Persistent Snow Research Articles

Persistence of Fungicides Applied to Control Snow Mold of Turfgrass as Influenced by Snow Cover and Antitranspirants.

Turfgrass managers rely on fungicides to suppress snow mold diseases in areas with persistent snow cover, which are commonly applied once or twice in late fall prior to snow cover. Fungicide applications targeting snow mold are expected to control snow mold diseases for the duration of winter; however, climate change is increasing the frequency of winter rainfall and snowmelts and may alter the duration of snow mold control. A 3-year study was conducted in Madison, WI, to assess the impact of snow cover and antitranspirants on the persistence of the commonly used fungicides chlorothalonil and propiconazole. Snow cover, fungicide treatment, and the use of antitranspirants had minimal impacts on fungicide persistence and disease control. The most important factors influencing persistence of the fungicides were snowmelt and winter rainfall events, yet antitranspirants demonstrated modest evidence of extending chlorothalonil persistence in certain years. These results demonstrate that increasing snowmelt and winter rainfall events associated with climate change will increase dissipation of fungicides used for snow mold suppression, which will likely impact disease control.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The hydrologic and geochemical contributions from snow to streamflow in the McMurdo Dry Valleys of Antarctica

AbstractThe glacial meltwater streams in the McMurdo Dry Valleys (MDVs), Antarctica only flow during the austral summer and contain abundant algal mats which grow at the onset of flow. Their relative abundance in stream channels of this polar desert make the streams biogeochemical hot spots. The MDVs receive minimal precipitation as snow, which is redistributed by wind and deposited in distinct locations, some of which become persistent snow patches each year. Previous studies identified that MDV streamflow comes from a combination of glacier ice and snow, although snow was assumed to contribute little to the overall water budget. This study uses a combination of satellite imagery, terrain analysis, and field measurements to determine where snow patches accumulate and persist across MDV watersheds, and to quantify the potential hydrologic and biogeochemical contributions of snow patches to streams. Watersheds near the coast have the highest snow‐covered area and longest snow persistence. Many of these snow patches accumulate within the stream channels, which results in the potential to contribute to streamflow. During the summer of 2021–2022, stream channel snow patches had the potential to contribute anywhere between <1% and 90% of the total annual discharge in Lake Fryxell Basin streams, and may increase with different hydrometeorological conditions. On average the potential inputs from snow patches to streamflow was between 12% and 25% of the annual discharge during the 2021–2022 season, as determined by snow area and SWE. Snow patches in the majority of the watersheds had higher nitrogen and phosphorous concentrations than stream water, and six streams contained snow with higher N:P ratios than the average N:P in the stream water. This suggests that if such patches melt early in the summer, these nutrient and water inputs could occur at the right time and stoichiometry to be crucial for early season algal mat growth.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

AbstractAccurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive.

Assessing snow cover patterns in the Indus-Ganga-Brahmaputra River Basins of the Hindu Kush Himalayas using snow persistence and snow line as metrics

This study investigates the utilization of snow persistence (SP) as a metric to assess snow cover patterns in the Upper Indus-Ganga-Brahmaputra (UIGB) river basins, an area notably susceptible to climate change. The objectives of the study are to establish seasonal snow persistence, calculate seasonal snow lines, and quantify the temporal trend of snow lines. It also underscores the importance of accounting for climate zones, elevation, and seasonal variations when investigating snow persistence. Furthermore, the study estimates the snow line elevation using the Regional Snow Line Elevation (RSLE) approach with remote sensing data. Over a span of 19 years (2003–2021), the analysis of SP% in UIGB basins indicates a notable reduction in the mean annual permanent snow zone area, decreasing by -2.61 %, -2.37 %, and -4.18 % from winter (DJF) to summer (JJA). The results uncover considerable fluctuations in snow cover across various climate zones and seasons. The impact of warming is clearly visible on snow cover, instigating a transition from permanent and seasonal snow zones towards intermittent and minimal or no snow zones. The seasonal analysis of the snow line from 2003 to 2021 illustrates fluctuation in the snow line for UIGB basins, from 2349 ± 117 m to 4802 ± 51.7 m, 3003 ± 286 m to 4871 ± 64.4 m, and 3238 ± 127 m to 5047 ± 73 m between winter and summer seasons, with the most significant fluctuation occurring in the Brahmaputra basin. A substantial increase in Snow Line Elevation (SLE) is observed for Ganga (6.67 m yr−1), and Brahmaputra (7.26 m yr−1) at the conclusion of the melting season. These findings offer invaluable insights for formulating targeted mitigation strategies in the snow accumulation regions of each river basin. They equip us with the ability to respond effectively to the spatiotemporal variability of snow cover caused by climate change.

