Articles | Volume 29, issue 11
https://doi.org/10.5194/hess-29-2445-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2445-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jun 2025
Research article |
| 11 Jun 2025
| 11 Jun 2025
Cold climates, complex hydrology: can a land surface model accurately simulate deep percolation?
Alireza Amani
CORRESPONDING AUTHOR
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Marie-Amélie Boucher
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Alexandre R. Cabral
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada
Vincent Vionnet
Environmental Numerical Weather Prediction Research, Environment and Climate Change Canada, Dorval, Quebec, Canada
Étienne Gaborit
Environmental Numerical Weather Prediction Research, Environment and Climate Change Canada, Dorval, Quebec, Canada
Related authors
No articles found.
Benjamin Bouchard, Vincent Vionnet, Étienne Gaborit, and Vincent Fortin
Hydrol. Earth Syst. Sci., 30, 4823–4846, https://doi.org/10.5194/hess-30-4823-2026, https://doi.org/10.5194/hess-30-4823-2026, 2026
Short summary
Short summary
Enhancing frozen soil infiltration based on soil liquid water significantly improves streamflow simulation in the SVS land surface model. Tested at over 580 stations in the Great-Lakes and Saint-Lawrence region for five years, this approach can be easily transferred to other land surface models (LSM). Our results suggest that promoting frozen soil infiltration in LSM is key for realistic large-scale streamflow simulations and critical for operational hydrological prediction in cold regions.
Matthieu Lafaysse, Marie Dumont, Basile De Fleurian, Mathieu Fructus, Rafife Nheili, Léo Viallon-Galinier, Matthieu Baron, Aaron Boone, Axel Bouchet, Julien Brondex, Carlo Carmagnola, Bertrand Cluzet, Kévin Fourteau, Ange Haddjeri, Pascal Hagenmuller, Giulia Mazzotti, Marie Minvielle, Samuel Morin, Louis Quéno, Léon Roussel, Pierre Spandre, François Tuzet, and Vincent Vionnet
Geosci. Model Dev., 19, 6273–6334, https://doi.org/10.5194/gmd-19-6273-2026, https://doi.org/10.5194/gmd-19-6273-2026, 2026
Short summary
Short summary
This article is a comprehensive description of the 3.0.2 stable release of the Crocus snowpack model. It describes various new implementations since the last reference article in 2012 and a review of the available scientific evaluations and applications of the model. This provides guidance for the future of numerical snow modelling.
Nicolas R. Leroux, Vincent Vionnet, Courtney Bayer, Julien Meloche, Arlan Dirkson, Franck Lespinas, Mark Buehner, Marco Carrera, Benoit Montpetit, Bernard Bilodeau, Maria Abrahamowicz, and Chris Derksen
The Cryosphere, 20, 2773–2792, https://doi.org/10.5194/tc-20-2773-2026, https://doi.org/10.5194/tc-20-2773-2026, 2026
Short summary
Short summary
This study evaluates the assimilation of Ku-band radar backscatter into a multilayer snowpack model to support the upcoming Terrestrial Snow Mass Mission. Synthetic experiments were conducted at Arctic, continental, and alpine sites over three winters using a particle filter. Results show that assimilating backscatter improves estimates of snow water equivalent, depth, and vertical snow properties, laying the groundwork for future satellite missions focused on radar-based snow monitoring.
Mickaël Lalande, Alexandre Roy, Libo Wang, Diana Verseghy, Vincent Vionnet, Florent Dominé, and Christophe Kinnard
EGUsphere, https://doi.org/10.5194/egusphere-2026-492, https://doi.org/10.5194/egusphere-2026-492, 2026
Short summary
Short summary
This study enhances a snow model for Arctic environments by improving the heat exchanges within the snowpack and at its interfaces, revising the compaction scheme, and adding consideration of blowing snow sublimation losses. Simulations at ten Arctic, mid-latitude, and Alpine sites show significant reductions in simulated soil and snow temperature biases and improved simulated snow depth and density, which are key features to improve simulated energy, water, and carbon budgets in the Arctic.
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
The Cryosphere, 20, 1315–1338, https://doi.org/10.5194/tc-20-1315-2026, https://doi.org/10.5194/tc-20-1315-2026, 2026
Short summary
Short summary
The impact of uncertainties in the simulation of density and specific surface area (SSA) by the snow model Crocus (embedded in the Soil, Vegetation and Snow v2 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 backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg−1).
