Extensive Snow Research Articles

Juvenile survival of little owls decreases with snow cover.

Global environmental changes are associated with warmer average temperatures and more extreme weather events, potentially affecting wildlife population dynamics by altering demographic processes. Extreme weather events can reduce food resources and survival in all seasons of the year. Estimates of season-specific survival probabilities are therefore crucial to understand the moderating effect of extreme events on annual mortality. Here, we analysed survival probabilities of 307 radio-tracked juvenile little owls (Athene noctua) over two-week periods from fledging to their first breeding attempt in the following spring to assess the contribution of extreme weather events. Survival probabilities were typically lowest during the first weeks after fledging in summer but were moderated by seasonal extremes in winter. The duration of snow cover in winter had a strong negative effect on survival probability, while being food supplemented during the nestling stage increased survival during the first weeks after fledging in summer and ultimately led to a larger proportion of birds surviving the first year. Overall annual survival probability over the first year varied by 34.3% between 0.117 (95% credible interval 0.052-0.223) and 0.178 (0.097-0.293) depending on the severity of the winter, and was as high as 0.233 (0.127-0.373) for food-supplemented fledglings. In years with mild winters, the season with the lowest survival was the summer post-fledging period (0.508; 0.428-0.594), but in years with extensive snow cover the winter was the season with the lowest survival (0.481; 0.337-0.626). We therefore show that extreme weather events occurring in a particular season reduced the proportion of first-year survivors. Increasing extreme weather events can moderate seasonal survival probability through altering food supply of juvenile little owls either during the nestling period or in winter, with similarly large effects on annual survival and the viability of populations.

From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea

Abstract. Year-round snow cover is a characteristic of the entire Antarctic sea ice cover, which has significant implications for the energy and mass budgets of sea ice, e.g., by preventing surface melt in summer and enhancing sea ice growth through extensive snow ice formation. However, substantial observational gaps in the seasonal cycle of Antarctic sea ice and its snow cover limit the understanding of important processes in the ice-covered Southern Ocean. They also introduce large uncertainties in satellite remote sensing applications and climate studies. Here we present results from 10 years of autonomous snow observations from Snow Buoys in the Weddell Sea. To distinguish between actual snow depth and potential snow ice thickness within the accumulated snowpack, a one-dimensional thermodynamic sea ice model is applied along the drift trajectories of the buoys. The results show that potential snow ice formation, with an average maximum thickness of 35 cm, was detected along 41 % of the total track length of the analyzed Snow Buoy tracks, which corresponds to about one-quarter of the snow accumulation. In addition, we simulate the evolution of internal snow properties along the drift trajectories with the more complex SNOWPACK model, which results in superimposed ice thicknesses between 0 and 14 cm on top of the snow ice layer. These estimates will provide an important reference dataset for both snow depth and meteoric ice rates in the Southern Ocean.

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time. Codes and models are available at https://github.com/StephenLinn/LMQFormer.

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle.

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Significance and issues of automatic snow cover measurement – case study for the Šumava Mountains region

The study evaluates the contribution of automatic snow measuring stations for snow monitoring in the Šumava region. Using climatological maps, the results of spatial analysis of the maximum depth of the snow cover for six winter seasons were compared, namely first from the measured data of the basic CHMI stations and second from the data of all stations. The biggest differences occurred in the comparison at the highest locations on the windward side of the Šumava (>1 200 m a.s.l.), where there is no corresponding network of basic meteorological stations. In the case of the snow-poor winter season 2020/21, these differences were relatively higher than in the 2018/19 season, with more extensive snow cover in all positions. The evaluation of the entire period (2017/18–2022/23) produced similar results, as snow-poor winter seasons prevailed here. We can therefore assume that the differences in snow characteristics will continue to be rather significant in the Šumava region. Accordingly, the importance of automatic snow measurement will also increase, especially in higher altitudes of the windward border area. In addition to the expansion of the measuring network and the significant refinement of information about the snow cover, especially in the mountains, the main benefit of data on the depth of snow from automatic stations is their operability and immediate use for other applications.

