Articles | Volume 13, issue 9
https://doi.org/10.5194/essd-13-4603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Sep 2021
Data description paper |
| 24 Sep 2021
| 24 Sep 2021
Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)
Meteorological Research Division, Environment and Climate Change Canada, Dorval, Canada
Colleen Mortimer
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Mike Brady
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
Louise Arnal
Coldwater Laboratory, University of Saskatchewan, Canmore, Alberta, Canada
Ross Brown
Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
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Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2029, https://doi.org/10.5194/egusphere-2025-2029, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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The Northwest Passage provides a shorter transit route connecting the Atlantic Ocean to the Pacific Ocean but ever-present sea ice has prevented its practical navigation. Sea ice area in the northern route of the Northwest Passage on September 30, 2024 fell to a minimum of 4x103 km2, the lowest ice area observed since 1960. This paper describes the unique processes that contributed to the record low sea ice area in the northern route of the Northwest Passage in 2024.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
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The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
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Earth Syst. Sci. Data, 17, 423–434, https://doi.org/10.5194/essd-17-423-2025, https://doi.org/10.5194/essd-17-423-2025, 2025
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3643, https://doi.org/10.5194/egusphere-2024-3643, 2025
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Satellite data-based estimation of large SWE values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, https://doi.org/10.5194/tc-19-201-2025, 2025
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We evaluate and rank 23 different datasets on their ability to accurately estimate historical snow amounts. The evaluation uses new a set of surface snow measurements with improved spatial coverage, enabling evaluation across both mountainous and nonmountainous regions. Performance measures vary tremendously across the products: while most perform reasonably in nonmountainous regions, accurate representation of snow amounts in mountainous regions and of historical trends is much more variable.
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Science communication in geosciences has societal and scientific value but often operates in “shadowlands”. This editorial highlights these issues and proposes potential solutions. Our objective is to create a transparent and responsible geoscience communication landscape, fostering scientific progress, the well-being of scientists, and societal benefits.
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
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We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
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The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
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Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
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We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
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Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
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Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield
Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, https://doi.org/10.5194/essd-12-2381-2020, 2020
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Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Cited articles
Beaumont, R. T.: Mt. Hood pressure pillow snow gage, J. Appl. Meteorol., 4, 626–631, https://doi.org/10.1175/1520-0450(1965)004<0626:MHPPSG>2.0.CO;2, 1965.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant flood generating mechanisms across the United States, Geophys. Res. Lett., 43, 4382–4390, https://doi.org/10.1002/2016GL068070, 2016.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., and Immerzeel, W. W.: Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain, Nature Sustainability, 2, 594–601, https://doi.org/10.1038/s41893-019-0305-3, 2019.
Braaten, R.: Canadian Snow Water Equivalent Database Main Documentation, Environment Canada, Climate Processes and Earth Observation Division, Main Documentation, 25 pp., http://data.ec.gc.ca/data/climate/systems/canadian-historical-snow-survey-data/Braaten_1998_Canadian_SWE_Database.pdf (last access: 21 September 2021), 1998.
Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016, Atmos. Ocean, 57, 149–156, https://doi.org/10.1080/07055900.2019.1598843, 2019.
Brown, R. D., Smith, C., Derksen, C., and Mudryk, L.: Canadian in situ snow cover trends for 1955–2017 including an assessment of the impact of Automation, Atmos. Ocean, 59, 77–92 https://doi.org/10.1080/07055900.2021.1911781, 2021.
Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette, R., Karbou, F., and Morin, S.: Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses, J. Hydrometeorol., 14, 203–219, https://doi.org/10.1175/JHM-D-12-012.1, 2013.
Bulygina, O., Groisman, P. Y., Razuvaev, V., and Korshunova, N.: Changes in snow cover characteristics over Northern Eurasia since 1966, Environ. Res. Lett., 6, 045204, https://doi.org/10.1088/1748-9326/6/4/045204, 2011.
Clark, M. P., Serreze, M. C., and McCabe, G. J.: Historical effects of El Nino and La Nina events on the seasonal evolution of the montane snowpack in the Columbia and Colorado River Basins, Water Resour. Res., 37, 741–757, https://doi.org/10.1029/2000WR900305, 2001.
Choquette, Y., Ducharme, P., and Rogoza, J.: CS725, An Accurate Sensor for the Snow Water Equivalent and Soil Moisture Measurements, in: Proceedings ISSW 2013, 2013 International Snow Science Workshop, Grenoble – Chamonix Mont-Blanc, France, 931–936, 2013.
Dixon, D. and Boon, S.: Comparison of the SnowHydro sampler with existing snow tube designs, Hydrol. Process., 26, 2555–2562, https://doi.org/10.1002/hyp.9317, 2012.
ECCC (Environment and Climate Change Canada): Canadian
Environmental Sustainability Indicators: Snow cover:
https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/snow-cover.html (last access: 23 March 2021), 2020.
