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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Glaciers and ice caps outside Greenland

USGS Publications Warehouse

Sharp, Marin; Wolken, G.; Burgess, D.; Cogley, J.G.; Copland, L.; Thomson, L.; Arendt, A.; Wouters, B.; Kohler, J.; Andreassen, L.M.; O'Neel, Shad; Pelto, M.

2015-01-01

Mountain glaciers and ice caps cover an area of over 400 000 km2 in the Arctic, and are a major influence on global sea level (Gardner et al. 2011, 2013; Jacob et al. 2012). They gain mass by snow accumulation and lose mass by meltwater runoff. Where they terminate in water (ocean or lake), they also lose mass by iceberg calving. The climatic mass balance (Bclim, the difference between annual snow accumulation and annual meltwater runoff) is a widely used index of how glaciers respond to climate variability and change. The total mass balance (ΔM) is defined as the difference between annual snow accumulation and annual mass losses (by iceberg calving plus runoff).

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

NASA Technical Reports Server (NTRS)

Yang, Wenze; Huang, Dong; Tan, Bin; Stroeve, Julienne C.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2006-01-01

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery

NASA Astrophysics Data System (ADS)

Cristea, Nicoleta C.; Breckheimer, Ian; Raleigh, Mark S.; HilleRisLambers, Janneke; Lundquist, Jessica D.

2017-08-01

Reliable maps of snow-covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ˜500 m data, to derive higher-resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne-derived high-resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site-specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Evolution of the snow area index of the subarctic snowpack in central Alaska over a whole season. consequences for the air to snow transfer of pollutants.

PubMed

Taillandier, A S; Domine, F; Simpson, W R; Sturm, M; Douglas, T A; Severin, K

2006-12-15

The detailed physical characteristics of the subarctic snowpack must be known to quantify the exchange of adsorbed pollutants between the atmosphere and the snow cover. For the first time, the combined evolutions of specific surface area (SSA), snow stratigraphy, temperature, and density were monitored throughout winter in central Alaska. We define the snow area index (SAI) as the vertically integrated surface area of snow crystals, and this variable is used to quantify pollutants' adsorption. Intense metamorphism generated by strong temperature gradients formed a thick depth hoar layer with low SSA (90 cm(2) g-1) and density (200 kg m(-3)), resulting in a low SAI. After snowpack buildup in autumn, the winter SAI remained around 1000 m(2)/m(2) of ground, much lower than the SAI of the Arctic snowpack, 2500 m(2) m-(2). With the example of PCBs 28 and 180, we calculate that the subarctic snowpack is a smaller reservoir of adsorbed pollutants than the Arctic snowpack and less efficiently transfers adsorbed pollutants from the atmosphere to ecosystems. The difference is greater for the more volatile PCB 28. With climate change, snowpack structure will be modified, and the snowpack's ability to transfer adsorbed pollutants from the atmosphere to ecosystems may be reduced, especially for the more volatile pollutants.

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

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

Monitoring and projecting snow on Hawaii Island

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Hamilton, Kevin; Wang, Yuqing

2017-05-01

The highest mountain peaks on Hawaii Island are snow covered for part of almost every year. This snow has aesthetic and recreational value as well as cultural significance for residents and visitors. Thus far there have been almost no systematic observations of snowfall, snow cover, or snow depth in Hawaii. Here we use satellite observations to construct a daily index of Hawaii Island snow cover starting from 2000. The seasonal mean of our index displays large interannual variations that are correlated with the seasonal mean freezing level and frequency of trade wind inversions as determined from nearby balloon soundings. Our snow cover index provides a diagnostic for monitoring climate variability and trends within the extensive area of the globe dominated by the North Pacific trade wind meteorological regime. We have also conducted simulations of the Hawaii climate with a regional atmospheric model. Retrospective simulations for 1990-2015 were run with boundary conditions prescribed from gridded observational analyses. Simulations for the end of 21st century employed boundary conditions based on global climate model projections that included standard scenarios for anticipated anthropogenic climate forcing. The future projections indicate that snowfall will nearly disappear by the end of the current century.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

How much can a single webcam tell to the operation of a water system?

NASA Astrophysics Data System (ADS)

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

2017-04-01

Recent advances in environmental monitoring are making a wide range of hydro-meteorological data available with a great potential to enhance understanding, modelling and management of environmental processes. Despite this progress, continuous monitoring of highly spatiotemporal heterogeneous processes is not well established yet, especially in inaccessible sites. In this context, the unprecedented availability of user-generated data on the web might open new opportunities for enhancing real-time monitoring and modeling of environmental systems based on data that are public, low-cost, and spatiotemporally dense. In this work, we focus on snow and contribute a novel crowdsourcing procedure for extracting snow-related information from public web images, either produced by users or generated by touristic webcams. A fully automated process fetches mountain images from multiple sources, identifies the peaks present therein, and estimates virtual snow indexes representing a proxy of the snow-covered area. The operational value of the obtained virtual snow indexes is then assessed for a real-world water-management problem, where we use these indexes for informing the daily control of a regulated lake supplying water for multiple purposes. Numerical results show that such information is effective in extending the anticipation capacity of the lake operations, ultimately improving the system performance. Our procedure has the potential for complementing traditional snow-related information, minimizing costs and efforts for obtaining the virtual snow indexes and, at the same time, maximizing the portability of the procedure to several locations where such public images are available.

Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models

USDA-ARS?s Scientific Manuscript database

Proper characterizations of snow melt and accumulation processes in the snow-dominated mountain environment are needed to understand and predict spatiotemporal distribution of water cycle components. Two commonly used strategies in modeling of snow accumulation and melt are the full energy based and...

Snow Coverage Analysis Using ASTER over the Sierra Nevada Mountain Range

NASA Astrophysics Data System (ADS)

Ross, B.

2017-12-01

Snow has strong impacts on human behavior, state and local activities, and the economy. The Sierra Nevada snowpack is California's most important natural reservoir of water. Such snow is melting sooner and faster. A recent California drought study showed that there was a deficit of 1.5 million acre-feet of water in 2014 due to the fast melting rates. Scientists have been using the Moderate Resolution Imaging Spectrometer (MODIS) which is available at the spatial resolution of 500-meter, to analyze the changes in snow coverage. While such analysis provides us with the valuable information, it would be more beneficial to employ the imageries at a higher spatial resolution for snow studies. Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), which acquires the high-resolution imageries ranging from 15-meter to 90-meter, has recently become freely available to the public. Our study utilized two scenes obtained from ASTER to investigate the changes in snow extent over the Sierra Nevada's mountain area for an 8-year period. These two scenes were collected on April 11, 2007 and April 16, 2015 covering the same geographic region. Normalized Difference Snow Index (NDSI) was adopted to delineate the snow coverage in each scene. Our study shows a substantial decrease of snow coverage in the studied geographic region by pixel count.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

Assessment of Consistencies and Uncertainties between the NASA MODIS and VIIRS Snow-Cover Maps

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; DiGirolamo, N. E.; Roman, M. O.

2017-12-01

Snow cover has great climatic and economic importance in part due to its high albedo and low thermal conductivity and large areal extent in the Northern Hemisphere winter, and its role as a freshwater source for about one-sixth of the world's population. The Rutgers University Global Snow Lab's 50-year climate-data record (CDR) of Northern Hemisphere snow cover is invaluable for climate studies, but, at 25-km resolution, the spatial resolution is too coarse to provide accurate snow information at the basin scale. Since 2000, global snow-cover maps have been produced from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites at 500-m resolution, and from the Suomi-National Polar Program (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) since 2011 at 375-m resolution. Development of a moderate-resolution (375 - 500 m) earth system data record (ESDR) that utilizes both MODIS and VIIRS snow maps is underway. There is a 6-year overlap between the data records. In late 2017 the second in a series of VIIRS sensors will be launched on the Joint Polar Satellite System-1 (JPSS-1), with the JPSS-2 satellite scheduled for launch in 2021, providing the potential to extend NASA's snow-cover ESDR for decades into the future and to create a CDR. Therefore it is important to investigate the continuity between the MODIS and VIIRS NASA snow-cover data products and evaluate whether there are any inconsistencies and biases that would affect their value as CDR. Time series of daily normalized-difference snow index (NDSI) Terra and Aqua MODIS Collection 6 (C6) and NASA VIIRS Collection 1 (C1) snow-cover tile maps (MOD10A1 and VNP10A1) are studied for North America to identify NDSI differences and possible biases between the datasets. Developing a CDR using the MODIS and VIIRS records is challenging. Though the instruments and orbits are similar, differences in bands, viewing geometry, spatial resolution, and cloud- and snow-mapping algorithms affect snow detection.

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.

Snow Cover Distribution and Variation using MODIS in the Himalayas of India

NASA Astrophysics Data System (ADS)

Mondal, A.; Lakshmi, V.; Jain, S. K.; Kansara, P. H.

2017-12-01

Snow cover variation plays a big role in river discharge, permafrost distribution and mass balance of glaciers in mountainous watersheds. Spatial distribution and temporal variation of snow cover varies with elevation and climate. We study the spatial distribution and temporal change of snow cover that has been observed using Terra Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A2 version 5) from 2001 to 2016. This MODIS product is based on normalized-difference snow index (NDSI) using band 4 (0.545-0.565 μm) and band 6 (1.628-1.652 μm). The spatial resolution of MOD10A2 is 500 m and composited over 8 days. The study area is the Indian Himalayas, major snow covered part of which is located in the states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, West Bengal, Sikkim, Assam and Arunachal Pradesh. Distribution and variation in snow cover is examined on monthly and annual time scales in this study. The temporal changes in snow cover has been compared with terrain attributes (elevation, slope and aspect). The snow cover depletion and accumulation have been observed during April-August and September-March. The snow cover is highest in the March and lowest in the August in the Himachal region. This study will be helpful to identify the amount of water stored in the glaciers of the Indian Himalaya and also important for water resources management of river basins, which are located in this area. Key words: Snow cover, MODIS, NDSI, terrain attribute

Microbial sequences retrieved from environmental samples from seasonal arctic snow and meltwater from Svalbard, Norway.

PubMed

Larose, Catherine; Berger, Sibel; Ferrari, Christophe; Navarro, Elisabeth; Dommergue, Aurélien; Schneider, Dominique; Vogel, Timothy M

2010-03-01

16S rRNA gene (rrs) clone libraries were constructed from two snow samples (May 11, 2007 and June 7, 2007) and two meltwater samples collected during the spring of 2007 in Svalbard, Norway (79 degrees N). The libraries covered 19 different microbial classes, including Betaproteobacteria (21.3%), Sphingobacteria (16.4%), Flavobacteria (9.0%), Acidobacteria (7.7%) and Alphaproteobacteria (6.5%). Significant differences were detected between the two sets of sample libraries. First, the meltwater libraries had the highest community richness (Chao1: 103.2 and 152.2) and Shannon biodiversity indices (between 3.38 and 3.59), when compared with the snow libraries (Chao1: 14.8 and 59.7; Shannon index: 1.93 and 3.01). Second, integral-LIBSHUFF analyses determined that the bacterial communities in the snow libraries were significantly different from those of the meltwater libraries. Despite these differences, our data also support the theory that a common core group of microbial populations exist within a variety of cryohabitats. Electronic supplementary material The online version of this article (doi:10.1007/s00792-009-0299-2) contains supplementary material, which is available to authorized users.

Snow drift: acoustic sensors for avalanche warning and research

NASA Astrophysics Data System (ADS)

Lehning, M.; Naaim, F.; Naaim, M.; Brabec, B.; Doorschot, J.; Durand, Y.; Guyomarc'h, G.; Michaux, J.-L.; Zimmerli, M.

Based on wind tunnel measurements at the CSTB (Jules Verne) facility in Nantes and based on field observations at the SLF experimental site Versuchsfeld Weissfluhjoch, two acoustic wind drift sensors are evaluated against different mechanical snow traps and one optical snow particle counter. The focus of the work is the suitability of the acoustic sensors for applications such as avalanche warning and research. Although the acoustic sensors have not yet reached the accuracy required for typical research applications, they can, however, be useful for snow drift monitoring to help avalanche forecasters. The main problem of the acoustic sensors is a difficult calibration that has to take into account the variable snow properties. Further difficulties arise from snow fall and high wind speeds. However, the sensor is robust and can be operated remotely under harsh conditions. It is emphasized that due to the lack of an accurate reference method for snow drift measurements, all sensors play a role in improving and evaluating snow drift models. Finally, current operational snow drift models and snow drift sensors are compared with respect to their usefulness as an aid for avalanche warning. While drift sensors always make a point measurement, the models are able to give a more representative drift index that is valid for a larger area. Therefore, models have the potential to replace difficult observations such as snow drift in operational applications. Current models on snow drift are either only applicable in flat terrain, are still too complex for an operational application (Lehning et al., 2000b), or offer only limited information on snow drift, such as the SNOWPACK drift index (Lehning et al., 2000a). On the other hand, snow drift is also difficult to measure. While mechanical traps (Mellor 1960; Budd et al., 1966) are probably still the best reference, they require more or less continuous manual operation and are thus not suitable for remote locations or long-term monitoring. Optical sensors (Schmidt, 1977; Brown and Pomeroy, 1989; Sato and Kimura, 1993) have been very successful for research applications, but suffer from the fact that they give a single flux value at one specific height. In addition, they have not been used, to our knowledge, for long-term monitoring applications or at remote sites. New developments of acoustic sensors have taken place recently (Chritin et al., 1999; Font et al., 1998). Jaedicke (2001) gives examples of possible applications of acoustic snow drift sensors. He emphasizes the advantages of acoustic sensors for snow drift monitoring at remote locations, but could not present any evaluation of the accuracy of the measurements. We present a complete evaluation of the new acoustic sensors for snow drift and discuss their applications for research or avalanche warning. We compare the suitability of sensors for operational applications.

Albedo and flux extinction coefficient of impure snow for diffuse shortwave radiation

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Mo, T.; Wang, J. R.; Chang, A. T. C.

1981-01-01

Impurities enter a snowpack as a result of fallout of scavenging by falling snow crystals. Albedo and flux extinction coefficient of soot contaminated snowcovers were studied using a two stream approximation of the radiative transfer equation. The effect of soot was calculated by two methods: independent scattering by ice grains and impurities and average refractive index for ice grains. Both methods predict a qualitatively similar effect of soot; the albedo is decreased and the extinction coefficient is increased compared to that for pure snow in the visible region; the infrared properties are largely unaffected. Quantitatively, however, the effect of soot is more pronounced in the average refractive index method. Soot contamination provides a qualitative explanation for several snow observations.

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

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

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.

A new approach to assess the skier additional stress within a multi-layered snowpack

NASA Astrophysics Data System (ADS)

Monti, Fabiano; Gaume, Johan; van Herwijnen, Alec; Schweizer, Jürg

2014-05-01

The physical and mechanical processes of dry-snow slab avalanche formation can be distinguished into two subsequent phases: failure initiation and crack propagation. Several approaches tried to quantify slab avalanche release probability in terms of failure initiation, based on a simple strength-of-material approach (strength vs. stress). Even if it is known that both weak layer and slab properties play a major role in avalanche release, apart from weak layer characteristics, often only the slab thickness and its average density were considered. For calculating the amount of additional stress (e.g. due to a skier) at the depth of the weak layer, the snow cover was often assumed to be a semi-infinite elastic half space in order to apply Boussinesq's theory. However, finite element (FE) calculations have shown that slab layering strongly influences the stress at depth. To avoid FE calculations, we suggest a new approach based on a simplification of multi-layered elasticity theory. It allows computing 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. The proposed approach was first tested on simplified snow profiles and compared reasonably well with FE calculations. We then implemented the method to refine the classical skier stability index. Using manually observed snow profiles, classified in different stability classes using stability tests, we obtained a satisfactory discrimination power. Lastly, the refined skier stability index was implemented into the 1-D snow cover model SNOWPACK and presented on two case studies. In the future, it will be interesting to implement the proposed method 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.

Measuring and modelling the impact of the bark beetle forest disturbance on snow accumulation and ablation at a plot scale

NASA Astrophysics Data System (ADS)

Jenicek, Michal; Matejka, Ondrej; Hotovy, Ondrej

2017-04-01

The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest structures on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. The forest canopy structure was described using parameters calculated from hemispherical photographs, such as canopy closure, leaf area index (LAI) and potential irradiance. Additionally, we used shortwave radiation measured using CNR4 Net radiometers placed in plots with different canopy structure. Two snow accumulation and ablation models were set-up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. First model was physically-based using the energy balance approach, second model was conceptual and it was based on the degree-day approach. Both models accounted for snow interception in different forest types using LAI as a parameter. The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. Both models simulated sufficiently compared to observations with slightly accurate simulations in open area compared to healthy forest. This was expected, since both models were forced to fit with observations. However, the energy balance approach simulated snowmelt in the forest environment accurately since it accounts also for longwave radiation which might largely influence snowmelt in the forested plots. Both models showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest compared to the healthy coniferous forest.

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

NASA Astrophysics Data System (ADS)

Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo

2016-10-01

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Snow cover monitoring over French Alps based on Spot-Vegetation S-10 products. Application to the Vercors area for the time period 1998-2008.

NASA Astrophysics Data System (ADS)

Bigot, S.; Dedieu, Jp.; Rome, S.

2009-04-01

Sylvain.bigot@ujf-grenoble.fr Jean-pierre.dedieu@hmg.inpg.fr Sandra.rome@ujf-grenoble.fr Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the SPOT-4 and -5 VEGETATION sensors are used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas. However, as the pixel size is 1km x 1km, a VGT pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the sub-pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Application of this approach in the French Alps is presented over the Vercors Natural Park area (N 44°.50' / E 05°.30'), based on 10-day Synthetic products for the 1998-2008 time period, and using the NDSII (Normalized Difference Snow/Ice Index) as numerical threshold. This work performs an analysis of climate impact on snow cover spatial and temporal variability, at mid-elevation mountain range (1500 m asl) under temperate climate conditions. The results indicates (i) a increasing temporal and spatial variability of snow coverage, and (ii) a high sensitivity to low variation of air temperature, often close to 1° C. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s.

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

NASA Technical Reports Server (NTRS)

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

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

Radiative forcing and climate change greatly depends on earth's surface albedo and its temporal and spatial variation. The surface albedo varies greatly depending on the surface characteristics ranging from 5-10% for calm ocean waters to 80% for some snow-covered areas. Clean and fresh snow surfaces have the highest albedo and are most sensitive to contamination with light absorbing impurities that can greatly reduce surface albedo and change overall radiative forcing estimates. Accurate estimation of snow albedo as well as understanding of feedbacks on climate from changes in snow-covered areas is important for radiative forcing, snow energy balance, predicting seasonal snowmelt, and run off rates. Such information is essential to inform timely decision making of stakeholders and policy makers. Light absorbing particles deposited onto the snow surface can greatly alter snow albedo and have been identified as a major contributor to regional climate forcing if seasonal snow cover is involved. However, uncertainty associated with quantification of albedo reduction by these light absorbing particles is high. Here, we use Mie theory (under the assumption of spherical snow grains) to reconstruct the single scattering parameters of snow (i.e., single scattering albedo ῶ and asymmetry parameter g) from observation-based size distribution information and retrieved refractive index values. The single scattering parameters of impurities are extracted with the same approach from datasets obtained during laboratory combustion of biomass samples. Instead of using plane-parallel approximation methods to account for multiple scattering, we have used the simple "Monte Carlo ray/photon tracing approach" to calculate the snow albedo. This simple approach considers multiple scattering to be the "collection" of single scattering events. Using this approach, we vary the effective snow grain size and impurity concentrations to explore the evolution of snow albedo over a wide wavelength range (300 nm - 2000 nm). Results will be compared with the SNICAR model to better understand the differences in snow albedo computation between plane-parallel methods and the statistical Monte Carlo methods.

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period

NASA Astrophysics Data System (ADS)

Cea, C.; Cristóbal, J.; Pons, X.

2009-04-01

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period. C. Cea (1), J. Cristóbal (1), X. Pons (1, 2) (1) Department of Geography. Autonomous University of Barcelona. Cerdanyola del Vallès, 08193. Cristina.Cea@uab.cat, (2) Center for Ecological Research and Forestry Applications (CREAF) Cerdanyola del Vallès, 08193. Water resources and its management are essential in many alpine mountainous areas. Snow cover monitoring in the Mediterranean zone requires obtaining accurate snow cartography to estimate the volume of water derived from snow melting and species distribution modelling. Snow data is usually obtained by field campaigns, but to obtain a spatial and temporal cover of enough detail and quality it is necessary collect an important number of data. However, when a continuous surface is needed, Remote Sensing could provide better snow cover estimation due to its spatial and temporal resolution. The aim of this study is to map snow cover and analyse its spatial and temporal dynamics using medium and coarse remote sensing data at a regional scale over an heterogeneous area, the Catalan Pyrenees (NE of the Iberian Peninsula). The seasonal snow cover period is from October to June. In this period, regular snowfalls usually take place from December to April, although during the rest of the period, punctual but important episodes of snowfalls are frequent. To perform this analysis, a set of 96 Landsat images (36 Landsat-5 TM and 60 Landsat-7 ETM+) of path 197 and 198 and rows 31 and 32 from January 2002 to April 2007, and 90 Terra-MODIS images from October 2007 to July 2008, with a different percentage of cloudiness, have been chosen. The computation of the Landsat-5 TM and Landsat-7 ETM+ data used in snow cover mapping has been carried out by means of the following methodologies. Images have been geometrically corrected by means of techniques based on first order polynomials taking into account the effect of the relief of the land surface using a Digital Elevation Model. Radiometric correction (non-thermal bands) has been done following the methodology which allows us to reduce the number of undesired artefacts that are due to the effects of the atmosphere or to the differential illumination. Finally, cloud removal has been carried out by means of a semi-automatic methodology. In the case of MODIS images, these have been imported by reading all the necessary metadata to document and interpret them. These products have already been radiometrically and geometrically corrected by USGS in a sinusoidal projection. Snow cover mapping has been carried out by means of the Normalized Snow Cover Index (NDSI), one of the most widely methodologies used. This methodology proposes a normalized index using green band 2 and Medium Infrared band because of snow reflectance is higher in the visible bands than in the medium infrared bands. NDSI pixels greater than 0.4 are select as snow cover. Although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. In shadow cast areas, we have used a hybrid classification to obtain the snow cover. Preliminary results show the snow surface evolution of the Pyrenean basis during the hydrological period, as well as the differences between the different years of study. The obtained data shows how the basins with Mediterranean influence, placed in the east, receive less nival contribution than those with Atlantic influence, situated in the west. In addition, this data allow establishing the beginning and the ending of the snowfall cycle. Eastern basins usually have a shorter period than west basins, where the snow line is lower. The snow cover area average of the studied period is about 1200 km2 and stands out that for the particular period 2006-2007, this area was only about 55% of the average, due to the lack of snow precipitation. It affected the winter sports, an important sector in this area, the vegetation and the extensive livestock, just as the volume of water that flows towards river basins and reservoir when snow melts. Technical characteristics are different between both sensors. On the one hand, Landsat spatial resolution is better to obtain accuracy cartography, however it is not suitable to determine the frequency of snow falls. On the one other hand, MODIS with its daily temporal resolution allows better temporal monitoring, but with coarse spatial resolution.

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

NASA Astrophysics Data System (ADS)

Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

2017-12-01

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

NASA Astrophysics Data System (ADS)

Härer, Stefan; Bernhardt, Matthias; Siebers, Matthias; Schulz, Karsten

2018-05-01

Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50 % in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500 m or larger, underlining the general applicability of the standard threshold at larger scales.

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.

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.

Field Measured Spectral Albedo-Four Years of Data from the Western U.S. Prairie

NASA Astrophysics Data System (ADS)

Michalsky, Joseph J.; Hodges, Gary B.

2013-01-01

This paper presents an initial look at four years of spectral measurements used to calculate albedo for the Colorado prairie just east of the Rocky Mountain range foothills. Some issues associated with calculating broadband albedo from thermopile sensors are discussed demonstrating that uncorrected instrument issues have led to incorrect conclusions. Normalized Difference Vegetative Index (NDVI) is defined for the spectral instruments in this study and used to demonstrate the dramatic changes that can be monitored with this very sensitive product. Examples of albedo wavelength and solar-zenith angle dependence for different stages of vegetative growth and senescence are presented. The spectral albedo of fresh snow and its spectral and solar-zenith angle dependence are discussed and contrasted with other studies of these dependencies. We conclude that fresh snow is consistent with a Lambertian reflector over the solar incidence angles measured; this is contrary to most snow albedo results. Even a slope of a degree or two in the viewed surface can explain the asymmetry in the morning and afternoon albedos for snow and vegetation. Plans for extending these spectral measurements for albedo to longer wavelengths and to additional sites are described.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Sustainability of winter tourism in a changing climate over Kashmir Himalaya.

PubMed

Dar, Reyaz Ahmad; Rashid, Irfan; Romshoo, Shakil Ahmad; Marazi, Asif

2014-04-01

Mountain areas are sensitive to climate change. Implications of climate change can be seen in less snow, receding glaciers, increasing temperatures, and decreasing precipitation. Climate change is also a severe threat to snow-related winter sports such as skiing, snowboarding, and cross-country skiing. The change in climate will put further pressure on the sensitive environment of high mountains. Therefore, in this study, an attempt has been made to know the impact of climate change on the snow precipitation, water resources, and winter tourism in the two famous tourist resorts of the Kashmir Valley. Our findings show that winters are getting prolonged with little snow falls on account of climate change. The average minimum and maximum temperatures are showing statistically significant increasing trends for winter months. The precipitation is showing decreasing trends in both the regions. A considerable area in these regions remains under the snow and glacier cover throughout the year especially during the winter and spring seasons. However, time series analysis of LandSat MODIS images using Normalized Difference Snow Index shows a decreasing trend in snow cover in both the regions from past few years. Similarly, the stream discharge, comprising predominantly of snow- and glacier-melt, is showing a statistically significant declining trend despite the melting of these glaciers. The predicted futuristic trends of temperature from Predicting Regional Climates for Impact Studies regional climate model are showing an increase which may enhance snow-melting in the near future posing a serious threat to the sustainability of winter tourism in the region. Hence, it becomes essential to monitor the changes in temperature and snow cover depletion in these basins in order to evaluate their effect on the winter tourism and water resources in the region.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

KARBILSIS is an online platform which is developed in order to provide end-users with daily remote sensing snow products for Turkey (www.karbilsis.com). The project has been started as a research activity after an award by Ministry of Science and Technology has been granted to our company. At the first stage of our project MODIS atmospherically corrected reflectance data has been downloaded covering the period of 2000-2011 which makes more than ten years of satellite imagery for Turkey. The archived MODIS data that have been obtained from National Snow and Ice Data Center (NSIDC) is mainly MOD09GA product that includes seven spectral bands. Only the tiles which are covering Turkey have been archived namely 19&20 horizontal and 4&5 vertical ones. In order to provide scientists with a website giving the availability of analysis of snow covered area for long terms based on their area of interests, a fractional snow extent (FSE) product has been generated. For FSE product a normalized difference snow index (NDSI) based algorithm has been developed using daily land surface reflectance values (MOD09GA). In addition to MODIS data, four different Landsat images belonging to different days of snowy period (January, March, and May) have been used during algorithm development taking into account a better representation of different reflectance values of snow which highly varies depending on the accumulation and melting periods. Landsat images were used as reference images. First the Landsat images were orthorectified and mapped to a cartographic projection. Then image segmentation was applied to obtain homogeneous tiles, where the homogeneity is defined as similarity in pixel values. The mean-shift segmentation approach, where each pixel was associated with a significant mode of the joint domain density located in its neighborhood, was applied. After segmentation, the image was classified into snow and no-snow classes with Maximum Likelihood Classification Method. FSE products have been produced for around 12 years from 2000 to 2012 and it is being produced daily as the data is available. 72% overall accuracy was obtained from the validation analysis. Our website will be available to give service to our users to make analysis on snow extent with a long time series database for free. By the help of WEBGIS interface it is going to be possible to produce time series of snow cover areas, and produce graphs and summary statistics for a better management of information on snow cover in various fields from flood forecast integration, energy production planning of hydropower plants which are fed from snow melting, and producing input for climate models.

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

NASA Technical Reports Server (NTRS)

Gladkova, Irina; Grossberg, Michael; Bonev, George; Romanov, Peter; Riggs, George; Hall, Dorothy

2013-01-01

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

An experience of the Snow-Littler procedure.

PubMed

Rider, M A; Grindel, S I; Tonkin, M A; Wood, V E

2000-08-01

This paper reviews the results of the Snow-Littler procedure performed in twelve hands with classical central longitudinal deficiency and in one hand with symbrachydactyly, cleft type. There were no instances of major flap necrosis although two flaps showed tip ischaemia. The width of the first web was, in the main, satisfactory but four webspace revisions were performed. Supplementary skin grafting at the time of surgery was necessary in complete and/or complex thumb index syndactylies and in the patient with symbrachydactyly. In eight cases, a transverse metacarpal ligament was reconstructed. In the five other cases, no clinical instability or radiological divergence of the index and ring fingers occurred, in spite of no transverse metacarpal ligament reconstruction. Three de-rotational osteotomies of transposed index fingers were performed in patients who had a transverse metacarpal ligament reconstruction. These results indicated significantly improved appearance and improved function following the Snow-Littler procedure.

Research on the resilience of husbandry economy to snow disaster

NASA Astrophysics Data System (ADS)

Zhao, Shuang; Fang, Yiping

2017-04-01

Snow disaster always makes adverse influence on the pastoral economy in alpine area. Resilience theory could efficiently enhance the capacities of resisting disaster and mitigating loss of animal husbandry economy. In order to distinguish the weak parts of existed resilience system and strengthen the construction of disaster mitigating in the source of Changjiang-Yellow River, this paper has developed two methods of comprehensive index and relationship model to measure the resilience from 1980 to 2014. The comprehensive index method is based on the conceptual framework of resilience assessment. And relationship model is derived from the internal relationship between vulnerability and resilience. Through the index system of resilience, this paper also summarizes the mean influencing indicator to husbandry economy resilience. The results show:(1)From time dimension, the resilience of snow disaster in Changjiang-Yellow River is rising with fluctuations. Based on the rate, the changes could be divided into slow(1980-1996) and fast(1997-2014) growing phases. The disaster-mitigating capacity of livestock has been markedly improved; (2)From spatial dimension, the magnitude and frequency of snow disaster change weakly. But the gap of resilience in Changjiang-Yellow River has shrunk in 35 years and the resilience in source of Changjiang is distinctly better than Yellow River; (3)Among all the indicators, snow disaster plays a decisive role in the changes of resilience. The resisting capacity including infrastructure construction makes significant effects on resilience and the reducing measures consisted of income, education and agricultural finance could effectively regulate the level. Key words: husbandry economy; snow disaster; resilience; mitigation

Estimation of the spatiotemporal dynamics of snow covered area by using cellular automata models

NASA Astrophysics Data System (ADS)

Pardo-Igúzquiza, Eulogio; Collados-Lara, Antonio-Juan; Pulido-Velazquez, David

2017-07-01

Given the need to consider the cryosphere in water resources management for mountainous regions, the purpose of this paper is to model the daily spatially distributed dynamics of snow covered area (SCA) by using calibrated cellular automata models. For the operational use of the calibrated model, the only data requirements are the altitude of each cell of the spatial discretization of the area of interest and precipitation and temperature indexes for the area of interest. For the calibration step, experimental snow covered area data are needed. Potential uses of the model are to estimate the snow covered area when satellite data are absent, or when they provide a temporal resolution different from the operational resolution, or when the satellite images are useless because they are covered by clouds or because there has been a sensor failure. Another interesting application is the simulation of SCA dynamics for the snow covered area under future climatic scenarios. The model is applied to the Sierra Nevada mountain range, in southern Spain, which is home to significant biodiversity, contains important water resources in its snowpack, and contains the most meridional ski resort in Europe.

Vegetation Cover Change in Yellowstone National Park Detected Using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2015-01-01

Results from Landsat satellite image analysis since 1987 in all unburned areas (since the 1880s) of Yellowstone National Park (YNP) showed that consistent decreases in the normalized difference vegetation index (NDVI) have been strongly dependent on periodic variations in peak annual snow water equivalents (SWE).

Time budgets of Snow Geese Chen caerulescens and Ross's Geese Chen rossii in mixed flocks: Implications of body size, ambient temperature and family associations

USGS Publications Warehouse

Jonsson, J.E.; Afton, A.D.

2009-01-01

Body size affects foraging and forage intake rates directly via energetic processes and indirectly through interactions with social status and social behaviour. Ambient temperature has a relatively greater effect on the energetics of smaller species, which also generally are more vulnerable to predator attacks than are larger species. We examined variability in an index of intake rates and an index of alertness in Lesser Snow Geese Chen caerulescens caerulescens and Ross's Geese Chen rossii wintering in southwest Louisiana. Specifically we examined variation in these response variables that could be attributed to species, age, family size and ambient temperature. We hypothesized that the smaller Ross's Geese would spend relatively more time feeding, exhibit relatively higher peck rates, spend more time alert or raise their heads up from feeding more frequently, and would respond to declining temperatures by increasing their proportion of time spent feeding. As predicted, we found that Ross's Geese spent more time feeding than did Snow Geese and had slightly higher peck rates than Snow Geese in one of two winters. Ross's Geese spent more time alert than did Snow Geese in one winter, but alert rates differed by family size, independent of species, in contrast to our prediction. In one winter, time spent foraging and walking was inversely related to average daily temperature, but both varied independently of species. Effects of age and family size on time budgets were generally independent of species and in accordance with previous studies. We conclude that body size is a key variable influencing time spent feeding in Ross's Geese, which may require a high time spent feeding at the expense of other activities. ?? 2008 The Authors.

Simulations of snow distribution and hydrology in a mountain basin

USGS Publications Warehouse

Hartman, Melannie D.; Baron, Jill S.; Lammers, Richard B.; Cline, Donald W.; Band, Larry E.; Liston, Glen E.; Tague, Christina L.

1999-01-01

We applied a version of the Regional Hydro-Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind-driven sublimation to Loch Vale Watershed (LVWS), an alpine-subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind-driven sublimation was necessary to predict moisture losses.

Remote estimation of crown size and tree density in snowy areas

NASA Astrophysics Data System (ADS)

Kishi, R.; Ito, A.; Kamada, K.; Fukita, T.; Lahrita, L.; Kawase, Y.; Murahashi, K.; Kawamata, H.; Naruse, N.; Takahashi, Y.

2017-12-01

Precise estimation of tree density in the forest leads us to understand the amount of carbon dioxide fixed by plants. Aerial photographs have been used to measure the number of trees. Campaign using aircraft, however, is expensive ( $50,000/1 campaign flight) and the research area is limited in drone. In addition, previous studies estimating the density of trees from aerial photographs have been performed in the summer, so there was a gap of 15% in the estimation due to the overlapping of the leaves. Here, we have proposed a method to accurately estimate the number of forest trees from the satellite images of snow-covered deciduous forest area, using the ratio of branches to snow. The advantages of our method are as follows; 1) snow area could be excluded easily due to the high reflectance, 2) tree branches are small overlapping compared to leaves. Although our method can use only in the snowfall region, the area covered with snow in the world becomes more than 12,800,000 km2. Our proposition should play an important role in discussing global warming. As a test area, we have chosen the forest near Mt. Amano in Iwate prefecture in Japan. First, we made a new index of (Band1-Band5)/(Band1+Band5), which will be suitable to distinguish between the snow and the tree trunk using the corresponding spectral reflection data. Next, the index values of changing the ratio in 1% increments were listed. From the satellite image analysis at 4 points, the ratio of snow to tree trunk showed the following values, I:61%, II:65%, III:66% and IV:65%. To confirm the estimation, we used the aerial photograph from Google earth; the rate was I:42.05%, II:48.89%, III:50.64%, IV:49.05%, respectively. There is a correlation between the numerical values of both, but there are differences. We will discuss in detail at this point, focusing on the effect of shadows.

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

PubMed

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

2003-01-01

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

Impact of the springtime Himalayan-Tibetan Plateau on the onset on the Indian summer monsoon in coupled forecasts

NASA Astrophysics Data System (ADS)

Orsolini, Yvan; Senan, Retish; Weisheimer, Antje; Vitart, Frederic; Balsamo, Gianpaolo; Doblas-Reyes, Francisco; Stockdale, Timothy; Dutra, Emanuel

2016-04-01

The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region has long been suggested to be an influential factor on the onset of the Indian summer monsoon. In the frame of the SPECS project, we have assessed the impact of realistic snow initialization in springtime over HTP on the onset of the Indian summer monsoon. We examine a suite of coupled ocean-atmosphere 4-month ensemble reforecasts made at the European Centre for Medium-Range Weather Forecasts (ECMWF), using the Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (i) the onset is delayed by about 8 days, and (ii) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by either the El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

Trends in annual minimum exposed snow and ice cover in High Mountain Asia from MODIS

NASA Astrophysics Data System (ADS)

Rittger, Karl; Brodzik, Mary J.; Painter, Thomas H.; Racoviteanu, Adina; Armstrong, Richard; Dozier, Jeff

2016-04-01

Though a relatively short record on climatological scales, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000-2014 can be used to evaluate changes in the cryosphere and provide a robust baseline for future observations from space. We use the MODIS Snow Covered Area and Grain size (MODSCAG) algorithm, based on spectral mixture analysis, to estimate daily fractional snow and ice cover and the MODICE Persistent Ice (MODICE) algorithm to estimate the annual minimum snow and ice fraction (fSCA) for each year from 2000 to 2014 in High Mountain Asia. We have found that MODSCAG performs better than other algorithms, such as the Normalized Difference Index (NDSI), at detecting snow. We use MODICE because it minimizes false positives (compared to maximum extents), for example, when bright soils or clouds are incorrectly classified as snow, a common problem with optical satellite snow mapping. We analyze changes in area using the annual MODICE maps of minimum snow and ice cover for over 15,000 individual glaciers as defined by the Randolph Glacier Inventory (RGI) Version 5, focusing on the Amu Darya, Syr Darya, Upper Indus, Ganges, and Brahmaputra River basins. For each glacier with an area of at least 1 km2 as defined by RGI, we sum the total minimum snow and ice covered area for each year from 2000 to 2014 and estimate the trends in area loss or gain. We find the largest loss in annual minimum snow and ice extent for 2000-2014 in the Brahmaputra and Ganges with 57% and 40%, respectively, of analyzed glaciers with significant losses (p-value<0.05). In the Upper Indus River basin, we see both gains and losses in minimum snow and ice extent, but more glaciers with losses than gains. Our analysis shows that a smaller proportion of glaciers in the Amu Darya and Syr Darya are experiencing significant changes in minimum snow and ice extent (3.5% and 12.2%), possibly because more of the glaciers in this region are smaller than 1 km2 than in the Indus, Ganges, and Brahmaputra making analysis from MODIS (pixel area ~0.25 km2) difficult. Overall, we see 23% of the glaciers in the 5 river basins with significant trends (in either direction). We relate these changes in area to topography and climate to understand the driving processes related to these changes. In addition to annual minimum snow and ice cover, the MODICE algorithm also provides the date of minimum fSCA for each pixel. To determine whether the surface was snow or ice we use the date of minimum fSCA from MODICE to index daily maps of snow on ice (SOI), or exposed glacier ice (EGI) and systematically derive an equilibrium line altitude (ELA) for each year from 2000-2014. We test this new algorithm in the Upper Indus basin and produce annual estimates of ELA. For the Upper Indus basin we are deriving annual ELAs that range from 5350 m to 5450 m which is slightly higher than published values of 5200 m for this region.

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

NASA Astrophysics Data System (ADS)

Wang, X.; Wu, C.

2017-12-01

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

78 FR 76387 - Notice of Passenger Facility Charge (PFC) Approvals and Disapprovals

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-17

... Collection and Use: Security enhancements. Rehabilitate runway. Snow removal equipment--broom. Design and... Collection and Use: Snow removal equipment acquisition (broom). Terminal jet bridge modification (design). Pavement condition index maintenance update. Taxilane construction/conversion. Terminal jet bridge and gate...

Climate Change Impacts on the Cryosphere of Mountain Regions: Validation of a Novel Model Using the Alaska Range

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2015-12-01

Mountain regions are natural water towers, storing water seasonally as snowpack and for much longer as glaciers. Understanding the response of these systems to climate change is necessary in order to make informed decisions about prevention or mitigation measures. Yet, mountain regions are often data sparse, leading many researchers to implement simple or enhanced temperature index (ETI) models to simulate cryosphere processes. These model structures do not account for the thermal inertia of snowpack and glaciers and do not robustly capture differences in system response to climate regimes that differ from those the model was calibrated for. For instance, a temperature index calibration parameter will differ substantially in cold-dry conditions versus warm-wet ones. To overcome these issues, we have developed a cryosphere hydrology model, called the Significantly Enhanced Temperature Index (SETI), which uses an energy balance structure but parameterizes energy balance components in terms of minimum, maximum and mean temperature, precipitation, and geometric inputs using established relationships. Additionally, the SETI model includes a glacier sliding model and can therefore be used to estimate long-term glacier response to climate change. Sensitivity of the SETI model to changing climate is compared with an ETI and a simple temperature index model for several partially-glaciated watersheds within Alaska, including Wolverine glacier where multi-decadal glacier stake measurements are available, to highlight the additional fidelity attributed to the increased complexity of the SETI structure. The SETI model is then applied to the entire Alaska Range region for an ensemble of global climate models (GCMs), using representative concentration pathways 4.5 and 8.5. Comparing model runs based on ensembles of GCM projections to historic conditions, total annual snowfall within the Alaska region is not expected to change appreciably, but the spatial distribution of snow shifts towards higher elevations and for a large portion of the region the duration of snow cover decreases. The changes in temperature and snow distribution also lead to spatially heterogeneous responses by glaciers within the region. The SETI model is designed to be easy to apply for any mountain region where cryospheric processes dominate.

Monitoring the spatio-temporal evolution of the snow cover in the eastern Alps from MODIS data

NASA Astrophysics Data System (ADS)

Cianfarra, P.; Salvini, F.; Valt, M.

2009-04-01

Estimating the snow cover extent in mountain ranges is important for a wide variety purposes including of scientific studies, environmental and meteo-climatic applications, as well as predicting water availability for energy resource and agriculture. Moreover, the monitoring of the spatio-temporal variation of the snow cover thickness, coupled with ground data from weather stations, allows to identify avalanche risk areas after heavy snowfall. The aim of this study is to test an automatic procedure to identify and map the snow coverage for different altitude interval in the eastern part of the Alpine range. There has been much progress since 1966 when the first operational snow mapping was done by NOAA with spaceborne sensors that provide daily, global observations to monitor the variability in space and time in the extent of snow cover. MODIS sensors offer increased improvements relative to the AVHRR that has been operational for many years on the NOAA Polar Operational Environmental Satellite System. In this context the MODIS provides observations at a nominal spatial resolution of 500 m versus the 1.1 km spatial resolution of the AVHRR and continuously available (spatially and temporally), spectral band observation that span the visible and short-wave infrared wavelengths, including those useful for recognize snow cover. The other advantage of using MODIS data is its availability and cost by the NASA's server. In this work we used MOD02 (L1B) data providing calibrated radiance values at the sensor (without atmospheric correction). Snow cover map production included the following steps: selection of the images with clear sky conditions, geometric correction and georeferencing to UTM zone 32 ,WSG 84 ellipsoid, to eliminate the distortion of and the typical bow-tie effect that produces the observed not alignment of the scan lines in the row image; spatial sub setting to produce an image covering an area of about 200 x 120 km; identification of the snow cover was done by computing the Normalised Difference Snow Index (NDSI) knowing that snow reflectance is higher in the visible (0.5-0.7 mm) wavelengths and has lower reflectance in the short wave infrared (1-4 mm) wavelengths. This allowed to separate snow from clouds and other non-snow-covered pixels. The NDSI for MODIS images is defined as the difference of reflectances observed in the visible band 4 (0.555 mm) and the short wave infrared band 6 (1.640 mm) divided by the sum of the two reflectances: NDSI=(B4 - B6)/ (B4 + B6) This approach allowed to reduce (yet not totally eliminate) the influence of the atmospheric effects and lighting conditions. A series of thresholds were tested to the ratio image to establish the best value for snow cover identification. Eventually, the snow cover extent was computed for 6 altitude intervals. Results from the different processed images were compared and statistically analysed. A complete set of ground truth of these preliminary results is still missing; yet we are confident that once the tuning of the processing will be completed, the automated processing of MODIS data will provide low cost, near real-time estimates of the snow cover distribution over the eastern Alps. This product would be a valuable tool for public administrations and authorities for environmental protection, control and risk management.

Transitions in High-Arctic Vegetation Growth Patterns and Ecosystem Productivity from 2000-2013 Tracked with Cameras

NASA Astrophysics Data System (ADS)

Westergaard-Nielsen, A.; Hansen, B. U.; Klosterman, S.; Pedersen, S. H.; Schmidt, N. M.; Abermann, J.; Lund, M.

2015-12-01

The changes in vegetation seasonality in high northern latitudes resulting from changes atmospheric temperatures and precipitation are still not well understood. Continued monitoring and research is therefore needed. In this study we use 13 years of time lapse camera data and climate data from high-Arctic Northeast Greenland to assess the seasonal response of a dwarf shrub heath, grassland, and fens to snow cover, soil moisture, and atmospheric and soil temperatures. Based on the camera data, we computed a greenness index which was subsequently used to analyze transition dates in vegetation seasonality. We show that snow cover and subsequent water from the melting snow pack is highly important for the seasonality. We found a significant advancement in start of growing season of 12 days but not a significant increase in growing season length. Both the timing and greenness index value of peak of growing season was significantly correlated to the available water in the pre-melt snow pack, mostly pronounced in vegetation with limited soil water. The end of growing season was likewise significantly correlated to the water equivalents in the pre-melt snowpack. Moreover, the vegetation greenness was highly correlated to GPP, and shifts in seasonality as tracked by the greenness index are thus expected to have direct influence on ecosystem productivity.

Optical bio-chemical sensors on SNOW ring resonators.

PubMed

Khorasaninejad, Mohammadreza; Clarke, Nigel; Anantram, M P; Saini, Simarjeet Singh

2011-08-29

In this paper, we propose and analyze novel ring resonator based bio-chemical sensors on silicon nanowire optical waveguide (SNOW) and show that the sensitivity of the sensors can be increased by an order of magnitude as compared to silicon-on-insulator based ring resonators while maintaining high index contrast and compact devices. The core of the waveguide is hollow and allows for introduction of biomaterial in the center of the mode, thereby increasing the sensitivity of detection. A sensitivity of 243 nm/refractive index unit (RIU) is achieved for a change in bulk refractive index. For surface attachment, the sensor is able to detect monolayer attachments as small as 1 Å on the surface of the silicon nanowires.

Optical bio-chemical sensors on SNOW ring resonators

NASA Astrophysics Data System (ADS)

Khorasaninejad, Mohammadreza; Clarke, Nigel; Anantram, M. P.; Singh Saini, Simarjeet

2011-08-01

In this paper, we propose and analyze novel ring resonator based bio-chemical sensors on silicon nanowire optical waveguide (SNOW) and show that the sensitivity of the sensors can be increased by an order of magnitude as compared to silicon-on-insulator based ring resonators while maintaining high index contrast and compact devices. The core of the waveguide is hollow and allows for introduction of biomaterial in the center of the mode, thereby increasing the sensitivity of detection. A sensitivity of 243 nm/refractive index unit (RIU) is achieved for a change in bulk refractive index. For surface attachment, the sensor is able to detect monolayer attachments as small as 1 Å on the surface of the silicon nanowires.

Changing Seasonality of Tundra Vegetation and Associated Climatic Variables

NASA Astrophysics Data System (ADS)

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

2014-12-01

This study documents changes in the seasonality of tundra vegetation productivity and its associated climate variables using long-term data sets. An overall increase of Pan-Arctic tundra greenness potential corresponds to increased land surface temperatures and declining sea ice concentrations. While sea ice has continued to decline, summer land surface temperature and vegetation productivity increases have stalled during the last decade in parts of the Arctic. To understand the processes behind these features we investigate additional climate parameters. This study employs remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2013. 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), ocean heat content (PIOMAS, model incorporating ocean data assimilation), and snow water equivalent (GlobSnow, assimilated snow data set) are explored. We analyzed the data for the full period (1982-2013) and for two sub-periods (1982-1998 and 1999-2013), which were chosen based on the declining Pan-Arctic SWI since 1998. MaxNDVI has increased from 1982-2013 over most of the Arctic but has declined from 1999 to 2013 over western Eurasia, northern Canada, and southwest Alaska. TI-NDVI has trends that are similar to those for MaxNDVI for the full period but displays widespread declines over the 1999-2013 period. Therefore, as the MaxNDVI has continued to increase overall for the Arctic, TI-NDVI has been declining since 1999. SWI has large relative increases over the 1982-2013 period in eastern Canada and Greenland and strong declines in western Eurasia and southern Canadian tundra. Weekly Pan-Arctic tundra land surface temperatures warmed throughout the summer during the 1982-1998 period but display midsummer declines from 1999-2013. Weekly snow water equivalent over Arctic tundra has declined over most seasons but shows slight increases in spring in North America and during fall over Eurasia. Later spring or earlier fall snow cover can both lead to reductions in TI-NDVI. The time-varying spatial patterns of NDVI trends can be largely explained using either snow cover or land surface temperature trends.

Sentinels for snow science

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Bark Beetles Modify the Impact of Snow Drought on Remotely Sensed Forest Productivity in the Southern Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Knowles, J. F.; Lestak, L.; Molotch, N. P.

2016-12-01

We evaluated the long term (1989-2012) relationship between the satellite-observed Normalized Difference Vegetation Index (NDVI), snowpack accumulation, and atmospheric demand throughout the Southern Rocky Mountain Ecoregion, USA. Deviations from this relationship were further explored during pre- and post-disturbance conditions associated with bark beetles and drought. Over the entire study area, both the snow water equivalent (SWE) and a snow aridity index (SAI), which used the SWE to normalize potential evapotranspiration (PET), were significant predictors of the long-term AVHRR NDVI, but the SAI was a better predictor of NDVI relative to SWE regardless of disturbance. Since these relationships were weaker in disturbed areas, we also introduced a metric of tree mortality, and subsequent multiple linear regression of SAI and cumulative mortality best predicted the NDVI from a pair of heavily impacted focus areas within the larger study area. The post-disturbance NDVI was systematically reduced per unit SAI in these areas, and the difference between the observed and predicted (from pre-disturbance regressions) post-disturbance NDVI was significantly correlated with the cumulative forest mortality. At the Ecoregion scale, these disturbance effects were not clearly evident, and we attribute this to spatial variability of both SAI and NDVI throughout the large study area as evidenced by spatial analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived data. These results constrain the expected reduction in forest productivity due to disturbance and demonstrate that this reduction can be particularly evident during drought conditions resultant from low snow accumulation during the winter. Hence, terrestrial carbon uptake may decrease non-linearly post disturbance. This work has implications for predicting the ecohydrological response to climate change in the southern Rocky Mountains, as reductions in SWE and increases in PET are predicted for this area in the future, and therefore changes in the terrestrial carbon, water, and energy cycles should be expected.

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.

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

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

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

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

PubMed

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

2010-01-01

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

DIRECT IMAGING OF THE WATER SNOW LINE AT THE TIME OF PLANET FORMATION USING TWO ALMA CONTINUUM BANDS

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

Banzatti, A.; Pontoppidan, K. M.; Pinilla, P.

