Maximum Snow Research Articles

Metabolic Diversity of Xylariaceous Fungi Associated with Leaf Litter Decomposition.

Fungi in the family Xylariaceae are primary agents of leaf litter decomposition. However, the diversity of carbon source utilization by xylariaceous fungi and the relative effects on this from environmental and phylogenetic factors are largely unknown. This study assessed the metabolic diversity and redundancy of xylariaceous fungi, associated with leaf litter decomposition, by measuring their in vitro capacity to utilize multiple carbon sources. The work identified the relative influences of geographic and climatic sources, as well as the taxonomic and phylogenetic relatedness, of the fungi. Using Biolog EcoPlateTM, 43 isolates belonging to Nemania, Xylaria, Nodulisporium, Astrocystis, and Hypoxylon, isolated from Castanopsis sieboldii leaf litter at eight sites in Japan, were found to have the capacity to utilize a variety of carbohydrates, amino acids/amines, carboxylic acids, and polymers. The genera of xylariaceous fungi and their origins significantly affected their metabolic diversity and utilization of carbon sources. Variation partitioning demonstrated that dissimilarities in carbon utilization among fungal isolates were mostly attributable to site differences, especially climatic factors: mean annual temperature and precipitation, and maximum snow depth. Moreover, xylariaceous isolates that originated from adjacent sites tended to have similar patterns of carbon source utilization, suggesting metabolic acclimation to local environmental conditions.

Climate change impact on snow loads in northern Europe

Ongoing and future climate change will affect our built environment. It will have an impact on how we should design our structures to withstand the future environment. One important load when designing buildings in high latitudes and high elevations is the snow load, which in many countries is the governing load for roofs and other structural parts. A warmer climate may lead to a reduction in snow cover. How this reduction relates to extreme values that occur very seldom, however, is unclear. For some regions an increase in precipitation may lead to an increase in snow fall over the winter, and thus to higher snow loads even in a warmer climate.The annual maximum snow water equivalents available from an ensemble of regional climate projections representing the three emission scenarios, RCP 2.6, RCP 4.5, and RCP 8.5, were used to project how the snow load will change in northern Europe until the end of the century. The variability between the different models is studied by analyzing results for individual cities as well as the overall regional variation. The results show that the spatial difference is large, where the mountainous and colder areas may experience an increase in extreme snow loads in the future for all climate scenarios, while the warmer parts of the region may have a significant decrease. The study shows that it is possible to project future snow loads with reasonable accuracy. The variability is still large, though, and must be considered when using climate projections for structural design.

On the Detection of Snow Cover Changes over the Australian Snowy Mountains Using a Dynamic OBIA Approach

This study detected the spatial changes in Snow Cover Area (SCA) over the Snowy Mountains in New South Wales, Australia. We applied a combination of Object-Based Image Analysis (OBIA) algorithms by segmentation, classification, and thresholding rules to extract the snow, water, vegetation, and non-vegetation land covers. For validation, the Maximum Snow Depths (MSDs) were collected at three local snow observation sites (namely Three Mile Dam, Spencer Creek, and Deep Creek) from 1984 to 2020. Multiple Landsat 5, 7, and 8 imageries extracted daily MSDs. The process was followed by applying an Estimation Scale Parameter (ESP) tool to build the local variance (LV) of object heterogeneity for each satellite scene. By matching the required segmentation parameters, the optimal separation step of the image objects was weighted for each of the image bands and the Digital Elevation Model (DEM). In the classification stage, a few land cover classes were initially assigned, and three different indices—Normalized Differential Vegetation Index (NDVI), Surface Water Index (SWI), and a Normalized Differential Snow Index (NDSI)—were created. These indices were used to adjust a few classification thresholds and ruleset functions. The resulting MSDs in all snow observation sites proves noticeable reduction trends during the study period. The SCA classified maps, with an overall accuracy of nearly 0.96, reveal non-significant trends, although with considerable fluctuations over the past 37 years. The variations concentrate in the north and south-east directions, to some extent with a similar pattern each year. Although the long-term changes in SCA are not significant, since 2006, the pattern of maximum values has decreased, with fewer fluctuations in wet and dry episodes. A preliminary analysis of climate drivers’ influences on MSD and SCA variability has also been performed. A dynamic indexing OBIA indicated that continuous processing of satellite images is an effective method of obtaining accurate spatial–temporal SCA information, which is critical for managing water resources and other geo-environmental investigations.

