Snow Conditions Research Articles

Benchmark of estimated solar irradiance data at high latitude locations

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

Trade-Offs Between Forage Availability, Accessibility, and Predation Risk on Winter Foraging Strategies of Wood Bison (Bison bison athabascae).

Optimal foraging theory (OFT) and the energy maximization hypothesis (EMH) have long been essential when examining wildlife habitat selection. At high latitudes and altitudes, animals in winter face greater limitations in availability and accessibility of forage. Here we explore the foraging behavior of wood bison (Bison bison athabascae) during winter within the Ronald Lake bison herd in northeastern Alberta, Canada, and examine the trade-offs they face due to limitations in forage abundance and availability (snow conditions), as well as the need to minimize predation risk. We used Global Positioning System (GPS) location data collected from 70 female wood bison to identify winter foraging sites and craters selected by bison to access forage beneath the snow. Within wetlands used by bison we selected 190 pairs of used (foraged) and random (available) sites to test eight a priori hypotheses explaining how bison traded-off between forage availability, accessibility, and minimizing predation risk. We found with matched-paired logistic regression that Carex atherodes was 1.21-times more likely to be selected per unit increase in ground cover, compared to 1.17-times per unit ground cover for C. aquatilis and C. utriculata. However, all Carex species showed an increase in selection when cover was > 50% cover within individual craters. While the importance of Carex was clear, forage site selection was still inversely related to snow depth. There is also a neutralizing combined effect of snow depth and Carex species ground cover which suggests that bison maximized their energy return by avoiding areas with deep snow (> 30 cm) that demanded intensive cratering, even when highly selected forage was accessible beneath. Avoidance of forage areas with deep snow demonstrates that wood bison employed a foraging strategy that considers both forage availability and environmental conditions, with snow depth being a limiting factor. We highlight the relationship between optimal foraging based on food availability and the trade-offs within an energy restrictive winter season, furthering the understanding of how large herbivores forage strategically to maximize energy intake in northern environments.

Predicting snow structures relevant to reindeer husbandry

ABSTRACT Snow conditions in the High North are an important control on wintertime forage availability for reindeer, and under climate change, they are changing rapidly. In the European Arctic, this has the potential to disrupt traditional reindeer herding practices, reindeer health, and local culture (including that of Indigenous communities like the Sámi). At the same time, Norwegian coastal cities are competing to act as multi-model transportation hubs as sea-ice retreat creates expectations for increased marine accessibility. An “Arctic Railway” connecting Rovaniemi to Kirkenes has been proposed to support these port developments, but this route passes through rangelands managed by Sámi and local herding communities. This study develops an assessment of past and future snow characteristics relevant to reindeer health to provide a context for understanding the impacts of this infrastructure development. Climate model and detailed snowpack simulations were performed for 1950 to 2100 along the proposed route. Results show that deep snow becomes less frequent and spring thaw advances, favorable to reindeer. However, icy snow conditions become more frequent, potentially forcing herds from tundra and farmland to forested areas. This suggests policy alternatives that focus on the development and maintenance of migration corridors to allow appropriate movement of reindeer herds.

Reconstructing 169 years of historical atmospheric and hydrologic conditions in the American River Watershed through the Watershed Environmental Hydrology Hydro-Climate Model system

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987–1992 and 2012–2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed.

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Summer snow on Arctic sea ice modulated by the Arctic Oscillation

Since the 1970s, Arctic sea ice has undergone unprecedented change, becoming thinner, less extensive and less resilient to summer melt. Snow’s high albedo greatly reduces solar absorption in sea ice and the upper ocean, which mitigates sea–ice melt and ocean warming. However, the drivers of summertime snow depth variability are unknown. The Arctic Oscillation is a mode of natural climate variability, influencing Arctic snowfall and air temperatures. Thus, it may affect summertime snow conditions on Arctic sea ice. Here we examine the role of the Arctic Oscillation in summer snow depth variability on Arctic sea ice in 1980–2020 using atmospheric reanalysis, snow modelling and satellite data. The positive phase leads to greater snow accumulation, ranging up to ~4.5 cm near the North Pole, and higher surface albedo in summer. There are more intense, frequent Arctic cyclones, cooler temperatures aloft and greater snowfall relative to negative and neutral phases; these conditions facilitate a more persistent summer snow cover, which may lessen sea-ice melt and ocean warming. The Arctic Oscillation influence on summertime snow weakens after 2007, which suggests that future warming and Arctic sea-ice loss might modify the relationship between the Arctic Oscillation and snow on Arctic sea ice.

