Snow Water Equivalent Accumulation Research Articles

Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet

AbstractA 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a−1 matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

The implications of permafrost thaw and land cover change on snow water equivalent accumulation, melt and runoff in discontinuous permafrost peatlands

AbstractIn the discontinuous permafrost zone of the Northwest Territories (NWT), Canada, snow covers the ground surface for half the year. Snowmelt constitutes a primary source of moisture supply for the short growing season and strongly influences stream hydrographs. Permafrost thaw has changed the landscape by increasing the proportional coverage of permafrost‐free wetlands at the expense of permafrost‐cored peat plateau forests. The biophysical characteristics of each feature affect snow water equivalent (SWE) accumulation and melt rates. In headwater streams in the southern Dehcho region of the NWT, snowmelt runoff has significantly increased over the past 50 years, despite no significant change in annual SWE. At the Fort Simpson A climate station, we found that SWE measurements made by Environment and Climate Change Canada using a Nipher precipitation gauge were more accurate than the Adjusted and Homogenized Canadian Climate Dataset which was derived from snow depth measurements. Here, we: (a) provide 13 years of snow survey data to demonstrate differences in end‐of‐season SWE between wetlands and plateau forests; (b) provide ablation stake and radiation measurements to document differences in snow melt patterns among wetlands, plateau forests, and upland forests; and (c) evaluate the potential impact of permafrost‐thaw induced wetland expansion on SWE accumulation, melt, and runoff. We found that plateaus retain significantly (p < 0.01) more SWE than wetlands. However, the differences are too small (123 mm and 111 mm, respectively) to cause any substantial change in basin SWE. During the snowmelt period in 2015, wetlands were the first feature to become snow‐free in mid‐April, followed by plateau forests (7 days after wetlands) and upland forests (18 days after wetlands). A transition to a higher percentage cover of wetlands may lead to more rapid snowmelt and provide a more hydrologically‐connected landscape, a plausible mechanism driving the observed increase in spring freshet runoff.

Snow Water Equivalent Accumulation Patterns from a Trajectory Approach over the U.S. Southern Rocky Mountains

The spatial characteristics and patterns of snow accumulation and ablation inform the amount of water stored and subsequently available for runoff and the timing of snowmelt. This paper characterizes the snow accumulation phase to investigate the spatiotemporal snow water equivalent (SWE) distribution by fitting a function to the trajectory plot of the standard deviation versus mean SWE across a domain. Data were used from 90 snow stations for a 34-year period across the Southern Rocky Mountains in the western United States. The stations were divided into sub-sets based on elevation, latitude, and the mean annual maximum SWE. The best function was a linear fit, excluding the first 35 mm of SWE. There was less variability with SWE data compared to snow depth data. The trajectory of the accumulation phase was consistent for most years, with limited correlation to the amount of accumulation. These trajectories are more similar for the northern portion of the domain and for below average snow years. This work could inform where to locate new stations, or be applied to other earth system variables.

Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Factors Governing Winter Snow Accumulation and Ablation Susceptibility Across the Sierra Nevada, U.S.A.

AbstractSeasonal snow water equivalent (SWE) accumulation in California’s Sierra Nevada is primarily governed by a few orographically enhanced snowstorms. However, as air temperatures gradually rise, resulting in a shift from snow to rain, the governing processes determining SWE accumulation versus ablation become ambiguous. Using a network of 28 snow pillow measurements to represent an elevational and latitudinal gradient across the Sierra Nevada, we identify distributions of critical temperatures and corresponding storm and snowpack properties that describe how SWE accumulation varies across the range at an hourly timescale for water years 2010 through 2019. We also describe antecedent and prevailing conditions governing whether SWE accumulates or ablates during warm storms. Results show that atmospheric moisture regulates a temperature dependence of SWE accumulation. Conditions balancing precipitable water and snow formation requirements produce the most seasonal SWE, which was observed in the (low-elevation) northern and (middle-elevation) central Sierra Nevada. The high southern Sierra Nevada conservatively accumulates SWE with colder, drier air, resulting in less midwinter ablation. These differences explain a tendency for deep, low-density snowpacks to accumulate rather than ablate SWE during warm storms (having median temperatures exceeding 1.0°C), reflecting counteracting liquid storage and internal energy deficits. The storm events themselves in these cases are brief with modest moisture supplies or are otherwise followed immediately by ablation.

Relative snowpack response to elevation, temperature and precipitation in the Crown of the Continent region of North America 1980-2013.