Gasset, N., Fortin, V., Dimitrijevic, M., Carrera, M., Bilodeau, B., Muncaster, R., Gaborit, É., Roy, G., Pentcheva, N., Bulat, M., Wang, X., Pavlovic, R., Lespinas, F., Khedhaouiria, D., and Mai, J.: A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model, Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, 2021.
Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The National Elevation Dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.
Gesch, D. B., Oimoen, M. J., and Evans, G. A.: Accuracy
assessment of the U. S. Geological Survey National Elevation
Dataset, and comparison with other large-area elevation
datasets—SRTM and ASTER, US Geological Survey, Open File Rep. 2014–1008, 10 pp., https://doi.org/10.3133/ofr20141008, 2014.
Goodison, B., Glynn, J., Harvey, K., and Slater, J.: Snow
Surveying in Canada: A Perspective, Can. Water Resour. J., 12, 27–42, https://doi.org/10.4296/cwrj1202027, 1987.
Gnanadesikan, R. and Kettenring, J: Robust estimates, residuals, and outlier detection with multiresponse data, Biometrics, 28, 81–214, https://doi.org/10.2307/2528963, 1972.
Haberkorn, A. (Ed.): European Snow Booklet – an Inventory of Snow Measurements in Europe, EnviDat, 363 pp., https://doi.org/10.16904/envidat.59, 2019.
Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.
Henkel, P., Koch, F., Appel, F., Bach, H., Prasch, M., Schmid, L., Schweizer, J., and Mauser, W.: Snow water equivalent of dry snow derived from GNSS carrier phases, IEEE T. Geosci. Remote, 56, 3561–3572, https://doi.org/10.1109/TGRS.2018.2802494, 2018.
Hill, D. F., Burakowski, E. A., Crumley, R. L., Keon, J., Hu, J. M., Arendt, A. A., Wikstrom Jones, K., and Wolken, G. J.: Converting snow depth to snow water equivalent using climatological variables, The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, 2019.
Hill, J.: Snow CD Archive Update Journal, Environment Canada, Documentation, March 2004, 8 pp., 2004.
Johnson, J. B.: A theory of pressure sensor performance in snow, Hydrol. Process., 18, 53–64, https://doi.org/10.1002/hyp.1310, 2004.
Johnson, J. B. and Marks, D.: The detection and correction of snow water equivalent pressure sensor errors, Hydrol. Process., 18, 3513–3525, https://doi.org/10.1002/hyp.5795, 2004.
Jörg-Hess, S., Griessinger, N., and Zappa, M.: Probabilistic forecasts of snow water equivalent and runoff in mountainous areas, J. Hydrometeorol., 16, 2169–2186, https://doi.org/10.1175/jhm-d-14-0193.1, 2015.
Karagulle, D., Frye, C., Sayre, R., Breyer, S., Aniello, P., Vaughan, R., and Wright, D.: Modeling global Hammond landform regions from 250 m elevation data, T. GIS, 21, 1040–1060, https://doi.org/10.1111/tgis.12265 , 2017.
Kinar, N. J. and Pomeroy, J. W.: Measurements of the physical properties of the snowpack, Rev. Geophys., 53, 481–544, https://doi.org/10.1002/2015RG000481, 2015.
Kodama, M., Nakai, K., Kawasaki, S., and Wada, M.: An application of cosmic-ray neutron measurements to the determination of the snow-water equivalent, J. Hydrol., 41, 85–92, https://doi.org/10.1016/0022-1694(79)90107-0, 1979.
Leys, C., Klein, O., Dominicy, Y., and Ley, C.: Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance, J. Exp. Soc. Psychol., 74, 150–156, https://doi.org/10.1016/j.jesp.2017.09.011, 2018.
López Moreno, J. I., Leppänen, L., Luks, B., Holko, L., Picard, G., Sanmiguel Vallelado, A., Alonso González, E., Finger, D. C., Arslan, A. N., Gillemot, K., Sensoy, A., Sorman, A., ErtaÅ, M. C., Fassnacht, S. R., Fierz, C., and Marty, C.: Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers, Hydrol. Process., 34, 3120–3133, https://doi.org/10.1002/hyp.13785, 2020.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Sci. Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Mahalanobis, P. C.: On tests and measures of groups
divergence, Journal of Asiatic Sociology of Bengal, 26, 541–588, 1930.
Magnusson, J., Nævdal, G., Matt, F., Burkhart, J. F., and Winstral, A.: Improving hydropower inflow forecasts by assimilating snow data, Hydrol. Res., 51, 226–237, https://doi.org/10.2166/nh.2020.025, 2020.
McKay, M., Wilks, D. S., and Schmidlin, T. W.: Quality-controlled snow water equivalent data for the Northeastern United States, Northeast Regional Climate Center Data Set DS 93-1, 5 pp., 1994.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R: Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219, https://doi.org/10.1038/nclimate3225, 2017.
