Progress in radar snow research

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

Tri-Frequency Synthetic Aperture Radar for the Measurements of Snow Water Equivalent

SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) is an airborne instrument developed at the NASA Goddard Space Flight Center for the retrieval of Snow Water Equivalent. SWESARR was specifically designed to measure co-located active and passive signals using a high resolution and multi-frequency Synthetic Aperture Radar (SAR) and a multifrequency radiometer. SWESARRs Synthetic Aperture Radar (SAR) system is made up of three independent radar units that operate in the X, Ku-Low, and Ku-High bands with bandwidths up to 200 MHz, and acquires data in two polarizations (dual-polarization radar). The difference in sensitivity of the backscatter signals to snow microstructure, in conjunctions with radiometer measurements, permits an accurate estimation of the snow water equivalent (SWE)

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

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

Ensemble Modeling of SWE Distribution in the Bitterroot Mountains, Montana, USA

The spatial distribution of snow remains poorly understood at the landscape scale, particularly at high elevation where snow can be under-represented by our current system of monitoring. The transferability of the processes and physiography that drive the spatial distribution of snow require further study. We apply an ensemble of three semi-independent snow models to the down-sloping side of the Bitterroot Mountains of Western Montana, which features an array of drainages of remarkably similar size and aspect. We modeled the snow water equivalent equal to the maximum snow accumulation plus any positive contributions to the snowpack during the melt season, for the years 2000-2010. The three models yield similar magnitudes and patterns of snow water equivalent distribution. We find that upwards of 70% of the snow water equivalent is found above the elevation of 1950 meters and this snow water equivalent is represented by a single SNOTEL station within our 1200 km2 study area. The difference in snow water equivalent on north and south facing aspects within in individual drainages is found to be small. At lower elevations snow water equivalent increases with elevation, while above the elevation of 2000 meters snow water equivalent remains constant as elevation increases. The difference in snow water equivalent at lower and higher elevations within the study area is driven by snow accumulation processes that differ between valleys and valley sidewalls/ridgetops within the study area. The processes that control the spatial distribution of snow water equivalent within this study area are site specific and are likely not transferable to other regions

Snow water equivalent determination by microwave radiometry

One of the most important parameters for accurate snowmelt runoff prediction is snow water equivalent (SWE) which is contentionally monitored using observations made at widely scattered points in or around specific watersheds. Remote sensors which provide data with better spatial and temporal coverage can be used to improve the SWE estimates. Microwave radiation, which can penetrate through a snowpack, may be used to infer the SWE. Calculations made from a microscopic scattering model were used to simulate the effect of varying SWE on the microwave brightness temperature. Data obtained from truck mounted, airborne and spaceborne systems from various test sites were studied. The simulated SWE compares favorable with the measured SWE. In addition, whether the underlying soil is frozen or thawed can be discriminated successfully on the basis of the polarization of the microwave radiation

Snow Survey Results for the Central Alaskan Arctic, Arctic Circle to Arctic Ocean: Spring 2013

