Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-5753-2024
© Author(s) 2024. 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-18-5753-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Dec 2024
Research article |
| 11 Dec 2024
| 11 Dec 2024
Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Jan Magnusson
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Louis Quéno
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Giulia Mazzotti
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Rebecca Mott
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Tobias Jonas
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
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Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-374, https://doi.org/10.5194/essd-2024-374, 2024
Preprint under review for ESSD
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In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land-surface, and hydrological models, with potential applications in similar high-alpine catchments.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
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Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
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The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
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This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Luca Palchetti, Marco Barucci, Claudio Belotti, Giovanni Bianchini, Bertrand Cluzet, Francesco D'Amato, Samuele Del Bianco, Gianluca Di Natale, Marco Gai, Dina Khordakova, Alessio Montori, Hilke Oetjen, Markus Rettinger, Christian Rolf, Dirk Schuettemeyer, Ralf Sussmann, Silvia Viciani, Hannes Vogelmann, and Frank Gunther Wienhold
Earth Syst. Sci. Data, 13, 4303–4312, https://doi.org/10.5194/essd-13-4303-2021, https://doi.org/10.5194/essd-13-4303-2021, 2021
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The FIRMOS far-infrared (IR) prototype, developed for the preparation of the ESA FORUM mission, was deployed for the first time at Mt. Zugspitze at 3000 m altitude to measure the far-IR spectrum of atmospheric emissions. The measurements, including co-located radiometers, lidars, radio soundings, weather, and surface properties, provide a unique dataset to study radiative properties of water vapour, cirrus clouds, and snow emissivity over the IR emissions, including the under-explored far-IR.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
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In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
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
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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.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-374, https://doi.org/10.5194/essd-2024-374, 2024
Preprint under review for ESSD
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land-surface, and hydrological models, with potential applications in similar high-alpine catchments.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
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Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Johanna Teresa Malle, Giulia Mazzotti, Dirk Nikolaus Karger, and Tobias Jonas
Earth Syst. Dynam., 15, 1073–1115, https://doi.org/10.5194/esd-15-1073-2024, https://doi.org/10.5194/esd-15-1073-2024, 2024
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Land surface processes are crucial for the exchange of carbon, nitrogen, and energy in the Earth system. Using meteorological and land use data, we found that higher resolution improved not only the model representation of snow cover but also plant productivity and that water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the Earth system.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
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Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
Short summary
Short summary
The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary
Short summary
This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Luca Palchetti, Marco Barucci, Claudio Belotti, Giovanni Bianchini, Bertrand Cluzet, Francesco D'Amato, Samuele Del Bianco, Gianluca Di Natale, Marco Gai, Dina Khordakova, Alessio Montori, Hilke Oetjen, Markus Rettinger, Christian Rolf, Dirk Schuettemeyer, Ralf Sussmann, Silvia Viciani, Hannes Vogelmann, and Frank Gunther Wienhold
Earth Syst. Sci. Data, 13, 4303–4312, https://doi.org/10.5194/essd-13-4303-2021, https://doi.org/10.5194/essd-13-4303-2021, 2021
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The FIRMOS far-infrared (IR) prototype, developed for the preparation of the ESA FORUM mission, was deployed for the first time at Mt. Zugspitze at 3000 m altitude to measure the far-IR spectrum of atmospheric emissions. The measurements, including co-located radiometers, lidars, radio soundings, weather, and surface properties, provide a unique dataset to study radiative properties of water vapour, cirrus clouds, and snow emissivity over the IR emissions, including the under-explored far-IR.
K. Koutantou, G. Mazzotti, and P. Brunner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 477–484, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021, 2021
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
Short summary
Short summary
In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
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Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
Louis Quéno, Fatima Karbou, Vincent Vionnet, and Ingrid Dombrowski-Etchevers
Hydrol. Earth Syst. Sci., 24, 2083–2104, https://doi.org/10.5194/hess-24-2083-2020, https://doi.org/10.5194/hess-24-2083-2020, 2020
Short summary
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In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. Satellite-derived products of incoming radiation were assessed in the French Alps and the Pyrenees and compared to meteorological forecasts, reanalyses and in situ measurements. We showed their good quality in mountains. The different radiation datasets were used as radiative forcing for snowpack simulations with the detailed model Crocus. Their impact on the snowpack evolution was explored.
