High Mountain Regions Research Articles

Humic fractions as support for the classification of high-mountain Organossolos in the southeast of Brazil

ABSTRACT Brazilian Soil Classification System (SiBCS) adopts a hierarchical approach to classify soils using specific diagnostic attributes. Organossolos (Histosols) class is differentiated according to its genesis, especially because the parent material is organic, thus requiring diagnostic attributes that describe the unique properties of soil organic matter (SOM). This study aimed to propose the use of labile organic carbon and the C and N contents of humic fractions and their ratios for the family and series levels of the Brazilian Soil Classification System for Organossolos in high mountainous regions. Quantitative chemical fractionation of SOM was performed to obtain the humic fractions and determine the labile oxidizable carbon in 16 Organossolos profiles from Itatiaia National Park, RJ. Carbon and nitrogen contents of the humic acid, fulvic acid, and humin fractions were obtained, as well as the percentages of these fractions in relation to the total carbon and nitrogen in the soil. Carbon and nitrogen ratios were calculated for each fraction. Results showed little variation in the levels of labile organic carbon between the profiles but a large variation in total carbon and nitrogen levels, especially in the Organossolo Fólico Hêmico lítico profile. The ratios between the carbon and nitrogen of humic acids and fulvic acids (means of HAC/FAC = 1.61 and AHN/FACN = 1.05), carbon and nitrogen of the alkaline extract and humin (means of AEC/HUMC = 0.71 and AEN/HUMN = 0.38), carbon and nitrogen of the alkaline extract, and total carbon and total nitrogen (means of AEC/TC = 0.28 and AEN/TN = 0.19) were effective in determining the humification level of the profiles. This study proposes that the attributes evaluated, especially the ratio between the carbon of the alkaline extract of the humic substances (carbon of the fulvic acid fraction + carbon of the humic acid fraction) and the total soil carbon, as well as the ratio between the C and N of the humin fraction, should be used to define lower categorical levels of Organossolos. This new approach could facilitate the classification of these soils and contribute to a better understanding of the composition of Organossolos in Brazil.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of −0.02 and 0.13 K yr−1, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr−1. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr−1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

Changes in water regime in the high-mountain region of the Terek River (North Caucasus) in connection with climate change and degradation of glaciation

In this study, we adapted the ECOMAG model of the runoff formation for analysis of the Terek River basin using comprehensive hydrometeorological information as well as data on soils, landscape, and glaciation. To take account of regional characteristics of the glaciation, the additional ice module was used with the model. This improvement has resulted in a satisfactory agreement between the modeled runoff hydrographs and the observed ones. In our simulations we used the updated glacier cover predictions from the- global glaciological model GloGEMflowdebris together with regional climate projections from the CORDEX experiment to determine possible future changes in the Terek River flow in the 21st century. The results show that the runoff will change between −2% and +5% according to the RCP2.6 scenario, and from −8% to +14% in the RCP8.5 scenario. The directedness of the runoff changes in particular subbasins of the River will essentially depend on the altitude position of the snow and glacier feeding zones, that is responsible for the intensity of their degradation. Thus, in the RCP8.5 scenario, the flow of the Chegem River will begin to decrease significantly in the second half of the 21st century. In contrast, the predicted increasing of the runoff in Malka and Baksan rivers, which are primarily fed by meltwater from glaciers and snow on Elbrus and other high-mountain zones, is expected to be continued until the end of the century. But this increase may be caused only by a growth of a part of the snowmelt feeding due to greater winter precipitation. The model estimates confirm the present-day observed trends within the intra-annual runoff distribution, demonstrating the earlier start of the spring flood, a decrease in summer runoff volumes and then its increase in the autumn months. The results of the research may be used for more efficient management of water resources in the North Caucasus in the future, including electricity generation and water supply.