2015-12-10

Molecular snow lines in protoplanetary disks have been studied theoretically for decades because of their importance in shaping planetary architectures and compositions. The water snow line lies in the planet formation region at ≲10 AU, and so far its location has been estimated only indirectly from spatially unresolved spectroscopy. This work presents a proof-of-concept method to directly image the water snow line in protoplanetary disks through its physical and chemical imprint on the local dust properties. We adopt a physical disk model that includes dust coagulation, fragmentation, drift, and a change in fragmentation velocities of a factor of 10 betweenmore » dry silicates and icy grains as found by laboratory work. We find that the presence of a water snow line leads to a sharp discontinuity in the radial profile of the dust emission spectral index α{sub mm} due to replenishment of small grains through fragmentation. We use the ALMA simulator to demonstrate that this effect can be observed in protoplanetary disks using spatially resolved ALMA images in two continuum bands. We explore the model dependence on the disk viscosity and find that the spectral index reveals the water snow line for a wide range of conditions, with opposite trends when the emission is optically thin rather than thick. If the disk viscosity is low (α{sub visc} < 10{sup −3}), the snow line produces a ringlike structure with a minimum at α{sub mm} ∼ 2 in the optically thick regime, possibly similar to what has been measured with ALMA in the innermost region of the HL Tau disk.« less

Statistical analysis and trends of wet snow avalanches in the French Alps over the period 1959-2010

NASA Astrophysics Data System (ADS)

Naaim, Mohamed

2017-04-01

Since an avalanche contains a significant proportion of wet snow, its characteristics and its behavior change significantly (heterogeneous and polydisperse). Even if on a steep given slope, wet snow avalanches are slow. They can flow over gentle slopes and reach the same extensions as dry avalanches. To highlight the link between climate warming and the proliferation of wet snow avlanches, we crossed two well-documented avalanche databases: the permanent avalanche chronicle (EPA) and the meteorological re-analyzes. For each avalanche referenced in EPA, a moisture index I is buit. It represents the ratio of the thickness of the wet snow layer to the total snow thickness, at the date of the avalanche on the concerned massif at 2400 m.a.s.l. The daily and annual proportion of avalanches exceeding a given threshold of I are calculated for each massif of the French alps. The statistical distribution of wet avalanches per massif is calculated over the period 1959-2009. The statistical quantities are also calculated over two successive periods of the same duration 1959-1984 and 1984-2009, and the annual evolution of the proportion of wet avalanches is studied using time-series tools to detect potential rupture or trends. This study showed that about 77% of avalanches on the French alpine massif mobilize dry snow. The probability of having an avalanche of a moisture index greater than 10 % in a given year is 0.2. This value varies from one massif to another. The analysis between the two successive periods showed a significant growth of wet avalanches on 20 massifs and a decrease on 3 massifs. The study of time-series confirmed these trends, which are of the inter-annual variability level.

Snow cover retrieval over Rhone and Po river basins from MODIS optical satellite data (2000-2009).

NASA Astrophysics Data System (ADS)

Dedieu, Jean-Pierre, ,, Dr.; Boos, Alain; Kiage, Wiliam; Pellegrini, Matteo

2010-05-01

Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the MODIS optical satellite sensor can be used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas (2,500 km range). However, as the pixel size is 500m x 500m, a MODIS pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the Sub-Pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Integrated into the EU-FP7 ACQWA Project (www.acqwa.ch), this approach was first applied over Alpine area of Rhone river basin upper Geneva Lake: Canton du Valais, Switzerland (5 375 km²). In a second step over Alps, rolling hills and plain areas in Po catchment for Val d'Aosta and Piemonte regions, Italy (37 190 km²). Watershed boundaries were provided respectively by GRID (Ch) and ARPA (It) partners. The complete satellite images database was extracted from the U.S. MODIS/NASA website (http://modis.gsfc.nasa.gov/) for MOD09_B1 Reflectance images, and from the MODIS/NSIDC website (http://nsidc.org/index.html) for MOD10_A2 snow cover images. Only the Terra platform was used because images are acquired in the morning and are therefore better correlated with dry snow surface, avoiding cloud coverage of the afternoon (Aqua Platform). The MOD9 Image reflectance and MOD10_A2 products were respectively analyzed to retrieve (i) Fractional Snow cover at sub-pixel scale, and (ii) maximum snow cover. All products were retrieved at 8-days over a complete time period of 10 years (2000-2009), giving 500 images for each river basin. Digital Model Elevation was given by NASA/SRTM database at 90-m resolution and used (i) for illumination versus topography correction on snow cover, (ii) geometric rectification of images. Geographic projection is WGS84, UTM 32. Fractional Snow cover mapping was derived from the NDSI linear regression method (Salomonson et al., 2004). Cloud mask was given by MODIS-NASA library (radiometric threshold) and completed by inverse slope regression to avoid lowlands fog confusing with thin snow cover (Po river basin). Maximum Snow Cover mapping was retrieved from the NSIDC database classification (Hall et al., 2001). Validation step was processed using comparison between MODIS Snow maps outputs and meteorological data provided by network of 87 meteorological stations: temperature, precipitation, snow depth measurement. A 0.92 correlation was observed for snow/non snow cover and can be considered as quite satisfactory, given the radiometric problems encountered in mountainous areas, particularly in snowmelt season. The 10-years time period results indicates a main difference between (i) regular snow accumulation and depletion in Rhone and (ii) the high temporal and spatial variability of snow cover for Po. Then, a high sensitivity to low variation of air temperature, often close to 1° C was observed. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s, particularly for Po river basin. Results will be combined with two hydrological models: Topkapi and Fest.

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

NASA Astrophysics Data System (ADS)

Varade, D. M.; Dikshit, O.

2017-12-01

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

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.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. (Principal Investigator); Ostrem, G.

1977-01-01

The author has identified the following significant results. It was shown that if the snow cover extent was determined from all four LANDSAT bands, there were significant differences in results. The MSS 4 gave the largest snow cover, but only slightly more than MSS 5, whereas MSS 6 and 7 gave the smallest snow area. A study was made to show that there was a relationship between the last date of snow fall and the area covered with snow, as determined from different bands. Imagery obtained shortly after a snow fall showed no significant difference in the snow-covered area when the four bans were compared, whereas, pronounced differences in the snow-covered area were found in images taken after a long period without precipitation.

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.

Snow conditions as an estimator of the breeding output in high-Arctic pink-footed geese Anser brachyrhynchus

USGS Publications Warehouse

Jensen, Gitte Høj; Madsen, Jesper; Johnson, Fred A.; Tamstorf, Mikkel P.

2014-01-01

The Svalbard-breeding population of pink-footed geese Anser brachyrhynchus has increased during the last decades and is giving rise to agricultural conflicts along their migration route, as well as causing grazing impacts on tundra vegetation. An adaptive flyway management plan has been implemented, which will be based on predictive population models including environmental variables expected to affect goose population development, such as weather conditions on the breeding grounds. A local study in Svalbard showed that snow cover prior to egg laying is a crucial factor for the reproductive output of pink-footed geese, and MODIS satellite images provided a useful estimator of snow cover. In this study, we up-scaled the analysis to the population level by examining various measures of snow conditions and compared them with the overall breeding success of the population as indexed by the proportion of juveniles in the autumn population. As explanatory variables, we explored MODIS images, satellite-based radar measures of onset of snow melt, winter NAO index, and the May temperature sum and May thaw days. To test for the presence of density dependence, we included the number of adults in the population. For 2000–2011, MODIS-derived snow cover (available since 2000) was the strongest indicator of breeding conditions. For 1981–2011, winter NAO and May thaw days had equal weight. Interestingly, there appears to have been a phase shift from density-dependent to density-independent reproduction, which is consistent with a hypothesis of released breeding potential due to the recent advancement of spring in Svalbard.

Stress distribution calculations through a snow slab of varying elastic modulus; comparison with stability evaluation in the field

NASA Astrophysics Data System (ADS)

Swinkels, Laura; Borstad, Chris

2017-04-01

Field observations are the main tools for assessing the snow stability concerning dry snow slab avalanche release. Often, theoretical studies cannot directly be translated into useful information for avalanche recreationists and forecasters in the field, and vice versa; field observations are not always objective and quantifiable for theoretical studies. Moreover, numerical models often simplify the snowpack and generally use an isotropic single layer slab which is not representative of the real-life situation. The aim of this study is to investigate the stress distribution in a snowpack with an elastic modulus that continuously varies with depth. The focus lies on the difference between a slab with a gradient in hardness and a slab with isotropic hardness and the effect on the calculated maximum stress and the stability evaluation in the field. Approximately 20 different snow pits were evaluated in the mountains around Tromsø, Norway and Longyearbyen, Svalbard. In addition to the standard snowpack observations, the hardness was measured using a thin-blade gauge. Extended column tests were executed for stability evaluation. Measurements from the field were used as input for stress calculations for each snow pit using a line load solution for a sloping half space with a non-homogeneous elastic modulus. The hardness measurements were used to calculate the elastic modulus and a power law relation was fit through the modulus in the slab. The calculated shear stress was compared to the estimated stability and character of the specific snowpack The results show that the approach used for this study improves the calculation of stress at a given depth, although many assumptions and simplifications were still needed. Comparison with the snow profiles indicate that calculated stresses correlate well with the observed snowpack properties and stability. The calculated shear stresses can be introduced in the standard stability index and give a better indication for the snowpack stability. Further research is required to delimit the stresses needed for propagation of a weak layer fracture.

California's transition from conventional snowpack measurements to a developing remote sensing capability for water supply forecasting

NASA Technical Reports Server (NTRS)

Brown, A. J.; Peterson, N.

1980-01-01

California's Snow Survey Program and water supply forecasting procedures are described. A review is made of current activities and program direction on such matters as: the growing statewide network of automatic snow sensors; restrictions on the gathering hydrometeorological data in areas designated as wilderness; the use of satellite communications, which both provides a flexible network without mountaintop repeaters and satisfies the need for unobtrusiveness in wilderness areas; and the increasing operational use of snow covered area (SCA) obtained from satellite imagery, which, combined with water equivalent from snow sensors, provides a high correlation to the volumes and rates of snowmelt runoff. Also examined are the advantages of remote sensing; the anticipated effects of a new input of basin wide index of water equivalent, such as the obtained through microwave techniques, on future forecasting opportunities; and the future direction and goals of the California Snow Surveys Program.

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

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

The endangered snow leopard is a large felid that is distributed over 1.83 million km2 globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica), blue sheep (Pseudois nayaur), Himalayan tahr (Hemitragus jemlahicus), argali (Ovis ammon) and marmots (Marmota spp). The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36–76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation. PMID:24533080

Snow Cover and Vegetation-Induced Decrease in Global Albedo From 2002 to 2016

NASA Astrophysics Data System (ADS)

Li, Qiuping; Ma, Mingguo; Wu, Xiaodan; Yang, Hong

2018-01-01

Land surface albedo is an essential parameter in regional and global climate models, and it is markedly influenced by land cover change. Variations in the albedo can affect the surface radiation budget and further impact the global climate. In this study, the interannual variation of albedo from 2002 to 2016 was estimated on the global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. The presence and causes of the albedo changes for each specific region were also explored. From 2002 to 2016, the MODIS-based albedo decreased globally, snow cover declined by 0.970 (percent per pixel), while the seasonally integrated normalized difference vegetation index increased by 0.175. Some obvious increases in the albedo were detected in Central Asia, northeastern China, parts of the boreal forest in Canada, and the temperate steppe in North America. In contrast, noticeable decreases in the albedo were found in the Siberian tundra, Europe, southeastern Australia, and northeastern regions of North America. In the Northern Hemisphere, the greening trend at high latitudes made more contribution to the decline in the albedo. However, the dramatic fluctuation of snow-cover at midlatitudes predominated in the change of albedo. Our analysis can help to understand the roles that vegetation and snow cover play in the variation of albedo on global and regional scales.

Spectral reflectance characteristics of different snow and snow-covered land surface objects and mixed spectrum fitting

USGS Publications Warehouse

Zhang, J.-H.; Zhou, Z.-M.; Wang, P.-J.; Yao, F.-M.; Yang, L.

2011-01-01

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area. The result showed that for a pure snow spectrum, the snow reflectance peaks appeared from visible to 800 nm band locations; there was an obvious absorption valley of snow spectrum near 1030 nm wavelength. Compared with fresh snow, the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300~1300, 1700~1800 and 2200~2300 nm, the lowest was from the compacted snow and frozen ice. For the vegetation and snow mixed spectral characteristics, it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350~1300 nm. However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic. In the end, based on the spectrum analysis of snow, vegetation, and mixed snow/vegetation pixels, the mixed spectral fitting equations were established, and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.9509).

What Does a Multilayer Canopy Model Tell Us About Our Current Understanding of Snow-Canopy Unloading?

NASA Astrophysics Data System (ADS)

McGowan, L. E.; Paw U, K. T.; Dahlke, H. E.

2017-12-01

In the Western U.S., future water resources depend on the forested mountain snowpack. The variations in and estimates of forest mountain snow volume are vital to projecting annual water availability; yet, snow forest processes are not fully known. Most snow models calculate snow-canopy unloading based on time, temperature, Leaf Area Index (LAI), and/or wind speed. While models crudely consider the canopy shape via LAI, current models typically do not consider the vertical canopy structure or varied energetics within multiple canopy layers. Vertical canopy structure influences the spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability. Within the canopy both the snowpack and energetic exposures to the snowpack (wind, shortwave and longwave radiation, turbulent heat fluxes etc.) vary widely in the vertical. The water and energy balance in each layer is dependent on all other layers. For example, increased snow canopy content in the top of the canopy will reduce available shortwave radiation at the bottom and snow unloading in a mid-layer can cascade and remove snow from all the lower layers. We examined vertical interactions and structures of the forest canopy on the impact of unloading utilizing the Advanced Canopy-Atmosphere-Soil-Algorithm (ACASA), a multilayer soil-vegetation-atmosphere numerical model based on higher-order closure of turbulence equations. Our results demonstrate how a multilayer model can be used to elucidate the physical processes of snow unloading, and could help researchers better parameterize unloading in snow-hydrology models.

Hand injuries from snow blowers: a report of an epidemic.

PubMed

Tetz, D J; Aghababian, R

1995-01-01

During a record snowfall in Worcester, Massachusetts, 11-13 December 1992, 37 male patients with hand injuries suffered during snow blower operation were seen at three area hospitals. Two previous reports describe 13 patients seen over a 3-year period and 28 patients over a 12-year period. This report describes the largest number of hand injuries from snow blowers that have occurred over a 48-hour period. The snow was unusual because of the high water density in the initial 9 inches (23 cm) that fell at an average temperature of 33 degrees F (0.6 degree C) with the final depth of 30 inches (76 cm), causing the machines to become clogged. Patients admitted to reaching into a running machine in 35/37 (95%) cases, 11/37 (30%) claimed the auger and impeller blades were disengaged, and 2/37 (5%) patients claimed their injuries occurred with the engines turned off. All injuries occurred when the patients placed their hands down the chute, contacting the impeller blades. Injuries involved 32 long, 15 ring, 13 index, and five small fingers and ranged from simple lacerations to partial phalangeal amputations. The majority, 27/37 (73%), were managed in emergency departments without interventions in the operating suites. Infection occurred in one patient who had the lesion repaired in the operating suite. As in previous studies, no differences were found for the variables of snow-blower age, type, or horsepower, or on experience level or age of the operators.(ABSTRACT TRUNCATED AT 250 WORDS)

Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery

NASA Astrophysics Data System (ADS)

Korzeniowska, Karolina; Bühler, Yves; Marty, Mauro; Korup, Oliver

2017-10-01

Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km-2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79-0.85. Testing the method for a larger area of 226.3 km-2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.

Accuracy assessment of a net radiation and temperature index snowmelt model using ground observations of snow water equivalent in an alpine basin

NASA Astrophysics Data System (ADS)

Molotch, N. P.; Painter, T. H.; Bales, R. C.; Dozier, J.

2003-04-01

In this study, an accumulated net radiation / accumulated degree-day index snowmelt model was coupled with remotely sensed snow covered area (SCA) data to simulate snow cover depletion and reconstruct maximum snow water equivalent (SWE) in the 19.1-km2 Tokopah Basin of the Sierra Nevada, California. Simple net radiation snowmelt models are attractive for operational snowmelt runoff forecasts as they are computationally inexpensive and have low input requirements relative to physically based energy balance models. The objective of this research was to assess the accuracy of a simple net radiation snowmelt model in a topographically heterogeneous alpine environment. Previous applications of net radiation / temperature index snowmelt models have not been evaluated in alpine terrain with intensive field observations of SWE. Solar radiation data from two meteorological stations were distributed using the topographic radiation model TOPORAD. Relative humidity and temperature data were distributed based on the lapse rate calculated between three meteorological stations within the basin. Fractional SCA data from the Landsat Enhanced Thematic Mapper (5 acquisitions) and the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) (2 acquisitions) were used to derive daily SCA using a linear regression between acquisition dates. Grain size data from AVIRIS (4 acquisitions) were used to infer snow surface albedo and interpolated linearly with time to derive daily albedo values. Modeled daily snowmelt rates for each 30-m pixel were scaled by the SCA and integrated over the snowmelt season to obtain estimates of maximum SWE accumulation. Snow surveys consisting of an average of 335 depth measurements and 53 density measurements during April, May and June, 1997 were interpolated using a regression tree / co-krig model, with independent variables of average incoming solar radiation, elevation, slope and maximum upwind slope. The basin was clustered into 7 elevation / average-solar-radiation zones for SWE accuracy assessment. Model simulations did a poor job at estimating the spatial distribution of SWE. Basin clusters where the solar radiative flux dominated the melt flux were simulated more accurately than those dominated by the turbulent fluxes or the longwave radiative flux.

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Forecasting of wet snow avalanche activity: Proof of concept and operational implementation

NASA Astrophysics Data System (ADS)

Gobiet, Andreas; Jöbstl, Lisa; Rieder, Hannes; Bellaire, Sascha; Mitterer, Christoph

2017-04-01

State-of-the-art tools for the operational assessment of avalanche danger include field observations, recordings from automatic weather stations, meteorological analyses and forecasts, and recently also indices derived from snowpack models. In particular, an index for identifying the onset of wet-snow avalanche cycles (LWCindex), has been demonstrated to be useful. However, its value for operational avalanche forecasting is currently limited, since detailed, physically based snowpack models are usually driven by meteorological data from automatic weather stations only and have therefore no prognostic ability. Since avalanche risk management heavily relies on timely information and early warnings, many avalanche services in Europe nowadays start issuing forecasts for the following days, instead of the traditional assessment of the current avalanche danger. In this context, the prognostic operation of detailed snowpack models has recently been objective of extensive research. In this study a new, observationally constrained setup for forecasting the onset of wet-snow avalanche cycles with the detailed snow cover model SNOWPACK is presented and evaluated. Based on data from weather stations and different numerical weather prediction models, we demonstrate that forecasts of the LWCindex as indicator for wet-snow avalanche cycles can be useful for operational warning services, but is so far not reliable enough to be used as single warning tool without considering other factors. Therefore, further development currently focuses on the improvement of the forecasts by applying ensemble techniques and suitable post processing approaches to the output of numerical weather prediction models. In parallel, the prognostic meteo-snow model chain is operationally used by two regional avalanche warning services in Austria since winter 2016/2017 for the first time. Experiences from the first operational season and first results from current model developments will be reported.

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.

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.

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.

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.

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.

Epidemic of fractures during a period of snow and ice: has anything changed 33 years on?

PubMed Central

Al-Azzani, Waheeb; Adam Maliq Mak, Danial; Hodgson, Paul; Williams, Rhodri

2016-01-01

Objectives We reproduced a frequently cited study that was published in the British Medical Journal (BMJ) in 1981 assessing the extent of ‘snow-and-ice’ fractures during the winter period. Setting This study aims to provide an insight into how things have changed within the same emergency department (ED) by comparing the findings of the BMJ paper published 33 years ago with the present date. Participants As per the original study, all patients presenting to the ED with a radiological evidence of fracture during three different 4-day periods were included. The three 4-day periods included 4 days of snow-and-ice conditions and two control 4-day periods when snow and ice was not present; the first was 4 days within the same year, with a similar amount of sunshine hours, and the second was 4 days 1 calendar year later. Primary and secondary outcome measures To identify the frequency, distribution and pattern of fractures sustained in snow-and-ice conditions compared to control conditions as well as comparisons with the index study 33 years ago. Results A total of 293 patients with fractures were identified. Overall, there was a 2.20 (CI 1.7 to 3.0, p <0.01) increase in risk of fracture during snow-and-ice periods compared to control conditions. There was an increase (p <0.01) of fractures of the arm, forearm and wrist (RR 3.2 (CI 1.4 to 7.6) and 2.9 (CI 1.5 to 5.4) respectively). Conclusions While the relative risk was not of the magnitude 33 years ago, the overall number of patients presenting with a fracture during snow-and-ice conditions remains more than double compared to control conditions. This highlights the need for improved understanding of the impact of increased fracture burden on hospitals and more effective preventative measures. PMID:27630066

[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.

MODIS Snow and Sea Ice Products

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

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.

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

PubMed

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

2016-03-01

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

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

NASA Astrophysics Data System (ADS)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

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

Soil erosion by snow gliding - a first quantification attempt in a subalpine area in Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-09-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959-2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha-1 yr-1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha-1 yr-1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.

Snow mass and river flows modelled using GRACE total water storage observations

NASA Astrophysics Data System (ADS)

Wang, S.

2017-12-01

Snow mass and river flow measurements are difficult and less accurate in cold regions due to the hash environment. Floods in cold regions are commonly a result of snowmelt during the spring break-up. Flooding is projected to increase with climate change in many parts of the world. Forecasting floods from snowmelt remains a challenge due to scarce and quality issues in basin-scale snow observations and lack of knowledge for cold region hydrological processes. This study developed a model for estimating basin-level snow mass (snow water equivalent SWE) and river flows using the total water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The SWE estimation is based on mass balance approach which is independent of in situ snow gauge observations, thus largely eliminates the limitations and uncertainties with traditional in situ or remote sensing snow estimates. The model forecasts river flows by simulating surface runoff from snowmelt and the corresponding baseflow from groundwater discharge. Snowmelt is predicted using a temperature index model. Baseflow is predicted using a modified linear reservoir model. The model also quantifies the hysteresis between the snowmelt and the streamflow rates, or the lump time for water travel in the basin. The model was applied to the Red River Basin, the Mackenzie River Basin, and the Hudson Bay Lowland Basins in Canada. The predicted river flows were compared with the observed values at downstream hydrometric stations. The results were also compared to that for the Lower Fraser River obtained in a separate study to help better understand the roles of environmental factors in determining flood and their variations with different hydroclimatic conditions. This study advances the applications of space-based time-variable gravity measurements in cold region snow mass estimation, river flow and flood forecasting. It demonstrates a relatively simple method that only needs GRACE TWS and temperature data for river flow or flood forecasting. The model can be particularly useful for regions with spare observation networks, and can be used in combination with other available methods to help improve the accuracy in river flow and flood forecasting over cold regions.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

Knowledge about snow distribution and snow accumulation patterns is important and valuable for different applications such as the prediction of seasonal water resources or avalanche forecasting. Furthermore, accumulated snow on the ground is an important ground truth for validating meteorological and climatological model predictions of precipitation in high mountains and polar regions. Snow accumulation patterns are determined by many different processes from ice crystal nucleation in clouds to snow redistribution by wind and avalanches. In between, snow precipitation undergoes different dynamical and microphysical processes, such as ice crystal growth, aggregation and riming, which determine the growth of individual particles and thereby influence the intensity and structure of the snowfall event. In alpine terrain the interaction of different processes and the topography (e.g. lifting condensation and low level cloud formation, which may result in a seeder-feeder effect) may lead to orographic enhancement of precipitation. Furthermore, the redistribution of snow particles in the air by wind results in preferential deposition of precipitation. Even though orographic enhancement is addressed in numerous studies, the relative importance of micro-physical and dynamically induced mechanisms on local snowfall amounts and especially snow accumulation patterns is hardly known. To better understand the relative importance of different processes on snow precipitation and accumulation we analyze snowfall and snow accumulation between January and March 2016 in Davos (Switzerland). We compare MeteoSwiss operational weather radar measurements on Weissfluhgipfel to a spatially continuous snow accumulation map derived from airborne digital sensing (ADS) snow height for the area of Dischma valley in the vicinity of the weather radar. Additionally, we include snow height measurements from automatic snow stations close to the weather radar. Large-scale radar snow accumulation patterns show a snowfall gradient consistent with the prevailing wind direction. Deriving snow accumulation based on radar data is challenging as the close-ground precipitation patters cannot be resolved by the radar due to shielding and ground clutter in highly complex terrain. Nonetheless, radar measurements show distinct patterns of snowfall and accumulation, which may be the result of orographic enhancement. Station-based snow accumulation measurements are in reasonable agreement with the estimated large-scale radar snow accumulation. The ADS-based snow accumulation maps feature much smaller scale snow accumulation patterns likely due to close-ground wind effects and snow redistribution on top of an altitudinal gradient. To evaluate microphysical processes and patterns influenced by the topography we run a hydrometeor classification on the radar data. The relative importance of topographically induced effects on snow accumulation patterns is investigated based on vertical cross sections of hydrometeor data and corresponding snow accumulation.

Automatic Calibration of a Distributed Rainfall-Runoff Model, Using the Degree-Day Formulation for Snow Melting, Within DMIP2 Project

NASA Astrophysics Data System (ADS)

Frances, F.; Orozco, I.

2010-12-01

This work presents the assessment of the TETIS distributed hydrological model in mountain basins of the American and Carson rivers in Sierra Nevada (USA) at hourly time discretization, as part of the DMIP2 Project. In TETIS each cell of the spatial grid conceptualizes the water cycle using six tanks connected among them. The relationship between tanks depends on the case, although at the end in most situations, simple linear reservoirs and flow thresholds schemes are used with exceptional results (Vélez et al., 1999; Francés et al., 2002). In particular, within the snow tank, snow melting is based in this work on the simple degree-day method with spatial constant parameters. The TETIS model includes an automatic calibration module, based on the SCE-UA algorithm (Duan et al., 1992; Duan et al., 1994) and the model effective parameters are organized following a split structure, as presented by Francés and Benito (1995) and Francés et al. (2007). In this way, the calibration involves in TETIS up to 9 correction factors (CFs), which correct globally the different parameter maps instead of each parameter cell value, thus reducing drastically the number of variables to be calibrated. This strategy allows for a fast and agile modification in different hydrological processes preserving the spatial structure of each parameter map. With the snowmelt submodel, automatic model calibration was carried out in three steps, separating the calibration of rainfall-runoff and snowmelt parameters. In the first step, the automatic calibration of the CFs during the period 05/20/1990 to 07/31/1990 in the American River (without snow influence), gave a Nash-Sutcliffe Efficiency (NSE) index of 0.92. The calibration of the three degree-day parameters was done using all the SNOTEL stations in the American and Carson rivers. Finally, using previous calibrations as initial values, the complete calibration done in the Carson River for the period 10/01/1992 to 07/31/1993 gave a NSE index of 0.86. The temporal and spatial validation using five periods must be considered in both rivers excellent for discharges (NSEs higher than 0.76) and good for snow distribution (daily spatial coverage errors ranging from -10 to 27%). In conclusion, this work demonstrates: 1.- The viability of automatic calibration of distributed models, with the corresponding personal time saving and maximum exploitation of the available information. 2.- The good performance of the degree-day snowmelt formulation even at hourly time discretization, in spite of its simplicity.

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.

Assessing the Snow Advance Index as potential predictor of winter streamflow of the Iberian Peninsula Rivers

NASA Astrophysics Data System (ADS)

Hidalgo-Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2015-04-01

This study examines the ability of the Eurasian snow cover increase during the previous October as potential predictor of winter streamflow in the Iberian Peninsula Rivers. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauging stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 382 streamflow time series homogeneously distributed over the Iberian Peninsula, covering the period 1975-2008. For this streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. The recently proposed Snow Advance Index (SAI) was employed to monitor the snow cover increase during previous October. The stability of the correlations was the criterion followed to establish if SAI could be considered as potential predictor of winter streamflow at each gauging station. Winter streamflow is predicted using a linear regression model. A leave-one-out cross validation approach was adopted to create calibration and validations subsets. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS) and the Gerrity Skill Score (GSS) were used to evaluate the forecasting skill. From the 382 stations evaluated, significant and stable correlations with SAI were found in 238 stations, covering most of the IP (except for the Cantabrian and Mediterranean slopes). Some forecasting skill was found in 223 of them, being this skill moderate (RHO>0.44, RMSESS>10%, GSS>0.2) in 141 of them, and particularly good (RHO>0.5, RMSESS>20%, GSS>0.4) in 23. This study shows that the SAI of previous October is a reliable predictor of following winter streamflow for the Iberian Peninsula Rivers, providing useful information, which, in turn, helps in better management of water resources. KEYWORDS Snow Advance Index, streamflow, forecasting, Iberian Peninsula. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

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

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

USGS Publications Warehouse

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

2013-01-01

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.

Small-area snow surveys on the northern plains of North Dakota

USGS Publications Warehouse

Emerson, Douglas G.; Carroll, T.R.; Steppuhn, Harold

1985-01-01

Snow-cover data are needed for many facets of hydrology. The variation in snow cover over small areas is the focus of this study. The feasibility of using aerial surveys to obtain information on the snow water equivalent of the snow cover in order to minimize the necessity of labor intensive ground snow surveys was- evaluated. A low-flying aircraft was used to measure attenuations of natural terrestrial gamma radiation by snow cover. Aerial and ground snow surveys of eight 1-mile snow courses and one 4-mile snow course were used in the evaluation, with ground snow surveys used as the base to evaluate aerial data. Each of the 1-mile snow courses consisted of a single land use and all had the same terrain type (plane). The 4-mile snow course consists of a variety of land uses and the same terrain type (plane). Using the aerial snow-survey technique, the snow water equivalent of the 1-mile snow courses was. measured with three passes of the aircraft. Use of more than one pass did not improve the results. The mean absolute difference between the aerial- and ground-measured snow water equivalents for the 1-mile snow courses was 26 percent (0.77 inches). The aerial snow water equivalents determined for the 1-mile snow courses were used to estimate the variations in the snow water equivalents over the 4-mile snow course. The weighted mean absolute difference for the 4-mile snow course was 27 percent (0.8 inches). Variations in snow water equivalents could not be verified adequately by segmenting the aerial snow-survey data because of the uniformity found in the snow cover. On the 4-mile snow coirse, about two-thirds of the aerial snow-survey data agreed with the ground snow-survey data within the accuracy of the aerial technique ( + 0.5 inch of the mean snow water equivalent).

Detecting ice lenses and melt-refreeze crusts using satellite passive microwaves (Invited)

NASA Astrophysics Data System (ADS)

Montpetit, B.; Royer, A.; Roy, A.

2013-12-01

With recent winter climate warming in high latitude regions, rain-on-snow and melt-refreeze events are more frequent creating ice lenses or ice crusts at the surface or even within the snowpack through drainage. These ice layers create an impermeable ice barrier that reduces vegetation respiration and modifies snow properties due to the weak thermal diffusivity of ice. Winter mean soil temperatures increase due to latent heat being released during the freezing process. When ice layers freeze at the snow-soil interface, they can also affect the feeding habits of the northern wild life. Ice layers also significantly affect satellite passive microwave signals that are widely used to monitor the spatial and temporal evolution of snow. Here we present a method using satellite passive microwave brightness temperatures (Tb) to detect ice lenses and/or ice crusts within a snowpack. First the Microwave Emission Model for Layered Snowpacks (MEMLS) was validated to model Tb at 10.7, 19 and 37 GHz using in situ measurements taken in multiple sub-arctic environments where ice layers where observed. Through validated modeling, the effects of ice layer insertion were studied and an ice layer index was developed using the polarization ratio (PR) at all three frequencies. The developed ice index was then applied to satellite passive microwave signals for reported ice layer events.

Snow micro-structure at Kongsvegen glacier, Svalbard

NASA Astrophysics Data System (ADS)

Bilgeri, F.; Karner, F.; Steinkogler, W.; Fromm, R.; Obleitner, F.; Kohler, J.

2012-04-01

Measurements of physical snow properties have been performed at several sites at Kongsvegen glacier, which is a key Arctic glacier in western Spitzbergen (79N, 13E). The data were collected at six locations along the flow line of the glacier at different elevations (161 to 741m asl.) and describe snow that was deposited during winter 2010/11. We basically consider the vertical profiles of snow temperature, density, hardness, grain size and crystal shapes derived from standard stratigraphic methods (snow pits)and measurements using advanced instruments like Snow Micropen® and NIR imagery. Some parameters were measured repeatedly and with different instruments which proves a high quality as well as long-term and spatial representativeness of the data. The general snow conditions at the end of winter are characterized by a linear increase of snow depth and water equivalent with elevation. Snow hardness also increases with elevation while density remains remarkably constant. At most sites the snow temperature, density, hardness and grain size increase from the surface towards the snow-ice interface. The surface and the bottom layers stand out by specific changes in snow signature (crystal types) and delineate the bulk of the snow pack which itself features a rather complex layering. Comparison of the high-resolution profiles measured at different elevations at the glacier suggests some principal correlations of the signatures of hardness, grain size and crystal type. Thus, some major features (e.g. particularly hard layers) can be traced along the glacier, but the high-resolution layering can not straightforwardly be related from one site to the other. This basically reflects a locally different history of the snow pack in terms of precipitation events and post-depositional snow metamorphism. The issue is investigated more quantitatively by enhanced statistical processing of the observed signatures and simulation of the history of individual layers. These studies are supported by meteorological measurements at the snow observation sites.

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

Soil erosion by snow gliding - a first quantification attempt in a sub-alpine area, Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-03-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as soil erosion agent for four different land use/land cover types in a sub-alpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide deposits, the fallout radionuclide 137Cs, and modelling with the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model is suitable to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due to all erosion agents involved. Thus, we hypothesise that the soil erosion rates determined with the 137Cs method are higher and that the observed discrepancy between the soil erosion rate of RUSLE and the 137Cs method is related to snow gliding and sediment concentrations in the snow glide deposits. Cumulative snow glide distance was measured for the sites in the winter 2009/10 and modelled for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow glide distance ranged from 2 to 189 cm, with lower values at the north facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is important information with respect to conservation planning and expected land use changes in the Alps. Our hypothesis was confirmed: the difference of RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2= 0.64; p < 0.005) and snow sediment yields (R2 = 0.39; p = 0.13). A high difference (lower proportion of water erosion compared to total net erosion) was observed for high snow glide rates and vice versa. The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding is a key process impacting soil erosion pattern and magnitude in sub-alpine areas with similar topographic and climatic conditions.

Differences in Bacterial Diversity and Communities Between Glacial Snow and Glacial Soil on the Chongce Ice Cap, West Kunlun Mountains.

PubMed

Yang, Guang Li; Hou, Shu Gui; Le Baoge, Ri; Li, Zhi Guo; Xu, Hao; Liu, Ya Ping; Du, Wen Tao; Liu, Yong Qin

2016-11-04

A detailed understanding of microbial ecology in different supraglacial habitats is important due to the unprecedented speed of glacier retreat. Differences in bacterial diversity and community structure between glacial snow and glacial soil on the Chongce Ice Cap were assessed using 454 pyrosequencing. Based on rarefaction curves, Chao1, ACE, and Shannon indices, we found that bacterial diversity in glacial snow was lower than that in glacial soil. Principal coordinate analysis (PCoA) and heatmap analysis indicated that there were major differences in bacterial communities between glacial snow and glacial soil. Most bacteria were different between the two habitats; however, there were some common bacteria shared between glacial snow and glacial soil. Some rare or functional bacterial resources were also present in the Chongce Ice Cap. These findings provide a preliminary understanding of the shifts in bacterial diversity and communities from glacial snow to glacial soil after the melting and inflow of glacial snow into glacial soil.

Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

USGS Publications Warehouse

Liang, D.; Xu, X.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.

2008-01-01

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

Intercomparison of Satellite-Derived Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Tait, Andrew B.; Foster, James L.; Chang, Alfred T. C.; Allen, Milan

1999-01-01

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40%. The highest percent change (less than 100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the percent change ranged from 0 to 40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Epidemic of fractures during a period of snow and ice: has anything changed 33 years on?

PubMed

Al-Azzani, Waheeb; Adam Maliq Mak, Danial; Hodgson, Paul; Williams, Rhodri

2016-09-14

We reproduced a frequently cited study that was published in the British Medical Journal (BMJ) in 1981 assessing the extent of 'snow-and-ice' fractures during the winter period. This study aims to provide an insight into how things have changed within the same emergency department (ED) by comparing the findings of the BMJ paper published 33 years ago with the present date. As per the original study, all patients presenting to the ED with a radiological evidence of fracture during three different 4-day periods were included. The three 4-day periods included 4 days of snow-and-ice conditions and two control 4-day periods when snow and ice was not present; the first was 4 days within the same year, with a similar amount of sunshine hours, and the second was 4 days 1 calendar year later. To identify the frequency, distribution and pattern of fractures sustained in snow-and-ice conditions compared to control conditions as well as comparisons with the index study 33 years ago. A total of 293 patients with fractures were identified. Overall, there was a 2.20 (CI 1.7 to 3.0, p <0.01) increase in risk of fracture during snow-and-ice periods compared to control conditions. There was an increase (p <0.01) of fractures of the arm, forearm and wrist (RR 3.2 (CI 1.4 to 7.6) and 2.9 (CI 1.5 to 5.4) respectively). While the relative risk was not of the magnitude 33 years ago, the overall number of patients presenting with a fracture during snow-and-ice conditions remains more than double compared to control conditions. This highlights the need for improved understanding of the impact of increased fracture burden on hospitals and more effective preventative measures. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

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

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

Impacts of the 2014 Drought on Vegetation Processes in the Sierra Nevada of California

NASA Astrophysics Data System (ADS)

Loik, M. E.; Wade, C. E.; Reed, C. C.

2014-12-01

Sierra Nevada snowpack provides over 60 percent of California's freshwater supplies. The drought of 2014 has been unprecedented in the state's history, and followed below-average precipitation for the hydrologic years 2012 and 2013. Record-low precipitation has resulted in minimal Sierra Nevada snow pack and runoff, and massive reductions in reservoir storage, which has triggered widespread drought adaptation measures for one of the world's largest economies. We assessed the impacts of the 2014 drought on vegetation processes in the headwaters of the Owens River, which is one of the main watersheds for the city of Los Angeles. We monitored water relations, photosynthesis, growth and Leaf Area Index of tree, shrub, herb, and grass species. In order to better understand the effects of drought, we examined responses to watering manipulations, long-term snow fences, elevation gradient analysis, and comparisons to previous wetter years. 1 April 2014 snow pack depth was 330 mm (average for 1928 - 2012 = 1344 mm, CV = 49%). Despite widespread mortality of Pinus jeffreyi saplings (mean 1.5 m tall) at 2300 m, older trees as well as saplings of Pinus contorta showed new growth. There were no significant differences in water potential (Ψ) for the two conifer species in a wet year (2006, 1 April snow depth = 2240 mm) vs. 2014. Water potential for P. contorta in 2014 was higher at 2900 m than at 2300 m but photosynthetic CO2 assimilation (A) and stomatal conductance (gs), were not different. By contrast, Ψ, A, gs, Vcmax and Jmax for the widespread shrub Artemisia tridentata increased along a gradient from 2100 m to 2900 m in 2014. Watering only significantly increased these photosynthetic parameters at the lowest, driest elevation. At the middle elevation, Leaf Area Index in 2014 was about 20% of the 2006 value for the N-fixing shrub Purshia tridentata. Results show reductions in photosynthesis and growth for some species but not others in response to the severe drought conditions of 2014. The ability to tolerate drought may be due to utilization of deep water for some species, or an ability to survive and grow on very little precipitation for other species. Incorporation of functional group survival, photosynthesis and growth responses to severe, ongoing drought stress should help to improve global models of carbon cycling.

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.

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.

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.

Observed Differences between North American Snow Extent and Snow Depth Variability

NASA Astrophysics Data System (ADS)

Ge, Y.; Gong, G.

2006-12-01

Snow extent and snow depth are two related characteristics of a snowpack, but they need not be mutually consistent. Differences between these two variables at local scales are readily apparent. However at larger scales which interact with atmospheric circulation and climate, snow extent is typically the variable used, while snow depth is often assumed to be minor and/or mutually consistent compared to snow extent, though this is rarely verified. In this study, a new regional/continental-scale gridded dataset derived from field observations is utilized to quantitatively evaluate the relationship between snow extent and snow depth over North America. Various statistical methods are applied to assess the mutual consistency of monthly snow depth vs. snow extent, including correlations, composites and principal components. Results indicate that snow depth variations are significant in their own rights, and that depth and extent anomalies are largely unrelated, especially over broad high latitude regions north of the snowline. In the vicinity of the snowline, where precipitation and ablation can affect both snow extent and snow depth, the two variables vary concurrently, especially in autumn and spring. It is also found that deeper winter snow translates into larger snow-covered area in the subsequent spring/summer season, which suggests a possible influence of winter snow depth on summer climate. The observed lack of mutual consistency at continental/regional scales suggests that snowpack depth variations may be of sufficiently large magnitude, spatial scope and temporal duration to influence regional-hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations.

Parameterization of single-scattering properties of snow

NASA Astrophysics Data System (ADS)

Räisänen, P.; Kokhanovsky, A.; Guyot, G.; Jourdan, O.; Nousiainen, T.

2015-02-01

Snow consists of non-spherical grains of various shapes and sizes. Still, in many radiative transfer applications, single-scattering properties of snow have been based on the assumption of spherical grains. More recently, second-generation Koch fractals have been employed. While they produce a relatively flat phase function typical of deformed non-spherical particles, this is still a rather ad-hoc choice. Here, angular scattering measurements for blowing snow conducted during the CLimate IMpacts of Short-Lived pollutants In the Polar region (CLIMSLIP) campaign at Ny Ålesund, Svalbard, are used to construct a reference phase function for snow. Based on this phase function, an optimized habit combination (OHC) consisting of severely rough (SR) droxtals, aggregates of SR plates and strongly distorted Koch fractals is selected. The single-scattering properties of snow are then computed for the OHC as a function of wavelength λ and snow grain volume-to-projected area equivalent radius rvp. Parameterization equations are developed for λ = 0.199-2.7 μm and rvp = 10-2000 μm, which express the single-scattering co-albedo β, the asymmetry parameter g and the phase function P11 as functions of the size parameter and the real and imaginary parts of the refractive index. The parameterizations are analytic and simple to use in radiative transfer models. Compared to the reference values computed for the OHC, the accuracy of the parameterization is very high for β and g. This is also true for the phase function parameterization, except for strongly absorbing cases (β > 0.3). Finally, we consider snow albedo and reflected radiances for the suggested snow optics parameterization, making comparisons to spheres and distorted Koch fractals.

Parameterization of single-scattering properties of snow

NASA Astrophysics Data System (ADS)

Räisänen, P.; Kokhanovsky, A.; Guyot, G.; Jourdan, O.; Nousiainen, T.

2015-06-01

Snow consists of non-spherical grains of various shapes and sizes. Still, in many radiative transfer applications, single-scattering properties of snow have been based on the assumption of spherical grains. More recently, second-generation Koch fractals have been employed. While they produce a relatively flat phase function typical of deformed non-spherical particles, this is still a rather ad hoc choice. Here, angular scattering measurements for blowing snow conducted during the CLimate IMpacts of Short-Lived pollutants In the Polar region (CLIMSLIP) campaign at Ny Ålesund, Svalbard, are used to construct a reference phase function for snow. Based on this phase function, an optimized habit combination (OHC) consisting of severely rough (SR) droxtals, aggregates of SR plates and strongly distorted Koch fractals is selected. The single-scattering properties of snow are then computed for the OHC as a function of wavelength λ and snow grain volume-to-projected area equivalent radius rvp. Parameterization equations are developed for λ = 0.199-2.7 μm and rvp = 10-2000 μm, which express the single-scattering co-albedo β, the asymmetry parameter g and the phase function P11 as functions of the size parameter and the real and imaginary parts of the refractive index. The parameterizations are analytic and simple to use in radiative transfer models. Compared to the reference values computed for the OHC, the accuracy of the parameterization is very high for β and g. This is also true for the phase function parameterization, except for strongly absorbing cases (β > 0.3). Finally, we consider snow albedo and reflected radiances for the suggested snow optics parameterization, making comparisons to spheres and distorted Koch fractals.

Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

NASA Astrophysics Data System (ADS)

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

2016-12-01

Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications for improving land surface models that do not currently resolve or parameterize fine-scale canopy structure. In addition, these findings have implications for understanding the potential of different forest management strategies (i.e. thinning) based on local topography and climate to maximize the amount and retention of snow.

Multispectral index for the remote detection of human skin signatures

NASA Astrophysics Data System (ADS)

Baranoski, Gladimir V. G.; Chen, Tenn F.

2015-07-01

We propose a multispectral index to assist the detection of human signatures in complex natural environments. Differently from previously proposed indices, it takes into account the spectral responses of human skin not only in the near infrared, but also in the visible region of the light spectrum. As a result, it can contribute to mitigate the chances of false alarms during time-critical search and rescue operations carried out in such environments. Our investigation is supported by the use of reflectance data measured for different skin specimens and natural materials such as sand, ocean water, melting snow, and forest vegetation. We believe that the observations reported in this work can be incorporated into the design of more effective procedures and devices for the differentiation of human targets from background materials commonly found in nature.

Snow cover distribution over elevation zones in a mountainous catchment

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Panagopoulos, Y.

2009-04-01

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

Effects of different temperature treatments on biological ice nuclei in snow samples

NASA Astrophysics Data System (ADS)

Hara, Kazutaka; Maki, Teruya; Kakikawa, Makiko; Kobayashi, Fumihisa; Matsuki, Atsushi

2016-09-01

The heat tolerance of biological ice nucleation activity (INA) depends on their types. Different temperature treatments may cause varying degrees of inactivation on biological ice nuclei (IN) in precipitation samples. In this study, we measured IN concentration and bacterial INA in snow samples using a drop freezing assay, and compared the results for unheated snow and snow treated at 40 °C and 90 °C. At a measured temperature of -7 °C, the concentration of IN in untreated snow was 100-570 L-1, whereas the concentration in snow treated at 40 °C and 90 °C was 31-270 L-1 and 2.5-14 L-1, respectively. In the present study, heat sensitive IN inactivated by heating at 40 °C were predominant, and ranged 23-78% of IN at -7 °C compared with untreated samples. Ice nucleation active Pseudomonas strains were also isolated from the snow samples, and heating at 40 °C and 90 °C inactivated these microorganisms. Consequently, different temperature treatments induced varying degrees of inactivation on IN in snow samples. Differences in the concentration of IN across a range of treatment temperatures might reflect the abundance of different heat sensitive biological IN components.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

Snow drought in western U.S. mountains: proximate causes, regional differences, and implications for streamflow and forests

NASA Astrophysics Data System (ADS)

Harpold, A. A.; Dettinger, M. D.; Rajagopal, S.

2017-12-01

Although drought is a recurring problem, recent extreme snow droughts have refocused attention on the interaction of meteorological extremes and snow accumulation in mountains. Only recently have two distinct types of snow drought been defined that help to differentiate a variety of water management implications. Dry snow drought is caused by deficits of winter precipitation and resulting low snow accumulation. Warm snow drought is characterized by temperature extremes causing faster and earlier snowmelt and/or shifts from snow to rain. Here we use 462 Snow Telemetry (SNOTEL) sites in the western U.S. to quantify snow drought as 75% of the long-term average snow water equivalent (SWE). We further subdivide dry snow droughts using SWE to winter precipitation (SWE/P) ratios that were near normal from warm snow droughts where SWE/P ratios were below normal and experienced SWE losses (warm-melt) or received unusual amounts of winter rain (warm-rain snow drought). Using this method we show clear regional patterns in the type and frequency of snow drought. Warm snow droughts on April 1st were most common in all but the highest elevations of the Rocky Mountains. The middle Rocky Mountains sites also experienced less frequent snow drought than the maritime and southern mountains. Warm-melt snow droughts were the primary cause in the Cascade Mountains and the southwestern sites, with only the Sierra Nevada and Wasatch mountains showing consistent warm-rain snow drought. These regional differences limited the predictability of snow drought with simple models of temperature and precipitation. We will discuss the effects of snow drought type and magnitude on streamflow forecasting skill using empirical relationships developed by water management agencies. We expect these types of snow drought to differentially affect streamflow regime and its predictability, as well as forest growth and mortality during and following drought.

Modeling the Soil Water and Energy Balance of a Mixed Grass Rangeland and Evaluating a Soil Water Based Drought Index in Wyoming

NASA Astrophysics Data System (ADS)

Engda, T. A.; Kelleners, T. J.; Paige, G. B.

2013-12-01

Soil water content plays an important role in the complex interaction between terrestrial ecosystems and the atmosphere. Automated soil water content sensing is increasingly being used to assess agricultural drought conditions. A one-dimensional vertical model that calculates incoming solar radiation, canopy energy balance, surface energy balance, snow pack dynamics, soil water flow, snow-soil heat exchange is applied to calculate water flow and heat transport in a Rangeland soil located near Lingel, Wyoming. The model is calibrated and validated using three years of measured soil water content data. Long-term average soil water content dynamics are calculated using a 30 year historical data record. The difference between long-term average soil water content and observed soil water content is compared with plant biomass to evaluate the usefulness of soil water content as a drought indicator. Strong correlation between soil moisture surplus/deficit and plant biomass may prove our hypothesis that soil water content is a good indicator of drought conditions. Soil moisture based drought index is calculated using modeled and measured soil water data input and is compared with measured plant biomass data. A drought index that captures local drought conditions proves the importance of a soil water monitoring network for Wyoming Rangelands to fill the gap between large scale drought indices, which are not detailed enough to assess conditions at local level, and local drought conditions. Results from a combined soil moisture monitoring and computer modeling, and soil water based drought index soil are presented to quantify vertical soil water flow, heat transport, historical soil water variations and drought conditions in the study area.

Response of seasonal soil freeze depth to climate change across China

NASA Astrophysics Data System (ADS)

Peng, Xiaoqing; Zhang, Tingjun; Frauenfeld, Oliver W.; Wang, Kang; Cao, Bin; Zhong, Xinyue; Su, Hang; Mu, Cuicui

2017-05-01

The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of -0.18 ± 0.03 cm yr-1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967-2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr-1 in most parts of China during 1950-2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze-thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

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.

[Effects of snow cover on water soluble and organic solvent soluble components during foliar litter decomposition in an alpine forest].

PubMed

Xu, Li-Ya; Yang, Wan-Qin; Li, Han; Ni, Xiang-Yin; He, Jie; Wu, Fu-Zhong

2014-11-01

Seasonal snow cover may change the characteristics of freezing, leaching and freeze-thaw cycles in the scenario of climate change, and then play important roles in the dynamics of water soluble and organic solvent soluble components during foliar litter decomposition in the alpine forest. Therefore, a field litterbag experiment was conducted in an alpine forest in western Sichuan, China. The foliar litterbags of typical tree species (birch, cypress, larch and fir) and shrub species (willow and azalea) were placed on the forest floor under different snow cover thickness (deep snow, medium snow, thin snow and no snow). The litterbags were sampled at snow formation stage, snow cover stage and snow melting stage in winter. The results showed that the content of water soluble components from six foliar litters decreased at snow formation stage and snow melting stage, but increased at snow cover stage as litter decomposition proceeded in the winter. Besides the content of organic solvent soluble components from azalea foliar litter increased at snow cover stage, the content of organic solvent soluble components from the other five foliar litters kept a continue decreasing tendency in the winter. Compared with the content of organic solvent soluble components, the content of water soluble components was affected more strongly by snow cover thickness, especially at snow formation stage and snow cover stage. Compared with the thicker snow covers, the thin snow cover promoted the decrease of water soluble component contents from willow and azalea foliar litter and restrain the decrease of water soluble component content from cypress foliar litter. Few changes in the content of water soluble components from birch, fir and larch foliar litter were observed under the different thicknesses of snow cover. The results suggested that the effects of snow cover on the contents of water soluble and organic solvent soluble components during litter decomposition would be controlled by litter quality.

21st century projections of snowfall and winter severity across central-eastern North America

NASA Astrophysics Data System (ADS)

Notaro, M.; Lorenz, D. J.; Hoving, C.; Schummer, M.

2014-12-01

Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the mid- and late 21st century. Here, we use projections of a cumulative WSI (CWSI) known to influence autumn-winter waterfowl migration to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes Basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December-January), leading to a delay in the mean onset of the snow season. Due to a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common, but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the -5°C isotherm in DJFM, around 44°N. The CWSI is projected to decline substantially during December-January, leading to increased likelihood of delays in timing and intensity of autumn-winter waterfowl migrations.

Impact of snow gliding on soil redistribution for a sub-alpine area in Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Alewell, C.

2013-07-01

The aim of this study is to assess the importance of snow gliding as soil erosion agent for four different land use/land cover types in a sub-alpine area in Switzerland. The 14 investigated sites are located close to the valley bottom at approximately 1500 m a.s.l., while the elevation of the surrounding mountain ranges is about 2500 m a.s.l. We used two different approaches to estimate soil erosion rates: the fallout radionuclide 137Cs and the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model is suitable to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due to all erosion agents involved. Thus, we hypothesise that the soil erosion rates determined with the 137Cs method are higher and that the observed discrepancy between the erosion rate of RUSLE and the 137Cs method is related to snow gliding. Cumulative snow glide distance was measured for the sites in the winter 2009/2010 and modelled for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow glide distance range from 0 to 189 cm with lower values for the north exposed slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected land use changes in the Alps. Our hypothesis was confirmed, the difference of RUSLE and 137Cs erosion rates was correlated to the measured snow glide distance (R2 = 0.73; p < 0.005). A high difference (lower proportion of water erosion compared to total net erosion) was observed for high snow glide rates and vice versa. The SSGM reproduced the relative difference of the measured snow glide values between different land use/land cover types. The resulting map highlights the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding is a key process impacting soil erosion pattern and magnitude in sub-alpine areas with similar topographic and climatic conditions.

