Snow Mass Research Articles

Analysis of railway transport snow-blowing equipment operating modes by diesel generator operation parameters

Ensuring the transportation process in the Siberian region during the winter months involves protecting the railway infrastructure from external climatic factors, such as snow drifts and icing. To ensure timely clearing of tracks, a wide fleet of snow removal equipment on rails is used, the most numerous of which is the snow removal train type SM-2 and its modifications. The technological cycle of such trains can be reduced to three operations: clearing the track and loading snow mass, transportation to the unloading dead end, unloading and returning to the work site. Reducing both fuel and time resources in each technological link is an urgent task for railways. The purpose of the study is to evaluate the capabilities of monitoring the parameters of a diesel generator for more effective planning of operating modes of snow removal equipment. The current strength of three phases of a diesel generator was measured in various operating modes. The values of the current strength when starting electric drives of various train powers are estimated. The duration of operation of a diesel generator in nominal mode is compared with the operational need to set the operating mode in the technological cycle of clearing tracks from snow.

Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance

Abstract. The presence of a ski resort modifies the snow cover at the local scale, due to snow management practices on ski pistes, especially grooming and snowmaking. Snow management exerts 2-fold effects on the local hydrological cycle, through (i) abstraction and transfer of water used for snowmaking, and (ii) changes in water runoff due to added snow mass through snowmaking and/or delayed melting of the snowpack due to snow grooming. This induces a local pressure on water resources, which has seldom been addressed in scientific studies hitherto. Here we introduce a method to compute the hydrological effects of snow management on ski pistes and we apply and illustrate its results for the case study of the La Plagne ski resort in the Northern French Alps. The approach mainly relies on snow cover modelling using the Crocus snow cover driven by SAFRAN reanalysis and climate projections. Model results are evaluated against in-situ hydrological observations and show that the modelling approach, although very simplified for many hydrological processes, provides relevant information and insights in terms of the influence of snow-related processes on water resources. Our study shows a visible impact of grooming, virtually eliminating snowmelt in winter, thus delaying the onset of snowmelt. This results is a lower snowmelt flux during the wintertime, low flow period, on the order of −10 % to −20 %, compensated by higher amounts when snow melts. While about 10 % of the water used for snowmaking is estimated to be lost by evaporation through the ice formation process from the liquid water droplets, we find that, in the case studied, the annual scale alteration of water resources is limited and estimated to be on the order of 1 % to 2 %. This is due to the fact that the amount of water used for snowmaking on ski pistes represents a fraction of 10 % to 20 % of total annual precipitation, that ski pistes cover typically 10 % of the surface area of catchments within which ski resorts are located, and that snowmaking equipment covers, in the case of La Plagne, 40 % of the surface area of ski pistes. Therefore, in this case, snowmaking mainly leads to a moderate shift in snow cover formation and snowmelt processes and plays, for example, a smaller role than the influence of future climate change on mountain hydrology. This study provides an initial overview of the influence of grooming and snowmaking on river flows in a mountain catchment, which can inform future studies on water management and climate change adaptation in areas with ski tourism facilities. This study does not discuss long-term sustainability challenges of ski tourism and other aspects of the local environmental impacts (landscape, biodiversity) of snow management, such as the construction and use of mountain water reservoirs and other earthworks in ski resorts.

Changes in power generation performance of an undershot cross-flow-type hydraulic turbine in an irrigation channel due to snow masses passing through the rotor

This study is focused on the development of a microhydraulic turbine that can stably and efficiently generate electricity even in channel where snow masses frequently flow down. A hydraulic turbine of an undershot cross-flow type was installed in an irrigation channel, and the change in the turbine performance was measured when spherical snowballs were released one by one from the upstream. The observation of the snowballs passing through the turbine was also conducted. Consequently, the variations in the power generated by the rotor were classified into three modes based on the motion of the snowballs, and could be organized by the ratio of the snowballs' cross-sectional area to the product of the rotor width and blade interval. Furthermore, the emergence of the power output overshoot phenomenon, in which the power output temporarily increases compared to clear water when the rotor restarts after stopping, was identified, and the relationship between the amount of loss when the rotor stops and that of electric energy gained during the overshoot was clarified. Certain guidelines for the installation of the undershot cross-flow type in irrigation channels of snowy regions was successfully obtained.