Combining modelled snowpack stability with machine learning to predict avalanche activity

Abstract. Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.

Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain

Land surface albedo plays an extremely important role in the surface energy budget, by determining the proportion of incoming solar radiation, which is available to drive photosynthesis and surface heating, and that which is reflected directly back to space. In northern high latitude regions, the albedo of snow-covered vegetation and open, leafless forest canopies in winter, is quite high, while the albedo of boreal evergreen conifers can either be quite low (even with extensive snow lying under the canopy) or rather bright depending on the structure and density of the canopy. Here, we present the further development and evaluation of a 30 m Landsat albedo product, including an operational blue-sky albedo product, for application in the circumpolar domain. The surface reflectances from the Landsat satellite constellation are coupled with surface anisotropy information (Bidirectional Reflectance Distribution Function, BRDF) from the MODerate-resolution Imaging Spectroradiometer (MODIS). The product is extensively validated across diverse land cover and conditions and performs well with root mean squared error of 0.0395 and negligible bias when compared to coincident tower-based albedo measurements. The development of this Landsat albedo products allows for better capture of ephemeral, heterogeneous and dynamic surface conditions at the landscape scale across the circumpolar domain.

Are periodic (intra-annual) tangential bands of vessels in diffuse-porous tree species the equivalent of flood rings in ring-porous species? Reproducibility and cause

Tree rings from ring-porous species have often been used as flood proxy. Many ring-porous species produce characteristic flood rings in response to stem submersion during vessel formation. Flood rings have earlywood vessels that are more numerous and/or of smaller cross-sectional area than “normal” rings. This study aimed at determining if diffuse-porous balsam poplar and trembling aspen, like ring-porous black ash, produce anatomically distinct annual tree rings in response to flooding. More specifically, we asked (i) if periodic tangential bands of vessels (hereafter PTBV) could be as easily identified/quantified as flood rings and (ii) if PTBV could be associated with spring flooding. Sampling of black ash, balsam poplar and trembling aspen trees took place along a flood exposure gradient in the floodplain of Lake Duparquet in northwestern Québec. Two observers recorded flood rings and PTBV. Consistency between observers was greatest when identifying flood rings. In both diffuse-porous species, PTBV occurred less abundantly than flood rings in black ash. They also occurred less often in balsam poplar than in trembling aspen. Years in which PTBV were initiated early in the growing season were associated with years in which flood rings occurred. Like flood rings, early occurring PTBV were more abundant in springs characterized by high mean river discharge, extensive snow cover, cold temperatures and heavy precipitation. Early-occurring PTBV dominated in flooded sites and late-occurring ones dominated in the control site. However, PTBV of the late-types were also observed in both flood exposures indicating that spring flood may not be the only factor modulating their formation. While flood rings seem to be associated with a change in the transport of growth regulators resulting from stem submergence and excess water, PTBV may be reflective of rhythmic alterations in the transport of growth regulators resulting from either water excess or deficit. Despite promising findings, many questions remain before PTBV in riparian diffuse-porous species can be widely used as a flood proxy. Why do species and individual trees differ in their ability to record them? What is the full range of environmental conditions triggering PTBV’s formation especially in unflooded sites and in the late growing season?

Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

AbstractMountain snow is a fundamental freshwater supply in the arid regions. Climate warming alters the timing of snowmelt and shortens the snow cover duration, which greatly influences the regional climate and water management. However, a reliable estimation of snow mass in the Tianshan Mountains (TS) is still unclear due to the scarcity of extensive continuous surface observations and a complex spatial heterogeneity. Therefore, a long‐time snow simulation from 1982 to 2018 was performed in WRF/Noah‐MP to quantify snow mass in the TS, forced by ERA5 reanalysis data and real‐time updated leaf area index and green vegetation fraction. Meanwhile, March snow mass (close to the annual peak snow mass), snow cover fraction (SCF), and their associated trends were investigated in the TS. The results indicated a good accuracy of the simulated snow water equivalent (root mean square error [RMSE]: 7.82 mm/day) with a slight overestimation (2.84 mm/day). Compared with ERA5 data set, the RMSE and mean bias of the daily snow depth from WRF/Noah‐MP downscaling were significantly reduced by 95.74% and 93.02%, respectively. The climatological March snow mass measured 97.85 (±16.60) Gt in the TS and exhibited a negligible tendency during the study period. The total precipitation during the cold season controlled the variations of March snow mass. The increased precipitation in the high‐altitude regions contributed to an extensive snow mass, which could offset the loss in the TS lowland. In contrast, rapidly rising air temperature caused a significant reduction of March SCF, particularly in the Southern TS.

