Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water-coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determined the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (Discrete Ordinate Radiative Transfer model, DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the lowest residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ∼1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field by imaging a snowpit sidewall during melt conditions and mapping LWC distribution in unprecedented detail, allowing for visualization of pooling water and flow features.

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

Detrainment and braking of snow avalanches interacting with forests

Mountain forests provide natural protection against avalanches. They can both prevent avalanche formation in release zones and reduce avalanche mobility in runout areas. Although the braking effect of forests has been previously explored through global statistical analyses on documented avalanches, little is known about the mechanism of snow detrainment in forests for small and medium avalanches. In this study, we investigate the detrainment and braking of snow avalanches in forested terrain, by performing three-dimensional simulations using the Material Point Method (MPM) and a large strain elastoplastic snow constitutive model based on Critical State Soil Mechanics. First, the snow internal friction is evaluated using existing field measurements based on the detrainment mass, showing the feasibility of the numerical framework and offering a reference case for further exploration of different snow types. Then, we systematically investigate the influence of snow properties and forest parameters on avalanche characteristics. Our results suggest that, for both dry and wet avalanches, the detrainment mass decreases with the square of the avalanche front velocity before it reaches a plateau value. Furthermore, the detrainment mass significantly depends on snow properties. It can be as much as ten times larger for wet snow compared to dry snow. By examining the effect of forest configurations, it is found that forest density and tree diameter have cubic and square relations with the detrainment mass, respectively. The outcomes of this study may contribute to the development of improved formulations of avalanche–forest interaction models in popular operational simulation tools and thus improve hazard assessment for alpine geophysical mass flows in forested terrain.

Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements

It is well understood that the distribution and quantity of liquid water in snow is relevant for snow hydrology and avalanche forecasting, yet detecting and quantifying liquid water in snow remains a challenge from the micro- to the macro-scale. Using near-infrared (NIR) spectral reflectance measurements, previous case studies have demonstrated the capability to retrieve surface liquid water content (LWC) of wet snow by leveraging shifts in the complex refractive index between ice and water. However, different models to represent mixed-phase optical properties have been proposed, including (1) internally mixed ice and water spheres, (2) internally mixed water coated ice spheres, and (3) externally mixed interstitial ice and water spheres. Here, from within a controlled laboratory environment, we determine the optimal mixed-phase optical property model for simulating wet snow reflectance using a combination of NIR hyperspectral imaging, radiative transfer simulations (DISORT), and an independent dielectric LWC measurement (SLF Snow Sensor). Maps of LWC were produced by finding the least residual between measured reflectance and simulated reflectance in spectral libraries, generated for each model with varying LWC and grain size, and assessed against the in situ LWC sensor. Our results show that the externally mixed model performed the best, retrieving LWC with an uncertainty of ~1 %, while the simultaneously retrieved grain size better represented wet snow relative to the established scaled band area method. Furthermore, the LWC retrieval method was demonstrated in the field, imaging a snowpit sidewall during melt conditions, mapping pooling water, flow features, and LWC distribution in unprecedented detail.

VISUAL LANDSCAPE EVALUATION OF THE PARK AT THE OPERA THEATER IN YEKATERINBURG

Статья содержит результаты визуально-эстетической оценки сквера у Оперного театра. В мире одно- тонных конструкции, похожих фасадов, повторяющихся деталей становится важным критерием сохране- ние физического и психоэмоционального состояния человека. Урбанизированное пространство негативно влияет на здоровье, подрывает нервную систему, дисгармонирует мозговую активность. Перспективным направлением становится визуально-эстетическая оценка окружающей среды. Данная оценка необходима для определения комфортных мест отдыха жителей. В работе взяли за основу методику Федосовой С. И. Локальные пейзажи выбирались с учётом максимальной концентрации людей либо в местах транзита. Существует несколько путей подхода к изучению визуально-эстетической оценки, но они не предлагают единой методики. Каждый направлен на выявление определённых характеристик, не даёт обобщённого анализа разнотипных ландшафтов. К эстетическому ландшафту следует относить территории, обладаю- щие уникальными свойствами, отличающими их от других. Эстетическая оценка воспринимается людьми с долей субъективности, данная закономерность складывается из различных факторов: этнических кано- нов, предпочтения возрастных групп, образовательного уровня. Суть проводимого исследования – оценка агрессивности визуального поля. На фотоснимок с локальным пейзажем накладывалась сетка. На полу- ченной плоскости определялся коэффициент агрессивности, рассчитывалась доля ячеек с двумя и более повторяющимися элементами. Исследование проводилось в ранневесенний период при пасмурной погоде и осадках в виде мокрого снега. Степень агрессивности на объекте исследования варьируется от 0,12 до 0,44. Полученные результаты демонстрируют, что сквер у Оперного театра комфортен для отдыха горожан. The article contains the results of the visual and aesthetic assessment of the square at the opera house. In the world of single-tone structures, similar facades, repeated details, it becomes an important criterion for maintaining the physical and psychoemotional state of a person. Urbanized space negatively affects health, undermines the nervous system, disharmonizes brain activity. A perspective direction is the visual and aesthetic assessment of the environment. This assessment is necessary to determine comfortable places of rest for residents. The method of Fedosova S.I. took the basis in the work. Local landscapes were chosen taking into account the maximum concentration of people, or in transit places. There are several ways to approach the study of visual and aesthetic assessment, but they do not offer a single methodology. Each is aimed at identifying certain characteristics, does not give a generalized analysis of different types of landscapes. The aesthetic landscape should include territories with unique properties that distinguish it from others. Aesthetic assessment is perceived by people with a degree of subjectivity, this pattern consists of various factors: ethnic canons, preferences of age groups, and the level of educational censorship. The essence of the study is the assessment of the aggressiveness of the visual fi eld. A grid was superimposed on a photograph with a local landscape. On the obtained plane, the aggressiveness coeffi cient was determined, the proportion of cells with two or more repeating elements was calculated. The study was conducted in the early spring, with cloudy weather and precipitation in the form of wet snow. The degree of aggressiveness at the study site varies from 0.12 to 0.44. The results obtained demonstrate that the square at the opera house is comfortable for the rest of citizens.