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Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterization. [ABSTRACT FROM AUTHOR]

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

This study provides the first contemporaneous measurements of the concentration and speciation of dissolved nitrogen (N) in snow, meltwater and ice during the onset of the ablation season at a site within the Dark Zone of the Greenland Ice Sheet. The decaying, partially leached snow pack near S6 in south-west Greenland produced meltwater with relatively constant nitrate (NO3−) concentrations, approximating the snow pack average (1.1 µM). By contrast, ammonium (NH4+) (0–∼ 4 µM) and dissolved organic nitrogen (DON) (0–∼ 3 µM) concentrations were more variable, and sometimes higher than the average snow pack concentrations of 1.0 and 0.4 µM, respectively. This variability could be the result of microbial uptake and production within the melting snow pack. We observed pooled meltwater at the snow-ice interface that appeared to scavenge DON (∼1–8 µM) and possibly NO3− (∼1–2 µM) from the underlying ice, whose initial surface was a continuum of superimposed ice and weathering crust from the previous season. The shallow meteoric ice (∼10 cm–1 m) beneath the snow pack had high concentrations of DON and NH4+(6.5 and 2.6 µM, respectively), while NO3− concentrations were similar to the snow pack (1.1 µM). The absence of NH4+ in the snowmelt traversing the snow-ice interface may also point to microbial activity occurring at this boundary layer. Melt modelling indicated the presence of liquid meltwater at the snow-ice interface and that at least 10 cm of the surface ice below the snow pack was at 0°C. Solar radiation transmitted through the thin snow pack to the ice surface is absorbed by pigmented glacier algae causing melt of the surrounding ice, allowing the possibly of photosynthesis to begin under the thinning snowpack in these micro-melt environments. Hence, we conjecture that glacier algal blooms can commence before the snow pack has completely melted, occuring at a time when meltwaters are enhanced in nutrients scavenged from the snowpack, superimposed ice and the remnants of the weathering crust from the previous year.

Snow packs cover large expanses of Earth’s land surface, making them integral components of the cryosphere in terms of past climate and atmospheric proxies, surface albedo regulators, insulators for other Arctic environments and habitats for diverse microbial communities such as algae, bacteria and fungi. Yet, most of our current understanding of snow pack environments, specifically microbial activity and community interaction, is limited to the main microbial growing season during spring ablation. At present, little is known about microbial activity and its influence on nutrient cycling during the subfreezing temperatures and 24-h darkness of the polar winter. Here, we examined microbial dynamics in a simulated cold (−5°C), dark snow pack to determine polar winter season microbial activity and its dependence on critical nutrients. Snow collected from Ny-Ålesund, Svalbard was incubated in the dark over a 5-week period with four different nutrient additions, including glacial mineral particles, dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP) and a combined treatment of DIN plus DIP. Data indicate a consumption of dissolved inorganic nutrients, particularly DIN, by heterotrophic communities, suggesting a potential nitrogen limitation, contradictory to phosphorus limitations found in most aquatic environments. 16S amplicon sequencing also reveal a clear difference in microbial community composition in the particulate mineral treatment compared to dissolved nutrient treatments and controls, suggesting that certain species of heterotrophs living within the snow pack are more likely to associate with particulates. Particulate phosphorus analyses indicate a potential ability of heterotrophic communities to access particulate sources of phosphorous, possibly explaining the lack of phosphorus limitation. These findings have importance for understanding microbial activity during the polar winter season and its potential influences on the abundance and bioavailability of nutrients released to surface ice and downstream environments during the ablation season.