Water availability in western Canada and the United States is dependent on the accumulation of snowpack in the montane regions and threatened by increased winter temperature and more precipitation as rain linked to climate change. In order to make reasoned decisions to adapt to climate change managers require knowledge of the role of temperature and precipitation in SWE development and data to distinguish the relative retention response of snowpack regions to expected temperature and precipitation regime shifts at the watershed scale. Using the Daymet interpolated 1 km2 dataset, effects of elevation, temperature (Tmax, Tmin and Tavg) and precipitation on April 1 SWE in the Crown of the Continent were tested by linear regression and Kendall correlation. Changes in Daymet estimated snow water equivalent (SWE) in response to increased temperatures and changes in precipitation were estimated in two ways: 1) comparing April 1SWE in the 11 warmest (mean Tmax February) and driest (mean precipitation January to March) years with the 22 cooler/wetter years 1981–2013 and 2) SWE retention from April 1 to June 1 over the period 1980 to 2013 across 120 watersheds in a major continental headwater region, the Crown of the Continent of North America. Historical analysis of period warm year April 1 SWE was assumed to indicate the recent impact of warmer winter temperatures. Changes in snowpack April 1 to June 1 reflected likely effects on peak runoff and were, therefore, also relevant for future climate change adaptation considerations. Winter (JFM) precipitation proved more influential than temperature in shaping April 1 SWE response at the regional scale. Of the three factors, elevation was most positively associated with April 1 SWE at the watershed scale. Temperature and precipitation influenced SWE accumulation and persistence at the watershed scale, but higher precipitation was more closely associated with higher April 1 SWE retention. Ranking of watershed snowpack retention in warm and dry years, combined with spring snowpack retention offers data to assist identification of watersheds with greatest snowpack persistence in the face of anticipated climate change effects.

Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes.

Within‐Stand Boundary Effects on Snow Water Equivalent Distribution in Forested Areas

AbstractForested areas exhibit high spatial variability in the distribution of snow water equivalent (SWE). Previous work has focused on forested areas with respect to snow accumulation in adjacent clearings. There is generally less snow in forested areas with greater variability relative to open areas due to the influence of tree canopies. However, the length scale of the transition from open areas to forested conditions is uncertain. Hence, the goal of this paper is to determine the length scales associated with forest boundary effects on SWE accumulation distribution patterns within forest stands. To accomplish this, we utilize a unique ground‐penetrating radar data set collected during the NASA SnowEx campaign on Grand Mesa, Colorado, in February 2017 to determine spatial SWE distribution patterns of areas under canopy and in clearings, and the length scales of transitions between these patterns (i.e., the size of within‐stand boundary areas). We define within‐stand boundary areas as the transitional zone from a clearing to relatively stable SWE distribution, or background distribution patterns, within forest stands. The largest within‐stand boundary effect occurred on the leeward side of stands with a mean extent of 44 m, or 4.3 mean canopy heights. In contrast, windward within‐stand boundary effects showed a mean extent of 28 m, or 3.7 mean canopy heights. We present a conceptual framework of the complex wind dynamics that occur in forest stands to explain the within‐stand boundary effects on SWE distribution. Future investigations could improve understanding of this complex process and associated driving variables.

Patterns of trends in niveograph characteristics across the western United States from snow telemetry data

The snowpack is changing across the globe, as the climate warms and changes. We used daily snow water equivalent (SWE) niveograph (time series of SWE) data from 458 snow telemetry (SNOTEL) stations for the period 1982 through 2012. Nineteen indices based on amount, timing, time length, and rates were used to describe the annual temporal evolution in SWE accumulation and ablation. The trends in these annual indices were computed over the time period for each station using the Theil-Sen slope. These trends were then clustered into four groups to determine the spatial pattern of SWE trends. Temperature and precipitation data were extracted from the PRISM data set, due to the shorter time period of temperature measurement at the SNOTEL stations. Results show that SNOTEL stations can be clustered in four clusters according to the observed trends in snow indices. Cluster 1 stations are mostly located in the Eastern- and South-eastern most parts of the study area and they exhibit a generalized decrease in the indices related with peak SWE and snow accumulation. Those stations recorded a negative trend in precipitation and an increase in temperature. Cluster 4 that is mostly restricted to the North and North-west of the study area shows an almost opposite pattern to cluster 1, due to months with positive trends and a more moderate increase of temperature. Stations grouped in clusters 2 and 3 appear mixed with clusters 1 and 4, in general they show very little trends in the snow indices.

Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees

AbstractSnowpacks and forests have complex interactions throughout the large range of altitudes where they co‐occur. However, there are no reliable data on the spatial and temporal interactions of forests with snowpacks, such as those that occur in nearby areas that have different environmental conditions and those that occur during different snow seasons. This study monitored the interactions of forests with snowpacks in four forest stands in a single valley of the central Spanish Pyrenees during three consecutive snow seasons (2015/2016, 2016/2017 and 2017/2018). Daily snow depth data from time‐lapse cameras were compared with snow data from field surveys that were performed every 10–15 days. These data thus provided information on the spatial and temporal changes of snow–water equivalent (SWE). The results indicated that forest had the same general effects on snowpack in each forest stand and during each snow season. On average, forest cover reduced the duration of snowpack by 17 days, reduced the cumulative SWE of the snowpack by about 60% and increased the spatial heterogeneity of snowpack by 190%. Overall, forest cover reduced SWE total accumulation by 40% and the rate of SWE accumulation by 25%. The forest‐mediated reduction of the accumulation rate, in combination with the occasional forest‐mediated enhancement of melting rate, explained the reduced duration of snowpacks beneath forest canopies. However, the magnitude and timing of certain forest effects on snowpack had significant spatial and temporal variations. This variability must be considered when selecting the location of an experimental site in a mountainous area, because the study site should be representative of surrounding areas. The same considerations apply when selecting a time period for study.

High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.

Management Implications of Snowpack Sensitivity to Temperature and Atmospheric Moisture Changes in Yosemite National Park, CA

AbstractIn order to investigate snowpack sensitivity to temperature increases and end‐member atmospheric moisture conditions, we applied a well‐constrained energy‐ and mass‐balance snow model across the full elevation range of seasonal snowpack using forcing data from recent wet and dry years. Humidity scenarios examined were constant relative humidity (high) and constant vapor pressure between storms (low). With minimum calibration, model results captured the observed magnitude and timing of snowmelt. April 1 snow water equivalent (SWE) losses of 38%, 73%, and 90% with temperature increases of 2, 4, and 6°C in a dry year centered on areas of greatest SWE accumulation. Each 2°C increment of warming also resulted in seasonal snowline moving upslope by 300 m. The zone of maximum melt was compressed upward 100–500 m with 6°C warming, with the range reflecting differences in basin hypsometry. Melt contribution by elevations below 2,000 m disappeared with 4°C warming. The constant‐relative‐humidity scenario resulted in 0–100 mm less snowpack in late spring vs. the constant‐vapor‐pressure scenario in a wet year, a difference driven by increased thermal radiation (+1.2 W/m2) and turbulent energy fluxes (+1.2 W/m2) to the snowpack for the constant‐relative‐humidity case. Loss of snowpack storage and potential increases in forest evapotranspiration due to warming will result in a substantial shift in forest water balance and present major challenges to land management in this mountainous region.

Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada

Regional-scale estimates of snow water equivalent (SWE) are challenging in alpine regions, particularly in areas of high accumulation and dense forest cover, suggesting efforts to improve these estimates may benefit from an evaluation of existing gridded products. Gridded SWE products comprising four reanalysis datasets (ERA-Interim, ERA-Interim/Land, MERRA and MERRA-Land), two land data assimilation system datasets (GLDAS1 and GLDAS2) and two observation-based products (CMC and GlobSnow) have been compared with in-situ measurements over five physiographic regions in British Columbia (BC), Canada. Time series were generated for each survey month (January through June), and median correlation, bias and mean absolute error (MAE) values were found for each product and physiographic region. The best performance in correlation and magnitude of bias and MAE was seen in areas of lowest SWE accumulation and land relief (e.g. the Interior Plains of northern BC), while poorer performance was seen in regions of high accumulation and complex topography (Columbia and Rocky Mountains and Coast Mountains). Overall, the performance ranking order is ERA-Interim/Land (best), GLDAS2, MERRA, CMC, GLDAS1, MERRA-Land, GlobSnow and ERA-Interim.

Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover

Abstract. Seasonal snow cover is the primary water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) snow water equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000–5000 m a.s.l. elevation band is significant for snow accumulation, despite having a smaller surface area than the 3000–4000 m a.s.l. band. On average, maximum snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

Biases of April 1 snow water equivalent records in the Sierra Nevada and their associations with large‐scale climate indices

AbstractDo the April 1 snow course measurements underestimate the snow accumulation peak in the Sierra Nevada, thus possibly introducing biases in studies of trends and interannual variability that use the April 1 data? Although their period of record is many decades shorter, measurements of snow water equivalent (SWE) from automated snow pillows provide daily records throughout each year, enabling us to examine the timing and magnitude of the maximum SWE accumulation. Using generalized additive mixed models, on average, we find that the peak occurs earlier than April 1 at the lowest elevations and later than April 1 at the highest elevations and that the degree to which April 1 SWE under‐measures maximum SWE is greatest at the lowest and highest elevations. In addition, our results show that the biases are associated with cold and warm phase interactions of certain climate indices.

Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the Northern Hemisphere

Gridded time-series data are increasingly available for climatology research. Microwave imagery of snow water equivalent (SWE) has been accumulated at daily basis for over two decades, but complex spatial-temporal patterns in SWE dataset pose great challenges for exploration and interpretation. This paper introduces the use of several perspectives from a tri-space conceptualization of a time series of SWE grids combined with dimensionality reduction via the self-organizing map (SOM) method. While SOM has been predominantly viewed as a clustering mechanism within climatology research, we expand the visual-analytic potential of SOM for climate research with a series of conceptual, computational, and visual transformations. Specifically, we apply a medium-resolution SOM to an SWE dataset covering the Northern Hemisphere over a 20-year period, with high temporal resolution. Through clustering and visualization a number of distinct SWE patterns are identified, including mountainous, coastal, and continental regions. A subset of cells from six areas are selected for transition analysis, including mountainous (Sierra Nevada, Western Himalaya, Eastern Himalaya) and continental (central Siberia, western Russia and Midwest United States) regions. By combining with trajectory analysis, this SOM documents notable transitions in seasonal SWE accumulation and melt patterns in mountain ranges, suggesting that SWE in some geographic locations alternates between different discrete annual patterns. In the Sierra Nevada, transitions to classes with high SWE are shown to be related to the Southern Oscillation Index, demonstrating that the annual patterns and transitions in SWE regime identified by the SOM correspond to synoptic climate conditions.

Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements

Narrow bands of strong atmospheric water vapor transport, referred to as “atmospheric rivers” (ARs), are responsible for the majority of wintertime extreme precipitation events with important contributions to the seasonal water balance. We investigate relationships between snow water equivalent (SWE), precipitation, and surface air temperature (SAT) across the Sierra Nevada for 45 wintertime AR events. Analysis of assimilated and in situ data for water years 2004–2010 indicates that ARs on average generate ∼4 times daily SWE accumulation of non‐AR storms. In addition, AR events contributed ∼30–40% of total seasonal SWE accumulation in most years, with the contribution dominated by just 1–2 extreme events in some cases. In situ and remotely sensed observations show that SWE changes associated with ARs are closely related to SAT. These results reveal the previously unexplored significance of ARs with regard to the snowpack and associated sensitivities of AR precipitation to SAT.

A Bayesian approach to snow water equivalent reconstruction

Snow water equivalent (SWE) reconstruction methods have previously been used to characterize seasonal SWE accumulation using mass and energy balance models and time series of remotely sensed snow‐covered area (SCA). Recognizing that the spatial signature of the seasonal SWE accumulation is an integration of a series of snowfall events, we have formulated a Bayesian SWE reconstruction that combines time series of remote sensing estimates of SCA with a land surface model to estimate storm‐specific snowfall distribution with a retrospective data assimilation scheme. The analysis is identical in form to the ensemble Kalman filter or ensemble Kalman smoother. A snow depletion curve is used to relate SCA and SWE. We perform a series of synthetic tests to assess how much information concerning snowfall accumulation patterns can be extracted from a time series of SCA measurements taken during the ablation season. The posterior estimate of SWE calculated via our method effectively recovers the true SWE: the mean and standard deviation of the SWE estimate error improve by 86% and by 78%, respectively, over the prior for pixels with vegetation fraction less than 90%. The sensitivity of the method to climatic and physiographic variables and input errors is investigated. The technique shows promise for future work in characterizing spatial patterns of snowfall over mountainous regions.

The Canadian boreal snow water equivalent band

Satellite and conventional snow water equivalent (SWE) dataseis reveal a well‐defined zone of high winter season SWE (>100 mm) that extends across the northern boreal forest of Canada. SWE coefficient of variation (CV) patterns derived from a monthly averaged (1978–2002) passive microwave derived time series show a high degree of interannual variability across open prairie, southern boreal, and open tundra regions of North America while SWE across the northern boreal forest was highly invariant. The potential existence of a consistent SWE zone resistant to interannual climatic variability over the past 25 years is intriguing in the context of the sensitivity of snow cover to climate variability and change. A ground sampling campaign conceived specifically to evaluate SWE distribution across the northern boreal forest was conducted in northern Manitoba during the 2003–04 winter season. Data from this survey confirmed the SWE gradient across the boreal forest, although satellite‐derived retrievals for the tundra were consistently low. A series of Canadian Regional Climate Model (CRCM) simulations were conducted to identify feedbacks between the atmosphere and land surface for a domain focused on the northern boreal forest. A control simulation produced monthly patterns of SWE distribution that closely matched the passive microwave retrievals. Water budget computations showed the SWE accumulation pattern to be a function of the modelled regional precipitation pattern, and not the result of surface processes such as melt or evaporation/sublimation. Mean monthly patterns of 850‐hPa fronto genesis forcing corresponded closely to the patterns of accumulated SWE suggesting that lower tropospheric frontal activity was responsible for the snowfall events that led directly to the deposition of the northern boreal SWE band. CRCM sensitivity experiments were conducted with perturbed land cover and terrain. Only subtle differences in SWE accumulation and frontogenesis patterns relative to the control run were found when complete grassland cover was prescribed, though removing orography greatly enhanced the magnitude and zonal extent of the SWE band.