetic capacity (Amax) of Eriophorum vaginatum as well as the total microbial biomass compared to spring and summer values. Amax of Sphagnum magellanicum and uptake of 15N-labelled ammonium-nitrate by vascular plants were, however, almost as high or higher in mid- and late-winter as in summer. Snow removal increased the number of freeze-thaw cycles in mid-winter but also increased the minimum soil temperature in late-winter before ambient snow-melt. This strongly reduced all measured ecosystem processes in mid-winter compared to control and to spring and summer values. Plant 15N-uptake, Amax of Eriophorum and total microbial biomass returned to, or exceeded, control values soon before or after snowmelt. However, Sphagnum Amax and its length growth, as well as the structure of the microbial community showed clear carry-over effects of the reduced winter snow cover into next summer. Altogether, our data indicate that peatlands are active in winter. However, a continuous snow cover is crucial for ecosystem processes both in winter and in the subsequent summer and a reduction of snow thickness or duration due to climate change may impact on peatland ecosystem functioning at various levels.

Monitoring Mountain Meteorology without Much Money (Invited)

NASA Astrophysics Data System (ADS)

Lundquist, J. D.

2009-12-01

Mountains are the water towers of the world, storing winter precipitation in the form of snow until summer, when it can be used for agriculture and cities. However, mountain weather is highly variable, and measurements are sparsely distributed. In order adequately sample snow and climate variables in complex terrain, we need as many measurements as possible. This means that instruments must be inexpensive and relatively simple to deploy. Here, we demonstrate how dime-sized temperature sensors developed for the refrigeration industry can be used to monitor air temperature (using evergreen trees as radiation shields) and snow cover duration (using the diurnal cycle in near-surface soil temperature). Together, these measurements can be used to recreate accumulated snow water equivalent over the prior year. We also demonstrate how buckets of water may be placed under networked acoustic snow depth sensors to provide an index of daily evaporation rates at SNOTEL stations. (a) Temperature sensor sealed for deployment in the soil. (b) Launching a temperature sensor into a tree. (c) Pulley system to keep sensor above the snow. (a) Photo of bucket underneath acoustic snow depth sensor. (b) Water depth in the bucket as calculated by the snow depth sensor and by a pressure sensor inside the bucket.

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

USGS Publications Warehouse

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.

Snowmelt in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhang, F.

2016-12-01

Snow accumulation and melting are important hydrological processes in the Tibetan Plateau (TP). Qualification of snow dynamics is helpful for water resources management. In this study, a case study of snow and runoff modeling in a glaciated catchment in south Tibet was firstly conducted and showed that MODIS snow cover data can be successfully used for snow model calibration. Following the method, snow accumulation and melting in the TP was simulated using a distributed degree-day model through zonal calibration. The simulation results showed that the spatial pattern of snowmelt is basically in accordance with that of precipitation with discrepancy mainly introduced by elevation and temperature lapse. During 1979-2010, average annual precipitation and snowmelt in the TP was 394 and 80 mm/yr, respectively, indicating that about 1/5 of the precipitation in the TP supplied the rivers, lakes, and groundwater etc in the form of snowmelt. Seasonal snowmelt accounted for 35%, 37%, 26%, and 2% of the annual gross in spring, summer, fall, and winter, respectively, with net accumulation of snow in fall and winter added to the snowmelt in the following spring and summer. The overall changing trends of annual precipitation and snowmelt in the TP were 4.1 and 0.4 mm/yr, respectively, with the most intensive snowmelt increase of about 3.0 mm/yr in the upstream of Tarim river basin (UTA) but decrease of about -1.4 mm/yr in the upstream of Mekong river basin (UME) due to the interacting impacts of temperature and precipitation. Significant increasing trend of snowmelt in spring shown in the UTA may benefit the local water use for irrigation etc.

Monitoring global snow cover

NASA Technical Reports Server (NTRS)

Armstrong, Richard; Hardman, Molly

1991-01-01

A snow model that supports the daily, operational analysis of global snow depth and age has been developed. It provides improved spatial interpolation of surface reports by incorporating digital elevation data, and by the application of regionalized variables (kriging) through the use of a global snow depth climatology. Where surface observations are inadequate, the model applies satellite remote sensing. Techniques for extrapolation into data-void mountain areas and a procedure to compute snow melt are also contained in the model.

Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields

Treesearch

Ernesto Trujillo; Jorge A. Ramirez; Kelly J. Elder

2007-01-01

In this study, LIDAR snow depths, bare ground elevations (topography), and elevations filtered to the top of vegetation (topography + vegetation) in five 1-km2 areas are used to determine whether the spatial distribution of snow depth exhibits scale invariance, and the control that vegetation, topography, and winds exert on such behavior. The one-dimensional and mean...

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

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Freeboard, Snow Depth and Sea-Ice Roughness in East Antarctica from In Situ and Multiple Satellite Data

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Masson, Robert; Worby, Anthony; Lytle, Victoria; Kurtz, Nathan; Maksym, Ted

2011-01-01

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

GPS interferometric reflectometry for ground-based remote sensing of snow depth and density

NASA Astrophysics Data System (ADS)

Nievinski, F. G.; Larson, K. M.; Gutmann, E. D.; Zavorotny, V.; Williams, M. W.

2011-12-01

GPS interferometric reflectometry (GPS-IR) is a method that exploits multipath for ground-based remote sensing in the surroundings of a GPS antenna. It operates on L-band, leveraging hundreds of conventional GPS sites existing in the U.S., with a typical footprint of 30-meter radius. Multipath is the coherent interference of line-of-sight and reflected signals; as the two go in and out of phase, the power recorded by a GPS interferometer goes through peaks and troughs that can be related to land surface characteristics, such as soil moisture and snow depth. GPS-IR has been demonstrated to be capable of retrieving snow depth during extended periods at various locations, as validated by comparisons with a continuously-operating terrestrial scanning laser, an airborne LIDAR campaign, manual stake surveys, and ultrasonic depth sensors. Here we explore the possibility of retrieving snow density, too. This will determine the feasibility and limitations of GPS-IR for monitoring of snow water equivalent (SWE). Data were collected at Niwot Ridge LTER in Colorado, at a 3,500-m altitude alpine tundra site. Niwot receives around 1,000 mm of precipitation per year and has a mean annual air temperature of -3.8°C. Snow density and temperature is measured in 10-cm vertical increments at snowpits dug approximately every week. A continuously-operating GPS system established in 2009 allows for measurement of the snowpack several times a day at multiple azimuths as satellites rise and set. The typical peak snow depth at the GPS site is 1.5 m, with a peak depth during the study period of 1.7 m in 2009/2010 and 2.5 m in 2010/2011; density ranged 200-600 kg/m3. We employ a forward/inverse model originally developed for snow depth and recently extended to account for layering to study both synthetic and real observations. We present comparisons of density estimates obtained using GPS-IR observations to snowpit field data, focusing initially on dry snow. In addition, we explore the sensitivity of the model to roughness, density, snow depth, and random noise. Synthetic observations derived from the forward model based on realistic snow profiles are utilized in the inverse model to quantify both the formal uncertainty and the expected error in parameter retrievals.

Routine Mapping of the Snow Depth Distribution on Sea Ice

NASA Astrophysics Data System (ADS)

Farrell, S. L.; Newman, T.; Richter-Menge, J.; Dattler, M.; Paden, J. D.; Yan, S.; Li, J.; Leuschen, C.

2016-12-01

The annual growth and retreat of the polar sea ice cover is influenced by the seasonal accumulation, redistribution and melt of snow on sea ice. Due to its high albedo and low thermal conductivity, snow is also a controlling parameter in the mass and energy budgets of the polar climate system. Under a changing climate scenario it is critical to obtain reliable and routine measurements of snow depth, across basin scales, and long time periods, so as to understand regional, seasonal and inter-annual variability, and the subsequent impacts on the sea ice cover itself. Moreover the snow depth distribution remains a significant source of uncertainty in the derivation of sea ice thickness from remote sensing measurements, as well as in numerical model predictions of future climate state. Radar altimeter systems flown onboard NASA's Operation IceBridge (OIB) mission now provide annual measurements of snow across both the Arctic and Southern Ocean ice packs. We describe recent advances in the processing techniques used to interpret airborne radar waveforms and produce accurate and robust snow depth results. As a consequence of instrument effects and data quality issues associated with the initial release of the OIB airborne radar data, the entire data set was reprocessed to remove coherent noise and sidelobes in the radar echograms. These reprocessed data were released to the community in early 2016, and are available for improved derivation of snow depth. Here, using the reprocessed data, we present the results of seven years of radar measurements collected over Arctic sea ice at the end of winter, just prior to melt. Our analysis provides the snow depth distribution on both seasonal and multi-year sea ice. We present the inter-annual variability in snow depth for both the Central Arctic and the Beaufort/Chukchi Seas. We validate our results via comparison with temporally and spatially coincident in situ measurements gathered during many of the OIB surveys. The results will influence future sensor suite development for sea ice studies, and they provide a new metric for comparison with other sea ice observations. Integrating these novel snow depth observations with modeling studies will help inform model development, and advance our predictive capabilities to help better understand how sea ice is responding to a changing climate.

Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qian, Yun; Flanner, M G; Leung, Lai-Yung R

2011-03-02

The Tibetan Plateau (TP), the highest and largest plateau in the world, has long been identified to be critical in regulating the Asian monsoon climate and hydrological cycle. The snowpack and glaciers over the TP provide fresh water to billions of people in Asian countries, but the TP glaciers have been retreating extensively at a speed faster than any other part of the world. In this study a series of experiments with a global climate model are designed to simulate black carbon (BC) and dust in snow and their radiative forcing and to assess the relative impacts of anthropogenic COmore » 2 and carbonaceous particles in the atmosphere and snow, respectively, on the snowpack over the TP, as well as their subsequent impacts on the Asian monsoon climate and hydrological cycle. Results show a large BC content in snow over the TP, especially the southern slope, with concentration larger than 100 µk/kg. Because of the high aerosol content in snow and large incident solar radiation in the low latitude and high elevation, the TP exhibits the largest surface radiative forcing induced by aerosols (e.g. BC, Dust) in snow compared to other snow-covered regions in the world. The aerosol-induced snow albedo perturbations generate surface radiative forcing of 5-25 W m -2 during spring, with a maximum in April or May. BC-in-snow increases the surface air temperature by around 1.0°C averaged over the TP and reduces snowpack over the TP more than that induced by pre-industrial to present CO 2 increase and carbonaceous particles in the atmosphere during spring. As a result, runoff increases during late winter and early spring but decreases during late spring and early summer (i.e. a trend toward earlier melt dates). The snowmelt efficacy, defined as the snowpack reduction per unit degree of warming induced by the forcing agent, is 1-4 times larger for BC-in-snow than CO 2 increase during April-July, indicating that BC-in-snow more efficiently accelerates snowmelt because the increased net solar radiation induced by reduced albedo melts the snow more efficiently than snow melt due to warming in the air. The TP also influences the South (SAM) and East (EAM) Asian monsoon through its dynamical and thermal forcing. During boreal spring, aerosols are transported by the southwesterly and reach the higher altitude and/or deposited in the snowpack over the TP. While BC and OM in the atmosphere directly absorb sunlight and warm the air, the darkened snow surface polluted by BC absorbs more solar radiation and increases the skin temperature, which warms the air above by the increased sensible heat flux over the TP. Both effects enhance the upward motion of air and spur deep convection along the TP during pre-monsoon season, resulting in earlier onset of the SAM and increase of moisture, cloudiness and convective precipitation over northern India. BC-in-snow has a more significant impact on the EAM in July than CO 2 increase and carbonaceous particles in the atmosphere. Contributed by the significant increase of both sensible heat flux associated with the warm skin temperature and latent heat flux associated with increased soil moisture with long memory, the role of the TP as a heat pump is elevated from spring through summer as the land-sea thermal contrast increases to strengthen the EAM. As a result, both southern China and northern China become wetter, but central China (i.e. Yangtze River Basin) becomes drier - a near zonal anomaly pattern that is consistent with the dominant mode of precipitation variability in East Asia.« less

Changes in snow cover over Northern Eurasia in the last few decades

NASA Astrophysics Data System (ADS)

Bulygina, O. N.; Razuvaev, V. N.; Korshunova, N. N.

2009-10-01

Daily snow depth (SD) and snow cover extent around 820 stations are used to analyse variations in snow cover characteristics in Northern Eurasia, a region that encompasses the Russian Federation. These analyses employ nearly five times more stations than in the previous studies and temporally span forty years. A representative judgement on the changes of snow depth over most of Russia is presented here for the first time. The number of days with greater than 50% of the near-station territory covered with snow, and the number of days with the snow depth greater than 1.0 cm, are used to characterize the duration of snow cover (SCD) season. Linear trends of the number of days and snow depth are calculated for each station from 1966 to 2007. This investigation reveals regional features in the change of snow cover characteristics. A decrease in the duration of snow cover is demonstrated in the northern regions of European Russia and in the mountainous regions of southern Siberia. An increase in SCD is found in Yakutia and in the Far East. In the western half of the Russian Federation, the winter-averaged SD is shown to increase, with the maximum trends being observed in Northern West Siberia. In contrast, in the mountainous regions of southern Siberia, the maximum SD decreases as the SCD decreases. While both snow cover characteristics (SCD and SD) play an important role in the hydrological cycle, ecosystems dynamics and societal wellbeing are quite different roles and the differences in their systematic changes (up to differences in the signs of changes) deserve further attention.

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

NASA Astrophysics Data System (ADS)

Roth, Travis R.; Nolin, Anne W.

2017-11-01

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

Understanding Snow Depth Variability with Respect to the Canopy in Multiple Climates Using Airborne LiDAR

NASA Astrophysics Data System (ADS)

Currier, W. R.; Giulia, M.; Pflug, J. M.; Jonas, T.; Jessica, L.

2017-12-01

Snow depth within a typical hydrologic model grid cell (150 m or 1 km) can vary from 0.5 meters to 6 meters, or more. This variability is driven by the meteorological conditions throughout the winter as well as the forest architecture. To better understand this variability, we used airborne LiDAR from Olympic National Park, WA, Yosemite National Park, CA, Jemez Caldera, NM, and Niwot Ridge, CO to determine unique spatial patterns of snow depth in forested regions. Specifically, we compared snow depth distributions along north facing forest edges and south facing forest edges to those in the open or directly under the canopy. When categorizing the north facing and south facing edges based on distance from the canopy, distances relative to tree height, and distances relative to the fraction of the sky that is visible (sky view factor) we found unique snow depth patterns for each of these regions. In all regions besides Olympic National Park, WA, north facing edges contained more snow than open areas, forested areas, or along the south facing edges. These snow distributions were relatively consistent regardless of the metric used to define the forest edge and the size of the domain (150 m through 1 km). The absence of the forest edge effect in Olympic National Park was attributed to the meteorological data and climate conditions, which showed significantly less incoming shortwave radiation and more incoming longwave radiation. Furthermore, this study evaluated the effect that wind speed and direction have on the spatial distribution of snow depth.

Validation of EOS Aqua AMSR Sea Ice Products for East Antarctica

NASA Technical Reports Server (NTRS)

Massom, Rob; Lytle, Vicky; Allison, Ian; Worby, Tony; Markus, Thorsten; Scambos, Ted; Haran, Terry; Enomoto, Hiro; Tateyama, Kazu; Pfaffling, Andi

2004-01-01

This paper presents results from AMSR-E validation activities during a collaborative international cruise onboard the RV Aurora Australis to the East Antarctic sea ice zone (64-65 deg.S, 110-120 deg.E) in the early Austral spring of 2003. The validation strategy entailed an IS-day survey of the statistical characteristics of sea ice and snowcover over a Lagrangian grid 100 x 50 km in size (demarcated by 9 drifting ice beacons) i.e. at a scale representative of Ah4SR pixels. Ice conditions ranged h m consolidated first-year ice to a large polynya offshore from Casey Base. Data sets collected include: snow depth and snow-ice interface temperatures on 24 (?) randomly-selected floes in grid cells within a 10 x 50 km area (using helicopters); detailed snow and ice measurements at 13 dedicated ice stations, one of which lasted for 4 days; time-series measurements of snow temperature and thickness at selected sites; 8 aerial photography and thermal-IR radiometer flights; other satellite products (SAR, AVHRR, MODIS, MISR, ASTER and Envisat MERIS); ice drift data; and ancillary meteorological (ship-based, meteorological buoys, twice-daily radiosondes). These data are applied to a validation of standard AMSR-E ice concentration, snowcover thickness and ice-temperature products. In addition, a validation is carried out of ice-surface skin temperature products h m the NOAA AVHRR and EOS MODIS datasets.

Towards Understanding the Timing and Frequency of Rain-on-Snow (ROS) Events in Alaska

NASA Astrophysics Data System (ADS)

Pan, C.; Kirchner, P. B.; Kimball, J. S.; Kim, Y.; Kamp, U.

2017-12-01

Rain-on-snow (ROS) events affect ecosystem processes at multiple spatial and temporal scales including hydrology, carbon cycling, wildlife movement and human transportation and result in marked changes to snowpack processes including enhanced snow melt, surface albedo and energy balance. Changes in the surface structure of the snowpack are visible through optical remote sensing and changes in the relative content and distribution of water, air and ice in the snowpack are detectable using passive microwave remote sensing. This project aims to develop ROS products to elucidate changes in frequency and distribution in ROS events using satellite data products derived from both optical and passive microwave satellite records. To detect ROS events, we use downscaled brightness temperature measurements derived from vertical and horizontal polarizations at 19 and 37 GHz from the Advanced Microwave Scanning Radiometer (AMSR-E/2) passive microwave satellites. Preliminary results indicate an overall classification accuracy of 77.6% relative to in situ weather observations including surface air temperature, precipitation, and snow depth. ROS events are spatially distributed largely to elevations below 500 m and occur most frequently on northern to western aspects in addition to southeastern. Regional ROS hot spots occur in southwest Alaska characterized by warmer climates and transient snowcover. The seasonal timing of ROS events indicates increasing frequency during the fall and spring months.

Mapping snow depth from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Gascoin, S.; Marti, R.; Berthier, E.; Houet, T.; de Pinel, M.; Laffly, D.

2016-12-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km²) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km²). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available. Based on this method we have initiated a multi-year survey of the peak snow depth in the Bassiès catchment.

Climatic controls on the snowmelt hydrology of the northern Rocky Mountains

USGS Publications Warehouse

Pederson, G.T.; Gray, S.T.; Ault, T.; Marsh, W.; Fagre, D.B.; Bunn, A.G.; Woodhouse, C.A.; Graumlich, L.J.

2011-01-01

The northern Rocky Mountains (NRMs) are a critical headwaters region with the majority of water resources originating from mountain snowpack. Observations showing declines in western U.S. snowpack have implications for water resources and biophysical processes in high-mountain environments. This study investigates oceanic and atmospheric controls underlying changes in timing, variability, and trends documented across the entire hydroclimatic-monitoring system within critical NRM watersheds. Analyses were conducted using records from 25 snow telemetry (SNOTEL) stations, 148 1 April snow course records, stream gauge records from 14 relatively unimpaired rivers, and 37 valley meteorological stations. Over the past four decades, midelevation SNOTEL records show a tendency toward decreased snowpack with peak snow water equivalent (SWE) arriving and melting out earlier. Temperature records show significant seasonal and annual decreases in the number of frost days (days ???0??C) and changes in spring minimum temperatures that correspond with atmospheric circulation changes and surface-albedo feedbacks in March and April. Warmer spring temperatures coupled with increases in mean and variance of spring precipitation correspond strongly to earlier snowmeltout, an increased number of snow-free days, and observed changes in streamflow timing and discharge. The majority of the variability in peak and total annual snowpack and streamflow, however, is explained by season-dependent interannual-to-interdecadal changes in atmospheric circulation associated with Pacific Ocean sea surface temperatures. Over recent decades, increased spring precipitation appears to be buffering NRM total annual streamflow from what would otherwise be greater snow-related declines in hydrologic yield. Results have important implications for ecosystems, water resources, and long-lead-forecasting capabilities. ?? 2011 American Meteorological Society.

Comparative spring-staging ecology of sympatric arctic-nesting geese in south-central Nebraska

USGS Publications Warehouse

Pearse, Aaron T.; Krapu, Gary L.; Cox, Robert R.

2013-01-01

The Rainwater Basin in Nebraska has been a historic staging area for midcontinent greater white-fronted geese (Anser albifrons frontalis) since the 1950s and, in the mid-1990s, millions of midcontinent lesser snow geese (Chen caerulescens caerulescens) expanded their spring migration route to include this region. In response to speculation that snow geese may be in direct competition with white-fronted geese, we compared staging ecology by quantifying diet, habitat use, movement patterns, and time budgets during springs 1998–1999. Collected white-fronted geese (n  =  190) and snow geese (n  =  203) consumed primarily corn (Zea mays; 97–98% aggregate dry mass) while staging in Nebraska; thus, diet overlap was nearly complete. Both species used cornfields most frequently during the morning (54–55%) and wetlands more during the afternoon (51–65%). When found grouped together, snow goose abundance was greater than white-fronted goose abundance by an average of 57 times (se  =  11, n  =  131 groups) in crop fields and 28 times (se  =  9, n  =  84 groups) in wetlands. Snow geese and white-fronted geese flew similar distances between roosting and feeding sites, leaving and returning to wetland roost sties at similar times in mornings and afternoons. Overlap in habitat-specific time budgets was high; resting was the most common behavior on wetlands, and foraging was a common behavior in fields. We observed 111 interspecific agonistic interactions while observing white-fronted and snow geese. White-fronted geese initiated and dominated more interactions with other waterfowl species than did snow geese (32 vs. 14%). Certain aspects of spring-staging niches (i.e., diet, habitat use, movement patterns, and habitat-specific behavior) of white-fronted and snow geese overlapped greatly at this mid-latitude staging site, creating opportunity for potential food- and habitat-based competition between species. Snow geese did not consistently dominate interactions with white-fronted geese; yet large differences in their numbers coupled with high degrees of spatial, temporal, and ecological overlap support potential for exploitative competition during years when waste corn may be in short supply and dry years when few wetlands are available for staging waterfowl.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

Characterization and predictability of basin scale SWE distributions using ASO snow depth and SWE retrievals

NASA Astrophysics Data System (ADS)

Bormann, K.; Hedrick, A. R.; Marks, D. G.; Painter, T. H.

2017-12-01

The spatial and temporal distribution of snow water resources (SWE) in the mountains has been examined extensively through the use of models, in-situ networks and remote sensing techniques. However, until the Airborne Snow Observatory (http://aso.jpl.nasa.gov), our understanding of SWE dynamics has been limited due to a lack of well-constrained spatial distributions of SWE in complex terrain, particularly at high elevations and at regional scales (100km+). ASO produces comprehensive snow depth measurements and well-constrained SWE products providing the opportunity to re-examine our current understanding of SWE distributions with a robust and rich data source. We collected spatially-distributed snow depth and SWE data from over 150 individual ASO acquisitions spanning seven basins in California during the five-year operational period of 2013 - 2017. For each of these acquisitions, we characterized the spatial distribution of snow depth and SWE and examined how these distributions changed with time during snowmelt. We compared these distribution patterns between each of the seven basins and finally, examined the predictability of the SWE distributions using statistical extrapolations through both space and time. We compare and contrast these observationally-based characteristics with those from a physically-based snow model to highlight the strengths and weaknesses of the implementation of our understanding of SWE processes in the model environment. In practice, these results may be used to support or challenge our current understanding of mountain SWE dynamics and provide techniques for enhanced evaluation of high-resolution snow models that go beyond in-situ point comparisons. In application, this work may provide guidance on the potential of ASO to guide backfilling of sparse spaceborne measurements of snow depth and snow water equivalent.

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

NASA Astrophysics Data System (ADS)

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 snow in 2017, the snowpack was also covered with far greater impurities (dust, black carbon) across the snowmelt period than in the previous years, as expressed in the snow albedo and radiative forcing by dust and BC in snow from the ASO imaging spectrometer. In this presentation, we explore the importance of this opportunity for water cycle science and water management.

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

NASA Astrophysics Data System (ADS)

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 equivalent (SWE) using microwave remote sensing and the CREST Snow Depth Regression Tree Model (SDRTM) was developed. Data from AMSR2 onboard the JAXA GCOM-W1 satellite is used to produce daily global snow depth and SWE maps in automated fashion at a 10-km resolution.

Changes in DOM Character and Composition during Spring Snow Melt in the Jemez River Basin Critical Zone Observatory.

NASA Astrophysics Data System (ADS)

Thompson, M.; Olshansky, Y.; Chorover, J.

2017-12-01

Dynamics of dissolved organic matter (DOM) in stream waters are important indicators of internal processes in the critical zone, such as decomposition and mobilization of soil organic matter, hydrologic flow paths, potential for metal mobilization and nutrient redistribution. Previous studies indicate that DOM concentration was highest during peak snow melt in the La Jara catchment located in the Jemez River Basin Critical Zone Observatory (Perdrial et al., 2014). We postulate that the molecular composition and character of DOM changes with the advance of spring snow melt. Water samples were collected from two flumes located at the outlets of the La Jara Creek and from a zero order basin within this catchment through the spring snowmelt from March 1 to May 15 2017. DOM concentration increased with stream discharge. Quantification of molecular changes was conducted using Fourier transform infrared spectroscopy (FTIR), which showed the variation in carboxyl abundance (wavenumbers 1680, 1600 and 1410 cm-1) correlated with dissolved organic carbon concentration, indicating that this component is relatively a constant fraction of the organic carbon exported through the stream during spring snowmelt. In contrast, amide vibrations (3550, 1640 and 670 cm-1) were shown to decrease with the advance of spring snowmelt. This trend further corresponded to a decrease in the ratio of carboxylic acid (above) to aromatic (1622, 1490, 955 cm-1) moieties, suggesting either a flush of compounds accumulated prior to spring snow melt, or increased decomposition of plant derived material in the soil that was then transported to the stream. Aliphatic components (2965, 2925 and 2865 cm-1) decreased from the beginning to the middle of sampling period, then showed an increase toward the end of snowmelt. O-Alkyl peak (1150 and 1073 cm-1) varied without a clear trend during the spring snowmelt. These changes in O-Alkyl and aliphatic compounds may be related to microbial derived compounds and indicate changes in microbial activity during the spring snowmelt. These results will be combined with concentration discharge analysis and data from fluorescence and UV-vis spectroscopy for evaluation and modeling of CZ processes dominated by spring snowmelt.

Seasonal prediction and predictability of Eurasian spring snow water equivalent in NCEP Climate Forecast System version 2 reforecasts

NASA Astrophysics Data System (ADS)

He, Qiong; Zuo, Zhiyan; Zhang, Renhe; Zhang, Ruonan

2018-01-01

The spring snow water equivalent (SWE) over Eurasia plays an important role in East Asian and Indian monsoon rainfall. This study evaluates the seasonal prediction capability of NCEP Climate Forecast System version 2 (CFSv2) retrospective forecasts (1983-2010) for the Eurasian spring SWE. The results demonstrate that CFSv2 is able to represent the climatological distribution of the observed Eurasian spring SWE with a lead time of 1-3 months, with the maximum SWE occurring over western Siberia and Northeastern Europe. For a longer lead time, the SWE is exaggerated in CFSv2 because the start of snow ablation in CFSv2 lags behind that of the observation, and the simulated snowmelt rate is less than that in the observation. Generally, CFSv2 can simulate the interannual variations of the Eurasian spring SWE 1-5 months ahead of time but with an exaggerated magnitude. Additionally, the downtrend in CFSv2 is also overestimated. Because the initial conditions (ICs) are related to the corresponding simulation results significantly, the robust interannual variability and the downtrend in the ICs are most likely the causes for these biases. Moreover, CFSv2 exhibits a high potential predictability for the Eurasian spring SWE, especially the spring SWE over Siberia, with a lead time of 1-5 months. For forecasts with lead times longer than 5 months, the model predictability gradually decreases mainly due to the rapid decrease in the model signal.

Preliminary Evaluation of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Kelly, Richard E. J.; Chien, Janet Y. L.; Montesano, Paul M.

2009-01-01

The Air Force Weather Agency (AFWA) - NASA (ANSA) blended-snow product utilizes EOS standard snow products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE. with SWE values calculated from snow depths reported at approx.1500 National Climatic Data Center (NCDC) coop stations in the Lower Great Lakes basin. Our preliminary results show that conversion of snow depth to SWE is very sensitive to the choice of snow density (we used either 0.2 or 03 as conversion factors). We found overall better agreement between the ANSA-derived SWE and the co-op station data when we use a snow density of 0.3 to convert the snow depths to SWE. In addition, we show that the ANSA underestimates SWE in densely-forested areas, using January and February 2008 ANSA and co-op data. Furthermore, apparent large SWE changes from one day to the next may be caused by thaw-re-freeze events, and do not always represent a real change in SWE. In the near future we will continue the analysis in the 2006-07 and 2007-08 snow seasons.

Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors

Treesearch

Kevin S. McKelvey; Jeffrey P. Copeland; Michael K. Schwartz; Jeremy S. Littell; Keith B. Aubry; John R. Squires; Sean A. Parks; Marketa M. Elsner; Guillaume S. Mauger

2011-01-01

Boreal species sensitive to the timing and duration of snow cover are particularly vulnerable to global climate change. Recent work has shown a link between wolverine (Gulo gulo) habitat and persistent spring snow cover through 15 May, the approximate end of the wolverine's reproductive denning period. We modeled the distribution of snow cover within the Columbia...

Permeability measurements on new and equitemperature snow

Treesearch

R. A. Sommerfeld; J. R. Rocchio

1993-01-01

During the month of February, between 46 and 53% of the land area of the northern hemisphere is snow covered. Continental snowpacks act as chemical reservoirs; pollutants can accumulate in the pack over the entire winter and are released during a relatively short spring melt period. Interactions between the snow and the atmosphere can change the quantities of different...

Aerosol optical properties over the Svalbard region of Arctic: ground-based measurements and satellite remote sensing

NASA Astrophysics Data System (ADS)

Gogoi, Mukunda M.; Babu, S. Suresh

2016-05-01

In view of the increasing anthropogenic presence and influence of aerosols in the northern polar regions, long-term continuous measurements of aerosol optical parameters have been investigated over the Svalbard region of Norwegian Arctic (Ny-Ålesund, 79°N, 12°E, 8 m ASL). This study has shown a consistent enhancement in the aerosol scattering and absorption coefficients during spring. The relative dominance of absorbing aerosols is more near the surface (lower single scattering albedo), compared to that at the higher altitude. This is indicative of the presence of local anthropogenic activities. In addition, long-range transported biomass burning aerosols (inferred from the spectral variation of absorption coefficient) also contribute significantly to the higher aerosol absorption in the Arctic spring. Aerosol optical depth (AOD) estimates from ground based Microtop sun-photometer measurements reveals that the columnar abundance of aerosols reaches the peak during spring season. Comparison of AODs between ground based and satellite remote sensing indicates that deep blue algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over Arctic snow surfaces overestimate the columnar AOD.

Towards Snowpack Characterization using C-band Synthetic Aperture Radar (SAR)

NASA Astrophysics Data System (ADS)

Park, J.; Forman, B. A.

2017-12-01

Sentinel 1A and 1B, operated by the European Space Agency (ESA), carries a C-band synthetic aperture radar (SAR) sensor that can be used to monitor terrestrial snow properties. This study explores the relationship between terrestrial snow-covered area, snow depth, and snow water equivalent with Sentinel 1 backscatter observations in order to better characterize snow mass. Ground-based observations collected by the National Oceanic and Atmospheric Administration - Cooperative Remote Sensing Science and Technology Center (NOAA-CREST) in Caribou, Maine in the United States are also used in the comparative analysis. Sentinel 1 Ground Range Detected (GRD) imagery with Interferometric Wide swath (IW) were preprocessed through a series of steps accounting for thermal noise, sensor orbit, radiometric calibration, speckle filtering, and terrain correction using ESA's Sentinel Application Platform (SNAP) software package, which is an open-source module written in Python. Comparisons of dual-polarized backscatter coefficients (i.e., σVV and σVH) with in-situ measurements of snow depth and SWE suggest that cross-polarized backscatter observations exhibit a modest correlation between both snow depth and SWE. In the case of the snow-covered area, a multi-temporal change detection method was used. Results using Sentinel 1 yield similar spatial patterns as when using hyperspectral observations collected by the MODerate Resolution Imaging Spectroradiometer (MODIS). These preliminary results suggest the potential application of Sentinel 1A/1B backscatter coefficients towards improved discrimination of snow cover, snow depth, and SWE. One goal of this research is to eventually merge C-band SAR backscatter observations with other snow information (e.g., passive microwave brightness temperatures) as part of a multi-sensor snow assimilation framework.

A distributed snow-evolution modeling system (SnowModel)

Treesearch

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...

Snow depth manipulation experiments in a dry and a moist tundra

NASA Astrophysics Data System (ADS)

Kwon, M. J.; Czimczik, C. I.; Jung, J. Y.; Kim, M.; Lee, Y. K.; Nam, S.; Wagner, I.

2017-12-01

As a result of global warming, precipitation in the Arctic is expected to increase by 25-50% by the end of this century, mostly in the form of snow. However, precipitation patterns vary considerable in space and time, and future precipitation patterns are highly uncertain at local and regional scales. The amount of snowfall (or snow depth) influences a number of ecosystem properties in Arctic ecosystems, such as soil temperature over winter and soil moisture in the following growing season. These modifications then affect rates of carbon-related soil processes and photosynthesis, thus CO2 exchange rates between terrestrial ecosystems and the atmosphere. In this study, we investigate the effects of snow depth on the magnitude, sources and temporal dynamics of CO2 fluxes. We installed snow fences in a dry dwarf-shrub (Cambridge Bay, Canada; 69° N, 105° W) and a moist low-shrub (Council, Alaska, USA; 64° N, 165° W) tundra in summer 2017, and established control, and increased and reduced snow depth plots at each snow fence. Summertime CO2 flux rates (net ecosystem exchange, ecosystem respiration, gross primary production) and the fractions of autotrophic and heterotrophic respiration to ecosystem respiration were measured using manual chambers and radiocarbon signatures. Wintertime CO2 flux rates will be measured using soda lime adsorption technique and forced diffusion chambers. Soil temperature and moisture at multiple depths, as well as changes in soil properties and microbial communities will be also observed, to research whether these changes affect CO2 flux rates or patterns. Our study will elucidate how future snow depth and its impact on soil physical and biogeochemical properties influence the magnitude and sources of tundra-atmosphere CO2 exchange in the rapidly warming Arctic.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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 interaction with the snowpack being complex, the methodology for using InSAR to estimate SWE shows great promise when considering NASA's proposed L-Band, weekly repeat time interval, interferometric DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) mission.

The Costs of Climate Change: Impact of Future Snow Cover Projections on Valuation of Albedo in Forest Management

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Lutz, D. A.

2014-12-01

Surface albedo provides an important climate regulating ecosystem service, particularly in the mid-latitudes where seasonal snow cover influences surface radiation budgets. In the case of substantial seasonal snow cover, the influence of albedo can equal or surpass the climatic benefits of carbon sequestration from forest growth. Climate mitigation platforms should therefore consider albedo in their framework in order to integrate these two climatic services in an economic context for the effective design and implementation of forest management projects. Over the next century, the influence of surface albedo is projected to diminish under higher emissions scenarios due to an overall decrease in snow depth and duration of snow cover in the mid-latitudes. In this study, we focus on the change in economic value of winter albedo in the northeastern United States projected through 2100 using the Special Report on Emissions Scenarios (SRES) a1 and b1 scenarios. Statistically downscaled temperature and precipitation are used as input to the Variable Infiltration Capacity (VIC) model to provide future daily snow depth fields through 2100. Using VIC projections of future snow depth, projected winter albedo fields over deforested lands were generated using an empirical logarithmic relationship between snow depth and albedo derived from a volunteer network of snow observers in New Hampshire over the period Nov 2011 through 2014. Our results show that greater reductions in snow depth and the number of winter days with snow cover in the a1 compared to the b1 scenario reduce wintertime albedo when forested lands are harvested. This result has implications on future trade-offs among albedo, carbon storage, and timber value that should be investigated in greater detail. The impacts of forest harvest on radiative forcing associated with energy redistribution (e.g., latent heat and surface roughness length) should also be considered in future work.

Mapping snow depth in open alpine terrain from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

2016-07-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

The Development and Validation of a New Land Surface Model for Regional and Global Climate Modeling

NASA Astrophysics Data System (ADS)

Lynch-Stieglitz, Marc

1995-11-01

A new land-surface scheme intended for use in mesoscale and global climate models has been developed and validated. The ground scheme consists of 6 soil layers. Diffusion and a modified tipping bucket model govern heat and water flow respectively. A 3 layer snow model has been incorporated into a modified BEST vegetation scheme. TOPMODEL equations and Digital Elevation Model data are used to generate baseflow which supports lowland saturated zones. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts watershed evapotranspiration, the partitioning of surface fluxes, and the development of the storm hydrograph. Five years of meteorological and hydrological data from the Sleepers river watershed located in the eastern highlands of Vermont where winter snow cover is significant were then used to drive and validate the new scheme. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and the seasonal development of watershed evapotranspiration and soil moisture. By including topographic effects, not only are the main spring hydrographs and individual storm hydrographs adequately resolved, but the mechanisms generating runoff are consistent with current views of hydrologic processes. The seasonal movement of the mean water table depth and the saturated area of the watershed are consistent with site data and the overall model hydroclimatology, including the surface fluxes, seems reasonable.

Quantifying the potential for low-level transport of black carbon emissions from cropland burning in Russia to the snow-covered Arctic.

NASA Astrophysics Data System (ADS)

Hall, Joanne V.; Loboda, Tatiana V.

2017-12-01

Short-lived aerosols and pollutants transported from northern mid-latitudes have amplified the short term warming in the Arctic region. Among those black carbon is recognized as the second most important human emission in regards to climate forcing, behind carbon dioxide, with a total climate forcing of +1.1Wm-2. Studies have suggested that cropland burning may be a large contributor to the black carbon emissions which are directly deposited on the snow in the Arctic. However, commonly applied atmospheric transport models rely on estimates of black carbon emissions from cropland burning which are known to be highly inaccurate in both the amount and the timing of release. Instead, this study quantifies the potential for the deposition of hypothetical black carbon emissions from known cropland burning in Russia, identified by the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detections, through low-level transport to the snow in the Arctic using wind vectors from the European Centre for Medium-Range Weather Forecasts’ ERA-Interim Reanalysis product. Our results confirm that Russian cropland burning is a potentially significant source of black carbon deposition on the Arctic snow in the spring despite the low injection heights associated with cropland burning. Approximately 10% of the observed spring (March - May) cropland active fires (7% annual) likely contribute to black carbon deposition on the Arctic snow from as far south as at least 40°N. Furthermore, our results show that potential spring black carbon emissions from cropland burning in Russia can be deposited beyond 80°N, however, the majority ( 90% - depending on injection height) of all potential spring deposition occurs below 75°N.

An Evaluation of Arctic Ocean Precipitation from Reanalyses for use in Snow Accumulation and Melt Models over Sea Ice

NASA Astrophysics Data System (ADS)

Barrett, A. P.; Stroeve, J.; Liston, G. E.; Tschudi, M. A.; Stewart, S.

2017-12-01

Retrievals of sea ice thickness from satellite- and air-borne sensors require knowledge of snow depth and density. Early retrievals used climatologies of snow depth and density - "The Warren Climatology" - based on observations from 31 Soviet drifting stations between 1957 and 1991. This climatology was the best available Arctic-wide data set at the time. However, it does not account for year-to-year variations in spatial and temporal patterns of snow depth, nor does it account for changes in snow depth over longer time periods. Recent efforts to retrieve ice thickness have used output from global and regional atmospheric reanalyses directly or as input to snow accumulation, density evolution, and melt models to estimate snow depth. While such efforts represent the state-of-the-art in terms of Arctic-wide snow depth fields, there can be large differences between precipitation (and other variables) from reanalyses. Knowledge about these differences and about biases in precipitation magnitude are important for getting the best-possible retrievals of ice thickness. Here, we evaluate fields of total precipitation and snow fall from the NASA MERRA and MERRA2, NOAA CFSR and CFSR version 2, ECMWF ERA-Interim, and Arctic System (ASR) reanalyses with a view to understanding differences in the magnitude, and temporal and spatial patterns of precipitation. Where possible we use observations to understand biases in the reanalysis output. Time series of annual total precipitation for the central Arctic correlate well with all reanalyses showing similar year-to-year variability. Time series for MERRA, MERRA2 and CFSR show no evidence of long-term trends. By contrast ERA-Interim appears to be wetter in the most recent decade. The ASR records only spans 2000 to 2012 but is similar to ERA-Interim. CFSR and MERRA2 are wetter than the other five reanalyses, especially over the eastern Arctic and North Atlantic.

Advances in Airborne Altimetric Techniques for the Measurement of Snow on Arctic Sea Ice

NASA Astrophysics Data System (ADS)

Newman, T.; Farrell, S. L.; Richter-Menge, J.; Elder, B. C.; Ruth, J.; Connor, L. N.

2014-12-01

Current sea ice observations and models indicate a transition towards a more seasonal Arctic ice pack with a smaller, and geographically more variable, multiyear ice component. To gain a comprehensive understanding of the processes governing this transition it is important to include the impact of the snow cover, determining the mechanisms by which snow is both responding to and forcing changes to the sea ice pack. Data from NASA's Operation IceBridge (OIB) snow radar system, which has been making yearly surveys of the western Arctic since 2009, offers a key resource for investigating the snow cover. In this work, we characterize the OIB snow radar instrument response to ascertain the location of 'side-lobes', aiding the interpretation of snow radar data. We apply novel wavelet-based techniques to identify the primary reflecting interfaces within the snow pack from which snow depth estimates are derived. We apply these techniques to the range of available snow radar data collected over the last 6 years during the NASA OIB mission. Our results are validated through comparison with a range of in-situ data. We discuss the impact of sea ice surface morphology on snow radar returns (with respect to ice type) and the topographic conditions over which accurate snow-radar-derived snow depths may be obtained. Finally we present improvements to in situ survey design that will allow for both an improved sampling of the snow radar footprint and more accurate assessment of the uncertainties in radar-derived snow depths in the future.

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.; Chang, A. T. C.; Allison, L. J.; Diesen, B. C., III

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia have been studied in an effort to determine the utility of spaceborne microwave radiometers for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In each of the study areas investigated in this paper, Nimbus-6 (0.81 cm) ESMR data produced higher correlations than Nimbus-5 (1.55 cm) ESMR data in relating microwave brightness temperature to snow depth. It is difficult to extrapolate relationships between microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

Winter and early spring CO2 efflux from tundra communities of northern Alaska

NASA Astrophysics Data System (ADS)

Fahnestock, J. T.; Jones, M. H.; Brooks, P. D.; Walker, D. A.; Welker, J. M.

