Variability Of Snow Water Equivalent Research Articles

Sensitivity of the Northern Hemisphere Warming Trend to Snowpack Variability

Abstract A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model’s SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates.

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (>1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm.

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Abstract. Simulations by the EURO-CORDEX (European branch of the Coordinated Regional Climate Downscaling Experiment) regional climate models indicate a widespread future decrease in snow water equivalent (SWE) in northern Europe. This concurs with the negative interannual correlation between SWE and winter temperature in the southern parts of the domain but not with the positive correlation observed further north and over the Scandinavian mountains. To better understand these similarities and differences, interannual variations and projected future changes in SWE are attributed to anomalies or changes in three factors: total precipitation, the snowfall fraction of precipitation and the fraction of accumulated snowfall that remains on the ground (the snow-on-ground fraction). In areas with relatively mild winter climate, the latter two terms govern both the long-term change and interannual variability, resulting in less snow with higher temperatures. In colder areas, however, interannual SWE variability is dominated by variations in total precipitation. Since total precipitation is positively correlated with temperature, more snow tends to accumulate in milder winters. Still, even in these areas, SWE is projected to decrease in the future due to the reduced snowfall and snow-on-ground fractions in response to higher temperatures. Although winter total precipitation is projected to increase, its increase is smaller than would be expected from the interannual covariation of temperature and precipitation and is therefore insufficient to compensate the lower snowfall and snow-on-ground fractions. Furthermore, interannual SWE variability in northern Europe in the simulated warmer future climate is increasingly governed by variations in the snowfall and snow-on-ground fractions and less by variations in total precipitation.

A multimillennial snow water equivalent reconstruction from giant sequoia tree rings

The first dendroclimatic reconstruction of May 1 snow water equivalent (SWE) was developed from a Sequoiadendron giganteum regional tree-ring chronology network of 23 sites in central California for the period 90–2012 CE. The reconstruction is based on a significant relationship between May 1 SWE and tree-ring growth and shows climate variability from interannual to intercentennial time scales. A regression-based reconstruction equation explains up to 55% of the variance of SWE for 1940–2012. Split-sample validation supports our use of a reconstruction model based on the full period of reliable observational data (1940–2012). Thresholds for May 1 SWE low (15 percentile) and high (80 percentile) years were selected based on the exploratory scatterplots relationship between observed and reconstructed data for the period 1940–2012. The longest period of consecutive low-SWE years in the reconstruction is 2 years and the frequency of the lowest SWE years is highest during the period 710–809 CE. The longest high-SWE period, defined by consecutive wet years, is 3 years (558–560 CE). SWE and its reconstruction positively correlate with northeastern Pacific sea surface temperatures, the low-frequency variability of which may provide some predictive ability. Ultimately, the instrumental record and reconstruction suggest that unlike other sites in the region, twentieth century SWE variability in these Sequoia groves has remained within historical boundaries and relatively buffered from extremes and severe declines, though this is likely to change in coming decades with potentially negative effects on water availability for these trees.

Three hundred years of snowpack variability in southwestern British Columbia reconstructed from tree‐rings

AbstractRecent snow droughts in southwestern British Columbia (BC), Canada, have reduced seasonal streamflow during the typically dry late‐spring and summer months, leading to socio‐economic and ecological impacts that draw attention to the impending consequences of climate change. Knowledge of annual winter snowfall variability within this region is largely derived from a sparse network of short‐duration (≤50 years) snow survey stations. In this paper, we develop an annual April 1 snow water equivalent (SWE) reconstruction from living tree‐ring chronologies that offer a perspective on long‐term natural snowpack variability. The dendrohydrological model estimates the first principal component April 1 SWE for the southwestern regions of BC to 1711. Spectral analysis identified dominant multidecadal April 1 SWE variability over the pre‐instrumental period. The reconstruction successfully captures known instrumental period influences of La Niña oscillations on reconstructed SWE, suggesting that our tree‐ring based the reconstruction has the potential to provide insights on pre‐instrumental ocean–atmosphere links with southwestern BC snowpack dynamics. Runs analysis suggests pre‐instrumental snow droughts have been more than twice as long in duration and severity than during the observed period which indicates the instrumental record may not capture the full range of April 1 SWE variability. The reconstruction provides the first high‐resolution description of SWE over the past 300 years in southwestern BC and is of immediate use to regional water resource managers.

