Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5371-2021
© 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/tc-15-5371-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Dec 2021
Research article |
| 06 Dec 2021
| 06 Dec 2021
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Achut Parajuli
CORRESPONDING AUTHOR
Department of Civil and Water Engineering, Université Laval,
Québec, Canada
CentrEau, Quebec Water Research Centre, Université Laval,
Québec, Canada
Department of Environmental Science, Université du Québec
à Trois Rivières, Trois-Rivières, Canada
Daniel F. Nadeau
Department of Civil and Water Engineering, Université Laval,
Québec, Canada
CentrEau, Quebec Water Research Centre, Université Laval,
Québec, Canada
François Anctil
Department of Civil and Water Engineering, Université Laval,
Québec, Canada
CentrEau, Quebec Water Research Centre, Université Laval,
Québec, Canada
Marco Alves
Department of Civil and Water Engineering, Université Laval,
Québec, Canada
CentrEau, Quebec Water Research Centre, Université Laval,
Québec, Canada
Related authors
No articles found.
Jérémie Labelle, Pierre-Erik Isabelle, Benjamin Bouchard, Nicolas Breton, André Bégin-Drolet, and Daniel F. Nadeau
EGUsphere, https://doi.org/10.5194/egusphere-2026-2196, https://doi.org/10.5194/egusphere-2026-2196, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Snow accumulation on radiometer lenses can obstruct radiation measurements. In this paper, we propose the Snow Removal Device (SRD) for remote and off-grid radiometers affected by this problem. Its average energy cost per cycle (for 1472 events) is comparable to 17s of heating. Two side-by-side radiometers were used for this study: one with the SRD and the another without. For the full winter, results show that radiometer measurements differed by +37.20 MJ m⁻² for LW↓ and −1.79 MJ m⁻² for SW↓.
Daniel Bellamy, Martina Klose, Daniel F. Nadeau, Sebastian Engelstaedter, Richard Washington, and James King
EGUsphere, https://doi.org/10.5194/egusphere-2026-1343, https://doi.org/10.5194/egusphere-2026-1343, 2026
Short summary
Short summary
Mineral dust emissions are simulated for the first time from proglacial valleys of the St. Elias Mountains (western Canada/Alaska) to elucidate their contributions to regional climate forcing and global mineral dust loading. We implement a regional-scale dust emission model and validate surface meteorology, dust emissions, and boundary layer processes controlling aerosol dispersion for a holistic evaluation of the model.
Pascal Matte, Xavier Chartrand, David Purnell, Marc Simard, Alexandra Christensen, Francis Fortin-Legault, Jonathan Morin, Yann Côté-Nadeau, Élaine Desrosiers, Anthony Ouellet, Cédric Chavanne, François Anctil, Gabriela Siles, Scott Bedart, Silvia Innocenti, Mohammed Dabboor, Daniel L. Peters, and Mélanie Trudel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-30, https://doi.org/10.5194/essd-2026-30, 2026
Preprint under review for ESSD
Short summary
Short summary
We present a new public collection of water level and wave measurements from the St. Lawrence Estuary and Saguenay Fjord in Canada, gathered using state-of-the-art instruments and the largest network ever deployed in this region. Collected to assess the accuracy of the Surface Water and Ocean Topography satellite, these data will help scientists, decision-makers, and communities better study floods and coastal changes in estuaries, especially as the climate shifts and water levels rise.