1998-11-01

Carbon dioxide concentrations through snow were measured in different arctic tundra communities on the North Slope of Alaska during winter and early spring of 1996. Subnivean CO2 concentrations were always higher than atmospheric CO2. A steady state diffusion model was used to generate conservative estimates of CO2 flux to the atmosphere. The magnitude of CO2 efflux differed with tundra community type, and rates of carbon release increased from March to May. Winter CO2 efflux was highest in riparian and snow bed communities and lowest in dry heath, upland tussock, and wet sedge communities. Snow generally accrues earlier in winter and is deeper in riparian and snow bed communities compared with other tundra communities, which are typically windswept and do not accumulate much snow during the winter. These results support the hypothesis that early and deep snow accumulation may insulate microbial populations from very cold temperatures, allowing sites with earlier snow cover to sustain higher levels of activity throughout winter compared to communities that have later developing snow cover. Extrapolating our estimates of CO2 efflux to the entire snow-covered season indicates that total carbon flux during winter in the Arctic is 13-109 kg CO2-C ha-1, depending on the vegetation community type. Wintertime CO2 flux is a potentially important, yet largely overlooked, part of the annual carbon cycle of tundra, and carbon release during winter should be accounted for in estimates of annual carbon balance in arctic ecosystems.

Impact of spatial variation in snow water equivalent and snow ablation on spring snowcover depletion over an alpine ridge

NASA Astrophysics Data System (ADS)

Schirmer, Michael; Harder, Phillip; Pomeroy, John

2016-04-01

The spatial and temporal dynamics of mountain snowmelt are controlled by the spatial distribution of snow accumulation and redistribution and the pattern of melt energy applied to this snowcover. In order to better quantify the spatial variations of accumulation and ablation, Structure-from-Motion techniques were applied to sequential aerial photographs of an alpine ridge in the Canadian Rocky Mountains taken from an Unmanned Aerial Vehicle (UAV). Seven spatial maps of snow depth and changes to depth during late melt (May-July) were generated at very high resolutions covering an area of 800 x 600 m. The accuracy was assessed with over 100 GPS measurements and RMSE were found to be less than 10 cm. Low resolution manual measurements of density permitted calculation of snow water equivalent (SWE) and change in SWE (ablation rate). The results indicate a highly variable initial SWE distribution, which was five times more variable than the spatial variation in ablation rate. Spatial variation in ablation rate was still substantial, with a factor of two difference between north and south aspects and small scale variations due to local dust deposition. However, the impact of spatial variations in ablation rate on the snowcover depletion curve could not be discerned. The reason for this is that only a weak spatial correlation developed between SWE and ablation rate. These findings suggest that despite substantial variations in ablation rate, snowcover depletion curve calculations should emphasize the spatial variation of initial SWE rather than the variation in ablation rate. While there is scientific evidence from other field studies that support this, there are also studies that suggest that spatial variations in ablation rate can influence snowcover depletion curves in complex terrain, particularly in early melt. The development of UAV photogrammetry has provided an opportunity for further detailed measurement of ablation rates, SWE and snowcover depletion over complex terrain and UAV field studies are recommended to clarify the relative importance of SWE and melt variability on snowcover depletion in various environmental conditions.

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

NASA Astrophysics Data System (ADS)

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 itself.

Assessment of the Barren Ground Caribou Die-off During Winter 2015-2016 Using Passive Microwave Observations

NASA Astrophysics Data System (ADS)

Dolant, C.; Montpetit, B.; Langlois, A.; Brucker, L.; Zolina, O.; Johnson, C. A.; Royer, A.; Smith, P.

2018-05-01

In summer 2016, more than 50 Arctic Barren Ground caribous were found dead on Prince Charles Island (Nunavut, Canada), a species recently classified as threatened. Neither predator nor sign of diseases was observed and reported. The main hypothesis is that caribous were not able to access food due to a very dense snow surface, created by a strong storm system in spring. Using satellite microwave data, a significant increase in brightness temperature polarization ratio at 19 and 37 GHz was observed in spring 2016 (60% higher than previous two winter seasons). Based on microwave radiative transfer simulations, such anomaly can be explained with a very dense snow surface. This is consistent with the succession of storms and strong winds highlighted in ERA-Interim over Prince Charles Island in spring 2016. Using several sources of data, this study shows that changes in snow conditions explain the caribou die-off due to restricted foraging.

View of portion of Western United States as seen by Skylab

NASA Image and Video Library

1974-01-10

SL4-139-4040 (10 Jan. 1974) --- An oblique view of a portion of the Western United States, as photographed from the Skylab space station in Earth orbit by one of the Skylab 4 crewmen. The camera used was a hand-held 70mm Hasselblad, with SO-368 medium-speed Ektachrome film. This photograph is one of a stereo pair (the other being 4039) taken to support the hydrological studies of the changing snow patterns in several watersheds. Stereo analysis will enable snow to be distinguished from clouds quantitatively. In a qualitative sense, the clouds are the fuzzy white, whereas the snow is distinct white. The area covered is from the Colorado Springs, Colorado area at the south to (and beyond) the Black Hills, South Dakota area. The Black Forest between Colorado Springs and Denver is evident as are the mountains west of these cities. South Park, west of Colorado Springs, and the South Platte River running north and east from Denver are two other conspicuous features. Photo credit: NASA

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

NASA Astrophysics Data System (ADS)

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.

Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

2017-04-01

A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed between LAI-2200 and UAV-based analyses. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

Comparing springtime ice-algal chlorophyll a and physical properties of multi-year and first-year sea ice from the Lincoln Sea.

PubMed

Lange, Benjamin A; Michel, Christine; Beckers, Justin F; Casey, J Alec; Flores, Hauke; Hatam, Ido; Meisterhans, Guillaume; Niemi, Andrea; Haas, Christian

2015-01-01

With near-complete replacement of Arctic multi-year ice (MYI) by first-year ice (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of sea ice associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln Sea during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-ice portions of MYI, upper old-ice portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial extent and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic sea ice algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus ice) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom ice algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest ice with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on ice-associated production than generally assumed.

Comparing Springtime Ice-Algal Chlorophyll a and Physical Properties of Multi-Year and First-Year Sea Ice from the Lincoln Sea

PubMed Central

Lange, Benjamin A.; Michel, Christine; Beckers, Justin F.; Casey, J. Alec; Flores, Hauke; Hatam, Ido; Meisterhans, Guillaume; Niemi, Andrea; Haas, Christian

2015-01-01

With near-complete replacement of Arctic multi-year ice (MYI) by first-year ice (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of sea ice associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln Sea during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-ice portions of MYI, upper old-ice portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial extent and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic sea ice algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus ice) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom ice algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest ice with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on ice–associated production than generally assumed. PMID:25901605

A Citizen Science Campaign to Validate Snow Remote-Sensing Products

NASA Astrophysics Data System (ADS)

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 this project we will focus on expanding our group of participants to a larger geographic area in Alaska, further develop our partnership with Mountain Hub, and build relationships in new communities as we conduct a photogrammetric survey in a different region next year.

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

USGS Publications Warehouse

Guo, J.; Tsang, L.; Josberger, E.G.; Wood, A.W.; Hwang, J.-N.; Lettenmaier, D.P.

2003-01-01

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case Study

NASA Technical Reports Server (NTRS)

Durand, Michael; Kim, Edward; Margulis, Steve

2008-01-01

We demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.

The Effects of Snow Depth Forcing on Southern Ocean Sea Ice Simulations

NASA Technical Reports Server (NTRS)

Powel, Dylan C.; Markus, Thorsten; Stoessel, Achim

2003-01-01

The spatial and temporal distribution of snow on sea ice is an important factor for sea ice and climate models. First, it acts as an efficient insulator between the ocean and the atmosphere, and second, snow is a source of fresh water for altering the already weak Southern Ocean stratification. For the Antarctic, where the ice thickness is relatively thin, snow can impact the ice thickness in two ways: a) As mentioned above snow on sea ice reduces the ocean-atmosphere heat flux and thus reduces freezing at the base of the ice flows; b) a heavy snow load can suppress the ice below sea level which causes flooding and, with subsequent freezing, a thickening of the sea ice (snow-to-ice conversion). In this paper, we compare different snow fall paramterizations (incl. the incorporation of satellite-derived snow depth) and study the effect on the sea ice using a sea ice model.

Responses of ecosystem water use efficiency to spring snow and summer water addition with or without nitrogen addition in a temperate steppe

PubMed Central

Zhai, Penghui; Huang, Jianhui; Zhao, Xiang; Dong, Kuanhu

2018-01-01

Water use efficiency (WUE) is an important indicator of ecosystem functioning but how ecosystem WUE responds to climate change including precipitation and nitrogen (N) deposition increases is still unknown. To investigate such responses, an experiment with a randomized block design with water (spring snowfall or summer water addition) and nitrogen addition was conducted in a temperate steppe of northern China. We investigated net ecosystem CO2 production (NEP), gross ecosystem production (GEP) and evapotranspiration (ET) to calculate ecosystem WUE (WUEnep = NEP/ET or WUEgep = GEP/ET) under spring snow and summer water addition with or without N addition from 2011 to 2013. The results showed that spring snow addition only had significant effect on ecosystem WUE in 2013 and summer water addition showed positive effect on ecosystem WUE in 2011 and 2013, as their effects on NEP and GEP is stronger than ET. N addition increased ecosystem WUE in 2012 and 2013 both in spring snow addition and summer water addition for its increasing effects on NEP and GEP but no effect on ET. Summer water addition had less but N addition had greater increasing effects on ecosystem WUE as natural precipitation increase indicating that natural precipitation regulates ecosystem WUE responses to water and N addition. Moreover, WUE was tightly related with atmospheric vapor-pressure deficit (VPD), photosynthetic active radiation (PAR), precipitation and soil moisture indicating the regulation of climate drivers on ecosystem WUE. In addition, it also was affected by aboveground net primary production (ANPP). The study suggests that ecosystem WUE responses to water and N addition is determined by the change in carbon process rather than that in water process, which are regulated by climate change in the temperate steppe of northern China. PMID:29529082

Responses of ecosystem water use efficiency to spring snow and summer water addition with or without nitrogen addition in a temperate steppe.

PubMed

Zhang, Xiaolin; Zhai, Penghui; Huang, Jianhui; Zhao, Xiang; Dong, Kuanhu

2018-01-01

Water use efficiency (WUE) is an important indicator of ecosystem functioning but how ecosystem WUE responds to climate change including precipitation and nitrogen (N) deposition increases is still unknown. To investigate such responses, an experiment with a randomized block design with water (spring snowfall or summer water addition) and nitrogen addition was conducted in a temperate steppe of northern China. We investigated net ecosystem CO2 production (NEP), gross ecosystem production (GEP) and evapotranspiration (ET) to calculate ecosystem WUE (WUEnep = NEP/ET or WUEgep = GEP/ET) under spring snow and summer water addition with or without N addition from 2011 to 2013. The results showed that spring snow addition only had significant effect on ecosystem WUE in 2013 and summer water addition showed positive effect on ecosystem WUE in 2011 and 2013, as their effects on NEP and GEP is stronger than ET. N addition increased ecosystem WUE in 2012 and 2013 both in spring snow addition and summer water addition for its increasing effects on NEP and GEP but no effect on ET. Summer water addition had less but N addition had greater increasing effects on ecosystem WUE as natural precipitation increase indicating that natural precipitation regulates ecosystem WUE responses to water and N addition. Moreover, WUE was tightly related with atmospheric vapor-pressure deficit (VPD), photosynthetic active radiation (PAR), precipitation and soil moisture indicating the regulation of climate drivers on ecosystem WUE. In addition, it also was affected by aboveground net primary production (ANPP). The study suggests that ecosystem WUE responses to water and N addition is determined by the change in carbon process rather than that in water process, which are regulated by climate change in the temperate steppe of northern China.

The influence of changing seasonality and snow cover on arctic ground squirrel phenology.

NASA Astrophysics Data System (ADS)

Barnes, B.; Sheriff, M.; Kenagy, J.; Buck, L.; Team Squirrel

2011-12-01

A warming climate in the Arctic may have asymmetrical effects on seasonality, depending on the timing and extent of snow cover. Warm autumns that delay the onset of persistent snow cover will lengthen growing seasons of some plants and, combined with continuing access to fallen seeds, berries, and leaves, extend feeding opportunities for ground foragers. Warming in spring should advance when the ground becomes snow free and the onset of plant productivity, leading overall to a longer growing season. However, if winter and spring precipitation increase, as is predicted in climate models, the amount and seasonal extent of snow pack will increase, which will delay melt and lead to delayed springs. Either of these scenarios may develop regionally, depending on local weather, snow, and wind. Since 1996, we have been investigating the timing of annual events in natural populations of arctic ground squirrels, Urocitellus parryii, living at two nearby sites (Toolik and Atigun, 68o38'N) in arctic Alaska that greatly differ in timing and duration of snow cover. Since arctic ground squirrels are highly dependent on snow free ground for foraging, we predicted that these environmental differences will have had major impacts on life histories and timing of annual events on the local populations. Precision in dates of the beginning and end of hibernation, use of heterothermy, and birth of young were determined by temperature-sensitive data loggers implanted into juvenile and adult animals of both sexes. Weather stations, snow cameras, and transects for plant phenology are in place at both locations, although record lengths differ. While across the past 15 years annual timing of hibernation and breeding has not shown significant trends at either site, the two populations have differed consistently in hibernation timing and length of active season, and they show a 13 day difference in average timing of reproduction. These results reveal a substantial flexibility of timing of the annual cycle in ground squirrel populations in response to local conditions. Current trends of change in weather show an increase in active season temperatures at Atigun but snowier springs at Toolik. We predict that if these trends continue, further separation in annual timing will develop between the two populations with negative impacts on survival and population density at Toolik and positive impacts at Atigun. In addition, to test for the pace of additional flexibility and gender specific differences in annual timing, we have begun a food addition experiment at Atigun that extends foraging opportunities in autumn.

Forest restoration as a strategy to mitigate climate impacts on wildfire, vegetation, and water in semi-arid forests of the southwestern U.S.

NASA Astrophysics Data System (ADS)

O'Donnell, F. C.; Flatley, W. T.; Masek Lopez, S.; Fulé, P. Z.; Springer, A. E.

2017-12-01

Climate change and fire suppression are interacting to reduce forest health, drive high-intensity wildfires, and potentially reduce water quantity and quality in high-elevation forests of the southwestern US. Forest restoration including thinning and prescribed fire, is a management approach that reduces fire risk. It may also improve forest health by increasing soil moisture through the combined effects of increased snow pack and reduced evapotranspiration (ET), though the relative importance of these mechanisms is unknown. It is also unclear how small-scale changes in the hydrologic cycle will scale-up to influence watershed dynamics. We conducted field and modeling studies to investigate these issues. We measured snow depth, snow water equivalent (SWE), and soil moisture at co-located points in paired restoration-control plots near Flagstaff, AZ. Soil moisture was consistently higher in restored plots across all seasons. Snow depth and SWE were significantly higher in restored plots immediately after large snow events with no difference one week after snowfall, suggesting that restoration leads to both increased accumulation and sublimation. At the point scale, there was a small (ρ=0.28) but significant correlation between fall-to-spring soil moisture increase and peak SWE during the winter. Consistent with previous studies, soil drying due to ET was more rapid in recently restored sites than controls, but there was no difference 10 years after restoration. In addition to the small role played by snow and ET, we also observed more rapid soil moisture loss in the 1-2 days following rain or rapid snowmelt in control than in restoration plots. We hypothesize that this is due to a loss of macropores when woody plants are replaced by herbaceous vegetation and warrants further study. To investigate watershed-scale dynamics, we combined spatially-explicit vegetation and fire modeling with statistical water and sediment yield models for a large forested landscape on the Kaibab Plateau, AZ. Our results predicted that climate-induced vegetation changes will result in annual runoff declines of 2%-10% in the next century, but that restoration reversed these declines. We also predict that restoration treatments will protect water quality by reducing the incidence of high severity fire and the associated erosion.

Synoptic controls on precipitation pathways and snow delivery to high-accumulation ice core sites in the Ross Sea region, Antarctica

NASA Astrophysics Data System (ADS)

Sinclair, K. E.; Bertler, N. A. N.; Trompetter, W. J.

2010-11-01

Dominant storm tracks to two ice core sites on the western margin of the Ross Sea, Antarctica (Skinner Saddle (SKS) and Evans Piedmont Glacier), are investigated to establish key synoptic controls on snow accumulation. This is critical in terms of understanding the seasonality, source regions, and transport pathways of precipitation delivered to these sites. In situ snow depth and meteorological observations are used to identify major accumulation events in 2007-2008, which differ considerably between sites in terms of their magnitude and seasonal distribution. While snowfall at Evans Piedmont Glacier occurs almost exclusively during summer and spring, Skinner Saddle receives precipitation year round with a lull during the months of April and May. Cluster analysis of daily back trajectories reveals that the highest-accumulation days at both sites result from fast-moving air masses, associated with synoptic-scale low-pressure systems. There is evidence that short-duration pulses of snowfall at SKS also originate from mesocyclone development over the Ross Ice Shelf and local moisture sources. Changes in the frequency and seasonal distribution of these mechanisms of precipitation delivery will have a marked impact on annual accumulation over time and will therefore need careful consideration during the interpretation of stable isotope and geochemical records from these ice cores.

BOREAS HYD-3 Snow Measurements

NASA Technical Reports Server (NTRS)

Hardy, Janet P.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Davis, Robert E.; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-3 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of snow depth, snow density in three cm intervals, an integrated snow pack density and snow water equivalent (SWE), and snow pack physical properties from snow pit evaluation taken in 1994 and 1996. The data were collected from several sites in both the southern study area (SSA) and the northern study area (NSA). A variety of standard tools were used to measure the snow pack properties, including a meter stick (snow depth), a 100 cc snow density cutter, a dial stem thermometer, and the Canadian snow sampler as used by HYD-4 to obtain a snow pack-integrated measure of SWE. This study was undertaken to predict spatial distributions of snow properties important to the hydrology, remote sensing signatures, and the transmissivity of gases through the snow. The data are available in tabular ASCII files. The snow measurement data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Observation of Snow cover glide on Sub-Alpine Coniferous Forests in Mount Zao, Northeastern Japan

NASA Astrophysics Data System (ADS)

Sasaki, A.; Suzuki, K.

2017-12-01

This is the study to clarify the snow cover glide behavior in the sub-alpine coniferous forests on Mount Zao, Northeastern Japan, in the winter of 2014-2015. We installed the glide-meter which is sled type, and measured the glide motion on the slope of Abies mariesii forest and its surrounding slope. In addition, we observed the air temperature, snow depth, density of snow, and snow temperature to discuss relationship between weather conditions and glide occurrence. The snow cover of the 2014-15 winter started on November 13th and disappeared on April 21st. The maximum snow depth was 242 cm thick, it was recorded at February 1st. The snow cover glide in the surrounding slope was occurred first at February 10th, although maximum snow depth recorded on February 1st. The glide motion in the surrounding slope is continuing and its velocity was 0.4 cm per day. The glide in the surrounding slope stopped at March 16th. The cumulative amount of the glide was 21.1 cm. The snow cover glide in the A. mariesii forest was even later occurred first at February 21st. The glide motion of it was intermittent and extremely small. On sub-alpine zone of Mount Zao, snow cover glide intensity is estimated to be 289 kg/m2 on March when snow water equivalent is maximum. At same period, maximum snow cover glide intensity is estimated to be about 1000 kg/m2 at very steep slopes where the slope angle is about 35 degree. Although potential of snow cover glide is enough high, the snow cover glide is suppressed by stem of A. mariesii trees, in the sub-alpine coniferous forest.

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

NASA Technical Reports Server (NTRS)

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.

Snow and Frost Depths on North and South Slopes

Treesearch

Richard S. Sartz

1973-01-01

Aspect affects soil frost depth by influencing the amount of solar radiation received at the ground or snow surface. Depending on the conditions, frost can be of equal depth on north and south slopes, deeper on north slopes, or deeper on south slopes. Data illustrate all three conditions

State of Arctic Sea Ice North of Svalbard during N-ICE2015

NASA Astrophysics Data System (ADS)

Rösel, Anja; King, Jennifer; Gerland, Sebastian

2016-04-01

The N-ICE2015 cruise, led by the Norwegian Polar Institute, was a drift experiment with the research vessel R/V Lance from January to June 2015, where the ship started the drift North of Svalbard at 83°14.45' N, 21°31.41' E. The drift was repeated as soon as the vessel drifted free. Altogether, 4 ice stations where installed and the complex ocean-sea ice-atmosphere system was studied with an interdisciplinary Approach. During the N-ICE2015 cruise, extensive ice thickness and snow depth measurements were performed during both, winter and summer conditions. Total ice and snow thickness was measured with ground-based and airborne electromagnetic instruments; snow depth was measured with a GPS snow depth probe. Additionally, ice mass balance and snow buoys were deployed. Snow and ice thickness measurements were performed on repeated transects to quantify the ice growth or loss as well as the snow accumulation and melt rate. Additionally, we collected independent values on surveys to determine the general ice thickness distribution. Average snow depths of 32 cm on first year ice, and 52 cm on multi-year ice were measured in January, the mean snow depth on all ice types even increased until end of March to 49 cm. The average total ice and snow thickness in winter conditions was 1.92 m. During winter we found a small growth rate on multi-year ice of about 15 cm in 2 months, due to above-average snow depths and some extraordinary storm events that came along with mild temperatures. In contrast thereto, we also were able to study new ice formation and thin ice on newly formed leads. In summer conditions an enormous melt rate, mainly driven by a warm Atlantic water inflow in the marginal ice zone, was observed during two ice stations with melt rates of up to 20 cm per 24 hours. To reinforce the local measurements around the ship and to confirm their significance on a larger scale, we compare them to airborne thickness measurements and classified SAR-satellite scenes. The here presented data set is important for understanding the ocean-ice-atmosphere interactions, for calculating energy fluxes, and biogeochemical processes.

Influence of winter season climate variability on snow-precipitation ratio in the western United States

Treesearch

Mohammad Safeeq; Shraddhanand Shukla; Ivan Arismendi; Gordon E. Grant; Sarah L. Lewis; Anne Nolin

2015-01-01

In the western United States, climate warming poses a unique threat to water and snow hydrology because much of the snowpack accumulates at temperatures near 0 °C. As the climate continues to warm, much of the region's precipitation is expected to switch from snow to rain, causing flashier hydrographs, earlier inflow to reservoirs, and reduced spring and summer...

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

NASA Astrophysics Data System (ADS)

Zhang, Yinsheng; Ma, Ning

2018-04-01

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

Continuous Snow Depth, Intensive Site 1, Barrow, Alaska

DOE Data Explorer

Bob Busey; Larry Hinzman; Vladimir Romanovsky; William Cable

2014-11-06

Continuous Snow depth data are being collected at several points within four intensive study areas in Barrow, Alaska. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements at this location. The data could also be used as supporting measurements for other research and modeling activities.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Snow loads on roofs in areas of heavy snowfall

Treesearch

Robert D. Doty; Glenn H. Deitschman

1966-01-01

This study tested the feasibility of estimating snow loads on roofs from measurements of depth and water content of snow on nearby ground. The water content, and therefore the weight, of snow on the ground proved comparable to that of snow on roofs.

Water uptake of Alaskan tundra evergreens during the winter-spring transition.

PubMed

Moser, Jonathan G; Oberbauer, Steven F; Sternberg, Leonel da S L; Ellsworth, Patrick Z; Starr, Gregory; Mortazavi, Behzad; Olivas, Paulo C

2016-02-01

The cold season in the Arctic extends over 8 to 9 mo, yet little is known about vascular plant physiology during this period. Evergreen species photosynthesize under the snow, implying that they are exchanging water with the atmosphere. However, liquid water available for plant uptake may be limited at this time. The study objective was to determine whether evergreen plants are actively taking up water while under snow and/or immediately following snowmelt during spring thaw. In two in situ experiments, one at the plot level and another at the individual species level, (2)H-labeled water was used as a tracer injected beneath the snow, after which plant stems and leaves were tested for the presence of the label. In separate experiments, excised shoots of evergreen species were exposed to (2)H-labeled water for ∼5 s or 60 min and tested for foliar uptake of the label. In both the plot-level and the species-level experiments, some (2)H-labeled water was found in leaves and stems. Additionally, excised individual plant shoots exposed to labeled water for 60 min took up significantly more (2)H-label than shoots exposed ∼5 s. Evergreen tundra plants take up water under snow cover, some via roots, but also likely by foliar uptake. The ability to take up water in the subnivean environment allows evergreen tundra plants to take advantage of mild spring conditions under the snow and replenish carbon lost by winter respiration. © 2016 Botanical Society of America.

Quantifying the variability of snowpack properties and processes in a small-forested catchment representative of the boreal zone

NASA Astrophysics Data System (ADS)

Parajuli, A.; Nadeau, D.; Anctil, F.; Parent, A. C.; Bouchard, B.; Jutras, S.

2017-12-01

In snow-fed catchments, it is crucial to monitor and to model snow water equivalent (SWE), particularly to simulate the melt water runoff. However, the distribution of SWE can be highly heterogeneous, particularly within forested environments, mainly because of the large variability in snow depths. Although the boreal forest is the dominant land cover in Canada and in a few other northern countries, very few studies have quantified the spatiotemporal variability of snow depths and snowpack dynamics within this biome. The objective of this paper is to fill this research gap, through a detailed monitoring of snowpack dynamics at nine locations within a 3.57 km2 experimental forested catchment in southern Quebec, Canada (47°N, 71°W). The catchment receives 6 m of snow annually on average and is predominantly covered with balsam fir stand with some traces of spruce and white birch. In this study, we used a network of nine so-called `snow profiling stations', providing automated snow depth and snowpack temperature profile measurements, as well as three contrasting sites (juvenile, sapling and open areas) where sublimation rates were directly measured with flux towers. In addition, a total of 1401 manual snow samples supported by 20 snow pits measurements were collected throughout the winter of 2017. This paper presents some preliminary analyses of this unique dataset. Simple empirical relations relying SWE with easy-to-determine proxies, such as snow depths and snow temperature, are tested. Then, binary regression trees and multiple regression analysis are used to model SWE using topographic characteristics (slope, aspect, elevation), forest features (tree height, tree diameter, forest density and gap fraction) and meteorological forcing (solar radiation, wind speed, snow-pack temperature profile, air temperature, humidity). An analysis of sublimation rates comparing open area, saplings and juvenile forest is also presented in this paper.

From Drought to Flood: An Analysis of the Water Balance of the Tuolumne River Basin During Extreme Conditions (2015 - 2017)

NASA Astrophysics Data System (ADS)

Hedrick, A. R.; Marks, D. G.; Havens, S.; Robertson, M.; Johnson, M.; Sandusky, M.; Bormann, K. J.; Painter, T. H.

2017-12-01

Closing the water balance of a snow-dominated mountain basin has long been a focal point of the hydrologic sciences. This study attempts to more precisely quantify the solid precipitation inputs to a basin using the iSnobal energy balance snowmelt model and assimilated snow depth information from the Airborne Snow Observatory (ASO). Throughout the ablation seasons of three highly dissimilar consecutive water years (2015 - 2017), the ASO captured high resolution snow depth snapshots over the Tuolumne River Basin in California's Central Sierra Nevada. These measurements were used to periodically update the snow depth state variable of iSnobal, thereby nudging the estimates of water storage (snow water equivalent, or SWE) and melt (surface water input, or SWI) toward a more accurate solution. Once precipitation inputs and streamflow outputs are better constrained, the additional loss terms of the water mass balance equation (i.e. groundwater recharge and evapotranspiration) can be estimated with less uncertainty.

Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the Northern Great Plains

USGS Publications Warehouse

Chang, A.T.C.; Kelly, R.E.J.; Josberger, E.G.; Armstrong, R.L.; Foster, J.L.; Mognard, N.M.

2005-01-01

Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 ?? 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1?? ?? 1?? in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations. ?? 2005 American Meteorological Society.

Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

NASA Astrophysics Data System (ADS)

Adams, Marc S.; Bühler, Yves; Fromm, Reinhard

2017-12-01

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km2 large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

Assimilating MODIS-based albedo and snow cover fraction into the Common Land Model to improve snow depth simulation with direct insertion and deterministic ensemble Kalman filter methods

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Shu, Hong

2014-09-01

This study assesses the analysis performance of assimilating the Moderate Resolution Imaging Spectroradiometer (MODIS)-based albedo and snow cover fraction (SCF) separately or jointly into the physically based Common Land Model (CoLM). A direct insertion method (DI) is proposed to assimilate the black and white-sky albedos into the CoLM. The MODIS-based albedo is calculated with the MODIS bidirectional reflectance distribution function (BRDF) model parameters product (MCD43B1) and the solar zenith angle as estimated in the CoLM for each time step. Meanwhile, the MODIS SCF (MOD10A1) is assimilated into the CoLM using the deterministic ensemble Kalman filter (DEnKF) method. A new DEnKF-albedo assimilation scheme for integrating the DI and DEnKF assimilation schemes is proposed. Our assimilation results are validated against in situ snow depth observations from November 2008 to March 2009 at five sites in the Altay region of China. The experimental results show that all three data assimilation schemes can improve snow depth simulations. But overall, the DEnKF-albedo assimilation shows the best analysis performance as it significantly reduces the bias and root-mean-square error (RMSE) during the snow accumulation and ablation periods at all sites except for the Fuyun site. The SCF assimilation via DEnKF produces better results than the albedo assimilation via DI, implying that the albedo assimilation that indirectly updates the snow depth state variable is less efficient than the direct SCF assimilation. For the Fuyun site, the DEnKF-albedo scheme tends to overestimate the snow depth accumulation with the maximum bias and RMSE values because of the large positive innovation (observation minus forecast).

Regional sensitivities of seasonal snowpack to elevation, aspect, and vegetation cover in western North America

NASA Astrophysics Data System (ADS)

Tennant, Christopher J.; Harpold, Adrian A.; Lohse, Kathleen Ann; Godsey, Sarah E.; Crosby, Benjamin T.; Larsen, Laurel G.; Brooks, Paul D.; Van Kirk, Robert W.; Glenn, Nancy F.

2017-08-01

In mountains with seasonal snow cover, the effects of climate change on snowpack will be constrained by landscape-vegetation interactions with the atmosphere. Airborne lidar surveys used to estimate snow depth, topography, and vegetation were coupled with reanalysis climate products to quantify these interactions and to highlight potential snowpack sensitivities to climate and vegetation change across the western U.S. at Rocky Mountain (RM), Northern Basin and Range (NBR), and Sierra Nevada (SNV) sites. In forest and shrub areas, elevation captured the greatest amount of variability in snow depth (16-79%) but aspect explained more variability (11-40%) in alpine areas. Aspect was most important at RM sites where incoming shortwave to incoming net radiation (SW:NetR↓) was highest (˜0.5), capturing 17-37% of snow depth variability in forests and 32-37% in shrub areas. Forest vegetation height exhibited negative relationships with snow depth and explained 3-6% of its variability at sites with greater longwave inputs (NBR and SNV). Variability in the importance of physiography suggests differential sensitivities of snowpack to climate and vegetation change. The high SW:NetR↓ and importance of aspect suggests RM sites may be more responsive to decreases in SW:NetR↓ driven by warming or increases in humidity or cloud cover. Reduced canopy-cover could increase snow depths at SNV sites, and NBR and SNV sites are currently more sensitive to shifts from snow to rain. The consistent importance of aspect and elevation indicates that changes in SW:NetR↓ and the elevation of the rain/snow transition zone could have widespread and varied effects on western U.S. snowpacks.

Estimating Catchment-Scale Snowpack Variability in Complex Forested Terrain, Valles Caldera National Preserve, NM

NASA Astrophysics Data System (ADS)

Harpold, A. A.; Brooks, P. D.; Biederman, J. A.; Swetnam, T.

2011-12-01

Difficulty estimating snowpack variability across complex forested terrain currently hinders the prediction of water resources in the semi-arid Southwestern U.S. Catchment-scale estimates of snowpack variability are necessary for addressing ecological, hydrological, and water resources issues, but are often interpolated from a small number of point-scale observations. In this study, we used LiDAR-derived distributed datasets to investigate how elevation, aspect, topography, and vegetation interact to control catchment-scale snowpack variability. The study area is the Redondo massif in the Valles Caldera National Preserve, NM, a resurgent dome that varies from 2500 to 3430 m and drains from all aspects. Mean LiDAR-derived snow depths from four catchments (2.2 to 3.4 km^2) draining different aspects of the Redondo massif varied by 30%, despite similar mean elevations and mixed conifer forest cover. To better quantify this variability in snow depths we performed a multiple linear regression (MLR) at a 7.3 by 7.3 km study area (5 x 106 snow depth measurements) comprising the four catchments. The MLR showed that elevation explained 45% of the variability in snow depths across the study area, aspect explained 18% (dominated by N-S aspect), and vegetation 2% (canopy density and height). This linear relationship was not transferable to the catchment-scale however, where additional MLR analyses showed the influence of aspect and elevation differed between the catchments. The strong influence of North-South aspect in most catchments indicated that the solar radiation is an important control on snow depth variability. To explore the role of solar radiation, a model was used to generate winter solar forcing index (SFI) values based on the local and remote topography. The SFI was able to explain a large amount of snow depth variability in areas with similar elevation and aspect. Finally, the SFI was modified to include the effects of shading from vegetation (in and out of canopy), which further explained snow depth variability. The importance of SFI for explaining catchment-scale snow depth variability demonstrates that aspect is not a sufficient metric for direct radiation in complex terrain where slope and remote topographic shading are significant. Surprisingly, the net effects of interception and shading by vegetation on snow depths were minimal compared to elevation and aspect in these catchments. These results suggest that snowpack losses from interception may be balanced by increased shading to reduce the overall impacts from vegetation compared to topographic factors in this high radiation environment. Our analysis indicated that elevation and solar radiation are likely to control snow variability in larger catchments, with interception and shading from vegetation becoming more important at smaller scales.

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

NASA Astrophysics Data System (ADS)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding the rock glacier. Additionally, one of the challenges with processing snow cover imagery with SfM-MVS is dealing with the general homogeneity of the surface, which makes is difficult for automated-feature detection algorithms to identify key features for point matching. This challenge depends on the snow cover surface conditions, such as scale, lighting conditions (high vs. low contrast), and availability of snow-free features within a scene, among others. We attempt to explore this aspect by spatial modelling the factors influencing the precision and bias of the DEMs from image, flight, and terrain attributes.

Seasonal variations in the major chemical species of snow at the South East Dome in Greenland

NASA Astrophysics Data System (ADS)

Oyabu, Ikumi; Matoba, Sumito; Yamasaki, Tetsuhide; Kadota, Moe; Iizuka, Yoshinori

2016-03-01

We analyze snow-pit samples collected in May 2015 at the South East Dome (SE Dome) on the Greenland ice sheet. The analysis includes high-resolution records of δD and δ18O, as well as the major ions, CH3SO3-, Cl-, NO3-, SO42-, Na+, NH4+, K+, Ma2+, and Ca2+. We find that the 3.55-m snow pit recorded temperature and aerosol proxies back to summer or autumn of 2014. This indicates a higher accumulation rate than those at other major drilling sites in Greenland. Due to this high accumulation rate, ion concentrations except Na+ are lower than those typical of the central Greenland ice sheet. Concerning seasonal variability, the Na+, Cl-, Ca2+, Mg2+, and NO3- vary similarly to other sites in Greenland, with the Na+ and Cl- peaking in winter to early spring, Ca2+ peaking in spring, Mg2+ peaking in winter to spring, and NO3- towards a peak in summer while showing smaller peaks in winter to spring. The NH4+ increased in spring, and SO42- increased in autumn to winter at SE Dome. On the other hand, the seasonal trend in the Cl-/Na+ ratio differs from those in the inland region. As we did not fully recover one seasonal cycle, some seasonal peaks may have been missed.

Wind tunnel experiments: influence of erosion and deposition on wind-packing of new snow

NASA Astrophysics Data System (ADS)

Sommer, Christian G.; Lehning, Michael; Fierz, Charles

2018-01-01

Wind sometimes creates a hard, wind-packed layer at the surface of a snowpack. The formation of such wind crusts was observed during wind tunnel experiments with combined SnowMicroPen and Microsoft Kinect sensors. The former provides the hardness of new and wind-packed snow and the latter spatial snow depth data in the test section. Previous experiments showed that saltation is necessary but not sufficient for wind-packing. The combination of hardness and snow depth data now allows to study the case with saltation in more detail. The Kinect data requires complex processing but with the appropriate corrections, snow depth changes can be measured with an accuracy of about 1 mm. The Kinect is therefore well suited to quantify erosion and deposition. We found that no hardening occurred during erosion and that a wind crust may or may not form when snow is deposited. Deposition is more efficient at hardening snow in wind-exposed than in wind-sheltered areas. The snow hardness increased more on the windward side of artificial obstacles placed in the wind tunnel. Similarly, the snow was harder in positions with a low Sx parameter. Sx describes how wind-sheltered (high Sx) or wind-exposed (low Sx) a position is and was calculated based on the Kinect data. The correlation between Sx and snow hardness was -0.63. We also found a negative correlation of -0.4 between the snow hardness and the deposition rate. Slowly deposited snow is harder than a rapidly growing accumulation. Sx and the deposition rate together explain about half of the observed variability of snow hardness.

Using Opposing Slope Aspects to Understand Water and Energy Flow Controls on Critical Zone Architecture

NASA Astrophysics Data System (ADS)

Anderson, S. P.; Barnhart, K. R.; Kelly, P. K.; Foster, M. A.; Langston, A. L.

2014-12-01

A long-standing problem is to understand how climate controls the structure of the critical zone, including the depth of weathering, thickness and character of soils, and morphology of hillslopes. We exploit microclimates on opposing aspects in a watershed in the Boulder Creek CZO to investigate the role of water and energy fluxes on development of critical zone architectures. The 2.6 km2 Gordon Gulch, located at ~2500 m a.s.l. at 40°N latitude, is elongated east-west, and consequently is predominantly composed of north and south-facing soil-mantled slopes, dotted with tors, developed on Precambrian gneiss. The depth to fresh rock ranges from about 8 to 12 m, and is up to 2 m deeper on north-facing slopes. In addition to greater thickness, weathered rock is measurably lower in tensile strength on north-facing slopes. While characteristics of weathered rock vary with aspect, the overlying mobile regolith is relatively uniform in thickness at ~0.5 m across the catchment, and its mineralogy shows only minor chemical alteration from parent rock. These features of the critical zone architecture arise in the face of systematic differences in energy and water delivery by aspect. About 40-50% of the ~500 mm annual precipitation is delivered as snow. During spring, the south-facing slopes receive up to 50% greater direct solar radiation than the north-facing slopes. Consequently, snow cover is ephemeral in the open Ponderosa forests on south-facing slopes, and soil wetting and drying events are frequent. Frost penetration is shallow, and short lived. On north-facing slopes, less direct radiation and a dense Lodgepole pine forest cover leads to snowpack retention. Soils are colder and soil moisture stays elevated for long periods in spring on these slopes. We postulate that deeper and more sustained frost penetration on north-facing slopes enhances the damage rate by frost cracking. Deeper water delivery further aids this process, and supports chemical alteration processes. The uniformity of mobile regolith depths suggests equal mobility on these slopes despite differing conditions.

Effects of Vegetation and of Heat and Vapor Fluxes from Soil on Snowpack Evolution and Radiobrightness

NASA Technical Reports Server (NTRS)

Chung, Y. C.; England, A. W.; DeRoo, R. D.; Weininger, Etai

2006-01-01

The radiobrightness of a snowpack is strongly linked to the snow moisture content profile, to the point that the only operational inversion algorithms require dry snow. Forward dynamic models do not include the effects of freezing and thawing of the soil beneath the snowpack and the effect of vegetation within the snow or above the snow. To get a more realistic description of the evolution of the snowpack, we reported an addition to the Snow-Soil-Vegetation-Atmosphere- Transfer (SSVAT) model, wherein we coupled soil processes of the Land Surface Process (LSP) model with the snow model SNTHERM. In the near future we will be adding a radiobrightness prediction based on the modeled moisture, temperature and snow grain size profiles. The initial investigations with this SSVAT for a late winter and early spring snow pack indicate that soil processes warm the snowpack and the soil. Vapor diffusion needs to be considered whenever the ground is thawed. In the early spring, heat flow from the ground into a snow and a strong temperature gradient across the snow lead to thermal convection. The buried vegetation can be ignored for a late winter snow pack. The warmer surface snow temperature will affect radiobrightness since it is most sensitive to snow surface characteristics. Comparison to data shows that SSVAT provides a more realistic representation of the temperature and moisture profiles in the snowpack and its underlying soil than SNTHERM. The radiobrightness module will be optimized for the prediction of brightness when the snow is moist. The liquid water content of snow causes considerable absorption compared to dry snow, and so longer wavelengths are likely to be most revealing as to the state of a moist snowpack. For volumetric moisture contents below about 7% (the pendular regime), the water forms rings around the contact points between snow grains. Electrostatic modeling of these pendular rings shows that the absorption of these rings is significantly higher than a sphere of the same volume. The first implementation of the radiobrightness module will therefore be a simple radiative transfer model without scattering.

Global Mercury Pathways in the Arctic Ecosystem

NASA Astrophysics Data System (ADS)

Lahoutifard, N.; Lean, D.

2003-12-01

The sudden depletions of atmospheric mercury which occur during the Arctic spring are believed to involve oxidation of gaseous elemental mercury, Hg(0), rendering it less volatile and more soluble. The Hg(II) oxidation product(s) are more susceptible to deposition, consistent with the observation of dramatic increases in snow mercury levels during depletion events. Temporal correlations with ozone depletion events and the proliferation of BrO radicals support the hypothesis that oxidation of Hg(0) occurs in the gas phase and results in its conversion to RGM (Reactive Gaseous Mercury). The mechanisms of Hg(0) oxidation and particularly Hg(II) reduction are as yet unproven. In order to evaluate the feasibility of proposed chemical processes involving mercury in the Arctic atmosphere and its pathway after deposition on the snow from the air, we investigated mercury speciation in air and snow pack at Resolute, Nunavut, Canada (latitude 75° N) prior to and during snow melt during spring 2003. Quantitative, real-time information on emission, air transport and deposition were combined with experimental studies of the distribution and concentrations of different mercury species, methyl mercury, anions, total organic carbon and total inorganic carbon in snow samples. The effect of solar radiation and photoreductants on mercury in snow samples was also investigated. In this work, we quantify mercury removed from the air, and deposited on the snow and the transformation to inorganic and methyl mercury.

Winter and early spring CO2 efflux from tundra communities of northern Alaska

USGS Publications Warehouse

Fahnestock, J.T.; Jones, M.H.; Brooks, P.D.; Walker, D.A.; Welker, J.M.

