Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling

2020 ◽  
Vol 34 (11) ◽  
pp. 2628-2644
Author(s):  
Achut Parajuli ◽  
Daniel F. Nadeau ◽  
François Anctil ◽  
Annie-Claude Parent ◽  
Benjamin Bouchard ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Forest Catchment ◽  
Snow Water

scholarly journals The Swiss snow climatology as seen by reanalyses and further gridded datasets

2021 ◽  
Author(s):  
Monika Goeldi ◽  
Stefanie Gubler ◽  
Christian Steger ◽  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Data Sets ◽  
Data Set ◽  
Climate Services ◽  
Snow Cover Extent ◽  
Snow Water

<p>Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.</p><p>In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.</p><p>The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.</p>

scholarly journals Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

2021 ◽  
Vol 15 (2) ◽  
pp. 771-791
Author(s):  
Rhae Sung Kim ◽  
Sujay Kumar ◽  
Carrie Vuyovich ◽  
Paul Houser ◽  
Jessica Lundquist ◽  
...  
Keyword(s):  
North America ◽  
Land Surface ◽  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Considerable Uncertainty ◽  
Mountainous Regions ◽  
Snow Storage ◽  
Snow Water

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes.

scholarly journals Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

2020 ◽  
Author(s):  
Rhae Sung Kim ◽  
Sujay Kumar ◽  
Carrie Vuyovich ◽  
Paul Houser ◽  
Jessica Lundquist ◽  
...  
Keyword(s):  
North America ◽  
Land Surface ◽  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Considerable Uncertainty ◽  
Mountainous Regions ◽  
Snow Storage ◽  
Snow Water

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009&ndashl2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

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