Potential Incoming Solar Radiation Research Articles

Rock glacier inventory and predictive modeling in the Mackenzie Mountains: predicting rock glacier likelihood with a generalized additive model

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal

Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.

The influence of spatial variability of solar radiation on the mass balance of glaciers in the Grønfjorden Bay area (the Svalbard archipelago)

In this article, we investigate how the irregular insolation of two low-elevated Svalbard glaciers exerts effect on rates of their surface melting. We compare the spatial distribution of rates of the surface lowering of glaciers Vøringbreen (0.76 km2) and Aldegondabreen (5.5 km2), both are located near Barentsburg settlement in the western part of Nordenskiöld Land (the Spitsbergen Island). As an approximation of the solar radiation flux, we used the potential incoming solar radiation calculated by the ArcticDEM digital elevation model for the period July 15–September 15, which is a typical time of ice ablation in the region under consideration. Motions of both glaciers are extremely slow, which allows assuming that lowering of their surfaces are identical to the rates of surface melting. We have found that both glaciers are distinctly divided into two parts, more and less sunlit. The spatial pattern of insolation of the Vøringbreen glacier is controlled by the shading of the walls surrounding the cirque, while the Aldegondabreen one due to its concave shape has two different areas with a more southern and more northern exposure. The lowering of the surface shows that the more and less illuminated parts differ significantly in ice ablation. The maximum differences in melting caused by the irregular insolation are 2.1 m of ice depth over five years for the Aldegondabreen Glacier (2008–2013 and 2013–2018) and 2.2 m over six years for the Vøringbreen Glacier (2013–2019), that is 40, 30 and 25% of the total values of the surface depression for the corresponding periods. Within every 50-meter altitude interval, correlation coefficients between surface ablation and insolation vary from –0.33 to –0.62 for the Aldegondabreen and from –0.50 to –0.92 for the Vøringbreen glacier. When compared with the vertical gradient of the ice melting, the variability of ablation caused by the irregular insolation correspond to a difference in altitudes of 45–50 m in vertical for the Aldegondabreen and 60 m for Vøringbreen. These values are significant taking into account the small altitudinal range of the glaciers in that part of Spitsbergen.

10Be exposure ages and paleoenvironmental significance of rock glaciers in the Western Tatra Mts., Western Carpathians

Relict rock glaciers are well-preserved features of high-elevated valleys in the Western Tatra Mts., Western Carpathians, but their chronology remained poorly constrained with numerical dating methods. We present the first robust set of 10Be exposure ages for eight rock glaciers with front elevation of 1376–1819 m asl. The results suggest that the rock glaciers stabilized throughout the Weichselian Lateglacial from ∼16.5 ka to 11 ka. This timing is consistent with the period of rock glacier stabilization in European mountain regions, but it extends the age span previously determined for rock glaciers in the Tatra Mts. There are differences between north- and south-facing valleys in the elevation and time of the stabilization of the rock glaciers, which were probably caused by contrasts in potential incoming solar radiation that affected the retreat of former glaciers and the subsequent formation of the rock glaciers. The initiation altitude of ∼1610 and ∼1830 m asl determined for the oldest and youngest dated rock glaciers is generally consistent with previous estimates of the glacier equilibrium line altitude during the Greenland Stadials 2.1 and 1, respectively. However, the lower limit of the rock glaciers is well above regional paleopermafrost features from the same periods, which suggests that rock glaciers may underestimate past permafrost extents and temperature declines disputing their regional validity for paleoenvironmental reconstructions.

Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy

Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries–Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective.

Mapping areas prone to piping using random forest with key explanatory variables

In previous studies, areas prone to soil piping were mapped on the catchment scale by integrating data-mining algorithms and geographical databases. However, this left the question of which input data layers are most influential on soil pipe distribution in semiarid regions, and how they can be exploited for accurate predictions of areas prone to piping. Here, a random forest (RF) procedure was applied to classify areas prone to piping using input data layers selected based on knowledge gained through previous theoretical modeling and empirical measurements. The input data layers were: potential incoming solar radiation; distance from the closest streambank; streambank slope gradient; topographic wetness index; flow accumulation; and vegetation cover as a proxy for the shading effect. An extensive dataset of field measurements (N = 774) was used to train and validate the RF classification procedure. Our results indicated that: (i) RF—based on carefully selected influential input data layers—can be used to map areas prone to soil piping at the catchment scale with high accuracy; (ii) the effect of potential solar radiation and—to a lesser extent—tree shading is noteworthy for soil cracking and subsequent piping development; and (iii) soil pipes were found at short distances (<30 m) from steep bank slopes due to the effect of tension cracks in the streambanks. These observations, over a wide region, further establish the effect of soil cracking, and drying and wetting processes, on soil pipe development in semiarid regions. Future studies applying piping susceptibility mapping at the catchment scale may benefit from using these input layers.