1998-01-01

Carbon dioxide concentrations through snow were measured in different arctic tundra communities on the North Slope of Alaska during winter and early spring of 1996. Subnivean CO2 concentrations were always higher than atmospheric CO2. A steady state diffusion model was used to generate conservative estimates of CO2 flux to the atmosphere. The magnitude of CO2 efflux differed with tundra community type, and rates of carbon release increased from March to May. Winter CO2 efflux was highest in riparian and snow bed communities and lowest in dry heath, upland tussock, and wet sedge communities. Snow generally accrues earlier in winter and is deeper in riparian and snow bed communities compared with other tundra communities, which are typically windswept and do not accumulate much snow during the winter. These results support the hypothesis that early and deep snow accumulation may insulate microbial populations from very cold temperatures, allowing sites with earlier snow cover to sustain higher levels of activity throughout winter compared to communities that have later developing snow cover. Extrapolating our estimates of CO2 efflux to the entire snow-covered season indicates that total carbon flux during winter in the Arctic is 13-109 kg CO2-C ha-1, depending on the vegetation community type. Wintertime CO2 flux is a potentially important, yet largely overlooked, part of the annual carbon cycle of tundra, and carbon release during winter should be accounted for in estimates of annual carbon balance in arctic ecosystems. Copyright 1998 by the American Geophysical Union.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

About uncertainties in sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

NASA Astrophysics Data System (ADS)

Kern, S.; Khvorostovsky, K.; Skourup, H.; Rinne, E.; Parsakhoo, Z. S.; Djepa, V.; Wadhams, P.; Sandven, S.

2014-03-01

One goal of the European Space Agency Climate Change Initiative sea ice Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time sea ice thickness distribution. An important step to achieve this goal is to assess the accuracy of sea ice thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea ice freeboard from Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea ice draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year ice the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea ice draft agrees with the mean sea ice draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in sea ice draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain sea ice thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an ice-type dependent sea ice density is as mandatory as a snow depth with centimetre accuracy.

NOAA's National Snow Analyses

NASA Astrophysics Data System (ADS)

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

2005-12-01

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

End-of-winter snow depth variability on glaciers in Alaska

NASA Astrophysics Data System (ADS)

McGrath, Daniel; Sass, Louis; O'Neel, Shad; Arendt, Anthony; Wolken, Gabriel; Gusmeroli, Alessio; Kienholz, Christian; McNeil, Christopher

2015-08-01

A quantitative understanding of snow thickness and snow water equivalent (SWE) on glaciers is essential to a wide range of scientific and resource management topics. However, robust SWE estimates are observationally challenging, in part because SWE can vary abruptly over short distances in complex terrain due to interactions between topography and meteorological processes. In spring 2013, we measured snow accumulation on several glaciers around the Gulf of Alaska using both ground- and helicopter-based ground-penetrating radar surveys, complemented by extensive ground truth observations. We found that SWE can be highly variable (40% difference) over short spatial scales (tens to hundreds of meters), especially in the ablation zone where the underlying ice surfaces are typically rough. Elevation provides the dominant basin-scale influence on SWE, with gradients ranging from 115 to 400 mm/100 m. Regionally, total accumulation and the accumulation gradient are strongly controlled by a glacier's distance from the coastal moisture source. Multiple linear regressions, used to calculate distributed SWE fields, show that robust results require adequate sampling of the true distribution of multiple terrain parameters. Final SWE estimates (comparable to winter balances) show reasonable agreement with both the Parameter-elevation Relationships on Independent Slopes Model climate data set (9-36% difference) and the U.S. Geological Survey Alaska Benchmark Glaciers (6-36% difference). All the glaciers in our study exhibit substantial sensitivity to changing snow-rain fractions, regardless of their location in a coastal or continental climate. While process-based SWE projections remain elusive, the collection of ground-penetrating radar (GPR)-derived data sets provides a greatly enhanced perspective on the spatial distribution of SWE and will pave the way for future work that may eventually allow such projections.

Prominent features in isotopic, chemical and dust stratigraphies from coastal East Antarctic ice sheet (Eastern Wilkes Land).

PubMed

Caiazzo, L; Baccolo, G; Barbante, C; Becagli, S; Bertò, M; Ciardini, V; Crotti, I; Delmonte, B; Dreossi, G; Frezzotti, M; Gabrieli, J; Giardi, F; Han, Y; Hong, S-B; Hur, S D; Hwang, H; Kang, J-H; Narcisi, B; Proposito, M; Scarchilli, C; Selmo, E; Severi, M; Spolaor, A; Stenni, B; Traversi, R; Udisti, R

2017-06-01

In this work we present the isotopic, chemical and dust stratigraphies of two snow pits sampled in 2013/14 at GV7 (coastal East Antarctica: 70°41' S - 158°51' E, 1950 m a.s.l.). A large number of chemical species are measured aiming to study their potentiality as environmental changes markers. Seasonal cluster backward trajectories analysis was performed and compared with chemical marker stratigraphies. Sea spray aerosol is delivered to the sampling site together with snow precipitation especially in autumn-winter by air masses arising from Western Pacific Ocean sector. Dust show maximum concentration in spring when the air masses arising from Ross Sea sector mobilize mineral dust from ice-free areas of the Transantarctic mountains. The clear seasonal pattern of sulfur oxidized compounds allows the dating of the snow-pit and the calculation of the mean accumulation rate, which is 242 ± 71 mm w.e. for the period 2008-2013. Methanesulfonic acid and NO 3 - do not show any concentration decreasing trend as depth increases, also considering a 12 m firn core record. Therefore these two compounds are not affected by post-depositional processes at this site and can be considered reliable markers for past environmental changes reconstruction. The rBC snow-pit record shows the highest values in summer 2012 likely related to large biomass burning even occurred in Australia in this summer. The undisturbed accumulation rate for this site is demonstrated by the agreement between the chemical stratigraphies and the annual accumulation rate of the two snow-pits analysed in Italian and Korean laboratories. Copyright © 2017 Elsevier Ltd. All rights reserved.

DC-8 Airborne Laboratory in flight over snow-capped Sierra Nevada mountain range

NASA Image and Video Library

1998-02-25

NASA's DC-8 Airborne Laboratory during a flight over the snow-covered Sierra Nevada Mountains. Over the past several years the DC-8 has conducted research missions in such diverse places as the Pacific in spring and Sweden in winter.

Effects of Changing Climate During the Snow Ablation Season on Seasonal Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Gutzler, D. S.; Chavarria, S. B.

2017-12-01

Seasonal forecasts of total surface runoff (Q) in snowmelt-dominated watersheds derive most of their prediction skill from the historical relationship between late winter snowpack (SWE) and subsequent snowmelt runoff. Across the western US, however, the relationship between SWE and Q is weakening as temperatures rise. We describe the effects of climate variability and change during the springtime snow ablation season on water supply outlooks (forecasts of Q) for southwestern rivers. As snow melts earlier, the importance of post-snow rainfall increases: interannual variability of spring season precipitation accounts for an increasing fraction of the variability of Q in recent decades. The results indicate that improvements to the skill of S2S forecasts of spring season temperature and precipitation would contribute very significantly to water supply outlooks that are now based largely on observed SWE. We assess this hypothesis using historical data from several snowpack-dominated basins in the American Southwest (Rio Grande, Pecos, and Gila Rivers) which are undergoing rapid climate change.

Projecting the Dependence of Sage-steppe Vegetation on Redistributed Snow in a Warming Climate.

NASA Astrophysics Data System (ADS)

Soderquist, B.; Kavanagh, K.; Link, T. E.; Seyfried, M. S.; Strand, E. K.

2015-12-01

In mountainous regions, the redistribution of snow by wind can increase the effective precipitation available to vegetation. Moisture subsidies caused by drifting snow may be critical to plant productivity in semi-arid ecosystems. However, with increasing temperatures, the distribution of precipitation is becoming more uniform as rain replaces drifting snow. Understanding the ecohydrological interactions between sagebrush steppe vegetation communities and the heterogeneous distribution of soil moisture is essential for predicting and mitigating future losses in ecosystem diversity and productivity in regions characterized by snow dominated precipitation regimes. To address the dependence of vegetation productivity on redistributed snow, we simulated the net primary production (NPP) of aspen, sagebrush, and C3 grass plant functional types spanning a precipitation phase (rain:snow) gradient in the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO). The biogeochemical process model Biome-BGC was used to simulate NPP at three sites located directly below snowdrifts that provide melt water late into the spring. To assess climate change impacts on future plant productivity, mid-century (2046-2065) NPP was simulated using the average temperature increase from the Multivariate Adaptive Constructed Analogs (MACA) data set under the RCP 8.5 emission scenario. At the driest site, mid-century projections of decreased snow cover and increased growing season evaporative demand resulted in limiting soil moisture up to 30 and 40 days earlier for aspen and sage respectively. While spring green up for aspen occurred an average of 13 days earlier under climate change scenarios, NPP remained negative up to 40 days longer during the growing season. These results indicate that the loss of the soil moisture subsidy stemming from prolonged redistributed snow water resources can directly influence ecosystem productivity in the rain:snow transition zone.

Regional patterns and proximal causes of the recent snowpack decline in the Rocky Mountains, U.S.

USGS Publications Warehouse

Pederson, Gregory T.; Betancourt, Julio L.; McCabe, Gregory J.

2013-01-01

We used a first-order, monthly snow model and observations to disentangle seasonal influences on 20th century,regional snowpack anomalies in the Rocky Mountains of western North America, where interannual variations in cool-season (November–March) temperatures are broadly synchronous, but precipitation is typically antiphased north to south and uncorrelated with temperature. Over the previous eight centuries, regional snowpack variability exhibits strong, decadally persistent north-south (N-S) antiphasing of snowpack anomalies. Contrary to the normal regional antiphasing, two intervals of spatially synchronized snow deficits were identified. Snow deficits shown during the 1930s were synchronized north-south by low cool-season precipitation, with spring warming (February–March) since the 1980s driving the majority of the recent synchronous snow declines, especially across the low to middle elevations. Spring warming strongly influenced low snowpacks in the north after 1958, but not in the south until after 1980. The post-1980, synchronous snow decline reduced snow cover at low to middle elevations by ~20% and partly explains earlier and reduced streamflow and both longer and more active fire seasons. Climatologies of Rocky Mountain snowpack are shown to be seasonally and regionally complex, with Pacific decadal variability positively reinforcing the anthropogenic warming trend.

Assessing the ability of operational snow models to predict snowmelt runoff extremes (Invited)

NASA Astrophysics Data System (ADS)

Wood, A. W.; Restrepo, P. J.; Clark, M. P.

2013-12-01

In the western US, the snow accumulation and melt cycle of winter and spring plays a critical role in the region's water management strategies. Consequently, the ability to predict snowmelt runoff at time scales from days to seasons is a key input for decisions in reservoir management, whether for avoiding flood hazards or supporting environmental flows through the scheduling of releases in spring, or for allocating releases for multi-state water distribution in dry seasons of year (using reservoir systems to provide an invaluable buffer for many sectors against drought). Runoff forecasts thus have important benefits at both wet and dry extremes of the climatological spectrum. The importance of the prediction of the snow cycle motivates an assessment of the strengths and weaknesses of the US's central operational snow model, SNOW17, in contrast to process-modeling alternatives, as they relate to simulating observed snowmelt variability and extremes. To this end, we use a flexible modeling approach that enables an investigation of different choices in model structure, including model physics, parameterization and degree of spatiotemporal discretization. We draw from examples of recent extreme events in western US watersheds and an overall assessment of retrospective model performance to identify fruitful avenues for advancing the modeling basis for the operational prediction of snow-related runoff extremes.

Multi-species patterns of avian cholera mortality in Nebraska's rainwater basin

USGS Publications Warehouse

Blanchong, Julie A.; Samuel, M.D.; Mack, G.

2006-01-01

Nebraska's Rainwater Basin (RWB) is a key spring migration area for millions of waterfowl and other avian species. Avian cholera has been endemic in the RWB since the 1970s and in some years tens of thousands of waterfowl have died from the disease. We evaluated patterns of avian cholera mortality in waterfowl species using the RWB during the last quarter of the 20th century. Mortality patterns changed between the years before (1976 - 1988) and coincident with (1989 - 1999) the dramatic increases in lesser snow goose abundance and mortality. Lesser snow geese (Chen caerulescens caerulescens) have commonly been associated with mortality events in the RWB and are known to carry virulent strains of Pasteurella multocida, the agent causing avian cholera. Lesser snow geese appeared to be the species most affected by avian cholera during 1989 - 1999; however, mortality in several other waterfowl species was positively correlated with lesser snow goose mortality. Coincident with increased lesser snow goose mortality, spring avian cholera outbreaks were detected earlier and ended earlier compared to 1976 - 1988. Dense concentrations of lesser snow geese may facilitate intraspecific disease transmission through bird-to-bird contact and wetland contamination. Rates of interspecific avian cholera transmission within the waterfowl community, however, are difficult to determine.

Evaluating indices of lipid and protein content in lesser snow and Ross's geese during spring migration

USGS Publications Warehouse

Webb, Elisabeth B.; Fowler, Drew N.; Woodall, Brendan A.; Vrtiska, Mark P.

2018-01-01

Assessing nutrient stores in avian species is important for understanding the extent to which body condition influences success or failure in life‐history events. We evaluated predictive models using morphometric characteristics to estimate total body lipids (TBL) and total body protein (TBP), based on traditional proximate analyses, in spring migrating lesser snow geese (Anser caerulescens caerulescens) and Ross's geese (A. rossii). We also compared performance of our lipid model with a previously derived predictive equation for TBL developed for nesting lesser snow geese. We used external and internal measurements on 612 lesser snow and 125 Ross's geese collected during spring migration in 2015 and 2016 within the Central and Mississippi flyways to derive and evaluate predictive models. Using a validation data set, our best performing lipid model for snow geese better predicted TBL (root mean square error [RMSE] of 23.56) compared with a model derived from nesting individuals (RMSE = 48.60), suggesting the importance of season‐specific models for accurate lipid estimation. Models that included body mass and abdominal fat deposit best predicted TBL determined by proximate analysis in both species (lesser snow goose, R2 = 0.87, RMSE = 23.56: Ross's geese, R2 = 0.89, RMSE = 13.75). Models incorporating a combination of external structural measurements in addition to internal muscle and body mass best predicted protein values (R2 = 0.85, RMSE = 19.39 and R2 = 0.85, RMSE = 7.65, lesser snow and Ross's geese, respectively), but protein models including only body mass and body size were also competitive and provided extended utility to our equations for field applications. Therefore, our models indicated the importance of specimen dissection and measurement of the abdominal fat pad to provide the most accurate lipid estimates and provide alternative dissection‐free methods for estimating protein.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, northwest China

NASA Astrophysics Data System (ADS)

Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G.

2017-12-01

Snowmelt water is an important freshwater resource in the Altay Mountains in northwest China, and it is also crucial for local ecological system, economic and social sustainable development; however, warming climate and rapid spring snowmelt can cause floods that endanger both eco-environment and public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature-index model based on remote sensing coupled with high-resolution meteorological data obtained from NCEP reanalysis fields that were downscaled using Weather Research Forecasting model, then bias-corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of temperature-index model based on remote sensing were calibrated for spring 2014, and model performance was validated using MODIS snow cover and snow observations from spring 2012. The results show that the temperature-index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutchliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt runoff was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt runoff accounts for 72% of spring runoff and 21% of annual runoff. Snowmelt is the main source of runoff for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt runoff predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data is lacking.

Long distance migratory songbirds respond to extremes in arctic seasonality

NASA Astrophysics Data System (ADS)

Boelman, N.; Asmus, A.; Chmura, H.; Krause, J.; Perez, J. H.; Sweet, S. K.; Gough, L.; Wingfield, J.

2017-12-01

Arctic regions are warming rapidly, with extreme weather events increasing in frequency, duration and intensity, as in other regions. Many studies have focused on how shifting seasonality in environmental conditions affect the phenology and productivity of vegetation, while far fewer have examined how arctic fauna responds. We studied two species of long-distance migratory songbirds, Lapland longspurs, Calcarius lapponicus, and White-crowned sparrows, Zonotrichia leucophrys gambelii, across seven consecutive breeding seasons in northern Alaskan tundra. We aimed to understand how spring environmental conditions affected breeding cycle phenology, food availability, body condition, stress physiology, and ultimately, reproductive success. Spring temperatures, precipitation, storm frequency, and snow-free dates differed significantly among years, with 2013 characterized by unusually late snow cover, and 2015 and 2016 characterized by unusually early snow-free dates and several late spring snowstorms. In response, we found that relative to other study years, there was a significant delay in breeding cycle phenology for both study species in 2013, while breeding cycle phenology was significantly earlier in 2015 only. For both species, we also found significant variation among years in: the seasonal patterns of arthropod availability during the nesting stage; body condition, and; stress physiology. Finally, we found significant variation in reproductive success of both species across years, and that daily survival rates were decreased by snow storm events. Our findings suggest that arctic-breeding passerine communities may be able to adjust phenology to unpredictable shifts in the timing of spring, but extreme conditions during the incubation and nestling stages are detrimental to reproductive success.

Timing of wet snow avalanche activity: An analysis from Glacier National Park, Montana, USA.

USGS Publications Warehouse

Peitzsch, Erich H.; Hendrikx, Jordy; Fagre, Daniel B.

2012-01-01

Wet snow avalanches pose a problem for annual spring road opening operations along the Going-to-the-Sun Road (GTSR) in Glacier National Park, Montana, USA. A suite of meteorological metrics and snow observations has been used to forecast for wet slab and glide avalanche activity. However, the timing of spring wet slab and glide avalanches is a difficult process to forecast and requires new capabilities. For the 2011 and 2012 spring seasons we tested a previously developed classification tree model which had been trained on data from 2003-2010. For 2011, this model yielded a 91% predictive rate for avalanche days. For 2012, the model failed to capture any of the avalanche days observed. We then investigated these misclassified avalanche days in the 2012 season by comparing them to the misclassified days from the original dataset from which the model was trained. Results showed no significant difference in air temperature variables between this year and the original training data set for these misclassified days. This indicates that 2012 was characterized by avalanche days most similar to those that the model struggled with in the original training data. The original classification tree model showed air temperature to be a significant variable in wet avalanche activity which implies that subsequent movement of meltwater through the snowpack is also important. To further understand the timing of water flow we installed two lysimeters in fall 2011 before snow accumulation. Water flow showed a moderate correlation with air temperature later in the season and no synchronous pattern associated with wet slab and glide avalanche activity. We also characterized snowpack structure as the snowpack transitioned from a dry to a wet snowpack throughout the spring. This helped to assess potential failure layers of wet snow avalanches and the timing of avalanches compared to water moving through the snowpack. These tools (classification tree model and lysimeter data), combined with standard meteorological and avalanche observations, proved useful to forecasters regarding the timing of wet snow avalanche activity along the GTSR.

Confounded winter and spring phenoclimatology on large herbivore ranges

USGS Publications Warehouse

Christianson, David; Klaver, Robert W.; Middleton, Arthur; Kauffman, Matthew

2013-01-01

Annual variation in winter severity and growing season vegetation dynamics appear to influence the demography of temperate herbivores but parsing winter from spring effects requires independent metrics of environmental conditions specific to each season. We tested for independence in annual variation amongst four common metrics used to describe winter severity and early growing season vegetation dynamics across the entire spatial distribution of elk (Cervus elaphus) in Wyoming from 1989 to 2006. Winter conditions and early growing season dynamics were correlated in a specific way. Winters with snow cover that ended early tended to be followed by early, but slow, rises in the normalized difference vegetation index (NDVI), while long winters with extended periods of snow cover were often followed by late and rapid rises in NDVI. Across the 35 elk ranges, 0.4–86.8 % of the variation in the rate of increase in NDVI’s in spring was explained by the date snow cover disappeared from SNOTEL stations. Because phenoclimatological metrics are correlated across seasons and shifting due to climate change, identifying environmental constraints on herbivore fitness, particularly migratory species, is more difficult than previously recognized.

Large-area surveys for black carbon and other light-absorbing impurities in snow: Arctic, Antarctic, North America, China (Invited)

NASA Astrophysics Data System (ADS)

Warren, S. G.; Doherty, S. J.; Hegg, D.; Dang, C.; Zhang, R.; Grenfell, T. C.; Brandt, R. E.; Clarke, A. D.; Zatko, M.

2013-12-01

Absorption of radiation by ice is extremely weak at visible and near-UV wavelengths, so small amounts of light-absorbing impurities (LAI) in snow can dominate the absorption of sunlight at these wavelengths, reducing the albedo relative to that of pure snow and leading to earlier snowmelt. Snow samples were collected in Alaska, Canada, Greenland, Svalbard, Norway, Russia, and the Arctic Ocean, on tundra, glaciers, ice caps, sea ice, and frozen lakes, and in boreal forests. Snow was collected mostly in spring, when the entire winter snowpack was accessible for sampling. Snow was also collected at 67 sites in western North America. Expeditions from Lanzhou University obtained black carbon (BC) amounts at 84 sites in northeast and northwest China. BC was measured at 3 locations on the Antarctic Plateau, and at 5 sites on East Antarctic sea ice. The snow is melted and filtered; the filters are analyzed in a spectrophotometer. Median BC mixing ratios in snow range over 4 orders of magnitude from 0.2 ng/g in Antarctica to 1000 ng/g in northeast China. Chemical analyses, input to a receptor model, indicate that the major source of BC in most of the Arctic is biomass burning, but industrial sources dominate in Svalbard and the central Arctic Ocean. Non-BC impurities, principally brown (organic) carbon, are typically responsible for ~40% of the visible and ultraviolet absorption. In northeast China BC is the dominant LAI, but in Inner Mongolia soil dominates. When the snow surface layer melts, much of the BC is left at the top of the snowpack rather than carried away in meltwater, thus causing a positive feedback on snowmelt. This process was quantified through field studies in Greenland, Alaska, and Norway, where we found that only 10-30% of the BC is removed with meltwater. The BC content of the Arctic atmosphere has declined markedly since 1989, according to the continuous measurements of near-surface air in Canada, Alaska, and Svalbard. Correspondingly, our recent BC amounts for Arctic snow are lower than those reported by Clarke and Noone for 1983-4. It is therefore doubtful that BC in Arctic snow has contributed to the rapid decline of Arctic sea ice in recent years. In much of the Arctic the snow cover, even at its maximum depth in April before melting begins, is thin and patchy; in these regions the albedo is determined more by snow thickness than by impurities. Satellite remote sensing will not be useful to detect BC in Arctic snow, for several reasons, particularly because thin snow has the same spectral signature as sooty snow.

No Snow No Flow: How Montane Stream Networks Respond to Drought

NASA Astrophysics Data System (ADS)

Grant, G.; Nolin, A. W.; Selker, J. S.; Lewis, S.; Hempel, L. A.; Jefferson, A.; Walter, C.; Roques, C.

2015-12-01

Hydrologic extremes, such as drought, offer an exceptional opportunity to explore how runoff generation mechanisms and stream networks respond to changing precipitation regimes. The winter of 2014-2015 was the warmest on record in western Oregon, US, with record low snowpacks, and was followed by an anomalously warm, dry spring, resulting in historically low streamflows. But a year like 2015 is more than an outlier meteorological year. It provides a unique opportunity to test fundamental hypotheses for how montane hydrologic systems will respond to anticipated changes in amount and timing of recharge. In particular, the volcanic Cascade Mountains represent a "landscape laboratory" comprised of two distinct runoff regimes: the surface-flow dominated Western Cascade watersheds, with flashy streamflow regimes, rapid baseflow recession, and very low summer flows; and (b) the spring-fed High Cascade watersheds, with a slow-responding streamflow regime, and a long and sustained baseflow recession that maintains late summer streamflow through deep-groundwater contributions to high volume, coldwater springs. We hypothesize that stream network response to the extremely low snowpack and recharge varies sharply in these two regions. In surface flow dominated streams, the location of channel heads can migrate downstream, contracting the network longitudinally; wetted channel width and depth contract laterally as summer recession proceeds and flows diminish. In contrast, in spring-fed streams, channel heads "jump" to the next downstream spring when upper basin spring flow diminishes to zero. Downstream of flowing springs, wetted channel width and depth contract laterally as flows recede. To test these hypotheses, we conducted a field campaign to measure changing discharge, hydraulic geometry, and channel head location in both types of watersheds throughout the summer and early fall. Multiple cross-section sites were established on 6 streams representing both flow regime types on either side of the Cascade crest. We also took Isotopic water samples to determine recharge elevations of receding streams. Taken together these measurements reveal the processes by which drainage networks contract as flows diminish - a fundamental property of montane stream systems both now and in the future.

The layered evolution of fabric and microstructure of snow at Point Barnola, Central East Antarctica

NASA Astrophysics Data System (ADS)

Calonne, Neige; Montagnat, Maurine; Matzl, Margret; Schneebeli, Martin

2017-02-01

Snow fabric, defined as the distribution of the c-axis orientations of the ice crystals in snow, is poorly known. So far, only one study exits that measured snow fabric based on a statistically representative technique. This recent study has revealed the impact of temperature gradient metamorphism on the evolution of fabric in natural snow, based on cold laboratory experiments. On polar ice sheets, snow properties are currently investigated regarding their strong variability in time and space, notably because of their potential influence on firn processes and consequently on ice core analysis. Here, we present measurements of fabric and microstructure of snow from Point Barnola, East Antarctica (close to Dome C). We analyzed a snow profile from 0 to 3 m depth, where temperature gradients occur. The main contributions of the paper are (1) a detailed characterization of snow in the upper meters of the ice sheet, especially by providing data on snow fabric, and (2) the study of a fundamental snow process, never observed up to now in a natural snowpack, namely the role of temperature gradient metamorphism on the evolution of the snow fabric. Snow samples were scanned by micro-tomography to measure continuous profiles of microstructural properties (density, specific surface area and pore thickness). Fabric analysis was performed using an automatic ice texture analyzer on 77 representative thin sections cut out from the samples. Different types of snow fabric could be identified and persist at depth. Snow fabric is significantly correlated with snow microstructure, pointing to the simultaneous influence of temperature gradient metamorphism on both properties. We propose a mechanism based on preferential grain growth to explain the fabric evolution under temperature gradients. Our work opens the question of how such a layered profile of fabric and microstructure evolves at depth and further influences the physical and mechanical properties of snow and firn. More generally, it opens the way to further studies on the influence of the snow fabric in snow processes related to anisotropic properties of ice such as grain growth, mechanical response, electromagnetic behavior.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

Wind and the associated snow drift are dominating factors determining the snow distribution and accumulation in alpine areas, resulting in a high spatial variability of snow depth that is difficult to evaluate and quantify. The terrain-based parameter Sx characterizes the degree of shelter or exposure of a grid point provided by the upwind terrain, without the computational complexity of numerical wind field models. The parameter has shown to qualitatively predict snow redistribution with good reproduction of spatial patterns, but has failed to quantitatively describe the snow redistribution, and correlations with measured snow heights were poor. The objective of our research was to a) identify the sources of poor correlations between predicted and measured snow re-distribution and b) improve the parameters ability to qualitatively and quantitatively describe snow redistribution in our research area, the Col du Lac Blanc in the French Alps. The area is at an elevation of 2700 m and particularly suited for our study due to its constant wind direction and the availability of data from a meteorological station. Our work focused on areas with terrain edges of approximately 10 m height, and we worked with 1-2 m resolution digital terrain and snow surface data. We first compared the results of the terrain-based parameter calculations to measured snow-depths, obtained by high-accuracy terrestrial laser scan measurements. The results were similar to previous studies: The parameter was able to reproduce observed patterns in snow distribution, but regression analyses showed poor correlations between terrain-based parameter and measured snow-depths. We demonstrate how the correlations between measured and calculated snow heights improve if the parameter is calculated based on a snow surface model instead of a digital terrain model. We show how changing the parameter's search distance and how raster re-sampling and raster smoothing improve the results. To improve the parameter's quantitative abilities, we modified the parameter, based on the comparisons with TLS data and the terrain and wind conditions specific to the research site. The modification is in a linear form f(x) = a * Sx, where a is a newly introduced parameter; f(x) yields the estimates for the snow height. We found that the parameter depends on the time period between the compared snow surfaces and the intensity of drifting snow events, which are linked to wind velocities. At the Col du Lac Blanc test side, blowing snow flux is recorded with snow particle counters (SPC). Snow flux is the number of drifting snow particles per time and area. Hence, the SPC provide data about the duration and intensity of drifting snow events, two important factors not accounted for by the terrain parameter Sx. We analyse how the SPC snow flux data can be used to estimate the magnitude of the new variable parameter a. We could improve the parameters' correlations with measured snow heights and its ability to quantitatively describe snow distribution in the Col du Lac Blanc area. We believe that our work is also a prerequisite to further improve the parameter's ability to describe snow redistribution.

Neutral Poly-/perfluoroalkyl Substances in Air and Snow from the Arctic

PubMed Central

Xie, Zhiyong; Wang, Zhen; Mi, Wenying; Möller, Axel; Wolschke, Hendrik; Ebinghaus, Ralf

2015-01-01

Levels of neutral poly-/perfluoroalkyl substances (nPFASs) in air and snow collected from Ny-Ålesund were measured and their air-snow exchange was determined to investigate whether they could re-volatilize into the atmosphere driven by means of air-snow exchange. The total concentration of 12 neutral PFASs ranged from 6.7 to 39 pg m−3 in air and from 330 to 690 pg L−1 in snow. A significant log-linear relationship was observed between the gas/particle partition coefficient and vapor pressure of the neutral PFASs. For fluorotelomer alcohol (FTOHs) and fluorotelomer acrylates (FTAs), the air-snow exchange fluxes were positive, indicating net evaporative from snow into air, while net deposition into snow was observed for perfluorooctane sulfonamidoethanols (Me/EtFOSEs) in winter and spring of 2012. The air-snow exchange was snow-phase controlled for FTOHs and FTAs, and controlled by the air-phase for FOSEs. Air-snow exchange may significantly interfere with atmospheric concentrations of neutral PFASs in the Arctic. PMID:25746440

Southeast Michigan Snow and Ice Management (SEMSIM) : final evaluation at end of winter season, year 2004

DOT National Transportation Integrated Search

2004-08-01

In the early spring of 1999, the Detroit Department of Public Works, the Road Commission of Macomb County, the Road Commission for Oakland County, and the Wayne County Department of Public Services formed the Southeast Michigan Snow and Ice Managemen...

Assessment of the sensitivity of radar backscatter to seasonal snow and vegetation thaw dynamics in a boreal ecosystem

NASA Technical Reports Server (NTRS)

McDonald, K. C.; Qualls, B.; Hardy, J.

2002-01-01

We examine the sensitivity of ERS-1 C-band synthetic aperture radar (SAR) backscatter to springtime snow and vegetation thaw dynamics for boreal forest stands within the BOREAS Southern Study Area (SSA) in Canada during the 1994 winter-spring thaw transition.

Anatomy of a late spring snowfall on sea ice

NASA Astrophysics Data System (ADS)

Perovich, Donald; Polashenski, Christopher; Arntsen, Alexandra; Stwertka, Carolyn

2017-03-01

Spring melt initiation is a critical process for Arctic sea ice. Melting conditions decrease surface albedo at a time of high insolation, triggering powerful albedo feedback. Weather events during melt initiation, such as new snowfalls, can stop or reverse the albedo decline, however. Here we present field observations of such a snow event and demonstrate its enduring impact through summer. Snow fell 3-6 June 2014 in the Chukchi Sea, halting melt onset. The snow not only raised albedo but also provided a significant negative latent heat flux, averaging -51 W m-2 from 3 to 6 June. The snowfall delayed sustained melt by 11 days, creating cascading impacts on surface energy balance that totaled some 135 MJ/m2 by mid-August. The findings highlight the sensitivity of sea ice conditions on seasonal time scales to melt initiation processes.

A World of Snowy Dunes

NASA Image and Video Library

2017-08-21

It is spring in the Northern hemisphere when NASA's Mars Reconnaissance Orbiter took this image. Over the winter, snow and ice have inexorably covered the dunes. Unlike on Earth, this snow and ice is carbon dioxide, better known to us as dry ice. When the sun starts shining on it in the spring, the ice on the smooth surface of the dune cracks and escaping gas carries dark sand out from the dune below, often creating beautiful patterns. On the rough surface between the dunes, frost is trapped behind small sheltered ridges. https://photojournal.jpl.nasa.gov/catalog/PIA21882

Quantifying Source Sector and Region Contributions of BC and Dust Deposition on the Arctic Snow and the Resulting Albedo Reduction

NASA Astrophysics Data System (ADS)

Sobhani, N.; Gregory, C.; Kulkarni, S.

2017-12-01

Long-range transport of atmospheric particulate matter (PM) from mid-latitude sources to the Arctic is the main contributor to the Arctic PM loadings and deposition. Light absorbing particles such as Black Carbon (BC) and dust are considered of great climatic importance and are the main absorbers of sunlight in the atmosphere. Wet and dry deposition of light absorbing particles (LAPs) on snow and ice cause reduction of snow and ice albedo. LAPs have significant radiative forcing and effect on snow albedo causing snow and ice to warm and melt more quickly. There are large uncertainties in estimating radiative forcing of LAPs. In this study, the potential impacts of LAPs from different emission source regions and sectors on snow albedo in the Arctic are studied. A modeling framework including Weather Research and Forecasting Model (WRF) and the University of Iowa's Sulfur Transport and dEpostion model (STEM) is used to simulate the seasonality and transport of LAPs from different geographical sources and sectors (i.e. transportation, residential, industry, biomass burning and power) to the Arctic. The main geographical source contributor to the Arctic BC annual deposition flux is China. However, there is a distinct seasonal variation for the contributions of geographical source emissions to BC deposition. During the spring, when the deposition flux is highest, the contribution of biomass burning attributes for up to 40% of total deposition at Alert and Barrow. However, during the winter, the anthropogenic sectors contribute up to 95% of total BC deposition. The simulated snow BC mixing ratios are evaluated using the observed BC snow concentration values from previous studies including Doherty et al., 2010. The simulations show the BC deposition causes 0.6% snow albedo decrease during spring 2008 over the Arctic.

Some relationships among air, snow, and soil temperatures and soil frost

Treesearch

George Hart; Howard W. Lull

1963-01-01

Each winter gives examples of the insulating properties of snow cover. Seeds and soil fauna are protected from the cold by snow. Underground water pipes are less likely to freeze under snow cover. And, according to many observers, the occurrence, penetration, and thaw of soil frost are affected by snow cover. The depth of snow necessary to protect soil from freezing...

Estimated snow parameters for vehicle mobility modeling in Korea, Germany and interior Alaska

DOT National Transportation Integrated Search

1995-09-01

Snow is a crucial factor affecting the U.S. Army's operations in cold regions. Values for snow depth and snow density are needed for vehicle mobility studies, but unfortunately the available historical records of these parameters tend to be relativel...

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

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

1973-01-01

The author has identified the following significant results. New fracture detail within New England test area has been interpreted from ERTS-1 images. Comparative analysis of snow-free imagery (1096-15065 and 1096-15072) has demonstrated that MSS bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images (1132-15074 and 1168-15065) in correlation with ground snow depth data indicates that a heavy blanket of snow (less than 9 inches) accentuates major structural features while a light dusting (greater than 1 inch) accentuates more subtle topographic expressions. Snow cover was found to accentuate drainage patterns which are indicative of lithological and/or structural variations. Snow cover provided added enhancement for viewing and detecting topographically expressed fractures and faults. A recent field investigation was conducted within the New England test area to field check lineaments observed from analysis of ERTS-1 imagery, collect snow depth readings, and obtain structural joint readings at key locations in the test area.

Snowcover influence on backscattering from terrain

NASA Technical Reports Server (NTRS)

Ulaby, F. T.; Abdelrazik, M.; Stiles, W. H.

1984-01-01

The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g/cu cm). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.

Using wireless sensor networks to improve understanding of rain-on-snow events across the Sierra Nevada

NASA Astrophysics Data System (ADS)

Maurer, T.; Avanzi, F.; Oroza, C.; Malek, S. A.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2017-12-01

We use data gathered from Wireless Sensor Networks (WSNs) between 2008 and 2017 to investigate the temporal/spatial patterns of rain-on-snow events in three river basins of California's Sierra Nevada. Rain-on-snow transitions occur across a broad elevation range (several hundred meters), both between storms and within a given storm, creating an opportunity to use spatially and temporally dense data to forecast and study them. WSNs collect snow depth; meteorological data; and soil moisture and temperature data across relatively dense sensor clusters. Ten to twelve measurement nodes per cluster are placed across 1-km2 areas in locations representative of snow patterns at larger scales. Combining precipitation and snow data from snow-pillow and climate stations with an estimation of dew-point temperature from WSNs, we determine the frequency, timing, and geographic extent of rain-on-snow events. We compare these results to WSN data to evaluate the impact of rain-on-snow events on snowpack energy balance, density, and depth as well as on soil moisture. Rain-on-snow events are compared to dry warm-weather days to identify the relative importance of rain and radiation as the primary energy input to the snowpack for snowmelt generation. An intercomparison of rain-on-snow events for the WSNs in the Feather, American, and Kings River basins captures the behavior across a 2° latitudinal range of the Sierra Nevada. Rain-on-snow events are potentially a more important streamflow generation mechanism in the lower-elevation Feather River basin. Snowmelt response to rain-on-snow events changes throughout the wet season, with later events resulting in more melt due to snow isothermal conditions, coarser grain size, and more-homogeneous snow stratigraphy. Regardless of snowmelt response, rain-on-snow events tend to result in decreasing snow depth and a corresponding increase in snow density. Our results demonstrate that strategically placed WSNs can provide the necessary data at high temporal resolution to investigate how hydrologic responses evolve in both space and time, data not available from operational networks.

Inventory of File sref_em.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 0-6 hour surface WEASD 0-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour -6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 415 surface SNOM 0-6 hour acc Snow

Improved quantification of mountain snowpack properties using observations from Unmanned Air Vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Shea, J. M.; Harder, P.; Pomeroy, J. W.; Kraaijenbrink, P. D. A.

2017-12-01

Mountain snowpacks represent a critical seasonal reservoir of water for downstream needs, and snowmelt is a significant component of mountain hydrological budgets. Ground-based point measurements are unable to describe the full spatial variability of snow accumulation and melt rates, and repeat Unmanned Air Vehicle (UAV) surveys provide an unparalleled opportunity to measure snow accumulation, redistribution and melt in alpine environments. This study presents results from a UAV-based observation campaign conducted at the Fortress Mountain Snow Laboratory in the Canadian Rockies in 2017. Seven survey flights were conducted between April (maximum snow accumulation) and mid-July (bare ground) to collect imagery with both an RGB camera and thermal infrared imager with the sensefly eBee RTK platform. UAV imagery are processed with structure from motion techniques, and orthoimages, digital elevation models, and surface temperature maps are validated against concurrent ground observations of snow depth, snow water equivalent, and snow surface temperature. We examine the seasonal evolution of snow depth and snow surface temperature, and explore the spatial covariances of these variables with respect to topographic factors and snow ablation rates. Our results have direct implications for scaling snow ablation calculations and model resolution and discretization.

A novel approach for automatic snow depth estimation using UAV-taken images without ground control points

NASA Astrophysics Data System (ADS)

Mizinski, Bartlomiej; Niedzielski, Tomasz

2017-04-01

Recent developments in snow depth reconstruction based on remote sensing techniques include the use of photographs of snow-covered terrain taken by unmanned aerial vehicles (UAVs). There are several approaches that utilize visible-light photos (RGB) or near infrared images (NIR). The majority of the methods in question are based on reconstructing the digital surface model (DSM) of the snow-covered area with the use of the Structure-from-Motion (SfM) algorithm and the stereo-vision software. Having reconstructed the above-mentioned DSM it is straightforward to calculate the snow depth map which may be produced as a difference between the DSM of snow-covered terrain and the snow-free DSM, known as the reference surface. In order to use the aforementioned procedure, the high spatial accuracy of the two DSMs must be ensured. Traditionally, this is done using the ground control points (GCPs), either artificial or natural terrain features that are visible on aerial images, the coordinates of which are measured in the field using the Global Navigation Satellite System (GNSS) receiver by qualified personnel. The field measurements may be time-taking (GCPs must be well distributed in the study area, therefore the field experts should travel over long distances) and dangerous (the field experts may be exposed to avalanche risk or cold). Thus, there is a need to elaborate methods that enable the above-mentioned automatic snow depth map production without the use of GCPs. One of such attempts is shown in this paper which aims to present the novel method which is based on real-time processing of snow-covered and snow-free dense point clouds produced by SfM. The two stage georeferencing is proposed. The initial (low accuracy) one assigns true geographic, and subsequently projected, coordinates to the two dense point clouds, while the said initially-registered dense point clouds are matched using the iterative closest point (ICP) algorithm in the final (high accuracy) stage. The stable reference is offered by specially-selected trees which are located in the vicinity of the terrain of interest. The method has already been implemented and along with the presentation of its concept, a few case studies from the Izerskie Mountains (southwestern Poland) are discussed. Although the method reveals several constraints, it may serve the purpose of generating the snow depth maps with reasonable accuracy, in particular in the absence of GCPs. The snow depth estimation algorithm has been elaborated in frame of the research grant no. LIDER/012/223/L-5/13/NCBR/2014 financed by the National Centre for Research and Development of Poland.

Optimum soil frost depth to alleviate climate change effects in cold region agriculture

NASA Astrophysics Data System (ADS)

Yanai, Yosuke; Iwata, Yukiyoshi; Hirota, Tomoyoshi

2017-03-01

On-farm soil frost control has been used for the management of volunteer potatoes (Solanum tuberosum L.), a serious weed problem caused by climate change, in northern Japan. Deep soil frost penetration is necessary for the effective eradication of unharvested small potato tubers; however, this process can delay soil thaw and increase soil wetting in spring, thereby delaying agricultural activity initiation and increasing nitrous oxide emissions from soil. Conversely, shallow soil frost development helps over-wintering of unharvested potato tubers and nitrate leaching from surface soil owing to the periodic infiltration of snowmelt water. In this study, we synthesised on-farm snow cover manipulation experiments to determine the optimum soil frost depth that can eradicate unharvested potato tubers without affecting agricultural activity initiation while minimising N pollution from agricultural soil. The optimum soil frost depth was estimated to be 0.28-0.33 m on the basis of the annual maximum soil frost depth. Soil frost control is a promising practice to alleviate climate change effects on agriculture in cold regions, which was initiated by local farmers and further promoted by national and local research institutes.

Optimum soil frost depth to alleviate climate change effects in cold region agriculture.

PubMed

Yanai, Yosuke; Iwata, Yukiyoshi; Hirota, Tomoyoshi

2017-03-21

On-farm soil frost control has been used for the management of volunteer potatoes (Solanum tuberosum L.), a serious weed problem caused by climate change, in northern Japan. Deep soil frost penetration is necessary for the effective eradication of unharvested small potato tubers; however, this process can delay soil thaw and increase soil wetting in spring, thereby delaying agricultural activity initiation and increasing nitrous oxide emissions from soil. Conversely, shallow soil frost development helps over-wintering of unharvested potato tubers and nitrate leaching from surface soil owing to the periodic infiltration of snowmelt water. In this study, we synthesised on-farm snow cover manipulation experiments to determine the optimum soil frost depth that can eradicate unharvested potato tubers without affecting agricultural activity initiation while minimising N pollution from agricultural soil. The optimum soil frost depth was estimated to be 0.28-0.33 m on the basis of the annual maximum soil frost depth. Soil frost control is a promising practice to alleviate climate change effects on agriculture in cold regions, which was initiated by local farmers and further promoted by national and local research institutes.