Rainy Days in the Arctic

Abstract The Arctic is warming faster than anywhere on Earth, and with these warming temperatures, there is likely to be more precipitation falling as rain. This precipitation phase change will have profound impacts on the hydrologic cycle, energy balance, and snow and sea ice mass budgets. Here, we examine the number of rainfall days in the Arctic from three reanalyses, ERA-Interim, ERA5, and MERRA-2, over 1980–2016. We show that the number of rainfall days has increased over this period, predominantly in the autumn and in the North Atlantic and peripheral seas, and the length of the rain season has increased in all reanalyses. This is positively correlated to the number of days with above freezing air temperatures and a lengthening of the warm season. ERA-Interim produces significantly more rainfall days than other reanalyses and CloudSat observations, as well as significantly more rainfall when temperatures are below freezing. Investigation into the cloud microphysics schemes revealed that the scheme employed by ERA-Interim allowed for mixed-phase clouds to form rain at temperatures below freezing following a temperature-dependent phase partitioning function between 250 and 273 K. This simple diagnostic treatment erroneously overestimates rain at temperatures below 273 K and produces unrealistic rainfall compared to ERA5 and MERRA-2. This work highlights the importance of having accurate physics and improving microphysical schemes in models for simulating precipitation in the Arctic and the caution that is warranted for interpreting reanalysis trends.

PECULIARITIES OF WATER AND SEDIMENT RUN-OFF IN A RAVINE AT THE BEGINNING OF THE FLOOD

The formation of water and sediment run-off in a ravine in different natural zones with humid climate begins before the main flood event. The type of spring (advective or solar type) determines the volume of water and sediment run-off, which is formed depending on the volume of snow masses in the ravine and in its catchment area. The density parameters of the snow mass, formed depending on the alternation of thaws and snow compaction under the influence of wind, determine the nature of the water flow through the snow mass or under the snow. At the same time, with the advective type of spring, the water flow does not produce any erosive work. The solar type of spring corresponds to active erosion of the sides and top of the ravine with a slight impact on the thalweg. The purpose of this work is to show the role of snow masses in the catchment and in the ravine and their influence on the formation of water and sediment run-off in the pre-water period with different types of spring. The work was carried out on the basis of observations during field work in different regions of the country and showed that the flow of water in the ravine at the initial stage of high water depends on the conditions for the formation of snow masses in the ravine. It has been established that snow masses accumulated during winter have an ambiguous effect on the water flow in the pre-water area and largely depend on specific weather conditions and anthropogenic impact on the gully catchment.

Air pollution reductions caused by the COVID-19 lockdown open up a way to preserve the Himalayan glaciers

Abstract. The rapid melting of glaciers in the Hindu Kush Himalayas (HKH) during recent decades poses an alarming threat to water security for larger parts of Asia. If this melting persists, the entirety of the Himalayan glaciers are estimated to disappear by end of the 21st century. Here, we assess the influence of the spring 2020 COVID-19 lockdown on the HKH, demonstrating the potential benefits of a strict emission reduction roadmap. Chemistry–climate model simulations, supported by satellite and ground measurements, show that lower levels of gas and aerosol pollution during lockdown led to changes in meteorology and to a reduction in black carbon in snow (2 %–14 %) and thus a reduction in snowmelt (10 %–40 %). This caused increases in snow cover (6 %–12 %) and mass (2 %–20 %) and a decrease in runoff (5 %–55 %) over the HKH and Tibetan Plateau, ultimately leading to an enhanced snow-equivalent water (2 %–55 %). We emphasize the necessity for immediate anthropogenic pollution reductions to address the hydro-climatic threat to billions of people in southern Asia.

Ice bottom evolution derived from thermistor string-based ice mass balance buoy observations

