Snow Survey Research Articles

On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N.N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17% of winters; while in 58% of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78% of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.

The snow depth and its dynamics on the continental part of the Russian Arctic under conditions of the present-day climate

Based on the data of route snow surveys for the period 1966–2020, the comparison of the average long-term maximum depths of snow cover, the depths of snow cover for individual months and the dynamics of snow accumulation (the ratio of the depths of snow cover to the maximum value) on the continental part of the Russian Arctic for two representative periods (1966–1990 and 1991–2020) was made. Maps of snow cover depths and snow accumulation dynamics have been constructed for both periods. These maps made possible to analyze influence of the climatic changes on the depths of snow cover and the dynamics of snow accumulation. A comparison of these values for the first (1991–2020) period with the same of the second one showed that in October–November in the European part of the Russian Arctic, the snow depths decreased by an average of 22% (first period) and 8% (second period), and in some areas the decrease reached 70 and 20%. In the Arctic part of Western Siberia, these characteristics of snow cover increased. Growth of snow cover depths in November/January/March averaged as 11/20/23%, and in some areas it exceeded 40%. The dynamics of snow accumulation in the Arctic for the whole period 1991–2020 averages 18/37%, in October/November, and 71/91% in January/March. In the European part of the Arctic, these values are smaller: 13/29% and 68/90%, respectively. The dynamics of snow accumulation in the west and in the center of the European Arctic territory by the end of the autumn period does not reach 30%, while in the Arctic part of Siberia this mainly exceeds 50%. In October/November 1991–2020, the dynamics of snow accumulation decreased in several regions of the European Arctic and the Arctic part of Western Siberia as compared to 1966–1990. On average over the entire territory of the Arctic, the decrease in the dynamics of snow accumulation compared to 1966–1990 amounted to 13/4% in October/November, and 3/1% in January/March.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

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.

Investigating hydrological recovery in regenerating coniferous stands in snow‐dominated watersheds using simultaneous localization and mapping‐enabled mobile terrestrial LiDAR

AbstractThe return of snow accumulation and ablation processes in regenerating forests to pre‐disturbance conditions, collectively referred to as hydrological recovery, has been investigated in past decades through manual snow surveys in adjacent open, juvenile, and mature stands. The outcomes of such studies provide a general understanding of hydrological recovery but lack transferability to areas where stand structure and terrain conditions differ from the reference sites. The application of mobile terrestrial LiDAR to investigate peak snow water equivalent (SWE) and ablation rates beneath regenerating trees in a space‐for‐time substitution study design provides new insights on the process of hydrological recovery in snowmelt forests of British Columbia, Canada. Outcomes of this study better quantify the influence of tree growth on peak SWE and ablation rate at both the tree and stand level for north aspect mixed conifer stands. Recovery of these two processes differ with recovery of Peak SWE beginning when the trees in a stand reach 3 m in height and recovery of ablation rates beginning once trees reach 5 m in height. Additionally, the process of negative ablation recovery in early juvenile stands reported in previous studies is herein clearly observed, providing an improved understanding of forest canopy effects on hydrological recovery in juvenile stands. The methods used in this study, which are internationally applicable, increase transferability of outcomes to stands where canopy characteristics (i.e., height, crown cover, and heterogeneity) are not represented in reference sites.

Snow survey within the copper ore deposits in the mining regions of the Urals

The paper presents the results of studies of dust residues of snow cover within various climatic zones and copper deposits of the Ural region. Snow cover research is one of the methods for environmental assessment of territories. This concerns the study of dust residues from snow cover samples, as shown by the example of the territories of the Northern and Southern Urals at different stages of work on copper deposits. The initial stage of study and development is considered using the example of porphyry copper deposits in the Chelyabinsk region, where snow samples reveal organic matter in dust residues, typical birch seeds, a small amount of minerals at a low dust load (P ˂ 100 kg/day per km2). For the deposits of the Ivdel region (Northern Urals), the dust residues largely contain organic dust, fragments of ore minerals (pyrite, chalcopyrite) and minerals of volcanic rocks and metasomatic rocks. The maximum value of the dust load is set for the Novo-Shemurskoye deposit currently being developed. A study of dust residues in the zone of influence of the Karabash copper smelter (before its reconstruction) has shown that in some parts of the sanitary protection zone the level of snow cover pollution is very high with dust load values up to 12,633 kg/day per km2, which is determined primarily by the widespread use of crushed copper smelting slag. In general, the study of dust residues of snow cover provides information about both the ecological state of the territories and the mineral composition of the residues, which must be used for practical purposes.

Snow mechanical property variability at the slope scale – implication for snow mechanical modelling

Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.

