Physical Properties Of Snow Research Articles

Constraining snowmelt in a temperature-index model using simulated snow densities

Current snowmelt parameterisation schemes are largely untested in warmer maritime snowfields, where physical snow properties can differ substantially from the more common colder snow environments. Physical properties such as snow density influence the thermal properties of snow layers and are likely to be important for snowmelt rates. Existing methods for incorporating physical snow properties into temperature-index models (TIMs) require frequent snow density observations. These observations are often unavailable in less monitored snow environments. In this study, previous techniques for end-of-season snow density estimation (Bormann et al., 2013) were enhanced and used as a basis for generating daily snow density data from climate inputs. When evaluated against 2970 observations, the snow density model outperforms a regionalised density-time curve reducing biases from −0.027 g cm−3 to −0.004 g cm−3 (7%). The simulated daily densities were used at 13 sites in the warmer maritime snowfields of Australia to parameterise snowmelt estimation. With absolute snow water equivalent (SWE) errors between 100 and 136 mm, the snow model performance was generally lower in the study region than that reported for colder snow environments, which may be attributed to high annual variability. Model performance was strongly dependent on both calibration and the adjustment for precipitation undercatch errors, which influenced model calibration parameters by 150–200%. Comparison of the density-based snowmelt algorithm against a typical temperature-index model revealed only minor differences between the two snowmelt schemes for estimation of SWE. However, when the model was evaluated against snow depths, the new scheme reduced errors by up to 50%, largely due to improved SWE to depth conversions. While this study demonstrates the use of simulated snow density in snowmelt parameterisation, the snow density model may also be of broad interest for snow depth to SWE conversion. Overall, the study responds to recent calls for broader testing of TIMs across different snow environments, improves existing snow modelling in Australia and proposes a new method for introducing physically-based constraints on snowmelt rates in data-poor regions.

Observations, theory, and modeling of the differential accumulation of Antarctic megadunes

Antarctic megadunes are characterized by significant spatial differences in accumulation rate, with higher accumulation on the windward side and near-zero accumulation on the lee side. This leads to spatial differences in physical properties of snow and surface roughness, as well as to the upwind migration of the megadunes. While previous studies agree that megadunes are a result of complex interactions between wind, topography, and snow, it is not clear how they form or why they accumulate on the windward side. Here we use ICESat observations, dimensional analysis, and atmospheric flow modeling to investigate what conditions are responsible for the accumulation patterns and upwind migration of the megadunes. First, we use ICESat data to quantify the pattern of differential surface elevation change across the megadunes. We then use dimensional analysis based on supercritical-flow theory and atmospheric flow modeling to show that the megadunes topography and a stable atmosphere will always lead to upwind dune migration. We show that a combination of persistent katabatic winds, strong stability, and spatial variability in surface roughness is responsible for the accumulation on the upwind slope and hence the upwind migration of the megadunes. We further show that spatial differences in surface roughness are the primary control on accumulation magnitudes and hence dune migration velocity. The dune migration velocity in turn influences the degree of snow-metamorphism and the physical properties of snow that are relevant for paleoclimate records. Our findings pertain to the ongoing evolution of the megadunes, but their genesis remains an open question.

Snow gliding and loading under two different forest stands: a case study in the north-western Italian Alps

The presence of a thick snowpack could interfere with forest stability, especially on steep slopes with potential damages for young and old stands. The study of snow gliding in forests is rather complex because this phenomenon could be influenced not only by forest features, but also by snow/soil interface characteristics, site morphology, meteorological conditions and snow physical properties. Our starting hypothesis is that different forest stands have an influence on the snowpack evolution and on the temperature and moisture at the snow/soil interface, which subsequently could affect snow gliding processes and snow forces. The aim of this work is therefore to analyse the snowpack evolution and snow gliding movements under different forest covers, in order to determine the snow forces acting on single trees. The study site is located in a subalpine forest in Aosta Valley (NW-Italy) and includes two plots at the same altitude, inclination and aspect but with different tree composition: Larch (Larix decidua) and Spruce (Picea abies). The plots were equipped with moisture and temperature sensors placed at the snow/soil interface and glide shoes for continuous monitoring of snow gliding. The recorded data were related to periodically monitored snowpack and snow/soil interface properties. Data were collected during two winter seasons (2009–10 and 2010–11). The snow forces on trees were analytically calculated either from snowpack data and site morphology or also from measured snow gliding rates. Different snow accumulations were observed under the two different forest stands, with a significant effect on temperature and moisture at the snow/soil interface. The highest snow gliding rates were observed under Larch and were related to rapid increases in moisture at the snow/soil interface. The calculated snow forces were generally lower than the threshold values reported for tree uprooting due to snow gliding, as confirmed by the absence of tree damages in the study areas.