Design and development of a wireless sensor network to monitor snow depth in multiple catchments in the American River basin, California: hardware selection and sensor placement techniques

NASA Astrophysics Data System (ADS)

Kerkez, B.; Rice, R.; Glaser, S. D.; Bales, R. C.; Saksa, P. C.

2010-12-01

A 100-node wireless sensor network (WSN) was designed for the purpose of monitoring snow depth in two watersheds, spanning 3 km2 in the American River basin, in the central Sierra Nevada of California. The network will be deployed as a prototype project that will become a core element of a larger water information system for the Sierra Nevada. The site conditions range from mid-elevation forested areas to sub-alpine terrain with light forest cover. Extreme temperature and humidity fluctuations, along with heavy rain and snowfall events, create particularly challenging conditions for wireless communications. We show how statistics gathered from a previously deployed 60-node WSN, located in the Southern Sierra Critical Zone Observatory, were used to inform design. We adapted robust network hardware, manufactured by Dust Networks for highly demanding industrial monitoring, and added linear amplifiers to the radios to improve transmission distances. We also designed a custom data-logging board to interface the WSN hardware with snow-depth sensors. Due to the large distance between sensing locations, and complexity of terrain, we analyzed network statistics to select the location of repeater nodes, to create a redundant and reliable mesh. This optimized network topology will maximize transmission distances, while ensuring power-efficient network operations throughout harsh winter conditions. At least 30 of the 100 nodes will actively sense snow depth, while the remainder will act as sensor-ready repeaters in the mesh. Data from a previously conducted snow survey was used to create a Gaussian Process model of snow depth; variance estimates produced by this model were used to suggest near-optimal locations for snow-depth sensors to measure the variability across a 1 km2 grid. We compare the locations selected by the sensor placement algorithm to those made through expert opinion, and offer explanations for differences resulting from each approach.

Variability in snowpack accumulation and ablation associated with mountain pine beetle infestation in western forests

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Harpold, A. A.; Gochis, D. J.; Reed, D.; Brooks, P. D.

2010-12-01

Seasonal snowcover is a primary source of water to urban and agricultural regions in the western United States, where Mountain Pine Beetle (MPB) has caused rapid and extensive changes to vegetation in montane forests. Levels of MPB infestation in these seasonally snow-covered systems are unprecedented, and it is unknown how this will affect water yield, especially in changing climate conditions. To address this unknown we ask: How does snow accumulation and ablation vary across forest with differing levels of impact? Our study areas in the Rocky Mountains of CO and WY are similar in latitude, elevation and forest structure before infestation, but they vary in the intensity and timing of beetle infestation and tree mortality. We present a record for winter 2010 that includes continuous snow depth as well as stand-scale snow surveys at maximum accumulation. Additional measurements include snowfall, net radiation, temperature and wind speed as well as characterization of forest structure by leaf area index. In a stand uninfested by MPB, maximum snow depth was fairly uniform under canopy (mean = 86 cm, coefficient of variation = 0.021), while canopy gaps showed greater and more variable depth (mean = 117 cm, CV = 0.111). This is consistent with several studies demonstrating that snowfall into canopy gaps depends upon gap size, orientation, wind speed and storm size. In a stand impacted in 2007, snow depth under canopy was less uniform, and there were smaller differences in both mean depth and variability between canopy (mean = 93 cm, CV = 0.072) and gaps (mean = 97 cm, CV = 0.070), consistent with decreased canopy density. In a more recently infested (2009) stand with an intermediate level of MPB impact, mean snow depths were similar between canopy (96 cm, CV = 0.016) and gaps (95 cm, CV = 0.185) but gaps showed much greater variability, suggesting controls similar to those in effect in the uninfested stand. We further use these data to model snow accumulation and ablation as a function of vegetation, topography and fine-scale climate variability, with preliminary results presented at the meeting.

Insights into seasonal active layer dynamics by monitoring relative velocity changes using ambient seismic noise

NASA Astrophysics Data System (ADS)

James, S. R.; Knox, H. A.; Cole, C. J.; Abbott, R. E.; Screaton, E.

2016-12-01

Seasonal freeze and thaw of the active layer above permafrost results in dramatic changes in seismic velocity. We used daily cross correlations of ambient seismic noise recorded at Poker Flat Research Range in central Alaska to create a nearly continuous 2-year record of relative velocity changes. This analysis required that we modify the Moving Window Cross-spectral Analysis technique used in the Python package MSNoise to reduce the occurrence of cycle skipping. Results show relative velocity variations follow a seasonal pattern, where velocities decrease in late spring through the summer months and increase through the fall and winter months. This timing is consistent with active layer freeze and thaw in this region. These results were compared to a suite of ground- and satellite-based measurements to identify relationships. A decrease in relative velocities in late spring closely follows the timing of snow melt recorded in nearby ground temperatures and snow-depth logs. This transition also aligns with a decrease in the Normalized Difference Snow Index (NDSI) derived from multi-temporal Landsat 8 satellite imagery collected over the study site. A gradual increase in relative velocity through the fall months occurs when temperatures below ground surface remain near zero. We suggest this is due to latent heat feedbacks that keep temperatures constant while active layer velocities increase from continued ice formation. This highlights the value in velocity variations for capturing details on the freezing process. In addition, spatial variations in the magnitude of velocity changes are consistent with thaw probe surveys. Exploring relationships with remote sensing may allow indirect measurements of thaw over larger areas and further surface wave analysis may allow for thickness evolution measurements. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

NASA Satellite Images Annual Spring Thaw, Red River, North Dakota

NASA Image and Video Library

2011-04-21

NASA Terra spacecraft shows the annual spring thaw in the upper Midwest is underway. Snow-covered ground contrasts with the dark tones of water under broken cloud cover. Along the Red River in North Dakota, floodwaters are moving northward into Canada.

Snow Depth Calibrations for Electromagnetic Induction Investigations at a Former Munitions Waste Disposal Site in Alaska

NASA Astrophysics Data System (ADS)

Glaser, D. R., II; Wagner, A. M.; Gelvin, A.; Saari, S.; Staples, A.; Larsen, G.

2017-12-01

A US Army legacy munitions waste site was identified adjacent to a river near a small arms range in Alaska. As part of remediation efforts, geophysical studies were conducted to characterize the extent of buried metal debris at the site. Time-domain electromagnetic surveys were completed over the site to meet the regulatory guidance for site cleanup. Time-domain and frequency-domain electromagnetic induction, magnetic gradiometry, and ground penetrating radar subsurface geophysical studies were deployed over soil, water, and snow surface conditions throughout the impacted area. The time-domain electromagnetic induction results acquired during summer months, presented clear indications of trenches located directly perpendicular to and adjacent to the river. However, in the follow up investigation where the snow-pack was greater than one meter, the response amplitude of the metallic debris was dampened and possible targets were missed. This was confirmed by the subsequent magnetic gradiometry survey which identified a suspected extension of one of the trenches through the river on to the seasonal sand bar island. The region is subject to extremely cold temperatures as well as significant snow pack and permafrost soil conditions. The snow presented a negative impact to the accurate assessment of the site by changing the effective investigation depth. To address this we developed an approach using ground penetrating radar data calibrated with physical snow depth measurements to generate continuous estimates of snow depth and spatially correct the electromagnetic induction data to the corresponding regulatory amplitude limit as if the snow were not present. Limitations of the approach as related to the signal floor of the electromagnetic induction response were also assessed.

Inventory of File sref_nmb.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour (non-convective) [kg/m^2] 417 surface SNOM 3-6 hour acc Snow Melt [kg/m^2] 418 surface LHTFL 3-6 hour

Inventory of File sref_nmm.t03z.pgrb212.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour (non-convective) [kg/m^2] 417 surface SNOM 3-6 hour acc Snow Melt [kg/m^2] 418 surface LHTFL 0-6 hour

7 CFR 612.3 - Data collected and forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.3 Data..., and wind. (b) Water supply forecasts in the western states area are generally made monthly from.... Data sites generally include a snow course where both snow depth and water equivalent of snow are...

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Foster, J. L.; Rango, A.; Hall, D. K.

1980-01-01

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia were studied in an effort to determine the utility of spaceborne electrical scanning microwave radiometers (ESMR) for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In the areas investigated, Nimbus 6 (.081 cm) ESMR data produced higher correlations than Nimbus 5 (1.55 cm) ESMR data in relating microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.

Snowmelt sensitivity to warmer temperatures: a field-validated model analysis, southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Musselman, K. N.; Molotch, N. P.; Margulis, S. A.

2014-12-01

We present model simulations of climate change impacts on snowmelt processes over a 1600 km2 area in the southern Sierra Nevada, including western Sequoia National Park. The domain spans a 3600 m elevation gradient and ecosystems ranging from semi-arid grasslands to giant sequoia groves to alpine tundra. Three reference years were evaluated: a moderately dry snow season (23% below average SWE), an average snow season (7% above average SWE), and a moderately wet snow season (54% above average SWE). The Alpine3D model was run for the reference years and results were evaluated against data from a multi-scale measurement campaign that included repeated manual snow courses and basin-scale snow surveys, dozens of automated snow depth sensors, and automated SWE stations. Compared to automated measurements, the model represented the date of snow disappearance within two days. Compared to manual measurements, model SWE RMSE values for the average and wet snow seasons were highly correlated (R2=0.89 and R2=0.73) with the distance of SWE measurements from the nearest precipitation gauge used to force the model; no significant correlation was found with elevation. The results suggest that Alpine3D is highly accurate during the melt season and that precipitation uncertainty may critically limit snow model accuracy. The air temperature measured at 19 regional stations for the three reference years was modified by +1°C to +6°C to simulate the impact of warmer temperatures on snowmelt dynamics over the 3600 m elevation gradient. For all years, progressively warmer temperatures caused the seasonal SWE centroid to shift earlier and higher in elevation. At forested middle elevations, 70 - 80% of the present-day snowpack volume is lost in a +2°C scenario; 30 - 40% of that change is a result of precipitation phase shift and the remainder is due to enhanced melt. At all elevations, spring and fall snowpack was most sensitive to warmer temperatures; mid-winter sensitivity was least for elevations >3100 m. Interestingly, the dominant effect of warmer temperatures on snowmelt was a reduction in daily melt rates. The drier year was most sensitive to temperature changes with a greater decrease in the number of days with high melt rates. The results offer insight into the sensitivity of snowmelt processes to warmer temperatures in the Sierra Nevada.

Assessment of the timing of daily peak streamflow during melt season in a snow dominated watershed

USDA-ARS?s Scientific Manuscript database

Previous studies have shown that gauge-observed daily streamflow peak times (DPT) during spring snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three processes that affect the timing of snowmelt arrival: 1) melt flux translation through the snow...

7 CFR 916.356 - California Nectarine Grade and Size Regulation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... causes. Damage to any nectarine is serious when it causes a waste of 10 percent or more, by volume, of... container of Arctic Star, Burnectone (Spring Ray®), Burnecttwelve (Sweet Flair® 21), Burnectthirteen (Snow Flare® 22), Burnectfourteen (Snow Flare® 21), Diamond Bright, Diamond Pearl, Early Pearl, Gee Sweet...

7 CFR 916.356 - California Nectarine Grade and Size Regulation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... causes. Damage to any nectarine is serious when it causes a waste of 10 percent or more, by volume, of..., Burnectone (Spring Ray®), Burnecttwelve (Sweet Flair® 21), Burnectthirteen (Snow Flare® 22), Burnectfourteen (Snow Flare® 21), Diamond Bright, Diamond Pearl, Gee Sweet, Honey Lite, June Pearl, June Sweet, Kay...

7 CFR 917.459 - California Peach Grade and Size Regulation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... serious when it causes a waste of 10 percent or more, by volume, of the individual peach. (iii) Tolerances... Earlitreat, Snow Angel, Supechfifteen, or Super Lady variety peaches unless: (i) Such peaches when packed in... Prince, Snow Peak, Spring Princess, or Super Rich variety peaches unless: (i) Such peaches when packed in...

Mapping snow depth in alpine terrain with remotely piloted aerial systems and structure-from-motion photogrammetry - first results from a pilot study

NASA Astrophysics Data System (ADS)

Adams, Marc; Fromm, Reinhard; Bühler, Yves; Bösch, Ruedi; Ginzler, Christian

2016-04-01

Detailed information on the spatio-temporal distribution of seasonal snow in the alpine terrain plays a major role for the hydrological cycle, natural hazard management, flora and fauna, as well as tourism. Current methods are mostly only valid on a regional scale or require a trade-off between the data's availability, cost and resolution. During a one-year pilot study, we investigated the potential of remotely piloted aerial systems (RPAS) and structure-from-motion photogrammetry for snow depth mapping. We employed multi-copter and fixed-wing RPAS, equipped with different low-cost, off-the shelf sensors, at four test sites in Austria and Switzerland. Over 30 flights were performed during the winter 2014/15, where different camera settings, filters and lenses, as well as data collection routines were tested. Orthophotos and digital surface models (DSM) where calculated from the imagery using structure-from-motion photogrammetry software. Snow height was derived by subtracting snow-free from snow-covered DSMs. The RPAS-results were validated against data collected using a variety of well-established remote sensing (i.e. terrestrial laser scanning, large frame aerial sensors) and in-situ measurement techniques. The results show, that RPAS i) are able to map snow depth within accuracies of 0.07-0.15 m root mean square error (RMSE), when compared to traditional in-situ data; ii) can be operated at lower cost, easier repeatability, less operational constraints and higher GSD than large frame aerial sensors on-board manned aircraft, while achieving significantly higher accuracies; iii) are able to acquire meaningful data even under harsh environmental conditions above 2000 m a.s.l. (turbulence, low temperature and high irradiance, low air density). While providing a first prove-of-concept, the study also showed future challenges and limitations of RPAS-based snow depth mapping, including a high dependency on correct co-registration of snow-free and snow-covered height measurements, as well as a significant impact of the underlying vegetation and illumination of the snow surface on the fidelity of the results.

Impacts of peatland forestation on regional climate conditions in Finland

NASA Astrophysics Data System (ADS)

Gao, Yao; Markkanen, Tiina; Backman, Leif; Henttonen, Helena M.; Pietikäinen, Joni-Pekka; Laaksonen, Ari

2014-05-01

Climate response to anthropogenic land cover change happens more locally and occurs on a shorter time scale than the global warming due to increased GHGs. Over the second half of last Century, peatlands were vastly drained in Finland to stimulate forest growth for timber production. In this study, we investigate the biophysical effects of peatland forestation on near-surface climate conditions in Finland. For this, the regional climate model REMO, developed in Max Plank Institute (currently in Climate Service Center, Germany), provides an effective way. Two sets of 15-year climate simulations were done by REMO, using the historic (1920s; The 1st Finnish National Forest Inventory) and present-day (2000s; the 10th Finnish National Forest Inventory) land cover maps, respectively. The simulated surface air temperature and precipitation were then analyzed. In the most intensive peatland forestation area in Finland, the differences in monthly averaged daily mean surface air temperature show a warming effect around 0.2 to 0.3 K in February and March and reach to 0.5 K in April, whereas a slight cooling effect, less than 0.2 K, is found from May till October. Consequently, the selected snow clearance dates in model gridboxes over that area are advanced 0.5 to 4 days in the mean of 15 years. The monthly averaged precipitation only shows small differences, less than 10 mm/month, in a varied pattern in Finland from April to September. Furthermore, a more detailed analysis was conducted on the peatland forestation area with a 23% decrease in peatland and a 15% increase in forest types. 11 day running means of simulated temperature and energy balance terms, as well as snow depth were averaged over 15 years. Results show a positive feedback induced by peatland forestation between the surface air temperature and snow depth in snow melting period. This is because the warmer temperature caused by lower surface albedo due to more forest in snow cover period leads to a quicker and earlier snow melting. Meanwhile, surface albedo is reduced and consequently surface air temperature is increased. Additionally, the maximum difference from individual gridboxes in this area over 15 years of 11 day running means of daily mean surface air temperature reaches 2 K, which is four times as much as the maximum difference of 15-year regional average of that. This illustrates that the spring warming effect from peatland forestation in Finland is highly heterogeneous spatially and temporally.

The Impacts of Pine Tree Die-Off on Snow Accumulation and Distribution at Plot to Catchment Scales

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Harpold, A. A.; Gutmann, E. D.; Reed, D. E.; Gochis, D. J.; Brooks, P. D.

2011-12-01

Seasonal snow cover is a primary water source throughout much of Western North America, where insect-induced tree die-off is changing the montane landscape. Widespread mortality from insects or drought differs from well-studied cases of fire and logging in that tree mortality is not accompanied by other immediate biophysical changes. Much of the impacted landscape is a mosaic of stands of varying species, structure, management history and health overlain on complex terrain. To address the challenge of predicting the effects of forest die-off on snow water input, we quantified snow accumulation and ablation at scales ranging from individual trees, through forest stands, to nested small catchments. Our study sites in Northern Colorado and Southern Wyoming are dominated by lodgepole pine, but they include forest stands that are naturally developed, managed and clear-cut with varying mortality from Mountain Pine Beetle (MPB). Our record for winters 2010 and 2011 includes continuous meteorological data and snow depth in plots with varying MPB impact as well as stand- to catchment-scale snow surveys mid-winter and near maximal accumulation. At the plot scale, snow depth sensors in healthy stands recorded greater inputs during storms (21-42% of depth) and greater seasonal accumulation (15-40%) in canopy gaps than under trees, whereas no spatial effects of canopy geometry were observed in stands with heavy mortality. Similar patterns were observed in snow surveys near peak accumulation. At both impacted and thinned sites, spatial variability in snow depth was more closely associated with larger scale topography and changes in stand structure than with canopy cover. The role of aspect in ablation was observed to increase in impacted stands as both shading and wind attenuation decreased. Evidence of wind-controlled snow distribution was found 80-100 meters from exposed stand edges in impacted forest as compared to 10-15 meters in healthy forest. Integrating from the scale of stands to small catchments, maximal snow water equivalent (SWE) as a fraction of winter precipitation (P) ranged from 62 to 74%. Despite an expectation of decreased interception and increased snow accumulation with advanced mortality, surveys at stand and catchment scales found no significant increases in net snow water input between healthy and impacted forests. These observations suggest that the spatial scale of processes affecting net snow accumulation and ablation increase following die-off. Using data from our sites and other studies, this presentation will develop a predictive model of how interception, shading, and wind redistribution interact to control net snow water input following large-scale forest mortality.

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

Quantifying Temperature Effects on Snow, Plant and Streamflow Dynamics in Headwater Catchments

NASA Astrophysics Data System (ADS)

Wainwright, H. M.; Sarah, T.; Siirila-Woodburn, E. R.; Newcomer, M. E.; Williams, K. H.; Hubbard, S. S.; Enquist, B. J.; Steltzer, H.; Carroll, R. W. H.

2017-12-01

Quantifying Temperature Effects on Snow, Plant and Streamflow Dynamics in Headwater Catchments Snow-dominated headwater catchments are critical for water resource throughout the world; particularly in Western US. Under climate change, temperature increases are expected to be amplified in mountainous regions. We use a data-driven approach to better understand the coupling among inter-annual variability in temperature, snow and plant community dynamics and stream discharge. We apply data mining methods (e.g., principal component analysis, random forest) to historical spatiotemporal datasets, including the SNOTEL data, Landsat-based normalized difference vegetation index (NDVI) and airborne LiDAR-based snow distribution. Although both snow distribution and NDVI are extremely heterogeneous spatially, the inter-annual variability and temporal responses are spatially consistent, providing an opportunity to quantify the effect of temperature in the catchment-scale. We demonstrate our approach in the East River Watershed of the Upper Colorado River Basin, including Rocky Mountain Biological Laboratory, where the changes in plant communities and their dynamics have been extensively documented. Results indicate that temperature - particularly spring temperature - has a significant control not only on the timing of snowmelt, plant NDVI and peak flow but also on the magnitude of peak NDVI, peak flow and annual discharge. Monthly temperature in spring explains the variability of snowmelt by the equivalent standard deviation of 3.4-4.4 days, and total discharge by 10-11%. In addition, the high correlation among June temperature, peak NDVI and annual discharge suggests a primary role of spring evapotranspiration on plant community phenology, productivity, and streamflow volume. On the other hand, summer monsoon precipitation does not contribute significantly to annual discharge, further emphasizing the importance of snowmelt. This approach is mostly based on a set of datasets typically available throughout the US, providing a powerful approach to link remote sensing techniques with long-term monitoring of temperature, snowfall, plant, and streamflow dynamics.

Further observations of snow and frost in the Adirondacks

Treesearch

Howard W. Lull; Francis M. Rushmore

1961-01-01

Snow-depth and water-content measurements were made in March and April 1960 in the vicinity of Paul Smiths, New York, to check on procedures developed the previous year for predicting snow accumulation and melt.

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

NASA Technical Reports Server (NTRS)

Petrich, Chris; Eicken, Hajo; Polashenski, Christopher M.; Sturm, Matthew; Harbeck, Jeremy P.; Perovich, Donald K.; Finnegan, David C.

2012-01-01

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

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

USGS Publications Warehouse

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

1986-01-01

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

Improving streamflow prediction using remotely-sensed soil moisture and snow depth

USDA-ARS?s Scientific Manuscript database

The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) ret...

Microwave remote sensing of snow experiment description and preliminary results

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Stiles, W. H.; Hanson, B. C.

1977-01-01

The active and passive microwave responses to snow were investigated at a site near Steamboat Springs, Colorado during the February and March winter months. The microwave equipment was mounted atop truck-mounted booms. Data were acquired at numerous frequencies, polarizations, and angles of incidence for a variety of snow conditions. The experiment description, the characteristics of the microwave and ground truth instruments, and the results of a preliminary analysis of a small portion of the total data volume acquired in Colorado are documented.

On the formation of glide-snow avalanches

NASA Astrophysics Data System (ADS)

Mitterer, C.; Schweizer, J.

2012-12-01

On steep slopes the full snowpack can glide on the ground; tension cracks may open and eventually the slope may fail as a glide-snow avalanche. Due to their large mass they have considerable destructive potential. Glide-snow avalanches typically occur when the snow-soil interface is moist or wet so that basal friction is reduced. The occurrence, however, of glide cracks and their evolution to glide avalanches are still poorly understood. Consequently, glides are difficult to predict as (i) not all cracks develop into an avalanche, and (ii) for those that do, the time between crack opening and avalanche event might vary from hours to weeks - or on the other hand be so short that there is no warning at all by crack opening. To improve our understanding we monitored several slopes and related glide snow activity to meteorological data. In addition, we explored conditions that favor the formation of a thin wet basal snowpack layer with a physical-based model representing water and heat flux at the snow-soil interface. The statistical analyses revealed that glide-snow avalanche activity might be associated to an early season and a spring condition. While early season conditions tend to have warm and dry autumns followed by heavy snowfalls, spring conditions showed good agreement with increasing air temperature. The model indicates that energy (summer heat) stored in the ground might be sufficient to melt snow at the bottom of the snowpack. Due to capillary forces, water will rise for a few centimeters into the snowpack and thereby reduce friction at the interface. Alternatively, we demonstrate that also in the absence of melt water production at the bottom of the snowpack water may accumulate in the bottom layer due to an upward flux into the snowpack if a dry snowpack overlies a wet soil. The particular conditions that are obviously required at the snow-soil interface explain the strong winter-to-winter variations in snow gliding.

The influence of sea ice on Antarctic ice core sulfur chemistry and on the future evolution of Arctic snow depth: Investigations using global models

NASA Astrophysics Data System (ADS)

Hezel, Paul J.

Observational studies have examined the relationship between methanesulfonic acid (MSA) measured in Antarctic ice cores and sea ice extent measured by satellites with the aim of producing a proxy for past sea ice extent. MSA is an oxidation product of dimethylsulfide (DMS) and is potentially linked to sea ice based on observations of very high surface seawater DMS in the sea ice zone. Using a global chemical transport model, we present the first modeling study that specifically examines this relationship on interannual and on glacial-interglacial time scales. On interannual time scales, the model shows no robust relationship between MSA deposited in Antarctica and sea ice extent. We show that lifetimes of MSA and DMS are longer in the high latitudes than in the global mean, interannual variability of sea ice is small (<25%) as a fraction of sea ice area, and sea ice determines only a fraction of the variability (<30%) of DMS emissions from the ocean surface. A potentially larger fraction of the variability in DMS emissions is determined by surface wind speed (up to 46%) via the parameterization for ocean-to-atmosphere gas exchange. Furthermore, we find that a significant fraction (up to 74%) of MSA deposited in Antarctica originates from north of 60°S, north of the seasonal sea ice zone. We then examine the deposition of MSA and non-sea-salt sulfate (nss SO2-4 ) on glacial-interglacial time scales. Ice core observations on the East Antarctic Plateau suggest that MSA increases much more than nss SO2-4 during the last glacial maximum (LGM) compared to the modern period. It has been suggested that high MSA during the LGM is indicative of higher primary productivity and DMS emissions in the LGM compared to the modern day. Studies have also shown that MSA is subject to post-depositional volatilization, especially during the modern period. Using the same chemical transport model driven by meteorology from a global climate model, we examine the sensitivity of MSA and nss SO2-4 deposition to differences between the modern and LGM climates, including sea ice extent, sea surface temperatures, oxidant concentrations, and meteorological conditions. We are unable to find a mechanism whereby MSA deposition fluxes are higher than nss SO2-4 deposition fluxes on the East Antarctic Plateau in the LGM compared the modern period. We conclude that the observed differences between MSA and nss SO2-4 on glacial-interglacial time scales are due to post-depositional processes that affect the ice core MSA concentrations. We can not rule out the possibility of increased DMS emissions in the LGM compared to the modern day. If oceanic DMS production and ocean-to-air fluxes in the sea ice zone are significantly enhanced by the presence of sea ice as indicated by observations, we suggest that the potentially larger amplitude of the seasonal cycle in sea ice extent in the LGM implies a more important role for sea ice in modulating the sulfur cycle during the LGM compared to the modern period. We then shift our focus to study the evolution of snow depth on sea ice in global climate model simulations of the 20th and 21st centuries from the Coupled Model Intercomparison Project 5 (CMIP5). Two competing processes, decreasing sea ice extent and increasing precipitation, will affect snow accumulation on sea ice in the future, and it is not known a priori which will dominate. The decline in Arctic sea ice extent is a well-studied problem in future scenarios of climate change. Moisture convergence into the Arctic is also expected to increase in a warmer world, which may result in increasing snowfall rates. We show that the accumulated snow depth on sea ice in the spring declines as a result of decreased ice extent in the early autumn, in spite of increased winter snowfall rates. The ringed seal (Phoca hispida ) depends on accumulated snow in the spring to build subnivean birth lairs, and provides one of the motivations for this study. Using an empirical threshold of 20 cm of snow depth on level sea ice for ringed seal lair success, we estimate a decline of potential ringed seal habitat of nearly 70%.

Persistency in monthly mean temperatures in Europe

NASA Astrophysics Data System (ADS)

Rasol, Dubravka; Ólafsson, Haraldur

2016-04-01

Time series from a number of weather stations in Europe have been studied in order to assess the persistency of montly mean temperatures. In most regions, the correlation between the mean temperatures of two months next to each other in time has maxima in the summer and in the winter, while there are minima in the sping and the autumn. An exception from this is in the oceanic warm climate in the southwest, where the spring minimum is missing. A plausible explanation for the positive correlation in the winter may be related to snow on the ground. The snow is associated with cold spells and increases the albedo, contributing to extension of the low temperatures. The summertime correlation may be related to the water content of the soil. A cold and rainy period results in wet soil and subsequently, relatively large part of the energy of the incoming solar radiation is consumed by evaporation, rather than sensible heating. In the spring, there is generally no snow on the ground and in the autumn, the air temperature is not as sensitive to the water content of the soil as in the summer. This may explain the low correlation in spring and autumn.

The Influence of a Record Heat Wave on Environmental Change in Barrow, Alaska

NASA Astrophysics Data System (ADS)

Stanitski, Diane; Cox, Christopher; Stone, Robert; Divoky, George

2016-04-01

The May 2015 average temperature at the NOAA Global Monitoring Division's Barrow Observatory (BRW), Alaska, set a 90+ year record high, averaging -2.2°C (28°F), nearly 5°C (9°F) above average. The 2015 spring transition in Barrow was notable with the second earliest date of snow melt on record (JD148, May 28) and earliest ice free conditions on a local lagoon (JD178, June 27). Anomalous early snowmelt was also observed at nearby Cooper Island where a colony of sea birds, the Black Guillemot, nests each year once snow disappears. The appearance of "first egg" is well correlated with the date of snowmelt at BRW (Fig. 1), as is the ice-out date at the Isaktoak Lagoon (ISK). In 2015, the first egg was observed on JD159 (June 8), the earliest in the 40-year record (source: Friends of Cooper Island, http://cooperisland.org/). The 2015 melt at BRW was very early due mainly to an unusually intense heat wave affecting all of Alaska. Each day of advance in the melt date at BRW results in an annual net radiation increase at the surface of about 1%. The documented changes can influence biogeochemical cycles, permafrost temperatures, and potentially the release of stored carbon. BRW permafrost temperatures were warmer than the three previous years; the active layer depth (ALD) was ~6 cm deeper in 2015 than in 2014; and the temperature at 120 cm was ~0.5°C warmer. The anomalous warmth that prevailed during spring 2015 can be primarily attributed to atmospheric circulation. Abnormal warmth of the North Pacific and a perturbed jet stream underlie the heat wave and advection of warm air into the Arctic. Warming was likely amplified locally as the early melting of snow increased absorption of solar radiation. Key factors contributing to the anomalous 2015 spring at BRW and the impact early melt had on the 2015 summer surface radiation budget will be discussed. The role of circulation anomalies reported by reanalysis data over the course of the Barrow observational record will be presented. Analysis of interactions underlying this anomaly will aid in developing strategies for improving predictability of interannual variability of the melt season and long-term change.

Using Deep Learning to Assess Future Flood Magnitude and Frequency in the Semi-arid and Snowmelt-dominated Missouri River Headwater Catchments

NASA Astrophysics Data System (ADS)

Francois, B.; Wi, S.; Brown, C.

2017-12-01

There has been growing interest for hydrologists and water resources managers about the emergence of non-stationarities associated with the hydro-meteorological processes driving floods. Among the potential causes of non-stationarity, climate change is deemed a major one. Understanding the effects of climate change on hydrological regimes of the Missouri River is challenging. In this region, floods are mainly triggered by snow melting, either when temperatures get mild in spring/summer, or when rain falls over snow in early spring and fall. The sparsely gauged and topographically complex area degrades the value of hydrological modeling that otherwise might foreshadow the evolution of hydro-meteorological interactions between precipitation, temperature and snow. In this work, we explore the utility of Deep Learning (DL) for assessing flood magnitude change under climate change. By using multiple hidden layers within artificial neural networks (ANNs), DL allows modeling complex interactions between inputs (i.e. precipitation, temperature and snow water equivalent) and outputs (i.e. water discharge). The objective is to develop a parsimonious model of the flood processes that maintain the contribution of nonstationary factors and their potential evolution under climate change, while reducing extraneous factors not central to flood generation. By comparing ANN's performance with outputs from two hydrological models of differing complexity (i.e. VIC, SAC-SMA), we evaluate the modeling capability of ANNs for three snow-dominated catchments that represent different flood regimes (Yellowstone River at Billings (MT; USGS 06214500), Powder River near Locate (MT; USGS 06326500) and James River near Scotland (SD; USGS 06478500)). Nonstationary inputs for each flood process model are derived from dynamically downscaled climate projections (from the NARCCAP experiment) to project floods in the three selected catchments. The uncertainty of future snow projections as well as its impact on spring flooding are explored. Future flood frequency obtained with ANNs is compared with the one obtained thanks to hydrological models and with the traditional approach as described in Bulletin 17C. Keywords: Flood, Climate-change, Snow, Neural Networks

The Development of Snow Properties and Its Effect on Trafficability.

DTIC Science & Technology

1980-04-01

preferred to the horizontally applied NRC snow hardness tester. Hence the latter does not enter into graphical representation of the snow cover...depth was broken or cracked during vehicle passage. With the air temperature at 0°C, snow density was meassured in the trace of the right track: TABLE I

On the absorption of solar radiation in a layer of oil beneath a layer of snow

NASA Technical Reports Server (NTRS)

Larsen, J. C.; Barkstrom, B. R.

1976-01-01

Solar energy deposition in oil layers covered by snow is calculated for three model snow types using radiative transfer theory. It is suggested that excess absorbed energy is unlikely to escape, so that some melting is likely to occur for snow depths less than about 4 cm.

McChord AFB, Tacoma, Washington. Revised Uniform Summary of Surface Weather Observations (RUSSWO)

DTIC Science & Technology

1974-03-19

depth at 0W LST Jan L6-MaY 57 Snow depth at 1230 (JOT Jun 57-present Snow depth at 1.200 G CO’ U. S. Nlavy :mad Wather From neginning of record thru Jun...8217;. Wather Bureau and Navy stations did not report ceilings within the range 10,000 feet and higher prior to jant.ary 19h9. Summaries prepared from

The extent and intensity of the urban heat island in Iași city, Romania

NASA Astrophysics Data System (ADS)

Sfîcă, Lucian; Ichim, Pavel; Apostol, Liviu; Ursu, Adrian

2017-10-01

The study underlines the characteristics of the urban heat island of Iași (Iași's UHI) on the basis of 3 years of air temperature measurements obtained by fixed-point observations. We focus on the identification of UHI development and intensity as it is expressed by the temperature differences between the city centre and the rural surroundings. Annual, seasonal and daily characteristics of Iaşi's UHI are investigated at the level of the classical weather observation. In brief, an intensity of 0.8 °C of UHI and a spatial extension which corresponds to the densely built area of the city were delineated. The Iaşi UHI is stronger during summer calm nights—when the inner city is warmer with 2.5-3 °C than the surroundings—and is weaker during windy spring days. The specific features of Iași's UHI bear a profound connection to the specificity of the urban structure, the high atmospheric stability in the region and the local topography. Also, the effects of Iași's UHI upon some environmental aspects are presented as study cases. For instance, under the direct influence of UHI, we have observed that in the city centre, the apricot tree blossoms earlier (with up to 4 days) and the depth of the snow cover is significantly lower (with up to 10 cm for a rural snow depth of 30 cm) than in the surrounding areas.

Radiative habitable zones in martian polar environments.

PubMed

Córdoba-Jabonero, Carmen; Zorzano, María-Paz; Selsis, Franck; Patel, Manish R; Cockell, Charles S

2005-06-01

The biologically damaging solar ultraviolet (UV) radiation (quantified by the DNA-weighted dose) reaches the martian surface in extremely high levels. Searching for potentially habitable UV-protected environments on Mars, we considered the polar ice caps that consist of a seasonally varying CO2 ice cover and a permanent H2O ice layer. It was found that, though the CO2 ice is insufficient by itself to screen the UV radiation, at approximately 1 m depth within the perennial H2O ice the DNA-weighted dose is reduced to terrestrial levels. This depth depends strongly on the optical properties of the H2O ice layers (for instance snow-like layers). The Earth-like DNA-weighted dose and Photosynthetically Active Radiation (PAR) requirements were used to define the upper and lower limits of the northern and southern polar Radiative Habitable Zone (RHZ) for which a temporal and spatial mapping was performed. Based on these studies we conclude that photosynthetic life might be possible within the ice layers of the polar regions. The thickness varies along each martian polar spring and summer between approximately 1.5 and 2.4 m for H2O ice-like layers, and a few centimeters for snow-like covers. These martian Earth-like radiative habitable environments may be primary targets for future martian astrobiological missions. Special attention should be paid to planetary protection, since the polar RHZ may also be subject to terrestrial contamination by probes. c2004 Elsevier Inc. All rights reserved.

Estimating snow depth in real time using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Witek, Matylda; Spallek, Waldemar; Szymanowski, Mariusz

2016-04-01

In frame of the project no. LIDER/012/223/L-5/13/NCBR/2014, financed by the National Centre for Research and Development of Poland, we elaborated a fully automated approach for estimating snow depth in real time in the field. The procedure uses oblique aerial photographs taken by the unmanned aerial vehicle (UAV). The geotagged images of snow-covered terrain are processed by the Structure-from-Motion (SfM) method which is used to produce a non-georeferenced dense point cloud. The workflow includes the enhanced RunSFM procedure (keypoint detection using the scale-invariant feature transform known as SIFT, image matching, bundling using the Bundler, executing the multi-view stereo PMVS and CMVS2 software) which is preceded by multicore image resizing. The dense point cloud is subsequently automatically georeferenced using the GRASS software, and the ground control points are borrowed from positions of image centres acquired from the UAV-mounted GPS receiver. Finally, the digital surface model (DSM) is produced which - to improve the accuracy of georeferencing - is shifted using a vector obtained through precise geodetic GPS observation of a single ground control point (GCP) placed on the Laboratory for Unmanned Observations of Earth (mobile lab established at the University of Wroclaw, Poland). The DSM includes snow cover and its difference with the corresponding snow-free DSM or digital terrain model (DTM), following the concept of the digital elevation model of differences (DOD), produces a map of snow depth. Since the final result depends on the snow-free model, two experiments are carried out. Firstly, we show the performance of the entire procedure when the snow-free model reveals a very high resolution (3 cm/px) and is produced using the UAV-taken photographs and the precise GCPs measured by the geodetic GPS receiver. Secondly, we perform a similar exercise but the 1-metre resolution light detection and ranging (LIDAR) DSM or DTM serves as the snow-free model. Thus, the main objective of the paper is to present the performance of the new procedure for estimating snow depth and to compare the two experiments.

Snow depth spatial structure from hillslope to basin scale

NASA Astrophysics Data System (ADS)

Deems, J. S.

2017-12-01

Knowledge of spatial patterns of snow accumulation is required for understanding the hydrology, climatology, and ecology of mountain regions. Spatial structure in snow accumulation patterns changes with the scale of observation, a feature that has been characterized using fractal dimensions calculated from lidar-derived snow depth maps: fractal scaling structure at short length scales, with a `scale break' transition to more stochastic patterns at longer separation distances. Previous work has shown that this fractal structure of snow depth distributions differs between sites with different vegetation and terrain characteristics. Forested areas showed a transition to a nearly random spatial distribution at a much shorter lag distance than do unforested sites, enabling a statistical characterization. Alpine areas, however, showed strong spatial structure for a much wider scale range, and were the source of the dominant spatial pattern observable over a wider area. These spatial structure characteristics suggest that the choice of measurement or model resolution (satellite sensor, DEM, field survey point spacing, etc.) will strongly affect the estimates of snow volume or mass, as well as the magnitude of spatial variability. These prior efforts used data sets that were high resolution ( 1 m laser point spacing) but of limited extent ( 1 km2), constraining detection of scale features such as fractal dimension or scale breaks to areas of relatively similar characteristics and to lag distances of under 500 m. New datasets available from the NASA JPL Airborne Snow Observatory (ASO) provide similar resolution but over large areas, enabling assessment of snow spatial structure across an entire watershed, or in similar vegetation or physiography but in different parts of the basin. Additionally, the multi-year ASO time series allows an investigation into the temporal stability of these scale characteristics, within a single snow season and between seasons of strongly varying accumulation totals and patterns. This presentation will explore initial results from this study, using data from the Tuolumne River Basin in California, USA. Fractal scaling characteristics derived from ASO lidar snow depth measurements are examined at the basin scale, as well as in varying topographic and forest cover environments.

Quantifying the accuracy of snow water equivalent estimates using broadband radar signal phase

NASA Astrophysics Data System (ADS)

Deeb, E. J.; Marshall, H. P.; Lamie, N. J.; Arcone, S. A.

2014-12-01

Radar wave velocity in dry snow depends solely on density. Consequently, ground-based pulsed systems can be used to accurately measure snow depth and snow water equivalent (SWE) using signal travel-time, along with manual depth-probing for signal velocity calibration. Travel-time measurements require a large bandwidth pulse not possible in airborne/space-borne platforms. In addition, radar backscatter from snow cover is sensitive to grain size and to a lesser extent roughness of layers at current/proposed satellite-based frequencies (~ 8 - 18 GHz), complicating inversion for SWE. Therefore, accurate retrievals of SWE still require local calibration due to this sensitivity to microstructure and layering. Conversely, satellite radar interferometry, which senses the difference in signal phase between acquisitions, has shown a potential relationship with SWE at lower frequencies (~ 1 - 5 GHz) because the phase of the snow-refracted signal is sensitive to depth and dielectric properties of the snowpack, as opposed to its microstructure and stratigraphy. We have constructed a lab-based, experimental test bed to quantify the change in radar phase over a wide range of frequencies for varying depths of dry quartz sand, a material dielectrically similar to dry snow. We use a laboratory grade Vector Network Analyzer (0.01 - 25.6 GHz) and a pair of antennae mounted on a trolley over the test bed to measure amplitude and phase repeatedly/accurately at many frequencies. Using ground-based LiDAR instrumentation, we collect a coordinated high-resolution digital surface model (DSM) of the test bed and subsequent depth surfaces with which to compare the radar record of changes in phase. Our plans to transition this methodology to a field deployment during winter 2014-2015 using precision pan/tilt instrumentation will also be presented, as well as applications to airborne and space-borne platforms toward the estimation of SWE at high spatial resolution (on the order of meters) over large regions (> 100 square kilometers).

Microwave signatures of snow and fresh water ice

NASA Technical Reports Server (NTRS)

Schmugge, T.; Wilheit, T. T.; Gloersen, P.; Meier, M. F.; Frank, D.; Dirmhirn, I.

1973-01-01

During March of 1971, the NASA Convair 990 Airborne Observatory carrying microwave radiometers in the wavelength range 0.8 to 21 cm was flown over dry snow with different substrata: Lake ice at Bear Lake in Utah; wet soil in the Yampa River Valley near Steamboat Springs, Colorado; and glacier ice, firm and wet snow on the South Cascade Glacier in Washington. The data presented indicate that the transparency of the snow cover is a function of wavelength. False-color images of microwave brightness temperatures obtained from a scanning radiometer operating at a wavelength of 1.55 cm demonstrate the capability of scanning radiometers for mapping snowfields.

Multi-scale responses of scattering layers to environmental variability in Monterey Bay, California

NASA Astrophysics Data System (ADS)

Urmy, Samuel S.; Horne, John K.

2016-07-01

A 38 kHz upward-facing echosounder was deployed on the seafloor at a depth of 875 m in Monterey Bay, CA, USA (36° 42.748‧N, 122° 11.214‧W) from 27 February 2009 to 18 August 2010. This 18-month record of acoustic backscatter was compared to oceanographic time series from a nearby data buoy to investigate the responses of animals in sound-scattering layers to oceanic variability at seasonal and sub-seasonal time scales. Pelagic animals, as measured by acoustic backscatter, moved higher in the water column and decreased in abundance during spring upwelling, attributed to avoidance of a shoaling oxycline and advection offshore. Seasonal changes were most evident in a non-migrating scattering layer near 500 m depth that disappeared in spring and reappeared in summer, building to a seasonal maximum in fall. At sub-seasonal time scales, similar responses were observed after individual upwelling events, though they were much weaker than the seasonal relationship. Correlations of acoustic backscatter with oceanographic variability also differed with depth. Backscatter in the upper water column decreased immediately following upwelling, then increased approximately 20 days later. Similar correlations existed deeper in the water column, but at increasing lags, suggesting that near-surface productivity propagated down the water column at 10-15 m d-1, consistent with sinking speeds of marine snow measured in Monterey Bay. Sub-seasonal variability in backscatter was best correlated with sea-surface height, suggesting that passive physical transport was most important at these time scales.