Assessments of CMIP3 Climate Models and Projected Climate Changes of Precipitation and Temperature for Vietnam and the Southeast Asia

The main objective of this study is to assess the information of climate simulation and projection of temperature and precipitation based on the results of 23 models from the World Climate Research Programme’s (WCRP) Coupled Model Inter-comparison Project phase three (CMIP3) for Southeast Asian Countries with a more focus on Vietnam. The research is divided into two parts: i) Validation of the simulation of the 1948-2002 period with observations (gridded NCEP and CRU data and Vietnam’s surface station data) and ii) Assessments of projected climate changes for both precipitation and temperature variables. For the first part, depending on areas, main results showed a very systematic bias for the simulations of temperature and precipitation. CMIP3’s simulations have significant issues regarding high altitude regions, which can be explained by coarse of model resolutions to represent the effects of the complex topography, land-surface interactions, and the distorted albedo feedbacks due to extensive snow cover. In the second part, regarding the climate projection for temperature, It is projected to get warmer from 3.5 to 6 degrees in 21st Century in all scenarios. For the details in 2030 and 2050, the average annual temperature will clearly increase in the whole Vietnam. The trend of annual precipitation conditions at multi-scenarios in Southeast Asia compared to the current climate condition is not as clear as that of the temperature. The remarkable issue for Vietnam is in the A2 scenario, which provides the most change for the whole country.

Fast Ice Prediction System (FIPS) for land-fast sea ice at Prydz Bay, East Antarctica: an operational service for CHINARE

AbstractA Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/VXuelongwhen optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

Predictability of the early winter Arctic oscillation from autumn Eurasian snowcover in subseasonal forecast models

The response of the early winter Northern Hemisphere stratospheric polar vortex and tropospheric Arctic Oscillation to anomalous autumn snow cover in Eurasia is evaluated in four operational subseasonal forecasting models. Of these four models, the two with finer stratospheric resolution simulate a weakened vortex for hindcasts initialized with more extensive snow as compared to those with less extensive snow, consistent with the observed effect, though the modeled effect is significantly weaker than that observed. The other two models fail to capture the local Western Eurasian ridge in response to enhanced snow, and hence their failure to simulate a stratospheric response may be due to biases in representing surface–atmosphere coupling rather than their coarser stratospheric resolution per se. There is no evidence of a tropospheric Arctic Oscillation response in early winter in any of these models, which may be related to the weakness of the stratospheric response or (in one model) to too-weak coupling from the stratosphere down to the surface. Overall, the possible contribution of autumn snowcover over Eurasia to improved predictability of the winter Arctic Oscillation in subseasonal forecast models has not yet been realized even in a probabilistic sense.

Snow processes in mountain forests: interception modeling for coarse-scale applications

Abstract. Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

Clinging on to alpine life: Investigating factors driving the uphill range contraction and population decline of a mountain breeding bird.