A general treatment of snow microstructure exemplified by an improved relation for thermal conductivity

Abstract. Finding relevant microstructural parameters beyond density is a longstanding problem which hinders the formulation of accurate parameterizations of physical properties of snow. Towards a remedy, we address the effective thermal conductivity tensor of snow via anisotropic, second-order bounds. The bound provides an explicit expression for the thermal conductivity and predicts the relevance of a microstructural anisotropy parameter Q, which is given by an integral over the two-point correlation function and unambiguously defined for arbitrary snow structures. For validation we compiled a comprehensive data set of 167 snow samples. The set comprises individual samples of various snow types and entire time series of metamorphism experiments under isothermal and temperature gradient conditions. All samples were digitally reconstructed by micro-computed tomography to perform microstructure-based simulations of heat transport. The incorporation of anisotropy via Q considerably reduces the root mean square error over the usual density-based parameterization. The systematic quantification of anisotropy via the two-point correlation function suggests a generalizable route to incorporate microstructure into snowpack models. We indicate the inter-relation of the conductivity to other properties and outline a potential impact of Q on dielectric constant, permeability and adsorption rate of diffusing species in the pore space.

Snow spectral albedo at Summit, Greenland: measurements and numerical simulations based on physical and chemical properties of the snowpack

Abstract. The broadband albedo of surface snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow were carried out at Summit Camp, Greenland (72°36´ N, 38°25´ W, 3210 m a.s.l.) in May and June 2011, along with spectral albedo measurements. One of the main objectives of the field campaign was to test our ability to predict snow spectral albedo by comparing the measured albedo to the albedo calculated with a radiative transfer model, using measured snow physical and chemical properties. To achieve this goal, we made daily measurements of the snow spectral albedo in the range 350–2200 nm and recorded snow stratigraphic information down to roughly 80 cm. The snow specific surface area (SSA) was measured using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement, Gallet et al., 2009). Samples were also collected for chemical analyses including black carbon (BC) and dust, to evaluate the impact of light absorbing particulate matter in snow. This is one of the most comprehensive albedo-related data sets combining chemical analysis, snow physical properties and spectral albedo measurements obtained in a polar environment. The surface albedo was calculated from density, SSA, BC and dust profiles using the DISORT model (DIScrete Ordinate Radiative Transfer, Stamnes et al., 1988) and compared to the measured values. Results indicate that the energy absorbed by the snowpack through the whole spectrum considered can be inferred within 1.10%. This accuracy is only slightly better than that which can be obtained considering pure snow, meaning that the impact of impurities on the snow albedo is small at Summit. In the near infrared, minor deviations in albedo up to 0.014 can be due to the accuracy of radiation and SSA measurements and to the surface roughness, whereas deviations up to 0.05 can be explained by the spatial heterogeneity of the snowpack at small scales, the assumption of spherical snow grains made for DISORT simulations and the vertical resolution of measurements of surface layer physical properties. At 1430 and around 1800 nm the discrepancies are larger and independent of the snow properties; we propose that they are due to errors in the ice refractive index at these wavelengths. This work contributes to the development of physically based albedo schemes in detailed snowpack models, and to the improvement of retrieval algorithms for estimating snow properties from remote sensing data.