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev., 19, 1559–1580, https://doi.org/10.5194/gmd-19-1559-2026, https://doi.org/10.5194/gmd-19-1559-2026, 2026
Short summary
Short summary
Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
Clarence Gagnon, Daniel F. Nadeau, Alejandro Di Luca, Benoit Brault, Romane Hamon, Nicolas L. Roy, Marc-André Bourgault, and François Anctil
EGUsphere, https://doi.org/10.5194/egusphere-2025-6192, https://doi.org/10.5194/egusphere-2025-6192, 2026
Short summary
Short summary
This article studies the contribution of extratropical cyclones to nearly 500 river floods that caused material damage in the province of Quebec, in eastern Canada, between 1991 and 2020. By bringing significant amounts of precipitation, extratropical cyclones played a key role in the majority of floods. During the period, over half of the flood-contributing storms originated in the central United States and the Gulf of Mexico, and just a handful caused most of the financial damage observed.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-585, https://doi.org/10.5194/essd-2025-585, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1,000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Amélie Pouliot, Isabelle Laurion, Antoine Thiboult, and Daniel F. Nadeau
Biogeosciences, 22, 5413–5433, https://doi.org/10.5194/bg-22-5413-2025, https://doi.org/10.5194/bg-22-5413-2025, 2025
Short summary
Short summary
Small thermokarst lakes release greenhouse gases (GHGs) as permafrost thaws, but most studies focus on diurnal measurements, potentially overlooking significant variations. We measured GHG fluxes from two lakes in Nunavik over two summers – one colder, one warmer – alongside 2 years of continuous water column monitoring. Fluxes were higher in the warmer summer, with strong day–night differences. Our findings show that accurate GHG estimates require full diel measurements and seasonal considerations.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
Short summary
Short summary
Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
Short summary
Short summary
Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
Florent Domine, Denis Sarrazin, Daniel F. Nadeau, Georg Lackner, and Maria Belke-Brea
Earth Syst. Sci. Data, 16, 1523–1541, https://doi.org/10.5194/essd-16-1523-2024, https://doi.org/10.5194/essd-16-1523-2024, 2024
Short summary
Short summary
The forest–tundra ecotone is the transition region between the boreal forest and Arctic tundra. It spans over 13 000 km across the Arctic and is evolving rapidly because of climate change. We provide extensive data sets of two sites 850 m apart, one in tundra and one in forest in this ecotone for use in various models. Data include meteorological and flux data and unique snow and soil physics data.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
Short summary
Short summary
A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
Short summary
Short summary
We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
Short summary
Short summary
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
Short summary
Short summary
We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Simon Ricard, Philippe Lucas-Picher, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-451, https://doi.org/10.5194/hess-2021-451, 2021
Revised manuscript not accepted
Short summary
Short summary
We propose a simplified hydroclimatic modelling workflow for producing hydrologic scenarios without resorting to meteorological observations. This innovative approach preserves trends and physical consistency between simulated climate variables, allows the implementation of modelling cascades despite observation scarcity, and supports the participation of end-users in producing and interpreting climate change impacts on water resources.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
Short summary
Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Cited articles
Alves, M., Nadeau, D. F., Music, B., Anctil, F., and Parajuli, A.: On the
performance of the Canadian Land Surface Scheme driven by the ERA5
reanalysis over the Canadian boreal forest, J. Hydrometeorol., 21,
1383–1404, https://doi.org/10.1175/jhm-d-19-0172.1, 2020.
Anderson, E. A.: A point energy and mass balance model of a snow cover, US
Department of Commerce, National Oceanic and Atmospheric Administration,
National Weather Service, Office of Hydrology, Washington DC, USA, 1976.
Andreadis, K. M., Storck, P., and Lettenmaier, D. P.: Modeling snow
accumulation and ablation processes in forested environments, Water Resour.
Res., 45, 1–13, https://doi.org/10.1029/2008WR007042, 2009.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature,
438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Bartlett, P. A. and Verseghy, D. L.: Modified treatment of intercepted snow
improves the simulated forest albedo in the Canadian Land Surface Scheme,
Hydrol. Process., 29, 3208–3226, https://doi.org/10.1002/hyp.10431, 2015.
Bartlett, P. A., MacKay, M. D., and Verseghy, D. L.: Modified snow algorithms
in the Canadian land surface scheme: Model runs and sensitivity analysis at
three boreal forest stands, Atmos. Ocean, 44, 207–222,
https://doi.org/10.3137/ao.440301, 2006.
Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C.: An energy and
mass model of snow cover suitable for operational avalanche forecasting, J.
Glaciol., 35, 333–342, https://doi.org/10.1017/S0022143000009254, 1989.