Global Snow Seasonality Regimes from Satellite Records of Snow Cover

Abstract Snow cover provides distinct seasonal controls on the exchange of energy between Earth’s surface and atmosphere, and on hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover–based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-yr record of snow cover (2000–22) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km2 (11%), respectively. Using the multidecadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70 000 km2 yr−1) as well as apparent losses in perennial (−3600 km2 yr−1) and seasonal snow regime coverage (−38 000 km2 yr−1). The resulting classification maps have strong agreement with in situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework’s ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality. Significance Statement Following a review of existing snow classification approaches, this study focuses on improving our understanding of snow-cover variability on Earth by using satellite-based observations of snow-covered area to derive a new snow classification. Satellite observations provide the best means of measuring snow cover at a global scale, helping to identify regions where its influence on energy, water, and climate is changing. The results are compared to existing climate and snow summaries, ground observations of snow depth, and include trends in snow cover over recent decades (2000–22). The resulting datasets are also made available to the broader scientific community.

Predicting Streamflow Duration From Crowd‐Sourced Flow Observations

AbstractStreamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes.

Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas

Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous regions. It calls for further efforts to improve the representation of subgrid-scale processes affecting snowpack in climate models.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile

Abstract. Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32∘ S and ∼ 69 to ∼ 71∘ W). This region has elevations up to 6000 m, is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as “snowmelt hotspots”. Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation.

Recolonization following past persecution questions the importance of persistent snow cover as a range limiting factor for wolverines.

Globally, climate is changing rapidly, which causes shifts in many species' distributions, stressing the need to understand their response to changing environmental conditions to inform conservation and management. Northern latitudes are expected to experience strongest changes in climate, with milder winters and decreasing snow cover. The wolverine (Gulo gulo) is a circumpolar, threatened carnivore distributed in northern tundra, boreal, and subboreal habitats. Previous studies have suggested that wolverine distribution and reproduction are constrained by a strong association with persistent spring snow cover. We assess this hypothesis by relating spatial distribution of 1589 reproductive events, a fitness-related proxy for female reproduction and survival, to snow cover over two decades. Wolverine distribution has increased and number of reproductive events increased 20 times in areas lacking spring snow cover during our study period, despite low monitoring effort where snow is sparse. Thus, the relationship between reproductive events and persistent spring snow cover weakened during this period. These findings show that wolverine reproductive success and hence distribution are less dependent on spring snow cover than expected. This has important implications for projections of future habitat availability, and thus distribution, of this threatened species. Our study also illustrates how past persecution, or other factors, that have restricted species distribution to remote areas can mask actual effects of environmental parameters, whose importance reveals when populations expand beyond previously restricted ranges. Overwhelming evidence shows that climate change is affecting many species and ecological processes, but forecasting potential consequences on a given species requires longitudinal data to revisit hypotheses and reassess the direction and magnitude of climate effects with new data. This is especially important for conservation-oriented management of species inhabiting dynamic systems where environmental factors and human activities interact, a common scenario for many species in different ecosystems around the globe.

Local snow and fluvial conditions drive taxonomic, functional and phylogenetic plant diversity in tundra