Assessing hydrological sensitivity of grassland basins in the Canadian Prairies to climate using a basin classification-based virtual modelling approach

Abstract. Significant challenges from changes in climate and land use face sustainable water use in the Canadian Prairies ecozone. The region has experienced significant warming since the mid-20th century, and continued warming of an additional 2 ∘C by 2050 is expected. This paper aims to enhance understanding of climate controls on Prairie basin hydrology through numerical model experiments. It approaches this by developing a basin-classification-based virtual modelling framework for a portion of the Prairie region and applying the modelling framework to investigate the hydrological sensitivity of one Prairie basin class (High Elevation Grasslands) to changes in climate. High Elevation Grasslands dominate much of central and southern Alberta and parts of south-western Saskatchewan, with outliers in eastern Saskatchewan and western Manitoba. The experiments revealed that High Elevation Grassland snowpacks are highly sensitive to changes in climate but that this varies geographically. Spring maximum snow water equivalent in grasslands decreases 8 % ∘C−1 of warming. Climate scenario simulations indicated that a 2 ∘C increase in temperature requires at least an increase of 20 % in mean annual precipitation for there to be enough additional snowfall to compensate for enhanced melt losses. The sensitivity in runoff is less linear and varies substantially across the study domain: simulations using 6 ∘C of warming, and a 30 % increase in mean annual precipitation yields simulated decreases in annual runoff of 40 % in climates of the western Prairie but 55 % increases in climates of eastern portions. These results can be used to identify those areas of the region that are most sensitive to climate change and highlight focus areas for monitoring and adaptation. The results also demonstrate how a basin classification-based virtual modelling framework can be applied to evaluate regional-scale impacts of climate change with relatively high spatial resolution in a robust, effective and efficient manner.

Where the wild beavers are: Climate and landscape controls on beaver pond area in snow‐dominated rangeland headwaters

AbstractBeavers are of increasing interest as a mechanism for ecosystem restoration. However, the physical watershed characteristics that allow for successful beaver re‐establishment are not consistent across habitat suitability models. We delineated the extent of beaver impounded surface water in the Pole Mountain Area (PMA) of south‐eastern Wyoming with six National Agricultural Imagery Program acquisitions over an 18‐year time period via a random forest classification algorithm. We computed beaver pond area for first‐ and second‐order watersheds within the PMA and related the impounded pond area to climatic metrics such as maximum annual snow water equivalent and snowmelt and rain input into the ground. There was no apparent relationship between annual climate metrics and beaver pond area. Further, we developed a random forest regression model to correlate beaver pond area with topographically derived watershed characteristics (e.g. hillslope size and riparian area size) to explore physical controls on impounded area at the watershed scale. Controls differed for first‐ and second‐order watersheds, with pond area in smaller watersheds largely being controlled by lateral water contributions from the hillslopes and in the larger watersheds by water from upstream and available space in the riparian areas. Interestingly, vegetation height did not emerge as an important predictor for pond area, likely because of generally good availability of forage material in all watersheds. This study improves our understanding of watershed characteristics that contribute to beaver habitat suitability and can be used to guide beaver reintroduction efforts through analysis targeted at identifying watersheds most appropriate to beaver pond construction.

Fluctuations of Winter Floods in Small Austrian and Ukrainian Catchments

Studying the changes in extreme river runoff induced by climate change is of utmost importance, as the variability of floods directly affects life and human activities. This study examines the fluctuations and persistence of winter floods in 14 catchments in the Rika River Basin (Ukraine) and ten catchments in the Steyr River Basin (Austria). The catchments represent typical hydrological regimes in the Danube River region. The fluctuations and persistence of floods are analyzed by the hydro-genetic method and a seasonality analysis for the period 1951–2015. The results show a much more pronounced fluctuation pattern in the upper Rika catchments than in the upper Steyr catchments. This pattern indicates an increase in winter flood magnitudes between the mid-1960s and the 1990s, followed by a decrease until recently. The flood seasonality shows a large inter-annual variability in both regions. The most significant winter floods tend to occur in November and December. The winter flood fluctuations are compared with changes in associated climate characteristics, i.e., seven-day maximum precipitation, a melt index, and annual maximum snow depth. The seasonality of these characteristics has a strong inter-annual variability and only partly explains the winter flood fluctuations.