USDA (U.S. Department of Agriculture): The History of Snow Survey and Water Supply Forecasting, Interviews With U.S. Department of Agriculture Pioneers, edited By: Helms, D., Phillips, S., and Reich, P., Natural Resources Conservation Service, U.S. Department of Agriculture, Natural Resources Conservation Service, Washington D.C., USA, available at: https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1043910.pdf (last access: 21 September 2021), 2008.
Ntokas, K. F. F., Odry, J., Boucher, M.-A., and Garnaud, C.: Investigating ANN architectures and training to estimate snow water equivalent from snow depth, Hydrol. Earth Syst. Sci., 25, 3017–3040, https://doi.org/10.5194/hess-25-3017-2021, 2021.
Paquet, E., Laval, M., Basalaev, L. M., Belov, A.,
Eroshenko, E., Kartyshov, V., Struminsky, A., and Yanke, V.: An
Application of Cosmic-Ray Neutron Measurements to the Determination
of the Snow Water Equivalent, in: Proceedings of the 30th International Cosmic Ray Conference, Merida, Mexico, 3–11 July 2008, https://indico.nucleares.unam.mx/event/4/session/39/contribution/1000/material/paper/0.pdf (last access: 21 September 2021), 2008.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Gutmann, E.: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate, J. Climate, 24, 3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011.
Rousseeuw, P.: Least Median of Squares Regression, J. Am. Stat. Assoc., 79, 871–880, https://doi.org/10.1080/01621459.1984.10477105, 1984.
Royer, A., Domine, F., Roy, A., Langlois, A., Marchand, N., and Davesne, G: New northern snowpack classification linked to vegetation cover on a latitudinal mega-transect across northeastern Canada, Écoscience, https://doi.org/10.1080/11956860.2021.1898775, online first, 2021a.
Royer, A., Picard, G., Vargel, C., Langlois, A.,
Gouttevin, I., and Dumont, M.: Improved Simulation of Arctic
Circumpolar Land Area Snow Properties and Soil
Temperatures, Front. Earth Sci., 9, 685140, https://doi.org/10.3389/feart.2021.685140, 2021b.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, https://doi.org/10.1029/1999WR900090, 1999.
Smith, C. D., Kontu, A., Laffin, R., and Pomeroy, J. W.: An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment, The Cryosphere, 11, 101–116, https://doi.org/10.5194/tc-11-101-2017, 2017.
Steiner, L., Meindl, M., Fierz, C., Marty, C., and Geiger, A.: Monitoring snow water equivalent using low-cost GPS antennas buried underneath a snowpack, in: 13th European Conference on Antennas and Propagation (EuCAP), Krakow, Poland, 31 March–5 April 2019, 1–5, 2019.
Stone, D. C.: Application of median filtering to noisy data, Can. J. Chem., 73, 1573–1581, https://doi.org/10.1139/v95-195, 1995.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating snow water equivalent using snow depth data and climate classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010.
Sun, N., Yan, H., Wigmosta, M., Skaggs, R., Leung, R.,
and Hou, Z.: Regional snow parameters estimation for large-domain hydrological applications in the western United States, J. Geophys. Res.-Atmos., 124, 5296–5313, https://doi.org/10.1029/2018JD030140, 2019.
Venäläinen, P., Luojus, K., Lemmetyinen, J., Pulliainen, J., Moisander, M., and Takala, M.: Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy, The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, 2021.
Vionnet, V., Fortin, V., Gaborit, E., Roy, G., Abrahamowicz, M., Gasset, N., and Pomeroy, J. W.: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins, Hydrol. Earth Syst. Sci., 24, 2141–2165, https://doi.org/10.5194/hess-24-2141-2020, 2020.
Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and
Brown, R.: Canadian historical Snow Water Equivalent dataset
(CanSWE, 1928–2020), Zenodo [data set], https://doi.org/10.5281/zenodo.4734371, 2021.
WMO (Ed.): Guide to instruments and methods of observation: Volume II – Measurement of Cryospheric Variables, 2018th edn., World Meteorological Organization, Geneva, WMO-No., 8, 52 pp., 2018.
WMO (Ed.): Manual on Codes: International codes – Part B – Binary Codes; Part C – Common Features to Binary and Alphanumeric Codes, 2019th edn., World Meteorological Organization WMO, Geneva, Switzerland, 1180 pp., 2019a.
WMO (Ed.): Manual on the WMO Integrated Gloval Observing System: Annex VIII to the WMO Technical Regulatsion, 2019th edn., World Meteorological Organization WMO, Geneva, Switzerland, 1180 pp., 2019b.
Yan, H., Sun, N., Wigmosta, M., Skaggs, R., Hou, Z., and Leung, R.: Next-generation intensity-duration-frequency curves for hydrologic design in snow-dominated environments, Water Resour. Res., 54, 1093–1108, https://doi.org/10.1002/2017WR021290, 2018.
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological...
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