Vincent Vionnet, Nicolas R. Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
Geosci. Model Dev., 18, 9119–9147, https://doi.org/10.5194/gmd-18-9119-2025, https://doi.org/10.5194/gmd-18-9119-2025, 2025
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Woolley, Nicolas R. Leroux, Paul Siqueira, J. Max Adam, and Mike Brady
The Cryosphere, 19, 5465–5484, https://doi.org/10.5194/tc-19-5465-2025, https://doi.org/10.5194/tc-19-5465-2025, 2025
Short summary
Short summary
This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, Terrestrial Snow Mass Mission. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–2019. We detail important considerations to have in the context of a satellite mission over a vast region such as Canada. Results show that it is possible to achieve the desired accuracy over an Arctic environment.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
Short summary
Short summary
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.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Valérie Jean, Marie-Amélie Boucher, Anissa Frini, and Dominic Roussel
Hydrol. Earth Syst. Sci., 27, 3351–3373, https://doi.org/10.5194/hess-27-3351-2023, https://doi.org/10.5194/hess-27-3351-2023, 2023
Short summary
Short summary
Flood forecasts are only useful if they are understood correctly. They are also uncertain, and it is difficult to present all of the information about the forecast and its uncertainty on a map, as it is three dimensional (water depth and extent, in all directions). To overcome this, we interviewed 139 people to understand their preferences in terms of forecast visualization. We propose simple and effective ways of presenting flood forecast maps so that they can be understood and useful.
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
Short summary
Short summary
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.
Jean Odry, Marie-Amélie Boucher, Simon Lachance-Cloutier, Richard Turcotte, and Pierre-Yves St-Louis
The Cryosphere, 16, 3489–3506, https://doi.org/10.5194/tc-16-3489-2022, https://doi.org/10.5194/tc-16-3489-2022, 2022
Short summary
Short summary
The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
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
Short summary
Short summary
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.
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).
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
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.
Cited articles
Abdolahzadeh, A. M., Lacroix Vachon, B., and Cabral, A. R.: Assessment of the design of an experimental cover with capillary barrier effect using 4 years of field data, Geotechnical and Geological Engineering, 29, 783–802, 2011. a
Agnihotri, J., Behrangi, A., Tavakoly, A., Geheran, M., Farmani, M. A., and Niu, G.-Y.: Higher frozen soil permeability represented in a hydrological model improves spring streamflow prediction from river basin to continental scales, Water Resour. Res., 59, e2022WR033075, https://doi.org/10.1029/2022WR033075, 2023. a
Al Atawneh, D., Cartwright, N., and Bertone, E.: Climate change and its impact on the projected values of groundwater recharge: A review, J. Hydrol., 601, 126602, https://doi.org/10.1016/j.jhydrol.2021.126602, 2021. a
Al-Houri, Z., Barber, M., Yonge, D., Ullman, J., and Beutel, M.: Impacts of frozen soils on the performance of infiltration treatment facilities, Cold Reg. Sci. Technol., 59, 51–57, 2009. a
Bakker, M., Bartholomeus, R. P., and Ferré, T. P. A.: Preface “Groundwater recharge: processes and quantification”, Hydrol. Earth Syst. Sci., 17, 2653–2655, https://doi.org/10.5194/hess-17-2653-2013, 2013. a
Baringhaus, L. and Franz, C.: On a new multivariate two-sample test, J. Multivariate Anal., 88, 190–206, 2004. a
Bethune, M., Selle, B., and Wang, Q.: Understanding and predicting deep percolation under surface irrigation, Water Resour. Res., 44, 12, https://doi.org/10.1029/2007WR006380, 2008. a