RECONSTRUCTIONS OF HYDROLOGIC VARIABLES IN THE NORTH PLATTE RIVER BASIN

Reconstructions of hydrologic variables are commonly created using tree-ring chronologies (TRCs) to generate information about historic climate and potential future variability. This study used TRCs to reconstruct annual streamflow, April 1st Snow Water Equivalent (SWE), and soil moisture in the North Platte River Basin (NPRB). Stepwise linear regression was performed to determine which of the 55 moisture sensitive TRCs were the best predictors of hydrologic variation. The regressions explained 63% of the variability in streamflow, 55% of the variability in SWE, and 66% of the variability in soil moisture. This study then maximized the overlapping period of records which resulted in a decrease in the percent of variability explained but indicated that the regression models were stable for long reconstruction periods. This study successfully reconstructed all three hydrologic variables for NPRB to 1438 or earlier. Temporal wet and dry periods for streamflow and SWE were closely aligned while soil moisture did not follow similar temporal patterns. This was likely due to a natural “lag” between soil moisture and streamflow / SWE given soil moisture tends to retain antecedent signals. The availability of reconstructed hydrologic data in NPRB allows for a better understanding of the long-term hydrologic variability in the region.

Spatio-temporal variability of snow water equivalent over the Vestre Broggerbreen and Feiringbreen glaciers, Ny-Ålesund, Svalbard

Snow water equivalent (SWE) is important for understanding the hydrological significance of glaciers. In this study, the spatial and temporal variability in SWE and its impact over the Vestre Broggerbreen and Feiringbreen glaciers around Ny-Ålesund in Svalbard (high Arctic) were investigated in the early snow season for the period 2012–2017. The physical properties like depth and density were measured directly in the field and spatial characteristics curvature, slope and aspect were extracted from the digital elevation model. The Vestre Broggerbreen (4.1 km2) is a NE flowing glacier, situated around 3 km SW to Ny-Ålesund village while the Feiringbreen (7.5 km2) is a SW flowing glacier, situated around 14 km NE across the Kongsfjorden. The SWE for the studied period (2012–2017) varied from 141 to 1188 mm. The significant (R2 = 0.97) correlation indicated a possible control of snow depth over SWE compared to altitude (R2 = 0.65) and other spatial characteristics. The glaciers have experienced negative balance and lost a significant amount of ice (~4 m.w.e.) since 2012. The observations suggest that the increased liquid precipitation and temperature in the early snow season have reduced SWE over both these valley glaciers. The reduced SWE has also contributed to decreases in the mass balance of these glaciers.

Critical Analysis of the Snow Survey Network According to the Spatial Variability of Snow Water Equivalent (SWE) on Eastern Mainland Canada

In Eastern Canada, the snow survey network is highly optimized at the operational scale. However, it is commonly accepted that the network is limited when it comes to studying the spatial variability of the snow water equivalent (SWE), which forms different spatial structures that are active at multiple scales—from local to regional. The main objective of this study was to conduct a critical analysis of the existing snow survey network, based on the spatial variability of the existing SWE structures. To do so, we must (1) assess the snow survey network’s capacity to model spatial variability structures of SWE, and (2) study the spatial distribution based on the spatial variability structures of SWE. Initially, the snow survey network’s capacity to model the spatial variability structures of the SWE was evaluated by a variogram analysis. Second, the spatial distribution of the snow survey network’s data was analyzed through the Lorenz index curve and by measuring the spatial distribution using the Gini index. The results showed that, at a regional scale, the snow survey stations were evenly distributed within the spatial structures. However, at the local scale, the snow survey network was inadequate to model the spatial variability of SWE due to the reduced and uneven number of snow survey stations.

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

AbstractIn snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.

