Snow Cover Characteristics Research Articles

Quality of Snow Cover Characteristics Derived from ERA 5-Land Reanalysis for the Territory of Perm Krai

Received March 31, 2023; revised June 6, 2023; accepted June 27, 2023Agreement between values of the mean monthly snow depth provided by the ERA 5-Land reanalysis and similar characteristics of snow cover calculated on the basis of the extended hydrometeorological monitoring performed in the Perm Region for 1990–2020 is analyzed. It was found that ERA 5-Land in 73% of cases reproduces the presence/absence of snow during the onset period, and in 53% – during the period of snow loss. The conclusions made in the authors' previous studies based on more limited material were generally confirmed. It is shown that the reanalysis values of the snow depth are overestimated in relation to instrumental measurements for most of the hydrometeorological stations of the Perm Region. In space, the magnitude of the reanalysis error increases from the southwest to the northeast of the region, with the exception of its central part, where the influence of the Kama water reservoir is perceptible. But the interannual variability of the average snow depth in the Perm Region was reproduced by the ERA 5-Land reanalysis adequately. For 30 years, the magnitude of the reanalysis error decreased as it was compared with 61% observation points. The analysis of seasonal variability showed that in ERA 5-Land time of the maximum snow depth was shifted to earlier onset. The complete coincidence of the seasonal course was recorded only in 5% of hydrometeorological monitoring sites. The value of the average monthly discrepancies between the data of the reanalysis and the information of the posts as a whole exceeds the similar characteristic for the stations, which is especially evident during the period of active snowmelt.

Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin

AbstractThe genesis of floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels, threatening cities and communities along the riverbanks. For better understanding the mechanisms (origin and composition) of flood events in large and complex basins, we capture and share the story behind major historic and projected streamflow peaks in the Rhine River basin. Our analysis is based on hydrological simulations with the mesoscale Hydrological Model forced with both meteorological observations and an ensemble of climate projections. The spatio‐temporal analysis of the flood events includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, areal extents of events, and the above‐average runoff from major sub‐basins up to 10 days before a streamflow peak. We introduce and assess the analytical setup by presenting the flood genesis of the two well‐known Rhine floods that occurred in January 1995 and May 1999. We share our extensive collection of event‐based Rhine River flood genesis, which can be used in‐ and outside the scientific community to explore the complexity and diversity of historic and projected flood genesis in the Rhine basin. An interactive web‐based viewer provides easy access to all major historic and projected streamflow peaks at four locations along the Rhine. The comparison of peak flow genesis depending on different warming levels elucidates the role of changes in snow cover and precipitation characteristics in the (pre‐)Alps for flood hazards along the entire channel of the Rhine. Furthermore, our results suggest a positive correlation between flood magnitudes and areal extents of an event. Further hydro‐climatological research is required to improve the understanding of the climatic impact on the Rhine and beyond.

A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

ABSTRACT A high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporating geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here, we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indices of the fused (best original) dataset yielded a coefficient of determination R2 of 0.81 (0.23), Root Mean Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31%) between the fused snow depth and in situ observations was in the range of −5 cm to 5 cm. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision for snow depths of less than 100 cm with a relatively homogeneous surrounding environment. The results of random point selection and independent in situ site validation show that the accuracy of the fused snow depth product is not significantly improved in deep snow areas and areas with complex terrain. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher precision, with R2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment, snow disaster and hazard prevention.

Changes in the Structure of the Snow Cover of Hansbreen (S Spitsbergen) Derived from Repeated High-Frequency Radio-Echo Sounding

This paper explores the potential of ground-penetrating radar (GPR) monitoring for an advanced understanding of snow cover processes and structure. For this purpose, the study uses the Hansbreen (SW Spitsbergen) records that are among the longest and the most comprehensive snow-cover GPR monitoring records available on Svalbard. While snow depth (HS) is frequently the only feature derived from high-frequency radio-echo sounding (RES), this study also offers an analysis of the physical characteristics (grain shape, size, hardness, and density) of the snow cover structure. We demonstrate that, based on GPR data (800 MHz) and a single snow pit, it is possible to extrapolate the detailed features of snow cover to the accumulation area. Field studies (snow pits and RES) were conducted at the end of selected accumulation seasons in the period 2008–2019, under dry snow conditions and HS close to the maximum. The paper shows that although the snow cover structure varies in space and from season to season, a single snow pit site can represent the entire center line of the accumulation zone. Numerous hard layers (HLs) (up to 30% of the snow column) were observed that reflect progressive climate change, but there is no trend in quantity, thickness, or percentage contribution in total snow depth in the study period. HLs with strong crystal bonds create a “framework” in the snowpack, which reduces compaction and, consequently, the ice formation layers slow down the rate of snowpack metamorphosis. The extrapolation of snow pit data through radar profiling is a novel solution that can improve spatial recognition of snow cover characteristics and the accuracy of calculation of snow water equivalent (SWE).