Snow packs cover large expanses of Earth's land surface, making them integral components of the cryosphere in terms of past climate and atmospheric proxies, surface albedo regulators, insulators for other Arctic environments and habitats for diverse microbial communities such as algae, bacteria and fungi. Yet, most of our current understanding of snow pack environments, specifically microbial activity and community interaction, is limited to the main microbial growing season during spring ablation. At present, little is known about microbial activity and its influence on nutrient cycling during the subfreezing temperatures and 24-h darkness of the polar winter. Here, we examined microbial dynamics in a simulated cold (−5°C), dark snow pack to determine polar winter season microbial activity and its dependence on critical nutrients. Snow collected from Ny-Ålesund, Svalbard was incubated in the dark over a 5-week period with four different nutrient additions, including glacial mineral particles, dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP) and a combined treatment of DIN plus DIP. Data indicate a consumption of dissolved inorganic nutrients, particularly DIN, by heterotrophic communities, suggesting a potential nitrogen limitation, contradictory to phosphorus limitations found in most aquatic environments. 16S amplicon sequencing also reveal a clear difference in microbial community composition in the particulate mineral treatment compared to dissolved nutrient treatments and controls, suggesting that certain species of heterotrophs living within the snow pack are more likely to associate with particulates. Particulate phosphorus analyses indicate a potential ability of heterotrophic communities to access particulate sources of phosphorous, possibly explaining the lack of phosphorus limitation. These findings have importance for understanding microbial activity during the polar winter season and its potential influences on the abundance and bioavailability of nutrients released to surface ice and downstream environments during the ablation season. [ABSTRACT FROM AUTHOR]

Abstract Subsurface geophysical characterization efforts at a US Army legacy munitions waste site were negatively impacted by dynamic cold regions surface processes including increased snow depth, surface soil erosion and deposition, and seasonal water levels. One concern identified in the characterization efforts showed the time-domain (TD) electromagnetic induction (EMI) response from the buried munitions was diminished due to an increased snow pack. It is well known that increasing the distance between the ground surface and the time-domain electromagnetics (TDEMI) sensor will result in a decreased response, all other factors remaining the same. However, in cold regions or other locations where seasonally dynamic changes in surface elevations occur, the depth to target may also change, potentially resulting in false negatives when site based regulatory thresholds are used for detection criteria. Here, we present measured and synthetic data illustrating the impact of dynamic cold regions environments on subsurface EMI geophysical surveys. We model the TDEMI response from a single target at varying depths and relate this to snow pack thickness in increments of 0.10 m. Results suggest for a target at an original depth of 0.86 m, 0.10 m of snow accumulation, or sensor height change, will result in a false negative for this size munition at this site. Next, a model from the synthetic data to correct for the diminished response due to increased sensor offset is developed, and effectiveness of the model as applied to potentially missed targets is demonstrated. The synthetic data were plotted against the modeled data and exhibited a close correlation. Additionally, we examine the effects of dynamic surface grade on the TDEMI results, demonstrating a significant difference between soil and snow surface grade impacts on target detection and locating. Finally, an approach to correct the TDEMI response based on the change in surface elevation resulting from increased snow depth, surface soil erosion and deposition, or water levels is proposed. Highlights • Cold regions dynamic terrain can negatively impact TDEMI UXO signatures. • Surface grades can affect the detection and location of UXO in TDEMI investigations. • Exponential models are used to correct signal decay from TDEMI sensor height change. • Small variations in TDEMI sensor height can result in false negatives. [ABSTRACT FROM AUTHOR]

Many arctic ecological processes are regulated by soil temperature that is tightly interconnected with snow cover distribution and persistence. Recently, various climate-induced changes have been observed in arctic tundra ecosystems, e.g. shrub expansion, resulting in reduction in albedo and greater C fixation in aboveground vegetation as well as increased rates of soil C mobilization by microbes. Importantly, the net effects of these shifts are unknown, in part because our understanding of belowground processes is limited. Here, we focus on the effects of increased snow depth, and as a consequence, increased winter soil temperature on ectomycorrhizal ( ECM) fungal communities in dry and moist tundra. We analyzed deep DNA sequence data from soil samples taken at a long-term snow fence experiment in Northern Alaska. Our results indicate that, in contrast with previously observed responses of plants to increased snow depth at the same experimental site, the ECM fungal community of the dry tundra was more affected by deeper snow than the moist tundra community. In the dry tundra, both community richness and composition were significantly altered while in the moist tundra, only community composition changed significantly while richness did not. We observed a decrease in richness of Tomentella, Inocybe and other taxa adapted to scavenge the soil for labile N forms. On the other hand, richness of Cortinarius, and species with the ability to scavenge the soil for recalcitrant N forms, did not change. We further link ECM fungal traits with C soil pools. If future warmer atmospheric conditions lead to greater winter snow fall, changes in the ECM fungal community will likely influence C emissions and C fixation through altering N plant availability, fungal biomass and soil-plant C-N dynamics, ultimately determining important future interactions between the tundra biosphere and atmosphere. [ABSTRACT FROM AUTHOR]