Design and performance of the NVE snow scales

Continuous information on snow water equivalent (SWE) is of great importance in the natural hazard assessment (i.e. snow melt floods), in power production planning, water supply forecasting and more. In 2009 NVE designed and developed a new electronic snow sensor: The M2525. The main goal was to develop a sensor that was better than a snow pillow with respect to measurement errors as well as being more robust and less prone to defects. This study investigates the performance of the M2525 and the two smaller snow scales, the M604 and M910, at six study sites from 2009 to 2023. The performance of the scales is validated against manual snow surveys. I addition we have used exploratory analysis of the time series with the purpose to detect and discover typical patterns of measurement error.The largest snow scale, the M2525, seems to overestimate SWE by a ratio of +0,14 compared to manual surveys. However, M2525 does not seem to have trouble with pressure relief of the same magnitude as a snow pillow. Data analysis from the smallest scales showed both over- and underestimation, as well as small signs of measurement errors indicating that further observations and analyses are required.

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

AbstractDepth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Impacts of shrub removal on snow and near-surface thermal conditions in permafrost terrain adjacent to the Dempster Highway, NT, Canada

The Peel Plateau, NT, Canada, is an area underlain by warm continuous permafrost where changes in soil moisture, snow conditions, and shrub density have increased ground temperatures next to the Dempster Highway. In this study, ground temperatures, snow, and thaw depth were monitored before and after tall shrub removal (2014). A snow survey after tall shrub removal indicated that snow depth decreased by a third and lowered winter ground temperatures when compared with control tall shrub sites. The response of ground temperatures to shrub removal depended on soil type. The site with organic soils had cooler winter temperatures and no apparent change in summer temperatures following shrub removal. At sites with mineral soil, moderate winter ground cooling insufficiently counteracted increases in summer ground heat flux caused by canopy removal. Given the predominance of mineral soil along the Dempster, these observations suggest tall shrub removal is not a viable short-term permafrost management strategy. Additionally, the perpendicular orientation of the Highway to prevailing winter winds stimulates snow drift formation and predisposes the site to warmer permafrost temperatures, altered hydrology, and tall shrub proliferation. Subsequent research should explore the effectiveness of tall shrub removal at sites with colder winter conditions or different snow accumulation patterns.

Uncertainty Assessment for Mean Snow Cover Depth Derived from Direct Measurements on Aldegondabreen Glacier (Svalbard)

Received March 27, 2022; revised May 5, 2023; accepted June 27, 2023This study introduces an empirical equation allowing to estimate an uncertainty of area-averaged snow depth on the Aldegondabreen Glacier, computed from standard snow surveys and made by an avalanche probe or by similar equipment. The two-decade history of the ongoing mass-balance monitoring program on this glacier shows that the methodology of field work on snow-measuring survey varies somewhat from year to year: the number and location of measurement points change. To identify and quantify long-term trends and variations in snow cover, it is crucial to assess the inter-comparability of the data in the obtained measurement series. The proposed equation was intended to solve this task basing on the collected data only, allowing to estimate the uncertainty even retrospectively. To build this equation, we applied a bootstrap statistical approach to the results of snow surveys carried out in Svalbard in 2015–2021. After interpolating the field measurements, obtained rasters were sampled sequentially with different numbers of points, simulating the real snow survey. The points were initially located in a form of a quasiregular grid and then randomly shifted between the iterations. After a thousand simulations for each number of points, the standard deviations were calculated relative to the “true” values, derived from corresponding rasters. These standard deviations, which we admit to be a random error of the area-averaged snow depth value, expectedly decrease with the number of sampling points and increase with the coefficient of variation (\({{{\text{C}}}_{\user1{v}}}\)). The well-known \({{{\text{C}}}_{\user1{v}}}\) index indirectly characterizes the irregularity of snow cover. After approximating the bootstrap results, the authors derived an equation that yields a relative error. The equation includes only two predictors which are the probing density per area unit and the \({{{\text{C}}}_{\user1{v}}}\), which potentially allows using it for the other glaciers. However, the universality of the empirically obtained coefficients is debatable, since they may vary due toa glacier size, its morphology and other parameters.

