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 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 A model for the spatial distribution of snow water equivalent parameterised from the spatial variability of precipitation

2016 ◽  
Author(s):  
Thomas Skaugen ◽  
Ingunn H. Weltzien
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Snow Distribution ◽  
Rainfall Runoff Model ◽  
Log Normal

Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation (SD_G), is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The latter model (SD_LN) has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution. The two models are implemented in the already parameter parsimonious rainfall runoff model Distance Distribution Dynamics (DDD) and their capability for predicting runoff, SWE and snow covered area (SCA) are tested and compared for 71 Norwegian catchments. Results show that SD_G better simulates SCA when compared with MODIS satellite derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash-Sutcliffe and Kling Gupta Criterion of 0.01, but it is shown that high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

scholarly journals Modelling the spatial variability of snow water equivalent at the catchment scale

2007 ◽  
Vol 11 (5) ◽  
pp. 1543-1550 ◽  
Author(s):  
T. Skaugen
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Gamma Distribution ◽  
Snow Water Equivalent ◽  
Temporal Correlation ◽  
Catchment Scale ◽  
Snow Season ◽  
Dynamical Parameters ◽  
Snow Water ◽  
Alpine Catchments

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and melting events and the temporal correlation of accumulation and melting events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season and showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the melting period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and melting events. However, at the start of melting, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

scholarly journals The spatial variability of snow water equivalent

2007 ◽  
Vol 4 (3) ◽  
pp. 1465-1489 ◽  
Author(s):  
T. Skaugen
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Gamma Distribution ◽  
Snow Water Equivalent ◽  
Temporal Correlation ◽  
Snow Season ◽  
Dynamical Parameters ◽  
Snow Water ◽  
Alpine Catchments ◽  
Southern Norway

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and ablation events and the temporal correlation of accumulation and ablation events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season, which showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the ablation period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and ablation events. However, at the start of ablation, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

scholarly journals A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

2016 ◽  
Vol 10 (5) ◽  
pp. 1947-1963 ◽  
Author(s):  
Thomas Skaugen ◽  
Ingunn H. Weltzien
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Efficiency Criterion ◽  
Covered Area ◽  
Snow Water ◽  
Snow Distribution ◽  
Rainfall Runoff Model ◽  
Log Normal

Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall–runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985–2000 and validation period is 2000–2014. Results show that SD_G better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash–Sutcliffe and Kling–Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

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.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

&lt;p&gt;Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &amp;#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km&lt;sup&gt;2&lt;/sup&gt; grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.&lt;/p&gt;

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.

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