Spatiotemporal changes in snow depth and the influence factors in China from 1979 to 2019.

Snow depth is an important parameter to characterize the characteristics of snow cover, and it is also one of the most sensitive response factors to regional climate change. However, the extent of snow depth variability and its driving mechanisms are still unknown in China. Therefore, in this study, we used the regression analysis, root-mean-square error analysis, anomalous year analysis, and correlation analysis methods to explore the spatiotemporal variation characteristics of snow depth in China from 1979 to 2019 based on the reanalysis snow depth dataset. The results show that (1) the snow distribution in China is obviously spatially heterogeneous, and the southeastern, western, and southern regions of the Qinghai-Tibet Plateau, northern Xinjiang, and northeastern China have high values of snow depth; (2) the high-value regions are also the sensitive regions for anomalous variations in snow depth in China; (3) in the past 41years, the interannual variability of snow depth in China has shown a significantly decreasing trend, and the linear tendency of snow depth is - 0.093cm/10 a (p < 0.01) and the snow depth in four seasons showed a decreasing trend (p < 0.05); and (4) the driving factors of snow heterogeneity are dissimilar in different regions and seasons. In temperate zones, average air temperature is the main factor affecting snow depth in cold temperature, mid temperature, and warm temperature zones; the maximum air temperature is the main factor affecting snow depth in mid temperate and warm temperate zones. Both the minimum air temperature and the average land-surface temperature are important factors affecting the snow depth in the cold temperate, mid temperate and warm temperate zones, and all passed the significance test of 0.01.

Snow cover dynamics: an overlooked yet important feature of winter bird occurrence and abundance across the United States

Snow cover dynamics (i.e. depth, duration and variability) are dominant drivers of ecological processes during winter. For overwintering species, changes and gradients in snow cover may impact survival and population dynamics (e.g. facilitating survival via thermal refugia or limiting survival via reduced resource acquisition). However, snow cover dynamics are rarely used in species distribution modelling, especially for overwintering birds. Currently, we lack understanding of how snow cover gradients affect overwintering bird distributions and which functional traits drive these associations at regional and continental scales. Using observations from eBird, a global community science network, we explored the effects of snow cover dynamics on continental patterns of occurrence and counts for 150 bird species. We quantified the relative importance, species‐specific responses and trait‐based relationships of bird occurrence and abundance patterns to ecologically relevant snow cover dynamics across the United States. Snow cover dynamics were important environmental predictors in species distributions models, ranking within the top three predictors for most species occurrence (&gt; 90%) and count (&gt; 79%) patterns across the contiguous United States. Species exhibited a gradient of responses to snow cover from snow association to snow avoidance, yet most birds were limited by long, persistent snow seasons. Duration of winter and percent frozen ground without snow structured species distributions in the eastern USA, whereas snow cover variability was a stronger driver in the western USA. Birds associated with long, persistent snow seasons had traits associated with greater dispersal capacity and dietary diversity, whereas birds inhabiting regions with variable snow cover were generally habitat generalists. Our results suggest that various snow cover dynamics are important ecological filters of species distributions during winter. Global climate change is rapidly degrading key characteristics of seasonal snow cover. A changing cryosphere may elicit variable distributional changes for many overwintering birds, potentially accelerating range shifts and novel community assemblages.

Assessment of Arctic snow stratigraphy and water equivalent using a portable Frequency Modulated Continuous Wave RADAR

There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement.

Characteristics of snowfall and snow cover in Iwamizawa, Hokkaido, Japan, during 2020/21 winter season

Characteristics of snowfall and snow cover in Iwamizawa, Hokkaido, Japan, during 2020/21 winter season

A Synthetic Aperture Radar Based Method for Long Term Monitoring of Seasonal Snowmelt and Wintertime Rain-On-Snow Events in Svalbard

