Snow Cover Variability Research Articles

Local variability of a taiga snow cover due to vegetation and microtopography

ABSTRACT The taiga snow cover accumulates in relatively stable and windless weather. This should produce a uniform snow cover with continuous, laterally homogeneous stratigraphy and snow properties when the snow is deposited on a level, smooth substrate. However, such substrates are rare, and local variations in vegetation and ground surface topography alter the structure of the snow cover and produce irregular snow layers. In this study, we investigated the effects of vegetation, microtopography, and microclimatic variability on the taiga snow near Fairbanks, Alaska. Through the winter of 2019–2020, in situ measurements were made at three locations with distinctly different local microtopographic features and radically different (but typical) vegetation. One site was an open grassy field, the second a mature spruce forest, and the third a birch forest located on thermokarst terrain with steep microrelief where ice wedges had degraded. Widely different canopy interception processes proved to have the strongest impact on the resulting snow cover heterogeneity by altering the initial deposition, with surface microtopography having the second strongest influence through postdepositional processes. In this article, we suggest a conceptual framework for understanding and modeling taiga snow variability in terms of the vegetation and microtopography that created it.

On the role of local and large-scale atmospheric variability in snow cover duration: a case study of Montevergine Observatory (Southern Italy)

Snow cover plays an important role in Earth’s climate, hydrological and biological systems as well as in socio-economical dynamics, especially in mountain regions. The objective of this work is to provide the first evidence about snow cover variability in the Italian Southern Apennines and investigate the forcing mechanisms controlling it. To this purpose, we present a new historical long-term (from 1931 to 2008) series of snow cover duration data observed at Montevergine Observatory, a mountainous site located at 1280 m above sea level. From the analysis of this series, it emerged a strong interannual variability, an overall reduction over time of snow cover days until mid-1990s and a recovery in the last 10-years. We model snow cover duration employing a multiple linear regression, considering both local and large-scale climate factors as explanatory variables. Our findings show that snow cover duration appears to be primarily dependent on temperature, which exhibits a positive trend in the considered time interval. However, the interannual and decadal fluctuations of the examined parameter are also strongly modulated by two large-scale patterns, the Arctic Oscillation and the Eastern Mediterranean Pattern. In the last segment of the considered time interval, the increase in temperature is not consistent with the dominant patterns of large-scale indices, which proved to be more effective in capturing the recent rebound in snow cover duration. The results demonstrate that snow cover duration is linked to the global warming by a non-trivial relationship and that its behaviour, in specific periods, can be largely independent from rising temperature tendency, according to the prevailing phase of large-scale atmospheric patterns.

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.

An integrated approach to reconstructing snow cover under clouds and cloud shadows on Sentinel-2 Time-Series images in a mountainous area

Snow is an important type of surface cover and an important freshwater resource in the arid regions of northwest China, and optical satellites are an important means for monitoring variation in snow cover. However, the radiation values measured by satellite sensors are affected by cloud cover and cloud shadows; thus, the reconstruction of snow cover under clouds and cloud shadows becomes a bottleneck problem especially in mountains areas. Based on the digital elevation model (DEM), in this paper, an integrated method of snow cover reconstruction on Sentinel-2 images is created by combining the improved SNOWL (snow line) algorithm considering unstable snow cover areas, Fmask cloud detection algorithms, and the Sen2Cor clear sky snow cover detection tool with a geospatial processing platform, GEE (Google Earth Engine). Considering the Babao River Basin as the study area, 474 Sentinel-2 scenes (10 m) were selected to obtain the snow cover spatiotemporal variations of 158 days from November 2016 to March 2021. Compared with the high-resolution GF-2 satellite images, the integrated method can extract snow under clouds and cloud shadows well, and the improved SNOWL algorithm improves the overall accuracy from 66.05 % to 84.26 %. The experimental results show that approximately half of the Babao River Basin is unstable snow cover, and areas above 3977 m are covered with snow for at least half a year.