Beven, K. and Young, P.: A guide to good practice in modeling semantics for authors and referees, Water Resour. Res., 49, 5092–5098, 2013. a
Bhumralkar, C. M.: Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model, J. Appl. Meteorol. Climatol., 14, 1246–1258, 1975. a
Blackadar, A. K.: Modelng the nocturnal boundary layer, in: Preprints, Third Symp. on Atmospheric Turbulence, Diffusion, and Air Quality, Raleigh, Amer. Meteor. Soc., edited by: Hanna, S. R., Talley, W. K., Blackadar, A. K., and Blair, F., https://www.jstor.org/stable/26217779 (last access: 20 March 2025), 1976. a
Boussetta, S., Balsamo, G., Arduini, G., Dutra, E., McNorton, J., Choulga, M., Agustí-Panareda, A., Beljaars, A., Wedi, N., Munõz-Sabater, J., de Rosnay, P., Sandu, I., Hadade, I., Carver, G., Mazzetti, C., Prudhomme, C., Dai, Y., and Zsoter, E.: ECLand: The ECMWF land surface modelling system, Atmosphere, 12, 723, https://doi.org/10.3390/atmos12060723, 2021. a, b
Cabral, A. R., Amani, A., Kahale, T., Ouédraogo,, O., and Duarte Neto, M.: Integrated Lysimeter Study (Deep Percolation) Data from Saint-Nicéphore, Quebec, Zenodo [data set], https://doi.org/10.5281/zenodo.10582140, 2024. a
Cao, G., Scanlon, B. R., Han, D., and Zheng, C.: Impacts of thickening unsaturated zone on groundwater recharge in the North China Plain, J. Hydrol., 537, 260–270, 2016. a
Chadburn, S., Burke, E., Essery, R., Boike, J., Langer, M., Heikenfeld, M., Cox, P., and Friedlingstein, P.: An improved representation of physical permafrost dynamics in the JULES land-surface model, Geosci. Model Dev., 8, 1493–1508, https://doi.org/10.5194/gmd-8-1493-2015, 2015. a
Chamberlain, E., Iskandar, I., and Hunsicker, S.: Effect of freeze-thaw cycles on the permeability and macrostructure of soils, Cold Reg, Res, Eng, Lab,, 90, 145–155, 1990. a
Chamberlain, E. J. and Gow, A. J.: Effect of freezing and thawing on the permeability and structure of soils, in: Developments in Geotechnical Engineering, Elsevier, 26, 73–92, 1979. a
Charrois, L., Cosme, E., Dumont, M., Lafaysse, M., Morin, S., Libois, Q., and Picard, G.: On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model, The Cryosphere, 10, 1021–1038, https://doi.org/10.5194/tc-10-1021-2016, 2016. a, b
Cohen, J., Ye, H., and Jones, J.: Trends and variability in rain-on-snow events, Geophys. Res. Lett., 42, 7115–7122, 2015. a
Colli, M., Lanza, L., and Chan, P.: Co-located tipping-bucket and optical drop counter RI measurements and a simulated correction algorithm, Atmos. Res., 119, 3–12, 2013. a
Cordeiro, M. R. C., Wilson, H. F., Vanrobaeys, J., Pomeroy, J. W., Fang, X., and The Red-Assiniboine Project Biophysical Modelling Team: Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model, Hydrol. Earth Syst. Sci., 21, 3483–3506, https://doi.org/10.5194/hess-21-3483-2017, 2017. a
Dalin, C., Wada, Y., Kastner, T., and Puma, M. J.: Groundwater depletion embedded in international food trade, Nature, 543, 700–704, 2017. a
Decharme, B. and Douville, H.: Introduction of a sub-grid hydrology in the ISBA land surface model, Clim. Dynam., 26, 65–78, 2006. a
Demand, D., Selker, J. S., and Weiler, M.: Influences of macropores on infiltration into seasonally frozen soil, Vadose Zone J., 18, 1–14, 2019. a
Denager, T., Sonnenborg, T. O., Looms, M. C., Bogena, H., and Jensen, K. H.: Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables, Hydrol. Earth Syst. Sci., 27, 2827–2845, https://doi.org/10.5194/hess-27-2827-2023, 2023. a
Dore, M. H.: Climate change and changes in global precipitation patterns: what do we know?, Environ. Int., 31, 1167–1181, 2005. a
Fayer, M. J.: UNSAT-H version 3.0: Unsaturated soil water and heat flow model theory, user manual, and examples, Tech. rep., Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2000. a
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020. a