Assessing the impacts of climate change on high mountain land-based livelihoods: An empirical investigation in District Nagar, Gilgit-Baltistan, Pakistan

Land-based livelihoods in Pakistan's high mountain regions are highly vulnerable to climate change due to the complex interactions between people and their natural environment. This study uses a mixed-method approach to explore how climate change affects land-based livelihoods in the high mountain Nagar District, Pakistan. Data were collected using a structured household survey of 430 randomly selected farmers, supplemented by focus group discussions and key informant interviews. The findings reveal that 87.7 % of farmers have observed negative impacts of climate change, such as increased crop diseases, reduced water for irrigation, and lower crop yields. Bivariate results indicate that factors related to farming practices, such as farming experience and cropping zones, significantly influence farmers' perceptions of impacts. The study emphasizes the urgent need for targeted government intervention and agricultural planning to boost the resilience of farmers in Nagar District. It calls for improved irrigation, crop disease management, and support tailored to high-mountain farming practices. The research highlights the importance of developing adaptation strategies to protect vulnerable farming communities from climate change impacts and supports the need for effective autonomous adaptation measures. This research contributes to a better understanding of climate change impacts on high-mountain agriculture and emphasizes the need to safeguard vulnerable farming communities.

A century of variability of heatwave-driven streamflow in melt-driven basins and implications under climate change

Abstract Meltwater runoff from snow and glaciers in high mountain regions supports the water needs of hundreds of millions of people, but extreme events such as heatwaves modify the timing and magnitude of water available for downstream communities and ecosystems. The streamflow response to heatwaves depends strongly on heatwave timing and temperature, and the amount of snow and glacier ice available to melt. However, as ongoing climate change continues to alter both seasonal melt patterns and the frequency and intensity of heatwaves, it is not well understood how such streamflow responses will evolve relative to the seasonal cycle of streamflow. We address this knowledge gap by using long-term meteorological and hydrological datasets to characterize spatial and temporal heterogeneity in the streamflow sensitivity to heatwaves at six basins with >80 years of observations in Western Canada. We use years with earlier freshets and less snowfall as proxies of anticipated climate change, and apply a metric to describe how the streamflow sensitivity to heatwaves varies across years with different hydro-climatological characteristics. We find that in future proxy years relative to baseline years, nival streamflow is more sensitive to early spring heatwaves but less sensitive to late spring and summer heatwaves. Relative to baseline years, late spring heatwaves in future proxy years generate a smaller fraction of peak streamflow that is diminished as the freshet progresses. Our findings imply that future heatwave-driven peak flows in spring may be lessened by the diminished streamflow sensitivity to heatwaves in late spring, but this may be partially offset by excess melt during future heatwaves that are longer and hotter.

A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks

Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<4 km2) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to the higher uncertainties associated with these data. However, the approach we propose remains potentially very helpful in addressing the problem of scarcity of ground observations of precipitation and snow in high-mountain regions.

Comparison of Three Remote Sensing Indices in Revealing the Vegetation Growth Dynamics in Nepal from 2000 to 2020

Remote sensing indices have been widely used to monitor the vegetation growth dynamics induced by climate change and human activities, and yet the consistency of the vegetation dynamics revealed by different remote sensing indices in mountains is unclear. Using Nepal as a case study, this study explored the spatial-termporal consistencies of the three widely-used remote sensing indices （i.e., normalized difference vegetation index （NDVI）, leaf area index （LAI）, and net primary production （NPP）） in quantifying the vegetation growth dynamics in mountainous regions. The results indicated that the spatial distributions of the multi-year mean estimates varied greatly by remote sensing index, especially in the low-altitude regions. The maximum NDVI, LAI, and NPP occurred in the low, medium, and high mountain regions, respectively. Although all three indices showed an overall increasing tendency from a long-term perspective, the area percentage of the lands with a significant trend was obviously larger in NDVI （82%） than that in NPP （58%） and LAI （56%）. In addition, the land area percentages with vegetation growth enhancement decreased gradually by the rise of altitude for both the NDVI and LAI indices but decreased after an increase for the NPP index. Only 9.6% of the lands showed consistent long-term trends （with the same change directions and significant levels） in the three indices on a per-pixel basis. Our findings highlight the large uncertainties of remote sensing indices in monitoring vegetation growth dynamics in mountainous areas, and the importance of developing reinforced remote sensing products in future efforts.