To understand, how the diversity and hence functioning of tundra ecosystems might respond to altering environmental conditions, fine‐scale studies are needed as local conditions may buffer broad‐scale environmental changes. Furthermore, species functional traits and phylogeny may provide complementary insights to taxonomic diversity patterns as they link plant communities to ecosystem processes often more closely than species count. Here, we examined taxonomic, functional and phylogenetic plant diversity in relation to fundamental environmental factors, namely, growing degree days, snow persistence, soil moisture, pH and fluvial disturbance in northern Norway. The relationships between eight diversity metrics and environmental predictors were investigated using hierarchical generalised additive models. Our results indicated that taxonomic, functional and phylogenetic plant diversity in tundra are all strongly linked to local snow and fluvial conditions, with average variable importance of 0.19 and 0.14, respectively, whereas the importance of other predictors was low (average variable importance < 0.06). The average explained deviance by the models was 0.23. Predicted hotspots of different diversity metrics overlapped notably and were mostly located along the streams. However, when the effect of taxonomic richness was removed from the phylogenetic and functional diversities their connections with environmental predictors were weaker but indicated strongest relationships with snow and soil pH showing distinct diversity hotspots in areas with low species richness. Our study demonstrates that investigating multiple facets of biodiversity enhances understanding on community patterns and their drivers. Furthermore, our results highlight the importance of addressing local hydrological conditions that represent both resources and disturbances for vegetation. As arctic and alpine areas are probably shifting from snow to rain dominated, incorporating snow and fluvial information into the models might be particularly important to better understand tundra ecosystems under global change.

The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis

Snow persistence (SP) is a widely available remotely-sensed measure of snowpack accumulation and ablation, reflecting the duration of snow presence on the ground in a given year. Available local-scale studies showed that SP is associated with the average magnitude of streamflow. However, despite the intuitive relationship between SP and catchment storage/release functioning, the spatial and temporal links between the persistence of snow on the ground and the shape and functionality of streamflow hydrographs were not studied empirically and were not generalized to diverse climatic settings. This study empirically explores the spatial and temporal links that SP has with measures of hydrograph shape and variability during low-flow and high-flow conditions across continent-wide gradients of aridity and seasonality. In arid in-phase and wet out-of-phase climates, higher SP is spatially associated with a damper (i.e., less flashy) streamflow hydrograph during low-flow and high-flow conditions. This is shown by a larger ratio of baseflow to average flow, a larger ratio of extreme low-flow to average flow, lower low-flow variability, and lower high-flow variability. While SP is spatially associated with a damped hydrograph in both arid/in-phase and wet/out-of-phase climates, this effect is stronger in the former region. For example, the size of the nonlinear impact of SP on reducing low-flow and high-flow variabilities is larger in arid in-phase climates (−7.64, −3.44, respectively) than in wet out-of-phase climates (−4.34, −2.02, respectively). Temporal analyses for “typical snow-rich” catchments show that years with relatively higher SP may lead to relatively flashier streamflow hydrographs, with lower baseflow indices, lower ratios of extreme low-flow to average flow, higher ratios of extreme high-flow to average flow and higher high-flow variability. Such results 1) demonstrate the utility of SP as a globally available descriptor of streamflow hydrograph shape and variability in a wide diversity of climatic conditions, 2) highlight that climate-driven snow loss may lead to substantial changes to hydrograph form and functionality, and 3) indicate that space-time symmetry may not be a valid assumption in hydrology.

Effects of vegetation and topography on snowshoe hare relative abundance at the southern range periphery

AbstractThe conservation of snowshoe hares (Lepus americanus) and their habitat is a major focus in the American West, largely because of their importance to the federally threatened Canada lynx (Lynx canadensis). Understanding the habitat relationships of snowshoe hare populations at the southern periphery of their distribution is particularly important because a warming climate is reducing the mesic forests and persistent snow cover they require. Using fecal pellet density as an index, we examined the factors influencing snowshoe hare relative abundance at 2 study areas in the Southern Rocky Mountains by comparing suites of candidate models containing fine‐scale vegetation and topographical covariates. The Birdseye Gulch study area is within an extensive area containing high elevations, mesic forests, and Canada lynx occurrence. The Three Peaks study area consists of an isolated zone of high elevation and mesic forests surrounded by inadequate habitat and is absent of Canada lynx. Mean hare pellet density in Birdseye Gulch was 2.48 pellets ± 0.46 (SE)/ (n = 49) versus 2.24 ± 0.48/m2 (n = 57) at Three Peaks. Models containing fine‐scale vegetation variables best explained pellet densities at Birdseye Gulch. Pellet densities at Three Peaks were best explained by topographical variables, with much unexplained variation within all models. The differing trends in these areas may be due to the absence of resident Canada lynx at Three Peaks and differences in topography between the areas. Our results indicate that snowshoe hare populations can persist in the type of isolated habitat that is increasingly common at the southern range periphery; however, the use of vegetation management to conserve habitat in these areas may be less effective because of weaker associations with vegetation structure.

