Snow Accumulation Research Articles

Comprehensive Hydrological Analysis of the Buha River Watershed with High-Resolution SHUD Modeling

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region’s water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model’s applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake’s water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin’s importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River’s hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research.

Dendrochronological analysis of conifers in avalanche catchments of the Northwestern Altai (Korgon River basin)

The purpose of the study was to date the powerful snow avalanches occurred in the avalanche catchment area in the river Korgon basin (Northwestern Altai) by the dendrochronological method. The paper presents the results of the analysis of a number of dendrochronological indicators of avalanches: the age of trees, the relation between widths of annual rings from opposite sides of the trunk, the presence of reactive (compressional) wood and traumatic resin canals, the dates of death and formation of wounds in trees, the presence of clearing effects (a sharp increase in growth) in three avalanche catchments, as well as a complex dendrochronological index of the avalanche activity. The dates of releases of the powerful avalanches were established down to 1570. It has been found that against the background of increasing amount of winter precipitation in these catchments, there are different trends in occurrence of the avalanches, which is determined by their morphological properties. In the avalanche areas with steeper slopes, due to repeated unloading of the avalanche centers in winter, the probability of releasing of powerful avalanches decreases. For the same reason, the powerful avalanches do not release during the years of maximum snow accumulation. The trends in the avalanche activity in the region under consideration had been obtained for the first time. The dendrochronological index of the avalanche activity, which is the ratio of the number of the tree growth failures to the number of the examined trees, is a good indicator of a release of a powerful avalanche even at a value of 0.85, provided that the number of examined trees in the lower part of the transit zone and in the zone of accumulation is 25–35 units. The results of this study may be used to predict the territorial differentiation of changes in the avalanche activity due to climate change. In connection with the recreational development of the Altai territory, they may also be of practical importance.

A complete environmental wind tunnel for studying the evolution of snow accumulations under the effects of wind, rain, and heat

Given the escalating frequency of extreme cold weather worldwide, snow-induced building failures have increased substantially, especially in large-span space structures. The reason for engineering accidents lies in a lack of understanding of the intricate process of the mechanism of full-period evolution of snow “accumulation-melt-crystallization- accumulation” under the coupling actions of wind, rain, and heat. It is significant to develop a comprehensive and accurate environmental wind tunnel for conducting snow-relevant experiments under the multi-factors, which are barely studied. Hence, this paper proposes and establishes an experimental system called “Simulator of Natural Action of Wind-Rain-Heat-Snow for Space Structures (SNOW).” The system consists of an atmospheric boundary layer cryogenic wind tunnel subsystem, snowfall simulation subsystem, rain simulation subsystem, solar and building heating simulation subsystem, and high-precision multi-task monitoring and control subsystem. The facility can provide a 0–20 m/s stable wind field, −15 °C–0 °C low-temperature conditions, and a 2 m × 2 m experimental section. The calibrations of each subsystem were conducted in SNOW to ensure each component can successfully provide basic conditions for snow-relevant experiments. The SNOW can accurately simulate the entire process of natural snowfall and snow evolution under the coupling effects of wind, rain, and heat.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

Influence of Slope Aspect and Vegetation on the Soil Moisture Response to Snowmelt in the German Alps

Snow, especially in mountainous regions, plays a major role acting as a quasi-reservoir, as it gradually releases fresh water during the melting season and thereby fills rivers, lakes, and groundwater aquifers. For vegetation and irrigation, the timing of the snowmelt is crucial. Therefore, it is necessary to understand how snowmelt varies under different local conditions. While differences in slope aspect and vegetation (individually) were linked to differences in snow accumulation and ablation, this study connects the two and describes their influence on the soil moisture response to snowmelt. This research focuses on the catchment of the “Brunnenkopfhütte” (BKH) in Bavaria, southern Germany, where an automatic weather station (AWS) has operated since 2016. In addition, soil temperature and moisture monitoring systems in the surrounding area on a south aspect slope on an open field (SO), on a south aspect slope in the forest (SF), and a north aspect slope in the forest (NF) have operated since 2020. On snow-free days in winter, the soil temperature at the SF site was on average 1 °C lower than on the open site. At the NF site, this soil temperature difference increased to 2.3 °C. At the same time, for a 1 °C increase in the air temperature, the soil temperature increases by 0.35 °C at the NF site. In addition, at this site, snow cover disappeared approximately one week later than on the south aspect slopes. Snow cover at the SF site disappeared even earlier than at the SO site. Finally, a significant difference in the soil moisture response was found between the sites. While the vegetation cover dampens the magnitude of the soil moisture increases, at the NF site, no sharp increases in soil moisture were observed.

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.