Tree growth history, legacy of large-scale forest management and climatic variability in North Finland

Tree growth history was investigated in North Finland to study the recently observed reduction in Scots pine growth. Tree-ring records from the National Forest Inventory (NFI) were compared to changes that have occurred in forest management activities and to variations in the North Atlantic Oscillation (NAO), as similar comparisons have not been previously made with NFI data. A phase of increased growth was dated to the early 1970s when the forest management activities were intensified on a large scale. More recently, growth has notably declined since the early 2000s which coincides with reduced activity to clean the ditches to ensure they are draining the substrate adequately. The growth reduction appeared greater for trees from stands with higher basal area. Trees representing sites where the first cleaning of the stand (thinning) had been carried out did not show reduced growth. NAO indices correlated positively with growth, especially during the cold season. An interpretation on the value of snow conditions in a dynamic interaction with management history/stand density is put forth to explain the growth decline observed in NFI data over the past two decades.

Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model

Abstract Blowing snow is a hazard for motorists because it may rapidly reduce visibility. Numerical weather prediction models in the United States do not capture the movement of snow once it reaches the ground, but visibility reductions due to blowing snow can be diagnosed based on model-predicted land surface and environmental conditions that correlate with blowing snow occurrence. A recently developed diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction is applied to High-Resolution Rapid Refresh (HRRR) and Rapid Refresh Forecast System (RRFS) model output including surface snow conditions to predict surface visibility reduction due to blowing snow. Twelve blowing snow events around Wyoming from 2018 to 2023 are examined. The analysis shows that visibility reductions due to blowing snow tend to be overpredicted, caused by the initial assumption of full driftability of the snowpack. This study refines the aging of the blowing snow reservoir with two methods. The first method estimates driftability based on time-varying snow density from the Rapid Update Cycle land surface model (RUC LSM) used in the HRRR and experimental RRFS models and is evaluated in a real-time context with the RRFS model. The second, complementary method diagnoses snowpack driftability using a process-based approach that requires data for recent snowfall, wind speed, and skin temperature. Compared to the full driftability assumption, this method shows limited improvements in forecasting skill. To improve model-based diagnosis of visibility reduction due to blowing snow, empirical work is needed to determine the relation between snowpack driftability and the recent history of snowfall and other weather conditions. Significance Statement Blowing snow presents a significant hazard to motorists and airport operations through sometimes very rapid and intense reductions in visibility, yet little predictive guidance exists for blowing snow. This study aims to improve the prediction of blowing snow occurrence and associated surface visibility reduction using diagnostics from an operational high-resolution weather model. One key challenge regards the question of driftability of the snowpack. This study evaluates two approaches to quantify driftability in terms of visibility reduction due to blowing snow and acknowledges that more measurements are needed to improve the representation of blowing snow physics in NWP models.

An enclosed solid-liquid triboelectric nanogenerator based on Janus-type TPU nanofibers

Various environmental factors such as high humidity, rain, snow, and other conditions restrict the practical use of triboelectric nanogenerator (TENG). Herein, Janus-type thermoplastic polyurethane (TPU) nanofiber films with good hydrophobicity were fabricated by side-by-side electrospinning. The output performance of TENG based on Janus-type TPU nanofiber films was much higher than that made of normal TPU nanofiber films. A flexible enclosed TENG was prepared that maintained its output performance regardless of external humidity. What’s more, liquid metal gallium (Ga) was injected into the enclosed TENG and used as the tribo-material. The addition of Ga to the enclosed TENG increased its output performance to reach a peak voltage of 282 V and current of 6.0 μA. The enclosed TENG was able to power light-emitting diodes (LEDs) even when immersed in water, demonstrating its potential for using under high-humidity conditions.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Understanding the barriers and facilitators of urban greenway use among older and disadvantaged adults: A mixed-methods study in Québec city