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

NASA Astrophysics Data System (ADS)

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.

Snowmelt and Infiltration Deficiencies of SSiB and Their Resolution with a New Snow-Physics Scheme

NASA Technical Reports Server (NTRS)

Sud, Y. C.; Mocko, David M.

1999-01-01

A two-year 1987-1988 integration of SSiB forced with ISLSCP Initiative I surface data (as part of the Global Soil Wetness Project, GSWP, evaluation and intercomparison) produced generally realistic land surface fluxes and hydrology. Nevertheless, the evaluation also helped to identify some of the deficiencies of the current version of the Simplified Simple Biosphere (SSiB) model. The simulated snowmelt was delayed in most regions, along with excessive runoff and lack of an spring soil moisture recharge. The SSIB model had previously been noted to have a problem producing accurate soil moisture as compared to observations in the Russian snowmelt region. Similarly, various GSWP implementations of SSIB found deficiencies in this region of the simulated soil moisture and runoff as compared to other non-SSiB land-surface models (LSMs). The origin of these deficiencies was: 1) excessive cooling of the snow and ground, and 2) deep frozen soil disallowing snowmelt infiltration. The problem was most severe in regions that experience very cold winters. In SSiB, snow was treated as a unified layer with the first soil layer, causing soil and snow to cool together in the winter months, as opposed to snow cover acting as an insulator. In the spring season, a large amount of heat was required to thaw a hard frozen snow plus deep soil layers, delaying snowmelt and causing meltwater to become runoff over the frozen soil rather than infiltrate into it.

Tree-Ring Widths and Snow Cover Depth in High Tauern

NASA Astrophysics Data System (ADS)

Falarz, Malgorzata

2017-12-01

The aim of the study is to examine the correlation of Norway spruce tree-ring widths and the snow cover depth in the High Tauern mountains. The average standardized tree-ring widths indices for Nowary spruce posted by Bednarz and Niedzwiedz (2006) were taken into account. Increment cores were collected from 39 Norway spruces growing in the High Tauern near the upper limit of the forest at altitude of 1700-1800 m, 3 km from the meteorological station at Sonnblick. Moreover, the maximum of snow cover depth in Sonnblick (3105 m a.s.l.) for each winter season in the period from 1938/39 to 1994/95 (57 winter seasons) was taken into account. The main results of the research are as follows: (1) tree-ring widths in a given year does not reveal statistically significant dependency on the maximum snow cover depth observed in the winter season, which ended this year; (2) however, the tested relationship is statistically significant in the case of correlating of the tree-ring widths in a given year with a maximum snow cover depth in a season of previous year. The correlation coefficient for the entire period of the study is not very high (r=0.27) but shows a statistical significance at the 0.05 level; (3) the described relationship is not stable over time. 30-year moving correlations showed no significant dependencies till 1942 and after 1982 (probably due to the so-called divergence phenomenon). However, during the period of 1943-1981 the values of correlation coefficient for moving 30-year periods are statistically significant and range from 0.37 to 0.45; (4) the correlation coefficient between real and calibrated (on the base of the regression equation) values of maximum snow cover depth is statistically significant for calibration period and not significant for verification one; (5) due to a quite short period of statistically significant correlations and not very strict dependencies, the reconstruction of snow cover on Sonnblick for the period before regular measurements seems to be not reasonable.

Evaluation of forest snow processes models (SnowMKIP2)

Treesearch

Nick Rutter; Richard Essery; John Pomeroy; Nuria Altimir; Kostas Andreadis; Ian Baker; Alan Barr; Paul Bartlett; Aaron Boone; Huiping Deng; Herve Douville; Emanuel Dutra; Kelly Elder; others

2009-01-01

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation...

Atmospheric mercury speciation and mercury in snow over time at Alert, Canada

NASA Astrophysics Data System (ADS)

Steffen, A.; Bottenheim, J.; Cole, A.; Ebinghaus, R.; Lawson, G.; Leaitch, W. R.

2014-03-01

Ten years of atmospheric mercury speciation data and 14 years of mercury in snow data from Alert, Nunavut, Canada, are examined. The speciation data, collected from 2002 to 2011, includes gaseous elemental mercury (GEM), particulate mercury (PHg) and reactive gaseous mercury (RGM). During the winter-spring period of atmospheric mercury depletion events (AMDEs), when GEM is close to being completely depleted from the air, the concentration of both PHg and RGM rise significantly. During this period, the median concentrations for PHg is 28.2 pgm-3 and RGM is 23.9 pgm-3, from March to June, in comparison to the annual median concentrations of 11.3 and 3.2 pgm-3 for PHg and RGM, respectively. In each of the ten years of sampling, the concentration of PHg increases steadily from January through March and is higher than the concentration of RGM. This pattern begins to change in April when the levels of PHg peak and RGM begin to increase. In May, the high PHg and low RGM concentration regime observed in the early spring undergoes a transition to a regime with higher RGM and much lower PHg concentrations. The higher RGM concentration continues into June. The transition is driven by the atmospheric conditions of air temperature and particle availability. Firstly, a high ratio of the concentrations of PHg to RGM is reported at low temperatures which suggests that oxidized gaseous mercury partitions to available particles to form PHg. Prior to the transition, the median air temperature is -24.8 °C and after the transition the median air temperature is -5.8 °C. Secondly, the high PHg concentrations occur in the spring when high particle concentrations are present. The high particle concentrations are principally due to Arctic haze and sea salts. In the snow, the concentrations of mercury peak in May for all years. Springtime deposition of total mercury to the snow at Alert peaks in May when atmospheric conditions favour higher levels of RGM. Therefore, the conditions in the atmosphere directly impact when the highest amount of mercury will be deposited to the snow during the Arctic spring.

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

NASA Astrophysics Data System (ADS)

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.

Date of Snowmelt at High Latitudes as Determined from Visible Satellite Data and Relationship with the Arctic Oscillation

NASA Technical Reports Server (NTRS)

Foster, James; Robinson, Dave; Estilow, Tom; Hall, Dorothy

2012-01-01

Spring snow cover across Arctic lands has, on average, retreated approximately five days earlier since the late 1980s compared to the previous twenty years. However, it appears that since about 1990, the date the snowline first retreats north during the spring has remained nearly unchanged--in the last twenty years, the date of snow disappearance has not been occurring noticeably earlier. Snowmelt changes observed in the 1980s was step-like in nature, unlike a more continuous downward trend seen in Arctic sea ice extent. At latitude 70 deg N, several latitudinal segments (of 10 degrees) show significant (negative) trends. However, only two latitudinal segments at 60 deg N show significant trends, one positive and one negative. These variations appear to be related to variations in the Arctic Oscillation (AO). Additional observations and modeling investigations are needed to better explain past and present spring melt characteristics and peculiarities.

Controls on summer low flow

NASA Astrophysics Data System (ADS)

Graham, C. B.; McNamara, J. P.

2012-12-01

Summer low flow has significant impacts on aquatic flora and fauna, municipal water use, and power generation. However, the controls on the minimum annual summer discharge are complex, including a combination of snowmelt dynamics, summer evapotranspiration demand, and spring, summer precipitation patterns and surface - groundwater interactions. This is especially true in the Rocky Mountain West of the United States, where snowpack provides the majority of water available for spring runoff and groundwater replenishment. In this study, we look at summer low flow conditions at four snow dominated catchments (26 km2 - 2200 km2) in South-central Idaho currently feeling the effects of climate change. Measures of snowmelt dynamics, summer evapotranspiration demand and spring and summer precipitation are used to determine the dominant controls on late summer low flow magnitude, timing and duration. These analyses show that the controls vary between watersheds, with significant implications for the impacts of climate change in snow dominated areas of the Rocky Mountain West.

Decorrelation of L-band and C-band interferometry to volcanic risk prevention

NASA Astrophysics Data System (ADS)

Malinverni, E. S.; Sandwell, D.; Tassetti, A. N.; Cappelletti, L.

2013-10-01

SAR has several strong key features: fine spatial resolution/precision and high temporal pass frequency. Moreover, the InSAR technique allows the accurate detection of ground deformations. This high potential technology can be invaluable to study volcanoes: it provides important information on pre-eruption surface deformation, improving the understanding of volcanic processes and the ability to predict eruptions. As a downside, SAR measurements are influenced by artifacts such as atmospheric effects or bad topographic data. Correlation gives a measure of these interferences, quantifying the similarity of the phase of two SAR images. Different approaches exists to reduce these errors but the main concern remain the possibility to correlate images with different acquisition times: snow-covered or heavily-vegetated areas produce seasonal changes on the surface. Minimizing the time between passes partly limits decorrelation. Though, images with a short temporal baseline aren't always available and some artifacts affecting correlation are timeindependent. This work studies correlation of pairs of SAR images focusing on the influence of surface and climate conditions, especially snow coverage and temperature. Furthermore, the effects of the acquisition band on correlation are taken into account, comparing L-band and C-band images. All the chosen images cover most of the Yellowstone caldera (USA) over a span of 4 years, sampling all the seasons. Interferograms and correlation maps are generated. To isolate temporal decorrelation, pairs of images with the shortest baseline are chosen. Correlation maps are analyzed in relation to snow depth and temperature. Results obtained with ENVISAT and ERS satellites (C-band) are compared with the ones from ALOS (L-band). Results show a good performance during winter and a bad attitude towards wet snow (spring and fall). During summer both L-band and C-band maintain a good coherence with L-band performing better over vegetation.

Long-range-transported bioaerosols captured in snow cover on Mount Tateyama, Japan: impacts of Asian-dust events on airborne bacterial dynamics relating to ice-nucleation activities

NASA Astrophysics Data System (ADS)

Maki, Teruya; Furumoto, Shogo; Asahi, Yuya; Lee, Kevin C.; Watanabe, Koichi; Aoki, Kazuma; Murakami, Masataka; Tajiri, Takuya; Hasegawa, Hiroshi; Mashio, Asami; Iwasaka, Yasunobu

2018-06-01

The westerly wind travelling at high altitudes over eastern Asia transports aerosols from the Asian deserts and urban areas to downwind areas such as Japan. These long-range-transported aerosols include not only mineral particles but also microbial particles (bioaerosols), that impact the ice-cloud formation processes as ice nuclei. However, the detailed relations of airborne bacterial dynamics to ice nucleation in high-elevation aerosols have not been investigated. Here, we used the aerosol particles captured in the snow cover at altitudes of 2450 m on Mt Tateyama to investigate sequential changes in the ice-nucleation activities and bacterial communities in aerosols and elucidate the relationships between the two processes. After stratification of the snow layers formed on the walls of a snow pit on Mt Tateyama, snow samples, including aerosol particles, were collected from 70 layers at the lower (winter accumulation) and upper (spring accumulation) parts of the snow wall. The aerosols recorded in the lower parts mainly came from Siberia (Russia), northern Asia and the Sea of Japan, whereas those in the upper parts showed an increase in Asian dust particles originating from the desert regions and industrial coasts of Asia. The snow samples exhibited high levels of ice nucleation corresponding to the increase in Asian dust particles. Amplicon sequencing analysis using 16S rRNA genes revealed that the bacterial communities in the snow samples predominately included plant associated and marine bacteria (phyla Proteobacteria) during winter, whereas during spring, when dust events arrived frequently, the majority were terrestrial bacteria of phyla Actinobacteria and Firmicutes. The relative abundances of Firmicutes (Bacilli) showed a significant positive relationship with the ice nucleation in snow samples. Presumably, Asian dust events change the airborne bacterial communities over Mt Tateyama and carry terrestrial bacterial populations, which possibly induce ice-nucleation activities, thereby indirectly impacting climate change.

Snow distribution throughout small subalpine catchment post-insect infestation of spruce and pine beetle.

NASA Astrophysics Data System (ADS)

Beverly, D.; Ewers, B. E.; Hyde, K.; Ohara, N.; Speckman, H. N.

2015-12-01

High elevation watersheds of the Rocky Mountains region contribute over 70% of the streamflow needed for infrastructure, agriculture, and ecological processes. Snow-water yields are heterogeneous in space and time and are driven by a multitude of snow distribution processes, including snowpack evolution driven by physical and biological factors. Quantifying heterogeneity of snowpack is further complicated by vegetation perturbations; much of the Rocky Mountains have experienced significant tree mortality due to bark beetle outbreaks. Reduction of living crown area decreases canopy interception while increasing radiation to snow surfaces, which alters snowpack distribution throughout the catchment. We hypothesize that, in a complex watershed, topographic variation (i.e., slope and aspect) will have a greater effect on snowpack evolution and distribution than densities of canopy mortality due to beetle infestation. The 120 ha No Name watershed, located in southern Wyoming at 3000 m elevation was divided into twenty-one 175 m2 parcels, in which plots were randomly assigned within each parcel. Peak snow was measured in April; in the 50 m2 plots, depths were measured every 2 m along north-south and east-west transects. Twenty-one snow pits were excavated to quantify snow densities in 10 cm increments throughout the pit profile. Forest inventories occurred the following summer. Peak snowpack levels occurred in April with mean depth of 92.3 ­­± 2.4 cm and peak SWE of 34.0 ± 0.84 cm. Binary decision trees accounted for 63% of the variability after including topographic indices, beetle condition of the trees, LAI, and basal area. Snow depth showed a slight positive relationship with increased in beetle mortality on slopes less than 11 degrees. Overall, topographic indices are greater drivers for snow distributions compared to effects of tree mortality.

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

NASA Technical Reports Server (NTRS)

Foster, J. L.; Chang, A. T. C.; Hall, D. K.

1997-01-01

While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.

Climate model assessment of changes in winter-spring streamflow timing over North America

USGS Publications Warehouse

Kam, Jonghun; Knutson, Thomas R.; Milly, Paul C. D.

2018-01-01

Over regions where snow-melt runoff substantially contributes to winter-spring streamflows, warming can accelerate snow melt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, a detection/attribution of changes in mid-latitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. In this study, robustness across models, start/end dates for trends, and assumptions about internal variability is evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central U.S., where winter-spring streamflows have been coming earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western U.S./southwestern Canada and in extreme northeastern U.S./Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow-free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

In the last decades, ski resort managers have massively improved their snow management practices, in order to adapt their strategies to the inter-annual variability in snow conditions and to the effects of climate change. New real-time informations, such as snow depth measurements carried out on the ski slopes by grooming machines during their daily operations, have become available, allowing high saving, efficiency and optimization gains (reducing for instance the groomer fuel consumption and operation time and the need for machine-made snow production). In order to take a step forward in improving the grooming techniques, it would be necessary to keep into account also the snow erosion by skiers, which depends mostly on the snow surface properties and on the skier attendance. Today, however, most ski resort managers have only a vague idea of the evolution of the skier flows on each slope during the winter season. In this context, we have developed a new sensor (named Skiflux) able to measure the skier attendance using an infrared beam crossing the slopes. Ten Skiflux sensors have been deployed during the 2016/17 winter season at Val Thorens ski area (French Alps), covering a whole sector of the resort. A dedicated software showing the number of skier passages in real time as been developed as well. Combining this new Skiflux dataset with the snow depth measurements from grooming machines (Snowsat System) and the snow and meteorological conditions measured in-situ (Liberty System from Technoalpin), we were able to create a "real-time skiability index" accounting for the quality of the surface snow and its evolution during the day. Moreover, this new framework allowed us to improve the preparation of ski slopes, suggesting new strategies for adapting the grooming working schedule to the snow quality and the skier attendance. In the near future, this work will benefit from the advances made within the H2020 PROSNOW project ("Provision of a prediction system allowing for management and optimization of snow in Alpine ski resorts"), which has been funded for the period 2017-2020.

Trace metal concentrations in snow from the Yukon River Basin, Alaska and Canada

USGS Publications Warehouse

Wang, B.; Gough, L.; Hinkley, T.; Garbarino, J.; Lamothe, P.

2005-01-01

We report here on metal concentrations in snow collected from the Yukon River basin. Atmospheric transport of metals and subsequent deposition is a known mechanism for introducing metals into the northern environment. Potential sources of airborne elements are locally generated terrestrial sources, locally derived anthropogenic sources, and long range atmospheric transport. Sites were distributed along the Yukon River corridor and within the southeastern, central, and western basin areas. Snow samples were taken in the spring of 2001 and 2002 when the snow pack was at its maximum. Total-depth composite samples were taken from pits using clean techniques. Mercury was analyzed using cold vapor atomic fluorescence spectrometry. All other elements were analyzed by inductively coupled plasma-mass spectrometry. In samples from remote sites, the concentration for selected metals ranged from: 0.015 - 0.34 ug/L for V, 0.01 - 0.22 ug/L for Ni, < 0.05 - 0.52 ug/L for Cu, 0.14 - 2.8 ug/L for Zn, 0.002 - 0.046 ug/L for Cd, 0.03 - 0.13 ug/L for Pb, 0.00041 - 0.0023 ug/L for filtered-Hg. Because the entire snow pack was sampled and there was no evidence of mid-season thaw, these concentrations represent the seasonal deposition. There was no significant difference in the seasonal deposition of V, Ni, Cu, Zn, Cd, and Pb at these sites between 2001 and 2002, and no north-south or east-west trend in concentrations. Samples taken from within communities, however, had significantly higher concentrations of V, Ni, Cu, Zn, and Cd in 2001, and Ni, Cu, and Pb in 2002 relative to the remote sites. Our data indicate that the atmospheric deposition of metals in the Yukon River basin is relatively uniform both spatially and temporally. However, communities have a measurable but variable effect on metal concentrations. Copyright ASCE 2005.

Measurements of the effects of forest cover upon the conservation of snow waters

Treesearch

W. R. Mattoon

1909-01-01

The large treeless openings or "parks" in the western yellow pine forests of the southwest, which form a well known characteristic, afford an excellent opportunity for a comparative study of the effect of a forest canopy upon local snow conditions. During the late winter and spring of 1909, the writer had an exceptionally favorable opportunity for observing...

Estimation of snow and glacier melt contribution to Liddar stream in a mountainous catchment, western Himalaya: an isotopic approach.

PubMed

Jeelani, Gh; Shah, Rouf A; Jacob, Noble; Deshpande, Rajendrakumar D

2017-03-01

Snow- and glacier-dominated catchments in the Himalayas are important sources of fresh water to more than one billion people. However, the contribution of snowmelt and glacier melt to stream flow remains largely unquantified in most parts of the Himalayas. We used environmental isotopes and geochemical tracers to determine the source water and flow paths of stream flow draining the snow- and glacier-dominated mountainous catchment of the western Himalaya. The study suggested that the stream flow in the spring season is dominated by the snowmelt released from low altitudes and becomes isotopically depleted as the melt season progressed. The tracer-based mixing models suggested that snowmelt contributed a significant proportion (5-66 %) to stream flow throughout the year with the maximum contribution in spring and summer seasons (from March to July). In 2013 a large and persistent snowpack contributed significantly (∼51 %) to stream flow in autumn (September and October) as well. The average annual contribution of glacier melt to stream flow is little (5 %). However, the monthly contribution of glacier melt to stream flow reaches up to 19 % in September during years of less persistent snow pack.

Water survey of Canada: Application for use of ERTS-A for retransmission of water resources data

NASA Technical Reports Server (NTRS)

Halliday, R. A. (Principal Investigator); Reid, I. A.

1974-01-01

The author has identified the following significant results. Water resources data were retransmitted from nine data collection platforms (DCP) located in remote regions of Canada. The DCPs located in the Arctic operated in temperatures lower than -40 C and the DCP antennas have survived wind speeds of greater than 80 kph and snow loads of a depth of one metre. Ice-out indicators were installed at a few DCP sites. The purpose of these indicators was to enable the detection of the movement of ice out of river channel during spring break-up. The suitability of satellite retransmission as a means of obtaining data from remote areas of Canada continues to be demonstrated. A modest expansion of the DCP network is planned.

A prediction and damage assessment model for snowmelt flood events in middle and high latitudes Region

NASA Astrophysics Data System (ADS)

Qiao, C.; Huang, Q.; Chen, T.; Zhang, X.

2017-12-01

In the context of global warming, the snowmelt flood events in the mountainous area of the middle and high latitudes are increasingly frequent and create severe casualties and property damages. Carrying out the prediction and risk assessment of the snowmelt flood is of great importance in the water resources management, the flood warning and prevention. Based on the remote sensing and GIS techniques, the relationships of the variables influencing the snowmelt flood such as the snow area, the snow depth, the air temperature, the precipitation, the land topography and land covers are analyzed and a prediction and damage assessment model for snowmelt floods is developed. This model analyzes and predicts the flood submerging area, flood depth, flood grade, and the damages of different underlying surfaces in the study area in a given time period based on the estimation of snowmelt amount, the snowmelt runoff, the direction and velocity of the flood. Then it was used to predict a snowmelt flood event in the Ertis River Basin in northern Xinjiang, China, during March and June, 2005 and to assess its damages including the damages of roads, transmission lines, settlements caused by the floods and the possible landslides using the hydrological and meteorological data, snow parameter data, DEM data and land use data. A comparison was made between the prediction results from this model and observation data including the flood measurement and its disaster loss data, which suggests that this model performs well in predicting the strength and impact area of snowmelt flood and its damage assessment. This model will be helpful for the prediction and damage assessment of snowmelt flood events in the mountainous area in the middle and high latitudes in spring, which has great social and economic significance because it provides a relatively reliable method for snowmelt flood prediction and reduces the possible damages caused by snowmelt floods.

The impact of changing the land surface scheme in ACCESS(v1.0/1.1) on the surface climatology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kowalczyk, Eva A.; Stevens, Lauren E.; Law, Rachel M.

The Community Atmosphere Biosphere Land Exchange (CABLE) model has been coupled to the UK Met Office Unified Model (UM) within the existing framework of the Australian Community Climate and Earth System Simulator (ACCESS), replacing the Met Office Surface Exchange Scheme (MOSES). Here we investigate how features of the CABLE model impact on present-day surface climate using ACCESS atmosphere-only simulations. The main differences attributed to CABLE include a warmer winter and a cooler summer in the Northern Hemisphere (NH), earlier NH spring runoff from snowmelt, and smaller seasonal and diurnal temperature ranges. The cooler NH summer temperatures in canopy-covered regions aremore » more consistent with observations and are attributed to two factors. Firstly, CABLE accounts for aerodynamic and radiative interactions between the canopy and the ground below; this placement of the canopy above the ground eliminates the need for a separate bare ground tile in canopy-covered areas. Secondly, CABLE simulates larger evapotranspiration fluxes and a slightly larger daytime cloud cover fraction. Warmer NH winter temperatures result from the parameterization of cold climate processes in CABLE in snow-covered areas. In particular, prognostic snow density increases through the winter and lowers the diurnally resolved snow albedo; variable snow thermal conductivity prevents early winter heat loss but allows more heat to enter the ground as the snow season progresses; liquid precipitation freezing within the snowpack delays the building of the snowpack in autumn and accelerates snow melting in spring. Altogether we find that the ACCESS simulation of surface air temperature benefits from the specific representation of the turbulent transport within and just above the canopy in the roughness sublayer as well as the more complex snow scheme in CABLE relative to MOSES.« less

The impact of changing the land surface scheme in ACCESS(v1.0/1.1) on the surface climatology

DOE PAGES

Kowalczyk, Eva A.; Stevens, Lauren E.; Law, Rachel M.; ...

2016-08-23

The Community Atmosphere Biosphere Land Exchange (CABLE) model has been coupled to the UK Met Office Unified Model (UM) within the existing framework of the Australian Community Climate and Earth System Simulator (ACCESS), replacing the Met Office Surface Exchange Scheme (MOSES). Here we investigate how features of the CABLE model impact on present-day surface climate using ACCESS atmosphere-only simulations. The main differences attributed to CABLE include a warmer winter and a cooler summer in the Northern Hemisphere (NH), earlier NH spring runoff from snowmelt, and smaller seasonal and diurnal temperature ranges. The cooler NH summer temperatures in canopy-covered regions aremore » more consistent with observations and are attributed to two factors. Firstly, CABLE accounts for aerodynamic and radiative interactions between the canopy and the ground below; this placement of the canopy above the ground eliminates the need for a separate bare ground tile in canopy-covered areas. Secondly, CABLE simulates larger evapotranspiration fluxes and a slightly larger daytime cloud cover fraction. Warmer NH winter temperatures result from the parameterization of cold climate processes in CABLE in snow-covered areas. In particular, prognostic snow density increases through the winter and lowers the diurnally resolved snow albedo; variable snow thermal conductivity prevents early winter heat loss but allows more heat to enter the ground as the snow season progresses; liquid precipitation freezing within the snowpack delays the building of the snowpack in autumn and accelerates snow melting in spring. Altogether we find that the ACCESS simulation of surface air temperature benefits from the specific representation of the turbulent transport within and just above the canopy in the roughness sublayer as well as the more complex snow scheme in CABLE relative to MOSES.« less

Siting, design and operational controls for snow disposal sites.

PubMed

Wheaton, S R; Rice, W J

2003-01-01

The Municipality of Anchorage (MOA), at 61 degrees north latitude, ploughs and hauls snow from urban streets throughout the winter, incorporating grit and chloride applied to street surfaces for traffic safety. Hauled snow is stored at snow disposal facilities, where it melts at ambient spring temperatures. MOA studies performed from 1998 through 2001 show that disposal site melt processes can be manipulated, through site design and operation practices, to control chloride and turbidity in meltwater. An experimental passive "V-swale" pad configuration tested by MOA investigators reduced site meltwater turbidity by an order of magnitude (to about 50 NTU from the 500 NTU typical of more conventional planar pad geometry). The MOA has developed new siting, design and operational criteria for snow disposal facilities to conform to the tested V-swale pad configuration.

Monitoring the snowpack volume in a sinkhole on Mount Lebanon using time lapse Photogrammetry

NASA Astrophysics Data System (ADS)

Abou Chakra, C.; Gascoin, S.; Somma, J.; Drapeau, L.; Fanise, P.

2017-12-01

Lebanon is one of the richest country in the Middle East for water resources, thanks to its mountain ranges that trigger precipitation from the moist air masses coming from the Mediterranean Sea. Snowpack acts as natural water storage in winter and supply fresh water during spring and summer. Yet, Lebanon is facing a serious water scarcity problem due to: i) decreasing amount of precipitation and climate change; ii) major growth of population of original residence and large number of refugees during regional wars. Therefore, continuous and systematic monitoring of the Lebanese water resources is becoming crucial. The Mount Lebanon is made of karstic depressions named "sinkholes". It is important to monitor the snowmelt process inside these sinkholes because of their key role as "containers" of seasonal snow. By isolating the snowpack from sun radiation and wind, they slow down the natural melting process and sublimation, thus delaying as well the low water flow period. An observatory is set up to monitor the snowpack evolution in a pilot sinkhole located in Mount Lebanon. The system uses three time-lapse cameras and structure-from-motion principles to reconstruct the snow volume within the sinkhole. The approach is validated by standard topographic surveys. The results indicate that snow depth can be retrieved with an accuracy between 20 and 60 cm (residuals standard deviation) and a low bias of 50 cm after coregistration of the digital elevation models.

Overcoming the stauchwall: Viscoelastic stress redistribution and the start of full-depth gliding snow avalanches

NASA Astrophysics Data System (ADS)

Bartelt, P.; Feistl, T.; Bühler, Y.; Buser, O.

2012-08-01

When a full-depth tensile crack opens in the mountain snowcover, internal forces are transferred from the fracture crown to the stauchwall. The stauchwall is located at the lower limit of a gliding zone and must carry the weight of the snowcover. The stauchwall can fail, leading to full-depth snow avalanches, or, it can withstand the stress redistribution. The snowcover often finds a new static equilibrium, despite the initial crack. We present a model describing how the snowcover reacts to the sudden transfer of the forces from the crown to the stauchwall. Our goal is to find the conditions for failure and the start of full-depth avalanches. The model balances the inertial forces of the gliding snowcover with the viscoelastic response of the stauchwall. We compute stresses, strain-rates and deformations during the stress redistribution and show that a new equilibrium state is not found directly, but depends on the viscoelastic properties of the snow, which are density and temperature dependent. During the stress redistribution the stauchwall encounters stresses and strain-rates that can be much higher than at the final equilibrium state. Because of the excess strain-rates, the stauchwall can fail in brittle compression before reaching the new equilibrium. Snow viscosity and the length of the gliding snow region are the two critical parameters governing the transition from stable snowpack gliding to avalanche flow. The model reveals why the formation of gliding snow avalanches is height invariant and how technical measures to prevent snowpack glide can be optimized to improve avalanche mitigation.

Colonization in the Photic Zone and Subsequent Changes during Sinking Determine Bacterial Community Composition in Marine Snow

PubMed Central

Thiele, Stefan; Fuchs, Bernhard M.; Amann, Rudolf

2014-01-01

Due to sampling difficulties, little is known about microbial communities associated with sinking marine snow in the twilight zone. A drifting sediment trap was equipped with a viscous cryogel and deployed to collect intact marine snow from depths of 100 and 400 m off Cape Blanc (Mauritania). Marine snow aggregates were fixed and washed in situ to prevent changes in microbial community composition and to enable subsequent analysis using catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH). The attached microbial communities collected at 100 m were similar to the free-living community at the depth of the fluorescence maximum (20 m) but different from those at other depths (150, 400, 550, and 700 m). Therefore, the attached microbial community seemed to be “inherited” from that at the fluorescence maximum. The attached microbial community structure at 400 m differed from that of the attached community at 100 m and from that of any free-living community at the tested depths, except that collected near the sediment at 700 m. The differences between the particle-associated communities at 400 m and 100 m appeared to be due to internal changes in the attached microbial community rather than de novo colonization, detachment, or grazing during the sinking of marine snow. The new sampling method presented here will facilitate future investigations into the mechanisms that shape the bacterial community within sinking marine snow, leading to better understanding of the mechanisms which regulate biogeochemical cycling of settling organic matter. PMID:25527538

Alaska North Slope Tundra Travel Model and Validation Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harry R. Bader; Jacynthe Guimond

2006-03-01

The Alaska Department of Natural Resources (DNR), Division of Mining, Land, and Water manages cross-country travel, typically associated with hydrocarbon exploration and development, on Alaska's arctic North Slope. This project is intended to provide natural resource managers with objective, quantitative data to assist decision making regarding opening of the tundra to cross-country travel. DNR designed standardized, controlled field trials, with baseline data, to investigate the relationships present between winter exploration vehicle treatments and the independent variables of ground hardness, snow depth, and snow slab thickness, as they relate to the dependent variables of active layer depth, soil moisture, and photosyntheticallymore » active radiation (a proxy for plant disturbance). Changes in the dependent variables were used as indicators of tundra disturbance. Two main tundra community types were studied: Coastal Plain (wet graminoid/moist sedge shrub) and Foothills (tussock). DNR constructed four models to address physical soil properties: two models for each main community type, one predicting change in depth of active layer and a second predicting change in soil moisture. DNR also investigated the limited potential management utility in using soil temperature, the amount of photosynthetically active radiation (PAR) absorbed by plants, and changes in microphotography as tools for the identification of disturbance in the field. DNR operated under the assumption that changes in the abiotic factors of active layer depth and soil moisture drive alteration in tundra vegetation structure and composition. Statistically significant differences in depth of active layer, soil moisture at a 15 cm depth, soil temperature at a 15 cm depth, and the absorption of photosynthetically active radiation were found among treatment cells and among treatment types. The models were unable to thoroughly investigate the interacting role between snow depth and disturbance due to a lack of variability in snow depth cover throughout the period of field experimentation. The amount of change in disturbance indicators was greater in the tundra communities of the Foothills than in those of the Coastal Plain. However the overall level of change in both community types was less than expected. In Coastal Plain communities, ground hardness and snow slab thickness were found to play an important role in change in active layer depth and soil moisture as a result of treatment. In the Foothills communities, snow cover had the most influence on active layer depth and soil moisture as a result of treatment. Once certain minimum thresholds for ground hardness, snow slab thickness, and snow depth were attained, it appeared that little or no additive effect was realized regarding increased resistance to disturbance in the tundra communities studied. DNR used the results of this modeling project to set a standard for maximum permissible disturbance of cross-country tundra travel, with the threshold set below the widely accepted standard of Low Disturbance levels (as determined by the U.S. Fish and Wildlife Service). DNR followed the modeling project with a validation study, which seemed to support the field trial conclusions and indicated that the standard set for maximum permissible disturbance exhibits a conservative bias in favor of environmental protection. Finally DNR established a quick and efficient tool for visual estimations of disturbance to determine when investment in field measurements is warranted. This Visual Assessment System (VAS) seemed to support the plot disturbance measurements taking during the modeling and validation phases of this project.« less

Concentrations of Reactive Trace Gases In The Interstitial Air of Surface Snow

NASA Astrophysics Data System (ADS)

Jacobi, H.-W.; Honrath, R. E.; Peterson, M. C.; Lu, Y.; Dibb, J. E.; Arsenault, M. A.; Swanson, A. L.; Blake, N. J.; Bales, R. C.; Schrems, O.

Several measurements at Arctic and Antarctic sites have demonstrated that unexpected photochemical reactions occur in irradiated surface snow influencing the composi- tion of the boundary layer over snow-covered areas. The results of these reactions are probably most obvious in the interstitial air of the surface snow since it constitutes the interface between the surface snow and the boundary layer. Therefore, measurements of concentrations of nitrogen oxide and dioxide, nitrous acid, formaldehyde, hydro- gen peroxide, formic acid, acetic acid, and other organic compounds were performed in the interstitial air of the surface snow of the Greenland ice sheet. Concentrations were measured at variable depths between - 10 cm and - 50 cm during the summer field season in 2000 at the Summit Environmental Observatory. At shallow depths, the system NO-NO2-O3 exhibits large deviations from the calculated photostationary state. Using steady-state analyses applied to OH-HO2-CH3O2 cycling indicated the presence of high concentrations of OH and peroxy radicals in the firn air. Maximum concentrations calculated for a depth of - 10 cm are in the order of 6 105 molecules cm-3 and 1.4 * 107 molecules cm-3 for OH and HO2, respectively, although radia- tion levels at - 10 cm are reduced by approximately 50 % compared to levels above the snow surface. By far the most important OH source is the photolysis of HONO while the photolysis of ozone contributes less than 2 % to the overall production of OH in the firn air.

Exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. R.

1973-01-01

Photogeological analysis of ERTS-simulation and ERTS-1 imagery of snowcovered terrain within the ERAP Feather River site and within the New England (ERTS) test area provided new fracture detail which does not appear on available geological maps. Comparative analysis of snowfree ERTS-1 images has demonstrated that MSS Bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images in correlation with ground snow depth data indicates that a heavy blanket of snow (more than 9 inches) accentuates major structural features while a light "dusting", (less than 1 inch) accentuates more subtle topographic expressions. An effective mail-based method for acquiring timely ground-truth (snowdepth) information was established and provides a ready correlation of fracture detail with snow depth so as to establish the working limits of the technique. The method is both efficient and inexpensive compared with the cost of similarly scaled direct field observations.

[Spectrum similarities-based analysis of spatial difference of snow cover for multi-scale satellite data-a case study of MODIS and HJ-1B data].

PubMed

Liu, Yan; Li, Yang; Yang, Yun; Jian, Ji

2014-05-01

Vegetation and bare soil were collected in the areas of Miyaluo district in northwest of Sichuan province, the Qilian Mountains in Qinghai province and northern areas of Xinjiang during the years of 2007 and 2013. Then these data were converted to spectral reflectance by applying sensor response function of MODIS and HJ-1B respectively within the range of visible light, near-infrared and shortwave infrared. Comprehensive analysis was made on spectral characteristics and reflectivity similarities and differences of different sensors between old and new snowmelt, under the condition of different snow depth and different snow cover. The conclusions can be drawn That is, there exists high consistency of spectral response between new snow and dirty snow for each sensor in the visible wavelength range, also it is true for bare soil and low vegetation. However, low consistency happens to other types of snow; especially snowmelt and frozen snow. The range of NDSI is relatively stable under the condition of different snow depth for full snow cover and the trend of NDSI shows great consistency for different sensors; NDSI threshold method for monitoring snow by using MODIS and HJ-1B data showed very obvious difference in spatial scales, which is a reasonable explanation of the existence of mixed pixels.

Soil moisture response to experimentally altered snowmelt timing is mediated by soil, vegetation, and regional climate patterns

USGS Publications Warehouse

Conner, Lafe G; Gill, Richard A.; Belnap, Jayne

2016-01-01

Soil moisture in seasonally snow-covered environments fluctuates seasonally between wet and dry states. Climate warming is advancing the onset of spring snowmelt and may lengthen the summer-dry state and ultimately cause drier soil conditions. The magnitude of either response may vary across elevation and vegetation types. We situated our study at the lower boundary of persistent snow cover and the upper boundary of subalpine forest with paired treatment blocks in aspen forest and open meadow. In treatments plots, we advanced snowmelt timing by an average of 14 days by adding dust to the snow surface during spring melt. We specifically wanted to know whether early snowmelt would increase the duration of the summer-dry period and cause soils to be drier in the early-snowmelt treatments compared with control plots. We found no difference in the onset of the summer-dry state and no significant differences in soil moisture between treatments. To better understand the reasons soil moisture did not respond to early snowmelt as expected, we examined the mediating influences of soil organic matter, texture, temperature, and the presence or absence of forest. In our study, late-spring precipitation may have moderated the effects of early snowmelt on soil moisture. We conclude that landscape characteristics, including soil, vegetation, and regional weather patterns, may supersede the effects of snowmelt timing in determining growing season soil moisture, and efforts to anticipate the impacts of climate change on seasonally snow-covered ecosystems should take into account these mediating factors. 

American River Hydrologic Observatory

NASA Astrophysics Data System (ADS)

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

2016-12-01

We have set up fourteen large wireless sensor networks to measure hydrologic parameters over physiographical representative regions of the snow-dominated portion of the river basin. This is perhaps the largest wireless sensor network in the world. Each network covers about a 1 km2 area and consists of about 45 elements. We measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time at ten locations per site, as opposed to the traditional once-a-month snow course. As part of the multi-PI SSCZO, we have installed a 62-node wireless sensor network to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time. This network has been operating for approximately six years. We are now installing four large wireless sensor networks to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in East Branch of the North Fork of the Feather River, CA. The presentation will discuss the planning and operation of the networks as well as some unique results. It will also present information about the networking hardware designed for these installations, which has resulted in a start-up, Metronome Systems.

The Alpine snow-albedo feedback in regional climate models

NASA Astrophysics Data System (ADS)

Winter, Kevin J.-P. M.; Kotlarski, Sven; Scherrer, Simon C.; Schär, Christoph

2017-02-01

The effect of the snow-albedo feedback (SAF) on 2m temperatures and their future changes in the European Alps is investigated in the ENSEMBLES regional climate models (RCMs) with a focus on the spring season. A total of 14 re-analysis-driven RCM experiments covering the period 1961-2000 and 10 GCM-driven transient climate change projections for 1950-2099 are analysed. A positive springtime SAF is found in all RCMs, but the range of the diagnosed SAF is large. Results are compared against an observation-based SAF estimate. For some RCMs, values very close to this estimate are found; other models show a considerable overestimation of the SAF. Net shortwave radiation has the largest influence of all components of the energy balance on the diagnosed SAF and can partly explain its spatial variability. Model deficiencies in reproducing 2m temperatures above snow and ice and associated cold temperature biases at high elevations seem to contribute to a SAF overestimation in several RCMs. The diagnosed SAF in the observational period strongly influences the estimated SAF contribution to twenty first century temperature changes in the European Alps. This contribution is subject to a clear elevation dependency that is governed by the elevation-dependent change in the number of snow days. Elevations of maximum SAF contribution range from 1500 to 2000 m in spring and are found above 2000 m in summer. Here, a SAF contribution to the total simulated temperature change between 0 and 0.5 °C until 2099 (multi-model mean in spring: 0.26 °C) or 0 and 14 % (multi-model mean in spring: 8 %) is obtained for models showing a realistic SAF. These numbers represent a well-funded but only approximate estimate of the SAF contribution to future warming, and a remaining contribution of model-specific SAF misrepresentations cannot be ruled out.

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

NASA Astrophysics Data System (ADS)

Stehr, Alejandra; Aguayo, Mauricio

2017-10-01

Andean watersheds present important snowfall accumulation mainly during the winter, which melts during the spring and part of the summer. The effect of snowmelt on the water balance can be critical to sustain agriculture activities, hydropower generation, urban water supplies and wildlife. In Chile, 25 % of the territory between the region of Valparaiso and Araucanía comprises areas where snow precipitation occurs. As in many other difficult-to-access regions of the world, there is a lack of hydrological data of the Chilean Andes related to discharge, snow courses, and snow depths, which complicates the analysis of important hydrological processes (e.g. water availability). Remote sensing provides a promising opportunity to enhance the assessment and monitoring of the spatial and temporal variability of snow characteristics, such as the snow cover area (SCA) and snow cover dynamic (SCD). With regards to the foregoing questions, the objective of the study is to evaluate the spatiotemporal dynamics of the SCA at five watersheds (Aconcagua, Rapel, Maule, Biobío and Toltén) located in the Chilean Andes, between latitude 32.0 and 39.5° S, and to analyse its relationship with the precipitation regime/pattern and El Niño-Southern Oscillation (ENSO) events. Those watersheds were chosen because of their importance in terms of their number of inhabitants, and economic activities depending on water resources. The SCA area was obtained from MOD10A2 for the period 2000-2016, and the SCD was analysed through a number of statistical tests to explore observed trends. In order to verify the SCA for trend analysis, a validation of the MOD10A2 product was done, consisting of the comparison of snow presence predicted by MODIS with ground observations. Results indicate that there is an overall agreement of 81 to 98 % between SCA determined from ground observations and MOD10A2, showing that the MODIS snow product can be taken as a feasible remote sensing tool for SCA estimation in southern-central Chile. Regarding SCD, no significant reduction in SCA for the period 2000-2016 was detected, with the exception of the Aconcagua and Rapel watersheds. In addition to that, an important decline in SCA in the five watersheds for the period of 2012 and 2016 was also evident, which is coincidental with the rainfall deficit for the same years. Findings were compared against ENSO episodes that occurred during 2010-2016, detecting that Niña years are coincident with maximum SCA during winter in all watersheds.

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

NASA Astrophysics Data System (ADS)

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 1:10 approximation (i.e. 100 kgm-3), which is mainly based on daily values in the Alps. Variations in new snow density could not be explained in a satisfactory manner using meteorological data measured at the same location. Likewise, some of the tested parametrizations of new snow density, which primarily use air temperature as a proxy, result in median new snow densities close to the ones from automated measurements, but show only a low correlation between calculated and measured new snow densities. The case study on the influence of snow settling on HN resulted on average in an underestimation of HN by 17%, which corresponds to 2-3% of the cumulated HN from the previous 24 hours. Therefore, the mean hourly new snow densities may be overestimated by 14%. The analysis in this study is especially limited with respect to the meteorological influence on the HS measurement using ultra-sonic rangers. Nevertheless, the reasonable mean values encourage calculating new snow densities from standard hydro-meteorological measurements using more precise observation devices such as optical snow depth sensors and more sensitive scales for SWE measurements also on sub-daily time-scales.