Variability of light transmission through Arctic land-fast sea ice during spring

Abstract. The amount of solar radiation transmitted through Arctic sea ice is determined by the thickness and physical properties of snow and sea ice. Light transmittance is highly variable in space and time since thickness and physical properties of snow and sea ice are highly heterogeneous on variable time and length scales. We present field measurements of under-ice irradiance along transects under undeformed land-fast sea ice at Barrow, Alaska (March, May, and June 2010). The measurements were performed with a spectral radiometer mounted on a floating under-ice sled. The objective was to quantify the spatial variability of light transmittance through snow and sea ice, and to compare this variability along its seasonal evolution. Along with optical measurements, snow depth, sea ice thickness, and freeboard were recorded, and ice cores were analyzed for chlorophyll a and particulate matter. Our results show that snow cover variability prior to onset of snow melt causes as much relative spatial variability of light transmittance as the contrast of ponded and white ice during summer. Both before and after melt onset, measured transmittances fell in a range from one third to three times the mean value. In addition, we found a twentyfold increase of light transmittance as a result of partial snowmelt, showing the seasonal evolution of transmittance through sea ice far exceeds the spatial variability. However, prior melt onset, light transmittance was time invariant and differences in under-ice irradiance were directly related to the spatial variability of the snow cover.

The human and the physical in Debussy’s depictions of snow

This article explores Debussy’s musical engagement of the topic of snow in two representative works for piano, “Des pas sur la neige” from Préludes, Book I and “The Snow is Dancing” from Children’s Corner. In the analysis of “Des pas,” a narrative is put forth based on the interpretation of the work’s opening short-long rhythmic figure as a representation of contact with a frozen, encrusted surface followed by an immediate collapse of that frozen surface and contact with the ground underneath. As the work’s protagonist engages in walking meditation to confront a troubling memory, a state of contemplation is achieved whereby the musical portrayal of the footsteps are momentarily suppressed in strategic portions of the work designed to portray recollection, as Steven Rings (2008) has noted. In my analysis, I argue that the closing measures’ portrayal of footsteps is shifted into binary alterations of two notes, D and G, in steady quarter notes, so as to illustrate the protagonist’s emergence from an off beaten path to a more trodden one. In “The Snow is Dancing,” Debussy shifts focus from that of human emotion to the actual physical properties of snow. Interestingly, the snow is anthropomorphized nonetheless into dancers. I trace shifts in rhythm, line and register, and engage gestures of motion to illustrate Debussy’s compositional approach to both gravitationally suspending and animating wind-driven snowflakes.

Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China

It is difficult and time consuming to use traditional measurement methods to estimate the physical properties of snow. However, the emergence of hyperspectral imagery for estimating the physical properties of snow provides a powerful tool. Snow albedo, grain size, and temperature are important factors for evaluating the surface energy balance. Using the spectrum-reflection curves of the different grain sizes of snow measured in the fields of the Binggou watershed of the Heihe River Basin, China, we analyzed the spectral reflection characteristics of snow. A statistical detection method was used to choose the most sensitive bands in the field spectra and find the corresponding band (band 89) in the Hyperion imagery. The bands near 1033 nm were sensitive to the snow grain size. According to the relationship between the snow grain size and the measured spectrum, we built a snow grain-size estimation model. The results showed that the snow reflectance had a good linear and exponential relationship with the snow grain size. The correlation coefficients of the two models were 0.81 and 0.84, respectively. We obtained the location of the absorption valley at the near-infrared wavelength, and the results showed that 6.9% of the pixels were affected by the snow water content. The locations of the absorption valley moved 1–4 bands from band 89 to shorter wavelengths. The accuracy of the snow grain size estimates based on the Hyperion imagery was relatively high.