Snow cover characteristics are highly sensitive to variations in temperature and precipitation. In Svalbard, these parameters are undergoing significant change in response to a rapidly warming climate and its associated positive feedback processes. The occurrence of wintertime rain-on-snow (ROS) events are expected to increase in frequency and intensity across the Arctic as a result of climate change. ROS events can dramatically alter snow cover characteristics, by saturating the snowpack and enhancing surface runoff as well as causing widespread formation of ground ice, which can negatively impact many ecosystems as well as infrastructure. Knowledge of the spatial and temporal variations in ROS occurrence across Svalbard, both past and present is needed to understand which areas are most vulnerable to ROS impacts and how this may change in the future. Until now comparatively few studies have exploited remote sensing methods to detect and monitor ROS occurrence and even these have been limited to relatively coarse spatial resolutions. This work has utilized C-band Synthetic Aperture Radar (SAR) observations to produce a 17-year dataset (2004–2020) of wet snow cover observations for Svalbard, from which a method for detecting and mapping both spring melt onset and ROS frequency has been developed. The mean spatial variations in melt onset and ROS occurrence reflect the geographical gradients in temperature and precipitation across the archipelago and are largely in agreement with current knowledge. The timing of ROS onset as detected using the SAR observations coincide well with in-situ measurements of rainfall, however in some cases the duration of a ROS event cannot be reliably estimated using SAR observations of wet snow, in particular after phase transitions from rain to snow. Linear trends derived from the limited time series of observations suggests that ROS frequency is increasing over most of the archipelago, but significant and large increases are confined to the south and west coast of the archipelago. However, low elevation areas in the central parts of the archipelago also exhibit a significant and decreasing trend in ROS occurrence over the time period studied.

Avalanche risk analysis using airborne LiDAR data

Natural and man-made disasters are a justified concern causing life-threats damages, affects existing budgets or disrupts national economic and social development plans. In this paper, a digital terrain model (DTM) and a geographic information system (GIS) are used to identify areas with a high risk of avalanches in the Râmeț mountain area of Alba County. Avalanches are the result of a complex interaction between terrain, weather conditions and snow cover characteristics. The most important factor among those listed is the terrain, being the only constant parameter over time. Therefore, the present study focused in particular on the determination of topographic parameters and the realization of a digital terrain model with a resolution of 0.5 m, which allows the small-scale identification of slope angle variations, which are of great interest for avalanche study. This digital terrain model was generated after processing LiDAR data obtained from a scanning campaign for the analyzed area with the Riegl LMS Q680i LiDAR system, located aboard the Hawker Beechcraft King Air C90 GTx aircraft from INCAS. The result of the analysis consisted in mapping the risk of avalanches in the Râmeț mountain area in Alba County.

МЕТОДИКА РАСЧЕТА ПРОСТРАНСТВЕННОГО РАСПРЕДЕЛЕНИЯ ВЫСОТЫ СНЕЖНОГО ПОКРОВА В УСЛОВИЯХ СЛОЖНОГО РЕЛЬЕФА ПРИ НЕДОСТАТОЧНОЙ МЕТЕОРОЛОГИЧЕСКОЙ ИНФОРМАЦИИ (НА ПРИМЕРЕ АНЮЙСКОГО ХРЕБТА)

In modern conditions of intensive economic development of the Eastern Siberia mountain regions, the spatial variability of the snow cover characteristics in complex terrain needs to be assessed. Such assessment is difficult due to the small number of weather stations in mountain regions. Indirect methods for calculating the snow cover characteristics for the areas with complex terrain have not been developed yet. A method is proposed for calculating the snow depth in mountain terrain, which is based on using microclimatic methods for obtaining detailed climatic information. The quantification of changes in application-specific characteristics of the snow cover depending on altitude in mountain terrain of the Anyuy Range is carried out.

Assessment of snow cover variability and its sensitivity to hydrometeorological factors in the Karakoram and Himalayan region

ABSTRACT Snow cover plays an important role in hydrological modelling and influences the energy balance. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow products are widely used for snow cover monitoring; however, the cloud cover causes a discontinuity in spatial and temporal scale. Therefore, we have implemented a composite (five-step) methodology for cloud removal over the Karakoram and Himalayan (KH) region during 2000–2019. The cloud-gap-filled snow cover area (SCA) was compared with Landsat-8 data, and the relationship with in situ observations (snowfall and temperature) was examined. The annual SCA illustrates an increasing trend for the entire KH except in the eastern Himalayas. The snow cover day and nine snow cover timing indices were assessed to explore the snow cover characteristics. The relationships between SCA and meteorological variables were evaluated, suggesting a higher correlation with temperature and shortwave radiation. A sensitivity analysis was performed, indicating higher sensitivity of SCA to radiation than to other selected variables.