Improved Passive Microwave Algorithms for North America and Eurasia

NASA Technical Reports Server (NTRS)

Foster, James; Chang, Alfred; Hall, Dorothy

1997-01-01

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

PULSE: A numerical model for the simulation of snowpack solute dynamics to capture runoff ionic pulses during snowmelt

NASA Astrophysics Data System (ADS)

Costa, D.; Pomeroy, J. W.; Wheater, H. S.

2017-12-01

Early ionic pulses in spring snowmelt can cause the temporary acidification of streams and account for a significant portion of the total annual nutrient export, particularly in seasonally snow-covered areas where the frozen ground may limit runoff-soil contact and cause the rapid delivery of these ions to streams. Ionic pulses are a consequence of snow ion exclusion, a process induced by snow metamorphism where ions are segregated from the snow grains losing mass to the surface of the grains gaining mass. While numerous studies have been successful in providing quantitative evidence of this process, few mechanistic mathematical models have been proposed for diagnostic and prediction. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity, however their implementation is difficult because they require complex models of snow physics to resolve the evolution of in-snow properties and processes during snowmelt, such as heat conduction, metamorphism, melt and water flow. Furthermore, initial snowpack to snow-surface ion concentration ratios are difficult to measure but are required to initiate these models and ion exclusion processes are not represented in a physically-based transparent fashion. In this research, a standalone numerical model has been developed to capture ionic pulses in snowmelt by emulating solute leaching from snow grains during melt and its subsequent transport by the percolating meltwater. Estimating snow porosity and water content dynamics is shown to be a viable alternative to deployment of complex snow physics models for this purpose. The model was applied to four study sites located in the Arctic and in Sierra Nevada to test for different climatic and hydrological conditions. The model compares very well with observations and could capture both the timing and magnitude of early melt ionic pulses accurately. This study demonstrates how physically based approaches can provide successful simulations of the spatial and temporal fluxes of snowmelt ions, which can be used to improve the prediction of nutrient export in cold regions for the spring freshet.

PULSE: A numerical model for the simulation of snowpack solute dynamics to capture runoff ionic pulses during snowmelt

NASA Astrophysics Data System (ADS)

Clark, M. P.; Nijssen, B.; Lundquist, J. D.; Luce, C. H.; Musselman, K. N.; Wayand, N. E.; Ou, M.; Lapo, K. E.

2016-12-01

Early ionic pulses in spring snowmelt can cause the temporary acidification of streams and account for a significant portion of the total annual nutrient export, particularly in seasonally snow-covered areas where the frozen ground may limit runoff-soil contact and cause the rapid delivery of these ions to streams. Ionic pulses are a consequence of snow ion exclusion, a process induced by snow metamorphism where ions are segregated from the snow grains losing mass to the surface of the grains gaining mass. While numerous studies have been successful in providing quantitative evidence of this process, few mechanistic mathematical models have been proposed for diagnostic and prediction. A few early modelling attempts have been successful in capturing this process assuming transport through porous media with variable porosity, however their implementation is difficult because they require complex models of snow physics to resolve the evolution of in-snow properties and processes during snowmelt, such as heat conduction, metamorphism, melt and water flow. Furthermore, initial snowpack to snow-surface ion concentration ratios are difficult to measure but are required to initiate these models and ion exclusion processes are not represented in a physically-based transparent fashion. In this research, a standalone numerical model has been developed to capture ionic pulses in snowmelt by emulating solute leaching from snow grains during melt and its subsequent transport by the percolating meltwater. Estimating snow porosity and water content dynamics is shown to be a viable alternative to deployment of complex snow physics models for this purpose. The model was applied to four study sites located in the Arctic and in Sierra Nevada to test for different climatic and hydrological conditions. The model compares very well with observations and could capture both the timing and magnitude of early melt ionic pulses accurately. This study demonstrates how physically based approaches can provide successful simulations of the spatial and temporal fluxes of snowmelt ions, which can be used to improve the prediction of nutrient export in cold regions for the spring freshet.

Coupled long-term summer warming and deeper snow alters species composition and stimulates gross primary productivity in tussock tundra.

PubMed

Leffler, A Joshua; Klein, Eric S; Oberbauer, Steven F; Welker, Jeffrey M

2016-05-01

Climate change is expected to increase summer temperature and winter precipitation throughout the Arctic. The long-term implications of these changes for plant species composition, plant function, and ecosystem processes are difficult to predict. We report on the influence of enhanced snow depth and warmer summer temperature following 20 years of an ITEX experimental manipulation at Toolik Lake, Alaska. Winter snow depth was increased using snow fences and warming was accomplished during summer using passive open-top chambers. One of the most important consequences of these experimental treatments was an increase in active layer depth and rate of thaw, which has led to deeper drainage and lower soil moisture content. Vegetation concomitantly shifted from a relatively wet system with high cover of the sedge Eriophorum vaginatum to a drier system, dominated by deciduous shrubs including Betula nana and Salix pulchra. At the individual plant level, we observed higher leaf nitrogen concentration associated with warmer temperatures and increased snow in S. pulchra and B. nana, but high leaf nitrogen concentration did not lead to higher rates of net photosynthesis. At the ecosystem level, we observed higher GPP and NEE in response to summer warming. Our results suggest that deeper snow has a cascading set of biophysical consequences that include a deeper active layer that leads to altered species composition, greater leaf nitrogen concentration, and higher ecosystem-level carbon uptake.

Phenological change in a spring ephemeral: implications for pollination and plant reproduction.

PubMed

Gezon, Zachariah J; Inouye, David W; Irwin, Rebecca E

2016-05-01

Climate change has had numerous ecological effects, including species range shifts and altered phenology. Altering flowering phenology often affects plant reproduction, but the mechanisms behind these changes are not well-understood. To investigate why altering flowering phenology affects plant reproduction, we manipulated flowering phenology of the spring herb Claytonia lanceolata (Portulacaceae) using two methods: in 2011-2013 by altering snow pack (snow-removal vs. control treatments), and in 2013 by inducing flowering in a greenhouse before placing plants in experimental outdoor arrays (early, control, and late treatments). We measured flowering phenology, pollinator visitation, plant reproduction (fruit and seed set), and pollen limitation. Flowering occurred approx. 10 days earlier in snow-removal than control plots during all years of snow manipulation. Pollinator visitation patterns and strength of pollen limitation varied with snow treatments, and among years. Plants in the snow removal treatment were more likely to experience frost damage, and frost-damaged plants suffered low reproduction despite lack of pollen limitation. Plants in the snow removal treatment that escaped frost damage had higher pollinator visitation rates and reproduction than controls. The results of the array experiment supported the results of the snow manipulations. Plants in the early and late treatments suffered very low reproduction due either to severe frost damage (early treatment) or low pollinator visitation (late treatment) relative to control plants. Thus, plants face tradeoffs with advanced flowering time. While early-flowering plants can reap the benefits of enhanced pollination services, they do so at the cost of increased susceptibility to frost damage that can overwhelm any benefit of flowering early. In contrast, delayed flowering results in dramatic reductions in plant reproduction through reduced pollination. Our results suggest that climate change may constrain the success of early-flowering plants not through plant-pollinator mismatch but through the direct impacts of extreme environmental conditions. © 2016 John Wiley & Sons Ltd.

Differences between the MEMLS and the multiple-layer HUT model and their comparisons with in-situ snowpack observations

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Sandells, M. J.; Lemmetyinen, J.; Kim, E. J.

2013-12-01

Application of passive microwave (PM) brightness temperature for snow water equivalent retrieval requires deep understanding of snow emission models, not only for their performance to reproduce in-situ PM observations, but also for their theoretical differences to approximate radiative transfer theory. In this paper, differences between the multiple-layer HUT (or TKK) model and the Microwave Emission Model of Layered Snowpacks (MEMLS) were listed, and the two models were compared with snow ground-based PM observations at Streamboat Springs, Colorado, USA; Churchill, Canada; and Sodankyla, Finland. The two models were chosen for their multiple-layer schemes are close to actual layer-by-layer snow measurements. Both the two models are semi-empirical models; whereas the HUT model uses the mean snow grain size, MEMLS uses the correlation length to relate the snow microstructure with the scattering coefficients. The two parameters are related according to previous studies. The Specific Surface Area (SSA) was measured at three test sites to derive the correlation length, while the mean snow grain sizes was available at Stream Springs and Sodankyla. It was shown that with different apparent forms of radiative transfer equations, the different parts of the two models have one-to-one correspondence however, and intermediate parameters are comparable. Regarding the multiple-layer structure of the models, it was found that the HUT model considers the internal reflectivity of each snow layer to be zero. The two-flux radiative transfer equations of the two models were compared, and the correspondence of the semi-empirical parameter q in the HUT model was found in the MEMLS. The effect of consideration of transverse radiation scattered into the direction under consideration via the six-flux approximation in MEMLS is compared. Based on model comparisons, we analyzed the differences of TB predictions at the three test sites.

Drivers and environmental responses to the changing annual snow cycle of northern Alaska

USGS Publications Warehouse

Cox, Christopher J.; Stone, Robert S.; Douglas, David C.; Stanitski, Diane; Divoky, George J.; Dutton, Geoff S.; Sweeney, Colm; George, J. Craig; Longenecker, David U.

2017-01-01

On the North Slope of Alaska, earlier spring snowmelt and later onset of autumn snow accumulation are tied to atmospheric dynamics and sea ice conditions, and result in environmental responses.Linkages between atmospheric, ecological and biogeochemical variables in the changing Arctic are analyzed using long-term measurements near Utqiaġvik (formerly Barrow), Alaska. Two key variables are the date when snow disappears in spring, as determined primarily by atmospheric dynamics, precipitation, air temperature, winter snow accumulation and cloud cover, as well as the date of onset of snowpack in autumn that is additionally influenced by ocean temperature and sea ice extent. In 2015 and 2016 the snow melted early at Utqiaġvik due mainly to anomalous warmth during May of both years attributed to atmospheric circulation patterns, with 2016 having the record earliest snowmelt. These years are discussed in the context of a 115-year snowmelt record at Utqiaġvik with a trend toward earlier melting since the mid- 1970s (-2.86 days/decade, 1975-2016). At nearby Cooper Island, where a colony of seabirds, Black Guillemots, have been monitored since 1975, timing of egg laying is correlated with Utqiaġvik snowmelt with 2015 and 2016 being the earliest years in the 42-year record. Ice-out at a nearby freshwater lagoon is also correlated with Utqiaġvik snowmelt. The date when snow begins to accumulate in autumn at Utqiaġvik shows a trend towards later dates (+4.6 days/decade, 1975-2016), with 2016 the latest on record. The relationships between the lengthening snow-free season and regional phenology, soil temperatures, fluxes of gases from the tundra, and to regional sea ice conditions are discussed. Better understanding of these interactions is needed to predict the annual snow cycles in the region at seasonal to decadal scales, and to anticipate coupled environmental responses.

Spatial and seasonal variations of elemental composition in Mt. Everest (Qomolangma) snow/firn

NASA Astrophysics Data System (ADS)

Kang, Shichang; Zhang, Qianggong; Kaspari, Susan; Qin, Dahe; Cong, Zhiyuan; Ren, Jiawen; Mayewski, Paul A.

In May 2005, a total of 14 surface snow (0-10 cm) samples were collected along the climbing route from the advanced base camp to the summit (6500-8844 m a.s.l.) on the northern slope of Mt. Everest (Qomolangma). A 108 m firn/ice core was retrieved from the col of the East Rongbuk Glacier (28.03°N, 86.96°E, 6518 m a.s.l.) on the north eastern saddle of Mt. Everest in September 2002. Surface snow and the upper 3.5 m firn samples from the core were analyzed for major and trace elements by inductively coupled plasma mass spectroscopy (ICP-MS). Measurements show that crustal elements dominated both surface snow and the firn core, suggesting that Everest snow chemistry is mainly influenced by crustal aerosols from local rock or prevalent spring dust storms over southern/central Asia. There are no clear trends for element variations with elevation due to local crustal aerosol inputs or redistribution of surface snow by strong winds during the spring. Seasonal variability in snow/firn elements show that high elemental concentrations occur during the non-monsoon season and low values during the monsoon season. Ca, Cr, Cs, and Sr display the most distinct seasonal variations. Elemental concentrations (especially for heavy metals) at Mt. Everest are comparable with polar sites, generally lower than in suburban areas, and far lower than in large cities. This indicates that anthropogenic activities and heavy metal pollution have little effect on the Mt. Everest atmospheric environment. Everest firn core REE concentrations are the first reported in the region and seem to be comparable with those measured in modern and Last Glacial Maximum snow/ice samples from Greenland and Antarctica, and with precipitation samples from Japan and the East China Sea. This suggests that REE concentrations measured at Everest are representative of the background atmospheric environment.

Are Sierran Lakes Warming as a Result of Climate Change? The Effects of Climate Warming and Variation in Precipitation on Water Temperature in a Snowmelt-Dominated Lake

NASA Astrophysics Data System (ADS)

Sadro, S.; Melack, J. M.; Sickman, J. O.; Skeen, K.

2016-12-01

Water temperature regulates a broad range of fundamental ecosystem processes in lakes. While climate can be an important factor regulating lake temperatures, heterogeneity in the warming response of lakes is large, and variation in precipitation is rarely considered. We analyzed three decades of climate and water temperature data from a high-elevation catchment in the southern Sierra Nevada of California to illustrate the magnitude of warming taking place during different seasons and the role of precipitation in regulating lake temperatures. Significant climate warming trends were evident during all seasons except spring. Nighttime rates of climate warming were approximately 25% higher than daytime rates. Spatial patterns in warming were elevation dependent, with rates of temperature increase higher at sites above 2800 m.a.s.l. than below. Although interannual variation in snow deposition was high, the frequency and severity of recent droughts has contributed to a significant 3.4 mm year -1 decline in snow water equivalent over the last century. Snow accumulation, more than any other climate factor, regulated lake temperature; 94% of variation in summer lake temperature was regulated by precipitation as snow. For every 100 mm decrease in snow water equivalent there was a 0.62 ° increase in lake temperature. Drought years amplify warming in lakes by reducing the role of cold spring meltwaters in lake energy budgets and prolonging the ice-free period during which lakes warm. The combination of declining winter snowpack and warming air temperatures has the capacity to amplify the effect of climate warming on lake temperatures during drought years. Interactions among climatic factors need to be considered when evaluating ecosystem level effects, especially in mountain regions. For mountain lakes already affected by drought, continued climate warming during spring and autumn has the greatest potential to impact mean lake temperatures.

Retention and radiative forcing of black carbon in eastern Sierra Nevada snow

NASA Astrophysics Data System (ADS)

Sterle, K. M.; McConnell, J. R.; Dozier, J.; Edwards, R.; Flanner, M. G.

2013-02-01

When contaminated by absorbing particles, such as refractory black carbon (rBC) and continental dust, snow's albedo decreases and thus its absorption of solar radiation increases, thereby hastening snowmelt. For this reason, an understanding of rBC's affect on snow albedo, melt processes, and radiation balance is critical for water management, especially in a changing climate. Measurements of rBC in a sequence of snow pits and surface snow samples in the eastern Sierra Nevada of California during the snow accumulation and ablation seasons of 2009 show that concentrations of rBC were enhanced sevenfold in surface snow (~25 ng g-1) compared to bulk values in the snowpack (~3 ng g-1). Unlike major ions, which were preferentially released during the initial melt, rBC and continental dust were retained in the snow, enhancing concentrations well into late spring, until a final flush occurred during the ablation period. We estimate a combined rBC and continental dust surface radiative forcing of 20 to 40 W m-2 during April and May, with dust likely contributing a greater share of the forcing.

Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin

NASA Astrophysics Data System (ADS)

Chen, S.; Cui, X.; Liang, T.

2018-04-01

Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Picard, Ghislain; Brucker, Ludovic; Roy, Alexandre; DuPont, FLorent; Champollion, Nicolas; Morin, Samuel

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).

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

NASA Astrophysics Data System (ADS)

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

1995-11-01

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

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

NASA Astrophysics Data System (ADS)

Pimentel, Rafael; Herrero, Javier; José Polo, María

2017-02-01

Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation-depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009-2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m-2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

Passive microwave studies of snowpack properties. [Walden and Steamboat Spring, Colorado

NASA Technical Reports Server (NTRS)

Hall, D. K.; Chang, A. T. C.; Foster, J. L.; Rango, A.; Schmugge, T.

1978-01-01

Microwave brightness temperatures were measured for the snowpacks at Walden and Steamboat Springs, Colorado during 1976 and 1977 aircraft experiments. Variations in measured brightness temperatures are attributed to snow grain and crystal sizes, liquid water content, and snowpack temperature. Results demonstrate that shorter wavelength radiation is scattered more strongly than longer wavelength radiation.

An Ultra-Wideband, Microwave Radar for Measuring Snow Thickness on Sea Ice and Mapping Near-Surface Internal Layers in Polar Firn

NASA Technical Reports Server (NTRS)

Panzer, Ben; Gomez-Garcia, Daniel; Leuschen, Carl; Paden, John; Rodriguez-Morales, Fernando; Patel, Azsa; Markus, Thorsten; Holt, Benjamin; Gogineni, Prasad

2013-01-01

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100mW signal is swept from 2 to 8GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 c range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Autotrophic and Heterotrophic Controls over Winter Soil Carbon Cycling in a Subalpine Forest Ecosystem

NASA Astrophysics Data System (ADS)

Monson, R. K.; Scott-Denton, L. E.; Lipson, D. A.; Weintrub, M. N.; Rosenstiel, T. N.; Schmidt, S. K.; Williams, M. W.; Burns, S. P.; Delany, A. E.; Turnipseed, A. A.

2005-12-01

Studies were conducted at the Niwot Ridge Ameriflux site to understand wintertime soil carbon cycling and its control over ecosystem respiration. Wintertime respiration in this ecosystem results in the loss of 60-90% of the carbon assimilated the previous growing season. Thus, an understanding of the controls over winter carbon cycling is required to understand controls over the annual carbon budget. Trees were girdled to prevent the transport of photosynthates to the rhizosphere. In plots with non-girdled trees a large mid-winter pulse of sucrose was observed to enter the soil. In plots with girdled trees, no sucrose pulse was observed. Trees of this ecosystem are not photosynthetically active during the winter, leading us to conclude that the sucrose pulse is due to the death of fine roots that had accumulated sucrose the previous autumn. The sucrose pulse is potentially utilized by a novel winter community of microbes. Using DNA fingerprinting we discovered that the dominant isolates from the winter soils were from Jathinobacter, whereas the summer isolates were from Burkholderia. The winter community was capable of high rates of respiration and exponential growth at low temperatures, whereas the summer community was not. Our winter observations also indicated high activity of N-acetyl-C-glucosaminidase, one of the principal enzymes involved in chitin degradation. The presence of such high chitinase activities implicates decomposing fungal biomass as a principle source of CO2 beneath the snow pack. Using a novel in situ, beneath-snow CO2 measurement system, we observed unprecedented Q10 values for winter respiration, being 98 and 8.44 x 104 for the soil next to tree boles or within the open spaces between trees, respectively. These high Q10 values are likely the result of fractional changes in the availability of liquid water below 0°C and responses of microbial biomass to changes in the liquid water fraction. Using six-years of eddy covariance data, we showed that interannual variation in winter ecosystem respiration is positively correlated to interannual variation in the spring snow depth. Years with a with a deeper spring snow pack exhibited higher soil temperatures, and concomitantly higher soil respiration rates. Given the recently reported decadal-scale trend in decreasing snow pack in the Western U.S., which is coupled to warm climate anomalies, our observations indicate the potential for higher wintertime soil carbon sequestration due to lower winter ecosystem respiration rates in subalpine forests. Our studies of processes beneath the winter snow pack demonstrate that contrary to previous assumptions, winter biogeochemical processing of soil organic matter is an important component of ecosystem carbon budgets. Despite low temperatures and an inactive plant rhizosphere, winter microbial communities and exoenzymes appear to be active, carbon substrates appear to be in relatively high abundance and soil respiration rates appear to be sensitive to seasonal and interannual winter climate variability.

The influences of changing weather patterns and land management on runoff biogeochemistry in a snowmelt dominated agricultural region

NASA Astrophysics Data System (ADS)

Wilson, H. F.; Elliott, J. A.; Glenn, A. J.

2017-12-01

Runoff generation and the associated export of nitrogen, phosphorus, and organic carbon on the Northern Great Plains have historically been dominated by snowmelt runoff. In this region the transport of elements primarily occurs in dissolved rather than particulate forms, so cropland management practices designed to reduce particulate losses tend to be ineffective in reducing nutrient runoff. Over the last decade a higher frequency of high volume and intensity rainfall has been observed, leading to rainfall runoff and downstream flooding. To evaluate interactions between tillage, crop residue management, fertilization practices, weather, and runoff biogeochemistry a network of 18 single field scale watersheds (2-6 ha.) has been established in Manitoba, Canada over a range of fertilization (no input to high input) and tillage (zero tillage to frequent tillage). Soils in this network are typical of cropland in the region with clay or clay loam textures, but soil phosphorus differs greatly depending on input practices (3 to 25 mg kg-1 sodium bicarbonate extractable P). Monitoring of runoff chemistry and hydrology at these sites was initiated in 2013 and over the course of 5 years high volume snowmelt runoff from deep snowpack (125mm snow water equivalent), low volume snowmelt from shallow snowpack (25mm snow water equivalent) and extreme rainfall runoff events in spring have all been observed. Event based analyses of the drivers of runoff chemistry indicate that spring fertilization practices (depth, amount, and timing) influence concentrations of N and P in runoff during large rainfall runoff events, but for snowmelt runoff the near surface soil chemistry, tillage, and crop residue management are of greater importance. Management recommendations that might be suggested to reduce nutrient export and downstream eutrophication in the region differ for snowmelt and rainfall, but are not mutually exclusive.

A hierarchical perspective on the diversity of butterfly species' responses to weather in the Sierra Nevada Mountains.

PubMed

Nice, Chris C; Forister, Matthew L; Gompert, Zachariah; Fordyce, James A; Shapiro, Arthur M

2014-08-01

An important and largely unaddressed issue in studies of biotic-abiotic relationships is the extent to which closely related species, or species living in similar habitats, have similar responses to weather. We addressed this by applying a hierarchical, Bayesian analytical framework to a long-term data set for butterflies which allowed us to simultaneously investigate responses of the entire fauna and individual species. A small number of variables had community-level effects. In particular, higher total annual snow depth had a positive effect on butterfly occurrences, while spring minimum temperature and El Niño-Southern Oscillation (ENSO) sea-surface variables for April-May had negative standardized coefficients. Our most important finding was that variables with large impacts at the community-level did not necessarily have a consistent response across all species. Species-level responses were much more similar to each other for snow depth compared to the other variables with strong community effects. This variation in species-level responses to weather variables raises important complications for the prediction of biotic responses to shifting climatic conditions. In addition, we found that clear associations with weather can be detected when considering ecologically delimited subsets of the community. For example, resident species and non-ruderal species had a much more unified response to weather variables compared to non-resident species and ruderal species, which suggests local adaptation to climate. These results highlight the complexity of biotic-abiotic interactions and confront that complexity with methodological advances that allow ecologists to understand communities and shifting climates while simultaneously revealing species-specific variation in response to climate.

Retention and radiative forcing of black carbon in Eastern Sierra Nevada snow

NASA Astrophysics Data System (ADS)

Sterle, K. M.; McConnell, J. R.; Dozier, J.; Edwards, R.; Flanner, M. G.

2012-06-01

Snow and glacier melt water contribute water resources to a fifth of Earth's population. Snow melt processes are sensitive not only to temperature changes, but also changes in albedo caused by deposition of particles such as refractory black carbon (rBC) and continental dust. The concentrations, sources, and fate of rBC particles in seasonal snow and its surface layers are uncertain, and thus an understanding of rBC's effect on snow albedo, melt processes, and radiation balance is critical for water management in a changing climate. Measurements of rBC in a sequence of snow pits and surface snow samples in the Eastern Sierra Nevada of California during the snow accumulation and melt seasons of 2009 show that concentrations of rBC were enhanced seven fold in surface snow (~25 ng g-1) compared to bulk values in the snow pack (~3 ng g-1). Unlike major ions which are preferentially released during initial melt, rBC and continental dust are retained in the snow, enhancing concentrations late into spring, until a final flush well into the melt period. We estimate a combined rBC and continental dust surface radiative forcing of 20 to 40 W m-2 during April and May, with dust likely contributing a greater share of the forcing than rBC.

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

NASA Astrophysics Data System (ADS)

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

1991-04-01

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

Altitudinal gradients, midwinter melt, and wind effects on snow accumulation in semiarid midlatitude Andes under La Niña conditions

NASA Astrophysics Data System (ADS)

Ayala, A.; McPhee, J.; Vargas, X.

2014-04-01

The Andes Cordillera remains a sparsely monitored and studied snow hydrology environment in comparison to similar mountain ranges in the Northern Hemisphere. In order to uncover some of the key processes driving snow water equivalent (SWE) spatial variability, we present and analyze a distributed SWE data set, sampled at the end of accumulation season 2011. Three representative catchments across the region were monitored, obtaining measurements in an elevation range spanning 2000 to 3900 m asl and from 32.4° to 34.0°S in latitude. Climatic conditions during this season corresponded to a moderate La Niña phenomenon, which is generally correlated with lower-than normal accumulation. Collected measurements can be described at the regional and watershed extents by altitudinal gradients that imply an increase by a factor of two in snow depth between 2200 and 3000 m asl, though with significant variability at the upper sites. In these upper sites, we found north-facing, wind-sheltered slopes showing 25% less average SWE values than south-facing, wind-exposed ones. This suggests that under these conditions, solar radiation dominated wind transport effects in controlling end-of-winter variability. Nevertheless, we found clusters of snow depth measurements above 3000 m asl that can be explained by wind exposure differences. This is the first documented snow depth data set of this spatial extent for this region, and it is framed within an ongoing research effort aimed at improving understanding and modeling of snow hydrology in the extratropical Andes Cordillera.

Effects of climate and snow depth on Bromus tectorum population dynamics at high elevation.

PubMed

Griffith, Alden B; Loik, Michael E

2010-11-01

Invasive plants are thought to be especially capable of range shifts or expansion in response to climate change due to high dispersal and colonization abilities. Although highly invasive throughout the Intermountain West, the presence and impact of the grass Bromus tectorum has been limited at higher elevations in the eastern Sierra Nevada, potentially due to extreme wintertime conditions. However, climate models project an upward elevational shift of climate regimes in the Sierra Nevada that could favor B. tectorum expansion. This research specifically examined the effects of experimental snow depth manipulations and interannual climate variability over 5 years on B. tectorum populations at high elevation (2,175 m). Experimentally-increased snow depth had an effect on phenology and biomass, but no effect on individual fecundity. Instead an experimentally-increased snowpack inhibited population growth in 1 year by reducing seedling emergence and early survival. A similar negative effect of increased snow was observed 2 years later. However, a strong negative effect on B. tectorum was also associated with a naturally low-snow winter, when seedling emergence was reduced by 86%. Across 5 years, winters with greater snow cover and a slower accumulation of degree-days coincided with higher B. tectorum seedling density and population growth. Thus, we observed negative effects associated with both experimentally-increased and naturally-decreased snowpacks. It is likely that the effect of snow at high elevation is nonlinear and differs from lower elevations where wintertime germination can be favorable. Additionally, we observed a doubling of population size in 1 year, which is alarming at this elevation.

Satellite Snow-Cover Mapping: A Brief Review

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1995-01-01

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow and ice cover using Earth Algorithms to map global snow cover using passive-microwave data have also cover and of snow grain size, globally, limits the utility of a single algorithm to map global snow cover.

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1975-01-01

LANDSAT and NOAA satellites data were used to study snow depth. These snow measurements were used to help forecast runoff and flooding. Many areas of California, Arizona, Colorado, and Wyoming were emphasized.

Changes in water supply in Alpine regions due to glacier retreat

NASA Astrophysics Data System (ADS)

Pelto, Mauri S.

1992-06-01

In the late 1970s global temperature rose abruptly, and between 1977 and 1990 has averaged 0.4 °C above the 1940-76 mean. In 1980, 50% of the the alpine glaciers observed in the Swiss Alps, Peruvian Andes, Norwegian Coast Range, Northern Caucasus and Washington's North Cascades were advancing. By 1990 in response to the warming only 15% were still advancing. During the peak non-glacier snow melt period glaciers are unsaturated aquifers soaking up and holding meltwater for the first two-six weeks of the melt season. This storage acts as a buffer for spring snow melt flooding, and spreads the peak spring flow over a longer period. In the late summer glaciers buffer low flow periods by providing large volumes of meltwater. As glaciers retreat the amount of water they can store decreases raising spring flood danger and the areal extend exposed for late summer meltwater generation decreases, thus reducing late summer flow.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, Fabiano; Gaume, Johan; van Herwijnen, Alec; Schweizer, Jürg

2016-03-01

The process of dry-snow slab avalanche formation can be divided into two phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation based on shear stress and strength. Though it is known that both the properties of the weak layer and the slab play a major role in avalanche release, most previous approaches only considered slab properties in terms of slab depth, average density and skier penetration. For example, for the skier stability index, the additional stress (e.g. due to a skier) at the depth of the weak layer is calculated by assuming that the snow cover can be considered a semi-infinite, elastic, half-space. We suggest a new approach based on a simplification of the multi-layered elasticity theory in order to easily compute the additional stress due to a skier at the depth of the weak layer, taking into account the layering of the snow slab and the substratum. We first tested the proposed approach on simplified snow profiles, then on manually observed snow profiles including a stability test and, finally, on simulated snow profiles. Our simple approach reproduced the additional stress obtained by finite element simulations for the simplified profiles well - except that the sequence of layering in the slab cannot be replicated. Once implemented into the classical skier stability index and applied to manually observed snow profiles classified into different stability classes, the classification accuracy improved with the new approach. Finally, we implemented the refined skier stability index into the 1-D snow cover model SNOWPACK. The two study cases presented in this paper showed promising results even though further verification is still needed. In the future, we intend to implement the proposed approach for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, F.; Gaume, J.; van Herwijnen, A.; Schweizer, J.

2015-08-01

The process of dry-snow slab avalanche formation can be divided into two phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation based on shear stress and strength. Though it is known that both the properties of the weak layer and the slab play a major role in avalanche release, most previous approaches only considered slab properties in terms of slab depth, average density and skier penetration. For example, for the skier stability index, the additional stress (e.g. due to a skier) at the depth of the weak layer is calculated by assuming that the snow cover can be considered a semi-infinite, elastic half-space. We suggest a new approach based on a simplification of the multi-layered elasticity theory in order to easily compute the additional stress due to a skier at the depth of the weak layer taking into account the layering of the snow slab and the substratum. We first tested the proposed approach on simplified snow profiles, then on manually observed snow profiles including a stability test and, finally, on simulated snow profiles. Our simple approach well reproduced the additional stress obtained by finite element simulations for the simplified profiles - except that the sequence of layering in the slab cannot be replicated. Once implemented into the classical skier stability index and applied to manually observed snow profiles classified into different stability classes, the classification accuracy improved with the new approach. Finally, we implemented the refined skier stability index into the 1-D snow cover model SNOWPACK. For the two study cases presented in this paper, this approach showed promising results even though further verification is still needed. In the future, we intend to implement the proposed approach for describing skier-induced stress within a multi-layered snowpack into more complex models which take into account not only failure initiation but also crack propagation.

Natural hazards in the Alps triggered by ski slope engineering and artificial snow production

NASA Astrophysics Data System (ADS)

de Jong, C.

2012-04-01

In the Alps there is increasing concern of man-made triggering of natural hazards in association with ski slope engineering and pressures from climate change. However literature on the topic is rare. Ski run development has been intensified in the past decade to accommodate a higher density of skiers. In order to absorb the increased flux of skiers promoted by continually increasing lift capacity, ski runs are subject to more and more enlargement, straightening and leveling. This has required large-scale re-leveling of slopes with the removal of soil and protective vegetation using heavy machinery during the summer season. Slope-ward incision on steep slopes, creation of artificial embankments by leeward deposition and development of straight ski runs perpendicular to steep slopes have resulted in both shallow and deep erosion, gullying, triggering of small landslides and even bedload transport in marginal channels. Other natural hazards have been triggered directly or indirectly due to intensification of artificial snow production. This has increased exponentially in the last decade in order to secure the skiing season under increasingly warm temperatures and erratic snowfall and decreasing snow depth and snow duration in association with climate change. The consequences are multiple. Firstly, in order to economize both costs and quantity of artificial snow production, ski runs are leveled as far as possible in order to avoid topographical irregularities, protruding vegetation or rocks. The combination of topsoil removal and prolonged duration of artificial snow cover results in a decreased vegetation cover and period as well as species alteration. Together with greatly decreased permeability of the underground, snowmelt and intensive summer precipitation trigger surface runoff, erosion and even small landslides. After more than a decade of intensive cover by artificial snow, most such steep ski runs at altitudes above 1400 m are reduced into highly erosive, vegetation-poor scree slopes in summertime. Secondly, the production of artificial snow requires increasingly large quantities of water during low flow periods and causes an exponential increase in the construction of water reservoirs and pipelines. Such reservoirs are often constructed in depressions occupied by wetlands but also on slopes, hilltops and in proglacial locations at high altitudes up to 3000m. Reservoir construction removes vegetation, soil and regolith over surface areas of up to 150 000 m2 and depths of more than 20 m. During their construction, the temporary or permanent storage of large quantities of sediment on steep slopes has lead in several cases to the production of debris flows. Each reservoir requires road construction and vehicle parking areas for heavy weight vehicle access. These are frequently subject to erosion, gullying, and small landslides. Some reservoirs are vulnerable to catastrophic drainage triggered by earthquakes, avalanches and other natural hazards typical for mountain environments since they are only sealed with plastic membranes. Thirdly, the melt of artificial snow introduced by water transfers from other catchments can cause a relatively large local surplus of water which in turn increases spring and summer flood peaks as well as sediment transport. Most steep ski runs have introduced artificial drainage canals across the ski runs to avoid concentration of surface flow and to prevent erosion. Slopes are also covered with organic soils and re-vegetated where possible. However, given the present trends of intensification of use and precipitation extremes, it is unlikely that erosion and mass movements can be prevented in the next few decades for the duration of the amortization of investments.

Differential ecophysiological response of deciduous shrubs and a graminoid to long-term experimental snow reductions and additions in moist acidic tundra, northern Alaska

Treesearch

Robert R. Pattison; Jeffrey M. Welker

2014-01-01

Changes in winter precipitation that include both decreases and increases in winter snow are underway across the Arctic. In this study, we used a 14-year experiment that has increased and decreased winter snow in the moist acidic tussock tundra of northern Alaska to understand impacts of variation in winter snow depth on summer leaf-level ecophysiology of two deciduous...

Radiative transfer in falling snow: A two-stream approximation

NASA Astrophysics Data System (ADS)

Koh, Gary

1989-04-01

Light transmission measurements through falling snow have produced results unexplainable by single scattering arguments. A two-stream approximation to radiative transfer is used to derive an analytical expression that describes the effects of multiple scattering as a function of the snow optical depth and the snow asymmetry parameter. The approximate solution is simple and it may be as accurate as the exact solution for describing the transmission measurements within the limits of experimental uncertainties.

Growing Season Conditions Mediate the Dependence of Aspen on Redistributed Snow Under Climate Change.

NASA Astrophysics Data System (ADS)

Soderquist, B.; Kavanagh, K.; Link, T. E.; Seyfried, M. S.; Strand, E. K.

2016-12-01

Precipitation regimes in many semiarid ecosystems are becoming increasingly dominated by winter rainfall as a result of climate change. Across these regions, snowpack plays a vital role in the distribution and timing of soil moisture availability. Rising temperatures will result in a more uniform distribution of soil moisture, advanced spring phenology, and prolonged growing seasons. Productive and wide ranging tree species like aspen, Populus tremuloides, may experience increased vulnerability to drought and mortality resulting from both reduced snowpack and increased evaporative demand during the growing season. We simulated the net primary production (NPP) of aspen stands spanning the rain:snow transition zone in the Reynolds Creek Critical Zone Observatory (RCCZO) in southwest Idaho, USA. Within the RCCZO, the total amount of precipitation has remained unchanged over the past 50 years, however the percentage of the precipitation falling as snow has declined by approximately 4% per decade at mid-elevation sites. The biogeochemical process model Biome-BGC was used to simulate aspen NPP at three stands located directly below snowdrifts that provide melt water late into the spring. After adjusting precipitation inputs to account for the redistribution of snow, we assessed climate change impacts on future aspen productivity. Mid-century (2046-2065) aspen NPP was simulated using temperature projections from a multi-model average under high emission conditions using the Multivariate Adaptive Constructed Analogs (MACA) data set. While climate change simulations indicated over a 20% decrease in annual NPP for some years, NPP rates for other mid-century years remained relatively unchanged due to variations in growing season conditions. Mid-century years with the largest decreases in NPP typically showed increased spring transpiration rates resulting from earlier leaf flush combined with warmer spring conditions. During these years, the onset of drought stress occurred earlier due to increased early season soil moisture use and higher summer vapor pressure deficits. These results indicate that vegetation response to decreased snowpack can result in significant drought stress although phenological shifts that better align leaf production and precipitation ameliorate this response in some years. Precipitation regimes in many semiarid ecosystems are becoming increasingly dominated by winter rainfall as a result of climate change. Across these regions, snowpack plays a vital role in the distribution and timing of soil moisture availability. Rising temperatures will result in a more uniform distribution of soil moisture, advanced spring phenology, and prolonged growing seasons. Productive and wide ranging tree species like aspen, Populus tremuloides, may experience increased vulnerability to drought and mortality resulting from both reduced snowpack and increased evaporative demand during the growing season. We simulated the net primary production (NPP) of aspen stands spanning the rain:snow transition zone in the Reynolds Creek Critical Zone Observatory (RCCZO) in southwest Idaho, USA. Within the RCCZO, the total amount of precipitation has remained unchanged over the past 50 years, however the percentage of the precipitation falling as snow has declined by approximately 4% per decade at mid-elevation sites. The biogeochemical process model Biome-BGC was used to simulate aspen NPP at three stands located directly below snowdrifts that provide melt water late into the spring. After adjusting precipitation inputs to account for the redistribution of snow, we assessed climate change impacts on future aspen productivity. Mid-century (2046-2065) aspen NPP was simulated using temperature projections from a multi-model average under high emission conditions using the Multivariate Adaptive Constructed Analogs (MACA) data set. While climate change simulations indicated over a 20% decrease in annual NPP for some years, NPP rates for other mid-century years remained relatively unchanged due to variations in growing season conditions. Mid-century years with the largest decreases in NPP typically showed increased spring transpiration rates resulting from earlier leaf flush combined with warmer spring conditions. During these years, the onset of drought stress occurred earlier due to increased early season soil moisture use and higher summer vapor pressure deficits. These results indicate that vegetation response to decreased snowpack can result in significant drought stress although phenological shifts that better align leaf production and precipitation ameliorate this response in some years.

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Picard, Ghislain; Roy, Alexandre; Dupont, Florent; Fily, Michel; Royer, Alain

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

Investigation of radar backscattering from second-year sea ice

NASA Technical Reports Server (NTRS)

Lei, Guang-Tsai; Moore, Richard K.; Gogineni, S. P.

1988-01-01

The scattering properties of second-year ice were studied in an experiment at Mould Bay in April 1983. Radar backscattering measurements were made at frequencies of 5.2, 9.6, 13.6, and 16.6 GHz for vertical polarization, horizontal polarization and cross polarizations, with incidence angles ranging from 15 to 70 deg. The results indicate that the second-year ice scattering characteristics were different from first-year ice and also different from multiyear ice. The fading properties of radar signals were studied and compared with experimental data. The influence of snow cover on sea ice can be evaluated by accounting for the increase in the number of independent samples from snow volume with respect to that for bare ice surface. A technique for calculating the snow depth was established by this principle and a reasonable agreement has been observed. It appears that this is a usable way to measure depth in snow or other snow-like media using radar.

Influence of Projected Changes in North American Snow Cover Extent on Mid-Latitude Cyclone Progression

NASA Astrophysics Data System (ADS)

Clare, R. M.; Desai, A. R.; Martin, J. E.; Notaro, M.; Vavrus, S. J.

2017-12-01

It has long been hypothesized that snow cover and snow extent have an influence on the development or steering of synoptic mid-latitude cyclones (MLCs). Rydzik and Desai (2014) showed a robust statistical relationship among snow cover extent, generation of low-level baroclinicity, and MLC tracks. Though snow cover extent is highly variable year to year, the changing global climate is expected to continue an already observed pattern of poleward retreat of mean snow cover in North America, particularly in late winter and spring. For this experiment, large ensemble simulations with the Weather Research and Forecasting model (WRF) were forced with output from the Community Earth System Model (CESM) to test the effect contributed solely by snow cover and the projected effects of a changing climate. Our experiment induces an adjustment to the extent of snow cover in North America according to CESM RCP 8.5 projections for each decade from 2020 to 2100 before and during several cases of MLCs moving east across the Great Plains near the snow line. To evaluate mechanisms of pre-existing and current snow influence on MLCs, model cases are started with snow line adjustment occurring from three days prior up to the storm's arrival over the Great Plains. We demonstrate that snow cover changes do alter MLC intensity and path via modification of low-level potential vorticity.

Quantifying the variability of potential black carbon transport from cropland burning in Russia driven by atmospheric blocking events

NASA Astrophysics Data System (ADS)

Hall, Joanne; Loboda, Tatiana

2018-05-01

The deposition of short-lived aerosols and pollutants on snow above the Arctic Circle transported from northern mid-latitudes have amplified the short term warming in the Arctic region. Specifically, black carbon has received a great deal of attention due to its absorptive efficiency and its fairly complex influence on the climate. Cropland burning in Russia is a large contributor to the black carbon emissions deposited directly onto the snow in the Arctic region during the spring when the impact on the snow/ice albedo is at its highest. In this study, our focus is on identifying a possible atmospheric pattern that may enhance the transport of black carbon emissions from cropland burning in Russia to the snow-covered Arctic. Specifically, atmospheric blocking events are large-scale patterns in the atmospheric pressure field that are nearly stationary and act to block migratory cyclones. The persistent low-level wind patterns associated with these mid-latitude weather patterns are likely to accelerate potential transport and increase the success of transport of black carbon emissions to the snow-covered Arctic during the spring. Our results revealed that overall, in March, the transport time of hypothetical black carbon emissions from Russian cropland burning to the Arctic snow is shorter (in some areas over 50 hours less at higher injection heights) and the success rate is also much higher (in some areas up to 100% more successful) during atmospheric blocking conditions as compared to conditions without an atmospheric blocking event. The enhanced transport of black carbon has important implications for the efficacy of deposited black carbon. Therefore, understanding these relationships could lead to possible mitigation strategies for reducing the impact of deposition of black carbon from crop residue burning in the Arctic.