Populations of the Caucasus as an object for studying the process of adaptation to conditions of high-altitude hypoxia

The work examines the main mechanisms responsible for the process of acclimatization of the population of high mountain regions to the conditions of hypobaric hypoxia. The purpose of this review is to describe the pathways of genetic, epigenetic and physiological control in the adaptation of indigenous populations of highlands to reduced barometric pressure and oxygen tension in the environment. It has been shown that populations living in different high-mountain regions demonstrate different ways of adaptation in response to a decrease in the partial pressure of oxygen in the inspired air. The changes that occur in the body in response to stressful conditions are extremely diverse. These include changes in the respiratory, cardiovascular, hematological systems and cellular adaptation. In this review, we examine genomic variations leading to evolutionary adaptation to life at high altitudes, gene expression, pathophysiological and metabolic features, and long-term adaptation in various high-altitude populations. We also consider the peoples of the Caucasus as one of the most promising populations for further study of complex adaptation mechanisms.

Induction of ductile modes of ice fracture and drastic enhancement of its fracture energy by means of introduction of nanoscale additives

Ice brittleness and low strength limits its usage as a construction material in cold climate regions on Earth (Arctics, Antarctic, high mountain regions on other continents) as well as in construction of habitable colonies at Moon and Mars planned by several countries despite attractiveness of its other properties. The paper presents experimental study of enhancement of ice carrying capacity and fracture energy by introduction of SiO2 nanoparticles and polyvinyl alcohol into it. Concentration dependences of these properties enhancement are found. Quantitative characteristics of transition from brittle fracture mode in pure ice to ductile one in ice composite caused by growing content of additives are revealed. This transition results in 2–3 orders of magnitude increase in ice fracture energy.

Co-analysis of total suspended particles and discharge reveals the dynamic of mercury input into glacier meltwater runoff in the northern Tibetan Plateau

Climate warming has accelerated glacier melting, releasing legacy pollutants such as mercury (Hg) into aquatic ecosystems. While the relationship between Hg in glacier meltwater runoff, total suspended particles (TSP), and runoff discharges has been established, the underlying inter-relationships and governing factors remain poorly understood. To address this knowledge gap, we conducted a continuous fixed-point sampling at Laohugou No. 12 Glacier in the northern Tibetan Plateau from June to September 2019 spanning the entire glacier ablation season. Our study analyzed the variations of Hg partition in the meltwater runoff and conducted a comprehensive co-analysis of Hg with TSP and discharge to uncover the dominant factors of Hg input into meltwater runoff. The concentration of total Hg (THg) in the meltwater runoff ranged from 0.7 to 112.6 ng/L, with an average concentration of 26.6 ± 25.1 ng/L. Particulate Hg (PHg) was found to be the predominant partition, while dissolved Hg (DHg) exhibited a notable increase in June and September. THg concentration significantly correlated with TSP concentration (r = 0.94, P < 0.01), exceeding the correlation with discharge (r = 0.76, P < 0.01) during the entire ablation period. However, further examination during varying hydrological periods revealed differing associations among Hg speciation concentrations, TSP concentration, and discharge. During the rising limb of the hydrograph, THg (r = 0.86, P < 0.01) and PHg concentrations (r = 0.87, P < 0.01) exhibited a significant correlation with TSP concentration, primarily driven by TSP, implying that Hg availability determines the Hg input into meltwater runoff. Conversely, during the recession limb of the hydrograph, THg concentration was primarily influenced by discharge (r = 0.85, P < 0.01). PHg (r = 0.84, P < 0.01) and TSP (r = 0.97, P < 0.01) concentrations were strongly influenced by discharge, indicating that hydraulic action is the dominant factor affecting Hg input. Our study elucidated the impact of glacier hydrological processes on Hg transport, revealing the dominant factors of Hg input during different hydrological periods. This contributes to a deeper understanding of Hg input into meltwater runoff and improves predictions of Hg export through glacier melt in high mountain regions.