Gaborit, É., Fortin, V., Xu, X., Seglenieks, F., Tolson, B., Fry, L. M., Hunter, T., Anctil, F., and Gronewold, A. D.: A hydrological prediction system based on the SVS land-surface scheme: efficient calibration of GEM-Hydro for streamflow simulation over the Lake Ontario basin, Hydrol. Earth Syst. Sci., 21, 4825–4839, https://doi.org/10.5194/hess-21-4825-2017, 2017. a
Ganji, A., Sushama, L., Verseghy, D., and Harvey, R.: On improving cold region hydrological processes in the Canadian Land Surface Scheme, Theor. Appl. Climatol., 127, 45–59, 2017. a
Gao, C., Ye, W.-M., Lu, P.-H., Liu, Z.-R., Wang, Q., and Chen, Y.-G.: An infiltration model for inclined covers with consideration of capillary barrier effect, Eng. Geol., 326, 107318, https://doi.org/10.1016/j.enggeo.2023.107318, 2023. a
Ghasemizade, M., Moeck, C., and Schirmer, M.: The effect of model complexity in simulating unsaturated zone flow processes on recharge estimation at varying time scales, J. Hydrol., 529, 1173–1184, 2015. a
Gleeson, T., Wada, Y., Bierkens, M. F., and Van Beek, L. P.: Water balance of global aquifers revealed by groundwater footprint, Nature, 488, 197–200, 2012. a
Graham, S. L., Srinivasan, M., Faulkner, N., and Carrick, S.: Soil hydraulic modeling outcomes with four parameterization methods: Comparing soil description and inverse estimation approaches, Vadose Zone J., 17, 1–10, 2018. a
Green, T. R., Taniguchi, M., Kooi, H., Gurdak, J. J., Allen, D. M., Hiscock, K. M., Treidel, H., and Aureli, A.: Beneath the surface of global change: Impacts of climate change on groundwater, J. Hydrol., 405, 532–560, 2011. a
Grimit, E. P., Gneiting, T., Berrocal, V. J., and Johnson, N. A.: The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification, Q. J. Roy. Meteor. Soc., 132, 2925–2942, 2006. a
Gurdak, J. J. and Roe, C. D.: Recharge rates and chemistry beneath playas of the High Plains aquifer, USA, Hydrogeol. J., 18, 1747, https://doi.org/10.1007/s10040-010-0672-3, 2010. a
Harpold, A. A. and Molotch, N. P.: Sensitivity of soil water availability to changing snowmelt timing in the western US, Geophys. Res. Lett., 42, 8011–8020, 2015. a
He, H., Liu, L., Dyck, M., Si, B., and Lv, J.: Modelling dry soil thermal conductivity, Soil Till. Res., 213, 105093, https://doi.org/10.1016/j.still.2021.105093, 2021. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a
Ho, C. K., Arnold, B. W., Cochran, J. R., Taira, R. Y., and Pelton, M. A.: A probabilistic model and software tool for evaluating the long-term performance of landfill covers, Environ. Model. Softw., 19, 63–88, 2004. a
Huang, F., Zhang, Y., Zhang, D., and Chen, X.: Environmental groundwater depth for groundwater-dependent terrestrial ecosystems in arid/semiarid regions: A review, Int. J. Env. Res. Pub. He., 16, 763, https://doi.org/10.3390/ijerph16050763, 2019. a
Hübner, R., Günther, T., Heller, K., Noell, U., and Kleber, A.: Impacts of a capillary barrier on infiltration and subsurface stormflow in layered slope deposits monitored with 3-D ERT and hydrometric measurements, Hydrol. Earth Syst. Sci., 21, 5181–5199, https://doi.org/10.5194/hess-21-5181-2017, 2017. a
Iwata, Y., Hayashi, M., Suzuki, S., Hirota, T., and Hasegawa, S.: Effects of snow cover on soil freezing, water movement, and snowmelt infiltration: A paired plot experiment, Water Resour. Res., 46, 9, https://doi.org/10.1029/2009WR008070, 2010. a
Iwata, Y., Yanai, Y., Yazaki, T., and Hirota, T.: Effects of a snow-compaction treatment on soil freezing, snowmelt runoff, and soil nitrate movement: A field-scale paired-plot experiment, J. Hydrol., 567, 280–289, 2018. a
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial variation of the rain–snow temperature threshold across the Northern Hemisphere, Nat. Commun., 9, 1–9, 2018. a
Jiang, R., Li, T., Liu, D., Fu, Q., Hou, R., Li, Q., Cui, S., and Li, M.: Soil infiltration characteristics and pore distribution under freezing–thawing conditions, The Cryosphere, 15, 2133–2146, https://doi.org/10.5194/tc-15-2133-2021, 2021. a