Many remote areas of Alaska lack meteorological data; this is especially true for solid precipitation. Researchers at the University of Alaska Fairbanks, Water and Environmental Research Center have been collecting end-of-winter snow cover observations (depth, density, snow water equivalent and ablation) since the year 2000. These observations do not document the total snowfall during the winter, but provide quantitative estimate of cold season precipitation on the ground at winter’s end after sublimation and redistribution by wind. This report provides summary of snow cover data collected during cold season of 2012–2013. There are two main areas of study. One includes drainage areas of the western Sagavanirktok, Kuparuk, Itkillik, Anaktuvuk and Chandler Rivers located north of the continental divide in the Brooks Range. While the number of sites has varied each year, we visited 76 sites in April of 2013 on the North Slope of Alaska. Second study area was established in 2012 in the drainage areas of the Kogoluktuk, Mauneluk, Reed, Alatna, and Koyukuk Rivers south of the Brooks Range. Fifty seven new snow survey sites were visited south of the Brooks Range in April 2013. The cold season of 2012-2013 experienced heavy snowfalls (record amounts since 2000) north of the Brooks Range. This was the first year of data collection south of the Brooks Range, thus no comparison can be made. SWE averaged over entire study area was 13.1 cm in 2013, ranging from 1.2 cm to 35.2 cm. Generally, higher SWEs were found in the western portion of the study area. Ablation was later than normal in spring 2013. Ablation window extended from May 8, 2013 in the far south of the study area to middle June at higher elevations on the north side of the Brooks Range.LIST OF FIGURES ....................................................................................................................... iii LIST OF TABLES ........................................................................................................................ vii DISCLAIMER ............................................................................................................................. viii UNITS, ABBREVIATIONS, AND SYMBOLS ........................................................................... ix ACKNOWLEDGMENTS ...............................................................................................................x ABSTRACT ................................................................................................................................... xi 1. INTRODUCTION .......................................................................................................................1 2. STUDY AREA ............................................................................................................................3 3. SAMPLING METHODS .............................................................................................................5 3.1 Snow Survey ..........................................................................................................................5 3.2 Snow Ablation .......................................................................................................................6 3.2.1 Observations from 1985 to 2012 .................................................................................... 8 3.2.2 Observations from 2013 ................................................................................................. 9 3.3 Snow Depth Sensors ............................................................................................................10 4. ACCURACY OF OBSERVATIONS ........................................................................................12 4.1 Snow Water Equivalent .......................................................................................................12 4.2 Snow Depth Sensors ............................................................................................................13 5. SPATIAL DISTRIBUTION OF SNOW SURVEY SITES.......................................................15 6. SNOW SURVEY DATA AT WATERSHED SCALE .............................................................18 7. SONIC SNOW DEPTH DATA .................................................................................................25 7.1 North of the Brooks Range Divide ......................................................................................25 7.2 South of the Brooks Range Divide ......................................................................................50 8. SURFACE WEATHER ANALYSIS ........................................................................................62 9. SWE CORRECTIONS ..............................................................................................................66 9.1 Snow Depth Increase in the Umiat Study Area ...................................................................66 9.2 Snow Depth Increase in the Ambler Study Area .................................................................67 10. ABLATION DATA .................................................................................................................68 11. SUMMARY .............................................................................................................................71 12. REFERENCES ........................................................................................................................73 APPENDIX A. Snow survey data .................................................................................................75 Appendix A1. Measured snow survey data for the Umiat Study Area, April 18-24, 2013. ...................................................................................................................................... 76 Appendix A2. Adjustment of the snow water equivalent for the Umiat Study Area, spring 2013. ........................................................................................................................... 78 Appendix A3. Measured Snow Survey Data for the Ambler Study Area, April 3‐9, 2013. ...................................................................................................................................... 80 Appendix A4. Adjustment of the snow water equivalent data for the Ambler Study Area, spring 2013. ................................................................................................................. 82 APPENDIX B. Ablation data ........................................................................................................84 Appendix B1a. Snow water equivalent (cm) in the Imnavait Creek basin 85-99 (basin average). ..................................................................................................................... 84 Appendix B1b. Snow water equivalent (cm) in the Imnavait Creek basin 00-13 (basin average). ..................................................................................................................... 85 Appendix B2. Snow water equivalent (cm) at the Upper Kuparuk (UK) site. ..................... 86 Appendix B3. Snow water equivalent (cm) at the Happy Valley (HV) site. ........................ 87 Appendix B4. Snow water equivalent (cm) at the Sagwon (SH) site. .................................. 89 Appendix B5. Snow water equivalent (cm) at the Franklin Bluffs (FR) site........................ 90 Appendix B6. Snow water equivalent (cm) at the Betty Pingo (BP) site. ............................ 92 Appendix B7. Snow water equivalent (cm) at the West Dock (WD) site. ........................... 93 Appendix B8. 2010 Snow water equivalent (cm) at the Atigun, Galbraith Lake and Oil Spill Hill sites. ................................................................................................................. 94 Appendix B9. 2011 and 2013 snow water equivalent (cm) at the Anaktuvuk River, Chandler River, Upper Itkilik River and Lower Itkillik meteorological sites. ..................... 95 Appendix B10. 2013 snow water equivalent (cm) at the Ambler Road Corridor project meteorological sites. ................................................................................................. 9

Spatio-temporal influence of tundra snow properties on Ku-band (17.2 GHz) backscatter

During the 2010/11 boreal winter, a distributed set of backscatter measurements was collected using a ground-based Ku-band (17.2 GHz) scatterometer system at 26 open tundra sites. A standard snow-sampling procedure was completed after each scan to evaluate local variability in snow layering, depth, density and water equivalent (SWE) within the scatterometer field of view. The shallow depths and large basal depth hoar encountered presented an opportunity to evaluate backscatter under a set of previously untested conditions. Strong Ku-band response was found with increasing snow depth and snow water equivalent (SWE). In particular, co-polarized vertical backscatter increased by 0.82 dB for every 1 cm increase in SWE (R2 = 0.62). While the result indicated strong potential for Ku-band retrieval of shallow snow properties, it did not characterize the influence of sub-scan variability. An enhanced snow-sampling procedure was introduced to generate detailed characterizations of stratigraphy within the scatterometer field of view using near-infrared photography along the length of a 5m trench. Changes in snow properties along the trench were used to discuss variations in the collocated backscatter response. A pair of contrasting observation sites was used to highlight uncertainties in backscatter response related to short length scale spatial variability in the observed tundra environment

Remote sensing of snow and ice: A review of the research in the United States 1975 - 1978

Research work in the United States from 1975-1978 in the field of remote sensing of snow and ice is reviewed. Topics covered include snowcover mapping, snowmelt runoff forecasting, demonstration projects, snow water equivalent and free water content determination, glaciers, river and lake ice, and sea ice. A bibliography of 200 references is included

LANDSAT-D investigations in snow hydrology

Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7

Snow water equivalent modeling components in NewAge-JGrass

This paper presents a package of modified temperature-index-based snow water equivalent models as part of the hydrological modeling system NewAge-JGrass. Three temperature-based snow models are integrated into the NewAge-JGrass modeling system and use many of its components such as those for radiation balance (short wave radiation balance, SWRB), kriging (KRIGING), automatic calibration algorithms (particle swarm optimization) and tests of goodness of fit (NewAge-V), to build suitable modeling solutions (MS). Similarly to all the NewAge-JGrass components, the models can be executed both in raster and in vector mode. The simulation time step can be daily, hourly or sub-hourly, depending on user needs and availability of input data. The MS are applied on the Cache la Poudre River basin (CO, USA) using three test applications. First, daily snow water equivalent is simulated for three different measurement stations for two snow model formulations. Second, hourly snow water equivalent is simulated using all the three different snow model formulae. Finally, a raster mode application is performed to compute snow water equivalent maps for the whole Cache la Poudre Basin