Urban greenways are multipurpose and multi-user trails that provide a range of socio-ecological and health benefits, including active transportation, social interactions, and increased well-being. However, despite their numerous benefits, barriers exist that limit urban greenway access and use, particularly among older and disadvantaged adults. This study addresses a significant research gap by examining the nuanced factors that influence the choices and experiences of these specific user groups in Québec City, Canada. We use a mixed-methods’ approach to explore the facilitators of and barriers to access and use of two urban greenway trails among older and disadvantaged adults. Our methods included a greenway user count, 96 observation time slots, and 15 semi-structured user interviews. The results revealed significant use of greenway trails by older adults for afternoon walks in both seasons studied (autumn and winter). We also observed variations in use patterns, such as higher levels of solitary walking, reduced levels of winter cycling, and the impracticality of the secondary greenway trail owing to snow conditions. In addition, the findings revealed a wide range of factors that influence greenway access and use, categorized as individual or personal, physical or built environment, social environment, and meteorological or climatic dimensions. Future research can build on these insights to design and assess interventions that capitalize on the facilitators and address any barriers, enhancing the value of urban greenways for older and disadvantaged adults.

Aerodynamic simulation on roof-mounted intelligent awareness system of intelligent vehicle

With the rapid development of intelligent driving technology, it is urgent to conduct research on the distribution and motion characteristics of snow particles in the area of the intelligent vehicle awareness system under harsh snow and wind conditions. In this paper, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) was first used to conduct 1:12 scaled aerodynamic simulation of the intelligent vehicle. It is found that the error of aerodynamic drag coefficient between simulation and test is 5.6%, indicating that the current numerical simulation model can predict the aerodynamic characteristics of intelligent vehicle accurately. Based on this, URANS was used coupled with Lagrangian Multiphase Model (LMP) to investigate the snow accumulation near the roof-mounted intelligent awareness system under different vehicle speeds and snowfall intensities. The study found that with the increase of snowfall intensity from moderate to heavy and vehicle speed at 40 km/h, the incidence of snow particles on various components significantly increases, and the number of adhered particles also increased accordingly. The particle adhesion rate of the awareness system has decreased by 1.24%, while the number of adhered snow particles increased significantly at the back of the roof-mounted intelligent awareness system and the wake window. Under moderate snow condition, as the vehicle speed increased from 40 to 60 km/h, the number of incident particles and adhered particles in the awareness system have significantly decreased by 24,592 and 317,019, respectively, but the particle adhesion rate has increased by a significant 2.88%. Compared to the same snowfall intensity but low speed, the range of particle distribution is significantly reduced and more scattered.

Modelling climate change impacts on lake ice and snow demonstrates breeding habitat decline of the endangered Saimaa ringed seal

Snowdrifts on lake ice provide vital breeding habitats for the endangered Saimaa ringed seal. In this study, a lake ice model of Watershed Simulation and Forecasting System (WSFS-Ice) was developed for improved estimation of ice and snow conditions in Lake Saimaa during the pupping season of the Saimaa ringed seal. The WSFS-Ice model is based on energy balance, enabling reliable estimation of the ice cover evolution in current and future climate. In addition, a simple snowdrift model was used to simulate formation of snowdrifts, which are essential for the seals breeding success in Lake Saimaa. The model was calibrated against ice thickness, ice type and snow depth measurements. According to our results based on climate scenarios with intermediate representative concentration pathway (RCP4.5), the breeding habitat of the Saimaa ringed seal is significantly deteriorating during the twenty-first century. The mean depth of the snowdrifts is projected to decrease approximately to half from the 1981–2010 to 2070–99 period and at the same time the ice-covered period is reduced by one and a half months. During the mildest winters the ice cover is projected to melt even before the pupping season has ended. The results highlight the importance of climate change mitigation and active conservation measures to enhance seal population growth, enabling it to survive in a changing climate.