ABSTRACT Digital information on sea ice extent, thickness, volume, and distribution is crucial for understanding Earth's climate system. The Snow and Ice Mass Balance Apparatus (SIMBA) is used to determine snow and ice temperatures in Arctic, Antarctic, ice-covered seas, and boreal lakes. Snow depth and ice thickness are derived from SIMBA temperature regimes (SIMBA_ET and SIMBA_HT). In warm conditions, SIMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile. SIMBA_HT provides a visible ice-bottom interface for manual quantification. We propose an unmanned approach, combining neural networks, wavelet analysis, and Kalman filtering (NWK), to mathematically establish NWK and retrieve ice bottoms from various SIMBA_HT datasets. In the Arctic, NWK-derived total thickness showed a bias range of −5.64 cm to 4.01 cm and a correlation coefficient of 95%−99%. For Baltic Sea ice, values ranged from 1.31 cm to 2.41 cm (88%−98% correlation), and for boreal lake ice, −0.7 cm to 2.6 cm (75%−83% correlation). During ice growth, thermal equilibrium, and melting, the bias varied from −3.93 cm to 2.37 cm, −1.92 cm to 0.04 cm, and −4.90 cm to 3.96 cm, with correlation coefficients of 76%−99%. These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.

Seasonal Forecasting of Precipitation, Temperature, and Snow Mass over the Western United States by Combining Ensemble Postprocessing with Empirical Ocean–Atmosphere Teleconnections

Abstract Accurate and reliable seasonal forecasts are important for water and energy supply management. Recognizing the important role of snow water equivalent (SWE) for water management, here we include the seasonal forecast of SWE in addition to precipitation (P) and 2-m temperature (T2m) over hydrologically defined regions of the western United States. A two-stage process is applied to seasonal predictions from two models (NCEP CFSv2 and ECMWF SEAS5) through 1) postprocessing to remove biases in the mean, variance, and ensemble spread and 2) further reducing the residual errors by linear regression using climate indices. The adjusted forecasts from the two models are combined to form a superensemble using weights based on their prior skill. The adjusted forecasts are consistently improved over raw model forecasts probabilistically for all variables and deterministically for SWE forecasts. Overall skill of the superensemble usually improves upon the skill of forecasts from individual models; however, the percentage of seasons and regions with increased skill was approximately the same as those with decreased skill relative to the top performing postprocessed individual model. Seasonal SWE has the highest prediction skill, followed by T2m, with P showing lower prediction skill. Persistence contributes strongly to the skill of SWE and moderately to the skill of T2m. Furthermore, a distinct seasonality in the skill is seen in SWE, with a higher skill from late spring through early summer. Significance Statement Here we test the postprocessing of seasonal forecasts from two state-of-the-art seasonal prediction models for traditionally forecasted elements of precipitation and temperature as well as snowpack, which is important for water management. A two-stage procedure is utilized, including ocean and atmospheric teleconnection indices that have been shown to impact seasonal weather across the western United States. First, we adjust model output based on the average error in historic runs and then relate the remaining error to these teleconnection indices. A final step combines each adjusted model based on its historic performance. Forecasts are shown to improve upon the original models when assessed probabilistically. The snowpack forecasts perform better than temperature and precipitation forecasts with the best performance from late winter through early summer. Persistence is found to contribute strongly to the skill of snowpack and moderately to the skill of temperature.

More Snow Accelerates Legacy Carbon Emissions From Arctic Permafrost

AbstractSnow is critically important to the energy budget, biogeochemistry, ecology, and people of the Arctic. While climate change continues to shorten the duration of the snow cover period, snow mass (the depth of the snow pack) has been increasing in many parts of the Arctic. Previous work has shown that deeper snow can rapidly thaw permafrost and expose the large amounts of ancient (legacy) organic matter contained within it to microbial decomposition. This process releases carbonaceous greenhouse gases but also nutrients, which promote plant growth and carbon sequestration. The net effect of increased snow depth on greenhouse gas emissions from Arctic ecosystems remains uncertain. Here we show that 25 years of snow addition turned tussock tundra, one of the most spatially extensive Arctic ecosystems, into a year‐round source of ancient carbon dioxide. More snow quadrupled the amount of organic matter available to microbial decomposition, much of it previously preserved in permafrost, due to deeper seasonal thaw, soil compaction and subsidence as well as the proliferation of deciduous shrubs that lead to 10% greater carbon uptake during the growing season. However, more snow also sustained warmer soil temperatures, causing greater carbon loss during winter (+200% from October to May) and year‐round. We find that increasing snow mass will accelerate the ongoing transformation of Arctic ecosystems and cause earlier‐than‐expected losses of climate‐warming legacy carbon from permafrost.

Atmospheric Circulation Anomalies and Key Physical Processes behind Two Categories of Anomalous Eurasian Spring Snowmelt

Abstract Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker. Significance Statement Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals.

Calorimetric determination of wet snow liquid water content: The effect of test conditions on the calorimeter constant and its impact on the measurement uncertainty

The liquid water content (lwc) of wet snow is a fundamental parameter in determining snow properties like its strength and adhesion force to surfaces. Among the different methods available for the measurement of the lwc, this paper focuses on melting calorimetry: known masses of hot water and snow are mixed into a thermally insulated container, and the lwc is obtained from the difference between the initial hot water temperature and the final mixing temperature; it is a fast and easy to implement method that requires little equipment, making it suitable for both laboratory and field measurements. Tests with “equivalent” wet snow samples whose liquid water content was known in advance were carried out to assess the method, showing that the liquid water content was overestimated by a variable amount that seemed to depend on the test parameters. To account for the heat capacity of the calorimeter, which was found not to be negligible, a constant in terms of equivalent hot water mass (E) was introduced. However, its value also depended on the test conditions. Hence, a correlation between E and the test parameters was found, and the measurements were repeated using a container of a different material, showing a similar behavior. Eventually, a discussion about the effects of choosing different hot water masses and hot water-to-snow mass ratios on measurement accuracy is provided.

Capabilities assessment for working bodies of rotary snowblowers through design and technological parameters study

Introduction. At the initial stages of designing snow blower feeders, it is necessary to have relatively simple ratios on the basis of which one can judge their efficiency and their potential. The presence of such indicators makes it possible to reasonably choose the design scheme, geometric and kinematic parameters of the working bodies of rotary snowplows. The solution of such problems, in turn, can significantly reduce the time for designing machines and their working bodies. Thus, the main goal of this study is to assess the possibility of substantiating the effectiveness of design solutions for snow blowers in the early stages of design. Methods and materials. To achieve this goal, such tasks as the substantiation of the indicators on the basis of which the effectiveness will be assessed, description of the mechanism for obtaining their theoretical values were solved. Results. As a result of the research, analytical expressions were obtained for the volumes of snow mass cut by horizontal and vertical cutters of the rotary snowplow feeder. The time of transporting the cut snow mass has been estimated. The degree of influence of a number of design and technological parameters on the size of the cut snow mass and the time of its transportation is shown. Discussion and conclusion. The equations for the lengths of the trajectories of motion of an arbitrary point of the cutter of the feeder of a rotary snowplow are obtained for its vertical and horizontal arrangement. The equations make it possible to determine the duration of the interaction between the transported snow mass and the working equipment from the moment of its capture to the moment of its unloading. As a result of the research, the range of values was determined, in which the length of the cutting path of the snow mass for a horizontal cutter is less than the length of the cutting path for a vertical cutter.

Performance evaluation of seasonal solar irradiation models—case study: Karapınar town, Turkey

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial Neural Networks (ANNs) and Bagged Tree (BT) methods for forecasting seasonal solar irradiance in Karapınar town Turkey. These methods, namely ANN, PSO and BT methods are widely used for solar irradiance estimation. The study utilizes input data from 2007 to 2020, including air temperature, precipitation, snow mass, air density, and cloud cover fraction, to estimate solar irradiance as the output value. The performance of these methods is analysed according to error indicators such as mean absolute error (MAE), root mean square error (RMSE) and R2. The results demonstrate that the BT method yields the lowest statistical error metrics. Additionally, the correlation matrix reveals a strong positive linear relationship (correlation coefficient of 0.9045) between air temperature and solar irradiance. The investigation of the impact of utilizing independent inputs on the forecast output has been conducted via the employment of multiple linear regression (MLR) subsets and diverse combinations.

Comparison of two 2-D numerical models for snow avalanche simulation

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. The results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.

Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia

Abstract. The acceleration of climate warming has led to a faster solid–liquid water cycle and a decrease in solid water storage in cold regions of the Earth. Although snowfall is the most critical input for the cryosphere, the phenology of snowfall, or potential snowfall phenology (PSP), has not been thoroughly studied, and there is a lack of indicators for PSP. For this reason, we have proposed three innovative indicators, namely, the start of potential snowfall season (SPSS), the end of potential snowfall season (EPSS), and the length of potential snowfall season (LPSS), to characterize the PSP. We then explored the spatial–temporal variation in all three PSP indicators in the past, present, and future across the Chinese Tianshan mountainous region (CTMR) based on the observed daily air temperature from 26 meteorological stations during 1961–2017/2020 combined with data from 14 models from CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) under four different scenarios (SSP126, SSP245, SSP370, and SSP585, where SSP represents Shared Socioeconomic Pathway) during 2021–2100. The study showed that the SPSS, EPSS, and LPSS indicators could accurately describe the PSP characteristics across the study area. In the past and present, the potential snowfall season started on 2 November, ended on 18 March, and lasted for about 4.5 months across the CTMR on average. During 1961–2017/2020, the rate of advancing the EPSS (−1.6 d per decade) was faster than that of postponing the SPSS (1.2 d per decade). It was also found that there was a significant delay in the starting time (2–13 d) and advancement in the ending time (1–13 d), respectively, resulting in a reduction of 3–26 d for the LPSS. The potential snowfall season started earlier, ended later, and lasted longer in the north and center compared with the south. Similarly, the SPSS, EPSS, and LPSS indicators are also expected to vary under the four emission scenarios during 2021–2100. Under the highest emission scenario, SSP585, the starting time is expected to be postponed by up to 41 d, while the ending time is expected to be advanced by up to 23 d across the study area. This change is expected to reduce the length of the potential snowfall season by up to 61 d (about 2 months), and the length of the potential snowfall season will only last 2.5 months in the 2100s under the SSP585 scenario. The length of the potential snowfall season in the west and southwest of the CTMR will be compressed by more days due to a more delayed starting time and an advanced ending time under all four scenarios. This suggests that, with constant snowfall intensity, annual total snowfall may decrease, including the amount and frequency, leading to a reduction in snow cover or mass, which will ultimately contribute to more rapid warming through the lower reflectivity to solar radiation. This research provides new insights into capturing the potential snowfall phenology in the alpine region and can be easily extended to other snow-dominated areas worldwide. It can also help inform snowfall monitoring and early warning for solid water resources.

The COVID-19 lockdown: a unique perspective into heterogeneous impacts of transboundary pollution on snow and ice darkening across the Himalayas.

The Tibetan Plateau holds the largest mass of snow and ice outside of the polar regions. The deposition of light-absorbing particles (LAPs) including mineral dust, black carbon and organic carbon and the resulting positive radiative forcing on snow (RFS) substantially contributes to glacier retreat. Yet how anthropogenic pollutant emissions affect Himalayan RFS through transboundary transport is currently not well known. The COVID-19 lockdown, resulting in a dramatic decline in human activities, offers a unique test to understand the transboundary mechanisms of RFS. This study employs multiple satellite data from the moderate resolution imaging spectroradiometer and ozone monitoring instrument, as well as a coupled atmosphere-chemistry-snow model, to reveal the high spatial heterogeneities in anthropogenic emissions-induced RFS across the Himalaya during the Indian lockdown in 2020. Our results show that the reduced anthropogenic pollutant emissions during the Indian lockdown were responsible for 71.6% of the reduction in RFS on the Himalaya in April 2020 compared to the same period in 2019. The contributions of the Indian lockdown-induced human emission reduction to the RFS decrease in the western, central, and eastern Himalayas were 46.8%, 81.1%, and 110.5%, respectively. The reduced RFS might have led to 27 Mt reduction in ice and snow melt over the Himalaya in April 2020. Our findings allude to the potential for mitigating rapid glacial threats by reducing anthropogenic pollutant emissions from economic activities.

The current state of anti-erosion pine plantations of the Dnieper Right Bank Forest Steppe