Historical Trends and Projections of Snow Cover over the High Arctic: A Review

Snow is the dominant form of precipitation and the main cryospheric feature of the High Arctic (HA) covering its land, sea, lake and river ice surfaces for a large part of the year. The snow cover in the HA is involved in climate feedbacks that influence the global climate system, and greatly impacts the hydrology and the ecosystems of the coldest biomes of the Northern Hemisphere. The ongoing global warming trend and its polar amplification is threatening the long-term stability of the snow cover in the HA. This study presents an extensive review of the literature on observed and projected snow cover conditions in the High Arctic region. Several key snow cover metrics were reviewed, including snowfall, snow cover duration (SCD), snow cover extent (SCE), snow depth (SD), and snow water equivalent (SWE) since 1930 based on in situ, remote sensing and simulations results. Changes in snow metrics were reviewed and outlined from the continental to the local scale. The reviewed snow metrics displayed different sensitivities to past and projected changes in precipitation and air temperature. Despite the overall increase in snowfall, both observed from historical data and projected into the future, some snow cover metrics displayed consistent decreasing trends, with SCE and SCD showing the most widespread and steady decreases over the last century in the HA, particularly in the spring and summer seasons. However, snow depth and, in some regions SWE, have mostly increased; nevertheless, both SD and SWE are projected to decrease by 2030. By the end of the century, the extent of Arctic spring snow cover will be considerably less than today (10–35%). Model simulations project higher winter snowfall, higher or lower maximum snow depth depending on regions, and a shortened snow season by the end of the century. The spatial pattern of snow metrics trends for both historical and projected climates exhibit noticeable asymmetry among the different HA sectors, with the largest observed and anticipated changes occurring over the Canadian HA.

Responses of spring soil moisture of different land use types to snow cover in Northeast China under climate change background

To explore the impact of climate change on snow cover and spring soil moisture (SM) in areas with seasonally frozen soil, snow cover parameters and spring SM for different land use types in Northeast China are extracted based on remote sensing snow cover and SM products. Snow cover parameters include snow days (SD), first day of snow cover (FSD), last day of snow cover (LSD), maximum snow depth (MSD) and average snow depth (ASD). The spatiotemporal variations and correlations between snow cover parameters and spring SM for different land use types are analyzed. The results showed that the average spring SM for different land use types was ordered woodland > farmland > grassland, with obvious woodland and farmland increases. Woodlands had many SD and large snow depths (MSD and ASD) that eventually decreased. Farmland SM increased significantly in spring, which aided crop development. The decrease in grassland spring SM was not obvious, but the snow cover parameters of certain areas decreased notably. Snow cover significantly impacted farmland SM, and correlation coefficients were highest between all snow cover parameters and SM in spring. The correlations between grassland snow cover parameters and SM in April were higher than those in May, but woodland snow cover parameters and spring SM were not correlated. Among the five snow parameters, FSD had the lowest correlation with spring SM, and SD had the greatest impact on SM. These results show the significant relationship between snow cover and SM and reveal relevant patterns. As future climate warming may introduce drought risk to woodland and grassland areas, advance preparations should be made. Farmland areas will continue to maintain appropriate SM, which is beneficial for agricultural development.

Climatology and extreme cases of sea‐effect snowfall on the southern Baltic Sea coast