Louis Quéno, Vincent Vionnet, Ingrid Dombrowski-Etchevers, Matthieu Lafaysse, Marie Dumont, and Fatima Karbou
The Cryosphere, 10, 1571–1589, https://doi.org/10.5194/tc-10-1571-2016, https://doi.org/10.5194/tc-10-1571-2016, 2016
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Simulations are carried out in the Pyrenees with the snowpack model Crocus, driven by meteorological forecasts from the model AROME at kilometer resolution. The evaluation is done with ground-based measurements, satellite data and reference simulations. Studying daily snow depth variations allows to separate different physical processes affecting the snowpack. We show the benefits of AROME kilometric resolution and dynamical behavior in terms of snowpack spatial variability in a mountain range.
Related subject area
Discipline: Snow | Subject: Snow Hydrology
Impact of intercepted and sub-canopy snow microstructure on snowpack response to rain-on-snow events under a boreal canopy
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Drone-based ground-penetrating radar (GPR) application to snow hydrology
Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events
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Huadong Wang, Xueliang Zhang, Pengfeng Xiao, Tao Che, Zhaojun Zheng, Liyun Dai, and Wenbo Luan
The Cryosphere, 17, 33–50, https://doi.org/10.5194/tc-17-33-2023, https://doi.org/10.5194/tc-17-33-2023, 2023
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The geographically and temporally weighted neural network (GTWNN) model is constructed for estimating large-scale daily snow density by integrating satellite, ground, and reanalysis data, which addresses the importance of spatiotemporal heterogeneity and a nonlinear relationship between snow density and impact variables, as well as allows us to understand the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China from 2013 to 2020.
Eole Valence, Michel Baraer, Eric Rosa, Florent Barbecot, and Chloe Monty
The Cryosphere, 16, 3843–3860, https://doi.org/10.5194/tc-16-3843-2022, https://doi.org/10.5194/tc-16-3843-2022, 2022
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The internal properties of the snow cover shape the annual hygrogram of northern and alpine regions. This study develops a multi-method approach to measure the evolution of snowpack internal properties. The snowpack hydrological property evolution was evaluated with drone-based ground-penetrating radar (GPR) measurements. In addition, the combination of GPR observations and time domain reflectometry measurements is shown to be able to be adapted to monitor the snowpack moisture over winter.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
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Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
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We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
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The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Ryan L. Crumley, David F. Hill, Jordan P. Beamer, and Elizabeth R. Holzenthal
The Cryosphere, 13, 1597–1619, https://doi.org/10.5194/tc-13-1597-2019, https://doi.org/10.5194/tc-13-1597-2019, 2019
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In this study we investigate the historical (1980–2015) and projection scenario (2070–2099) components of freshwater runoff to Glacier Bay, Alaska, using a modeling approach. We find that many of the historically snow-dominated watersheds in Glacier Bay National Park and Preserve may transition towards rainfall-dominated hydrographs in a projection scenario under RCP 8.5 conditions. The changes in timing and volume of freshwater entering Glacier Bay will affect bay ecology and hydrochemistry.
Cited articles
Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating Satellite Retrieved Fractional Snow-Covered Area at a High-Arctic Site Using Terrestrial Photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., Bouchet, M., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area, Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, 2021. a
Alonso-González, E., Aalstad, K., Pirk, N., Mazzolini, M., Treichler, D., Leclercq, P., Westermann, S., López-Moreno, J. I., and Gascoin, S.: Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation, Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, 2023. a
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating Remotely Sensed Snow Observations into a Macroscale Hydrology Model, Adv. Water Resour., 29, 872–886, https://doi.org/10.1016/j.advwatres.2005.08.004, 2006. a
Baba, M., Gascoin, S., and Hanich, L.: Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco, Remote Sensing, 10, 1982, https://doi.org/10.3390/rs10121982, 2018. a, b