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Assessment of mechanical performance of electrically conductive asphalt pavements using accelerated pavement testing

ABSTRACT Electrically Conductive Asphalt (ECA) pavements are a promising technology for preventing snow and ice accumulation on roadways. This study assesses the mechanical performance of ECA pavements using Accelerated Pavement Testing (APT). Three pavement test strips were constructed in New Jersey: a traditional asphalt layer as test strip-I, an ECA mastic prepared using a combination of asphalt binder and conductive fibres (strip-II), and an ECA-HPTO (high performance thin overlay mixture) layer containing graphite and carbon fibres (strip-III). Each test strip was instrumented with asphalt strain gauges (ASGs), pressure cells, and thermocouples to evaluate the structural responses. A Heavy Vehicle Simulator (HVS) was used to apply 300,000 passes on each test strip using a 40-kN truck tire. The structural integrity of the test strips was evaluated by conducting Heavy Weight Deflectometer (HWD) testing before and after HVS loading. Data from the ASGs revealed that strip III exhibited the highest cracking resistance (nearly 2 times less tensile strain). The rut depths in all test strips were negligible (less than 2 mm). ECA mastic and steel electrodes in the ECA layer impact the structural integrity of test strips, with variations in HWD deflection behaviour indicating the role of electrode spacing in maintaining pavement stability.

Detection of land surface albedo changes over Iran using remote sensing data

AbstractAlbedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre‐processing, the long‐term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non‐parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow.

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.

Habitat suitability of biocrust communities in a cold desert ecosystem.

Drylands are unique among terrestrial ecosystems in that they have a significant proportion of primary production facilitated by non-vascular plants such as colonial cyanobacteria, moss, and lichens, i.e., biocrusts, which occur on and in the surface soil. Biocrusts inhabit all continents, including Antarctica, an increasingly dynamic continent on the precipice of change. Here, we describe in-situ field surveying and sampling, remote sensing, and modeling approaches to assess the habitat suitability of biocrusts in the Lake Fryxell basin of Taylor Valley, Antarctica, which is the main site of the McMurdo Dry Valleys Long-Term Ecological Research Program. Soils suitable for the development of biocrusts are typically wetter, less alkaline, and less saline compared to unvegetated soils. Using random forest models, we show that gravimetric water content, electrical conductivity, and snow frequency are the top predictors of biocrust presence and biomass. Areas most suitable for the growth of dense biocrusts are soils associated with seasonal snow patches. Using geospatial data to extrapolate our habitat suitability model to the whole basin predicts that biocrusts are present in 2.7 × 105 m2 and contain 11-72 Mg of aboveground carbon, based on the 90% probability of occurrence. Our study illustrates the synergistic effect of combining field and remote sensing data for understanding the distribution and biomass of biocrusts, a foundational community in the carbon balance of this region. Extreme weather events and changing climate conditions in this region, especially those influencing snow accumulation and persistence, could have significant effects on the future distribution and abundance of biocrusts and therefore soil organic carbon storage in the McMurdo Dry Valleys.

Freeze-thaw characteristics and deformation monitoring of topsoil in highland pastoral areas under the influence of snow accumulation

Soil freeze–thaw processes and snowmelt infiltration have a significant impact on the hydrological cycle and ecosystem productivity in alpine pastoral areas. To investigate the driving mechanism of snow on soil freeze-thaw processes, a meteorological station was established in the southern part of Namatso Lake, Tibet to collect environmental data. The study included analyzing the relationship between soil temperature and humity, as well as assessing soil freeze-thaw characteristics and the evolution of soil frost heave over time. The freeze-thaw frequency was significantly 43% lower at the surface soil, and the freeze-thaw intensity decreased when the ground was snow-covered. During the initial freezing period, precipitation and soil humidity remain relatively low, and soil moisture decreases linearly with decreasing soil temperature, which occurs in the phenomenon of soil freeze-shrink. During the initial thawing period, snowmelt infiltration impacts soil humidity, and soil frost heave increases logarithmically, reaching a maximum of 5.2 mm, driven by freeze-thaw. Soil moisture decreases under topsoil evaporation in the later stage and again follows a linear trend with the soil temperature. This study not only reveals the correlation between soil temperature and humidity during different freeze-thaw periods but also enhances the understanding of soil deformation patterns under the influence of snow accumulation.

Research progress of multifunctional anti‐icing composites materials

AbstractThe accumulation of ice and snow will have a great impact on industry and people's lives. People usually adopted active deicing method for deicing in the past, which is characterized by high energy consumption, low efficiency, and high cost. Passive anti‐icing materials are the most widely used due to their low icing adhesion strength and energy saving, but passive anti‐icing materials tend to lose anti‐icing performance after mechanical damage or following exposure to harsh environments resembling low temperatures, high humidity. Recently, new anti‐icing materials have been rationally designed through the combination of passive and active anti‐icing, exhibiting high‐efficiency and energy‐saving anti‐icing properties. The design idea is that low surface energy polymers combine photothermal and electrothermal particles to achieve photothermal and electrothermal superhydrophobic anti‐icing. Anti‐icing materials are easily damaged after long‐term outdoor use, so the self‐healing capabilities of the materials are important. This review briefly introduces the ice formation mechanism and anti‐icing methods, focusing on the recent progress in the structural design of superhydrophobic, photothermal, electrothermal, and self‐healing anti‐icing materials. The design of the new generation of anti‐icing materials will be a combination of active and passive anti‐icing, which can achieve ideal all‐weather anti‐icing and deicing and repair the damage during long‐term use.

