Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains

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

<p>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.</p><p>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.</p><p><strong>Key words</strong>: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5</p>

scholarly journals Estimated seasonal snow cover and snowfall in Japan

1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Heavy Snowfall ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Prone Area ◽  
Snow Water ◽  
Seasonal Snow Cover

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.

scholarly journals Estimated seasonal snow cover and snowfall in Japan

1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Heavy Snowfall ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Prone Area ◽  
Snow Water ◽  
Seasonal Snow Cover

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.

scholarly journals Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

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

2009 ◽  
Vol 10 (6) ◽  
pp. 1447-1463 ◽  
Author(s):  
A. Langlois ◽  
J. Kohn ◽  
A. Royer ◽  
P. Cliche ◽  
L. Brucker ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Reanalysis Data ◽  
Regional Variability ◽  
The North ◽  
Snow Water ◽  
Regional Reanalysis

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.

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

2016 ◽  
Vol 17 (5) ◽  
pp. 1467-1488 ◽  
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.

scholarly journals Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor

2013 ◽  
Vol 54 (62) ◽  
pp. 205-213 ◽  
Author(s):  
Yoshihiro Asaoka ◽  
Yuji Kominami
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Sensible Heat Flux ◽  
Spatial Uncertainty ◽  
Large Area ◽  
Snow Cover Area ◽  
Degree Day ◽  
Snow Water ◽  
Snowmelt Modeling

AbstractSpatial degree-day factors (DDFs) are required for spatial snowmelt modeling over large areas by the degree-day method. We propose a method to obtain DDFs by incorporating snow disappearance dates (SDDs), derived from 10 day composites of Satellite Pour l’Observation de la Terre (SPOT)/VEGETATION data, into the degree-day method. This approach allowed determination of DDFs for each gridpoint so as to better reflect regional characteristics than use of spatially constant DDFs obtained from point measurements. Simulations at six observation sites successfully accounted for variations in snow water equivalent (SWE), even at elevations different from the closest measurement site. These results suggest that incorporating satellite-derived SDDs into the degree-day method decreases spatial uncertainty compared with the use of spatially constant DDFs. Application of our method to Japanese cold regions revealed that gridded DDFs were negatively correlated with accumulated positive degree-days (APDDs) and were high only when APDDs were low. These results imply that high DDFs resulted from the dominant contribution of solar radiation to snowmelt at low temperatures and that low DDFs resulted from a relatively high contribution of sensible heat flux at high temperatures. The proposed method seems to adequately account for the main energetic components of snowmelt during the snow-cover season over large areas.

scholarly journals The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

2019 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin
Keyword(s):  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Water Percolation ◽  
Snow Water ◽  
Snow Model ◽  
Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

scholarly journals Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

2020 ◽  
Vol 163 ◽  
pp. 06003
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeykin ◽  
Natalia Frolova ◽  
Inna Krylenko
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Hydrological Models ◽  
In Situ Data ◽  
Use Of Data ◽  
Complex Approach ◽  
Snow Water ◽  
Seasonal Snow Cover

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

scholarly journals Vertical variability in the position of the zero isochion in geomorphologic regions of Czechia

Geografie ◽  
2014 ◽  
Vol 119 (2) ◽  
pp. 145-160
Author(s):  
Libor Ducháček
Keyword(s):  
Snow Cover ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Field Measurements ◽  
Mountainous Areas ◽  
Snow Cover Area ◽  
Interconnected System ◽  
Central Highland ◽  
Snow Water ◽  
Source Of Information

Knowledge of the volume of water retained in mountainous areas serves as an important source of information for the anticipation of spring floods, as well as for other purposes, such as those related to agricultural management. Similarly, the extent and distribution of snow coverage (snow cover area – SCA) in lowlands are factors influencing the threat of large-scale floods caused by the melting of even a thin layer of snow cover. Every week during the winter months, the Czech Hydrometeorological Institute (CHMI) provides up to date information on the snow water equivalent present in Czech regions and especially within important hydrological basins. This information comes predominantly from an observation of net and field measurements. The position of the zero isohione, determined through remote sensing, helps to increase the accuracy of the calculations of such spatial distribution in Czechia. As a consequence of this practical use, changes in the accumulation and distribution of snow cover can be readily observed via remote senzing. This is further made easier by Czechia’s orographic disposition, specifically its interconnected system of border mountains and a central highland. As a result, the position of the zero isohione can be determined with an accuracy of 50 m a.s.l. If we compare selected geomorphological regions, we can find statistically substantiated differences in the position of the zero isohione of more than 200 m.

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