Dense avalanche friction coefficients: influence of physical properties of snow

AbstractThe values of the Voellmy friction parameters of 735 historical avalanches that have occurred along 26 paths in the Chamonix valley, France, since 1958 are back-analyzed with a depth-averaged hydraulic model, including sub-models for erosion, entrainment and deposition. For each path, the longitudinal and crosswise topographic profiles were derived from a high-resolution digital elevation model acquired by laser scanning. The initial snow depth and snow cohesion, as well as various physical properties of snow, were computed from numerical simulations of the detailed snowpack model Crocus fed by the SAFRAN meteorological analysis. For each event, the full ranges of the two friction parameters were scanned and the pairs of friction parameters for which the run-out altitude is found close enough to the observed one (with an uncertainty of ±5 m) were retained. Statistical class analysis was used to investigate the correlation between the obtained friction coefficients and the snow physical properties. No evident trend with the snow parameters was found for the inertial friction coefficient. For the static friction coefficient, an increasing trend with temperature and density was observed, as well as a decreasing trend with liquid water content and initial snow depth. Although modeling assumptions and limitations regarding data and the calibration procedure should be kept in mind, these trends are worth noting, allowing avalanche simulations to be refined to take into account prevailing weather and snow conditions.

Evolution of crystal orientation in snow during temperature gradient metamorphism

AbstractThe physical properties of snow are tied to its microstructure. Especially for the slow, plastic deformation of snow and firn, the crystal orientation is an important factor in addition to the geometry of the ice matrix. While micro-computed tomography measures the snow microstructure precisely, it gives no information about the orientation of the ice crystals. In this study, we applied a temperature gradient of 50 K m−1 to large blocks of undisturbed decomposed snow and sieved snow during 3 months. The mean temperature of the snow samples during the temperature gradient experiment was −20°C. Two closely spaced snow samples were taken before the experiment, then every week during the first month and afterwards every month. From each sampling, one sample was analyzed by micro-computed tomography and the other was used for thin sections. The orientation of the c-axis was measured in the thin sections using an automatic ice texture analyzer. Initial density was 30% higher in the sieved snow sample. Density and specific surface area evolved alike, while the fabric showed a different evolution between the two samples. The undisturbed snow evolved from a weak single-maximum fabric towards a weak girdle fabric, while the sieved sample showed no evolution. The undisturbed snow sample converged toward the sieved sample fabric after 6 weeks, but continued its evolution thereafter. We suggest that the main factor causing this different behavior is the difference in density and in pore size.

Energy-based binary segmentation of snow microtomographic images

AbstractX-ray microtomography has become an essential tool for investigating the mechanical and physical properties of snow, which are tied to its microstructure. To allow a quantitative characterization of the microstructure, the grayscale X-ray attenuation coefficient image has to be segmented into a binary ice/pore image. This step, called binary segmentation, is crucial and affects all subsequent analysis and modeling. Common segmentation methods are based on thresholding. In practice, these methods present some drawbacks and often require time-consuming manual post-processing. Here we present a binary segmentation algorithm based on the minimization of a segmentation energy. This energy is composed of a data fidelity term and a regularization term penalizing large interface area, which is of particular interest for snow where sintering naturally tends to reduce the surface energy. The accuracy of the method is demonstrated on a synthetic image. The method is then successfully applied on microtomographic images of snow and compared to the threshold-based segmentation. The main advantage of the presented approach is that it benefits from local spatial information. Moreover, the effective resolution of the segmented image is clearly defined and can be chosen a priori.