Climate change and anthropogenic nitrogen deposition are widely regarded as important drivers of environmental change in alpine habitats. However, due to the difficulties working in high-elevation mountain systems, the impacts of these drivers on alpine breeding species have rarely been investigated. The Eurasian dotterel (Charadrius morinellus) is a migratory wader, which has been the subject of uniquely long-term and spatially widespread monitoring effort in Scotland, where it breeds in alpine areas in dwindling numbers. Here we analyse data sets spanning three decades, to investigate whether key potential drivers of environmental change in Scottish mountains (snow lie, elevated summer temperatures and nitrogen deposition) have contributed to the population decline of dotterel. We also consider the role of rainfall on the species' wintering grounds in North Africa. We found that dotterel declines-in both density and site occupancy of breeding males-primarily occurred on low and intermediate elevation sites. High-elevation sites mostly continued to be occupied, but males occurred at lower densities in years following snow-rich winters, suggesting that high-elevation snow cover displaced dotterel to lower sites. Wintering ground rainfall was positively associated with densities of breeding males two springs later. Dotterel densities were reduced at low and intermediate sites where nitrogen deposition was greatest, but not at high-elevation sites. While climatic factors explained variation in breeding density between years, they did not seem to explain the species' uphill retreat and decline. We cannot rule out the possibility that dotterel have increasingly settled on higher sites previously unavailable due to extensive snow cover, while changes associated with nitrogen deposition may also have rendered lower lying sites less suitable for breeding. Causes of population and range changes in mountain-breeding species are thus liable to be complex, involving multiple anthropogenic drivers of environmental change acting widely across annual and migratory life cycles.

The first record of raccoon dog ( Nyctereutes procyonoides ) in Turkey

The raccoon dog Nyctereutes procyonoides was recorded for the first time in Turkey on May 10, 2019, and June 5, 2019, in the same location after 4668 nights of camera trapping in the forests of the Sarikamis region and Allahuekber Mountains in eastern Turkey. It was recorded in a Scots pine Pinus sylvestris forest at 2340 m above sea level with extensive snow cover. Given that the nearest known population is in the forests of Georgia approximately 105 km away, there may already be a viable population in Turkey. As an omnivorous species with a high adaptive ability and high reproductive potential, the raccoon dog has strong dispersal capability. It is considered an invasive species in Europe and there are some ongoing eradication activities in several countries. Therefore, we strongly advise that the raccoon dog in Turkey be evaluated as an invasive species and that the relevant authorities conduct extensive research and any necessary management, especially where the habitat and local climate are more favorable for the species’ reproduction and range expansion.

Interaction of wind and cold‐season hydrologic processes on erosion from complex topography following wildfire in sagebrush steppe

AbstractWildfire is a natural component of sagebrush (Artemisia spp.) steppe rangelands that induces temporal shifts in plant community physiognomy, ground surface conditions, and erosion rates. Fire alteration of the vegetation structure and ground cover in these ecosystems commonly amplifies soil losses by wind‐ and water‐driven erosion. Much of the fire‐related erosion research for sagebrush steppe has focused on either erosion by wind over gentle terrain or water‐driven erosion under high‐intensity rainfall on complex topography. However, many sagebrush rangelands are geographically positioned in snow‐dominated uplands with complex terrain in which runoff and sediment delivery occur primarily in winter months associated with cold‐season hydrology. Current understanding is limited regarding fire effects on the interaction of wind‐ and cold‐season hydrologic‐driven erosion processes for these ecosystems. In this study, we evaluated fire impacts on vegetation, ground cover, soils, and erosion across spatial scales at a snow‐dominated mountainous sagebrush site over a 2‐year period post‐fire. Vegetation, ground cover, and soil conditions were assessed at various plot scales (8 m2 to 3.42 ha) through standard field measures. Erosion was quantified through a network of silt fences (n = 24) spanning hillslope and side channel or swale areas, ranging from 0.003 to 3.42 ha in size. Sediment delivery at the watershed scale (129 ha) was assessed by suspended sediment samples of streamflow through a drop‐box v‐notch weir. Wildfire consumed nearly all above‐ground live vegetation at the site and resulted in more than 60% bare ground (bare soil, ash, and rock) in the immediate post‐fire period. Widespread wind‐driven sediment loading of swales was observed over the first month post‐fire and extensive snow drifts were formed in these swales each winter season during the study. In the first year, sediment yields from north‐ and south‐facing aspects averaged 0.99–8.62 t ha−1 at the short‐hillslope scale (~0.004 ha), 0.02–1.65 t ha−1 at the long‐hillslope scale (0.02–0.46 ha), and 0.24–0.71 t ha−1 at the swale scale (0.65–3.42 ha), and watershed scale sediment yield was 2.47 t ha−1. By the second year post fire, foliar cover exceeded 120% across the site, but bare ground remained more than 60%. Sediment yield in the second year was greatly reduced across short‐ to long‐hillslope scales (0.02–0.04 t ha−1), but was similar to first‐year measures for swale plots (0.24–0.61 t ha−1) and at the watershed scale (3.05 t ha−1). Nearly all the sediment collected across all spatial scales was delivered during runoff events associated with cold‐season hydrologic processes, including rain‐on‐snow, rain‐on‐frozen soils, and snowmelt runoff. Approximately 85–99% of annual sediment collected across all silt fence plots each year was from swales. The high levels of sediment delivered across hillslope to watershed scales in this study are attributed to observed preferential loading of fine sediments into swale channels by aeolian processes in the immediate post‐fire period and subsequent flushing of these sediments by runoff from cold‐season hydrologic processes. Our results suggest that the interaction of aeolian and cold‐season hydrologic‐driven erosion processes is an important component for consideration in post‐fire erosion assessment and prediction and can have profound implications for soil loss from these ecosystems. © 2019 John Wiley & Sons, Ltd.