The results of the snow measurement at logged sites in fir-Siberian pine forests of the Yenisey Ridge in 2006-2011 are presented. Orographic and biotopic features of snow accumulation in the stands of different forest regeneration stages are considered. The snow cover depth and snow pack are determined by the altitude and topography of this region that form 'barrier-shadow' effects. On the local level, the characteristics of snow cover are related to duration after logging, the species composition of young forests, and the logging technology. [ABSTRACT FROM AUTHOR]

Regional operational modeling systems that support forecasters for the real-time warning of flash flood events often suffer from lack of adequate real-time surface air temperature data to force their accumulation and ablation snow model. The Land Surface Temperature (LST) product from MODIS, which provides four instantaneous readings per day, was tested for its feasibility to be used in real-time to derive spatially distributed surface air temperature ( T a ) forcing for the operational snow model. The study was conducted in the Southeast region of Turkey using an atypically dense network of hourly T a , daily snow depth, snow water equivalent (SWE), and rainfall datasets for the period: October 2002–September 2010. A comparison between the T a and the corresponding LST grid-cell data indicated close associations that are different in nature for periods with and without snow on the ground. The LST-derived T a was compared with that obtained from on-site gauge-based interpolation procedures and climatological time series. The LST-derived T a was found inferior only to the T a derived from the interpolation of the dense gauge network (31-gauges). Snow-pack simulations using estimated T a time series were compared to simulations that were forced by the observed T a at each site of 18 sites. The LST-derived T a performed well in simulating snow mass and maximum SWE magnitude, while it did not represent well the timing of the annual peak of SWE and the duration of spring melt. Our study concluded that the MODIS/LST product can be a valuable additional source of real time forcing data for regional operational snow models, especially in remote mountainous areas with sparse telemetric data. [ABSTRACT FROM AUTHOR]

This study characterized the dynamics of the activities of urease, nitrate reductase and nitrite reductase in both soil organic layer and mineral soil layer under three depths of snow pack (deep snowpack, moderate snowpack and shallow snowpack) over the three critical periods (snow formed period, snow stable period, and snow melt period) in the subalpine Abies faxoniana forest of western Sichuan in the winter of 2012 and 2013. Throughout the winter, soil temperature under deep snowpack increased by 46. 2% and 26. 2%, respectively in comparison with moderate snowpack and shallow snowpack. In general, the three nitrogen-related soil enzyme activities under shallow snowpack were 0. 8 to 3. 9 times of those under deep snowpack during the winter. In the beginning and thawing periods of seasonal snow pack, shallow snowpack significantly increased the activities of urease, nitrate and nitrite reductase enzyme in both soil organic layer and mineral soil layer. Although the activities of the studied enzymes in soil organic layer and mineral soil layer were observed to be higher than those under deep-and moderate snowpacks in deep winter, no significant difference was found under the three snow packs. Meanwhile, the effects of snowpack on the activities of the measured enzymes were related with season, soil layer and enzyme type. Significant variations of the activities of nitrogen-related enzymes were found in three critical periods over the winter, and the three measured soil enzymes were significantly higher in organic layer than in mineral layer. In addition, the activities of the three measured soil enzymes were closely related with temperature and moisture in soils. In conclusion, the decrease of snow pack induced by winter warming might increase the activities of soil enzymes related with nitrogen transformation and further stimulate the process of wintertime nitrogen transformation in soils of the subalpine forest. [ABSTRACT FROM AUTHOR]