Integrating multisource data and machine learning for supraglacial lake detection: Implications for environmental management and sustainable development goals in high mountainous regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument (MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

Morphological and Molecular Characterization of Trichoderma Isolates from Vegetable Crop Rhizospheres in Nepal

Background Trichoderma spp. hold significant potential as biocontrol agents in agriculture due to their antagonistic properties against plant pathogens. The study aimed to characterize and identify Trichoderma isolates from rhizospheric regions of vegetable crops. Methods In this study, Trichoderma isolates were collected from rhizospheric soil samples of vegetable crops from different ecological zones and were selected for comprehensive morphological and molecular characterization. The isolates were visually assessed for colony color, growth pattern, aerial mycelium presence, phialide and conidial morphology, and chlamydospore presence. Molecular analysis was employed based on ITS and tef-1α sequences. Diversity indices were also computed for different ecological zones. Results The morphological characteristics and phylogenetic trees for both regions provided a clear species resolution, with four main clades: Harzianum, Viride, Brevicompactum and Longibrachiatum with 12 species T. harzinaum, T. afroharzianum, T. lentiforme, T. inhamatum, T. camerunense, T. azevedoi, T. atroviride, T. asperellum, T. asperelloides, T. koningii, T. longibrachiatum and T. brevicompactum and nine species as a new country record. Diversity indices indicated that high mountain regions displayed the highest species diversity and evenness (H = 1.724 [0.28], J = 0.84, D = 0.28), followed by hilly regions (H = 1.563 [0.28], J = 0.72, D = 0.28). Plains, on the other hand, exhibited lower species diversity (H = 1.515, J = 0.66, D = 0.33). The calculated species abundance values showed that plains (E = 2.11), mid-hills (E = 1.95), and high mountains (E = 1.99) each had their unique diversity profiles. Notably, T. afroharzianum and T. asperellum were predominant. Conclusions Overall, the study unveiled a rich diversity of Trichoderma species in different agricultural zones of Nepal. These findings shed light on the ecological distribution and diversity of Trichoderma spp., which could have significant implications for sustainable agriculture and biological control strategies.

Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI &gt; 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.

Cold mountain stream chironomids (Diptera) of the genus Diamesa indicate both historical and recent climate change.

Chironomids of the genus Diamesa (Meigen, 1835, Diptera: Chironomidae) inhabit cold, oxygen-rich running waters. We have investigated the presence of Diamesa and other freshwater macroinvertebrates at 22 stream sampling sites in 3 European high mountain regions (the Central Pyrenees, the Ötztal Alps, and the Tatra Mountains) to establish suitable temperature conditions for Diamesa dominance. It has been generally accepted that their high abundance was linked to the presence of glaciers; however, we have shown that in the Tatra Mountains, where there are no glaciers, the conditions for the dominance of Diamesa species are created due to permanent snowfields, the geographical orientation of the valley and shading by the surrounding high peaks. The historical connection of Diamesa to glaciers was investigated from the paleolimnological records of subfossil chironomid assemblages from the Bohemian Forest, where glaciers disappeared before or during the Late Glacial period. As expected, water temperature seems to be the main driver of Diamesa distribution, and we determined that the relative abundance of Diamesa species was significantly higher at the sites with a mean July water temperature below 6.5 °C. The Diamesa-dominated stream communities seems to be endangered due to ongoing climate warming and this assumption is supported by our paleolimnological results from the Bohemian Forest lakes, where Diamesa has disappeared due to warming of lake inflows at the beginning of the Holocene. These findings strengthen the former suggestions that some Diamesa species could be used as an indicator for tracking recent environmental changes in vulnerable ecosystems of cold mountain streams.

