Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation

Brown, Ross; Smith, Charles W.; Derksen, Chris; Mudryk, Lawrence

doi:10.1080/07055900.2021.1911781

Cited by 14 publications

(10 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SCD increase in northern Fennoscandia in the boreal spring shown in Figure 9 confirms previous studies [26]. For the continental areas in Canada, the SCD increase found by [27] in the boreal spring (Figure 9) could be confirmed. However, e.g., the Nelson River showed a significant SCD increase of +0.31 days/year even in November, where [27] found a general decrease.…”

Section: Discussionsupporting

confidence: 90%

“…Our study will focus on changes in the SCD and also on different time periods-since drastic changes in the SCD can occur on a small scale and are limited to a few months, such as an increase in the SCD during the boreal spring in the Sápmi region (North Fennoscandia) [26]. A shift in the SCD was found particularly in continental areas; [27] analyzed snow data in Canada from 185 stations and found an SCD decrease of −1.68 days/decade in the boreal autumn and an increase of +0.28 days/decade in the boreal spring.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Roessler

Dietz

2022

Earth

View full text Add to dashboard Cite

Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Roessler

Dietz

2022

Earth

View full text Add to dashboard Cite

show abstract

“…In situ SWE data from several of these networks are used for a number of research and development applications. For example, they serve as reference data for the evaluation of a variety of largescale gridded SWE products (e.g., Mortimer et al, 2020) including (i) snowpack models driven by meteorological reanalysis (e.g., Brun et al, 2013), (ii) passive microwave estimates combined with surface snow depth observation such as the GlobSnow product (Pulliainen et al, 2020) and (iii) regional climate models (e.g., Rasmussen et al, 2011). Gridded snow products can also be derived from manual and automatic in situ SWE measurements (e.g., Brown et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Vionnet

Mortimer

Brady

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. In situ measurements of water equivalent of snow cover (SWE) – the vertical depth of water that would be obtained if all the snow cover melted completely – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth (SD) and bulk snow density (defined as the ratio of SWE to SD) are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020, and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro-Québec were included. CanSWE includes over 1 million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734371 (Vionnet et al., 2021).

show abstract

“…Improving snow depth observations and retrieving an accurate higher spatial resolution snow depth is essential for hydrological and lake ice studies (Kheyrollah Pour et al, 2017;Marsh et al, 2020). Daily snow depths are reported across Canada using instruments, such as a manual ruler or a sonic sensor, at weather stations located on land (Brown et al, 2021). However, the depth of snow on land does not compare to snow over lake ice (Sturm & Liston, 2003).…”

mentioning

confidence: 99%

Mapping snow depth over lake ice in Canada’s sub-arctic using ground-penetrating radar

Pouw

Pour

MacLean

2022

Preprint

View full text Add to dashboard Cite

Abstract. Ice thickness across lake ice is influenced mainly by the presence of snow and its distribution, as it directly impacts the rate of lake ice growth. The spatial distribution of snow depth over lake ice varies and is driven by wind redistribution and snowpack metamorphism, creating variability in the lake ice thickness. The accuracy and consistency of snow depth measurement data on lake ice are challenging and sparse to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models. Such information is required to improve the knowledge and understanding of snow depth distribution over lake ice. This study maps snow depth distribution over lake ice using ground-penetrating radar (GPR) two-way travel-time (TWT) with ~9 cm spatial resolution along transects totalling ~44 km over four freshwater lakes in Canada’s sub-arctic. The accuracy of the snow depth retrieval is assessed using in situ snow depth observations (n =2,430). On average, the snow depth derived from GPR TWTs for the early winter season is estimated with a root mean square error (RMSE) of 1.58 cm and a mean bias error of -0.01 cm. For the late winter season on a deeper snowpack, the accuracy is estimated with RMSE of 2.86 cm and a mean bias error of 0.41 cm. The GPR-derived snow depths are interpolated to create 1 m spatial resolution snow depth maps. Overall, this study improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information, which is essential for the lake ice modelling community.

show abstract

Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation

Cited by 14 publications

References 48 publications

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Mapping snow depth over lake ice in Canada’s sub-arctic using ground-penetrating radar

Contact Info

Product

Resources

About