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. a
Baron, M., Haddjeri, A., Lafaysse, M., Le Toumelin, L., Vionnet, V., and Fructus, M.: SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme, Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, 2024. a
Barrou Dumont, Z., Gascoin, S., Hagolle, O., Ablain, M., Jugier, R., Salgues, G., Marti, F., Dupuis, A., Dumont, M., and Morin, S.: Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service, The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, 2021. a
Brauchli, T., Trujillo, E., Huwald, H., and Lehning, M.: Influence of Slope-Scale Snowmelt on Catchment Response Simulated With the Alpine3D Model, Water Resour. Res., 53, 10723–10739, https://doi.org/10.1002/2017WR021278, 2017. a
Buchelt, S., Skov, K., Rasmussen, K. K., and Ullmann, T.: Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland, The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, 2022. a
Buchhorn, M.: Copernicus Global Land Service: Global Biome Cluster Layer for the 100m Global Land Cover Processing Line, Zenodo [data set], https://doi.org/10.5281/zenodo.5848610, 2022. a
Cluzet, B., Revuelto, J., Lafaysse, M., Tuzet, F., Cosme, E., Picard, G., Arnaud, L., and Dumont, M.: Towards the Assimilation of Satellite Reflectance into Semi-Distributed Ensemble Snowpack Simulations, Cold Reg. Sci. Technol, 170, 102918, https://doi.org/10.1016/j.coldregions.2019.102918, 2020. a, b, c
Cluzet, B., Lafaysse, M., Cosme, E., Albergel, C., Meunier, L.-F., and Dumont, M.: CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework, Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, 2021. a, b
Cluzet, B., Lafaysse, M., Deschamps-Berger, C., Vernay, M., and Dumont, M.: Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network, The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, 2022. a
Cristea, N. C., Bennett, A., Nijssen, B., and Lundquist, J. D.: When and Where Are Multiple Snow Layers Important for Simulations of Snow Accumulation and Melt?, Water Resour. Res., 58, e2020WR028993, https://doi.org/10.1029/2020WR028993, 2022. a, b
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale Assimilation of Advanced Microwave Scanning Radiometer–EOS Snow Water Equivalent and Moderate Resolution Imaging Spectroradiometer Snow Cover Fraction Observations in Northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011wr010588, 2012. a, b
Diffenbaugh, N. S., Scherer, M., and Ashfaq, M.: Response of Snow-Dependent Hydrologic Extremes to Continued Global Warming, Nat. Clim. Change, 3, 379–384, https://doi.org/10.1038/nclimate1732, 2013. a
Dingman, S. L.: Physical Hydrology: Third Edition, Waveland Press, ISBN 978-1-4786-2807-1, 2015. a
Douville, H., Royer, J. F., and Mahfouf, J. F.: A New Snow Parameterization for the Météo-France Climate Model: Part I: Validation in Stand-Alone Experiments, Clim. Dynam., 12, 21–35, https://doi.org/10.1007/BF00208760, 1995. a, b
Dujardin, J. and Lehning, M.: Wind-Topo: Downscaling Near-surface Wind Fields to High-resolution Topography in Highly Complex Terrain with Deep Learning, Q. J. Roy. Meteor. Soc., 148, 1368–1388, https://doi.org/10.1002/qj.4265, 2022. a
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015. a, b
Essery, R., Morin, S., Lejeune, Y., and Bauduin-Ménard, C.: A Comparison of 1701 Snow Models Using Observations from an Alpine Site, Adv. Water Res., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a
Fiddes, J., Aalstad, K., and Westermann, S.: Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering, Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, 2019. a
Freudiger, D., Kohn, I., Seibert, J., Stahl, K., and Weiler, M.: Snow Redistribution for the Hydrological Modeling of Alpine Catchments, WIREs Water, 4, e1232, https://doi.org/10.1002/wat2.1232, 2017. a
Gardner, A. S. and Sharp, M. J.: A Review of Snow and Ice Albedo and the Development of a New Physically Based Broadband Albedo Parametrization, J. Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010. a, b
Gaudard, L., Romerio, F., Dalla Valle, F., Gorret, R., Maran, S., Ravazzani, G., Stoffel, M., and Volonterio, M.: Climate Change Impacts on Hydropower in the Swiss and Italian Alps, Sci. Total Environ., 493, 1211–1221, https://doi.org/10.1016/j.scitotenv.2013.10.012, 2014. a
Griessinger, N., Schirmer, M., Helbig, N., Winstral, A., Michel, A., and Jonas, T.: Implications of Observation-Enhanced Energy-Balance Snowmelt Simulations for Runoff Modeling of Alpine Catchments, Adv. Water Resour., 133, 103410, https://doi.org/10.1016/j.advwatres.2019.103410, 2019. a