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

Modelling Permafrost Distribution in Western Himalaya Using Remote Sensing and Field Observations

The presence and extent of permafrost in the Himalaya, which is a vital component of the cryosphere, remains severely under-researched with its future climatic-driven trajectory only partly understood and the future consequences on high-altitude ecosystem tentatively sketched out. Previous studies and available permafrost maps for the Himalaya relied primarily upon the modelled meteorological inputs to further model the likelihood of permafrost. Here, as a maiden attempt, we have quantified the distribution of permafrost at 30 m grid-resolution in the Western Himalaya using observations from multisource satellite datasets for estimating input parameters, namely temperature, potential incoming solar radiation (PISR), slope, aspect and land use, and cover. The results have been compared to previous studies and have been validated through field investigations and geomorphological proxies associated with permafrost presence. A large part of the study area is barren land (~69%) due to its extremely resistive climate condition with ~62% of the total area having a mean annual air temperature of (MAAT) &lt;1 °C. There is a high inter-annual variability indicated by varying standard deviation (1–3 °C) associated with MAAT with low standard deviation in southern part of the study area indicating low variations in areas with high temperatures and vice-versa. The majority of the study area is northerly (~36%) and southerly (~38%) oriented, receiving PISR between 1 and 2.5 MW/m2. The analysis of permafrost distribution using biennial mean air temperature (BMAT) for 2002-04 to 2018-20 suggests that the ~25% of the total study area has continuous permafrost, ~35% has discontinuous permafrost, ~1.5% has sporadic permafrost, and ~39% has no permafrost presence. The temporal analysis of permafrost distribution indicates a significant decrease in the permafrost cover in general and discontinuous permafrost in particular, from 2002-04 to 2018-20, with a loss of around 3% for the total area (~8340.48 km2). The present study will serve as an analogue for future permafrost studies to help understand the permafrost dynamics associated with the effects of the recent abrupt rise in temperature and change in precipitation pattern in the region.

Rock glacier and protalus rampart inventory in Las Salinas river basin, Central Andes of Argentina

This paper presents a detailed rock glacier and protalus rampart inventory from Las Salinas river basin, an arid subtropical mountain hydrological system (between 31°02’ and 31°22’ S latitude) located in the northern sector of the Central Andes of Argentina, where permafrost and cryogenic processes prevail. The inventory is based on a geomorphological characterization by means of optical remote sensing and field description data. The study region covers 630 km2, with 3.25% of the area containing 405±8.2 rock glaciers and protalus ramparts in total, of which 231±2.5 are considered protalus rampart and 49±2 are considered active, 61±1 inactive and 64±3 fossil rock glaciers. Frequency ratio and logistic regression were used as statistical methods to determine the relationship between the distribution of these periglacial landforms and different geological, morphometric and climatic variables as elevation, potential incoming solar radiation, slope, aspect and lithology. Results show that elevation, lithology, and aspect are the most influencing factors for the occurrence of active rock glaciers. According to rock glaciers and protalus ramparts distribution, the permafrost occurrence probability is above 3690 m a.s.l. (current and high periglacial environment). However, some inactive rock glaciers and protalus rampart were found below this elevation, thus between 3300 and 3690 m a.s.l. the landscapes are dominated by an unstable periglacial environment.

Ice-covered ponds in the Untersee Oasis (East Antarctica): Distribution, chemical composition, and trajectory under a warming climate

ABSTRACT Climate change is impacting lakes and ponds in Antarctica. To that end, we investigated the distribution and chemical composition of ice-covered ponds in Untersee Oasis, East Antarctica. Thirty-nine ponds were inventoried, and the thirteen sampled ponds have low total dissolved solutes (TDS) with a Ca(Na)-SO4 or Na-Cl geochemical facies. Tritium and radiocarbon measurements of the total inorganic carbon (TIC) suggest that the ponds are recharged by modern snowmelt. Hierarchical cluster analysis grouped the ponds based on the amount of potential incoming solar radiation reaching their surface. Ponds receiving higher insolation develop moats or completely lose their ice cover during summer, have lower pH, and higher TDS, δ18O, total inorganic carbon, and δ13CTIC,which suggest CO2 availability is not limiting benthic photosynthetic activity. The ponds that receive lower insolation retain their full ice cover and remain well-sealed to direct exchanges with the atmosphere, have high pH, and lower TDS and TIC, and likely host a CO2-starved benthic microbial ecosystem. Therefore, the state of the ponds’ ice cover evolved their hydrochemistry along different trajectories. The findings can be used to predict the trajectory of the chemical composition of the ponds and effect on biological productivity as the phenology of their ice cover shifts under a warming climate.