Inventory of File sref_em.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 0-6 hour surface WEASD 0-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour hour fcst Specific Humidity [kg/kg] 401 surface NCPCP 0-6 hour acc Large-Scale Precipitation (non

Inventory of File nam.t00z.awip2000.tm00.grib2

Science.gov Websites

analysis Pressure Reduced to MSL [Pa] 002 1 hybrid level RIME analysis Rime Factor [non-dim] 003 surface Temperature [K] 014 surface WEASD analysis Water Equivalent of Accumulated Snow Depth [kg/m^2] 015 2 m above ^2] 021 surface WEASD 0-0 day acc f Water Equivalent of Accumulated Snow Depth [kg/m^2] 022 surface

Mean wind patterns and snow depths in an alpine-subalpine ecosystem as measured by damage to coniferous trees

Treesearch

G. L. Wooldridge; R. C. Musselman; R. A. Sommerfeld; D. G. Fox; B. H. Connell

1996-01-01

1. Deformations of Engelmann spruce and subalpine fir trees were surveyed for the purpose of determining climatic wind speeds and directions and snow depths in the Glacier Lakes Ecosystem Experiments Site (GLEES) in the Snowy Range of southeastern Wyoming, USA. Tree deformations were recorded at 50- and 100-m grid intervals over areas of c. 30 ha and 300 ha,...

Influence of overstory on snow depth and density in hemlock-spruce stands: implications for management of deer habitat in Southeastern Alaska.

Treesearch

Thomas A. Hanley; Cathy L. Rose

1987-01-01

Snow depth and density were measured in 33 stands of western hemlock-Sitka spruce (Tsuga heterophylla [Rat] Sarg.-Picea sitchensis [Bong.] Carr.) over a 3-year period. The stands, near Juneau, Alaska, provided broad ranges of species composition, age, over-story canopy coverage, tree density, and wood volume. Stepwise multiple regression analyses indicated that both...

Modelling high Arctic deep permafrost temperature sensitivity in Northeast Greenland based on experimental and field observations

NASA Astrophysics Data System (ADS)

Rasmussen, Laura Helene; Zhang, Wenxin; Hollesen, Jørgen; Cable, Stefanie; Hvidtfeldt Christiansen, Hanne; Jansson, Per-Erik; Elberling, Bo

2017-04-01

Permafrost affected areas in Greenland are expected to experience a marked temperature increase within decades. Most studies have considered near-surface permafrost sensitivity, whereas permafrost temperatures below the depths of zero annual amplitude is less studied despite being closely related to changes in near-surface conditions, such as changes in active layer thermal properties, soil moisture and snow depth. In this study, we measured the sensitivity of thermal conductivity (TC) to gravimetric water content (GWC) in frozen and thawed permafrost sediments from fine-sandy and gravelly deltaic and fine-sandy alluvial deposits in the Zackenberg valley, NE Greenland. We further calibrated a coupled heat and water transfer model, the "CoupModel", for one central delta sediment site with average snow depth and further forced it with meteorology from a nearby delta sediment site with a topographic snow accumulation. With the calibrated model, we simulated deep permafrost thermal dynamics in four 20-year scenarios with changes in surface temperature and active layer (AL) soil moisture: a) 3 °C warming and AL water table at 0.5 m depth; b) 3 °C warming and AL water table at 0.1 m depth; c) 6 °C warming and AL water table at 0.5 m depth and d) 6 °C warming and AL water table at 0.1 m depth. Our results indicate that frozen sediments have higher TC than thawed sediments. All sediments show a positive linear relation between TC and soil moisture when frozen, and a logarithmic one when thawed. Gravelly delta sediments were highly sensitive, but never reached above 12 % GWC, indicating a field effect of water retention capacity. Alluvial sediments are less sensitive to soil moisture than deltaic (fine and coarse) sediments, indicating the importance of unfrozen water in frozen sediment. The deltaic site with snow accumulation had 1 °C higher mean annual ground temperature than the average snow depth site. Permafrost temperature at the depth of 18 m increased with 1.5 °C and 3.5 °C in the scenarios with 3 °C and 6 °C warming, respectively. Increasing the soil moisture had no important additional effect to warming, although an increase in thermal offset was indicated. We conclude that below-ground sediment properties affect the sensitivity of TC to GWC, that surface temperature changes can influence the deep permafrost within a short time scale, and that differences in snow depth affect surface temperatures. Sediment type and the type of precipitation should thus be considered when estimating future High Arctic deep permafrost sensitivity.

Using a Multi-Method Approach to Examine Social-Ecological Vulnerability to Climate Change and Natural Resource Policies on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Klein, J.; Hopping, K. A.; Yeh, E.; Nyima, Y.; Galvin, K.; Boone, R.; Dorje, T.; Ojima, D. S.

2012-12-01

Pastoralists and ecosystems on the Tibetan Plateau are facing a suite of novel stresses. Temperatures are increasing several times more than the global average. The frequency and severity of severe snowstorms, which lead to critical losses of livestock, are also increasing. Pastoralists are also experiencing changes to their livelihood activities, including reduced mobility and severe grazing restrictions. We are using interdisciplinary frameworks and methods that integrate results from a multifactor ecological experiment, household interviews, remote sensing, and a coupled ecosystem and household decision-making model to examine herder and ecosystem vulnerability to climate change and extreme weather events (snow disasters) within the context of changing natural resource management policies in China. The fully factorial ecological experiment includes two climate changes (warming and spring snow additions) and two types of grazing (yak and pika) that are being affected by current policy. We established the experiment in 2008 within the Tibet Autonomous Region. We are monitoring microclimate, vegetation, nutrient availability, ecosystem carbon fluxes and stable isotope signatures of select plant species. Through this experiment, we are investigating the sensitivity of the system, whether it can cross critical thresholds, and how resilient this system may be to predicted future climate and land use changes. Semi-structured, in-depth interviews on indigenous knowledge and vulnerability complement the ecological experimental work. We are asking herders about climate and ecological change and their drivers and are also conducting interviews on vulnerability to snow disasters across a three site, 300-500mm precipitation gradient. We are using remote sensing to identify biophysical landscape change over time. To integrate our ecological and social findings, we are coupling the Savanna ecosystem model to the DECUMA agent-based pastoral household model. Our results to date from the experiment and the indigenous knowledge study suggest that Kobresia pygmaea, the dominant plant species and the primary grazing resource, is vulnerable to warming. Moreover, several lines of evidence suggest that warming is causing delayed spring phenology, with important ecosystem and livelihood implications. Herders are observing climatic and ecological changes, knowledge which is important for adaptation, but people whose livelihoods are most directly derived from the rangelands, those situated at higher elevations, and those who are more mobile across the landscape are most attuned to these changes. These results suggest that rangeland degradation and delayed spring phenology are occurring, and that climate warming may be responsible for these changes. While additional snow may improve ecological conditions, the warming-induced degradation may make the social-ecological system more vulnerable to large snowstorm events. Our findings suggest that climate adaptation strategies should address the effects of both climate warming and the changing nature of extreme weather events and should also encourage land use policies that will maintain these systems under change. Moreover, policies that encourage mobility and rangeland-based livelihoods will enhance adaptation to climate change.

[Monitoring on spatial and temporal changes of snow cover in the Heilongjiang Basin based on remote sensing].

PubMed

Yu, Ling-Xue; Zhang, Shu-Wen; Guan, Cong; Yan, Feng-Qin; Yang, Chao-Bin; Bu, Kun; Yang, Jiu-Chun; Chang, Li-Ping

2014-09-01

This paper extracted and verified the snow cover extent in Heilongjiang Basin from 2003 to 2012 based on MODIS Aqua and Terra data, and the seasonal and interannual variations of snow cover extent were analyzed. The result showed that the double-star composite data reduced the effects of clouds and the overall accuracy was more than 91%, which could meet the research requirements. There existed significant seasonal variation of snow cover extent. The snow cover area was almost zero in July and August while in January it expanded to the maximum, which accounted for more than 80% of the basin. According to the analysis on the interannual variability of snow cover, the maximum winter snow cover areas in 2003-2004 and 2009-2010 (>180 x 10(4) km2) were higher than that of 2011 (150 x 10(4) km2). Meanwhile, there were certain correlations between the interannual fluctuations of snow cover and the changes of average annual temperature and precipitation. The year with the low snow cover was corresponding to less annual rainfall and higher average temperature, and vice versa. The spring snow cover showed a decreasing trend from 2003 to 2012, which was closely linked with decreasing precipitation and increasing temperature.

The generation of spring peak flows by short-term meteorological events

Treesearch

Harold F. Haupt

1968-01-01

Spring peak flows recorded over a 25-year period in Benton Creek, a small forested watershed in northern Idaho, were studied in their relation to meteorological events. More peak flows were generated by rain-on-snow than by clear-weather snowmelt; the two types of peaks differ in magnitude and in other characteristics. Two rather simple techniques were used to...

Arctic Stratus Cloud Properties and Their Effect on the Surface Radiation Budget: Selected Cases from FIRE ACE

NASA Technical Reports Server (NTRS)

Doug, Xiquan; Mace, Gerald G.; Minnis, Patrick; Young, David F.

2001-01-01

To study Arctic stratus cloud properties and their effect on the surface radiation balance during the spring transition season, analyses are performed using data taken during three cloudy and two clear days in May 1998 as part of the First ISCCP Regional Experiment (FIRE) Arctic Cloud Experiment (ACE). Radiative transfer models are used in conjunction with surface- and satellite-based measurements to retrieve the layer-averaged microphysical and shortwave radiative properties. The surface-retrieved cloud properties in Cases 1 and 2 agree well with the in situ and satellite retrievals. Discrepancies in Case 3 are due to spatial mismatches between the aircraft and the surface measurements in a highly variable cloud field. Also, the vertical structure in the cloud layer is not fully characterized by the aircraft measurements. Satellite data are critical for understanding some of the observed discrepancies. The satellite-derived particle sizes agree well with the coincident surface retrievals and with the aircraft data when they were collocated. Optical depths derived from visible-channel data over snow backgrounds were overestimated in all three cases, suggesting that methods currently used in satellite cloud climatologies derive optical depths that are too large. Use of a near-infrared channel with a solar infrared channel to simultaneously derive optical depth and particle size appears to alleviate this overestimation problem. Further study of the optical depth retrieval is needed. The surface-based radiometer data reveal that the Arctic stratus clouds produce a net warming of 20 W m(exp -2) in the surface layer during the transition season suggesting that these clouds may accelerate the spring time melting of the ice pack. This surface warming contrasts with the net cooling at the top of the atmosphere (TOA) during the same period. All analysis of the complete FIRE ACE data sets will be valuable for understanding the role of clouds during the entire melting and refreezing process that occurs annually in the Arctic.

Snowmelt discharge characteristics Sierra Nevada, California

USGS Publications Warehouse

Peterson, David; Smith, Richard; Stewart, Iris; Knowles, Noah; Soulard, Chris; Hager, Stephen

2005-01-01

Alpine snow is an important water resource in California and the western U.S. Three major features of alpine snowmelt are the spring pulse (the first surge in snowmelt-driven river discharge in spring), maximum snowmelt discharge, and base flow (low river discharge supported by groundwater in fall). A long term data set of hydrologic measurements at 24 gage locations in 20 watersheds in the Sierra Nevada was investigated to relate patterns of snowmelt with stream discharge In wet years, the daily variations in snowmelt discharge at all the gage locations in the Sierra Nevada correlate strongly with the centrally located Merced River at Happy Isles, Yosemite National Park (i.e., in 1983, the mean of the 23 correlations was R= 0.93 + 0.09) ; in dry years, however, this correlation breaks down (i.e., in year 1977, R=0.72 + 0.24). A general trend towards earlier snowmelt was found and modeled using correlations with the timing of the spring pulse and the river discharge center of mass. For the 24 river and creek gage locations in this study, the spring pulse appeared to be a more sensitive measure of early snowmelt than the center of mass. The amplitude of maximum daily snowmelt discharge correlates strongly with initial snow water equivalent. Geologic factors, base rock permeability and soil-to-bedrock ratio, influence snowmelt flow pathways. Although both surface and ground water flows and water levels increase in wet years compared to dry years, the increase was greater for surface water in a watershed with relatively impermeable base rock than for surface water in a watershed with highly permeable base rock The relation was the opposite for base flow (ground water). The increase was greater for groundwater in a watershed with permeable rock compared to ground water in a watershed with impermeable rock. A similar, but weaker, surface/groundwater partitioning was observed in relatively impermeable granitic watersheds with differing soil-to-bedrock ratios. The increase in surface flow was greater in a watershed with a low, compared to a high, soil-to-bedrock ratio; whereas the increase in ground water flow was greater in a watershed with a high, compared to a low, soil-to-bedrock ratio. Transects that include long-term observations of shallow well-water depth and chemistry would complement traditional hydroclimate data and provide a more complete understanding of hydrologic controls of snowmelt.

Seasonal frost conditions in different periglacial landforms in the Eastern Pyrenees from 2003 to 2015

NASA Astrophysics Data System (ADS)

Salvador-Franch, Ferran; Salvà-Catarineu, Montserrat; Oliva, Marc; Gómez-Ortiz, Antonio

2016-04-01

Glaciers shaped the headwaters and valley floors in the Eastern Pyrenees during the Last Glaciation at elevations above 2100-2200 m. Since the deglaciation of these areas, periglacial processes have generated a wide range of periglacial landforms, such as rock glaciers, patterned ground and debris slopes. The role of soil temperatures is decisive for the degree of activity of periglacial processes: cryoturbation, solifluction, frost weathering, etc. Nowadays, periglacial processes in the Eastern Pyrenees are driven by a seasonal frozen layer extending 5-7 months. In general, at 2100 m the seasonal frost reaches 20 cm depth, while at 2700 m reaches 50 cm depth. However, soil temperatures, and thus, periglacial processes are strongly controlled by the large interannual variability of the snow cover. With the purpose of understanding the rhythm and intensity of soil freezing/thawing in 2003 we set up several monitoring sites along a vertical transect from the valley floors (1100 m) to the high plateaus (2700 m) across the southern slope of the Puigpedrós massif (2914 m), in the Eastern Pyrenees. The monitoring of soil temperatures has been conducted from 2003 to 2015 in different periglacial landforms using UTL and Hobo loggers. These loggers were installed at depths of 5, 20 and 50 cm at five sites: Calmquerdós (2730 m), Malniu (2230 m), La Feixa (2150 m), Meranges (1600 m) and Das (1097 m). Air temperatures used as reference come from two automatic stations of the Catalan Meteorological Survey in Malniu and Das, and with two loggers installed in La Feixa and Meranges. No permafrost regime was detected in none of the sites. Data shows evidence of the control of snow cover on the depth of the frozen layer and on the number of freeze-thaw cycles. Air temperatures at 2000-2200 m show a mean of 150 freeze-thaw cycles per year. In La Feixa, with very thin snow cover, only 67 cycles are recorded at 5 cm depth and 5 cycles at 50 cm depth. In Malniu, located at a higher elevation showing a thicker and longer snow cover, only 17 freeze-thaw cycles per year are recorded at 5 cm depth, with no cycles recorded at 50 cm depth. Soils remain unfrozen during years with a very thick snow cover. The snow cover is also largely conditioned by the microtopography and exposure to the dominant winds. These factors condition the distribution, duration and intensity of the frozen ground and, thus, determine the intensity of periglacial processes in these areas.

Ground thermal conditions along a vertical transect with contrasted topography in a high mountain Mediterranean environment (Puigpedrós massif, eastern Pyrenees), from 2003 to 2014

NASA Astrophysics Data System (ADS)

Salvador-Franch, Ferran; Salvà-Catarineu, Montserrat; Oliva, Marc; Gómez-Ortiz, Antonio

2015-04-01

During the Last Glaciation glaciers shaped the headwaters and valley floors in the Eastern Pyrenees above 2100-2200 m. Since the deglaciation of these high mountain environments, periglacial processes have generated rock glaciers, patterned ground and debris slopes. The role of soil temperatures is decisive regarding the contemporary activity of several processes: cryoturbation, solifluction, frost weathering, etc. Nowadays, periglacial processes are driven by a seasonal frozen layer extending 4-5 months. At 2100 m the seasonal frost reaches 20 cm depth, while at 2700 m reaches 50 cm depth. However, soil temperatures, and thus, periglacial processes are strongly controlled by the large interannual variability of the snow cover. With the purpose of understanding the rhythm and intensity of soil freezing/thawing we have set up several monitoring sites along a vertical transect from the high plateaus (2700 m) to the valley floors (1100 m) across the southern slope of the Puigpedrós massif (2914 m), in the Eastern Pyrenees. The monitoring of soil temperatures extends from 2003 to 2014. TinyTalk, UTL and Hobo loggers have been used in this study. These loggers were installed at depths of -5, -20 and -50 cm at five sites: Calmquerdós (2730 m), Malniu (2230 m), La Feixa (2150 m), Meranges (1600 m) and Das (1097 m). Air temperatures used as reference come from two automatic stations of the Catalan Meteorological Survey (Malniu, Das) as well as from two loggers installed in La Feixa and Meranges. Data shows the control of snow cover on the depth of the frozen layer and on the number of freeze-thaw cycles. Air temperatures at 2000-2200 m show a mean of 150 freeze-thaw cycles per year. In La Feixa, with very thin snow cover, only 67 cycles are recorded at 5 cm depth and 5 cycles at 50 cm depth. In Malniu, located at a higher elevation showing a thicker and longer snow cover, only 17 freeze-thaw cycles per year are recorded at 5 cm depth, with no cycles recorded at 50 cm depth. Soils remain unfrozen during years with a very thick snow cover. The snow cover is also largely conditioned by the microtopography and exposure to the dominant winds. These factors condition the distribution, duration and intensity of the frozen ground and, thus, determine the intensity of periglacial processes in these areas.

Characteristics and Limitations of Submerged GPS L1 Observations

NASA Astrophysics Data System (ADS)

Steiner, Ladina; Geiger, Alain

2017-04-01

Extensive amount of water stored in snow covers has a high impact on flood development during snow melting periods. Early assessment of these parameters in mountain environments enhance early-warning and thus prevention of major impacts. Sub-snow GNSS techniques are lately suggested to determine liquid water content, snow water equivalent or considered for avalanche rescue. This technique is affordable, flexible, and provides accurate and continuous observations independent on weather conditions. However, the characteristics of GNSS observations for applications within a snow-pack still need to be further investigated. The magnitude of the main interaction processes involved for the GPS wavelength propagating through different layers of snow, ice or water is theoretically examined. Liquid water exerts the largest influence on GPS signal propagation through a snow-pack. Therefore, we focus on determining the characteristics of GNSS observables under water. An experiment was set-up to investigate the characteristics and limitations of submerged GPS observations using a pool, a level control by communicating pipes, a geodetic and a low-cost GPS antenna, and a water level sensor. The GPS antennas were placed into the water. The water level was increased daily by a step of two millimeters up to thirty millimeters above the antenna. Based on this experiment, the signal penetration depth, satellite availability, the attenuation of signal strength and the quality of solutions are analyzed. Our experimental results show an agreement with the theoretically derived attenuation parameter and signal penetration depth. The assumption of water as the limiting parameter for GPS observations within a snow-pack can be confirmed. Higher wetness in a snow-pack leads to less transmission, higher refraction, higher attenuation and thus a decreased penetration depth as well as a reduced quality of the solutions. In consequence, GPS applications within a snow-pack are heavily impacted by wetness which is even more pronounced during melting period. In this poster, we present a short introduction to the principle, explain the developed algorithms and show results of experiments dedicated to the signal propagation in water.

The temporal dynamics of carbon dioxide under snow in a high elevation Rocky Mountain subalpine forest and meadow

Treesearch

R. C. Musselman; W. J. Massman; J. M. Frank; J. L. Korfmacher

2005-01-01

Carbon dioxide (CO2) concentration under snow was examined through two winter seasons at a 3100 m elevation subalpine site in the Snowy Range of Wyoming. CO2 was monitored every half hour at the soil/snow interface, and at about 25 cm soil depth the second year, in a meadow and in an adjacent forest. CO2 under snow in the meadow was significantly higher than that in...

Investigation of features in radon soil dynamics and search for influencing factors

NASA Astrophysics Data System (ADS)

Yakovlev, Grigorii; Cherepnev, Maxim; Nagorskiy, Petr; Yakovleva, Valentina

2018-03-01

The features in radon soil dynamics at two depths were investigated and the main influencing factors were revealed. The monitoring of radon volumetric activity in soil air was performed at experimental site of Tomsk Observatory of Radioactivity and Ionizing Radiation with using radon radiometers and scintillation detectors of alpha-radiation with 10 min sampling frequency. The detectors were installed into boreholes of 0.5 and 1 m depths. The analysis of the soil radon monitoring data has allowed revealing some dependencies at daily and annual scales and main influencing factors. In periods with clearly defined daily radon variations in the soil were revealed the next: 1) amplitude of the daily variations of the soil radon volumetric activity damps with the depth, that is related with the influence of convective fluxes in the soil; 2) temporal shift between times of occurrence of radon volumetric activity maximum (or minimum) values at 0.5 m and 1 m depths can reach 3 hours. In seasonal dynamics of the soil radon the following dependences were found: 1) maximal values are observed in winter, but minimal - in summer; 2) spring periods of snow melting are accompanied by anomaly increasing of radon volumetric activity in the soil up to about 3 times. The main influencing factors are atmospheric precipitations, temperature gradient in the soil and the state of upper soil layer.

Meteorological factors affecting scrub typhus occurrence: a retrospective study of Yamagata Prefecture, Japan, 1984-2014.

PubMed

Seto, J; Suzuki, Y; Nakao, R; Otani, K; Yahagi, K; Mizuta, K

2017-02-01

Climate change, by its influence on the ecology of vectors might affect the occurrence of vector-borne diseases. This study examines the effects of meteorological factors in Japan on the occurrence of scrub typhus, a mite-borne zoonosis caused by Orientia tsutsugamushi. Using negative binomial regression, we analysed the relationships between meteorological factors (including temperature, rainfall, snowfall) and spring-early summer cases of scrub typhus in Yamagata Prefecture, Japan, during 1984-2014. The average temperature in July and August of the previous year, cumulative rainfall in September of the previous year, snowfall throughout the winter, and maximum depth of snow cover in January and February were positively correlated with the number of scrub typhus cases. By contrast, cumulative rainfall in July of the previous year showed a negative relationship to the number of cases. These associations can be explained by the life-cycle of Leptotrombidium pallidum, a predominant vector of spring-early summer cases of scrub typhus in northern Japan. Our findings show that several meteorological factors are useful to estimate the number of scrub typhus cases before the endemic period. They are applicable to establish an early warning system for scrub typhus in northern Japan.

Sensitivity of modelled snow cover to turbulent flux parameterization and forcing data: a case study in a high altitude basin of the dry Andes, northern Chile

NASA Astrophysics Data System (ADS)

Kinnard, C.; Irarrazaval, I.; Campos, C.; Gascoin, S.; MacDonell, S.; Macdonell, S.; Herrero, J.

2016-12-01

Snow cover in the central-northern Andes of Chile is the main runoff source, providing water for the irrigation of cultures in the fertile valleys downstream. The prospect of adverse climate warming impacts on the hydrological cycle calls for a better understanding of the snow cover dynamics in response to climate, an aspect that has been little studied in the dry Andes. The heterogeneous and often thin snow cover, as well as the paucity of long-term hydrometeorological data makes snow modelling a challenging task in these regions. In this work we applied a physically-based, spatially-distributed snow model (Wimmed) to the La Laguna headwater catchment in the dry Andes (30°S, 70°W) during three hydrological years (2010-2013) when forcing data was available. Model testing at the point scale revealed a large sensitivity of simulated snow depths to the choice of snow roughness parameter (z0), which controls turbulent fluxes, while wind-induced snow erosion at the station in 2010 and 2011 complicated model validation. The inclusion of a mean wind speed map from a previous simulation with the WRF atmospheric model was found to improve the simulation results, while excluding the highest mountain ridge weather station had detrimental effects on the results. A snow roughness (z0) of 1 mm yielded the best comparison between the simulated and observed snow depth at the reference weather station, and between the simulated and MODIS-derived snow cover at the catchment scale. The simulation resulted in large sublimation losses (up to 4 mm day-1), corresponding to more than 80% of snow ablation in the catchment. While such high sublimation rates have been reported before in this region, remaining uncertainties in precipitation data and snow compaction processes call for more detailed studies and increased instrumentation in order to improve future modelling efforts.

On the performance of updating Stochastic Dynamic Programming policy using Ensemble Streamflow Prediction in a snow-covered region

NASA Astrophysics Data System (ADS)

Martin, A.; Pascal, C.; Leconte, R.

2014-12-01

Stochastic Dynamic Programming (SDP) is known to be an effective technique to find the optimal operating policy of hydropower systems. In order to improve the performance of SDP, this project evaluates the impact of re-updating the policy at every time step by using Ensemble Streamflow Prediction (ESP). We present a case study of the Kemano's hydropower system on the Nechako River in British Columbia, Canada. Managed by Rio Tinto Alcan (RTA), this system is subject to large streamflow volumes in spring due to important amount of snow depth during the winter season. Therefore, the operating policy should not only maximize production but also minimize the risk of flooding. The hydrological behavior of the system is simulated with CEQUEAU, a distributed and deterministic hydrological model developed by the Institut national de la recherche scientifique - Eau, Terre et Environnement (INRS-ETE) in Quebec, Canada. On each decision time step, CEQUEAU is used to generate ESP scenarios based on historical meteorological sequences and the current state of the hydrological model. These scenarios are used into the SDP to optimize the new release policy for the next time steps. This routine is then repeated over the entire simulation period. Results are compared with those obtained by using SDP on historical inflow scenarios.

Examination of snowmelt over Western Himalayas using remote sensing data

NASA Astrophysics Data System (ADS)

Tiwari, Sarita; Kar, Sarat C.; Bhatla, R.

2016-07-01

Snowmelt variability in the Western Himalayas has been examined using remotely sensed snow water equivalent (SWE) and snow-covered area (SCA) datasets. It is seen that climatological snowfall and snowmelt amount varies in the Himalayan region from west to east and from month to month. Maximum snowmelt occurs at the elevation zone between 4500 and 5000 m. As the spring and summer approach and snowmelt begins, a large amount of snow melts in May. Strength and weaknesses of temperature-based snowmelt models have been analyzed for this region by computing the snowmelt factor or the degree-day factor (DDF). It is seen that average DDF in the Himalayas is more in April and less in July. During spring and summer months, melting rate is higher in the areas that have height above 2500 m. The region that lies between 4500 and 5000 m elevation zones contributes toward more snowmelt with higher melting rate. Snowmelt models have been developed to estimate interannual variations of monthly snowmelt amount using the DDF, observed SWE, and surface air temperature from reanalysis datasets. In order to further improve the estimate snowmelt, regression between observed and modeled snowmelt has been carried out and revised DDF values have been computed. It is found that both the models do not capture the interannual variability of snowmelt in April. The skill of the model is moderate in May and June, but the skill is relatively better in July. In order to explain this skill, interannual variability (IAV) of surface air temperature has been examined. Compared to July, in April, the IAV of temperature is large indicating that a climatological value of DDF is not sufficient to explain the snowmelt rate in April. Snow area and snow amount depletion curves over Himalayas indicate that in a small area at high altitude, snow is still observed with large SWE whereas over most of the region, all the snow has melted.

Winter ecology of a subalpine grassland: Effects of snow removal on soil respiration, microbial structure and function.

PubMed

Gavazov, Konstantin; Ingrisch, Johannes; Hasibeder, Roland; Mills, Robert T E; Buttler, Alexandre; Gleixner, Gerd; Pumpanen, Jukka; Bahn, Michael

2017-07-15

Seasonal snow cover provides essential insulation for mountain ecosystems, but expected changes in precipitation patterns and snow cover duration due to global warming can influence the activity of soil microbial communities. In turn, these changes have the potential to create new dynamics of soil organic matter cycling. To assess the effects of experimental snow removal and advanced spring conditions on soil carbon (C) and nitrogen (N) dynamics, and on the biomass and structure of soil microbial communities, we performed an in situ study in a subalpine grassland in the Austrian Alps, in conjunction with soil incubations under controlled conditions. We found substantial winter C-mineralisation and high accumulation of inorganic and organic N in the topsoil, peaking at snowmelt. Soil microbial biomass doubled under the snow, paralleled by a fivefold increase in its C:N ratio, but no apparent change in its bacteria-dominated community structure. Snow removal led to a series of mild freeze-thaw cycles, which had minor effects on in situ soil CO 2 production and N mineralisation. Incubated soil under advanced spring conditions, however, revealed an impaired microbial metabolism shortly after snow removal, characterised by a limited capacity for C-mineralisation of both fresh plant-derived substrates and existing soil organic matter (SOM), leading to reduced priming effects. This effect was transient and the observed recovery in microbial respiration and SOM priming towards the end of the winter season indicated microbial resilience to short-lived freeze-thaw disturbance under field conditions. Bacteria showed a higher potential for uptake of plant-derived C substrates during this recovery phase. The observed temporary loss in microbial C-mineralisation capacity and the promotion of bacteria over fungi can likely impede winter SOM cycling in mountain grasslands under recurrent winter climate change events, with plausible implications for soil nutrient availability and plant-soil interactions. Copyright © 2017 Elsevier B.V. All rights reserved.

Water, ice, and meteorological measurements at South Cascade Glacier, Washington, 1986-1991 balance years

USGS Publications Warehouse

Krimmel, Robert M.

2000-01-01

Mass balance and climate variables are reported for South Cascade Glacier, Washington, for the years 1986-91. These variables include air temperature, precipitation, water runoff, snow accumulation, snow and ice melt terminus position, surface level, and ice speed. Data are reduced to daily and monthly values where appropriate. The glacier-averaged values of spring snow accumulation and fall net balance given in this report differ from previous results because amore complete analysis is made. Snow accumulation values for the1986-91 period ranged from 3.54 (water equivalent) meters in 1991 to2.04 meters in 1987. Net balance values ranged from 0.07 meters in1991 to -2.06 meters in 1987. The glacier became much smaller during the 1986-91 period and retreated a cumulative 50 meters.

SWEAT: Snow Water Equivalent with AlTimetry

NASA Astrophysics Data System (ADS)

Agten, Dries; Benninga, Harm-Jan; Diaz Schümmer, Carlos; Donnerer, Julia; Fischer, Georg; Henriksen, Marie; Hippert Ferrer, Alexandre; Jamali, Maryam; Marinaci, Stefano; Mould, Toby JD; Phelan, Liam; Rosker, Stephanie; Schrenker, Caroline; Schulze, Kerstin; Emanuel Telo Bordalo Monteiro, Jorge

2017-04-01

To study how the water cycle changes over time, satellite and airborne remote sensing missions are typically employed. Over the last 40 years of satellite missions, the measurement of true water inventories stored in sea and land ice within the cryosphere have been significantly hindered by uncertainties introduced by snow cover. Being able to determine the thickness of this snow cover would act to reduce such error, improving current estimations of hydrological and climate models, Earth's energy balance (albedo) calculations and flood predictions. Therefore, the target of the SWEAT (Snow Water Equivalent with AlTimetry) mission is to directly measure the surface Snow Water Equivalent (SWE) on sea and land ice within the polar regions above 60°and below -60° latitude. There are no other satellite missions currently capable of directly measuring SWE. In order to achieve this, the proposed mission will implement a novel combination of Ka- and Ku-band radioaltimeters (active microwave sensors), capable of penetrating into the snow microstructure. The Ka-band altimeter (λ ≈ 0.8 cm) provides a low maximum snow pack penetration depth of up to 20 cm for dry snow at 37 GHz, since the volume scattering of snow dominates over the scattering caused by the underlying ice surface. In contrast, the Ku-band altimeter (λ ≈ 2 cm) provides a high maximum snowpack penetration depth of up to 15 m in high latitudes regions with dry snow, as volume scattering is decreased by a factor of 55. The combined difference in Ka- and Ku-band signal penetration results will provide more accurate and direct determination of SWE. Therefore, the SWEAT mission aims to improve estimations of global SWE interpreted from passive microwave products, and improve the reliability of numerical snow and climate models.

Inventory of File sref_nmb.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour surface NCPCP 3-6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 404 surface SNOM 3-6 hour

Inventory of File sref_nmm.t03z.pgrb221.p1.f06.grib2

Science.gov Websites

surface WEASD 6 hour fcst Water Equivalent of Accumulated Snow Depth [kg/m^2] 016 surface APCP 3-6 hour surface WEASD 3-6 hour acc Water Equivalent of Accumulated Snow Depth [kg/m^2] 019 surface CSNOW 6 hour surface NCPCP 3-6 hour acc Large-Scale Precipitation (non-convective) [kg/m^2] 404 surface SNOM 3-6 hour

Snow cover and extreme winter warming events control flower abundance of some, but not all species in high arctic Svalbard

PubMed Central

Semenchuk, Philipp R; Elberling, Bo; Cooper, Elisabeth J

2013-01-01

Abstract The High Arctic winter is expected to be altered through ongoing and future climate change. Winter precipitation and snow depth are projected to increase and melt out dates change accordingly. Also, snow cover and depth will play an important role in protecting plant canopy from increasingly more frequent extreme winter warming events. Flower production of many Arctic plants is dependent on melt out timing, since season length determines resource availability for flower preformation. We erected snow fences to increase snow depth and shorten growing season, and counted flowers of six species over 5 years, during which we experienced two extreme winter warming events. Most species were resistant to snow cover increase, but two species reduced flower abundance due to shortened growing seasons. Cassiope tetragona responded strongly with fewer flowers in deep snow regimes during years without extreme events, while Stellaria crassipes responded partly. Snow pack thickness determined whether winter warming events had an effect on flower abundance of some species. Warming events clearly reduced flower abundance in shallow but not in deep snow regimes of Cassiope tetragona, but only marginally for Dryas octopetala. However, the affected species were resilient and individuals did not experience any long term effects. In the case of short or cold summers, a subset of species suffered reduced reproductive success, which may affect future plant composition through possible cascading competition effects. Extreme winter warming events were shown to expose the canopy to cold winter air. The following summer most of the overwintering flower buds could not produce flowers. Thus reproductive success is reduced if this occurs in subsequent years. We conclude that snow depth influences flower abundance by altering season length and by protecting or exposing flower buds to cold winter air, but most species studied are resistant to changes. Winter warming events, often occurring together with rain, can substantially remove snow cover and thereby expose plants to cold winter air. Depending on morphology, different parts of the plant can be directly exposed. On this picture, we see Dryas octopetala seed heads from the previous growing season protrude through the remaining ice layer after a warming event in early 2010. The rest of the plant, including meristems and flower primordia, are still somewhat protected by the ice. In the background we can see a patch of Cassiope tetragona protruding through the ice; in this case, the whole plant including flower primordia is exposed, which might be one reason why this species experienced a loss of flowers the following season. Photograph by Philipp Semenchuk. PMID:24567826

Improvement of high floods predictability in the Red River of the North basin using combined remote-sensed, gauge-based and assimilated precipitation data

NASA Astrophysics Data System (ADS)

Semenova, O.; Restrepo, P. J.

2011-12-01

The Red River of the North basin (USA) is considered to be under high risk of flood danger, having experienced serious flooding during the last few years. The region climate can be characterized as cold and, during winter, it exhibits continuous snowcover modified by wind redistribution. High-hazard runoff regularly occurs as a major spring snowmelt event resulting from the relatively rapid release of water from the snowpack on frozen soils. Although in summer/autumn most rainfall occurs from convective storms over small areas and does not generate dangerous floods, the pre-winter state of the soils may radically influence spring maximum flows. Large amount of artificial agricultural tiles and numerous small post-glacial depressions influencing the redistribution of runoff complicates the predictions of high floods. In such conditions any hydrological model would not be successful without proper precipitation input. In this study the simulation of runoff processes for two watersheds in the basin of the Red River of the North, USA, was undertaken using the Hydrograph model developed at the State Hydrological Institute (St. Petersburg, Russia). The Hydrograph is a robust process-based model, where the processes have a physical basis combined with some strategic conceptual simplifications that give it the ability to be applied in the conditions of low information availability. It accounts for the processes of frost and thaw of soils, snow redistribution and depression storage impacts. The assessment of the model parameters was conducted based on the characteristics of soil and vegetation cover. While performing the model runs, the parameters of depression storage and the parameters of different types of flow were manually calibrated to reproduce the observed flow. The model provided satisfactory simulation results in terms not only of river runoff but also variable sates of soil like moisture and temperature over a simulation period 2005 - 2010. For experimental runs precipitation from different sources was used as forcing data to the hydrological model: 1) data of ground meteorological stations; 2) the Snow Data Assimilation System (SNODAS) products containing several variables: snow water equivalent, snow depth, solid and liquid precipitation; 3) MAPX precipitation data which is mean areal precipitation for a watershed calculated using the radar- and gauge-based information. The results demonstrated that in the conditions of high uncertainty of model parameters combining precipitation information from different sources (the SNODAS precipitation in winter with the MAPX precipitation in summer) significantly improves the model performance and predictability of high floods.

Snow-mediated ptarmigan browsing and shrub expansion in arctic Alaska

Treesearch

Ken D. Tape; Rachel Lord; Hans-Peter Marshall; Roger W. Ruess

2010-01-01

Large, late-winter ptarmigan migrations heavily impact the shoot, plant, and patch architecture of shrubs that remain above the snow surface. Ptarmigan browsing on arctic shrubs was assessed in the vicinity of Toolik Lake, on the north side of the Brooks Range in Alaska. Data were collected in early May 2007, at maximum snow depth, after the bulk of the ptarmigan...

Estimating snowpack density from Albedo measurement

Treesearch

James L. Smith; Howard G. Halverson

1979-01-01

Snow is a major source of water in Western United States. Data on snow depth and average snowpack density are used in mathematical models to predict water supply. In California, about 75 percent of the snow survey sites above 2750-meter elevation now used to collect data are in statutory wilderness areas. There is need for a method of estimating the water content of a...

Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA

USDA-ARS?s Scientific Manuscript database

Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1 °C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent, and snow cover duration. Paradoxically, it has been su...

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Chang, A. T. C.; Hall, D. K.; Kelly, R.; Houser, Paul (Technical Monitor)

2001-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming

USGS Publications Warehouse

Euskirchen, E.S.; McGuire, A.D.; Chapin, F.S.

2007-01-01

The warming associated with changes in snow cover in northern high-latitude terrestrial regions represents an important energy feedback to the climate system. Here, we simulate snow cover-climate feedbacks (i.e. changes in snow cover on atmospheric heating) across the Pan-arctic over two distinct warming periods during the 20th century, 1910-1940 and 1970-2000. We offer evidence that increases in snow cover-climate feedbacks during 1970-2000 were nearly three times larger than during 1910-1940 because the recent snow-cover change occurred in spring, when radiation load is highest, rather than in autumn. Based on linear regression analysis, we also detected a greater sensitivity of snow cover-climate feedbacks to temperature trends during the more recent time period. Pan-arctic vegetation types differed substantially in snow cover-climate feedbacks. Those with a high seasonal contrast in albedo, such as tundra, showed much larger changes in atmospheric heating than did those with a low seasonal contrast in albedo, such as forests, even if the changes in snow-cover duration were similar across the vegetation types. These changes in energy exchange warrant careful consideration in studies of climate change, particularly with respect to associated shifts in vegetation between forests, grasslands, and tundra. ?? 2007 Blackwell Publishing Ltd.

Changes in agriculture and abundance of snow geese affect carrying capacity of sandhill cranes in Nebraska

USGS Publications Warehouse

Pearse, A.T.; Krapu, G.L.; Brandt, D.A.; Kinzel, P.J.

2010-01-01

The central Platte River valley (CPRV) in Nebraska, USA, is a key spring-staging area for approximately 80 of the midcontinent population of sandhill cranes (Grus canadensis; hereafter cranes). Evidence that staging cranes acquired less lipid reserves during the 1990s compared to the late 1970s and increases in use of the CPRV by snow geese (Chen caerulescens) prompted us to investigate availability of waste corn and quantify spatial and temporal patterns of crane and waterfowl use of the region. We developed a predictive model to assess impacts of changes in availability of corn and snow goose abundance under past, present, and potential future conditions. Over a hypothetical 60-day staging period, predicted energy demand of cranes and waterfowl increased 87 between the late 1970s and 19982007, primarily because peak abundances of snow geese increased by 650,000 and cranes by 110,000. Compared to spring 1979, corn available when cranes arrived was 20 less in 1998 and 68 less in 1999; consequently, the area of cornfields required to meet crane needs increased from 14,464 ha in 1979 to 32,751 ha in 1998 and 90,559 ha in 1999. Using a pooled estimate of 88 kg/ha from springs 19981999 and 20052007, the area of cornfields needed to supply food requirements of cranes and waterfowl increased to 65,587 ha and was greatest in the eastern region of the CPRV, where an estimated 54 of cranes, 47 of Canada geese (Branta canadensis), 45 of greater white-fronted geese (Anser albifrons), and 46 of snow geese occurred during ground surveys. We estimated that a future reduction of 25 in available corn or cornfields would increase daily foraging flight distances of cranes by 2738. Crane use and ability of cranes to store lipid reserves in the CPRV could be reduced substantially if flight distance required to locate adequate corn exceeded a physiological maximum distance cranes could fly in search of food. Options to increase carrying capacity for cranes include increasing accessibility of cornfields by restoring degraded river channels to disperse roosting cranes and increasing wetland availability in the Rainwater Basin to attract snow geese using the CPRV. ?? The Wildlife Society.

Putting the Capital 'A' in CoCoRAHS: A Pilot Program to Measure Albedo using the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Stampone, M. D.; Wake, C. P.; Dibb, J. E.

2012-12-01

The Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network, started in 1998 as a community-based network of volunteer weather observer in Colorado, is the single largest provider of daily precipitation observations in the United States. We embrace the CoCoRaHS mission to use low-cost measurement tools, provide training and education, and utilize an interactive website to collect high quality albedo data for research and education applications. We trained a select sub-set of CoCoRaHS's eighteen most enthusiastic, self-proclaimed 'weather nuts' in the state of New Hampshire to collect surface albedo, snow depth, and snow density measurements between 23-Nov-2011 and 15-Mar-2012. At less than 700 per observer, the low-cost albedo data falls within ±0.05 of albedo values collected from a First Class Kipp and Zonen Albedometer (CMA6) at local solar noon. CoCoRaHS albedo values range from 0.99 for fresh snow to 0.34 for shallow, aged snow. Snow-free albedo ranges from 0.09 to 0.39, depending on ground cover. Albedo is found to increase logarithmically with snow depth and decrease linearly with snow density. The latter relationship with snow density is inferred to be a proxy for increasing snow grain size as snowpack ages and compacts, supported by spectral albedo measurements collected with an ASD FieldSpec4 spectrometer. The newly established albedo network also serves as a development test bed for interactive online mapping and graphing applications for CoCoRaHS observers to investigate spatial and temporal patterns in albedo, snow depth, and snow density (www.cocorahs-albedo.org). The 2012-2013 field season will include low-cost infrared temperature guns (<40 each) to investigate the relationship between surface albedo and skin temperature. We have also recruited middle- and high-schools as volunteer observers and are working with the teachers to develop curriculum and lesson plans that utilize the low-cost measurement tools provided by CoCoRAHS. CoCoRAHS data will provide critical spatially distributed measurements of surface data that will be used to validate and improve land surface modeling of New Hampshire climate under different land cover scenarios. Building on the success of the first season, the newly established albedo network shows promise to put the capital 'A' in CoCoRAHS.Figure 1. (a) Map of Community Collaborative Rain, Hail, and Snow (CoCoRAHS) volunteers participating in the pilot albedo project, and (b) CoCoRAHS snow measurement kit.