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

Abstract. From the micro- to the mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, and elevation, together with associated meteorological forcings such as precipitation, solar radiation, and wind speed. Those topographic features govern the snow deposition, melting, and transport, which further impacts the overall water cycle. However, this microscale variability is not well represented in Earth system models due to coarse resolutions. This study explores the impact of precipitation, shortwave radiation, and wind speed on the water budget distribution over a 15.28 ha small, mid-elevation (2000–2200 m) alpine catchment at Col du Lautaret (France). The grass-dominated catchment remains covered with snow for 5 to 6 months per year. The surface–subsurface coupled distributed hydrological model ParFlow-CLM is used at a very high resolution (10 m) to simulate the impacts on the water cycle of meteorological variability at very small spatial and temporal scales. These include 3D simulations of hydrological fluxes with spatially distributed forcing of precipitation, shortwave radiation, and wind speed compared to 3D simulations of hydrological fluxes with non-distributed forcing. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the dynamics and spatial variability of snow cover using the Common Land Model (CLM) energy balance module and under different combinations of distributed forcing. The resulting subsurface and surface water transfers are computed by the ParFlow module. Distributed forcing leads to spatially heterogeneous snow cover simulation, which becomes patchy at the end of the melt season and shows a good agreement with the remote sensing images (mean bias error (MBE) = 0.22). This asynchronous melting results in a longer melting period compared to the non-distributed forcing, which does not generate any patchiness. Among the distributed meteorological forcings tested, precipitation distribution, including snow transport, has the greatest impact on spatial snow cover (MBE = 0.06) and runoff. Shortwave radiation distribution has an important impact, reducing evapotranspiration as a function of the slope orientation (decreasing the slope between observed and simulated evapotranspiration from 1.55 to 1.18). For the primarily east-facing catchment studied here, distributing shortwave radiation helps generate realistic timing and spatial heterogeneity in the snowmelt at the expense of an increase in the mean bias error (from 0.06 to 0.22) for all distributed forcing simulations compared to the simulation with only distributed precipitation. Distributing wind speed in the energy balance calculation has a more complex impact on our catchment, as it accelerates snowmelt when meteorological conditions are favorable but does not generate snow patches at the end of our test case. This shows that slope- and aspect-based meteorological distribution can improve the spatio-temporal representation of snow cover and evapotranspiration in complex mountain terrain.

Increasing landslide activity in the Taxkorgan River Basin (eastern Pamirs Plateau, China) driven by climate change

Global warming promotes glacier retreat, snow melting, and permafrost thawing, thereby increasing landslide activity in alpine mountain regions. However, our understanding of the effects of climate change on landslides is restricted by the sparse nature of landslide records. In this study, taking the Taxkorgan River basin in the eastern Pamirs as the study area, we assessed the interaction between landslides and climate change over the past 20 years using a reconstructed landslide inventory and a set of downscaled meteorological data, and we projected the future impacts of climate change on landslide activity. From 2000 to 2019, the number of landslides increased exponentially under global warming. Especially in 2000 and 2014, the abrupt increases in temperature and precipitation caused a more significant increase in the number of landslides. Ninety-four percent of the landslides occurred in the warm season (April to October), which is attributed to the diminishing snow cover in the warm season and the increase in the annual snow cover variation. Spatially, the increase in the number of landslides with elevation was due to the increase in the rate of temperature rise, suggesting that the high-elevation region (4500 m above sea level) responds more significantly to climate change and is more susceptible to landslides. The future climate is predicted to exhibit a warming and wetting trend, which will likely induce more frequent landslides for some time. We suggest that in alpine mountain areas, there is an urgent need to improve the comprehensive ground observation network and reveal the underlying relationship between the variable climate and landslides using multiple datasets, which would contribute to the accurate assessment of the risks posed by landslides induced by climate change and the adequate management of this risk under the warming climate in the future.