Interannual variability of March snow mass over Northern Eurasia and its relation to the concurrent and preceding surface air temperature, precipitation and atmospheric circulation

The interannual variability of March snow water equivalent (SWE) in Northern Eurasia and its influencing factors are studied. The surface air temperature (SAT) and precipitation are the dominant factors for the snow accumulation in northern Europe and the remaining region, respectively. The strongest contribution of SAT to snow accumulation is mainly found in those months with moderate mean SAT. The strongest contribution of precipitation is not collocated with the climatological maxima in precipitation. The leading mode of March SWE variability is obtained and characterized by a spatial dipole. Anomalies in atmospheric water vapor divergence, storm activity and the associated atmospheric circulation can explain many of the associated precipitation and SAT features. Anomalies in autumn Arctic sea ice concentration (SIC) over the Barents Sea and Kara Sea (B/K Sea) and a dipole pattern of November snow cover (SC) in Eurasia are also observed. The atmospheric circulation anomalies that resemble a negative phase of North Atlantic Oscillation (NAO)/Arctic Oscillation (AO) are strongly projected onto the wintertime atmospheric circulation. Both observations and model experiment support that the autumn B/K Sea SIC has some impacts on the autumn and AO/NAO-like wintertime atmospheric circulation patterns. The dipole pattern of November Eurasian SC seems to be strongly forced by the autumn B/K Sea SIC and its feedback to the atmospheric circulation is important. Therefore, the impacts of autumn B/K Sea SIC on the autumn/wintertime atmospheric circulation and thus the March SWE variability may be modulated by both constructive and destructive interference of autumn Eurasian SC.

Intercomparison of snow water equivalent observations in the Northern Great Plains

AbstractIn the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = −3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = −9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km2 vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.

Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain

Abstract. In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow–soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4 % of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170 % at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together.

Remote Sensing of Snow Water Equivalent Using Coherent Reflection From Satellite Signals of Opportunity: Theoretical Modeling

A model for the remote sensing of snow water equivalent (SWE) using coherent detection of reflected communication satellite “signals of opportunity” is described in this paper. We modeled the snowpack by a layered medium and compute the phase of specularly reflected signals from the snowpack. Phase change of the reflected signal is predicted to be strongly dependent on SWE for dry snow and on snow depth for wet snow. Phase sensitivity to SWE increases with frequency. Reflected signals with frequencies above S-band, however, will experience rapid phase wrapping (360° change) and become more susceptible to fringe washing due to spatial variability of SWE across the footprint. We also examined the impact of snow grain size, snow density, snow layering, ground surface scattering, and incoherent scattering from ice grains embedded in the snowpack, concluding that these factors do not have a substantial impact on the phase-SWE relationship. However, the snow wetness of more than a few percent can make the reflection from air–snow interface dominant, leading to a correlation between phase change and snow depth. To address the path delay due to the ionosphere, dual-frequency observations would have to be considered. Modeling analysis indicates that the coherent change detection is a promising technique for remote sensing of SWE.

Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N‐ICE2015) expedition

AbstractSnow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N‐ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid‐June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second‐year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first‐year ice. The average density was 350–400 kg m−3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.

Effect of Forest Ecosystems on the Snow Water Equivalent in Relation to Aspect And Elevation in the Hučava River Watershed, Poľana Biosphere Reserve (Slovakia)