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.

Airborne Spectral Measurements of Surface-Atmosphere Anisotropy for Arctic Sea Ice and Tundra

NASA Technical Reports Server (NTRS)

Arnold, G. Thomas; Tsay, Si-Chee; King, Michael D.; Li, Jason Y.; Soulen, Peter F.

1999-01-01

Angular distributions of spectral reflectance for four common arctic surfaces: snow-covered sea ice, melt-season sea ice, snow-covered tundra, and tundra shortly after snowmelt were measured using an aircraft based, high angular resolution (1-degree) multispectral radiometer. Results indicate bidirectional reflectance is higher for snow-covered sea ice than melt-season sea ice at all wavelengths between 0.47 and 2.3 pm, with the difference increasing with wavelength. Bidirectional reflectance of snow-covered tundra is higher than for snow-free tundra for measurements less than 1.64 pm, with the difference decreasing with wavelength. Bidirectional reflectance patterns of all measured surfaces show maximum reflectance in the forward scattering direction of the principal plane, with identifiable specular reflection for the melt-season sea ice and snow-free tundra cases. The snow-free tundra had the most significant backscatter, and the melt-season sea ice the least. For sea ice, bidirectional reflectance changes due to snowmelt were more significant than differences among the different types of melt-season sea ice. Also the spectral-hemispherical (plane) albedo of each measured arctic surface was computed. Comparing measured nadir reflectance to albedo for sea ice and snow-covered tundra shows albedo underestimated 5-40%, with the largest bias at wavelengths beyond 1 pm. For snow-free tundra, nadir reflectance underestimates plane albedo by about 30-50%.

Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

NASA Astrophysics Data System (ADS)

Otto, M.; Scherer, D.; Richters, J.

2011-05-01

High Altitude Wetlands of the Andes (HAWA) belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km2 situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6 %). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual changes in spatial extend of perennial HAWA indicate alterations in annual water supply generated from snow melt.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

NASA Astrophysics Data System (ADS)

Hammond, John C.; Saavedra, Freddy A.; Kampf, Stephanie K.

2018-04-01

With climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins.

Laws of distribution of the snow cover on the greater Caucasus (Soviet Union)

NASA Technical Reports Server (NTRS)

Gurtovaya, Y. Y.; Sulakvelidze, G. K.; Yashina, A. V.

1985-01-01

The laws of the distribution of the snow cover on the mountains of the greater Caucasus are discussed. It is shown that an extremely unequal distribution of the snow cover is caused by the complex orography of this territory, the diversity of climatic conditions and by the difference in altitude. Regions of constant, variable and unstable snow cover are distinguished because of the clearly marked division into altitude layers, each of which is characterized by climatic differences in the nature of the snow accumulation.

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.

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.

Integrated assessment on the vulnerability of animal husbandry to snow disasters under climate change in the Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wei, Yanqiang; Wang, Shijin; Fang, Yiping; Nawaz, Zain

2017-10-01

Animal husbandry is a dominant and traditional source of livelihood and income in the Qinghai-Tibetan Plateau. The Qinghai-Tibetan Plateau is the third largest snow covered area in China and is one of the main snow disaster regions in the world. It is thus imperative to urgently address the issue of vulnerability of the animal husbandry sector to snow disasters for disaster mitigation and adaptation under growing risk of these disasters as a result of future climate change. However, there is very few literature reported on the vulnerability of animal husbandry in the Qinghai-Tibetan Plateau. This assessment aims at identifying vulnerability of animal husbandry at spatial scale and to identify the reasons for vulnerability for adaptive planning and disaster mitigation. First, historical snow disaster characteristics have been analyzed and used for the spatial weight for vulnerability assessment. Second, indicator-based vulnerability assessment model and indicator system have been established. We combined risk of snow hazard, sensitivity of livestock to disaster, physical exposure to disaster, and community capacity to adapt to snow disaster in an integrated vulnerability index. Lastly, vulnerability of animal husbandry to snow disaster on the Qinghai-Tibetan Plateau has been evaluated. Results indicate that high vulnerabilities are mainly concentrated in the eastern and central plateau and that vulnerability decreases gradually from the east to the west. Due to global warming, the vulnerability trend has eased to some extent during the last few decades. High livestock density exposure to blizzard-prone regions and shortages of livestock barn and forage are the main reasons of high vulnerability. The conclusion emphasizes the important role of the local government and community to help local pastoralists for reducing vulnerability to snow disaster and frozen hazard. The approaches presented in this paper can be used for snow disaster mitigation, resilience enhancement and effectively reducing vulnerability to natural hazards in other regions.

Global mountain snow and ice loss driven by dust and black carbon radiative forcing

NASA Astrophysics Data System (ADS)

Painter, T. H.

2014-12-01

Changes in mountain snow and glaciers have been our strongest indicators of the effects of changing climate. Earlier melt of snow and losses of glacier mass have perturbed regional water cycling, regional climate, and ecosystem dynamics, and contributed strongly to sea level rise. Recent studies however have revealed that in some regions, the reduction of albedo by light absorbing impurities in snow and ice such as dust and black carbon can be distinctly more powerful than regional warming at melting snow and ice. In the Rocky Mountains, dust deposition has increased 5 to 7 fold in the last 150 years, leading to ~3 weeks earlier loss of snow cover from forced melt. In absolute terms, in some years dust radiative forcing there can shorten snow cover duration by nearly two months. Remote sensing retrievals are beginning to reveal powerful dust and black carbon radiative forcing in the Hindu Kush through Himalaya. In light of recent ice cores that show pronounced increases in loading of dust and BC during the Anthropocene, these forcings may have contributed far more to glacier retreat than previously thought. For example, we have shown that the paradoxical end of the Little Ice Age in the European Alps beginning around 1850 (when glaciers began to retreat but temperatures continued to decline and precipitation was unchanged) very likely was driven by the massive increases in deposition to snow and ice of black carbon from industrialization in surrounding nations. A more robust understanding of changes in mountain snow and ice during the Anthropocene requires that we move past simplistic treatments (e.g. temperature-index modeling) to energy balance approaches that assess changes in the individual forcings such as the most powerful component for melt - net solar radiation. Remote sensing retrievals from imaging spectrometers and multispectral sensors are giving us more powerful insights into the time-space variation of snow and ice albedo.

Unusual radar echoes from the Greenland ice sheet

NASA Technical Reports Server (NTRS)

Rignot, E. J.; Vanzyl, J. J.; Ostro, S. J.; Jezek, K. C.

1993-01-01

In June 1991, the NASA/Jet Propulsion Laboratory airborne synthetic-aperture radar (AIRSAR) instrument collected the first calibrated data set of multifrequency, polarimetric, radar observations of the Greenland ice sheet. At the time of the AIRSAR overflight, ground teams recorded the snow and firn (old snow) stratigraphy, grain size, density, and temperature at ice camps in three of the four snow zones identified by glaciologists to characterize four different degrees of summer melting of the Greenland ice sheet. The four snow zones are: (1) the dry-snow zone, at high elevation, where melting rarely occurs; (2) the percolation zone, where summer melting generates water that percolates down through the cold, porous, dry snow and then refreezes in place to form massive layers and pipes of solid ice; (3) the soaked-snow zone where melting saturates the snow with liquid water and forms standing lakes; and (4) the ablation zone, at the lowest elevations, where melting is vigorous enough to remove the seasonal snow cover and ablate the glacier ice. There is interest in mapping the spatial extent and temporal variability of these different snow zones repeatedly by using remote sensing techniques. The objectives of the 1991 experiment were to study changes in radar scattering properties across the different melting zones of the Greenland ice sheet, and relate the radar properties of the ice sheet to the snow and firn physical properties via relevant scattering mechanisms. Here, we present an analysis of the unusual radar echoes measured from the percolation zone.

The Contribution to High Asia Runoff from Ice and Snow (CHARIS): Understanding the source and trends of cryospheric contributions to the water balance

NASA Astrophysics Data System (ADS)

Rittger, K.; Armstrong, R. L.; Bair, N.; Racoviteanu, A.; Brodzik, M. J.; Hill, A. F.; Wilson, A. M.; Khan, A. L.; Ramage, J. M.; Khalsa, S. J. S.; Barrett, A. P.; Raup, B. H.; Painter, T. H.

2017-12-01

The Contribution to High Asia Runoff from Ice and Snow, or CHARIS, project is systematically assessing the role that glaciers and seasonal snow play in the freshwater resources of Central and South Asia. The study area encompasses roughly 3 million square kilometers of the Himalaya, Karakoram, Hindu Kush, Pamir and Tien Shan mountain ranges that drain to five major rivers: the Ganges, Brahmaputra, Indus, Amu Darya and Syr Darya. We estimate daily snow and glacier ice contributions to the water balance. Our automated partitioning method generates daily maps of 1) snow over ice (SOI), 2) exposed glacier ice (EGI), 3) debris covered glacier ice (DGI) and 4) snow over land (SOL) using fractional snow cover, snow grain size, and annual minimum ice and snow from the 500 m MODIS-derived MODSCAG and MODICE products. Maps of snow and ice cover are validated using high-resolution (30 m) maps of snow, ice, and debris cover from Landsat. The probability of detection is 0.91 and precision is 0.85 for MODICE. We examine trends in annual and monthly snow and ice maps and use daily maps as inputs to a calibrated temperature-index model and an uncalibrated energy balance model, ParBal. Melt model results and measurements of isotopes and specific ions used as an independent validation of melt modeling indicate a sharp geographic contrast in the role of snow and ice melt to downstream water supplies between the arid Tien Shan and Pamir ranges of Central Asia, where melt water dominates dry season flows, and the monsoon influenced central and eastern Himalaya where rain controls runoff. We also compare melt onset and duration from the melt models to the Calibrated, Enhanced Resolution Passive Microwave Brightness Temperature Earth Science Data Record. Trend analysis of annual and monthly area of permanent snow and ice (the union of SOI and EGI) for 2000 to 2016 shows statistically significant negative trends in the Ganges and Brahmaputra basins. There are no statistically significant trends in permanent snow and ice in the other basins and no statistically significant trends in SOL, the renewable and seasonal component of snow and ice cover, in any of the five basins. This work gives a better understanding of the current hydrologic regime to guide realistic estimates of the future availability and vulnerability of water resources in these regions.

The spatial and seasonal variations in mineral particle composition on the snow surface and their possible effect on snow algae in the Tateyama Mountains, Japan

NASA Astrophysics Data System (ADS)

Umino, T.; Takeuchi, N.

2012-12-01

Snow algae are autotrophic microbes and play an important role as primary producers in food chain of glaciers and snowfield. Although their reproduction requires nutrients, snow and ice is extreamly poor in nutrients. One of the possible sources of nutrients is mineral particles blown by wind and deposited on the snow. They may contain variable elements and provide nutrients for snow algae. However, we scarcely know about the relationship between mineral particles and snow algae. In this study, we described spatial and seasonal variations in mineral particle composition and also snow algae on the snow surface in the Tateyama Mountains, Japan. We discussed the possible effect of mineral particles on snow algae. Tateyama Mountains are located in middle-north part of Japan ranging from 2000 - 3000 m above sea level and have heavy snow fall in winter due to strong monsoon wind from Siberia. The snow starts to thaw in April and remains until late summer as perennial snow patches in some valleys. Kosa eolian dust is known to be blown from Chinese deserts and deposited on the snow every spring. Also, snow algal bloom is often observed as red-colored snow in summer. Samples were collected from the snow surface during summer in 2008 - 2011 at four different sites (A - D) in this area. We examined them by X-ray diffractometer (XRD) and microscope to obtain composition of mineral particles and structure of snow algae community. XRD analysis revealed mineral particles on the snow surface were mainly composed of quartz, plagioclase, hornblende, mica, chlorite, and amorphous. In April, mineral compositions of all sites were almost similar to that of Kosa eolian dust, indicating that these mineral particles were derived from Chinese arid regions. After May, the mineral compositions changed according to sites. The proportion of hornblende at the site C significantly increased whereas that of mica increased at the site D. Since the site C was located near geological features mainly composed of hornblende, the supply of mineral particles from local sources is likely to increase after May. These results indicate mineral particles on the snow surface were blown from distant Chinese deserts in April when snow covered entire ground surface, and they may change to be supplied from the local exposed ground surface after May. Microscopy revealed over 90 % of snow algal cells attached mineral particles on their surfaces, suggesting mineral particles may be beneficial for their growth. Furthermore, algal community structure was different among study sites. The difference may be due to different composition of mineral particles. This suggests composition of mineral particles may affect algal community structure. Hornblende, which was abundant at the site C, is usually rich in Fe or Mg, and it may affect algal growth in the site.

First Satellite-detected Perturbations of Outgoing Longwave Radiation Associated with Blowing Snow Events over Antarctica

NASA Technical Reports Server (NTRS)

Yang, Yuekui; Palm, Stephen P.; Marshak, Alexander; Wu, Dong L.; Yu, Hongbin; Fu, Qiang

2014-01-01

We present the first satellite-detected perturbations of the outgoing longwave radiation (OLR) associated with blowing snow events over the Antarctic ice sheet using data from Cloud-Aerosol Lidar with Orthogonal Polarization and Clouds and the Earth's Radiant Energy System. Significant cloud-free OLR differences are observed between the clear and blowing snow sky, with the sign andmagnitude depending on season and time of the day. During nighttime, OLRs are usually larger when blowing snow is present; the average difference in OLRs between without and with blowing snow over the East Antarctic Ice Sheet is about 5.2 W/m2 for the winter months of 2009. During daytime, in contrast, the OLR perturbation is usually smaller or even has the opposite sign. The observed seasonal variations and day-night differences in the OLR perturbation are consistent with theoretical calculations of the influence of blowing snow on OLR. Detailed atmospheric profiles are needed to quantify the radiative effect of blowing snow from the satellite observations.

Collateral Damage

DTIC Science & Technology

1978-10-01

stated, they are, in reality , indexed to a single aspect of the weapon phenomena, e.g., damage levels for airblast-sensitive objects are indexed to...of Burst Propagation Related Airblast Representation Target Altitude Wheather (snow/rain) Terrain Temperature Air Pressure Around Structures 1. d) 3-38...with opaque material. Simply closing a shutter can be quite effective in virtually eliminating all possibility of interior fire starts from a single

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Importance of climatological downscaling and plant phenology for red deer in heterogeneous landscapes

PubMed Central

Pettorelli, Nathalie; Mysterud, Atle; Yoccoz, Nigel G; Langvatn, Rolf; Stenseth, Nils Chr

2005-01-01

Understanding how climate influences ecosystems represents a challenge in ecology and natural resource management. Although we know that climate affects plant phenology and herbivore performances at any single site, no study has directly coupled the topography–climate interaction (i.e. the climatological downscaling process) with large-scale vegetation dynamics and animal performances. Here we show how climatic variability (measured by the North Atlantic oscillation ‘NAO’) interacts with local topography in determining the vegetative greenness (as measured by the normalized difference vegetation index ‘NDVI’) and the body masses and seasonal movements of red deer (Cervus elaphus) in Norway. Warm springs induced an earlier onset of vegetation, resulting in earlier migration and higher body masses. Increasing values of the winter-NAO corresponded to less snow at low altitude (warmer, more precipitation results in more rain), but more snow at high altitude (colder, more precipitation corresponds to more snow) relative to winters with low winter-NAO. An increasing NAO thus results in a spatially more variable phenology, offering migrating deer an extended period with access to high-quality forage leading to increased body mass. Our results emphasize the importance of incorporating spring as well as the interaction between winter climate and topography when aiming at understanding how plant and animal respond to climate change. PMID:16243701

Air temperature thresholds to evaluate snow melting at the surface of Alpine glaciers by T-index models: the case study of Forni Glacier (Italy)

NASA Astrophysics Data System (ADS)

Senese, A.; Maugeri, M.; Vuillermoz, E.; Smiraglia, C.; Diolaiuti, G.

2014-03-01

The glacier melt conditions (i.e.: null surface temperature and positive energy budget) can be assessed by analyzing meteorological and energy data acquired by a supraglacial Automatic Weather Station (AWS). In the case this latter is not present the assessment of actual melting conditions and the evaluation of the melt amount is difficult and simple methods based on T-index (or degree days) models are generally applied. These models require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 273.15 K. In this paper, to detect the most indicative threshold witnessing melt conditions in the April-June period, we have analyzed air temperature data recorded from 2006 to 2012 by a supraglacial AWS set up at 2631 m a.s.l. on the ablation tongue of the Forni Glacier (Italian Alps), and by a weather station located outside the studied glacier (at Bormio, a village at 1225 m a.s.l.). Moreover we have evaluated the glacier energy budget and the Snow Water Equivalent (SWE) values during this time-frame. Then the snow ablation amount was estimated both from the surface energy balance (from supraglacial AWS data) and from T-index method (from Bormio data, applying the mean tropospheric lapse rate and varying the air temperature threshold) and the results were compared. We found that the mean tropospheric lapse rate permits a good and reliable reconstruction of glacier air temperatures and the major uncertainty in the computation of snow melt is driven by the choice of an appropriate temperature threshold. From our study using a 5.0 K lower threshold value (with respect to the largely applied 273.15 K) permits the most reliable reconstruction of glacier melt.

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.

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.

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

USGS Publications Warehouse

Reed, Bradley C.; Budde, Michael E.; Spencer, Page; Miller, Amy E.

2009-01-01

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3??months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Ni??o winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.

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.

Measuring Snow Grain Size with the Near-Infrared Emitting Reflectance Dome (NERD)

NASA Astrophysics Data System (ADS)

Schneider, A. M.; Flanner, M.

2014-12-01

Because of its high visible albedo, snow plays a large role in Earth's surface energy balance. This role is a subject of intense study, but due to the wide range of snow albedo, variations in the characteristics of snow grains can introduce radiative feedbacks in a snow pack. Snow grain size, for example, is one property which directly affects a snow pack's absorption spectrum. Previous studies model and observe this spectrum, but potential feedbacks induced by these variations are largely unknown. Here, we implement a simple and inexpensive technique to measure snow grain size in an instrument we call the Near-infrared Emitting Reflectance Dome (NERD). A small black styrene dome (~17cm diameter), fitted with two narrowband light-emitting diodes (LEDs) centered around 1300nm and 1550nm and three near-infrared reverse-biased photodiodes, is placed over the snow surface enabling a multi-spectral measurement of the hemispheric directional reflectance factor (HDRF). We illuminate the snow at each wavelength, measure directional reflectance, and infer grain size from the difference in HDRFs measured on the same snow crystals at fixed viewing angles. We validate measurements from the NERD using two different reflectance standards, materials designed to be near perfect Lambertian reflectors, having known, constant reflectances (~99% and ~55%) across a wide range of wavelengths. Using a 3D Monte Carlo model simulating photon pathways through a pack of spherical snow grains, we calculate the difference in HDRFs at 1300nm and 1550nm to predict the calibration curve for a wide range of grain sizes. This theoretically derived curve gives a relationship between effective radius and the difference in HDRFs and allows us to approximate grain sizes using the NERD in just a few seconds. Further calibration requires knowledge of truth values attainable using a previously validated instrument or measurements from an inter-comparison workshop.

Are We Biologically Safe with Snow Precipitation? A Case Study in Beijing

PubMed Central

Shen, Fangxia; Yao, Maosheng

2013-01-01

In this study, the bacterial and fungal abundances, diversities, conductance levels as well as total organic carbon (TOC) were investigated in the snow samples collected from five different snow occurrences in Beijing between January and March, 2010. The collected snow samples were melted and cultured at three different temperatures (4, 26 and 37°C). The culturable bacterial concentrations were manually counted and the resulting colony forming units (CFUs) at 26°C were further studied using V3 region of 16 S rRNA gene-targeted polymerase chain reaction -denaturing gradient gel electrophoresis (PCR-DGGE). The clone library was constructed after the liquid culturing of snow samples at 26°C. And microscopic method was employed to investigate the fungal diversity in the samples. In addition, outdoor air samples were also collected using mixed cellulose ester (MCE) filters and compared with snow samples with respect to described characteristics. The results revealed that snow samples had bacterial concentrations as much as 16000 CFU/ml for those cultured at 26°C, and the conductance levels ranged from 5.6×10−6 to 2.4×10−5 S. PCR-DGGE, sequencing and microscopic analysis revealed remarkable bacterial and fungal diversity differences between the snow samples and the outdoor air samples. In addition, DGGE banding profiles for the snow samples collected were also shown distinctly different from one another. Absent from the outdoor air, certain human, plant, and insect fungal pathogens were found in the snow samples. By calculation, culturable bacteria accounted for an average of 3.38% (±1.96%) of TOC for the snow samples, and 0.01% for that of outdoor air samples. The results here suggest that snow precipitations are important sources of fungal pathogens and ice nucleators, thus could affect local climate, human health and agriculture security. PMID:23762327

Are we biologically safe with snow precipitation? A case study in beijing.

PubMed

Shen, Fangxia; Yao, Maosheng

2013-01-01

In this study, the bacterial and fungal abundances, diversities, conductance levels as well as total organic carbon (TOC) were investigated in the snow samples collected from five different snow occurrences in Beijing between January and March, 2010. The collected snow samples were melted and cultured at three different temperatures (4, 26 and 37°C). The culturable bacterial concentrations were manually counted and the resulting colony forming units (CFUs) at 26°C were further studied using V3 region of 16 S rRNA gene-targeted polymerase chain reaction -denaturing gradient gel electrophoresis (PCR-DGGE). The clone library was constructed after the liquid culturing of snow samples at 26°C. And microscopic method was employed to investigate the fungal diversity in the samples. In addition, outdoor air samples were also collected using mixed cellulose ester (MCE) filters and compared with snow samples with respect to described characteristics. The results revealed that snow samples had bacterial concentrations as much as 16000 CFU/ml for those cultured at 26°C, and the conductance levels ranged from 5.6×10(-6) to 2.4×10(-5) S. PCR-DGGE, sequencing and microscopic analysis revealed remarkable bacterial and fungal diversity differences between the snow samples and the outdoor air samples. In addition, DGGE banding profiles for the snow samples collected were also shown distinctly different from one another. Absent from the outdoor air, certain human, plant, and insect fungal pathogens were found in the snow samples. By calculation, culturable bacteria accounted for an average of 3.38% (±1.96%) of TOC for the snow samples, and 0.01% for that of outdoor air samples. The results here suggest that snow precipitations are important sources of fungal pathogens and ice nucleators, thus could affect local climate, human health and agriculture security.

Parameterization of single-scattering properties of snow

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Kokhanovsky, Alexander; Guyot, Gwennole; Jourdan, Olivier; Nousiainen, Timo

2015-04-01

Snow consists of non-spherical ice grains of various shapes and sizes, which are surrounded by air and sometimes covered by films of liquid water. Still, in many studies, homogeneous spherical snow grains have been assumed in radiative transfer calculations, due to the convenience of using Mie theory. More recently, second-generation Koch fractals have been employed. While they produce a relatively flat scattering phase function typical of deformed non-spherical particles, this is still a rather ad-hoc choice. Here, angular scattering measurements for blowing snow conducted during the CLimate IMpacts of Short-Lived pollutants In the Polar region (CLIMSLIP) campaign at Ny Ålesund, Svalbard, are used to construct a reference phase function for snow. Based on this phase function, an optimized habit combination (OHC) consisting of severely rough (SR) droxtals, aggregates of SR plates and strongly distorted Koch fractals is selected. The single-scattering properties of snow are then computed for the OHC as a function of wavelength λ and snow grain volume-to-projected area equivalent radius rvp. Parameterization equations are developed for λ=0.199-2.7 μm and rvp = 10-2000 μm, which express the single-scattering co-albedo β, the asymmetry parameter g and the phase function as functions of the size parameter and the real and imaginary parts of the refractive index. Compared to the reference values computed for the OHC, the accuracy of the parameterization is very high for β and g. This is also true for the phase function parameterization, except for strongly absorbing cases (β > 0.3). Finally, we consider snow albedo and reflected radiances for the suggested snow optics parameterization, making comparisons with spheres and distorted Koch fractals. Further evaluation and validation of the proposed approach against (e.g.) bidirectional reflectance and polarization measurements for snow is planned. At any rate, it seems safe to assume that the OHC selected here provides a substantially better basis for representing the single-scattering properties of snow than spheres do. Moreover, the parameterizations developed here are analytic and simple to use, and they can also be applied to the treatment of dirty snow following (e.g.) the approach of Kokhanovsky (The Cryosphere, 7, 1325-1331, doi:10.5194/tc-7-1325-2013, 2013). This should make them an attractive option for use in radiative transfer applications involving snow.

Research on snow cover monitoring of Northeast China using Fengyun Geostationary Satellite

NASA Astrophysics Data System (ADS)

Wu, Tong; Gu, Lingjia; Ren, Ruizhi; Zhou, TIngting

2017-09-01

Snow cover information has great significance for monitoring and preventing snowstorms. With the development of satellite technology, geostationary satellites are playing more important roles in snow monitoring. Currently, cloud interference is a serious problem for obtaining accurate snow cover information. Therefore, the cloud pixels located in the MODIS snow products are usually replaced by cloud-free pixels around the day, which ignores snow cover dynamics. FengYun-2(FY-2) is the first generation of geostationary satellite in our country which complements the polar orbit satellite. The snow cover monitoring of Northeast China using FY-2G data in January and February 2016 is introduced in this paper. First of all, geometric and radiometric corrections are carried out for visible and infrared channels. Secondly, snow cover information is extracted according to its characteristics in different channels. Multi-threshold judgment methods for the different land types and similarity separation techniques are combined to discriminate snow and cloud. Furthermore, multi-temporal data is used to eliminate cloud effect. Finally, the experimental results are compared with the MOD10A1 and MYD10A1 (MODIS daily snow cover) product. The MODIS product can provide higher resolution of the snow cover information in cloudless conditions. Multi-temporal FY-2G data can get more accurate snow cover information in cloudy conditions, which is beneficial for monitoring snowstorms and climate changes.

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.

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.

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.

Aeolian snow transport from wind tunnel experiments

NASA Astrophysics Data System (ADS)

Paterna, E.; Crivelli, P.; Lehning, M.

2016-12-01

Aeolian snow transport has a significant impact on snow redistribution in mountains, prairies as well as on glaciers, ice shelves, and sea ice. In all these environments, the local mass balance is highly influenced by Aeolian snow transport. The dynamics of snow saltation has a high impact on the land surface processes shaping these regions. More specifically, the observed high intermittency of saltation fluxes poses a problem for saltation models and needs to be better understood. We therefore aimed at unveiling the mechanisms underlying snow saltation at different saltation strengths and its coupling with the turbulent fluctuations of the wind. We conducted wind tunnel measurements of the momentum and mass-fluxes during snow saltation. For the mass-flux measurements we employed a shadowgraphy system which acquires images of the snow particle's shadows at high spatial and temporal resolution. The size and displacement of the particles are then determined by means of image analysis and Particle Tracking Velocimetry (PTV), allowing to estimate both snow mass-flux and flow velocity. Our controlled wind tunnel experiments revealed the existence of two regimes of saltation. In a turbulence-dependent regime occurring during weak saltation activity, we observed a strong coupling between snow transport and turbulent flow. Conversely during stronger saltation activity a turbulence-independent regime emerges, where the saltation develops its own length scale and it efficiently decouples from the wind fluctuations. We argue that different entrainment mechanisms could explain the existence of the two different saltation regimes as well as the observed high level of mass-flux intermittency.

An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data

USGS Publications Warehouse

Tan, B.; Morisette, J.T.; Wolfe, R.E.; Gao, F.; Ederer, G.A.; Nightingale, J.; Pedelty, J.A.

2011-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. ?? 2010 IEEE.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Impact of Land Cover Characterization and Properties on Snow Albedo in Climate Models

NASA Astrophysics Data System (ADS)

Wang, L.; Bartlett, P. A.; Chan, E.; Montesano, P.

2017-12-01

The simulation of winter albedo in boreal and northern environments has been a particular challenge for land surface modellers. Assessments of output from CMIP3 and CMIP5 climate models have revealed that many simulations are characterized by overestimation of albedo in the boreal forest. Recent studies suggest that inaccurate representation of vegetation distribution, improper simulation of leaf area index, and poor treatment of canopy-snow processes are the primary causes of albedo errors. While several land cover datasets are commonly used to derive plant functional types (PFT) for use in climate models, new land cover and vegetation datasets with higher spatial resolution have become available in recent years. In this study, we compare the spatial distribution of the dominant PFTs and canopy cover fractions based on different land cover datasets, and present results from offline simulations of the latest version Canadian Land Surface Scheme (CLASS) over the northern Hemisphere land. We discuss the impact of land cover representation and surface properties on winter albedo simulations in climate models.

Use of 2d-video Disdrometer to Derive Mean Density-size and Ze-SR Relations: Four Snow Cases from the Light Precipitation Validation Experiment

NASA Technical Reports Server (NTRS)

Huang, Gwo-Jong; Bringi, V. N.; Moisseev, Dmitri; Petersen, Walter A.; Bliven, Francis L.; Hudak, David

2014-01-01

The application of the 2D-video disdrometer to measure fall speed and snow size distribution and to derive liquid equivalent snow rate, mean density-size and reflectivity-snow rate power law is described. Inversion of the methodology proposed by Böhm provides the pathway to use measured fall speed, area ratio and '3D' size measurement to estimate the mass of each particle. Four snow cases from the Light Precipitation Validation Experiment are analyzed with supporting data from other instruments such as Precipitation Occurrence Sensor System (POSS), Snow Video Imager (SVI), a network of seven snow gauges and three scanning C9 band radars. The radar-based snow accumulations using the 2DVD-derived Ze-SR relation are in good agreement with a network of seven snow gauges and outperform the accumulations derived from a climatological Ze-SR relation used by the Finnish Meteorological Institute (FMI). The normalized bias between radar-derived and gauge accumulation is reduced from 96% when using the fixed FMI relation to 28% when using the Ze-SR relations based on 2DVD data. The normalized standard error is also reduced significantly from 66% to 31%. For two of the days with widely different coefficients of the Ze-SR power law, the reflectivity structure showed significant differences in spatial variability. Liquid water path estimates from radiometric data also showed significant differences between the two cases. Examination of SVI particle images at the measurement site corroborated these differences in terms of unrimed versus rimed snow particles. The findings reported herein support the application of Böhm's methodology for deriving the mean density-size and Ze-SR power laws using data from 2D-video disdrometer.

Evaluation of the most suitable threshold value for modelling snow glacier melt through T- index approach: the case study of Forni Glacier (Italian Alps)

NASA Astrophysics Data System (ADS)

Senese, Antonella; Maugeri, Maurizio; Vuillermoz, Elisa; Smiraglia, Claudio; Diolaiuti, Guglielmina

2014-05-01

Glacier melt occurs whenever the surface temperature is null (273.15 K) and the net energy budget is positive. These conditions can be assessed by analyzing meteorological and energy data acquired by a supraglacial Automatic Weather Station (AWS). In the case this latter is not present at the glacier surface the assessment of actual melting conditions and the evaluation of melt amount is difficult and degree-day (also named T-index) models are applied. These approaches require the choice of a correct temperature threshold. In fact, melt does not necessarily occur at daily air temperatures higher than 273.15 K, since it is determined by the energy budget which in turn is only indirectly affected by air temperature. This is the case of the late spring period when ablation processes start at the glacier surface thus progressively reducing snow thickness. In this study, to detect the most indicative air temperature threshold witnessing melt conditions in the April-June period, we analyzed air temperature data recorded from 2006 to 2012 by a supraglacial AWS (at 2631 m a.s.l.) on the ablation tongue of the Forni Glacier (Italy), and by a weather station located nearby the studied glacier (at Bormio, 1225 m a.s.l.). Moreover we evaluated the glacier energy budget (which gives the actual melt, Senese et al., 2012) and the snow water equivalent values during this time-frame. Then the ablation amount was estimated both from the surface energy balance (MEB from supraglacial AWS data) and from degree-day method (MT-INDEX, in this latter case applying the mean tropospheric lapse rate to temperature data acquired at Bormio changing the air temperature threshold) and the results were compared. We found that the mean tropospheric lapse rate permits a good and reliable reconstruction of daily glacier air temperature conditions and the major uncertainty in the computation of snow melt from degree-day models is driven by the choice of an appropriate air temperature threshold. Then, to assess the most suitable threshold, we firstly analyzed hourly MEB values to detect if ablation occurs and how long this phenomenon takes (number of hours per day). The largest part of the melting (97.7%) resulted occurring on days featuring at least 6 melting hours thus suggesting to consider their minimum average daily temperature value as a suitable threshold (268.1 K). Then we ran a simple T-index model applying different threshold values. The threshold which better reproduces snow melting results the value 268.1 K. Summarizing using a 5.0 K lower threshold value (with respect to the largely applied 273.15 K) permits the best reconstruction of glacier melt and it results in agreement with findings by van den Broeke et al. (2010) in Greenland ice sheet. Then probably the choice of a 268 K value as threshold for computing degree days amount could be generalized and applied not only on Greenland glaciers but also on Mid latitude and Alpine ones. This work was carried out under the umbrella of the SHARE Stelvio Project funded by the Lombardy Region and managed by FLA and EvK2-CNR Committee.

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

NASA Astrophysics Data System (ADS)

Omiya, S.; Sato, A.

2010-12-01

Blowing snow particles are known to have an electrostatic charge. This charge may be a contributing factor in the formation of snow drifts and snow cornices and changing of the trajectory of blowing snow particles. These formations and phenomena can cause natural disaster such as an avalanche and a visibility deterioration, and obstruct transportation during winter season. Therefore, charging phenomenon of the blowing snow particles is an important issue in terms of not only precise understanding of the particle motion but disaster prevention. The primary factor of charge accumulation to the blowing snow particles is thought to be due to “saltation” of them. The “saltation” is one of movement forms of blowing snow: when the snow particles are transported by the wind, they repeat frictional collisions with the snow surface. In previous studies, charge-to-mass ratios measured in the field were approximately -50 to -10 μC/kg, and in the wind tunnel were approximately -0.8 to -0.1 μC/kg. While there were qualitatively consistent in sign, negative, there were huge gaps quantitatively between them. One reason of those gaps is speculated to be due to differences in fetch. In other words, the difference of the collision frequency of snow particles to the snow surface has caused the gaps. But it is merely a suggestion and that has not been confirmed. The purpose of this experiment is to measure the charge of blowing snow particles focusing on the collision frequency and clarify the relationship between them. Experiments were carried out in the cryogenic wind tunnel of Snow and Ice Research Center (NIED, JAPAN). A Faraday cage and an electrometer were used to measure the charge of snow particles. These experiments were conducted over the hard snow surface condition to prevent the erosion of the snow surface and the generation of new snow particles from the surface. The collision frequency of particle was controlled by changing the wind velocity (4.5 to 7 m/s) under the fixed fetch (12m). The number of collisions of particle was converted from the wind velocity using an equation obtained by Kosugi et al. (2004). Blowing snow particles tend to accumulate negative charges gradually with increase of the number of collisions to the snow surface. As a result, it is demonstrated that the gaps between the field values and the wind tunnel ones were due to difference of the collision frequency of snow particles. Assuming a logarithmic relationship as first approximation between the measured charges and the number of collisions, the charge-to-mass ratios will reach roughly the same value which was obtained in the field with several hundreds collisions. For instance, fetch is needed roughly 200m for blowing snow particles to gain -30 μC/kg under the following conditions: air temperature -20 degrees Celsius, wind velocity 7m/s and hard snow surface. REFERENCE: Kosugi et al., (2004): Dependence of drifting snow saltation length on snow surface hardness. Cold Reg. Sci. Technol., 39, 133-139.

Modelling hazardous surface hoar layers in the mountain snowpack over space and time

NASA Astrophysics Data System (ADS)

Horton, Simon Earl

Surface hoar layers are a common failure layer in hazardous snow slab avalanches. Surface hoar crystals (frost) initially form on the surface of the snow, and once buried can remain a persistent weak layer for weeks or months. Avalanche forecasters have difficulty tracking the spatial distribution and mechanical properties of these layers in mountainous terrain. This thesis presents numerical models and remote sensing methods to track the distribution and properties of surface hoar layers over space and time. The formation of surface hoar was modelled with meteorological data by calculating the downward flux of water vapour from the atmospheric boundary layer. The timing of surface hoar formation and the modelled crystal size was verified at snow study sites throughout western Canada. The major surface hoar layers over several winters were predicted with fair success. Surface hoar formation was modelled over various spatial scales using meteorological data from weather forecast models. The largest surface hoar crystals formed in regions and elevation bands with clear skies, warm and humid air, cold snow surfaces, and light winds. Field surveys measured similar regional-scale patterns in surface hoar distribution. Surface hoar formation patterns on different slope aspects were observed, but were not modelled reliably. Mechanical field tests on buried surface hoar layers found layers increased in shear strength over time, but had persistent high propensity for fracture propagation. Layers with large crystals and layers overlying hard melt-freeze crusts showed greater signs of instability. Buried surface hoar layers were simulated with the snow cover model SNOWPACK and verified with avalanche observations, finding most hazardous surface hoar layers were identified with a structural stability index. Finally, the optical properties of surface hoar crystals were measured in the field with spectral instruments. Large plate-shaped crystals were less reflective at shortwave infrared wavelengths than other common surface snow grains. The methods presented in this thesis were developed into operational products that model hazardous surface hoar layers in western Canada. Further research and refinements could improve avalanche forecasts in regions prone to hazardous surface hoar layers.

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

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Makkonen, Risto; Kirkevåg, Alf

2017-04-01

Snow grains are non-spherical and generally irregular in shape. Still, in radiative transfer calculations, they are often treated as spheres. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this work, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (≈ 0.78 in the visible region) than in the spherical case (≈ 0.89). Therefore, for a given snow grain size, the use of non-spherical snow grains yields a higher snow broadband albedo, typically by ≈0.03. Consequently, considering the spherical case as the baseline, the use of non-spherical snow grains results in a negative radiative forcing (RF), with a global-mean top-of-the-model value of ≈ -0.22 W m-2. Although this global-mean RF is modest, it has a rather substantial impact on the climate simulated by NoRESM. In particular, the global annual-mean 2-m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further found that the difference between NONSPH and SPH could be largely "tuned away" by adjusting the snow grain size in the NONSPH experiment by ≈ 70%. The impact of snow grain shape on the radiative effect (RE) of absorbing aerosols in snow (black carbon and mineral dust) is also discussed. For an optically thick snowpack with a given snow grain effective size, the absorbing aerosol RE is smaller for non-spherical than for spherical snow grains. The reason for this is that due to the lower asymmetry parameter of the non-spherical snow grains, solar radiation does not penetrate as deep in snow as in the case of spherical snow grains. However, in a climate model simulation, the RE is sensitive to patterns of aerosol deposition and simulated snow cover. In fact, the global land-area mean absorbing aerosol RE is larger in the NONSPH than SPH experiment (0.193 vs. 0.168 W m-2), owing to later snowmelt in spring.

Application of the Markov Chain Monte Carlo method for snow water equivalent retrieval based on passive microwave measurements

NASA Astrophysics Data System (ADS)

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

2015-12-01

Markov Chain Monte Carlo (MCMC) method is a retrieval algorithm based on Bayes' rule, which starts from an initial state of snow/soil parameters, and updates it to a series of new states by comparing the posterior probability of simulated snow microwave signals before and after each time of random walk. It is a realization of the Bayes' rule, which gives an approximation to the probability of the snow/soil parameters in condition of the measured microwave TB signals at different bands. Although this method could solve all snow parameters including depth, density, snow grain size and temperature at the same time, it still needs prior information of these parameters for posterior probability calculation. How the priors will influence the SWE retrieval is a big concern. Therefore, in this paper at first, a sensitivity test will be carried out to study how accurate the snow emission models and how explicit the snow priors need to be to maintain the SWE error within certain amount. The synthetic TB simulated from the measured snow properties plus a 2-K observation error will be used for this purpose. It aims to provide a guidance on the MCMC application under different circumstances. Later, the method will be used for the snowpits at different sites, including Sodankyla, Finland, Churchill, Canada and Colorado, USA, using the measured TB from ground-based radiometers at different bands. Based on the previous work, the error in these practical cases will be studied, and the error sources will be separated and quantified.

Finland Validation of the New Blended Snow Product

NASA Technical Reports Server (NTRS)

Kim, E. J.; Casey, K. A.; Hallikainen, M. T.; Foster, J. L.; Hall, D. K.; Riggs, G. A.

2008-01-01

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

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.

Field observations of the electrostatic charges of blowing snow in Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Omiya, S.; Sato, A.

2011-12-01

An electrostatic charge of blowing snow may be a contributing factor in the formation of a snow drift and a snow cornice, and changing of the trajectory of own motion. However, detailed electrification characteristics of blowing snow are not known as there are few reports of charge measurements. We carried out field observations of the electrostatic charges of blowing snow in Tobetsu, Hokkaido, Japan in the mid winter of 2011. An anemovane and a thermohygrometer were used for the meteorological observation. Charge-to-mass ratios of blowing snow were obtained by a Faraday-cage, an electrometer and an electric balance. In this observation period, the air temperature during the blowing snow event was -6.5 to -0.5 degree Celsius. The measured charges in this observation were consistent with the previous studies in sign, which is negative, but they were smaller than the previous one. In most cases, the measured values increased with the temperature decrease, which corresponds with previous studies. However, some results contradicted the tendency, and the maximum value was obtained on the day of the highest air temperature of -0.5 degree Celsius. This discrepancy may be explained from the difference of the snow surface condition on observation day. The day when the maximum value was obtained, the snow surface was covered with old snow, and hard. On the other hand, in many other cases, the snow surface was covered with the fresh snow, and soft. Blowing snow particles on the hard surface can travel longer distance than on the soft one. Therefore, it can be surmised that the hard surface makes the blowing snow particles accumulate a lot of negative charges due to a large number of collisions to the surface. This can be supported by the results of the wind tunnel experiments by Omiya and Sato (2011). By this field observation, it was newly suggested that the electrostatic charge of blowing snow are influenced greatly by the difference of the snow surface condition. REFERENCE: Omiya and Sato,(2010):An electrostatic charge measurement of blowing snow particles focusing on collision frequency to the snow surface. AGU Abstract Database, 2010 Fall Meeting.

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

NASA Astrophysics Data System (ADS)

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

2002-11-01

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

Evaluation of an improved intermediate complexity snow scheme in the ORCHIDEE land surface model

NASA Astrophysics Data System (ADS)

Wang, Tao; Ottlé, Catherine; Boone, Aaron; Ciais, Philippe; Brun, Eric; Morin, Samuel; Krinner, Gerhard; Piao, Shilong; Peng, Shushi

2013-06-01

Snow plays an important role in land surface models (LSM) for climate and model applied over Fran studies, but its current treatment as a single layer of constant density and thermal conductivity in ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) induces significant deficiencies. The intermediate complexity snow scheme ISBA-ES (Interaction between Soil, Biosphere and Atmosphere-Explicit Snow) that includes key snow processes has been adapted and implemented into ORCHIDEE, referred to here as ORCHIDEE-ES. In this study, the adapted scheme is evaluated against the observations from the alpine site Col de Porte (CDP) with a continuous 18 year data set and from sites distributed in northern Eurasia. At CDP, the comparisons of snow depth, snow water equivalent, surface temperature, snow albedo, and snowmelt runoff reveal that the improved scheme in ORCHIDEE is capable of simulating the internal snow processes better than the original one. Preliminary sensitivity tests indicate that snow albedo parameterization is the main cause for the large difference in snow-related variables but not for soil temperature simulated by the two models. The ability of the ORCHIDEE-ES to better simulate snow thermal conductivity mainly results in differences in soil temperatures. These are confirmed by performing sensitivity analysis of ORCHIDEE-ES parameters using the Morris method. These features can enable us to more realistically investigate interactions between snow and soil thermal regimes (and related soil carbon decomposition). When the two models are compared over sites located in northern Eurasia from 1979 to 1993, snow-related variables and 20 cm soil temperature are better reproduced by ORCHIDEE-ES than ORCHIDEE, revealing a more accurate representation of spatio-temporal variability.

Application of satellite data for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. A.; Andersen, T.; Ostrem, G. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A close quantitative relationship was found between snow covered areas and subsequent runoff for different parts of the country despite climate differences. Digital LANDSAT data can be used for areas down to approximately 10 sq km to 20 sq km for accurate measurement of snow cover extent. On large watersheds (more than 500 sq km), digital NOAA/TIROS imagery can be used for snow mapping if the area/runoff relationship is determined by using observations from previous years.

Chosen risk level during car-following in adverse weather conditions.

PubMed

Hjelkrem, Odd André; Ryeng, Eirin Olaussen

2016-10-01

This study examines how precipitation, light conditions and surface conditions affect the drivers' risk perception. An indicator CRI (Chosen Risk Index) is defined, which describes the chosen risk level for drivers in a car-following situation. The dataset contains about 70 000 observations of driver behaviour and weather status on a rural road. Based on the theory of risk homeostasis and an assumption that driving behaviour in situations with daylight, dry road and no precipitation reflects drivers' target level of risk, generalised linear models (GLM) were estimated for cars and trucks separately to reveal the effect of adverse weather conditions on risk perception. The analyses show that both car and truck drivers perceive the highest risk when driving on snow covered roads. For car drivers, a snow covered road in combination with moderate rain or light snow are the factors which lowers the CRI the most. For trucks, snow cover and partially covered roads significantly lowers the CRI, while precipitation did not seem to impose any higher risk. Interaction effects were found for car drivers only. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

Simulation of Surface Energy Fluxes and Snow Interception Using a Higher Order Closure Multi-Layer Soil-Vegetation-Atmospheric Model: The Effect of Canopy Shape and Structure

NASA Astrophysics Data System (ADS)

McGowan, L. E.; Dahlke, H. E.; Paw U, K. T.

2015-12-01

Snow cover is a critical driver of the Earth's surface energy budget, climate change, and water resources. Variations in snow cover not only affect the energy budget of the land surface but also represent a major water supply source. In California, US estimates of snow depth, extent, and melt in the Sierra Nevada are critical to estimating the amount of water available for both California agriculture and urban users. However, accurate estimates of snow cover and snow melt processes in forested area still remain a challenge. Canopy structure influences the vertical and spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability in forested regions. In this study we use the Advanced Canopy-Atmosphere-Soil algorithm (ACASA), a multi-layer soil-vegetation-atmosphere numerical model, to simulate the effect of different snow-covered canopy structures on the energy budget, and temperature and other scalar profiles within different forest types in the Sierra Nevada, California. ACASA incorporates a higher order turbulence closure scheme which allows the detailed simulation of turbulent fluxes of heat and water vapor as well as the CO2 exchange of several layers within the canopy. As such ACASA can capture the counter gradient fluxes within canopies that may occur frequently, but are typically unaccounted for, in most snow hydrology models. Six different canopy types were modeled ranging from coniferous forests (e.g. most biomass near the ground) to top-heavy (e.g. most biomass near the top of the crown) deciduous forests to multi-layered forest canopies (e.g. mixture of young and mature trees). Preliminary results indicate that the canopy shape and structure associated with different canopy types fundamentally influence the vertical scalar profiles (including those of temperature, moisture, and wind speed) in the canopy and thus alter the interception and snow melt dynamics in forested land surfaces. The turbulent transport dynamics, including counter-gradient fluxes, and radiation features including land surface albedo, are discussed in the context of the snow energy balance.

Intraseasonal Characteristics Of North Atlantic Oscillation

NASA Astrophysics Data System (ADS)

Bojariu, R.; Gimeno, L..; de La Torre, L.; Nieto, R.

There is evidence of a temporal structure of regional response to the NAO variability in the cold season (e.g. NAO-related climate fluctuations reveal their strongest signal in January). To document the details of NAO intraseasonal characteristics we anal- ysed surface and upper air variables (air surface temperature, sea-ice concentration, sea surface temperature, and sea level pressure and geopotential heights at 700 hPa level) in individual months, from November to April. The data consist of 40 years of monthly reanalyses (1961-2000) extracted from the NCAR-NCEP data set. In ad- dition, snow cover data are used (monthly snow cover frequencies from the Climate Prediction Centre and number of days with snow cover from the Former Soviet Union Hydrological Snow Surveys available at the National Snow and Ice Data Centre). A NAO-related signal with predictive potential has been identified in November air surface temperature over Europe and SLP and geopotential heights over Eurasia. Neg- ative thermal anomalies over the Central Europe and positive geopotential anomalies at 700 hPa over a latitudinal belt from Arabic Peninsula to Pacific Ocean are associated with a high NAO index in the following winter. The November thermal anomalies that seem to be related to the NAO interannual persistence are also linked with the fluctu- ations of snow cover over Europe. Both tropical and high latitude influences may play a role in the onset of the November signal and in further NAO development.

The ASMEx snow slab experiment: snow microwave radiative transfer (SMRT) model evaluation

NASA Astrophysics Data System (ADS)

Sandells, Melody; Löwe, Henning; Picard, Ghislain; Dumont, Marie; Essery, Richard; Floury, Nicolas; Kontu, Anna; Lemmetyinen, Juha; Maslanka, William; Mätzler, Christian; Morin, Samuel; Wiesmann, Andreas

2017-04-01

A major uncertainty in snow microwave modelling to date has been the treatment of the snow microstructure. Although observations of microstructural parameters such as the optical grain diameter, specific surface area and correlation length have improved drastically over the last few years, scale factors have been used to derive the parameters needed in microwave emission models from these observations. Previous work has shown that a major difference between electromagnetic models of scattering coefficients is due to the specific snow microstructure models used. The snow microwave radiative transfer model (SMRT) is a new model developed to advance understanding of the role of microstructure and isolate different assumptions in existing microwave models that collectively hinder interpretation of model intercomparison studies. SMRT is implemented in Python and is modular, thus allows switching between different representations in its various components. Here, the role of microstructure is examined with the Improved Born Approximation electromagnetic model. The model is evaluated against scattering and absorption coefficients derived from radiometer measurements of snow slabs taken as part of the Arctic Snow Microstructure Experiment (ASMEx), which took place in Sodankylä, Finland over two seasons. Microtomography observations of slab samples were used to determine parameters for five microstructure models: spherical, exponential, sticky hard sphere, Teubner-Strey and Gaussian random field. SMRT brightness temperature simulations are also compared with radiometric observations of the snow slabs over a reflector plate and an absorber substrate. Agreement between simulations and observations is generally good except for slabs that are highly anisotropic.

Sensitivity of snow process simulations to precipitation-phase transition method in forested and open areas

NASA Astrophysics Data System (ADS)

Lundberg, A.; Gustafsson, D.

2009-04-01

Modeling of forest snow processes is complicated and especially problematic seems to be the separation of precipitation phase in climates where a large part of the precipitation falls at temperatures near zero degrees Celsius. When the precipitation is classified as snow, the tree crowns can carry an order of magnitude more canopy storage as compared to when the precipitation is classified as rain, and snow in the trees also alters the albedo of the forest while rain does not. Many different schemes for the precipitation phase separation are used by various snow models. Some models use just one air temperature threshold (TR/S) below which all precipitation is assumed to be snow and above which all precipitation is classified as rain. A more common approach for forest snow models is to use two temperature thresholds. The snow fraction (SF) is then set to one below the snow threshold (TS) and to zero above the rain threshold (TR) and SF is assumed to decrease linearly between these two thresholds. Also more sophisticated schemes exist, but three seems to be a lack of agreement on how the precipitation phase separations should be performed. The aim with this study is to use a hydrological model including canopy snow processes to illustrate the sensitivity for different formulations of the precipitation phase separation on a) the simulated maximum snow pack storage b) the interception evaporation loss and c) snow melt runoff. In other words, to investigate of the choice of precipitation phase separation has an impact on the simulated wintertime water balance. Simulations are made for sites in different climates and for both open fields and forest sites in different regions of Sweden from north to south. In general, precipitation phase separation methods that classified snowfall at higher temperatures resulted in a larger proportion of the precipitation lost by interception evaporation as a result of the increased interception capacity. However, the maximum snow accumulation was also increased in some cases due to the overall increased snowfall, depending on canopy density and precipitation and temperature regimes. Results show that the choice of precipitation phase separation method can have an significant impact on the simulated wintertime water balance, especially in forested regions.

[Effects of seasonal snow cover on soil nitrogen transformation in alpine ecosystem: a review].

