scholarly journals Now you see it, now you don't: a case study of ephemeral snowpacks and soil moisture response in the Great Basin, USA

2018 ◽  
Vol 22 (9) ◽  
pp. 4891-4906 ◽  
Author(s):  
Rose Petersky ◽  
Adrian Harpold
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Great Basin ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Deep Soil ◽  
Time Period ◽  
Snow Distribution ◽  
Snow Cover Duration ◽  
Strong Control

Abstract. Ephemeral snowpacks, or those that persist for < 60 continuous days, are challenging to observe and model because snow accumulation and ablation occur during the same season. This has left ephemeral snow understudied, despite its widespread extent. Using 328 site years from the Great Basin, we show that ephemeral snowmelt causes a 70-days-earlier soil moisture response than seasonal snowmelt. In addition, deep soil moisture response was more variable in areas with seasonal snowmelt. To understand Great Basin snow distribution, we used MODIS and Snow Data Assimilation System (SNODAS) data to map snow extent. Estimates of maximum continuous snow cover duration from SNODAS consistently overestimated MODIS observations by >25 days in the lowest (<1500 m) and highest (>2500 m) elevations. During this time period snowpack was highly variable. The maximum seasonal snow cover during water years 2005–2014 was 64 % in 2010 and at a minimum of 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal except those on south-facing slopes. Additionally, we used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain–snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, physics-based modeling is needed that can account for the complex energetics of shallow snowpacks in complex terrain. These modeling efforts will need to be supported by field observations of mass and energy and linked to finer remote sensing snow products in order to track ephemeral snow dynamics.

scholarly journals Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

2018 ◽  
Author(s):  
Rose Petersky ◽  
Adrian Harpold
Keyword(s):  
Soil Moisture ◽  
Great Basin ◽  
Blowing Snow ◽  
Time Period ◽  
Moderate Resolution ◽  
Snow Distribution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Strong Control

Abstract. Ephemeral snowpacks, or those that routinely experience accumulation and ablation at the same time and persist for <60 days, are challenging to observe and model. Using 328 site years from the Great Basin, we show that ephemeral snowmelt delivers water earlier than seasonal snowmelt. For example, we found that day of peak soil moisture preceded day of last snowmelt in the Great Basin by 79 days for shallow soil moisture in ephemeral snowmelt compared to 5 days for seasonal snowmelt. To understand Great Basin snow distribution, we used moderate resolution imaging spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) data from water years 2005–2014 to map snow extent. During this time period snowpack was highly variable. The maximum seasonal snow cover was 64 % in 2010 and the minimum was 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal. Snowpacks were more likely to be ephemeral on south facing slopes than north facing slopes at elevations above 2500 m. Additionally, we used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain-snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, we put forward several challenges and recommendations to bolster physics based modeling of ephemeral snow such as better metrics for snow ephemerality and more ground-based observations.

scholarly journals High resolution snow distribution data from complex Arctic terrain: a tool for model validation

2002 ◽  
Vol 2 (3/4) ◽  
pp. 147-155 ◽  
Author(s):  
Ch. Jaedicke ◽  
A. D. Sandvik
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Numerical Models ◽  
The Arctic ◽  
Distribution Data ◽  
Blowing Snow ◽  
Data Set ◽  
Meteorological Model ◽  
Drift Model ◽  
Snow Distribution

Abstract. Blowing snow and snow drifts are common features in the Arctic. Due to sparse vegetation, low temperatures and high wind speeds, the snow is constantly moving. This causes severe problems for transportation and infrastructure in the affected areas. To minimise the effect of drifting snow already in the designing phase of new structures, adequate models have to be developed and tested. In this study, snow distribution in Arctic topography is surveyed in two study areas during the spring of 1999 and 2000. Snow depth is measured by ground penetrating radar and manual methods. The study areas encompass four by four kilometres and are partly glaciated. The results of the surveys show a clear pattern of erosion, accumulation areas and the evolution of the snow cover over time. This high resolution data set is valuable for the validation of numerical models. A simple numerical snow drift model was used to simulate the measured snow distribution in one of the areas for the winter of 1998/1999. The model is a two-level drift model coupled to the wind field, generated by a mesoscale meteorological model. The simulations are based on five wind fields from the dominating wind directions. The model produces a satisfying snow distribution but fails to reproduce the details of the observed snow cover. The results clearly demonstrate the importance of quality field data to detect and analyse errors in numerical simulations.