Small Glaciers of the Putorana Plateau under Climate Change

Received November 14, 2022; revised May 24, 2023; accepted June 27, 2023The characteristic feature of the Putorana Plateau is that the glaciological objects here are mostly represented by small glaciers and perennial snow patches. Their regime and morphology have so much common features that separation of these two categories of nival glacial formations from one another is extremely difficult problem. The distinctions between results of earlier studies carried out in the 1970s (the USSR Glacier Inventory estimated local resources at 22 glaciers with a total area of 2.5 km2) and at the beginning of the current century (in 2005, V.A. Sarana identified 61 glaciers with a total area of 7 km2) are too large to make any reliable conclusions about the current trends of the Putoran glaciation. In such conditions, the materials of rare field monitoring work performed on individual nival-glacial bodies become very valuable. Three expedition seasons of 2002–2004 included thorough geodetic and mass-balance measurements on 3 reference objects on the northern ledge of Lama Mts. – Prives (No. 30) Glacier, Marlborough (No. 31) Glacier and Strudoms snow patch. Similar field monitoring was repeated in summer’2019. The change in their configuration according to digital photography data, including the results of the analysis of satellite images, make possible to reveal that interannual fluctuations in the area of each glacier occur due to different vector displacements of its different sections along their entire perimeters. In 2019, the somewhat unexpected good budget state of all 3 monitored objects was detected. It manifested itself in growth of their areas and increased fraction of firn basin as well as in larger water equivalent of the firn residue as this followed from results of snow surveys. This fact contradicts the previous conclusion about the steady trend of deglaciation on the Putorana Plateau. The favourable condition for small glaciers occurred here in 2019 was mainly caused by reduced ablation owing to the weakened insolation that resulted from tremendous forest fires in Siberia, remarkable by their abnormal intensity in this year.

ХИМИЧЕСКИЙ СОСТАВ АТМОСФЕРНЫХ ВЫПАДЕНИЙ НА ТЕРРИТОРИИ БАЙКАЛЬСКОГО ГОСУДАРСТВЕННОГО ПРИРОДНОГО БИОСФЕРНОГО ЗАПОВЕДНИКА (ВОСТОЧНОЕ ПОБЕРЕЖЬЕ ЮЖНОГО БАЙКАЛА)

The results of studying the chemical composition of atmospheric aerosol, precipitation, and snow cover on the territory of the Baikal State Biospherical Nature Reserve during 2017-2021 are presented. The principal ions in atmospheric aerosol and precipitation were sulfates and ammonium and those for snow cover were sulfates, nitrates, and calcium. The series of detailed simulations with the HYSPLIT model and the methods for statistical data processing (ArcGIS 10.2 and RStudio software) enable the identification of natural and anthropogenic groups of sources affecting the chemical compound of atmospheric deposition on the eastern coast of Lake Baikal. Based on snow survey data, the accumulation of the substances, which are the indicators of anthropogenic load, in the snow cover of the Baikal State Biospherical Nature Reserve was evaluated.

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

AbstractThe Snow Survey and Water Supply Forecasting (SSWSF) Program and the Soil Climate Analysis Network (SCAN) of the United States Department of Agriculture's Natural Resources Conservation Service (NRCS) generate key observational and predictive information for water managers. Examples include mountain climate and snow monitoring through manual snow surveys and the SNOw TELemetry (SNOTEL) and SNOtel LITE networks, in situ soil moisture data acquisition through the SCAN and SNOTEL networks, and water supply forecasting using river runoff prediction models. The SSWSF Program has advanced continuously over the decades and is a major source of valuable water management information across the western United States, and the SCAN network supports agricultural and other water users nationwide. Product users and their management goals are diverse, and use‐cases range from guiding crop selection to seasonal flood risk assessment, drought monitoring and prediction, avalanche and fire prediction, hydropower optimization, tracking climate variability and change, environmental management, satisfying international treaty and domestic legal requirements, and more. Priorities going forward are to continue innovating to enhance the accuracy and completeness of the observational and model‐generated data products these programs deliver, including expanded synergies with the remote sensing community and uptake of artificial intelligence while maintaining long‐term operational reliability and consistency at scale.

Influence of protective forest belts on snow accumulation in agricultural landscapes

In semi-arid climate conditions, where farming is complicated by a lack of atmospheric moisture, the preservation of snow in an agroforest landscape serves as an additional source of moisture for the growth and development of tree and shrub vegetation. The paper investigates the role of forest belts of a combined structure on the characteristics of snow deposition depending on different patterns of shrub placement (along the top edge, along the lower edge, on both sides). The results of the conducted snow surveys show that experimental sites with shrubs along the top edge are characterized by the highest level of snow accumulation both in the forest belt and in the adjacent field. The snow-retaining function in the forest belt zone is weaker in the presence of shrubs on both sides. It has been established that the values of snow density increase with approaching the forest stand. The highest values were recorded in the forest belt with shrubs along the top edge (up to 0.5 g cm-3). The accumulation of snow and its density eventually affected the amount of snow reserves. The highest values of snow reserves were observed in the forest belt with shrubs along the top edge with a row width of up to 1 m. This contributed to the accumulation of 82-203 mm of snow in the forest belt area (at 43 mm of snowfall). Shrub placement along the lower edge provoked a loss of moisture in the forest belt itself, which made this pattern ineffective. The results obtained can be applied in the design of protective forest belts in the areas with insufficient moisture.