Characterization of production and commercialization systems of camedor palm (Chamaedorea elegans Mart.).

Objective: To describe the cultivation systems of C. elegans through the analysis of production and commercialization in three municipalities in the high mountain region of Veracruz, Mexico. Design/Methodology/Approach: The study was conducted in Tepatlaxco, Zongolica, and Omealca, Veracruz, Mexico. From January to July 2022, a survey was administered to 84 producers of Camedor palm using convenience sampling. In addition, interviews with key informants and participant observation were conducted. Results: Camedor palm production occurs in small production units ranging from 1 ± 0.05 to 7.0 hectares, primarily managed by male producers (93%) with an average age of 48 years and 6 years of schooling. Planting densities range from 35,000 to 100,000 plants per hectare, and leaf cutting is conducted on a quarterly basis, yielding between 1000 to 3700 rolls per hectare at an average price of $14.00 Mexican pesos per roll paid to the producer. Prior to cultivating Camedor palm, 56% of producers were growing coffee. The main driving factor for cultivation is the steady generation of income. Limitations/Implications: This is a specific case study; therefore, the results are limited to descriptive statements about the study area. Findings/Conclusions: Producers utilize non-timber forest products as alternatives to crises in other crops and investment constraints within production units. The production of C. elegans is accessible, requires low investment, and is compatible with family farming. However, yields in some cases are low, and the marketing network shows high intermediation.

Impact of the UNB topographical density model on precise geoid determination in the high mountainous region

A precise gravimetric geoid model is computed utilising Stokes’s formula, supposing an absence of topography above the geoid. Subsequently, the geoid model undergoes a simple correction for topographic masses, the constant density is taken as 2670 kg/m3. Notably, the true density of topographical mass deviates by approximately ±20% from this constant value. Recently, the University of New Brunswick in Canada released a global topographical density model at a 30 arc-second resolution. This paper investigates the impact of incorporating this model on the precision of the gravimetric geoid within a mountainous region in the Colorado test region. Numerical findings reveal that variations in geoid undulation attributable to this model can extend to a few decimetres, a discrepancy that cannot be neglected in geoid modelling with one-centimetre precision. It is therefore recommended that the considerable impact of topographic density fluctuations on geoid determination be taken into account, particularly in mountainous regions.

Cryosphere degradation in a changing climate

AbstractOver the last few decades, the instrumental climate record shows a progressive global warming. As one of the most sensitive elements of the Earth's climate system, the cryosphere is significantly affected by this trend. As a result, its various components are readjusting in a situation of disequilibrium with the climate through a series of dynamics. These include the thinning and retreat of glaciers that may lead to the formation of new lakes; the thawing of ground ice, leading to the deformation of terrain; the reduction of snow cover; and the occurrence of mass movements that threaten populations and infrastructures. This Special Issue contains 23 scientific papers with case studies that explore the above issues in the Arctic, Antarctic and high mountain regions.

Multitemporal monitoring of paramos as critical water sources in Central Colombia

Paramos, unique and biodiverse ecosystems found solely in the high mountain regions of the tropics, are under threat. Despite their crucial role as primary water sources and significant carbon repositories in Colombia, they are deteriorating rapidly and garner less attention than other vulnerable ecosystems like the Amazon rainforest. Their fertile soil and unique climate make them prime locations for agriculture and cattle grazing, often coinciding with economically critical deposits such as coal which has led to a steady decline in paramo area. Anthropic impact was evaluated using multispectral images from Landsat and Sentinel over 37 years, on the Guerrero and Rabanal paramos in central Colombia which have experienced rapid expansion of mining and agriculture. Our analysis revealed that since 1984, the Rabanal and Guerrero paramos have lost 47.96% and 59.96% of their native vegetation respectively, replaced primarily by crops, pastures, and planted forests. We detected alterations in the spectral signatures of native vegetation near coal coking ovens, indicating a deterioration of paramo health and potential impact on ecosystem services. Consequently, human activity is reducing the extent of paramos and their efficiency as water sources and carbon sinks, potentially leading to severe regional and even global consequences.