scholarly journals Topographic control of snow distribution in an alpine watershed of western Canada inferred from spatially-filtered MODIS snow products

2009 ◽  
Vol 13 (3) ◽  
pp. 319-326 ◽  
Author(s):  
J. Tong ◽  
S. J. Déry ◽  
P. L. Jackson
Keyword(s):  
Snow Cover ◽  
Correlation Coefficients ◽  
Time Correlation ◽  
Spatial Filter ◽  
Maximum Difference ◽  
Cloud Coverage ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Snow Cover Duration

Abstract. A spatial filter (SF) is used to reduce cloud coverage in Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow cover extent products (MOD10A2) from 2000–2007, which are obtained from MODIS daily snow cover extent products (MOD10A1), to assess the topographic control on snow cover fraction (SCF) and snow cover duration (SCD) in the Quesnel River Basin (QRB) of British Columbia, Canada. Results show that the SF reduces cloud coverage and improves by 2% the accuracy of snow mapping in the QRB. The new product developed using the SF method shows larger SCF and longer SCD than MOD10A2, with higher altitudes experiencing longer snow cover and perennial snow above 2500 m. The gradient of SCF with elevation (d(SCF)/dz) during the snowmelt season is 8% (100 m)−1. The average ablation rates of SCF are similar for different 100 m elevation bands at about 5.5% (8 days)−1 for altitudes <1500 m with decreasing values with elevation to near 0% (8 days)−1 for altitudes >2500 m. Different combinations of slopes and aspects also affect the SCF with a maximum difference of 20.9% at a given time. Correlation coefficients between SCD and elevation attain 0.96 (p<0.001). Mean gradients of SCD with elevation are 3.8, 4.3, and 11.6 days (100 m)−1 for the snow onset season, snowmelt season, and entire year, respectively. The SF decreases the standard deviations of SCDs compared to MOD10A2 with a maximum difference near 0.6 day, 0.9 day, and 1.0 day for the snow onset season, snowmelt season, and entire year, respectively.

scholarly journals Field-scale water balance closure in seasonally frozen conditions

2017 ◽  
Vol 21 (11) ◽  
pp. 5401-5413 ◽  
Author(s):  
Xicai Pan ◽  
Warren Helgason ◽  
Andrew Ireson ◽  
Howard Wheater
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Accumulation ◽  
Frozen Soil ◽  
Blowing Snow ◽  
Soil Drainage ◽  
Field Scale ◽  
Significant Term ◽  
The Common ◽  
Precipitation Gauge

Abstract. Hydrological water balance closure is a simple concept, yet in practice it is uncommon to measure every significant term independently in the field. Here we demonstrate the degree to which the field-scale water balance can be closed using only routine field observations in a seasonally frozen prairie pasture field site in Saskatchewan, Canada. Arrays of snow and soil moisture measurements were combined with a precipitation gauge and flux tower evapotranspiration estimates. We consider three hydrologically distinct periods: the snow accumulation period over the winter, the snowmelt period in spring, and the summer growing season. In each period, we attempt to quantify the residual between net precipitation (precipitation minus evaporation) and the change in field-scale storage (snow and soil moisture), while accounting for measurement uncertainties. When the residual is negligible, a simple 1-D water balance with no net drainage is adequate. When the residual is non-negligible, we must find additional processes to explain the result. We identify the hydrological fluxes which confound the 1-D water balance assumptions during different periods of the year, notably blowing snow and frozen soil moisture redistribution during the snow accumulation period, and snowmelt runoff and soil drainage during the melt period. Challenges associated with quantifying these processes, as well as uncertainties in the measurable quantities, caution against the common use of water balance residuals to estimate fluxes and constrain models in such a complex environment.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an alpine watershed