THE INFLUENCE OF LANDSCAPE ELEMENTS ON THE DISTRIBUTION OF SNOW COVER ACCORDING TO FIELD MEASUREMENTS AND ANALYSIS (ON THE EXAMPLE OF PERM)

The scientific interest in snow dynamics is growing steadily, especially in relation to climate variability. The ability to monitor snow cover is being improved, which makes it possible to take into account the characteristics of snow cover from hard-to-reach places in hydrodynamic models of the atmosphere. As a result, the quality of climate and weather forecasting improves. This paper presents an analysis of field observation data in small relief forms with observation data from a stationary network of ground-based observations and ERA5-Land reanalysis. It was found that the temporal variability of the height and density of the snow cover in various elements of the landscape is successfully reproduced by reanalysis. The closest values of the characteristics of the snow cover are observed during the period of its formation. The greatest unevenness in the distribution of snow cover is distinguished by negative landforms and forest edges, which seems to be determined by snowstorm transport. The height of the snow cover in the field during route observations in the vicinity of the meteorological station and stationary observations at the meteorological station has discrepancies on average by 1-4 cm. The height of the snow cover in the gully is 10-22% higher than in the field and 10% more than in the log. It is shown that the highest snow density is observed in open areas, since in open areas, the wind seems to play an important role in compaction of the snow cover. Comparison of field observation data with reanalysis on the distribution of snow cover density shows small discrepancies: no more than 0.08 g/ cm. The maximum deviation from the actual data is observed in the first half of March.

Comprehensive analysis of snow cover in the south of Russia

Snow cover is one of the components of the natural environment that determines the weather and climate. In recent decades, there have been changes in all characteristics of the snow cover, which are associated with climate warming. This work is devoted to the analysis of changes in various characteristics of snow cover in different climatic zones of the Caucasus region for the period 1960-2018 and the construction of a regression model of seasonal variability of snow cover thickness (SCT) in the south of the European territory of Russia (ETR). Based on the data from the series of snow cover thickness, the number of days from the snow cover (SC), the dates of the beginning and end of the SC descent, the dates of the establishment / destruction of the stable SC in the cold seasons of 1961-2018 in the south of the ETR, an assessment of the change in these characteristics was carried out. As a result of the analysis, it was found that, against the background of global warming, the number of days with snow cover decreases, the dates of the beginning and descent of a stable SC are shifted towards a reduction in the duration of the SC occurrence, and at the same time, an increase in the SC height is observed. A regression model of seasonal variability of the thickness of the snow cover has been built. The analysis of the seasonal variability of the SCT showed that the thickness of the snow cover for the southern regions of Russia has a pronounced seasonal variation with a determination coefficient no worse than 55%. The fields of the parameters of the seasonal variation are quite smooth, and their variability is well physically conditioned.

Variability of snow cover characteristics in the southern Rostov region (on the example of the Gigant settlement)

Snow cover affects many natural processes, including determining and regulating the water balance, determining spring runoff. The aridity of the climate in the southern Rostov region causes water scarcity in the region. The purpose of this work is to study the variability of snow cover in the settlement Gigant for the period from 1961 to 2019. The main task is to determine the changes in the dynamics of snow cover, analyze the number of days with snow and the average snow height for the study period. The analysis included the height of the snow cover, the number of days with snow, and the average annual temperatures. The data of route snow-measuring surveys were statistically analyzed. A decrease in the number of days with snow was revealed, which is explained by frequent thaws and instability of negative temperatures. There is a tendency to decrease the snow cover in winter and an increase in the number of thaws.

Сравнение характеристик снежного покрова по данным метеорологических станций и реанализа ERA 5-Land на территории Пермского края

Comparison of snow cover characteristics according to weather stations and ERA 5-Land reanalysis in the Perm region / Kryuchkov A.D., Kalinin N.A. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 95-110. The consistency of information on the snow depth contained in the ERA 5-Land reanalysis with data of weather stations of the Perm region is analyzed. The study is performed for the period from October 1990 to May 2020. It is shown that the interannual variability of the snow cover is generally successfully reflected by the current version of the reanalysis. Data on the snow availability are more accurately reproduced during the period of formation of the snow cover than during its melt. The performed calculations demonstrate a systematic overestimation of the snow depth in the ERA 5-Land reanalysis relative to the actual observations and a predominantly meridional error distribution on the territory of the Perm region. The maximum values in the seasonal variability of the snow cover occur earlier in the reanalysis than in the actual observations. Keywords: snow cover, reanalysis, weather stations, seasonal variability, interannual variability

A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.

Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

Numerical simulation of seasonal snow in Tianshan Mountains

Snowfall in the Tianshan Mountains in China is frequent during winter; thus, avalanches have become a severe issue in snow-covered areas. Accumulation and metamorphosis, as well as hydrothermal exchanges with the environment, considerably affect the stability of snow on slopes. Therefore, a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches. This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components. The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated. Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover. Temperature drives water movement in snow. The low-density area of the bottom snow is the result of temperature gradient. The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation. Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.