Geoinformation analysis of the period of a stable snow cover in the Baikal region

From 2000 to 2010, we carried out mapping and spatiotemporal analysis of the period of the stable snow cover (SSC) in the Baikal region and assessed the influence of various environmental factors on it. The dates of the onset and destruction of SSC were determined using the eight-day MODIS “snow cover” composites with a spatial resolution of 500m (MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 6), and the duration was calculated as the difference of dates. We also carried out a multiple regression analysis of the relationship of the SSC period and environmental factors: the altitude, slope, potential total incoming solar radiation (kWh/m2 per day) (according to digital elevation model from the WorldClim dataset) as well as land surface temperature (according to MODIS data). The results revealed a high positive linear relationship between the SSC period and the altitude of the entire area. In general, in the entire area, the calculated increment in the SSC period with the altitude is approximately 12 days every 100 m. There is no linear relationship between the SSC duration and the incoming solar radiation that is determined by the steepness and orientation of slopes. The SSC duration and temperature show the average negative linear relationship.

Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks

We have generated permafrost probability distribution maps (10 m resolution) for the north-eastern Himalayan region in Sikkim using remote sensing measurements and machine learning algorithms. Four machine learning algorithms, logistic regression, random forests, support vector machines and neural networks, and two different sets of input data set, were used to generate a total of 8 machine learning models and hence 8 permafrost probability distribution maps. The first set of input data set included surface reflectance from atmospherically corrected Sentinel-2A spectral bands, elevation and slope parameters while the second set of input data set included mean annul air temperature (MAAT) and potential incoming solar radiation (PISR). Permafrost probability distribution maps obtained from the 8 models show reasonable agreement in the total spatial extent of permafrost occurrence but dissimilarities in the pattern of probability distribution. Accuracy assessment results are more optimistic towards models developed using spectral reflectance, elevation and slope parameters as input data set. Nevertheless, 5 out of 8 models agree that around 60% of total area under observation is highly likely to contain permafrost. This congruence in outputs, despite the use of different machine learning algorithms and separate sets of input data set, establishes reliability in the application of machine learning models for the preliminary estimation of permafrost distribution for remote and data-scarce Himalayan region.

Snow as a parameter of bryophyte niche partitioning in snow-beds of the Tatra Mountains (Western Carpathians)

Abstract Understanding relationships between microclimate and bryophyte species diversity in alpine conditions creates a background for investigation into the response of vegetation to potential climate changes and anthropogenic pressure. This study presents the reaction of spore-bearing plants (liverworts and mosses) to the main thermal gradients in the snow-beds of the Tatra Mountains (Western Carpathians) and the mechanisms behind these response patterns. The results were based on direct, year-long temperature measurements in 47 permanent plots located at sites where the plant cover is formed largely by bryophytes. Data were gathered in 2010–2013 using Onset Hobo Pro v.2 temperature data loggers. Habitat variables i.e. seasonal snow cover duration (SCD), zero curtain period (ZC) and July mean ground surface temperature (MGST_VII) were determined using the ground surface temperature (GST) method. Additionally, the sum of potential incoming solar radiation in July (PISR_VII) was analysed on the basis of the localisation of permanent plots. Using detrended correspondence analysis (DCA) and redundancy analysis (RDA), we analysed the relationships between the species composition and the microclimatic variables. The generalised additive model (GAM) was employed to investigate the response curves of bryophyte species to snow parameters (SCD and ZC). Humidity (expressed as ZC temperature parameter) proved to have the highest impact on the distribution of bryophytes in snow-beds. Moreover, ground temperature in the vegetative season (in July, MGST_VII) and altitude had statistically significant effects. The responses of liverworts to microclimate gradients were more varied in comparison with those of the mosses. Humidity significantly affects the occurrence of the species of both groups in snow-beds. However, mosses respond to ground temperature (MGST_VII), whereas liverworts respond to SCD, and this distinguishes the niches of these groups. The ZC is a significant parameter affecting plant functional group composition, demonstrating a high relationship between cryptogamic plants and alpine climate. Pohlia ludwigii, Nardia breidleri and Moerckia blyttii prefer the high humidity of the snow-beds, while Marsupella condensata, M. apiculata and Pohlia drummondii are associated with dry habitats.