AbstractHeavy snowfall events of possible sea‐effect origin, occurring on the southern Baltic Sea coast, are described in this study. The analysed region is not very snowy, with an average of 40 days with snow cover in the winter season and a mean maximum snow depth of 8–12 cm. Twenty‐five snowfall events were selected using the threshold of 20 cm/2 days (i.e., 20 cm of snow depth increase during 2 days), and the synoptic climatology, as well as triggering factors of their occurrence, were defined. The bipolar pattern of sea level pressure with a high‐pressure over Scandinavia and a low‐pressure system southward of it was identified as favourable for sea‐effect snowfall on the southern Baltic Sea coast. This pressure field, causing northeastern circulation in the lower troposphere, brings cold air masses over the relatively warm sea basin, and the negative anomalies of air temperature at 850 hPa geopotential level, amounting to −9°C, encompass Scandinavia, as well as central and northeastern Europe. This makes the temperature difference between the air at the 850 hPa geopotential height and the sea surface exceeding 15°C. This large temperature difference establishes favourable conditions for convection in cold air masses, which become unstable while crossing over the warm sea. The formed clouds give snowfall in the coastal areas. In the further part of the study, three sea‐effect snowfall cases were chosen for detailed analysis, including atmospheric sounding, radar and satellite images, air parcel backward trajectories, and synoptic maps. They reveal high‐pressure wedges associated with low air temperature over Scandinavia and northern airflow over the Baltic Sea. The wind direction and the temperature of air masses are key components in the occurrence of sea‐effect snowfall, and global warming conditions seem to reduce the frequency of snow events over the Polish Baltic Sea coast.

Effect of Snow Cover on Spring Soil Moisture Content in Key Agricultural Areas of Northeast China

As an important source of soil moisture content during spring in high-latitude areas, snow cover affects the occurrence of spring drought and crop yield and quality. There has not been sufficient research on the effect of winter snow cover on spring soil moisture content. This paper focuses on the main agricultural areas of Northeast China—the Songnen Plain and the Sanjiang Plain. Using meteorological data of both spring soil moisture content and snow cover at 19 agricultural meteorological stations from 1983 to 2019, the effect of snow cover on spring soil moisture content in the Sanjiang Plain and Songnen Plain is studied by variance analysis, spatial analysis, and correlation analysis. The results show that: (1) Compared to the Sanjiang Plain, the Songnen Plain has a significantly lower content of soil moisture at the surface (0–10 cm) and deep layer (10–20 cm, 20–30 cm) during the entire spring and every month of spring (p < 0.05), and a greater interannual variation of soil moisture. (2) Snow cover has a significant effect on spring soil moisture in the Songnen Plain, but not as much as one in the Sanjiang Plain. For the Songnen Plain, snow-cover duration and the snow-cover onset date has a lasting influence on spring soil moisture until May, which can extend to as deep as 20–30 cm. As months go by, its influence on shallow-layer soil gradually wears off. Maximum snow depth and the snow-cover end date only influence the April surface soil. (3) Snow cover has a strong effect on soil moisture conservation in more arid areas. Delayed snow-cover onset date, earlier snow-cover end date, and significantly shortened snow-cover duration all contribute to a spring drought soil condition in the Songnen Plain.

Impacts of summer and winter conditions on summer river low flows in low elevation, snow-affected catchments

Reduced snow storage has been associated with lower river low flows in mountainous catchments, exacerbating summer hydrological droughts. However, the impacts of changing snow storage on summer low flows in low-elevation, snow-affected catchments has not yet been investigated. To address this knowledge gap, the dominant hydroclimate predictors of summer low flows were first identified through correlation analysis in 12 tributary catchments of the St. Lawrence River in the Canadian province of Quebec. The correlation results show that summer low flow is most sensitive to summer rainfall, while maximum snow water equivalent (SWE) is the dominant winter preconditioning factor of low flows, particularly at the end of summer. The multivariate sensitivity of summer low flow to hydroclimate predictors was then quantified by multilevel regression analysis, considering also the effect of catchment biophysical attributes. Accumulated rainfall since snow cover disappearance was found to be the prime control on summer low flow, as expected for the humid climate of Quebec. Maximum SWE had a secondary but significant positive influence on low flow, sometimes on the same order as the negative effect of evapotranspiration losses. As a whole, our results show that in these low elevation catchments, thicker winter snowpacks that last longer and melt slower in the spring are conducive to higher low flows in the following summer. More rugged and forested catchments with coarser soils were found to have higher summer low flows than flatter agricultural catchments with compacted clayed soils. This emphasizes the role of soils and geology on infiltration, aquifer recharge and related river baseflow in summer. Further climate warming and snowpack depletion could reduce future summer low flow, exacerbating hydrological droughts and impacting ecosystems integrity and ecological services.

