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.

Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps.

PubMed

Warscher, M; Strasser, U; Kraller, G; Marke, T; Franz, H; Kunstmann, H

2013-05-01

[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

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

Treesearch

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

2001-01-01

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

Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover

NASA Astrophysics Data System (ADS)

Cornwell, E.; Molotch, N. P.; McPhee, J.

2016-01-01

Seasonal snow cover is the primary water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) snow water equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000-5000 m a.s.l. elevation band is significant for snow accumulation, despite having a smaller surface area than the 3000-4000 m a.s.l. band. On average, maximum snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

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.

Linking laser scanning to snowpack modeling: Data processing and visualization

NASA Astrophysics Data System (ADS)

Teufelsbauer, H.

2009-07-01

SnowSim is a newly developed physical snowpack model that can use three-dimensional terrestrial laser scanning data to generate model domains. This greatly simplifies the input and numerical simulation of snow covers in complex terrains. The program can model two-dimensional cross sections of general slopes, with complicated snow distributions. The model predicts temperature distributions and snow settlements in this cross section. Thus, the model can be used for a wide range of problems in snow science and engineering, including numerical investigations of avalanche formation. The governing partial differential equations are solved by means of the finite element method, using triangular elements. All essential data for defining the boundary conditions and evaluating the simulation results are gathered by automatic weather and snow measurement sites. This work focuses on the treatment of these measurements and the simulation results, and presents a pre- and post-processing graphical user interface (GUI) programmed in Matlab.

Numerical simulation of distributed snow processes in complex terrain utilizing triangulated irregular networks (TINs)

NASA Astrophysics Data System (ADS)

Rinehart, A. J.; Vivoni, E. R.

2005-12-01

Snow processes play a significant role in the hydrologic cycle of mountainous and high-latitude catchments in the western United States. Snowmelt runoff contributes to a large percentage of stream runoff while snow covered regions remain highly localized to small portions of the catchment area. The appropriate representation of snow dynamics at a given range of spatial and temporal scales is critical for adequately predicting runoff responses in snowmelt-dominated watersheds. In particular, the accurate depiction of snow cover patterns is important as a range of topographic, land-use and geographic parameters create zones of preferential snow accumulation or ablation that significantly affect the timing of a region's snow melt and the persistence of a snow pack. In this study, we present the development and testing of a distributed snow model designed for simulations over complex terrain. The snow model is developed within the context of the TIN-based Real-time Integrated Basin Simulator (tRIBS), a fully-distributed watershed model capable of continuous simulations of coupled hydrological processes, including unsaturated-saturated zone dynamics, land-atmosphere interactions and runoff generation via multiple mechanisms. The use of triangulated irregular networks as a domain discretization allows tRIBS to accurately represent topography with a reduced number of computational nodes, as compared to traditional grid-based models. This representation is developed using a Delauney optimization criterion that causes areas of topographic homogeneity to be represented at larger spatial scales than the original grid, while more heterogeneous areas are represented at higher resolutions. We utilize the TIN-based terrain representation to simulate microscale (10-m to 100-m) snow pack dynamics over a catchment. The model includes processes such as the snow pack energy balance, wind and bulk redistribution, and snow interception by vegetation. For this study, we present tests from a distributed one-layer energy balance model as applied to a northern New Mexico hillslope in a ponderosa pine forest using both synthetic and real meteorological forcing. We also provide tests of the model's capability to represent spatial patterns within a small watershed in the Jemez Mountain region. Finally, we discuss the interaction of the tested snow process module with existing components in the watershed model and additional applications and capabilities under development.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Impacts of Recent Climatic Wetting on Distributed Snow and Streamflow Responses in a Terminal Lake Basin.

NASA Astrophysics Data System (ADS)

Van Hoy, D.; Mahmood, T. H.; Jeannotte, T.; Todhunter, P. E.

2017-12-01

The recent shift in hydroclimatic conditions in the Northern Great Plains (NGP) has led to an increase in precipitation, rainfall rate, and wetland connectivity over the last few decades. These changes yield an integrated response resulting in high mean annual streamflow and subsequent flooding in many NGP basins such as the terminal Devils Lake Basin (DLB). In this study, we investigate the impacts of recent climatic wetting on distributed hydrologic responses such as snow processes and streamflow using a field-tested and physically-based cold region hydrologic model (CRHM). CHRM is designed for cold prairie regions and has modules to simulate major processes such as blowing snow transport, sublimation, interception, frozen soil infiltration, snowmelt and subsequent streamflow generation. Our modeling focuses on a tributary basin of the DLB known as the Mauvais Coulee Basin (MCB). Since there were no snow observations in the MCB, we conducted a detailed snow survey at distributed locations estimating snow depth, density, and snow water equivalent (SWE) using a prairie snow tube four times during winter of 2016-17. The MCB model was evaluated against distributed snow observations and streamflow measured at the basin outlet (USGS) for the year 2016-2017. Preliminary results indicate that the simulated SWEs at distributed locations and streamflow (NSE ≈ 0.70) are in good agreement with observations. The simulated SWE maps exhibit large spatiotemporal variation during 2016-17 winter due to spatial variability in precipitation, snow redistribution from stubble field to wooded areas, and snow accumulations in small depressions across the subbasins. The main source of snow appears to be the hills and ridges of the eastern and western edges of the basin, while the main sink is the large flat central valleys. The model will be used to examine the effect of recent changes to precipitation and temperature on snow processes and subsequent streamflow for 2004-2017 season. We will also investigate the hydrologic sensitivity to precipitation and temperature changes by altering input temperature and precipitation. Finally, our findings will point toward future process-based studies and simulated hydrologic responses that can be used to prepare flood hazard maps for cities around Devils Lake.

Snow on Sea Ice Workshop - An Icy Meeting of the Minds: Modelers and Measurers

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Snow on Sea Ice Workshop - An Icy Meeting of the Minds...workshop was to promote more seamless and better integration between measurements and modeling of snow on sea ice , thereby improving our predictive...capabilities for sea ice . OBJECTIVES The key objective was to improve the ability of modelers and measurers work together closely. To that end, we

Distributed energy-balance modeling of snow-cover evolution and melt in rugged terrain: Tobacco Root Mountains, Montana, USA

USGS Publications Warehouse

Letsinger, S.L.; Olyphant, G.A.

2007-01-01

A distributed energy-balance model was developed for simulating snowpack evolution and melt in rugged terrain. The model, which was applied to a 43-km2 watershed in the Tobacco Root Mountains, Montana, USA, used measured ambient data from nearby weather stations to drive energy-balance calculations and to constrain the model of Liston and Sturm [Liston, G.E., Sturm, M., 1998. A snow-transport model for complex terrain. Journal of Glaciology 44 (148), 498-516] for calculating the initial snowpack thickness. Simulated initial snow-water equivalent ranged between 1 cm and 385 cm w.e. (water equivalent) with high values concentrated on east-facing slopes below tall summits. An interpreted satellite image of the snowcover distribution on May 6, 1998, closely matched the simulated distribution with the greatest discrepancy occurring in the floor of the main trunk valley. Model simulations indicated that snowmelt commenced early in the melt season, but rapid meltout of snow cover did not occur until after the average energy balance of the entire watershed became positive about 45 days into the melt season. Meltout was fastest in the lower part of the watershed where warmer temperatures and tree cover enhanced the energy income of the underlying snow. An interpreted satellite image of the snowcover distribution on July 9, 1998 compared favorably with the simulated distribution, and melt curves for modeled canopy-covered cells mimicked the trends measured at nearby snow pillow stations. By the end of the simulation period (August 3), 28% of the watershed remained snow covered, most of which was concentrated in the highest parts of the watershed where initially thick accumulations had been shaded by surrounding summits. The results of this study provide further demonstration of the critical role that topography plays in the timing and magnitude of snowmelt from high mountain watersheds. ?? 2006 Elsevier B.V. All rights reserved.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

Combining model and satellite data to investigate the effect of light absorbing impurities on snow melt and discharge generation

NASA Astrophysics Data System (ADS)

Matt, F.; Burkhart, J. F.

2017-12-01

Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of solar radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the effects as simulated in numerical models have large uncertainties. These uncertainties originate mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI (the so called radiative forcing of LAISI), a key variable in understanding snowpack energy-balance dynamics. In this study, we present an approach combining distributed model simulations on the catchment scale and remotely sensed radiative forcing from LAISI in order to evaluate and improve model predictions. In a case study, we assess the effect of LAISI on snow melt and discharge generation in a high mountain catchment located in the western Himalaya using the distributed hydrologic model, Shyft. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of solar radiation by LAISI into account. LAISI mixing ratios in snow are determined from atmospheric aerosol deposition rates. To asses the quality of our simulations, we model the instantaneous clear sky radiative forcing at MODIS overpass times, and compare it to the MODIS Dust Radiative Forcing in Snow (MODDRFS) satellite product. By scaling the deposition input to the model, we can optimize the simulated radiative forcing towards the satellite observations.

PBSM3D: A finite volume, scalar-transport blowing snow model for use with variable resolution meshes

NASA Astrophysics Data System (ADS)

Marsh, C.; Wayand, N. E.; Pomeroy, J. W.; Wheater, H. S.; Spiteri, R. J.

2017-12-01

Blowing snow redistribution results in heterogeneous snowcovers that are ubiquitous in cold, windswept environments. Capturing this spatial and temporal variability is important for melt and runoff simulations. Point scale blowing snow transport models are difficult to apply in fully distributed hydrological models due to landscape heterogeneity and complex wind fields. Many existing distributed snow transport models have empirical wind flow and/or simplified wind direction algorithms that perform poorly in calculating snow redistribution where there are divergent wind flows, sharp topography, and over large spatial extents. Herein, a steady-state scalar transport model is discretized using the finite volume method (FVM), using parameterizations from the Prairie Blowing Snow Model (PBSM). PBSM has been applied in hydrological response units and grids to prairie, arctic, glacier, and alpine terrain and shows a good capability to represent snow redistribution over complex terrain. The FVM discretization takes advantage of the variable resolution mesh in the Canadian Hydrological Model (CHM) to ensure efficient calculations over small and large spatial extents. Variable resolution unstructured meshes preserve surface heterogeneity but result in fewer computational elements versus high-resolution structured (raster) grids. Snowpack, soil moisture, and streamflow observations were used to evaluate CHM-modelled outputs in a sub-arctic and an alpine basin. Newly developed remotely sensed snowcover indices allowed for validation over large basins. CHM simulations of snow hydrology were improved by inclusion of the blowing snow model. The results demonstrate the key role of snow transport processes in creating pre-melt snowcover heterogeneity and therefore governing post-melt soil moisture and runoff generation dynamics.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

Shulski, Martha D.; Seeley, Mark W.

2004-11-01

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

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

Treesearch

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

2011-01-01

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

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

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

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Janet Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500 m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5 km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Chien, Y. L.; Houser, Paul R. (Technical Monitor)

2001-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500-m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5-km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

How Much Water is in That Snowpack? Improving Basin-wide Snow Water Equivalent Estimates from the Airborne Snow Observatory

NASA Astrophysics Data System (ADS)

Bormann, K.; Painter, T. H.; Marks, D. G.; Kirchner, P. B.; Winstral, A. H.; Ramirez, P.; Goodale, C. E.; Richardson, M.; Berisford, D. F.

2014-12-01

In the western US, snowmelt from the mountains contribute the vast majority of fresh water supply, in an otherwise dry region. With much of California currently experiencing extreme drought, it is critical for water managers to have accurate basin-wide estimations of snow water content during the spring melt season. At the forefront of basin-scale snow monitoring is the Jet Propulsion Laboratory's Airborne Snow Observatory (ASO). With combined LiDAR /spectrometer instruments and weekly flights over key basins throughout California, the ASO suite is capable of retrieving high-resolution basin-wide snow depth and albedo observations. To make best use of these high-resolution snow depths, spatially distributed snow density data are required to leverage snow water equivalent (SWE) from the measured depths. Snow density is a spatially and temporally variable property and is difficult to estimate at basin scales. Currently, ASO uses a physically based snow model (iSnobal) to resolve distributed snow density dynamics across the basin. However, there are issues with the density algorithms in iSnobal, particularly with snow depths below 0.50 m. This shortcoming limited the use of snow density fields from iSnobal during the poor snowfall year of 2014 in the Sierra Nevada, where snow depths were generally low. A deeper understanding of iSnobal model performance and uncertainty for snow density estimation is required. In this study, the model is compared to an existing climate-based statistical method for basin-wide snow density estimation in the Tuolumne basin in the Sierra Nevada and sparse field density measurements. The objective of this study is to improve the water resource information provided to water managers during ASO operation in the future by reducing the uncertainty introduced during the snow depth to SWE conversion.

Developing the snow component of a distributed hydrological model: a step-wise approach based on multi-objective analysis

NASA Astrophysics Data System (ADS)

Dunn, S. M.; Colohan, R. J. E.

1999-09-01

A snow component has been developed for the distributed hydrological model, DIY, using an approach that sequentially evaluates the behaviour of different functions as they are implemented in the model. The evaluation is performed using multi-objective functions to ensure that the internal structure of the model is correct. The development of the model, using a sub-catchment in the Cairngorm Mountains in Scotland, demonstrated that the degree-day model can be enhanced for hydroclimatic conditions typical of those found in Scotland, without increasing meteorological data requirements. An important element of the snow model is a function to account for wind re-distribution. This causes large accumulations of snow in small pockets, which are shown to be important in sustaining baseflows in the rivers during the late spring and early summer, long after the snowpack has melted from the bulk of the catchment. The importance of the wind function would not have been identified using a single objective function of total streamflow to evaluate the model behaviour.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Distributed Assimilation of Satellite-based Snow Extent for Improving Simulated Streamflow in Mountainous, Dense Forests: An Example Over the DMIP2 Western Basins

NASA Technical Reports Server (NTRS)

Yatheendradas, Soni; Peters-Lidard, Christa D.; Koren, Victor; Cosgrove, Brian A.; DeGoncalves, Luis G. D.; Smith, Michael; Geiger, James; Cui, Zhengtao; Borak, Jordan; Kumar, Sujay V.;

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.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

Xiong, Chuan; Shi, Jiancheng

2014-01-01

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

Evaluation of Model Microphysics within Precipitation Bands of Extratropical Cyclones

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Yu, Ruyi; Molthan, Andrew L.; Nesbitt, Steven

2013-01-01

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is approx 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were approx 0.25 m/s too slow, while the SBU-YLIN was 0.25 to 0.5 m/s too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Evaluation of Model Microphysics Within Precipitation Bands of Extratropical Cyclones

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Molthan, Andrew; Yu, Ruyi; Stark, David; Yuter, Sandra; Nesbitt, Steven

2013-01-01

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is 0.25 meters per second too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were 0.25 meters per second too slow, while the SBU-YLIN was 0.25 to 0.5 meters per second too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach.

PubMed

Ala-Aho, Pertti; Tetzlaff, Doerthe; McNamara, James P; Laudon, Hjalmar; Kormos, Patrick; Soulsby, Chris

2017-07-01

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer-aided modeling. In snow-influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer-aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process-based snow interception-accumulation-melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof-of-concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long-term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer-aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow-influenced catchments.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

The Airborne Snow Observatory: fusion of imaging spectrometer and scanning lidar for studies of mountain snow cover (Invited)

NASA Astrophysics Data System (ADS)

Painter, T. H.; Andreadis, K.; Berisford, D. F.; Goodale, C. E.; Hart, A. F.; Heneghan, C.; Deems, J. S.; Gehrke, F.; Marks, D. G.; Mattmann, C. A.; McGurk, B. J.; Ramirez, P.; Seidel, F. C.; Skiles, M.; Trangsrud, A.; Winstral, A. H.; Kirchner, P.; Zimdars, P. A.; Yaghoobi, R.; Boustani, M.; Khudikyan, S.; Richardson, M.; Atwater, R.; Horn, J.; Goods, D.; Verma, R.; Boardman, J. W.

2013-12-01

Snow cover and its melt dominate regional climate and water resources in many of the world's mountainous regions. However, we face significant water resource challenges due to the intersection of increasing demand from population growth and changes in runoff total and timing due to climate change. Moreover, increasing temperatures in desert systems will increase dust loading to mountain snow cover, thus reducing the snow cover albedo and accelerating snowmelt runoff. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still poorly quantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. Recognizing this need, JPL developed the Airborne Snow Observatory (ASO), an imaging spectrometer and imaging LiDAR system, to quantify snow water equivalent and snow albedo, provide unprecedented knowledge of snow properties, and provide complete, robust inputs to snowmelt runoff models, water management models, and systems of the future. Critical in the design of the ASO system is the availability of snow water equivalent and albedo products within 24 hours of acquisition for timely constraint of snowmelt runoff forecast models. In spring 2013, ASO was deployed for its first year of a multi-year Demonstration Mission of weekly acquisitions in the Tuolumne River Basin (Sierra Nevada) and monthly acquisitions in the Uncompahgre River Basin (Colorado). The ASO data were used to constrain spatially distributed models of varying complexities and integrated into the operations of the O'Shaughnessy Dam on the Hetch Hetchy reservoir on the Tuolumne River. Here we present the first results from the ASO Demonstration Mission 1 along with modeling results with and without the constraint by the ASO's high spatial resolution and spatially complete acquisitions. ASO ultimately provides a potential foundation for coming spaceborne missions.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

NASA Astrophysics Data System (ADS)

Garavaglia, Federico; Le Lay, Matthieu; Gottardi, Fréderic; Garçon, Rémy; Gailhard, Joël; Paquet, Emmanuel; Mathevet, Thibault

2017-08-01

Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration-validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

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.

Modelization of the Current and Future Habitat Suitability of Rhododendron ferrugineum Using Potential Snow Accumulation

PubMed Central

Komac, Benjamin; Esteban, Pere; Trapero, Laura; Caritg, Roger

2016-01-01

Mountain areas are particularly sensitive to climate change. Species distribution models predict important extinctions in these areas whose magnitude will depend on a number of different factors. Here we examine the possible impact of climate change on the Rhododendron ferrugineum (alpenrose) niche in Andorra (Pyrenees). This species currently occupies 14.6 km2 of this country and relies on the protection afforded by snow cover in winter. We used high-resolution climatic data, potential snow accumulation and a combined forecasting method to obtain the realized niche model of this species. Subsequently, we used data from the high-resolution Scampei project climate change projection for the A2, A1B and B1 scenarios to model its future realized niche model. The modelization performed well when predicting the species’s distribution, which improved when we considered the potential snow accumulation, the most important variable influencing its distribution. We thus obtained a potential extent of about 70.7 km2 or 15.1% of the country. We observed an elevation lag distribution between the current and potential distribution of the species, probably due to its slow colonization rate and the small-scale survey of seedlings. Under the three climatic scenarios, the realized niche model of the species will be reduced by 37.9–70.1 km2 by the end of the century and it will become confined to what are today screes and rocky hillside habitats. The particular effects of climate change on seedling establishment, as well as on the species’ plasticity and sensitivity in the event of a reduction of the snow cover, could worsen these predictions. PMID:26824847

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.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

A comparison between modeled and measured permafrost temperatures at Ritigraben borehole, Switzerland

NASA Astrophysics Data System (ADS)

Mitterer-Hoinkes, Susanna; Lehning, Michael; Phillips, Marcia; Sailer, Rudolf

2013-04-01

The area-wide distribution of permafrost is sparsely known in mountainous terrain (e.g. Alps). Permafrost monitoring can only be based on point or small scale measurements such as boreholes, active rock glaciers, BTS measurements or geophysical measurements. To get a better understanding of permafrost distribution, it is necessary to focus on modeling permafrost temperatures and permafrost distribution patterns. A lot of effort on these topics has been already expended using different kinds of models. In this study, the evolution of subsurface temperatures over successive years has been modeled at the location Ritigraben borehole (Mattertal, Switzerland) by using the one-dimensional snow cover model SNOWPACK. The model needs meteorological input and in our case information on subsurface properties. We used meteorological input variables of the automatic weather station Ritigraben (2630 m) in combination with the automatic weather station Saas Seetal (2480 m). Meteorological data between 2006 and 2011 on an hourly basis were used to drive the model. As former studies showed, the snow amount and the snow cover duration have a great influence on the thermal regime. Low snow heights allow for deeper penetration of low winter temperatures into the ground, strong winters with a high amount of snow attenuate this effect. In addition, variations in subsurface conditions highly influence the temperature regime. Therefore, we conducted sensitivity runs by defining a series of different subsurface properties. The modeled subsurface temperature profiles of Ritigraben were then compared to the measured temperatures in the Ritigraben borehole. This allows a validation of the influence of subsurface properties on the temperature regime. As expected, the influence of the snow cover is stronger than the influence of sub-surface material properties, which are significant, however. The validation presented here serves to prepare a larger spatial simulation with the complex hydro-meteorological 3-dimensional model Alpine 3D, which is based on a distributed application of SNOWPACK.

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.

Physics-based distributed snow models in the operational arena: Current and future challenges

NASA Astrophysics Data System (ADS)

Winstral, A. H.; Jonas, T.; Schirmer, M.; Helbig, N.

2017-12-01

The demand for modeling tools robust to climate change and weather extremes along with coincident increases in computational capabilities have led to an increase in the use of physics-based snow models in operational applications. Current operational applications include the WSL-SLF's across Switzerland, ASO's in California, and USDA-ARS's in Idaho. While the physics-based approaches offer many advantages there remain limitations and modeling challenges. The most evident limitation remains computation times that often limit forecasters to a single, deterministic model run. Other limitations however remain less conspicuous amidst the assumptions that these models require little to no calibration based on their foundation on physical principles. Yet all energy balance snow models seemingly contain parameterizations or simplifications of processes where validation data are scarce or present understanding is limited. At the research-basin scale where many of these models were developed these modeling elements may prove adequate. However when applied over large areas, spatially invariable parameterizations of snow albedo, roughness lengths and atmospheric exchange coefficients - all vital to determining the snowcover energy balance - become problematic. Moreover as we apply models over larger grid cells, the representation of sub-grid variability such as the snow-covered fraction adds to the challenges. Here, we will demonstrate some of the major sensitivities of distributed energy balance snow models to particular model constructs, the need for advanced and spatially flexible methods and parameterizations, and prompt the community for open dialogue and future collaborations to further modeling capabilities.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

NOAA's National Snow Analyses

NASA Astrophysics Data System (ADS)

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

2005-12-01

NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products. The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

Species associations and habitat influence the range-wide distribution of breeding Canada Geese (Branta canadensis interior) on Western Hudson Bay

USGS Publications Warehouse

Reiter, Matthew E.; Andersen, David E.; Raedeke, Andrew H.; Humburg, Dale D.

2017-01-01

Inter- and intra-specific interactions are potentially important factors influencing the distribution of populations. Aerial survey data, collected during range-wide breeding population surveys for Eastern Prairie Population (EPP) Canada Geese (Branta canadensis interior), 1987–2008, were evaluated to assess factors influencing their nesting distribution. Specifically, associations between nesting Lesser Snow Geese (Chen caerulescens caerulescens) and EPP Canada Geese were quantified; and changes in the spatial distribution of EPP Canada Geese were identified. Mixed-effects Poisson regression models of EPP Canada Goose nest counts were evaluated within a cross-validation framework. The total count of EPP Canada Goose nests varied moderately among years between 1987 and 2008 with no long-term trend; however, the total count of nesting Lesser Snow Geese generally increased. Three models containing factors related to previous EPP Canada Goose nest density (representing recruitment), distance to Hudson Bay (representing brood-habitat), nesting habitat type, and Lesser Snow Goose nest density (inter-specific associations) were the most accurate, improving prediction accuracy by 45% when compared to intercept-only models. EPP Canada Goose nest density varied by habitat type, was negatively associated with distance to coastal brood-rearing areas, and suggested density-dependent intra-specific effects on recruitment. However, a non-linear relationship between Lesser Snow and EPP Canada Goose nest density suggests that as nesting Lesser Snow Geese increase, EPP Canada Geese locally decline and subsequently the spatial distribution of EPP Canada Geese on western Hudson Bay has changed.

A spatially distributed energy balance snowmelt model for application in mountain basins

USGS Publications Warehouse

Marks, D.; Domingo, J.; Susong, D.; Link, T.; Garen, D.

1999-01-01

Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model ISNOBAL is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2, with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management.Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model ISNOBAL is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2, with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Dependence of Snowmelt Simulations on Scaling of the Forcing Processes (Invited)

NASA Astrophysics Data System (ADS)

Winstral, A. H.; Marks, D. G.; Gurney, R. J.

2009-12-01

The spatial organization and scaling relationships of snow distribution in mountain environs is ultimately dependent on the controlling processes. These processes include interactions between weather, topography, vegetation, snow state, and seasonally-dependent radiation inputs. In large scale snow modeling it is vital to know these dependencies to obtain accurate predictions while reducing computational costs. This study examined the scaling characteristics of the forcing processes and the dependency of distributed snowmelt simulations to their scaling. A base model simulation characterized these processes with 10m resolution over a 14.0 km2 basin with an elevation range of 1474 - 2244 masl. Each of the major processes affecting snow accumulation and melt - precipitation, wind speed, solar radiation, thermal radiation, temperature, and vapor pressure - were independently degraded to 1 km resolution. Seasonal and event-specific results were analyzed. Results indicated that scale effects on melt vary by process and weather conditions. The dependence of melt simulations on the scaling of solar radiation fluxes also had a seasonal component. These process-based scaling characteristics should remain static through time as they are based on physical considerations. As such, these results not only provide guidance for current modeling efforts, but are also well suited to predicting how potential climate changes will affect the heterogeneity of mountain snow distributions.

Snow hydrology in a general circulation model

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

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.

Two-threshold model for scaling laws of noninteracting snow avalanches

USGS Publications Warehouse

Faillettaz, J.; Louchet, F.; Grasso, J.-R.

2004-01-01

A two-threshold model was proposed for scaling laws of noninteracting snow avalanches. It was found that the sizes of the largest avalanches just preceding the lattice system were power-law distributed. The proposed model reproduced the range of power-law exponents observe for land, rock or snow avalanches, by tuning the maximum value of the ratio of the two failure thresholds. A two-threshold 2D cellular automation was introduced to study the scaling for gravity-driven systems.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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.

Use of distributed snow cover information to update snow storages of a lumped rainfall-runoff model operationally

NASA Astrophysics Data System (ADS)

Lisniak, D.; Meissner, D.; Klein, B.; Pinzinger, R.

2013-12-01

The German Federal Institute of Hydrology (BfG) offers navigational water-level forecasting services on the Federal Waterways, like the rivers Rhine and Danube. In cooperation with the Federal States this mandate also includes the forecasting of flood events. For the River Rhine, the most frequented inland waterway in Central Europe, the BfG employs a hydrological model (HBV) coupled to a hydraulic model (SOBEK) by the FEWS-framework to perform daily forecasts of water-levels operationally. Sensitivity studies have shown that the state of soil water storage in the hydrological model is a major factor of uncertainty when performing short- to medium-range forecasts some days ahead. Taking into account the various additional sources of uncertainty associated with hydrological modeling, including measurement uncertainties, it is essential to estimate an optimal initial state of the soil water storage before propagating it in time, forced by meteorological forecasts, and transforming it into discharge. We show, that using the Ensemble Kalman Filter these initial states can be updated straightforward under certain hydrologic conditions. However, this approach is not sufficient if the runoff is mainly generated by snow melt. Since the snow cover evolution is modeled rather poorly by the HBV-model in our operational setting, flood events caused by snow melt are consistently underestimated by the HBV-model, which has long term effects in basins characterized by a nival runoff regime. Thus, it appears beneficial to update the snow storage of the HBV-model with information derived from regionalized snow cover observations. We present a method to incorporate spatially distributed snow cover observations into the lumped HBV-model. We show the plausibility of this approach and asses the benefits of a coupled snow cover and soil water storage updating, which combine a direct insertion with an Ensemble Kalman Filter. The Ensemble Kalman Filter used here takes into account the internal routing mechanism of the HBV-model, which causes a delayed response of the simulated discharge at the catchment outlet to changes in internal states.

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.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

Modeling runoff generation in a small snow-dominated mountainous catchment

USDA-ARS?s Scientific Manuscript database

Snowmelt in mountainous areas is an important contributor to river water flows in the western United States. We developed a distributed model that calculates solar radiation, canopy energy balance, surface energy balance, snow pack dynamics, soil water flow, snow–soil–bedrock heat exchange, soil wat...

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Investigating the effect and uncertainties of light absorbing impurities in snow and ice on snow melt and discharge generation using a hydrologic catchment model and satellite data

NASA Astrophysics Data System (ADS)

Matt, Felix; Burkhart, John F.

2017-04-01

Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of short wave radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the magnitude of these effects as simulated in numerical models have large uncertainties, originating mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters and evaluate the simulated variables connected with the representation of LAISI. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI, a key variable in understanding snowpack energy-balance dynamics. In this study, we assess the effect of LAISI on snow melt and discharge generation and the involved uncertainties in a high mountain catchment located in the western Himalayas by using a distributed hydrological catchment model with focus on the representation of the seasonal snow pack. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of short wave radiation by LAISI into account. Meteorological forcing data is generated from an assimilation of observations and high resolution WRF simulations, and LAISI mixing ratios from deposition rates of Black Carbon simulated with the FLEXPART model. To asses the quality of our simulations and the related uncertainties, we compare the simulated additional energy absorbed by the snow due to the presence of LAISI to the MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithm satellite product.

Snow water equivalent mapping in Norway

NASA Astrophysics Data System (ADS)

Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

2003-04-01

In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

The application of depletion curves for parameterization of subgrid variability of snow

Treesearch

C. H. Luce; D. G. Tarboton

2004-01-01

Parameterization of subgrid-scale variability in snow accumulation and melt is important for improvements in distributed snowmelt modelling. We have taken the approach of using depletion curves that relate fractional snowcovered area to element-average snow water equivalent to parameterize the effect of snowpack heterogeneity within a physically based mass and energy...

What are the controls on mountain snowmelt and runoff around the globe?

NASA Astrophysics Data System (ADS)

Painter, T. H.

2017-12-01

The Anthropocene has seen a marked expulsion of mass from mountain glaciers to oceans and earlier snowmelt that evacuates the mountains earlier in the year. The loss of ice mass and snow cover is often attributed to increasing temperatures. However, process studies across the last two decades indicate that acceleration of melt by dust/black carbon (BC) may dominate in some regions. Process studies with detailed energy balance measurements around the globe are relatively sparse but strongly suggestive of the impact of dust and BC. Mesoscale and global scale modeling have recently taken on radiative transfer modeling of snow albedo that accounts for changes in grain size and dust/BC concentrations and optical properties. However, our understanding of metamorphism and changes in grain growth still has considerable range of uncertainty that, when passed through radiative transfer modeling, far exceeds in magnitude the at-surface greenhouse gas forcing of 3 W m-2. Likewise, it is a rare study that provides the quantitative knowledge of seasonal variation of dust and BC concentrations, let alone the range of optical properties. Therefore, the energy balance of snow in mountains around the globe is poorly understood and our capacity to model past, present, and future hydrologic responses is relatively weak. Atop the energy balance uncertainties, we also still do not know the spatio-temporal distributions of snow water equivalent in mountain basins around the globe. With the advent of the NASA Airborne Snow Observatory in 2013, we entered a new era of understanding mountain basin SWE. ASO uses scanning lidar, imaging spectrometer, and physical modeling to map distributions across basins in California, Colorado, and the Swiss Alps. The program is expanding in these and other regions for water management. However, in the science realm, in addition to providing the capacity to understand distributed SWE and its change, ASO is also pathfinding through the NASA Snow Experiment (SnowEx) for a spaceborne snow depth and SWE mission that can provide the global perspective we need. The next few decades hold enormous potential to quantify mountain snow pack and to constrain physically-based climate models to allow us to answer the title question here and where the cryosphere-water cycles are heading.

Climate Sensitivity to Realistic Solar Heating of Snow and Ice

NASA Astrophysics Data System (ADS)

Flanner, M.; Zender, C. S.

2004-12-01

Snow and ice-covered surfaces are highly reflective and play an integral role in the planetary radiation budget. However, GCMs typically prescribe snow reflection and absorption based on minimal knowledge of snow physical characteristics. We performed climate sensitivity simulations with the NCAR CCSM including a new physically-based multi-layer snow radiative transfer model. The model predicts the effects of vertically resolved heating, absorbing aerosol, and snowpack transparency on snowpack evolution and climate. These processes significantly reduce the model's near-infrared albedo bias over deep snowpacks. While the current CCSM implementation prescribes all solar radiative absorption to occur in the top 2 cm of snow, we estimate that about 65% occurs beneath this level. Accounting for the vertical distribution of snowpack heating and more realistic reflectance significantly alters snowpack depth, surface albedo, and surface air temperature over Northern Hemisphere regions. Implications for the strength of the ice-albedo feedback will be discussed.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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.

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.

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.

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.

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.

Simulations of a Canadian snowpack brightness temperatures using SURFEX-Crocus for Snow Water Equivalent (SWE) retrievals

NASA Astrophysics Data System (ADS)

Larue, Fanny; Royer, Alain; De Sève, Danielle; Langlois, Alexandre; Roy, Alexandre; Saint-Jean-Rondeau, Olivier

2016-04-01

In Quebec, the water associated to snowmelt represents 30% of the annual electricity production so that the snow cover evaluation in real time is of primary interest. The key variable is snow water equivalent (SWE) which describes the evolution of a global seasonal snow cover. However, the sparse distribution of meteorological stations in northern Québec generates great uncertainty in the extrapolation of SWE. On the contrary, the spatial and temporal coverage of satellite data offer a source of information with a high potential when considered as an alternative to the poor spatial distribution of in-situ information. Thus, this project aims to improve the prediction of SWE by assimilation of satellite passive microwave brightness temperatures (Tb) observations, independently of any ground observations. The snowpack evolution is simulated by the French snow model SURFEX-Crocus, driven by the Canadian atmospheric model GEM with a spatial resolution of 10 km. The bias of the atmospheric model and the impact of initialization errors on the simulated SWE were quantified from our ground measurements. To assimilate satellite observations, the multi-layered snow model is first coupled with a radiative transfer model using the Dense Media Radiative transfer theory (the DMRT-ML model) to estimate the microwave snow emission of the simulated snowpack. In order to retrieve simulated Tb in frequencies of interest (i.e. sensitive to snow dielectric properties), the snow microstructure needs to be well parameterized. It was shown in previous studies that the specific surface area (SSA) of snow grains is a well-defined parameter to describe the size and the shape of snow grains and which allows reproducible field measurements. SURFEX-Crocus estimates a SSA for each simulated snow layer, however, the snow microstructure in DMRT-ML is defined per layer by monodisperse optical radius of grain (~ 1/SSA) and by the stickiness which is not known. It thus becomes necessary to introduce an empirical factor (noted φ) due to the simplification of the representation of snow as non-sticky spheres of ice in the model. In other words, the measured and simulated SSA has to be converted in an effective snow grain metric by optimizing this scaling factor to minimize the root-mean-square error between the measured and simulated brightness temperatures. The φ factor scaling the Crocus simulated SSA was estimated using ground-based radiometric measurements made during several field campaigns in the James Bay territory, Nunavik (in 2013 and 2015), and Churchill, Manitoba in 2010. This new parameterization, adapted to the Canadian arctic and subarctic snowpack, represents an essential step to optimize SWE maps in this remote region which have yet to be proven accurate.

MODIS Snow and Ice Products from the NSIDC DAAC

NASA Technical Reports Server (NTRS)

Scharfen, Greg R.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information on snow and ice processes, especially pertaining to interactions among snow, ice, atmosphere and ocean, in support of research on global change detection and model validation, and provides general data and information services to cryospheric and polar processes research community. The NSIDC DAAC is an integral part of the multi-agency-funded support for snow and ice data management services at NSIDC. The Moderate Resolution Imaging Spectroradiometer (MODIS) will be flown on the first Earth Observation System (EOS) platform (AM-1) in 1998. The MODIS Instrument Science Team is developing geophysical products from data collected by the MODIS instrument, including snow and ice products which will be archived and distributed by NSIDC DAAC. The MODIS snow and ice mapping algorithms will generate global snow, lake ice, and sea ice cover products on a daily basis. These products will augment the existing record of satellite-derived snow cover and sea ice products that began about 30 years ago. The characteristics of these products, their utility, and comparisons to other data set are discussed. Current developments and issues are summarized.

General Report of the Researches of Snowpack Properties, Snowmelt Runoff and Evapotranspiration in Japan

NASA Technical Reports Server (NTRS)

Takeda, K.

1985-01-01

A method was developed for estimating the distribution of snow and the snow water equivalent in Japan by combining LANDSAT data with the degree day method. A snow runoff model was improved and applied to the Okutadami River basin. The Martinec Rango model from the U.S. was applied to Japanese river basins to verify its applicability. This model was then compared with the Japanese model. Analysis of microwave measurements obtained by a radiometer on a tower over dry snow in Hokkaido indicate a certain correlation between brightness temperature and snowpack properties. A correlation between brightness temperature and depth of dry snow in an inland plain area was revealed in NIMBUS SMMR data obtained from the U.S. Calculation of evaporation using airborne remote sensing data and a Priestley-Taylor type of equation shows that the differentiation of evaporation with vegetation type is not remarkable because of little evapotransportation in winter.

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.

Advances in snow cover distributed modelling via ensemble simulations and assimilation of satellite data

NASA Astrophysics Data System (ADS)

Revuelto, J.; Dumont, M.; Tuzet, F.; Vionnet, V.; Lafaysse, M.; Lecourt, G.; Vernay, M.; Morin, S.; Cosme, E.; Six, D.; Rabatel, A.

2017-12-01

Nowadays snowpack models show a good capability in simulating the evolution of snow in mountain areas. However singular deviations of meteorological forcing and shortcomings in the modelling of snow physical processes, when accumulated on time along a snow season, could produce large deviations from real snowpack state. The evaluation of these deviations is usually assessed with on-site observations from automatic weather stations. Nevertheless the location of these stations could strongly influence the results of these evaluations since local topography may have a marked influence on snowpack evolution. Despite the evaluation of snowpack models with automatic weather stations usually reveal good results, there exist a lack of large scale evaluations of simulations results on heterogeneous alpine terrain subjected to local topographic effects.This work firstly presents a complete evaluation of the detailed snowpack model Crocus over an extended mountain area, the Arve upper catchment (western European Alps). This catchment has a wide elevation range with a large area above 2000m a.s.l. and/or glaciated. The evaluation compares results obtained with distributed and semi-distributed simulations (the latter nowadays used on the operational forecasting). Daily observations of the snow covered area from MODIS satellite sensor, seasonal glacier surface mass balance evolution measured in more than 65 locations and the galciers annual equilibrium line altitude from Landsat/Spot/Aster satellites, have been used for model evaluation. Additionally the latest advances in producing ensemble snowpack simulations for assimilating satellite reflectance data over extended areas will be presented. These advances comprises the generation of an ensemble of downscaled high-resolution meteorological forcing from meso-scale meteorological models and the application of a particle filter scheme for assimilating satellite observations. Despite the results are prefatory, they show a good potential improving snowpack forecasting capabilities.

[Multi-Scale Convergence of Cold-Land Process Representation in Land-Surface Models, Microwave Remote Sensing, and Field Observations

NASA Technical Reports Server (NTRS)

Shi, Jiancheng

2005-01-01

The cryosphere is a major component of the hydrosphere and interacts significantly with the global climate system, the geosphere, and the biosphere. Measurement of the amount of water stored in the snow pack and forecasting the rate of melt are thus essential for managing water supply and flood control systems. Snow hydrologists are confronted with the dual problems of estimating both the quantity of water held by seasonal snow packs and time of snow melt. Monitoring these snow parameters is essential for one of the objectives of the Earth Science Enterprise-understanding of the global hydrologic cycle. Measuring spatially distributed snow properties, such as snow water equivalence (SWE) and wetness, from space is a key component for improvement of our understanding of coupled atmosphere-surface processes. Through the GWEC project, we have significantly advanced our understandings and improved modeling capabilities of the microwave signatures in response to snow and underground properties.

Sensitivity of modelled snow cover to turbulent flux parameterization and forcing data: a case study in a high altitude basin of the dry Andes, northern Chile

NASA Astrophysics Data System (ADS)

Kinnard, C.; Irarrazaval, I.; Campos, C.; Gascoin, S.; MacDonell, S.; Macdonell, S.; Herrero, J.

2016-12-01

Snow cover in the central-northern Andes of Chile is the main runoff source, providing water for the irrigation of cultures in the fertile valleys downstream. The prospect of adverse climate warming impacts on the hydrological cycle calls for a better understanding of the snow cover dynamics in response to climate, an aspect that has been little studied in the dry Andes. The heterogeneous and often thin snow cover, as well as the paucity of long-term hydrometeorological data makes snow modelling a challenging task in these regions. In this work we applied a physically-based, spatially-distributed snow model (Wimmed) to the La Laguna headwater catchment in the dry Andes (30°S, 70°W) during three hydrological years (2010-2013) when forcing data was available. Model testing at the point scale revealed a large sensitivity of simulated snow depths to the choice of snow roughness parameter (z0), which controls turbulent fluxes, while wind-induced snow erosion at the station in 2010 and 2011 complicated model validation. The inclusion of a mean wind speed map from a previous simulation with the WRF atmospheric model was found to improve the simulation results, while excluding the highest mountain ridge weather station had detrimental effects on the results. A snow roughness (z0) of 1 mm yielded the best comparison between the simulated and observed snow depth at the reference weather station, and between the simulated and MODIS-derived snow cover at the catchment scale. The simulation resulted in large sublimation losses (up to 4 mm day-1), corresponding to more than 80% of snow ablation in the catchment. While such high sublimation rates have been reported before in this region, remaining uncertainties in precipitation data and snow compaction processes call for more detailed studies and increased instrumentation in order to improve future modelling efforts.

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

Treesearch

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

2011-01-01

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

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.