Plant trait‐environment relationships in tundra are consistent across spatial scales

Patterns and processes shaping ecosystems vary across spatiotemporal scales. As plant functional traits reflect ecosystem properties, investigating their relationships with environment provides an important tool to understand and predict ecosystem structure and functioning. This is particularly important in the tundra where a changing climate may trigger severe alterations in plant communities as both summer and winter conditions are changing. Here, we investigate the relationships between key environmental drivers including summer temperature, snow persistence, topographic position and soil pH, and species height, specific leaf area (SLA) and seed mass as plant traits. The study is carried out at three spatial extents in the arctic–alpine region of Fennoscandia, modelling the trait‐environment relationships at each scale to investigate whether the relationships are scale dependent. Our results show that summer temperature and snow persistence are the most important variables explaining community trait composition. Temperature is important especially to vegetation height, which increased towards higher temperatures, whereas seed mass and SLA are related to snow persistence. Seed mass decreased towards longer snow persistence, while SLA responded in scale‐dependent ways. Topographic position and soil pH affect community trait composition moderately. Overall, our study demonstrates that trait‐environment relationships in the tundra are largely consistent across spatial scales. Our findings highlight the ecological relevance of snow for all three functional traits regardless of scale, showing that snow information could be particularly important to better understand large‐scale trends in plant community composition and ecosystem functioning as seasonal snow cover is shrinking globally.

Compositional Changes in Sediment Microbiota Are Associated with Seasonal Variation of the Water Column in High-Altitude Hyperarid Andean Lake Systems.

The lacustrine systems of La Brava and La Punta, located in the Tilopozo sector in the extreme south of Salar de Atacama, are pristine high-altitude Andean lakes found along the central Andes of South America. This shallow ecosystem suffers from permanent evaporation, leading to falling water levels, causing it to recede or disappear during the dry season. This dynamic causes physicochemical changes in lakes, such as low nutrient availability, pH change, and dissolved metals, which can influence the composition of the microbial community. In this study, we used a metataxonomic approach (16S rRNA hypervariable regions V3 to V4) to characterize the sedimentary microbiota of these lakes. To understand how the water column affects and is structured in the microbiota of these lakes, we combined the analysis of the persistence of the water column through satellite images and physicochemical characterization. Our results show a significant difference in abiotic factors and microbiota composition between La Punta and La Brava lakes. In addition, microbiota analysis revealed compositional changes in the ecological disaggregation (main and isolated bodies) and antagonistic changes in the abundance of certain taxa between lakes. These findings are an invaluable resource for understanding the microbiological diversity of high Andean lakes using a multidisciplinary approach that evaluates the microbiota behavior in response to abiotic factors. IMPORTANCE In this study, we analyzed the persistence of the water column through satellite images and physicochemical characterization to investigate the composition and diversity in High Andean Lake Systems in a hyperarid environment. In addition to the persistence of the water column, this approach can be used to analyze changes in the morphology of saline accumulations and persistence of snow or ice; for example, for establishing variable plant cover over time and evaluating the microbiota associated with soils with seasonal changes in plants. This makes it an ideal approach to search for novel extremophilic microorganisms with unique properties. In our case, it was used to study microorganisms capable of resisting desiccation and water restriction for a considerable period and adapting to survive in ecological niches, such as those with high UV irradiation, extreme drought, and high salt concentration.

Mechanisms of cold-induced immunity in plants.

Overwintering plants acquire substantial levels of freezing tolerance through cold acclimation or winter hardening. This process is essential for the plants survival to harsh winter conditions. In the areas where persistent snow cover lasts several months, plants are protected from freezing but are, however, exposed to other harsh conditions such as dark, cold, and high humidity. These conditions facilitate infection of psychrophilic pathogens, which are termed 'snow molds'. To fight against infection of snow molds, overwintering plants develop disease resistance via the process of cold acclimation. Compared with pathogen-induced disease resistance, the molecular mechanisms of cold-induced disease resistance have yet to be fully elucidated. In this review, we outline the recent progress in our understanding of disease resistance acquired through cold acclimation. This article is protected by copyright. All rights reserved.