Simulation of Daily Snow Depth Data in China Based on the NEX-GDDP

In this study, a backpropagation artificial neural network snow simulation model (BPANNSIM) is built using data collected from the National Climate Reference Station to obtain simulation data of China’s future daily snow depth in terms of representative concentration pathways (RCP4.5 and RCP8.5). The input layer of the BPANNSIM comprises the current day’s maximum temperature, minimum temperature, snow depth, and precipitation data, and the target layer comprises snow depth data of the following day. The model is trained and validated based on data from the National Climate Reference Station over a baseline period of 1986–2005. Validation results show that the temporal correlations of the observed and the model iterative simulated values are 0.94 for monthly cumulative snow cover duration and 0.88 for monthly cumulative snow depth. Subsequently, future daily snow depth data (2016–2065) are retrieved from the NEX-GDPP dataset (Washington, DC/USA: the National Aeronautics and Space Administration(NASA)Earth Exchange/Global Daily Downscaled Projections data), revealing that the simulation data error is highly correlated with that of the input data; thus, a validation method for gridded meteorological data is proposed to verify the accuracy of gridded meteorological data within snowfall periods and the reasonability of hydrothermal coupling for gridded meteorological data.

Altitudinal Gradient Characteristics of Spatial and Temporal Variations of Snowpack in the Changbai Mountain and Their Response to Climate Change

The variations in the snowpack in water towers of the world due to climate change have threatened the amount and timing of freshwater supplied downstream. However, it remains to be further investigated whether snowpack variation in water towers exhibits elevational heterogeneity at different altitude gradients and which climatic factors mainly influence these differences. Therefore, Changbai Mountain, a high-latitude water tower, was selected to analyze the changes in the snowpack by the methods of modified Mann–Kendall based on the daily meteorological data from the China Meteorological Data Service Centre. Meanwhile, the responses of snowpack change to climatic factors over recent decades were assessed and generalized using additive models. The results showed that the snow depth was greater in the higher altitude areas than in the lower elevation areas at different times. Areas with a snow depth of over 70 mm increased significantly in the 2010s. Increasing trends were shown at different altitudes from December to March of the next year during 1960~2018. However, a significant decreasing trend was shown in April, except for altitudes of 600–2378 m. The snow cover time at different altitudes showed a trend of first increasing and then decreasing during 1960~2018. The date of maximum snow depth appears to be more lagged as the altitude increases. In addition, the spring snowpack melted significantly faster in the 2010s than that in the 1960s. The snowpack variation in low-altitude regions is mainly influenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with the increase in altitude. Therefore, the results of this study will be beneficial to the ecological protection and sustainable development of water towers.

Spatiotemporal characteristics of the soil freeze-thaw state and its variation under different land use types - A case study in Northeast China

With global warming, the soil freeze-thaw state has greatly changed, which has profoundly impacted the balance between water resources, ecological security and regional agriculture and forestry production in frozen regions. Based on daily soil freezing/thawing depth data in Heilongjiang Province over the last 60 years, Mann-Kendall trend analysis, Sen's slope and Hurst exponent were performed to analyze the spatiotemporal characteristics of the soil freeze-thaw state, and the effects of nonclimatic factors "land use types" and snow cover on the change in the soil freeze-thaw state were discussed. The results indicated that (1) the soil freeze-thaw state significantly changed over the past 60 years. The maximum seasonally frozen depth decreased approximately by 48 cm, the first freezing date was delayed by approximately 10 d, the first thawing date and last date advanced approximately by 6 and 27 d, respectively, the freezing and thawing periods decreased by approximately 17 and 21 d, respectively, and the duration and actual number of freezing days decreased by 38 and 34 d, respectively. (2) The soil freeze-thaw state changed abruptly in 1988 and exhibited a significant trend after the 1990s. The Hurst exponent of the soil freeze-thaw state variables at approximately 2/3 of the stations reached the strong persistence level. (3) In forestland and grassland areas, vegetation coverage reduced the climate warming impact on the soil freeze-thaw state. In addition, the maximum snow depth was significantly correlated with the soil freezing depth and first thawing date, and the change in snow depth also offset the effects of temperature rise on soil freezing depth and first thawing date to a certain extent. Changes in the soil freeze-thaw state have both advantages and disadvantages on agricultural production. Therefore, it is necessary to strengthen agricultural monitoring and use territorial knowledge to reasonably regulate agricultural production.