Abstract The aim of this work was to assess how forest ecosystems dominated by Norway spruce (Picea abies (L.) or European beech (Fagus sylvatica L.) affect snow water equivalent (SWE) in relation to aspect and elevation. The research plots were established in a small headwater watershed of the Hučava flow belonging to the Poľana Biosphere Reserve (Central Europe, Inner Western Carpathians). The SWE values in this watershed (approximately 580–1270 m a.s.l.) were monitored during the three winter seasons starting from 2012–2013 to 2014–2015. The results revealed high variability in SWE and in snow cover duration between the studied seasons. The spatial variability was significantly affected by the forest ecosystem, aspect and elevation. The seasonal mean SWE value was lower by about 50–60% in the spruce forests and by about 21–30% in the beech forests compared to the open areas (100%). Over the whole seasons, the whole watershed mean SWE value on the slopes with the northern aspect was mostly higher compared to the slopes with the southern aspect. The effect of aspect was significant mainly in the open areas and in the forests dominated by European beech during the ablation periods of every season. In the case of the sufficient snow cover, the mean SWE value always increased with elevation. The elevation gradient of SWE was steepest at the open areas of the watershed in the peaks of the winter seasons. The three-season mean value of SWE elevation gradient (per 100 m) at the time of snow accumulation peak was equal to 16 mm in the spruce forests, 20 mm in the beech forests and 26 mm in the open areas. The research revealed that SWE is significantly affected by the forest ecosystem whilst its effect is dependent on the occurrence of dominant deciduous or coniferous tree species. However, the effect of forests is closely related to topographic characteristics (aspect and elevation) of a locality.

Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado

Snow is an important environmental variable in headwater systems that controls hydrological processes such as streamflow, groundwater recharge, and evapotranspiration. These processes will be affected by both the amount of snow available for melt and the rate at which it melts. Snow water equivalent (SWE) and snowmelt are known to vary within complex subalpine terrain due to terrain and canopy influences. This study assesses this variability during the melt season using ground penetrating radar to survey multiple plots in northwestern Colorado near a snow telemetry (SNOTEL) station. The plots include south aspect and flat aspect slopes with open, coniferous (subalpine fir, Abies lasiocarpa and engelman spruce, Picea engelmanii), and deciduous (aspen, populous tremuooides) canopy cover. Results show the high variability for both SWE and loss of SWE during spring snowmelt in 2014. The coefficient of variation for SWE tended to increase with time during snowmelt whereas loss of SWE remained similar. Correlation lengths for SWE were between two and five meters with melt having correlation lengths between two and four meters. The SNOTEL station regularly measured higher SWE values relative to the survey plots but was able to reasonably capture the overall mean loss of SWE during melt. Ground Penetrating Radar methods can improve future investigations with the advantage of non-destructive sampling and the ability to estimate depth, density, and SWE.

Developing a representative snow-monitoring network in a forested mountain watershed

Abstract. A challenge in establishing new ground-based stations for monitoring snowpack accumulation and ablation is to locate the sites in areas that represent the key processes affecting snow accumulation and ablation. This is especially challenging in forested montane watersheds where the combined effects of terrain, climate, and land cover affect seasonal snowpack. We present a coupled modeling approach used to objectively identify representative snow-monitoring locations in a forested watershed in the western Oregon Cascades mountain range. We used a binary regression tree (BRT) non-parametric statistical model to classify peak snow water equivalent (SWE) based on physiographic landscape characteristics in an average snow year, an above-average snow year, and a below-average snow year. Training data for the BRT classification were derived using spatially distributed estimates of SWE from a validated physically based model of snow evolution. The optimal BRT model showed that elevation and land cover type were the most significant drivers of spatial variability in peak SWE across the watershed (R2 = 0.93, p value < 0.01). Geospatial elevation and land cover data were used to map the BRT-derived snow classes across the watershed. Specific snow-monitoring sites were selected randomly within the dominant BRT-derived snow classes to capture the range of spatial variability in snowpack conditions in the McKenzie River basin. The Forest Elevational Snow Transect (ForEST) is a result of this coupled modeling approach and represents combinations of forested and open land cover types at low, mid-, and high elevations. After 5 years of snowpack monitoring, the ForEST network provides a valuable and detailed dataset of snow accumulation, snow ablation, and snowpack energy balance in forested and open sites from the rain–snow transition zone to the upper seasonal snow zone in the western Oregon Cascades.

Representation of Snow in the Canadian Seasonal to Interannual Prediction System. Part I: Initialization

Abstract The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency’s GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions.

Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia

Abstract Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow-water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite sensors — the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987–2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E, 2002–2011), AMSR2 (2012–2015), and the Global Precipitation Measurement (GPM, 2014–2015) — in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multi-year snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low ( While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor-specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas.