Snow Density Research Articles

Experimental determination of the coefficient of viscosity of the dry snow

Snow compaction is a natural process in the evolution of any snow cover, and this obviously changes the physical and mechanical properties of a snow thickness. The quantitative estimate of this process is a coefficient of the snow viscosity, which is linearly related to the rate of the snow cover deformation The technique for preparation and testing of samples with the fine-grain snow under condition of uniaxial compression at negative temperatures is described. It was determined that values of the snow viscosity coefficient fall within the range from 0.10 to 0.30 g/cm³ for three temperature intervals. The viscosity coefficient of the fine-grain snow varies from 10⁸ до 10¹⁰ Па. The data obtained made possible to find the limits of variability of this coefficient for winter conditions on the Russian plain. A dependence of the viscosity coefficient on the snow density and temperature is proposed, and it describes the series of the above shown experimental data. It is shown that influence of changes in the viscosity coefficient of fine-grain snow on the snow properties depending on its density are comparable in scale to the influence of the temperature. The result may be useful for modeling the evolution of snow cover.

Mechanism of ion migration from the substrate material into snow cover at the end of the cold period

Earlier, it was established that maximum mineralization of the contact layer of snow occurs in spring at the interface with substrates (soil or ice). This study analyzes the temperature and moisture conditions during this period at the interface of the contact layer of snow with substrates by examining frozen sand blocks saturated with a solution containing complex gold ions, or blocks filled with polystyrene containing ions of molybdenum, copper, etc. It is assumed that the migration of ions from the underlying substrate into the contact layer of snow cover in spring occurs along quasi-liquid films on the surface of snow crystals, the thickness of which exceeds the equilibrium one. Migration becomes noticeable when the temperature at the snow–substrate contact reaches −13 °С and above. The appearance of quasi-liquid films on the surface of snow particles under variable temperature and moisture conditions is possible due to the condensation of water vapor, which during the day, with general heating of the system, can enter the contact layer of snow both from above and below. With an increase in snow density in the spring, the mineralization of the near-contact layer of snow cover increases. At the same time, linear relationships were revealed between the content of substrate components migrating into the near-contact layer of snow and the gradient of water vapor density in it. The reliability of the approximation of these dependencies for the gold thiosulfate complex is 0.98; for copper ions – 0.52; for hydrogen ions – 0.88; for sodium ions – 0.69, for chloride anions – 0.89. The results of the study substantiate the increased efficiency of geochemical prospecting for mineral deposits using snow cover in the spring.

Estimating the snow density using collocated Parsivel and Micro-Rain Radar measurements: a preliminary study from ICE-POP 2017/2018

Abstract. A new method is developed to derive the bulk density and bulk water fraction of a population of particles from collocated measurements from the Micro-Rain Radar (MRR) and Particle Size and Velocity disdrometer (Parsivel). A rigorous particle-scattering simulation, namely the T-matrix method, is applied to Parsivel's particle size distribution data to calculate the reflectivity (ZHH). The possible combinations of the particle's ice, air, and water are derived to compare them with the MRR-measured ZHH. The combination of the minimum water fraction and maximum ice fraction subsequently determines the bulk density (ρbulk). The proposed method is applied to the data collected from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018) projects and its pre-campaign. The estimated ρbulk was examined independently by a comparison of the liquid-equivalent snowfall rate (SR) of collocated Pluvio devices. The bias values are adequately low (SR: −0.25–0.06 mm h−1). The retrieved bulk density also shows good consistency with collocated Precipitation Imaging Package (PIP) retrievals. The results indicate the capability of the proposed algorithm to derive reliable ρbulk, leveraging the compact and easily deployable designs of MRR and Parsivel. The derived bulk density of the two warm–low cases (28 February and 7 March 2018) shares a similar transition as the systems were decaying. The higher bulk density and bulk water fraction were found in the coastal sites (BKC and GWU have a median value of ρbulk and are 0.05 to 0.12 g cm−3), typically accompanied by higher liquid-water constituents (mean values of the top 5 % bulk water fraction are 0.07 to 0.45) than the inland sites (YPO and MHS have a median value of ρbulk and are 0.06 to 0.10, and mean values of the top 5 % bulk water fraction are 0.001 to 0.008) during such synoptic conditions.

Развитие модели динамики снежного покрова Гидрометцентра России

Conceptual models of runoff formation used in the operational practice of hydrological forecasting by the Hydrometeorological Center of Russia include simplified parameterization of snow cover dynamics based on the use of snow cover melting coefficient, water-retaining capacity of snow and secondary freezing of meltwater in case of return of negative air temperatures. This schematization of the process is well established for the reliable calculation of the snow water equivalent for subsequent use in the calculation of meltwater inflow to the catchment surface and the calculation of streamflow characteristics. At the same time, the lack of calculation of snow density and snow height in the used schematization restricts the use of schemes for calculating of soil freezing depth in hydrological models, which seems to be extremely important for modelling runoff on mid-latitudes rivers, i.e. for the most Russian rivers. To overcome this disadvantage, the snow density and snow height parameterization was added to the calculation scheme of the model, which was verified using Roshydromet’s snow measurement routes data and shown a good and satisfactory quality of modeling of model characteristics.

Relationship between Newly Fallen Snow Density and Polarimetric Parameters Obtained from X-Band Radar Observations along the Sea of Japan Coast in January 2021

Abstract The density of newly fallen snow ρN is an important parameter for assessing accumulated snowfall depth. We examined the relationships between polarimetric parameters of X-band radar and the ρN in dry snow cases with ground temperatures less than 0°C. Our study was based on observations at Niigata Prefecture, Japan, along the coastal region of the Sea of Japan. This region is subjected primarily to sea-effect snow during the winter monsoon season, and convective clouds and rimed snow are common. We assumed that snow particles that accumulated on the ground originated from altitudes above an altitude with a temperature of −15°C, and we focused on the ratio of the differential phase KDP to radar reflectivity Zh, which is influenced by both aspect ratio and inverse particle size. We found that KDP/Zh at an altitude with a temperature of −15°C exhibited a greater magnitude for lower ρN values. Its correlation coefficient was the best among the polarimetric parameters that we examined. The difference in ice crystal flatness is highlighted rather than the difference in size because aggregation growth has not progressed at this altitude. On the basis of this result, we propose an empirical relationship between KDP/Zh at an altitude with a temperature of −15°C and ρN on the ground, thereby facilitating the estimation of snowfall depth by combining the estimated ρN with the liquid equivalent snowfall rate from, for example, Zh or KDP. Significance Statement This study aims to estimate the density of newly fallen (just-accumulated) snow from polarimetric radar observations. Understanding the newly fallen snow density will help to determine the exact snowfall depth. Focusing on polarimetric parameters at an altitude with a temperature of −15°C, we conducted radar and ground-based observations of snow particles and found that the newly fallen snow density of dry snow can be estimated. We were able to highlight the difference in ice crystal flatness before aggregation growth progressed by focusing on higher altitudes.

Optimization of snow-related processes in Noah-MP land surface model over the mid-latitudes of Asian region

Snow plays a critical role in modulating surface energy, water cycles, and climate prediction. Optimizing snow-related parameterizations can enhance the model behaviors in simulating snow-related physical processes and reduce the cold bias observed in winter climate simulations in the Northern Hemisphere. In this study, the topographic complexity and wind speed were incorporated into the parameterization of the snow cover fraction (SCF) and newly fallen snow density (SFD) respectively within the Noah with multi-parameterization (Noah-MP) LSM to optimize the simulation of snow-related processes in the mid-latitude regions of East Asia. A control simulation and three sensitivity experiments were conducted to investigate and quantify the effects of topographic complexity and wind speed on the simulation of snow-related processes and land surface temperature (LST) against MODIS products. The results showed that modifications to the two schemes effectively mitigated the overestimation of snow cover and albedo, and alleviated cold biases in the most of study area. The influence of SFD scheme considering wind speed was more pronounced in regions with more snowfall and higher wind speed, while the SCF scheme considering topographic complexity showed a more widespread effect. The combination of these two modified schemes yielded the best performance. The mean biases of SCF, albedo, and LST over the entire study region with both modified schemes were reduced by 0.126 (∼63 %), 0.044 (∼41 %), and 0.584 °C (∼18 %), respectively. Their RMSEs were reduced by 0.119 (∼36 %), 0.036 (∼22 %), and 0.489 °C (∼10 %), respectively. This study highlights the importance of wind conditions and topographic complexity in the simulations of snow-related characteristics over the mid-latitudes of Asian region.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model

Abstract Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions. Significance Statement Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Tower-based C-band radar measurements of an alpine snowpack

Abstract. To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals.

Comparison of bulk snow density measurements using different methods

Snow density is one of the basic properties used to describe snow cover characteristics, and it is critical for remote sensing retrieval, water resources assessment and modeling inputs. There are many instruments available to measure snow density in situ. However, there are measurement errors of snow density for bulk and layers or gravimetric and electronic instruments, which may affect the accuracy of remote sensing retrieval and model simulation. Especially in China, due to the noticeable heterogeneity of snowpacks, it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack conditions. This study evaluated the performance of different snow density instruments: the Federal Sampler, the model VS–43 snow density cylinder (VS–43), the wedge snow density cutter (WC1000 and WC250), and the Snow Fork. The average bulk snow density of all instrument measurements was set as the reference value for evaluation. The results showed that as compared with the reference, the VS–43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE (11 kg m−3), BIAS (3 kg m−3), and MRE (1.6%). For layer observation, bulk snow density was overestimated by 8.1% with WC1000 and underestimated by 11.4% with Snow Fork which was the worst performance compared with the reference value, and there were greater measurement errors of snow density in the depth hoar than other snow layers. Compared with grassland, the uncertainty of snow density measurements was slightly lower in forests. Overall, the Federal Sampler and VS–43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China, and it is recommended to use WC1000, WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

Estimating Daily Snow Density Through a Spatiotemporal Random Forest Model

AbstractSnow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology.

Relationship between Snow and Temperature over Some Iraqi Meteorological Stations

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. Objective: It is a myth that it needs to be below 0 °C (273.15) K to snow. In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region.

Modeling seasonal ice and its impact on the thermal regime of a shallow boreal lake using the Canadian small Lake model

At high latitudes, lake-atmosphere interactions are disrupted for several months of the year by the presence of an ice cover. By isolating the water column from the atmosphere, ice, typically topped by snow, drastically alters albedo, surface roughness, and heat exchanges relative to the open water period, with major climatic, ecological, and hydrological implications. Lake models used to simulate the appearance and disappearance of the ice cover have rarely been validated with detailed in situ observations of snow and ice. In this study, we investigate the ability of the physically-based 1D Canadian Small Lake Model (CSLM) to simulate the freeze-up, ice-cover growth, and breakup of a small boreal lake. The model, driven offline by local weather observations, is run on Lake Piché, 0.15 km2 and 4 m deep (47.32°N; 71.15°W) from 25 October 2019 to 20 July 2021, and compared to observations of the temperature profile and ice and snow cover properties. Our results show that the CSLM is able to reproduce the total ice thickness (average error of 15 cm) but not the ice type-specific thickness, underestimating clear ice and overestimating snow ice. CSLM manages to reproduce snow depth (errors less than 10 cm). However, it has an average cold bias of 2°C and an underestimation of average snow density of 34 kg m−3. Observed and model freeze-up and break-up dates are very similar, as the model is able to predict the longevity of the ice cover to within 2 weeks. CSLM successfully reproduces seasonal stratification, the mixed layer depth, and surface water temperatures, while it shows discrepancies in simulating bottom waters especially during the open water period.

A comparison of machine learning methods for estimation of snow density using satellite images

AbstractLow snow density causes snow to melt quickly, so there is no runoff during the warmer months of the year. Therefore, knowing the snow density can be useful in determining the amount of water. To predict snow density, this study used seven machine learning methods, including adaptive neural‐fuzzy inference system (ANFIS), M5P, multivariate adaptive regression spline (MARS), random forest (RF), support vector regression (SVR), gene expression programming (GEP) and eXtreme gradient boosting (XGBoost). Nine factors expected to affect snow density were considered. These factors were extracted using Google Earth Engine (GEE) from 1983 to 2022. The results showed that the surface temperature had the highest correlation (coefficient = −0.7), and the wind speed had the lowest correlation (coefficient = 0.3) among the considered factors on the snow density. Also, the best method was XGBoost (Nash–Sutcliffe efficiency [NSE] = 0.978, R = 0.957), and the worst method is SVR (NSE = 0.7, R = 0.9). Therefore, snow density can be estimated with good accuracy using a combination of machine learning methods and remote sensing.

Demonstrating a Hybrid Machine Learning Approach for Snow Characteristic Estimation Throughout the Western United States

AbstractSnow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a “hybrid” model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone.

Optical effect of Andean mineral dust onto snow surface spectral albedo

Light absorbing particles (LAPs) present high absorbance and contribute to reducing the snow albedo when deposited on snow surfaces. This deposition can be caused by aerosols transported from natural or anthropogenic, either distant or nearby sources. In this study, snow was artificially contaminated with soil samples collected in the Central Andes (near El Yeso dam) to simulate the most common nearby source of Mineral Dust (MD) deposition onto snow surface. Andean soil samples previously conditioned were characterized through Single Particle Optical Sizing (SPOS), X-ray diffraction (XRD) analysis, and Scanning Electron Microscope (SEM) for the determination of optical properties. Spectral snow albedo was measured in situ with a spectroradiometric system. To evaluate the heterogeneity of the particle distribution over the snow surface, aerial photographs were taken with a drone to apply a visual color segmentation of the surface and to determine the equivalent MD concentration. Experimental snow albedo was compared with theoretical values obtained with the OptiPar radiative transfer model. Inputs for the model were: the MD refractive index (calculated from the mineralogical composition and morphology of MD) and particle size, cloudiness, snow density, surface roughness, snow grain size, and LAPs concentration (obtained from the snow samples collected during the experiments and analyzed in the laboratory). Small black carbon concentrations were found in natural snow and considered in the simulations. Spectral albedo measurements showed high albedo reductions in the UV and VIS range (300–800 nm), being less significant in the NIR range (800–1700 nm). A nonlinear behavior was observed in broadband albedo when increasing MD concentration. For lower values of MD concentration (lower than 1500 mg⋅kg−1), a significant albedo reduction rate of 0.1 units per 1000 mg⋅kg−1 was found, while at higher concentrations (¿ 3500 mg⋅kg−1), such reduction tends to the minimum. Simulated values with OptiPar are in agreement with measured albedo, but some differences are observed, probably due to the refractive index considered, the snow surface roughness, and the non-uniform MD concentration in snow.

Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One

This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.