Assessments of long-term precipitation and temperature variations during snow cover periods using high-elevation AMeDAS data in central Japan

Long-term hourly data records of the Automated Meteorological Data Acquisition System (AMeDAS) with snow depth measurements at three high elevation sites were analyzed in central Japan to assess the decadal scale winter climate variabilities with the effects of observation biases. Year-to-year variations of persistent snow cover periods (PSCPs) did not show a significant shortening trend with the delays of starting the PSCP associated with El Niño in December and forward to the ending of the PSCP with nationwide warming in March. Significant warming trends in the surface air temperature averaged in PSCPs were not identified, and length of the PSCP strongly contributed to the year-to-year variations of monthly averaged winter air temperatures. Long-term variabilities of precipitation records were primarily affected by changing the gauge type and data resolutions. After a simple correction of gauge undercatch by air temperature and wind speed, the amount of precipitation increased by around 20 % at the northern station but only by 3 % in the south due to frequent rain-on-snow events. In the two northern stations, snowfall amounts on the order of around 10 % were estimated to be missing in the precipitation records, where a long-term decreasing trend in occurrences of freezing precipitation was also found. The statistics calculated by AMeDAS data were verified using the data of the Vaisala Present Weather Detector in the Sugadaira Highland.

Temporal dynamics in alpine snowpatch plants along a snowmelt gradient explained by functional traits and strategies.

Alpine snowpatches are characterised by persistent snow cover, short growing seasons and periglacial processes, which has resulted in highly specialised plant communities. Hence, these snowpatch communities are among the most threatened from climate change. However, temporal dynamics in snowpatch microclimate and plant composition are rarely explored, especially in the marginal alpine environments of Australia. Seven snowpatches were categorised into early, mid and late snowmelt zones based on growing season length, with soil temperatures recorded from 2003 to 2020 and plant composition surveyed in 84 1m2 quadrats in 2007, 2013 and 2020. Microclimate, species diversity, plant cover and composition, along with community-weighted trait means and plant strategies were assessed to understand snowpatch dynamics in response to climate change. We found that growing season length and temperatures have increased in late melt zones, while changes were less consistent in early and mid melt zones. There were few changes in species diversity, but increases in graminoids and declines in snowpatch specialists in mid and late melt zones. Community-weighted plant height, leaf area and leaf weight also increased, particularly in mid and late melt zones, while plant strategies shifted from compositions of ruderal-tolerant to stress-tolerant. Here, we show that snowpatch communities are rapidly changing in response to longer growing seasons and warmer temperatures, with the greatest changes occurring where snow persists the longest. The results highlight the climate-induced loss of defining biotic and abiotic characteristics of snowpatches, as temporal convergence of compositions along snowmelt gradients threatens the distinctiveness of snowpatch plant communities.

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 (> 90%) and count (> 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.

The spatial and temporal influence of infrastructure and road dust on seasonal snowmelt, vegetation productivity, and early season surface water cover in the Prudhoe Bay Oilfield

Increased industrial development in the Arctic has led to a rapid expansion of infrastructure in the region. Localized impacts of infrastructure on snow distribution, road dust, and snowmelt timing and duration feeds back into the coupled Arctic system causing a series of cascading effects that remain poorly understood. We quantify spatial and temporal patterns of snow-off dates in the Prudhoe Bay Oilfield, Alaska, using Sentinel-2 data. We derive the Normalized Difference Snow Index to quantify snow persistence in 2019–2020. The Normalized Difference Vegetation Index and Normalized Difference Water Index were used to show linkages of vegetation and surface hydrology, in relationship to patterns of snowmelt. Newly available infrastructure data were used to analyze snowmelt patterns in relation infrastructure. Results show a relationship between snowmelt and distance to infrastructure varying by use and traffic load, and orientation relative to the prevailing wind direction (up to 1 month difference in snow-free dates). Post-snowmelt surface water area showed a strong negative correlation (up to −0.927) with distance to infrastructure. Results from field observations indicate an impact of infrastructure on winter near-surface ground temperature and snow depth. This study highlights the impact of infrastructure on a large area beyond the direct human footprint and the interconnectedness between snow-off timing, vegetation, surface hydrology, and near-surface ground temperatures.