Influence of Slope-Scale Snowmelt on Catchment Response Simulated With the Alpine3D Model

NASA Astrophysics Data System (ADS)

Brauchli, Tristan; Trujillo, Ernesto; Huwald, Hendrik; Lehning, Michael

2017-12-01

Snow and hydrological modeling in alpine environments remains challenging because of the complexity of the processes affecting the mass and energy balance. This study examines the influence of snowmelt on the hydrological response of a high-alpine catchment of 43.2 km2 in the Swiss Alps during the water year 2014-2015. Based on recent advances in Alpine3D, we examine how snow distributions and liquid water transport within the snowpack influence runoff dynamics. By combining these results with multiscale observations (snow lysimeter, distributed snow depths, and streamflow), we demonstrate the added value of a more realistic snow distribution at the onset of melt season. At the site scale, snowpack runoff is well simulated when the mass balance errors are corrected (R2 = 0.95 versus R2 = 0.61). At the subbasin scale, a more heterogeneous snowpack leads to a more rapid runoff pulse originating in the shallower areas while an extended melting period (by a month) is caused by snowmelt from deeper areas. This is a marked improvement over results obtained using a traditional precipitation interpolation method. Hydrological response is also improved by the more realistic snowpack (NSE of 0.85 versus 0.74), even though calibration processes smoothen out the differences. The added value of a more complex liquid water transport scheme is obvious at the site scale but decreases at larger scales. Our results highlight not only the importance but also the difficulty of getting a realistic snowpack distribution even in a well-instrumented area and present a model validation from multiscale experimental data sets.

Analyzing dynamics of snow distribution and melt runoff in a meso-scaled watershed using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Kunz, A.; Helmschrot, J.; Green, T. R.

2013-12-01

The seasonal snow cover in the western mountain regions of the United States functions as the primary supply and storage of water. Water management in these areas is often based on empirical relationships between point measurements of snow water equivalent (SWE) at selected sites and associated stream discharge. With a climate shifting towards more rain and less snow, due to the global warming, the patterns of snow deposition, and consequently the timing of melt, soil water content and the flow in streams and rivers will most likely alter. As a consequence, the established relationships between measured SWE and runoff will become unstable and unreliable, and consequently impacting the water resource management in this area. To better assess and understand the spatial and temporal dimension of altered snow cover on runoff generation in the intermountain region of the western United States, we set up the distributed hydrological AgroEcoSystem-Watershed (AgES-W) model for the Reynolds Creek Experimental Watershed (239 km2) in the Owyhee Mountains of Idaho. The study area with elevations ranging from 1101 to 2241 m is dominated by granitic and volcanic rocks and lake sediments. Deep moist soils allowing for mountain big sagebrush aspen and subalpine fir are found at higher elevations, whereas shallow, arid soils supporting sagebrush-grassland communities are common at lower elevations. Precipitation in the region varies from 230 mm at the lower elevations in the north up to 1100 mm in the higher regions at the southern margin south. The mean annual streamflow at the outlet is 0.56 m3/s. Since the Reynolds Creek Experimental Watershed (RCEW) was selected as a test basin in 1959, a comprehensive hydro-climatological network provides long-term records of daily snow, precipitation, temperature and streamflow measurements. Thus, we used a 30-year data record to calibrate and validate the AgES-W model to three nested sub-basins within the test site. First results show declining discharge volumes for RCEW, while volumes remain fairly constant for the 0.4 km2 Reynolds Mountain East (RME) headwater basin. Comparing simulated snow cover with snow-depth records measured across RME, the model was initially tested regarding its reliability to estimate spatio-temporal snow cover. AgES-W was able to simulate snow-depth dynamics quite well (>0.7 Nash-Sutcliffe Efficiency) for single measurement points, which were cross-validated using additional measurement points as well as stream discharge. The obtained parameter set was then used to model snow distribution for the entire RME basin for a period of 12 years. Applying the calibrated model to all catchments, we analyzed temporal shifts of seasonal runoff within and between the three nested subwatersheds to identify possible changes in the spatio-temporal pattern of snow accumulation and snowmelt. The model results were further used to analyze and map simulated snow water equivalents along a topographic gradient to identify spatial shifts of the snowline during the last 30 years. First results for RME indicate a decline of snow-covered area based on the course of monthly averages, with the largest declines in January and February.

Routine Mapping of the Snow Depth Distribution on Sea Ice

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

USGS Publications Warehouse

Artan, G.A.; Verdin, J.P.; Lietzow, R.

2013-01-01

We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.

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).

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Granulation of snow: From tumbler experiments to discrete element simulations

NASA Astrophysics Data System (ADS)

Steinkogler, Walter; Gaume, Johan; Löwe, Henning; Sovilla, Betty; Lehning, Michael

2015-06-01

It is well known that snow avalanches exhibit granulation phenomena, i.e., the formation of large and apparently stable snow granules during the flow. The size distribution of the granules has an influence on flow behavior which, in turn, affects runout distances and avalanche velocities. The underlying mechanisms of granule formation are notoriously difficult to investigate within large-scale field experiments, due to limitations in the scope for measuring temperatures, velocities, and size distributions. To address this issue we present experiments with a concrete tumbler, which provide an appropriate means to investigate granule formation of snow. In a set of experiments at constant rotation velocity with varying temperatures and water content, we demonstrate that temperature has a major impact on the formation of granules. The experiments showed that granules only formed when the snow temperature exceeded -1∘C. No evolution in the granule size was observed at colder temperatures. Depending on the conditions, different granulation regimes are obtained, which are qualitatively classified according to their persistence and size distribution. The potential of granulation of snow in a tumbler is further demonstrated by showing that generic features of the experiments can be reproduced by cohesive discrete element simulations. The proposed discrete element model mimics the competition between cohesive forces, which promote aggregation, and impact forces, which induce fragmentation, and supports the interpretation of the granule regime classification obtained from the tumbler experiments. Generalizations, implications for flow dynamics, and experimental and model limitations as well as suggestions for future work are discussed.

Snow model design for operational purposes

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2017-04-01

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

Studies of Malagasy Eugenia - IV: Seventeen new endemic species, a new combination, and three lectotypifications; with comments on distribution, ecological and evolutionary patterns.

PubMed

Snow, Neil; Callmander, Martin; Phillipson, Peter B

2015-01-01

Seventeen new endemic species of the genus Eugenia L. (Myrtaceae) are proposed from Madagascar, including: Eugeniaandapae N. Snow, Eugeniabarriei N. Snow, Eugeniabemangidiensis N. Snow, Eugeniacalciscopulorum N. Snow, Eugeniadelicatissima N. Snow, Callm. & Phillipson, Eugeniaechinulata N. Snow, Eugeniagandhii N. Snow, Eugeniahazonjia N. Snow, Eugeniaiantarensis N. Snow, Eugeniamalcomberi N. Snow, Eugeniamanomboensis N. Snow, Eugeniaobovatifolia N. Snow, Eugeniaranomafana N. Snow & D. Turk, Eugeniaravelonarivoi N. Snow & Callm., Eugeniarazakamalalae N. Snow & Callm., Eugeniatiampoka N. Snow & Callm., and Eugeniawilsoniana N. Snow, and one new combination, Eugeniarichardii (Blume) N. Snow, Callm. & Phillipson is provided. Detailed descriptions, information on distribution and ecology, distribution maps, vernacular names (where known), digital images of types, comparisons to morphologically similar species. Preliminary assessment of IUCN risk of extinction and conservation recommendations are provided, including Vulnerable (4 species), Endangered (2 species), and Critically Endangered (4 species). Lectotpyes are designated for Eugeniahovarum H. Perrier, Eugenianompa H. Perrier, and Eugeniascottii H. Perrier respectively.

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.

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.

A Mass Diffusion Model for Dry Snow Utilizing a Fabric Tensor to Characterize Anisotropy

NASA Astrophysics Data System (ADS)

Shertzer, Richard H.; Adams, Edward E.

2018-03-01

A homogenization algorithm for randomly distributed microstructures is applied to develop a mass diffusion model for dry snow. Homogenization is a multiscale approach linking constituent behavior at the microscopic level—among ice and air—to the macroscopic material—snow. Principles of continuum mechanics at the microscopic scale describe water vapor diffusion across an ice grain's surface to the air-filled pore space. Volume averaging and a localization assumption scale up and down, respectively, between microscopic and macroscopic scales. The model yields a mass diffusivity expression at the macroscopic scale that is, in general, a second-order tensor parameterized by both bulk and microstructural variables. The model predicts a mass diffusivity of water vapor through snow that is less than that through air. Mass diffusivity is expected to decrease linearly with ice volume fraction. Potential anisotropy in snow's mass diffusivity is captured due to the tensor representation. The tensor is built from directional data assigned to specific, idealized microstructural features. Such anisotropy has been observed in the field and laboratories in snow morphologies of interest such as weak layers of depth hoar and near-surface facets.

Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

NASA Astrophysics Data System (ADS)

Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

Evaluation of Mixed-Phase Microphysics Within Winter Storms Using Field Data and In Situ Observations

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Molthan, Andrew; Yu, Ruyi; Nesbitt, Steven

2014-01-01

Snow prediction within models is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Cold-season Precipitation Experiment (GCPEx) experiment over southern Ontario, as well as a few years (12 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. The GCPex presentation will focus on verification using aircraft spirals through warm frontal snow band event on 18 February 2012. All the BMPs realistically simulated the structure of the band and the vertical distribution of snow/ice aloft, except the SBU-YLIN overpredicted slightly and Thompson (THOM) underpredicted somewhat. The Morrison (MORR) scheme produced the best slope size distribution for snow, while the Stony Brook (SBU) underpredicted and the THOM slightly overpredicted. Those schemes that have the slope intercept a function of temperature (SBU and WSM6) tended to perform better for that parameter than others, especially the fixed intercept in Goddard. Overall, the spread among BMPs was smaller than in other studies, likely because there was limited riming with the band. For the 15 cases at SBNY, which include moderate and heavy riming events, the non-spherical snow assumption (THOM and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM is approx. 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM, and MORR schemes were approx. 0.25 m/s too slow, while the SBU was 0.25 to 0.5 m/s too fast because of some excessive cloud water issues.

Studies of Malagasy Eugenia – IV: Seventeen new endemic species, a new combination, and three lectotypifications; with comments on distribution, ecological and evolutionary patterns

PubMed Central

Snow, Neil; Callmander, Martin; Phillipson, Peter B.

2015-01-01

Abstract Seventeen new endemic species of the genus Eugenia L. (Myrtaceae) are proposed from Madagascar, including: Eugenia andapae N. Snow, Eugenia barriei N. Snow, Eugenia bemangidiensis N. Snow, Eugenia calciscopulorum N. Snow, Eugenia delicatissima N. Snow, Callm. & Phillipson, Eugenia echinulata N. Snow, Eugenia gandhii N. Snow, Eugenia hazonjia N. Snow, Eugenia iantarensis N. Snow, Eugenia malcomberi N. Snow, Eugenia manomboensis N. Snow, Eugenia obovatifolia N. Snow, Eugenia ranomafana N. Snow & D. Turk, Eugenia ravelonarivoi N. Snow & Callm., Eugenia razakamalalae N. Snow & Callm., Eugenia tiampoka N. Snow & Callm., and Eugenia wilsoniana N. Snow, and one new combination, Eugenia richardii (Blume) N. Snow, Callm. & Phillipson is provided. Detailed descriptions, information on distribution and ecology, distribution maps, vernacular names (where known), digital images of types, comparisons to morphologically similar species. Preliminary assessment of IUCN risk of extinction and conservation recommendations are provided, including Vulnerable (4 species), Endangered (2 species), and Critically Endangered (4 species). Lectotpyes are designated for Eugenia hovarum H. Perrier, Eugenia nompa H. Perrier, and Eugenia scottii H. Perrier respectively. PMID:25987885

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Simulating the Permafrost Distribution on the Seward Peninsula, Alaska

NASA Astrophysics Data System (ADS)

Busey, R.; Hinzman, L. D.; Yoshikawa, K.; Liston, G. E.

2005-12-01

Permafrost extent has been estimated using an equivalent latitude / elevation model based upon good climate, terrain and soil property data. This research extends a previously developed model to a relatively data sparse region. We are applying the general equivalent attitude model developed for Caribou-Poker Creeks Research Watershed over the much larger area of the Seward Peninsula, Alaska. This region of sub-Arctic Alaska is a proxy for a warmer Arctic due to the broad expanses of tussock tundra, invading shrubs and fragile permafrost with average temperatures just below freezing. The equivalent latitude model combines elevation, slope, and aspect with snow cover, where the snow cover distribution was defined using MicroMet and SnowModel. Source data for the distributed snow model came from meteorological stations across the Seward Peninsula from the National Weather Service, SNOTEL, RAWS, and our own stations. Simulations of permafrost extent will enable us to compare the current distribution to that existing during past climates and estimate the future state of permafrost on the Seward Peninsula. The broadest impacts to the terrestrial arctic regions will result through consequent effects of changing permafrost structure and extent. As the climate differentially warms in summer and winter, the permafrost will become warmer, the active layer (the layer of soil above the permafrost that annually experiences freeze and thaw) will become thicker, the lower boundary of permafrost will become shallower and permafrost extent will decrease in area. These simple structural changes will affect every aspect of the surface water and energy balances. As permafrost extent decreases, there is more infiltration to groundwater. This has significant impacts on large and small scales.

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

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Jeyaratnam, Jeyavinoth

2016-01-01

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

Imaging of the CO snow line in a solar nebula analog.

PubMed

Qi, Chunhua; Öberg, Karin I; Wilner, David J; D'Alessio, Paola; Bergin, Edwin; Andrews, Sean M; Blake, Geoffrey A; Hogerheijde, Michiel R; van Dishoeck, Ewine F

2013-08-09

Planets form in the disks around young stars. Their formation efficiency and composition are intimately linked to the protoplanetary disk locations of "snow lines" of abundant volatiles. We present chemical imaging of the carbon monoxide (CO) snow line in the disk around TW Hya, an analog of the solar nebula, using high spatial and spectral resolution Atacama Large Millimeter/Submillimeter Array observations of diazenylium (N2H(+)), a reactive ion present in large abundance only where CO is frozen out. The N2H(+) emission is distributed in a large ring, with an inner radius that matches CO snow line model predictions. The extracted CO snow line radius of ~30 astronomical units helps to assess models of the formation dynamics of the solar system, when combined with measurements of the bulk composition of planets and comets.

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

NASA Astrophysics Data System (ADS)

Gafurov, Abror; Lüdtke, Stefan; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Schöne, Tilo; Schmidt, Sebastian; Kalashnikova, Olga; Merz, Bruno

2017-04-01

Spatially distributed snow cover information in mountain areas is extremely important for water storage estimations, seasonal water availability forecasting, or the assessment of snow-related hazards (e.g. enhanced snow-melt following intensive rains, or avalanche events). Moreover, spatially distributed snow cover information can be used to calibrate and/or validate hydrological models. We present the MODSNOW-Tool - an operational monitoring tool offers a user-friendly application which can be used for catchment-based operational snow cover monitoring. The application automatically downloads and processes freely available daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. The MODSNOW-Tool uses a step-wise approach for cloud removal and delivers cloud-free snow cover maps for the selected river basins including basin specific snow cover extent statistics. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps. The MODSNOW-Tool is currently implemented and in use at the national hydrometeorological services of four Central Asian states - Kazakhstan, Kyrgyzstan, Uzbekistan and Turkmenistan and used for seasonal water availability forecast.

Estimating time and spatial distribution of snow water equivalent in the Hakusan area

NASA Astrophysics Data System (ADS)

Tanaka, K.; Matsui, Y.; Touge, Y.

2015-12-01

In the Sousei program, on-going Japanese research program for risk information on climate change, assessing the impact of climate change on water resources is attempted using the integrated water resources model which consists of land surface model, irrigation model, river routing model, reservoir operation model, and crop growth model. Due to climate change, reduction of snowfall amount, reduction of snow cover and change in snowmelt timing, change in river discharge are of increasing concern. So, the evaluation of snow water amount is crucial for assessing the impact of climate change on water resources in Japan. To validate the snow simulation of the land surface model, time and spatial distribution of the snow water equivalent was estimated using the observed surface meteorological data and RAP (Radar Analysis Precipitation) data. Target area is Hakusan. Hakusan means 'white mountain' in Japanese. Water balance of the Tedori River Dam catchment was checked with daily inflow data. Analyzed runoff was generally well for the period from 2010 to 2012. From the result for 2010-2011 winter, maximum snow water equivalent in the headwater area of the Tedori River dam reached more than 2000mm in early April. On the other hand, due to the underestimation of RAP data, analyzed runoff was under estimated from 2006 to 2009. This underestimation is probably not from the lack of land surface model, but from the quality of input precipitation data. In the original RAP, only the rain gauge data of JMA (Japan Meteorological Agency) were used in the analysis. Recently, other rain gauge data of MLIT (Ministry of Land, Infrastructure, Transport and Tourism) and local government have been added in the analysis. So, the quality of the RAP data especially in the mountain region has been greatly improved. "Reanalysis" of the RAP precipitation is strongly recommended using all the available off-line rain gauges information. High quality precipitation data will contribute to validate hydrological model, satellite based precipitation product, GCM output, etc.

High Resolution Electro-Optical Aerosol Phase Function Database PFNDAT2006

DTIC Science & Technology

2006-08-01

snow models use the gamma distribution (equation 12) with m = 0. 3.4.1 Rain Model The most widely used analytical parameterization for raindrop size ...Uijlenhoet and Stricker (22), as the result of an analytical derivation based on a theoretical parameterization for the raindrop size distribution ...6 2.2 Particle Size Distribution Models

Retrieval of Snow Properties for Ku- and Ka-band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro/macro-physical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) duringthe winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

Retrieval of Snow Properties for Ku- and Ka-Band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro-macrophysical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow-scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) during the winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

Cartographic modelling of aerotechnogenic pollution in snow cover in the landscapes of the Kola Peninsula.

PubMed

Ratkin, N E; Asming, V E; Koshkin, V V

2001-01-01

The goal of this work was to develop computational techniques for sulphates, nickel and copper accumulation in the snow in the local pollution zone. The main task was to reveal the peculiarities of formation and pollution of snow cover on the region with complex cross-relief. A digital cartographic model of aerotechnogenic pollution of snow cover in the landscapes of the local zone has been developed, based on five-year experimental data. Data regarding annual emissions from the industrial complex, information about distribution of wind and the sum of precipitation from meteostation "Nikel" for the winter period, allowed the model to ensure: * material presentation in the form of maps of water capacity and accumulation of sulphates, nickel and copper in the snow over any winter period in retrospective; * calculation of water capacity and accumulation of pollutants for watersheds and other natural-territorial complexes; * solution of the opposite problem about the determination of the emissions of sulphates, nickel and copper from the enterprise by measuring snow pollution in datum points. The model can be used in other northern regions of the Russian Federation with similar physical-geographical and climatic conditions. The relationships between the sum of precipitation and water capacity in the landscapes of the same type and also the relationships between pollution content in snow and relief, pollution content in snow and distance from the source of emissions, were used as the basis for the model.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Overview of SnowEx Year 1 Activities

NASA Technical Reports Server (NTRS)

Kim, Edward; Gatebe, Charles; Hall, Dorothy; Newlin, Jerry; Misakonis, Amy; Elder, Kelly; Marshall, Hans Peter; Heimstra, Chris; Brucker, Ludovic; De Marco, Eugenia;

MODIS Collection 6 Data at the National Snow and Ice Data Center (NSIDC)

NASA Astrophysics Data System (ADS)

Fowler, D. K.; Steiker, A. E.; Johnston, T.; Haran, T. M.; Fowler, C.; Wyatt, P.

2015-12-01

For over 15 years, the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) has archived and distributed snow and sea ice products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Earth Observing System (EOS) Aqua and Terra satellites. Collection 6 represents the next revision to NSIDC's MODIS archive, mainly affecting the snow-cover products. Collection 6 specifically addresses the needs of the MODIS science community by targeting the scenarios that have historically confounded snow detection and introduced errors into the snow-cover and fractional snow-cover maps even though MODIS snow-cover maps are typically 90 percent accurate or better under good observing conditions, Collection 6 uses revised algorithms to discriminate between snow and clouds, resolve uncertainties along the edges of snow-covered regions, and detect summer snow cover in mountains. Furthermore, Collection 6 applies modified and additional snow detection screens and new Quality Assessment protocols that enhance the overall accuracy of the snow maps compared with Collection 5. Collection 6 also introduces several new MODIS snow products, including a daily Climate Modelling Grid (CMG) cloud gap-filled (CGF) snow-cover map which generates cloud-free maps by using the most recent clear observations.. The MODIS Collection 6 sea ice extent and ice surface temperature algorithms and products are much the same as Collection 5; however, Collection 6 updates to algorithm inputs—in particular, the L1B calibrated radiances, land and water mask, and cloud mask products—have improved the sea ice outputs. The MODIS sea ice products are currently available at NSIDC, and the snow cover products are soon to follow in 2016 NSIDC offers a variety of methods for obtaining these data. Users can download data directly from an online archive or use the NASA Reverb Search & Order Tool to perform spatial, temporal, and parameter subsetting, reformatting, and re-projection of the data.

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

NASA Technical Reports Server (NTRS)

Palm, Stephen P.; Kayetha, Vinay; Yang, Yuekui; Pauly, Rebecca

2017-01-01

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

A Physical Model to Determine Snowfall over Land by Microwave Radiometry

NASA Technical Reports Server (NTRS)

Skofronick-Jackson, G.; Kim, M.-J.; Weinman, J. A.; Chang, D.-E.

2003-01-01

Because microwave brightness temperatures emitted by snow covered surfaces are highly variable, snowfall above such surfaces is difficult to observe using window channels that occur at low frequencies (v less than 100 GHz). Furthermore, at frequencies v less than or equal to 37 GHz, sensitivity to liquid hydrometeors is dominant. These problems are mitigated at high frequencies (v greater than 100 GHz) where water vapor screens the surface emission and sensitivity to frozen hydrometeors is significant. However the scattering effect of snowfall in the atmosphere at those higher frequencies is also impacted by water vapor in the upper atmosphere. This work describes the methodology and results of physically-based retrievals of snow falling over land surfaces. The theory of scattering by randomly oriented dry snow particles at high microwave frequencies appears to be better described by regarding snow as a concatenation of equivalent ice spheres rather than as a sphere with the effective dielectric constant of an air-ice mixture. An equivalent sphere snow scattering model was validated against high frequency attenuation measurements. Satellite-based high frequency observations from an Advanced Microwave Sounding Unit (AMSU-B) instrument during the March 5-6, 2001 New England blizzard were used to retrieve snowfall over land. Vertical distributions of snow, temperature and relative humidity profiles were derived from the Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) fifth-generation Mesoscale Model (MM5). Those data were applied and modified in a radiative transfer model that derived brightness temperatures consistent with the AMSU-B observations. The retrieved snowfall distribution was validated with radar reflectivity measurements obtained from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) ground-based radar network.

Monitoring of snowpack dynamics in mountainous terrain by cosmic-ray neutron sensing compared to Terrestrial Laser Scanning observations

NASA Astrophysics Data System (ADS)

Schattan, P.; Baroni, G.; Schrön, M.; Köhli, M.; Oswald, S. E.; Huttenlau, M.; Achleitner, S.

2017-12-01

Monitoring a mountain snowpack in a representative domain of several hectares is challenging due to its high heterogeneity in time and space. Recent studies have suggested cosmic-ray neutron sensing (CRNS) as a promising method for monitoring snow representatively at these scales. Little is known however about the depth of sensitivity, the effects of fractional snow coverage in complex terrain or the influence of snow density profiles. Therefore, a field campaign in the Austrian Alps was conducted from March 2014 to June 2016. The main scope was to evaluate the characteristics of CRNS for monitoring a snowpack in a relatively wet and mountainous environment. During the experiment, the study site experienced a peak snow accumulation in terms of snow water equivalent (SWE) of up to 600 mm in the 2014/2015 winter season. Snow depth (SD) and SWE measurements from an automatic weather station were compared to CRNS neutron counts. Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used to cope with the spatial heterogeneity of the site. Furthermore, an URANOS neutron transport model was set up to provide additional insights into the response of CRNS to the presence of a complex snowpack. Therein, spatially distributed SWE scenarios and different snow density assumptions are used for hypothesis testing. The field measurements revealed an unexpectedly high potential of CRNS for monitoring heterogeneous snowpack dynamics beyond shallow snowpacks. A clear, nonlinear relation was found for both SD and SWE with neutron counts. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, complete signal saturation was observed only for SWE values beyond 500 to 600 mm. In addition, first modelling results highlight the effects of snow density profiles, small-scale changes in SWE, and the complex patterns of fractional snow cover on neutron counts. Understanding the interactions between neutrons and snow cover in complex terrain potentially improves the transferability of the results to other locations.

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.

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

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.

Performance of the Goddard Multiscale Modeling Framework with Goddard Ice Microphysical Schemes

NASA Technical Reports Server (NTRS)

Chern, Jiun-Dar; Tao, Wei-Kuo; Lang, Stephen E.; Matsui, Toshihisa; Li, J.-L.; Mohr, Karen I.; Skofronick-Jackson, Gail M.; Peters-Lidard, Christa D.

2016-01-01

The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has become a new approach for climate modeling. The embedded CRMs make it possible to apply CRM-based cloud microphysics directly within a GCM. However, most such schemes have never been tested in a global environment for long-term climate simulation. The benefits of using an MMF to evaluate rigorously and improve microphysics schemes are here demonstrated. Four one-moment microphysical schemes are implemented into the Goddard MMF and their results validated against three CloudSat/CALIPSO cloud ice products and other satellite data. The new four-class (cloud ice, snow, graupel, and frozen drops/hail) ice scheme produces a better overall spatial distribution of cloud ice amount, total cloud fractions, net radiation, and total cloud radiative forcing than earlier three-class ice schemes, with biases within the observational uncertainties. Sensitivity experiments are conducted to examine the impact of recently upgraded microphysical processes on global hydrometeor distributions. Five processes dominate the global distributions of cloud ice and snow amount in long-term simulations: (1) allowing for ice supersaturation in the saturation adjustment, (2) three additional correction terms in the depositional growth of cloud ice to snow, (3) accounting for cloud ice fall speeds, (4) limiting cloud ice particle size, and (5) new size-mapping schemes for snow and graupel. Despite the cloud microphysics improvements, systematic errors associated with subgrid processes, cyclic lateral boundaries in the embedded CRMs, and momentum transport remain and will require future improvement.

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

USGS Publications Warehouse

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

2002-01-01

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

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

Treesearch

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

2008-01-01

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

Assessment and application of a snowblow modelling approach for identifying enhanced snow accumulation in areas of former glaciation

NASA Astrophysics Data System (ADS)

Mills, Stephanie; Smith, Michael; Le Brocq, Anne; Ardakova, Ekaterina; Hillier, John; Boston, Clare

2016-04-01

The redistribution of snow by wind can play an important role in providing additional mass to the surface of glaciers and can, therefore, have an impact on the glacier's surface mass balance. In areas of marginal glaciation, this local topo-climatic effect may be prove crucial for the initiation and survival of glaciers, whilst it can also increase heterogeneity in the distribution of snow on ice caps and ice sheets. We present a newly developed snowblow model which calculates spatial variations in relative snow accumulation that result from variations in topography. We apply this model to areas of former marginal glaciation in the Brecon Beacons, Wales and an area of former plateau icefield glaciation in the Monadhliath, Scotland. We can then determine whether redistribution by snow can help explain variations in the estimated equilibrium line altitudes (ELAs) of these former glaciers. Specifically, we compare the areas where snow is modelled as accumulating, to the reconstructed glacier surface, which is based on mapped moraines believed to be of Younger Dryas age. The model is applied to 30 m resolution DEMs and potential snow accumulation is simulated from different wind directions in order to determine the most likely contributing sector. Total snow accumulation in sub-set areas is then calculated and compared to the reconstructed glacier area. The results suggest that areas with larger amounts of snow accumulation often correspond with those where the ELA is lower than surrounding glaciers and vice versa, in both the marginal and icefield setting, suggesting that the role of snowblow in supplying additional mass to the surface of glaciers is significant.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J. (Principal Investigator); Davis, R. E.; Dubayah, R. O.; Frew, J. E.; Li, S.; Marks, D.; Milliff, R. F.; Rousseau, D. D.; Wan, Z. M.

1985-01-01

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover.

Improvement of Mars Surface Snow Albedo Modeling in LMD Mars GCM With SNICAR

NASA Astrophysics Data System (ADS)

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

2018-03-01

The current version of Laboratoire de Météorologie Dynamique (LMD) Mars GCM (original-MGCM) uses annually repeating (prescribed) CO2 snow albedo values based on the Thermal Emission Spectrometer observations. We integrate the Snow, Ice, and Aerosol Radiation (SNICAR) model with MGCM (SNICAR-MGCM) to prognostically determine H2O and CO2 snow albedos interactively in the model. Using the new diagnostic capabilities of this model, we find that cryospheric surfaces (with dust) increase the global surface albedo of Mars by 0.022. Over snow-covered regions, SNICAR-MGCM simulates mean albedo that is higher by about 0.034 than prescribed values in the original-MGCM. Globally, shortwave flux into the surface decreases by 1.26 W/m2, and net CO2 snow deposition increases by about 4% with SNICAR-MGCM over one Martian annual cycle as compared to the original-MGCM simulations. SNICAR integration reduces the mean global surface temperature and the surface pressure of Mars by about 0.87% and 2.5%, respectively. Changes in albedo also show a similar distribution to dust deposition over the globe. The SNICAR-MGCM model generates albedos with higher sensitivity to surface dust content as compared to original-MGCM. For snow-covered regions, we improve the correlation between albedo and optical depth of dust from -0.91 to -0.97 with SNICAR-MGCM as compared to the original-MGCM. Dust substantially darkens Mars's cryosphere, thereby reducing its impact on the global shortwave energy budget by more than half, relative to the impact of pure snow.

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.

Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013

USGS Publications Warehouse

Driscoll, Jessica; Hay, Lauren E.; Bock, Andrew R.

2017-01-01

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes.

Role of Tibetan Buddhist monasteries in snow leopard conservation.

PubMed

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

2014-02-01

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

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.

Uncertainty Evaluation and Appropriate Distribution for the RDHM in the Rockies

NASA Astrophysics Data System (ADS)

Kim, J.; Bastidas, L. A.; Clark, E. P.

2010-12-01

The problems that hydrologic models have in properly reproducing the processes involved in mountainous areas, and in particular the Rocky Mountains, are widely acknowledged. Herein, we present an application of the National Weather Service RDHM distributed model over the Durango River basin in Colorado. We focus primarily in the assessment of the model prediction uncertainty associated with the parameter estimation and the comparison of the model performance using parameters obtained with a priori estimation following the procedure of Koren et al., and those obtained via inverse modeling using a variety of Markov chain Monte Carlo based optimization algorithms. The model evaluation is based on traditional procedures as well as non-traditional ones based on the use of shape matching functions, which are more appropriate for the evaluation of distributed information (e.g. Hausdorff distance, earth movers distance). The variables used for the model performance evaluation are discharge (with internal nodes), snow cover and snow water equivalent. An attempt to establish the proper degree of distribution, for the Durango basin with the RDHM model, is also presented.

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Demek, Scott R.

2010-01-01

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

NASA Astrophysics Data System (ADS)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Snow Water Equivalent estimation based on satellite observation

NASA Astrophysics Data System (ADS)

Macchiavello, G.; Pesce, F.; Boni, G.; Gabellani, S.

2009-09-01

The availability of remotely sensed images and them analysis is a powerful tool for monitoring the extension and typology of snow cover over territory where the in situ measurements are often difficult. Information on snow are fundamental for monitoring and forecasting the available water above all in regions at mid latitudes as Mediterranean where snowmelt may cause floods. The hydrological model requirements and the daily acquisitions of MODIS (Moderate Resolution Imaging Spectroradiometer), drove, in previous research activities, to the development of a method to automatically map the snow cover from multi-spectral images. But, the major hydrological parameter related to the snow pack is the Snow Water Equivalent (SWE). This represents a direct measure of stored water in the basin. Because of it, the work was focused to the daily estimation of SWE from MODIS images. But, the complexity of this aim, based only on optical data, doesn’t find any information in literature. Since, from the spectral range of MODIS data it is not possible to extract a direct relation between spectral information and the SWE. Then a new method, respectful of the physic of the snow, was defined and developed. Reminding that the snow water equivalent is the product of the three factors as snow density, snow depth and the snow covered areas, the proposed approach works separately on each of these physical behaviors. Referring to the physical characteristic of snow, the snow density is function of the snow age, then it was studied a new method to evaluate this. Where, a module for snow age simulation from albedo information was developed. It activates an age counter updated by new snow information set to estimate snow age from zero accumulation status to the end of melting season. The height of the snow pack, can be retrieved by adopting relation between vegetation and snow depth distributions. This computes snow height distribution by the relation between snow cover fraction and the forest canopy density. Finally, the SWE has to be calculated for the snow covered areas, detected by means of a previously developed decision tree classifier able to classify snow cover by self selecting rules in a statistically optimum way. The advantages introduced from this work are many. Firstly, applying a suitable method with data features, it is possible to automatically obtain snow cover description with high frequency. Moreover, the advantages of the modularity in the proposed approach allows to improve the three factors estimation in an independent way. Limitations lie into clouds problem that affects results by obscuring the observed territory, that is bounded by fusing temporal and spatial information. Then the spatial resolution of data, satisfactory with the scale of hydrological models, mismatch with the available in situ point information, causing difficulties for a method validation or calibration. However this working flow results computationally cost-effectiveness, robust to the radiometric noise of the original data, provides spatially extended and frequent information.

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

NASA Technical Reports Server (NTRS)

Kim, Min-Jeong; Weinman, J. A.; Olson, W. S.; Chang, D.-E.; Skofronick-Jackson, G.; Wang, J. R.

2008-01-01

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.

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

Using mid-altitude regions as observatories of change in snow areas: the Natural Park of Cazorla, Segura y las Villas (South Spain) as study case for early snow regression.

NASA Astrophysics Data System (ADS)

Montilla, Soledad; Pimentel, Rafael; José Pérez-Palazón, María; Aguillar, Cristina; José Polo, María

2017-04-01

Snow plays a key role at the hydrological cycle in semiarid mountainous areas, modifying the energy and water balances that govern the regime of stored water in the snowpack, a key resource for the spring and summer river flow. The Natural and National Park of Sierra Nevada (SNNP), a coastal mountain range up to 3450 m a.s.l. in southern Spain, is a representative example of snow areas in Mediterranean-climate regions; its high altitudinal gradient results in a wide variety of eco-climatic environments, and it is part of the global monitoring network to study climate change. Both monitoring and modelling efforts have been performed to assess this variability and its significant scales; whereas increasing temperature trends have been found, no significant trends are observed so far regarding the precipitation regime both on a seasonal and annual basis, with a highly variable impact on the snow regime in this area, especially in the mid-altitude range. In this context, the study of the snow cover in the neighbouring Natural Park of Cazorla, Segura and Las Villas (CSLVNP), with similar climatic conditions but lower altitudes (up to 2107 m a.s.l.) is proposed as a parallel monitoring site for early warning of impacts of climate change on the snow regime. The CSLVNP is the most extensive protected area in Spain and it is located to the north of SNPN, with less influence of the Mediterranean Sea. This study carried out a first quantification of the snow importance in this area, which exhibits a large transitional zone with a dominant alpine environment, and its relationship with the observed local precipitation-temperature trends. For this, the snow cover fraction on a 30x30 m gridded resolution has been studied during a 5-yr period combining on-site meteorological observations and remote-sensing data analysis, and snow modelling by the distributed and physically based approach for Mediterranean regions proposed by Herrero et al. (2009; 2010). The analysis of the available series of satellite images Landsat 8 OLI/TIRS, Landsat 7 ETM+ and Landsat 4-5 TM were used to obtain snow cover fraction maps with 30x30 m resolution. The study period 2010-2015 was simulated with the distributed snow model and the results were compared against these snow map series. Additionally, the annual and seasonal trends of precipitation, mean daily temperature and global radiation were obtained from the available local data sets. Globally, the simulated results overestimate the snow presence in the study area, very likely due to the estimation of snowfall. However, on a local scale, the model performance improves in the region between 1750 and 2250 m altitude. On the other hand, those zones at lower altitudes, which are a transition of the clearly alpine environment above, present a high variability of results related to the spatial patterns of precipitation, temperature and radiation. Regarding the precipitation-temperature regime, an increasing 0.05 °/yr over the last 30 years (1970-2010) was found, but no significant conclusion can be achieved on precipitation trends. This is also observed in the SNNP, which confirms the potential representativeness of PNCSV as an early warning site. Further work is being carried out to improve the snow modelling at this site and generate longer snow cover fraction maps series and other characteristic variables of the snow in this area.

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.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vinvent V.; DiGirolamo, Nicolo; Bayr, Klaus J.; Houser, Paul (Technical Monitor)

2001-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.

Severe snow loads on mountain afforestation in Japan

Treesearch

Ryuzo Nitta; Yoshio Ozeki; Shoichi Niwano

1991-01-01

A simple device for estimating snow settling force on tree branches was used to determine the distribution of snow settling force at various heights in a snowy mountainous region in Japan. A trapezoidal distribution of snow settling force was found to exist at all sites tested. It is thought that a zoning scheme based on the damaging potential of snow on young man-made...

The application of ERTS imagery to mapping snow cover in the western United States. [Salt Verde in Arizona and Sierra Nevada California

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Bowley, C. J.; Simmes, D. A.

1974-01-01

The author has identified the following significant results. In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models. The application of ERTS-1 imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde watershed in central Arizona and the southern Sierra Nevada in California. Techniques have been developed to identify snow and to differentiate between snow and cloud. The snow extent for these two drainage areas has been mapped from the MSS-5 (0.6 - 0.7 microns) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements. The results indicate that ERTS imagery has substantial practical applications for snow mapping. Snow extent can be mapped from ERTS-1 imagery in more detail than is depicted on aerial survey snow charts. Moreover, in Arizona and southern California cloud obscuration does not appear to be a serious deterrent to the use of satellite data for snow survey. The costs involved in deriving snow maps from ERTS-1 imagery appear to be very reasonable in comparison with existing data collection methods.

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

NASA Astrophysics Data System (ADS)

Kushner, Paul J.; Mudryk, Lawrence R.; Merryfield, William; Ambadan, Jaison T.; Berg, Aaron; Bichet, Adéline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; Fyfe, John C.; Gillett, Nathan; Haas, Christian; Howell, Stephen; Laliberté, Frédéric; McCusker, Kelly; Sigmond, Michael; Sospedra-Alfonso, Reinel; Tandon, Neil F.; Thackeray, Chad; Tremblay, Bruno; Zwiers, Francis W.

2018-04-01

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Climate change impacts on maritime mountain snowpack in the Oregon Cascades

Treesearch

E. Sproles; A.W. Nolin; K. Rittger; T.H. Painter

2013-01-01

This study investigates the effect of projected temperature increases on maritime mountain snowpack in the McKenzie River Basin (MRB; 3041 km2) in the Cascades Mountains of Oregon, USA. We simulated the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with SnowModel, a spatiallydistributed, process-based...

Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling

NASA Astrophysics Data System (ADS)

Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc

2010-05-01

Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (< 30 m) they have been considered as an evaluation tool. The snow cover maps are then compared with the hydrological GEOtop model outputs. The main objectives of this work are: 1. Evaluation of the MODIS snow cover algorithm using LANDSAT data 2. Investigation of snow cover, and snow cover duration for the area of interest for South Tyrol 3. Derivation and interpretation of the snow line for the seven winter seasons 4. An evaluation of the model outputs in order to determine the situations in which the remotely sensed data can be used to improve the model prediction of snow coverage and related variables References [1] Rigon R., Bertoldi G. and Over T.M. 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, Journal of Hydrometeorology, 7: 371-388. [2] Rastner P., Irsara L., Schellenberger T., Della Chiesa S., Bertoldi G., Endrizzi S., Notarnicola C., Steurer C., Zebisch M. 2009. Monitoraggio del manto nevoso in aree alpine con dati MODIS multi-temporali e modelli idrologici, 13th ASITA National Conference, 1-4.12.2009, Bari, Italy. [3] Zanotti F., Endrizzi S., Bertoldi G. and Rigon R. 2004. The GEOtop snow module. Hydrological Processes, 18: 3667-3679. DOI:10.1002/hyp.5794.

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.

Snow Pattern Delineation, Scaling, Fidelity, and Landscape Factors

NASA Astrophysics Data System (ADS)

Hiemstra, C. A.; Wagner, A. M.; Deeb, E. J.; Morriss, B. F.; Sturm, M.

2014-12-01

In many snow-covered landscapes, snow tends to be shallow or deep in the same locations year after year. As snowmelt progresses in spring, areas of shallow snow become snow-free earlier than areas with deep snow. This pattern (Sturm and Wagner 2010) could likely be used to inform or improve modeled snow depth estimates where ground measurements are not collected; however, we must be certain of their utility before ingesting them into model calculations. Do patterns, as we detect them, have a relationship with earlier measured snow distributions? Second, are certain areas on the landscape likely to yield patterns that are influenced too highly by melting to be useful? Our Imnavait Creek Study Area (11 by 19 km) is on Alaska's North Slope, where we have examined a vast library of spring satellite imagery (ranging from mostly snow-covered to mostly snow-free). Landsat TM Imagery has been collected from the early 1980s-present, and the temporal and spatial resolution is roughly two weeks and 30 m, respectively. High resolution satellite imagery (WorldView 1, WorldView 2, IKONOS) has been obtained from 2010-2013 for the same area with almost daily- to monthly-temporal and at 2.5 m spatial resolutions, respectively. We found that there is a striking similarity among patterns from year to year across the span of decades and resolutions. However, the relationship of pattern with observed snow depths was strong in some areas and less clear in others. Overall, we suspect spatial scaling, spatial mismatch, sampling errors, and melt patterns explain most of the areas of pattern and depth disparity.

Validation of snow characteristics and snow albedo feedback in the Canadian Regional Climate Model simulations over North America

NASA Astrophysics Data System (ADS)

Fang, B.; Sushama, L.; Diro, G. T.