Evaluating methods for reconstructing large gaps in historic snow depth time series

Abstract. Historic measurements are often temporally incomplete and may contain longer periods of missing data, whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, for which even whole winters can be missing in a station record, and suitable methods have to be found to reconstruct the missing data. Daily in situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level are used to compare six different methods for reconstructing long gaps in manual HS time series by performing a “leave-one-winter-out” cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias-corrected data from the best-correlated neighboring station (BSC), inverse distance-weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, elastic net (ENET) regression, random forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. The median root mean square error (RMSE) in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be reproduced by ENET, RF, SM and WNR well, with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS>1 cm (dHS1) are clearly weaker and, except for BCS, positively biased with RMSE of 18–33 d. SM reveals the best performance with r2 of 0.93 and RMSE of 15 d for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements

Abstract. Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

Snow Temperature Behind Sliding Skis as an Indicator for Frictional Meltwater

For many years, a frictional meltwater film has been assumed to be the reason for the low friction between skis and snow, but experimental studies have been inconclusive. Therefore, the aim of our study was to find indications or evidence for the presence of frictional meltwater. The friction between snow at −4°C and an XC ski as well as a flat ski was measured on a large-scale linear snow tribometer at realistic skiing speeds from 5 to 25 m/s. We used an infrared camera to analyze the snow temperature behind the skis. From the maximum snow surface temperature, we estimated the temperature at the spots where ski and snow contacted. Assuming that the contact spot temperature does not notably exceed 0°C, we calculated the relative contact area between ski and snow. Maximum snow surface temperatures were very close to 0°C. Given that not the entire snow surface is in contact with the ski, this finding is a strong indication for snow melting. Heat flow considerations led to the conclusion that there must be energy dissipation beyond the heat flow into ski and snow. The most obvious mechanism for the additional energy dissipation is snow melting. Presuming that the contact spot temperatures are at most slightly above 0°C, we calculated relative contact areas of 21–98%. Previous research has reported much lower values; however, most studies were conducted under conditions that are not realistic for skiing.

Contribution of conspecific negative density dependence to species diversity is increasing towards low environmental limitation in Japanese forests

Species coexistence is a result of biotic interactions, environmental and historical conditions. The Janzen-Connell hypothesis assumes that conspecific negative density dependence (CNDD) is one of the local processes maintaining high species diversity by decreasing population growth rates at high densities. However, the contribution of CNDD to species richness variation across environmental gradients remains unclear. In 32 large forest plots all over the Japanese archipelago covering > 40,000 individual trees of > 300 species and based on size distributions, we analysed the strength of CNDD of individual species and its contribution to species number and diversity across altitude, mean annual temperature, mean annual precipitation and maximum snow depth gradients. The strength of CNDD was increasing towards low altitudes and high tree species number and diversity. The effect of CNDD on species number was changing across altitude, temperature and snow depth gradients and their combined effects contributed 11–18% of the overall explained variance. Our results suggest that CNDD can work as a mechanism structuring forest communities in the Japanese archipelago. Strong CNDD was observed to be connected with high species diversity under low environmental limitations where local biotic interactions are expected to be stronger than in niche-based community assemblies under high environmental filtering.

Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

Determination of intraspecific variation in seed weight, leaf functional traits, and sapling size of Betula ermanii using a common garden experiment

ABSTRACT Habitat loss of forest tree species distributed in cold temperate and alpine zones is a serious issue in the context of global warming. Considering that intraspecific variation is essential for adaptation to changing environments, intraspecific variation in seed weight, leaf traits, and sapling size of a sub-alpine broad-leaved tree species, Betula ermanii, was examined in this study using seeds collected from 11 populations across its natural distribution. Seeds were sown in seed beds, and containerized seedlings were grown in a greenhouse with a uniform environment (a common garden experiment). The effects of four climate variables, i.e. warmth index (WI), minimum temperature of the coldest month (TMC), summer precipitation (PRS), and maximum snow depth (MSD) were evaluated on seed weight, leaf functional traits, and sapling size using a generalized linear mixed model (GLMM). Positive latitudinal clines were detected for seed weight, whereas negative clines were detected for both specific leaf area and leaf nitrogen. These clines were not consistent with the general trends observed in previous studies. The reasons might be partly explained by geographic variation in climatic variables and nutrient availability. The findings obtained from this study should be useful for conservation of mountainous forest ecosystems in the age of climate change.