PubMed

Liu, Lin; Wu, Yan; He, Yi-xin; Wu, Ning; Sun, Geng; Zhang, Lin; Xu, Jun-jun

2011-08-01

Seasonal snow cover has pronounced effects on the soil nitrogen concentration and transformation in alpine ecosystem. Snowfall is an important form of nitrogen deposition, which directly affects the content of soil available nitrogen. Different depths and different duration of snow cover caused by snowfall may lead the heterogeneity of abiotic factors (soil temperature and moisture) and biotic factors (soil microbes, alpine plants, and alpine animals), and further, produce complicated effects on the mineralization and immobilization of soil nitrogen. This paper introduced in emphasis the inherent mechanisms of soil nitrogen mineralization and leaching under the effects of frequent freeze-thaw events during the durative melting of snow cover, and summarized the main research results of field in situ experiments about the effects of seasonal snow cover on soil nitrogen in alpine ecosystem based on the possible changes in snow cover in the future. Some suggestions with regard to the effects of seasonal snow cover on soil nitrogen were put forward.

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.

Reproducing snow making strategies with deterministic modeling and image-based validation

NASA Astrophysics Data System (ADS)

Allamano, P.; Claps, P.; Poggi, D.

2012-04-01

Almost all winter resorts rely on artificial snow production as a surrogate for natural snow when the natural snow cover is missing or inadequate. The sustainability of snowmaking practices represents a debated issue, with two contrasting views: on the one hand the need for enhancing the value of mountain regions in terms of touristic appeal; on the other hand, the question whether the production of artificial snow is sustainable from an environmental point of view. We present here the outcomes of a pilot study aimed at assessing the impact of snowmaking practices on water resources management in the Gressoney valley. The study area is located in the Aosta Valley (North-Western Italy). The total area covered by ski runs is of about 95 ha, with an elevation range of 2000 m and an average snow production over the last 5 seasons of 200.000 m3 of water per year. Daily records of water volume used for artificial snow making were made available by the ski runs administrators for the last 5 seasons along with webcam images taken for the last 2 years. Daily meteorological records (of temperature and precipitation) were retrieved in 5 meteo stations within the district area since 1928 (83 years). The snowpack evolution in the skiable domain is modeled by means of a distributed water balance model which adopts a radiation-temperature index representation to describe snowmelt, and accounts for the topographic complexity of the area by modeling radiation over a very fine terrain grid (10 by 10 m cells). The model requires distributed daily temperature and precipitation as inputs. The snowmelt module is calibrated locally at the five stations. The snow-making module, aimed at synthesizing the production strategies at the district scale, is calibrated by keeping the required average snow cover depths on the ski runs as a free parameter. After calibrating the model parameters, also with the aid of visual comparison of modeled and real snow patterns registered by the webcams, we were able to reconstruct the seasonal evolution of natural and artificial snow cover over the whole district since 1928. A 83 years-long synthetic record of seasonal volumes potentially allocated for artificial snow production was obtained and a preliminary evaluation of the probability to have insufficient resource to face both domestic and snow production needs was performed. The system was found to have a 10% probability of deficiency, with deficit volumes ranging from 10.000 to 100.000 m3.

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.

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.

Windblown Snow

NASA Image and Video Library

2011-04-11

On April 11, 2011, IceBridge finally got the clear weather necessary to fly over glaciers in southeast Greenland, but with clear skies came winds of up to 70 knots. What looks like clouds is actually wind-blown snow. The data could help scientists to evaluate the impact of wind-blown snow on satellite-based laser altimetry measurements. Operation IceBridge, now in its third year, makes annual campaigns in the Arctic and Antarctic where science flights monitor glaciers, ice sheets and sea ice. Credit: NASA/GSFC/Michael Studinger To learn more about Ice Bridge go to: www.nasa.gov/mission_pages/icebridge/news/spr11/index.html NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Join us on Facebook

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.

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.

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

Monitoring the snow parameters (e.g. snow cover area, snow water equivalent) is a challenging work. Because of its natural physical properties, snow highly affects the evolution of weather from daily basis to climate on a longer time scale. The derivation of snow products over mountainous regions has been considered very challenging. This can be done by periodic and precise mapping of the snow cover. However inaccessibility and scarcity of the ground observations limit the snow cover mapping in the mountainous areas. Today, it is carried out operationally by means of optical satellite imagery and microwave radiometry. In retrieving the snow cover area from satellite images bring the problem of topographical variations within the footprint of satellite sensors and spatial and temporal variation of snow characteristics in the mountainous areas. Most of the global and regional operational snow products use generic algorithms for flat and mountainous areas. However the non-uniformity of the snow characteristics can only be modeled with different algorithms for mountain and flat areas. In this study the early findings of Satellite Application Facilities on Hydrology (H-SAF) project, which is financially supported by EUMETSAT, will be presented. Turkey is a part of the H-SAF project, both in product generation (eg. snow recognition, fractional snow cover and snow water equivalent) for mountainous regions for whole Europe, cal/val of satellite-derived snow products with ground observations and cal/val studies with hydrological modeling in the mountainous terrain of Europe. All the snow products are operational on a daily basis. For the snow recognition product (H10) for mountainous areas, spectral thresholding methods were applied on sub pixel scale of MSG-SEVIRI images. The different spectral characteristics of cloud, snow and land determined the structure of the algorithm and these characteristics were obtained from subjective classification of known snow cover features in the MSG/SEVIRI images. The fractional snow cover area (H12) algorithm is based on a sub-pixel reflectance model applied on METOP-AVHRR data. Knowing the effects of topography on satellite-measured radiances for rough terrain, the sun zenith and azimuth angles, as well as direction of observation relative to these are taken into account in estimating the target reflectances from the satellite images. The values of SWE products (H13) were obtained using an assimilation process based on the Helsinki University of Technology model using Advanced Microwave Scanning Radiometer for EOS (AMSR-E) daily brightness-temperature values. The validation studies for three products have been performed for the water years 2010 and 2011. Average values of 70% of probability of detection for snow recognition product, 60% of overall accuracy for the fractional snow cover product and 45 mm RMSE for the snow water equivalent product have been obtained from the validation studies. Final versions of these three products will be presented and discussed. Key words: snow, satellite images, mountain, HSAF, snow cover, snow water equivalent

Snow distribution and heat flow in the taiga

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

Sturm, M.

1992-05-01

The trees of the taiga intercept falling snow and cause it to become distributed in an uneven fashion. Around aspen and birch, cone-shaped accumulations form. Beneath large spruce trees, the snow cover is depleted, forming a bowl-shaped depression called a tree well. Small spruce trees become covered with snow, creating cavities that funnel cold air to the snow/ground interface. The depletion of snow under large spruce trees results in greater heat loss from the ground. A finite difference model suggests that heat flow from tree wells can be more than twice that of undisturbed snow. In forested watersheds, this increasemore » can be a significant percentage of the total winter energy exchange.« less

Thaw depth spatial and temporal variability at the Limnopolar Lake CALM-S site, Byers Peninsula, Livingston Island, Antarctica.

PubMed

de Pablo, M A; Ramos, M; Molina, A; Prieto, M

2018-02-15

A new Circumpolar Active Layer Monitoring (CALM) site was established in 2009 at the Limnopolar Lake watershed in Byers Peninsula, Livingston Island, Antarctica, to provide a node in the western Antarctic Peninsula, one of the regions that recorded the highest air temperature increase in the planet during the last decades. The first detailed analysis of the temporal and spatial evolution of the thaw depth at the Limnopolar Lake CALM-S site is presented here, after eight years of monitoring. The average values range between 48 and 29cm, decreasing at a ratio of 16cm/decade. The annual thaw depth observations in the 100×100 m CALM grid are variable (Variability Index of 34 to 51%), although both the Variance Coefficient and the Climate Matrix Analysis Residual point to the internal consistency of the data. Those differences could be explained then by the terrain complexity and node-specific variability due to the ground properties. The interannual variability was about 60% during 2009-2012, increasing to 124% due to the presence of snow in 2013, 2015 and 2016. The snow has been proposed here as one of the most important factors controlling the spatial variability of ground thaw depth, since its values correlate with the snow thickness but also with the ground surface temperature and unconfined compression resistance, as measured in 2010. The topography explains the thaw depth spatial distribution pattern, being related to snowmelt water and its accumulation in low-elevation areas (downslope-flow). Patterned grounds and other surface features correlate well with high thaw depth patterns as well. The edaphic factor (E=0.05842m 2 /°C·day; R 2 =0.63) is in agreement with other permafrost environments, since frozen index (F>0.67) and MAAT (<-2°C) denote a continuous permafrost existence in the area. All these characteristics provided the basis for further comparative analyses between others nearby CALM sites. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of Different Tree canopies on the Brightness Temperature of Snowpack

NASA Astrophysics Data System (ADS)

Mousavi, S.; De Roo, R. D.; Brucker, L.

2017-12-01

Snow stores the water we drink and is essential to grow food that we eat. But changes in snow quantities such as snow water equivalent (SWE) are underway and have serious consequences. So, effective management of the freshwater reservoir requires to monitor frequently (weekly or better) the spatial distribution of SWE and snowpack wetness. Both microwave radar and radiometer systems have long been considered as relevant remote sensing tools in retrieving globally snow physical parameters of interest thanks to their all-weather operation capability. However, their observations are sensitive to the presence of tree canopies, which in turns impacts SWE estimation. To address this long-lasting challenge, we parked a truck-mounted microwave radiometer system for an entire winter in a rare area where it exists different tree types in the different cardinal directions. We used dual-polarization microwave radiometers at three different frequencies (1.4, 19, and 37 GHz), mounted on a boom truck to observe continuously the snowpack surrounding the truck. Observations were recorded at different incidence angles. These measurements have been collected in Grand Mesa National Forest, Colorado as part of the NASA SnowEx 2016-17. In this presentation, the effect of Engelmann Spruce and Aspen trees on the measured brightness temperature of snow is discussed. It is shown that Engelmann Spruce trees increases the brightness temperature of the snowpack more than Aspen trees do. Moreover, the elevation angular dependence of the measured brightness temperatures of snowpack with and without tree canopies is investigated in the context of SWE retrievals. A time-lapse camera was monitoring a snow post installed in the sensors' field of view to characterize the brightness temperature change as snow depth evolved. Also, our study takes advantage of the snowpit measurements that were collected near the radiometers' field of view.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.

2015-07-01

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China

NASA Astrophysics Data System (ADS)

Maimaitiaili, Ayisulitan; Aji, xiaokaiti; Kondoh, Akihiko

2016-04-01

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China Ayisulitan Maimaitiaili1, Xiaokaiti Aji2 Akihiko Kondoh2 1Graduate School of Science, Chiba University, Japan 2Center for Environmental Remote Sensing, Chiba University The spatio-temporal changes of Land Use/Cover (LUCC) and its driving forces in Kashgar region, Xinjiang Province, China, are investigated by using satellite remote sensing and a geographical information system (GIS). Main goal of this paper is to quantify the drivers of LUCC. First, considering lack of the Land Cover (LC) map in whole study area, we produced LC map by using Landsat images. Land use information from Landsat data was collected using maximum likelihood classification method. Land use change was studied based on the change detection method of land use types. Second, because the snow provides a key water resources for stream flow, agricultural production and drinking water for sustaining large population in Kashgar region, snow cover are estimated by Spot Vegetation data. Normalized Difference Snow Index (NDSI) algorithm are applied to make snow cover map, which is used to screen the LUCC and climate change. The best agreement is found with threshold value of NDSI≥0.2 to generate multi-temporal snow cover and snowmelt maps. Third, driving forces are systematically identified by LC maps and statistical data such as climate and socio-economic data, regarding to i) the climate changes and ii) socioeconomic development that the spatial correlation among LUCC, snow cover change, climate and socioeconomic changes are quantified by using liner regression model and negative / positive trend analysis. Our results showed that water bodies, bare land and grass land have decreasing notably. By contrast, crop land and urban area have continually increasing significantly, which are dominated in study area. The area of snow/ice have fluctuated and has strong seasonal trends, total annual snow cover has two peaks in 2005 and 2009. With increasing population from 2,324,375 in 1984 to 4,228,200 in 2014 and crop land reclamation from 6031.4 km2 in 1972 to 16549km2 in 2014 at the study area. Water resources consumption increased with support to large population and irrigate whole crop land area, caused the water shortages that the surface water bodies decreased from 2531.43km2 in the 1972s to 1067.05km2 in the 2014. The grass land with an acreage larger than 6749km2 in 1972 decreased to 922.6 km2 in 2014. The transformations between water bodies, garss land and bare land are remarkbale. The results also suggested high linearity between the LUCC and socioeconomic changes that specific land cover change be cause of the fact that socioeconomic development. In the recent 42 years, average annual temperature have been increasing significantly, although, precipitation have increased but partly weaken effect of the rising temperature, in addition snow cover more sensitive to precipitation than temperature. Results the change of climate showed a nagitive relationship between the NDSI with decrased of the snow cover and climate with increasing of the tempreature. Morover, the relationship between the LUCC and snow cover recorded higher linearity, because the temperature have increased, consequence influence on snow cover that provides melt water for study area which expanding crop land.

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.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Introduction of A New Toolbox for Processing Digital Images From Multiple Camera Networks: FMIPROT

NASA Astrophysics Data System (ADS)

Melih Tanis, Cemal; Nadir Arslan, Ali

2017-04-01

Webcam networks intended for scientific monitoring of ecosystems is providing digital images and other environmental data for various studies. Also, other types of camera networks can also be used for scientific purposes, e.g. usage of traffic webcams for phenological studies, camera networks for ski tracks and avalanche monitoring over mountains for hydrological studies. To efficiently harness the potential of these camera networks, easy to use software which can obtain and handle images from different networks having different protocols and standards is necessary. For the analyses of the images from webcam networks, numerous software packages are freely available. These software packages have different strong features not only for analyzing but also post processing digital images. But specifically for the ease of use, applicability and scalability, a different set of features could be added. Thus, a more customized approach would be of high value, not only for analyzing images of comprehensive camera networks, but also considering the possibility to create operational data extraction and processing with an easy to use toolbox. At this paper, we introduce a new toolbox, entitled; Finnish Meteorological Institute Image PROcessing Tool (FMIPROT) which a customized approach is followed. FMIPROT has currently following features: • straightforward installation, • no software dependencies that require as extra installations, • communication with multiple camera networks, • automatic downloading and handling images, • user friendly and simple user interface, • data filtering, • visualizing results on customizable plots, • plugins; allows users to add their own algorithms. Current image analyses in FMIPROT include "Color Fraction Extraction" and "Vegetation Indices". The analysis of color fraction extraction is calculating the fractions of the colors in a region of interest, for red, green and blue colors along with brightness and luminance parameters. The analysis of vegetation indices is a collection of indices used in vegetation phenology and includes "Green Fraction" (green chromatic coordinate), "Green-Red Vegetation Index" and "Green Excess Index". "Snow cover fraction" analysis which detects snow covered pixels in the images and georeference them on a geospatial plane to calculate the snow cover fraction is being implemented at the moment. FMIPROT is being developed during the EU Life+ MONIMET project. Altogether we mounted 28 cameras at 14 different sites in Finland as MONIMET camera network. In this paper, we will present details of FMIPROT and analysis results from MONIMET camera network. We will also discuss on future planned developments of FMIPROT.

Fractional Snowcover Estimates from Earth Observing System (EOS) Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)

NASA Technical Reports Server (NTRS)

Salomonson, Vincent V.

2002-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua missions has shown considerable capability for mapping snowcover. The typical approach that has used, along with other criteria, the Normalized Snow Difference Index (NDSI) that takes the difference between 500 meter observations at 1.64 micrometers (MODIS band 6) and 0.555 micrometers (MODIS band 4) over the sum of these observations to determine whether MODIS pixels are snowcovered or not in mapping the extent of snowcover. For many hydrological and climate studies using remote sensing of snowcover, it is desirable to assess if the MODIS snowcover observations could not be enhanced by providing the fraction of snowcover in each MODIS observation (pixel). Pursuant to this objective studies have been conducted to assess whether there is sufficient "signal%o in the NDSI parameter to provide useful estimates of fractional snowcover in each MODIS 500 meter pixel. To accomplish this objective high spatial resolution (30 meter) Landsat snowcover observations were used and co-registered with MODIS 500 meter pixels. The NDSI approach was used to assess whether a Landsat pixel was or was not snowcovered. Then the number of snowcovered Landsat pixels within a MODIS pixel was used to determine the fraction of snowcover within each MODIS pixel. The e results were then used to develop statistical relationships between the NDSI value for each 500 meter MODIS pixel and the fraction of snowcover in the MODIS pixel. Such studies were conducted for three widely different areas covered by Landsat scenes in Alaska, Russia, and the Quebec Province in Canada. The statistical relationships indicate that a 10 percent accuracy can be attained. The variability in the statistical relationship for the three areas was found to be remarkably similar (-0.02 for mean error and less than 0.01 for mean absolute error and standard deviation). Independent tests of the relationships were accomplished by taking the relationship of fractional snow-cover to NDSI from one area (e.g., Alaska) and testing it on the other two areas (e.g. Russia and Quebec). Again the results showed that fractional snow-cover can be estimated to 10 percent. The results have been shown to have advantages over other published fractional snowcover algorithms applied to MODIS data. Most recently the fractional snow-cover algorithm has been applied using 500-meter observations over the state of Colorado for a period spanning 25 days. The results exhibit good behavior in mapping the spatial and temporal variability in snowcover over that 25-day period. Overall these studies indicate that robust estimates of fractional snow-cover can be attained using the NDSI parameter over areas extending in size from watersheds relatively large compared to MODIS pixels to global land cover. Other refinements to this approach as well as different approaches are being examined for mapping fractional snow-cover using MODIS observations.

Mesoscale variability of the Upper Colorado River snowpack

USGS Publications Warehouse

Ling, C.-H.; Josberger, E.G.; Thorndike, A.S.

1996-01-01

In the mountainous regions of the Upper Colorado River Basin, snow course observations give local measurements of snow water equivalent, which can be used to estimate regional averages of snow conditions. We develop a statistical technique to estimate the mesoscale average snow accumulation, using 8 years of snow course observations. For each of three major snow accumulation regions in the Upper Colorado River Basin - the Colorado Rocky Mountains, Colorado, the Uinta Mountains, Utah, and the Wind River Range, Wyoming - the snow course observations yield a correlation length scale of 38 km, 46 km, and 116 km respectively. This is the scale for which the snow course data at different sites are correlated with 70 per cent correlation. This correlation of snow accumulation over large distances allows for the estimation of the snow water equivalent on a mesoscale basis. With the snow course data binned into 1/4?? latitude by 1/4?? longitude pixels, an error analysis shows the following: for no snow course data in a given pixel, the uncertainty in the water equivalent estimate reaches 50 cm; that is, the climatological variability. However, as the number of snow courses in a pixel increases the uncertainty decreases, and approaches 5-10 cm when there are five snow courses in a pixel.

Recovery of snow-bent young western larch

Treesearch

Wyman C. Schmidt; Jack A. Schmidt

1979-01-01

This paper illustrates how much, how long it takes, and the method in which young western larch responds to snow bend. A 13-year-old vigorous larch stand was flattened by a heavy, wet snow in June 1966 in northwestern Montana. We recorded photographically how two different crown classes (dominant and nondominant) of young larch subjected to four levels of snow beud (...

Resolving Size Distribution of Black Carbon Internally Mixed With Snow: Impact on Snow Optical Properties and Albedo

NASA Astrophysics Data System (ADS)

He, Cenlin; Liou, Kuo-Nan; Takano, Yoshi

2018-03-01

We develop a stochastic aerosol-snow albedo model that explicitly resolves size distribution of aerosols internally mixed with various snow grains. We use the model to quantify black carbon (BC) size effects on snow albedo and optical properties for BC-snow internal mixing. Results show that BC-induced snow single-scattering coalbedo enhancement and albedo reduction decrease by a factor of 2-3 with increasing BC effective radii from 0.05 to 0.25 μm, while polydisperse BC results in up to 40% smaller visible single-scattering coalbedo enhancement and albedo reduction compared to monodisperse BC with equivalent effective radii. We further develop parameterizations for BC size effects for application to climate models. Compared with a realistic polydisperse assumption and observed shifts to larger BC sizes in snow, respectively, assuming monodisperse BC and typical atmospheric BC effective radii could lead to overestimates of 24% and 40% in BC-snow albedo forcing averaged over different BC and snow conditions.

Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model

USGS Publications Warehouse

Dressler, K.A.; Leavesley, G.H.; Bales, R.C.; Fassnacht, S.R.

2006-01-01

The USGS precipitation-runoff modelling system (PRMS) hydrologic model was used to evaluate experimental, gridded, 1 km2 snow-covered area (SCA) and snow water equivalent (SWE) products for two headwater basins within the Rio Grande (i.e. upper Rio Grande River basin) and Salt River (i.e. Black River basin) drainages in the southwestern USA. The SCA product was the fraction of each 1 km2 pixel covered by snow and was derived from NOAA advanced very high-resolution radiometer imagery. The SWE product was developed by multiplying the SCA product by SWE estimates interpolated from National Resources Conservation Service snow telemetry point measurements for a 6 year period (1995-2000). Measured SCA and SWE estimates were consistently lower than values estimated from temperature and precipitation within PRMS. The greatest differences occurred in the relatively complex terrain of the Rio Grande basin, as opposed to the relatively homogeneous terrain of the Black River basin, where differences were small. Differences between modelled and measured snow were different for the accumulation period versus the ablation period and had an elevational trend. Assimilating the measured snowfields into a version of PRMS calibrated to achieve water balance without assimilation led to reduced performance in estimating streamflow for the Rio Grande and increased performance in estimating streamflow for the Black River basin. Correcting the measured SCA and SWE for canopy effects improved simulations by adding snow mostly in the mid-to-high elevations, where satellite estimates of SCA are lower than model estimates. Copyright ?? 2006 John Wiley & Sons, Ltd.

A multi-objective approach to improve SWAT model calibration in alpine catchments

NASA Astrophysics Data System (ADS)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

Snow survey and vegetation growth in high mountains (Swiss Alps) and additional ERTS investigations in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Two different methods, an analog and a digital one, have been developed for rapid and accurate mapping of the areal extent and changes in snow cover in high mountains. The quick-look method is based on individual visual control of each image using a photo quantizer which provides exact references for density slicing with high resolution lith-film. The digital snow classification system is based on discriminant analysis with the data of the four multispectral bands as variables and contains all preprocessing, feature extraction, and mapping steps for an operational application. Two different sets of sampling groups were established which apply to different conditions of snow cover. The first one serves for the normal situation with a uniform dry and new cover. The second one serves for situations with partly thawing and/or frozen snow.

Influence of climate on alpine stream chemistry and water sources

USGS Publications Warehouse

Foks, Sydney; Stets, Edward; Singha, Kamini; Clow, David W.

2018-01-01

The resilience of alpine/subalpine watersheds may be viewed as the resistance of streamflow or stream chemistry to change under varying climatic conditions, which is governed by the relative size (volume) and transit time of surface and subsurface water sources. Here, we use end‐member mixing analysis in Andrews Creek, an alpine stream in Rocky Mountain National Park, Colorado, from water year 1994 to 2015, to explore how the partitioning of water sources and associated hydrologic resilience change in response to climate. Our results indicate that four water sources are significant contributors to Andrews Creek, including snow, rain, soil water, and talus groundwater. Seasonal patterns in source‐water contributions reflected the seasonal hydrologic cycle, which is driven by the accumulation and melting of seasonal snowpack. Flushing of soil water had a large effect on stream chemistry during spring snowmelt, despite making only a small contribution to streamflow volume. Snow had a large influence on stream chemistry as well, contributing large amounts of water with low concentrations of weathering products. Interannual patterns in end‐member contributions reflected responses to drought and wet periods. Moderate and significant correlations exist between annual end‐member contributions and regional‐scale climate indices (the Palmer Drought Severity Index, the Palmer Hydrologic Drought Index, and the Modified Palmer Drought Severity Index). From water year 1994 to 2015, the percent contribution from the talus‐groundwater end member to Andrews Creek increased an average of 0.5% per year (p < 0.0001), whereas the percent contributions from snow plus rain decreased by a similar amount (p = 0.001). Our results show how water and solute sources in alpine environments shift in response to climate variability and highlight the role of talus groundwater and soil water in providing hydrologic resilience to the system.

White water: Fifty years of snow research in WRR and the outlook for the future

NASA Astrophysics Data System (ADS)

Sturm, Matthew

2015-07-01

Over the past 50 years, 239 papers related to snow have been published in Water Resources Research (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

BOREAS HYD-4 Standard Snow Course Data

NASA Technical Reports Server (NTRS)

Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data during the winter focused field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. The data are provided in tabular ASCII files. The HYD-4 standard snow course 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).

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.

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Montpetit, B.; Bartlett, P. A.; Langlois, A.

2012-12-01

Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, whereas it considers the liquid water content for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, Arctic and sub-Arctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.1 m2 kg-1, and a RMSE of 4.9 m2 kg-1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

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.

On the Impact of Snow Salinity on CryoSat-2 First-Year Sea Ice Thickness Retrievals

NASA Astrophysics Data System (ADS)

Nandan, V.; Yackel, J.; Geldsetzer, T.; Mahmud, M.

2017-12-01

European Space Agency's Ku-band altimeter CryoSat-2 (CS-2) has demonstrated its potential to provide extensive basin-scale spatial and temporal measurements of Arctic sea ice freeboard. It is assumed that CS-2 altimetric returns originate from the snow/sea ice interface (assumed to be the main scattering horizon). However, in newly formed thin ice ( 0.6 m) through to thick first-year sea ice (FYI) ( 2 m), upward wicking of brine into the snow cover from the underlying sea ice surface produces saline snow layers, especially in the bottom 6-8 cm of a snow cover. This in turn modifies the brine volume at/or near the snow/sea ice interface, altering the dielectric and scattering properties of the snow cover, leading to strong Ku-band microwave attenuation within the upper snow volume. Such significant reductions in Ku-band penetration may substantially affect CS-2 FYI freeboard retrievals. Therefore, the goal of this study is to evaluate a theoretical approach to estimate snow salinity induced uncertainty on CS-2 Arctic FYI freeboard measurements. Using the freeboard-to-thickness hydrostatic equilibrium equation, we quantify the error differences between the CS-2 FYI thickness, (assuming complete penetration of CS-2 radar signals to the snow/FYI interface), and the FYI thickness based on the modeled Ku-band main scattering horizon for different snow cover cases. We utilized naturally occurring saline and non-saline snow cover cases ranging between 6 cm to 32 cm from the Canadian Arctic, observed during late-winter from 1993 to 2017, on newly-formed ice ( 0.6 m), medium ( 1.5 m) and thick FYI ( 2 m). Our results suggest that irrespective of the thickness of the snow cover overlaying FYI, the thickness of brine-wetted snow layers and actual FYI freeboard strongly influence the amount with which CS-2 FYI freeboard estimates and thus thickness calculations are overestimated. This effect is accentuated for increasingly thicker saline snow covers overlaying newly-formed ice, which accounted to an overestimated FYI thickness by 250%, when compared to 80% overestimations on thinner saline snow covers, and the error reduces with increase in FYI thickness. Our study recommends the CS-2 sea ice community to add snow salinity as a potential error source, affecting CS-2 Arctic FYI freeboard and thickness retrievals.

The Snowcloud System: Architecture and Algorithms for Snow Hydrology Studies

NASA Astrophysics Data System (ADS)

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

2013-12-01

Snowcloud is an embedded data collection system for snow hydrology field research campaigns conducted in harsh climates and remote areas. The system combines distributed wireless sensor network technology and computational techniques to provide data at lower cost and higher spatio-temporal resolution than ground-based systems using traditional methods. Snowcloud has seen multiple Winter deployments in settings ranging from high desert to arctic, resulting in over a dozen node-years of practical experience. The Snowcloud system architecture consists of multiple TinyOS mesh-networked sensor stations collecting environmental data above and, in some deployments, below the snowpack. Monitored data modalities include snow depth, ground and air temperature, PAR and leaf-area index (LAI), and soil moisture. To enable power cycling and control of multiple sensors a custom power and sensor conditioning board was developed. The electronics and structural systems for individual stations have been designed and tested (in the lab and in situ) for ease of assembly and robustness to harsh winter conditions. Battery systems and solar chargers enable seasonal operation even under low/no light arctic conditions. Station costs range between 500 and 1000 depending on the instrumentation suite. For remote field locations, a custom designed hand-held device and data retrieval protocol serves as the primary data collection method. We are also developing and testing a Gateway device that will report data in near-real-time (NRT) over a cellular connection. Data is made available to users via web interfaces that also provide basic data analysis and visualization tools. For applications to snow hydrology studies, the better spatiotemporal resolution of snowpack data provided by Snowcloud is beneficial in several aspects. It provides insight into snowpack evolution, and allows us to investigate differences across different spatial and temporal scales in deployment areas. It enables the relation of local variations in accumulation to environmental parameters such as slope, micro-topography and vegetation cover. The relative importance of such factors is likely to change over the snow season, for example as deposition fills depressions reducing surface roughness due to micro-topography and low lying vegetation. Snowcloud has also been deployed to support the acquisition of satellite imagery from Radarsat-2 and TanDEM-X missions. In capturing spatial variability Snowcloud provides better validation data than single node systems. An important use case of Snowcloud is its application to modeling snow-water equivalent SWE evolution, and more accurate areal average SWE estimation in locations with a typical paucity of snowpack data. We are especially interested in applying machine learning techniques to find relations between average SWE in remote areas and regional infrastructure data (e.g. regional snow pillows). These techniques leverage a combination of data provided by Snowcloud systems and periodic manual snow courses in short field campaigns. Furthermore, using machine learning we can explore the importance of snowpack variables for synthetic aperture radar backscatter at co- and cross-polarizations to better understand scattering processes and unify models of scattering with in situ observations at high resolution.

Conceptualisation of Snowpack Isotope Dynamics in Spatially Distributed Tracer-Aided Runoff Models in Snow Influenced Northern Cathments

NASA Astrophysics Data System (ADS)

Ala-aho, P. O. A.; Tetzlaff, D.; Laudon, H.; McNamara, J. P.; Soulsby, C.

2016-12-01

We use the Spatially distributed Tracer-Aided Rainfall-Runoff (STARR) modelling framework to explore non-stationary flow and isotope response in three northern headwater catchments. The model simulates dynamic, spatially variable tracer concentration in different water stores and fluxes within a catchment, which can constrain internal catchment mixing processes, flow paths and associated water ages. To date, a major limitation in using such models in snow-dominated catchments has been the difficulties in paramaterising the isotopic transformations in snowpack accumulation and melt. We use high quality long term datasets for hydrometrics and stable water isotopes collected in three northern study catchments for model calibration and testing. The three catchments exhibit different hydroclimatic conditions, soil and vegetation types, and topographic relief, which brings about variable degree of snow dominance across the catchments. To account for the snow influence we develop novel formulations to estimate the isotope evolution in the snowpack and melt. Algorithms for the isotopic evolution parameterize an isotopic offset between snow evaporation and melt fluxes and the remaining snow storage. The model for each catchment is calibrated to match both streamflow and tracer concentration at the stream outlet to ensure internal consistency of the system behaviour. The model is able to reproduce the streamflow along with the spatio-temporal differences in tracer concentrations across the three studies catchments reasonably well. Incorporating the spatially distributed snowmelt processes and associated isotope transformations proved essential in capturing the stream tracer reponse for strongly snow-influenced cathments. This provides a transferrable tool which can be used to understand spatio-temporal variability of mixing and water ages for different storages and flow paths in other snow influenced, environments.

Impact of Grain Shape and Multiple Black Carbon Internal Mixing on Snow Albedo: Parameterization and Radiative Effect Analysis

NASA Astrophysics Data System (ADS)

He, Cenlin; Liou, Kuo-Nan; Takano, Yoshi; Yang, Ping; Qi, Ling; Chen, Fei

2018-01-01

We quantify the effects of grain shape and multiple black carbon (BC)-snow internal mixing on snow albedo by explicitly resolving shape and mixing structures. Nonspherical snow grains tend to have higher albedos than spheres with the same effective sizes, while the albedo difference due to shape effects increases with grain size, with up to 0.013 and 0.055 for effective radii of 1,000 μm at visible and near-infrared bands, respectively. BC-snow internal mixing reduces snow albedo at wavelengths < 1.5 μm, with negligible effects at longer wavelengths. Nonspherical snow grains show less BC-induced albedo reductions than spheres with the same effective sizes by up to 0.06 at ultraviolet and visible bands. Compared with external mixing, internal mixing enhances snow albedo reduction by a factor of 1.2-2.0 at visible wavelengths depending on BC concentration and snow shape. The opposite effects on albedo reductions due to snow grain nonsphericity and BC-snow internal mixing point toward a careful investigation of these two factors simultaneously in climate modeling. We further develop parameterizations for snow albedo and its reduction by accounting for grain shape and BC-snow internal/external mixing. Combining the parameterizations with BC-in-snow measurements in China, North America, and the Arctic, we estimate that nonspherical snow grains reduce BC-induced albedo radiative effects by up to 50% compared with spherical grains. Moreover, BC-snow internal mixing enhances the albedo effects by up to 30% (130%) for spherical (nonspherical) grains relative to external mixing. The overall uncertainty induced by snow shape and BC-snow mixing state is about 21-32%.

Winter Insulation By Snow Accumulation in a Subarctic Treeline Ecosystem Increases Summer Carbon Cycling Rates

NASA Astrophysics Data System (ADS)

Parker, T.; Subke, J. A.; Wookey, P. A.

2014-12-01

The effect of snow accumulation on soil carbon and nutrient cycling is attracting substantial attention from researchers. We know that deeper snow accumulation caused by high stature vegetation increases winter microbial activity and therefore carbon and nitrogen flux rates. However, until now the effect of snow accumulation, by buffering winter soil temperature, on subsequent summer soil processes, has scarcely been considered. We carried out an experiment at an alpine treeline in subarctic Sweden in which soil monoliths, contained within PVC collars, were transplanted between forest (deep winter snow) and tundra heath (shallow winter snow). We measured soil CO2efflux over two growing seasons and quantified soil microbial biomass after the second winter. We showed that respiration rates of transplanted forest soil were significantly reduced compared with control collars (remaining in the forest) as a consequence of colder, but more variable, winter temperatures. We hypothesised that microbial biomass would be reduced in transplanted forests soils but found there was no difference compared to control. We therefore further hypothesised that the similarly sized microbial pool in the control is assembled differently to the transplant. We believe that the warmer winters in forests foster more active consortia of decomposer microbes as a result of different abiotic selection pressures. Using an ecosystem scale experimental approach, we have identified a mechanism that influences summer carbon cycling rates based solely on the amount of snow that accumulates the previous winter. We conclude that modification of snow depth as a consequence of changes in vegetation structure is an important mechanism influencing soil C stocks in ecosystems where snow persists for a major fraction of the year.

Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

2017-12-01

Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

How Can Polarization States of Reflected Light from Snow Surfaces Inform Us on Surface Normals and Ultimately Snow Grain Size Measurements?

NASA Astrophysics Data System (ADS)

Schneider, A. M.; Flanner, M.; Yang, P.; Yi, B.; Huang, X.; Feldman, D.

2016-12-01

The Snow Grain Size and Pollution (SGSP) algorithm is a method applied to Moderate Resolution Imaging Spectroradiometer data to estimate snow grain size from space-borne measurements. Previous studies validate and quantify potential sources of error in this method, but because it assumes flat snow surfaces, however, large scale variations in surface normals can cause biases in its estimates due to its dependence on solar and observation zenith angles. To address these variations, we apply the Monte Carlo method for photon transport using data containing the single scattering properties of different ice crystals to calculate polarization states of reflected monochromatic light at 1500nm from modeled snow surfaces. We evaluate the dependence of these polarization states on solar and observation geometry at 1500nm because multiple scattering is generally a mechanism for depolarization and the ice crystals are relatively absorptive at this wavelength. Using 1500nm thus results in a higher number of reflected photons undergoing fewer scattering events, increasing the likelihood of reflected light having higher degrees of polarization. In evaluating the validity of the model, we find agreement with previous studies pertaining to near-infrared spectral directional hemispherical reflectance (i.e. black-sky albedo) and similarities in measured bidirectional reflectance factors, but few studies exist modeling polarization states of reflected light from snow surfaces. Here, we present novel results pertaining to calculated polarization states and compare dependences on solar and observation geometry for different idealized snow surfaces. If these dependencies are consistent across different ice particle shapes and sizes, then these findings could inform the SGSP algorithm by providing useful relationships between measurable physical quantities and solar and observation geometry to better understand variations in snow surface normals from remote sensing observations.

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

PubMed

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

2013-12-01

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

Seasonal Snow Extent and Snow Mass in South America Using SMMR and SSM/I Passive Microwave Data (1979-2003)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Chiu, L.; Kelly, R. E.; Powell, H.; Chiu, L.

2007-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2003, both snow cover extent and snow depth (snow mass) were investigated during coldest months (May-September), primarily in the Patagonia area of Argentina and in Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and also usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average snow extent during the 25-year period of record, is 320,700 km(exp 2). In July of 1984, the average monthly snow cover was 701,250 km(exp 2) - the most extensive coverage observed between 1979 and 2003. However, in July of 1989, snow cover extent was only 120 km(exp 2). The 25-year period of record shows a sinusoidal like pattern, though there appears to be no obvious trend in either increasing or decreasing snow extent or snow mass between 1979 and 2003.

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.

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.

Observations of Precipitation Size and Fall Speed Characteristics within Coexisting Rain and Wet Snow

NASA Technical Reports Server (NTRS)

Yuter, Sandra E.; Kingsmill, David E.; Nance, Louisa B.; Loeffler-Mang, Martin

2006-01-01

Ground-based measurements of particle size and fall speed distributions using a Particle Size and Velocity (PARSIVEL) disdrometer are compa red among samples obtained in mixed precipitation (rain and wet snow) and rain in the Oregon Cascade Mountains and in dry snow in the Rock y Mountains of Colorado. Coexisting rain and snow particles are distinguished using a classification method based on their size and fall sp eed properties. The bimodal distribution of the particles' joint fall speed-size characteristics at air temperatures from 0.5 to 0 C suggests that wet-snow particles quickly make a transition to rain once mel ting has progressed sufficiently. As air temperatures increase to 1.5 C, the reduction in the number of very large aggregates with a diame ter > 10 mm coincides with the appearance of rain particles larger than 6 mm. In this setting. very large raindrops appear to be the result of aggregates melting with minimal breakup rather than formation by c oalescence. In contrast to dry snow and rain, the fall speed for wet snow has a much weaker correlation between increasing size and increasing fall speed. Wet snow has a larger standard deviation of fall spee d (120%-230% relative to dry snow) for a given particle size. The ave rage fall speed for observed wet-snow particles with a diameter great er than or equal to 2.4 mm is 2 m/s with a standard deviation of 0.8 m/s. The large standard deviation is likely related to the coexistence of particles of similar physical size with different percentages of melting. These results suggest that different particle sizes are not required for aggregation since wet-snow particles of the same size can have different fall speeds. Given the large standard deviation of fa ll speeds in wet snow, the collision efficiency for wet snow is likely larger than that of dry snow. For particle sizes between 1 and 10 mm in diameter within mixed precipitation, rain constituted I % of the particles by volume within the isothermal layer at 0 C and 4% of the particles by volume for the region just below the isothermal layer where air temperatures rise from 0" to 0.5"C. As air temperatures increa sed above 0.5 C, the relative proportions of rain versus snow particl es shift dramatically and raindrops become dominant. The value of 0.5 C for the sharp transition in volume fraction from snow to rain is sl ightly lower than the range from 1 .l to 1.7 C often used in hydrolog ical models.

Snow deposition and melt under different vegetative covers in central New York

Treesearch

A. R. Eschner; D. R. Satterlund

1963-01-01

Two-thirds of the annual runoff from watersheds in the Allegheny Plateau of central New York comes from the snow-or snow and rain that falls in December through April. Although the amounts of precipitation in this period are fairly uniform from year to year, the proportion that falls as snow varies; so does the amount that accumulates on the ground, and its duration...

Sensitivity of Alpine Snow and Streamflow Regimes to Climate Changes

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Numerical run-out modelling used for reassessment of existing permanent avalanche paths in the Krkonose Mts., Czechia

NASA Astrophysics Data System (ADS)

Blahut, Jan; Klimes, Jan; Balek, Jan; Taborik, Petr; Juras, Roman; Pavlasek, Jiri

2015-04-01

Run-out modelling of snow avalanches is being widely applied in high mountain areas worldwide. This study presents application of snow avalanche run-out calculation applied to mid-mountain ranges - the Krkonose, Jeseniky and Kralicky Sneznik Mountains. All mentioned mountain ranges lie in the northern part of Czechia, close to the border with Poland. Its highest peak reaches only 1602 m a.s.l. However, climatic conditions and regular snowpack presence are the reason why these mountain ranges experience considerable snow avalanche activity every year, sometimes resulting in injuries or even fatalities. Within the aim of an applied project dealing with snow avalanche hazard prediction a re-assessment of permanent snow avalanche paths has been performed based on extensive statistics covering period from 1961/62 till present. On each avalanche path different avalanches with different return periods were modelled using the RAMMS code. As a result, an up-to-date snow avalanche hazard map was prepared.

A Generalized Framework for Different Drought Indices: Testing its Suitability in a Simulation of the last two Millennia for Europe

NASA Astrophysics Data System (ADS)

Raible, Christoph C.; Baerenbold, Oliver; Gomez-Navarro, Juan Jose

2016-04-01

Over the past decades, different drought indices have been suggested in the literature. This study tackles the problem of how to characterize drought by defining a general framework and proposing a generalized family of drought indices that is flexible regarding the use of different water balance models. The sensitivity of various indices and its skill to represent drought conditions is evaluated using a regional model simulation in Europe spanning the last two millennia as test bed. The framework combines an exponentially damped memory with a normalization method based on quantile mapping. Both approaches are more robust and physically meaningful compared to the existing methods used to define drought indices. Still, framework is flexible with respect to the water balance, enabling users to adapt the index formulation to the data availability of different locations. Based on the framework, indices with different complex water balances are compared with each other. The comparison shows that a drought index considering only precipitation in the water balance is sufficient for Western to Central Europe. However, in the Mediterranean temperature effects via evapotranspiration need to be considered in order to produce meaningful indices representative of actual water deficit. Similarly, our results indicate that in north-eastern Europe and Scandinavia, snow and runoff effects needs to be considered in the index definition to obtain accurate results.

A full year of snow on sea ice observations and simulations - Plans for MOSAiC 2019/20

NASA Astrophysics Data System (ADS)

Nicolaus, M.; Geland, S.; Perovich, D. K.

2017-12-01

The snow cover on sea on sea ice dominates many exchange processes and properties of the ice covered polar oceans. It is a major interface between the atmosphere and the sea ice with the ocean underneath. Snow on sea ice is known for its extraordinarily large spatial and temporal variability from micro scales and minutes to basin wide scales and decades. At the same time, snow cover properties and even snow depth distributions are among the least known and most difficult to observe climate variables. Starting in October 2019 and ending in October 2020, the international MOSAiC drift experiment will allow to observe the evolution of a snow pack on Arctic sea ice over a full annual cycle. During the drift with one ice floe along the transpolar drift, we will study snow processes and interactions as one of the main topics of the MOSAiC research program. Thus we will, for the first time, be able to perform such studies on seasonal sea ice and relate it to previous expeditions and parallel observations at different locations. Here we will present the current status of our planning of the MOSAiC snow program. We will summarize the latest implementation ideas to combine the field observations with numerical simulations. The field program will include regular manual observations and sampling on the main floe of the central observatory, autonomous recordings in the distributed network, airborne observations in the surrounding of the central observatory, and retrievals of satellite remote sensing products. Along with the field program, numerical simulations of the MOSAiC snow cover will be performed on different scales, including large-scale interaction with the atmosphere and the sea ice. The snow studies will also bridge between the different disciplines, including physical, chemical, biological, and geochemical measurements, samples, and fluxes. The main challenge of all measurements will be to accomplish the description of the full annual cycle.

Snow Leopard and Himalayan Wolf: Food Habits and Prey Selection in the Central Himalayas, Nepal.

PubMed

Chetri, Madhu; Odden, Morten; Wegge, Per

2017-01-01

Top carnivores play an important role in maintaining energy flow and functioning of the ecosystem, and a clear understanding of their diets and foraging strategies is essential for developing effective conservation strategies. In this paper, we compared diets and prey selection of snow leopards and wolves based on analyses of genotyped scats (snow leopards n = 182, wolves n = 57), collected within 26 sampling grid cells (5×5 km) that were distributed across a vast landscape of ca 5000 km2 in the Central Himalayas, Nepal. Within the grid cells, we sampled prey abundances using the double observer method. We found that interspecific differences in diet composition and prey selection reflected their respective habitat preferences, i.e. snow leopards significantly preferred cliff-dwelling wild ungulates (mainly bharal, 57% of identified material in scat samples), whereas wolves preferred typically plain-dwellers (Tibetan gazelle, kiang and argali, 31%). Livestock was consumed less frequently than their proportional availability by both predators (snow leopard = 27%; wolf = 24%), but significant avoidance was only detected among snow leopards. Among livestock species, snow leopards significantly preferred horses and goats, avoided yaks, and used sheep as available. We identified factors influencing diet composition using Generalized Linear Mixed Models. Wolves showed seasonal differences in the occurrence of small mammals/birds, probably due to the winter hibernation of an important prey, marmots. For snow leopard, occurrence of both wild ungulates and livestock in scats depended on sex and latitude. Wild ungulates occurrence increased while livestock decreased from south to north, probably due to a latitudinal gradient in prey availability. Livestock occurred more frequently in scats from male snow leopards (males: 47%, females: 21%), and wild ungulates more frequently in scats from females (males: 48%, females: 70%). The sexual difference agrees with previous telemetry studies on snow leopards and other large carnivores, and may reflect a high-risk high-gain strategy among males.

Snow Leopard and Himalayan Wolf: Food Habits and Prey Selection in the Central Himalayas, Nepal

PubMed Central

Odden, Morten; Wegge, Per

2017-01-01

Top carnivores play an important role in maintaining energy flow and functioning of the ecosystem, and a clear understanding of their diets and foraging strategies is essential for developing effective conservation strategies. In this paper, we compared diets and prey selection of snow leopards and wolves based on analyses of genotyped scats (snow leopards n = 182, wolves n = 57), collected within 26 sampling grid cells (5×5 km) that were distributed across a vast landscape of ca 5000 km2 in the Central Himalayas, Nepal. Within the grid cells, we sampled prey abundances using the double observer method. We found that interspecific differences in diet composition and prey selection reflected their respective habitat preferences, i.e. snow leopards significantly preferred cliff-dwelling wild ungulates (mainly bharal, 57% of identified material in scat samples), whereas wolves preferred typically plain-dwellers (Tibetan gazelle, kiang and argali, 31%). Livestock was consumed less frequently than their proportional availability by both predators (snow leopard = 27%; wolf = 24%), but significant avoidance was only detected among snow leopards. Among livestock species, snow leopards significantly preferred horses and goats, avoided yaks, and used sheep as available. We identified factors influencing diet composition using Generalized Linear Mixed Models. Wolves showed seasonal differences in the occurrence of small mammals/birds, probably due to the winter hibernation of an important prey, marmots. For snow leopard, occurrence of both wild ungulates and livestock in scats depended on sex and latitude. Wild ungulates occurrence increased while livestock decreased from south to north, probably due to a latitudinal gradient in prey availability. Livestock occurred more frequently in scats from male snow leopards (males: 47%, females: 21%), and wild ungulates more frequently in scats from females (males: 48%, females: 70%). The sexual difference agrees with previous telemetry studies on snow leopards and other large carnivores, and may reflect a high-risk high-gain strategy among males. PMID:28178279

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

Snowpack is a critical freshwater reservoir that sustains ecosystem, natural habitat, hydropower, agriculture, and urban water supply in many areas around the world. Accurate estimation of basin scale snow water equivalent (SWE), through both measurement and modeling, has been significantly recognized to improve regional water resource management. Recent advances in remote data acquisition techniques have improved snow measurements but our ability to model snowpack evolution is largely hampered by poor knowledge of inherently variable high-elevation precipitation patterns. For a variety of reasons, majority of the precipitation gages are located in low and mid-elevation range and function as drivers for basin scale hydrologic modeling. Here, we blend observed gage precipitation from low and mid-elevation with point observations of SWE from high-elevation snow pillow into a physically based snow evolution model (SnowModel) to better represent the basin-scale precipitation field and improve snow simulations. To do this, we constructed two scenarios that differed in only precipitation. In WTH scenario, we forced the SnowModel using spatially distributed gage precipitation data. In WTH+SP scenario, the model was forced with spatially distributed precipitation data derived from gage precipitation along with observed precipitation from snow pillows. Since snow pillows do not directly measure precipitation, we uses positive change in SWE as a proxy for precipitation. The SnowModel was implemented at daily time step and 100 m resolution for the Kings River Basin, USA over 2000-2014. Our results show an improvement in snow simulation under WTH+SP as compared to WTH scenario, which can be attributed to better representation in high-elevation precipitation patterns under WTH+SP. The average Nash Sutcliffe efficiency over all snow pillow and course sites was substantially higher for WTH+SP (0.77) than for WTH scenario (0.47). The maximum difference in observed and simulated peak SWE was 810 mm for WTH and 380 mm for WTH+SP, which led to underestimation of snow season length and melt rate by up to 30 days and 12 mm/day, respectively, in WTH scenario. These results indicate that point scale snow observations at higher elevation can be used to improve precipitation input to hydrologic modeling in mountainous basins.

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.

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

NASA Astrophysics Data System (ADS)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

Archival processes of the water stable isotope signal in East Antarctic ice cores

NASA Astrophysics Data System (ADS)

Casado, Mathieu; Landais, Amaelle; Picard, Ghislain; Münch, Thomas; Laepple, Thomas; Stenni, Barbara; Dreossi, Giuliano; Ekaykin, Alexey; Arnaud, Laurent; Genthon, Christophe; Touzeau, Alexandra; Masson-Delmotte, Valerie; Jouzel, Jean

2018-05-01

The oldest ice core records are obtained from the East Antarctic Plateau. Water isotopes are key proxies to reconstructing past climatic conditions over the ice sheet and at the evaporation source. The accuracy of climate reconstructions depends on knowledge of all processes affecting water vapour, precipitation and snow isotopic compositions. Fractionation processes are well understood and can be integrated in trajectory-based Rayleigh distillation and isotope-enabled climate models. However, a quantitative understanding of processes potentially altering snow isotopic composition after deposition is still missing. In low-accumulation sites, such as those found in East Antarctica, these poorly constrained processes are likely to play a significant role and limit the interpretability of an ice core's isotopic composition. By combining observations of isotopic composition in vapour, precipitation, surface snow and buried snow from Dome C, a deep ice core site on the East Antarctic Plateau, we found indications of a seasonal impact of metamorphism on the surface snow isotopic signal when compared to the initial precipitation. Particularly in summer, exchanges of water molecules between vapour and snow are driven by the diurnal sublimation-condensation cycles. Overall, we observe in between precipitation events modification of the surface snow isotopic composition. Using high-resolution water isotopic composition profiles from snow pits at five Antarctic sites with different accumulation rates, we identified common patterns which cannot be attributed to the seasonal variability of precipitation. These differences in the precipitation, surface snow and buried snow isotopic composition provide evidence of post-deposition processes affecting ice core records in low-accumulation areas.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Preferential Deposition of Snow in Mountains Revisited

NASA Astrophysics Data System (ADS)

Lehning, M.; Comola, F.

2017-12-01

Inhomogeneous snow accumulation in mountainous terrain is caused by precipitation gradients, spatial deposition differences as well as snow transport. The effect of spatially varying deposition as a function of near-surface flow - particle interactions has had some attention in the last decade but different groups have found conflicting results on both the relative magnitude of the effect as well as the resulting snow distribution patterns. Since in the field and through measurements it is difficult to separate preferential deposition from the other two processes, the investigation needs to rely on modellig. We present a new and complete model of flow - particle dynamics, which combines large eddy flow field simulations (LES) with Lagrangian stochastic modelling (LSM) over topography of varying complexity. Using a non-dimensionalized formulation of flow - particle interactions, we present systematic investigations on how particle properties (inertia, shape), flow properties (wind speed) and topography (height, width) influence the magnitude and distribution pattern of snow deposition. It is shown that dependent on Froude and Stokes numbers, very different deposition patterns can result with maximum deposition either in the windward or lee of a ridge and that dendridic snow is behaving similar to inertialess tracers.

Snow farming: conserving snow over the summer season

NASA Astrophysics Data System (ADS)

Grünewald, Thomas; Wolfsperger, Fabian; Lehning, Michael

2018-01-01

Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63 % of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9 % of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters thickness of the sawdust layer, air temperature, precipitation and wind speed were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for mid-elevation (approx. 1500 m a.s.l.) Alpine climates and can be managed with reasonable effort.

