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

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.

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

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 55
Author(s):  
Sena ◽  
Chokmani ◽  
Gloaguen ◽  
Bernier
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Critical Analysis ◽  
Snow Water Equivalent ◽  
Multiple Scales ◽  
Regional Scale ◽  
Spatial Structures ◽  
Eastern Canada ◽  
Snow Water ◽  
Index Curve

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

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

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.

scholarly journals Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

2016 ◽  
Vol 64 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Pavel Krajčí ◽  
Michal Danko ◽  
Jozef Hlavčo ◽  
Zdeněk Kostka ◽  
Ladislav Holko
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Flood Forecasting ◽  
Snow Accumulation ◽  
Small Scale ◽  
Climatic Data ◽  
Tatra Mountains ◽  
Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

A Detailed and in Situ Assessment of the Snowpack Physical Properties in a Discontinuous Humid Boreal Forest

2021 ◽  
Author(s):  
Benjamin Bouchard ◽  
Daniel F. Nadeau ◽  
Florent Domine
Keyword(s):  
Physical Properties ◽  
Snow Cover ◽  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Weather Forecasting ◽  
Large Fraction ◽  
Heat Fluxes ◽  
Homogeneous Layer ◽  
Eastern Canada ◽  
Snow Water

<p>Boreal forests occupy a large fraction of the continental surfaces and receive a lot of solid precipitation in winter. Evergreen canopies are often represented as a single and homogeneous layer in hydrological and weather forecasting models. However, in reality, boreal canopies are composed of a rather complex mosaic of trees unevenly spaced apart, with gaps of various sizes. Therefore, mass and energy inputs to the snowpack show remarkable variability at small scales resulting not only in strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE), but also in the vertical temperature gradient in the snow column (<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="48" height="17">). Unlike SD and SWE, <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="48" height="17"> has been little documented in discontinuous needleleaf forests, despite its impact on snow cover metamorphism and on a range of physical properties of snow such as density (<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.d6e05221ecff56228040161/sdaolpUECMynit/12UGE&app=m&a=0&c=e83ed3b230a37b46d23b9b7d13655568&ct=x&pn=gnp.elif&d=1" alt="" width="17" height="16">), specific surface area (SSA) and effective thermal conductivity (k<sub>eff</sub>). This work investigates the snowpack underneath the canopy and inside small forest gaps using continuous measurements of SD and k<sub>eff</sub> and weekly snow pit surveys during winter 2018-19 in a juvenile balsam fir stand of eastern Canada (47°17’18’’N, 71°10’05’’W). This site receives an average of almost 1600 mm of precipitation annually, including 40 % falling as snow. Snow cover typically lasts over 6 months. Observations show that less snow accumulates in the subcanopy and therefore <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="46" height="16"> is more pronounced than inside the gaps. Moreover, <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.d6e05221ecff56228040161/sdaolpUECMynit/12UGE&app=m&a=0&c=e83ed3b230a37b46d23b9b7d13655568&ct=x&pn=gnp.elif&d=1" alt="" width="17" height="16"> and SSA are lower underneath the canopy where faceted crystals are observed. Large <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="49" height="17"> in that environment results in a decreasing k<sub>eff</sub> over time. Overall, kinetic grain growth takes place in the subcanopy whereas settlement and isothermal conditions prevail inside the gaps. This research provides accurate observations of the snowpack in forested environments needed for a better representation of SWE, heat fluxes and ground thermal regime in hydrological and meteorological models.</p>

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

2016 ◽  
Vol 20 (1) ◽  
pp. 411-430 ◽  
Author(s):  
E. Cornwell ◽  
N. P. Molotch ◽  
J. McPhee
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Snow Accumulation ◽  
Model Assessment ◽  
Continental Divide ◽  
Average Maximum ◽  
Snow Water ◽  
Andes Cordillera

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.

scholarly journals Spatial Variability of Snow Water Equivalent – The Case Study from the Research Site in Khibiny Mountains, Russia

2019 ◽  
Vol 67 (1) ◽  
pp. 110-112 ◽  
Author(s):  
Anton Yu. Komarov ◽  
Yury G. Seliverstov ◽  
Pavel B. Grebennikov ◽  
Sergey A. Sokratov
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Density ◽  
Hydrological Characteristic ◽  
Snow Water ◽  
Exact Position ◽  
Seasonal Snow Cover ◽  
Uniform Area

Abstract The aim of the investigation was assessment of spatial variability of the characteristics of snowpack, including the snow water equivalent (SWE) as the main hydrological characteristic of a seasonal snow cover. The study was performed in Khibiny Mountains (Russia), where snow density and snow cover stratigraphy were documented with the help of the SnowMicropen measurements, allowing to determine the exact position of the snow layers’ boundaries with accuracy of 0.1 cm. The study site was located at the geomorphologically and topographically uniform area with uniform vegetation cover. The measurement was conducted at maximum seasonal SWE on 27 March 2016. Twenty vertical profiles were measured along the 10 m long transect. Vertical resolution depended on the thickness of individual layers and was not less than 10 cm. The spatial variation of the measured snowpack characteristics was substantial even within such a homogeneous landscape. Bulk snow density variability was similar to the variability in snow height. The total variation of the snowpack SWE values along the transect was about 20%, which is more than the variability in snow height or snow density, and should be taken into account in analysis of the results of normally performed in operational hydrology snow course SWE estimations by snow tubes.

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

Water ◽  
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.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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