SWAT模型融雪模块的改进

PDF HTML阅读 XML下载 导出引用 引用提醒 SWAT模型融雪模块的改进 DOI: 10.5846/stxb201207110977 作者: 作者单位: 南京师范大学 地理科学学院 南京,中国科学院寒区旱区环境与工程研究所 兰州,西北师范大学 地理与环境科学学院 兰州,南京师范大学 地理科学学院 南京 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上资助项目（91125006）；国家重点实验室开放基金资助项目（SKLFSE201009） Improvement of snowmelt implementation in the SWAT hydrologic model Author: Affiliation: School of Geography Science,Nanjing Normal University,Nanjing,Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou,College of Geography and Environment Science,Northwest Normal University,Lanzhou,School of Geography Science,Nanjing Normal University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:水土评价工具模型（SWAT）是一个具有物理基础的分布式水文模型，利用SCS径流曲线数方法计算地表径流，而采用相对简单的度日因子方法计算融雪径流。因此在湿润半湿润、雨量丰富的平原地区应用SWAT模型进行径流模拟时可以得到较好的模拟结果，但是在干旱半干旱、降水稀少，且春汛期间融雪径流是重要补给来源的高寒山区，模拟的融雪径流明显偏小，不能很好的反映这些地区的融雪过程，导致河道径流模拟精度偏低。FASST模型是具有物理机制的陆面过程模型，其采用能量平衡的方法计算融雪径流，能够较好的模拟复杂地形山区流域的融雪径流。以黑河山区流域为研究区，将FASST模型集成到SWAT模型，改善SWAT模型融雪径流的计算方法。通过对比SWAT模型集成前后莺落峡出山口的河道月径流、融雪径流和地表径流对河道的贡献等几个方面，表明了集成FASST融雪模块的SWAT模型能更好的反映黑河山区流域的融雪径流过程，从而提高河道径流的整体模拟精度。 Abstract:SWAT is a physically explicit distributed hydrologic model, which could simulate hydrological processes such as surface runoff, snowmelt runoff and infiltration with geographic information system(GIS).The surface runoff component in SWAT is implemented by using the SCS curve number and snowmelt is calculated by a relatively simple, empirical degree-day method. Those approaches work well in humid and semi-humid areas where precipitation dominantly controls runoff. In cold and arid regions, for example, in a case study in the Heihe river basin (HRB) of northwest China, however, it revealed those implementations cannot represent the effects of snowmelt in springs and thus impact surface runoff simulations. At large, snowmelt in such areas is underestimated and more discrepancies are consequently introduced to the overall simulation accuracy. The FASST model is a surface process model with explicit physical base, including a snowmelt runoff component that makes use of mass and energy balance equations. There is a snowmelt implementation in FASST, which takes topography, vegetation, soil type, and snow physical properties into account. Existing applications of FASST show good simulations of snowmelt in terrain-complex mountainous watershed and its applicability is also confirmed by an application to the Tangula site of northwest China located in a similar cold and alpine area. This paper proposes a coupling approach to improve the simulation of snowmelt by integrating FASST snowmelt to SWAT. In this approach, when the snowmelt begins to be calculated in SWAT, it will call FASST snowmelt to calculate, return its value to the SWAT corresponding variable and continue remainder SWAT logics. The technical implementation is presented in detail. An application to the upper mountainous HRB is set up to test its performance. There are abundant snow falls in Upper HRB in winters and snowmelt is the primary water source to river in springs that cannot be simulated well by original SWAT. The improvements in comparison to the original were examined from three aspects, namely, streamflow, snowmelt runoff and surface runoff contribution to streamflow. Both snowmelt and surface runoff estimates with the coupled model were increased so that streamflow estimate was more close to that observed. An improvement of monthly streamflow estimation by 0.11 in Nash-Sutcliffe coefficient (NSE) can be achieved. Examination of surface runoff contribution to streamflow also supports its feasibility in estimating snowmelt by the coupled approach. Moreover, by using optimized parameter sets, the monthly streamflow simulation accuracy in the validation period of 2000-2009 can be up to 0.83 in NSE. The results confirm the applicability of the SWAT-FASST coupled approach in cold and alpine watersheds where snowmelt should be taken into account and suggest its significance in improving the simulations in such areas. 参考文献 相似文献 引证文献

Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography

Abstract. Dry snow metamorphism under an external temperature gradient is the most common type of recrystallization of snow on the ground. The changes in snow microstructure modify the physical properties of snow, and therefore an understanding of this process is essential for many disciplines, from modeling the effects of snow on climate to assessing avalanche risk. We directly imaged the microstructural changes in snow during temperature gradient metamorphism (TGM) under a constant gradient of 50 K m−1, using in situ time-lapse X-ray micro-tomography. This novel and non-destructive technique directly reveals the amount of ice that sublimates and is deposited during metamorphism, in addition to the exact locations of these phase changes. We calculated the average time that an ice volume stayed in place before it sublimated and found a characteristic residence time of 2–3 days. This means that most of the ice changes its phase from solid to vapor and back many times in a seasonal snowpack where similar temperature conditions can be found. Consistent with such a short timescale, we observed a mass turnover of up to 60% of the total ice mass per day. The concept of hand-to-hand transport for the water vapor flux describes the observed changes very well. However, we did not find evidence for a macroscopic vapor diffusion enhancement. The picture of {temperature gradient metamorphism} produced by directly observing the changing microstructure sheds light on the micro-physical processes and could help to improve models that predict the physical properties of snow.

Snow physical parameters estimation using space-based Synthetic Aperture Radar

This study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the snow physical properties (snow wetness and snow density), inManali sub-basin of Himachal Pradesh, India. The SAR data used are of Radarsat-2 (RS2) and Environmental Satellite, Advanced Synthetic Aperture Radar (ASAR). SAR-based inversion models were implemented in Mathematica and MATLAB, and have been used for finding out wet and dry snow dielectric constant, snow wetness and snow density. The SAR-based inversion models were implemented separately for fully polarimetric RS2 and dual polarimetric ASAR Alternate Polarization System datasets. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. The overall accuracy in terms of R 2 value comes out to be 0.86 for snow wetness and 0.84–0.72 for snow density based on the ground truth data for subset area of Manali sub-basin of Beas river up to Manali.

High‐frequency observations of melt effects on snowpack stratigraphy, Kahiltna Glacier, Central Alaska Range

AbstractWith an increased volume of melt on many of the world's glaciers, study of how meltwater affects the properties of glacial snowpack becomes essential to our understanding of how glaciers will respond to climate change. We address this problem by studying how snow properties changed on sub‐daily timescales on the Kahiltna Glacier, Alaska, between May 26 and June 10, 2010. During this period, we dug 1.8‐m‐deep snow pits twice daily to record the stratigraphy of melt layers, snow hardness, grain size, and density and sampled for hydrogen isotopic composition (δD) on four occasions. From these data, we show that 65% of the melted surface snow infiltrates and refreezes in the snowpack. This leads to a densification of the snow, a 729% increase in volume of melt layers, and a homogenization of isotopic and physical snow properties. From visual and stratigraphic observations, we show that meltwater flow within the snowpack is conducted primarily along lenses and pipes, where melt layers later form, but that more homogeneous capillary‐based flow is also important. Finally, we show using isotope ratios that post‐depositional alteration is exacerbated with increased melt extent, using the δD profile below a volcanic ash layer as a case study. In the future, similar studies would benefit from this high‐frequency monitoring approach to assessing snowpack evolution, as it allows for a greater understanding of short duration processes. New directions for study would include longer‐term monitoring efforts over a wider spatial snow pit network. Copyright © 2012 John Wiley & Sons, Ltd.

Structure, specific surface area and thermal conductivity of the snowpack around Barrow, Alaska

The structure of the snowpack near Barrow was studied in March–April 2009. Vertical profiles of density, specific surface area (SSA) and thermal conductivity were measured on tundra, lakes and landfast ice. The average thickness was 41 cm on tundra and 21 cm on fast ice. Layers observed were diamond dust or recent wind drifts on top, overlaying wind slabs, occasional faceted crystals and melt‐freeze crusts, and basal depth hoar layers. The top layer had a SSA between 45 and 224 m2 kg−1. All layers at Barrow had SSAs higher than at many other places because of the geographical and climatic characteristics of Barrow. In particular, a given snow layer was remobilized several times by frequent winds, which resulted in SSA increases each time. The average snow area index (SAI, the dimensionless vertically integrated SSA) on tundra was 3260, higher than in the Canadian High Arctic or in the Alaskan taiga. This high SAI, combined with low snow temperatures, imply that the Barrow snowpack efficiently traps persistent organic pollutants, as illustrated with simple calculations for PCB 28 and PCB 180. The average thermal conductivity was 0.21 Wm−1 K−1, and the average thermal resistance on tundra was 3.25 m2 K W−1. This low value partly explains why the snow‐ground interface was cold, around −19°C. The high SAI and low thermal resistance values illustrate the interplay between climate, snow physical properties, and their potential impact on atmospheric chemistry, and the need to describe these relationships in models of polar climate and atmospheric chemistry, especially in a climate change context.