Johansen, O.: Varmeledningsevne av jordarter (Thermal conductivity of soils). Trondheim, Norway: University of Trondheim US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory, Hanover, N.H., CRREL draft English Translation 637, 1975. a
Juras, R., Würzer, S., Pavlásek, J., Vitvar, T., and Jonas, T.: Rainwater propagation through snowpack during rain-on-snow sprinkling experiments under different snow conditions, Hydrol. Earth Syst. Sci., 21, 4973–4987, https://doi.org/10.5194/hess-21-4973-2017, 2017. a
Kahale, T. and Cabral, A. R.: Field and numerical evaluation of breakthrough suction effects in lysimeter design, Environ. Technol., 45, 1169–1182, 2024. a
Kahale, T., Ouédraogo, O., Duarte Neto, M., Simard, V., and Cabral, A. R.: Field-based assessment of the design of lysimeters for landfill final cover seepage control, J. Air Waste Manage. A., 72, 1477–1488, 2022. a
Khire, M. V., Benson, C. H., and Bosscher, P. J.: Water balance modeling of earthen final covers, J. Geotech. Geoenviron., 123, 744–754, 1997. a
Kochendorfer, J., Rasmussen, R., Wolff, M., Baker, B., Hall, M. E., Meyers, T., Landolt, S., Jachcik, A., Isaksen, K., Brækkan, R., and Leeper, R.: The quantification and correction of wind-induced precipitation measurement errors, Hydrol. Earth Syst. Sci., 21, 1973–1989, https://doi.org/10.5194/hess-21-1973-2017, 2017. a
Kurylyk, B. L., MacQuarrie, K. T., and McKenzie, J. M.: Climate change impacts on groundwater and soil temperatures in cold and temperate regions: Implications, mathematical theory, and emerging simulation tools, Earth-Sci. Rev., 138, 313–334, 2014. a
Lanza, L., Leroy, M., Alexandropoulos, C., Stagi, L., and Wauben, W.: Laboratory intercomparison of rainfall intensity gauges, World Meteorological Organisation–Instruments and Observing Methods Rep, 84, 2005. a
Leonardini, G., Anctil, F., Vionnet, V., Abrahamowicz, M., Nadeau, D. F., and Fortin, V.: Evaluation of the Snow Cover in the Soil, Vegetation, and Snow (SVS) Land Surface Model, J. Hydrometeorol., 22, 1663–1680, https://doi.org/10.1175/jhm-d-20-0249.1, 2021. a, b, c
Li, D. and Shao, M.: Temporal stability analysis for estimating spatial mean soil water storage and deep percolation in irrigated maize crops, Agr. Water Manage., 144, 140–149, 2014. a
Li, Y., Fu, Q., Li, T., Liu, D., Hou, R., Li, Q., Yi, J., Li, M., and Meng, F.: Snow melting water infiltration mechanism of farmland freezing-thawing soil and determination of meltwater infiltration parameter in seasonal frozen soil areas, Agr. Water Manage., 258, 107165, https://doi.org/10.1016/j.agwat.2021.107165, 2021. a
Loh, W.-L.: On Latin hypercube sampling, Ann. Stat., 24, 2058–2080, 1996. a
Malusis, M. A. and Benson, C. H.: Lysimeters versus water-content sensors for performance monitoring of alternative earthen final covers, Unsaturated Soils 2006, 741–752, 2006. a
Manabe, S.: Climate and the ocean circulation: II. The atmospheric circulation and the effect of heat transfer by ocean currents, Mon. Weather Rev., 97, 775–805, 1969. a
Mancarella, D. and Simeone, V.: Capillary barrier effects in unsaturated layered soils, with special reference to the pyroclastic veneer of the Pizzo d’Alvano, Campania, Italy, B. Eng. Geol. Environ., 71, 791–801, 2012. a
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Management Science, 22, 1087–1096, 1976. a
May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T.: MetPy: A meteorological Python library for data analysis and visualization, B. Am. Meteorol. Soc., 103, E2273–E2284, 2022. a
Mazurkiewicz, A. B., Callery, D. G., and McDonnell, J. J.: Assessing the controls of the snow energy balance and water available for runoff in a rain-on-snow environment, J. Hydrol., 354, 1–14, 2008. a
McCabe, G. J., Clark, M. P., and Hay, L. E.: Rain-on-snow events in the western United States, B. Am. Meteorol. Soc., 88, 319–328, 2007. a
McCartney, J. S. and Zornberg, J. G.: Effects of infiltration and evaporation on geosynthetic capillary barrier performance, Can. Geotech. J., 47, 1201–1213, 2010. a