Understanding the hydrological performance of green and grey roofs during winter in cold climate regions

Green and grey roofs have emerged as promising and sustainable measures for effectively managing stormwater in urban catchments. However, there is a gap in the literature in understanding and modelling the hydrological performance of these roofs during winter and snow-covered periods in cold climate regions. The present study attempted to address this gap by validating the use of a snow module in simulating the dynamics of snow accumulation and melting of green and grey roofs. Then, the validated model was used to identify and separate the different events that occur in winter (melt only, rainfall only, rain-on-snow) to assess the hydrological performance of six different configurations of green and grey roofs in Trondheim, Norway. The snow module accurately simulated snow accumulation and melting of green and grey roofs. The results showed that rain-on-snow events in winter have longer duration compared to other events including rainfall events in summer. Consequently, rain-on-snow events yield a higher amount of inflow to the roofs compared to rainfall events in summer, despite summer events having higher intensities. The retention and detention performances of green and grey roofs were found to be lowest for rain-on-snow events compared to other types of events, but still yielding significantly lower peak runoffs when compared to standard black roofs. The decrease in retention and detention performances in winter were attributed to the long duration of events, accumulation effect of snow, freezing of roof surface layers, and reduction of evapotranspiration. The study highlights the importance of considering winter conditions in the design of green and grey roofs in cold climates to enhance stormwater management.

Economics of Snow Accumulation on Photovoltaic Modules

The growth in photovoltaic (PV) module installations over the past decade has prompted a critical need to examine the economic implications of snow accumulation on solar energy production. The aim of this study is to quantify the economic impact of snow accumulation on PV modules in different regions and environmental conditions and to identify effective mitigation strategies for enhancing power generation efficiency and reliability of PV systems. It was found that snow accumulation on PV modules can lead to annual losses of 1% to 12% depending on the environmental conditions and geographic location. A financial analysis related to maintenance costs associated with snow accumulation on PV modules is also presented. A two-fold methodology of quantitative data analysis and interviews conducted with PV system operators is used for this purpose. In addition, the extent of snow accumulation financial losses in the U.S. is categorized based on the snowfall amount and solar market segment, suggesting an annual loss of at least USD 313M in utility and residential solar sectors. Furthermore, various currently employed active and passive snow mitigation strategies are presented in detail, describing their shortcomings and advantages. Finally, prospects on the need for developing reliable and cost-effective snow mitigation strategies for solar panels are discussed, paving the path for future studies.

Effect of phase change materials on thermophysical properties of asphalt mixtures and snow melting performance

The increased Thermal Conductivity and Thermal Diffusion Coefficient of asphalt mixtures means that the material is able to conduct and disperse heat more efficiently. In winter, this property can be used to accelerate the melting process of snow and ice, reducing the risk of road sliding due to snow and ice build-up. In this study, the thermal conductivity, thermal diffusion coefficient, and specific heat capacity of phase change asphalt (PCA) mixtures were investigated using a transient flat-plate heat source method. Analysis was performed with a thermal constant analyzer, a custom-designed thermal conductivity experiment, and a differential scanning calorimeter. Additionally, a phase change asphalt mixture thermoregulation rutting plate was prepared to evaluate its thermoregulation and snow and ice resistance under actual winter and indoor conditions. Results indicated that the thermal conductivity and thermal diffusion coefficient of PCA mixtures were superior to those of conventional asphalt mixtures. Notably, the specific heat capacity of these mixtures increased significantly within the phase change temperature range. The integration of phase change materials enhanced heat transfer within the asphalt mixtures, allowing for a delayed cooling process when temperatures decreased, with the maximum cooling range observed to be 2.8°C. Snow melting tests confirmed the early snowfall efficacy of the phase change asphalt mixture rutting plate, effectively achieving minimal snow accumulation and demonstrating the capability of 'melting light snow.'

Distinct Central and Eastern Pacific El Niño Influence on Antarctic Surface Mass Balance

AbstractThe El Niño‐Southern Oscillation causes anomalous atmospheric circulation, temperature and precipitation across southern polar latitudes, but the influence of Central and Eastern Pacific El Niño events on Antarctic surface mass balance and snow accumulation has not yet been assessed. Here, we use reanalysis and reanalysis‐forced regional climate model output and find that Central Pacific El Niño results in significantly increased snow accumulation in the western Ross Sea sector and significantly decreased snow accumulation in the Amundsen Sea sector. Eastern Pacific El Niño is associated with similar but weaker patterns, with some regional exceptions. In some areas, like Dronning Maud Land, or the Wilkes Subglacial Basin, the effect of El Niño on snow accumulation changes from increased to reduced accumulation depending on the type of El Niño. Our results show that projecting El Niño types is important for constraining future changes in Antarctic surface mass balance.

An interpretable Bayesian deep learning-based approach for sustainable clean energy

Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.