Grünewald, T. and Lehning, M.: Are Flat-Field Snow Depth Measurements Representative? A Comparison of Selected Index Sites with Areal Snow Depth Measurements at the Small Catchment Scale, Hydrol. Process., 29, 1717–1728, https://doi.org/10.1002/hyp.10295, 2015. a
Guiot, A., Karbou, F., James, G., and Durand, P.: Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection, Geosciences, 13, 193, https://doi.org/10.3390/geosciences13070193, 2023. a
Gupta, R. P.: Dry and Wet Snow Line/Zone, in: Encyclopedia of Snow, Ice and Glaciers, edited by Singh, V. P., Singh, P., and Haritashya, U. K., Encyclopedia of Earth Sciences Series, Springer Netherlands, Dordrecht, 240–241, ISBN 978-90-481-2642-2, https://doi.org/10.1007/978-90-481-2642-2_125, 2011. a
Helbig, N., Schirmer, M., Magnusson, J., Mäder, F., van Herwijnen, A., Quéno, L., Bühler, Y., Deems, J. S., and Gascoin, S.: A seasonal algorithm of the snow-covered area fraction for mountainous terrain, The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, 2021. a, b
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M., Lassègues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo, G., and Vertačnik, G.: The Climate of Daily Precipitation in the Alps: Development and Analysis of a High-Resolution Grid Dataset from Pan-Alpine Rain-Gauge Data, Int. J. Climatol., 34, 1657–1675, https://doi.org/10.1002/joc.3794, 2014. a, b
Jonas, T., Webster, C., Mazzotti, G., and Malle, J.: HPEval: A Canopy Shortwave Radiation Transmission Model Using High-Resolution Hemispherical Images, Agr. Forest Meteorol., 284, 107903, https://doi.org/10.1016/j.agrformet.2020.107903, 2020. a
Karbou, F., Veyssière, G., Coleou, C., Dufour, A., Gouttevin, I., Durand, P., Gascoin, S., and Grizonnet, M.: Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations, Remote Sensing, 13, 381, https://doi.org/10.3390/rs13030381, 2021. a, b, c
Karbou, F., James, G., Fructus, M., and Marti, F.: On the Evaluation of the SAR-Based Copernicus Snow Products in the French Alps, Geosciences, 12, 420, https://doi.org/10.3390/geosciences12110420, 2022. a, b, c
Keuris, L., Hetzenecker, M., Nagler, T., Mölg, N., and Schwaizer, G.: An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales, Remote Sensing, 15, 1231, https://doi.org/10.3390/rs15051231, 2023. a, b
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a
Lafaysse, M., Hingray, B., Mezghani, A., Gailhard, J., and Terray, L.: Internal Variability and Model Uncertainty Components in Future Hydrometeorological Projections: The Alpine Durance Basin, Water Resour. Res., 50, 3317–3341, https://doi.org/10.1002/2013WR014897, 2014. a
Lamare, M., Dumont, M., Picard, G., Larue, F., Tuzet, F., Delcourt, C., and Arnaud, L.: Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain, The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, 2020. a
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Frontiers in Earth Science, 8, https://doi.org/10.3389/feart.2020.00325, 2020. a, b
Liston, G. E. and Sturm, M.: A Snow-Transport Model for Complex Terrain, J. Glaciol., 44, 498–516, https://doi.org/10.1017/S0022143000002021, 1998. a
Lund, J., Forster, R. R., Deeb, E. J., Liston, G. E., Skiles, S. M., and Marshall, H.-P.: Interpreting Sentinel-1 SAR Backscatter Signals of Snowpack Surface Melt/Freeze, Warming, and Ripening, through Field Measurements and Physically-Based SnowModel, Remote Sensing, 14, 4002, https://doi.org/10.3390/rs14164002, 2022. a
Lundquist, J. D. and Dettinger, M. D.: How Snowpack Heterogeneity Affects Diurnal Streamflow Timing, Water Resour. Res., 41, W05007, https://doi.org/10.1029/2004WR003649, 2005. a
Magand, C., Ducharne, A., Le Moine, N., and Gascoin, S.: Introducing Hysteresis in Snow Depletion Curves to Improve the Water Budget of a Land Surface Model in an Alpine Catchment, J. Hydrometeorol., 15, 631–649, https://doi.org/10.1175/JHM-D-13-091.1, 2014. a
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation of Point SWE Data into a Distributed Snow Cover Model Comparing Two Contrasting Methods, Water Resour. Res., 50, 7816–7835, https://doi.org/10.1002/2014WR015302, 2014. a
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: Evaluating Snow Models with Varying Process Representations for Hydrological Applications, Water Resour. Res., 51, 2707–2723, https://doi.org/10.1002/2014WR016498, 2015. a
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A Particle Batch Smoother Approach to Snow Water Equivalent Estimation, J. Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015. a
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.: A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015), J. Hydrometeorol., 17, 1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a
Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, 2020. a, b, c, d, e
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview, Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020, 2020. a
Mary, A., Dumont, M., Dedieu, J.-P., Durand, Y., Sirguey, P., Milhem, H., Mestre, O., Negi, H. S., Kokhanovsky, A. A., Lafaysse, M., and Morin, S.: Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model, The Cryosphere, 7, 741–761, https://doi.org/10.5194/tc-7-741-2013, 2013. a, b
Mätzler, C.: Applications of the Interaction of Microwaves with the Natural Snow Cover, Remote Sensing Reviews, 2, 259–387, https://doi.org/10.1080/02757258709532086, 1987. a
Mott, R., Egli, L., Grünewald, T., Dawes, N., Manes, C., Bavay, M., and Lehning, M.: Micrometeorological processes driving snow ablation in an Alpine catchment, The Cryosphere, 5, 1083–1098, https://doi.org/10.5194/tc-5-1083-2011, 2011. a
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes, Frontiers in Earth Science, 6, https://doi.org/10.3389/feart.2018.00197, 2018. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Jan Magnusson, GiuliaMazzotti, Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: oshd-slf/FSM2oshd: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.14290600, 2023b. a
Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M.: Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR, Remote Sensing, 8, 348, https://doi.org/10.3390/rs8040348, 2016. a, b
Oberrauch, M., Cluzet, B., Magnusson, J., and Jonas, T.: Improving Fully Distributed Snowpack Simulations by Mapping Perturbations of Meteorological Forcings Fnferred from Particle Filter Assimilation of Snow Monitoring Data, Water Resour. Res., 60, e2023WR036994, https://doi.org/10.1029/2023WR036994, 2024. a
Parajka, J., Pepe, M., Rampini, A., Rossi, S., and Blöschl, G.: A Regional Snow-Line Method for Estimating Snow Cover from MODIS during Cloud Cover, J. Hydrol., 381, 203–212, https://doi.org/10.1016/j.jhydrol.2009.11.042, 2010. a
Qin, Y., Abatzoglou, J. T., Siebert, S., Huning, L. S., AghaKouchak, A., Mankin, J. S., Hong, C., Tong, D., Davis, S. J., and Mueller, N. D.: Agricultural Risks from Changing Snowmelt, Nat. Clim. Change, 10, 459–465, https://doi.org/10.1038/s41558-020-0746-8, 2020. a
Quéno, L., Fierz, C., van Herwijnen, A., Longridge, D., and Wever, N.: Deep ice layer formation in an alpine snowpack: monitoring and modeling, The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, 2020. a
Quéno, L., Mott, R., Morin, P., Cluzet, B., Mazzotti, G., and Jonas, T.: Snow redistribution in an intermediate-complexity snow hydrology modelling framework, The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, 2024. 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
Scipión, D. E., Mott, R., Lehning, M., Schneebeli, M., and Berne, A.: Seasonal Small-Scale Spatial Variability in Alpine Snowfall and Snow Accumulation, Water Resour. Res., 49, 1446–1457, https://doi.org/10.1002/wrcr.20135, 2013. a
Techel, F. and Pielmeier, C.: Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects, The Cryosphere, 5, 405–418, https://doi.org/10.5194/tc-5-405-2011, 2011. a
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.: The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022. a
Veyssière, G., Karbou, F., Morin, S., Lafaysse, M., and Vionnet, V.: Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations, Remote Sensing, 11, 8, https://doi.org/10.3390/rs11010008, 2019. a
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021. a
Vögeli, C., Lehning, M., Wever, N., and Bavay, M.: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution, Frontiers in Earth Science, 4, 2016. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a, b
Winstral, A., Magnusson, J., Schirmer, M., and Jonas, T.: The Bias-Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics-Based, Multilayer Snow Models, Water Resour. Res., 55, 613–631, https://doi.org/10.1029/2018WR024521, 2019. a, b
Würzer, S., Wever, N., Juras, R., Lehning, M., and Jonas, T.: Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow, Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, 2017. a
Short summary
We use novel wet-snow maps from Sentinel-1 to evaluate simulations of a snow-hydrological model over Switzerland. These data are complementary to available in situ snow depth observations as they capture a broad diversity of topographic conditions. Wet-snow maps allow us to detect a delayed melt onset in the model, which we resolve thanks to an improved parametrization. This paves the way to further evaluation, calibration, and data assimilation using wet-snow maps.
We use novel wet-snow maps from Sentinel-1 to evaluate simulations of a snow-hydrological model...