EXPERIMENTAL STUDY OF SNOW LOAD DISTRIBUTION ON A SHELL OF THE GRAND SPORTS ARENA OF LUZHNIKI OLYMPIC COMPLEX

Introduction. The mechanism of the formation of snow deposits on the shell of the Luzhniki GSA and their redistribution in winter were established on the basis of the data obtained during the monitoring of the snow load for over 20 years.Aim. In this article, the mechanism of the formation of snow deposits and their distribution on the shell of the Luzhniki GSA were determined, along with the numerical values of the form factor μ characterizing the transition from the ground snow load to the snow load on the shell.Materials and methods. The measurements of the load and density of snow deposits on the shell of the Luzhniki GSA were carried out from 1998 to 2019. The obtained results were compared with the parallel measurements of ground snow load (GSL) in Luzhniki. In addition, the snow load, as well as statistical data analysis on the maximum annual values of the GSL, were analyzed using the hydrometeorological data of decadic snow surveys in Moscow, performed by the V.A. Mikhelson Meteorological Observatory following the dates of field measurements. The graphs of the repeatability of wind directions for the month preceding the observation dates were plotted.Results. The maximum values of the form factor μ for each section of the shell were obtained. The areas characterized by increased snow deposition on the shell during various periods of snow accumulation andthe dependence of their formation on wind speeds and directions in winter were revealed. Graphs depicting the distribution of snow load on the surface by observation years were plotted. It was established that the values of snow loads on the GSA shell during the observation period generally lay within the design values, except for local zones near the internal contour during the installation of the canopy.Conclusion. It was shown that the formation, accumulation, and redistribution of snow deposits on the shell comprise a complex and uneven process, varying from winter to winter. When selecting the analytical models of snow loads for calculating unique load-bearing structures, it is necessary to account for the most unfavorable wind flow directions, at which an uneven snow deposition pattern occurs, as well as the physical properties of the shell and field observations.

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

AbstractSnowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.

Prospecting terrain for surface geochemical exploration of oil and gas in West Siberia

ABSTRACT The relevance of this research is necessitated by the need to advance geochemical prospecting methods for raw hydrocarbon deposits. The study aimed to design new migration models by the interpretation of surface geochemical survey data. In particular, the relationship behavior needs to be identified between the hydrocarbon migration intensity and the presence of oil-bearing reservoirs within the section. To reveal quantitative relationships between the oil show and the oil-bearing capacity of the section, a substantial body of geochemical data across different fields in West Siberia was processed herein. Field-type and in-office operations were being performed by the snow survey procedure. The СН4–С6Н14 saturated hydrocarbons were employed as the major measured parameter. No direct quantitative connection was found between the intensity of surface hydrocarbon anomalies and the section productivity. Nonetheless, the two basic factors were found to affect the structure of the surface geochemical field: first, oil-bearing reservoirs present in the section and, second, fluid-resistant properties of the above-productive strata. Despite a direct quantitative relationship between the oil-bearing capacity of the section and the intensity of surface hydrocarbon anomalies being absent, the large sample made it possible to determine quantitative measures of the surface geochemical field that credibly characterize territories whose sections hold oil-bearing horizons.

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Featured Collection Introduction: Severe Sustained Drought Revisited: Managing the Colorado River System in Times of Water Shortage 25 Years Later — Part I

Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles

Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts are initialized from state variables that are as representative of reality as possible, and also to estimate the uncertainty of the state variables. There are several data assimilation methods, and particle filters are increasingly popular because of their minimal assumptions. The baseline idea is to produce an ensemble of scenarios (i.e. the particles) using perturbations of the forcing variables and/or state variables of the model. The different particles are weighted using the observations when they become available. However, implementing a particle filter over a domain with large spatial dimensions remains challenging, as the number of required particles rises exponentially as the domain size increases. Such a situation is referred to as the “curse of dimensionality”, or a “dimensionality limit”. A common solution to overcome this curse is to localize the particle filter. This consists in dividing the large spatial domain into smaller portions, or “blocks”, and applying the particle filter separately for each block. This can solve the above-mentioned dimensionality problem because it reduces the spatial scale at which each particle filter must be applied. However, it can also cause spatial discontinuities when the blocks are reassembled to form the whole domain. This issue can become even more problematic when additional data are assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering them. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local SWE observations from manual snow surveys. We apply two reordering strategies based on (1) a simple ascending-order sorting and (2) the Schaake shuffle and evaluate their ability to maintain the spatial structure of the particles. To increase the amount of assimilated data, we investigate the inclusion of a second data set (SR50), in which the SWE is indirectly estimated from automatic measurements of snow depth using sonic sensors. The two reordering solutions maintain the spatial structure of the individual particles throughout the winter season, which significantly reduces the spatial random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. The Schaake shuffle proves to be a better tool for maintaining a realistic spatial structure for all particles, although we also found that sorting provides a simpler and satisfactory solution. The assimilation of the secondary data set improved SWE estimates in ungauged sites when compared with the deterministic model, but we noted no significant improvement when both snow courses and the SR50 data were assimilated.