Simulation of Snow Water Equivalent (SWE) Using Thermodynamic Snow Models in Québec, Canada

Abstract Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale. In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Québec. Results show that the SWE simulations are in agreement with ground measurements through three complete winter seasons (2004/05, 2005/06, and 2007/08) in southern Québec, with higher error for 2007/08. The correlation coefficients between measured and predicted SWE values ranged between 0.72 and 0.99 for the three models and three seasons evaluated in southern Québec. In subarctic regions, predicted SWE driven with the North American Regional Reanalysis (NARR) data fall within the range of measured regional variability. NARR data allow snow models to be used regionally, and this paper represents a first step for the regionalization of thermodynamic multilayered snow models driven by reanalysis data for improved global SWE evolution retrievals.

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The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009)

Journal of Climate ◽

10.1175/jcli-d-11-00081.1 ◽

2011 ◽

Vol 24 (21) ◽

pp. 5691-5712 ◽

Cited By ~ 158

Author(s):

Glen E. Liston ◽

Christopher A. Hiemstra

Keyword(s):

Snow Cover ◽

Air Temperature ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

The Arctic ◽

Regional Variability ◽

Time Step ◽

Snow Season ◽

Regional Trends ◽

Dominant Feature

Abstract Arctic snow presence, absence, properties, and water amount are key components of Earth’s changing climate system that incur far-reaching physical and biological ramifications. Recent dataset and modeling developments permit relatively high-resolution (10-km horizontal grid; 3-h time step) pan-Arctic snow estimates for 1979–2009. Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events. Regional variability is a dominant feature of the modeled snow-property trends. Both positive and negative regional trends are distributed throughout the pan-Arctic domain, featuring, for example, spatially distinct areas of increasing and decreasing SWE or snow season length. In spite of strong regional variability, the data clearly show a general snow decrease throughout the Arctic: maximum winter SWE has decreased, snow-cover onset is later, the snow-free date in spring is earlier, and snow-cover duration has decreased. The domain-averaged air temperature trend when snow was on the ground was 0.17°C decade−1 with minimum and maximum regional trends of −0.55° and 0.78°C decade−1, respectively. The trends for total number of snow days in a year averaged −2.49 days decade−1 with minimum and maximum regional trends of −17.21 and 7.19 days decade−1, respectively. The average trend for peak SWE in a snow season was −0.17 cm decade−1 with minimum and maximum regional trends of −2.50 and 5.70 cm decade−1, respectively.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0223.1 ◽

2016 ◽

Vol 17 (5) ◽

pp. 1467-1488 ◽

Cited By ~ 14

Author(s):

Reinel Sospedra-Alfonso ◽

Lawrence Mudryk ◽

William Merryfield ◽

Chris Derksen

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Water Equivalent ◽

Initial Conditions ◽

Reanalysis Data ◽

Prediction System ◽

Snow Water ◽

System Part ◽

Surface Scheme

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.

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Estimating Snow Cover Duration from Ground Temperature

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0006.1 ◽

2015 ◽

Vol 54 (5) ◽

pp. 959-965 ◽

Cited By ~ 4

Author(s):

Irene E. Teubner ◽

Leopold Haimberger ◽

Michael Hantel

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Rank Correlation ◽

Correlation Coefficients ◽

Ground Temperature ◽

Large Dataset ◽

Diurnal Range ◽

Snow Water ◽

Snow Cover Duration ◽

Primary Variable

AbstractSnow cover duration is commonly derived from snow depth, snow water equivalent, or satellite data. Snow cover duration has more recently also been inferred from ground temperature data. In this study, a probabilistic snow cover duration (SCD) model is introduced that estimates the conditional probability for snow cover given the daily mean and the diurnal range of ground temperature. For the application of the SCD model, 87 Austrian sites in the Alpine region are investigated in the period of 2000 to 2011. The daily range of ground temperature is identified to represent the primary variable in determining the snow cover duration. In the case of a large dataset, however, the inclusion of the daily mean ground temperature as the second given parameter improves results. Rank correlation coefficients of predicted versus observed snow cover duration are typically between 0.8 and 0.9.

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Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-082.1 ◽

2012 ◽

Vol 13 (5) ◽

pp. 1475-1492 ◽

Cited By ~ 26

Author(s):

Steven J. Fletcher ◽

Glen E. Liston ◽

Christopher A. Hiemstra ◽

Steven D. Miller

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Evolution Model ◽

The South ◽

Earth Observing System ◽

The North ◽

Moderate Resolution ◽

Snow Water ◽

Moderate Resolution Imaging Spectroradiometer

Abstract In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.

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Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains

10.5194/egusphere-egu21-486 ◽

2021 ◽

Author(s):

Wassim Mohamed Baba ◽

Abdelghani Boudhar ◽

Simon Gascoin ◽

Lahoucine Hanich ◽

Ahmed Marchane ◽

...