Meissner, R., Rupp, H., and Seyfarth, M.: Advances in out door lysimeter techniques, Water Air Soil Poll. Focus, 8, 217–225, 2008. a
Mittelbach, H., Lehner, I., and Seneviratne, S. I.: Comparison of four soil moisture sensor types under field conditions in Switzerland, J. Hydrol., 430, 39–49, 2012. a
Mohammed, A. A., Kurylyk, B. L., Cey, E. E., and Hayashi, M.: Snowmelt infiltration and macropore flow in frozen soils: Overview, knowledge gaps, and a conceptual framework, Vadose Zone J., 17, 1–15, 2018. a
Mohammed, A. A., Pavlovskii, I., Cey, E. E., and Hayashi, M.: Effects of preferential flow on snowmelt partitioning and groundwater recharge in frozen soils, Hydrol. Earth Syst. Sci., 23, 5017–5031, https://doi.org/10.5194/hess-23-5017-2019, 2019. a
Mohammed, G. A., Hayashi, M., Farrow, C. R., and Takano, Y.: Improved characterization of frozen soil processes in the Versatile Soil Moisture Budget model, Can. J. Soil Sci., 93, 511–531, 2013. a
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, 2017. a
Naik, A. P., Ghosh, B., and Pekkat, S.: Estimating soil hydraulic properties using mini disk infiltrometer, ISH J. Hydraul. Eng., 25, 62–70, 2019. a
Niu, G.-Y. and Yang, Z.-L.: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins, J. Geophys. Res.-Atmos., 112, D21101, https://doi.org/10.1029/2007JD008674, 2007. a, b, c
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015139, 2011. a
Noori, R., Maghrebi, M., Jessen, S., Bateni, S. M., Heggy, E., Javadi, S., Noury, M., Pistre, S., Abolfathi, S., and AghaKouchak, A.: Decline in Iran’s groundwater recharge, Nat. Commun., 14, 6674, https://doi.org/10.1038/s41467-023-42411-2, 2023. a
Nygren, M., Giese, M., and Barthel, R.: Recent trends in hydroclimate and groundwater levels in a region with seasonal frost cover, J. Hydrol., 602, 126732, https://doi.org/10.1016/j.jhydrol.2021.126732, 2021. a
Ogorzalek, A., Bohnhoff, G., Shackelford, C., Benson, C., and Apiwantragoon, P.: Comparison of field data and water-balance predictions for a capillary barrier cover, J. Geotech. Geoenviron., 134, 470–486, 2008. a
Oldecop, L. A., Rodari, G. J., and Muñoz, J. J.: Atmosphere interaction and capillary barrier in filtered tailings, Geotech. Geol. Eng., 35, 1803–1817, 2017. a
Orellana, F., Verma, P., Loheide, S. P., and Daly, E.: Monitoring and modeling water-vegetation interactions in groundwater-dependent ecosystems, Rev. Geophys., 50, https://doi.org/10.1029/2011RG000383, 2012. a
Oreskes, N., Shrader-Frechette, K., and Belitz, K.: Verification, validation, and confirmation of numerical models in the earth sciences, Science, 263, 641–646, 1994. a
Ouédraogo, O., Duarte, M., Kahale, T., Abichou, T., and Cabral, A.: Parametric analysis of the efficacy of lysimeter designs using numerical modelling, Geotech. Geol. Eng., 40, 5361–5375, 2022. a
Payero, J. O. and Irmak, S.: Construction, installation, and performance of two repacked weighing lysimeters, Irrig. Sci., 26, 191–202, 2008. a
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Peters-Lidard, C., Blackburn, E., Liang, X., and Wood, E. F.: The effect of soil thermal conductivity parameterization on surface energy fluxes and temperatures, J. Atmos. Sci., 55, 1209–1224, 1998. a
Pomeroy, J., Brown, T., Fang, X., Shook, K. R., Pradhananga, D., Armstrong, R., Harder, P., Marsh, C., Costa, D., Krogh, S., Aubry-Wake, C., Annand, H., Lawford, P., He, Z., Kompanizare, M., and Lopez Moreno, J. I.: The cold regions hydrological modelling platform for hydrological diagnosis and prediction based on process understanding, J. Hydrol., 615, 128711, https://doi.org/10.1016/j.jhydrol.2022.128711, 2022. a
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. a
Sammartino, S., Lissy, A.-S., Bogner, C., Van Den Bogaert, R., Capowiez, Y., Ruy, S., and Cornu, S.: Identifying the functional macropore network related to preferential flow in structured soils, Vadose Zone J., 14, vzj2015–05, https://doi.org/10.2136/vzj2015.05.0070, 2015. a