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.

Grey Tienshan Urumqi Glacier No.1 and light-absorbing impurities.

PubMed

Ming, Jing; Xiao, Cunde; Wang, Feiteng; Li, Zhongqin; Li, Yamin

2016-05-01

The Tienshan Urumqi Glacier No.1 (TUG1) usually shows "grey" surfaces in summers. Besides known regional warming, what should be responsible for largely reducing its surface albedo and making it look "grey"? A field campaign was conducted on the TUG1 on a selected cloud-free day of 2013 after a snow fall at night. Fresh and aged snow samples were collected in the field, and snow densities, grain sizes, and spectral reflectances were measured. Light-absorbing impurities (LAIs) including black carbon (BC) and dust, and number concentrations and sizes of the insoluble particles (IPs) in the samples were measured in the laboratory. High temperatures in summer probably enhanced the snow ageing. During the snow ageing process, the snow density varied from 243 to 458 kg m(-3), associated with the snow grain size varying from 290 to 2500 μm. The concentrations of LAIs in aged snow were significantly higher than those in fresh snow. Dust and BC varied from 16 ppm and 25 ppb in fresh snow to 1507 ppm and 1738 ppb in aged snow, respectively. Large albedo difference between the fresh and aged snow suggests a consequent forcing of 180 W m(-2). Simulations under scenarios show that snow ageing, BC, and dust were responsible for 44, 25, and 7 % of the albedo reduction in the accumulation zone, respectively.

Early formation of preferential flow in a homogeneous snowpack observed by micro-CT

NASA Astrophysics Data System (ADS)

Avanzi, Francesco; Petrucci, Giacomo; Matzl, Margret; Schneebeli, Martin; De Michele, Carlo

2017-05-01

We performed X-ray microtomographic observations of wet-snow metamorphism during controlled continuous melting and melt-freeze events in the laboratory. Three blocks of snow were sieved into boxes and subjected to cyclic, superficial heating or heating-cooling to reproduce vertical water infiltration patterns in snow similarly to natural conditions. Periodically, samples were taken at different heights and scanned. Results suggest that wet-snow metamorphism dynamics are highly heterogeneous even in an initially homogeneous snowpack. Consistent with previous work, we observed an increase with time in the thickness of the ice structure, which is a measure of grain size. However, this was coupled with large temporal scatter between consecutive measurements of the specific surface area and of the statistical moments of grain thickness distributions. Because of marked differences in the right tail, grain thickness distributions did not show shape invariance with time, contrary to previous analyses. In our experiments, wet-snow metamorphism showed two strikingly different patterns: homogeneous coarsening superimposed by faster heterogeneous coarsening in areas that were affected by preferential percolation of water. Liquid water movement in snow and fast structural evolution may be thus intrinsically coupled by early formation of preferential flow at local scale. These observations suggest that further experiments are highly needed to fully understand wet-snow metamorphism and infiltration patterns in a natural snowpack.

Seasonal Snow Extent and Snow Mass in South America using SMMR and SSM/I Passive Microwave Data (1979-2006)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Kelly, R. E. J.; Chiu, L.

2008-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and the Special Sensor Microwave Imagers (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow water equivalent (snow mass) were investigated during the coldest months (May-September), primarily in the Patagonia area of Argentina and in the Andes of Chile, Argentina and Bolivia, where most of the seasonal snow is found. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 km(exp 2). In July of 1984, the average monthly snow cover extent was 701,250 km(exp 2) the most extensive coverage observed between 1979 and 2006. However, in July of 1989, snow cover extent was only 120,000 km(exp 2). The 28-year period of record shows a sinusoidal like pattern for both snow cover and snow mass, though neither trend is significant at the 95% level.

Interannual changes in snow cover and its impact on ground surface temperatures in Livingston Island (Antarctica)

NASA Astrophysics Data System (ADS)

Nieuwendam, Alexandre; Ramos, Miguel; Vieira, Gonçalo

2015-04-01

In permafrost areas the seasonal snow cover is an important factor on the ground thermal regime. Snow depth and timing are important in ground insulation from the atmosphere, creating different snow patterns and resulting in spatially variable ground temperatures. The aim of this work is to characterize the interactions between ground thermal regimes and snow cover and the influence on permafrost spatial distribution. The study area is the ice-free terrains of northwestern Hurd Peninsula in the vicinity of the Spanish Antarctic Station "Juan Carlos I" and Bulgarian Antarctic Station "St. Kliment Ohridski". Air and ground temperatures and snow thickness data where analysed from 4 sites along an altitudinal transect in Hurd Peninsula from 2007 to 2012: Nuevo Incinerador (25 m asl), Collado Ramos (110 m), Ohridski (140 m) and Reina Sofia Peak (275 m). The data covers 6 cold seasons showing different conditions: i) very cold with thin snow cover; ii) cold with a gradual increase of snow cover; iii) warm with thick snow cover. The data shows three types of periods regarding the ground surface thermal regime and the thickness of snow cover: a) thin snow cover and short-term fluctuation of ground temperatures; b) thick snow cover and stable ground temperatures; c) very thick snow cover and ground temperatures nearly constant at 0°C. a) Thin snow cover periods: Collado Ramos and Ohridski sites show frequent temperature variations, alternating between short-term fluctuations and stable ground temperatures. Nuevo Incinerador displays during most of the winter stable ground temperatures; b) Cold winters with a gradual increase of the snow cover: Nuevo Incinerador, Collado Ramos and Ohridski sites show similar behavior, with a long period of stable ground temperatures; c) Thick snow cover periods: Collado Ramos and Ohridski show long periods of stable ground, while Nuevo Incinerador shows temperatures close to 0°C since the beginning of the winter, due to early snow cover, which prevents cooling. Reina Sofia shows a very different behavior from the other sites, with a frequent stabilization of ground temperatures during all the winters, and last until late-fall. This situation could be related to the structure, and physical and thermal properties of snow cover. The analysis of the Freezing Degree Days (FDDs) and freezing n-factor reveals significant interannual variations. Ohridski shows the highest FDDs values followed by Reina Sofia. Nuevo Incinerador showed the lowest FDDs values. The freezing n-factor shows highest values at Ohridski, followed by Collado Ramos and Reina Sofia with very similar values. Nuevo Incinerador shows the lowest n-factor values. Snow cover doesn't insulate the ground from freezing, but depending on its thickness, density and the amount of heat in the ground, it decreases ground temperatures amplitudes and increases delays relative to air temperature changes. Even where snow cover remains several centimeters thick for several months, slow decrease of bottom temperature is possible, reaching a minimum value at the end of the winter. The results demonstrate that Reina Sofia and Ohridski sites, because of the seasonal behavior, FDDs and freezing n-factor, demonstrate higher winter ground cooling. This research was funded by PERMANTAR-3 (PTDC/AAG-GLO/3908/2012) project (Fundação para a Ciência e a Tecnologia of Portugal)

Interactions Between Ice Thickness, Bottom Ice Algae, and Transmitted Spectral Irradiance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Arntsen, A. E.; Perovich, D. K.; Polashenski, C.; Stwertka, C.

2015-12-01

The amount of light that penetrates the Arctic sea ice cover impacts sea-ice mass balance as well as ecological processes in the upper ocean. The seasonally evolving macro and micro spatial variability of transmitted spectral irradiance observed in the Chukchi Sea from May 18 to June 17, 2014 can be primarily attributed to variations in snow depth, ice thickness, and bottom ice algae concentrations. This study characterizes the interactions among these dominant variables using observed optical properties at each sampling site. We employ a normalized difference index to compute estimates of Chlorophyll a concentrations and analyze the increased attenuation of incident irradiance due to absorption by biomass. On a kilometer spatial scale, the presence of bottom ice algae reduced the maximum transmitted irradiance by about 1.5 orders of magnitude when comparing floes of similar snow and ice thicknesses. On a meter spatial scale, the combined effects of disparities in the depth and distribution of the overlying snow cover along with algae concentrations caused maximum transmittances to vary between 0.0577 and 0.282 at a single site. Temporal variability was also observed as the average integrated transmitted photosynthetically active radiation increased by one order of magnitude to 3.4% for the last eight measurement days compared to the first nine. Results provide insight on how interrelated physical and ecological parameters of sea ice in varying time and space may impact new trends in Arctic sea ice extent and the progression of melt.

Topography significantly influencing low flows in snow-dominated watersheds

NASA Astrophysics Data System (ADS)

Li, Qiang; Wei, Xiaohua; Yang, Xin; Giles-Hansen, Krysta; Zhang, Mingfang; Liu, Wenfei

2018-03-01

Watershed topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Few studies have quantified the role of topography in various flow variables. In this study, 28 watersheds with snow-dominated hydrological regimes were selected with daily flow records from 1989 to 1996. These watersheds are located in the Southern Interior of British Columbia, Canada, and range in size from 2.6 to 1780 km2. For each watershed, 22 topographic indices (TIs) were derived, including those commonly used in hydrology and other environmental fields. Flow variables include annual mean flow (Qmean), Q10 %, Q25 %, Q50 %, Q75 %, Q90 %, and annual minimum flow (Qmin), where Qx % is defined as the daily flow that occurred each year at a given percentage (x). Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, multiple linear regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important role in low flows (flow magnitudes ≤ Q75 %) than high flows. However, the effects of TIs on different flow magnitudes are not consistent. Our analysis also determined five significant TIs: perimeter, slope length factor, surface area, openness, and terrain characterization index. These can be used to compare watersheds when low flow assessments are conducted, specifically in snow-dominated regions with the watershed size less than several thousand square kilometres.

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.

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 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.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Low-flow characteristics of streams in the Puget Sound region, Washington

USGS Publications Warehouse

Hidaka, F.T.

1973-01-01

Periods of low streamflow are usually the most critical factor in relation to most water uses. The purpose of this report is to present data on low-flow characteristics of streams in the Puget Sound region, Washington, and to briefly explain some of the factors that influence low flow in the various basins. Presented are data on low-flow frequencies of streams in the Puget Sound region, as gathered at 150 gaging stations. Four indexes were computed from the flow-flow-frequency curves and were used as a basis to compare the low-flow characteristics of the streams. The indexes are the (1) low-flow-yield index, expressed in unit runoff per square mile; (2) base-flow index, or the ratio of the median 7-day low flow to the average discharge; (3) slope index, or slope of annual 7-day low-flow-frequency curve; and (4) spacing index, or spread between the 7-day and 183-day low-flow-frequency curves. The indexes showed a wide variation between streams due to the complex interrelation between climate, topography, and geology. The largest low-flow-yield indexes determined--greater than 1.5 cfs (cubic feet per second) per square mile--were for streams that head at high altitudes in the Cascade and Olympic Mountains and have their sources at glaciers. The smallest low-flow-yield indexes--less than 0.5 cfs per square mile--were for the small streams that drain the lowlands adjacent to Puget Sound. Indexes between the two extremes were for nonglacial streams that head at fairly high altitudes in areas of abundant precipitation. The base-flow index has variations that can be attributed to a basin's hydrogeology, with very little influence from climate. The largest base-flow indexes were obtained for streams draining permeable unconsolidated glacial and alluvial sediments in parts of the lowlands adjacent to Puget Sound. Large volume of ground water in these materials sustain flows during late summer. The smallest indexes were computed for streams draining areas underlain by relatively impermeable igneous, sedimentary, and metamorphic rocks or by relatively impermeable glacial till. Melt water from snow and ice influences the index for streams which originate at glaciers, and result in fairly large indexes--0.25 or greater. The slope index is influenced principally by the character of the geologic materials that underlie the basin. The largest slope indexes were computed for small streams that drain areas underlain by compact glacial till or consolidated sedimentary rocks. In contrast, lowland streams that flow through areas underlain by unconsolidated alluvia and glacial deposits have the smallest indexes. Small slope indexes also are characteristic of glacial streams and show the moderating effect of the snow and ice storage in the high mountain basins. The spacing indexes are similar to the slope indexes in that they are affected by the character of the geologic materials underlying a basin. The largest spacing indexes are characteristic of small streams whose basins are underlain by glacial till or by consolidated sedimentary rocks. The smallest indexes were computed for some lowland streams draining areas underlain by permeable glacial and alluvial sediments. The indexes do not appear to have a definite relation to each other. The low-flow-yield indexes are not related to either the slope or spacing indexes because snow and ice storage has a great influence on the low-flow-yield index, while the character of the geologic materials influences the slope and spacing indexes. A relation exists between the slope and spacing indexes but many anomalies occur that cannot be explained by the geology of the basins.

Effects of roadside habitat and fox density on a snow track survey for foxes in Ohio

USGS Publications Warehouse

Stanley, Thomas R.; Bart, Jonathan

1991-01-01

Many methods have been used to survey red fox (Vulpes vulpes) and gray fox (Urocyon dnereoargenteus) populations. However, none has proven entirely satisfactory, and wild foxes remain one of the most difficult economically important wildlife species to monitor. In this study we evaluated the reliability of a snow track survey method for foxes by investigating whether the average number of road crossings per fox is influenced by changes in roadside habitat or changes in fox density. Several snow track surveys were conducted in two Ohio counties during January and February, 1984. Data on roadside habitat, relative fox densities, and fox crossings were collected. Results suggested that changes in roadside habitat could influence the average number of crossings per fox and, therefore, changes in the index could occur independent of actual population changes. We found no evidence that crossings per fox varied with fox density, but further research is needed to substantiate this finding.

A comparison study of two snow models using data from different Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Riboust, Philippe; Campo, Lorenzo; Cremonese, Edoardo; Gabellani, Simone; Le Moine, Nicolas; Morra di Cella, Umberto; Ribstein, Pierre; Thirel, Guillaume

2017-04-01

The hydrological balance of an Alpine catchment is strongly affected by snowpack dynamics. Melt-water supplies a significant component of the annual water budget, both in terms of soil moisture and runoff, which play a critical role in floods generation and impact water resource management in snow-dominated basins. Several snow models have been developed with variable degrees of complexity, mainly depending on their target application and the availability of computational resources and data. According to the level of detail, snow models range from statistical snowmelt-runoff and degree-day methods using composite snow-soil or explicit snow layer(s), to physically-based and energy balance snow models, consisting of detailed internal snow-process schemes. Intermediate-complexity approaches have been widely developed resulting in simplified versions of the physical parameterization schemes with a reduced snowpack layering. Nevertheless, an increasing model complexity does not necessarily entail improved model simulations. This study presents a comparison analysis between two snow models designed for hydrological purposes. The snow module developed at UPMC and IRSTEA is a mono-layer energy balance model analytically resolving heat and phase change equations into the snowpack. Vertical mass exchange into the snowpack is also analytically resolved. The model is intended to be used for hydrological studies but also to give a realistic estimation of the snowpack state at watershed scale (SWE and snow depth). The structure of the model allows it to be easily calibrated using snow observation. This model is further presented in EGU2017-7492. The snow module of SMASH (Snow Multidata Assimilation System for Hydrology) consists in a multi-layer snow dynamic scheme. It is physically based on mass and energy balances and it reproduces the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide an estimation of the snowpack state. In this study, no DA is used. For more details on the DA scheme, please see EGU2017-7777. Observed data supplied by meteorological stations located in three experimental Alpine sites are used: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). Performances of the two models are compared through evaluations of snow mass, snow depth, albedo and surface temperature simulations in order to better understand and pinpoint limits and potentialities of the analyzed schemes and the impact of different parameterizations on models simulations.

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.

Seasonal albedo of an urban/rural landscape from satellite observations

NASA Technical Reports Server (NTRS)

Brest, Christopher L.

1987-01-01

Using data from 27 calibrated Landsat observations of the Hartford, Connecticut area, the spatial distribution and seasonal variation of surface reflectance and albedo were examined. Mean values of visible reflectance, near-IR reflectance, and albedo are presented (for both snow-free and snow-cover observations) according to 14 land use/land cover categories. A diversity of albedo values was found to exist in this type of environment, associated with land cover. Many land-cover categories display a seasonal dependence, with intracategory seasonal differences being of comparable magnitude to intercategory differences. Key factors in determining albedo (and its seasonal dynamics) are the presence or absence of vegetation and the canopy structure. Snow-cover/snow-free differences range from a few percent (for urban land covers) to over 40 percent (for low-canopy vegetation).

The Spectral and Chemical Measurement of Pollutants on Snow Near South Pole, Antarctica

NASA Technical Reports Server (NTRS)

Casey, K. A.; Kaspari, S. D.; Skiles, S. M.; Kreutz, K.; Handley, M. J.

2017-01-01

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350-2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014-2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from less than 1 W m(exp. -2) for clean snow to approximately 70 W m(exp. -2) for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models.

The spectral and chemical measurement of pollutants on snow near South Pole, Antarctica

NASA Astrophysics Data System (ADS)

Casey, K. A.; Kaspari, S. D.; Skiles, S. M.; Kreutz, K.; Handley, M. J.

2017-06-01

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350-2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014-2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from <1 W m-2 for clean snow to 70 W m-2 for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models.

A Comparison of the SNICAR Radiative Transfer Model to In Situ Snow Characterization Measurements at Sites in New England, USA

NASA Astrophysics Data System (ADS)

Adolph, A. C.; Albert, M. R.; Dibb, J. E.; Lazarcik, J.; Amante, J.

2016-12-01

As a highly reflective material, snow serves as an important control on surface energy balance. Given the current changes in climate and the sensitivity of snow cover to rising temperatures, it is critical that we understand the role of snow and its associated feedbacks in the climate system. Much of snow albedo research has focused on polar or high altitude snow packs, but rapid changes are also occurring in temperate regions; in the northeastern United States of America, changing climate has resulted in shallower snow packs and fewer days of snow cover. As these changes occur and we seek to understand the associated implications for snow albedo within climate dynamics, it is imperative that we are able to accurately represent snow in models. The SNow, ICe, and Aerosol Radiation model (SNICAR), developed by Flanner and Zender (2005) and used in the IPCC assessments, provides upward and downward radiative fluxes of one or many snow layers based on the following inputs: snow depth, density, grain size, and impurity content; solar zenith angle; lighting conditions; and albedo of the surface beneath the snowpack. To our knowledge, the SNICAR model has not been validated with data from a mid-latitude temperate region. Through a measurement campaign that occurred from winter 2013-2016, we have collected over 400 independent observations of a suite of snow characterization measurements and spectral snow albedo from three different sites in New Hampshire, USA. Comparison of our spectral albedo measurements to the SNICAR albedo derived from measured snow properties and illumination conditions will allow for validation of the model or recommendations for improvement based on the sensitivities found in the data.

Net primary productivity of subalpine meadows in Yosemite National Park in relation to climate variability

USGS Publications Warehouse

Moore, Peggy E.; Van Wagtendonk, Jan W.; Yee, Julie L.; McClaran, Mitchel P.; Cole, David N.; McDougald, Neil K.; Brooks, Matthew L.

2013-01-01

Subalpine meadows are some of the most ecologically important components of mountain landscapes, and primary productivity is important to the maintenance of meadow functions. Understanding how changes in primary productivity are associated with variability in moisture and temperature will become increasingly important with current and anticipated changes in climate. Our objective was to describe patterns and variability in aboveground live vascular plant biomass in relation to climatic factors. We harvested aboveground biomass at peak growth from four 64-m2 plots each in xeric, mesic, and hydric meadows annually from 1994 to 2000. Data from nearby weather stations provided independent variables of spring snow water content, snow-free date, and thawing degree days for a cumulative index of available energy. We assembled these climatic variables into a set of mixed effects analysis of covariance models to evaluate their relationships with annual aboveground net primary productivity (ANPP), and we used an information theoretic approach to compare the quality of fit among candidate models. ANPP in the xeric meadow was negatively related to snow water content and thawing degree days and in the mesic meadow was negatively related to snow water content. Relationships between ANPP and these 2 covariates in the hydric meadow were not significant. Increasing snow water content may limit ANPP in these meadows if anaerobic conditions delay microbial activity and nutrient availability. Increased thawing degree days may limit ANPP in xeric meadows by prematurely depleting soil moisture. Large within-year variation of ANPP in the hydric meadow limited sensitivity to the climatic variables. These relationships suggest that, under projected warmer and drier conditions, ANPP will increase in mesic meadows but remain unchanged in xeric meadows because declines associated with increased temperatures would offset the increases from decreased snow water content.

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.

76 FR 70809 - Notice of Passenger Facility Charge (PFC) Approvals and Disapprovals

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-15

.... Pavement condition index airfield inspection. Brief Description of Withdrawn Projects: Safety management... 1U19R and 7R/25L intersection repaving, construction. Perimeter road bridge over Howell Avenue, design. Inline baggage security, construction. Gate D56 improvements, design and construction. Snow removal...

Satellite Detection of Smoke Aerosols Over a Snow/Ice Surface by TOMS

NASA Technical Reports Server (NTRS)

Hsu, N. Christina; Herman, Jay R.; Gleason, J. F.; Torres, O.; Seftor, C. J.

1998-01-01

The use of TOMS (Total Ozone Mapping Spectrometer) satellite data demonstrates the recently developed technique of using satellite UV radiance measurements to detect absorbing tropospheric aerosols is effective over snow/ice surfaces. Instead of the traditional single wavelength (visible or infrared) method of measuring tropospheric aerosols, this method takes advantage of the wavelength dependent reduction in the backscattered radiance due to the presence of absorbing aerosols over snow/ice surfaces. An example of the resulting aerosol distribution derived from TOMS data is shown for an August 1998 event in which smoke generated by Canadian forest fires drifts over and across Greenland. As the smoke plume moved over Greenland, the TOMS observed 380 nm reflectivity over the snow/ice surface dropped drastically from 90-100% down to 30-40%. To study the effects of this smoke plume in both the UV and visible regions of the spectrum, we compared a smoke-laden spectrum taken over Greenland by the high spectral resolution (300 to 800 nm) GOME instrument with one that is aerosol-free. We also discuss the results of modeling the darkening effects of various types of absorbing aerosols over snow/ice surfaces using a radiative transfer code. Finally, we investigated the history of such events by looking at the nearly twenty year record of TOMS aerosol index measurements and found that there is a large interannual variability in the amount of smoke aerosols observed over Greenland. This information will be available for studies of radiation and transport properties in the Arctic.

Catchment response to bark beetle outbreak and dust-on-snow in the Colorado Rocky Mountains

NASA Astrophysics Data System (ADS)

Livneh, Ben; Deems, Jeffrey S.; Buma, Brian; Barsugli, Joseph J.; Schneider, Dominik; Molotch, Noah P.; Wolter, K.; Wessman, Carol A.

2015-04-01

Since 2002, the headwaters of the Colorado River and nearby basins have experienced extensive changes in land cover at sub-annual timescales. Widespread tree mortality from bark beetle infestation has taken place across a range of forest types, elevation, and latitude. Extent and severity of forest structure alteration have been observed through a combination of aerial survey, satellite remote-sensing, and in situ measurements. Additional perturbations have resulted from deposition of dust from regional dry-land sources on mountain snowpacks that strongly alter the snow surface albedo, driving earlier and faster snowmelt runoff. One challenge facing past studies of these forms of disturbance is the relatively small magnitude of the disturbance signals within the larger climatic signal. The combined impacts of forest disturbance and dust-on-snow are explored within a hydrologic modeling framework. We drive the Distributed Hydrology Soil and Vegetation Model (DHSVM) with observed meteorological data, time-varying maps of leaf area index and forest properties to emulate bark beetle impacts, and parameterizations of snow albedo based on observations of dust forcing. Results from beetle-killed canopy alteration suggest slightly greater snow accumulation as a result of less interception and reduced canopy sublimation and evapotranspiration, contributing to overall increases in annual water yield between 8% and 13%. However, understory regeneration roughly halves the changes in water yield. A purely observation-based estimate of runoff coefficient change with cumulative forest mortality shows comparable sensitivities to simulated results; however, positive water yield changes are not statistically significant (p ⩽ 0.05). The primary hydrologic impact of dust-on-snow forcing is an increased rate of snowmelt associated with more extreme dust deposition, producing earlier peak streamflow rates on the order of 1-3 weeks. Simulations of combined bark beetle and dust-on-snow produced little compounding effects, due to the relatively exclusive nature of their impacts. Potential changes in water yield and peak streamflow timing have important implications for regional water management decisions.

No evidence of widespread decline of snow cover on the Tibetan Plateau over 2000-2015.

PubMed

Wang, Xiaoyue; Wu, Chaoyang; Wang, Huanjiong; Gonsamo, Alemu; Liu, Zhengjia

2017-11-07

Understanding the changes in snow cover is essential for biological and hydrological processes in the Tibetan Plateau (TP) and its surrounding areas. However, the changes in snow cover phenology over the TP have not been well documented. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data, we reported daily cloud-free snow cover product over the Tibetan Plateau (TP) for 2000-2015. Snow cover start (SCS), melt (SCM) and duration (SCD) dates were calculated for each hydrological year, and their spatial and temporal variations were analyzed with elevation variations. Our results show no widespread decline in snow cover over the past fifteen years and the trends of snow cover phenology over the TP has high spatial heterogeneity. Later SCS, earlier SCM, and thus decreased SCD mainly occurred in the areas with elevation below 3500 m a.s.l., while regions in central and southwestern edges of the TP showed advanced SCS, delayed SCM and consequently longer SCD. The roles of temperature and precipitation on snow cover penology varied in different elevation zones, and the impact of both temperature and precipitation strengthened as elevation increases.

Oxygen isotope and deuterium composition of snow cover on the profile of Western Siberia from Tomsk to the Gulf of Ob

NASA Astrophysics Data System (ADS)

Vasil'chuk, Yu. K.; Shevchenko, V. P.; Lisitzin, A. P.; Budantseva, N. A.; Vorobiov, S. N.; Kirpotin, S. N.; Krizkov, I. V.; Manasypov, R. M.; Pokrovsky, O. S.; Chizhova, Ju. N.

2016-12-01

The purpose of this work is to study the variability of the isotope composition (δ18O, δD, d exc) of the snow cover on a long transect of Western Siberia from the southern taiga to the tundra. The study of the snow cover is of paleogeographic, paleogeocryological, and paleohydrological value. The snow cover of western Siberia was sampled on a broadly NS transzonal profile from the environs of Tomsk (southern taiga zone) to the eastern coast of the Gulf of Ob (tundra zone) from February 19 to March 4, 2014. Snow samples were collected at 31 sites. Most of the samples represented by fresh snow, i.e., snow that had fallen a day before the moment of sampling were collected in two areas. In the area of Yamburg, the snow specimens collected from the surface are most probably settled snow of different ages. The values of δ18O in the snow from Tomsk to Yamburg varied from-21.89 to-32.82‰, and the values of δD, from-163.3 to-261.2‰. The value of deuterium excess was in the range of 4.06-19.53‰.

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.

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.

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.

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

PubMed

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

1995-04-01

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

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.

The influence of canopy shading of snow on effective albedo in forested environments

NASA Astrophysics Data System (ADS)

Webster, C.; Jonas, T.

2017-12-01

The overlap of highly reflective snow and absorbent forested areas creates strong heterogeneity in the effective surface albedo compared to forest-free areas. Current errors in calculations of effective forest snow albedo arise due to uncertainties in how models should treat masking of snow by vegetation but improvement of local and large scale models is currently limited by a lack of measurements that demonstrate both spatial and temporal variability over forests. We present above-canopy measurements of winter-time effective forest snow albedo using up- and down-looking radiometers mounted on an octocopter UAV for a total of fifteen flights on eight different days. Ground-view fractions across the flight path were between 0.12 and 0.81. Correlations between effective albedo and both ground-view fraction and canopy height were statistically significant during 14 out of 15 flights, but varied between flights due to solar angle and snow cover. Measured effective albedo across the flight path differed by up to 0.33 during snow-on canopy conditions. A comparison between maximum interception and no interception showed effective albedo varied by up 0.17, which was the same variation between effective albedo during high (46°) and low (23°) solar elevation angles. Temporal and spatial variations in effective albedo caused by canopy shading of the snow surface are therefore as important as temporal variations caused by interception of snow by the canopy. Calculation of effective albedo over forested areas therefore requires careful consideration of canopy height, canopy coverage, solar angle and interception load. The results of this study should be used to inform snow albedo and canopy structure parametrisations in local and larger scale land surface models.

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.

Function and culture requirements of snow leopard (Panthera uncia) spermatozoa in vitro.

PubMed

Roth, T L; Howard, J G; Donoghue, A M; Swanson, W F; Wildt, D E

1994-08-01

Electroejaculates from eight snow leopards were used to determine how the motility of spermatozoa was influenced by (i) type of media (Ham's F10, PBS, human tubal fluid or RPMI-1640); (ii) holding temperature (23 degrees C versus 37 degrees C); (iii) washing of spermatozoa and (iv) a sperm metabolic enhancer, pentoxifylline. The duration of sperm motility was assessed by evaluating samples in each treatment every hour for 6 h and a sperm motility index (a value combining percentage sperm motility and rate of forward progression) calculated. Spermatozoa from the Ham's F10, PBS and PBS plus pentoxifylline treatments were also co-incubated with zona-intact, domestic cat eggs that were fixed and evaluated for spermatozoa bound to the zona pellucida, penetrating the outer and inner layers of the zona pellucida and within the perivitelline space. During the 6 h co-incubation, the sperm motility index in PBS with pentoxifylline was greater (P < 0.05) than in PBS alone which, in turn, was greater (P < 0.05) than in the other three test media. Washing the spermatozoa enhanced (P < 0.05) motility in both PBS and PBS plus pentoxifylline relative to unwashed samples, but there was no effect (P > 0.05) of holding temperature. Pentoxifylline supplementation enhanced (P < 0.05) the proportion of cat eggs with bound, but not penetrated, snow leopard spermatozoa in the inner layer of the zona pellucida, and there were no spermatozoa in the perivitelline space.(ABSTRACT TRUNCATED AT 250 WORDS)

An eleven-year record of mass balance of Brewster Glacier, New Zealand, determined using a geostatistical approach

NASA Astrophysics Data System (ADS)

Cullen, N. J.; Anderson, B.; Sirguey, P. J.; Stumm, D.; Mackintosh, A.; Conway, J. P.; Horgan, H. J.; Dadic, R.; Fitzsimons, S.; Lorrey, A.

2016-12-01

Recognizing the scarcity of glacier mass balance data in the Southern Hemisphere, a mass balance measurement program was started at Brewster Glacier in 2004. Evolution of the measurement regime over the 11 years of data recorded means there are differences in the spatial density of data obtained. To ensure the temporal integrity of the dataset a new, geostatistical approach has been developed to calculate mass balance. Spatial co-variance between elevation and snow depth has enabled a digital elevation model to be used in a co-kriging approach to develop a snow depth index (SDI). By capturing the observed spatial variability in snow depth, the SDI is a more reliable predictor than elevation and is used to adjust each year of measurements consistently despite variability in sampling spatial density. The SDI also resolves the spatial structure of summer balance better than elevation. Co-kriging is used again to spatially interpolate a derived mean summer balance index using SDI as a co-variate, which yields a spatial predictor for summer balance. A similar approach is also used to create a predictor for annual balance, which allows us to revisit years where summer balance was not obtained. The average glacier-wide surface winter, summer and annual mass balances over the period 2005-2015 are 2484, -2586, and -102 mm w.e., respectively, with changes in summer balance explaining most of the variability in annual balance. On the whole, there is good agreement between our ELA and AAR values and those derived from the end-of-summer snowline (EOSS) program, though discrepancies in some years cannot be fully accounted for. A mass balance map of Brewster Glacier in an equilibrium state, which by definition has a glacier-wide mass balance equal to zero (a balanced-budget), is used to calculate values of ELA (1923 ±25 m) and AAR (0.40) representative of the observational period. The relationships between mass balance and ELA/AAR are explored, demonstrating they are mostly linear. On average, the mass balance gradients are found to be equal to 14.5 and 7.4 mm we m-1 in the ablation and accumulation zones, respectively. However, there is considerable variability in the gradients from year to year, as well as variability between different elevation bands. The largest variability in the mass balance gradient is observed in the ablation zone.

New estimates of changes in snow cover over Russia in recent decades

NASA Astrophysics Data System (ADS)

Bulygina, O.; Korshunova, N.; Razuvaev, V.; Groisman, P. Y.

2017-12-01

Snow covers plays critical roles in the energy and water balance of the Earth through its unique physical properties (high reflectivity and low thermal conductivity) and water storage. The main objective of this research is to monitoring snow cover change in Russia. The estimates of changes of major snow characteristics (snow cover duration, maximum winter snow depth, snow water equivalent) are described. Apart from the description of long-term averages of snow characteristics, the estimates of their change that are averaged over quasi-homogeneous climatic regions are derived and regional differences in the change of snow characteristics are studied. We used in our study daily snow observations for 820 Russian meteorological station from 1966 to 2017. All of these meteorological stations are of unprotected type. The water equivalent is analyzed from snow course survey data at 958 meteorological stations from 1966 to 2017. The time series are prepared by RIHMI-WDC. Regional analysis of snow cover data was carried out using quasi-homogeneous climatic regions. The area-averaging technique using station values converted to anomalies with respect to a common reference period (in this study, 1981-2010). Anomalies were arithmetically averaged first within 1°N x 2°E grid cells and thereafter by a weighted average value derived over the quasi-homogeneous climatic regions. This approach provides a more uniform spatial field for averaging. By using a denser network of meteorological stations, bringing into consideration snow course data and, we managed to specify changes in all observed major snow characteristics and to obtain estimates generalized for quasi-homogeneous climatic regions. The detected changes in the dates of the establishment and disappearance of the snow cover.

Limitations of using a thermal imager for snow pit temperatures

NASA Astrophysics Data System (ADS)

Schirmer, M.; Jamieson, B.

2013-10-01

Driven by temperature gradients, kinetic snow metamorphism is important for avalanche formation. Even when gradients appear to be insufficient for kinetic metamorphism, based on temperatures measured 10 cm apart, faceting close to a~crust can still be observed. Recent studies that visualized small scale (< 10 cm) thermal structures in a profile of snow layers with an infrared (IR) camera produced interesting results. The studies found melt-freeze crusts to be warmer or cooler than the surrounding snow depending on the large scale gradient direction. However, an important assumption within the studies was that a thermal photo of a freshly exposed snow pit was similar enough to the internal temperature of the snow. In this study, we tested this assumption by recording thermal videos during the exposure of the snow pit wall. In the first minute, the results showed increasing gradients with time, both at melt-freeze crusts and at artificial surface structures such as shovel scours. Cutting through a crust with a cutting blade or a shovel produced small concavities (holes) even when the objective was to cut a planar surface. Our findings suggest there is a surface structure dependency of the thermal image, which is only observed at times with large temperature differences between air and snow. We were able to reproduce the hot-crust/cold-crust phenomenon and relate it entirely to surface structure in a temperature-controlled cold laboratory. Concave areas cooled or warmed slower compared with convex areas (bumps) when applying temperature differences between snow and air. This can be explained by increased radiative transfer or convection by air at convex areas. Thermal videos suggest that such processes influence the snow temperature within seconds. Our findings show the limitations of the use of a thermal camera for measuring pit-wall temperatures, particularly in scenarios where large gradients exist between air and snow and the interaction of snow pit and atmospheric temperatures are enhanced. At crusts or other heterogeneities, we were unable to create a sufficiently homogenous snow pit surface and non-internal gradients appeared at the exposed surface. The immediate adjustment of snow pit temperature as it reacts with the atmosphere complicates the capture of the internal thermal structure of a snowpack even with thermal videos. Instead, the shown structural dependency of the IR signal may be used to detect structural changes of snow caused by kinetic metamorphism. The IR signal can also be used to measure near surface temperatures in a homogenous new snow layer.

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

NASA Technical Reports Server (NTRS)

Olson, William S.; Tian, Lin; Grecu, Mircea; Kuo, Kwo-Sen; Johnson, Benjamin; Heymsfield, Andrew J.; Bansemer, Aaron; Heymsfield, Gerald M.; Wang, James R.; Meneghini, Robert

2016-01-01

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

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.

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.

How much can we save? Impact of different emission scenarios on future snow cover in the Alps

NASA Astrophysics Data System (ADS)

Marty, Christoph; Schlögl, Sebastian; Bavay, Mathias; Lehning, Michael

2017-02-01

This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at a 200 m horizontal resolution for the reference period (1999-2012). Future temperature and precipitation changes have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2 °C target) and for three future time periods (2020-2049, 2045-2074, 2070-2099). By applying simple daily change values to measured time series of temperature and precipitation, small-scale climate scenarios have been calculated for the median estimate and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-intervention scenarios demonstrate a decrease of about 50 % even for elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to 1 month later and ends 1 to 3 months earlier in this last scenario period. The resulting snow cover changes may be roughly equivalent to an elevation shift of 500-800 or 700-1000 m for the two non-intervention emission scenarios. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and only 0-2 snow days are predicted in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period but clearly demonstrate a stabilization thereafter, comprising much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Enhancement of the MODIS Daily Snow Albedo Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Schaaf, Crystal B.; Wang, Zhuosen; Riggs, George A.

2009-01-01

The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we compare a daily version of MCD43B3 with the daily albedo from MOD10A1. and MCD43B3 with a 16-day average of MOD10A1, over Greenland. We also discuss some near-future planned enhancements to MOD10A1.

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.

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.

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.

Snowpack Regimes of the Western United States

NASA Astrophysics Data System (ADS)

Trujillo, E.; Molotch, N. P.

2011-12-01

Snow accumulation and melt patterns play a significant role in the water, energy, carbon and nutrient cycles in the montane environments of the Western United States. Recent studies have illustrated that changes in the snow/rainfall apportionments, and snow accumulation and melt patterns may occur as a consequence of changes in climate in the region. In order to understand how these changes may affect the snow regimes of the region, the current characteristics of the snow accumulation and melt patterns must be identified. Here, we characterize the snow water equivalent (SWE) curve formed by the daily SWE values at over seven hundred snow pillow stations in the Western U.S., focusing on several metrics of the yearly SWE curves and the cross relationships between the different metrics. The metrics include the initial snow accumulation and meltout dates, the peak accumulation and date of peak, the time from initial accumulation to peak, the time from peak to meltout, the accumulation and melt slopes, and the daily rates of accumulation and melt. Three distinct regimes emerge from these results: a maritime, an intermediate (intercontinental), and a continental regime. The maritime regime is characterized by higher maximum snow accumulations reaching 300 cm and shorter accumulation periods of less than 220 days, while on the other hand; the continental regime is characterized by lower maximum accumulations below 200 cm and longer accumulation periods reaching over 260 days. The intercontinental regime lies in between. Several other differences are identified between the metrics of the SWE curve in these regimes. The regions that show the characteristics of the maritime regime include the Cascade Mountains, the Klamath Mountains, and the Sierra Nevada Mountains. The intercontinental regime includes the Northern and Central basins and ranges, the Idaho Batholith, the Northern Rockies and the Blue Mountains. Lastly, the Continental regime includes the Middle and Southern Rockies, and the Wasatch and Uinta Mountains. The consequences of the differences between these snow regimes are discussed in the framework of the implications of possible changes in accumulation and melt patterns as a consequence of changes in climate.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

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.

Ecophysiology, secondary pigments and ultrastructure of Chlainomonas sp. (Chlorophyta) from the European Alps compared with Chlamydomonas nivalis forming red snow

PubMed Central

Remias, Daniel; Pichrtová, Martina; Pangratz, Marion; Lütz, Cornelius; Holzinger, Andreas

2016-01-01

Red snow is a well-known phenomenon caused by microalgae thriving in alpine and polar regions during the melting season. The ecology and biodiversity of these organisms, which are adapted to low temperatures, high irradiance and freeze–thaw events, are still poorly understood. We compared two different snow habitats containing two different green algal genera in the European Alps, namely algae blooming in seasonal rock-based snowfields (Chlamydomonas nivalis) and algae dominating waterlogged snow bedded over ice (Chlainomonas sp.). Despite the morphological similarity of the red spores found at the snow surface, we found differences in intracellular organization investigated by light and transmission electron microscopy and in secondary pigments investigated by chromatographic analysis in combination with mass spectrometry. Spores of Chlainomonas sp. show clear differences from Chlamydomonas nivalis in cell wall arrangement and plastid organization. Active photosynthesis at ambient temperatures indicates a high physiological activity, despite no cell division being present. Lipid bodies containing the carotenoid astaxanthin, which produces the red color, dominate cells of both species, but are modified differently. While in Chlainomonas sp. astaxanthin is mainly esterified with two fatty acids and is more apolar, in Chamydomonas nivalis, in contrast, less apolar monoesters prevail. PMID:26884467

Snow accumulation on Arctic sea ice: is it a matter of how much or when?

NASA Astrophysics Data System (ADS)

Webster, M.; Petty, A.; Boisvert, L.; Markus, T.

2017-12-01

Snow on sea ice plays an important, yet sometimes opposing role in sea ice mass balance depending on the season. In autumn and winter, snow reduces the heat exchange from the ocean to the atmosphere, reducing sea ice growth. In spring and summer, snow shields sea ice from solar radiation, delaying sea ice surface melt. Changes in snow depth and distribution in any season therefore directly affect the mass balance of Arctic sea ice. In the western Arctic, a decreasing trend in spring snow depth distribution has been observed and attributed to the combined effect of peak snowfall rates in autumn and the coincident delay in sea ice freeze-up. Here, we build on this work and present an in-depth analysis on the relationship between snow accumulation and the timing of sea ice freeze-up across all Arctic regions. A newly developed two-layer snow model is forced with eight reanalysis precipitation products to: (1) identify the seasonal distribution of snowfall accumulation for different regions, (2) highlight which regions are most sensitive to the timing of sea ice freeze-up with regard to snow accumulation, and (3) show, if precipitation were to increase, which regions would be most susceptible to thicker snow covers. We also utilize a comprehensive sensitivity study to better understand the factors most important in controlling winter/spring snow depths, and to explore what could happen to snow depth on sea ice in a warming Arctic climate.

Blowing Snow Sublimation at a High Altitude Alpine Site and Effects on the Surface Boundary Layer

NASA Astrophysics Data System (ADS)

Vionnet, V.; Guyomarc'h, G.; Sicart, J. E.; Deliot, Y.; Naaim-Bouvet, F.; Bellot, H.; Merzisen, H.

2017-12-01

In alpine terrain, wind-induced snow transport strongly influences the spatial and temporal variability of the snow cover. During their transport, blown snow particles undergo sublimation with an intensity depending on atmospheric conditions (air temperature and humidity). The mass loss due to blowing snow sublimation is a source of uncertainty for the mass balance of the alpine snowpack. Additionally, blowing snow sublimation modifies humidity and temperature in the surface boundary layer. To better quantify these effects in alpine terrain, a dedicated measurement setup has been deployed at the experimental site of Col du Lac Blanc (2720 m a.s.l., French Alps, Cryobs-Clim network) since winter 2015/2016. It consists in three vertical masts measuring the near-surface vertical profiles (0.2-5 m) of wind speed, air temperature and humidity and blowing snow fluxes and size distribution. Observations collected during blowing snow events without concurrent snowfall show only a slight increase in relative humidity (10-20%) and near-surface saturation is never observed. Estimation of blowing snow sublimation rates are then obtained from these measurements. They range between 0 and 5 mmSWE day-1 for blowing snow events without snowfall in agreement with previous studies in different environments (North American prairies, Antarctica). Finally, an estimation of the mass loss due to blowing snow sublimation at our experimental site is proposed for two consecutive winters. Future use of the database collected in this study includes the evaluation of blowing snow models in alpine terrain.

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.

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.

Comparison of AMSR-E and SSM/I snow parameter retrievals over the Ob river basin

USGS Publications Warehouse

Mognard, N.M.; Grippa, M.; LeToan, T.; Kelly, R.E.J.; Chang, A.T.C.; Josberger, E.G.

2004-01-01

Passive microwave observations from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2 975 000 km2). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

A conceptual snow model with an analytic resolution of the heat and phase change equations

NASA Astrophysics Data System (ADS)

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

2017-04-01

Compared to degree-day snow models, physically-based snow models resolve more processes in an attempt to achieve a better representation of reality. Often these physically-based models resolve the heat transport equations in snow using a vertical discretization of the snowpack. The snowpack is decomposed into several layers in which the mechanical and thermal states of the snow are calculated. A higher number of layers in the snowpack allow for better accuracy but it also tends to increase the computational costs. In order to develop a snow model that estimates the temperature profile of snow with a lower computational cost, we used an analytical decomposition of the vertical profile using eigenfunctions (i.e. trigonometric functions adapted to the specific boundary conditions). The mass transfer of snow melt has also been estimated using an analytical conceptualization of runoff fingering and matrix flow. As external meteorological forcing, the model uses solar and atmospheric radiation, air temperature, atmospheric humidity and precipitations. It has been tested and calibrated at point scale at two different stations in the Alps: Col de Porte (France, 1325 m) and Weissfluhjoch (Switzerland, 2540 m). A sensitivity analysis of model parameters and model inputs will be presented together with a comparison with measured snow surface temperature, SWE, snow depth, temperature profile and snow melt data. The snow model is created in order to be ultimately coupled with hydrological models for rainfall-runoff modeling in mountainous areas. We hope to create a model faster than physically-based models but capable to estimate more physical processes than degree-day snow models. This should help to build a more reliable snow model capable of being easily calibrated by remote sensing and in situ observation or to assimilate these data for forecasting purposes.

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

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.

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...

Peruvian Tropical Glacier May Survive Longer Than Previously Thought: Landsat Image Analysis of Nevado Coropuna Ice Cap, Peru

NASA Astrophysics Data System (ADS)

Kochtitzky, W. H.; Edwards, B. R.; Marino, J.; Manrique, N.

2015-12-01

Nevado Coropuna is a large volcanic complex in southern Peru (15.56°S, 72.62°N; 6,425 m). The complex is approximately 12 km east-west and 8 km north-south with elevation from ~4,500 m at the base to over 6,000 m at the highest points. This ice cap is the largest hosted by a volcano in the tropics, and one of the ten biggest ice masses in the tropics. Previous workers have predicted that the Coropuna ice cap will completely melt by 2050. We present a new analysis of historic satellite imagery to test this hypothesis. In this study, ice and snow are classified based on unique spectral signatures including spectral band thresholds, Normalized Difference Snow Index, and Band 4/5 ratio. Landsat scenes (L2, 4, 5, 7, and 8) from 1975 to present in addition to one SPOT scene (2013) are used. Previous workers used images from June and July, which are peak snow periods in southern Peru, leading to overestimates of ice area. This study uses November and December images when snow is at an annual minimum. Annual equilibrium line altitudes are calculated for each end of year image (November/December). The glaciers of Nevado Coropuna were found to be shrinking at ~0.5 km2/yr, which is ~1/3 the rate previously published. In this study, SPOT (1.5 m resolution) and Landsat 7 ETM scenes from November 23 and 26, 2013 respectively were used to calibrate the spectral band threshold classification. While this study suggests that the ice cap of Coropuna will persist until 2100 given current rates, water quantity and security remains a concern for Peruvian agriculture. Coropuna is an active volcano, so it poses great risk to surrounding inhabitants from lahars, flooding, and debris avalanches. Our new data suggest that these will continue to be risks late into this century.

Experimental manipulations of snow-depth: Effects on nutrient content of caribou forage

USGS Publications Warehouse

Walsh, Noreen E.; McCabe, Thomas R.; Welker, J.M.; Parsons, A.N.

1997-01-01

We investigated the potential effects of global climate change on arctic tundra vegetation used as caribou forage. A total of 96 experimental plots was established at six sites on the coastal plain of the Arctic National Wildlife Refuge, Alaska, in 1993 and 1994. We erected snow-fences to increase the amount of snow deposition, and therefore delay the date of the snowmelt on 48 plots (referred to as increased snow/late melting plots). We used black mesh netting on the surface of the snow to increase the rate of melting on 24 plots; the remaining 24 plots served as controls. In July 1994, we collected green leaves from Eriophorum vaginatum, Salix planifolia, and Betula nana and analysed these samples for total carbon and total nitrogen content. Ratios of carbon to nitrogen differed among treatments for all three species. Generally, C:N ratios for B. nana and E. vaginatum on increased snow/late melting plots were lower than on control plots. C:N ratios for S. planifolia on increased snow/late melting plots did not differ from controls, but were lower than on plots which started to melt early. These results may be due to the timing of nitrogen translocation from leaf and stem tissue into storage organs, or due to an increase in available nitrogen input to the system. Further sampling is needed to adequately determine the mechanism responsible for increased nitrogen content of caribou forage in areas with increased amount of snow and delayed snowmelt. ?? 1997 Blackwell Science Ltd.

Community variability of bacteria in alpine snow (Mont Blanc) containing Saharan dust deposition and their snow colonisation potential.

PubMed

Chuvochina, Maria S; Marie, Dominique; Chevaillier, Servanne; Petit, Jean-Robert; Normand, Philippe; Alekhina, Irina A; Bulat, Sergey A

2011-01-01

Microorganisms uplifted during dust storms survive long-range transport in the atmosphere and could colonize high-altitude snow. Bacterial communities in alpine snow on a Mont Blanc glacier, associated with four depositions of Saharan dust during the period 2006-2009, were studied using 16S rRNA gene sequencing and flow cytometry. Also, sand from the Tunisian Sahara, Saharan dust collected in Grenoble and Mont Blanc snow containing no Saharan dust (one sample of each) were analyzed. The bacterial community composition varied significantly in snow containing four dust depositions over a 3-year period. Out of 61 phylotypes recovered from dusty snow, only three phylotypes were detected in more than one sample. Overall, 15 phylotypes were recognized as potential snow colonizers. For snow samples, these phylotypes belonged to Actinobacteria, Proteobacteria and Cyanobacteria, while for Saharan sand/dust samples they belonged to Actinobacteria, Bacteroidetes, Deinococcus-Thermus and Proteobacteria. Thus, regardless of the time-scale, Saharan dust events can bring different microbiota with no common species set to alpine glaciers. This seems to be defined more by event peculiarities and aeolian transport conditions than by the bacterial load from the original dust source.

Developing a hydrological model in the absence of field data

NASA Astrophysics Data System (ADS)

Sproles, E. A.; Orrego Nelson, C.; Kerr, T.; Lopez Aspe, D.

2014-12-01

We present two runoff models that use remotely-sensed snow cover products from the Moderate Resolution Imaging Spectrometer (MODIS) as the first order hydrologic input. These simplistic models are the first step in developing an operational model for the Elqui River watershed located in northern Central Chile (30°S). In this semi-arid region, snow and glacier melt are the dominant hydrologic inputs where annual precipitation is limited to three or four winter events. Unfortunately winter access to the Andean Cordillera where snow accumulates is limited. While a monitoring network to measure snow where it accumulates in the upper elevations is under development, management decisions regarding water resources cannot wait. The two models we present differ in structure. The first applies a Monte Carlo approach to determine relationships between lagged changes in monthly snow cover frequency and monthly discharge. The second is a modified degree-day melt model, utilizing the MODIS snow cover product to determine where and when snow melt occurs. These models are not watershed specific and are applicable in other regions where snow dominates hydrologic inputs, but measurements are minimal.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

Variations in snow cover seasonality across the Kyrgyz Republic from 2000 to 2016 revealed through MODIS Terra and Aqua snow products

NASA Astrophysics Data System (ADS)

Tomaszewska, M. A.; Henebry, G. M.

2017-12-01

The vertical transhumance practiced by herders in the highlands of Kyrgyzstan is vulnerable to environmental change. Herd movements and pasture conditions are both affected by spatial and temporal variations in snow cover and the timing of snowmelt. Early growing season soil moisture conditions affect the phenology and growth of vegetation, especially in the high elevation pastures used for summer forage. To evaluate snow seasonality, we examined three snow cover variables—the first day of snow (FDoS), the last day of snow (LDoS), and the duration of snow cover (DoSC) over 17 years based on 8-day snow product from MODIS Terra and Aqua (MOD/MYD10A2) across the Kyrgyz Republic (KYR). To track the "snow season" efficiently in the presence of snow-capped peaks, we start each snow season at day of year (DOY) 169, approximately the summer solstice, and extend to DOY 168 of the following year. To track the interannual variation of these variables, we applied two nonparametric statistics: the Mann-Kendall trend test and the Theil-Sen linear trend estimator. Our preliminary results focusing on four rayons in two oblasts indicate both large swaths of positive and negative significant trends over the different regions of the country. Positive trends in FDoS, meaning later snow arrival, were detected in parts of central KYR. Negative trends in FDoS meaning earlier arrival were detected at lower elevations in southwestern KYR. Earlier snowmelt (negative trend in LDoS) in eastern KYR resulted in a shorter snow season (negative trend in DoSC); in contrast, later snowmelt in southwestern KYR (positive trend in LDoS) resulted in a longer period of snow cover (positive trend of DoSC). We extend the analysis to the entire country and explore the influence of terrain attribites (elevation, slope, and aspect) and MODIS IGBP land cover type (MCD12Q1) on trends in snow cover seasonality. Additionally, we ran the trend tests for the Terra and Aqua snow products separately to evaluate the effect of overpass time on snow cover retrieval.