Prediction of daily spring hydrographs for future climatic scenarios based on an integrated numerical modelling approach: Application on a snow-governed semi- arid karst catchment area.

NASA Astrophysics Data System (ADS)

Doummar, J.; Kassem, A.; Gurdak, J. J.

2017-12-01

In the framework of a three-year USAID/NSF- funded PEER Science project, flow in a karst system in Lebanon (Assal Spring; discharge 0.2-2.5 m3/s yearly volume of 22-30 Mm3) dominated by snow and semi arid conditions was simulated using an integrated numerical model (Mike She 2016). The calibrated model (Nash-Sutcliffe coefficient of 0.77) is based on high resolution input data (2014-2017) and detailed catchment characterization. The approach is to assess the influence of various model parameters on recharge signals in the different hydrological karst compartments (Atmosphere, unsaturated zone, and saturated zone) based on an integrated numerical model. These parameters include precipitation intensity and magnitude, temperature, snow-melt parameters, in addition to karst specific spatially distributed features such as fast infiltration points, soil properties and thickness, topographical slopes, Epikarst and thickness of unsaturated zone, and hydraulic conductivity among others. Moreover, the model is currently simulated forward using various scenarios for future climate (Global Climate Models GCM; daily downscaled temperature and precipitation time series for Lebanon 2020-2045) in order to depict the flow rates expected in the future and the effect of climate change on hydrographs recession coefficients, discharge maxima and minima, and total spring discharge volume . Additionally, a sensitivity analysis of individual or coupled major parameters allows quantifying their impact on recharge or indirectly on the vulnerability of the system (soil thickness, soil and rock hydraulic conductivity appear to be amongst the highly sensitive parameters). This study particularly unravels the normalized single effect of rain magnitude and intensity, snow, and temperature change on the flow rate (e.g., a change of temperature of 3° on the catchment yields a Residual Mean Square Error RMSE of 0.15 m3/s in the spring discharge and a 16% error in the total annual volume with respect to the calibrated model). Finally, such a study can allow decision makers to implement best informed management practices, especially in complex karst systems, to overcome impacts of climate change on water resources.

Modeling of Future Changes in Seasonal Snowpack and Impacts on Summer Low Flows in Alpine Catchments

NASA Astrophysics Data System (ADS)

Jenicek, Michal; Seibert, Jan; Staudinger, Maria

2018-01-01

It is expected that an increasing proportion of the precipitation will fall as rain in alpine catchments in the future. Consequently, snow storage is expected to decrease, which, together with changes in snowmelt rates and timing, might cause reductions in spring and summer low flows. The objectives of this study were (1) to simulate the effect of changing snow storage on low flows during the warm seasons and (2) to relate drought sensitivity to the simulated snow storage changes at different elevations. The Swiss Climate Change Scenarios 2011 data set was used to derive future changes in air temperature and precipitation. A typical bucket-type catchment model, HBV-light, was applied to 14 mountain catchments in Switzerland to simulate streamflow and snow in the reference period and three future periods. The largest relative decrease in annual maximum SWE was simulated for elevations below 2,200 m a.s.l. (60-75% for the period 2070-2099) and the snowmelt season shifted by up to 4 weeks earlier. The relative decrease in spring and summer minimum runoff that was caused by the relative decrease in maximum SWE (i.e., elasticity), reached 40-90% in most of catchments for the reference period and decreased for the future periods. This decreasing elasticity indicated that the effect of snow on summer low flows is reduced in the future. The fraction of snowmelt runoff in summer decreased by more than 50% at the highest elevations and almost disappeared at the lowest elevations. This might have large implications on water availability during the summer.

Comparison of snow melt properties across multiple spatial scales and landscape units in interior sub-Arctic boreal Alaskan watersheds

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Cherry, J. E.; Hiemstra, C. A.; Bolton, W. R.

2013-12-01

Interior sub-Arctic Alaskan snow cover is rapidly changing and requires further study for correct parameterization in physically based models. This project undertook field studies during the 2013 snow melt season to capture snow depth, snow temperature profiles, and snow cover extent to compare with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at four different sites underlain by discontinuous permafrost. The 2013 melt season, which turned out to be the latest snow melt period on record, was monitored using manual field measurements (SWE, snow depth data collection), iButtons to record temperature of the snow pack, GoPro cameras to capture time lapse of the snow melt, and low level orthoimagery collected at ~1500 m using a Navion L17a plane mounted with a Nikon D3s camera. Sites were selected across a range of landscape conditions, including a north facing black spruce hill slope, a south facing birch forest, an open tundra site, and a high alpine meadow. Initial results from the adjacent north and south facing sites indicate a highly sensitive system where snow cover melts over just a few days, illustrating the importance of high resolution temporal data capture at these locations. Field observations, iButtons and GoPro cameras show that the MODIS data captures the melt conditions at the south and the north site with accuracy (2.5% and 6.5% snow cover fraction present on date of melt, respectively), but MODIS data for the north site is less variable around the melt period, owing to open conditions and sparse tree cover. However, due to the rapid melt rate trajectory, shifting the melt date estimate by a day results in a doubling of the snow cover fraction estimate observed by MODIS. This information can assist in approximating uncertainty associated with remote sensing data that is being used to populate hydrologic and snow models (the Sacramento Soil Moisture Accounting model, coupled with SNOW-17, and the Variable Infiltration Capacity hydrologic model) and provide greater understanding of error and resultant model sensitivities associated with regional observations of snow cover across the sub-Arctic boreal landscape.

Local Variability in Firn Layering and Compaction Rates Using GPR Data, Depth-Density Profiles, and In-Situ Reflectors in the Dry Snow Zone Near Summit Station, Greenland

NASA Astrophysics Data System (ADS)

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

2017-12-01

Understanding the surface mass balance (SMB) of the Greenland ice sheet is critical to evaluating its response to a changing climate. A key factor in translating satellite and airborne elevation measurements of the ice sheet to SMB is understanding natural variability of firn layer depth and the relative compaction rate of these layers. A site near Summit Station, Greenland was chosen to investigate the variation in layering across a 100m by 100m grid using a 900 MHz and a 2.6 GHz ground penetrating radar (GPR) antenna. These radargrams were ground truthed by taking depth density profiles of five 2m snow pits and five 5m firn cores within the 100m by 100m grid. Combining these measurements with the accumulation data from the nearby ICECAPS weekly bamboo forest measurements, it's possible to see how the snow deposition from individual storm events can vary over a small area. Five metal reflectors were also placed on the surface of the snow in the bounds of the grid to serve as reference reflectors for similar measurements that will be taken in the 2018 field season at Summit Station. This will assist in understanding how one year of accumulation in the dry snow zone impacts compaction and how this rate can vary over a small area.

Canopy Effects on Macroscale Snow Sublimation

NASA Astrophysics Data System (ADS)

Svoma, B. M.

2015-12-01

Sublimation of snow cover directly affects snow accumulation, impacting ecosystem processes, soil moisture, soil porosity, biogeochemical processes, wildfire, and water resources. Available energy, the exposed surface area of a snow cover, and exposure time with the atmosphere vary greatly in complex terrain (e.g., aspect, elevation, forest cover), with latitude, and with continentality. It is therefore difficult to scale up results from site specific short term studies. Using the 32-km NARR, the 4-km PRISM, with 30-m terrain and forest cover data, meteorological variables are downscaled to simulate sublimation from canopy intercepted snow and from the snowpack over the Salt River Basin in Arizona for a wet and dry year. Simulations indicate that: (1) total sublimation is highly variable in response to variability in both sublimation rate and snow cover duration; (2) total canopy sublimation is similar for both years while ground sublimation is considerably greater during the wet year; (3) sublimation is a relatively greater contribution to the snow water budget during the dry year (28% vs. 20% of total snowfall); (4) at high elevations, ground sublimation is less in open areas than forested areas during the dry year, while the reverse is evident during the wet year as snowpack lasted longer into spring. While a reduction in leaf area index leads to a reduction of total sublimation due to less interception in both years, ground sublimation increases during the dry year, possibly due to less sheltering from solar radiation and wind. This reduction in sheltering results in a large decrease in snowpack duration (i.e., ten days in spring) at mid-elevations for the wet year, leading to a decrease in ground sublimation. This results in a 500 meter difference in the elevation of maximum sublimation reduction upon reduced leaf area index between the two years. Forest cover properties can vary considerably on short and long time scales through natural (wildfire, bark beetle infestation, drought) and anthropogenic (land management practices) processes. Therefore, understanding how small scale changes impact snow sublimation at larger spatial scales, and how this varies temporally, is critical from ecosystem function and water resources perspectives.

Comparison of snow depth retrieval algorithm in Northeastern China based on AMSR2 and FY3B-MWRI data

NASA Astrophysics Data System (ADS)

Fan, Xintong; Gu, Lingjia; Ren, Ruizhi; Zhou, Tingting

2017-09-01

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang's FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

Long-term deepened snow promotes tundra evergreen shrub growth and summertime ecosystem net CO2 gain but reduces soil carbon and nutrient pools.

PubMed

Christiansen, Casper T; Lafreniére, Melissa J; Henry, Gregory H R; Grogan, Paul

2018-02-07

Arctic climate warming will be primarily during winter, resulting in increased snowfall in many regions. Previous tundra research on the impacts of deepened snow has generally been of short duration. Here, we report relatively long-term (7-9 years) effects of experimentally deepened snow on plant community structure, net ecosystem CO 2 exchange (NEE), and soil biogeochemistry in Canadian Low Arctic mesic shrub tundra. The snowfence treatment enhanced snow depth from 0.3 to ~1 m, increasing winter soil temperatures by ~3°C, but with no effect on summer soil temperature, moisture, or thaw depth. Nevertheless, shoot biomass of the evergreen shrub Rhododendron subarcticum was near-doubled by the snowfences, leading to a 52% increase in aboveground vascular plant biomass. Additionally, summertime NEE rates, measured in collars containing similar plant biomass across treatments, were consistently reduced ~30% in the snowfenced plots due to decreased ecosystem respiration rather than increased gross photosynthesis. Phosphate in the organic soil layer (0-10 cm depth) and nitrate in the mineral soil layer (15-25 cm depth) were substantially reduced within the snowfences (47-70 and 43%-73% reductions, respectively, across sampling times). Finally, the snowfences tended (p = .08) to reduce mineral soil layer C% by 40%, but with considerable within- and among plot variation due to cryoturbation across the landscape. These results indicate that enhanced snow accumulation is likely to further increase dominance of R. subarcticum in its favored locations, and reduce summertime respiration and soil biogeochemical pools. Since evergreens are relatively slow growing and of low stature, their increased dominance may constrain vegetation-related feedbacks to climate change. We found no evidence that deepened snow promoted deciduous shrub growth in mesic tundra, and conclude that the relatively strong R. subarcticum response to snow accumulation may explain the extensive spatial variability in observed circumpolar patterns of evergreen and deciduous shrub growth over the past 30 years. © 2018 John Wiley & Sons Ltd.

Statistical prediction of seasonal discharge in the Naryn basin for water resources planning in Central Asia

NASA Astrophysics Data System (ADS)

Apel, Heiko; Gafurov, Abror; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Merkushkin, Aleksandr; Merz, Bruno

2016-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt water of the rivers originating in the mountains provides the only water resource available for agricultural production but also for water collection in reservoirs for energy production in winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources.. In fact, seasonal forecasts are mandatory tasks of national hydro-meteorological services in the region. Thus this study aims at a statistical forecast of the seasonal water availability, whereas the focus is put on the usage of freely available data in order to facilitate an operational use without data access limitations. The study takes the Naryn basin as a test case, at which outlet the Toktogul reservoir stores the discharge of the Naryn River. As most of the water originates form snow and glacier melt, a statistical forecast model should use data sets that can serve as proxy data for the snow masses and snow water equivalent in late spring, which essentially determines the bulk of the seasonal discharge. CRU climate data describing the precipitation and temperature in the basin during winter and spring was used as base information, which was complemented by MODIS snow cover data processed through ModSnow tool, discharge during the spring and also GRACE gravimetry anomalies. For the construction of linear forecast models monthly as well as multi-monthly means over the period January to April were used to predict the seasonal mean discharge of May-September at the station Uchterek. An automatic model selection was performed in multiple steps, whereas the best models were selected according to several performance measures and their robustness in a leave-one-out cross validation. It could be shown that the seasonal discharge can be predicted with exceptionally high skill reaching explained variances of 86% in the cross validation using ModSnow processed snow cover data and CRU temperature and precipitation data, i.e. freely available data only. Using antecedent discharge information from the Uchterek station over the period January to April the skill can be improved even further. Also the addition of latest EGSIEM GRACE products can improve this skill to > 90% explained variance by replacing the CRU temperature data in the forecast model. From all variables the ModSnow processed MODIS snow cover data proved to be the most important predictor. However, although the prediction models proved to be robust in the cross validation, it has to be mentioned that the models are based on a limited time spanning the period 2000-2012 only. Nevertheless it is believed that the models are reliable, as this time period shows a high variability in seasonal water availability spanning from exceptionally dry to wet years. In summary, the developed forecast model may be a valuable complementary tool for the seasonal discharge prediction in Central Asia for water resources planning, that does not suffer from limited data access required for other forecast methods.

Increased photosynthesis compensates for shorter growing season in subarctic tundra - seven years of snow accumulation manipulations

NASA Astrophysics Data System (ADS)

Bosiö, Julia; Johansson, Margareta; Njuabe, Herbert; Christensen, Torben R.

2013-04-01

This study was initiated to analyze the effect of snow cover on photosynthesis and plant growth in subarctic mires underlain by permafrost. Due to their narrow environmental window these raised bogs, often referred to as palsa mires, are highly sensitive to climatic changes. In Fennoscandia palsa mires are currently subjected to climate related thawing and shift in vegetational and hydrological patterns. Yet, we know little of how these subarctic permafrost mires react and feed back to such changes. By using snow fences to hinder snow drift the accumulation of snow was increased in six plots (10x20 m) in a snow manipulation experiment on a subarctic permafrost mire in northern Sweden. The thicker snow pack prolongs the duration of the snow cover in spring, causing a delay in the onset, as well as an overall shortening of the growing season. By measuring incoming and reflected photosynthetic active radiation (PAR) we wanted to address the question whether the increased snow thickness and associated delay of the growing season start affected the absorbed PAR and the accumulated gross primary production (GPP) over the season. The reflected PAR was measured at twelve plots where six of the plots experienced increased snow accumulation (treatment), and remaining six plots were untreated (control). Minikin QT sensors with integrated data loggers logged incoming and reflected PAR hourly throughout the growing seasons of 2011 and 2012. In July - September 2010 PAR measurements were coupled with flux chamber measurements to assess GPP and light use efficiency of the plots. The increased accumulation of snow prolonged the duration of the snow cover in spring, causing a delay in the onset, as well as an overall shortening of the growing season in the treated plots. The end of the growing season was not affected by the snow manipulation. The delay of the growing season start and hence overall shortening of the growing season in the treatment plots was 18 days in 2011 and 3 days in 2012 in relation to control plots. Results show higher PAR absorption together with almost 50% higher light use efficiency in treatment plots compared with control plots. Estimations of GPP suggest that the loss in early season photosynthesis due to the shortening of the growing season in the treatment plots is well compensated for by the increased absorption of PAR and higher light use efficiency throughout the whole growing seasons. This compensation is likely to be explained by increased soil moisture and nutrient availability together with a shift in vegetation composition associated with the accelerated permafrost thaw in the treatment plots. In our presentation implications and possible feedbacks of the increased absorbed PAR and estimated change in GPP will be discussed.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

Snow driven Radiative Forcing in High Latitude Areas of Disturbance Using Higher Resolution Albedo Products from Landsat and Sentinel-2

NASA Astrophysics Data System (ADS)

Erb, A.; Li, Z.; Schaaf, C.; Wang, Z.; Rogers, B. M.

2017-12-01

Land surface albedo plays an important role in the surface energy budget and radiative forcing by determining the proportion of absorbed incoming solar radiation available to drive photosynthesis and surface heating. In Arctic regions, albedo is particularly sensitive to land cover and land use change (LCLUC) and modeling efforts have shown it to be the primary driver of effective radiative forcing from the biogeophysical effects of LCLUC. In boreal forests, the effects of these changes are complicated during snow covered periods when newly exposed, highly reflective snow can serve as the primary driver of radiative forcing. In Arctic biomes disturbance scars from fire, pest and harvest can remain in the landscape for long periods of time. As such, understanding the magnitude and persistence of these disturbances, especially in the shoulder seasons, is critical. The Landsat and Sentinel-2 Albedo Products couple 30m and 20m surface reflectances with concurrent 500m BRDF Products from the MODerate resolution Imaging Spectroradiometer (MODIS). The 12 bit radiometric fidelity of Sentinel-2 and Landsat-8 allow for the inclusion of high-quality, unsaturated albedo calculations over snow covered surfaces at scales more compatible with fragmented landscapes. Recent work on the early spring albedo of fire scars has illustrated significant post-fire spatial heterogeneity of burn severity at the landscape scale and highlights the need for a finer spatial resolution albedo record. The increased temporal resolution provided by multiple satellite instruments also allows for a better understanding of albedo dynamics during the dynamic shoulder seasons and in historically difficult high latitude locations where persistent cloud cover limits high quality retrievals. Here we present how changes in the early spring albedo of recent boreal forest disturbance in Alaska and central Canada affects landscape-scale radiative forcing. We take advantage of the long historical Landsat record to examine pre-disturbance albedo trends and to link historical land cover and disturbance history to post-disturbance early spring albedo values. We examine the impact of landscape heterogeneity on albedo in the growing and dormant seasons and quantify the effects of snow exposure changes from over-story canopy loss.

Research of microwave scattering properties of snow fields

NASA Technical Reports Server (NTRS)

Angelakos, D. J.

1978-01-01

The results obtained in the research program of microwave scattering properties of snow fields are presented. Experimental results are presented showing backscatter dependence on frequency (5.8-8.0 GHz), angle of incidence (0-60 degrees), snow wetness (time of day), and frequency modulation (0-500 MHz). Theoretical studies are being made of the inverse scattering problem yielding some preliminary results concerning the determination of the dielectric constant of the snow layer. The experimental results lead to the following conclusions: snow layering affects backscatter, layer response is significant up to 45 degrees of incidence, wetness modifies snow layer effects, frequency modulation masks the layer response, and for the proper choice of probing frequency and for nominal snow depths, it appears to be possible to measure the effective dielectric constant and the corresponding water content of a snow pack.

Increased nitrogen leaching following soil freezing is due to decreased root uptake in a northern hardwood forest

Treesearch

John L. Campbell; Anne M. Socci; Pamela H. Templer

2014-01-01

The depth and duration of snow pack is declining in the northeastern United States as a result of warming air temperatures. Since snow insulates soil, a decreased snow pack can increase the frequency of soil freezing, which has been shown to have important biogeochemical implications. One of the most notable effects of soil freezing is increased inorganic nitrogen...

Forest influences on snow accumulation and snowmelt at the Hubbard Brook Experimental Forest, New Hampshire, USA

Treesearch

Colin A. Penn; Beverley C. Wemple; John L. Campbell

2012-01-01

Many factors influence snow depth, water content and duration in forest ecosystems. The effects of forest cover and canopy gap geometry on snow accumulation has been well documented in coniferous forests of western North America and other regions; however, few studies have evaluated these effects on snowpack dynamics in mixed deciduous forests of the northeastern USA....

Comparison of MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

NASA Astrophysics Data System (ADS)

Klein, A. G.; Thapa, S.

2017-12-01

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS. While it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage quantitative comparisons between their snow products are currently limited. Therefore this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI Snow Cover product at a snow cover fraction of 30% generated binary snow maps most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. Agreement was highest during the winter but lower during late fall and spring. Comparability was lowest over forest. MODIS and VIIRS often mapped snow/no-snow transition zones as cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicates that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67%, and cloud 65.02% and 59.91%, respectively. Visual comparisons depict good qualitative agreement between snow cover area visible in MODIS and VIIRS false color imagery and mapped in their respective snow cover products. While VIIRS and MODIS have similar capacity to map snow cover, VIIRS has the potential to more accurately map snow cover area for the successive development of climate data records.

Dynamics of glide avalanches and snow gliding

NASA Astrophysics Data System (ADS)

Ancey, Christophe; Bain, Vincent

2015-09-01

In recent years, due to warmer snow cover, there has been a significant increase in the number of cases of damage caused by gliding snowpacks and glide avalanches. On most occasions, these have been full-depth, wet-snow avalanches, and this led some people to express their surprise: how could low-speed masses of wet snow exert sufficiently high levels of pressure to severely damage engineered structures designed to carry heavy loads? This paper reviews the current state of knowledge about the formation of glide avalanches and the forces exerted on simple structures by a gliding mass of snow. One particular difficulty in reviewing the existing literature on gliding snow and on force calculations is that much of the theoretical and phenomenological analyses were presented in technical reports that date back to the earliest developments of avalanche science in the 1930s. Returning to these primary sources and attempting to put them into a contemporary perspective are vital. A detailed, modern analysis of them shows that the order of magnitude of the forces exerted by gliding snow can indeed be estimated correctly. The precise physical mechanisms remain elusive, however. We comment on the existing approaches in light of the most recent findings about related topics, including the physics of granular and plastic flows, and from field surveys of snow and avalanches (as well as glaciers and debris flows). Methods of calculating the forces exerted by glide avalanches are compared quantitatively on the basis of two case studies. This paper shows that if snow depth and density are known, then certain approaches can indeed predict the forces exerted on simple obstacles in the event of glide avalanches or gliding snow cover.

Uncertainty in Estimates of Net Seasonal Snow Accumulation on Glaciers from In Situ Measurements

NASA Astrophysics Data System (ADS)

Pulwicki, A.; Flowers, G. E.; Radic, V.

2017-12-01

Accurately estimating the net seasonal snow accumulation (or "winter balance") on glaciers is central to assessing glacier health and predicting glacier runoff. However, measuring and modeling snow distribution is inherently difficult in mountainous terrain, resulting in high uncertainties in estimates of winter balance. Our work focuses on uncertainty attribution within the process of converting direct measurements of snow depth and density to estimates of winter balance. We collected more than 9000 direct measurements of snow depth across three glaciers in the St. Elias Mountains, Yukon, Canada in May 2016. Linear regression (LR) and simple kriging (SK), combined with cross correlation and Bayesian model averaging, are used to interpolate estimates of snow water equivalent (SWE) from snow depth and density measurements. Snow distribution patterns are found to differ considerably between glaciers, highlighting strong inter- and intra-basin variability. Elevation is found to be the dominant control of the spatial distribution of SWE, but the relationship varies considerably between glaciers. A simple parameterization of wind redistribution is also a small but statistically significant predictor of SWE. The SWE estimated for one study glacier has a short range parameter (90 m) and both LR and SK estimate a winter balance of 0.6 m w.e. but are poor predictors of SWE at measurement locations. The other two glaciers have longer SWE range parameters ( 450 m) and due to differences in extrapolation, SK estimates are more than 0.1 m w.e. (up to 40%) lower than LR estimates. By using a Monte Carlo method to quantify the effects of various sources of uncertainty, we find that the interpolation of estimated values of SWE is a larger source of uncertainty than the assignment of snow density or than the representation of the SWE value within a terrain model grid cell. For our study glaciers, the total winter balance uncertainty ranges from 0.03 (8%) to 0.15 (54%) m w.e. depending primarily on the interpolation method. Despite the challenges associated with accurately and precisely estimating winter balance, our results are consistent with the previously reported regional accumulation gradient.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Role of Forcing Uncertainty and Background Model Error Characterization in Snow Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Dong, Jiarul; Peters-Lidard, Christa D.; Mocko, David; Gomez, Breogan

2017-01-01

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This article examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, the article demonstrates that the use of hybrid forcing input strategies (either through the use of an ensemble of forcing products or through the added use of the forcing climatology) provide a better characterization of the background model error, which leads to improved data assimilation results, especially during the snow accumulation and melt-time periods. The use of hybrid forcing ensembles is then employed for assimilating snow depth retrievals from the AMSR2 (Advanced Microwave Scanning Radiometer 2) instrument over two domains in the continental USA with different snow evolution characteristics. Over a region near the Great Lakes, where the snow evolution tends to be ephemeral, the use of hybrid forcing ensembles provides significant improvements relative to the use of a single forcing dataset. Over the Colorado headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results of the article demonstrate that improving the background model error through the use of a forcing ensemble enables the assimilation system to better incorporate the observational information.

Taking the pulse of mountains: Ecosystem responses to climatic variability

USGS Publications Warehouse

Fagre, Daniel B.; Peterson, David L.; Hessl, Amy E.

2003-01-01

An integrated program of ecosystem modeling and field studies in the mountains of the Pacific Northwest (U.S.A.) has quantified many of the ecological processes affected by climatic variability. Paleoecological and contemporary ecological data in forest ecosystems provided model parameterization and validation at broad spatial and temporal scales for tree growth, tree regeneration and treeline movement. For subalpine tree species, winter precipitation has a strong negative correlation with growth; this relationship is stronger at higher elevations and west-side sites (which have more precipitation). Temperature affects tree growth at some locations with respect to length of growing season (spring) and severity of drought at drier sites (summer). Furthermore, variable but predictable climate-growth relationships across elevation gradients suggest that tree species respond differently to climate at different locations, making a uniform response of these species to future climatic change unlikely. Multi-decadal variability in climate also affects ecosystem processes. Mountain hemlock growth at high-elevation sites is negatively correlated with winter snow depth and positively correlated with the winter Pacific Decadal Oscillation (PDO) index. At low elevations, the reverse is true. Glacier mass balance and fire severity are also linked to PDO. Rapid establishment of trees in subalpine ecosystems during this century is increasing forest cover and reducing meadow cover at many subalpine locations in the western U.S.A. and precipitation (snow depth) is a critical variable regulating conifer expansion. Lastly, modeling potential future ecosystem conditions suggests that increased climatic variability will result in increasing forest fire size and frequency, and reduced net primary productivity in drier, east-side forest ecosystems. As additional empirical data and modeling output become available, we will improve our ability to predict the effects of climatic change across a broad range of climates and mountain ecosystems in the northwestern U.S.A.

Comparison of glacial and non-glacial-fed streams to evaluate the loading of persistent organic pollutants through seasonal snow/ice melt.

PubMed

Bizzotto, E C; Villa, S; Vaj, C; Vighi, M

2009-02-01

The release of persistent organic pollutants (PCBs, HCB, HCHs and DDTs) accumulated in Alpine glaciers, was studied during spring-summer 2006 on the Frodolfo glacial-fed stream (Italian Alps). Samples were also taken on a non-glacial stream in the same valley, to compare POP contribution from different water sources (glacier ice, recent snow and spring). In late spring and early summer (May, June) recent snow melting is the most important process. POP contamination is more affected by local emissions and transport, and comparable levels have been measured in both streams for all studied compounds. In late summer and autumn (July-October), the contribution of ice melting strongly increases. In the glacial-fed stream the concentration of chlorinated pesticides (HCHs and DDTs) is about one order of magnitude higher than in the non-glacial-fed. A different behaviour was observed for PCBs, characterised by a peak in June showing, in both streams, concentrations three orders of magnitude higher than the background levels measured in May and in October. This result should be attributed to local emissions rather than long range atmospheric transport (LRAT). This hypothesis is supported by the PCB congener profile in June strictly comparable to the most commonly used Aroclor technical mixtures. The different seasonal behaviour observed for the different groups of chemicals indicates the POP loading in glacial streams is a combined role of long range atmospheric transport and local emission.

On the characterization of subpixel effects for passive microwave remote sensing of snow in montane environments

NASA Astrophysics Data System (ADS)

Vander Jagt, Benjamin John

Snow and its water equivalent plays a vital role in global water and energy balances, with particular relevance in mountainous areas with arid and semi-arid climate regimes. Spaceborne passive microwave (PM) remote sensing measurements are attractive for snowpack characterization due to their continuous global coverage and historical record; over 30 years of research has been invested in the development of methods to characterize large-scale snow water resources from PM-based measurements. Historically, use of PM data for snowpack characterization in montane enviroments has been obstructed by the complex subpixel variability of snow properties within the PM measurement footprint. The main subpixel effects can be grouped as: the effect of snow microstructure (e.g. snow grain size) and stratigraphy on snow microwave emission, vegetation attenuation of PM measurements, and the sensitivity PM brightness temperature (Tb) observation to the variability of different subpixel properties at spaceborne measurement scales. This dissertation is focused on a systematic examination of these issues, which thus far have prevented the widespread integration of snow water equivalent (SWE) retrieval methods. It is meant to further our comprehension of the underlying processes at work in these rugged, remote, a hydrologically important areas. The role that snow microstructure plays in the PM retrievals of SWE is examined first. Traditional estimates of grain size are subjective and prone to error. Objective techniques to characterize grain size are described and implemented, including near infrared (NIR), stereology, and autocorrelation based approaches. Results from an intensive Colorado field study in which independent estimates of grain size and their modeled brightness temperature (Tb) emission are evaluated against PM Tb observations are included. The coarse resolution of the passive microwave measurements provides additional challenges when trying to resolve snow states via remote sensing observations. The natural heterogeneity of snowpack (e.g. depth, stratigraphy, etc) and vegetative states within the PM footprint occurs at spatial scales smaller than PM observation scales. The sensitivity to changes in snow depth given sub-pixel variability in snow and vegetation is explored and quantified using the comprehensive dataset acquired during the Cold Land Processes experiment (CLPX). Lastly, vegetation has long been an obstacle in efforts to derive snow depth and mass estimates from passive microwave (PM) measurements of brightness temperature (Tb). We introduce a vegetation transmissivity model that is derived entirely from multi-scale and multi-temporal PM Tb observations and a globally available vegetation dataset, specifically the Leaf Area Index (LAI). This newly constructed model characterizes the attenuation of PM Tb observations at frequencies typically employed for snow retrieval algorithms, as a function of LAI. Additionally, the model is used to predict how much SWE is observable within the major river basins of Colorado and the central Rockies.

Timing of seasonal migration in mule deer: effects of climate, plant phenology, and life-history characteristics

USGS Publications Warehouse

Monteith, Kevin L.; Bleich, Vernon C.; Stephenson, Thomas R.; Pierce, Beck M.; Conner, Mary M.; Klaver, Robert W.; Bowyer, R. Terry

2011-01-01

Phenological events of plants and animals are sensitive to climatic processes. Migration is a life-history event exhibited by most large herbivores living in seasonal environments, and is thought to occur in response to dynamics of forage and weather. Decisions regarding when to migrate, however, may be affected by differences in life-history characteristics of individuals. Long-term and intensive study of a population of mule deer (Odocoileus hemionus) in the Sierra Nevada, California, USA, allowed us to document patterns of migration during 11 years that encompassed a wide array of environmental conditions. We used two new techniques to properly account for interval-censored data and disentangle effects of broad-scale climate, local weather patterns, and plant phenology on seasonal patterns of migration, while incorporating effects of individual life-history characteristics. Timing of autumn migration varied substantially among individual deer, but was associated with the severity of winter weather, and in particular, snow depth and cold temperatures. Migratory responses to winter weather, however, were affected by age, nutritional condition, and summer residency of individual females. Old females and those in good nutritional condition risked encountering severe weather by delaying autumn migration, and were thus risk-prone with respect to the potential loss of foraging opportunities in deep snow compared with young females and those in poor nutritional condition. Females that summered on the west side of the crest of the Sierra Nevada delayed autumn migration relative to east-side females, which supports the influence of the local environment on timing of migration. In contrast, timing of spring migration was unrelated to individual life-history characteristics, was nearly twice as synchronous as autumn migration, differed among years, was related to the southern oscillation index, and was influenced by absolute snow depth and advancing phenology of plants. Plasticity in timing of migration in response to climatic conditions and plant phenology may be an adaptive behavioral strategy, which should reduce the detrimental effects of trophic mismatches between resources and other life-history events of large herbivores. Failure to consider effects of nutrition and other life-history traits may cloud interpretation of phenological patterns of mammals and conceal relationships associated with climate change.

Soil Biogeochemical and Microbial Feedbacks along a Snowmelt-Dominated Hillslope-to-Floodplain Transect in Colorado.

NASA Astrophysics Data System (ADS)

Sorensen, P.; Beller, H. R.; Bill, M.; Bouskill, N.; Brodie, E.; Chakraborty, R.; Conrad, M. E.; Karaoz, U.; Polussa, A.; Steltzer, H.; Wang, S.; Williams, K. H.; Wilmer, C.; Wu, Y.

2017-12-01

Nitrogen export from mountainous watersheds is a product of multiple interactions among hydrological processes and soil-microbial-plant feedbacks along the continuum from terrestrial to aquatic environments. In snow-dominated systems, like the East River Watershed (CO), seasonal processes such as snowmelt exert significant influence on the annual hydrologic cycle and may also link spatially distinct catchment subsystems, such as hillslope and adjoining riparian floodplains. Further, snowmelt is occurring earlier each year and this is predicted to result in a temporal asynchrony between historically coupled microbial nutrient release and plant nutrient demand in spring, with the potential to increase N export from the East River Watershed. Here we summarize biogeochemical data collected along a hillslope-to-riparian floodplain transect at the East River site. Starting in Fall 2016, we sampled soils at 3 depths and measured dissolved pools of soil nutrients (e.g., NH4+, NO3-, DOC, P), microbial biomass CN, and microbial community composition over a seasonal time course, through periods of snow accumulation, snowmelt, and plant senescence. Soil moisture content in the top 5 cm of floodplain soils was nearly 4X greater across sampling dates, coinciding with 2X greater microbial biomass C, larger extractable pools of NH4+, and smaller pools of NO3- in floodplain vs. hillslope soils. These results suggest that microbially mediated redox processes played an important role in N cycling along the transect. Hillslope vs. floodplain location also appeared to be a key factor that differentiated soil microbial communities (e.g., a more important factor than seasonality or soil depth or type). Snow accumulation and snowmelt exerted substantial influence on soil biogeochemistry. For example, microbial biomass accumulation increased about 2X beneath the winter snowpack. Snowmelt resulted in a precipitous crash in the microbial population, with 2.5X reductions in floodplain and 2X reductions in hillslope soils. Immediately following snowmelt, NO3- concentrations in soil porewater and soil extracts increased dramatically. Overall, these results suggest that N export is strongly influenced by distinct soil biogeochemical and microbiological patterns along hillslope-to-floodplain transects at East River.

Dust on Snow Processes and Impacts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Skiles, M.; Painter, T. H.; Okin, G. S.

2015-12-01

In the Upper Colorado River Basin episodic deposition of mineral dust onto mountain snow cover frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau, and deposition rates have increased since the intensive settlement in the western USA in the mid 1880s. Dust deposition darkens the snow surface, and accelerates snowmelt through reduction of albedo and further indirect reduction of albedo by accelerating the growth of snow grain size. Observation and modeling of dust-on-snow processes began in 2005 at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, work which has shown that dust advances melt, shifts runoff timing and intensity, and reduces total water yield. The consistency of deposition and magnitude of impacts highlighted the need for more detailed understanding of the radiative impacts of dust-on-snow in this region. Here I will present results from a novel, high resolution, daily snow property dataset, collected at SBBSA over the 2013 ablation season, to facilitate physically based radiative transfer and snowmelt modeling. Measurements included snow albedo and vertical profiles of snow density, optical snow grain size, and dust/black carbon concentrations. This dataset was used to assess the relationship between episodic dust events, snow grain growth, and albedo over time, and observe the relation between deposited dust and melt water. Additionally, modeling results include the determination of the regionally specific dust-on-snow complex refractive index and radiative forcing partitioning between dust and black carbon, and dust and snow grain growth.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

2002-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Snow grain size and shape distributions in northern Canada

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Montpetit, B.; Roy, A.

2016-12-01

Pioneer snow work in the 1970s and 1980s proposed new approaches to retrieve snow depth and water equivalent from space using passive microwave brightness temperatures. Numerous research work have led to the realization that microwave approaches depend strongly on snow grain morphology (size and shape), which was poorly parameterized since recently, leading to strong biases in the retrieval calculations. Related uncertainties from space retrievals and the development of complex thermodynamic multilayer snow and emission models motivated several research works on the development of new approaches to quantify snow grain metrics given the lack of field measurements arising from the sampling constraints of such variable. This presentation focuses on the unknown size distribution of snow grain sizes. Our group developed a new approach to the `traditional' measurements of snow grain metrics where micro-photographs of snow grains are taken under angular directional LED lighting. The projected shadows are digitized so that a 3D reconstruction of the snow grains is possible. This device has been used in several field campaigns and over the years a very large dataset was collected and is presented in this paper. A total of 588 snow photographs from 107 snowpits collected during the European Space Agency (ESA) Cold Regions Hydrology high-resolution Observatory (CoReH2O) mission concept field campaign, in Churchill, Manitoba Canada (January - April 2010). Each of the 588 photographs was classified as: depth hoar, rounded, facets and precipitation particles. A total of 162,516 snow grains were digitized across the 588 photographs, averaging 263 grains/photo. Results include distribution histograms for 5 `size' metrics (projected area, perimeter, equivalent optical diameter, minimum axis and maximum axis), and 2 `shape' metrics (eccentricity, major/minor axis ratio). Different cumulative histograms are found between the grain types, and proposed fits are presented with the Kernel distribution function. Finally, a comparison with the Specific Surface Area (SSA) derived from reflectance values using the Infrared Integrating Sphere (IRIS) highlight different power statistical fits for the 5 `size' metrics.

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

NASA Astrophysics Data System (ADS)

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.

Snow Clouds and the Carbon Dioxide Cycle on Mars

NASA Astrophysics Data System (ADS)

Hayne, P. O.; Paige, D. A.

2009-12-01

The present climate of Mars is strongly influenced by the energy balance at the planet’s poles, with ~30% of the atmospheric mass exchanged seasonally with the polar ice caps. While the spring and summer sublimation process is observable in sunlight, the deposition process occurs in the darkness of polar night. We present direct radiometric observations of carbon dioxide snow clouds from the Mars Climate Sounder (MCS) and estimate the rate of deposition due to snowfall. We also present radiative transfer models capable of reproducing the observations and providing constraints on the radiative and thermal properties of the cap-atmosphere system. Snow clouds display a multi-layered structure with greatest opacity near the surface and extending to typical altitudes of about 20 km, with equivalent normal visible optical depths of ~0.1. Our modeling suggests the observed carbon dioxide snow grains are ~10 μm in radius, implying modest deposition rates, and suggesting the majority of the seasonal cap is deposited in a vertical region within one MCS field of view (or ~1 km) of the surface. Models reproducing the MCS limb observations only reproduce the nadir observations if the surface (or near-surface) is an optically thick layer of small (< 100 μm radius) carbon dioxide grains, which are therefore the primary cause of radiometrically cold areas (“cold spots”) observed since the Viking era. For the extreme polar regions, a persistent, ~500 km diameter snow cloud is strongly coupled to the most active cold spots, and smaller clouds (< 50 km diameter) in the latitude range 60-80°, though unobserved, cannot be ruled out by the MCS data. Based on this correlation, and observations of cold spots recurring near topographic slopes, we conclude that deposition is indeed linked to cloud formation, with the majority of material condensing below ~1 km altitude. Optically thin water ice layers are necessary to accurately model the MCS spectrum, particularly at altitudes above 20 km. This suggests water ice functions as the required condensation nucleus, consistent with earlier laboratory and theoretical studies. Important hemispherical differences are observed in the deposition process: 1) northern clouds are optically thicker at middle altitudes, ~5-15 km; 2) southern clouds are more often “detached”, showing a local maximum opacity near 20-25 km altitude; 3) mode particle radii are larger (~100 μm versus ~10 μm) in the north. Total normal optical depths are typically higher by a factor of ~2 in the north, and water ice content is relatively higher. Energy balance constraints can be placed on the system by MCS observations of outgoing infrared flux, which we map through time as an effective emissivity by taking account of the topography from MOLA and the expected frost point temperature.

Snowmobile impacts on snowpack physical and mechanical properties

NASA Astrophysics Data System (ADS)

Fassnacht, Steven R.; Heath, Jared T.; Venable, Niah B. H.; Elder, Kelly J.

2018-03-01

Snowmobile use is a popular form of winter recreation in Colorado, particularly on public lands. To examine the effects of differing levels of use on snowpack properties, experiments were performed at two different areas, Rabbit Ears Pass near Steamboat Springs and at Fraser Experimental Forest near Fraser, Colorado USA. Differences between no use and varying degrees of snowmobile use (low, medium and high) on shallow (the operational standard of 30 cm) and deeper snowpacks (120 cm) were quantified and statistically assessed using measurements of snow density, temperature, stratigraphy, hardness, and ram resistance from snow pit profiles. A simple model was explored that estimated snow density changes from snowmobile use based on experimental results. Snowpack property changes were more pronounced for thinner snow accumulations. When snowmobile use started in deeper snow conditions, there was less difference in density, hardness, and ram resistance compared to the control case of no snowmobile use. These results have implications for the management of snowmobile use in times and places of shallower snow conditions where underlying natural resources could be affected by denser and harder snowpacks.

Estimate of temperature change due to ice and snow accretion in the boreal forest regions

NASA Astrophysics Data System (ADS)

Sugiura, K.; Nagai, S.; Suzuki, R.; Eicken, H.; Maximov, T. C.

2016-12-01

Previous research has demonstrated that there is a wide difference between the surface albedo in winter/spring in snow-covered forest regions in various global climate models. If the forest is covered with snow, the surface albedo would increase. In this study, we carried out field observations to monitor the frequency of ice and snow accretion in the boreal forest regions. The time-lapse digital camera was set up on each side of the observation towers at the site located to the north of Fairbanks (USA) and at the site located to the north of Yakutsk (Russia). It was confirmed that both forests were not necessarily covered with snow without a break from the start of continuous snow cover until the end. In addition, the boreal forest at the Yakutsk site is covered with snow in comparison with the boreal forest at the Fairbanks site for a long term such as for about five month. Using a one-dimensional mathematics model about the energy flow including atmospheric multiple scattering, we estimated temperature change due to ice and snow accretion in the boreal forest regions. The result show that the mean surface temperature rises approximately 0.5 [oC] when the boreal forest is not covered with snow. In this presentation, we discuss the snow albedo parameterization in the boreal forest regions and the one-dimensional mathematics model to provide a basis for a better understanding of the role of snow in the climate system.