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Growing Season ◽

Reanalysis Data ◽

Water Runoff ◽

Snow Cover Area ◽

Seasonal Snow ◽

Atlas Mountains ◽

Snow Water ◽

Seasonal Snow Cover

The seasonal snow cover in the Altas mountains of Morocco is an important resource, mostly because it provides melt-water runoff for irrigation during the crop growing season. However, the knowledge on physical properties of the snowpack (e.g., snow water equivalent (SWE) and snowmelt) is still very limited due to the scarcity or the lack of ground measurements in the elevated area. In this study we suggest that the recent progresses of meteorological reanalysis data (e.g., MERRA-2 and ERA-5) open new perspectives to overcome this issue. We fed a distributed snowpack evolution model (SnowModel) with downscaled ERA-5 and MERRA-2 reanalyses and evaluate their performance to simulate snow cover. The modeling covers the period 1981 to 2019 (37 water years). SnowModel simulations were assessed using observations of river discharge, snow height and snow cover area derived from MODIS.For most of hydrological years, the results show a good performance for both MERRA-2 and ERA-5 with a slight superiority of ERA-5, to reproduce the snowpack state.Key words: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5

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Multi-scale spatialization of snow water equivalent (SWE) according to their spatial structures in eastern Canada

10.5194/tc-2020-316 ◽

2020 ◽

Author(s):

Noumonvi Yawu Sena ◽

Karem Chokmani ◽

Erwan Gloaguen ◽

Monique Bernier

Keyword(s):

Spatial Variability ◽

Snow Cover ◽

Snow Water Equivalent ◽

Regional Scale ◽

Local Scale ◽

Spatial Structures ◽

Eastern Canada ◽

Average Maximum ◽

Regression Approach ◽

Snow Water

Abstract. The spatial variability of snow plays a key role in snow water storage, spring runoff and hydraulic dam management. The snow survey network unequally distributed ability, to monitoring the spatial variability of the snow cover is limited. The spatial variability of the snow cover is explained by physiographic factors, which generate spatial structures at different scales. The variability of the snow cover is explained by physiographic factors, which generate structures at different scales. These structures of spatial variability of the snow cover were delimited by a functional approach at the local (300 × 300 m) and regional (10 × 10 km) scales on eastern Canada. The territory was segmented into regions, (called spatial structures,) with homogeneous average maximum annual snow water equivalent (SWE). The aim of this paper is to spatialize the average maximum annual snow water equivalent (SWE) according to spatial variability structures at both scales. Initially, at the regional scale, the average maximum annual SWE is estimated using the stepwise regression approach. Secondly, the SWE residuals are estimated using a regression approach on local physiographic meta-variables. The estimated SWE allows quantifying the spatial variability of the average maximum annual SWE for regional and local physiographic factors. Indeed, at the regional scale, the physiographic regional factors explain 68 % of the variance of the spatial variability of the average maximum annual SWE. At the local scale, physiographic factors improve the estimate of the average annual maximum SWE by 21 % (R = 89 %) for an unexplained share of 10 % of the variance. Local physiographic factors reorganize the regional residuals of average maximum annual SWE and contribute to the local variability. This study shows the role of altitude in snow accumulation at the regional scale, where the presence of high mountains increases the amount of rainfall from wet winds. In each geographical area, the highest values of the SWE are related to high mountain peaks. The impact is confirmed at the foothills of the Canadian Shield mountains. At the local scale, the regional residual value was reorganized based on local physiographic factors (slope, forms of catchment, distance to rivers, etc.); this adjustment led to high SWE values in the concave landscape and the ubacs away from sunlight. The SWE accumulation area corresponds to the depressions and concave sections at foothills.

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The Spatiotemporal Patterns and Interrelationships of Snow Cover and Climate Change in Tianshan Mountains

Water ◽

10.3390/w13040404 ◽

2021 ◽

Vol 13 (4) ◽

pp. 404

Author(s):

Tong Heng ◽

Xinlin He ◽

Lili Yang ◽

Jiawen Yu ◽

Yulin Yang ◽

...

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Warming Trend ◽

Tianshan Mountains ◽

Spatiotemporal Patterns ◽

Tianshan Mountain ◽

Mountainous Areas ◽

The Future ◽

Snow Water ◽

Nighttime Warming

To reveal the spatiotemporal patterns of the asymmetry in the Tianshan mountains’ climatic warming, in this study, we analyzed climate and MODIS snow cover data (2001–2019). The change trends of asymmetrical warming, snow depth (SD), snow coverage percentage (SCP), snow cover days (SCD) and snow water equivalent (SWE) in the Tianshan mountains were quantitatively determined, and the influence of asymmetrical warming on the snow cover activity of the Tianshan mountains were discussed. The results showed that the nighttime warming rate (0.10 °C per decade) was greater than the daytime, and that the asymmetrical warming trend may accelerate in the future. The SCP of Tianshan mountain has reduced by 0.9%. This means that for each 0.1 °C increase in temperature, the area of snow cover will reduce by 5.9 km2. About 60% of the region’s daytime warming was positively related to SD and SWE, and about 48% of the region’s nighttime warming was negatively related to SD and SWE. Temperature increases were concentrated mainly in the Pamir Plateau southwest of Tianshan at high altitudes and in the Turpan and Hami basins in the east. In the future, the western and eastern mountainous areas of the Tianshan will continue to show a warming trend, while the central mountainous areas of the Tianshan mountains will mainly show a cooling trend.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.1017/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 200

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

The Cryosphere ◽

10.5194/tc-11-1647-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1647-1664 ◽

Cited By ~ 29

Author(s):

Emmy E. Stigter ◽

Niko Wanders ◽

Tuomo M. Saloranta ◽

Joseph M. Shea ◽

Marc F. P. Bierkens ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Snow Cover ◽

Climate Sensitivity ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.3189/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 85

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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