Studying the growth characteristics of urban trees using an example from Szeged, Hungary

The spatial expansion of urbanised areas and the steady increase in the urban population, as well as climate change trends, are increasing the need for the development of adequate urban green infrastructure. The social demand for combating climate change is accompanied by the revaluation of green spaces, and in this context woody vegetation plays a key role. In a changing climatic context and under intense anthropogenic stress, the challenge of developing a tree population that is climate-friendly and resistant to disturbance is a major one. In our research, we investigate all growth parameters of the newly planted trees from the start of a street reconstruction involving a complete tree replacement (Gutenberg Street, Szeged). The structural analysis of the revealed not only the growth rate over the 8 years since planting, but also the significant differences between the two sides of the street. In order to find a possible reason for this, we examined (using SAGA GIS software) potential incoming solar radiation of the street, which could explain the significant difference in growth rate. The data collected also provided an opportunity to analyse the allometric relationships. This will partly allow the prediction of the growth rate and can provide baseline data for planning and decision-making processes in the dilemma of whether to retain older trees or plant new stocks.

Studying the growth characteristics of urban trees using an example from Szeged, Hungary

The spatial expansion of urbanised areas and the steady increase in the urban population, as well as climate change trends, are increasing the need for the development of adequate urban green infrastructure. The social demand for combating climate change is accompanied by the revaluation of green spaces, and in this context woody vegetation plays a key role. In a changing climatic context and under intense anthropogenic stress, the challenge of developing a tree population that is climate-friendly and resistant to disturbance is a major one. In our research, we investigate all growth parameters of the newly planted trees from the start of a street reconstruction involving a complete tree replacement (Gutenberg Street, Szeged). The structural analysis of the revealed not only the growth rate over the 8 years since planting, but also the significant differences between the two sides of the street. In order to find a possible reason for this, we examined (using SAGA GIS software) potential incoming solar radiation of the street, which could explain the significant difference in growth rate. The data collected also provided an opportunity to analyse the allometric relationships. This will partly allow the prediction of the growth rate and can provide baseline data for planning and decision-making processes in the dilemma of whether to retain older trees or plant new stocks.

Classification of the Tatra Mountain lakes in terms of the duration of their ice cover (Poland and Slovakia)

This paper presents the results of a classification of the Tatra lakes based on the duration of their ice cover, altitude, volume, and potential incoming solar radiation (PISR). It is embedded in the context of the impact of current climate change on the mountain environment. A digital elevation model, morphometric data, satellite imagery from the winter seasons of 2015-2017 and the Wrocław taxonomy method were used in the study. It was found that the order of freezing and thawing of the lakes investigated may change from year to year. The relationship between ice cover duration and altitude is clearly weakened by variations in lake volumes, with insolation having a noticeably lesser effect. Determining the duration of ice cover of the lakes over several seasons facilitates identifying the similarities and dissimilarities between them. Five groups of lakes displaying similar characteristics were identified as well as 2 groups of lakes with highly individual characteristics. Based on the data obtained, it can be concluded that the duration of ice cover on the Tatra lakes has been shortening noticeably over the last 100 years. Small high-altitude lakes seem to be most vulnerable to climate change.

TERRAIN MORPHOLOGY AS FACTOR OF LOCAL TEMPERATURES DISTRIBUTION IN UKRAINIAN CARPATHIANS

Ukrainian Carpathians like other mountainous areas are susceptible to the modern climate changes manifested mainly in accelerated warming. There are several main mechanisms of the orographic effects on temperature distribution, operating on different scales. The well-known and universally observed cooling with elevation is still often used as an over-simplified model of terrain relation to temperature regime, yet there are other significant orographic effects connected with the position of terrain features respective to incoming solar radiation angle and the large-scale movements of air masses. In Ukrainian Carpathians these effects are manifested mainly in more mild and less continental conditions on south-western macroslope that is also warmer due to larger solar radiation intake. Another important factor of temperature distribution is the formation of near-ground temperature inversions that often emerge in narrow valleys and inter-mountain troughs. The mapping of land surface temperatures can be effectively achieved by applying spatial imagery with thermal infrared bands; in our case, Landsat 8 images allowed to obtain detailed maps of temperature distribution in Bystrytsia river basin for three different seasons of the year. An analysis has been performed of relationships between temperatures and a set of morphometric parameters, by means of raster package and appropriate functions of R software environment. Rather strong negative relationship between elevation and temperature has been revealed in all the three cases. Direct relationship between slope and temperature was not confirmed, while potential incoming solar radiation values for the date and time of the imagery appeared to have a moderate effect on temperature, its effect varying from season to season. Key words: mountain climate; Carpathians; terrain morphology; temperature gradient; spatial analysis.