The current state of pine plantations created on the timber-beamed lands of the Right Bank Dnieper region is analyzed. It has been established that the most influential factors that significantly impair the condition of anti-erosion pine plantations are Heterobasidion annosum (Fr.) Bref. and grass fires. Based on the data of 26 test areas, which cover typical conditions of growth of anti-erosion plantations, indicators of sanitary condition were calculated. The analysis of the sanitary condition indicator showed that the plantations belong to the category of weakened. In separate test areas, where the damage of H. annosum is combined with the impact of fires, the plantations go from the category of weakened to the category of very weakened. Very weakened trees make up 21,3 %, their sanitary condition index ranges from 2,61 to 3,21. The mass of dry matter, windfall, and broken snow varies from 0,2 to 8,1 % of the total stock of plantations per 1 ha. No direct connection between the accelerated fall of Scots pine trees and the spread of forest pests was found. The best condition of anti-erosion plantings was observed in conditions of fresh sudibrow under mixing schemes 2rSz1–2rKlg. In fresh forests, the share of pine in the composition of plantations must be reduced in favor of deciduous species. In the process of formation of the species composition of the tree stand on eroded lands, an increase in the share of Scots pine by more than 6–7 units, and a corresponding decrease in the share of accompanying deciduous species leads to a significant deterioration in the sanitary condition of the plantation. In order to prevent the development of H. annosum, it is necessary to create mixed plantations of Scots pine with the introduction of deciduous species, which at the same time will act as a fire barrier. Considering the high level of fire danger and the wide distribution of H. annosum in pine plantations on the eroded lands of the Right Bank Dnieper region, it is necessary to apply a system of preventive measures aimed at preventing their negative impact. Key words: anti-erosion plantations, plowed-beam lands, sanitary state of plantations, Heterobasidion annosum, forest lowland fires, index of sanitary state.

Determining the cooling magnitude in the east of China during the Last Glacial Maximum using a degree-day model

There is a great controversy regarding the cooling magnitude and the existence of ancient glaciers during the Last Glacial Maximum (LGM) in the east of China. The cooling magnitude during the LGM can be seen as a basis for determining whether glaciers developed during the Quaternary. In this study, we chose 9 mountain areas where there are long-term meteorological records as the research sites. And there are different glacier-climate conditions at these 9 sites. The degree-day model was conducted to estimate the snow mass and the snowmelt at the altitude of the bottom of ancient cirques (snow line) based on the mean multiple-year-day temperature and mean multiple-year-day precipitation. The results showed that: (1) The altitude of the modern snow line of Tianshan Mountains was similar to the result estimated by the degree-day model, which indicated this model can be used to determine the altitude of ancient cirques, and it can also be used to calculate the cooling magnitude required to meet a condition for cirques to develop at a given altitude. (2) For the sites where ancient cirques existed in the monsoon zone, the cooling magnitude during the LGM was 6.4 °C, 6.4 °C, and 8.0 °C in Garze, Taibi Mountain, and Yu Mountain, respectively. For the site where ancient cirques existed in the non-monsoon zone, the cooling magnitude during the LGM was 3.3 °C in Tianshan Mountains. The mean cooling magnitude during the LGM was 5.9 °C in China. (3) A much greater cooling magnitude was required to develop cirques during the LGM than the average cooling magnitude of China in Huanggangliang, Wutai Mountain, Tai Mountain, Shenlongjia, and Lu Mountain. (4) The cooling magnitude was underestimated by the TraCE-21ka model compared with this study. The differences between the results from the TraCE-21ka model and this study were approximately 3.3 °C, 2.9 °C, and 5.7 °C for Garze, Taibai, and Yu Mountain, respectively, averaging 4.0 °C. (5) There was no sufficient evidence showing that the cooling magnitude during the LGM decreased with latitude.

Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)

Abstract. Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

Evaporation from snow surface: A multi-model evaluation with the FLUXNET2015 dataset

Evaporation from snow surfaces (Ess) is a critical component of snow mass balance, and its accurate estimation is essential for the water balance at high latitudes and altitudes. Selecting an appropriate model is important for achieving an accurate estimation of Ess. However, there have been few studies focused on the choice of Ess models. In this study, we evaluated six commonly used Ess models at twelve eddy covariance sites in the FLUXNET2015 dataset. The six models considered include an empirical method (EM), two bulk aerodynamic (BA) methods, two energy-based models (maximum entropy production (MEP) model and Priestley-Taylor (PT) model), and a combination model (Penman model). The results show that the performance of the six models differs significantly, and no single model consistently outperforms the others across all sites. Overall, the Penman method performs the best on both the hourly and daily scales with Kling-Gupta efficiency (KGE) coefficients of 0.75 and 0.65 on the two timescales, respectively. Therefore, we recommended the Penman model as the preferred model for Ess estimation. In the absence of net radiation data, we suggested using the EM model as it noticeably outperforms the other four models. The reason for the poor performance of the two BA models, with KGE values less than 0.4, may be attributed to their unreasonable parameterization of atmospheric stability. On the other hand, ignoring or simplifying the aerodynamic term is likely responsible for the poor performance of the MEP and PT models.