High Arctic Vegetation Communities With a Thick Moss Layer Slow Active Layer Thaw

AbstractSvalbards permafrost is thawing as a direct consequence of climate change. In the Low Arctic, vegetation has been shown to slow down and reduce the active layer thaw, yet it is unknown whether this also applies to High Arctic regions like Svalbard where vegetation is smaller, sparser, and thus likely less able to insulate the soil. Therefore, it remains unknown which components of High Arctic vegetation impact active layer thaw and at which temporal scale this insulation could be effective. Such knowledge is necessary to predict and understand future changes in active layer in a changing Arctic. In this study we used frost tubes placed in study grids located in Svalbard with known vegetation composition, to monitor the progression of active layer thaw and analyze the relationship between vegetation composition, vegetation structure and snow conditions, and active layer thaw early in summer. We found that moss thickness, shrub and forb height, and vascular vegetation cover delayed soil thaw immediately after snow melt. These insulating effects attenuated as thaw progressed, until no effect on thaw depth was present after 8 weeks. High Arctic mosses are expected to decline due to climate change, which could lead to a loss in insulating capacity, potentially accelerating early summer active layer thaw. This may have important repercussions for a wide range of ecosystem functions such as plant phenology and decomposition processes.

Using barcoding to reveal ecological patterns of nivicolous myxomycetes in the German Alps: How do they deal with varying snow conditions?

A transect in the German limestone Alps was monitored over ten years for nivicolous myxomycetes to test if species display stable altitudinal belts for fruiting. The data set comprised 1368 barcoded specimens assigned to 112 ribotypes forming 51 ribogroups. Ribogroups were largely consistent with 35 identified morphospecies, although in eleven cases a morphospecies included several ribogroups. Fructification abundance correlated with duration of the snow cover inferred from data loggers placed at ground height. Morphospecies, ribogroups, and ribotypes showed a peak of fructification abundance at different elevations in different years. Species composition, not abundances, showed a high overlap with soil metabarcoding data. Thirteen ribogroups detected in the metabarcoding data set were never found as fructifications. This survey demonstrates that nivicolous myxomycetes are opportunists, which are likely to persist as trophic or resting stages independent from snow cover, but fruit only in altitudes and years with snow cover stable over several months.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

AbstractEstimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.

Evaluation of PRISMA Products Over Snow in the Alps and Antarctica

AbstractPRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel‐2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel‐2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel‐2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values <10% between PRISMA L2D and field data, while Sentinel‐2 BOA reflectance showed higher values than other data sources.

Tracking the Evolution of Snow Drought in the U.S. Pacific Northwest at Variable Scales

AbstractLong‐term and year‐to‐year changes in climate can cause anomalously warm and/or dry conditions, leading to periods with snowfall deficits known as snow droughts. How the occurrence of distinct types of snow drought has evolved at different spatial and temporal scales is not well understood. Here, we investigate how snow droughts and associated seasonal snow conditions vary subseasonally in the US Pacific Northwest. We specifically consider differences with spatial unit (point vs. river basin), data set and intra‐annual timing of snow drought. Data sets included in the research come from in situ observations, gridded model outputs and optical remote sensing. We found that overall, snow drought occurrences have increased 10%–15% over the last 30 years based on decadal counts, but the change was non‐uniform across snow drought types and winter periods. Our research showed a decrease in dry snow droughts but an increase in warm/dry snow droughts according to in situ observations and only a modest increase in the model record. We also found that dry snow droughts were more prevalent in the eastern portion of our study region while warm/dry snow droughts were generally more common in the western part of the study area. With optical remote sensing data, it was possible to identify warm/dry snow drought conditions in the mid to late winter (January–May), but not possible to distinguish other types of snow drought in the early winter. This research points to the importance of data set choice, spatial resolution and spatial/temporal aggregation when selecting parameters for snow drought research.