Meteorological regime of the Elbrus high-mountain zone during the accumulation period

Unique automated meteorological observations were carried out on the southern slope of Elbrus, near Pastukhov Rocks, at 4700 m a.s.l., during the 2021–2022 accumulation season. Data were obtained on air temperature, humidity, wind speed and direction, snowdrift and radiation fluxes with a temporal resolution of 1 minute or less. Analysis of the data series showed that the representative winter air temperature at this altitude on the southern slope of Elbrus is –10 °С, and the minimum is –36.4 °С; the partial pressure of water vapor does not exceed 3.5 hPa. At the same time, the average daily maximum of wind speed amounted 13.1 m s–1 with the absolute maximum of 54.1 m/s. Snowstorms with a snow transport intensity of more 0.1 kg/m2s–1 are quite common phenomenon in winter, while the maximum average value of the transport reaches 0.87 kg/m2s–1. An empirical relationship was established between the average hourly wind speed and the maximum gust speed for the same period, and it was shown that for these conditions the wind gust exceeds the average hourly wind speed by 1.8 times, while the representative value of the standard deviation of wind speed is 5.8 m s–1. This information may be useful not only for the glaciologic problems and modeling, but also for construction and engineering surveys, which are relevant in view of the present-day active development of the mountain ski infrastructure on the southern macro-slope of the Elbrus. In addition, the obtained series of instrumental observations were used to assess the quality of reanalysis data for high mountain regions taking as an example the ERA5. The ERA5 reanalysis was demonstrated to reproduce rather successfully the air temperature, wind speed and humidity in high mountain conditions, but extreme values for all these parameters are underestimated. Thus, the minimum temperature in winter turned out to be overestimated by 2 °C, and the maximum was underestimated by 4 °C, while the wind speed, according to the ERA5 reanalysis, never exceeded 40 m/s during the above observation period. It is also shown that the FlowCapt4 acoustic blizzard gauge (driftometer) can be used to estimate average wind speeds since it is less sensitive to severe high-altitude conditions compared to acoustic and cup anemometers.

Clonal propagated 'Ek Pothi Lehsun' as a potential antifungal agent against Candida sp.

'Ek Pothi Lehsun', also known as snow mountain garlic, is a type of garlic grown in the high mountainous region of Jammu and Kashmir state of India. The present study aimed to develop a protocol for propagating snow mountain garlic in-vitro using corm seed as an explant. The study also assessed the antifungal potential of in vitro-grown bulbils against different Candida species. Four different concentrations of NAA and 2,4-D were tested for their effectiveness in promoting root formation, and eighteen different combinations of BAP (µM), KN (µM) and TDZ (µM) were investigated for effective proliferation of shoots with varied lengths. Shoot with maximum length (5.03±1.40) was obtained in MS medium containing 1.0 µM TDZ after 24 days of inoculation, whereas MS basal media was found effective for rooting plantlets. Rooted micro shoots were acclimatized successfully in hardening treys with a percent survival of nearly 80%. Seven different concentrations of Sucrose, i.e. 5%, 7%, 10%, 15%, 17%, 20%, and 25% were investigated for effective bulbil formation. Bulbil with a maximum diameter of 0.86 cm was obtained in 20% sucrose-containing MS media in 5 days. Further, the antifungal potential of aqueous extract (TC-SMG) of in vitro grown bulbils was investigated against three Candida sp. A zone of inhibition of 22.30±0.33 mm, 17.3±0.33 mm and 19.3±0.33 mm was observed against C. albicans, C. tropicalis and C. glabrata respectively, by using 200 mg/mL extract after 24 hrs depicting the remarkable potential of TC-SMG as an antifungal agent. In vitro culture of snow mountain garlic has demonstrated promising antifungal properties against Candida species.