Schindler, U., von Unold, G., Durner, W., and Mueller, L.: Recent progress in measuring soil hydraulic properties, in: Proceedings of the International Conference on Environment and Civil Engineering, 24–25 April 2015, Pattaya (Thailand), 24–25, https://doi.org/10.15242/iae.iae0415401, 2015. a
Schindler, U. G. and Müller, L.: Soil hydraulic functions of international soils measured with the Extended Evaporation Method (EEM) and the HYPROP device, Open Data Journal for Agricultural Research, 3, 2017. a
Schwemmle, R. and Weiler, M.: Consistent modeling of transport processes and travel times–coupling soil hydrologic processes with StorAge Selection functions, Water Resour. Res., 60, e2023WR034441, https://doi.org/10.1029/2023WR034441, 2024. a
Selim, T., Elkefafy, S. M., Berndtsson, R., Elkiki, M., and El-Kharbotly, A. A.: Heavy Metal Transport in Different Drip-Irrigated Soil Types with Potato Crop, Sustainability, 15, 10542, https://doi.org/10.3390/su151310542, 2023. a
Selle, B., Minasny, B., Bethune, M., Thayalakumaran, T., and Chandra, S.: Applicability of Richards' equation models to predict deep percolation under surface irrigation, Geoderma, 160, 569–578, 2011. a
Séré, G., Ouvrard, S., Magnenet, V., Pey, B., Morel, J. L., and Schwartz, C.: Predictability of the evolution of the soil structure using water flow modeling for a constructed technosol, Vadose Zone J., 11, 1, https://doi.org/10.2136/vzj2011.0069, 2012. a
Shirazi, T., Allen, D., Quinton, W., and Pomeroy, J.: Estimating soil thaw energy in sub-Alpine tundra at the hillslope scale, Wolf Creek, Yukon Territory, Canada, Hydrol. Res., 40, 1–18, 2009. a
Simunek, J., Van Genuchten, M. T., and Sejna, M.: The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media, University of California-Riverside Research Reports, 3, 1–240, 2005. a
Sobaga, A., Decharme, B., Habets, F., Delire, C., Enjelvin, N., Redon, P.-O., Faure-Catteloin, P., and Le Moigne, P.: Assessment of the interactions between soil–biosphere–atmosphere (ISBA) land surface model soil hydrology, using four closed-form soil water relationships and several lysimeters, Hydrol. Earth Syst. Sci., 27, 2437–2461, https://doi.org/10.5194/hess-27-2437-2023, 2023. a
Stähli, M., Jansson, P.-E., and Lundin, L.-C.: Preferential Water Flow in a Frozen Soil - a Two-Domain Model Approach, Hydrol. Process., 10, 1305–1316, 1996. a
Stumpp, C., Stichler, W., Kandolf, M., and Simunek, Jiri, J.: Effects of Land Cover and Fertilization Method on Water Flow and Solute Transport in Five Lysimeters: A Long-Term Study Using Stable Water Isotopes, Vadose Zone J., 11, 1, https://doi.org/10.2136/vzj2011.0075, 2012. a
Székely, G. J. and Rizzo, M. L.: A new test for multivariate normality, J. Multivariate Anal., 93, 58–80, 2005. a
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow, L., Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., MacDonald, A., Fan, Y., Maxwell, R. M., Yechieli, Y., Gurdak, J. J., Allen, D. M., Shamsudduha, M., Hiscock, K., Yeh, P. J.-F., Holman, I., and Treidel, H.: Ground water and climate change, Nat. Clim. Change, 3, 322–329, 2013. a
Trenberth, K. E.: Changes in precipitation with climate change, Climate Res., 47, 123–138, 2011. a
Trigo, I., Boussetta, S., Viterbo, P., Balsamo, G., Beljaars, A., and Sandu, I.: Comparison of model land skin temperature with remotely sensed estimates and assessment of surface-atmosphere coupling, J. Geophys. Res.-Atmos., 120, 12096–12111, 2015. a
Trubilowicz, J. W. and Moore, R. D.: Quantifying the role of the snowpack in generating water available for run-off during rain-on-snow events from snow pillow records, Hydrol. Process., 31, 4136–4150, 2017. a
Vásquez, V., Thomsen, A., Iversen, B. V., Jensen, R., Ringgaard, R., and Schelde, K.: Integrating lysimeter drainage and eddy covariance flux measurements in a groundwater recharge model, Hydrol. Sci. J., 60, 1520–1537, 2015. a