Application of Temperature Index Model to Assess the Future Hydrological Regime of the Glacierized Catchments in Nepal.

NASA Astrophysics Data System (ADS)

Kayastha, R.; Kayastha, R. B.

2017-12-01

Unavailability of hydro meteorological data in the Himalayan regions is challenging on understanding the flow regimes. Temperature index model is simple yet the powerful glacio-hydrological model to simulate the discharge in the glacierized basin. Modified Positive Degree Day (MPDD) Model Version 2.0 is a grid-ded based semi distributed model with baseflow module is a robust melt modelling tools to estimate the discharge. MPDD model uses temperature and precipitation as a forcing datasets to simulate the discharge and also to obtain the snowmelt, icemelt, rain and baseflow contribution on total discharge. In this study two glacierized, Marsyangdi and Langtang catchment were investigated for the future hydrological regimes. Marsyangdi encompasses an area of 4026.19 sq. km with 20% glaciated area, whereas Langtang catchment with area of 354.64 sq. km with 36% glaciated area is studied to examine for the future climatic scenarios. The model simulates discharge well for the observed period; (1992-1998) in Marsyangdi and from (2007-2013) in Langtang catchment. The Nash-Sutcliffe Efficiency (NSE) for the both catchment were above 0.75 with the volume difference less than - 8 %. The snow and ice melts contribution in Marsyangdi were 4.7% and 10.2% whereas in Langtang the contribution is 15.3% and 23.4%, respectively. Rain contribution ( 40%) is higher than the baseflow contribution in total discharge in both basins. The future river discharge is also predicted using the future climate data from the regional climate models (RCMs) of CORDEX South Asia experiments for the medium stabilization scenario RCP4.5 and very high radiative forcing scenario RCP8.5 after bias correction. The projected future discharge of both catchment shows slightly increase in both scenarios with increase of snow and ice melt contribution on discharge. The result generated from the model can be utilized to understand the future hydrological regimes of the glacierized catchment also the impact of climate change on the snow and ice contribution on discharge. The future discharge projection is also helpful for the water resource management and also for the strategic planners.

Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

NASA Astrophysics Data System (ADS)

Otto, M.; Scherer, D.; Richters, J.

2011-01-01

High Altitude Wetlands of the Andes (HAWA) are unique types of wetlands within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 11 000 km2 situated in the Northwest of Lake Titicaca. The multi temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6%). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the reletation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies to precipitation conditions. Strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual spatial patterns of perennial HAWA indicated spatial alteration of water supply for PAV up to several hundred metres at a single HAWA site.

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.

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.;

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.

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

PubMed

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

2007-09-01

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

Environmental impacts of urban snow management--the alpine case study of Innsbruck.

PubMed

Engelhard, C; De Toffol, S; Lek, I; Rauch, W; Dallinger, R

2007-09-01

In regions with colder climate, snow at roads can accumulate significant amounts of pollutant chemicals. In northern countries various efforts have been made to face this problem, but for the alpine region little is known about the pollution of urban snow. The present case study was carried out in the city of Innsbruck (Austria). It aimed at measuring pollution of roadside snow and estimating the impact of snow management practises on environmental quality. Concentrations of copper, zinc, lead, cadmium, suspended solids and chloride were determined during a series of sampling events. Various locations with low and high traffic densities and in different distances from a highway have been investigated. The concentrations of copper were generally higher at sites with high traffic density compared to locations with low traffic impact. In contrast to this, the concentrations of zinc and lead remained almost unvaried irrespective of traffic density at the different sampling sites. For cadmium, the picture was more diverse, showing moderately elevated concentrations of this metal also at the urban reference site not polluted by traffic. This indicates that there may be also other important sources for cadmium besides traffic. Suspended solids accumulated in the roadside snow, the highest concentrations were found at the sites with high traffic density. The chloride concentrations were considerable in the snow, especially at the highway. Based on the results of the present measurement campaign, the environmental impact of snow disposal in rivers was also estimated. A negative impact on rivers from snow disposal seems likely to occur, although the discharged loads could only be calculated with substantial uncertainty, considering the high variability of the measured pollutant concentrations. For a more accurate evaluation of this management practise on rivers, further investigations would be necessary.

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.

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.

Snow fraction products evaluation with Landsat-8/OLI data and its spatial scale effects over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Jiang, L.

2016-12-01

Snow cover is one of important elements in the water supply of large populations, especially in those downstream from mountainous watershed. The cryosphere process in the Tibetan Plateau is paid much attention due to rapid change of snow amount and cover extent. Snow mapping from MODIS has been increased attention in the study of climate change and hydrology. But the lack of intensive validation of different snow mapping methods especially at Tibetan Plateau hinders its application. In this work, we examined three MODIS snow products, including standard MODIS fractional snow product (MOD10A1) (Kaufman et al., 2002; Salomonson & Appel, 2004, 2006), two other fractional snow product, MODSCAG (Painter et al., 2009) and MOD_MESMA (Shi, 2012). Both these two methods are based on spectral mixture analysis. The difference between MODISCAG and MOD_MESMA was the endmember selection. For MODSCAG product, snow spectral endmembers of varying grain size was obtained both from a radiative transfer model and spectra of vegetation, rock and soil collected in the field and laboratory. MOD_MESMA was obtained from automated endmember extraction method using linear spectral mixture analysis. Its endmembers are selected in each image to enhance the computational efficiency of MESMA (Multiple Endmember Spectral Analysis). Landsat-8 Operatinal Land Imager (OLI) data from 2013-2015 was used to evaluate the performance of these three snow fraction products in Tibetan Plateau. The effect of land cover types including forest, grass and bare soil was analyzed to evaluate three products. In addition, the effects of relatively flat surface in internal plateau and high mountain areas of Himalaya were also evaluated on the impact of these snow fraction products. From our comparison, MODSCAG and MOD10A1 overestimated snow cover, while MOD_MESMA underestimated snow cover. And RMSE of MOD_MESMA at each land cover type including forest, grass and mountain area decreased with the spatial resolution increasing from 500m, 1km, 2km to 5km. The RMSE of MODSCAG and MOD10A1 is very similar. In Himalaya area, these two RMSEs of MODSCAG and MOD10A1 increased with the spatial resolution increasing from 500m to 5km. For forest, grass and bare soil, RMSE decreased from 500m to 1km, then increased from 1km to 2km.

Natural resources inventory and land evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Using MSS channels 5 and 7 and a supervised classification system with a PPD classification algorithm, it was possible to map the exact areal extent of the snow cover and of the transition zone with melting snow patches and snow free parts of various sizes over a large area under different aspects such as relief, exposure, shadows etc. A correlation of the data from ground control, areal underflights and earth resources satellites provided a very accurate interpretation of the melting procedure of snow in high mountains.

Warming effects on the urban hydrology in cold climate regions.

PubMed

Järvi, L; Grimmond, C S B; McFadden, J P; Christen, A; Strachan, I B; Taka, M; Warsta, L; Heimann, M

2017-07-19

While approximately 338 million people in the Northern hemisphere live in regions that are regularly snow covered in winter, there is little hydro-climatologic knowledge in the cities impacted by snow. Using observations and modelling we have evaluated the energy and water exchanges of four cities that are exposed to wintertime snow. We show that the presence of snow critically changes the impact that city design has on the local-scale hydrology and climate. After snow melt, the cities return to being strongly controlled by the proportion of built and vegetated surfaces. However in winter, the presence of snow masks the influence of the built and vegetated fractions. We show how inter-year variability of wintertime temperature can modify this effect of snow. With increasing temperatures, these cities could be pushed towards very different partitioning between runoff and evapotranspiration. We derive the dependency of wintertime runoff on this warming effect in combination with the effect of urban densification.

Measuring snow cover using satellite imagery during 1973 and 1974 melt season: North Santiam, Boise, and Upper Snake Basins, phase 1. [LANDSAT satellites, imaging techniques

NASA Technical Reports Server (NTRS)

Wiegman, E. J.; Evans, W. E.; Hadfield, R.

1975-01-01

Measurements are examined of snow coverage during the snow-melt season in 1973 and 1974 from LANDSAT imagery for the three Columbia River Subbasins. Satellite derived snow cover inventories for the three test basins were obtained as an alternative to inventories performed with the current operational practice of using small aircraft flights over selected snow fields. The accuracy and precision versus cost for several different interactive image analysis procedures was investigated using a display device, the Electronic Satellite Image Analysis Console. Single-band radiance thresholding was the principal technique employed in the snow detection, although this technique was supplemented by an editing procedure involving reference to hand-generated elevation contours. For each data and view measured, a binary thematic map or "mask" depicting the snow cover was generated by a combination of objective and subjective procedures. Photographs of data analysis equipment (displays) are shown.

Frost risk for overwintering crops in a changing climate

NASA Astrophysics Data System (ADS)

Vico, Giulia; Weih, Martin

2013-04-01

Climate change scenarios predict a general increase in daily temperatures and a decline in snow cover duration. On the one hand, higher temperature in fall and spring may facilitate the development of overwintering crops and allow the expansion of winter cropping in locations where the growing season is currently too short. On the other hand, higher temperatures prior to winter crop dormancy slow down frost hardening, enhancing crop vulnerability to temperature fluctuation. Such vulnerability may be exacerbated by reduced snow cover, with potential further negative impacts on yields in extremely low temperatures. We propose a parsimonious probabilistic model to quantify the winter frost damage risk for overwintering crops, based on a coupled model of air temperature, snow cover, and crop minimum tolerable temperature. The latter is determined by crop features, previous history of temperature, and snow cover. The temperature-snow cover model is tested against meteorological data collected over 50 years in Sweden and applied to winter wheat varieties differing in their ability to acquire frost resistance. Hence, exploiting experimental results assessing crop frost damage under limited temperature and snow cover realizations, this probabilistic framework allows the quantification of frost risk for different crop varieties, including in full temperature and precipitation unpredictability. Climate change scenarios are explored to quantify the effects of changes in temperature mean and variance and precipitation regime over crops differing in winter frost resistance and response to temperature.

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.

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.

Genetic differentiation between sympatric and allopatric wintering populations of Snow Geese

USGS Publications Warehouse

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

2009-01-01

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

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

PubMed

Blagojević, Jelena; Stamenković, Gorana; Vujosević, Mladen

2009-09-01

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

Discrimination Between Clouds and Snow in Landsat 8 Imagery: an Assessment of Current Methods and a New Approach

NASA Astrophysics Data System (ADS)

Stillinger, T.; Dozier, J.; Phares, N.; Rittger, K.

2015-12-01

Discrimination between snow and clouds poses a serious but tractable challenge to the consistent delivery of high-quality information on mountain snow from remote sensing. Clouds obstruct the surface from the sensor's view, and the similar optical properties of clouds and snow make accurate discrimination difficult. We assess the performance of the current Landsat 8 operational snow and cloud mask products (LDCM CCA and CFmask), along with a new method, using over one million manually identified snow and clouds pixels in Landsat 8 scenes. The new method uses physically based scattering models to generate spectra in each Landsat 8 band, at that scene's solar illumination, for snow and cloud particle sizes that cover the plausible range for each. The modeled spectra are compared to pixels' spectra via several independent ways to identify snow and clouds. The results are synthesized to create a final snow/cloud mask, and the method can be applied to any multispectral imager with bands covering the visible, near-infrared, and shortwave-infrared regions. Each algorithm we tested misidentifies snow and clouds in both directions to varying degrees. We assess performance with measures of Precision, Recall, and the F statistic, which are based on counts of true and false positives and negatives. Tests for significance in differences between spectra in the measured and modeled values among incorrectly identified pixels help ascertain reasons for misidentification. A cloud mask specifically designed to separate snow from clouds is a valuable tool for those interested in remotely sensing snow cover. Given freely available remote sensing datasets and computational tools to feasibly process entire mission histories for an area of interest, enabling researchers to reliably identify and separate snow and clouds increases the usability of the data for hydrological and climatological studies.

Monitoring snow cover variability (2000-2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method

NASA Astrophysics Data System (ADS)

Li, Xinghua; Fu, Wenxuan; Shen, Huanfeng; Huang, Chunlin; Zhang, Liangpei

2017-08-01

Monitoring the variability of snow cover is necessary and meaningful because snow cover is closely connected with climate and ecological change. In this work, 500 m resolution MODIS daily snow cover products from 2000 to 2014 were adopted to analyze the status in Hengduan Mountains. In order to solve the spatial discontinuity caused by clouds in the products, we propose an adaptive spatio-temporal weighted method (ASTWM), which is based on the initial result of a Terra and Aqua combination. This novel method simultaneously considers the temporal and spatial correlations of the snow cover. The simulated experiments indicate that ASTWM removes clouds completely, with a robust overall accuracy (OA) of above 93% under different cloud fractions. The spatio-temporal variability of snow cover in the Hengduan Mountains was investigated with two indices: snow cover days (SCD) and snow fraction. The results reveal that the annual SCD gradually increases and the coefficient of variation (CV) decreases with elevation. The pixel-wise trends of SCD first rise and then drop in most areas. Moreover, intense intra-annual variability of the snow fraction occurs from October to March, during which time there is abundant snow cover. The inter-annual variability, which mainly occurs in high elevation areas, shows an increasing trend before 2004/2005 and a decreasing trend after 2004/2005. In addition, the snow fraction responds to the two climate factors of air temperature and precipitation. For the intra-annual variability, when the air temperature and precipitation decrease, the snow cover increases. Besides, precipitation plays a more important role in the inter-annual variability of snow cover than temperature.

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.

Snow-atmosphere coupling and its impact on temperature variability and extremes over North America

NASA Astrophysics Data System (ADS)

Diro, G. T.; Sushama, L.; Huziy, O.

2018-04-01

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981-2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40-60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

Influence of snow cover changes on surface radiation and heat balance based on the WRF model

NASA Astrophysics Data System (ADS)

Yu, Lingxue; Liu, Tingxiang; Bu, Kun; Yang, Jiuchun; Chang, Liping; Zhang, Shuwen

2017-10-01

The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes, indicating the importance of snow cover changes in the surface-atmospheric feedback system.

Snowpack regimes of the Western United States

NASA Astrophysics Data System (ADS)

Trujillo, Ernesto; Molotch, Noah P.

2014-07-01

Snow accumulation and melt patterns play a significant role in the water, energy, carbon, and nutrient cycles in the montane environments of the Western United States. Recent studies have illustrated that changes in the snow/rainfall apportionments and snow accumulation and melt patterns may occur as a consequence of changes in climate in the region. In order to understand how these changes may affect the snow regimes of the region, the current characteristics of the snow accumulation and melt patterns must be identified. Here we characterize the snow water equivalent (SWE) curve formed by the daily SWE values at 766 snow pillow stations in the Western United States, focusing on several metrics of the yearly SWE curves and the relationships between the different metrics. The metrics are the initial snow accumulation and snow disappearance dates, the peak snow accumulation and date of peak, the length of the snow accumulation season, the length of the snowmelt season, and the snow accumulation and snowmelt slopes. Three snow regimes emerge from these results: a maritime, an intermountain, and a continental regime. The maritime regime is characterized by higher maximum snow accumulations reaching 300 cm and shorter accumulation periods of less than 220 days. Conversely, the continental regime is characterized by lower maximum accumulations below 200 cm and longer accumulation periods reaching over 260 days. The intermountain regime lies in between. The regions that show the characteristics of the maritime regime include the Cascade Mountains, the Klamath Mountains, and the Sierra Nevada Mountains. The intermountain regime includes the Eastern Cascades slopes and foothills, the Blue Mountains, Northern and Central basins and ranges, the Columbia Mountains/Northern Rockies, the Idaho Batholith, and the Canadian Rockies. Lastly, the continental regime includes the Middle and Southern Rockies, and the Wasatch and Uinta Mountains. The implications of snow regime classification are discussed in the context of possible changes in accumulation and melt patterns associated with regional warming.

Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

NASA Astrophysics Data System (ADS)

Ishimoto, Hiroshi; Adachi, Satoru; Yamaguchi, Satoru; Tanikawa, Tomonori; Aoki, Teruo; Masuda, Kazuhiko

2018-04-01

Sizes and shapes of snow particles were determined from X-ray computed microtomography (micro-CT) images, and their single-scattering properties were calculated at visible and near-infrared wavelengths using a Geometrical Optics Method (GOM). We analyzed seven snow samples including fresh and aged artificial snow and natural snow obtained from field samples. Individual snow particles were numerically extracted, and the shape of each snow particle was defined by applying a rendering method. The size distribution and specific surface area distribution were estimated from the geometrical properties of the snow particles, and an effective particle radius was derived for each snow sample. The GOM calculations at wavelengths of 0.532 and 1.242 μm revealed that the realistic snow particles had similar scattering phase functions as those of previously modeled irregular shaped particles. Furthermore, distinct dendritic particles had a characteristic scattering phase function and asymmetry factor. The single-scattering properties of particles of effective radius reff were compared with the size-averaged single-scattering properties. We found that the particles of reff could be used as representative particles for calculating the average single-scattering properties of the snow. Furthermore, the single-scattering properties of the micro-CT particles were compared to those of particle shape models using our current snow retrieval algorithm. For the single-scattering phase function, the results of the micro-CT particles were consistent with those of a conceptual two-shape model. However, the particle size dependence differed for the single-scattering albedo and asymmetry factor.

Spatiotemporal changes of snow cover over the Tibetan plateau based on cloud-removed moderate resolution imaging spectroradiometer fractional snow cover product from 2001 to 2011

NASA Astrophysics Data System (ADS)

Tang, Zhiguang; Wang, Jian; Li, Hongyi; Yan, Lili

2013-01-01

Snow cover changes over the Tibetan plateau (TP) are examined using moderate resolution imaging spectroradiometer (MODIS) daily fractional snow cover (FSC) data from 2001 to 2011 as well as in situ temperature data. First, the accuracy of the MODIS FSC data under clear sky conditions is evaluated by comparing with Landsat 30-m observations. Then we describe a cloud-gap-filled (CGF) method using cubic spline interpolation algorithm to fill in data gaps caused by clouds. Finally, the spatial and temporal changes of snow cover are analyzed on the basis of the MODIS-derived snow-covered area and snow-covered days (SCD) data. Results show that the mean absolute error of MODIS FSC data under clear sky condition is about 0.098 over the TP. The CGF method is efficient in cloud reduction (overall mean absolute error of the retrieved FSC data is 0.092). There is a very high inter-annual and intra-seasonal variability of snow cover in the 11 years. The higher snow cover corresponds well with the huge mountains. The accumulation and melt periods of snow cover vary in different elevation zones. About 34.14% (5.56% with a significant decline) and 24.75% (3.9% with a significant increase) of the study area presents declining and increasing trend in SCD, respectively. The inter-annual fluctuation of snow cover can be explained by the high negative correlations observed between the snow cover and the in situ temperature, especially in some elevations of February, April, May, August, and September.

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

USGS Publications Warehouse

Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.

2013-01-01

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

UAS applications in high alpine, snow-covered terrain

NASA Astrophysics Data System (ADS)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

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

Clear-Sky Narrowband Albedo Variations Derived from VIRS and MODIS Data

NASA Technical Reports Server (NTRS)

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

2004-01-01

A critical parameter for detecting clouds and aerosols and for retrieving their microphysical properties is the clear-sky radiance. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the visible (VIS; 0.63 m) and near-infrared (NIR; 1.6 or 2.13 m) channels available on same satellites as the CERES scanners. Another channel often used for cloud and aerosol, and vegetation cover retrievals is the vegetation (VEG; 0.86- m) channel that has been available on the Advanced Very High Resolution Radiometer (AVHRR) for many years. Generally, clear-sky albedo for a given surface type is determined for conditions when the vegetation is either thriving or dormant and free of snow. Snow albedo is typically estimated without considering the underlying surface type. The albedo for a surface blanketed by snow, however, should vary with surface type because the vegetation often emerges from the snow to varying degrees depending on the vertical dimensions of the vegetation. For example, a snowcovered prairie will probably be brighter than a snowcovered forest because the snow typically falls off the trees exposing the darker surfaces while the snow on a grassland at the same temperatures will likely be continuous and, therefore, more reflective. Accounting for the vegetation-induced differences should improve the capabilities for distinguishing snow and clouds over different surface types and facilitate improvements in the accuracy of radiative transfer calculations between the snow-covered surface and the atmosphere, eventually leading to improvements in models of the energy budgets over land. This paper presents a more complete analysis of the CERES spectral clear-sky reflectances to determine the variations in clear-sky top-of-atmosphere (TOA) albedos for both snow-free and snow-covered surfaces for four spectral channels using data from Terra and Aqua.. The results should be valuable for improved cloud retrievals and for modeling radiation fields.

Changes of glacier, glacier-fed rivers and lakes in Altai Tavan Bogd National Park, Western Mongolia, based on multispectral satellite data from 1990 to 2017

NASA Astrophysics Data System (ADS)

Batsaikhan, B.; Lkhamjav, O.; Batsaikhan, N.

2017-12-01

Impacts on glaciers and water resource management have been altering through climate changes in Mongolia territory characterized by dry and semi-arid climate with low precipitation. Melting glaciers are early indicators of climate change unlike the response of the forests which is slower and takes place over a long period of time. Mountain glaciers are important environmental components of local, regional, and global hydrological cycles. The study calculates an overview of changes for glacier, glacier-fed rivers and lakes in Altai Tavan Bogd mountain, the Western Mongolia, based on the indexes of multispectral data and the methods typically applied in glacier studies. Were utilized an integrated approach of Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI) to combine Landsat, MODIS imagery and digital elevation model, to identify glacier cover are and quantify water storage change in lakes, and compared that with and climate parameters including precipitation, land surface temperature, evaporation, moisture. Our results show that melts of glacier at the study area has contributed to significantly increase of water storage of lakes in valley of The Altai Tavan Bogd mountain. There is hydrologic connection that lake basin is directly fed by glacier meltwater.

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.

Physical and Chemical Properties of Seasonal Snow and the Impacts on Albedo in New Hampshire, USA

NASA Astrophysics Data System (ADS)

Adolph, A. C.; Albert, M. R.; Amante, J.; Dibb, J. E.

2014-12-01

Snow albedo is critical to surface energy budgets and thus to the timing of mid-winter and vernal melt events in seasonal snow packs. Timing of these melt events is important in predicting flooding, understanding plant and animal phenology, and the availability of winter recreational activity. The state of New Hampshire experiences large spatial and temporal variability in snow albedo as a result of differences in meteorological conditions, physical snow structure, and chemical impurities in the snow, particularly highly absorptive black carbon (BC) and dust particles. This work focuses on the winters of 2012-2013 and 2013-2014, comparing three intensive study sites. Data collected at these sites include sub-hourly meteorological data, near daily measurements of snow depth, snow density, surface IR temperature, specific surface area (SSA) from contact spectroscopy, and spectrally resolved snow albedo using an ASD FieldSpec4 throughout the winter season. Additionally, snow samples were analyzed for black carbon content and other chemical impurities including Cl-, NO3-, NH4 , K , Na , Mg2+ , Ca2+ and SO42-. For each storm event at the three intensive sites, moisture sources and paths were determined using HYPLIT back trajectory modeling to determine potential sources of black carbon and other impurities in the snow. Storms with terrestrial-based paths across the US Midwest and Canada resulted in higher BC content than storms with ocean-based paths and sources. In addition to the variable storm path between sites and between years, the second year of study was on average 2.5°C colder than the first year, impacting duration of snow cover at each site and the SSA of surface snow which is sensitive to frequency of snow events and relies on cold temperatures to reduce grain metamorphism. Combining an understanding of storm frequency and path with physical and chemical attributes of the snow allows us to investigate snow albedo sensitivities with implications for understanding the impacts of future climate change on snow albedo in the Northeastern US.

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.

A comparison of methods used to obtain age ratios of snow and Canada geese

USGS Publications Warehouse

Higgins, K.F.; Linder, R.L.; Springer, P.F.

1969-01-01

The validity of group counts, cannon-net catches, and hunter-bag checks for estimating productivity of lesser snow geese (Anser caerulescens caerulescens) and small Canada geese (Branta canadensis hutchinsii-parvipes complex) was studied at Sand Lake National Wildlife Refuge during the falls of 1965 and 1966. Age ratios of snow geese obtained from net-trapped samples were significantly higher (P < 0.01) than from group counts at the same site. Immature snow geese were shot in a significantly greater (P < 0.01) proportion than they existed in the population as determined by group counts. Cannon-net catches and hunter-bag checks of snow and Canada geese yielded age ratios which were biased because of behavioral characteristics of the geese. Immatures of both species were less wary of trap equipment and immature snow geese were more vulnerable to the gun than adults. It was believed that age ratios from group counts of snow geese were more representative of the population than those from net catches and hunter-bag checks. Sex ratios of net-trapped geese showed a preponderance of males for adult Canada and adult and immature snow geese, whereas females were predominant in the immature segment of Canada geese. Hunter selectivity of blue- or white-phase snow geese was not observed at Sand Lake Refuge. Differential vulnerability to hunting between snow and Canada geese resulted from differences m feeding-flight behavior.

Limitations of using a thermal imager for snow pit temperatures

NASA Astrophysics Data System (ADS)

Schirmer, M.; Jamieson, B.

2014-03-01

Driven by temperature gradients, kinetic snow metamorphism plays an import role in avalanche formation. When gradients based on temperatures measured 10 cm apart appear to be insufficient for kinetic metamorphism, faceting close to a crust can be observed. Recent studies that visualised small-scale (< 10 cm) thermal structures in a profile of snow layers with an infrared (IR) camera produced interesting results. The studies found melt-freeze crusts to be warmer or cooler than the surrounding snow depending on the large-scale gradient direction. However, an important assumption within these studies was that a thermal photo of a freshly exposed snow pit was similar enough to the internal temperature of the snow. In this study, we tested this assumption by recording thermal videos during the exposure of the snow pit wall. In the first minute, the results showed increasing gradients with time, both at melt-freeze crusts and artificial surface structures such as shovel scours. Cutting through a crust with a cutting blade or shovel produced small concavities (holes) even when the objective was to cut a planar surface. Our findings suggest there is a surface structure dependency of the thermal image, which was only observed at times during a strong cooling/warming of the exposed pit wall. We were able to reproduce the hot-crust/cold-crust phenomenon and relate it entirely to surface structure in a temperature-controlled cold laboratory. Concave areas cooled or warmed more slowly compared with convex areas (bumps) when applying temperature differences between snow and air. This can be explained by increased radiative and/or turbulent energy transfer at convex areas. Thermal videos suggest that such processes influence the snow temperature within seconds. Our findings show the limitations of using a thermal camera for measuring pit-wall temperatures, particularly during windy conditions, clear skies and large temperature differences between air and snow. At crusts or other heterogeneities, we were unable to create a sufficiently planar snow pit surface and non-internal gradients appeared at the exposed surface. The immediate adjustment of snow pit temperature as it reacts with the atmosphere complicates the capture of the internal thermal structure of a snowpack with thermal videos. Instead, the shown structural dependency of the IR signal may be used to detect structural changes of snow caused by kinetic metamorphism. The IR signal can also be used to measure near surface temperatures in a homogenous new snow layer.

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modelling

NASA Astrophysics Data System (ADS)

Sandells, M.; Rutter, N.; Derksen, C.; Langlois, A.; Lemmetyinen, J.; Montpetit, B.; Pulliainen, J. T.; Royer, A.; Toose, P.

2012-12-01

Remote sensing of snow mass remains a challenging area of research. Scattering of electromagnetic radiation is sensitive to snow mass, but is also affected by contrasts in the dielectric properties of the snow. Although the argument that errors from simple algorithms average out at large scales has been used to justify current retrieval methods, it is not obvious why this should be the case. This hypothesis needs to be tested more rigorously. A ground-based field experiment was carried out to assess the impact of sub-footprint snow heterogeneity on microwave brightness temperature, in Churchill, Canada in winter in early 2010. Passive microwave measurements of snow were made using sled-mounted radiometers at 75cm intervals over a 5m transect. Measurements were made at horizontal and vertical polarizations at frequencies of 19 and 37 GHz. Snow beneath the radiometer footprints was subsequently excavated, creating a snow trench wall along the centrepoints of adjacent footprints. The trench wall was carefully smoothed and photographed with a near-infrared camera in order to determine the positions of stratigraphic snow layer boundaries. Three one-dimensional vertical profiles of snowpack properties (density and snow specific surface area) were taken at 75cm, 185cm and 355cm from the left hand side of the trench. These profile measurements were used to derive snow density and grain size for each of the layers identified from the NIR image. Microwave brightness temperatures for the 2-dimensional map of snow properties was simulated with the Helsinki University of Technology (HUT) model at 1cm intervals horizontally across the trench. Where each of five ice lenses was identified in the snow stratigraphy, a decrease in brightness temperature was simulated. However, the median brightness temperature simulated across the trench was substantially higher than the observations, of the order of tens of Kelvin, dependent on frequency and polarization. In order to understand and quantify possible sources of error in the simulations, a number of experiments were carried out to investigate the sensitivity of the brightness temperature to: 1) uncertainties in field observations, 2) representation of ice lenses, 3) model layering structure, and 4) near-infrared derived grain size representing snow grain size at microwave wavelengths. Field measurement error made little difference to the simulated brightness temperature, nor did the representation of ice lenses as crusts of high density snow. As the number of layers in the snow was reduced to 3, 2, or 1, the simulated brightness temperature increased slightly. However, scaling of snow grain size had a dramatic effect on the simulated brightness temperatures, reducing the median bias of the simulations to within measurement error for the statistically different brightness temperature distributions. This indicated that further investigation is required to define what is meant by the microwave grain size, and how this relates to the grain size that is used in the microwave emission model.

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.

Assessment of snow modeling decisions in the extra-tropical Andes Cordillera

NASA Astrophysics Data System (ADS)

Mendoza, P. A.; Musselman, K. N.; Raleigh, M. S.; Clark, M. P.; McPhee, J. P.

2017-12-01

Improving model realism is an ongoing challenge for the cryosphere research community, not only to advance process understanding, but also to quantify and reduce uncertainty under global warming conditions. This work attempts to characterize the interplay and impact of user decisions about snow model structure and parameter specification on model uncertainty. Snow simulations were conducted in the extra-tropical Andes - a mountainous region that acts as a natural reservoir for Central Chile and Western Argentina. To address this topic, we apply the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to simulate seasonal snowpack dynamics at three sites with different hydroclimatic regimes (semi-arid, Mediterranean, and temperate humid). Results are verified against extensive ground-based observations. Site elevations decrease from north to south, whereas precipitation amounts increase with latitude. Results highlight the impact of different windflow and snow transport decisions on model skill during the accumulation period, and different parameterizations (e.g., albedo decay) on spring simulations. We anticipate that the outcomes from this study will have important implications on current and future research, in particular on the configuration of snow models used to quantify the availability of water resources in this region.

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E.; Riggs, George A.; Chang, Alfred T. C.; Foster, James L.; Houser, Paul (Technical Monitor)

2001-01-01

There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period October 23 - December 25, 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), which both rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, however discrepancies exist as to the location and extent of the snow cover among those maps. The large (approx. 30 km) footprint of the SSM/I data and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.32 million sq km in the amount of snow mapped using MODIS versus SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping ability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

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

PubMed

Karmacharya, Dibesh B; Thapa, Kamal; Shrestha, Rinjan; Dhakal, Maheshwar; Janecka, Jan E

2011-11-28

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

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

PubMed Central

2011-01-01

Background The endangered snow leopard is found throughout major mountain ranges of Central Asia, including the remote Himalayas. However, because of their elusive behavior, sparse distribution, and poor access to their habitat, there is a lack of reliable information on their population status and demography, particularly in Nepal. Therefore, we utilized noninvasive genetic techniques to conduct a preliminary snow leopard survey in two protected areas of Nepal. Results A total of 71 putative snow leopard scats were collected and analyzed from two different areas; Shey Phoksundo National Park (SPNP) in the west and Kangchanjunga Conservation Area (KCA) in the east. Nineteen (27%) scats were genetically identified as snow leopards, and 10 (53%) of these were successfully genotyped at 6 microsatellite loci. Two samples showed identical genotype profiles indicating a total of 9 individual snow leopards. Four individual snow leopards were identified in SPNP (1 male and 3 females) and five (2 males and 3 females) in KCA. Conclusions We were able to confirm the occurrence of snow leopards in both study areas and determine the minimum number present. This information can be used to design more in-depth population surveys that will enable estimation of snow leopard population abundance at these sites. PMID:22117538

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.

Characterization of Air and Ground Temperature Relationships within the CMIP5 Historical and Future Climate Simulations

NASA Astrophysics Data System (ADS)

García-García, A.; Cuesta-Valero, F. J.; Beltrami, H.; Smerdon, J. E.

2017-12-01

The relationships between air and ground surface temperatures across North America are examined in the historical and future projection simulations from 32 General Circulation Models (GCMs) included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The covariability between surface air (2 m) and ground surface temperatures (10 cm) is affected by simulated snow cover, vegetation cover and precipitation through changes in soil moisture at the surface. At high latitudes, the differences between air and ground surface temperatures, for all CMIP5 simulations, are related to the insulating effect of snow cover and soil freezing phenomena. At low latitudes, the differences between the two temperatures, for the majority of simulations, are inversely proportional to leaf area index and precipitation, likely due to induced-changes in latent and sensible heat fluxes at the ground surface. Our results show that the transport of energy across the air-ground interface differs from observations and among GCM simulations, by amounts that depend on the components of the land-surface models that they include. The large variability among GCMs and the marked dependency of the results on the choice of the land-surface model, illustrate the need for improving the representation of processes controlling the coupling of the lower atmosphere and the land surface in GCMs as a means of reducing the variability in their representation of weather and climate phenomena, with potentially important implications for positive climate feedbacks such as permafrost and soil carbon stability.

Snow and Glacier Hydrology

NASA Astrophysics Data System (ADS)

Brubaker, Kaye

The study of snow and ice is rich in both fundamental science and practical applications. Snow and Glacier Hydrology offers something for everyone, from resource practitioners in regions where water supply depends on seasonal snow pack or glaciers, to research scientists seeking to understand the role of the solid phase in the water cycle and climate. The book is aimed at the advanced undergraduate or graduate-level student. A perusal of online documentation for snow hydrology classes suggests that there is currently no single text or reference book on this topic in general use. Instructors rely on chapters from general hydrology texts or operational manuals, collections of journal papers, or their own notes. This variety reflects the fact that snow and ice regions differ in climate, topography, language, water law, hazards, and resource use (hydropower, irrigation, recreation). Given this diversity, producing a universally applicable book is a challenge.

Numerical simulation of microstructural damage and tensile strength of snow

NASA Astrophysics Data System (ADS)

Hagenmuller, Pascal; Theile, Thiemo C.; Schneebeli, Martin

2014-01-01

This contribution uses finite-element analysis to simulate microstructural failure processes and the tensile strength of snow. The 3-D structure of snow was imaged by microtomography. Modeling procedures used the elastic properties of ice with bond fracture assumptions as inputs. The microstructure experiences combined tensile and compressive stresses in response to macroscopic tensile stress. The simulated nonlocalized failure of ice lattice bonds before or after reaching peak stress creates a pseudo-plastic yield curve. This explains the occurrence of acoustic events observed in advance of global failure. The measured and simulated average tensile strengths differed by 35%, a typical range for strength measurements in snow given its low Weibull modulus. The simulation successfully explains damage, fracture nucleation, and strength according to the geometry of the microstructure of snow and the mechanical properties of ice. This novel method can be applied to more complex snow structures including the weak layers that cause avalanches.

Modelling the influence of elevation and snow regime on winter stream temperature in the rain-on-snow zone

NASA Astrophysics Data System (ADS)

Leach, J.; Moore, D.

2015-12-01

Winter stream temperature of coastal mountain catchments influences fish growth and development. Transient snow cover and advection associated with lateral throughflow inputs are dominant controls on stream thermal regimes in these regions. Existing stream temperature models lack the ability to properly simulate these processes. Therefore, we developed and evaluated a conceptual-parametric catchment-scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model provided reasonable estimates of observed stream temperature at three test catchments. We used the model to simulate winter stream temperature for virtual catchments located at different elevations within the rain-on-snow zone. The modelling exercise examined stream temperature response associated with interactions between elevation, snow regime, and changes in air temperature. Modelling results highlight that the sensitivity of winter stream temperature response to changes in climate may be dependent on catchment elevation and landscape position.

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.

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.

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.

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.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

NASA Technical Reports Server (NTRS)

Pan, Jinmei; Durand, Michael; Sandells, Melody; Lemmetyinen, Juha; Kim, Edward J.; Pulliainen, Jouni; Kontu, Anna; Derksen, Chris

2015-01-01

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer HUT (Helsinki University of Technology) model and MEMLS (Microwave Emission Model of Layered Snowpacks). By comparing the mathematical formulation side-by-side, three major differences were identified: (1) by assuming the scattered intensity is mostly (96) in the forward direction, the HUT model simplifies the radiative transfer (RT) equation into 1-flux; whereas MEMLS uses a 2-flux theory; (2) the HUT scattering coefficient is much larger than MEMLS; (3 ) MEMLS considers the trapped radiation inside snow due to internal reflection by a 6-flux model, which is not included in HUT. Simulation experiments indicate that, the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of 1-flux simplification and the trapped radiation still result in different T(sub B) simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyl, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that HUT model tends to under estimate T(sub B) for deep snow. MEMLS with the physically-based improved Born approximation performed best among the models, with a bias of -1.4 K, and an RMSE of 11.0 K.

Rainy Days in the New Arctic: A Comprehensive Look at Precipitation from 8 Reanalysis

NASA Astrophysics Data System (ADS)

Boisvert, L.; Webster, M.; Petty, A.; Markus, T.

2017-12-01

Precipitation in the Arctic plays an important role in the fresh water budget, and is the primary control of snow accumulation on sea ice. However, Arctic precipitation from reanalysis is highly uncertain due to differences in the atmospheric physics and use/approaches of data assimilation and sea ice concentrations across the different products. More specifically, yearly cumulative precipitation in some regions can vary by 100-150 mm across reanalyses. This creates problems for those modeling snow depth on sea ice, specifically for use in deriving sea ice thickness from satellite altimetry. In recent years, this new Arctic has become warmer and wetter, and evaporation from the ice-free ocean has been increasing, which leads to the question: is more precipitation falling and is more of this precipitation rain? This could pose a big problem for model and remote sensing applications and studies those modeling snow accumulation because rain events will can melt the existing snow pack, reduce surface albedo, and modify the ocean-to-atmosphere heat flux via snow densification. In this work we compare precipitation (both snow and rain) from 8 different reanalysis: MERRA, MERRA2, NCEP-R1, NCEP-R2, ERA-Interim, ERA-5, ASR and JRA-55. We examine the annual, seasonal, and regional differences and compare with buoy data to assess discrepancies between products during observed snowfall and rainfall events. Magnitudes and frequencies of these precipitation events are evaluated, as well as the "residual drizzle" between reanalyzes. Lastly, we will look at whether the frequency and magnitude of "rainy days" in the Arctic have been changing over recent decades.

Different sensitivities of snowpacks to warming in Mediterranean climate mountain areas

NASA Astrophysics Data System (ADS)

López-Moreno, J. I.; Gascoin, S.; Herrero, J.; Sproles, E. A.; Pons, M.; Alonso-González, E.; Hanich, L.; Boudhar, A.; Musselman, K. N.; Molotch, N. P.; Sickman, J.; Pomeroy, J.

2017-07-01

In this study we quantified the sensitivity of snow to climate warming in selected mountain sites having a Mediterranean climate, including the Pyrenees in Spain and Andorra, the Sierra Nevada in Spain and California (USA), the Atlas in Morocco, and the Andes in Chile. Meteorological observations from high elevations were used to simulate the snow energy and mass balance (SEMB) and calculate its sensitivity to climate. Very different climate sensitivities were evident amongst the various sites. For example, reductions of 9%-19% and 6-28 days in the mean snow water equivalent (SWE) and snow duration, respectively, were found per °C increase. Simulated changes in precipitation (±20%) did not affect the sensitivities. The Andes and Atlas Mountains have a shallow and cold snowpack, and net radiation dominates the SEMB; and explains their relatively low sensitivity to climate warming. The Pyrenees and USA Sierra Nevada have a deeper and warmer snowpack, and sensible heat flux is more important in the SEMB; this explains the much greater sensitivities of these regions. Differences in sensitivity help explain why, in regions where climate models project relatively greater temperature increases and drier conditions by 2050 (such as the Spanish Sierra Nevada and the Moroccan Atlas Mountains), the decline in snow accumulation and duration is similar to other sites (such as the Pyrenees and the USA Sierra Nevada), where models project stable precipitation and more attenuated warming. The snowpack in the Andes (Chile) exhibited the lowest sensitivity to warming, and is expected to undergo only moderate change (a decrease of <12% in mean SWE, and a reduction of < 7 days in snow duration under RCP 4.5). Snow accumulation and duration in the other regions are projected to decrease substantially (a minimum of 40% in mean SWE and 15 days in snow duration) by 2050.

Forecasts of 21st Century Snowpack and Implications for Snowmobile and Snowcoach Use in Yellowstone National Park

PubMed Central

Tercek, Michael; Rodman, Ann

2016-01-01

Climate models project a general decline in western US snowpack throughout the 21st century, but long-term, spatially fine-grained, management-relevant projections of snowpack are not available for Yellowstone National Park. We focus on the implications that future snow declines may have for oversnow vehicle (snowmobile and snowcoach) use because oversnow tourism is critical to the local economy and has been a contentious issue in the park for more than 30 years. Using temperature-indexed snow melt and accumulation equations with temperature and precipitation data from downscaled global climate models, we forecast the number of days that will be suitable for oversnow travel on each Yellowstone road segment during the mid- and late-21st century. The west entrance road was forecast to be the least suitable for oversnow use in the future while the south entrance road was forecast to remain at near historical levels of driveability. The greatest snow losses were forecast for the west entrance road where as little as 29% of the December–March oversnow season was forecast to be driveable by late century. The climatic conditions that allow oversnow vehicle use in Yellowstone are forecast by our methods to deteriorate significantly in the future. At some point it may be prudent to consider plowing the roads that experience the greatest snow losses. PMID:27467778

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.

Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

NASA Astrophysics Data System (ADS)

Marshall, Hans-Peter

The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution on a slope. The ability to accurately characterize snowpack properties at much higher resolutions and spatial extent than previously possible will hopefully help lead to a more complete understanding of spatial variability, its effect on remote sensing measurements and snow slope stability, and result in improvements in avalanche prediction and accuracy of SWE estimates from space.

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.

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

PubMed Central

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

2016-01-01

At high latitudes, the climate has warmed at twice the rate of the global average with most changes observed in autumn, winter and spring. Increasing winter temperatures and wide temperature fluctuations are leading to more frequent rain-on-snow events and freeze-thaw cycles causing snow compaction and formation of ice layers in the snowpack, thus creating ice encasement (IE). By decreasing the snowpack insulation capacity and restricting soil-atmosphere gas exchange, modification of the snow properties may lead to colder soil but also to hypoxia and accumulation of trace gases in the subnivean environment. To test the effects of these overwintering conditions changes on plant winter survival and growth, we established a snow manipulation experiment in a coniferous forest in Northern Finland with Norway spruce and Scots pine seedlings. In addition to ambient conditions and prevention of IE, we applied three snow manipulation levels: IE created by artificial rain-on-snow events, snow compaction and complete snow removal. Snow removal led to deeper soil frost during winter, but no clear effect of IE or snow compaction done in early winter was observed on soil temperature. Hypoxia and accumulation of CO2 were highest in the IE plots but, more importantly, the duration of CO2 concentration above 5% was 17 days in IE plots compared to 0 days in ambient plots. IE was the most damaging winter condition for both species, decreasing the proportion of healthy seedlings by 47% for spruce and 76% for pine compared to ambient conditions. Seedlings in all three treatments tended to grow less than seedlings in ambient conditions but only IE had a significant effect on spruce growth. Our results demonstrate a negative impact of winter climate change on boreal forest regeneration and productivity. Changing snow conditions may thus partially mitigate the positive effect of increasing growing season temperatures on boreal forest productivity. PMID:27254100

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

PubMed

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

2016-01-01

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

Summer snowmelt patterns in the South Shetlands using TerraSAR-X imagery

NASA Astrophysics Data System (ADS)

Mora, C.; Jimenez, J. J.; Catalao Fernades, J.; Ferreira, A.; David, A.; Ramos, M.; Vieira, G.

2014-12-01

Snow plays an important role in controlling ground thermal regime and thus influencing permafrost distribution in the lower areas of the South Shetlands archipelago, where late lying snowpatches protect the soil from summer warming. However, summer snow distribution is complex in the mountainous environments of the Maritime Antarctica and it is very difficult to obtain accurate mapping products of snow cover extent and also to monitor snowmelt. Field observations of snow cover in the region are currently based on: i) thickness data from a very scarce network of meteorological stations, ii) temperature poles allowing to estimate snow thickness, iii) and time-lapse cameras allowing for assessing snow distribution over relatively small areas. The high cloudiness of the Maritime Antarctic environment limits good mapping results from the analysis of optical remote sensing imagery such as Landsat, QuickBird or GeoEye. Therefore, microwave sensors provide the best imagery, since they are not influenced by cloudiness and are sensitive to wet-snow, typical of the melting season. We have acquired TerraSAR-X scenes for Deception and Livingston Islands for January-March 2014 in spotlight (HH, VV and HH/VV) and stripmap modes (HH) and analyse the radar backscattering for determining the differences between wet-snow, dry-snow and bare soil aiming at developing snow melt pattern maps. For ground truthing, snowpits were dug in order to characterize snow stratigraphy, grain size, grain type and snow density and to evaluate its effects on radar backscattering. Time-lapse cameras allow to identify snow patch boundaries in the field and ground surface temperatures obtained with minloggers, together with air temperatures, allow to identify the presence of snow cover in the ground. The current research is conducted in the framework of the project PERMANTAR-3 (Permafrost monitoring and modelling in Antarctic Peninsula - PTDC/AAG-GLO/3908/2012 of the FCT and PROPOLAR).

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

PubMed

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

2016-10-01

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

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

NASA Astrophysics Data System (ADS)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

What do We Know the Snow Darkening Effect Over Himalayan Glaciers?

NASA Technical Reports Server (NTRS)

Yasunari, T. J.; Lau, K.-U.; Koster, R. D.; Suarez, M.; Mahanama, S. P.; Gautam, R.; Kim, K. M.; Dasilva, A. M.; Colarco, P. R.

2011-01-01

The atmospheric absorbing aerosols such as dust, black carbon (BC), organic carbon (OC) are now well known warming factors in the atmosphere. However, when these aerosols deposit onto the snow surface, it causes darkening of snow and thereby absorbing more energy at the snow surface leading to the accelerated melting of snow. If this happens over Himalayan glacier surface, the glacier meltings are expected and may contribute the mass balance changes though the mass balance itself is more complicated issue. Glacier has mainly two parts: ablation and accumulation zones. Those are separated by the Equilibrium Line Altitude (ELA). Above and below ELA, snow accumulation and melting are dominant, respectively. The change of ELA will influence the glacier disappearance in future. In the Himalayan region, many glacier are debris covered glacier at the terminus (i.e., in the ablation zone). Debris is pieces of rock from local land and the debris covered parts are probably not affected by any deposition of the absorbing aerosols because the snow surface is already covered by debris (the debris covered parts have different mechanism of melting). Hence, the contribution of the snow darkening effect is considered to be most important "over non debris covered part" of the Himalayan glacier (i.e., over the snow or ice surface area). To discuss the whole glacier retreat, mass balance of each glacier is most important including the discussion on glacier flow, vertical compaction of glacier, melting amount, etc. The contribution of the snow darkening is mostly associated with "the snow/ice surface melting". Note that the surface melting itself is not always directly related to glacier retreats because sometimes melt water refreezes inside of the glacier. We should discuss glacier retreats in terms of not only the snow darkening but also other contributions to the mass balance.

Data Fusion of Gridded Snow Products Enhanced with Terrain Covariates and a Simple Snow Model

NASA Astrophysics Data System (ADS)

Snauffer, A. M.; Hsieh, W. W.; Cannon, A. J.

2017-12-01

Hydrologic planning requires accurate estimates of regional snow water equivalent (SWE), particularly areas with hydrologic regimes dominated by spring melt. While numerous gridded data products provide such estimates, accurate representations are particularly challenging under conditions of mountainous terrain, heavy forest cover and large snow accumulations, contexts which in many ways define the province of British Columbia (BC), Canada. One promising avenue of improving SWE estimates is a data fusion approach which combines field observations with gridded SWE products and relevant covariates. A base artificial neural network (ANN) was constructed using three of the best performing gridded SWE products over BC (ERA-Interim/Land, MERRA and GLDAS-2) and simple location and time covariates. This base ANN was then enhanced to include terrain covariates (slope, aspect and Terrain Roughness Index, TRI) as well as a simple 1-layer energy balance snow model driven by gridded bias-corrected ANUSPLIN temperature and precipitation values. The ANN enhanced with all aforementioned covariates performed better than the base ANN, but most of the skill improvement was attributable to the snow model with very little contribution from the terrain covariates. The enhanced ANN improved station mean absolute error (MAE) by an average of 53% relative to the composing gridded products over the province. Interannual peak SWE correlation coefficient was found to be 0.78, an improvement of 0.05 to 0.18 over the composing products. This nonlinear approach outperformed a comparable multiple linear regression (MLR) model by 22% in MAE and 0.04 in interannual correlation. The enhanced ANN has also been shown to estimate better than the Variable Infiltration Capacity (VIC) hydrologic model calibrated and run for four BC watersheds, improving MAE by 22% and correlation by 0.05. The performance improvements of the enhanced ANN are statistically significant at the 5% level across the province and in four out of five physiographic regions.

Future Change of Snow Water Equivalent over Japan

NASA Astrophysics Data System (ADS)

Hara, M.; Kawase, H.; Kimura, F.; Fujita, M.; Ma, X.

2012-12-01

Western side of Honshu Island and Hokkaido Island in Japan are ones of the heaviest snowfall areas in the world. Although a heavy snowfall often brings disaster, snow is one of the major sources for agriculture, industrial, and house-use in Japan. Even during the winter, the monthly mean of the surface air temperature often exceeds 0 C in large parts of the heavy snow areas along the Sea of Japan. Thus, snow cover may be seriously reduced in these areas as a result of the global warming, which is caused by an increase in greenhouse gases. The change in seasonal march of snow water equivalent, e.g., snowmelt season and amount will strongly influence to social-economic activities. We performed a series of numerical experiments including present and future climate simulations and much-snow and less-snow cases using a regional climate model. Pseudo-Global-Warming (PGW) method (Kimura and Kitoh, 2008) is applied for the future climate simulations. MIROC 3.2 medres 2070s output under IPCC SRES A2 scenario and 1990s output under 20c3m scenario used for PGW method. The precipitation, snow depth, and surface air temperature of the hindcast simulations show good agreement with the AMeDAS station data. In much-snow cases, The decreasing rate of maximum total snow water equivalent over Japan due to climate change was 49%. Main cause of the decrease of the total snow water equivalent is the air temperature rise due to global climate change. The difference in the precipitation amount between the present and the future simulations is small.

Comparison of Bi-directional Reflectance Distribution Functions of Black Spruce Forest in Snow and No-snow Seasons in Alaska

NASA Astrophysics Data System (ADS)

Suzuki, R.; Nagai, S.; Nakai, T.; Kim, Y.