High altitude accumulation and preserved climate information in the western Pamir, observations from the Fedchenko Glacier accumulation basin

AbstractThe accumulation region of Fedchenko Glacier represents an extensive snow reservoir in the Pamir Mountains feeding the longest glacier in Central Asia. Observed elevation changes indicate a continuous ice loss in the ablation region of Fedchenko Glacier since 1928, while the mass balance of the accumulation region is largely unknown. In this study, we show that accumulation varies considerably in the main accumulation basin, with accumulation rates up to 2400 mm w.e. a−1in the West, decreasing to <1000 mm w.e. a−1in the center, although the elevation difference is <200 m. The combination of snow/firn samples and ground-penetrating radar profiles suggests that this accumulation pattern is persistent during the recent past. The recent accumulation history is reconstructed from internal radar reflectors using a firn densification model and shows strong interannual variations, but near constant mean values since 2002. Modeling of trajectories, based on accumulation and glacier geometry, results in an estimate of the depth/age relation close to the main divide. This region provides one of the most suitable locations for retrieving climate information with temporal high resolution for the last millennium, with a potential to cover most of the Holocene in less detail.

Scientific Note: Facultative Perenniality in the Dwarf Sundew (Drosera brevifolia)

The dwarf sundew (Drosera brevifolia) occurs from Uruguay to Virginia. Disjunct populations occur in the southeastern U.S. with the northernmost in Kentucky. Despite this wide distribution, relatively little is known about the biology of this species. It has been described as both annual and biennial. The endangered Kentucky population is considered biennial, but occasionally, live, mature plants have dead flower stalks in early autumn suggesting some may be perennial. In 2013, 40 sundews that germinated in the fall of 2012 were marked as they flowered in 2013. They were observed into a third growing season in 2014 until the end of July. Sixteen plants (40%) died after setting seeds in their second growing season, while nine (22.5%) remained alive, flowered, and set seeds again the following year. Seven of these were still alive at the end of July 2014. Fifteen plants were puzzling as the leaves were dead after setting seeds in their second growing season, but were alive May 2014, of which 12 flowered. This study confirms that at least a portion of the Kentucky population of D. brevifolia is perennial. This is probably facultative perenniality because some plants only survive to a third growing season when precipitation falls evenly over the growing seasons and when no hot, dry periods occur. Mild winters without extensive snow cover may contribute to facultative perenniality as well.

Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model

AbstractSnow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service's Snow Data Assimilation System (SNODAS) provides snow products at high spatial (~1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79% in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.

Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”

Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate [...]