How to Use the Science of Snow to Engage Middle School Students in an Interdisciplinary Experience

ABSTRACT In this interdisciplinary and field-based activity, grade 5 to 9 students engage in a comprehensive scientific study of snow. Through a series of in-class and out-of-class structured interdisciplinary and team-teaching lesson progressions, students will collect data to be able to analyze and apply knowledge about weather, the physical properties of snow, and the structure of matter that will increase understanding about the nature of science.

Measurements and modeling of the vertical profile of specific surface area of an alpine snowpack

The specific surface area of snow (SSA) is a useful variable to describe the physical and chemical properties of snow, including a quantitative link to snow metamorphism and the optical properties of snow. Here we present a series of 16 weekly profiles of snow physical properties including SSA measured using the DUFISSS instrument spanning the period from January to April 2010 at the Col de Porte field site in the French Alps near Grenoble. Measured SSA values for dry snow ranged between ca. 5 and 80m2kg−1, and generally decreased over time in a given snow layer. Wet snow conditions encountered towards the end of the snow season show SSA values between 3 and 10m2kg−1. This unique dataset is compared with simulations carried out using the Crocus snowpack model, using two parameterizations of snow SSA: one simply derived from the internal computation of the optical radius in Crocus, and the other one determined from density and snow type. Both parameterizations perform rather satisfactorily qualitatively and quantitatively, compared to the performance in terms of snow density profile. Ample room for improvement exists, in particular through the implementation of SSA as a fully fledged prognostic variable in Crocus, which is currently in progress.

Effect of forests on the retrieval of snow parameters from backscatter measurements

In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. ARTT model capable of simulating scattering from a snow- covered vegetated terrain was developed and implemented to study the sensitivity to snow and vegetation parameters. A procedure for correcting the vegetation effect in the SWE retrieval algorithm has been suggested.

Estimation of snow depth and snow water equivalent distribution using airborne microwave radiometry in the Binggou Watershed, the upper reaches of the Heihe River basin

We estimated the spatial distribution of snow depth/snow water equivalent (SD/SWE) in a mountainous watershed (Binggou, which is in the upper reaches of the Heihe River basin) by an airborne microwave radiometry observational experiment. Two microwave radiometers measuring at K band (18.7GHz) and Ka band (36GHz) were used to estimate the volume scatter from snowpacks and infer SD and SWE. Simultaneously, the snow physical properties (such as snow depth, density, grain size and temperature) over four sites were measured, and a simple multi-layer sample scheme was adopted to obtain the stratigraphic information. The microwave emission model of layered snowpacks (MEMLS) was used to simulate the brightness temperatures of snow cover for each measurement point. By comparing TB data that were simulated by MEMLS and observed by radiometers on the aircraft over the four sites, we obtained the retrieval algorithms of SD and SWE based on brightness temperature differences (TBD) at the K- and Ka-bands that are suitable to the local snow properties. The validation shows that the mean absolute and relative errors of SD estimates are approximately 3.5cm and 14.8%, respectively. SWE from airborne microwave radiometers show that blowing snow and sun radiation are two main factors that determine the spatial distribution of SWE in Binggou Watershed.The local angle of incidence of the microwave radiometer observation can be influenced by mountainous topography, and a sensitivity analysis suggests that changes in the local angle of incidence (e.g., the nominal angle of incidence) will not significantly influence the estimation of SD/SWE. The snow's stratigraphic condition is not an important factor for estimating SD/SWE in this study because the snow was not very deep in the Binggou Watershed. However, the field sampling scheme should be given more attention to obtain the spatial variations of snow properties and to support pixel-by-pixel validation in next field campaign.