Brun, E., Martin, E., and Spiridonov, V.: Coupling a multi-layered snow model
with a GCM, Ann. Glaciol., 25, 66–72, https://doi.org/10.1017/s0260305500013811,
1997.
Davis, R. E., Hardy, J. P., Ni, W., Woodcock, C., McKenzie, J. C., Jordan,
R., and Li, X.: Variation of snow cover ablation in the boreal forest: A
sensitivity study on the effects of conifer canopy, J. Geophys. Res.,
102, 29389–29395, https://doi.org/10.1029/97JD01335, 1997.
DeWalle, D. R. and Rango, A.: Principles of snow hydrology, 1st ed.,
Cambridge University Press, New York, 1–410, ISBN 978 0 521 82362 3, 2008.
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of
1701 snow models using observations from an alpine site, Adv. Water Resour.,
55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Fujita, K., Hiyama, K., Iida, H., and Ageta, Y.: Self-regulated fluctuations
in the ablation of a snow patch over four decades, Water Resour. Res.,
46, 1–9, https://doi.org/10.1029/2009WR008383, 2010.
Gouttevin, I., Lehning, M., Jonas, T., Gustafsson, D., and Mölder, M.: A two-layer canopy model with thermal inertia for an improved snowpack energy balance below needleleaf forest (model SNOWPACK, version 3.2.1, revision 741), Geosci. Model Dev., 8, 2379–2398, https://doi.org/10.5194/gmd-8-2379-2015, 2015.
Harding, R. J. and Pomeroy, J. W.: The energy balance of the winter boreal
landscape, J. Climate, 9, 2778–2787,
https://doi.org/10.1175/1520-0442(1996)009<2778:TEBOTW>2.0.CO;2, 1996.
Hedstrom, N. R. and Pomeroy, J. W.: Measurements and modelling of snow
interception in the boreal forest, Hydrol. Process., 12, 1611–1625,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4, 1998.
Isabelle, P. E., Nadeau, D. F., Asselin, M. H., Harvey, R., Musselman, K.
N., Rousseau, A. N., and Anctil, F.: Solar radiation transmittance of a
boreal balsam fir canopy: Spatiotemporal variability and impacts on growing
season hydrology, Agric. For. Meteorol., 263, 1–14,
https://doi.org/10.1016/j.agrformet.2018.07.022, 2018.
Isabelle, P. E., Nadeau, D. F., Rousseau, A. N., Anctil, F., Jutras, S., and
Music, B.: Impacts of high precipitation on the energy and water budgets of
a humid boreal forest, Agric. For. Meteorol., 280, 1–13,
https://doi.org/10.1016/j.agrformet.2019.107813, 2020.
Jennings, K. S., Kittel, T. G. F., and Molotch, N. P.: Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget, The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, 2018.
Jost, G., Moore, R. D., Smith, R., and Gluns, D. R.: Distributed
temperature-index snowmelt modelling for forested catchments, J. Hydrol.,
420–421, 87–101, https://doi.org/10.1016/j.jhydrol.2011.11.045, 2012.
Koivusalo, H., Heikinheimo, M., and Karvonen, T.: Test of a simple two-layer
parameterisation to simulate the energy balance and temperature of a snow
pack, Theor. Appl. Climatol., 70, 65–79, https://doi.org/10.1007/s007040170006,
2001.
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model
for the Swiss avalanche warning Part III: Meteorological forcing, thin layer
formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184,
https://doi.org/10.1016/S0165-232X(02)00072-1, 2002.
Lundquist, J. D. and Lott, F.: Using inexpensive temperature sensors to
monitor the duration and heterogeneity of snow-covered areas, Water Resour.
Res., 44, 1–6, https://doi.org/10.1029/2008WR007035, 2008.
Mahat, V. and Tarboton, D. G.: Canopy radiation transmission for an energy
balance snowmelt model, Water Resour. Res., 48, 1–16,
https://doi.org/10.1029/2011WR010438, 2012.
Marks, D. and Winstral, A.: Comparison of snow deposition, the snow cover
energy balance, and snowmelt at two sites in a semiarid mountain basin, J.