2011-12-01

The Bidirectional Reflectance Distribution Function (BRDF) of the forest is an important clue for remote sensing to reveal the forest structure such as Leaf Area Index (LAI) and above-ground biomass. The BRDF is required for the robust development of forest radiative transfer model, which is applied to the forest structure analysis based on satellite data. To acquire in-situ BRDF of the forest, we carried out the field survey of BRDFs at a boreal forest in no-snow season (July 2010) and snow season (March 2011) in Alaska, and compared them. A black spruce forest, a typical boreal evergreen forest in Alaska, located in the Poker Flat Research Range of University of Alaska Fairbanks (65 07'24"N, 147 29'15"W, 210 m MSL) was targeted. Since the forest homogeneously extends about 500 m wide and the terrain is relatively even, this forest site is highly suitable for the validation of the remote sensing measurement. The tree stand density was about 4000 tree/ha, and the highest tree was 6.4 m. The forest floor is covered by the green vegetation such as moss and grass in summer, while the vegetation on the floor is completely covered by snow during winter and early spring. The observations of the BRDF taken place around the noon of July 7 and 8, 2010 (no-snow season) and March 16 and 17, 2011 (snow season) from the top of the tower (17 m) constructed in the forest. We measured the reflected irradiance from the forest by the spectroradiometer (MS-720; EKO Instruments) changing the viewing angle from 20 to 70 degrees and -20 to -70 degrees(off-nadir angle; positive and negative angles mean forward and back scatter angles, respectively) with 5 degrees interval in the principal plane. Irradiances in the orthogonal (cross) plane were also measured in the same manner. The global radiation was simultaneously measured by the other spectroradiometer for the calculation of the reflectance. The BRDF in the principal plane in the no-snow season showed a kind of bowl-shape distribution with its minimum and maximum at approximately 30 and -70 degrees in visible and near-infrared bands, respectively, that is, the forward scatter was generally smaller than the back scatter. By contrast, in the snow season, the back scatter was generally smaller than the forward scatter, that is, the reverse of that in the no-snow season. These results will be used for the development of the forest radiative transfer model aimed to evaluate the forest biodiversity and ecosystem functions.

Mapping snow cover using multi-source satellite data on big data platforms

NASA Astrophysics Data System (ADS)

Lhermitte, Stef

2017-04-01

Snowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.

Calibrating a surface mass-balance model for Austfonna ice cap, Svalbard

NASA Astrophysics Data System (ADS)

Schuler, Thomas Vikhamar; Loe, Even; Taurisano, Andrea; Eiken, Trond; Hagen, Jon Ove; Kohler, Jack

2007-10-01

Austfonna (8120 km2) is by far the largest ice mass in the Svalbard archipelago. There is considerable uncertainty about its current state of balance and its possible response to climate change. Over the 2004/05 period, we collected continuous meteorological data series from the ice cap, performed mass-balance measurements using a network of stakes distributed across the ice cap and mapped the distribution of snow accumulation using ground-penetrating radar along several profile lines. These data are used to drive and test a model of the surface mass balance. The spatial accumulation pattern was derived from the snow depth profiles using regression techniques, and ablation was calculated using a temperature-index approach. Model parameters were calibrated using the available field data. Parameter calibration was complicated by the fact that different parameter combinations yield equally acceptable matches to the stake data while the resulting calculated net mass balance differs considerably. Testing model results against multiple criteria is an efficient method to cope with non-uniqueness. In doing so, a range of different data and observations was compared to several different aspects of the model results. We find a systematic underestimation of net balance for parameter combinations that predict observed ice ablation, which suggests that refreezing processes play an important role. To represent these effects in the model, a simple PMAX approach was included in its formulation. Used as a diagnostic tool, the model suggests that the surface mass balance for the period 29 April 2004 to 23 April 2005 was negative (-318 mm w.e.).

Snow model analysis.

DOT National Transportation Integrated Search

2014-01-01

This study developed a new snow model and a database which warehouses geometric, weather and traffic : data on New Jersey highways. The complexity of the model development lies in considering variable road : width, different spreading/plowing pattern...

Comparison of antioxidant activities of different parts from snow chrysanthemum (Coreopsis tinctoria Nutt.) and identification of their natural antioxidants using high performance liquid chromatography coupled with diode array detection and mass spectrometry and 2,2'-azinobis(3-ethylbenzthiazoline-sulfonic acid)diammonium salt-based assay.

PubMed

Chen, L X; Hu, D J; Lam, S C; Ge, L; Wu, D; Zhao, J; Long, Z R; Yang, W J; Fan, B; Li, S P

2016-01-08

Snow chrysanthemum (Coreopsis tinctoria Nutt.), a world-widely well-known flower tea material, has attracted more and more attention because of its beneficial health effects such as antioxidant activity and special flavor. In this study, a high performance liquid chromatography coupled with diode array detection and mass spectrometry (HPLC-DAD-MS) and 2,2'-azinobis(3-ethylbenzthiazoline-sulfonic acid)diammonium salt (ABTS) based assay was employed for comparison and identification of antioxidants in different samples of snow chrysanthemum. The results showed that snow chrysanthemum flowers possessed the highest while stems presented the lowest antioxidant capacities. Fourteen detected peaks with antioxidant activity were temporarily identified as 3,4',5,6,7-pentahydroxyflavanone-O-hexoside, chlorogenic acid, 2R-3',4',8-trihydroxyflavanone-7-O-glucoside, flavanomarein, flavanocorepsin, flavanokanin, quercetagitin-7-O-glucoside, 3',5,5',7-tetrahydroxyflavanone-O-hexoside, marein, maritimein, 1,3-dicaffeoylquinic acid, coreopsin, okanin and acetyl-marein by comparing their UV spectra, retention times and MS data with standards or literature data. Antioxidants existed in snow chrysanthemum are quite different from those reported in Chrysanthemum morifolium, a well-known traditional beverage in China, which indicated that snow chrysanthemum may be a promising herbal tea material with obvious antioxidant activity. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-04-04

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

Altitude-dependent influence of snow cover on alpine land surface phenology

NASA Astrophysics Data System (ADS)

Xie, Jing; Kneubühler, Mathias; Garonna, Irene; Notarnicola, Claudia; De Gregorio, Ludovica; De Jong, Rogier; Chimani, Barbara; Schaepman, Michael E.

2017-05-01

Snow cover impacts alpine land surface phenology in various ways, but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of snow cover phenology (SCP), namely, first snow fall, last snow day, and snow cover duration (SCD), in combination with land surface phenology (LSP), namely, start of season (SOS), end of season, and length of season (LOS) for the period of 2003-2014. We tested the dependency of interannual differences (Δ) of SCP and LSP metrics with altitude (up to 3000 m above sea level) for seven natural vegetation types, four main climatic subregions, and four terrain expositions. We found that 25.3% of all pixels showed significant (p < 0.05) correlation between ΔSCD and ΔSOS and 15.3% between ΔSCD and ΔLOS across the entire study area. Correlations between ΔSCD and ΔSOS as well as ΔSCD and ΔLOS are more pronounced in the northern subregions of the Alps, at high altitudes, and on north and west facing terrain—or more generally, in regions with longer SCD. We conclude that snow cover has a greater effect on alpine phenology at higher than at lower altitudes, which may be attributed to the coupled influence of snow cover with underground conditions and air temperature. Alpine ecosystems may therefore be particularly sensitive to future change of snow cover at high altitudes under climate warming scenarios.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

In order to face climate change, meteorological variability and the recurrent lack of natural snow on the ground, ski resorts adaptation often rely on technical responses. Indeed, since the occurrence of episodes with insufficient snowfalls in the early 1990's, snowmaking has become an ordinary practice of snow management, comparable to grooming, and contributes to optimise the operation of ski resorts. It also participates to the growth of investments and is associated with significant operating costs, and thus represents a new source of vulnerability. The assessment of the actual effects of snowmaking and of snow management practices in general is a real concern for the future of the ski industry. The principal model use to simulate snow conditions in resorts, Ski Sim, has also been moving this way. Its developers introduced an artificial input of snow on ski area to complete natural snowfalls and considered different organisations of ski lifts (lower and upper zones). However the use of a degree-day model prevents them to consider the specific properties of artificial snow and the impact of grooming on the snowpack. A first proof of concept in the French Alps has shown the feasibility and the interest to cross the geographic model of ski areas and the output of the physically-based reanalysis of snow conditions SAFRAN - Crocus (François et al., CRST 2014). Since these initial developments, several ways have been explored to refine our model. A new model of ski areas has been developed. Our representation is now based on gravity derived from a DEM and ski lift localisation. A survey about snow management practices also allowed us to define criteria in order to model snowmaking areas given ski areas properties and tourism infrastructures localisation. We also suggest to revisit the assessment of ski resort viability based on the "one hundred days rule" based on natural snow depth only. Indeed, the impact of snow management must be considered so as to propose reliability indices that are both physically and socio-economically meaningful. Our contribution proposes to present these works in the context of a test resort in the French Alps and its snow conditions during the period 2000-2012. In order to show the impact of different management practices three configurations are considered: natural snowfalls, groomed natural snowfalls and managed snow (natural and artificial snowpack combined with grooming) and discuss the implications of the results in terms of the assessment of the climate vulnerability of ski resorts.

Modeling snow accumulation and ablation processes in forested environments

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

Physical and Optical Properties of Falling Snow

DTIC Science & Technology

1989-07-01

ments with those measured with a transmissometer .................................. 19 24. HSS forward-scatter meter used for measuring extinction in...snowfall conditions, the different ge- ometries of the transmission systems and discrep- | 2• a 2 n(a) da ancies in the snow precipitation rate measure ...J0 ments. Bet = Ms. (27) Table 3. Relationships between measured fn(a) mn(a) da extinction coefficient and snow precipita- ion rate . 091 This

A Microwave Radiance Assimilation Study for a Tundra Snowpack

NASA Technical Reports Server (NTRS)

Kim, Edward; Durand, Michael; Margulis, Steve; England, Anthony

2010-01-01

Recent studies have begun exploring the assimilation of microwave radiances for the modeling and retrieval of snow properties. At a point scale, and for short durations (i week), radiance assimilation (RA) results are encouraging. However, in order to determine how practical RA might be for snow retrievals when applied over longer durations, larger spatial scales, and/or different snow types, we must expand the scope of the tests. In this paper we use coincident microwave radiance measurements and station data from a tundra site on the North Slope of Alaska. The field data are from the 3rd Radio-brightness Energy Balance Experiment (REBEX-3) carried out in 1994-95 by the University of Michigan. This dataset will provide a test of RA over months instead of one week, and for a very different type of snow than previous snow RA studies. We will address the following questions: flow well can a snowpack physical model (SM), forced with local weather, match measured conditions for a tundra snowpack?; How well can a microwave emission model, driven by the snowpack model, match measured microwave brightnesses for a tundra snowpack?; How well does RA increase or decrease the fidelity of estimates of snow depth and temperatures for a tundra snowpack?

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.

Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic

NASA Astrophysics Data System (ADS)

Svensson, Jonas; Ström, Johan; Kivekäs, Niku; Dkhar, Nathaniel B.; Tayal, Shresth; Sharma, Ved P.; Jutila, Arttu; Backman, John; Virkkula, Aki; Ruppel, Meri; Hyvärinen, Antti; Kontu, Anna; Hannula, Henna-Reetta; Leppäranta, Matti; Hooda, Rakesh K.; Korhola, Atte; Asmi, Eija; Lihavainen, Heikki

2018-03-01

Light-absorbing impurities (LAIs) deposited in snow have the potential to substantially affect the snow radiation budget, with subsequent implications for snow melt. To more accurately quantify the snow albedo, the contribution from different LAIs needs to be assessed. Here we estimate the main LAI components, elemental carbon (EC) (as a proxy for black carbon) and mineral dust in snow from the Indian Himalayas and paired the results with snow samples from Arctic Finland. The impurities are collected onto quartz filters and are analyzed thermal-optically for EC, as well as with an additional optical measurement to estimate the light-absorption of dust separately on the filters. Laboratory tests were conducted using substrates containing soot and mineral particles, especially prepared to test the experimental setup. Analyzed ambient snow samples show EC concentrations that are in the same range as presented by previous research, for each respective region. In terms of the mass absorption cross section (MAC) our ambient EC surprisingly had about half of the MAC value compared to our laboratory standard EC (chimney soot), suggesting a less light absorptive EC in the snow, which has consequences for the snow albedo reduction caused by EC. In the Himalayan samples, larger contributions by dust (in the range of 50 % or greater for the light absorption caused by the LAI) highlighted the importance of dust acting as a light absorber in the snow. Moreover, EC concentrations in the Indian samples, acquired from a 120 cm deep snow pit (possibly covering the last five years of snow fall), suggest an increase in both EC and dust deposition. This work emphasizes the complexity in determining the snow albedo, showing that LAI concentrations alone might not be sufficient, but additional transient effects on the light-absorbing properties of the EC need to be considered and studied in the snow. Equally as imperative is the confirmation of the spatial and temporal representativeness of these data by comparing data from several and deeper pits explored at the same time.

Snow cover trend and hydrological characteristics of the Astore River basin (Western Himalayas) and its comparison to the Hunza basin (Karakoram region).

PubMed

Tahir, Adnan Ahmad; Chevallier, Pierre; Arnaud, Yves; Ashraf, Muhammad; Bhatti, Muhammad Tousif

2015-02-01

A large proportion of Pakistan's irrigation water supply is taken from the Upper Indus River Basin (UIB) in the Himalaya-Karakoram-Hindukush range. More than half of the annual flow in the UIB is contributed by five of its snow and glacier-fed sub-basins including the Astore (Western Himalaya - south latitude of the UIB) and Hunza (Central Karakoram - north latitude of the UIB) River basins. Studying the snow cover, its spatio-temporal change and the hydrological response of these sub-basins is important so as to better manage water resources. This paper compares new data from the Astore River basin (mean catchment elevation, 4100 m above sea level; m asl afterwards), obtained using MODIS satellite snow cover images, with data from a previously-studied high-altitude basin, the Hunza (mean catchment elevation, 4650 m asl). The hydrological regime of this sub-catchment was analyzed using the hydrological and climate data available at different altitudes from the basin area. The results suggest that the UIB is a region undergoing a stable or slightly increasing trend of snow cover in the southern (Western Himalayas) and northern (Central Karakoram) parts. Discharge from the UIB is a combination of snow and glacier melt with rainfall-runoff at southern part, but snow and glacier melt are dominant at the northern part of the catchment. Similar snow cover trends (stable or slightly increasing) but different river flow trends (increasing in Astore and decreasing in Hunza) suggest a sub-catchment level study of the UIB to understand thoroughly its hydrological behavior for better flood forecasting and water resources management. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

SMRT: A new, modular snow microwave radiative transfer model

NASA Astrophysics Data System (ADS)

Picard, Ghislain; Sandells, Melody; Löwe, Henning; Dumont, Marie; Essery, Richard; Floury, Nicolas; Kontu, Anna; Lemmetyinen, Juha; Maslanka, William; Mätzler, Christian; Morin, Samuel; Wiesmann, Andreas

2017-04-01

Forward models of radiative transfer processes are needed to interpret remote sensing data and derive measurements of snow properties such as snow mass. A key requirement and challenge for microwave emission and scattering models is an accurate description of the snow microstructure. The snow microwave radiative transfer model (SMRT) was designed to cater for potential future active and/or passive satellite missions and developed to improve understanding of how to parameterize snow microstructure. SMRT is implemented in Python and is modular to allow easy intercomparison of different theoretical approaches. Separate modules are included for the snow microstructure model, electromagnetic module, radiative transfer solver, substrate, interface reflectivities, atmosphere and permittivities. An object-oriented approach is used with carefully specified exchanges between modules to allow future extensibility i.e. without constraining the parameter list requirements. This presentation illustrates the capabilities of SMRT. At present, five different snow microstructure models have been implemented, and direct insertion of the autocorrelation function from microtomography data is also foreseen with SMRT. Three electromagnetic modules are currently available. While DMRT-QCA and Rayleigh models need specific microstructure models, the Improved Born Approximation may be used with any microstructure representation. A discrete ordinates approach with stream connection is used to solve the radiative transfer equations, although future inclusion of 6-flux and 2-flux solvers are envisioned. Wrappers have been included to allow existing microwave emission models (MEMLS, HUT, DMRT-QMS) to be run with the same inputs and minimal extra code (2 lines). Comparisons between theoretical approaches will be shown, and evaluation against field experiments in the frequency range 5-150 GHz. SMRT is simple and elegant to use whilst providing a framework for future development within the community.

The Dominant Snow-forming Process in Warm and Cold Mixed-phase Orographic Clouds: Effects of Cloud Condensation Nuclei and Ice Nuclei

NASA Astrophysics Data System (ADS)

Fan, J.; Rosenfeld, D.; Leung, L. R.; DeMott, P. J.

2014-12-01

Mineral dust aerosols often observed over California in winter and spring from long-range transport can be efficient ice nuclei (IN) and enhance snow precipitation in mixed-phase orographic clouds. On the other hand, local pollution particles can serve as good CCN and suppress warm rain, but their impacts on cold rain processes are uncertain. The main snow-forming mechanism in warm and cold mixed-phase orographic clouds (refer to as WMOC and CMOC, respectively) could be very different, leading to different precipitation response to CCN and IN. We have conducted 1-km resolution model simulations using the Weather Research and Forecasting (WRF) model coupled with a spectral-bin cloud microphysical model for WMOC and CMOC cases from CalWater2011. We investigated the response of cloud microphysical processes and precipitation to CCN and IN with extremely low to extremely high concentrations using ice nucleation parameterizations that connect with dust and implemented based on observational evidences. We find that riming is the dominant process for producing snow in WMOC while deposition plays a more important role than riming in CMOC. Increasing IN leads to much more snow precipitation mainly due to an increase of deposition in CMOC and increased rimming in WMOC. Increasing CCN decreases precipitation in WMOC by efficiently suppressing warm rain, although snow is increased. In CMOC where cold rain dominates, increasing CCN significantly increases snow, leading to a net increase in precipitation. The sensitivity of supercooled liquid to CCN and IN has also been analyzed. The mechanism for the increased snow by CCN and caveats due to uncertainties in ice nucleation parameterizations will be discussed.

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.

Using noble gas ratios to determine the origin of ground ice

NASA Astrophysics Data System (ADS)

Utting, Nicholas; Lauriol, Bernard; Lacelle, Denis; Clark, Ian

2016-01-01

Argon, krypton and xenon have different solubilities in water, meaning their ratios in water are different from those in atmospheric air. This characteristic is used in a novel method to distinguish between ice bodies which originate from the compaction of snow (i.e. buried snow banks, glacial ice) vs. ice which forms from the freezing of groundwater (i.e. pingo ice). Ice which forms from the compaction of snow has gas ratios similar to atmospheric air, while ice which forms from the freezing of liquid water is expected to have gas ratios similar to air-equilibrated water. This analysis has been conducted using a spike dilution noble gas line with gas extraction conducted on-line. Samples were mixed with an aliquot of rare noble gases while being melted, then extracted gases are purified and cryogenically separated. Samples have been analysed from glacial ice, buried snow bank ice, intrusive ice, wedge ice, cave ice and two unknown ice bodies. Ice bodies which have formed from different processes have different gas ratios relative to their formation processes.

SNOW LINES AS PROBES OF TURBULENT DIFFUSION IN PROTOPLANETARY DISKS

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

Owen, James E.

2014-07-20

Sharp chemical discontinuities can occur in protoplanetary disks, particularly at ''snow lines'' where a gas-phase species freezes out to form ice grains. Such sharp discontinuities will diffuse out due to the turbulence suspected to drive angular momentum transport in accretion disks. We demonstrate that the concentration gradient—in the vicinity of the snow line—of a species present outside a snow line but destroyed inside is strongly sensitive to the level of turbulent diffusion (provided the chemical and transport timescales are decoupled) and provides a direct measurement of the radial ''Schmidt number'' (the ratio of the angular momentum transport to radial turbulentmore » diffusion). Taking as an example the tracer species N{sub 2}H{sup +}, which is expected to be destroyed inside the CO snow line (as recently observed in TW Hya) we show that ALMA observations possess significant angular resolution to constrain the Schmidt number. Since different turbulent driving mechanisms predict different Schmidt numbers, a direct measurement of the Schmidt number in accretion disks would allow inferences to be made about the nature of the turbulence.« less

Using NASA Earth Observations to Assist the National Park Service in Assessing Snow Cover Distribution and Persistence Changes in the Sky Islands

NASA Astrophysics Data System (ADS)

Bayat, F.; Barrow, C., III; Gonsoroski, E.; Dutta, S.; Lynn, T.; Harville, K.; Spruce, J.

2017-12-01

Saguaro National Park in southeastern Arizona occupies one of several unique mountain ranges known collectively as the Sky Islands or the Madrean Archipelago. The Sky Islands are biodiversity hotspots and host different ecosystems, ranging from arid deserts to temperate forests. Snowmelt provides a source of water during the dry season for various flora and fauna inhabiting the region. Climate change and its effect on snow cover is of growing concern by resource managers in this location. Currently, the National Park Service (NPS) monitors water presence via stream gauges, but a synoptic record of snow presence does not exist due to the remote and rugged topography of the region. As a result, it is difficult to study how climate change has affected water resources in the Sky Islands and what effect this has on wildlife and vegetation. This project used NASA Earth observations (e.g., Landsat data) and GIS technology to help the NPS in understanding the role of snow cover in the Sky Islands. Historical snow cover maps were compiled using a combination of snow detection indices to provide spatio-temporal information on snow presence and phenology. With a more complete understanding of snow cover trends in the park, the NPS can further analyze snow cover impacts to improve future land management decisions.

Analysis of snow-cap pollution for air quality assessment in the vicinity of an oil refinery.

PubMed

Krastinyte, Viktorija; Baltrenaite, Edita; Lietuvninkas, Arvydas

2013-01-01

Snow-cap can be used as a simple and effective indicator of industrial air pollution. In this study snow-cap samples were collected from 11 sites located in the vicinity of an oil refinery in Mazeikiai, a region in the north-west of Lithuania, in the winter of 2011. Analysis of snowmelt water and snow-dust was used to determine anthropogenic pollutants such as: sulphates and chlorides, nitrites, nitrates, ammonium nitrogen, total carbon, total nitrogen; heavy metals: lead (Pb), copper (Cu), chromium (Cr), cadmium (Cd). Concentrations of heavy metals in snow-dust were detected thousands of times higher than those in the snowmelt water. In this study, analysis of heavy metal concentration was conducted considering different distances and the wind direction within the impact zone of the oil refinery. The sequence of heavy metals according to their mean concentrations in the snow-dust samples was the following: Pb > Cr > Cu > Cd. Heavy metals highly correlated among each other. The load of snow-dust was evaluated to determine the pollution level in the study area. The highest daily load of snow-dust was 45.81 +/- 12.35 mg/m2 in the north-western direction from the oil refinery. According to classification of the daily load of snow-dust a lower than medium-risk level of pollution was determined in the vicinity of the oil refinery.

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

NASA Astrophysics Data System (ADS)

Huang, Xiaodong; Deng, Jie; Ma, Xiaofang; Wang, Yunlong; Feng, Qisheng; Hao, Xiaohua; Liang, Tiangang

2016-10-01

By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

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.

a Physical Parameterization of Snow Albedo for Use in Climate Models.

NASA Astrophysics Data System (ADS)

Marshall, Susan Elaine

The albedo of a natural snowcover is highly variable ranging from 90 percent for clean, new snow to 30 percent for old, dirty snow. This range in albedo represents a difference in surface energy absorption of 10 to 70 percent of incident solar radiation. Most general circulation models (GCMs) fail to calculate the surface snow albedo accurately, yet the results of these models are sensitive to the assumed value of the snow albedo. This study replaces the current simple empirical parameterizations of snow albedo with a physically-based parameterization which is accurate (within +/- 3% of theoretical estimates) yet efficient to compute. The parameterization is designed as a FORTRAN subroutine (called SNOALB) which can be easily implemented into model code. The subroutine requires less then 0.02 seconds of computer time (CRAY X-MP) per call and adds only one new parameter to the model calculations, the snow grain size. The snow grain size can be calculated according to one of the two methods offered in this thesis. All other input variables to the subroutine are available from a climate model. The subroutine calculates a visible, near-infrared and solar (0.2-5 μm) snow albedo and offers a choice of two wavelengths (0.7 and 0.9 mu m) at which the solar spectrum is separated into the visible and near-infrared components. The parameterization is incorporated into the National Center for Atmospheric Research (NCAR) Community Climate Model, version 1 (CCM1), and the results of a five -year, seasonal cycle, fixed hydrology experiment are compared to the current model snow albedo parameterization. The results show the SNOALB albedos to be comparable to the old CCM1 snow albedos for current climate conditions, with generally higher visible and lower near-infrared snow albedos using the new subroutine. However, this parameterization offers a greater predictability for climate change experiments outside the range of current snow conditions because it is physically-based and not tuned to current empirical results.

Radar backscattering from snow facies of the Greenland ice sheet: Results from the AIRSAR 1991 campaign

NASA Technical Reports Server (NTRS)

Rignot, Eric; Jezek, K.; Vanzyl, J. J.; Drinkwater, Mark R.; Lou, Y. L.

1993-01-01

In June 1991, the NASA/JPL airborne SAR (AIRSAR) acquired C- (lambda = 5.6cm), L- (lambda = 24cm), and P- (lambda = 68m) band polarimetric SAR data over the Greenland ice sheet. These data are processed using version 3.55 of the AIRSAR processor which provides radiometrically and polarimetrically calibrated images. The internal calibration of the AIRSAR data is cross-checked using the radar response from corner reflectors deployed prior to flight in one of the scenes. In addition, a quantitative assessment of the noise power level at various frequencies and polarizations is made in all the scenes. Synoptic SAR data corresponding to a swath width of about 12 by 50 km in length (compared to the standard 12 x 12 km size of high-resolution scenes) are also processed and calibrated to study transitions in radar backscatter as a function of snow facies at selected frequencies and polarizations. The snow facies on the Greenland ice sheet are traditionally categorized based on differences in melting regime during the summer months. The interior of Greenland corresponds to the dry snow zone where terrain elevation is the highest and no snow melt occurs. The lowest elevation boundary of the dry snow zone is known traditionally as the dry snow line. Beneath it is the percolation zone where melting occurs in the summer and water percolates through the snow freezing at depth to form massive ice lenses and ice pipes. At the downslope margin of this zone is the wet snow line. Below it, the wet snow zone corresponds to the lowest elevations where snow remains at the end of the summer. Ablation produces enough meltwater to create areas of snow saturated with water, together with ponds and lakes. The lowest altitude zone of ablation sees enough summer melt to remove all traces of seasonal snow accumulation, such that the surface comprises bare glacier ice.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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

PubMed

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

2014-09-15

Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000-2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover duration increases by 4-5 days per 100 m elevation during the accumulation period, depending of the watershed, while during the melting season the snow depletion rate is 0.3% per day of surface loss for the upper Rhone catchment, 0.4%/day for the Syr Darya headwater basins, and 0.6%/day for the upper Po, respectively. Then, the annual STD maps of snow cover indicate higher values (more than 45 days difference compared to the mean values) for (i) the Po foothill region at medium elevation (SE orientation) and (ii) the Kyrgyzstan high plateaux (permafrost areas). These observations cover only a time-period of 10 years, but exhibit a signal under current climate that is already consistent with the expected decline in snow in these regions in the course of the 21st century. Copyright © 2014 Elsevier B.V. All rights reserved.

Validation of snow line estimations using MODIS images for the Elqui River basin, Chile

NASA Astrophysics Data System (ADS)

Vasquez, Nicolas; Lagos, Miguel; Vargas, Ximena

2015-04-01

Precipitation events in North-Central Chile are very important because the region has a Mediterranean climate, with a humid period, and an extensive dry one. Separation between solid and liquid precipitation (snow line) in each event is important information that allow to estimate 1) the available snow covered area for snow-melt forecasting, during the dry season (the only resource of water in this period) and 2) the area affected by rain for flood modelling and infrastructure design. In this work, snow line was estimated with a meteorological approach, considering precipitation, temperature, relative humidity and dew point information at a daily scale from 2004 to 2010 and hourly from 2010 to 2013. In both periods, different meteorological stations are considered due to the implementation of new stations in the study area, covering from 1000 to 3000 (m.a.s.l) approximately, with snow and rain meteorological stations. The methodology exposed in this research is based in vertical variation of dew point and temperature due to more stability variations compared to vertical relative humidity behavior. The results calculated from meteorological data are compared with MODIS images, considering three criteria: (1) the median altitude of the minimum specific fractional snow covered area (FSCA), (2) the mean elevation of pixels with a FSCA<10% and (3) the snow line estimation via snow covered area and hypsometric curve. Historically in Chile, snow line has been studied considering few specific precipitation and temperature observations, or estimations of zero isotherms from upper air soundings. A comparison between these estimations and the results validated through MOD10A1/MYD10A1 products was made in order to identify tendencies and/or variations of the snow line at an annually scale.

Combining point and distributed snowpack data with landscape-based discretization for hydrologic modeling of the snow-dominated Maipo River basin, in the semi-arid Andes of Central Chile.

NASA Astrophysics Data System (ADS)

McPhee, James; Videla, Yohann

2014-05-01

The 5000-km2 upper Maipo River Basin, in central Chile's Andes, has an adequate streamgage network but almost no meteorological or snow accumulation data. Therefore, hydrologic model parameterization is strongly subject to model errors stemming from input and model-state uncertainty. In this research, we apply the Cold Regions Hydrologic Model (CRHM) to the basin, force it with reanalysis data downscaled to an appropriate resolution, and inform a parsimonious basin discretization, based on the hydrologic response unit concept, with distributed data on snowpack properties obtained through snow surveys for two seasons. With minimal calibration the model is able to reproduce the seasonal accumulation and melt cycle as recorded in the one snow pillow available for the basin, and although a bias in maximum accumulation persists, snowpack persistence in time is appropriately simulated based on snow water equivalent and snow covered area observations. Blowing snow events were simulated by the model whenever daily wind speed surpassed 8 m/s, although the use of daily instead of hourly data to force the model suggests that this phenomenon could be underestimated. We investigate the representation of snow redistribution by the model, and compare it with small-scale observations of wintertime snow accumulation on glaciers, in a first step towards characterizing ice distribution within a HRU spatial discretization. Although built at a different spatial scale, we present a comparison of simulated results with distributed snow depth data obtained within a 40 km2 sub-basin of the main Maipo watershed in two snow surveys carried out at the end of winter seasons 2011 and 2012, and compare basin-wide SWE estimates with a regression tree extrapolation of the observed data.

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.

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.

Analysis of passive microwave signatures over snow-covered mountainous area

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.

2015-12-01

Accurate knowledge of snow distribution over mountainous area is critical for climate studies and the passive microwave(PM) measurements have been widely used and invested in order to obtain information about snowpack properties. Understanding and analyzing the signatures for the explicit inversion of the remote sensing data from land surfaces is required for successful using of passive microwave sensors but this task is often ambiguous due to the large variability of physical conditions and object types. In this paper, we discuss the pattern of measured brightness temperatures and emissivities at vertical and horizontal polarization over the frequency range of 10.7 to 89 GHz of land surfaces under various snow and vegetation conditions. The Multiband polarimetric Scanning Radiometer(PSR) imagery is used over NASA Cold Land Processes Field Experiment(CLPX) study area with ground-based measurements of snow depth and snow properties. Classification of snow under various conditions in mountainous area is implemented based on different patterns of microwave signatures.

Black carbon radiative forcing over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

He, Cenlin; Li, Qinbin; Liou, Kuo-Nan; Takano, Yoshi; Gu, Yu; Qi, Ling; Mao, Yuhao; Leung, L. Ruby

2014-11-01

We estimate the snow albedo forcing and direct radiative forcing (DRF) of black carbon (BC) in the Tibetan Plateau using a global chemical transport model in conjunction with a stochastic snow model and a radiative transfer model. The annual mean BC snow albedo forcing is 2.9 W m-2 averaged over snow-covered plateau regions, which is a factor of 3 larger than the value over global land snowpack. BC-snow internal mixing increases the albedo forcing by 40-60% compared with external mixing, and coated BC increases the forcing by 30-50% compared with uncoated BC aggregates, whereas Koch snowflakes reduce the forcing by 20-40% relative to spherical snow grains. The annual BC DRF at the top of the atmosphere is 2.3 W m-2 with uncertainties of -70-85% in the plateau after scaling the modeled BC absorption optical depth to Aerosol Robotic Network observations. The BC forcings are attributed to emissions from different regions.

Role of Marine Snows in Microplastic Fate and Bioavailability.

PubMed

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

2018-06-19

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

Snow extent measurements from geostationary satellites using an interactive computer system. [Salt and Verde River Basins, Arizona

NASA Technical Reports Server (NTRS)

Gird, R. S. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A time series of GOES full resolution visible image sectors was viewed on the McIDAS video component in chronological order and registered to within plus or minus 1 image pixel to compute real time snow melting rates. Synoptic scale clouds were eliminated to create a snow covered area from a composite image. Results show good agreement with NESS products although a significant difference was noted for one two-day period when the NESS products showed an increase in the snow cover for the Verde Basin, while the GOES/McIDAS product implied no change in the snow cover for approximately the same period. A check of NWS radar reports indicated no precipitation had occurred within the Verde basin. The use of the registered image sequence eliminates instrument error since small changes in the snow cover between any two days are easily detected.

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.

Evaluation of inlaid durable pavement markings in an Oregon snow zone.

DOT National Transportation Integrated Search

2006-04-01

The Oregon Department of Transportation (ODOT) evaluated the use of inlaid durable pavement markings within a snow zone. Three different durable pavement marking products were installed and evaluated: Dura-Stripe, a methyl methacrylate; Permaline...

Contribution of high resolution remote sensing data to the modeling of the snow cover the in Atlas Mountains

NASA Astrophysics Data System (ADS)

Baba, Wassim; Gascoin, Simon; Hanich, Lahoucine; Kinnard, Christophe

2017-04-01

Snow melt from the Atlas Mountains watersheds represent an important water resource for the semi-arid, cultivated, lowlands. Due to the high incoming solar radiation and low precipitation, the spatial-temporal variability of the snowpack is expected to be strongly influenced by the topography. We explore this hypothesis using a distributed energy balance snow model (SnowModel) in the experimental watershed of the Rheraya River in Morocco (225 km2). The digital elevation model (DEM) in SnowModel is used for the computation of the gridded meteorological forcing from the automatic weather stations data. We acquired three Pléiades stereo pairs in to produce an accurate, high resolution DEM of the Rheraya watershed at 4 m posting. Then, the DEM was resampled to different spatial resolutions (8 m, 30 m, 90 m, 250 m and 500 m) to simulate the snowpack evolution over 2008-2009 snow season. As validation data we used a time series of 15 maps of the snow cover area (SCA) from Formosat-2 imagery over the same snow season in the upper Rheraya watershed. These maps have a resolution of 8 m, which enables to capture small-scale variability in the snow cover. We found that the simulations at 90 m, 30 m and 8 m yield similar results at the catchment scale, with significant differences in areas of very steep topography only. From February to April, an overall good agreement was observed between the simulated SCA and the Formosat-2 SCA at 8 m and 90 m. Before the melting season, true positive (TP) column of confusion matrix is close to 1, but it drops to 0.6 during the melting season. Heidke Skill Score is higher than 0.7 for the most of the validation dates and averages 0.8. On the contrary, 500 m simulation underestimates the SCA throughout the snow season and the TP score is always inferior to the one obtained at 8 m and 90 m. We further analyzed the effect of topography by comparing the distribution of meteorological and snowpack variables along north-south and east-west transects. This analysis indicates that the impact of the topography on the simulated SWE and snow melt is mainly driven by changes in the solar radiations and the precipitations.

Operational warnings issued by the SMC in the 8th March snow event in Catalonia

NASA Astrophysics Data System (ADS)

Vilaclara, E.; Segalà, S.; Andrés, A.; Aran, M.

2010-09-01

The snowfall event of 8th March 2010 was one of the most important of the last years in Catalonia, with high societal impact in the communication and in the power distribution. Since 2002, after an agreement between Meteorological Service of Catalonia (SMC) and Civil Protection authority, the SMC is the agency responsible to carry out meteorological warnings in Catalonia. These warnings are issued depending on the thresholds which are expected to be exceeded (three different probability degrees are defined), and are characterized by a high spatial and temporal resolution. In the snow event of 8th March, forecasting team of the SMC did the first meteorological warning three days before, on 5th March. During the two following days broadcasted four different warnings, taking into account the high probability of exceeding 2 cm of snow above 200 meters, 15 above 400 m and 30 above 800 m. Furthermore, the SMC disseminated also two special press releases with the aim to extend the forecast to the public. In the afternoon of Sunday, 7th March, the snow precipitation started in some areas of Catalonia. From this moment, the SMC followed the surveillance of situation with the remote sensing tools and the Meteorological Observers Network data. This network is formed by a hundred of spotters with mobile phones able to transmit observations in real time to our web site. This surveillance and the conceptual model of snow events in Catalonia allowed the forecasters to improve the estimation of the snow level forecasted by mesoscale meteorological models. In this event, the snow level obtained from these models was higher than the real one. In spite of the accuracy of the forecast, the impact was very important in Catalonia. On one hand, it was due to the exceptionality of the event, with 3 cm of snow in Barcelona in the afternoon of a working day. On the other hand, the large amount of wet snow precipitation (until 100 mm), and the wind, contributed to an important snow accumulation in the electric power lines. The snow weight and the wind effect broke down a lot of electrical towers in the north-east of Catalonia and more than 450,000 customers were affected by power outages in the following days.

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.

Snow multivariable data assimilation for hydrological predictions in Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Thirel, Guillaume; Campo, Lorenzo; Gabellani, Simone; Stevenin, Hervè

2017-04-01

Snowpack dynamics (snow accumulation and ablation) strongly impacts on hydrological processes in Alpine areas. During the winter season the presence of snow cover (snow accumulation) reduces the drainage in the basin with a resulting lower watershed time of concentration in case of possible rainfall events. Moreover, the release of the significant water volume stored in winter (snowmelt) considerably contributes to the total discharge during the melting period. Therefore when modeling hydrological processes in snow-dominated catchments the quality of predictions deeply depends on how the model succeeds in catching snowpack dynamics. The integration of a hydrological model with a snow module allows improving predictions of river discharges. Besides the well-known modeling limitations (uncertainty in parameterizations; possible errors affecting both meteorological forcing data and initial conditions; approximations in boundary conditions), there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine several independent snow-related data sources (model simulations, ground-based measurements and remote sensed observations) in order to obtain the most likely estimate of snowpack state. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of a DA scheme enables to assimilate simultaneously ground-based observations of different snow-related variables (snow depth, snow density, surface temperature and albedo). SMASH performances are evaluated by using observed data supplied by meteorological stations located in three experimental Alpine sites: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). A comparison analysis between the resulting performaces of Particle Filter and Ensemble Kalman Filter schemes is shown.

In-situ measurements of light-absorbing impurities in snow of glacier on Mt. Yulong and implications for radiative forcing estimates.

PubMed

Niu, Hewen; Kang, Shichang; Shi, Xiaofei; Paudyal, Rukumesh; He, Yuanqing; Li, Gang; Wang, Shijin; Pu, Tao; Shi, Xiaoyi

2017-03-01

The Tibetan Plateau (TP) or the third polar cryosphere borders geographical hotspots for discharges of black carbon (BC). BC and dust play important roles in climate system and Earth's energy budget, particularly after they are deposited on snow and glacial surfaces. BC and dust are two kinds of main light-absorbing impurities (LAIs) in snow and glaciers. Estimating concentrations and distribution of LAIs in snow and glacier ice in the TP is of great interest because this region is a global hotspot in geophysical research. Various snow samples, including surface aged-snow, superimposed ice and snow meltwater samples were collected from a typical temperate glacier on Mt. Yulong in the snow melt season in 2015. The samples were determined for BC, Organic Carbon (OC) concentrations using an improved thermal/optical reflectance (DRI Model 2001) method and gravimetric method for dust concentrations. Results indicated that the LAIs concentrations were highly elevation-dependent in the study area. Higher contents and probably greater deposition at relative lower elevations (generally <5000masl) of the glacier was observed. Temporal difference of LAIs contents demonstrated that LAIs in snow of glacier gradually increased as snow melting progressed. Evaluations of the relative absorption of BC and dust displayed that the impact of dust on snow albedo and radiative forcing (RF) is substantially larger than BC, particularly when dust contents are higher. This was verified by the absorption factor, which was <1.0. In addition, we found the BC-induced albedo reduction to be in the range of 2% to nearly 10% during the snow melting season, and the mean snow albedo reduction was 4.63%, hence for BC contents ranging from 281 to 894ngg -1 in snow of a typical temperate glacier on Mt. Yulong, the associated instantaneous RF will be 76.38-146.96Wm -2 . Further research is needed to partition LAIs induced glacial melt, modeling researches in combination with long-term in-situ observations of LAIs in glaciers is also urgent needed in the future work. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Howse, Dana; Gautrin, Denyse; Neis, Barbara

Fish and shellfish processing employs many thousands of people globally, with shellfish processing becoming more important in recent years. Shellfish processing is associated with multiple occupational health and safety (OHS) risks. Snow crab occupational asthma (OA) is work-related asthma associated with processing snow crab. We present a gender analysis of findings from a 3-year multifaceted study of snow crab OA in Newfoundland and Labrador, Canada. The study was carried out in four snow crab processing communities between 2001 and 2004. An anonymous survey questionnaire on knowledge, beliefs, and concerns related to processing snow crab administered to 158 workers attending communitymore » meetings at the start of the research found that women were significantly more likely than men to associate certain health problems, especially chest tightness, difficulty breathing, and cough, with crab processing (P<0.001). Worker health assessments carried out with 215 processing workers (187 current/28 former; 120 female/95 male) found that female participants were more likely to be diagnosed as almost certain/highly probable snow crab OA and allergy (P=0.001) and to be sensitized to snow crab (P=0.01) than male participants. Work histories from the health assessments were used to classify processing jobs as male or female. Allergen sampling (211 allergen samples: 115 area, 96 personal breathing zone) indicated that the plant areas where these male jobs were concentrated were associated with lower levels of aerosolized crab allergens (the agents responsible for OA to snow crab) than areas associated with female jobs. This difference was statistically significant in the two plants with poor ventilation (p<0.001 and P=0.017 for these plants). A gender analysis of work history data showed that female health assessment participants were likely to have worked longer processing snow crab than males (5 years versus 3.5 years, respectively). Cross-referencing of work history results with allergen sampling data for male and female job areas showed a gender difference in median cumulative exposures (duration of exposurexlevel of exposures) for health assessment participants. Health assessment participants with estimated higher median cumulative exposures were more likely to receive a diagnosis of almost certain/highly probable OA and allergy. Semistructured interviews with 27 health assessment participants (24 female/ 3 male) with a diagnosis of almost certain/highly probable or possible snow crab OA indicated that these workers can experience substantial quality of life impacts while working and that they seek to reduce the economic impact of their illness by remaining at their jobs as long as possible. Indications of selection bias and other study limitations point to the need for more research exploring the relationship between the gender division of labor and knowledge, beliefs, and concerns about snow crab processing, as well as gender differences in prevalence, quality of life, and socioeconomic impact.« less

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

PubMed

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

2006-06-01

Fish and shellfish processing employs many thousands of people globally, with shellfish processing becoming more important in recent years. Shellfish processing is associated with multiple occupational health and safety (OHS) risks. Snow crab occupational asthma (OA) is work-related asthma associated with processing snow crab. We present a gender analysis of findings from a 3-year multifaceted study of snow crab OA in Newfoundland and Labrador, Canada. The study was carried out in four snow crab processing communities between 2001 and 2004. An anonymous survey questionnaire on knowledge, beliefs, and concerns related to processing snow crab administered to 158 workers attending community meetings at the start of the research found that women were significantly more likely than men to associate certain health problems, especially chest tightness, difficulty breathing, and cough, with crab processing (P<0.001). Worker health assessments carried out with 215 processing workers (187 current/28 former; 120 female/95 male) found that female participants were more likely to be diagnosed as almost certain/highly probable snow crab OA and allergy (P=0.001) and to be sensitized to snow crab (P=0.01) than male participants. Work histories from the health assessments were used to classify processing jobs as male or female. Allergen sampling (211 allergen samples: 115 area, 96 personal breathing zone) indicated that the plant areas where these male jobs were concentrated were associated with lower levels of aerosolized crab allergens (the agents responsible for OA to snow crab) than areas associated with female jobs. This difference was statistically significant in the two plants with poor ventilation (p<0.001 and P=0.017 for these plants). A gender analysis of work history data showed that female health assessment participants were likely to have worked longer processing snow crab than males (5 years versus 3.5 years, respectively). Cross-referencing of work history results with allergen sampling data for male and female job areas showed a gender difference in median cumulative exposures (duration of exposure x level of exposures) for health assessment participants. Health assessment participants with estimated higher median cumulative exposures were more likely to receive a diagnosis of almost certain/highly probable OA and allergy. Semistructured interviews with 27 health assessment participants (24 female/ 3 male) with a diagnosis of almost certain/highly probable or possible snow crab OA indicated that these workers can experience substantial quality of life impacts while working and that they seek to reduce the economic impact of their illness by remaining at their jobs as long as possible. Indications of selection bias and other study limitations point to the need for more research exploring the relationship between the gender division of labor and knowledge, beliefs, and concerns about snow crab processing, as well as gender differences in prevalence, quality of life, and socioeconomic impact.

Weather severity index on a mule deer winter range. [Odocoileus hemionus hemionus

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

Leckenby, D.A.; Adams, A.W.

1986-05-01

Temperature, wind, and snow conditions predictably affect the nutrition, behavior, distribution, productivity, and mortality of free-ranging cattle and big game in winter. Indexing of data obtained with commonly available weather instruments to reflect episodes of positive and negative energy balances of free-ranging ruminants could aid scheduling of feeding programs and planning of cover-forage manipulations. Such a weather severity index was developed and tested over 11 winters. Plausible levels of stress and episodes of relative severity were depicted during winters when mule deer exhibited low, moderate, and high mortality. The index curves mirrored over-winter declines of fat reserves probably sustained bymore » mule deer. Lesser weather severity was predicted and measured in a western juniper woodland than in an adjacent rabbitbrush steppe community in southcentral Oregon. 32 references, 3 figures, 2 tables.« less

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

Characterization of organic composition in snow and surface waters in the Athabasca Oil Sands Region, using ultrahigh resolution Fourier transform mass spectrometry.

PubMed

Yi, Y; Birks, S J; Cho, S; Gibson, J J

2015-06-15

This study was conducted to characterize the composition of dissolved organic compounds present in snow and surface waters in the Athabasca Oil Sands Region (AOSR) with the goal of identifying whether atmospherically-derived organic compounds present in snow are a significant contributor to the compounds detected in surface waters (i.e., rivers and lakes). We used electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) to characterize the dissolved organic compound compositions of snow and surface water samples. The organic profiles obtained for the snow samples show compositional differences between samples from near-field sites (<5 km from oil sands activities) and those from more distant locations (i.e., far-field sites). There are also significant compositional differences between samples collected in near-field sites and surface water samples in the AOSR. The composition of dissolved organic compounds at the upstream Athabasca River site (i.e., Athabasca River at Athabasca) is found to be different from samples obtained from downstream sites in the vicinity of oil sands operations (i.e., Athabasca River at Fort McMurray and Athabasca River at Firebag confluence). The upstream Athabasca River sites tended to share some compositional similarities with far-field snow deposition, while the downstream Athabasca River sites are more similar to local lakes and tributaries. This contrast likely indicates the relative role of regional snowmelt contributions to the Athabasca River vs inputs from local catchments in the reach downstream of Fort McMurray. Copyright © 2015 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. Global snow cover fluctuation can now be monitored over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere weekly snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Decadal trends and their significance are compared for the two data types. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as throughout the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Because the current generation of microwave snow algorithms is unable to consistently detect shallow and intermittent snow, we combine visible satellite data with the microwave data in a single blended product to overcome this problem. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets, both of which have greatly enhanced spatial resolution compared to the earlier data sources. Because it is not possible to determine snow depth or snow water equivalent from visible data, the regions where only the NOAA or MODIS data indicate snow are defined as "shallow snow". However, because our current blended product is being developed in the 25 km EASE-Grid and the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) the blended product also includes percent snow cover over the larger grid cell. A prototype version of the blended MODIS/AMSR-E product will be available in near real-time from NSIDC during the 2002-2003 winter season.

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

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.

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.

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Robinson, David A.; Riggs, George A.

2004-01-01

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

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.

Evolution of the Specific Surface Area of Snow in a High Temperature Gradient Metamorphism

NASA Astrophysics Data System (ADS)

Wang, X.; Baker, I.

2014-12-01

The structural evolution of low-density snow under a high temperature gradient over a short period usually takes place in the surface layers during diurnal recrystallization or on a clear, cold night. To relate snow microstructures with their thermal properties, we combined X-ray computed microtomography (micro-CT) observations with numerical simulations. Different types of snow were tested over a large range of TGs (100 K m-1- 500 K m-1). The Specific Surface Area (SSA) was used to characterize the temperature gradient metamorphism (TGM). The magnitude of the temperature gradient and the initial snow type both influence the evolution of SSA. The SSA evolution under TGM was dominated by grain growth and the formation of complex surfaces. Fresh snow experienced a logarithmic decrease of SSA with time, a feature been observed previously by others [Calonne et al., 2014; Schneebeli and Sokratov, 2004; Taillandier et al., 2007]. However, for initial rounded and connected snow structures, the SSA will increase during TGM. Understanding the SSA increase is important in order to predict the enhanced uptake of chemical species by snow or increase in snow albedo. Calonne, N., F. Flin, C. Geindreau, B. Lesaffre, and S. Rolland du Roscoat (2014), Study of a temperature gradient metamorphism of snow from 3-D images: time evolution of microstructures, physical properties and their associated anisotropy, The Cryosphere Discussions, 8, 1407-1451, doi:10.5194/tcd-8-1407-2014. Schneebeli, M., and S. A. Sokratov (2004), Tomography of temperature gradient metamorphism of snow and associated changes in heat conductivity, Hydrological Processes, 18(18), 3655-3665, doi:10.1002/hyp.5800. Taillandier, A. S., F. Domine, W. R. Simpson, M. Sturm, and T. A. Douglas (2007), Rate of decrease of the specific surface area of dry snow: Isothermal and temperature gradient conditions, Journal of Geophysical Research: Earth Surface (2003-2012), 112(F3), doi: 10.1029/2006JF000514.

A simple algorithm for identifying periods of snow accumulation on a radiometer

NASA Astrophysics Data System (ADS)

Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

2015-09-01

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

Feedback mechanisms between snow and atmospheric mercury: Results and observations from field campaigns on the Antarctic plateau.

PubMed

Spolaor, Andrea; Angot, Hélène; Roman, Marco; Dommergue, Aurélien; Scarchilli, Claudio; Vardè, Massimiliano; Del Guasta, Massimo; Pedeli, Xanthi; Varin, Cristiano; Sprovieri, Francesca; Magand, Olivier; Legrand, Michel; Barbante, Carlo; Cairns, Warren R L

2018-04-01

The Antarctic Plateau snowpack is an important environment for the mercury geochemical cycle. We have extensively characterized and compared the changes in surface snow and atmospheric mercury concentrations that occur at Dome C. Three summer sampling campaigns were conducted between 2013 and 2016. The three campaigns had different meteorological conditions that significantly affected mercury deposition processes and its abundance in surface snow. In the absence of snow deposition events, the surface mercury concentration remained stable with narrow oscillations, while an increase in precipitation results in a higher mercury variability. The Hg concentrations detected confirm that snowfall can act as a mercury atmospheric scavenger. A high temporal resolution sampling experiment showed that surface concentration changes are connected with the diurnal solar radiation cycle. Mercury in surface snow is highly dynamic and it could decrease by up to 90% within 4/6 h. A negative relationship between surface snow mercury and atmospheric concentrations has been detected suggesting a mutual dynamic exchange between these two environments. Mercury concentrations were also compared with the Br concentrations in surface and deeper snow, results suggest that Br could have an active role in Hg deposition, particularly when air masses are from coastal areas. This research presents new information on the presence of Hg in surface and deeper snow layers, improving our understanding of atmospheric Hg deposition to the snow surface and the possible role of re-emission on the atmospheric Hg concentration. Copyright © 2018 Elsevier Ltd. All rights reserved.

Snow-depth and water-equivalent data for the Fairbanks area, Alaska, spring 1995

USGS Publications Warehouse

Plumb, E.W.; Lilly, M.R.

1996-01-01

Snow depths at 34 sites and snow-water equivalents at 13 sites in the Fairbanks area were monitored during the 1995 snowmelt period (March 30 to April 26) in the spring of 1995. The U.S. Geological Survey conducted this study in cooperation with the Fairbanks International Airport, the University of Alaska Fairbanks, the Alaska Department of Natural Resources-Division of Mining and Water Management, the U.S Army, Alaska, and the U.S. Army Corps of Engineers-Alaska District. These data were collected to provide information about potential recharge of the ground-and surface-water systems during the snowmelt period in the Fairbanks area. This information is needed by companion geohydrologic studies of areas with known or suspected contaminants in the subsurface. Data-collection sites selected had open, boggy, wooded, or brushy vegetation cover and had different slope aspects. The deepest snow at any site, 27.1 inches, was recorded on April 1, 1995; the shallowest snow measured that day was 19.1 inches. The snow-water equivalents at these two sites were 5.9 inches and 4.5 inches, respectively. Snow depths and water equivalents were comparatively greater at open and bog sites than at wooded or brushy sites. Snow depths and water equivalents at all sites decreased throughout the measuring period. The decrease was more rapid at open and boggy sites than at wooded and brushy sites. Snow had completely disappeared from all sites by April 26, 1995.