Investigation of the Spatio-Temporal Heterogeneity of Snow Thickness at the Meteorological Site of the Lomonosov MSU in the Winter of 2022/2023

This paper presents the results of field studies conducted at the MSU meteorological site for the winter period of 2022/2023. The purpose of the observations was to study the development of the snow column and its spatial variability in one winter season. Field research consisted of analyzing stratigraphic layers of snow and measuring their density. The data obtained made it possible to characterize and evaluate changes in snow layers, structure, and density in spatiotemporal terms. The results of the work are displayed on the graphs of the spatial and temporal variability of the snow cover for 2022/2023. The evolution of the snow column over the winter period is analyzed. The analysis of observations reflects a high spatial and temporal variability of snow cover in winter, which allows not only to evaluate and compare the data obtained with past studies but also to supplement and improve the already available information on the heterogeneity of snow cover.

Spatial and temporal analyses of land use changes with special focus on seasonal variation in snow cover in District Chitral; a Hindu Kush mountain region of Pakistan

Snow and glacier changes and increase in temperature due to climate change in Hindu Kush Himalayas (HKH) and its impacts are important for water resource management in the world and specifically for Pakistan, which is a water-stressed country. Being a mountainous region, the people living in HKH are highly susceptible to the adverse impacts induced by climate change. This paper therefore mainly focused on monitoring snow cover changes in district Chitral using USGS satellite images (Landsat5-TM, Landsat7-ETM+, and Landsat8-OLI) for the years 2000, 2004, 2008, 2012, and 2016. The maximum likelihood method was used for Land Use Land Cover (LULC) classification. For a better understanding of the pattern of seasonal variation in snow cover over time, the total study period was distributed in four quadrennials, and the dataset for each period was taken only for early winter (Oct-Nov). A marked increase (266.7%) in the built-up area and 135.9 km2 of agriculture expansion in this remote and mountainous area is quite significant. Results of the current study showed that in the year 2000, more than half (51.6%) of the total land of District Chitral was covered with snow in early winter, but it drastically decreased to only 6.8% in the year 2008. During the first two quadrennials, 44.7% (5694.6 km2) of glaciated area exposed the barren rocks, while the overall changes in snow-cover areas are approximately 43.3% (5514.6 km2). The most significant impact of melting snow and glaciers is Glacial Lakes Outburst Floods (GLOFs). GLOFs may cause human deaths and injuries, while these are also a threat to agricultural land, forest, and residential areas. Based on the current study, it is recommended to enhance institutional capacity in the country to face different environmental hazards, including early warning systems on the local scale. Keeping in view the water stress and based agriculture-based economy of Pakistan, proper analysis and monitoring of snow should be carried out for the sack of water availability, drought or floods, and other environmental vulnerabilities.

Climate and elevation control snow depth and snow phenology on the Tibetan Plateau

Snowmelt from the Tibetan Plateau (TP) is the water source of many major Asian rivers, with significant importance for regional water supply and ecosystem services. The spatiotemporal variations of snow cover on the TP and its response to climate change at different altitudes are still unclear because of the lack of in situ observations on the western TP, and limitations of optical remote sensing due to clouds. Using a daily passive microwave remote sensing snow depth dataset from 1979 to 2020 across the TP, this study investigates the spatiotemporal variations of snow depth and snow phenology as well as their responses to climate change. The results showed that during the past 40 years, the maximum snow depth (SDmax) and the snow cover duration days (SCD) on the TP decreased significantly at a rate of 0.6 cm/decade and 2.9 d/decade, respectively. The snow cover starting date (SCS) was delayed at a rate of 1.1 d/decade, whereas the snow cover melt date (SCM) advanced at a rate of 1.1 d/decade. The decrease of SCD was driven by the advance of SCM, which was caused by the decrease of SDmax, whereas the decrease of SDmax was driven by the increase in snow accumulation season temperature (Ta). The TP showed significant elevation-dependent warming (EDW) (0.04 ℃ decade-1 km−1) and elevation-dependent decrease of SDmax (0.12 cm decade-1 km−1) and SCD (4.1 d decade-1 km−1). The EDW explains the elevation-dependent decrease of SDmax (r = −0.72, P < 0.05) and SCD (r = −0.83, P < 0.05). It is expected that the negative feedback effect of SDmax and SCD on EDW may further exacerbate the EDW of the TP, and the earlier snowmelt due to the continuous decrease of snow depth may increase the frequency of droughts and floods. Our findings support the enhancement of EDW on the TP, depletion of solid reservoirs, and intensification of the regional water crisis in a warmer future.

Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.

Damage detection on antarctic ice shelves using the normalised radon transform

Areas of structural damage mechanically weaken Antarctic ice shelves. This potentially preconditions ice shelves for disintegration and enhanced grounding line retreat. The development of damage and its feedback on marine ice sheet dynamics has been identified as key to future ice shelf stability and sea level contributions from Antarctica. However, it is one of the least understood processes that impact ice shelf instability since quantifying damage efficiently and accurately is a challenging task. Challenges relate to the complex surface of Antarctica, variations in viewing-illumination geometry, snow or cloud cover and variable signal-to-noise levels in satellite imagery. Therefore, automated damage assessment approaches require careful pre- and post-processing, lacking the option to be applied to wider spatiotemporal domains. Simultaneously, studies that use manual mapping are usually limited due to the effort required for extensive mapping, which either results in a limited spatial domain or the use of low resolution data.This study proposes the NormalisEd Radon transform Damage detection (NeRD) method to detect damage features and their orientations from multi-source satellite imagery. NeRD performs robust, high resolution, large-scale damage assessments. NeRD is applied to the ice shelves in the Amundsen Sea Embayment (ASE) and validated with both manually labelled and existing fracture maps. Validation shows that NeRD detects damage with high recall and provides an accurate physical representation of multi-scale damage features and their orientation. Sensitivity analyses indicate NeRD is robust to different resolution parameter settings. NeRD consistently detects damage for different data sources ranging from optical Landsat 7/8 and Sentinel-2 optical to Synthetic Aperture Radar Sentinel-1 data. Therefore, NeRD paves the way for synergistic multi-source damage detection that overcomes remaining limitations from individual sources. Results show varying damage patterns on the ice shelves in the ASE area in austral summer 2020–2021, with most damage located on the Pine Island, Crosson and Thwaites ice shelves. We show a damage increase on the Pine Island ice shelf between 2013–2019, and display advection and rotation of crevasses. The detected damage orientation can provide insight in the type of crevasse opening mode and the development of damage over time. The damage maps produced with NeRD can help evaluate ice sheet models or machine learning approaches, improving our understanding of damage evolution.

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

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

High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.

Influence of snow cover on soil freeze depth across China

Using 1980–2014 observation data of 378 meteorological stations, this study quantified the correlations between snow cover and the annual maximum seasonally freeze depth (MSFD) across China and the contributions of snow cover to MSFD. Snow cover exhibited a weak effect on MSFD across China, which may be related to the thinness and short duration of the snow cover. Regional differences in the effects of snow cover on MSFD were relatively large, and the effects of snow cover were more significant than those of air temperature (characterized by the air-freezing index) at stations in Northeast China, northeastern Inner Mongolia, and the north of the Tianshan Mountains. In regions with a thin snow cover or short snow cover duration (SCD), the cooling effect of snow cover can dominate, but it only slightly influences MSFD owing to the short SCD. With increasing average snow depth (ASD) and SCD, the relative contributions of snow cover to MSFD gradually increased, peaking at SCDs of 120–140 days or ASDs of 8–10 cm, which is attributable to the increasing insulating effect. However, with further increase in ASD and SCD, the insulating effect decreases because of high albedo and the latent heat effect of snow melting. This decrease resulted in declining MSFD–snow cover correlations and snow cover contributions to MSFD. Compared with SCD, ASD has a greater influence on MSFD. By adding an ASD variable to the Stefan formula, this modified formula outperformed the conventional formula in MSFD estimation at SCDs of 60–140 days. This study demonstrated the importance of snow cover variables for soil freeze depth analysis and simulation in areas with large snow cover and further elucidated the effects of snow cover on soil freeze depth.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data.