Hydrometeorol., 2, 213–227, https://doi.org/10.1175/1525-7541(2001)002<0213:COSDTS>2.0.CO;2, 2001.
Marks, D., Kimball, J., Tingey, D., and Link, T.: The sensitivity of snowmelt
processes to climate conditions and forest cover during rain-on-snow: a case
study of the 1996 Pacific Northwest flood, Hydrol. Process., 12, 1569–1587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L, 1998.
Molotch, N. P., Brooks, P. D., Burns, S. P., Litvak, M., Monson, R. K.,
McConnell, J. R., and Musselman, K. N.: Ecohydrological controls on snowmelt
partitioning in mixed-conifer sub-alpine forests, Ecohydrology, 2, 129–142,
https://doi.org/10.1002/eco.48, 2009.
Mosier, T. M., Hill, D. F., and Sharp, K. V.: How much cryosphere model complexity is just right? Exploration using the conceptual cryosphere hydrology framework, The Cryosphere, 10, 2147–2171, https://doi.org/10.5194/tc-10-2147-2016, 2016.
Mott, R., Paterna, E., Horender, S., Crivelli, P., and Lehning, M.: Wind tunnel experiments: cold-air pooling and atmospheric decoupling above a melting snow patch, The Cryosphere, 10, 445–458, https://doi.org/10.5194/tc-10-445-2016, 2016.
Musselman, K., Molotch, N., and Brooks, P.: Effect of vegetation on snow
accumulation and ablation in a mid-latitude sub-alpine forest, Hydrol.
Process., 22, 2267–2274, https://doi.org/10.1002/hyp.7050, 2008.
Oldroyd, H. J., Higgins, C. W., Huwald, H., Selker, J. S., and Parlange, M.
B.: Thermal diffusivity of seasonal snow determined from temperature
profiles, Adv. Water Resour., 55, 121–130,
https://doi.org/10.1016/j.advwatres.2012.06.011, 2013.
Parajuli, A., Nadeau, D. F., Anctil, F., Schilling, O. S., and Jutras, S.:
Does data availability constrain temperature-index snow model? A case study
in the humid boreal forest, Water, 12, 1–22, https://doi.org/10.3390/w12082284,
2020a.
Parajuli, A., Nadeau, D. F., Anctil, F., Parent, A.-C., Bouchard, B.,
Girard, M., and Jutras, S.: Exploring the spatiotemporal variability of the
snow water equivalent in a small boreal forest catchment through observation
and modelling, Hydrol. Process., 34, 2628–2644, https://doi.org/10.1002/hyp.13756,
2020b.
Pierre, A., Jutras, S., Smith, C., Kochendorfer, J., Fortin, V., and Anctil,
F.: Evaluation of catch efficiency transfer functions for unshielded and
single-alter-shielded solid precipitation measurements, J. Atmos. Ocean.
Technol., 36, 865–881, https://doi.org/10.1175/JTECH-D-18-0112.1, 2019.
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W.,
Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a
platform for basing process representation and model structure on physical
evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007.
Qi, J., Li, S., Jamieson, R., Hebb, D., Xing, Z., and Meng, F. R.: Modifying
SWAT with an energy balance module to simulate snowmelt for maritime
regions, Environ. Model. Softw., 93, 146–160,
https://doi.org/10.1016/j.envsoft.2017.03.007, 2017.
Raleigh, M. S. and Small, E. E.: Snowpack density modeling is the primary
source of uncertainty when mapping basin-wide SWE with lidar, Geophys. Res.
Lett., 44, 3700–3709, https://doi.org/10.1002/2016GL071999, 2017.
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015.
Raleigh, M. S., Livneh, B., Lapo, K., and Lundquist, J. D.: How does
availability of meteorological forcing data impact physically based snowpack
simulations?, J. Hydrometeorol., 17, 99–120,
https://doi.org/10.1175/JHM-D-14-0235.1, 2016.
Roy, A., Royer, A., Montpetit, B., Bartlett, P. A., and Langlois, A.: Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS), The Cryosphere, 7, 961–975, https://doi.org/10.5194/tc-7-961-2013, 2013.