2010 ◽  
Vol 7 (3) ◽  
pp. 3189-3211 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Bias Error ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote sensing data were used to simulate snow distribution and melt in an alpine watershed in Northwestern China within a complete snow accumulation-melt period. Spatial energy budgets were simulated using the meteorological observations and digital elevation model of the watershed. A linear interpolation method was used to discriminate daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer data. Hourly snow distribution and melt, snow cover extent, and daily discharge were included in the simulated results. The bias error between field snow water equivalent samplings and simulated results is −2.1 cm, and Root Mean Square Error is 33.9 cm. The Nash and Sutcliffe efficiency statistic (R2) between measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9%. Using the method introduced in this article, modeling spatial snow distribution and melt runoff will become relatively convenient.

The Response of Northern Hemisphere Snow Cover to a Changing Climate*

2009 ◽  
Vol 22 (8) ◽  
pp. 2124-2145 ◽  
Author(s):  
Ross D. Brown ◽  
Philip W. Mote
Keyword(s):  
Sensitivity Analysis ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
Sensitivity Study ◽  
Snow Accumulation ◽  
Snow Cover Duration

Abstract A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.

scholarly journals Modeling arctic snow distribution and melt at the 1 km grid scale*

2004 ◽  
Vol 35 (4-5) ◽  
pp. 295-307 ◽  
Author(s):  
Ming-ko Woo ◽  
Kathy L. Young
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Base Station ◽  
Snow Accumulation ◽  
The Arctic ◽  
Radiation Balance ◽  
Target Area ◽  
Snow Distribution ◽  
Terrain Units ◽  
Energy Balance Approach

Snow accumulation, re-distribution and melt are important hydrological considerations in the Arctic. This study presents a model of the late-winter snow cover and the ensuing snowmelt in a High Arctic environment at a scale of 1 km. Indexing is used to spread the snow data from a lowland weather station to various terrain units over a 16×13 km2 target area east of Resolute, Cornwallis Island, Canada. Meteorological variables measured at this base station are spatially extended by field derived empirical relationships for the computation of melt at various terrain units using the energy balance approach. These melt rates are weighted by the fractional coverage of various terrain unit within each 1×1 km2 cell. The snow distribution pattern is obtained daily and model performance was tested by comparing observed and computed dates of melt and the radiation balance over snow. The simulated snow pattern compared favourably with the snow cover imaged by LANDSAT. Daily changes in the probability distribution of snow water equivalent over the target area was examined and snow depletion curves were derived. They describe sub-grid variability over an area and our results point to several assumptions that should be scrutinized in sub-grid parameterization of snow distribution.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed

2011 ◽  
Vol 15 (7) ◽  
pp. 2195-2203 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE) between the measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

scholarly journals Snowfall and snow accumulation processes during the MOSAiC winter and spring season

2021 ◽  
Author(s):  
David N. Wagner ◽  
Matthew D. Shupe ◽  
Ola G. Persson ◽  
Taneil Uttal ◽  
Markus M. Frey ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Drifting Snow ◽  
Snow Water ◽  
Abstract Data ◽  
Good Agreement

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Phase Method ◽  
Annual Peak ◽  
Method Selection ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of modeled snow accumulation and melt to rain-snow partitioning at research sites in the western United States. Simulations using the physics-based SNOWPACK model and 12 different precipitation phase methods indicated maritime sites were the most sensitive to method selection. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California’s Sierra Nevada mountains. This led to ranges in annual peak snow water equivalent (SWE) typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration less than 18 %, 62 mm, 10 d, and 15 d, respectively. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 °C and 4 °C exhibited the greatest sensitivity to method selection. These findings have large implications for modeled snowpack water storage and land surface albedo at the warmer fringes of the seasonal snow zone.

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