2015-12-01

Snow characteristics and snow albedo feedback (SAF) over North America, as simulated by the fifth-generation Canadian Regional Climate Model (CRCM5), when driven by ERA-40/ERA-Interim, CanESM2 and MPI-ESM-LR at the lateral boundaries, are analyzed in this study. Validation of snow characteristics is performed by comparing simulations against available observations from MODIS, ISCCP and CMC. Results show that the model is able to represent the main spatial distribution of snow characteristics with some overestimation in snow mass and snow depth over the Canadian high Arctic. Some overestimation in surface albedo is also noted for the boreal region which is believed to be related to the snow unloading parameterization, as well as the overestimation of snow albedo. SAF is assessed both in seasonal and climate change contexts when possible. The strength of SAF is quantified as the amount of additional net shortwave radiation at the top of the atmosphere as surface albedo decreases in association with a 1°C increase in surface temperature. Following Qu and Hall (2007), this is expressed as the product of the variation in planetary albedo with surface albedo and the change in surface albedo for 1°C change in surface air temperature during the season, which in turn is determined by the strength of the snow cover and snowpack metamorphosis feedback loops. Analysis of the latter term in the seasonal cycle suggests that for CRCM5 simulations, the snow cover feedback loop is more dominant compared to the snowpack metamorphosis feedback loop, whereas for MODIS, the two feedback loops have more or less similar strength. Moreover, the SAF strength in the climate change context appears to be weaker than in the seasonal cycle and is sensitive to the driving GCM and the RCP scenario.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Modelling Ground Based X- and Ku-Band Observations of Tundra Snow

NASA Astrophysics Data System (ADS)

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

2012-12-01

As part of a radar-based remote sensing field experiment in Churchill, Manitoba ground based Ku- and X-band scatterometers were deployed to observe changing tundra snowpack conditions from November 2010 to March 2011. The research is part of the validation effort for the Cold Regions Hydrology High-resolution Observatory (CoReH2O) mission, a candidate in the European Space Agency's Earth Explorer program. This paper focuses on the local validation of the semi-empirical radiative transfer (sRT) model proposed for use in snow property retrievals as part of the CoReH2O mission. In this validation experiment, sRT was executed in the forward mode, simulating backscatter to assess the ability of the model. This is a necessary precursor to any inversion attempt. Two experiments are considered, both conducted in a hummocky tundra environment with shallow snow cover. In both cases, scatterometer observations were acquired over a field of view of approximately 10 by 20 meters. In the first experiment, radar observations were made of a snow field and then repeated after the snow had been removed. A ground-based scanning LiDAR system was used to characterize the spatial variability of snow depth through measurements of the snow and ground surface. Snow properties were determined in the field of view from two snow pits, 12 density core measurements, and Magnaprobe snow depth measurements. In the second experiment, a site was non-destructively observed from November through March, with snow properties measured out-of-scene, to characterize the snow evolution response. The model results from sRT fit the form of the observations from the two scatterometer field experiments but do not capture the backscatter magnitude. A constant offset for the season of 5 dB for X-band co- and cross-polarization response was required to match observations, in addition to a 3 dB X- and Ku-band co-polarization offset after the 6th of December. To explain these offsets, it is recognized that the two main physical processes represented by the model are snow volume scattering and ground surface reflectance. With a larger correction needed for X-band, where the ground portion of backscatter is expected to be larger, the contribution from the underlying soil is explored first. The ground contribution in sRT is computed using the semi-empirical Oh et al. (1992) model using permittivity from a temperate mineral soil based model. The ground response is tested against two observations of snow-removed tundra, and one observation of snow free tundra. A secondary analysis is completed using a modified sRT ground model, incorporating recent work on frozen organic permittivity by Mironov et al. (2010). Multi-scale surface roughness resulting from superimposed microtopography on regularly distributed hummocks is also addressed. These results demonstrate the applicability of microwave scattering models to tundra snowpacks underlain with peat, and demonstrate the applicability of the CoReH2O sRT model.

An AeroCom Assessment of Black Carbon in Arctic Snow and Sea Ice

NASA Technical Reports Server (NTRS)

Jiao, C.; Flanner, M. G.; Balkanski, Y.; Bauer, S. E.; Bellouin, N.; Bernsten, T. K.; Bian, H.; Carslaw, K. S.; Chin, M.; DeLuca, N.;

Evaluation TRMM Rainfall Data In Hydrological Modeling For An Ungaged In Lhasa River Basin

NASA Astrophysics Data System (ADS)

Ji, H. J.; Liu, J.

2017-12-01

Evaluation TRMM Rainfall Data In Hydrological Modeling For An Ungaged In Lhasa River BasinHaijuan Ji1* Jintao Liu1,2 Shanshan Xu1___________________ 1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, People's Republic of China 2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, People's Republic of China ___________________ * Corresponding author. Tel.: +86-025-83786973; Fax: +86-025-83786606. E-mail address: Hhu201510@163.com (H.J. Ji). Abstract: The Tibetan Plateau plays an important role in regulating the regional hydrological processes due to its high elevations and being the headwaters of many major Asian river basins. If familiar with the distribution of hydrological characteristics, will help us improve the level of development and utilization the water resources. However, there exist glaciers and snow with few sites. It is significance for us to understand the glacier and snow hydrological process in order to recognize the evolution of water resources in the Tibetan. This manuscript takes Lhasa River as the study area, taking use of ground, remote sensing and assimilation data, taking advantage of high precision TRMM precipitation data and MODIS snow cover data, first, according to the data from ground station evaluation of TRMM data in the application of the accuracy of the Lhasa River, and based on MODIS data fusion of multi source microwave snow making cloudless snow products, which are used for discriminant and analysis glacier and snow regulation mechanism on day scale, add snow and glacier unit into xinanjing model, this model can simulate the study region's runoff evolution, parameter sensitivity even spatial variation of hydrological characteristics the next ten years on region grid scale. The results of hydrological model in Lhasa River can simulate the glacier and snow runoff variation in high cold region better, to enhance the predictive ability of the spring snow disaster.

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.

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.

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

DOE PAGES

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

2017-08-18

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

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

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

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang

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

Experimental investigation of drifting snow in a wind tunnel

NASA Astrophysics Data System (ADS)

Crivelli, Philip; Paterna, Enrico; Horender, Stefan; Lehning, Michael

2015-11-01

Drifting snow has a significant impact on snow distribution in mountains, prairies as well as on glaciers and polar regions. In all these environments, the local mass balance is highly influenced by drifting snow. Despite most of the model approaches still rely on the assumption of steady-state and equilibrium saltation, recent advances have proven the mass-transport of drifting snow events to be highly intermittent. A clear understanding of such high intermittency has not yet been achieved. Therefore in our contribution we investigate mass- and momentum fluxes during drifting snow events, in order to better understand that the link between snow cover erosion and deposition. Experiments were conducted in a cold wind tunnel, employing sensors for the momentum flux measurements, the mass flux measurement and for the snow depth estimation over a certain area upstream of the other devices. Preliminary results show that the mass flux is highly intermittent at scales ranging from eddy turnover time to much larger scales. The former scales are those that contribute the most to the overall intermittency and we observe a link between the turbulent flow structures and the mass flux of drifting snow at those scales. The role of varying snow properties in inducing drifting snow intermittency goes beyond such link and is expected to occur at much larger scales, caused by the physical snow properties such as density and cohesiveness.

The influence of air temperature inversions on snowmelt and glacier mass-balance simulations, Ammassalik island, SE Greenland

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

Mernild, Sebastian Haugard; Liston, Glen

2009-01-01

In many applications, a realistic description of air temperature inversions is essential for accurate snow and glacier ice melt, and glacier mass-balance simulations. A physically based snow-evolution modeling system (SnowModel) was used to simulate eight years (1998/99 to 2005/06) of snow accumulation and snow and glacier ice ablation from numerous small coastal marginal glaciers on the SW-part of Ammassalik Island in SE Greenland. These glaciers are regularly influenced by inversions and sea breezes associated with the adjacent relatively low temperature and frequently ice-choked fjords and ocean. To account for the influence of these inversions on the spatiotemporal variation of airmore » temperature and snow and glacier melt rates, temperature inversion routines were added to MircoMet, the meteorological distribution sub-model used in SnowModel. The inversions were observed and modeled to occur during 84% of the simulation period. Modeled inversions were defined not to occur during days with strong winds and high precipitation rates due to the potential of inversion break-up. Field observations showed inversions to extend from sea level to approximately 300 m a.s.l., and this inversion level was prescribed in the model simulations. Simulations with and without the inversion routines were compared. The inversion model produced air temperature distributions with warmer lower elevation areas and cooler higher elevation areas than without inversion routines due to the use of cold sea-breeze base temperature data from underneath the inversion. This yielded an up to 2 weeks earlier snowmelt in the lower areas and up to 1 to 3 weeks later snowmelt in the higher elevation areas of the simulation domain. Averaged mean annual modeled surface mass-balance for all glaciers (mainly located above the inversion layer) was -720 {+-} 620 mm w.eq. y{sup -1} for inversion simulations, and -880 {+-} 620 mm w.eq. y{sup -1} without the inversion routines, a difference of 160 mm w.eq. y{sup -1}. The annual glacier loss for the two simulations was 50.7 x 10{sup 6} m{sup 3} y{sup -1} and 64.4 x 10{sup 6} m{sup 3} y{sup -1} for all glaciers - a difference of {approx}21%. The average equilibrium line altitude (ELA) for all glaciers in the simulation domain was located at 875 m a.s.l. and at 900 m a.s.l. for simulations with or without inversion routines, respectively.« less

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.

BOREAS HYD-3 Snow Measurements

NASA Technical Reports Server (NTRS)

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

2000-01-01

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

The shifting nature of vegetation controls on peak snowpack with varying slope and aspect

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Harpold, A. A.; Broxton, P. D.; Brooks, P. D.

2012-12-01

The controls on peak seasonal snowpack are known to shift between forested and open environments as well as with slope and aspect. Peak snowpack is predicted well by interception models under uniformly dense canopy, while topography, wind and radiation are strong predictors in open areas. However, many basins have complex mosaics of forest canopy and small gaps, where snowpack controls involve complex interactions among climate, topography and forest structure. In this presentation we use a new fully distributed tree-scale model to investigate vegetation controls on snowpack for a range of slope and aspect, and we evaluate the energy balance in forest canopy and gap environments. The model is informed by airborne LiDAR and ground-based observations of climate, vegetation and snowpack. It represents interception, snow distribution by wind, latent and sensible heat fluxes, and radiative fluxes above and below the canopy at a grid scale of 1 m square on an hourly time step. First, the model is minimally calibrated using continuous records of snow depth and snow water equivalent (SWE). Next, the model is evaluated using distributed observations at peak accumulation. Finally, the domain is synthetically altered to introduce ranges of slope and aspect. Northerly aspects accumulate greater peak SWE than southerly aspects (e.g. 275 mm vs. 250 mm at a slope of 28 %) but show lower spatial variability (e. g. CV = 0.14 vs. CV = 0.17 at slope of 28 %). On northerly aspects, most of the snowpack remains shaded by vegetation, whereas on southerly aspects the northern portions of gaps and southern forest edges receive direct insolation during late winter. This difference in net radiation makes peak SWE in forest gaps and adjacent forest edges more sensitive to topography than SWE in areas under dense canopy. Tree-scale modeling of snow dynamics over synthetic terrain offers extensive possibilities to test interactions among vegetation and topographic controls.

Supporting Snow Research: SnowEx Data and Services at the NASA National Snow and Ice Data Center DAAC

NASA Astrophysics Data System (ADS)

Leon, A.; Tanner, S.; Deems, J. S.

2017-12-01

The National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC), part of the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder, will archive and distribute all primary data sets collected during the NASA SnowEx campaigns. NSIDC DAAC's overarching goal for SnowEx data management is to steward the diverse SnowEx data sets to provide a reliable long-term archive, to enable effective data discovery, retrieval, and usage, and to support end user engagement. This goal will be achieved though coordination and collaboration with SnowEx project management and investigators. NSIDC DAAC's core functions for SnowEx data management include: Data Creation: Advise investigators on data formats and structure as well as metadata creation and content to enable preservation, usability, and discoverability. Data Documentation: Develop comprehensive data set documentation describing the instruments, data collection and derivation methods, and data file contents. Data Distribution: Provide discovery and access through NSIDC and NASA data portals to make SnowEx data available to a broad user community Data & User Support: Assist user communities with the selection and usage of SnowEx data products. In an effort to educate and broaden the SnowEx user community, we will present an overview of the SnowEx data products, tools, and services which will be available at the NSIDC DAAC. We hope to gain further insight into how the DAAC can enable the user community to seamlessly and effectively utilize SnowEx data in their research and applications.

Evolution of Snow-Size Spectra in Cyclonic Storms. Part I: Snow Growth by Vapor Deposition and Aggregation.

NASA Astrophysics Data System (ADS)

Mitchell, David L.

1988-11-01

Based on the stochastic collection equation, height- and time-dependent snow growth models were developed for unrimed stratiform snowfall. Moment conservation equations were parameterized and solved by constraining the size distribution to be of the form N(D)dD = N0 exp(D)dD, yielding expressions for the slope parameter, , and the y-intercept parameters, NO, as functions of height or time. The processes of vapor deposition and aggregation were treated analytically without neglecting changes in ice crystal habits, while the ice particle breakup process was dealt with empirically.The models were compared against vertical profiles of snow-size spectra, obtained from aircraft measurements, for three case studies. The predicted spectra are in good agreement with the observed evolution of snow-size spectra in all three cases, indicating the proposed scheme for ice particle aggregation was successful. The temperature dependence of aggregation was assumed to result from differences in ice crystal habit. Using data from an earlier study, the aggregation efficiency between two levels in a cloud was calculated. Finally, other height-dependent, steady-state snowfall models in the literature were compared against spectra from one of the above case studies. The agreement between the predicted and observed spectra regarding these models was less favorable than was obtained from the models presented here.

Assimilation of snow covered area information into hydrologic and land-surface models

USGS Publications Warehouse

Clark, M.P.; Slater, A.G.; Barrett, A.P.; Hay, L.E.; McCabe, G.J.; Rajagopalan, B.; Leavesley, G.H.

2006-01-01

This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy. ?? 2005 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

A new parameterization of the post-fire snow albedo effect

NASA Astrophysics Data System (ADS)

Gleason, K. E.; Nolin, A. W.

2013-12-01

Mountain snowpack serves as an important natural reservoir of water: recharging aquifers, sustaining streams, and providing important ecosystem services. Reduced snowpacks and earlier snowmelt have been shown to affect fire size, frequency, and severity in the western United States. In turn, wildfire disturbance affects patterns of snow accumulation and ablation by reducing canopy interception, increasing turbulent fluxes, and modifying the surface radiation balance. Recent work shows that after a high severity forest fire, approximately 60% more solar radiation reaches the snow surface due to the reduction in canopy density. Also, significant amounts of pyrogenic carbon particles and larger burned woody debris (BWD) are shed from standing charred trees, which concentrate on the snowpack, darken its surface, and reduce snow albedo by 50% during ablation. Although the post-fire forest environment drives a substantial increase in net shortwave radiation at the snowpack surface, driving earlier and more rapid melt, hydrologic models do not explicitly incorporate forest fire disturbance effects to snowpack dynamics. The objective of this study was to parameterize the post-fire snow albedo effect due to BWD deposition on snow to better represent forest fire disturbance in modeling of snow-dominated hydrologic regimes. Based on empirical results from winter experiments, in-situ snow monitoring, and remote sensing data from a recent forest fire in the Oregon High Cascades, we characterized the post-fire snow albedo effect, and developed a simple parameterization of snowpack albedo decay in the post-fire forest environment. We modified the recession coefficient in the algorithm: α = α0 + K exp (-nr) where α = snowpack albedo, α0 = minimum snowpack albedo (≈0.4), K = constant (≈ 0.44), -n = number of days since last major snowfall, r = recession coefficient [Rohrer and Braun, 1994]. Our parameterization quantified BWD deposition and snow albedo decay rates and related these forest disturbance effects to radiative heating and snow melt rates. We validated our parameterization of the post-fire snow albedo effect at the plot scale using a physically-based, spatially-distributed snow accumulation and melt model, and in-situ eddy covariance and snow monitoring data. This research quantified wildfire impacts to snow dynamics in the Oregon High Cascades, and provided a new parameterization of post-fire drivers to changes in high elevation winter water storage.

The Distribution of Snow Black Carbon observed in the Arctic and Compared to the GISS-PUCCINI Model

NASA Technical Reports Server (NTRS)

Dou, T.; Xiao, C.; Shindell, D. T.; Liu, J.; Eleftheriadis, K.; Ming, J.; Qin, D.

2012-01-01

In this study, we evaluate the ability of the latest NASA GISS composition-climate model, GISS-E2- PUCCINI, to simulate the spatial distribution of snow BC (sBC) in the Arctic relative to present-day observations. Radiative forcing due to BC deposition onto Arctic snow and sea ice is also estimated. Two sets of model simulations are analyzed, where meteorology is linearly relaxed towards National Centers for Environmental Prediction (NCEP) and towards NASA Modern Era Reanalysis for Research and Applications (MERRA) reanalyses. Results indicate that the modeled concentrations of sBC are comparable with presentday observations in and around the Arctic Ocean, except for apparent underestimation at a few sites in the Russian Arctic. That said, the model has some biases in its simulated spatial distribution of BC deposition to the Arctic. The simulations from the two model runs are roughly equal, indicating that discrepancies between model and observations come from other sources. Underestimation of biomass burning emissions in Northern Eurasia may be the main cause of the low biases in the Russian Arctic. Comparisons of modeled aerosol BC (aBC) with long-term surface observations at Barrow, Alert, Zeppelin and Nord stations show significant underestimation in winter and spring concentrations in the Arctic (most significant in Alaska), although the simulated seasonality of aBC has been greatly improved relative to earlier model versions. This is consistent with simulated biases in vertical profiles of aBC, with underestimation in the lower and middle troposphere but overestimation in the upper troposphere and lower stratosphere, suggesting that the wet removal processes in the current model may be too weak or that vertical transport is too rapid, although the simulated BC lifetime seems reasonable. The combination of observations and modeling provides a comprehensive distribution of sBC over the Arctic. On the basis of this distribution, we estimate the decrease in snow and sea ice albedo and the resulting radiative forcing. We suggest that the albedo reduction due to BC deposition presents significant space-time variations, with highest mean reductions of 1.25% in the Russian Arctic, which are much larger than those in other Arctic regions (0.39% to 0.64 %). The averaged value over the Arctic north of 66degN is 0.4-0.6% during spring, leading to regional surface radiative forcings of 0.7, 1.1 and 1.0Wm(exp-2) in spring 2007, 2008 and 2009, respectively.

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

An AeroCom assessment of black carbon in Arctic snow and sea ice

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

Jiao, C.; Flanner, M. G.; Balkanski, Y.

2014-01-01

Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. In this paper, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during whichmore » an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are -4.4 (-13.2 to +10.7) ng g -1 for an earlier phase of AeroCom models (phase I), and +4.1 (-13.0 to +21.4) ng g -1 for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g -1. Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model–measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60–90° N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07–0.25) W m -2 and 0.18 (0.06–0.28) W m -2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m -2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.« less

Evolution of the persistence of snow over Sierra Nevada Mountain (southern, Spain) in the last 55 years

NASA Astrophysics Data System (ADS)

Pimentel, Rafael; José Pérez-Palazón, María; Herrero, Javier; José Polo, María

2016-04-01

Snow plays a crucial role in mountainous areas, not only as water resources for human supply, irrigation and energy production, but also for the ecosystem, flora and fauna, over these areas. Sierra Nevada Mountains, southern Spain, constitutes a rich reservoir of endemic wildlife species, and it is considered the most important center of biodiversity in the wester Mediterranean region. The highest regions of the range were declared UNESCO Biosphere Reserve, Natural and National Parks. Climate trends over the last decades put a lot of pressure on both snowfall occurrence and snow persistence; this poses a risk for biodiversity and has led to its inclusion in the Global Change Observatory Network. This work quantifies the evolution of the persistence of snow over the Sierra Nevada area during the last fifty-five years (1960-2015) as a basis to assess the vulnerability of its ecosystem services. For this, the spatial distribution of the annual number of days with snow, SDS, was analyzed over a study area of 4583 km2 (140-3479 m.a.s.l.), which comprises the head of the five basins in these mountains. The following indicator variables were studied over the whole area and each one of the five head regions identified: 1) the trend of SDS; 2) the annual area where SDS exceeded selected percentiles in its distribution; and 3) the annual minimum altitude where SDS exceeded those percentiles. SDS was obtained during the study period by means of the snow module in WiMMed (Watershed Integrated Model in Mediterranean Environment), a physically-based hydrological model developed, calibrated and validated in the area; the model is based on an energy-mass balance over the snowpack that is spatially distributed through the use of depletion curves, and is operational at hourly and daily scales. A general decreasing trend of SDS (0.25 days year-1) was found over the whole study area for the study period. This value is higher in the more humid basins (0.45 and 0.41 days year-1) than in the drier ones (0.15 and 0.18 days year-1) where the snow is significant only in very wet years. The minimum altitude at which SDS is higher than the 25th percentile of SDS distribution is increasing at a mean rate of 0.0016 m year-1 during the 55-yr period; however, marked differences were found among these years, with and absolute range from 557 to 1594 m.a.s.l., showing the highly variable character of the climate in this region. The observed trend of temperature rather than precipitation seems to be more determining for the snow persistence, with an average correlation coefficient for the whole study period of -0.9 and 0.7 between SDS and the annual mean daily temperature and annual precipitation, respectively. The results led to the further identification of zones facing a significant reduction of the snow presence in the medium and long term, and they constitute a relevant basis to assess the decision-making process for both planning and adaptation actions in the Natural and National Park area.

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.

Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

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

2017-12-01

Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final forecasting framework developed during this project will be delivered to CBRFC and run operationally for a set of pilot basins.

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.

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

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Shu, Hong

2014-09-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Snow and Ice Products from the Moderate Resolution Imaging Spectroradiometer

NASA Technical Reports Server (NTRS)

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

2003-01-01

Snow and sea ice products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, flown on the Terra and Aqua satellites, are or will be available through the National Snow and Ice Data Center Distributed Active Archive Center (DAAC). The algorithms that produce the products are automated, thus providing a consistent global data set that is suitable for climate studies. The suite of MODIS snow products begins with a 500-m resolution, 2330-km swath snow-cover map that is then projected onto a sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to daily and 8-day composite climate-modeling grid (CMG) products at 0.05 resolution. A daily snow albedo product will be available in early 2003 as a beta test product. The sequence of sea ice products begins with a swath product at 1-km resolution that provides sea ice extent and ice-surface temperature (IST). The sea ice swath products are then mapped onto the Lambert azimuthal equal area or EASE-Grid projection to create a daily and 8-day composite sea ice tile product, also at 1 -km resolution. Climate-Modeling Grid (CMG) sea ice products in the EASE-Grid projection at 4-km resolution are planned for early 2003.

Snow distribution throughout small subalpine catchment post-insect infestation of spruce and pine beetle.

NASA Astrophysics Data System (ADS)

Beverly, D.; Ewers, B. E.; Hyde, K.; Ohara, N.; Speckman, H. N.

2015-12-01

High elevation watersheds of the Rocky Mountains region contribute over 70% of the streamflow needed for infrastructure, agriculture, and ecological processes. Snow-water yields are heterogeneous in space and time and are driven by a multitude of snow distribution processes, including snowpack evolution driven by physical and biological factors. Quantifying heterogeneity of snowpack is further complicated by vegetation perturbations; much of the Rocky Mountains have experienced significant tree mortality due to bark beetle outbreaks. Reduction of living crown area decreases canopy interception while increasing radiation to snow surfaces, which alters snowpack distribution throughout the catchment. We hypothesize that, in a complex watershed, topographic variation (i.e., slope and aspect) will have a greater effect on snowpack evolution and distribution than densities of canopy mortality due to beetle infestation. The 120 ha No Name watershed, located in southern Wyoming at 3000 m elevation was divided into twenty-one 175 m2 parcels, in which plots were randomly assigned within each parcel. Peak snow was measured in April; in the 50 m2 plots, depths were measured every 2 m along north-south and east-west transects. Twenty-one snow pits were excavated to quantify snow densities in 10 cm increments throughout the pit profile. Forest inventories occurred the following summer. Peak snowpack levels occurred in April with mean depth of 92.3 ­­± 2.4 cm and peak SWE of 34.0 ± 0.84 cm. Binary decision trees accounted for 63% of the variability after including topographic indices, beetle condition of the trees, LAI, and basal area. Snow depth showed a slight positive relationship with increased in beetle mortality on slopes less than 11 degrees. Overall, topographic indices are greater drivers for snow distributions compared to effects of tree mortality.

Snowmelt in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhang, F.

2016-12-01

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

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.

Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions

NASA Astrophysics Data System (ADS)

Oaida, C. M.; Andreadis, K.; Reager, J. T., II; Famiglietti, J. S.; Levoe, S.

2017-12-01

Accurately estimating how much snow water equivalent (SWE) is stored in mountainous regions characterized by complex terrain and snowmelt-driven hydrologic cycles is not only greatly desirable, but also a big challenge. Mountain snowpack exhibits high spatial variability across a broad range of spatial and temporal scales due to a multitude of physical and climatic factors, making it difficult to observe or estimate in its entirety. Combing remotely sensed data and high resolution hydrologic modeling through data assimilation (DA) has the potential to provide a spatially and temporally continuous SWE dataset at horizontal scales that capture sub-grid snow spatial variability and are also relevant to stakeholders such as water resource managers. Here, we present the evaluation of a new snow DA approach that uses a Local Ensemble Transform Kalman Filter (LETKF) in tandem with the Variable Infiltration Capacity macro-scale hydrologic model across the Western United States, at a daily temporal resolution, and a horizontal resolution of 1.75 km x 1.75 km. The LETKF is chosen for its relative simplicity, ease of implementation, and computational efficiency and scalability. The modeling/DA system assimilates daily MODIS Snow Covered Area and Grain Size (MODSCAG) fractional snow cover over, and has been developed to efficiently calculate SWE estimates over extended periods of time and covering large regional-scale areas at relatively high spatial resolution, ultimately producing a snow reanalysis-type dataset. Here we focus on the assessment of SWE produced by the DA scheme over several basins in California's Sierra Nevada Mountain range where Airborne Snow Observatory data is available, during the last five water years (2013-2017), which include both one of the driest and one of the wettest years. Comparison against such a spatially distributed SWE observational product provides a greater understanding of the model's ability to estimate SWE and SWE spatial variability, and highlights under which conditions snow cover DA can add value in estimating SWE.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment

NASA Astrophysics Data System (ADS)

DeBeer, Chris M.; Pomeroy, John W.

2017-10-01

The spatial heterogeneity of mountain snow cover and ablation is important in controlling patterns of snow cover depletion (SCD), meltwater production, and runoff, yet is not well-represented in most large-scale hydrological models and land surface schemes. Analyses were conducted in this study to examine the influence of various representations of snow cover and melt energy heterogeneity on both simulated SCD and stream discharge from a small alpine basin in the Canadian Rocky Mountains. Simulations were performed using the Cold Regions Hydrological Model (CRHM), where point-scale snowmelt computations were made using a snowpack energy balance formulation and applied to spatial frequency distributions of snow water equivalent (SWE) on individual slope-, aspect-, and landcover-based hydrological response units (HRUs) in the basin. Hydrological routines were added to represent the vertical and lateral transfers of water through the basin and channel system. From previous studies it is understood that the heterogeneity of late winter SWE is a primary control on patterns of SCD. The analyses here showed that spatial variation in applied melt energy, mainly due to differences in net radiation, has an important influence on SCD at multiple scales and basin discharge, and cannot be neglected without serious error in the prediction of these variables. A single basin SWE distribution using the basin-wide mean SWE (SWE ‾) and coefficient of variation (CV; standard deviation/mean) was found to represent the fine-scale spatial heterogeneity of SWE sufficiently well. Simulations that accounted for differences in (SWE ‾) among HRUs but neglected the sub-HRU heterogeneity of SWE were found to yield similar discharge results as simulations that included this heterogeneity, while SCD was poorly represented, even at the basin level. Finally, applying point-scale snowmelt computations based on a single SWE depth for each HRU (thereby neglecting spatial differences in internal snowpack energetics over the distributions) was found to yield similar SCD and discharge results as simulations that resolved internal energy differences. Spatial/internal snowpack melt energy effects are more pronounced at times earlier in spring before the main period of snowmelt and SCD, as shown in previously published work. The paper discusses the importance of these findings as they apply to the warranted complexity of snowmelt process simulation in cold mountain environments, and shows how the end-of-winter SWE distribution represents an effective means of resolving snow cover heterogeneity at multiple scales for modelling, even in steep and complex terrain.

Estimation of Coastal Freshwater Discharge into Prince William Sound using a High-Resolution Hydrological Model

NASA Astrophysics Data System (ADS)

Beamer, J. P.; Hill, D. F.; Liston, G. E.; Arendt, A. A.; Hood, E. W.

2013-12-01

In Prince William Sound (PWS), Alaska, there is a pressing need for accurate estimates of the spatial and temporal variations in coastal freshwater discharge (FWD). FWD into PWS originates from streamflow due to rainfall, annual snowmelt, and changes in stored glacier mass and is important because it helps establish spatial and temporal patterns in ocean salinity and temperature, and is a time-varying boundary condition for oceanographic circulation models. Previous efforts to model FWD into PWS have been heavily empirical, with many physical processes absorbed into calibration coefficients that, in many cases, were calibrated to streams and rivers not hydrologically similar to those discharging into PWS. In this work we adapted and validated a suite of high-resolution (in space and time), physically-based, distributed weather, snowmelt, and runoff-routing models designed specifically for snow melt- and glacier melt-dominated watersheds like PWS in order to: 1) provide high-resolution, real-time simulations of snowpack and FWD, and 2) provide a record of historical variations of FWD. SnowModel, driven with gridded topography, land cover, and 32 years (1979-2011) of 3-hourly North American Regional Reanalysis (NARR) atmospheric forcing data, was used to simulate snowpack accumulation and melt across a PWS model domain. SnowModel outputs of daily snow water equivalent (SWE) depth and grid-cell runoff volumes were then coupled with HydroFlow, a runoff-routing model which routed snowmelt, glacier-melt, and rainfall to each watershed outlet (PWS coastline) in the simulation domain. The end product was a continuous 32-year simulation of daily FWD into PWS. In order to validate the models, SWE and snow depths from SnowModel were compared with observed SWE and snow depths from SnoTel and snow survey data, and discharge from HydroFlow was compared with observed streamflow measurements. As a second phase of this research effort, the coupled models will be set-up to run in real-time, where daily measurements from weather stations in the PWS will be used to drive simulations of snow cover and streamflow. In addition, we will deploy a strategic array of instrumentation aimed at validating the simulated weather estimates and the calculations of freshwater discharge. Upon successful implementation and validation of the modeling system, it will join established and ongoing computational and observational efforts that have a common goal of establishing a comprehensive understanding of the physical behavior of PWS.

Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Zhang, Feifei; Zhao, Yi; Shu, Hong; Zhong, Kaiwen

2016-07-01

For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

2002-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Spatial Patterns of Snow Cover in North Carolina: Surface and Satellite Perspectives

NASA Technical Reports Server (NTRS)

Fuhrmann, Christopher M.; Hall, Dorothy K.; Perry, L. Baker; Riggs, George A.

2010-01-01

Snow mapping is a common practice in regions that receive large amounts of snowfall annually, have seasonally-continuous snow cover, and where snowmelt contributes significantly to the hydrologic cycle. Although higher elevations in the southern Appalachian Mountains average upwards of 100 inches of snow annually, much of the remainder of the Southeast U.S. receives comparatively little snowfall (< 10 inches). Recent snowy winters in the region have provided an opportunity to assess the fine-grained spatial distribution of snow cover and the physical processes that act to limit or improve its detection across the Southeast. In the present work, both in situ and remote sensing data are utilized to assess the spatial distribution of snow cover for a sample of recent snowfall events in North Carolina. Specifically, this work seeks to determine how well ground measurements characterize the fine-grained patterns of snow cover in relation to Moderate- Resolution Imaging Spectroradiometer (MODIS) snow cover products (in this case, the MODIS Fractional Snow Cover product).

Comparisons of Modeled and Observed Reflectivities and Fall Speeds for Snowfall of Varied Riming Degree During Winter Storms on Long Island, New York

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Colle, Brian A.; Yuter, Sandra E.; Stark, David

2016-01-01

Derived radar reflectivity and fall speed for four Weather Research and Forecasting model bulk microphysical parameterizations (BMPs) run at 1.33 km grid spacing are compared with ground-based, vertically-pointing Ku-band radar, scanning S- band radar, and in situ measurements at Stony Brook, NY. Simulations were partitioned into periods of observed riming degree as determined manually using a stereo microscope and camera during nine winter storms. Simulations were examined to determine whether the selected BMPs captured the effects of varying riming intensities, provided a reasonable match to the vertical structure of radar reflectivity or fall speed, and whether they produced reasonable surface fall speed distributions. Schemes assuming non spherical mass-diameter relationships yielded reflectivity distributions closer to observed values. All four schemes examined in this study provided a better match to the observed, vertical structure of reflectivity during moderate riming than light riming periods. The comparison of observed and simulated snow fall speeds had mixed results. One BMP produced episodes of excessive cloud water at times, resulting in fall speeds that were too large. However, most schemes had frequent periods of little or no cloud water during moderate riming periods and thus underpredicted the snow fall speeds at lower levels. Short, 1-4 hour periods with relatively steady snow conditions were used to compare BMP and observed size and fall speed distributions. These limited data suggest the examined BMPs underpredict fall speeds of cold-type snow habits and underrepresent aggregates larger than 4 mm diameter.

Wide-area mapping of snow water equivalent by Sentinel-1&2 data

NASA Astrophysics Data System (ADS)

Conde, Vasco; Nico, Giovanni; Catalao, Joao; Kontu, Anna; Gritsevich, Maria

2017-04-01

The mapping of snow physical properties over large mountain areas of remote areas is an important topic in both climatological studies and hydrological models where the effects of snow melting are modeled and used to forecast extreme flood events. Usually, these models are run using in-situ measurements of snow which are expensive and statistically not representative of the spatial distribution of snow properties due to slope orientation of terrain, local terrain morphology and height as well as vegetation cover. In this work we investigate the use of data acquired by Sentinel-1 and 2 missions using a C-band SAR and multispectral sensor, respectively. The Sentinel-1 SAR data are processed to estimate the Snow Water Equivalent (SWE) using both the radar amplitude and the output of the SAR interferometry processing. Both approaches need in-situ data to process SAR data and calibrate SWE estimates. The use of SAR amplitude to estimate the SWE is well established and the basic idea is that the radar signal backscattered by snow is related to the SWE so, after modeling the relationship between these two quantities at the site of in-situ measurements this relationship can be used to map the SWE at all site where the SAR amplitude information is available. The physical principle used by SAR interferometry is that of phase delay due to propagation in a non-dispersive medium. This implies that the snow is supposed to be dry in order to allow the propagation of the SAR signal. Sentinel-2 images have been used to get land-use maps and identify areas covered by vegetation. Finland has been chosen as a study region with in-situ measurements acquired thanks to the availability of rich database of in-situ measurements of SWE. Sentinel data used in this work have been acquired starting from November 2015. Publication supported by FCT- project UID/GEO/50019/2013 - Instituto Dom Luiz.

Snowmelt and water resources in a changing climate and dustier world

NASA Astrophysics Data System (ADS)

Painter, T. H.

2015-12-01

Snow cover and its melt dominate regional climate and water resources in the world's mountain regions, providing for critical agricultural and sustaining populations in otherwise dry regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known even in the best-instrumented mountain regions of the globe. In this talk, we discuss developments in the spaceborne and airborne remote sensing of snow properties, and the assimilation of these products into research water cycle modeling and operational forecasting. Our work with the NWS Colorado Basin River Forecast Center has shown marked improvements in runoff forecasting through inclusion of MODIS and VIIRS fractional snow covered area data. Moreover, the analyses have shown that the CBRFC forecasting errors are strongly sensitive to actual dust radiative forcing in snow with rising limb excursions as large as 40%. With MODIS retrievals of dust radiative forcing, the CBRFC will be implementing modifications to forecasts to reduce those errors to order < 10%. In the last few years, the NASA Airborne Snow Observatory has emerged to provide the first spatially explicit distributions of snow water equivalent and coincident snow albedo products for mountain basins. ASO is an imaging spectrometer and imaging LiDAR system, to quantify snow water equivalent and snow albedo, provide unprecedented knowledge of snow properties, and provide complete, robust inputs to snowmelt runoff models, water management models, and systems of the future. ASO has been flying in the Western US for three snowmelt seasons. In 2015, ASO provided complete basin coverage for the Tuolumne, Merced, Lakes, Rush Creek, and Middle+South Forks of Kings River Basins in the California Sierra Nevada and the Upper Rio Grande, Conejos, and Uncompahgre Basins in the Colorado Rocky Mountains. Analyses show that with ASO data, river flows and reservoir inflows from the ASO acquisition date to 1 July can be estimated with uncertainties of less than 2%. The synergy of the ASO and the satellite retrievals will ultimately allow extension of quantitative knowledge to addressing the snowmelt water resources and availability for agricultural regions in sparsely instrumented regions of the globe.

The role of sea ice dynamics in global climate change

NASA Technical Reports Server (NTRS)

Hibler, William D., III

1992-01-01

The topics covered include the following: general characteristics of sea ice drift; sea ice rheology; ice thickness distribution; sea ice thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic Sea ice; and sensitivity of sea ice models to climate change.

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.

Pebble pile-up and planetesimal formation at the snow line

NASA Astrophysics Data System (ADS)

Drazkowska, J.

2017-09-01

The planetesimal formation stage represents a major gap in our understanding of planet formation process. Because of this, the late-stage planet accretion models typically make arbitrary assumptions about planetesimals and pebbles distribution, while the state-of-the-art dust evolution models predict no or little planetesimal formation. With this contribution, I present a step toward bridging the gap between the early and late stages of planet formation by models that connect dust coagulation and planetesimal formation. With the aid of evaporation, outward diffusion, and re-condensation of water vapor, pile-up of large pebbles is formed outside of the snow line that facilitates planetesimal formation by streaming instability.

Data set: 31 years of spatially distributed air temperature, humidity, precipitation amount and precipitation phase from a mountain catchment in the rain-snow transition zone

USDA-ARS?s Scientific Manuscript database

Thirty one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed. The data are spatially distributed over a 10m Lidar-derived digital elevation model at ...

California's Snow Gun and its implications for mass balance predictions under greenhouse warming

NASA Astrophysics Data System (ADS)

Howat, I.; Snyder, M.; Tulaczyk, S.; Sloan, L.

2003-12-01

Precipitation has received limited treatment in glacier and snowpack mass balance models, largely due to the poor resolution and confidence of precipitation predictions relative to temperature predictions derived from atmospheric models. Most snow and glacier mass balance models rely on statistical or lapse rate-based downscaling of general or regional circulation models (GCM's and RCM's), essentially decoupling sub-grid scale, orographically-driven evolution of atmospheric heat and moisture. Such models invariably predict large losses in the snow and ice volume under greenhouse warming. However, positive trends in the mass balance of glaciers in some warming maritime climates, as well as at high elevations of the Greenland Ice Sheet, suggest that increased precipitation may play an important role in snow- and glacier-climate interactions. Here, we present a half century of April snowpack data from the Sierra Nevada and Cascade mountains of California, USA. This high-density network of snow-course data indicates that a gain in winter snow accumulation at higher elevations has compensated loss in snow volume at lower elevations by over 50% and has led to glacier expansion on Mt. Shasta. These trends are concurrent with a region-wide increase in winter temperatures up to 2° C. They result from the orographic lifting and saturation of warmer, more humid air leading to increased precipitation at higher elevations. Previous studies have invoked such a "Snow Gun" effect to explain contemporaneous records of Tertiary ocean warming and rapid glacial expansion. A climatological context of the California's "snow gun" effect is elucidated by correlation between the elevation distribution of April SWE observations and the phase of the Pacific Decadal Oscillation and the El Nino Southern Oscillation, both controlling the heat and moisture delivered to the U.S. Pacific coast. The existence of a significant "Snow Gun" effect presents two challenges to snow and glacier mass balance modeling. Firstly, the link between amplification of orographic precipitation and the temporal evolution of ocean-climate oscillations indicates that prediction of future mass balance trends requires consideration of the timing and amplitude of such oscillations. Only recently have ocean-atmosphere models begun to realistically produce such temporal variability. Secondly, the steepening snow mass-balance elevation-gradient associated with the "Snow Gun" implies greater spatial variability in balance with warming. In a warming climate, orographic processes at a scale finer that the highest resolution RCM (>20km grid) become increasingly important and predictions based on lower elevations become increasingly inaccurate for higher elevations. Therefore, thermodynamic interaction between atmospheric heat, moisture and topography must be included in downscaling techniques. In order to demonstrate the importance of the thermodynamic downscaling in mass balance predictions, we nest a high-resolution (100m grid), coupled Orographic Precipitation and Surface Energy balance Model (OPSEM) into the RegC2.5 RCM (40 km grid) and compare results. We apply this nesting technique to Mt. Shasta, California, an area of high topography (~4000m) relative to its RegCM2.5 grid elevation (1289m). These models compute average April snow volume under present and doubled-present Atmospheric CO2 concentrations. While the RegCM2.5 regional model predicts an 83% decrease in April SWE, OPSEM predicts a 16% increase. These results indicate that thermodynamic interactions between the atmosphere and topography at sub- RCM grid resolution must be considered in mass balance models.

Snowpack Estimates Improve Water Resources Climate-Change Adaptation Strategies

NASA Astrophysics Data System (ADS)

Lestak, L.; Molotch, N. P.; Guan, B.; Granger, S. L.; Nemeth, S.; Rizzardo, D.; Gehrke, F.; Franz, K. J.; Karsten, L. R.; Margulis, S. A.; Case, K.; Anderson, M.; Painter, T. H.; Dozier, J.

2010-12-01

Observed climate trends over the past 50 years indicate a reduction in snowpack water storage across the Western U.S. As the primary water source for the region, the loss in snowpack water storage presents significant challenges for managing water deliveries to meet agricultural, municipal, and hydropower demands. Improved snowpack information via remote sensing shows promise for improving seasonal water supply forecasts and for informing decadal scale infrastructure planning. An ongoing project in the California Sierra Nevada and examples from the Rocky Mountains indicate the tractability of estimating snowpack water storage on daily time steps using a distributed snowpack reconstruction model. Fractional snow covered area (FSCA) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were used with modeled snowmelt from the snowpack model to estimate snow water equivalent (SWE) in the Sierra Nevada (64,515 km2). Spatially distributed daily SWE estimates were calculated for 10 years, 2000-2009, with detailed analysis for two anamolous years, 2006, a wet year and 2009, an over-forecasted year. Sierra-wide mean SWE was 0.8 cm for 01 April 2006 versus 0.4 cm for 01 April 2009, comparing favorably with known outflow. Modeled SWE was compared to in-situ (observed) SWE for 01 April 2006 for the Feather (northern Sierra, lower-elevation) and Merced (central Sierra, higher-elevation) basins, with mean modeled SWE 80% of observed SWE. Integration of spatial SWE estimates into forecasting operations will allow for better visualization and analysis of high-altitude late-season snow missed by in-situ snow sensors and inter-annual anomalies associated with extreme precipitation events/atmospheric rivers. Collaborations with state and local entities establish protocols on how to meet current and future information needs and improve climate-change adaptation strategies.

Research relative to angular distribution of snow reflectance/snow cover characterization and microwave emission

NASA Technical Reports Server (NTRS)

Dozier, Jeff; Davis, Robert E.

1987-01-01

Remote sensing has been applied in recent years to monitoring snow cover properties for applications in hydrologic and energy balance modeling. In addition, snow cover has been recently shown to exert a considerable local influence on weather variables. Of particular importance is the potential of sensors to provide data on the physical properties of snow with high spatial and temporal resolution. Visible and near-infrared measurements of upwelling radiance can be used to infer near-surface properties through the calculation of albedo. Microwave signals usually come from deeper within the snow pack and thus provide depth-integrated information, which can be measured through clouds and does not relay on solar illumination.Fundamental studies examining the influence of snow properties on signals from various parts of the electromagnetic spectrum continue in part because of the promise of new remote sensors with higher spectral and spatial accuracy. Information in the visible and near-infrared parts of the spectrum comprise nearly all available data with high spatial resolution. Current passive microwave sensors have poor spatial resolution and the data are problematic where the scenes consist of mixed landscape features, but they offer timely observations that are independent of cloud cover and solar illumination.

In Situ Microphysical and Scattering Properties of Falling Snow in GPM-GCPEx

NASA Astrophysics Data System (ADS)

Duffy, G.; Nesbitt, S. W.; McFarquhar, G. M.; Poellot, M.; Chandrasekar, C. V.; Hudak, D. R.

2013-12-01

The Global Precipitation Measurement Cold-season Precipitation Experiment (GPM-GCPEx) field campaign was conducted near Egbert, Ontario, Canada in January-February 2012 to study the physical characteristics and microwave radiative properties of the column of hydrometeors in cold season precipitation events. Extensive in situ aircraft profiling was conducted with the University of North Dakota (UND) Citation aircraft within the volume of several remote sensing instruments within a wide variety of precipitation events, from snow to freezing drizzle. Several of the primary goals of GCPEx include improving our understanding of the microphysical characteristics of falling snow and how those characteristics relate to the multi-wavelength radiative characteristics In this study, particle size distribution parameters, effective particle densities, and habit distributions are determined using in-situ cloud measurements obtained on the UND citation using the High Volume Precipitation Spectrometer, the Cloud Particle Imager, and the Cloud Imaging Probe. These quantities are matched compared to multi-frequency radar measurements from the Environment Canada King City C-Band and NASA D3R Ku-Ka Band dual polarization radars. These analysis composites provide the basis for direct evaluation of particle size distributions and observed multi-wavelength and multi-polarization radar observations, including radar reflectivity, differential reflectivity, and dual wavelength ratio) in falling snow at weather radar and GPM radar frequencies. Theoretical predictions from Mie, Rayleigh-Gans, and more complex snowflake aggregate scattering model predictions using observed particle size distributions are compared with observed radar scattering characteristics along the Citation flight track.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Contribution of rainfall, snow and ice melt to the hydrological regime of the Arve upper catchment and to severe flood events

NASA Astrophysics Data System (ADS)

Lecourt, Grégoire; Revuelto, Jesús; Morin, Samuel; Zin, Isabella; Lafaysse, Matthieu; Condom, Thomas; Six, Delphine; Vionnet, Vincent; Charrois, Luc; Dumont, Marie; Gottardi, Frédéric; Laarman, Olivier; Coulaud, Catherine; Esteves, Michel; Lebel, Thierry; Vincent, Christian

2016-04-01

In Alpine catchments, the hydrological response to meteorological events is highly influenced by the precipitation phase (liquid or solid) and by snow and ice melt. It is thus necessary to simulate accurately the snowpack evolution and its spatial distribution to perform relevant hydrological simulations. This work is focused on the upper Arve Valley (Western Alps). This 205 km2 catchment has large glaciated areas (roughly 32% of the study area) and covers a large range of elevations (1000-4500 m a.s.l.). Snow presence is significant year-round. The area is also characterized by steep terrain and strong vegetation heterogeneity. Modelling hydrological processes in such a complex catchment is therefore challenging. The detailed ISBA land surface model (including the Crocus snowpack scheme) has been applied to the study area using a topography based discretization (classifying terrain by aspect, elevation, slope and presence of glacier). The meteorological forcing used to run the simulations is the reanalysis issued from the SAFRAN model which assimilates meteorological observations from the Meteo-France networks. Conceptual reservoirs with calibrated values of emptying parameters are used to represent the underground water storage. This approach has been tested to simulate the discharge on the Arve catchment and three sub-catchments over 1990-2015. The simulations were evaluated with respect to observed water discharges for several headwaters with varying glaciated areas. They allow to quantify the relative contribution of rainfall, snow and ice melt to the hydrological regime of the basin. Additionally, we present a detailed analysis of several particular flood events. For these events, the ability of the model to correctly represent the catchment behaviour is investigated, looking particularly to the relevance of the simulated snowpack. Particularly, its spatial distribution is evaluated using MODIS snow cover maps, punctual snowpack observations and summer glacier mass balance estimations.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Simulation of Current and Projected Montane Snowpacks for the Preservation of the Wolverine in the Western U.S.

NASA Astrophysics Data System (ADS)

Heldmyer, A.; Livneh, B.; Barsugli, J. J.; Dewes, C.; Ray, A. J.; Rangwala, I.; Guinotte, J. M.; Torbit, S.

2017-12-01

A major gap in research on the future of snowpack in the western United States is accounting for snow persistence in relation to topographical effects like terrain aspect and slope, which have important consequences for species that rely on snow for habitat in alpine regions, such as the wolverine (Gulo gulo). Previous work has shown a predicted loss of snow-covered area in Montana (which encompasses much of the Wolverine's extent range) ranging from 50 - 85%. However, these estimates use coarse model grid-boxes (6 - 12 km per side) that lack topographic shading, with mean elevations below the higher elevations where the wolverine tends to live. We address these informational gaps by applying a physically-based, high-resolution hydrologic model (250 m spatial resolution), the Distributed Hydrologic Soil and Vegetation Model (DHSVM), to project snow water equivalent (SWE) in two regions important to the survival of the wolverine within Glacier and Rocky Mountain National Parks. Because snowpack evolution is driven primarily by the energy balance at the surface, particularly during melt season, the inclusion of a realistic, physically-based energy balance together with topographic shading enables a clearer understanding of how projected climatic perturbations will affect future snowpack. We apply a diverse sample of future (2035-2064) climate conditions from CMIP5 General Circulation Models (GCMs) to meteorological forcing data from a baseline historical period (1998-2013) through the delta method, after validating historical simulations with SNOTEL and MODIS satellite data. Despite considerable variability across models, the results show a consistent decrease in Snow-Covered Area (SCA) across investigated future climate projections, an increased loss of snowpack during years of drought, and a fragmentation of land with deep snow available for refuge.

Analysis of Snow Line and Albedo Conditions By Means of Time-Lapse Photography on Tapado Glacier, Chile

NASA Astrophysics Data System (ADS)

Vivero, S.; MacDonell, S.; McPhee, J. P.

2014-12-01

In the semiarid Coquimbo Region of Chile, high-altitude glaciers and seasonal snow are important sources of freshwater for irrigated agriculture and urban consumption. Due to the aridity of the environment, losses due to sublimation are large which means that accurate melt modelling is essential in order to reliably estimate streamflow. Since 2008, the CEAZA glaciology group has been studying the energy and mass balance of the largest glacier in the catchment, the Tapado Glacier using field and remote sensing measurements, and numerical modelling. The Tapado glacier system (30°08' S, 69°55' W) is a complex assemblage of uncovered and debris-covered ice located at the head of the Elqui basin between 4500 and 5536 m a.s.l. Energy balance modelling studies at the site have been limited in scope due to the development of ice pinnacles or penitentes on snow and ice surfaces. These features complicate energy distribution across the surface, due to modifications of parameters such as albedo. In this paper, we use time-lapse photography and automatic weather station (AWS) measurements to investigate how the development of penitentes impacts the spatial and temporal variability of albedo across the glacier surface and whether terrestrial photography is appropriate for use at such locations. Oblique photographs obtained from a high vantage point were georeferenced using a high resolution digital elevation model available for the entire glacier and its environs. By comparing the photographic data with point albedo measurements made at an AWS, distributed albedo maps were produced. Preliminary results suggest that distributed albedo values may be underestimated by the formation and development of penitentes during the ablation season. Moreover, it was observed that the evolution of the snow line during summer was not only topographically controlled but also modified by occasional convective snowfalls. Time-lapse photography provided to be a cost-effective tool for monitoring remote and high-altitude glaciers at high temporal resolution.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Microwave Signatures of Melting/Refreezing Snow: Observations and Modeling Using Dense Medium Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Kim, Edward J.; England, Anthony; deRoo, Roger; Hardy, Janet

2005-01-01

Microwave brightness temperatures of snow covered terrains can be modeled by means of the Dense Radiative Transfer Medium Theory (DMRT). In a dense medium, such as snow, the assumption of independent scattering is no longer valid and the scattering of correlated scatterers must be considered. In the DMRT, this is done considering a pair distribution function of the particles position. In the electromagnetic model, the snowpack is simulated as a homogeneous layer having effective permittivity and albedo calculated through the DMRT. In order to account for clustering of snow crystals, a model of cohesive particles can be applied, where the cohesion between the particles is described by means of a dimensionless parameters called stickiness (z), representing a measure of the inversion of the attraction of the particles. The lower the z the higher the stickiness. In this study, microwave signatures of melting and refreezing cycles of seasonal snowpacks at high altitudes are studied by means of both experimental and modeling tools. Radiometric data were collected 24 hours per day by the University of Michigan Tower Mounted Radiometer System (TMRS). The brightness temperatures collected by means of the TMRS are simulated by means of a multi-layer electromagnetic model based on the dense medium theory with the inputs to the model derived from the data collected at the snow pits and from the meteorological station. The paper is structured as follows: in the first Section the temperature profiles recorded by the meteorological station and the snow pit data are presented and analyzed; in the second Section, the characteristics of the radiometric system used to collect the brightness temperatures are reported together with the temporal behavior of the recorded brightness temperatures; in the successive Section the multi-layer DMRT-based electromagnetic model is described; in the fourth Section the comparison between modeled and measured brightness temperatures is discussed. We dedicate the last Section to the conclusions and future works.

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.

Analysis of Snow Bidirectional Reflectance from ARCTAS Spring-2008 Campaign

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Gatebe, C. K.; Redemann, J.; Kahn, R.; Brandt, R.; Russell, P.; King, M. D.; Pedersen, C. A.; Gerland, S.; Poudyal, R.;

Conserving and managing the subnivium.

PubMed

Zuckerberg, Benjamin; Pauli, Jonathan N

2018-02-08

In regions where snowfall historically has been a defining seasonal characteristic of the landscape, warming winters have reduced the depth, duration, and extent of snowpack. However, most management and conservation has focused on how aboveground wildlife will be affected by altered snow conditions, even though the majority of species that persist through the winter do so under the snowpack in a thermally stable refugium: the subnivium. Shortened winters, forest management practices, and winter recreation can alter subnivium conditions by increasing snow compaction and compromising thermal stability at the soil-snow interface. To help slow the loss of the subnivium in the face of rapidly changing winter conditions, we suggest managers adopt regional conservation plans for identifying threatened snow-covered environments; measure and predict the effects land cover and habitat management has on local subnivium conditions; and control the timing and distribution of activities that disturb and compact snow cover (e.g., silvicultural practices, snow recreation, and road and trail maintenance). As a case study, we developed a spatially explicit model of subnivium presence in a working landscape of the Chequamegon National Forest, Wisconsin. We identified landscapes where winter recreation and management practices could threaten potentially important areas for subnivium persistence. Similar modeling approaches could inform management decisions related to subnivium conservation. Current climate projections predict that snow seasons will change rapidly in many regions, and as result, we advocate for the immediate recognition, conservation, and management of the subnivium and its dependent species. © 2018 Society for Conservation Biology.

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.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

Seasonal snow cover is a primary water source throughout much of Western North America, where insect-induced tree die-off is changing the montane landscape. Widespread mortality from insects or drought differs from well-studied cases of fire and logging in that tree mortality is not accompanied by other immediate biophysical changes. Much of the impacted landscape is a mosaic of stands of varying species, structure, management history and health overlain on complex terrain. To address the challenge of predicting the effects of forest die-off on snow water input, we quantified snow accumulation and ablation at scales ranging from individual trees, through forest stands, to nested small catchments. Our study sites in Northern Colorado and Southern Wyoming are dominated by lodgepole pine, but they include forest stands that are naturally developed, managed and clear-cut with varying mortality from Mountain Pine Beetle (MPB). Our record for winters 2010 and 2011 includes continuous meteorological data and snow depth in plots with varying MPB impact as well as stand- to catchment-scale snow surveys mid-winter and near maximal accumulation. At the plot scale, snow depth sensors in healthy stands recorded greater inputs during storms (21-42% of depth) and greater seasonal accumulation (15-40%) in canopy gaps than under trees, whereas no spatial effects of canopy geometry were observed in stands with heavy mortality. Similar patterns were observed in snow surveys near peak accumulation. At both impacted and thinned sites, spatial variability in snow depth was more closely associated with larger scale topography and changes in stand structure than with canopy cover. The role of aspect in ablation was observed to increase in impacted stands as both shading and wind attenuation decreased. Evidence of wind-controlled snow distribution was found 80-100 meters from exposed stand edges in impacted forest as compared to 10-15 meters in healthy forest. Integrating from the scale of stands to small catchments, maximal snow water equivalent (SWE) as a fraction of winter precipitation (P) ranged from 62 to 74%. Despite an expectation of decreased interception and increased snow accumulation with advanced mortality, surveys at stand and catchment scales found no significant increases in net snow water input between healthy and impacted forests. These observations suggest that the spatial scale of processes affecting net snow accumulation and ablation increase following die-off. Using data from our sites and other studies, this presentation will develop a predictive model of how interception, shading, and wind redistribution interact to control net snow water input following large-scale forest mortality.

Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps - a matter of model limitations or unpredictable nature?

NASA Astrophysics Data System (ADS)

Rössler, O.; Froidevaux, P.; Börst, U.; Rickli, R.; Martius, O.; Weingartner, R.

2014-06-01

A rain-on-snow flood occurred in the Bernese Alps, Switzerland, on 10 October 2011, and caused significant damage. As the flood peak was unpredicted by the flood forecast system, questions were raised concerning the causes and the predictability of the event. Here, we aimed to reconstruct the anatomy of this rain-on-snow flood in the Lötschen Valley (160 km2) by analyzing meteorological data from the synoptic to the local scale and by reproducing the flood peak with the hydrological model WaSiM-ETH (Water Flow and Balance Simulation Model). This in order to gain process understanding and to evaluate the predictability. The atmospheric drivers of this rain-on-snow flood were (i) sustained snowfall followed by (ii) the passage of an atmospheric river bringing warm and moist air towards the Alps. As a result, intensive rainfall (average of 100 mm day-1) was accompanied by a temperature increase that shifted the 0° line from 1500 to 3200 m a.s.l. (meters above sea level) in 24 h with a maximum increase of 9 K in 9 h. The south-facing slope of the valley received significantly more precipitation than the north-facing slope, leading to flooding only in tributaries along the south-facing slope. We hypothesized that the reason for this very local rainfall distribution was a cavity circulation combined with a seeder-feeder-cloud system enhancing local rainfall and snowmelt along the south-facing slope. By applying and considerably recalibrating the standard hydrological model setup, we proved that both latent and sensible heat fluxes were needed to reconstruct the snow cover dynamic, and that locally high-precipitation sums (160 mm in 12 h) were required to produce the estimated flood peak. However, to reproduce the rapid runoff responses during the event, we conceptually represent likely lateral flow dynamics within the snow cover causing the model to react "oversensitively" to meltwater. Driving the optimized model with COSMO (Consortium for Small-scale Modeling)-2 forecast data, we still failed to simulate the flood because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus we conclude that this rain-on-snow flood was, in general, predictable, but requires a special hydrological model setup and extensive and locally precise meteorological input data. Although, this data quality may not be achieved with forecast data, an additional model with a specific rain-on-snow configuration can provide useful information when rain-on-snow events are likely to occur.

Decadal trend of precipitation and temperature patterns and impacts on snow-related variables in a semiarid region, Sierra Nevada, Spain.

NASA Astrophysics Data System (ADS)

José Pérez-Palazón, María; Pimentel, Rafael; Herrero, Javier; José Polo, María

2016-04-01

In the current context of global change, mountainous areas constitute singular locations in which these changes can be traced. Early detection of significant shifts of snow state variables in semiarid regions can help assess climate variability impacts and future snow dynamics in northern latitudes. The Sierra Nevada mountain range, in southern Spain, is a representative example of snow areas in Mediterranean-climate regions and both monitoring and modelling efforts have been performed to assess this variability and its significant scales. This work presents a decadal trend analysis throughout the 50-yr period 1960-2010 performed on some snow-related variables over Sierra Nevada, in Spain, which is included in the global climate change observatories network around the world. The study area comprises 4583 km2 distributed throughout the five head basins influenced by these mountains, with altitude values ranging from 140 to 3479 m.a.s.l., just 40 km from the Mediterranean coastline. Meteorological variables obtained from 44 weather stations from the National Meteorological Agency were studied and further used as input to the distributed hydrological model WiMMed (Polo et al., 2010), operational at the study area, to obtain selected snow variables. Decadal trends were obtained, together with their statistical significance, over the following variables, averaged over the whole study area: (1) annual precipitation; (2) annual snowfall; annual (3) mean, (4) maximum and (5) minimum daily temperature; annual (6) mean and (7) maximum daily fraction of snow covered areas; (8) annual number of days with snow cover; (9) mean and (10) maximum daily snow water equivalent; (11) annual number of extreme precipitation events; and (12) mean intensity of the annual extreme precipitation events. These variables were also studied over each of the five regions associated to each basin in the range. Globally decreasing decadal trends were obtained for all the meteorological variables, with the exception of the average annual mean and maximum daily temperature. In the case of the snow-related variables, no significant trends are observed at this time scale; nonetheless, a global decreasing rate is predominant in most of the variables. The torrential events are more frequent in the last decades of the study period, with an apparently increasing associated dispersion. This study constitutes a first sound analysis of the long-term observed trends of the snow regime in this area under the context of increasing temperature and decreasing precipitation regimes. The results highlight the complexity of non-linearity in environmental processes in Mediterranean regions, and point out to a significant shift in the precipitation and temperature regime, and thus on the snow-affected hydrological variables in the study area.

Temporal trend of the snow-related variables in Sierra Nevada in the last years: An analysis combining Earth Observation and hydrological modelling

NASA Astrophysics Data System (ADS)

Pérez-Luque, Antonio J.; Herrero, Javier; Bonet, Francisco J.; Pérez-Pérez, Ramón

2016-04-01

Climate change is causing declines in snow-cover extent and duration in European mountain ranges. This is especially important in Mediterranean mountain ranges where the observed trends towards precipitation and higher temperatures can provoke problems of water scarcity. In this work, we analyzed temporal trends (2000 to 2014) of snow-related variables obtained from satellite and modelling data in Sierra Nevada, a Mediterranean high-mountain range located in Southern Spain, at 37°N. Snow cover indicators (snow-cover duration, snow-cover onset dates and snow-cover melting dates) were obtained by processing images of MOD10A2 MODIS product using an automated workflow. Precipitation data were obtained using WiMMed, a complete and fully distributed hydrological model that is used to map the annual rainfall and snowfall with a resolution of 30x30 m over the whole study area. It uses expert algorithms to interpolate precipitation and temperature at an hourly scale, and simulates partition of precipitation into snowfall with several methods. For each snow-related indicator (snow-covers and snowfall), a trend analysis was applied at the MODIS pixel scale during the study period (2000-2014). We applied Mann-Kendall test and Theil-Sen slope estimation in each of the pixels comprising Sierra Nevada. The trend analysis assesses the intensity, magnitude and degree of statistical significance during the period analysed. The spatial pattern of these trends was explored according to elevation ranges. Finally, we explored the relationship between trends of snow-cover related indicators and precipitation trends. Our results show that snow-cover has undergone significant changes in the last 14 years. 80 % of the pixels covering Sierra Nevada showed a negative trend in the duration of snow-cover. We also observed a delay in the snow-cover onset date (68.03 % pixels showing a positive trend in the snow-cover onset date) and an advance in the melt date (80.72 % of pixels followed a negative trend for the snow-cover melting date). Precipitation does not show a significant trend for these years, even though its inter-annual variability has been outstanding. The maximum mean annual precipitation of 906 mm/year doubles the mean precipitation, which somehow compensates for the occurrence of a sequence of dry years with a minimum of 250 mm/year. The assessment of the spatial pattern of snow cover duration shows that both the trend and the slope of the trend becomes more pronounced with elevation. At higher elevations the snow-cover duration decreased an average of 3 days from 2000-2014. This research has been funded by ECOPOTENTIAL (Improving future ecosystem benefits through Earth Observations) Horizon 2020 EU project, and Sierra Nevada Global Change Observatory (LTER-site)

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

THE INFLUENCE OF THE SPATIAL DISTRIBUTION OF SNOW ON BASIN-AVERAGED SNOWMELT. (R824784)

EPA Science Inventory

Spatial variability in snow accumulation and melt owing to topographic effects on solar radiation, snow drifting, air temperature and precipitation is important in determining the timing of snowmelt releases. Precipitation and temperature effects related to topography affect snow...

Properties of the surface snow in Princess Elizabeth Land, East Antarctica - climate and non-climate dependent variability of the surface mass balance and stable water isotopic composition

NASA Astrophysics Data System (ADS)

Vladimirova, D.; Ekaykin, A.; Lipenkov, V.; Popov, S. V.; Petit, J. R.; Masson-Delmotte, V.

2017-12-01

Glaciological and meteorological observations conducted during the past four decades in Princess Elizabeth Land, East Antarctica, are compiled. The database is used to investigate spatial patterns of surface snow isotopic composition and surface mass balance, including detailed information near subglacial lake Vostok. We show diverse relationships between snow isotopic composition and surface temperature. In the most inland part (elevation 3200-3400 m a.s.l.), surface snow isotopic composition varies independently from surface temperature, and is closely related to the distance to the open water source (with a slope of 0.98±0.17 ‰ per 100 km). Surface mass balance values are higher along the ice sheet slope, and relatively evenly distributed inland. The minimum values of snow isotopic composition and surface mass balance are identified in an area XX km southwestward from Vostok station. The spatial distribution of deuterium excess delineates regions influenced by the Indian Ocean and Pacific Ocean air masses, with Vostok area being situated close to their boundary. Anomalously high deuterium excess values are observed near Dome A, suggesting high kinetic fractionation for its moisture source, or specifically high post-deposition artifacts. The dataset is available for further studies such as the assessment of skills of general circulation or regional atmospheric models, and the search for the oldest ice.

The Open Source Snowpack modelling ecosystem

NASA Astrophysics Data System (ADS)

Bavay, Mathias; Fierz, Charles; Egger, Thomas; Lehning, Michael

2016-04-01

As a large number of numerical snow models are available, a few stand out as quite mature and widespread. One such model is SNOWPACK, the Open Source model that is developed at the WSL Institute for Snow and Avalanche Research SLF. Over the years, various tools have been developed around SNOWPACK in order to expand its use or to integrate additional features. Today, the model is part of a whole ecosystem that has evolved to both offer seamless integration and high modularity so each tool can easily be used outside the ecosystem. Many of these Open Source tools experience their own, autonomous development and are successfully used in their own right in other models and applications. There is Alpine3D, the spatially distributed version of SNOWPACK, that forces it with terrain-corrected radiation fields and optionally with blowing and drifting snow. This model can be used on parallel systems (either with OpenMP or MPI) and has been used for applications ranging from climate change to reindeer herding. There is the MeteoIO pre-processing library that offers fully integrated data access, data filtering, data correction, data resampling and spatial interpolations. This library is now used by several other models and applications. There is the SnopViz snow profile visualization library and application that supports both measured and simulated snow profiles (relying on the CAAML standard) as well as time series. This JavaScript application can be used standalone without any internet connection or served on the web together with simulation results. There is the OSPER data platform effort with a data management service (build on the Global Sensor Network (GSN) platform) as well as a data documenting system (metadata management as a wiki). There are several distributed hydrological models for mountainous areas in ongoing development that require very little information about the soil structure based on the assumption that in step terrain, the most relevant information is contained in the Digital Elevation Model (DEM). There is finally a set of tools making up the operational chain to automatically run, monitor and publish SNOWPACK simulations for operational avalanche warning purposes. This tool chain has been developed with the aim of offering very low maintenance operation and very fast deployment and to easily adapt to other avalanche services.

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.

Mountain Hydrology of the Semi-Arid Western U.S.: Research Needs, Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Bales, R.; Dozier, J.; Molotch, N.; Painter, T.; Rice, R.

2004-12-01

In the semi-arid Western U.S., water resources are being stressed by the combination of climate warming, changing land use, and population growth. Multiple consensus planning documents point to this region as perhaps the highest priority for new hydrologic understanding. Three main hydrologic issues illustrate research needs in the snow-driven hydrology of the region. First, despite the hydrologic importance of mountainous regions, the processes controlling their energy, water and biogeochemical fluxes are not well understood. Second, there exists a need to realize, at various spatial and temporal scales, the feedback systems between hydrological fluxes and biogeochemical and ecological processes. Third, the paucity of adequate observation networks in mountainous regions hampers improvements in understanding these processes. For example, we lack an adequate description of factors controlling the partitioning of snowmelt into runoff versus infiltration and evapotranspiration, and need strategies to accurately measure the variability of precipitation, snow cover and soil moisture. The amount of mountain-block and mountain-front recharge and how recharge patterns respond to climate variability are poorly known across the mountainous West. Moreover, hydrologic modelers and those measuring important hydrologic variables from remote sensing and distributed in situ sites have failed to bridge rifts between modeling needs and available measurements. Research and operational communities will benefit from data fusion/integration, improved measurement arrays, and rapid data access. For example, the hydrologic modeling community would advance if given new access to single rather than disparate sources of bundles of cutting-edge remote sensing retrievals of snow covered area and albedo, in situ measurements of snow water equivalent and precipitation, and spatio-temporal fields of variables that drive models. In addition, opportunities exist for the deployment of new technologies, taking advantage of research in spatially distributed sensor networks that can enhance data recovery and analysis.

Radiative effects of light-absorbing particles deposited in snow over Himalayas using WRF-Chem simulations

NASA Astrophysics Data System (ADS)

Sarangi, C.; Qian, Y.; Painter, T. H.; Liu, Y.; Lin, G.; Wang, H.

2017-12-01

Radiative forcing induced by light-absorbing particles (LAP) deposited on snow is an important surface forcing. It has been debated that an aerosol-induced increase in atmospheric and surface warming over Tibetan Plateau (TP) prior to the South Asian summer monsoon can have a significant effect on the regional thermodynamics and South Asian monsoon circulation. However, knowledge about the radiative effects due to deposition of LAP in snow over TP is limited. In this study we have used a high-resolution WRF-Chem (coupled with online chemistry and snow-LAP-radiation model) simulations during 2013-2014 to estimate the spatio-temporal variation in LAP deposition on snow, specifically black carbon (BC) and dust particles, in Himalayas. Simulated distributions in meteorology, aerosol concentrations, snow albedo, snow grain size and snow depth are evaluated against satellite and in-situ measurements. The spatio-temporal change in snow albedo and snow grain size with variation in LAP deposition is investigated and the resulting shortwave LAP radiative forcing at surface is calculated. The LAP-radiative forcing due to aerosol deposition, both BC and dust, is higher in magnitude over Himalayan slopes (terrain height below 4 km) compared to that over TP (terrain height above 4 km). We found that the shortwave aerosol radiative forcing efficiency at surface due to increase in deposited mass of BC particles in snow layer ( 25 (W/m2)/ (mg/m2)) is manifold higher than the efficiency of dust particles ( 0.1 (W/m2)/ (mg/m2)) over TP. However, the radiative forcing of dust deposited in snow is similar in magnitude (maximum 20-30 W/m2) to that of BC deposited in snow over TP. This is mainly because the amount of dust deposited in snow over TP can be about 100 times greater than the amount of BC deposited in snow during polluted conditions. The impact of LAP on surface energy balance, snow melting and atmospheric thermodynamics is also examined.

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)

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Planetesimal formation starts at the snow line

NASA Astrophysics Data System (ADS)

Drążkowska, J.; Alibert, Y.

2017-12-01

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

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

Treesearch

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

2007-01-01

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

BOREAS HYD-2 Estimated Snow Water Equivalent (SWE) from Microwave Measurements

NASA Technical Reports Server (NTRS)

Powell, Hugh; Chang, Alfred T. C.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The surface meteorological data collected at the Boreal Ecosystem-Atmosphere Study (BOREAS) tower and ancillary sites are being used as inputs to an energy balance model to monitor the amount of snow storage in the boreal forest region. The BOREAS Hydrology (HYD)-2 team used Snow Water Equivalent (SWE) derived from an energy balance model and in situ observed SWE to compare the SWE inferred from airborne and spaceborne microwave data, and to assess the accuracy of microwave retrieval algorithms. The major external measurements that are needed are snowpack temperature profiles, in situ snow areal extent, and SWE data. The data in this data set were collected during February 1994 and cover portions of the Southern Study Area (SSA), Northern Study Area (NSA), and the transect areas. The data are available from BORIS as comma-delimited tabular ASCII files. The SWE 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).

Estimation of Snow Parameters from Dual-Wavelength Airborne Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Iguchi, Toshio; Detwiler, Andrew

1997-01-01

Estimation of snow characteristics from airborne radar measurements would complement In-situ measurements. While In-situ data provide more detailed information than radar, they are limited in their space-time sampling. In the absence of significant cloud water contents, dual-wavelength radar data can be used to estimate 2 parameters of a drop size distribution if the snow density is assumed. To estimate, rather than assume, a snow density is difficult, however, and represents a major limitation in the radar retrieval. There are a number of ways that this problem can be investigated: direct comparisons with in-situ measurements, examination of the large scale characteristics of the retrievals and their comparison to cloud model outputs, use of LDR measurements, and comparisons to the theoretical results of Passarelli(1978) and others. In this paper we address the first approach and, in part, the second.

Assessing the snowmelt submodel of TETIS within the DMIP2 project

NASA Astrophysics Data System (ADS)

Orozco, Ismael; Francés, Félix

2010-05-01

Melt modelling is a crucial element in any attempt to predict runoff from snow-covered or glacierised areas, as well as to assess changes in the cryosphere associated with clime change. In mountainous regions, snow and ice significantly affect catchment hydrology by temporarily storing and releasing water on various time scales (Jansson et al., 2003). Hence, success of runoff modelling in such areas largely depends on accurate quantification of the melt process (Hock, 2003). Snowmelt modelling is complex and dependent on elevation, slope, vegetation type, surface roughness, radiation load, and energy exchange at the snow-air interface (Baron, 1992; Barros and Lettenmaier, 1993; Becker et al., 1994; Cline, 1995; Elder et al., 1991). This paper describes the application of the degree-day method for snowmelt-runoff at hourly time discretization, which is implemented in the distributed and conceptually based hydrological model TETIS, as well as the evaluation of results. In the TETIS model the natural basins are discretizated in grid cells according to drainage network. This conceptualization permits all parameters do not lose its physical meaning (Francés et al., 2007). At each cell the main soil properties need to be estimated previously using topographical, environmental, land use, geological and soil maps. The model has been applied to the Sierra Nevada basins, in USA: the American River (886 km2) and the Carson River (922 km2), as a part of the Distributed Model Intercomparison Project, second phase (DMIP2), of the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS), in which we are participating. These basins are geographically close, but their hidrological regimes are quite different: the Carson River is a high altitude basin with a snow dominated regime; while the American River drains an area that is lower in elevation with precipitation falling as rain and mixed snow and rain (Jeton et al., 1996). Details on the basins features are available in Smith et al. (2006). The degree-day method in the TETIS model, assuming an empirical relationship between air temperatures and melt rates, applied and refined (e.g. Clyde, 1931; Collins, 1934; Corps of Engineers, 1956; Hoinkes and Steinacker, 1975; Braithwaite, 1995). The snow (initial values) and temperature are interpolated at each cell with inverse distance squared algorithm with a linear correction with altitude. The degree-day method was implemented with a simple and parsimonious parameterization using one melting coefficient for rainy and another for not-rainy time. The evaluation of the modeling results was performed using the observed snow water equivalent (SWE) at daily scale, hourly discharges at the basin outlet and some snow-covered images provided by NOAA/NWS. As expected, the model does not reproduce the fluctuations observed in the outflow hydrograph, caused by diurnal melting. The results obtained are acceptable according to the Nash-Sutcliffe coefficient, but excellent at daily scale. Concerning the SWE, the results are very good, taking into account we are dealing with point observations in space. Also, it must be underline that such results are better at higher altitude stations than in lower altitude ones. In conclusion, the results are acceptable, but indicate the need to add information of radiation to the snowmelt model in order to improve the energy-balance and the sensitivity of the model against spatial-temporal changes in the energy fluxes and assess what degree of complexity is recommended for snowmelt model, based on the results and the principle of parsimony.

Projected changes in atmospheric heating due to changes in fire disturbance and the snow season in the western Arctic, 2003–2100

USGS Publications Warehouse

Euskirchen, E.S.; McGuire, A. David; Rupp, T.S.; Chapin, F. S.; Walsh, J.E.

2009-01-01

In high latitudes, changes in climate impact fire regimes and snow cover duration, altering the surface albedo and the heating of the regional atmosphere. In the western Arctic, under four scenarios of future climate change and future fire regimes (2003–2100), we examined changes in surface albedo and the related changes in regional atmospheric heating due to: (1) vegetation changes following a changing fire regime, and (2) changes in snow cover duration. We used a spatially explicit dynamic vegetation model (Alaskan Frame-based Ecosystem Code) to simulate changes in successional dynamics associated with fire under the future climate scenarios, and the Terrestrial Ecosystem Model to simulate changes in snow cover. Changes in summer heating due to the changes in the forest stand age distributions under future fire regimes showed a slight cooling effect due to increases in summer albedo (mean across climates of −0.9 W m−2 decade−1). Over this same time period, decreases in snow cover (mean reduction in the snow season of 4.5 d decade−1) caused a reduction in albedo, and a heating effect (mean across climates of 4.3 W m−2 decade−1). Adding both the summer negative change in atmospheric heating due to changes in fire regimes to the positive changes in atmospheric heating due to changes in the length of the snow season resulted in a 3.4 W m−2 decade−1 increase in atmospheric heating. These findings highlight the importance of gaining a better understanding of the influences of changes in surface albedo on atmospheric heating due to both changes in the fire regime and changes in snow cover duration.

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.

Simulating the Dependence of Aspen on Redistributed Snow

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Light-absorbing impurities in a southern Tibetan Plateau glacier: Variations and potential impact on snow albedo and radiative forcing

NASA Astrophysics Data System (ADS)

Li, Xiaofei; Kang, Shichang; Zhang, Guoshuai; Qu, Bin; Tripathee, Lekhendra; Paudyal, Rukumesh; Jing, Zhefan; Zhang, Yulan; Yan, Fangping; Li, Gang; Cui, Xiaoqing; Xu, Rui; Hu, Zhaofu; Li, Chaoliu

2018-02-01

Light-absorbing impurities (LAIs), such as organic carbon (OC), black carbon (BC), and mineral dust (MD), deposited on the surface snow of glacier can reduce the surface albedo. As there exists insufficient knowledge to completely characterize LAIs variations and difference in LAIs distributions, it is essential to investigate the behaviors of LAIs and their influence on the glaciers across the Tibetan Plateau (TP). Therefore, surface snow and snowpit samples were collected during September 2014 to September 2015 from Zhadang (ZD) glacier in the southern TP to investigate the role of LAIs in the glacier. LAIs concentrations were observed to be higher in surface aged snow than in the fresh snow possibly due to post-depositional processes such as melting or sublimation. The LAIs concentrations showed a significant spatial distribution and marked negative relationship with elevation. Impurity concentrations varied significantly with depth in the vertical profile of the snowpit, with maximum LAIs concentrations frequently occurred in the distinct dust layers which were deposited in non-monsoon, and the bottom of snowpit due to the eluviation in monsoon. Major ions in snowpit and backward trajectory analysis indicated that regional activities and South Asian emissions were the major sources. According to the SNow ICe Aerosol Radiative (SNICAR) model, the average simulated albedo caused by MD and BC in aged snow collected on 31 May 2015 accounts for about 13% ± 3% and 46% ± 2% of the albedo reduction. Furthermore, we also found that instantaneous RF caused by MD and BC in aged snow collected on 31 May 2015 varied between 4-16 W m- 2 and 7-64 W m- 2, respectively. The effect of BC exceeds that of MD on albedo reduction and instantaneous RF in the study area, indicating that BC played a major role on the surface of the ZD glacier.

Analyzing the importance of wind-blown snow accumulations on Mount

NASA Astrophysics Data System (ADS)

Nestler, Alexander; Huss, Matthias; Ambartsumian, Rouben; Hambarian, Artak; Mohr, Sandra; Santi, Flavio

2013-04-01

Armenia's climate has a predominantly continental character with high amounts of precipitation and low temperatures during wintertime and a lack of precipitation together with high temperatures during summer. On the volcano Mount Aragatz, snow is relocated by strong winds into massive accumulations between 2500 and 4100 m a.s.l. during the winter season. These snow accumulations appear every winter in regular patterns as cornices on the lee side of sharp edges, such as those of ridges and canyons, which are arranged in a radial manner around the central crater. The biggest cornices almost outlast the hot period and provide considerable amounts of melt water until they disappear completely by the end of August. Snow melt water is known to have a high economic importance for agriculture on the slopes of Mount Aragatz and in the surroundings of Armenia's captial Yerewan. The aim of this study is to estimate the quantity of water naturally stored as snow on Mount Aragatz, and to what degree the use of geotextiles can prolong the lives of these snow accumulations. The characteristics and the spatial distribution of snow cornices on Mount Aragatz were determined using classical glaciological methods in June/July 2011 and 2012, involving snow depth soundings, water equivalent measurements and snow melt monitoring using ablation stakes, together with GPS mappings and classifications obtained from satellite images of the snow cornices. The combination of these data with ASTER DEMs and local weather data allows the modelling of the formation of wind-driven snow accumulations. Statistical relationships between the measured extent and volume of the snow cornices and surface parameters such as slope, aspect and curvature are established. In order to analyze the meltdown of the snow accumulations and the consequent impacts on runoff generation and the hydrological regime, a glacio-hydrological model integrating topographic parameters and meteorological data is applied. The combination of in-situ field data and satellite information allows an estimation of the water volume that is stored in the form of snow on Mount Aragatz. Using numerical modelling, we extend these results to other years, and calculate past and future water yields from snow melt from Mount Aragatz. This study is performed in the frame of the Armenian-Swiss project "Freezwater" that aims at an artificial managing of snow melting to better time the release of melt water at low cost. In the past few years, an artificial glacier was built up successfully, and geotextiles were used to reduce the melt rates of snow cornices. In order to estimate the efficiency of geotextiles in delaying the melt-down, ablation rates of protected snow surfaces were compared to those of uncovered areas. This study will contribute to the understanding of aeolian processes within the cryosphere as well as it will help to gain engineering knowledge concerning a new and efficient water storage technique.

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.

Modelling Seasonally Freezing Ground Conditions

DTIC Science & Technology

1989-05-01

used as the ’snow input’ in the larger hydrological models, e.g. Pangburn (1987). The most advanced index model is Anderson’s (1973) model. This bases...source as the soils) is shown in figures 32 and 33. Table 10 shows the percentage areas of Hydrologic Soil Groups, Land Use and Slope Distribution for...C") z c~cu CYa) 65 table 10: Percentage areas of Hydrologic Soil Grouos, Land Use and Slope Distribution over W3 (?Pn!ke e: al., 1978) Parameter

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Laser Pulse Bidirectional Reflectance from CALIPSO Mission

NASA Technical Reports Server (NTRS)

Lu, Xiaomei; Hu, Yongxiang; Yang, Yuekui; Liu, Zhaoyan; Vaughan, Mark; Lucker, Patricia; Trepte, Charles

2017-01-01

In this Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) study, we present a simple way of determining laser pulse bidirectional reflectance over snow/ice surface using the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) 532 nanometer polarization channels' measurements. The saturated laser pulse returns from snow and ice surfaces are recovered based on surface tail information. The method overview and initial assessment of the method performance will be presented. The retrieved snow surface bidirectional reflectance is compared with reflectance from both CALIOP cloud cover regions and Moderate Resolution Imaging Spectroradiometer (Earth Observing System (EOS)) (MODIS) Bi-directional Reflectance Distribution Function (BRDF) / Albedo model parameters. The comparisons show that the snow surface bidirectional reflectance over Antarctica for saturation region are generally reliable with a mean value of about 0.90 plus or minus 0.10, while the mean surface reflectance from cloud cover region is about 0.84 plus or minus 0.13 and the calculated MODIS reflectance at 555 nanometers from the BRDF / Albedo model with near nadir illumination and viewing angles is about 0.96 plus or minus 0.04. The comparisons here demonstrate that the snow surface reflectance underneath the cloud with cloud optical depth of about 1 is significantly lower than that for a clear sky condition.

Assessing spatial and temporal snowpack evolution and melt with time-lapse photography

NASA Astrophysics Data System (ADS)

Bush, C. E.; Ewers, B. E.; Beverly, D.; Speckman, H. N.; Hyde, K.; Ohara, N.

2015-12-01

Snowpack supplies and stores water for many ecosystems of the greater Rocky Mountain region. In Wyoming the snowpack supplies water to 18 states east and west of the Continental Divide. The spatial variability in physical and biological processes creates a heterogeneous pattern of snow evolution. Understanding these processes within individual plots and throughout the entire watershed increases the predictive power of snow distribution, melt rates and contribution to streamflow. However, on site sampling of snow can be an expensive and arduous process. The objective of this experiment was to quantify spatial and temporal patterns of snowpack evolution and melt rates while minimizing perturbations to snowpack through the use of time-lapse photography via trail cameras. Field cameras were assessed as a method to quantify snow depths throughout the 120 ha No Name watershed at approximately 3000 m elevation in central Wyoming. RGB trail cameras were installed at three systematically chosen sites within the watershed to correlate physical and biological drivers of snow distribution. Five stakes were placed in each site in heterogeneous spots that remained in the frame of the camera. Stakes were divided into five centimeter increments, alternating black and white bars, with red bars denoting each half meter. Images were then taken at two-hour intervals over a period of three-months and analyzed with the ImageJ program. Snowpack distributions, as well as melt rates, were variable at both the plot and watershed scales. Meteorological and physical drivers, primarily topography and radiation, accounted for the greatest variability when comparing among plot across the watershed; however, LAI and soil and air temperature were the most significant drivers within plots. Snow-melt rate increased as soils and course woody debris became exposed increasing ground and soil temperature. These data will improve process model predictions of streamflow from the watershed.

Relating C-band Microwave and Optical Satellite Observations as A Function of Snow Thickness on First-Year Sea Ice during the Winter to Summer Transition

NASA Astrophysics Data System (ADS)

Zheng, J.; Yackel, J.

2015-12-01

The Arctic sea ice and its snow cover have a direct impact on both the Arctic and global climate system through their ability to moderate heat exchange across the ocean-sea ice-atmosphere (OSA) interface. Snow cover plays a key role in the OSA interface radiation and energy exchange, as it controls the growth and decay of first-year sea ice (FYI). However, meteoric accumulation and redistribution of snow on FYI is highly stochastic over space and time, which makes it poorly understood. Previous studies have estimated local-scale snow thickness distributions using in-situ technique and modelling but it is spatially limited and challenging due to logistic difficulties. Moreover, snow albedo is also critical for determining the surface energy balance of the OSA during the critical summer ablation season. Even then, due to persistent and widespread cloud cover in the Arctic at various spatio-temporal scales, it is difficult and unreliable to remotely measure albedo of snow cover on FYI in the optical spectrum. Previous studies demonstrate that only large-scale sea ice albedo was successfully estimated using optical-satellite sensors. However, space-borne microwave sensors, with their capability of all-weather and 24-hour imaging, can provide enhanced information about snow cover on FYI. Daily spaceborne C-band scatterometer data (ASCAT) and MODIS data are used to investigate the the seasonal co-evolution of the microwave backscatter coefficient and optical albedo as a function of snow thickness on smooth FYI. The research focuses on snow-covered FYI near Cambridge Bay, Nunavut (Fig.1) during the winter to advanced-melt period (April-June, 2014). The ACSAT time series (Fig.2) show distinct increase in scattering at melt onset indicating the first occurrence of melt water in the snow cover. The corresponding albedo exhibits no decrease at this stage. We show how the standard deviation of ASCAT backscatter on FYI during winter can be used as a proxy for surface roughness and subsequent snow thickness (ie. Rougher surfaces acquire thicker snow covers) and then how this surface manifests into statistically distinguishable surface melt pond fractions which largely governs the optical derived albedo. Such relationships are useful for modelling the subsequent summer melt pond fraction and albedo from winter snow cover.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

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.

Validation of snow depth reconstruction from lapse-rate webcam images against terrestrial laser scanner measurements in centrel Pyrenees

NASA Astrophysics Data System (ADS)

Revuelto, Jesús; Jonas, Tobias; López-Moreno, Juan Ignacio

2015-04-01

Snow distribution in mountain areas plays a key role in many processes as runoff dynamics, ecological cycles or erosion rates. Nevertheless, the acquisition of high resolution snow depth data (SD) in space-time is a complex task that needs the application of remote sensing techniques as Terrestrial Laser Scanning (TLS). Such kind of techniques requires intense field work for obtaining high quality snowpack evolution during a specific time period. Combining TLS data with other remote sensing techniques (satellite images, photogrammetry…) and in-situ measurements could represent an improvement of the available information of a variable with rapid topographic changes. The aim of this study is to reconstruct daily SD distribution from lapse-rate images from a webcam and data from two to three TLS acquisitions during the snow melting periods of 2012, 2013 and 2014. This information is obtained at Izas Experimental catchment in Central Spanish Pyrenees; a catchment of 33ha, with an elevation ranging from 2050 to 2350m a.s.l. The lapse-rate images provide the Snow Covered Area (SCA) evolution at the study site, while TLS allows obtaining high resolution information of SD distribution. With ground control points, lapse-rate images are georrectified and their information is rasterized into a 1-meter resolution Digital Elevation Model. Subsequently, for each snow season, the Melt-Out Date (MOD) of each pixel is obtained. The reconstruction increases the estimated SD lose for each time step (day) in a distributed manner; starting the reconstruction for each grid cell at the MOD (note the reverse time evolution). To do so, the reconstruction has been previously adjusted in time and space as follows. Firstly, the degree day factor (SD lose/positive average temperatures) is calculated from the information measured at an automatic weather station (AWS) located in the catchment. Afterwards, comparing the SD lose at the AWS during a specific time period (i.e. between two TLS acquisitions) to that melted on each grid cell, a coefficient is obtained for spatially distributing the SD loses. For 2012 and 2013, three TLS acquisition campaigns were available during each melting period. This way the first acquisitions of both melting periods were reserved for validation while the other two were considered for adjusting the reconstruction. Validation has revealed a very good performance of the reconstructed SD distribution when compared with the TLS data (r2 values between 0.74 and 0.8 respectively). When no calibration with TLS data was applied for distributing melt rates; this is, using the distribution coefficients for reconstructing SD of precedent years, rather similar accuracy was reached. With the spatial calibration of 2012 and 2013, the reconstructions for the two TLS acquisition dates in 2014, obtained r2 values that ranged between 0.73 and 0.76. This shows the usefulness of lapse-rate images to estimate not only SCA but also the spatial distribution of the SD when combined with TLS acquisition and punctual information on temperature and SD. In such a way it is shown the effectiveness of combining two remote sensing techniques for obtaining distributed information on snow depth.

The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales

NASA Technical Reports Server (NTRS)

Stieglitz, Marc; Ducharne, Agnes; Koster, Randy; Suarez, Max; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

The three-layer snow model is coupled to the global catchment-based Land Surface Model (LSM) of the NASA Seasonal to Interannual Prediction Project (NSIPP) project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period 1987-1988. The various snow processes included in the three-layer model, such as snow melting and re-freezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale.

Improving the Representation of Snow Crystal Properties within a Single-Moment Microphysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, Scott R.

2010-01-01

The assumptions of a single-moment microphysics scheme (NASA Goddard) were evaluated using a variety of surface, aircraft and radar data sets. Fixed distribution intercepts and snow bulk densities fail to represent the vertical variability and diversity of crystal populations for this event. Temperature-based equations have merit, but they can be adversely affected by complex temperature profiles that are inverted or isothermal. Column-based approaches can mitigate complex profiles of temperature but are restricted by the ability of the model to represent cloud depth. Spheres are insufficient for use in CloudSat reflectivity comparisons due to Mie resonance, but reasonable for Rayleigh scattering applications. Microphysics schemes will benefit from a greater range of snow crystal characteristics to accommodate naturally occurring diversity.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

(abstract) A Polarimetric Model for Effects of Brine Infiltrated Snow Cover and Frost Flowers on Sea Ice Backscatter

NASA Technical Reports Server (NTRS)

Nghiem, S. V.; Kwok, R.; Yueh, S. H.

1995-01-01

A polarimetric scattering model is developed to study effects of snow cover and frost flowers with brine infiltration on thin sea ice. Leads containing thin sea ice in the Artic icepack are important to heat exchange with the atmosphere and salt flux into the upper ocean. Surface characteristics of thin sea ice in leads are dominated by the formation of frost flowers with high salinity. In many cases, the thin sea ice layer is covered by snow, which wicks up brine from sea ice due to capillary force. Snow and frost flowers have a significant impact on polarimetric signatures of thin ice, which needs to be studied for accessing the retrieval of geophysical parameters such as ice thickness. Frost flowers or snow layer is modeled with a heterogeneous mixture consisting of randomly oriented ellipsoids and brine infiltration in an air background. Ice crystals are characterized with three different axial lengths to depict the nonspherical shape. Under the covering multispecies medium, the columinar sea-ice layer is an inhomogeneous anisotropic medium composed of ellipsoidal brine inclusions preferentially oriented in the vertical direction in an ice background. The underlying medium is homogeneous sea water. This configuration is described with layered inhomogeneous media containing multiple species of scatterers. The species are allowed to have different size, shape, and permittivity. The strong permittivity fluctuation theory is extended to account for the multispecies in the derivation of effective permittivities with distributions of scatterer orientations characterized by Eulerian rotation angles. Polarimetric backscattering coefficients are obtained consistently with the same physical description used in the effective permittivity calculation. The mulitspecies model allows the inclusion of high-permittivity species to study effects of brine infiltrated snow cover and frost flowers on thin ice. The results suggest that the frost cover with a rough interface significantly increases the backscatter from thin saline ice and the polarimetric signature becomes closer to the isotropic characteristics. The snow cover also modifies polarimetric signatures of thin sea ice depending on the snow mixture and the interface condition.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H.; Skorve, J. E. (Principal Investigator)

1977-01-01

The author has identified the following significant results. During the summer seasons of 1975 and 1976, the snow cover was successfully monitored and measured in the four basins. By using elevation distributions for these basins combined with the measured snow cover percentages, the equivalent snow line altitude was calculated. Equivalent snow line altitude was used in accordance with Mark Meier's definition. Cumulative runoff data were collected for the basins. Tables showing percentage snow cover versus cumulative runoff were worked out for 1975.

Prototyping and Testing a Wireless Sensor Network to Retrieve SWE at High Spatial Resolution

NASA Astrophysics Data System (ADS)

Kang, D.; Barros, A. P.

2007-12-01

A critical challenge in snow research from space is the ability to obtain measurements at the spatial and temporal resolution to characterize the statistical structure of the space-time variability of the physical properties of the snowpack within an area consistent with the pixel resolution in snow hydrology models or that expected from a future NASA mission dedicated to cold region processes. That is, observations of relevant snow dielectric properties are necessary at high spatial and temporal resolution during the accumulation and melt seasons. We present a new wireless sensor network prototype consisting of multiple antennas and buried low-power, multi- channel transmitters operating in L-band that communicate to a central pod equipped with a Vector Signal Analyzer (VSA) that receives, processes and manages the data. Only commercial off-the-shelf hard-ware parts were used to build the sensors. Because the sensors are very low cost and run autonomously, one envisions that self-organizing networks of large numbers of such sensors might be distributed over very large areas, therefore proving much needed data sets for scaling studies. The measurement strategy consists of placing the transmitters the land surface in the beginning of the snow season which are then run autonomously till the end of the spring and waken at pre-determined time-intervals to emit radio frequency signals and thus sample the snowpack. Along with the sensors, an important component of this work entails the development of an estimation algorithm to estimate snow dielectric properties, snow density, and volume fraction of snow (VF) from the time-of-travel, amplitude and phase modification of the multi-channel RF signals as they propagate through the snow-pack. Here, we present results from full system testing and evaluation of the sensors that were conducted at Duke University using ¢®¡Æsynthetic¢®¡¾ limited-area snowpacks (0.5 by 0.5 m2 and 1 by 2 m2) constructed of various combinations of foam layers of different porosities to simulate heterogeneous distributions of water. The existing sensors are currently being primed for field deployment. Discussion is also presented regarding further technology development including power usage, networking, and distribution and operations in remote regions.

Comparison of Two Conceptually Different Physically-based Hydrological Models - Looking Beyond Streamflows

NASA Astrophysics Data System (ADS)

Rousseau, A. N.; Álvarez; Yu, X.; Savary, S.; Duffy, C.

2015-12-01

Most physically-based hydrological models simulate to various extents the relevant watershed processes occurring at different spatiotemporal scales. These models use different physical domain representations (e.g., hydrological response units, discretized control volumes) and numerical solution techniques (e.g., finite difference method, finite element method) as well as a variety of approximations for representing the physical processes. Despite the fact that several models have been developed so far, very few inter-comparison studies have been conducted to check beyond streamflows whether different modeling approaches could simulate in a similar fashion the other processes at the watershed scale. In this study, PIHM (Qu and Duffy, 2007), a fully coupled, distributed model, and HYDROTEL (Fortin et al., 2001; Turcotte et al., 2003, 2007), a pseudo-coupled, semi-distributed model, were compared to check whether the models could corroborate observed streamflows while equally representing other processes as well such as evapotranspiration, snow accumulation/melt or infiltration, etc. For this study, the Young Womans Creek watershed, PA, was used to compare: streamflows (channel routing), actual evapotranspiration, snow water equivalent (snow accumulation and melt), infiltration, recharge, shallow water depth above the soil surface (surface flow), lateral flow into the river (surface and subsurface flow) and height of the saturated soil column (subsurface flow). Despite a lack of observed data for contrasting most of the simulated processes, it can be said that the two models can be used as simulation tools for streamflows, actual evapotranspiration, infiltration, lateral flows into the river, and height of the saturated soil column. However, each process presents particular differences as a result of the physical parameters and the modeling approaches used by each model. Potentially, these differences should be object of further analyses to definitively confirm or reject modeling hypotheses.

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

NASA Technical Reports Server (NTRS)

Petrich, Chris; Eicken, Hajo; Polashenski, Christopher M.; Sturm, Matthew; Harbeck, Jeremy P.; Perovich, Donald K.; Finnegan, David C.

2012-01-01

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

He, C.; Liou, K. N.; Takano, Y.

2017-12-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Close packing effects on clean and dirty snow albedo and associated climatic implications

NASA Astrophysics Data System (ADS)

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

2017-04-01

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close-packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC-contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to 0.05, whereas it enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC-snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

NASA Astrophysics Data System (ADS)

Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

2017-12-01

In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Runoff sensitivity to snowmelt process representation for the conterminous United States

NASA Astrophysics Data System (ADS)

Driscoll, J. M.; Sexstone, G. A.

2017-12-01

Watershed-scale hydrologic models that operate at a continental extent must balance detailed descriptions of spatiotemporal variability against simplified process representations across a diverse range of physiographic and climatic regimes. Some of these models describe the sub-grid variability of snow-cover extent and snowmelt processes using snow depletion curves (SDCs), which relate the snow covered area to the snow water equivalent (SWE). The U.S. Geological Survey's National Hydrologic Modeling (NHM) system run with the daily-timestep Precipitation Runoff Modeling System (PRMS), or NHM-PRMS, originally used two default SDCs to describe snowmelt processes: one for hydrologic response units with elevations above treeline and one for hydrologic response units with elevations below treeline. Seeking to improve upon this approach, spatially-distributed SWE, derived from Snow Data Assimilation System (SNODAS) over eleven years, was used to develop new, site-specific SDCs for each hydrologic response unit in the NHM-PRMS. This study investigates the sensitivity of NHM-PRMS to changes in SDCs for a 30-year historical period by first running the NHM-PRMS with the default binary SDCs and then with the site-specific SDCs. Comparison of simulated snowmelt and streamflow response during the snowmelt season allows for spatial analysis and grouping of the sensitivity of streamflow to changes in snowmelt dynamics. Site-specific SDCs allow for the identification and categorization of areas where faster or slower snowmelt could have a greater impact to water resources. These new SDCs can be used to identify locations where increased SWE observation density would be most useful for seasonal water availability assessments.

A comparison of two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund

NASA Astrophysics Data System (ADS)

Luks, B.; Osuch, M.; Romanowicz, R. J.

2012-04-01

We compare two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund. In the first approach we apply physically-based Utah Energy Balance Snow Accumulation and Melt Model (UEB) (Tarboton et al., 1995; Tarboton and Luce, 1996). The model uses a lumped representation of the snowpack with two primary state variables: snow water equivalence and energy. Its main driving inputs are: air temperature, precipitation, wind speed, humidity and radiation (estimated from the diurnal temperature range). Those variables are used for physically-based calculations of radiative, sensible, latent and advective heat exchanges with a 3 hours time step. The second method is an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow cover , based on daily meteorological measurements from the same area. A dynamic Stochastic Transfer Function model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. We focus on the analysis of uncertainty of both model outputs. In the time series approach, the applied techniques also provide estimates of the modeling errors and the uncertainty of the model parameters. In the first, physically-based approach the applied UEB model is deterministic. It assumes that the observations are without errors and that the model structure perfectly describes the processes within the snowpack. To take into account the model and observation errors, we applied a version of the Generalized Likelihood Uncertainty Estimation technique (GLUE). This technique also provide estimates of the modelling errors and the uncertainty of the model parameters. The observed snowpack water equivalent values are compared with those simulated with 95% confidence bounds. This work was supported by National Science Centre of Poland (grant no. 7879/B/P01/2011/40). Tarboton, D. G., T. G. Chowdhury and T. H. Jackson, 1995. A Spatially Distributed Energy Balance Snowmelt Model. In K. A. Tonnessen, M. W. Williams and M. Tranter (Ed.), Proceedings of a Boulder Symposium, July 3-14, IAHS Publ. no. 228, pp. 141-155. Tarboton, D. G. and C. H. Luce, 1996. Utah Energy Balance Snow Accumulation and Melt Model (UEB). Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station (http://www.engineering.usu.edu/dtarb/). 64 pp.

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

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

Flanner, M. G.; Liu, Xiaohong; Zhou, Cheng

2012-05-30

Here we explore light absorption by snowpack containing black carbon (BC) particles residing within ice grains. Basic considerations of particle volumes and BC/snow mass concentrations show that there are generally 0:05-109 BC particles for each ice grain. This suggests that internal BC is likely distributed as multiple inclusions within ice grains, and thus the dynamic effective medium approximation (DEMA) (Chylek and Srivastava, 1983) is a more appropriate optical representation for BC/ice composites than coated-sphere or standard mixing approximations. DEMA calculations show that the 460 nm absorption cross-section of BC/ice composites, normalized to the mass of BC, is typically enhanced bymore » factors of 1.8-2.1 relative to interstitial BC. BC effective radius is the dominant cause of variation in this enhancement, compared with ice grain size and BC volume fraction. We apply two atmospheric aerosol models that simulate interstitial and within-hydrometeor BC lifecycles. Although only {approx}2% of the atmospheric BC burden is cloud-borne, 71-83% of the BC deposited to global snow and sea-ice surfaces occurs within hydrometeors. Key processes responsible for within-snow BC deposition are development of hydrophilic coatings on BC, activation of liquid droplets, and subsequent snow formation through riming or ice nucleation by other species and aggregation/accretion of ice particles. Applying deposition fields from these aerosol models in offline snow and sea-ice simulations, we calculate that 32-73% of BC in global surface snow resides within ice grains. This fraction is smaller than the within-hydrometeor deposition fraction because meltwater flux preferentially removes internal BC, while sublimation and freezing within snowpack expose internal BC. Incorporating the DEMA into a global climate model, we simulate increases in BC/snow radiative forcing of 43-86%, relative to scenarios that apply external optical properties to all BC. We show that snow metamorphism driven by diffusive vapor transfer likely proceeds too slowly to alter the mass of internal BC while it is radiatively active, but neglected processes like wind pumping and convection may play much larger roles. These results suggest that a large portion of BC in surface snowpack may reside within ice grains and increase BC/snow radiative forcing, although measurements to evaluate this are lacking. Finally, previous studies of BC/snow forcing that neglected this absorption enhancement are not necessarily biased low, because of application of absorption-enhancing sulfate coatings to hydrophilic BC, neglect of coincident absorption by dust in snow, and implicit treatment of cloud-borne BC resulting in longer-range transport.« less

Distributed snow data as a tool to inform water management decisions: Using Airborne Snow Observatory (ASO) at the Hetch Hetchy Reservoir in Yosemite National Park, City and County of San Francisco.

NASA Astrophysics Data System (ADS)

Graham, C. B.

2016-12-01

The timing and magnitude of spring snowmelt and runoff is critical in managing reservoirs in the Western United States. The Hetch Hetchy Reservoir in Yosemite National Park provides drinking water for 2.6 million customers in over 30 communities in the San Francisco Bay Area. Power generation from Hetch Hetchy meets the municipal load of the City and County of San Francisco. Water from the Hetch Hetchy Reservoir is also released in the Tuolumne River, supporting critical ecosystems in Yosemite National Park and the Stanislaus National Forest. Better predictions of long (seasonal) and short (weekly) term streamflow allow for more secure water resource planning, earlier power generation and ecologically beneficial releases from the Reservoir. Hetch Hetchy Reservoir is fed by snow dominated watersheds in the Sierra Mountains. Better knowledge of snowpack conditions allow for better predictions of inflows, both at the seasonal and at the weekly time scales. The ASO project has provided the managers of Hetch Hetchy Reservoir with high resolution estimates of total snowpack and snowpack distribution in the 460 mi2 Hetch Hetchy. We show that there is a tight correlation between snowpack estimates and future streamflow, allowing earlier, more confident operational decisions. We also show how distributed SWE estimates were used to develop and test a hydrologic model of the system (PRMS). This model, calibrated directly to snowpack conditions, is shown to correctly simulate snowpack volume and distribution, as well as streamflow patterns.

Use of Moderate-Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos

NASA Astrophysics Data System (ADS)

Roesch, Andreas; Schaaf, Crystal; Gao, Feng

2004-06-01

Moderate-Resolution Imaging Spectroradiometer (MODIS) surface albedo at high spatial and spectral resolution is compared with other remotely sensed climatologies, ground-based data, and albedos simulated with the European Center/Hamburg 4 (ECHAM4) global climate model at T42 resolution. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterizations in weather forecast and climate models. The remotely sensed PINKER surface albedo climatology follows the MODIS estimates fairly well in both the visible and near-infrared spectra, whereas ECHAM4 simulates high positive albedo biases over snow-covered boreal forests and the Himalayas. In contrast, the ECHAM4 albedo is probably too low over the Sahara sand desert and adjacent steppes. The study clearly indicates that neglecting albedo variations within T42 grid boxes leads to significant errors in the simulated regional climate and horizontal fluxes, mainly in mountainous and/or snow-covered regions. MODIS surface albedo at 0.05 resolution agrees quite well with in situ field measurements collected at Baseline Surface Radiation Network (BSRN) sites during snow-free periods, while significant positive biases are found under snow-covered conditions, mainly due to differences in the vegetation cover at the BSRN site (short grass) and the vegetation within the larger MODIS grid box. Black sky (direct beam) albedo from the MODIS bidirectional reflectance distribution function model captures the diurnal albedo cycle at BSRN sites with sufficient accuracy. The greatest negative biases are generally found when the Sun is low. A realistic approach for relating albedo and zenith angle has been proposed. Detailed evaluations have demonstrated that ignoring the zenith angle dependence may lead to significant errors in the surface energy balance.

Expanded science and management utllity of SWE and albedo data from the NASA/JPL Airborne Snow Observatory

NASA Astrophysics Data System (ADS)

Painter, T. H.; Deems, J. S.; Marks, D. G.; Hedrick, A. R.; Bormann, K.; Skiles, S. M.; Boardman, J. W.; Graham, C. B.; McGurk, B. J.; Gehrke, F.; Berisford, D. F.; Ferraz, A.; Saatchi, S.; Schimel, D.

2016-12-01

The NASA Airborne Snow Observatory (ASO), an imaging spectrometer and imaging LiDAR system, to quantify snow water equivalent and snow albedo, provide unprecedented knowledge of snow properties, and provide complete, robust inputs to snowmelt runoff models, water management models, and systems of the future. This talk presents results from the fourth year of the ASO program, 2016, and the now four years of data record in the Western United States. Following on the heels of the most intense, sustained drought in California history, 2016 held promise of a large snowfall year due to an intense El Nino anomaly. Ultimately, the year had approximately 85% of average peak SWE. In the Sierra Nevada, ASO measured 10x greater SWE than near peak in the dramatic 2015 drought year, and twice that of the more moderate drought year of 2013. Water managers in the Sierra were using these data regularly and extending the dynamic range of newly established relationships between accumulated runoff (circa April through July runoff) and ASO total basin SWE acquisitions. ASO also participated in the NASA OLYMPEX project by flying the entire snow-covered reghions of the Olympic Peninsula for distributed SWE. These data are now being used to validate snowfall estimates from modeling and accumulation patterns as inferred from the NASA Global Precipitation Measurement mission (GPM). The ASO snow program expanded to acquire data in the McKenzie and Deschutes Rivers of Oregon in participation with university and state/federal agencies; Sagehen and Lee Vining basins in the Sierra Nevada, California; the Reynolds Creek Experimental Watershed in Idaho; and the East River, in the Colorado River Basin. These regions extend the existing program flying the Tuolumne, Merced, Lakes, Rush Creek, and Middle+South Forks of Kings River Basins in the California Sierra Nevada and the Upper Rio Grande, Conejos, and Uncompahgre Basins in the Colorado Rocky Mountains.The ASO SWE and albedo data are now being used to constrain various hydrologic models for water cycle science of varying complexity and expanding empirical and physically-based water management models.

Modelling CLPX IOP3 Radiometric Data by Means of the Dense Media Theory: Preliminary Results for the LSOS Test Site

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Kim, Edward J.; Cline, Don; Graf, Tobias; Koike, Toshio; Armstrong, Richard; Brodzik, Mary Jo; Hardy, Janet

2003-01-01

The capabilities of the Dense Media Radiative Transfer model using the Quasi Crystalline Approximation with Coherent Potential (QCA-CP) to reproduce measured radiometric data were tested using the University of Tokyo Ground Based Microwave Radiometer (GBMR-7) during the third Intensive Observation Period (IOP3) of the NASA Cold-land Processes Field Experiment (CLPX). The data were collected at the Local-Scale Observation Site (LSOS), a 0.8-ha study site consisting of two open meadows separated by trees. Intensive measurements were also made of snow depth and temperature, density, and grain size profiles. A DMRT model is needed to describe radiative transfer in a medium such as snow because the assumption of independent scattering used in classical radiative transfer theory (CRT) is not valid. Validation of the DMRT approach requires a relationship between measured snow grain size and the DMRT approximation of snow grain radius as spherical particles with a mean radius of the log-normal particle-size distribution. This relationship is very important for a better understanding of snow modelling and for practical applications. DMRT simulations were compared with observations of microwave brightnesses at 18.7, 36.5 and 89-GHz (V and H polarizations) collected on February-1 9-25, 2003. Observation angles ranged from 30\\deg to 70\\deg. Model inputs included measured snow parameters except mean grain size. The average snow temperature, fractional volume and depth were held constant, together with the ice and soil permittivities. The minimum and maximum measured mean grain sizes were used to test the capabilities of the DMRT to reproduce the brightnesses as upper and lower limits. The sensitivity to the largest and smallest measured grain size in the three classes of minimum, medium and maximum observed grain sizes was also investigated. DMRT particle sizes yielding a best-fit to the experimental data for each date were computed. Results show that the measured brightnesses fall within the range of simulated brightnesses using the smallest and largest measured grain size values. The DMRT best-fit radii are comparable to the average radii for the medium observed grain sizes.

Horizontal distribution and levels of heavy metals in the biggest snowstorm in a century in Shenyang, China.

PubMed

An, Jing; Zhou, Qixing; Liu, Weitao; Ren, Liping

2008-01-01

The horizontal distribution and levels of heavy metals in the biggest snowstorm in Shenyang since 1904 were investigated by analyzing 4 metals (As, Cd, Pb, and Cu) in a series of ultraclean samples collected from 17 sites distributed in different regions of the Shenyang area, China. The results showed that the concentrations of all the 4 heavy metals in snow from the industrial regions were high, up to 7.3 (As), 2.2 (Cd), 850.0 (Pb), and 0.197-20.2 (Cu) microg/kg, respectively. In the suburb, in contrast, their concentrations were not detected, except for As. Because of the long-term application of arsenical pesticides and herbicides, As was detected in the snow samples which collected on the farm. As, Cd, and Pb were also detected in the snow samples collected from the parks, the residential areas, and the commercial districts mainly by reason of human activities. In a sense, long-term industrial activities, traffic activities, coal combustion, and agricultural activities affected the horizontal distribution and levels of these heavy metals in snow differently. The data relating to the horizontal distribution and concentrations of heavy metals in the snow under extreme climatic conditions can provide with a unique snapshot of environmental pollution situation and behaviors in urban areas.

Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

NASA Astrophysics Data System (ADS)

Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

2016-12-01

Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

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

NASA Astrophysics Data System (ADS)

Kadlec, J.; Ames, D. P.

2014-12-01

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

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

PubMed

Braunisch, Veronika; Patthey, Patrick; Arlettaz, Raphaël

2011-04-01

Outdoor winter recreation exerts an increasing pressure upon mountain ecosystems, with unpredictable, free-ranging activities (e.g., ski mountaineering, snowboarding, and snowshoeing) representing a major source of stress for wildlife. Mitigating anthropogenic disturbance requires the spatially explicit prediction of the interference between the activities of humans and wildlife. We applied spatial modeling to localize conflict zones between wintering Black Grouse (Tetrao tetrix), a declining species of Alpine timberline ecosystems, and two free-ranging winter sports (off-piste skiing [including snow-boarding] and snowshoeing). Track data (snow-sports and birds' traces) obtained from aerial photographs taken over a 585-km transect running along the timberline, implemented within a maximum entropy model, were used to predict the occurrence of snow sports and Black Grouse as a function of landscape characteristics. By modeling Black Grouse presence in the theoretical absence of free-ranging activities and ski infrastructure, we first estimated the amount of habitat reduction caused by these two factors. The models were then extrapolated to the altitudinal range occupied by Black Grouse, while the spatial extent and intensity of potential conflict were assessed by calculating the probability of human-wildlife co-occurrence. The two snow-sports showed different distribution patterns. Skiers' occurrence was mainly determined by ski-lift presence and a smooth terrain, while snowshoers' occurrence was linked to hiking or skiing routes and moderate slopes. Wintering Black Grouse avoided ski lifts and areas frequented by free-ranging snow sports. According to the models, Black Grouse have faced a substantial reduction of suitable wintering habitat along the timberline transect: 12% due to ski infrastructure and another 16% when adding free-ranging activities. Extrapolating the models over the whole study area results in an overall habitat loss due to ski infrastructure of 10%, while there was a > 10% probability of human-wildlife encounters on 67% of the remaining area of suitable wintering habitat. Only 23% of the wintering habitat was thus free of anthropogenic disturbance. By identifying zones of potential conflict, while rating its relative intensity, our model provides a powerful tool to delineate and prioritize areas where wildlife winter refuges and visitor steering measures should be implemented.

SkiSim - A semi-distributed model to assess the impact of climate change on ski season length and snowmaking

NASA Astrophysics Data System (ADS)

Steiger, R.

2009-04-01

Winter tourism is highly sensitive to climate change. The altitudinally- dependent line of natural snow reliability (e.g. Abegg et al. 2007) is losing its relevance for skilift operators, as for example in Tyrol 70% of the ski slopes are already covered by technical snow production. Less snowfall at lower altitudes and rising temperatures increase the demand for technical snow. Simultaneously, periods cold enough for snowmaking will get shorter and less frequent. Studies incorporating snowmaking are still rare and there is need for an assessment of the suitability of snowmaking as a successful adaptation strategy. The aim of this study is to assess the development of ski season lengths and snowmaking requirements under different climate change scenarios for ski areas in North and South Tyrol. A semi-distributed ski season model "SkiSim" was developed based on the temperature-index snow model of Kleindienst (2000) and the ski season model of Scott et al. (2007). As input variables only daily minimum and maximum temperature and precipitation are needed. Additionally, measured snowfall and snow depth are used for the calibration of the snowfall temperature at each climate station. Further snow model parameters (e.g. degree-day factor, snow metamorphosis) were adopted from Kleindienst (2000) and tested at all stations. Snowmaking module parameters were derived from interviews with ski area managers based on the methodology of Scott et al. (2007). Daily data (1960-2100) of the high resolution climate model REMO (10x10km) was used to produce climate change scenarios for four time horizons (2020s, 2030s, 2050s, 2080s) under two emission scenarios (A1B and B1). The climate change signal for change in temperature, (in standard deviation of temperature, in precipitation and in dry and wet spells) was processed on climate station data by the LARS weather generator. In winter (DJF), the change signal for daily average temperature from the 2020s to 2080s compared to 1971-2000 is +1,4°C to +3,5°C in the B1 scenario, and from +1,2°C to 5,7°C in the A1B scenario. Precipitation change in the A1B scenario is between +5% and +10%, in B1 it is +5% to +20%. The synthetic daily data produced by LARS-WG is the input data for the ski season model. Temperature and precipitation are distributed for each 100m altitude band, ranging from the lowest to the highest point of the ski area. The ski season model produces results for the whole ski area and not just for one single point (e.g. the lowest point in a ski area). Thus it is possible to assess the impact of climate change on ski season length and snowmaking in much greater detail than in previous studies. Based on these results, the consequences for winter tourism destinations can be assessed. Following the methodology of Breiling et al. (1997, 2008), the contribution of winter tourism to the regional economy and the number employments in tourism serve as an indicator for the vulnerability of communities and regions to climate change. In total 120 ski areas in North and South Tyrol are investigated in the running project. Results of three ski areas, different in size and altitude, will be presented. Sources: Abegg, B., S. Agrawala, Crick, F., de Montfalcon, A. (2007): Climate change impacts and adaptation in winter tourism. In: S. Agrawala, Climate Change in the European Alps. Adapting Winter Tourism and Natural Hazards Management. Paris, OECD: 25-60. Breiling, M., Charamza, P., Skage, O. (1997): Klimasensibilität österreichischer Bezirke mit besonderer Berücksichtigung des Wintertourismus. Rapport 1,Department of Landscape Planning Alnarp, Swedish University of Agricultural Sciences. Breiling, M., Charamza, P.,Feilmayr, W. (2008): Klimasensibilität des Salzburger Wintertourismus nach Bezirken, TTL, TU Vienna. Kleindienst, H. (2000): Snow hydrological models as tools for snow cover assessment and water resources management, Bern. Phd. Scott, D., G. McBoyle, Minogue, A. (2007): Climate Change and Quebećs Ski Industry. In: Global Environmental Change 17, 181-190.

Detection and quantification of snow algae with an airborne imaging spectrometer.

PubMed

Painter, T H; Duval, B; Thomas, W H; Mendez, M; Heintzelman, S; Dozier, J

2001-11-01

We describe spectral reflectance measurements of snow containing the snow alga Chlamydomonas nivalis and a model to retrieve snow algal concentrations from airborne imaging spectrometer data. Because cells of C. nivalis absorb at specific wavelengths in regions indicative of carotenoids (astaxanthin esters, lutein, beta-carotene) and chlorophylls a and b, the spectral signature of snow containing C. nivalis is distinct from that of snow without algae. The spectral reflectance of snow containing C. nivalis is separable from that of snow without algae due to carotenoid absorption in the wavelength range from 0.4 to 0.58 microm and chlorophyll a and b absorption in the wavelength range from 0.6 to 0.7 microm. The integral of the scaled chlorophyll a and b absorption feature (I(0.68)) varies with algal concentration (C(a)). Using the relationship C(a) = 81019.2 I(0.68) + 845.2, we inverted Airborne Visible Infrared Imaging Spectrometer reflectance data collected in the Tioga Pass region of the Sierra Nevada in California to determine algal concentration. For the 5.5-km(2) region imaged, the mean algal concentration was 1,306 cells ml(-1), the standard deviation was 1,740 cells ml(-1), and the coefficient of variation was 1.33. The retrieved spatial distribution was consistent with observations made in the field. From the spatial estimates of algal concentration, we calculated a total imaged algal biomass of 16.55 kg for the 0.495-km(2) snow-covered area, which gave an areal biomass concentration of 0.033 g/m(2).

Detection and Quantification of Snow Algae with an Airborne Imaging Spectrometer

PubMed Central

Painter, Thomas H.; Duval, Brian; Thomas, William H.; Mendez, Maria; Heintzelman, Sara; Dozier, Jeff

2001-01-01

We describe spectral reflectance measurements of snow containing the snow alga Chlamydomonas nivalis and a model to retrieve snow algal concentrations from airborne imaging spectrometer data. Because cells of C. nivalis absorb at specific wavelengths in regions indicative of carotenoids (astaxanthin esters, lutein, β-carotene) and chlorophylls a and b, the spectral signature of snow containing C. nivalis is distinct from that of snow without algae. The spectral reflectance of snow containing C. nivalis is separable from that of snow without algae due to carotenoid absorption in the wavelength range from 0.4 to 0.58 μm and chlorophyll a and b absorption in the wavelength range from 0.6 to 0.7 μm. The integral of the scaled chlorophyll a and b absorption feature (I0.68) varies with algal concentration (Ca). Using the relationship Ca = 81019.2 I0.68 + 845.2, we inverted Airborne Visible Infrared Imaging Spectrometer reflectance data collected in the Tioga Pass region of the Sierra Nevada in California to determine algal concentration. For the 5.5-km2 region imaged, the mean algal concentration was 1,306 cells ml−1, the standard deviation was 1,740 cells ml−1, and the coefficient of variation was 1.33. The retrieved spatial distribution was consistent with observations made in the field. From the spatial estimates of algal concentration, we calculated a total imaged algal biomass of 16.55 kg for the 0.495-km2 snow-covered area, which gave an areal biomass concentration of 0.033 g/m2. PMID:11679355

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

Effects of snow on fisher and marten distributions in Idaho

Treesearch

Nathan Albrecht; C. Heusser; M. Schwartz; J. Sauder; R. Vinkey

2013-01-01

Studies have suggested that deep snow may limit fisher (Martes pennanti) distribution, and that fisher populations may in turn limit marten (Martes americana) distribution. We tested these hypotheses in the Northern Rocky Mountains of Idaho, a region which differs from previous study areas in its climate and relative fisher and marten abundance, but in which very...

76 FR 7238 - Pipeline Safety: Dangers of Abnormal Snow and Ice Build-Up on Gas Distribution Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-09

... been related to either the stress of snow and ice or the malfunction of pressure control equipment due... to have been related to either the stress of snow and ice or malfunction of pressure control... from the stresses imposed by the additional loading of the snow or ice. Damage to facilities may also...

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

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

Estimation de l'equivalent en eau de la neige en milieu subarctique du Quebec par teledetection micro-ondes passives

NASA Astrophysics Data System (ADS)

Vachon, Francois

The snow cover (extent, depth and water equivalent) is an important factor in assessing the water balance of a territory. In a context of deregulation of electricity, better knowledge of the quantity of water resulting from snowmelt that will be available for hydroelectric power generation has become a major challenge for the managers of Hydro-Quebec's generating plant. In fact, the snow on the ground represents nearly one third of Hydro-Quebec's annual energy reserve and the proportion is even higher for northern watersheds. Snowcover knowledge would therefore help optimize the management of energy stocks. The issue is especially important when one considers that better management of water resources can lead to substantial economic benefits. The Research Institute of Hydro-Quebec (IREQ), our research partner, is currently attempting to optimize the streamfiow forecasts made by its hydrological models by improving the quality of the inputs. These include a parameter known as the snow water equivalent (SWE) which characterizes the properties of the snow cover. At the present time, SWE data is obtained from in situ measurements, which are both sporadic and scattered, and does not allow the temporal and spatial variability of SWE to be characterized adequately for the needs of hydrological models. This research project proposes to provide the Quebec utility's hydrological models with distributed SWE information about its northern watersheds. The targeted accuracy is 15% for the proposed period of analysis covering the winter months of January, February and March of 2001 to 2006. The methodology is based on the HUT snow emission model and uses the passive microwave remote sensing data acquired by the SSM/I sensor. Monitoring of the temporal and spatial variations in SWE is done by inversion of the model and benefits from the assimilation of in situ data to characterize the state of snow cover during the season. Experimental results show that the assimilation technique of in situ data (density and depth) can reproduce the temporal variations in SWE with a RMSE error of 15.9% (R2=0.76). The analysis of land cover within the SSMI pixels can reduce this error to 14.6% ( R2=0.66) for SWE values below 300 mm. Moreover, the results show that the fluctuations of SWE values are driven by changes in snow depths. Indeed, the use of a constant value for the density of snow is feasible and makes it possible to get as good if not better results. These results will allow IREQ to assess the suitability of using snow cover information provided by the remote sensing data in its forecasting models. This improvement in SWE characterization will meet the needs of IREQ for its work on optimization of the quality of hydrological simulations. The originality and relevance of this work are based primarily on the type of method used to quantify SWE and the site where it is applied. The proposed method focuses on the inversion of the HUT model from passive remote sensing data and assimilates in situ data. Moreover, this approach allows high SWE values (> 300 mm) to be quantified, which was impossible with previous methods. These high SWE values are encountered in areas with large amounts of snow such as northern Quebec. Keywords. remote sensing, microwave, snow water equivalent (SWE), model, retrieval, data assimilation, SWE monitoring, spatialization Complete reference. Vachon, F. (2009) Snow water equivalent retrieval in a subartic environment of Quebec using passive microwave remote sensing. Ph.D. Thesis, Sherbrooke University, Sherbrooke, 211 p.

Ecosystem dynamics and disturbance in mountain wildernesses: assessing vulnerability of natural resources to change

Treesearch

Daniel B. Fagre; David L. Peterson

2000-01-01

An integrated program of ecosystem modeling and extensive field studies at Glacier and Olympic National Parks has quantified many of the ecological processes affected by climatic variability and disturbance. Models have successfully estimated snow distribution, annual watershed discharge, and stream temperature variation based on seven years of monitoring. Various...

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Evaluating precipitation in a regional climate model using ground-based radar measurements in Dronning Maud Land, East Antarctica

NASA Astrophysics Data System (ADS)

Gorodetskaya, Irina; Maahn, Maximilan; Gallée, Hubert; Souverijns, Niels; Gossart, Alexandra; Kneifel, Stefan; Crewell, Susanne; Van Lipzig, Nicole

2017-04-01

Occasional very intense snowfall events over Dronning Maud Land (DML) region in East Antarctica, contributed significantly to the entire Antarctic ice sheet surface mass balance (SMB) during the last years. The meteorological-cloud-precipitation observatory running at the Princess Elisabeth station (PE) in the DML escarpment zone since 2009 (HYDRANT/AEROCLOUD projects), provides unique opportunity to estimate contribution of precipitation to the local snow accumulation and new data for evaluating precipitation in climate models. Our previous work using PE measurements showed that occasional intense precipitation events determine the total local yearly SMB and account for its large interannual variability. Here we use radar measurements to evaluate precipitation in a regional climate model with a special focus on intense precipitation events together with the large-scale atmospheric dynamics responsible for these events. The coupled snow-atmosphere regional climate model MAR (Modèle Atmosphérique Régional) is used to simulate climate and SMB in DML at 5-km horizontal resolution during 2012 using initial and boundary conditions from the European Centre for Medium-range Weather Forecasts (ECMWF) Interim re-analysis atmospheric and oceanic fields. Two evaluation approaches are used: observations-to-model and model-to-observations. In the first approach, snowfall rate (S) is derived from the MRR (vertically profiling 24-GHz precipitation radar) effective reflectivity factor (Ze) at 400 m agl using various Ze-S relationships for dry snow. The uncertainty in Ze-S relationships is constrained using snow particle size distribution from Snow Video Imager - Precipitation Imaging Package (SVI/PIP) and information about particle shapes. For the second approach we apply the Passive and Active Microwave radiative TRAnsfer model (PAMTRA), which allows direct comparison of the radar-measured and climate model-based vertical profiles of the radar Ze and Doppler velocity. In MAR, the mass and terminal velocity of snow particles are defined as for the graupel-like snowflakes of hexagonal type, determining single scattering properties for snow hydrometeors used as input (along with cloud particle properties and atmospheric parameters) into PAMTRA. MAR simulates well the timing of major synoptic-scale precipitation events, while overestimating snowfall rate during the intense precipitation events beyond the Ze-S relationship uncertainty. This bias is also evident in significantly longer tail of the frequency distribution towards high values for MAR synthetic Ze near the surface compared to PE radar. This bias can be related to the differences both in the amount and type of snowflakes reaching the surface. The most intense precipitation event contributing almost 50% to the local yearly SMB occurred on 6 November 2012 and was associated with an atmospheric river. MAR model produced more than twice as much precipitation compared to PE radar measurements on this event. Reasons for this high bias are investigated by looking at the moisture transports, cloud properties (ice/liquid occurrence and cloud vertical structure), and precipitation formation efficiency especially related to the mixed-phase clouds (the Bergeron-Findeisen process).

Spatial scales of light transmission through Antarctic pack ice: Surface flooding vs. floe-size distribution

NASA Astrophysics Data System (ADS)

Arndt, S.; Meiners, K.; Krumpen, T.; Ricker, R.; Nicolaus, M.

2016-12-01

Snow on sea ice plays a crucial role for interactions between the ocean and atmosphere within the climate system of polar regions. Antarctic sea ice is covered with snow during most of the year. The snow contributes substantially to the sea-ice mass budget as the heavy snow loads can depress the ice below water level causing flooding. Refreezing of the snow and seawater mixture results in snow-ice formation on the ice surface. The snow cover determines also the amount of light being reflected, absorbed, and transmitted into the upper ocean, determining the surface energy budget of ice-covered oceans. The amount of light penetrating through sea ice into the upper ocean is of critical importance for the timing and amount of bottom sea-ice melt, biogeochemical processes and under-ice ecosystems. Here, we present results of several recent observations in the Weddell Sea measuring solar radiation under Antarctic sea ice with instrumented Remotely Operated Vehicles (ROV). The combination of under-ice optical measurements with simultaneous characterization of surface properties, such as sea-ice thickness and snow depth, allows the identification of key processes controlling the spatial distribution of the under-ice light. Thus, our results show how the distinction between flooded and non-flooded sea-ice regimes dominates the spatial scales of under-ice light variability for areas smaller than 100-by-100m. In contrast, the variability on larger scales seems to be controlled by the floe-size distribution and the associated lateral incidence of light. These results are related to recent studies on the spatial variability of Arctic under-ice light fields focusing on the distinctly differing dominant surface properties between the northern (e.g. summer melt ponds) and southern (e.g. year-round snow cover, surface flooding) hemisphere sea-ice cover.

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Picard, Ghislain; Brucker, Ludovic; Roy, Alexandre; DuPont, FLorent; Champollion, Nicolas; Morin, Samuel

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).

Precipitation Modeling over the Greenland and Antarctic Ice Sheets and the Relationship to the Surface Mass Balance and Climate

NASA Technical Reports Server (NTRS)

Bromwich, David H.; Chen, Qui-Shi

2005-01-01

Atmospheric numerical simulation and dynamic retrieval method with atmospheric numerical analyses are used to assess the spatial and temporal variability of Antarctic precipitation for the last two decades. First, the Polar MM5 has been run over Antarctica to study the Antarctic precipitation. With a horizontal resolution of 60km, the Polar MM5 has been run for the period of July 1996 through June 1999 in a series of short-term forecasts from initial and boundary conditions provided by the ECMWF operational analyses. In comparison with climatological maps, the major features of the spatial distribution of Antarctic precipitation are well captured by the Polar MM5. Drift snow effects on redistribution of surface accumulation over Antarctica are also assessed with surface wind fields from Polar MM5 in this study. There are complex divergence and convergence patterns of drift snow transport over Antarctica, especially along the coast. It is found that areas with large drift snow transport convergence and divergence are located around escarpment areas where there is large katabatic wind acceleration. In addition, areas with large snow transport divergence are generally accompanied by areas with large snow transport convergence nearby, indicating that drift snow transport is of local importance for the redistribution of the snowfall

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.

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

NASA Astrophysics Data System (ADS)

Aleinov, I.

2001-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Using CREST to Model Floods for the Upper Missouri Basin

NASA Astrophysics Data System (ADS)

Rodriguez, L.; Spelman, D.; Skym, P.; Oguamanam, M.

2012-12-01

The Upper Missouri River basin in Montana is prone to frequent floods during the months of May through June. During the summer of 2011, the duration of heavy rain and high snow melt caused floods in the Missouri Watershed to last for several weeks. Although organizations and citizens are aware of the flooding, the severity of any given flood is difficult to predict. Thus, a flood forecasting system would benefit the community by providing advance notice of areas prone to floods. The team addressed these issues by calibrating a fully distributed hydrological model on this region using the University of Oklahoma's Coupled Routing Excess STorage (CREST) 2.0 model. The CREST model contains ten internal parameters that must be optimized through calibration to in-situ data which was done using only remotely sensed data as inputs; these include: rainfall from TRMM, Digital Elevation Model (DEM) from SRTM, and potential evapotranspiration (PET). At a one kilometer spatial and three hour temporal resolutions, CREST was calibrated over a three year period. This calibration was unsuccessful, resulting in a maximum NSCE of just 0.24, due to the heavy impact of snow-pack melt on the hydrology of the Upper Missouri River watershed and the lack of snow melt inputs to the model. Because of this, a modeled snow melt product, which was selected to be the Snow Data Assimilation System (SNODAS), was implemented into the model for the first time. This implementation required substantial data processing prior to input as well as substantial troubleshooting within the CREST model. Additionally, the San Bernard watershed, which is in the absence of snow, was used and calibrated in the model for comparison. The arrangement of these calibrations was performed, and the necessary steps required were documented. However, the actual calibrations were not carried out due to lack of access needed in computational power and time. Significant progress was made in understanding the fundamental steps used in the implementation of various types of input data into the CREST model. This progress was documented in the aid of future calibrations and widespread use of the model. With the information and groundwork established by the team, final calibration and validation can be done on the Upper Missouri River which will allow insight for our partners to incorporate a global application of CREST.

SNOWMIP2: An evaluation of forest snow process simulations

Treesearch

Richard Essery; Nick Rutter; John Pomeroy; Robert Baxter; Manfred Stahli; David Gustafsson; Alan Barr; Paul Bartlett; Kelly Elder

2009-01-01

Models of terrestrial snow cover, or snow modules within land surface models, are used in many meteorological, hydrological, and ecological applications. Such models were developed first, and have achieved their greatest sophistication, for snow in open areas; however, huge tracts of the Northern Hemisphere both have seasonal snow cover and are forested (Fig. 1)....

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

Science.gov Websites

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

Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections

NASA Astrophysics Data System (ADS)

Luca, Alejandro Di; Evans, Jason P.; Ji, Fei

2017-10-01

In this study we evaluate the ability of an ensemble of high-resolution Regional Climate Model simulations to represent snow cover characteristics over the Australian Alps and go on to asses future projections of snowpack characteristics. Our results show that the ensemble presents a cold temperature bias and overestimates total precipitation leading to a general overestimation of the snow cover as compared with MODIS satellite data. We then produce a new set of snowpack characteristics by running a temperature based snow melt/accumulation model forced by bias corrected temperature and precipitation fields. While some positive snow cover biases remain, the bias corrected (BC) dataset show large improvements regarding the simulation of total amounts, seasonality and spatial distribution of the snow cover compared with MODIS products. Both the raw and BC datasets are then used to assess future changes in the snowpack characteristics. Both datasets show robust increases in near-surface temperatures and decreases in snowfall that lead to a substantial reduction of the snowpack over the Australian Alps. The snowpack decreases by about 15 and 60% by 2030 and 2070 respectively. While the BC data introduce large differences in the simulation of the present climate snowpack, in relative terms future changes appear to be similar to those obtained using the raw data. Future temperature projections show a clear dependence with elevation through the snow-albedo feedback effect that affects snowpack projections. Uncertainties in future projections of the snowpack are large in both datasets and are mainly dominated by the choice of the lateral boundary conditions.

Evaluation of the SMAP model calculated snow albedo at the SIGMA-A site, northwest Greenland, during the 2012 record surface melt event

NASA Astrophysics Data System (ADS)

Niwano, M.; Aoki, T.; Matoba, S.; Yamaguchi, S.; Tanikawa, T.; Kuchiki, K.; Motoyama, H.

2015-12-01

The snow and ice on the Greenland ice sheet (GrIS) experienced the extreme surface melt around 12 July, 2012. In order to understand the snow-atmosphere interaction during the period, we applied a physical snowpack model SMAP to the GrIS snowpack. In the SMAP model, the snow albedo is calculated by the PBSAM component explicitly considering effects of snow grain size and light-absorbing snow impurities such as black carbon and dust. Temporal evolution of snow grain size is calculated internally in the SMAP model, whereas mass concentrations of snow impurities are externally given from observations. In the PBSAM, the (shortwave) snow albedo is calculated from a weighted summation of visible albedo (primarily affected by snow impurities) and near-infrared albedo (mainly controlled by snow grain size). The weights for these albedos are the visible and near-infrared fractions of the downward shortwave radiant flux. The SMAP model forced by meteorological data obtained from an automated weather station at SIGMA-A site, northwest GrIS during 30 June to 14 July, 2012　(IOP) was evaluated in terms of surface (optically equivalent) snow grain size and snow albedo. Snow grain size simulated by the model was compared against that retrieved from in-situ spectral albedo measurements. Although the RMSE and ME were reasonable (0.21 mm and 0.17 mm, respectively), the small snow grain size associated with the surface hoar could not be simulated by the SMAP model. As for snow albedo, simulation results agreed well with observations throughout the IOP (RMSE was 0.022 and ME was 0.008). Under cloudy-sky conditions, the SMAP model reproduced observed rapid increase in the snow albedo. When cloud cover is present the near-infrared fraction of the downward shortwave radiant flux is decreased, while it is increased under clear-sky conditions. Therefore, the above mentioned performance of the SMAP model can be attributed to the PBSAM component driven by the observed near-infrared and visible fractions of the downward shortwave radiant flux. This result suggests that it is necessary for snowpack models to consider changes in the visible and near-infrared fractions of the downward shortwave radiant flux caused by the presence of cloud cover to reproduce realistic temporal changes in the snow albedo and consequently the surface energy balance.

Insights into mountain precipitation and snowpack from a basin-scale wireless-sensor network

NASA Astrophysics Data System (ADS)

Zhang, Z.; Glaser, S.; Bales, R.; Conklin, M.; Rice, R.; Marks, D.

2017-08-01

A spatially distributed wireless-sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water-year 2014, variability that was not apparent in the more limited operational data. Using daily dew-point temperature to track temporal elevational changes in the rain-snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew-point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher-elevation sensor-node data for each event to estimate total precipitation. Blended estimates were on average 15-30% higher than using either set of measurements alone. Using data from the current operational snow-pillow sites gives even lower estimates of basin-wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin-wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin.

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

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

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

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Picard, Ghislain; Roy, Alexandre; Dupont, Florent; Fily, Michel; Royer, Alain

2014-01-01

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

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.

Evidence of territoriality and species interactions from spatial point-pattern analyses of subarctic-nesting geese

USGS Publications Warehouse

Reiter, Matthew E.; Andersen, David E.

2013-01-01

Quantifying spatial patterns of bird nests and nest fate provides insights into processes influencing a species’ distribution. At Cape Churchill, Manitoba, Canada, recent declines in breeding Eastern Prairie Population Canada geese (Branta canadensis interior) has coincided with increasing populations of nesting lesser snow geese (Chen caerulescens caerulescens) and Ross’s geese (Chen rossii). We conducted a spatial analysis of point patterns using Canada goose nest locations and nest fate, and lesser snow goose nest locations at two study areas in northern Manitoba with different densities and temporal durations of sympatric nesting Canada and lesser snow geese. Specifically, we assessed (1) whether Canada geese exhibited territoriality and at what scale and nest density; and (2) whether spatial patterns of Canada goose nest fate were associated with the density of nesting lesser snow geese as predicted by the protective-association hypothesis. Between 2001 and 2007, our data suggest that Canada geese were territorial at the scale of nearest neighbors, but were aggregated when considering overall density of conspecifics at slightly broader spatial scales. The spatial distribution of nest fates indicated that lesser snow goose nest proximity and density likely influence Canada goose nest fate. Our analyses of spatial point patterns suggested that continued changes in the distribution and abundance of breeding lesser snow geese on the Hudson Bay Lowlands may have impacts on the reproductive performance of Canada geese, and subsequently the spatial distribution of Canada goose nests.

Water Futures for Cold Mountain Ecohydrology under Climate Change - Results from the North American Cordilleran Transect

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Fang, X.; Whitfield, P. H.; Marks, D. G.; Janowicz, J. R.

2017-12-01

A transect comprising three intensively researched mountain headwater catchments stretching from the northern US to northern Canada provides the basis to downscale climate models outputs for mountain hydrology and insight for an assessment of water futures under changing climate and vegetation using a physically based hydrological model. Reynolds Mountain East, Idaho; Marmot Creek, Alberta and Wolf Creek, Yukon are high mountain catchments dominated by forests and alpine shrub and grass vegetation with long-term snow, hydrometric and meteorological observations and extensive ecohydrological process studies. The physically based, modular, flexible and object-oriented Cold Regions Hydrological Modelling Platform (CRHM) was used to create custom spatially distributed hydrological models for these three catchments. Model parameterisations were based on knowledge of hydrological processes, basin physiography, soils and vegetation with minimal or no calibration from streamflow measurements. The models were run over multidecadal periods using high-elevation meteorological observations to assess the recent ecohydrological functioning of these catchments. The results showed unique features in each catchment, from snowdrift-fed aspen pocket forests in Reynolds Mountain East, to deep late-lying snowdrifts at treeline larch forests in Marmot Creek, and snow-trapping shrub tundra overlying discontinuous permafrost in Wolf Creek. The meteorological observations were then perturbed using the changes in monthly temperature and precipitation predicted by the NARCCAP modelling outputs for the mid-21st C. In all catchments there is a dramatic decline in snow redistribution and sublimation by wind and of snow interception by and sublimation from evergreen canopies that is associated with warmer winters. Reduced sublimation loss only partially compensated for greater rainfall fractions of precipitation. Under climate change, snowmelt was earlier and slower and at the lowest elevations and latitudes produced less proportion of runoff from snowmelt. Transient vegetation changes counteracted increasing streamflow yields from climate change partly due to increased snow retention by enhanced vegetation heights at high elevations and reduced vegetation canopy coverage at low elevations.

A modelling study of the seasonal snowpack energy balance at three sites along the Andes Cordillera. Regional climate and local effects.

NASA Astrophysics Data System (ADS)

McPhee, James; Mengual, Sebastian; MacDonell, Shelley

2017-04-01

Seasonal snowpack melt constitutes the main water source for large portions of extratropical South America, including central Chile and Western Argentina. The properties and distribution of snow in the Andes are threatened by rapid climate change, characterised by warming and drying. This study provides a first attempt at detailed description of the energy balance of the seasonal snowpack and its variability along a latitudinal gradient, which is also correlated with an elevation and precipitation gradient, in the Andes Cordillera. The Snowpack model was validated at semi-arid, Mediterranean and temperate humid sites, where meteorological and snowpack properties have been observed since year 2013. Site elevations decrease from north to south, whereas precipitation climatology increases with latitude. Results show that turbulent energy exchange becomes relatively more important in periods of low snow accumulation, with sensible heat fluxes having a greater effect in cooling the snowpack at the high-altitude, low latitude site. Likewise, daily melt-freeze cycles are important in maintaining positive cold contents throughout the accumulation season at this site, and contribute to extending the duration of snow cover despite low accumulation and high radiation loads. In contrast, the southernmost, lowest elevation site shows smaller daily temperature amplitude and a much more preponderant radiation component to the energy balance. This modelling exercise highlights the nonlinearities of snow dynamics at different geographical settings in a sparsely monitored mountain area of the world, as well as the need for further understanding in order to evaluate the sensitivity of snow-dominated watersheds to global warming and climate change.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

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.

Meltwater flux and runoff modeling in the abalation area of jakobshavn Isbrae, West Greenland

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

Mernild, Sebastian Haugard; Chylek, Petr; Liston, Glen

2009-01-01

The temporal variability in surface snow and glacier melt flux and runoff were investigated for the ablation area of lakobshavn Isbrae, West Greenland. High-resolution meteorological observations both on and outside the Greenland Ice Sheet (GrIS) were used as model input. Realistic descriptions of snow accumulation, snow and glacier-ice melt, and runoff are essential to understand trends in ice sheet surface properties and processes. SnowModel, a physically based, spatially distributed meteorological and snow-evolution modeling system was used to simulate the temporal variability of lakobshavn Isbrre accumulation and ablation processes for 2000/01-2006/07. Winter snow-depth observations and MODIS satellite-derived summer melt observations weremore » used for model validation of accumulation and ablation. Simulations agreed well with observed values. Simulated annual surface melt varied from as low as 3.83 x 10{sup 9} m{sup 3} (2001/02) to as high as 8.64 x 10{sup 9} m{sup 3} (2004/05). Modeled surface melt occurred at elevations reaching 1,870 m a.s.l. for 2004/05, while the equilibrium line altitude (ELA) fluctuated from 990 to 1,210 m a.s.l. during the simulation period. The SnowModel meltwater retention and refreezing routines considerably reduce the amount of meltwater available as ice sheet runoff; without these routines the lakobshavn surface runoff would be overestimated by an average of 80%. From September/October through May/June no runoff events were simulated. The modeled interannual runoff variability varied from 1.81 x 10{sup 9} m{sup 3} (2001/02) to 5.21 x 10{sup 9} m{sup 3} (2004/05), yielding a cumulative runoff at the Jakobshavn glacier terminus of {approx}2.25 m w.eq. to {approx}4.5 m w.eq., respectively. The average modeled lakobshavn runoff of {approx}3.4 km{sup 3} y{sup -1} was merged with previous estimates of Jakobshavn ice discharge to quantify the freshwater flux to Illulissat Icefiord. For both runoff and ice discharge the average trends are similar, indicating increasing (insignificant) influx of freshwater to the Illulissat Icefiord for the period 2000/01-2006/07. This study suggests that surface runoff forms a minor part of the overall Jakobshavn freshwater flux to the fiord: around 7% ({approx}3.4 km{sup 3} y{sup -1}) of the average annual freshwater flux of {approx}51.0 km{sup 3} y{sup -1} originates from the surface runoff.« less

Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada

NASA Astrophysics Data System (ADS)

Domine, Florent; Barrere, Mathieu; Sarrazin, Denis

2016-11-01

The values of the snow and soil thermal conductivity, ksnow and ksoil, strongly impact the thermal regime of the ground in the Arctic, but very few data are available to test model predictions for these variables. We have monitored ksnow and ksoil using heated needle probes at Bylot Island in the Canadian High Arctic (73° N, 80° W) between July 2013 and July 2015. Few ksnow data were obtained during the 2013-2014 winter, because little snow was present. During the 2014-2015 winter ksnow monitoring at 2, 12 and 22 cm heights and field observations show that a depth hoar layer with ksnow around 0.02 W m-1 K-1 rapidly formed. At 12 and 22 cm, wind slabs with ksnow around 0.2 to 0.3 W m-1 K-1 formed. The monitoring of ksoil at 10 cm depth shows that in thawed soil ksoil was around 0.7 W m-1 K-1, while in frozen soil it was around 1.9 W m-1 K-1. The transition between both values took place within a few days, with faster thawing than freezing and a hysteresis effect evidenced in the thermal conductivity-liquid water content relationship. The fast transitions suggest that the use of a bimodal distribution of ksoil for modelling may be an interesting option that deserves further testing. Simulations of ksnow using the snow physics model Crocus were performed. Contrary to observations, Crocus predicts high ksnow values at the base of the snowpack (0.12-0.27 W m-1 K-1) and low ones in its upper parts (0.02-0.12 W m-1 K-1). We diagnose that this is because Crocus does not describe the large upward water vapour fluxes caused by the temperature gradient in the snow and soil. These fluxes produce mass transfer between the soil and lower snow layers to the upper snow layers and the atmosphere. Finally, we discuss the importance of the structure and properties of the Arctic snowpack on subnivean life, as species such as lemmings live under the snow most of the year and must travel in the lower snow layer in search of food.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Bromine release from blowing snow and its impact on tropospheric chemistry

NASA Astrophysics Data System (ADS)

Griffiths, Paul; Yang, Xin; Abraham, N. Luke; Archibald, Alexander; Pyle, John

2016-04-01

In the last two decades, significant depletion of boundary layer ozone (ozone depletion events, ODEs) has been observed in both Arctic and Antarctic spring. ODEs are attributed to catalytic destruction by bromine radicals (Br plus BrO), especially during bromine explosion events (BEs), when high concentrations of BrO periodically occur. The source of bromine and the mechanism that sustains the high BrO levels are still the subject of study. Recent work by Pratt et al. (2013) posits Br2 production within saline snow and sea ice which leads to sudden ODEs. Previously, Yang et al. (2008) suggested snow could provide a source of (depleted) sea-salt aerosol if wicked from the surface of ice. They suggest that rapid depletion of bromide from the aerosol will constitute a source of photochemical Bry. Given the large sea ice extent in polar regions, this may constitute a significant source of sea salt and bromine in the polar lower atmosphere. While bromine release from blowing snow is perhaps less likely to trigger sudden ODEs, it may make a contribution to regional scale processes affecting ozone levels. Currently, the model parameterisations of Yang et al. assumes that rapid release of bromine occurs from fresh snow on sea ice during periods of strong wind. The parameterisation depends on an assumed sea-salt aerosol distribution generated via sublimation of the snow above the boundary layer, as well as taking into account the salinity of the snow. In this work, we draw on recent measurements by scientists from the British Antarctic Survey during a cruise aboard the Polarstern in the southern oceans. This has provided an extensive set of measurements of the chemical and physical characteristics of blowing snow over sea ice, and of the aerosol associated with it. Based on the observations, we have developed an improved parameterisation of the release of bromine from blowing snow. The paper presents results from the simulation performed using the United Kingdom Chemistry and Aerosols (UKCA) model, run as a component of the UK Met Office Unified Model, employing the updated parameterisation of Yang et al. We assess the performance of the parameterisation in simulating tropospheric BrO, a review of relevant parameters, as well as a quantitative assessment of the release of sea salt aerosol and its contribution to halogen chemistry in the polar and global atmosphere.

Soil Temperature Variability in Complex Terrain measured using Distributed a Fiber-Optic Distributed Temperature Sensing

NASA Astrophysics Data System (ADS)

Seyfried, M. S.; Link, T. E.

2013-12-01

Soil temperature (Ts) exerts critical environmental controls on hydrologic and biogeochemical processes. Rates of carbon cycling, mineral weathering, infiltration and snow melt are all influenced by Ts. Although broadly reflective of the climate, Ts is sensitive to local variations in cover (vegetative, litter, snow), topography (slope, aspect, position), and soil properties (texture, water content), resulting in a spatially and temporally complex distribution of Ts across the landscape. Understanding and quantifying the processes controlled by Ts requires an understanding of that distribution. Relatively few spatially distributed field Ts data exist, partly because traditional Ts data are point measurements. A relatively new technology, fiber optic distributed temperature system (FO-DTS), has the potential to provide such data but has not been rigorously evaluated in the context of remote, long term field research. We installed FO-DTS in a small experimental watershed in the Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains of SW Idaho. The watershed is characterized by complex terrain and a seasonal snow cover. Our objectives are to: (i) evaluate the applicability of fiber optic DTS to remote field environments and (ii) to describe the spatial and temporal variability of soil temperature in complex terrain influenced by a variable snow cover. We installed fiber optic cable at a depth of 10 cm in contrasting snow accumulation and topographic environments and monitored temperature along 750 m with DTS. We found that the DTS can provide accurate Ts data (+/- .4°C) that resolves Ts changes of about 0.03°C at a spatial scale of 1 m with occasional calibration under conditions with an ambient temperature range of 50°C. We note that there are site-specific limitations related cable installation and destruction by local fauna. The FO-DTS provide unique insight into the spatial and temporal variability of Ts in a landscape. We found strong seasonal trends in Ts variability controlled by snow cover and solar radiation as modified by topography. During periods of spatially continuous snow cover Ts was practically homogeneous throughout. In the absence of snow cover, Ts is highly variable, with most of the variability attributable to different topographic units defined by slope and aspect. During transition periods when snow melts out, Ts is highly variable within the watershed and within topographic units. The importance of accounting for these relatively small scale effects is underscored by the fact that the overall range of Ts in study area 600 m long is similar to that of the much large RCEW with 900 m elevation gradient.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

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

USGS Publications Warehouse

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

2014-01-01

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

Polar View Snow Service- Operational Snow Cover Mapping for Downstream Runoff Modeling and Hydropower Predictions

NASA Astrophysics Data System (ADS)

Bach, Heike; Appel, Florian; Rust, Felix; Mauser, Wolfram

2010-12-01

Information on snow cover and snow properties are important for hydrology and runoff modelling. Frequent updates of snow cover observation, especially for areas characterized by short-term snow dynamics, can help to improve water balance and discharge calculations. Within the GMES service element Polar View, VISTA offers a snow mapping service for Central Europe since several years [1, 2]. We outline the use of this near-real- time product for hydrological applications in Alpine environment. In particular we discuss the integration of the Polar View product into a physically based hydrological model (PROMET). This allows not only the provision of snow equivalent values, but also enhances river runoff modelling and its use in hydropower energy yield prediction. The GMES snow products of Polar View are thus used in a downstream service for water resources management, providing information services for renewable energy suppliers and energy traders.

Evaluation of Model Microphysics Within Precipitation Bands of Extratropical Cyclones

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Yu, Ruyi; Molthan, Andrew L.; Nesbitt, Steven

2014-01-01

It is hypothesized microphysical predictions have greater uncertainties/errors when there are complex interactions that result from mixed phased processes like riming. Use Global Precipitation Measurement (GPM) Mission ground validation studies in Ontario, Canada to verify and improve parameterizations. The WRF realistically simulated the warm frontal snowband at relatively short lead times (1014 h). The snowband structire is sensitive to the microphysical parameterization used in WRF. The Goddard and SBUYLin most realistically predicted the band structure, but overpredicted snow content. The double moment Morrison scheme best produced the slope of the snow distribution, but it underpredicted the intercept. All schemes and the radar derived (which used dry snow ZR) underpredicted the surface precipitation amount, likely because there was more cloud water than expected. The Morrison had the most cloud water and the best precipitation prediction of all schemes.

Forage site selection by lesser snow geese during autumn staging on the Arctic National Wildlife Refuge, Alaska

USGS Publications Warehouse

Hupp, Jerry W.; Robertson, Donna G.

1998-01-01

Lesser snow geese (Chen caerulescens caerulescens) of the Western Canadian Arctic Population feed intensively for 2-4 weeks on the coastal plain of the Beaufort Sea in Canada and Alaska at the beginning of their autumn migration. Petroleum leasing proposed for the Alaskan portion of the staging area on the Arctic National Wildlife Refuge (ANWR) could affect staging habitats and their use by geese. Therefore we studied availability, distribution, and use by snow geese of tall and russett cotton-grass (Eriophorum angustifolium and E. russeolum, respectively) feeding habitats on the ANWR. We studied selection of feeding habitats at 3 spatial scales (feeding sites [0.06 m2], feeding patches [ca. 100 m2], and feeding areas [>1 ha]) during 1990-93. We used logistic regression analysis to discriminate differences in soil moisture and vegetation between 1,548 feeding sites where snow geese exploited individual cotton-grass plants and 1,143 unexploited sites at 61 feeding patches in 1990. Feeding likelihood increased with greater soil moisture and decreased where nonforage species were present. We tested the logistic regression model in 1991 by releasing human-imprinted snow geese into 4 10 × 20-m enclosed plots where plant communities had been mapped, habitats sampled, and feeding probabilities calculated. Geese selected more feeding sites per square meter in areas of predicted high quality feeding habitat (feeding probability ≥ 0.6) than in medium (feeding probability = 0.3-0.59) or poor (feeding probability < 0.3) quality habitat (P < 0.0001). Geese increasingly used medium quality areas and spent more time feeding as trials progressed and forage was presumably reduced in high quality habitats. We examined relationships between underground biomass of plants, feeding probability, and surface microrelief at 474 0.06- m2 sites in 20 thermokarst pits in 1992. Feeding probability was correlated with the percentage of underground biomass composed of cotton-grass (r = 0.56). Feeding probability and relative availability of cotton-grass forage were highest in flooded soils along the ecotone of flooded and upland habitats. In 1992, we also used the logistic regression model to estimate availability of high quality feeding sites on 192 80 × 90-m plots that were randomly located on 24 study areas. A mean of 1.6% of the area sampled in each plot was classified as high quality feeding habitat at 23 of the study areas. Relative availability of high quality sites was highest in troughs, thermokarst pits, and water tracks because saturated soils in those microreliefs were dominated by cotton-grass. Relative availability of high quality sites was lower in saturated soils of basins (low-centered polygons, wet meadows, and strangmoor) because that microrelief was dominated by Carex spp. Most (63%) of the saturated area on the ANWR coastal plain was in basins. We examined distribution of feeding patches relative to microrelief in 49 snow goose feeding areas in 1993. Only 2.5% of the tundra in each feeding area was exploited by snow geese. Snow geese preferentially fed in thermokarst pits, water tracks, and troughs, and avoided basins and uplands. Feeding areas had more thermokarst pit but less basin microrelief than adjacent randomly-selected areas. Thermokarst pits and water tracks occurred most frequently in regions of the coastal plain where geese were observed most often during aerial surveys (1982-93). Microrelief influenced selection of feeding patches and feeding areas and may have affected snow goose distribution on the ANWR. Potential feeding patches were widely distributed but composed a small percentage (≤2.5%) of the tundra landscape and were highly interspersed with less suitable habitat. The Western Canadian Arctic Population probably used a large staging area on the Beaufort Sea coastal plain because snow geese exploited a spatially and temporally heterogeneous resource.

McSnow: A Monte-Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space

NASA Astrophysics Data System (ADS)

Brdar, S.; Seifert, A.

2018-01-01

We present a novel Monte-Carlo ice microphysics model, McSnow, to simulate the evolution of ice particles due to deposition, aggregation, riming, and sedimentation. The model is an application and extension of the super-droplet method of Shima et al. (2009) to the more complex problem of rimed ice particles and aggregates. For each individual super-particle, the ice mass, rime mass, rime volume, and the number of monomers are predicted establishing a four-dimensional particle-size distribution. The sensitivity of the model to various assumptions is discussed based on box model and one-dimensional simulations. We show that the Monte-Carlo method provides a feasible approach to tackle this high-dimensional problem. The largest uncertainty seems to be related to the treatment of the riming processes. This calls for additional field and laboratory measurements of partially rimed snowflakes.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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.

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

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

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.

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.

Seasonal Progression of the Deposition of Black Carbon by Snowfall at Ny-Ålesund, Spitsbergen

NASA Astrophysics Data System (ADS)

Sinha, P. R.; Kondo, Y.; Goto-Azuma, K.; Tsukagawa, Y.; Fukuda, K.; Koike, M.; Ohata, S.; Moteki, N.; Mori, T.; Oshima, N.; Førland, E. J.; Irwin, M.; Gallet, J.-C.; Pedersen, C. A.

2018-01-01

Deposition of black carbon (BC) aerosol in the Arctic lowers snow albedo, thus contributing to warming in the region. However, the processes and impacts associated with BC deposition are poorly understood because of the scarcity and uncertainties of measurements of BC in snow with adequate spatiotemporal resolution. We sampled snowpack at two sites (11 m and 300 m above sea level) at Ny-Ålesund, Spitsbergen, in April 2013. We also collected falling snow near the surface with a windsock from September 2012 to April 2013. The size distribution of BC in snowpack and falling snow was measured using a single-particle soot photometer combined with a characterized nebulizer. The BC size distributions did not show significant variations with depth in the snowpack, suggesting stable size distributions in falling snow. The BC number and mass concentrations (CNBC and CMBC) at the two sites agreed to within 19% and 10%, respectively, despite the sites' different snow water equivalent (SWE) loadings. This indicates the small influence of the amount of SWE (or precipitation) on these quantities. Average CNBC and CMBC in snowpack and falling snow at nearly the same locations agreed to within 5% and 16%, after small corrections for artifacts associated with the sampling of the falling snow. This comparison shows that the dry deposition was a small contributor to the total BC deposition. CMBC were highest (2.4 ± 3.0 μg L-1) in December-February and lowest (1.2 ± 1.2 μg L-1) in September-November.

Integration of snow management practices into a detailed snow pack model

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Snow Physics and Meltwater Hydrology of the SSiB Model Employed for Climate Simulation Studies with GEOS 2 GCM

NASA Technical Reports Server (NTRS)

Mocko, David M.; Sud, Y. C.; Einaudi, Franco (Technical Monitor)

2000-01-01

Present-day climate models produce large climate drifts that interfere with the climate signals simulated in modelling studies. The simplifying assumptions of the physical parameterization of snow and ice processes lead to large biases in the annual cycles of surface temperature, evapotranspiration, and the water budget, which in turn causes erroneous land-atmosphere interactions. Since land processes are vital for climate prediction, and snow and snowmelt processes have been shown to affect Indian monsoons and North American rainfall and hydrology, special attention is now being given to cold land processes and their influence on the simulated annual cycle in GCMs. The snow model of the SSiB land-surface model being used at Goddard has evolved from a unified single snow-soil layer interacting with a deep soil layer through a force-restore procedure to a two-layer snow model atop a ground layer separated by a snow-ground interface. When the snow cover is deep, force-restore occurs within the snow layers. However, several other simplifying assumptions such as homogeneous snow cover, an empirical depth related surface albedo, snowmelt and melt-freeze in the diurnal cycles, and neglect of latent heat of soil freezing and thawing still remain as nagging problems. Several important influences of these assumptions will be discussed with the goal of improving them to better simulate the snowmelt and meltwater hydrology. Nevertheless, the current snow model (Mocko and Sud, 2000, submitted) better simulates cold land processes as compared to the original SSiB. This was confirmed against observations of soil moisture, runoff, and snow cover in global GSWP (Sud and Mocko, 1999) and point-scale Valdai simulations over seasonal snow regions. New results from the current snow model SSiB from the 10-year PILPS 2e intercomparison in northern Scandinavia will be presented.

Formation, distribution and variability in snow cover on the Asian territory of the USSR

NASA Technical Reports Server (NTRS)

Pupkov, V. N.

1985-01-01

A description is given of maps compiled for annual and average multiple-year water reserves. The annual and average multiple-year maximum snow cover height for winter, extreme values of maximum snow reserves, and the average height and snow reserves at the end of each decade are shown. These maps were made for the entire Asian territory of the USSR, excluding Central Asia, Kamchatka Peninsula, and the Sakhalin Islands.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

The response of the cryosphere to climate forcings largely determines Earth's climate sensitivity. However, our understanding of the strength of the simulated snow albedo feedback varies by a factor of three in the GCMs used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, mainly caused by uncertainties in snow extent and the albedo of snow-covered areas from imprecise remote sensing retrievals. Additionally, the Western US and other regions of the globe depend predominantly on snowmelt for their water supply to agriculture, industry and cities, hydroelectric power, and recreation, against rising demand from increasing population. In the mountains of the Upper Colorado River Basin, dust radiative forcing in snow shortens snow cover duration by 3-7 weeks. Extended to the entire upper basin, the 5-fold increase in dust load since the late-1800s results in a 3-week earlier peak runoff and a 5% annual loss of total runoff. The remotely sensed dynamics of snow cover duration and melt however have not been factored into hydrological modeling, operational forecasting, and policymaking. To address these deficiencies in our understanding of snow properties, we have developed and validated a suite of MODIS snow products that provide accurate fractional snow covered area and radiative forcing of dust and carbonaceous aerosols in snow. The MODIS Snow Covered Area and Grain size (MODSCAG) and MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithms, developed and transferred from imaging spectroscopy techniques, leverage the complete MODIS surface reflectance spectrum. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. We have created the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties, and provide complete, robust inputs to water management models and systems of the future. In the push to better understand the physical and ecological processes of snowmelt and how they influence regional to global hydrologic and climatic cycles, these technologies and retrievals provide markedly improved detail. We have implemented a science computing facility anchored upon the open source Apache OODT data processing framework. Apache OODT provides adaptable, rapid, and effective workflow technologies that we leverage to execute 10s of thousands of MOD-DRFS and MODSCAG jobs in the Western US, Alaska, and High Asia, critical regions where snowmelt and runoff must be more accurately and precisely identified. Apache OODT also provides us data dissemination capabilities built upon the popular, open source WebDAV protocol that allow our system to disseminate over 20 TB of MOD-DRFS and MODSCAG to the decision making community. Our latest endeavor involves building out Apache OODT to support Geospatial exploration of our data, including providing a Leaflet.js based Map, Geoserver backed protocols, and seamless integration with our Apache OODT system. This framework provides the foundation for the ASO data system.

Spatial downscaling and correction of precipitation and temperature time series to high resolution hydrological response units in the Canadian Rocky Mountains

NASA Astrophysics Data System (ADS)

Kienzle, Stefan

2015-04-01

Precipitation is the central driving force of most hydrological processes, and is also the most variable element of the hydrological cycle. As the precipitation to runoff ratio is non-linear, errors in precipitation estimations are amplified in streamflow simulations. Therefore, the accurate estimate of areal precipitation is essential for watershed models and relevant impacts studies. A procedure is presented to demonstrate the spatial distribution of daily precipitation and temperature estimates across the Rocky Mountains within the framework of the ACRU agro-hydrological modelling system (ACRU). ACRU (Schulze, 1995) is a physical-conceptual, semi-distributed hydrological modelling system designed to be responsive to changes in land use and climate. The model has been updated to include specific high-mountain and cold climate routines and is applied to simulate impacts of land cover and climate change on the hydrological behaviour of numerous Rocky Mountain watersheds in Alberta, Canada. Both air temperature and precipitation time series need to be downscaled to hydrological response units (HRUs), as they are the spatial modelling units for the model. The estimation of accurate daily air temperatures is critical for the separation of rain and snow. The precipitation estimation procedure integrates a spatially distributed daily precipitation database for the period 1950 to 2010 at a scale of 10 by 10 km with a 1971-2000 climate normal database available at 2 by 2 km (PRISM). Resulting daily precipitation time series are further downscaled to the spatial resolution of hydrological response units, defined by 100 m elevation bands, land cover, and solar radiation, which have an average size of about 15 km2. As snow measurements are known to have a potential under-catch of up to 40%, further adjustment of snowfall may need to be increased using a procedure by Richter (1995). Finally, precipitation input to HRUs with slopes steeper than 10% need to be further corrected, because the true, sloped area, has a larger area than the planimetric area derived from a GIS. The omission of correcting for sloped areas would result in incorrect calculations of interception volumes, soil moisture storages, groundwater recharge rates, actual evapotranspiration volumes, and runoff coefficients. Daily minimum and maximum air temperatures are estimated for each HRU by downscaling the 10km time series to the HRUs by (a) applying monthly mean lapse rates, estimated either from surrounding climate stations or from the PRISM climate normal dataset in combination with a digital elevation model, (b) adjusting further for aspect of the HRU based on monthly mean incoming solar radiation, and (c) adjusting for canopy cover using the monthly mean leaf area indices. Precipitation estimates can be verified using independent snow water equivalent measurements derived from snow pillow or snow course observations, while temperature estimates are verified against either independent temperature measurements from climate stations, or from fire observation towers.

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

NASA Astrophysics Data System (ADS)

Valero, Cesar Vera; Wever, Nander; Christen, Marc; Bartelt, Perry

2018-03-01

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

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

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.

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.

Snow measurement system for airborne snow surveys (GPR system from helicopter) in high mountian areas.

NASA Astrophysics Data System (ADS)

Sorteberg, Hilleborg K.

2010-05-01

In the hydropower industry, it is important to have precise information about snow deposits at all times, to allow for effective planning and optimal use of the water. In Norway, it is common to measure snow density using a manual method, i.e. the depth and weight of the snow is measured. In recent years, radar measurements have been taken from snowmobiles; however, few energy supply companies use this method operatively - it has mostly been used in connection with research projects. Agder Energi is the first Norwegian power producer in using radar tecnology from helicopter in monitoring mountain snow levels. Measurement accuracy is crucial when obtaining input data for snow reservoir estimates. Radar screening by helicopter makes remote areas more easily accessible and provides larger quantities of data than traditional ground level measurement methods. In order to draw up a snow survey system, it is assumed as a basis that the snow distribution is influenced by vegetation, climate and topography. In order to take these factors into consideration, a snow survey system for fields in high mountain areas has been designed in which the data collection is carried out by following the lines of a grid system. The lines of this grid system is placed in order to effectively capture the distribution of elevation, x-coordinates, y-coordinates, aspect, slope and curvature in the field. Variation in climatic conditions are also captured better when using a grid, and dominant weather patterns will largely be captured in this measurement system.

Evaluating Multispectral Snowpack Reflectivity With Changing Snow Correlation Lengths

NASA Technical Reports Server (NTRS)

Kang, Do Hyuk; Barros, Ana P.; Kim, Edward J.

2016-01-01

This study investigates the sensitivity of multispectral reflectivity to changing snow correlation lengths. Matzler's ice-lamellae radiative transfer model was implemented and tested to evaluate the reflectivity of snow correlation lengths at multiple frequencies from the ultraviolet (UV) to the microwave bands. The model reveals that, in the UV to infrared (IR) frequency range, the reflectivity and correlation length are inversely related, whereas reflectivity increases with snow correlation length in the microwave frequency range. The model further shows that the reflectivity behavior can be mainly attributed to scattering rather than absorption for shallow snowpacks. The largest scattering coefficients and reflectivity occur at very small correlation lengths (approximately 10(exp -5 m) for frequencies higher than the IR band. In the microwave range, the largest scattering coefficients are found at millimeter wavelengths. For validation purposes, the ice-lamella model is coupled with a multilayer snow physics model to characterize the reflectivity response of realistic snow hydrological processes. The evolution of the coupled model simulated reflectivities in both the visible and the microwave bands is consistent with satellite-based reflectivity observations in the same frequencies. The model results are also compared with colocated in situ snow correlation length measurements (Cold Land Processes Field Experiment 2002-2003). The analysis and evaluation of model results indicate that the coupled multifrequency radiative transfer and snow hydrology modeling system can be used as a forward operator in a data-assimilation framework to predict the status of snow physical properties, including snow correlation length.

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.

Surface Snow Density of East Antarctica Derived from In-Situ Observations

NASA Astrophysics Data System (ADS)

Tian, Y.; Zhang, S.; Du, W.; Chen, J.; Xie, H.; Tong, X.; Li, R.

2018-04-01

Models based on physical principles or semi-empirical parameterizations have used to compute the firn density, which is essential for the study of surface processes in the Antarctic ice sheet. However, parameterization of surface snow density is often challenged by the description of detailed local characterization. In this study we propose to generate a surface density map for East Antarctica from all the filed observations that are available. Considering that the observations are non-uniformly distributed around East Antarctica, obtained by different methods, and temporally inhomogeneous, the field observations are used to establish an initial density map with a grid size of 30 × 30 km2 in which the observations are averaged at a temporal scale of five years. We then construct an observation matrix with its columns as the map grids and rows as the temporal scale. If a site has an unknown density value for a period, we will set it to 0 in the matrix. In order to construct the main spatial and temple information of surface snow density matrix we adopt Empirical Orthogonal Function (EOF) method to decompose the observation matrix and only take first several lower-order modes, because these modes already contain most information of the observation matrix. However, there are a lot of zeros in the matrix and we solve it by using matrix completion algorithm, and then we derive the time series of surface snow density at each observation site. Finally, we can obtain the surface snow density by multiplying the modes interpolated by kriging with the corresponding amplitude of the modes. Comparative analysis have done between our surface snow density map and model results. The above details will be introduced in the paper.

Use of the Water, Energy, and Biogeochemical Model (WEBMOD) to Simulate Water Quality at Five U.S. Geological Survey Research Watersheds

NASA Astrophysics Data System (ADS)

Webb, R. M.; Leavesley, G. H.; Shanley, J. B.; Peters, N. E.; Aulenbach, B. T.; Blum, A. E.; Campbell, D. H.; Clow, D. W.; Mast, M. A.; Stallard, R. F.; Larsen, M. C.; Troester, J. W.; Walker, J. F.; White, A. F.

2003-12-01

The Water, Energy, and Biogeochemical Model (WEBMOD) was developed as an aid to compare and contrast basic hydrologic and biogeochemical processes active in the diverse hydroclimatic regions represented by the five U.S. Geological Survey (USGS) Water, Energy, and Biogeochemical Budget (WEBB) sites: Loch Vale, Colorado; Trout Lake, Wisconsin; Sleepers River, Vermont; Panola Mountain, Georgia; and Luquillo Experimental Forest, Puerto Rico. WEBMOD simulates solute concentrations for vegetation canopy, snow pack, impermeable ground, leaf litter, unsaturated and saturated soil zones, riparian zones and streams using selected process modules coupled within the USGS Modular Modeling System (MMS). Source codes for the MMS hydrologic modules include the USGS Precipitation Runoff Modeling System, the National Weather Service Hydro-17 snow model, and TOPMODEL. The hydrologic modules distribute precipitation and temperature to predict evapotranspiration, snow accumulation, snow melt, and streamflow. Streamflow generation mechanisms include infiltration excess, saturated overland flow, preferential lateral flow, and base flow. Input precipitation chemistry, and fluxes and residence times predicted by the hydrologic modules are input into the geochemical module where solute concentrations are computed for a series of discrete well-mixed reservoirs using calls to the geochemical engine PHREEQC. WEBMOD was used to better understand variations in water quality observed at the WEBB sites from October 1991 through September 1997. Initial calibrations were completed by fitting the simulated hydrographs with those measured at the watershed outlets. Model performance was then refined by comparing the predicted export of conservative chemical tracers such as chloride, with those measured at the watershed outlets. The model succeeded in duplicating the temporal variability of net exports of major ions from the watersheds.

A spotlight on snow leopard conservation in China.

PubMed

Alexander, Justine S; Zhang, Chengcheng; Shi, Kun; Riordan, Philip

2016-07-01

China holds the greatest proportion of the snow leopard's (Panthera uncia) global range and is central to their conservation. The country is also undergoing unprecedented economic growth, which increases both the threats to the snow leopard and the opportunities for its conservation. In this paper we aim to review published literature (from 1950 to 2014) in English and Mandarin on snow leopard ecology and conservation in China in order to identify thematic and geographic research gaps and propose research priorities. We first retrieved all published items that considered snow leopards in China (n = 106). We extracted from these papers 274 reports of snow leopard presence in China. We then reviewed a subset of papers (n = 33) of this literature, which specifically focused on snow leopard ecology and conservation within China. We introduced a thematic framework that allows a structured and comprehensive assessment of findings. This framework recognizes 4 critical and interrelated topics underpinning snow leopard ecology and conservation: habitat (distribution and protected area coverage); prey (distribution and abundance, predator-prey relationships); human interactions (hunting and trade, livestock interactions and conflicts); and the underlying policy context. Significant gains in knowledge as well as research gaps and priorities are discussed with reference to our framework. The modest quantity and limited scope of published research on the snow leopard in China calls for a continued and intensified effort to inform and support national conservation policies. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Snow in Earth System Models: Recent Progress and Future Challenges

NASA Astrophysics Data System (ADS)

Clark, M. P.; Slater, A. G.

2016-12-01

Snow is the most variable of terrestrial boundary conditions. Some 50 million km^2 of the Northern Hemisphere typically sees periods of accumulation and ablation in the annual cycle. The wonderous properties of snow, such as high albedo, thermal insulation and its ability to act as a water store make it an integral part of the global climate system. Earliest inclusions of snow within climate models were simple adjustments to albedo and a moisture store. Modern Earth Syetem Models now represent snow through a myriad of model architectures and parameterizations that span a broad range of complexity. Understanding the impacts of modeling decisions upon simulation of snow and other Earth System components (either directly or via feedbacks) is an ongoing area of research. Snow models are progressing with multi-layer representations and capabilities such as complex albedo schemes that include contaminants. While considerable advances have been made, numerous challenges also remain. Simply getting a grasp on the mass of snow (seasonal or permanent) has proved more difficult than expected over the past 30 years. Snow interactions with vegetation has improved but the details of vegetation masking and emergence are still limited. Inclusion of blowing snow processes, in terms of transport and sublimation, is typically rare and sublimation remains a difficult quantity to measure. Contemplation of snow crystal form within models and integration with radiative transfer schemes for better understanding of full spectrum interations (from UV to long microwave) may simultaneously advance simulation and remote sensing. A series of international modeling experiments and directed field campaigns are planned in the near future with the aim of pushing our knowledge forward.

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Royer, Alain; Picard, Ghislain; Langlois, Alex; Fily, Michel

2014-01-01

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

NASA Astrophysics Data System (ADS)

Tuzet, Francois; Dumont, Marie; Lafaysse, Matthieu; Picard, Ghislain; Arnaud, Laurent; Voisin, Didier; Lejeune, Yves; Charrois, Luc; Nabat, Pierre; Morin, Samuel

2017-11-01

Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013-2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction.

Extreme flood estimation by the SCHADEX method in a snow-driven catchment: application to Atnasjø (Norway)

NASA Astrophysics Data System (ADS)

Paquet, Emmanuel; Lawrence, Deborah

2013-04-01

The SCHADEX method for extreme flood estimation was developed by Paquet et al. (2006, 2013), and since 2008, it is the reference method used by Electricité de France (EDF) for dam spillway design. SCHADEX is a so-called "semi-continuous" stochastic simulation method in that flood events are simulated on an event basis and are superimposed on a continuous simulation of the catchment saturation hazard usingrainfall-runoff modelling. The MORDOR hydrological model (Garçon, 1999) has thus far been used for the rainfall-runoff modelling. MORDOR is a conceptual, lumped, reservoir model with daily areal rainfall and air temperature as the driving input data. The principal hydrological processes represented are evapotranspiration, direct and indirect runoff, ground water, snow accumulation and melt, and routing. The model has been intensively used at EDF for more than 15 years, in particular for inflow forecasts for French mountainous catchments. SCHADEX has now also been applied to the Atnasjø catchment (463 km²), a well-documented inland catchment in south-central Norway, dominated by snowmelt flooding during spring/early summer. To support this application, a weather pattern classification based on extreme rainfall was first established for Norway (Fleig, 2012). This classification scheme was then used to build a Multi-Exponential Weather Pattern distribution (MEWP), as introduced by Garavaglia et al. (2010) for extreme rainfall estimation. The MORDOR model was then calibrated relative to daily discharge data for Atnasjø. Finally, a SCHADEX simulation was run to build a daily discharge distribution with a sufficient number of simulations for assessing the extreme quantiles. Detailed results are used to illustrate how SCHADEX handles the complex and interacting hydrological processes driving flood generation in this snow driven catchment. Seasonal and monthly distributions, as well as statistics for several thousand simulated events reaching a 1000 years return level value and assessment of snowmelt role in extreme floods are presented. This study illustrates the complexity of the extreme flood estimation in snow driven catchments, and the need for a good representation of snow accumulation and melting processes in simulations for design flood estimations. In particular, the SCHADEX method is able to represent a range of possible catchment conditions (representing both soil moisture and snowmelt) in which extreme flood events can occur. This study is part of a collaboration between NVE and EDF, initiated within the FloodFreq COST Action (http://www.cost-floodfreq.eu/). References: Fleig, A., Scientific Report of the Short Term Scientific Mission Anne Fleig visiting Électricité de France, FloodFreq COST action - STSM report, 2012 Garavaglia, F., Gailhard, J., Paquet, E., Lang, M., Garçon, R., and Bernardara, P., Introducing a rainfall compound distribution model based on weather patterns sub-sampling, Hydrol. Earth Syst. Sci., 14, 951-964, doi:10.5194/hess-14-951-2010, 2010 Garçon, R. Modèle global pluie-débit pour la prévision et la prédétermination des crues, La Houille Blanche, 7-8, 88-95. doi: 10.1051/lhb/1999088 Paquet, E., Gailhard, J. and Garçon, R. (2006), Evolution of the GRADEX method: improvement by atmospheric circulation classification and hydrological modeling, La Houille Blanche, 5, 80-90. doi: 10.1051/lhb/2006091 Paquet, E., Garavaglia, F., Garçon, R. and Gailhard, J. (2012), The SCHADEX method: a semi-continuous rainfall-runoff simulation for extreme food estimation, Journal of Hydrology, under revision

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.

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

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

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.

New nitrogen uptake strategy: specialized snow roots.

PubMed

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

2009-08-01

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

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.

Improving Running Times for the Determination of Fractional Snow-Covered Area from Landsat TM/ETM+ via Utilization of the CUDA® Programming Paradigm

NASA Astrophysics Data System (ADS)

McGibbney, L. J.; Rittger, K.; Painter, T. H.; Selkowitz, D.; Mattmann, C. A.; Ramirez, P.

2014-12-01

As part of a JPL-USGS collaboration to expand distribution of essential climate variables (ECV) to include on-demand fractional snow cover we describe our experience and implementation of a shift towards the use of NVIDIA's CUDA® parallel computing platform and programming model. In particular the on-demand aspect of this work involves the improvement (via faster processing and a reduction in overall running times) for determination of fractional snow-covered area (fSCA) from Landsat TM/ETM+. Our observations indicate that processing tasks associated with remote sensing including the Snow Covered Area and Grain Size Model (SCAG) when applied to MODIS or LANDSAT TM/ETM+ are computationally intensive processes. We believe the shift to the CUDA programming paradigm represents a significant improvement in the ability to more quickly assert the outcomes of such activities. We use the TMSCAG model as our subject to highlight this argument. We do this by describing how we can ingest a LANDSAT surface reflectance image (typically provided in HDF format), perform spectral mixture analysis to produce land cover fractions including snow, vegetation and rock/soil whilst greatly reducing running time for such tasks. Within the scope of this work we first document the original workflow used to assert fSCA for Landsat TM and it's primary shortcomings. We then introduce the logic and justification behind the switch to the CUDA paradigm for running single as well as batch jobs on the GPU in order to achieve parallel processing. Finally we share lessons learned from the implementation of myriad of existing algorithms to a single set of code in a single target language as well as benefits this ultimately provides scientists at the USGS.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

The notion of snow grain shape: Ambiguous definitions, retrievalfrom tomography and implications on remote sensing

NASA Astrophysics Data System (ADS)

Krol, Q. E.; Loewe, H.

2016-12-01

Grain shape is known to influence the effective physical properties of snow and therefore included in the international classification of seasonal snow. Accordingly, snowpack models account for phenomenological shape parameters (sphericity, dendricity) to capture shape variations. These parameters are however difficult to validate due to the lack of clear-cut definitions from the 3D microstucture and insufficient links to physical properties. While the definition of traditional shape was tailored to the requirements of observers, a more objective definition should be tailored to the requirements of physical properties, by analyzing geometrical (shape) corrections in existing theoretical formulations directly. To this end we revisited the autocorrelation function (ACF) and the chord length distribution (CLD) of snow. Both functions capture size distributions of the microstructure, can be calculated from X-ray tomography and are related to various physical properties. Both functions involve the optical equivalent diameter as dominant quantity, however the respective higher-order geometrical correction differ. We have analyzed these corrections, namely interfacial curvatures for the ACF and the second moment for the CLD, using an existing data set of 165 tomography samples. To unify the notion of shape, we derived various statistical relations between the length scales. Our analysis bears three key practical implications. First, we derived a significantly improved relation between the exponential correlation length and the optical diameter by taking curvatures into account. This adds to the understanding of linking "microwave grain size" and "optical grain size" of snow for remote sensing. Second, we retrieve the optical shape parameter (commonly referred to as B) from tomography images via the moment of the CLD. Third, shape variations seen by observers do not necessarily correspond to shape variations probed by physical properties.

The added value of remote sensing products in constraining hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Almeida, Susana; Pechlivanidis, Ilias; Capell, René; Gustafsson, David; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; Sleziak, Patrik; Parajka, Juraj; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model's ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.

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.

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2011-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2012-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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;

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.

Effects of spatial and temporal resolution on simulated feedbacks from polygonal tundra.

NASA Astrophysics Data System (ADS)

Coon, E.; Atchley, A. L.; Painter, S. L.; Karra, S.; Moulton, J. D.; Wilson, C. J.; Liljedahl, A.

2014-12-01

Earth system land models typically resolve permafrost regions at spatial resolutions grossly larger than the scales of topographic variation. This observation leads to two critical questions: How much error is introduced by this lack of resolution, and what is the effect of this approximation on other coupled components of the Earth system, notably the energy balance and carbon cycle? Here we use the Arctic Terrestrial Simulator (ATS) to run micro-topography resolving simulations of polygonal ground, driven by meteorological data from Barrow, AK, to address these questions. ATS couples surface and subsurface processes, including thermal hydrology, surface energy balance, and a snow model. Comparisons are made between one-dimensional "column model" simulations (similar to, for instance, CLM or other land models typically used in Earth System models) and higher-dimensional simulations which resolve micro-topography, allowing for distributed surface runoff, horizontal flow in the subsurface, and uneven snow distribution. Additionally, we drive models with meteorological data averaged over different time scales from daily to weekly moving windows. In each case, we compare fluxes important to the surface energy balance including albedo, latent and sensible heat fluxes, and land-to-atmosphere long-wave radiation. Results indicate that spatial topography variation and temporal variability are important in several ways. Snow distribution greatly affects the surface energy balance, fundamentally changing the partitioning of incoming solar radiation between the subsurface and the atmosphere. This has significant effects on soil moisture and temperature, with implications for vegetation and decomposition. Resolving temporal variability is especially important in spring, when early warm days can alter the onset of snowmelt by days to weeks. We show that high-resolution simulations are valuable in evaluating current land models, especially in areas of polygonal ground. This work was supported by LANL Laboratory Directed Research and Development Project LDRD201200068DR and by the The Next-Generation Ecosystem Experiments (NGEE Arctic) project. NGEE-Arctic is supported by the Office of Biological and Environmental Research in the DOE Office of Science. LA-UR-14-26227.

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.

Recruitment variation of eastern Bering Sea crabs: Climate-forcing or top-down effects?

NASA Astrophysics Data System (ADS)

Zheng, Jie; Kruse, Gordon H.

2006-02-01

During the last three decades, population abundances of eastern Bering Sea (EBS) crab stocks fluctuated greatly, driven by highly variable recruitment. In recent years, abundances of these stocks have been very low compared to historical levels. This study aims to understand recruitment variation of six stocks of red king ( Paralithodes camtschaticus), blue king ( P. platypus), Tanner ( Chionoecetes bairdi), and snow ( C. opilio) crabs in the EBS. Most crab recruitment time series are not significantly correlated with each other. Spatial distributions of three broadly distributed crab stocks (EBS snow and Tanner crabs and Bristol Bay red king crab) have changed considerably over time, possibly related in part to the regime shift in climate and physical oceanography in 1976-1977. Three climate-forcing hypotheses on larval survival have been proposed to explain crab recruitment variation of Bristol Bay red king crab and EBS Tanner and snow crabs. Some empirical evidence supports speculation that groundfish predation may play an important role in crab recruitment success in the EBS. However, spatial dynamics in the geographic distributions of groundfish and crabs over time make it difficult to relate crab recruitment strength to groundfish biomass. Comprehensive field and spatially explicit modeling studies are needed to test the hypotheses and better understand the relative importance and compound effects of bottom-up and top-down controls on crab recruitment.

Monitoring global snow cover

NASA Technical Reports Server (NTRS)

Armstrong, Richard; Hardman, Molly

1991-01-01

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

Snow: A New Model Diagnostic and Seasonal Forecast Influences

NASA Astrophysics Data System (ADS)

Slater, A. G.; Lawrence, D. M.; Koven, C.

2015-12-01

Snow is the most variable of terrestrial surface condition on the planet with the seasonal extent of snow cover varying by about 48% of land area in the Northern Hemisphere. Physical properties of snow such as high albedo, high insulation along with its ability to store moisture make it an integral component of mid- and high-latitude climates and it is therefore important that models capture these properties and associated processes. In this work we explore two items associated with snow and their role in the climate system. Firstly, a diagnostic measure of snow insulation that is rooted in the physics of heat transfer is introduced. Insulation of the ground during cold Arctic winters heavily influences the rate and depth of ground freezing (or thawing), which can then influence hydrologic and biogeochemical fluxes. The ability of models to simulate snow insulation varies widely. Secondly, the role of snow upon seasonal forecasts is demonstrated within a currently operational modeling system. Due to model system biases, mass and longevity of snow can vary with forecasts. In turn, a longer lasting and greater moisture store can have impacts upon the surface temperature. These impacts can linger for over two months after all snow has melted. The cause of the biases is identified and a solution posed.

Applying A Multi-Objective Based Procedure to SWAT Modelling in Alpine Catchments

NASA Astrophysics Data System (ADS)

Tuo, Y.; Disse, M.; Chiogna, G.

2017-12-01

In alpine catchments, water management practices can lead to conflicts between upstream and downstream stakeholders, like in the Adige river basin (Italy). A correct prediction of available water resources plays an important part, for example, in defining how much water can be stored for hydropower production in upstream reservoirs without affecting agricultural activities downstream. Snow is a crucial hydrological component that highly affects seasonal behavior of streamflow. Therefore, a realistic representation of snow dynamics is fundamental for water management operations in alpine catchments. The Soil and Water Assessment Tool (SWAT) model has been applied in alpine catchments worldwide. However, during model calibration of catchment scale applications, snow parameters were generally estimated based on streamflow records rather than on snow measurements. This may lead to streamflow predictions with wrong snow melt contribution. This work highlights the importance of considering snow measurements in the calibration of the SWAT model for alpine hydrology and compares various calibration methodologies. In addition to discharge records, snow water equivalent time series of both subbasin scale and monitoring station were also utilized to evaluate the model performance by comparing with the SWAT subbasin and elevation band snow outputs. Comparing model results obtained calibrating the model using discharge data only and discharge data along with snow water equivalent data, we show that the latter approach allows us to improve the reliability of snow simulations while maintaining good estimations of streamflow. With a more reliable representation of snow dynamics, the hydrological model can provide more accurate references for proposing adequate water management solutions. This study offers to the wide SWAT user community an effective approach to improve streamflow predictions in alpine catchments and hence support decision makers in water allocation.

Microphysical Retrievals Over Stratiform Rain Using Measurements from an Airborne Dual-Wavelength Radar-Radiometer

NASA Technical Reports Server (NTRS)

Meneghini, Robert; Kumagai, Hiroshi; Wang, James R.; Iguchi, Toshio; Kozu, Toshiaki

1997-01-01

The need to understand the complementarity of the radar and radiometer is important not only to the Tropical Rain Measuring Mission (TRMM) program but to a growing number of multi-instrumented airborne experiment that combine single or dual-frequency radars with multichannel radiometers. The method of analysis used in this study begins with the derivation of dual-wavelength radar equations for the estimation of a two-parameter drop size distribution (DSD). Defining a "storm model" as the set of parameters that characterize snow density, cloud water, water vapor, and features of the melting layer, then to each storm model there will usually correspond a set of range-profiled drop size distributions that are approximate solutions of the radar equations. To test these solutions, a radiative transfer model is used to compute the brightness temperatures for the radiometric frequencies of interest. A storm model or class of storm models is considered optimum if it provides the best reproduction of the radar and radiometer measurements. Tests of the method are made for stratiform rain using simulated storm models as well as measured airborne data. Preliminary results show that the best correspondence between the measured and estimated radar profiles usually can be obtained by using a moderate snow density (0.1-0.2 g/cu cm), the Maxwell-Garnett mixing formula for partially melted hydrometeors (water matrix with snow inclusions), and low to moderate values of the integrated cloud liquid water (less than 1 kg/sq m). The storm-model parameters that yield the best reproductions of the measured radar reflectivity factors also provide brightness temperatures at 10 GHz that agree well with the measurements. On the other hand, the correspondence between the measured and modeled values usually worsens in going to the higher frequency channels at 19 and 34 GHz. In searching for possible reasons for the discrepancies, It is found that changes in the DSD parameter Mu, the radar constants, or the path-integrated attenuation can affect the high frequency channels significantly. In particular, parameters that cause only modest increases in the median mass diameter of the snow, and which have a minor effect on the radar returns or the low frequency brightness temperature, can produce a strong cooling of the 34 GHz brightness temperature.

Simulating hydrological processes of a typical small mountainous catchment in Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xu, Y. P.; Bai, Z.; Fu, Q.; Pan, S.; Zhu, C.

2017-12-01

Water cycle of small watersheds with seasonal/permanent frozen soil and snow pack in Tibetan Plateau is seriously affected by climate change. The objective of this study is to find out how much and in what way the frozen soil and snow pack will influence the hydrology of small mountainous catchments in cold regions and how can the performance of simulation by a distributed hydrological model be improved. The Dong catchment, a small catchment located in Tibetan Plateau, is used as a case study. Two measurement stations are set up to collect basic meteorological and hydrological data for the modeling purpose. Annual and interannual variations of runoff indices are first analyzed based on historic data series. The sources of runoff in dry periods and wet periods are analyzed respectively. Then, a distributed hydrology soil vegetation model (DHSVM) is adopted to simulate the hydrological process of Dong catchment based on limited data set. Global sensitivity analysis is applied to help determine the important processes of the catchment. Based on sensitivity analysis results, the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) is finally added into the hydrological model to calibrate the hydrological model in a multi-objective way and analyze the performance of DHSVM model. The performance of simulation is evaluated with several evaluation indices. The final results show that frozen soil and snow pack do play an important role in hydrological processes in cold mountainous region, in particular in dry periods without precipitation, while in wet periods precipitation is often the main source of runoff. The results also show that although the DHSVM hydrological model has the potential to model the hydrology well in small mountainous catchments with very limited data in Tibetan Plateau, the simulation of hydrology in dry periods is not very satisfactory due to the model's insufficiency in simulating seasonal frozen soil.

Altitudinal gradients, midwinter melt, and wind effects on snow accumulation in semiarid midlatitude Andes under La Niña conditions

NASA Astrophysics Data System (ADS)

Ayala, A.; McPhee, J.; Vargas, X.

2014-04-01

The Andes Cordillera remains a sparsely monitored and studied snow hydrology environment in comparison to similar mountain ranges in the Northern Hemisphere. In order to uncover some of the key processes driving snow water equivalent (SWE) spatial variability, we present and analyze a distributed SWE data set, sampled at the end of accumulation season 2011. Three representative catchments across the region were monitored, obtaining measurements in an elevation range spanning 2000 to 3900 m asl and from 32.4° to 34.0°S in latitude. Climatic conditions during this season corresponded to a moderate La Niña phenomenon, which is generally correlated with lower-than normal accumulation. Collected measurements can be described at the regional and watershed extents by altitudinal gradients that imply an increase by a factor of two in snow depth between 2200 and 3000 m asl, though with significant variability at the upper sites. In these upper sites, we found north-facing, wind-sheltered slopes showing 25% less average SWE values than south-facing, wind-exposed ones. This suggests that under these conditions, solar radiation dominated wind transport effects in controlling end-of-winter variability. Nevertheless, we found clusters of snow depth measurements above 3000 m asl that can be explained by wind exposure differences. This is the first documented snow depth data set of this spatial extent for this region, and it is framed within an ongoing research effort aimed at improving understanding and modeling of snow hydrology in the extratropical Andes Cordillera.

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

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

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

An improved snow scheme for the ECMWF land surface model: Description and offline validation

Treesearch

Emanuel Dutra; Gianpaolo Balsamo; Pedro Viterbo; Pedro M. A. Miranda; Anton Beljaars; Christoph Schar; Kelly Elder

2010-01-01

A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and...

Evaluation of forest snow processes models (SnowMKIP2)

Treesearch

Nick Rutter; Richard Essery; John Pomeroy; Nuria Altimir; Kostas Andreadis; Ian Baker; Alan Barr; Paul Bartlett; Aaron Boone; Huiping Deng; Herve Douville; Emanuel Dutra; Kelly Elder; others

2009-01-01

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation...

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.

A Physical Based Formula for Calculating the Critical Stress of Snow Movement

NASA Astrophysics Data System (ADS)

He, S.; Ohara, N.

2016-12-01

In snow redistribution modeling, one of the most important parameters is the critical stress of snow movement, which is difficult to estimate from field data because it is influenced by various factors. In this study, a new formula for calculating critical stress of snow movement was derived based on the ice particle sintering process modeling and the moment balance of a snow particle. Through this formula, the influences of snow particle size, air temperature, and deposited time on the critical stress were explicitly taken into consideration. It was found that some of the model parameters were sensitive to the critical stress estimation through the sensitivity analysis using Sobol's method. The two sensitive parameters of the sintering process modeling were determined by a calibration-validation procedure using the observed snow flux data via FlowCapt. Based on the snow flux and metrological data observed at the ISAW stations (http://www.iav.ch), it was shown that the results of this formula were able to describe very well the evolution of the minimum friction wind speed required for the snow motion. This new formula suggested that when the snow just reaches the surface, the smaller snowflake can move easier than the larger particles. However, smaller snow particles require more force to move as the sintering between the snowflakes progresses. This implied that compact snow with small snow particles may be harder to erode by wind although smaller particles may have a higher chance to be suspended once they take off.

Satellite-observed snow cover variations over the Tibetan Plateau for the period 2001-2014

NASA Astrophysics Data System (ADS)

Long, D.; Chen, X.

2016-12-01

Snow is an integral component of the global climate system. Owing to its high albedo and thermal and water storage properties, snow has important linkages and feedbacks through its influence on surface energy and moisture fluxes, clouds, precipitation, hydrology, and atmospheric circulation. As the "Roof of the World" and the "Third Pole" with the highest mountains in middle latitudes, the Tibetan Plateau (TP) is one of the most hot spots in climate change and hydrological studies, in which seasonal snow cover is a critical aspect. Unlike large-scale snow cover and regional-scale glaciers over other cryospheric regions, changes in snow cover over the TP has been largely unknown due mostly to the quality of observations. Based on improved MODIS daily snow cover products, this study aims to quantify the distribution and changes in snow cover over the TP for the period 2001 to 2014. Results show that the spatial distribution of changes in snow cover fraction (SCF) over the 14-year study period exhibited a general negative trend over the TP driven primarily by increasing land surface temperature (LST), except some areas of the upper Golden-Sanded River and upper Brahmaputra River basins. However, decreased LST and increased precipitation in the accumulation season (September to the following February) resulted in increased SCF in the accumulation season, coinciding with large-scale cold snaps and heavy snowfall events at middle latitudes. Detailed analyses of the intra-annual variability of SCF in the TP regions show an increase in SCF in the accumulation season but a decrease in SCF in the melting season (March to August), indicating that the intra-annual amplitude of SCF increased during the study period and more snow cover was released as snowmelt in the spring season.

Photovoltaic cell electrical heating system for removing snow on panel including verification.

PubMed

Weiss, Agnes; Weiss, Helmut

2017-11-16

Small photovoltaic plants in private ownership are typically rated at 5 kW (peak). The panels are mounted on roofs at a decline angle of 20° to 45°. In winter time, a dense layer of snow at a width of e.g., 10 cm keeps off solar radiation from the photovoltaic cells for weeks under continental climate conditions. Practically, no energy is produced over the time of snow coverage. Only until outside air temperature has risen high enough for a rather long-time interval to allow partial melting of snow; the snow layer rushes down in an avalanche. Following this proposal, snow removal can be arranged electrically at an extremely positive energy balance in a fast way. A photovoltaic cell is a large junction area diode inside with a threshold voltage of about 0.6 to 0.7 V (depending on temperature). This forward voltage drop created by an externally driven current through the modules can be efficiently used to provide well-distributed heat dissipation at the cell and further on at the glass surface of the whole panel. The adhesion of snow on glass is widely reduced through this heating in case a thin water film can be produced by this external short time heating. Laboratory experiments provided a temperature increase through rated panel current of more than 10 °C within about 10 min. This heating can initiate the avalanche for snow removal on intention as described before provided the clamping effect on snow at the edge of the panel frame is overcome by an additional heating foil. Basics of internal cell heat production, heating thermal effects in time course, thermographic measurements on temperature distribution, power circuit opportunities including battery storage elements and snow-removal under practical conditions are described.

Spatially-resolved mean flow and turbulence help explain observed erosion and deposition patterns of snow over Antarctic sea ice

NASA Astrophysics Data System (ADS)

Trujillo, E.; Giometto, M. G.; Leonard, K. C.; Maksym, T. L.; Meneveau, C. V.; Parlange, M. B.; Lehning, M.

2014-12-01

Sea ice-atmosphere interactions are major drivers of patterns of sea ice drift and deformations in the Polar regions, and affect snow erosion and deposition at the surface. Here, we combine analyses of sea ice surface topography at very high-resolutions (1-10 cm), and Large Eddy Simulations (LES) to study surface drag and snow erosion and deposition patterns from process scales to floe scales (1 cm - 100 m). The snow/ice elevations were obtained using a Terrestrial Laser Scanner during the SIPEX II (Sea Ice Physics and Ecosystem eXperiment II) research voyage to East Antarctica (September-November 2012). LES are performed on a regular domain adopting a mixed pseudo-spectral/finite difference spatial discretization. A scale-dependent dynamic subgrid-scale model based on Lagrangian time averaging is adopted to determine the eddy-viscosity in the bulk of the flow. Effects of larger-scale features of the surface on wind flows (those features that can be resolved in the LES) are accounted for through an immersed boundary method. Conversely, drag forces caused by subgrid-scale features of the surface should be accounted for through a parameterization. However, the effective aerodynamic roughness parameter z0 for snow/ice is not known. Hence, a novel dynamic approach is utilized, in which z0 is determined using the constraint that the total momentum flux (drag) must be independent on grid-filter scale. We focus on three ice floe surfaces. The first of these surfaces (October 6, 2012) is used to test the performance of the model, validate the algorithm, and study the spatial distributed fields of resolved and modeled stress components. The following two surfaces, scanned at the same location before and after a snow storm event (October 20/23, 2012), are used to propose an application to study how spatially resolved mean flow and turbulence relates to observed patterns of snow erosion and deposition. We show how erosion and deposition patterns are correlated with the computed stresses, with modeled stresses having higher explanatory power. Deposition is mainly occurring in wake regions of specific ridges that strongly affect wind flow patterns. These larger ridges also lock in place elongated streaks of relatively high speeds with axes along the stream-wise direction, and which are largely responsible for the observed erosion.

Spatial distribution and potential sources of trace metals in insoluble particles of snow from Urumqi, China.

PubMed

Li, Xiaolan; Jiang, Fengqing; Wang, Shaoping; Turdi, Muyesser; Zhang, Zhaoyong

2015-01-01

The purpose of this work is to characterize trace elements in snow in urban-suburb gradient over Urumqi city, China. The spatial distribution patterns of 11 trace metals in insoluble particulate matters of snow were revealed by using 102 snow samples collected in and around urban areas of Urumqi, a city suffering from severe wintertime air pollution in China. Similar spatial distribution for Mn, Cu, Zn, Ni, and Pb was found and their two significant high-value areas located in the west and east, respectively, and a high-value area in the south, which were correlated with factory emissions, traffic activities, and construction fugitive dust. The high-value areas of Cr, Ni, and V occurred in the northeast corner and along main traffic paths, which were linked to oil refinery and vehicular emissions. High value of Be presented in the west of the city. The high-value area of Co in the northeast could be related to local soil. Cd and U displayed relatively even spatial patterns in the urban area. In view of distance from the urban center, e.g., from the first circular belt to the fourth circular belt, except Be, V, Cd, and U, the contents of other metals generally decreased from the first circular belt to the forth circular belt, implying the effect of human activity clearly. Additionally, prevailing northwesterly winds and occasionally southeasterly winds in winter were associated with decreased, generally, concentrations of trace metal in snow from the urban center to the southern suburb along a northwest and southeast transect. The information on concentrations and spatial distributions of these metals in insoluble particles of snow in winter will be valuable for further environmental protection and planning.

Orographic precipitation, wind-blown snow, and landscape evolution in glaciated mountain ranges

NASA Astrophysics Data System (ADS)

Brocklehurst, S. H.; Rowan, A. V.; Plummer, M. A.; Foster, D.; Schultz, D. M.; MacGregor, K. R.

2011-12-01

Orographic precipitation and wind-blown snow appear to significantly influence the evolution of glaciated mountain ranges, and in narrow ranges the effect is opposite from orographic precipitation in non-glaciated ranges. While fluvially-eroded ranges tend to be exhumed more on the windward side, glacially-eroded ranges can experience greater erosion on the leeward side. On the timescale of an individual glaciation, the distribution of precipitation and settling is a key component of glacier mass balance and ice extent, while on longer timescales, the interaction of precipitation and topography can play a major role in landscape evolution and range morphology. Numerical modelling of last glacial maximum (LGM) ice extents for catchments on the eastern side of the Southern Alps, New Zealand, highlights the importance of the distribution of precipitation. The accumulation areas of the glaciers would have experienced much greater precipitation than lower elevations, because of the pronounced orographic precipitation gradient, so glacier length is very sensitive to the precipitation distribution employed for any given temperature change. This is particularly challenging given the lack of modern snow monitoring at high altitudes within the Southern Alps, the likelihood of steep accumulation gradients amongst high topography, below the resolution of current datasets, and the difficulty of extrapolating modern values to the LGM. The Sangre de Cristo Range, southern Colorado, and the Bitterroot Range on the Idaho-Montana border both run close to north-south, cross-cutting the prevailing westerly winds. Drainage basins on both sides of the ranges cover similar areas, but moraines are much more substantial on the eastern sides, indicating greater glacial incision, which we suggest at least partly reflects snow blown over the range crest. The Uinta Mountains, Utah, run west-east, parallel to prevailing winds, and show topographic asymmetry across individual catchments, rather than at the range scale. Rapid rock uplift and significant glacial erosion of the north-south Teton Range, Wyoming, has created some of the highest relief in the conterminous US. While an initial topographic asymmetry would have arisen from the tectonic gradient imposed by the extensional Teton Fault on the east side of the range, the topographic asymmetry would have been exaggerated by feedbacks associated with glacial erosion. Slowly-falling snow would have been advected further into the range by prevailing westerlies, which would also have redistributed fallen snow from the subdued topography typical of the headwaters of west-draining basins. Greater topographic shading and cover by rock debris would have mitigated ablation of eastern glaciers bounded by high valley walls. Glacier size, ice flux and erosion would therefore have been enhanced in eastern-draining basins, though only the largest glaciers were capable of eroding at rates that kept pace with rock uplift. Preliminary numerical modelling results are consistent with these inferences of the importance of orographic precipitation and wind-blown snow based on topographic analysis.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

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.

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.

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.

Progress in radar snow research. [Brookings, South Dakota

NASA Technical Reports Server (NTRS)

Stiles, W. H.; Ulaby, F. T.; Fung, A. K.; Aslam, A.

1981-01-01

Multifrequency measurements of the radar backscatter from snow-covered terrain were made at several sites in Brookings, South Dakota, during the month of March of 1979. The data are used to examine the response of the scattering coefficient to the following parameters: (1) snow surface roughness, (2) snow liquid water content, and (3) snow water equivalent. The results indicate that the scattering coefficient is insensitive to snow surface roughness if the snow is drv. For wet snow, however, surface roughness can have a strong influence on the magnitude of the scattering coefficient. These observations confirm the results predicted by a theoretical model that describes the snow as a volume of Rayleig scatterers, bounded by a Gaussian random surface. In addition, empirical models were developed to relate the scattering coefficient to snow liquid water content and the dependence of the scattering coefficient on water equivalent was evaluated for both wet and dry snow conditions.

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.

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 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.

Estimation of snow emissivity via assimilation of multi-frequency passive microwave data into an ensemble-based data assimilation system

NASA Astrophysics Data System (ADS)

Farhadi, L.; Bateni, S. M.; Auligne, T.; Navari, M.

2017-12-01

Snow emissivity is a key parameter for the estimation of snow surface temperature, which is needed as an initial value in climate models and determination of the outgoing long-wave radiation. Moreover, snow emissivity is required for retrieval of atmospheric parameters (e.g., temperature and humidity profiles) from satellite measurements and satellite data assimilations in numerical weather prediction systems. Microwave emission models and remote sensing data cannot accurately estimate snow emissivity due to limitations attributed to each of them. Existing microwave emission models introduce significant uncertainties in their snow emissivity estimates. This is mainly due to shortcomings of the dense media theory for snow medium at high frequencies, and erroneous forcing variables. The well-known limitations of passive microwave data such as coarse spatial resolution, saturation in deep snowpack, and signal loss in wet snow are the major drawbacks of passive microwave retrieval algorithms for estimation of snow emissivity. A full exploitation of the information contained in the remote sensing data can be achieved by merging them with snow emission models within a data assimilation framework. Such an optimal merging can overcome the specific limitations of models and remote sensing data. An Ensemble Batch Smoother (EnBS) data assimilation framework was developed in this study to combine the synthetically generated passive microwave brightness temperatures at 1.4-, 18.7-, 36.5-, and 89-GHz frequencies with the MEMLS microwave emission model to reduce the uncertainty of the snow emissivity estimates. We have used the EnBS algorithm in the context of observing system simulation experiment (or synthetic experiment) at the local scale observation site (LSOS) of the NASA CLPX field campaign. Our findings showed that the developed methodology significantly improves the estimates of the snow emissivity. The simultaneous assimilation of passive microwave brightness temperatures at all frequencies (i.e., 1.4-, 18.7-, 36.5-, and 89-GHz) reduce the root-mean-square-error (RMSE) of snow emissivity at 1.4-, 18.7-, 36.5-, and 89-GHz (H-pol.) by 80%, 42%, 52%, 40%, respectively compared to the corresponding snow emissivity estimates from the open-loop model.