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snowcover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

On the Relevance of Aerosols to Snow Cover Variability Over High Mountain Asia

AbstractWhile meteorology and aerosols are identified as key drivers of snow cover (SC) variability in High Mountain Asia, complex non‐linear interactions between them are not adequately quantified. Here, we attempt to unravel these interactions through a simple relative importance (RI) analysis of meteorological and aerosol variables from ERA5/CAMS‐EAC4 reanalysis against satellite‐derived SC from Moderate Resolution Imaging Spectroradiometer across 2003–2018. Our results show a statistically significant 7% rise in the RI of aerosol‐meteorology interactions (AMI) in modulating SC during late snowmelt season (June and July), notably over low snow‐covered (LSC) regions. Sensitivity tests further reveal that the importance of meteorological interactions with individual aerosol species are more prominent than total aerosols over LSC regions. We find that the RI of AMI for LSC regions is clearly dominated by carbonaceous aerosols, on top of the expected importance of dynamic meteorology. These findings clearly highlight the need to consider AMI in hydrometeorological monitoring, modeling, and reanalyses.

Estimating Regional Snow Line Elevation Using Public Webcam Images

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements.

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

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

Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021

Accurate understanding the spatiotemporal variability of snow cover and snow phenology over High Mountain Asia (HMA) is of great interest because of their vital impacts on the hydrological processes, global climate system and regional water resource management. This study investigates the spatiotemporal variability of snow-covered extent (SCE), snow-covered days (SCD), snow onset date (SOD), snow end date (SED) and snow duration days (SDD) of HMA using the daily cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products from the hydrological year 2002 to 2020. The vertical difference of the effect of elevation on snow phenology and the response of snow phenology to climate change are explored. The results indicate that the monthly SCE over the whole HMA exhibited a slight decreasing in most months over the study period. The variability of snow cover and snow phenology over the whole HMA generally showed decreased SCD (significant decrease accounts for 15.73%), delayed SOD (significant delay accounts for 14.35%), advanced SED (significant advance accounts for 15.46%) and shortened SDD (significant shorten accounts for 16.01%). It is worth noting that the snow cover in the West Himalaya was enhancing with its increased SCE, increased SCD and lengthened SDD. There is an elevation dependence of snow phenology changes over HMA with the high-altitude mountain ranges generally having a higher SCD, earlier SOD, later SED and longer SDD than low altitude areas. The air temperature of snow accumulation period is the dominant factor affecting the interannual variation of SOD, and the air temperature of snow melt period is the dominant factor affecting the interannual variation of SED. The increase of air temperature leads to the delayed SOD, advanced SED and shortened SDD.

Untangling the impacts of socioeconomic and climatic changes on vegetation greenness and productivity in Kazakhstan

Studies examining the joint interactions and impacts of social-environmental system (SES) drivers on vegetation dynamics in Central Asia are scarce. We investigated seasonal trends and anomalies in drivers and their impacts on ecosystem structure and function (ESF). We explored the response of net primary production, evapotranspiration and normalized difference vegetation index (NDVI) to various SES drivers—climate, human influence, heat stress, water storage, and water content—and their latent relationships in Kazakhstan. We employed 13 predictor drivers from 2000 to 2016 to identify the interactions and impacts on ESF variables that reflect vegetation growth and productivity. We developed 12 models with different predictor–response variable combinations and separated them into two approaches. First, we considered the winter percent snow cover (SNOWc) and spring rainfall (P_MAM) as drivers and then as moderators in a structural equation model (SEM). SNOWc variability (SNOWcSD) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWcSD did not have a strong direct positive influence on summer vegetation growth and productivity; however, it was an important moderator between human influence and the ESF variables. Spring rainfall had a stronger impact on ESF variability than summer rainfall. We also found strong positive feedback between soil moisture (SM) and NDVI, as well as a strong positive influence of vegetation optical depth (VOD) and terrestrial water storage (TWS) on ESF. Livestock density (LSKD) exhibited a strong negative influence on ESF. Our results also showed a strong positive influence of socioeconomic drivers, including crop yield per hectare (CROPh), gross domestic product per capita (GDPca), and population density (POPD) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSKD and POPD than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.