Vereecken, H., Weihermüller, L., Assouline, S., Šimůnek, J., Verhoef, A., Herbst, M., Archer, N., Mohanty, B., Montzka, C., Vanderborght, J., Balsamo, G., Bechtold, M., Boone, A., Chadburn, S., Cuntz, M., Decharme, B., Ducharne, A., Ek, M., Garrigues, S., Goergen, K., Ingwersen, J., Kollet, S., Lawrence, D. M., Li, Q., Or, D., Swenson, S., de Vrese, P., Walko, R., Wu, Y., and Xue, Y.: Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling, Vadose Zone J., 18, 1–53, 2019. a
Verseghy, D. L.: CLASS – A Canadian land surface scheme for GCMs. I. Soil model, Int. J. Climatol., 11, 111–133, 1991. a
Wada, Y., Van Beek, L. P., Van Kempen, C. M., Reckman, J. W., Vasak, S., and Bierkens, M. F.: Global depletion of groundwater resources, Geophys. Res. Lett., 37, 20, https://doi.org/10.1029/2010GL044571, 2010. a
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing permafrost – A review, Vadose Zone J., 15, vzj2016–01, https://doi.org/10.2136/vzj2016.01.0010, 2016. a
Watanabe, K., Kito, T., Dun, S., Wu, J. Q., Greer, R. C., and Flury, M.: Water infiltration into a frozen soil with simultaneous melting of the frozen layer, Vadose Zone J., 12, vzj2011–0188, https://doi.org/10.2136/vzj2011.0188, 2013. a
Wheater, H. S., Pomeroy, J. W., Pietroniro, A., Davison, B., Elshamy, M., Yassin, F., Rokaya, P., Fayad, A., Tesemma, Z., Princz, D., Loukili, Y., DeBeer, C. M., Ireson, A. M., Razavi, S., Lindenschmidt, K.-E., Elshorbagy, A., MacDonald, M., Abdelhamed, M., Haghnegahdar, A., and Bahrami, A.: Advances in modelling large river basins in cold regions with Modélisation Environmentale Communautaire–Surface and Hydrology (MESH), the Canadian hydrological land surface scheme, Hydrol. Process., 36, e1457, https://doi.org/10.1002/hyp.14557, 2022. a, b
Willcox, K. E., Ghattas, O., and Heimbach, P.: The imperative of physics-based modeling and inverse theory in computational science, Nature Computational Science, 1, 166–168, 2021. a
Williams, M. R., McAfee, S. J., and Kent, B. E.: Dye tracers reveal potential edge-flow effects in undisturbed lysimeters sealed with petrolatum, Vadose Zone J., 18, 1–9, 2019. a
Williams, M. R., Coronel, O., McAfee, S. J., and Sanders, L. L.: Preferential flow of surface-applied solutes: Effect of lysimeter design and initial soil water content, Vadose Zone J., 19, e20052, https://doi.org/10.1002/vzj2.20052, 2020. a
Wilson, C., Guivarch, C., Kriegler, E., Van Ruijven, B., Van Vuuren, D. P., Krey, V., Schwanitz, V. J., and Thompson, E. L.: Evaluating process-based integrated assessment models of climate change mitigation, Clim. Change, 166, 1–22, 2021. a
Wu, W.-Y., Lo, M.-H., Wada, Y., Famiglietti, J. S., Reager, J. T., Yeh, P. J.-F., Ducharne, A., and Yang, Z.-L.: Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers, Nat. Commun., 11, 3710, https://doi.org/10.1038/s41467-020-17581-y, 2020. a
Yang, Y., Chen, R., Liu, G., Liu, Z., and Wang, X.: Trends and variability in snowmelt in China under climate change, Hydrol. Earth Syst. Sci., 26, 305–329, https://doi.org/10.5194/hess-26-305-2022, 2022. a
Yang, Z.-L.: Modeling land surface processes in short-term weather and climate studies, in: Observation, Theory And Modeling Of Atmospheric Variability: Selected Papers of Nanjing Institute of Meteorology Alumni in Commemoration of Professor Jijia Zhang, 288–313, World Scientific, 2004. a
Zhang, L., Mao, J., Shi, X., Ricciuto, D., He, H., Thornton, P., Yu, G., Li, P., Liu, M., Ren, X., Han, S., Li, Y., Yan, J., Hao, Y., and Wang, H.: Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites, Agr. Forest Meteorol., 226, 174–185, 2016. a
Zhao, L., Gray, D. M., and Male, D. H.: Numerical analysis of simultaneous heat and mass transfer during infiltration into frozen ground, J. Hydrol., 200, 345–363, 1997. a
Short summary
Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.
Accurately estimating groundwater recharge using numerical models is particularly difficult in...