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.

A Comparison of Sea Ice Type, Sea Ice Temperature, and Snow Thickness Distributions in the Arctic Seasonal Ice Zones with the DMSP SSM/I

NASA Technical Reports Server (NTRS)

St.Germain, Karen; Cavalieri, Donald J.; Markus, Thorsten

1997-01-01

Global climate studies have shown that sea ice is a critical component in the global climate system through its effect on the ocean and atmosphere, and on the earth's radiation balance. Polar energy studies have further shown that the distribution of thin ice and open water largely controls the distribution of surface heat exchange between the ocean and atmosphere within the winter Arctic ice pack. The thickness of the ice, the depth of snow on the ice, and the temperature profile of the snow/ice composite are all important parameters in calculating surface heat fluxes. In recent years, researchers have used various combinations of DMSP SSMI channels to independently estimate the thin ice type (which is related to ice thickness), the thin ice temperature, and the depth of snow on the ice. In each case validation efforts provided encouraging results, but taken individually each algorithm gives only one piece of the information necessary to compute the energy fluxes through the ice and snow. In this paper we present a comparison of the results from each of these algorithms to provide a more comprehensive picture of the seasonal ice zone using passive microwave observations.

Nitrate deposition and preservation in the snowpack along a traverse from coast to the ice sheet summit (Dome A) in East Antarctica

NASA Astrophysics Data System (ADS)

Shi, Guitao; Hastings, Meredith G.; Yu, Jinhai; Ma, Tianming; Hu, Zhengyi; An, Chunlei; Li, Chuanjin; Ma, Hongmei; Jiang, Su; Li, Yuansheng

2018-04-01

Antarctic ice core nitrate (NO3-) can provide a unique record of the atmospheric reactive nitrogen cycle. However, the factors influencing the deposition and preservation of NO3- at the ice sheet surface must first be understood. Therefore, an intensive program of snow and atmospheric sampling was made on a traverse from the coast to the ice sheet summit, Dome A, East Antarctica. Snow samples in this observation include 120 surface snow samples (top ˜ 3 cm), 20 snow pits with depths of 150 to 300 cm, and 6 crystal ice samples (the topmost needle-like layer on Dome A plateau). The main purpose of this investigation is to characterize the distribution pattern and preservation of NO3- concentrations in the snow in different environments. Results show that an increasing trend of NO3- concentrations with distance inland is present in surface snow, and NO3- is extremely enriched in the topmost crystal ice (with a maximum of 16.1 µeq L-1). NO3- concentration profiles for snow pits vary between coastal and inland sites. On the coast, the deposited NO3- was largely preserved, and the archived NO3- fluxes are dominated by snow accumulation. The relationship between the archived NO3- and snow accumulation rate can be depicted well by a linear model, suggesting a homogeneity of atmospheric NO3- levels. It is estimated that dry deposition contributes 27-44 % of the archived NO3- fluxes, and the dry deposition velocity and scavenging ratio for NO3- were relatively constant near the coast. Compared to the coast, the inland snow shows a relatively weak correlation between archived NO3- and snow accumulation, and the archived NO3- fluxes were more dependent on concentration. The relationship between NO3- and coexisting ions (nssSO42-, Na+ and Cl-) was also investigated, and the results show a correlation between nssSO42- (fine aerosol particles) and NO3- in surface snow, while the correlation between NO3- and Na+ (mainly associated with coarse aerosol particles) is not significant. In inland snow, there were no significant relationships found between NO3- and the coexisting ions, suggesting a dominant role of NO3- recycling in determining the concentrations.

Snow avalanche activity in the High Tatras Mountains: new data achieved by means of dendrogeomorphic methods

NASA Astrophysics Data System (ADS)

Tichavsky, R.

2016-12-01

The High Tatras Mountains are permanently affected by the occurrence of hazardous geomorphic processes. Snow avalanches represent a common hazard that threatens the infrastructure and humans living and visiting the mountains. So far, the spatio-temporal reconstruction of snow avalanche histories was based only on existing archival records, orthophoto interpretation and lichenometric dating in the High Tatras Mountains. Dendrogeomorphic methods allow for the intra-seasonal dating of scars on tree stems and branches and have been broadly used for the dating of snow avalanche events all over the world. We extracted the increment cores and cross sections from 189 individuals of Pinus mugo var. mugo growing on four tali in the Great Cold Valley and dated all the past scars that could correspond with the winter to early spring occurrence of snow avalanches. The dating was supported by the visual analysis of three orthophoto images from 2004, 2009 and 2014. In total, nineteen event years of snow avalanches (10 certain events, and 9 probable events) were identified since 1959. Historical archives provided evidence only for nine event years since 1987, and three of them were confirmed dendrogeomorphically. Geomorphic effect of recent snow avalanches identified by the spatial distribution of scarred trees in individual years corresponds with the extent of events visible from the orthophotos. We can confirm higher frequency of snow avalanche events since 1980s (17 out of 19 events) and significant increase during the last ten years. The future expected climatic changes associated with the changes in temperature and precipitation regime could significantly influence on the frequency of snow avalanches. Therefore, our results can become the starting line for more extensive dendrogeomorphic survey in the High Tatras Mountains in order to create a catalogue of all natural hazards for the future prediction and modelling of these phenomena in context of environmental changes.

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.

Light-absorbing impurities accelerate glacier melt in the Central Tibetan Plateau.

PubMed

Li, Xiaofei; Kang, Shichang; He, Xiaobo; Qu, Bin; Tripathee, Lekhendra; Jing, Zhefan; Paudyal, Rukumesh; Li, Yang; Zhang, Yulan; Yan, Fangping; Li, Gang; Li, Chaoliu

2017-06-01

Light-absorbing impurities (LAIs), such as organic carbon (OC), black carbon (BC), and mineral dust (MD) deposited on the glacier surface can reduce albedo, thus accelerating the glacier melt. Surface fresh snow, aged snow, granular ice, and snowpits samples were collected between August 2014 and October 2015 on the Xiao Dongkemadi (XDKMD) glacier (33°04'N, 92°04'E) in the central Tibetan Plateau (TP). The spatiotemporal variations of LAIs concentrations in the surface snow/ice were observed to be consistent, differing mainly in magnitudes. LAIs concentrations were found to be in the order: granular ice>snowpit>aged snow>fresh snow, which must be because of post-depositional effects and enrichment. In addition, more intense melting led to higher LAIs concentrations exposed to the surface at a lower elevation, suggesting a strong negative relationship between LAIs concentrations and elevation. The scavenging efficiencies of OC and BC were same (0.07±0.02 for OC, 0.07±0.01 for BC), and the highest enrichments was observed in late September and August for surface snow and granular ice, respectively. Meanwhile, as revealed by the changes in the OC/BC ratios, intense glacier melt mainly occurred between August and October. Based on the SNow ICe Aerosol Radiative (SNICAR) model simulations, BC and MD in the surface snow/ice were responsible for about 52%±19% and 25%±14% of the albedo reduction, while the radiative forcing (RF) were estimated to be 42.74±40.96Wm -2 and 21.23±22.08Wm -2 , respectively. Meanwhile, the highest RF was observed in the granular ice, suggesting that the exposed glaciers melt and retreat more easily than the snow distributed glaciers. Furthermore, our results suggest that BC was the main forcing factor compared with MD in accelerating glacier melt during the melt season in the Central TP. Copyright © 2017 Elsevier B.V. All rights reserved.

High-Resolution NU-WRF Simulations of a Deep Convective-Precipitation System During MC3E: Part I: Comparisons Between Goddard Microphysics Schemes and Observations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Wu, Di; Lang, Stephen; Chern, Jiun-Dar; Peters-Lidard, Christa; Fridlind, Ann; Matsui, Toshihisa

2016-01-01

The Goddard microphysics was recently improved by adding a fourth ice class (frozen dropshail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of icesnow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.

Comparing elevation and freeboard from IceBridge and four different CryoSat-2 retrackers for coincident sea ice observations

NASA Astrophysics Data System (ADS)

Yi, D.; Kurtz, N. T.; Harbeck, J.

2017-12-01

The airborne IceBridge and spaceborne Cryosat-2 missions observe polar sea ice at different altitudes with different footprint sizes and often at different time and locations. Many studies use different retrackers to derive Cryosat-2 surface elevation, which we find causes large differences in the elevation and freeboard comparisons of IceBridge and Cryosat-2. In this study, we compare sea ice surface elevation and freeboard using 8 coincident CryoSat-2, ATM, and LVIS observations with IceBridge airplanes under flying the Cryosat-2 ground tracks. We apply identical ellipsoid, geoid model, tide model, and atmospheric correction to CryoSat-2 and IceBridge data to reduce elevation bias due to their differences. IceBridge's ATM and LVIS elevation and freeboard and Snow Radar snow depth are averaged at each CryoSat-2 footprint for comparison. The four different Cryosat-2 retrackers (ESA, GSFC, AWI, and JPL) show distinct differences in mean elevation up to 0.35 meters over leads and over floes, which suggests that systematic elevation bias exists between the retrackers. The mean IceBridge elevation over leads is within the mean elevation distribution of the four Cryosat-2 retrackers. The mean IceBridge elevation over floes is above the mean elevation distribution of the four Cryosat-2 retrackers. After removing the snow depth from IceBridge elevation, over floe, the mean elevation of IceBridge is within the mean elevation distribution of the four Cryosat-2 retrackers. By identifying the strengths and weaknesses of the retrackers, this study provides a mechanism to improve freeboard retrievals from existing methods.

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.

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

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

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.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Evaluation of snow and frozen soil parameterization in a cryosphere land surface modeling framework in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.

2017-12-01

Snow and frozen soil are important components in the Tibetan Plateau, and influence the water cycle and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new cryosphere land surface model (LSM) with coupled snow and frozen soil parameterization was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Development of a land surface model with coupled snow and frozen soil physics

NASA Astrophysics Data System (ADS)

Wang, Lei; Zhou, Jing; Qi, Jia; Sun, Litao; Yang, Kun; Tian, Lide; Lin, Yanluan; Liu, Wenbin; Shrestha, Maheswor; Xue, Yongkang; Koike, Toshio; Ma, Yaoming; Li, Xiuping; Chen, Yingying; Chen, Deliang; Piao, Shilong; Lu, Hui

2017-06-01

Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Radiative transfer model of snow for bare ice regions

NASA Astrophysics Data System (ADS)

Tanikawa, T.; Aoki, T.; Niwano, M.; Hosaka, M.; Shimada, R.; Hori, M.; Yamaguchi, S.

2016-12-01

Modeling a radiative transfer (RT) for coupled atmosphere-snow-bare ice systems is of fundamental importance for remote sensing applications to monitor snow and bare ice regions in the Greenland ice sheet and for accurate climate change predictions by regional and global climate models. Recently, the RT model for atmosphere-snow system was implemented for our regional and global climate models. However, the bare ice region where recently it has been expanded on the Greenland ice sheet due to the global warming, has not been implemented for these models, implying that this region leads miscalculations in these climate models. Thus, the RT model of snow for bare ice regions is needed for accurate climate change predictions. We developed the RT model for coupled atmosphere-snow-bare ice systems, and conducted a sensitivity analysis of the RT model to know the effect of snow, bare ice and geometry parameters on the spectral radiant quantities. The RT model considers snow and bare-ice inherent optical properties (IOPs), including snow grain size, air bubble size and its concentration and bare ice thickness. The conventional light scattering theory, Mie theory, was used for IOP calculations. Monte Carlo method was used for the multiple scattering. The sensitivity analyses showed that spectral albedo for the bare ice increased with increasing the concentration of the air bubble in the bare ice for visible wavelengths because the air bubble is scatterer with no absorption. For near infrared wavelengths, spectral albedo has no dependence on the air bubble due to the strong light absorption by ice. When increasing solar zenith angle, the spectral albedo were increased for all wavelengths. This is the similar trend with spectral snow albedo. Cloud cover influenced the bare ice spectral albedo by covering direct radiation into diffuse radiation. The purely diffuse radiation has an effective solar zenith angle near 50°. Converting direct into diffuse radiation reduces the effective solar zenith angle, resulting in reducing the spectral albedo. This is also the similar trend with spectral snow albedo. Further work should focus on the validation of the RT model using in situ measurement data through field and laboratory experiments.

The value of snow cover

NASA Astrophysics Data System (ADS)

Sokratov, S. A.

2009-04-01

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

Evaluation of the satellite derived snow cover area - Runoff forecasting models for the inaccessible basins of western Himalayas

NASA Technical Reports Server (NTRS)

Dey, B.

1985-01-01

In this study, the existing seasonal snow cover area runoff forecasting models of the Indus, Kabul, Sutlej and Chenab basins were evaluated with the concurrent flow correlation model for the period 1975-79. In all the basins under study, correlation of concurrent flow model explained the variability in flow better than by the snow cover area runoff models. Actually, the concurrent flow correlation model explained more than 90 percent of the variability in the flow of these rivers. Compared to this model, the snow cover area runoff models explained less of the variability in flow. In the Himalayan river basins under study and at least for the period under observation, the concurrent flow correlation model provided a set of results with which to compare the estimates from the snow cover area runoff models.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Global land-atmosphere coupling associated with cold climate processes

NASA Astrophysics Data System (ADS)

Dutra, Emanuel

This dissertation constitutes an assessment of the role of cold processes, associated with snow cover, in controlling the land-atmosphere coupling. The work was based on model simulations, including offline simulations with the land surface model HTESSEL, and coupled atmosphere simulations with the EC-EARTH climate model. A revised snow scheme was developed and tested in HTESSEL and EC-EARTH. The snow scheme is currently operational at the European Centre for Medium-Range Weather Forecasts integrated forecast system, and in the default configuration of EC-EARTH. The improved representation of the snowpack dynamics in HTESSEL resulted in improvements in the near surface temperature simulations of EC-EARTH. The new snow scheme development was complemented with the option of multi-layer version that showed its potential in modeling thick snowpacks. A key process was the snow thermal insulation that led to significant improvements of the surface water and energy balance components. Similar findings were observed when coupling the snow scheme to lake ice, where lake ice duration was significantly improved. An assessment on the snow cover sensitivity to horizontal resolution, parameterizations and atmospheric forcing within HTESSEL highlighted the role of the atmospheric forcing accuracy and snowpack parameterizations in detriment of horizontal resolution over flat regions. A set of experiments with and without free snow evolution was carried out with EC-EARTH to assess the impact of the interannual variability of snow cover on near surface and soil temperatures. It was found that snow cover interannual variability explained up to 60% of the total interannual variability of near surface temperature over snow covered regions. Although these findings are model dependent, the results showed consistency with previously published work. Furthermore, the detailed validation of the snow dynamics simulations in HTESSEL and EC-EARTH guarantees consistency of the results.

49 CFR 1242.28 - Roadway machines, small tools and supplies, and snow removal (accounts XX-19-36 to XX-19-38...

Code of Federal Regulations, 2010 CFR

2010-10-01

... snow removal (accounts XX-19-36 to XX-19-38, inclusive). 1242.28 Section 1242.28 Transportation Other... tools and supplies, and snow removal (accounts XX-19-36 to XX-19-38, inclusive). Separate common expenses according to distribution of common expenses listed in § 1242.10, Administration—Track (account XX...

Snow Roads at McMurdo Station, Antarctica

DTIC Science & Technology

2010-05-01

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

Modeling Snow Regime in Cores of Small Planetary Bodies

NASA Astrophysics Data System (ADS)

Boukaré, C. E.; Ricard, Y. R.; Parmentier, E.; Parman, S. W.

2017-12-01

Observations of present day magnetic field on small planetary bodies such as Ganymede or Mercury challenge our understanding of planetary dynamo. Several mechanisms have been proposed to explain the origin of magnetic fields. Among the proposed scenarios, one family of models relies on snow regime. Snow regime is supported by experimental studies showing that melting curves can first intersect adiabats in regions where the solidifying phase is not gravitationaly stable. First solids should thus remelt during their ascent or descent. The effect of the snow zone on magnetic field generation remains an open question. Could magnetic field be generated in the snow zone? If not, what is the depth extent of the snow zone? How remelting in the snow zone drive compositional convection in the liquid layer? Several authors have tackled this question with 1D-spherical models. Zhang and Schubert, 2012 model sinking of the dense phase as internally heated convection. However, to our knowledge, there is no study on the convection structure associated with sedimentation and phase change at planetary scale. We extend the numerical model developped in [Boukare et al., 2017] to model snow dynamics in 2D Cartesian geometry. We build a general approach for modeling double diffusive convection coupled with solid-liquid phase change and phase separation. We identify several aspects that may govern the convection structure of the solidifying system: viscosity contrast between the snow zone and the liquid layer, crystal size, rate of melting/solidification and partitioning of light components during phase change.

A Coupled Snow Operations-Skier Demand Model for the Ontario (Canada) Ski Region

NASA Astrophysics Data System (ADS)

Pons, Marc; Scott, Daniel; Steiger, Robert; Rutty, Michelle; Johnson, Peter; Vilella, Marc

2016-04-01

The multi-billion dollar global ski industry is one of the tourism subsectors most directly impacted by climate variability and change. In the decades ahead, the scholarly literature consistently projects decreased reliability of natural snow cover, shortened and more variable ski seasons, as well as increased reliance on snowmaking with associated increases in operational costs. In order to develop the coupled snow, ski operations and demand model for the Ontario ski region (which represents approximately 18% of Canada's ski market), the research utilized multiple methods, including: a in situ survey of over 2400 skiers, daily operations data from ski resorts over the last 10 years, climate station data (1981-2013), climate change scenario ensemble (AR5 - RCP 8.5), an updated SkiSim model (building on Scott et al. 2003; Steiger 2010), and an agent-based model (building on Pons et al. 2014). Daily snow and ski operations for all ski areas in southern Ontario were modeled with the updated SkiSim model, which utilized current differential snowmaking capacity of individual resorts, as determined from daily ski area operations data. Snowmaking capacities and decision rules were informed by interviews with ski area managers and daily operations data. Model outputs were validated with local climate station and ski operations data. The coupled SkiSim-ABM model was run with historical weather data for seasons representative of an average winter for the 1981-2010 period, as well as an anomalously cold winter (2012-13) and the record warm winter in the region (2011-12). The impact on total skier visits and revenues, and the geographic and temporal distribution of skier visits were compared. The implications of further climate adaptation (i.e., improving the snowmaking capacity of all ski areas to the level of leading resorts in the region) were also explored. This research advances system modelling, especially improving the integration of snow and ski operations models with demand and socioeconomic implications. This innovative integrated systems model approach can be exported to other major ski tourism markets (e.g., Canada, USA, Western and Eastern Europe, Australia, Japan) to facilitate global comparative assessments of ski tourism vulnerability to climate change, establishing the standard for ski tourism vulnerability assessments and advancing scholarly work on sustainable tourism and climate-compatible development in mountain communities.

Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model

DOE PAGES

Frans, Chris D.; Clarke, Garry K. C.; Burns, P.; ...

2014-02-27

Here, we describe an integrated spatially distributed hydrologic and glacier dynamic model, and use it to investigate the effect of glacier recession on streamflow variations for the Upper Bow River basin, a tributary of the South Saskatchewan River. Several recent studies have suggested that observed decreases in summer flows in the South Saskatchewan River are partly due to the retreat of glaciers in the river's headwaters. Modeling the effect of glacier changes on streamflow response in river basins such as the South Saskatchewan is complicated due to the inability of most existing physically-based distributed hydrologic models to represent glacier dynamics.more » We compare predicted variations in glacier extent, snow water equivalent and streamflow discharge made with the integrated model with satellite estimates of glacier area and terminus position, observed streamflow and snow water equivalent measurements over the period of 1980 2007. Simulations with the coupled hydrology-glacier model reduce the uncertainty in streamflow predictions. Our results suggested that on average, the glacier melt contribution to the Bow River flow upstream of Lake Louise is about 30% in summer. For warm and dry years, however, the glacier melt contribution can be as large as 50% in August, whereas for cold years, it can be as small as 20% and the timing of glacier melt signature can be delayed by a month.« less

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

A better knowledge of the accumulated snow on the watersheds will help flood forecasting centres and hydro-power companies to predict the amount of water released during spring snowmelt. Since precipitations gauges are sparse at high elevations and integrative measurements of the snow accumulated on watershed surface are hard to obtain, using snow models is an adequate way to estimate snow water equivalent (SWE) on watersheds. In addition to short term prediction, simulating accurately SWE with snow models should have many advantages. Validating the snow module on both SWE and snowmelt should give a more reliable model for climate change studies or regionalization for ungauged watersheds. The aim of this study is to create a new snow module, which has a structure that allows the use of measured snow data for calibration or assimilation. Energy balance modelling seems to be the logical choice for designing a model in which internal variables, such as SWE, could be compared to observations. Physical models are complex, needing high computational resources and many different types of inputs that are not widely measured at meteorological stations. At the opposite, simple conceptual degree-day models offer to simulate snowmelt using only temperature and precipitation as inputs with fast computing. Its major drawback is to be empirical, i.e. not taking into account all of the processes of the energy balance, which makes this kind of model more difficult to use when willing to compare SWE to observed measurements. In order to reach our objectives, we created a snow model structured by a simplified energy balance where each of the processes is empirically parameterized in order to be calculated using only temperature, precipitation and cloud cover variables. This model's structure is similar to the one created by M.T. Walter (2005), where parameterizations from the literature were used to compute all of the processes of the energy balance. The conductive fluxes into the snowpack were modelled by using analytical solutions to the heat equation taking phase change into account. This approach has the advantage to use few forcing variables and to take into account all the processes of the energy balance. Indeed, the simulations should be quick enough to allow, for example, ensemble prediction or simulation of numerous basins, more easily than physical snow models. The snow module formulation has been completed and is in its validation phase using data from the experimental station of Col de Porte, Alpes, France. Data from the US SNOTEL product will be used in order to test the model structure on a larger scale and to test diverse calibration procedures, since the aim is to use it on a basin scale for discharge modelling purposes.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

DOE PAGES

Atchley, A. L.; Painter, S. L.; Harp, D. R.; ...

2015-04-14

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurementsmore » to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.« less

Snow Micro-Structure Model

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

Micah Johnson, Andrew Slaughter

PIKA is a MOOSE-based application for modeling micro-structure evolution of seasonal snow. The model will be useful for environmental, atmospheric, and climate scientists. Possible applications include application to energy balance models, ice sheet modeling, and avalanche forecasting. The model implements physics from published, peer-reviewed articles. The main purpose is to foster university and laboratory collaboration to build a larger multi-scale snow model using MOOSE. The main feature of the code is that it is implemented using the MOOSE framework, thus making features such as multiphysics coupling, adaptive mesh refinement, and parallel scalability native to the application. PIKA implements three equations:more » the phase-field equation for tracking the evolution of the ice-air interface within seasonal snow at the grain-scale; the heat equation for computing the temperature of both the ice and air within the snow; and the mass transport equation for monitoring the diffusion of water vapor in the pore space of the snow.« less

An integrated uncertainty analysis and data assimilation approach for improved streamflow predictions

NASA Astrophysics Data System (ADS)

Hogue, T. S.; He, M.; Franz, K. J.; Margulis, S. A.; Vrugt, J. A.

2010-12-01

The current study presents an integrated uncertainty analysis and data assimilation approach to improve streamflow predictions while simultaneously providing meaningful estimates of the associated uncertainty. Study models include the National Weather Service (NWS) operational snow model (SNOW17) and rainfall-runoff model (SAC-SMA). The proposed approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. An ensemble Kalman filter (EnKF) is configured with the DREAM-identified uncertainty structure and applied to assimilating snow water equivalent data into the SNOW17 model for improved snowmelt simulations. Snowmelt estimates then serves as an input to the SAC-SMA model to provide streamflow predictions at the basin outlet. The robustness and usefulness of the approach is evaluated for a snow-dominated watershed in the northern Sierra Mountains. This presentation describes the implementation of DREAM and EnKF into the coupled SNOW17 and SAC-SMA models and summarizes study results and findings.

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

USGS Publications Warehouse

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

2002-01-01

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

Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) Global Snow-Cover Maps

NASA Technical Reports Server (NTRS)

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

2000-01-01

Following the 1999 launch of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), the capability exists to produce global snow-cover maps on a daily basis at 500-m resolution. Eight-day composite snow-cover maps will also be available. MODIS snow-cover products are produced at Goddard Space Flight Center and archived and distributed by the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. The products are available in both orbital and gridded formats. An online search and order tool and user-services staff will be available at NSIDC to assist users with the snow products. The snow maps are available at a spatial resolution of 500 m, and 1/4 degree x 1/4 degree spatial resolution, and provide information on sub-pixel (fractional) snow cover. Pre-launch validation work has shown that the MODIS snow-mapping algorithms perform best under conditions of continuous snow cover in low vegetation areas, but can also map snow cover in dense forests. Post-launch validation activities will be performed using field and aircraft measurements from a February 2000 validation mission, as well as from existing satellite-derived snow-cover maps from NOAA and Landsat-7 Enhanced Thematic Mapper Plus (ETM+).

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.

A study of the break-up characteristics of Chena River Basin using ERTS imagery

NASA Technical Reports Server (NTRS)

Carlson, R. F. (Principal Investigator); Kane, D. L.; Wendler, G.

1973-01-01

The author has identified the following significant results. The Chena River Basin was selected because of the availability of ground truth data for comparison. Very good agreement for snow distribution and rates of ablation was found between the ERTS-1 imagery, the snowmelt model, and field measurements. Monitoring snowmelt rates for relatively small basins appears to be practical. The main limitation of the ERTS-1 imagery is the interval of coverage. More frequent overflights providing coverage are needed for the study of transient hydrologic events. ERTS-1 data is most useful when used in conjunction with snowmelt prediction models and existing snow course data. These results should prove very useful in preliminary assessment of hydrologic conditions in ungaged watersheds and will provide a tool for month-to-month volume forecasting.

View Angle Effects on MODIS Snow Mapping in Forests

NASA Technical Reports Server (NTRS)

Xin, Qinchuan; Woodcock, Curtis E.; Liu, Jicheng; Tan, Bin; Melloh, Rae A.; Davis, Robert E.

2012-01-01

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

Incorporation of the Mass Concentration and the New Snow Albedo Schemes into the Global Forecasting Model, GEOS-5 and the Impact of the New Schemes over Himalayan Glaciers

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2012-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et aL including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Characterization of Ice for Return-to-Flight of the Space Shuttle. Part 2; Soft Ice

NASA Technical Reports Server (NTRS)

Schulson, Erland M.; Iliescu, Daniel

2005-01-01

In support of characterizing ice debris for return-to-flight (RTF) of NASA's space shuttle, we have determined the microstructure, density and compressive strength (at -10 C at approximately 0.3 per second) of porous or soft ice that was produced from both atmospheric water and consolidated snow. The study showed that the atmospheric material was generally composed of a mixture of very fine (0.1 to 0.3 millimeters) and coarser (5 to 10 millimeter) grains, plus air bubbles distributed preferentially within the more finely-grained part of the microstructure. The snow ice was composed of even finer grains (approximately 0.05 millimeters) and contained more pores. Correspondingly, the snow ice was of lower density than the atmospheric ice and both materials were significantly less dense than hard ice. The atmospheric ice was stronger (approximately 3.8 MPa) than the snow ice (approximately 1.9 MPa), but weaker by a factor of 2 to 5 than pore-free hard ice deformed under the same conditions. Zero Values are given for Young's modulus, compressive strength and Poisson's ratio that can be used for modeling soft ice from the external tank (ET).

Influence of Projected Changes in North American Snow Cover Extent on Mid-Latitude Cyclone Progression

NASA Astrophysics Data System (ADS)

Clare, R. M.; Desai, A. R.; Martin, J. E.; Notaro, M.; Vavrus, S. J.

2017-12-01

It has long been hypothesized that snow cover and snow extent have an influence on the development or steering of synoptic mid-latitude cyclones (MLCs). Rydzik and Desai (2014) showed a robust statistical relationship among snow cover extent, generation of low-level baroclinicity, and MLC tracks. Though snow cover extent is highly variable year to year, the changing global climate is expected to continue an already observed pattern of poleward retreat of mean snow cover in North America, particularly in late winter and spring. For this experiment, large ensemble simulations with the Weather Research and Forecasting model (WRF) were forced with output from the Community Earth System Model (CESM) to test the effect contributed solely by snow cover and the projected effects of a changing climate. Our experiment induces an adjustment to the extent of snow cover in North America according to CESM RCP 8.5 projections for each decade from 2020 to 2100 before and during several cases of MLCs moving east across the Great Plains near the snow line. To evaluate mechanisms of pre-existing and current snow influence on MLCs, model cases are started with snow line adjustment occurring from three days prior up to the storm's arrival over the Great Plains. We demonstrate that snow cover changes do alter MLC intensity and path via modification of low-level potential vorticity.

Modeling of surface dust concentrations using neural networks and kriging

NASA Astrophysics Data System (ADS)

Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.

2016-12-01

Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.

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.

Role of snow and cold environment in the fate and effects of nanoparticles and select organic pollutants from gasoline engine exhaust.

PubMed

Nazarenko, Yevgen; Kurien, Uday; Nepotchatykh, Oleg; Rangel-Alvarado, Rodrigo B; Ariya, Parisa A

2016-02-01

Exposure to vehicle exhaust can drive up to 70 % of excess lifetime cancer incidences due to air pollution in urban environments. Little is known about how exhaust-derived particles and organic pollutants, implicated in adverse health effects, are affected by freezing ambient temperatures and the presence of snow. Airborne particles and (semi)volatile organic constituents in dilute exhaust were studied in a novel low-temperature environmental chamber system containing natural urban snow under controlled cold environmental conditions. The presence of snow altered the aerosol size distributions of dilute exhaust in the 10 nm to 10 μm range and decreased the number density of the nanoparticulate (<100 nm) fraction of exhaust aerosols, yet increased the 100-150 nm fraction. Upon 1 hour exhaust exposure, the total organic carbon increased in the natural snow from 0.218 ± 0.014 to 0.539 ± 0.009 mg L(-1), and over 40 additional (semi)volatile organic compounds and a large number of exhaust-derived carbonaceous and likely organic particles were identified. The concentrations of benzene, toluene, ethylbenzene, and xylenes (BTEX) increased from near the detection limit to 52.48, 379.5, 242.7, and 238.1 μg kg(-1) (± 10 %), respectively, indicating the absorption of exhaust-derived toxic organic compounds by snow. The alteration of exhaust aerosol size distributions at freezing temperatures and in the presence of snow, accompanied by changes of the organic pollutant content in snow, has potential to alter health effects of human exposure to vehicle exhaust.

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

NASA Astrophysics Data System (ADS)

Deeb, Elias J.

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

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.

A vertically integrated snow/ice model over land/sea for climate models. I - Development. II - Impact on orbital change experiments

NASA Technical Reports Server (NTRS)

Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

1988-01-01

A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.

Face Value: Towards Robust Estimates of Snow Leopard Densities.

PubMed

Alexander, Justine S; Gopalaswamy, Arjun M; Shi, Kun; Riordan, Philip

2015-01-01

When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

Face Value: Towards Robust Estimates of Snow Leopard Densities

PubMed Central

2015-01-01

When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality. PMID:26322682

Characterization of mercury concentrations in snow and potential sources, Shanghai, China.

PubMed

Zhang, Yanyan; Xiu, Guangli; Wu, Xuefang; Moore, Christopher W; Wang, Jiajia; Cai, Ji; Zhang, Danian; Shi, Chaoou; Zhang, Renjian

2013-04-01

This work focused on quantifying the total mercury (HgT) and major ion concentrations in snow samples to understand the importance of this pathway and sources of Hg deposited in Shanghai, China. Rare snow event samples were collected at 26 sites within the city of Shanghai on February 18, 2006, January 27, 2008 and January 20, 2011. The sites were distributed among four main functional area types (i.e., industrial impacted, residential impacted, traffic impacted sites and sites in the city center). Concentrations of HgT and major soluble ions, and pH values were determined for each site. Mean HgT concentrations for all sites were 78±52 ng L(-1), 277±184 ng L(-1), 189±123 ng L(-1) in 2006, 2008 and 2011, respectively. Values were higher in Shanghai than observed in other cities including Beijing which has a smaller population and is less industrial. Principle component analysis (PCA) indicated that secondary aerosols (SO4(2-), NO3(-) and NH4(+)), and biomass combustion (K(+), CH3COO(-), and HCOO(-)) were best related to mercury concentrations in the snow in 2008 and 2011. Although HYSPLIT back trajectory modeling indicated air mass transport from areas with significant coal combustion, results indicate that anthropogenic pollution from within Shanghai was the predominant source of Hg in snow. Copyright © 2013 Elsevier B.V. All rights reserved.

An Innovative Network to Improve Sea Ice Prediction in a Changing Arctic

DTIC Science & Technology

2014-09-30

sea ice volume. The EXP ensemble is initialized with 1/5 of CNTL snow depths, thus resulting in a reduced snow cover and lower summer albedo ... Sea Ice - Albedo Feedback in Sea Ice Predictions is also about understanding sea ice predictability. REFERENCES Blanchard-Wrigglesworth, E., K...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. An Innovative Network to Improve Sea Ice Prediction

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

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.

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.

Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area

USDA-ARS?s Scientific Manuscript database

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral m...

Utilizing Multiple Datasets for Snow Cover Mapping

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Hall, Dorothy K.; Foster, James L.; Armstrong, Richard L.

1999-01-01

Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

‘As simple as possible but not simpler': What is useful in a temperature-based snow-accounting routine? Part 2 - Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments

NASA Astrophysics Data System (ADS)

Valéry, Audrey; Andréassian, Vazken; Perrin, Charles

2014-09-01

This paper investigates the degree of complexity required in a snow accounting routine to ultimately simulate flows at the catchment outlet. We present a simple, parsimonious and general snow accounting routine (SAR), called Cemaneige, that can be associated with any precipitation-runoff model to simulate discharge at the catchment scale. To get results of general applicability, this SAR was tested on a large set of 380 catchments from four countries (France, Switzerland, Sweden and Canada) and combined with four different hydrological models. Our results show that five basic features provide a good reliability and robustness to the SAR, namely considering: (1) a transition range of temperature for the determination of the solid fraction of precipitation; (2) five altitudinal bands of equal area for snow accumulation; (3) the cold-content of the snowpack (with a parameter controlling snowpack inertia); (4) a degree-day factor controlling snowmelt; (5) uneven snow distribution in each band. This general SAR includes two internal states (the snowpack and its cold-content). Results also indicate that only two free parameters (snowmelt factor and cold-content factor) are warranted in a SAR at the daily time step and that further complexity is not supported by improvements in flow simulation efficiency. To justify the reasons for considering the five features above, a sensitivity analysis comparing Cemaneige with other SAR versions is performed. It analyses the snow processes which should be selected or not to bring significant improvement in model performances. Compared with the six existing SARs presented in the companion article (Valéry et al., 2014) on the 380 catchments set, Cemaneige shows better performance on average than five of these six SARs. It provides performance similar to the sixth SAR (MORD4) but with only half its number of free parameters. However, CemaNeige still appears perfectible on mountainous catchments (France and Switzerland) where the lumped SAR, MORD4, outperforms Cemaneige. Cemaneige can easily be adapted for simulation on ungauged catchments: fixing its two parameters to default values much less degrades performances than the other best performing SAR. This may partly due to the Cemaneige parsimony.

Climatic Effects of Black Carbon Aerosols Over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

He, Cenlin

Black carbon (BC), also known as soot, has been identified as the second most important anthropogenic emissions in terms of global climate forcing in the current atmosphere. Ample evidence has shown that BC deposition is an important driver of rapid snow melting and glacier retreat over the Tibetan Plateau, which holds the largest snow/ice mass outside polar regions. However, the climatic effects of BC over the Tibetan Plateau have not been thoroughly investigated in such a manner as to understand, quantify, and reduce large uncertainties in the estimate of radiative and hydrological effects. Thus, this Ph.D. study seeks to understand and improve key processes controlling BC life cycle in global and regional models and to quantify BC radiative effects over the Tibetan Plateau. First, the capability of a state-of-the-art global chemical transport model (CTM), GEOS-Chem, and the associated model uncertainties are systematically evaluated in simulating BC over the Tibetan Plateau, using in situ measurements of BC in surface air, BC in snow, and BC absorption optical depth. The effects of three key factors on the simulation are also delineated, including Asian anthropogenic emissions, BC aging process, and model resolution. Subsequently, a microphysics-based BC aging scheme that accounts for condensation, coagulation, and heterogeneous chemical oxidation processes is developed and examined in GEOS-Chem by comparing with aircraft measurements. Compared to the default aging scheme, the microphysical scheme reduces model-observation discrepancies by a factor of 3, particularly in the middle and upper troposphere. In addition, a theoretical BC aging-optics model is developed to account for three typical evolution stages, namely, freshly emitted aggregates, coated BC by soluble material, and BC particles undergoing further hygroscopic growth. The geometric-optics surface-wave (GOS) approach is employed to compute the BC single-scattering properties at each aging stage, which are subsequently compared with laboratory measurements. Results show large variations in BC optical properties caused by coating morphology and aging stages. Furthermore, a comprehensive intercomparison of the GOS approach, the superposition T-matrix method, and laboratory measurements is performed for optical properties of BC with complex structures during aging. Moreover, a new snow albedo model is developed for widely-observed close-packed snow grains internally mixed with BC. Results indicate that albedo simulations that account for snow close packing match closer to observations. Close packing enhances BC-induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, which suggests that BC-snow albedo forcing is underestimated in previous modeling studies without accounting for close packing. Finally, the snow albedo forcing and direct radiative forcing (DRF) of BC in the Tibetan Plateau are estimated using GEOS-Chem in conjunction with a stochastic snow model and a radiative transfer model. This, for the first time, accounts for realistic non-spherical snow grain shape and stochastic multiple inclusions of BC within snow in assessing BC-snow interactions. The annual mean BC snow albedo forcing is 2.9 W m-2 over snow-covered Plateau regions. BC-snow internal mixing increases the albedo forcing by 40-60% compared with external mixing, whereas Koch snowflakes reduce the forcing by 20-40% relative to spherical snow grains. BC DRF at the top of the atmosphere is 2.3 W m-2 with uncertainties of -70% - +85% in the Plateau. The BC forcings are further attributed to emissions from different regions.

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.

Data sets for snow cover monitoring and modelling from the National Snow and Ice Data Center

NASA Astrophysics Data System (ADS)

Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.

2003-04-01

A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and Ice Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The AMSR-E and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

NASA Astrophysics Data System (ADS)

Cook, Joseph M.; Hodson, Andrew J.; Gardner, Alex S.; Flanner, Mark; Tedstone, Andrew J.; Williamson, Christopher; Irvine-Fynn, Tristram D. L.; Nilsson, Johan; Bryant, Robert; Tranter, Martyn

2017-11-01

The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo (bioalbedo) and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

Intra-basin variability of snowmelt water balance calculations in a subarctic catchment

NASA Astrophysics Data System (ADS)

McCartney, Stephen E.; Carey, Sean K.; Pomeroy, John W.

2006-03-01

The intra-basin variability of snowmelt and melt-water runoff hydrology in an 8 km2 subarctic alpine tundra catchment was examined for the 2003 melt period. The catchment, Granger Creek, is within the Wolf Creek Research Basin, Yukon, which is typical of mountain subarctic landscapes in northwestern Canada. The study catchment was segmented into nine internally uniform zones termed hydrological response units (HRUs) based on their similar hydrological, physiographic, vegetation and soil properties. Snow accumulation exhibited significant variability among the HRUs, with greatest snow water equivalent in areas of tall shrub vegetation. Melt began first on southerly exposures and at lower elevations, yet average melt rates for the study period varied little among HRUs with the exception of those with steep aspects. In HRUs with capping organic soils, melt water first infiltrated this surface horizon, satisfying its storage capacity, and then percolated into the frozen mineral substrate. Infiltration and percolation into frozen mineral soils was restricted where melt occurred rapidly and organic soils were thin; in this case, melt-water delivery rates exceeded the frozen mineral soil infiltration rate, resulting in high runoff rates. In contrast, where there were slower melt rates and thick organic soils, infiltration was unlimited and runoff was suppressed. The snow water equivalent had a large impact on runoff volume, as soil storage capacity was quickly surpassed in areas of deep snow, diverting the bulk of melt water laterally to the drainage network. A spatially distributed water balance indicated that the snowmelt freshet was primarily controlled by areas with tall shrub vegetation that accumulate large quantities of snow and by alpine areas with no capping organic soils. The intra-basin water balance variability has important implications for modelling freshet in hydrological models.

Integration, Validation, and Application of a PV Snow Coverage Model in SAM

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

Freeman, Janine M.; Ryberg, David Severin

2017-08-01

Due to the increasing deployment of PV systems in snowy climates, there is significant interest in a method capable of estimating PV losses resulting from snow coverage that has been verified for a variety of system designs and locations. Many independent snow coverage models have been developed over the last 15 years; however, there has been very little effort verifying these models beyond the system designs and locations on which they were based. Moreover, major PV modeling software products have not yet incorporated any of these models into their workflows. In response to this deficiency, we have integrated the methodologymore » of the snow model developed in the paper by Marion et al. (2013) into the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM). In this work, we describe how the snow model is implemented in SAM and we discuss our demonstration of the model's effectiveness at reducing error in annual estimations for three PV arrays. Next, we use this new functionality in conjunction with a long term historical data set to estimate average snow losses across the United States for two typical PV system designs. The open availability of the snow loss estimation capability in SAM to the PV modeling community, coupled with our results of the nationwide study, will better equip the industry to accurately estimate PV energy production in areas affected by snowfall.« less