Russell, M., Eitel, J. U. H., Maguire, A. J., and Link, T. E.: Toward a novel
laser-based approach for estimating snow interception, Remote Sens., 12,
1–11, https://doi.org/10.3390/rs12071146, 2020.
Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I.,
Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder,
K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson,
D., Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V.,
Kuragina, A., Lettenmaier, D., Li, W. P., Luce, C., Martin, E., Nasonova,
O., Pumpanen, J., Pyles, R. D., Samuelsson, P., Sandells, M., Schädler,
G., Shmakin, A., Smirnova, T. G., Stähli, M., Stöckli, R., Strasser,
U., Su, H., Suzuki, K., Takata, K., Tanaka, K., Thompson, E., Vesala, T.,
Viterbo, P., Wiltshire, A., Xia, K., Xue, Y., and Yamazaki, T.: Evaluation of
forest snow processes models (SnowMIP2), J. Geophys. Res.-Atmos., 114,
1–18, https://doi.org/10.1029/2008JD011063, 2009.
Schaefli, B., Hingray, B., and Musy, A.: Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties, Hydrol. Earth Syst. Sci., 11, 1191–1205, https://doi.org/10.5194/hess-11-1191-2007, 2007.
Schilling, O. S., Parajuli, A., Tremblay Otis, C., Müller, T. U.,
Antolinez Quijano, W., Tremblay, Y., Brennwald, M. S., Nadeau, D. F.,
Jutras, S., Kipfer, R., and Therrien, R.: Quantifying groundwater recharge
dynamics and unsaturated zone processes in snow-dominated catchments via
on-site dissolved gas analysis, Water Resour. Res., 57, 1–24,
https://doi.org/10.1029/2020wr028479, 2021.
Seligman, Z. M., Harper, J. T., and Maneta, M. P.: Changes to snowpack energy
state from spring storm events, Columbia River headwaters, Montana, J.
Hydrometeorol., 15, 159–170, https://doi.org/10.1175/JHM-D-12-078.1, 2014.
Shrestha, M., Wang, L., Koike, T., Xue, Y., and Hirabayashi, Y.: Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites, Hydrol. Earth Syst. Sci., 14, 2577–2594, https://doi.org/10.5194/hess-14-2577-2010, 2010.
Smith, S. A., Brown, A. R., Vosper, S. B., Murkin, P. A., and Veal, A. T.:
Observations and simulations of cold air pooling in valleys, Boundary-Lay.
Meteorol., 134, 85–108, https://doi.org/10.1007/s10546-009-9436-9, 2010.
U.S. Army Corps of Engineers, U. S.: Snow hydrology: Summary report of the
snow investigations, North Pacific Division, Portland District, USA, 1956.
Valéry, A., Andréassian, V., and Perrin, C.: “As simple as possible
but not simpler”: What is useful in a temperature-based snow-accounting
routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting
routine on 380 catchments, J. Hydrol., 517, 1176–1187,
https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014.
Verseghy, D. L.: CLASS-A Canadian land surface scheme for GCMS. I. soil
model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
Verseghy, D. L., Brown, R., and Wang, L.: Evaluation of CLASS snow simulation
over Eastern Canada, J. Hydrometeorol., 18, 1205–1225,
https://doi.org/10.1175/JHM-D-16-0153.1, 2017.
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012.
Wigmosta, M., Nijssen, B., and Storck, P.: The distributed hydrology soil vegetation model, in: Mathematical Models of Small Watershed Hydrology and Applications, edited by: Singh, V. P., Frevert, D. K., Water Resources Publications LLC, Highlands Ranch Colorado, USA, 1, 7–42, available at: https://www.pnnl.gov/sites/default/files/media/file/The-distributed-hydrology-soil-vegetation-model.pdf (last access: 28 November 2021), 2002.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, https://doi.org/10.1029/94WR00436, 1994.
Williams, M. and Morse, J.: Snow cover profile data for Niwot Ridge and
Green Lakes Valley from 1993/2/26 – ongoing, weekly to biweekly,
available at: https://doi.org/10.6073/, 2020.
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the...