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

NASA Astrophysics Data System (ADS)

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

2007-06-01

Taxonomical and ecological analyses were performed on micro-autotrophs (cyanobacteria and algae together with remnants of diatom valves), micro-fungi (hyphae and spores), bacteria (rod, cocci and red clusters), yeast, and plant pollen extracted from various samples: Alps snow (Mt. Blank area), Andean snow (Illimani, Bolivia), Antarctic aerosol filters (Dumont d'Urville, Terre Adélie), and Antarctic inland ice (Terre Adélie). Three methods for ice and snow sample's pre-concentration were tested (filtration, centrifugation and lyophilisation). Afterwards, cultivation methods for terrestrial, freshwater and marine microorganisms (micro-autotrophs and micro-fungi) were used in combination with liquid and solid media. The main goal of the study was to find out if micro-autotrophs are commonly transported by air masses, and later stored in snow and icecaps around the world. The most striking result of this study was the absence of culturable micro-autotrophs in all studied samples. However, an unusual culturable pigmented prokaryote was found in both alpine snow and aerosol samples. Analyses of many samples and proper statistical analyses (PCA, RDA- Monte Carlo permutation tests) showed that studied treatments highly significantly differ in both microbial community and biotic remnants composition F=9.33, p=0.001. In addition, GLM showed that studied treatments highly significantly differ in numbers of categories of microorganisms and remnants of biological material F=11.45, p=0.00005. The Antarctic aerosol samples were characterised by having red clusters of bacteria, the unusual prokaryote and yeasts. The high mountain snow from the Alps and Andes contained much more culturable heterotrophs. The unusual prokaryote was very abundant, as were coccoid bacteria, red clusters of bacteria, as well as yeasts. The Antarctic ice samples were quite different. These samples had higher numbers of rod bacteria and fungal hyphae. The microbial communities and biological remnants of analysed samples comprises two communities, without a sharp boundary between them: i) the first community includes ubiquitous organisms including contaminants, ii) the second community represents individuals frequently occurring in remote terrestrial cold or hot desert/semi-desert and/or marginal soil-snow-ice ecosystems.

Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002

NASA Astrophysics Data System (ADS)

Khan, Asif; Naz, Bibi S.; Bowling, Laura C.

2015-02-01

The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7238 ± 724, 5226 ± 522, 4695 ± 469 and 2126 ± 212 km2 respectively. Thus total snow and glacier cover is 19,285 ± 1928 km2, out of which 12,075 ± 1207 km2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4800 and 5500 m, while the Accumulation Area Ratio (AAR) ranges between 7% and 80%. 0 °C isotherms during peak ablation months (July and August) range between ∼ 5500 and 6200 m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate.

Seventy-five years of masting and rodent population peaks in Norway: Why do wood mice not follow the rules?

PubMed

Selås, Vidar

2016-09-01

Wood mouse (Apodemus sylvaticus) populations are expected to show a peak in autumn in the year after a mast year of sessile oak (Quercus petraea), because stored acorns increase winter survival. In Aust-Agder, South Norway, only 16 of 34 mast years from 1939-2014 were followed by a year with a peak in the wood mouse population. For many of the remaining instances, there rather was a minor peak 2 or 3 years after the mast. In multiple logistic regression models, the probability of a wood mouse population peak after a mast year of sessile oak was positively related to a snow-corrected temperature index of the previous winter and negatively to a small rodent population index of the previous autumn. The present study thus supports the hypothesis that longer periods with snow-free ground and subzero temperatures negatively affect wood mouse winter survival. Because it may be difficult for wood mice to survive on a diet consisting of acorns alone, the negative relationship with the rodent population index of the previous year is most likely caused by an over-exploitation of necessary alternative food resources, such as other plant seeds and arthropods. Stored acorns not utilized during one winter are assumed to benefit wood mice in a succeeding winter, giving a delayed population peak relative to the mast year. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Early results from NASA's SnowEx campaign

NASA Astrophysics Data System (ADS)

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

2017-04-01

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focuses on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site is Grand Mesa and the secondary site is the Senator Beck Basin, both in western, Colorado, USA. Ten core sensors on four core aircraft will make observations using a broad suite of airborne sensors including active and passive microwave, and active and passive optical/infrared sensing techniques to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also includes an extensive range of ground truth measurements—in-situ samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some early results will be presented. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earth's surface (31 % of land areas) each year). This seasonal snow has major societal impacts in the areas of water resources, natural hazards (floods and droughts), water security, and weather and climate. The only practical way to estimate the quantity of snow on a consistent global basis is through satellites. Yet, current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly vs. estimates based on remotely-sensed observations. At peak coverage, as much as half of snow-covered terrestrial areas involve forested areas, so quantifying the challenge represented by forests is important to plan any future snow mission. Single-sensor approaches may work for certain snow types and certain conditions, but not for others. Snow simply varies too much. Thus, the snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation. What remains at issue, then, is how best to combine and use the various sensors in an optimal way. That requires field measurements. NASA's SnowEx airborne campaign is designed to do exactly that. A list of core sensors is as follows. All are from NASA unless otherwise noted. • Radar (volume scattering): European Space Agency's SnowSAR, operated by MetaSensing • Lidar & hyperspectral imager: Airborne Snow Observatory (ASO) • Passive microwave: Airborne Earth Science Microwave Imaging Radiometer (AESMIR) • Bi-directional Reflectance Function (BRDF): the Cloud Absorption Radiometer (CAR) • Thermal Infrared imager • Thermal infrared non-imager from U. Washington • Video camera The ASO suite flew on a King Air, and the other sensors flew on a Navy P-3. In addition, two NASA radars flew on G-III aircraft to test more experimental retrieval techniques: • InSAR altimetry: Glacier and Ice Surface Topography Interferometer (GLISTIN-A) • Radar phase delay: Uninhabited Aerial Vehicle Synthetic Aperture Radar, (UAVSAR)

Doomed Quest: "The Snows of Kilimanjaro."

ERIC Educational Resources Information Center

Satterfield, Ben

2002-01-01

Considers how "The Snow of Kilimanjaro" is one of Hemingway's most popular stories in terms of both readers and critics, offering as it does an abundance of symbols and imagery that has pleased, puzzled, and perplexed. Discusses its many different interpretations. (SG)

Greenland ice sheet surface mass-balance modeling in a 131-year perspective, 1950-2080

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

Mernild, Sebastian Haugard; Liston, Glen; Hiemstra, Christopher

2009-01-01

Fluctuations in the Greenland Ice Sheet (GrIS) surface mass-balance (SMB) and freshwater influx to the surrounding oceans closely follow climate fluctuations and are of considerable importance to the global eustatic sea level rise. SnowModel, a state-of-the-art snow-evolution modeling system, was used to simulate variations in the GrIS melt extent, surface water balance components, changes in SMB, and freshwater influx to the ocean. The simulations are based on the IPCC scenario AlB modeled by the HIRHAM4 RCM (using boundary conditions from ECHAM5 AOGCM) from 1950 through 2080. In-situ meteorological station (GC-Net and WMO DMI) observations from inside and outside the GrISmore » were used to validate and correct RCM output data before it was used as input for SnowModel. Satellite observations and independent SMB studies were used to validate the SnowModel output and confirm the model's robustness. We simulated a {approx}90% increase in end-of-summer surface melt extent (0.483 x 10{sup 6} km{sup 2}) from 1950 to 2080, and a melt index (above 2,000-m elevation) increase of 138% (1.96 x 10{sup 6} km{sup 2} x days). The greatest difference in melt extent occured in the southern part of the GrIS, and the greatest changes in the number of melt days was seen in the eastern part of the GrIS ({approx}50-70%) and was lowest in the west ({approx}20-30%). The rate of SMB loss, largely tied to changes in ablation processes, lead to an enhanced average loss of 331 km{sup 3} from 1950 to 2080, an average 5MB level of -99 km{sup 3} for the period 2070-2080. GrIS surface freshwater runoff yielded an eustatic rise in sea level from 0.8 {+-} 0.1 (1950-1959) to 1.9 {+-} 0.1 mm (2070-2080) sea level equivalent (SLE) y{sup -1}. The accumulated GrIS freshwater runoff contribution from surface melting equaled 160 mm SLE from 1950 through 2080.« less

3D documentation of footwear impressions and tyre tracks in snow with high resolution optical surface scanning.

PubMed

Buck, Ursula; Albertini, Nicola; Naether, Silvio; Thali, Michael J

2007-09-13

The three-dimensional documentation of footwear and tyre impressions in snow offers an opportunity to capture additional fine detail for the identification as present photographs. For this approach, up to now, different casting methods have been used. Casting of footwear impressions in snow has always been a difficult assignment. This work demonstrates that for the three-dimensional documentation of impressions in snow the non-destructive method of 3D optical surface scanning is suitable. The new method delivers more detailed results of higher accuracy than the conventional casting techniques. The results of this easy to use and mobile 3D optical surface scanner were very satisfactory in different meteorological and snow conditions. The method is also suitable for impressions in soil, sand or other materials. In addition to the side by side comparison, the automatic comparison of the 3D models and the computation of deviations and accuracy of the data simplify the examination and delivers objective and secure results. The results can be visualized efficiently. Data exchange between investigating authorities at a national or an international level can be achieved easily with electronic data carriers.

Colonization of Snow by Microorganisms as Revealed Using Miniature Raman Spectrometers-Possibilities for Detecting Carotenoids of Psychrophiles on Mars?

PubMed

Jehlička, Jan; Culka, Adam; Nedbalová, Linda

2016-12-01

We tested the potential of a miniaturized Raman spectrometer for use in field detection of snow algae pigments. A miniature Raman spectrometer, equipped with an excitation laser at 532 nm, allowed for the detection of carotenoids in cells of Chloromonas nivalis and Chlamydomonas nivalis at different stages of their life cycle. Astaxanthin, the major photoprotective pigment, was detected in algal blooms originating in snows at two alpine European sites that differed in altitude (Krkonoše Mts., Czech Republic, 1502 m a.s.l., and Ötztal Alps, Austria, 2790 m a.s.l.). Comparison is made with a common microalga exclusively producing astaxanthin (Haematococcus pluvialis). The handheld Raman spectrometer is a useful tool for fast and direct field estimations of the presence of carotenoids (mainly astaxanthin) within blooms of snow algae. Application of miniature Raman instruments as well as flight prototypes in areas where microbes are surviving under extreme conditions is an important stage in preparation for successful deployment of this kind of instrumentation in the framework of forthcoming astrobiological missions to Mars. Key Words: Snow algae-Chloromonas nivalis-Chlamydomonas nivalis-On-site field detection-Raman spectroscopy-Astaxanthin. Astrobiology 16, 913-924.

Lava-snow interactions at Tolbachik 2012-13 eruption: comparison to recent field observations and experiments

NASA Astrophysics Data System (ADS)

Edwards, B. R.; Belousov, A.; Belousova, M.; Izbekov, P. E.; Bindeman, I. N.; Gardeev, E.; Muravyev, Y. D.; Melnikov, D.

2013-12-01

More than a dozen volcanic eruptions in the past twenty years have produced lava interaction with snow or ice, some of which have produced damaging floods/lahars. However, the factors controlling melting during lava-snow/ice interactions is not well understood. Recent observations from the presently ongoing eruption at Tolbachik, Kamchatka confirm some general observations from large-scale experiments, and recent eruptions (2010 Fimmvorduhals; Edwards et al, 2012), but also show new types of behavior not before described. The new observations provide further constraints on heat transfer between ice/snow and three different lava morphologies: ';a'a, pahoehoe, and toothpaste. ';A'a flows at Tolbachik commonly were able to travel over seasonal snow cover (up to 4 m thick), especially where the snow was covered by tephra within 1.5 km of the vent area. Locally, heated meltwater discharge events issued from beneath the front of advancing lava, even though snow observation pits dug in front of advancing ';a'a flows also showed that in some areas melting was not as extensive. Once, an ';a'a flow was seen to collapse through snow, generating short-lived phreatomagmatic/phreatic activity. Closer to the vent, pahoehoe flow lobes and sheet flows occasionally spilled over onto snow and were able to rapidly transit snow with few obvious signs of melting/steam generation. Most of these flows did melt through basal snow layers within 24 hours however. We were also able to closely observe ';toothpaste' lava flows ';intruding' into snow in several locations, including snow-pits, and to watch it pushing up through snow forming temporary snow domes. Toothpaste lava caused the most rapid melting and most significant volumes of steam, as the meltwater drained down into the intruding lava. Behaviour seen at Tolbachik is similar to historic (e.g., Hekla 1947; Einarrson, 1949) and recent observations (e.g. Fimmvorduhals), as well as large-scale experiments (Edwards et al., 2013). While lava flows have been seen to eventually melt through up to 5 m of snow, melting generally is relatively slow (cm / hr); presence of ash cover on snow slows melting. Temperatures of meltwater discharging from beneath lava flows at Tolbachik were up to 40 deg C, which is similar to maximum temperatures measured during experiments. While meltwater discharge was documented on both subhorizontal and steeper slows (~10 degrees), the only explosive activity was observed where topography likely prevented fast meltwater escape from beneath lava. All of these observations hopefully will lead to a new and better understanding of the hazards associated with lava-ice/snow interactions. Meltwater discharge from beneath 'a'a flow.

How Do Alternative Ways of Responding Influence 3- and 4-Year-Olds' Performance on Tests of Executive Function and Theory of Mind?

ERIC Educational Resources Information Center

Carroll, Daniel J.; Riggs, Kevin J.; Apperly, Ian A.; Graham, Kate; Geoghegan, Ceara

2012-01-01

A total of 69 preschool children were tested on measures of false belief understanding (the Unexpected Transfer task), inhibitory control (the Grass/Snow task), and strategic reasoning (the Windows task). For each task, children indicated their response either by pointing with their index finger or by using a nonstandard response mode (pointing…

Tool and Method for Testing the Resistance of the Snow Road Cover to Destruction

NASA Astrophysics Data System (ADS)

Zhelykevich, R.; Lysyannikov, A.; Kaiser, Yu; Serebrenikova, Yu; Lysyannikova, N.; Shram, V.; Kravtsova, Ye; Plakhotnikova, M.

2016-06-01

The paper presents the design of the tool for efficient determination of the hardness of the snow road coating. The tool increases vertical positioning of the rod with the tip through replacement of the rod slide friction of the ball element by roll friction of its outer bearing race in order to enhance the accuracy of determining the hardness of the snow-ice road covering. A special feature of the tool consists in possibility of creating different impact energy by the change of the lifting height of the rod with the tip (indenter) and the exchangeable load mass. This allows the study of the influence of the tip shape and the impact energy on the snow strength parameters in a wide range, extends the scope of application of the durometer and makes possible to determine the strength of snow-ice formations by indenters with various geometrical parameters depending on climatic conditions.

Diffusion of nitrogen oxides and oxygenated volatile organic compounds through snow

NASA Astrophysics Data System (ADS)

Bartels-Rausch, T.; Ammann, M.; Schneebeli, M.; Riche, F.; Wren, S. N.

2013-12-01

Release of trace gases from surface snow on Earth drives atmospheric chemistry, especially in the Polar Regions. The exchange of atmospheric trace gases between snow or firn and atmosphere can also determine how these species are incorporated into glacial ice, which serves as archive. At low wind conditions, such fluxes between the porous surface snow and the overlaying atmosphere are driven by diffusion through the interstitial air. Here we present results from two laboratory studies where we looked at how the structure of the snowpack, the interaction of the trace gases with the snow surface, and the grain boundaries influence the diffusion of NO, NO2, HONO, methanol, and acetone on time scales up to 1 h. The diffusion through a snow sample was the direct observable of the experiments. Results for different snow types are presented, the structures of which were analysed by means of X-ray computed micro-tomography. Grain boundary content was quantified in one sample using a stereological method. The observed diffusion profiles were very well reproduced in simulations based on gas-phase diffusion and the known structure of the snow sample at temperatures above 253 K. At colder temperatures surface interactions start to dominate the diffusion. Parameterizing these in terms of adsorption to the solid ice surface gave much better agreement to the observations than the use of air - liquid partitioning coefficients. This is a central result as field and modelling studies have indicated that the partitioning to liquid water might describe the diffusion through snow much better even at cold temperatures. This will be discussed using our recent results from surface sensitive spectroscopy experiments. No changes in the diffusion was observed by increasing the number of grain boundaries in the snow sample by a factor of 7.

Experimental and numerical investigation of a RC wall loaded by snow-like avalanche pressure signal

NASA Astrophysics Data System (ADS)

Ousset, Isabelle; Bertrand, David; Brun, Michaël; Limam, Ali; Naaïm, Mohamed

2013-04-01

Nowadays, civil engineering structures exposed to snow avalanches are mostly designed considering static loadings involving large safety factors. These latters highlight the lack of knowledge about the effects of the loading generated by a snow flow, and generally lead to oversize the civil structure. Indeed, the transient nature of the loading signal and also the composition of the snow flow can generate dynamic phenomena which cannot be taken into account considering only static loadings. The case of the avalanche of the Taconnaz (France), which occurred in 1999 and where important parts of the defense structure were destroyed, showed that static design approaches can lead to underestimate the potential effect of the snow flow. Thus, in order to give some new insights about this issue, the effect of the temporal variations of the snow loading on the mechanical behavior of an idealized defense structure is investigated. Therefore, a reinforced concrete (RC) wall with a L-like shape has been considered which is supposed to represent a part of the defense structure situated in Taconnaz. Static pushover tests, carried out in laboratory conditions on 1/6 scale physical model of the RC structure, allowed obtaining the capacity of the tested structure (Berthet-Rambaud et al. (2007)). Finite Element (FE) models have been developed and calibrated from the previous experimental data. The FE approach allows simulating the dynamic mechanical response of the structure. The effect of the transient nature of the loading of the avalanche has been explored applying out-of-plan dynamic loadings on the RC wall. In order to be as close as possible of a "field" snow avalanche, the imposed time evolution of the loading has been generated from in situ measurements recorded at the French experimental site "le col du Lautaret" (Thibert et al. (2008)). The RC mechanical behaviour has been described by four nonlinear constitutive laws. The four behaviour laws are compared and analyzed for specific loading situations. Next, the influences of typical parameters characterizing the avalanche loading signal are proposed. In particular, a special focused is presented on the effect of the loading rate. Finally, the vulnerability of the RC wall is studied in a reliability framework. Damage index are proposed and the probability of failure of the RC wall is derived. These relations might be useful for risk analysis.

Methane fluxes during the cold season: distribution and mass transfer in the snow cover of bogs

NASA Astrophysics Data System (ADS)

Smagin, A. V.; Shnyrev, N. A.

2015-08-01

Fluxes and profile distribution of methane in the snow cover and different landscape elements of an oligotrophic West-Siberian bog (Mukhrino Research Station, Khanty-Mansiisk autonomous district) have been studied during a cold season. Simple models have been proposed for the description of methane distribution in the inert snow layer, which combine the transport of the gas and a source of constant intensity on the soil surface. The formation rates of stationary methane profiles in the snow cover have been estimated (characteristic time of 24 h). Theoretical equations have been derived for the calculation of small emission fluxes from bogs to the atmosphere on the basis of the stationary profile distribution parameters, the snow porosity, and the effective methane diffusion coefficient in the snow layer. The calculated values of methane emission significantly (by 2-3 to several tens of times) have exceeded the values measured under field conditions by the closed chamber method (0.008-0.25 mg C/(m2 h)), which indicates the possibility of underestimating the contribution of the cold period to the annual emission cycle of bog methane.

Snow and ice ecosystems: not so extreme.

PubMed

Maccario, Lorrie; Sanguino, Laura; Vogel, Timothy M; Larose, Catherine

2015-12-01

Snow and ice environments cover up to 21% of the Earth's surface. They have been regarded as extreme environments because of their low temperatures, high UV irradiation, low nutrients and low water availability, and thus, their microbial activity has not been considered relevant from a global microbial ecology viewpoint. In this review, we focus on why snow and ice habitats might not be extreme from a microbiological perspective. Microorganisms interact closely with the abiotic conditions imposed by snow and ice habitats by having diverse adaptations, that include genetic resistance mechanisms, to different types of stresses in addition to inhabiting various niches where these potential stresses might be reduced. The microbial communities inhabiting snow and ice are not only abundant and taxonomically diverse, but complex in terms of their interactions. Altogether, snow and ice seem to be true ecosystems with a role in global biogeochemical cycles that has likely been underestimated. Future work should expand past resistance studies to understanding the function of these ecosystems. Copyright © 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Microwave Observations of Snow-Covered Freshwater Lake Ice obtained during the Great Lakes Winter EXperiment (GLAWEX), 2017

NASA Astrophysics Data System (ADS)

Gunn, G. E.; Hall, D. K.; Nghiem, S. V.

2017-12-01

Studies observing lake ice using active microwave acquisitions suggest that the dominant scattering mechanism in ice is caused by double-bounce of the signal off vertical tubular bubble inclusions. Recent polarimetric SAR observations and target decomposition algorithms indicate single-bounce interactions may be the dominant source of returns, and in the absence of field observations, has been hypothesized to be the result of roughness at the ice-water interface on the order of incident wavelengths. This study presents in-situ physical observations of snow-covered lake ice in western Michigan and Wisconsin acquired during the Great Lakes Winter EXperiment in 2017 (GLAWEX'17). In conjunction with NASA's SnowEx airborne snow campaign in Colorado (http://snow.nasa.gov), C- (Sentinel-1, RADARSAT-2) and X-band (TerraSAR-X) synthetic aperture radar (SAR) observations were acquired coincidently to surface physical snow and ice observations. Small/large scale roughness features at the ice-water interface are quantified through auger transects and used as an input variable in lake ice backscatter models to assess the relative contributions from different scattering mechanisms.

Characterizing the spatiotemporal variability of groundwater levels of alluvial aquifers in different settings using drought indices

NASA Astrophysics Data System (ADS)

Haas, Johannes Christoph; Birk, Steffen

2017-05-01

To improve the understanding of how aquifers in different alluvial settings respond to extreme events in a changing environment, we analyze standardized time series of groundwater levels (Standardized Groundwater level Index - SGI), precipitation (Standardized Precipitation Index - SPI), and river stages of three subregions within the catchment of the river Mur (Austria). Using correlation matrices, differences and similarities between the subregions, ranging from the Alpine upstream part of the catchment to its shallow foreland basin, are identified and visualized. Generally, river stages exhibit the highest correlations with groundwater levels, frequently affecting not only the wells closest to the river, but also more distant parts of the alluvial aquifer. As a result, human impacts on the river are transferred to the aquifer, thus affecting the behavior of groundwater levels. Hence, to avoid misinterpretation of groundwater levels in this type of setting, it is important to account for the river and human impacts on it. While the river is a controlling factor in all of the subregions, an influence of precipitation is evident too. Except for deep wells found in an upstream Alpine basin, groundwater levels show the highest correlation with a precipitation accumulation period of 6 months (SPI6). The correlation in the foreland is generally higher than that in the Alpine subregions, thus corresponding to a trend from deeper wells in the Alpine parts of the catchment towards more shallow wells in the foreland. Extreme events are found to affect the aquifer in different ways. As shown with the well-known European 2003 drought and the local 2009 floods, correlations are reduced under flood conditions, but increased under drought. Thus, precipitation, groundwater levels and river stages tend to exhibit uniform behavior under drought conditions, whereas they may show irregular behavior during floods. Similarly, correlations are found to be weaker in years with little snow as compared with those with much snow. This is in agreement with typical aquifer response times over 1 month, suggesting that short events such as floods will not affect much of the aquifer, whereas a long-term event such as a drought or snow-rich winter will. Splitting the time series into periods of 12 years reveals a tendency towards higher correlations in the most recent time period from 1999 to 2010. This time period also shows the highest number of events with SPI values below -2. The SGI values behave in a similar way only in the foreland aquifer, whereas the investigated Alpine aquifers exhibit a contrasting behavior with the highest number of low SGI events in the time before 1986. This is a result of overlying trends and suggests that the groundwater levels within these subregions are more strongly influenced by direct human impacts, e.g., on the river, than by changes in precipitation. Thus, direct human impacts must not be ignored when assessing climate change impacts on alluvial aquifers situated in populated valleys.

Multi-RTM-based Radiance Assimilation to Improve Snow Estimates

NASA Astrophysics Data System (ADS)

Kwon, Y.; Zhao, L.; Hoar, T. J.; Yang, Z. L.; Toure, A. M.

2015-12-01

Data assimilation of microwave brightness temperature (TB) observations (i.e., radiance assimilation (RA)) has been proven to improve snowpack characterization at relatively small scales. However, large-scale applications of RA require a considerable amount of further efforts. Our objective in this study is to explore global-scale snow RA. In a RA scheme, a radiative transfer model (RTM) is an observational operator predicting TB; therefore, the quality of the assimilation results may strongly depend upon the RTM used as well as the land surface model (LSM). Several existing RTMs show different sensitivities to snowpack properties and thus they simulate significantly different TB. At the global scale, snow physical properties vary widely with local climate conditions. No single RTM has been shown to be able to accurately reproduce the observed TB for such a wide range of snow conditions. In this study, therefore, we hypothesize that snow estimates using a microwave RA scheme can be improved through the use of multiple RTMs (i.e., multi-RTM-based approaches). As a first step, here we use two snowpack RTMs, i.e., the Dense Media Radiative Transfer-Multi Layers model (DMRT-ML) and the Microwave Emission Model for Layered Snowpacks (MEMLS). The Community Land Model version 4 (CLM4) is used to simulate snow dynamics. The assimilation process is conducted by the Data Assimilation Research Testbed (DART), which is a community facility developed by the National Center for Atmospheric Research (NCAR) for ensemble-based data assimilation studies. In the RA experiments, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB at 18.7 and 36.5 GHz vertical polarization channels are assimilated into the RA system using the ensemble adjustment Kalman filter. The results are evaluated using the Canadian Meteorological Centre (CMC) daily snow depth, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, and in-situ snowpack and river discharge observations.

Determining and validating the effective snow grain size and pollution amount from satellite measurements in polar regions

NASA Astrophysics Data System (ADS)

Heygster, Georg; Wiebe, Heidrun; Zege, Eleonora; Aoki, Teruo; Kokhanovsky, Alexander; Katsev, I. L.; Prikhach, Alexander; Malinka, A. V.; Grudo, J. O.

Sea ice is part of the cryosphere, besides the ice sheets, ice shelves, and glaciers. Compared to the other components, it is small in volume but large in area. Snow on top of the sea ice is even less in mass, but strongly influences the albedo of the sea ice, and thus the local radiative balance which plays an essential role for the albedo feedback process. The albedo of snow does not have a constant value, but depends on the grain size (smaller grains have higher albedo) and the amount of pollution like soot and in fewer cases dust which both lower the albedo significantly. Our retrievals are based on an algorithm that uses optical satellite observations to calculate the size of the snow grains and its pollution, the Snow Grain Size and Pollution amount (SGSP) algorithm (Zege et al. 2009) Here we present the algorithm and its operational implementation, based on MODIS data, to calculate the snow grain size and pollution amount in near real time, and a destriping procedure. The resulting data are used for a validation study by comparing them to in situ data taken at several places near Hokkaido (Japan), Barrow (Alaska, USA) between 2002 and 2005 and in Antarctica in 2003. While each single set of observations, in the Arctic and in the Antarctic, shows encouraging correlations, the regression lines between in situ and satellite retrievals of the snow grain size are quite different, with slopes of 1.01 (Arctic and Japan) and 0.44 (Antarctica). The discrepancy remains unresolved, emphasizing the need for more in situ observations for validation. Among the potential reasons for the discrepancy are the different kinds of in situ measured snow grain sizes. The crystal size was measured in the Arctic (Barrow) and Japan (Hokkaido) using a lens and optical methods have been used in Antarctica.

Influence of projected snow and sea-ice changes on future climate in heavy snowfall region

NASA Astrophysics Data System (ADS)

Matsumura, S.; Sato, T.

2011-12-01

Snow/ice albedo and cloud feedbacks are critical for climate change projection in cryosphere regions. However, future snow and sea-ice distributions are significantly different in each GCM. Thus, surface albedo in cryosphere regions is one of the causes of the uncertainty for climate change projection. Northern Japan is one of the heaviest snowfall regions in the world. In particular, Hokkaido is bounded on the north by the Okhotsk Sea, where is the southernmost ocean in the Northern Hemisphere that is covered with sea ice during winter. Wintertime climate around Hokkaido is highly sensitive to fluctuations in snow and sea-ice. The purpose of this study is to evaluate the influence of global warming on future climate around Hokkaido, using the Pseudo-Global-Warming method (PGW) by a regional climate model. The boundary conditions of the PGW run were obtained by adding the difference between the future (2090s) and past (1990s) climates simulated by coupled general circulation model (MIROC3.2 medres), which is from the CMIP3 multi-model dataset, into the 6-hourly NCEP reanalysis (R-2) and daily OISST data in the past climate (CTL) run. The PGW experiments show that snow depth significantly decreases over mountainous areas and snow cover mainly decreases over plain areas, contributing to higher surface warming due to the decreased snow albedo. Despite the snow reductions, precipitation mainly increases over the mountainous areas because of enhanced water vapor content. However, precipitation decreases over the Japan Sea and the coastal areas, indicating the weakening of a convergent cloud band, which is formed by convergence between cold northwesteries from the Eurasian continent and anticyclonic circulation over the Okhotsk Sea. These results suggest that Okhotsk sea-ice decline may change the atmospheric circulation and the resulting effect on cloud formation, resulting in changes in winter snow or precipitation. We will also examine another CMIP3 model (MRI-CGCM2.3.2), which sensitivity of surface albedo to surface air temperature is the lowest in the CMIP3 models.

High resolution climate scenarios for snowmelt modelling in small alpine catchments

NASA Astrophysics Data System (ADS)

Schirmer, M.; Peleg, N.; Burlando, P.; Jonas, T.

2017-12-01

Snow in the Alps is affected by climate change with regard to duration, timing and amount. This has implications with respect to important societal issues as drinking water supply or hydropower generation. In Switzerland, the latter received a lot of attention following the political decision to phase out of nuclear electricity production. An increasing number of authorization requests for small hydropower plants located in small alpine catchments was observed in the recent years. This situation generates ecological conflicts, while the expected climate change poses a threat to water availability thus putting at risk investments in such hydropower plants. Reliable high-resolution climate scenarios are thus required, which account for small-scale processes to achieve realistic predictions of snowmelt runoff and its variability in small alpine catchments. We therefore used a novel model chain by coupling a stochastic 2-dimensional weather generator (AWE-GEN-2d) with a state-of-the-art energy balance snow cover model (FSM). AWE-GEN-2d was applied to generate ensembles of climate variables at very fine temporal and spatial resolution, thus providing all climatic input variables required for the energy balance modelling. The land-surface model FSM was used to describe spatially variable snow cover accumulation and melt processes. The FSM was refined to allow applications at very high spatial resolution by specifically accounting for small-scale processes, such as a subgrid-parametrization of snow covered area or an improved representation of forest-snow processes. For the present study, the model chain was tested for current climate conditions using extensive observational dataset of different spatial and temporal coverage. Small-scale spatial processes such as elevation gradients or aspect differences in the snow distribution were evaluated using airborne LiDAR data. 40-year of monitoring data for snow water equivalent, snowmelt and snow-covered area for entire Switzerland was used to verify snow distribution patterns at coarser spatial and temporal scale. The ability of the model chain to reproduce current climate conditions in small alpine catchments makes this model combination an outstanding candidate to produce high resolution climate scenarios of snowmelt in small alpine catchments.

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.

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.

Modification of Soil Temperature and Moisture Budgets by Snow Processes

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2006-12-01

Snow cover significantly influences the land surface energy and surface moisture budgets. Snow thermally insulates the soil column from large and rapid temperature fluctuations, and snow melting provides an important source for surface runoff and soil moisture. Therefore, it is important to accurately understand and predict the energy and moisture exchange between surface and subsurface associated with snow accumulation and ablation. The objective of this study is to understand the impact of land surface model soil layering treatment on the realistic simulation of soil temperature and soil moisture. We seek to understand how many soil layers are required to fully take into account soil thermodynamic properties and hydrological process while also honoring efficient calculation and inexpensive computation? This work attempts to address this question using field measurements from the Cold Land Processes Field Experiment (CLPX). In addition, to gain a better understanding of surface heat and surface moisture transfer process between land surface and deep soil involved in snow processes, numerical simulations were performed at several Meso-Cell Study Areas (MSAs) of CLPX using the Center for Ocean-Land-Atmosphere (COLA) Simplified Version of the Simple Biosphere Model (SSiB). Measurements of soil temperature and soil moisture were analyzed at several CLPX sites with different vegetation and soil features. The monthly mean vertical profile of soil temperature during October 2002 to July 2003 at North Park Illinois River exhibits a large near surface variation (<5 cm), reveals a significant transition zone from 5 cm to 25 cm, and becomes uniform beyond 25cm. This result shows us that three soil layers are reasonable in solving the vertical variation of soil temperature at these study sites. With 6 soil layers, SSiB also captures the vertical variation of soil temperature during entire winter season, featuring with six soil layers, but the bare soil temperature is underestimated and root-zone soil temperature is overestimated during snow melting; which leads to overestimated temperature variations down to 20 cm. This is caused by extra heat loss from upper soil level and insufficient heat transport from the deep soil. Further work will need to verify if soil temperature displays similar vertical thermal structure for different vegetation and soil types during snow season. This study provides insight to the surface and subsurface thermodynamic and hydrological processes involved in snow modeling which is important for accurate snow simulation.

Comparison of Commonly-Used Microwave Radiative Transfer Models for Snow Remote Sensing

NASA Technical Reports Server (NTRS)

Royer, Alain; Roy, Alexandre; Montpetit, Benoit; Saint-Jean-Rondeau, Olivier; Picard, Ghislain; Brucker, Ludovic; Langlois, Alexandre

2017-01-01

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D(sub max) measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

A Study on Feasibility of Dual-Wavelength Radar for Identification of Hydrometeor Phases

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert

2010-01-01

An important objective for the Dual-wavelength Ku-/Ka-band Precipitation Radar (DPR) that will be on board the Global Precipitation Measuring (GPM) core satellite, is to identify the phase state of hydrometeors along the range direction. To assess this, radar signatures are simulated in snow and rain to explore the relation between the differential frequency ratio (DFR), defined as the difference of radar reflectivity factors between Ku- and Ka-bands, and the radar reflectivity factor at Ku-band, ZKu, for different hydrometeor types. Model simulations indicate that there is clear separation between snow and rain in the ZKu-DFR plane assuming that the snow follows the Gunn-Marshall size distribution (1958) and rain follows the Marshall-Palmer size distribution (1948). In an effort to verify the simulated results, the data collected by the Airborne Second Generation Precipitation Radar (APR-2) in the Wakasa Bay AMSR-E campaign are employed. Using the signatures of Linear Depolarization Ratio (LDR) at Ku-band, the APR-2 data can be easily divided into the regions of snow, mixed phase and rain for stratiform storms. These results are then superimposed onto the theoretical curves computed from the model in the ZKu-DFR plane. It has been found that in 90% of the cases, snow and rain can be distinguished if the Ku-band radar reflectivity exceeds 18 dBZ (the minimum detectable level of GPM DPR at Ku-band). This is also the case for snow and mixed-phase hydrometeors. Although snow can be easily distinguished from rain and melting hydrometeors by using Ku- and Ka-band radar, the rain and mixed-phase particles are not always separable. It is concluded that Ku- and Ka-band dual-wavelength radar might provide a potential means to identify the phase state of hydrometeors.

Influence of snow temperature on avalanche impact pressure

NASA Astrophysics Data System (ADS)

Sovilla, Betty; Koehler, Anselm; Steinkogler, Walter; Fischer, Jan-Thomas

2015-04-01

The properties of the snow entrained by an avalanche during its motion (density, temperature) significantly affect flow dynamics and can determine whether the flowing material forms granules or maintains its original fine-grained structure. In general, a cold and light snow cover typically fluidizes, while warmer and more cohesive snow may form a granular denser layer in a flowing avalanche. This structural difference has a fundamental influence not only in the mobility of the flow but also on the impact pressure of avalanches. Using measurements of impact pressure, velocity, density and snow temperature performed at the Swiss Vallée de la Sionne full-scale test site, we show that, impact pressure fundamentally changes with snow temperature. A transition threshold of about -2°C is determined, the same temperature at which snow granulation starts. On the one hand warm avalanches, characterized by temperatures larger than -2°C, move as a plug and exert impact pressures linearly proportional to the avalanche depth. For Froude numbers larger than 1, an additional square-velocity dependent contribution cannot be neglected. On the other hand cold avalanches, characterized by a temperature smaller than -2°C, move as dense sheared flows, or completely dilute powder clouds and exert impact pressures, which are mainly proportional to the square of the flow velocity. For these avalanches the impact pressures strongly depend on density variations within the flow. We suggest that the proposed temperature threshold can be used as a criterion to define the transition between the impact pressures exerted by warm and cold avalanches, thus offering a new way to elude the notorious difficulties in defining the differences between wet and dry flow, respectively.

Laboratory-based observations of capillary barriers and preferential flow in layered snow

NASA Astrophysics Data System (ADS)

Avanzi, F.; Hirashima, H.; Yamaguchi, S.; Katsushima, T.; De Michele, C.

2015-12-01

Several evidences are nowadays available that show how the effects of capillary gradients and preferential flow on water transmission in snow may play a more important role than expected. To observe these processes and to contribute in their characterization, we performed observations on the development of capillary barriers and preferential flow patterns in layered snow during cold laboratory experiments. We considered three different layering (all characterized by a finer-over-coarser texture in grain size) and three different water input rates. Nine samples of layered snow were sieved in a cold laboratory, and subjected to a constant supply of dyed tracer. By means of visual inspection, horizontal sectioning and liquid water content measurements, the processes of ponding and preferential flow were characterized as a function of texture and water input rate. The dynamics of each sample were replicated using the multi-layer physically-based SNOWPACK model. Results show that capillary barriers and preferential flow are relevant processes ruling the speed of liquid water in stratified snow. Ponding is associated with peaks in LWC at the boundary between the two layers equal to ~ 33-36 vol. % when the upper layer is composed by fine snow (grain size smaller than 0.5 mm). The thickness of the ponding layer at the textural boundary is between 0 and 3 cm, depending on sample stratigraphy. Heterogeneity in water transmission increases with grain size, while we do not observe any clear dependency on water input rate. The extensive comparison between observed and simulated LWC profiles by SNOWPACK (using an approximation of Richards Equation) shows high performances by the model in estimating the LWC peak over the boundary, while water speed in snow is underestimated by the chosen water transport scheme.

Seasonal Change in Bacterial Flora and Biomass in Mountain Snow from the Tateyama Mountains, Japan, Analyzed by 16S rRNA Gene Sequencing and Real-Time PCR

PubMed Central

Segawa, Takahiro; Miyamoto, Koji; Ushida, Kazunari; Agata, Kiyokazu; Okada, Norihiro; Kohshima, Shiro

2005-01-01

The bacterial flora and biomass in mountain snow from the Tateyama Mountains, Toyama Prefecture, Japan, one of the heaviest snowfall regions in the world, were analyzed by amplified ribosomal DNA restriction analysis followed by 16S rRNA gene sequencing and DNA quantification by real-time PCR. Samples of surface snow collected in various months during the melting season contained a psychrophilic bacterium, Cryobacterium psychrophilum, and two psychrotrophic bacteria, Variovorax paradoxus and Janthinobacterium lividum. Bacterial colonies that developed in an in situ meltwater medium at 4°C were revealed to be V. paradoxus. The biomasses of C. psychrophilum, J. lividum, and V. paradoxus, as estimated by real-time PCR, showed large increases during the melting season from March to October (2.0 × 105-fold, 1.5 × 105-fold, and 1.0 × 104-fold increases, respectively), suggesting their rapid growth in the surface snow. The biomasses of C. psychrophilum and J. lividum increased significantly from March to April, reached a maximum in August, and dropped at the end of the melting season. In contrast, the biomass of V. paradoxus did not increase as rapidly during the early melting season but continued to increase from June until October. The differences in development observed among these bacterial species suggest that their growth was promoted by different nutrients and/or environmental conditions in the snow. Since these three types of bacteria have also been reported to be present in a glacier in Antarctica and a Greenland ice core, they seem to be specialized members of the snow biota that are distributed in snow and ice environments in various parts of the world. PMID:15640179

Seasonal change in bacterial flora and biomass in mountain snow from the Tateyama Mountains, Japan, analyzed by 16S rRNA gene sequencing and real-time PCR.

PubMed

Segawa, Takahiro; Miyamoto, Koji; Ushida, Kazunari; Agata, Kiyokazu; Okada, Norihiro; Kohshima, Shiro

2005-01-01

The bacterial flora and biomass in mountain snow from the Tateyama Mountains, Toyama Prefecture, Japan, one of the heaviest snowfall regions in the world, were analyzed by amplified ribosomal DNA restriction analysis followed by 16S rRNA gene sequencing and DNA quantification by real-time PCR. Samples of surface snow collected in various months during the melting season contained a psychrophilic bacterium, Cryobacterium psychrophilum, and two psychrotrophic bacteria, Variovorax paradoxus and Janthinobacterium lividum. Bacterial colonies that developed in an in situ meltwater medium at 4 degrees C were revealed to be V. paradoxus. The biomasses of C. psychrophilum, J. lividum, and V. paradoxus, as estimated by real-time PCR, showed large increases during the melting season from March to October (2.0 x 10(5)-fold, 1.5 x 10(5)-fold, and 1.0 x 10(4)-fold increases, respectively), suggesting their rapid growth in the surface snow. The biomasses of C. psychrophilum and J. lividum increased significantly from March to April, reached a maximum in August, and dropped at the end of the melting season. In contrast, the biomass of V. paradoxus did not increase as rapidly during the early melting season but continued to increase from June until October. The differences in development observed among these bacterial species suggest that their growth was promoted by different nutrients and/or environmental conditions in the snow. Since these three types of bacteria have also been reported to be present in a glacier in Antarctica and a Greenland ice core, they seem to be specialized members of the snow biota that are distributed in snow and ice environments in various parts of the world.

Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)

NASA Astrophysics Data System (ADS)

Jobst, Andreas M.; Kingston, Daniel G.; Cullen, Nicolas J.; Schmid, Josef

2018-06-01

As climate change is projected to alter both temperature and precipitation, snow-controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of a headwater sub-catchment of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: general circulation model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +29 to +84 % in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57 %) followed by emission scenario (16-49 %), bias correction (4-22 %) and snow model (3-10 %). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

NASA Technical Reports Server (NTRS)

Cortes, Gonzalo; Girotto, Manuela; Margulis, Steven

2016-01-01

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

Does Temperature Modify the Effects of Rain and Snow Precipitation on Road Traffic Injuries?

PubMed

Lee, Won-Kyung; Lee, Hye-Ah; Hwang, Seung-sik; Kim, Ho; Lim, Youn-Hee; Hong, Yun-Chul; Ha, Eun-Hee; Park, Hyesook

2015-01-01

There are few data on the interaction between temperature and snow and rain precipitation, although they could interact in their effects on road traffic injuries. The integrated database of the Korea Road Traffic Authority was used to calculate the daily frequency of road traffic injuries in Seoul. Weather data included rain and snow precipitation, temperature, pressure, and fog from May 2007 to December 2011. Precipitation of rain and snow were divided into nine and six temperature range categories, respectively. The interactive effects of temperature and rain and snow precipitation on road traffic injuries were analyzed using a generalized additive model with a Poisson distribution. The risk of road traffic injuries during snow increased when the temperature was below freezing. Road traffic injuries increased by 6.6% when it was snowing and above 0 °C, whereas they increased by 15% when it was snowing and at or below 0 °C. In terms of heavy rain precipitation, moderate temperatures were related to an increased prevalence of injuries. When the temperature was 0-20 °C, we found a 12% increase in road traffic injuries, whereas it increased by 8.5% and 6.8% when it was <0 °C and >20 °C, respectively. The interactive effect was consistent across the traffic accident subtypes. The effect of adverse weather conditions on road traffic injuries differed depending on the temperature. More road traffic injuries were related to rain precipitation when the temperature was moderate and to snow when it was below freezing.

Does Temperature Modify the Effects of Rain and Snow Precipitation on Road Traffic Injuries?

PubMed Central

Lee, Won-Kyung; Lee, Hye-Ah; Hwang, Seung-sik; Kim, Ho; Lim, Youn-Hee; Hong, Yun-Chul; Ha, Eun-Hee; Park, Hyesook

2015-01-01

Background There are few data on the interaction between temperature and snow and rain precipitation, although they could interact in their effects on road traffic injuries. Methods The integrated database of the Korea Road Traffic Authority was used to calculate the daily frequency of road traffic injuries in Seoul. Weather data included rain and snow precipitation, temperature, pressure, and fog from May 2007 to December 2011. Precipitation of rain and snow were divided into nine and six temperature range categories, respectively. The interactive effects of temperature and rain and snow precipitation on road traffic injuries were analyzed using a generalized additive model with a Poisson distribution. Results The risk of road traffic injuries during snow increased when the temperature was below freezing. Road traffic injuries increased by 6.6% when it was snowing and above 0°C, whereas they increased by 15% when it was snowing and at or below 0°C. In terms of heavy rain precipitation, moderate temperatures were related to an increased prevalence of injuries. When the temperature was 0–20°C, we found a 12% increase in road traffic injuries, whereas it increased by 8.5% and 6.8% when it was <0°C and >20°C, respectively. The interactive effect was consistent across the traffic accident subtypes. Conclusions The effect of adverse weather conditions on road traffic injuries differed depending on the temperature. More road traffic injuries were related to rain precipitation when the temperature was moderate and to snow when it was below freezing. PMID:26073021

Snow cover monitoring model and change over both time and space in pastoral area of northern China

NASA Astrophysics Data System (ADS)

Cui, Yan; Li, Suju; Wang, Ping; Zhang, Wei; Nie, Juan; Wen, Qi

2014-11-01

Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.

Evaluation and time series analysis of mountain snow from MODIS and VIIRS fractional snow cover products

NASA Astrophysics Data System (ADS)

Bormann, K.; Rittger, K.; Painter, T. H.

2016-12-01

The continuation of large-scale snow cover records into the future is crucial for monitoring the impacts of global pressures such as climate change and weather variability on the cryosphere. With daily MODIS records since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP platform, the consistency of information between the two optical sensors must be understood. First, we evaluated snow cover maps derived from both MODIS and VIIRS retrievals with coincident cloud-free Landsat 8 OLI maps across a range of locations. We found that both MODIS and VIIRS snow cover maps show similar errors when evaluated with Landsat OLI retrievals. Preliminary results also show a general agreement in regional snowline between the two sensors that is maintained during the spring snowline retreat where the proportion of mixed pixels is increased. The agreement between sensors supports the future use of VIIRS snow cover maps to continue the long-term record beyond the lifetime of MODIS. Second, we use snowline elevation to quantify large scale snow cover variability and to monitor potential changes in the rain/snow transition zone where climate change pressures may be enhanced. Despite the large inter-annual variability that is often observed in snow metrics, we expect that over the 16-year time series we will see a rise in seasonal elevation of the snowline and consequently an increasing rain/snow transition boundary in mountain environments. These results form the basis for global snowline elevation monitoring using optical remote sensing data and highlight regional differences in snowline elevation dynamics. The long-term variability in observed snowline elevation provides a recent climatology of mountain snowpack across several regions that will likely to be of interest to those interested in climate change impacts in mountain environments. This work will also be of interest to existing users of MODSCAG and VIIRSCAG snow cover products and those working in remote sensing of the mountain snowpack.

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.

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.

New, Improved Goddard Bulk-Microphysical Schemes for Studying Precipitation Processes in WRF

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2007-01-01

An improved bulk microphysical parameterization is implemented into the Weather Research and Forecasting ()VRF) model. This bulk microphysical scheme has three different options, 2ICE (cloud ice & snow), 3ICE-graupel (cloud ice, snow & graupel) and 3ICE-hail (cloud ice, snow & hail). High-resolution model simulations are conducted to examine the impact of microphysical schemes on two different weather events (a midlatitude linear convective system and an Atlantic hurricane). The results suggest that microphysics has a major impact on the organization and precipitation processes associated with a summer midlatitude convective line system. The Goddard 3ICE scheme with a cloud ice-snow-hail configuration agreed better with observations in terms of rainfall intensity and a narrow convective line than did simulations with a cloud ice-snow-graupel or cloud ice-snow (i.e., 2ICE) configuration. This is because the 3ICE-hail scheme includes dense ice precipitating (hail) particle with very fast fall speed (over 10 in For an Atlantic hurricane case, the Goddard microphysical schemes had no significant impact on the track forecast but did affect the intensity slightly. The improved Goddard schemes are also compared with WRF's three other 3ICE bulk microphysical schemes: WSM6, Purdue-Lin and Thompson. For the summer midlatitude convective line system, all of the schemes resulted in simulated precipitation events that were elongated in the southwest-northeast direction in qualitative agreement with the observed feature. However, the Goddard 3ICE scheme with the hail option and the Thompson scheme agree better with observations in terms of rainfall intensity, expect that the Goddard scheme simulated more heavy rainfall (over 48 mm/h). For the Atlantic hurricane case, none of the schemes had a significant impact on the track forecast; however, the simulated intensity using the Purdue-Lin scheme was much stronger than the other schemes. The vertical distributions of model simulated cloud species (i.e., snow) are quite sensitive to microphysical schemes, which is an important issue for future verification against satellite retrievals. Both the Purdue-Lin and WSM6 schemes simulated very little snow compared to the other schemes for both the midlatitude convective line and hurricane cases. Sensitivity tests are performed for these two WRF schemes to identify that snow productions could be increased by increasing the snow intercept, turning off the auto-conversion from snow to graupel and reducing the transfer processes from cloud-sized particles to precipitation-sized ice.

Studying Precipitation Processes in WRF with Goddard Bulk Microphysics in Comparison with Other Microphysical Schemes

NASA Technical Reports Server (NTRS)

Tao, W.K.; Shi, J.J.; Braun, S.; Simpson, J.; Chen, S.S.; Lang, S.; Hong, S.Y.; Thompson, G.; Peters-Lidard, C.

2009-01-01

A Goddard bulk microphysical parameterization is implemented into the Weather Research and Forecasting (WRF) model. This bulk microphysical scheme has three different options, 2ICE (cloud ice & snow), 3ICE-graupel (cloud ice, snow & graupel) and 3ICE-hail (cloud ice, snow & hail). High-resolution model simulations are conducted to examine the impact of microphysical schemes on different weather events: a midlatitude linear convective system and an Atlantic hurricane. The results suggest that microphysics has a major impact on the organization and precipitation processes associated with a summer midlatitude convective line system. The Goddard 3ICE scheme with the cloud ice-snow-hail configuration agreed better with observations ill of rainfall intensity and having a narrow convective line than did simulations with the cloud ice-snow-graupel and cloud ice-snow (i.e., 2ICE) configurations. This is because the Goddard 3ICE-hail configuration has denser precipitating ice particles (hail) with very fast fall speeds (over 10 m/s) For an Atlantic hurricane case, the Goddard microphysical scheme (with 3ICE-hail, 3ICE-graupel and 2ICE configurations) had no significant impact on the track forecast but did affect the intensity slightly. The Goddard scheme is also compared with WRF's three other 3ICE bulk microphysical schemes: WSM6, Purdue-Lin and Thompson. For the summer midlatitude convective line system, all of the schemes resulted in simulated precipitation events that were elongated in southwest-northeast direction in qualitative agreement with the observed feature. However, the Goddard 3ICE-hail and Thompson schemes were closest to the observed rainfall intensities although the Goddard scheme simulated more heavy rainfall (over 48 mm/h). For the Atlantic hurricane case, none of the schemes had a significant impact on the track forecast; however, the simulated intensity using the Purdue-Lin scheme was much stronger than the other schemes. The vertical distributions of model-simulated cloud species (e.g., snow) are quite sensitive to the microphysical schemes, which is an issue for future verification against satellite retrievals. Both the Purdue-Lin and WSM6 schemes simulated very little snow compared to the other schemes for both the midlatitude convective line and hurricane case. Sensitivity tests with these two schemes showed that increasing the snow intercept, turning off the auto-conversion from snow to graupel, eliminating dry growth, and reducing the transfer processes from cloud-sized particles to precipitation-sized ice collectively resulted in a net increase in those schemes' snow amounts.