Biocrust landscape‐scale spatial distribution is strongly controlled by terrain attributes: Topographic thresholds for colonization in a semiarid badland system

AbstractBiological soil crust, or biocrust communities, are the dominating life form in many extreme habitats, such as arid and semiarid badlands, where water scarcity and highly erodible substrates limit vegetation cover. While climate, soil and biotic factors have been described as environmental filters influencing biocrust distribution in such biomes, little is known about the effect of terrain attributes on creating specific microhabitats that promote or restrict biocrust colonization. This study aimed to identify the main terrain attributes controlling biocrust distribution in the driest badland system in Europe, the Tabernas Badlands (SE Spain). To do this, we analysed the influence of different terrain attributes related to landscape stability and microclimate formation on the spatial distribution of lichen and cyanobacteria, using field measurements and topographical information from a LiDAR survey. Our results showed that the spatial distribution of cyanobacteria‐dominated biocrusts, which are physiologically and morphologically adapted to extreme drought and high UVA radiation, was mostly associated with areas of high potential incoming solar radiation. The exception was bare south‐aspect hillslopes with very high sediment transport potential, where bare physically crusted soils were the dominant ground cover. Lichen‐dominated biocrusts, in contrast, colonized near the top of north‐aspect hillslopes, characterized by low potential incoming solar radiation and potential evapotranspiration, and their cover decreased downstream, as conditions became good enough for vascular plants. © 2019 John Wiley &amp; Sons, Ltd. © 2019 John Wiley &amp; Sons, Ltd.

Distinguishing ice-rich and ice-poor permafrost to map ground temperatures and ground ice occurrence in the Swiss Alps

Abstract. Mountain permafrost is invisible, and mapping it is still a challenge. Available permafrost distribution maps often overestimate the permafrost extent and include large permafrost-free areas in their permafrost zonation. In addition, the representation of the lower belt of permafrost consisting of ice-rich features such as rock glaciers or ice-rich talus slopes can be challenging. These problems are caused by considerable differences in genesis and thermal characteristics between ice-poor permafrost, occurring for example in rock walls, and ice-rich permafrost. While ice-poor permafrost shows a strong correlation of ground temperature with elevation and potential incoming solar radiation, ice-rich ground does not show such a correlation. Instead, the distribution of ice-rich ground is controlled by gravitational processes such as the relocation of ground ice by permafrost creep or by ground ice genesis from avalanche deposits or glacierets covered with talus. We therefore developed a mapping method which distinguishes between ice-poor and ice-rich permafrost and tested it for the entire Swiss Alps. For ice-poor ground we found a linear regression formula based on elevation and potential incoming solar radiation which predicts borehole ground temperatures at multiple depths with an accuracy higher than 0.6 ∘C. The zone of ice-rich permafrost was defined by modelling the deposition zones of alpine mass wasting processes. This dual approach allows the cartographic representation of permafrost-free belts, which are bounded above and below by permafrost. This enables a high quality of permafrost modelling, as is shown by the validation of our map. The dominating influence of the two rather simple connected factors, elevation (as a proxy for mean annual air temperature) and solar radiation, on the distribution of ice-poor permafrost is significant for permafrost modelling in different climate conditions and regions. Indicating temperatures of ice-poor permafrost and distinguishing between ice-poor and ice-rich permafrost on a national permafrost map provides new information for users.

Inventory of rock glaciers in Himachal Himalaya, India using high-resolution Google Earth imagery

Little information about the number, spatial distribution, and characteristics of rock glaciers in Himachal Himalaya is available. This information is crucial to assess the hydrological contribution of permafrost regimes and to understand its response to changing climate. Employing high-resolution satellite data freely accessible through Google Earth, the first comprehensive rock glacier inventory of Himachal Himalaya is presented in this work. The inventory reports 516 rock glaciers in the study area corresponding to an estimated area of 353 km2 of which 59% have glacier origin and 41% have talus origin. The frontal elevation of the lowest rock glacier in the Himachal Himalaya is 3052 m above sea level which was significantly low, whereas the highest rock glacier occurs at 5503 m above sea level. The mean minimum elevation of rock glaciers was 4484 m above sea level and the maximum was 4900 m above sea level. The majority of the rock glaciers have a northerly aspect (N, NE, NW) followed by westerly aspect signifying that slopes with lower potential incoming solar radiation favour the formation of rock glaciers. The mean annual surface temperature of the rock glaciers derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) product (MOD11B3) was −1.5 °C, with glacier-derived rock glaciers having colder surfaces than talus-derived rock glaciers. The topographical and climatological parameters greatly influenced the formation and development of rock glaciers.