Articles | Volume 14, issue 4
https://doi.org/10.5194/tc-14-1225-2020
© Author(s) 2020. 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-14-1225-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2020
Research article |
| 14 Apr 2020
| 14 Apr 2020
Multi-physics ensemble snow modelling in the western Himalaya
David M. W. Pritchard
CORRESPONDING AUTHOR
School of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, UK
Nathan Forsythe
School of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, UK
Greg O'Donnell
School of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, UK
Hayley J. Fowler
School of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, UK
Nick Rutter
Department of Geography and Environmental Sciences, Northumbria
University, Newcastle upon Tyne, NE1 8ST, UK
Related authors
Georgina Jean Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamund Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1237, https://doi.org/10.5194/egusphere-2024-1237, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of SVS2-Crocus and evaluated using density and SSA measurements at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and SSA were identified. Top performing ensemble members featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift and increase viscosity in basal layers.
Adrien Damseaux, Heidrun Matthes, Victoria R. Dutch, Leanne Wake, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-1412, https://doi.org/10.5194/egusphere-2024-1412, 2024
Short summary
Short summary
Models often underestimate the role of snow cover in permafrost regions, leading to soil temperatures and permafrost dynamics inaccuracies. Through the use of a snow thermal conductivity scheme better adapted to this region, we mitigated soil temperature biases and permafrost extent overestimation within a land surface model. Our study sheds light on the importance of refining snow-related processes in models to enhance our understanding of permafrost dynamics in the context of climate change.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
Short summary
Short summary
Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Georgina Jean Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamund Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1237, https://doi.org/10.5194/egusphere-2024-1237, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of SVS2-Crocus and evaluated using density and SSA measurements at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and SSA were identified. Top performing ensemble members featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift and increase viscosity in basal layers.
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
Short summary
Short summary
In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2926, https://doi.org/10.5194/egusphere-2023-2926, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modelers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modeling as an example, we examine the process behind the decisions to understand what motivates or limits modelers in their decision-making. We found that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency into our research practice.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
Short summary
Short summary
We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
EGUsphere, https://doi.org/10.5194/egusphere-2023-696, https://doi.org/10.5194/egusphere-2023-696, 2023
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from the snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed the airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow, and could unlock these data to improve forecasts.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
Short summary
Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
John Ewen and Greg Martin O'Donnell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-170, https://doi.org/10.5194/hess-2021-170, 2021
Revised manuscript not accepted
Short summary
Short summary
Models for the water flow from river catchments are used when making decisions about flooding, climate change, etc. The models should depend directly and transparently on things known about how catchments generate flow, but that is not always the case. In an example, all the knowledge in a model is made visible as a set of statements in everyday English. The aim is to help improve the quality of future decisions by exploring the problem and creating a concrete example for use as a benchmark.
Doug Richardson, Hayley J. Fowler, Christopher G. Kilsby, Robert Neal, and Rutger Dankers
Nat. Hazards Earth Syst. Sci., 20, 107–124, https://doi.org/10.5194/nhess-20-107-2020, https://doi.org/10.5194/nhess-20-107-2020, 2020
Short summary
Short summary
Models are not particularly skilful at forecasting rainfall more than 15 d in advance. However, they are often better at predicting atmospheric variables such as mean sea-level pressure (MSLP). Comparing a range of models, we show that UK winter and autumn rainfall and drought prediction skill can be improved by utilising forecasts of MSLP-based weather patterns (WPs) and subsequently estimating rainfall using the historical WP–precipitation relationships.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
Short summary
Short summary
Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
Short summary
Short summary
Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
Short summary
Short summary
Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Steve J. Birkinshaw, Selma B. Guerreiro, Alex Nicholson, Qiuhua Liang, Paul Quinn, Lili Zhang, Bin He, Junxian Yin, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 21, 1911–1927, https://doi.org/10.5194/hess-21-1911-2017, https://doi.org/10.5194/hess-21-1911-2017, 2017
Short summary
Short summary
The Yangtze River basin in China is home to more than 400 million people and susceptible to major floods. We used projections of future precipitation and temperature from 35 of the most recent global climate models and applied this to a hydrological model of the Yangtze. Changes in the annual discharge varied between a 29.8 % decrease and a 16.0 % increase. The main reason for the difference between the models was the predicted expansion of the summer monsoon north and and west into the basin.
Melody Sandells, Richard Essery, Nick Rutter, Leanne Wake, Leena Leppänen, and Juha Lemmetyinen
The Cryosphere, 11, 229–246, https://doi.org/10.5194/tc-11-229-2017, https://doi.org/10.5194/tc-11-229-2017, 2017
Short summary
Short summary
This study looks at a wide range of options for simulating sensor signals for satellite monitoring of water stored as snow, though an ensemble of 1323 coupled snow evolution and microwave scattering models. The greatest improvements will be made with better computer simulations of how the snow microstructure changes, followed by how the microstructure scatters radiation at microwave frequencies. Snow compaction should also be considered in systems to monitor snow mass from space.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
Short summary
Short summary
Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Martin Proksch, Nick Rutter, Charles Fierz, and Martin Schneebeli
The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, https://doi.org/10.5194/tc-10-371-2016, 2016
Short summary
Short summary
Density is a fundamental property of porous media such as snow. During the MicroSnow Davos 2014 workshop, different approaches (box-, wedge- and cylinder-type density cutters, micro-computed tomography) to measure snow density were applied in a controlled laboratory environment and in the field. In general, results suggest that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably.
John Gowing, Geoff Parkin, Nathan Forsythe, David Walker, Alemseged Tamiru Haile, and Demis Alamirew
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2015-549, https://doi.org/10.5194/hess-2015-549, 2016
Manuscript not accepted for further review
Short summary
Short summary
Shallow groundwater in sub-Saharan Africa represents a neglected opportunity for sustainable intensification of small-scale agriculture. However, it is important to consider constraints, since shallow groundwater resources are likely to be vulnerable to over-exploitation and climatic variability. We examine the opportunities and constraints and draw upon evidence from Ethiopia. We present a method for assessing shallow groundwater resources. We consider possible models for developing distributed
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
M. Sharif, D. R. Archer, H. J. Fowler, and N. Forsythe
Hydrol. Earth Syst. Sci., 17, 1503–1516, https://doi.org/10.5194/hess-17-1503-2013, https://doi.org/10.5194/hess-17-1503-2013, 2013
Related subject area
Discipline: Snow | Subject: Numerical Modelling
Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming
Exploring the sensitivity to precipitation, blowing snow, and horizontal resolution of the spatial distribution of simulated snow cover
Snow redistribution in an intermediate-complexity snow hydrology modelling framework
Microstructure-based modelling of snow mechanics: experimental evaluation on the cone penetration test
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting
Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect
Elements of future snowpack modeling – Part 1: A physical instability arising from the nonlinear coupling of transport and phase changes
Elements of future snowpack modeling – Part 2: A modular and extendable Eulerian–Lagrangian numerical scheme for coupled transport, phase changes and settling processes
Assessment of neutrons from secondary cosmic rays at mountain altitudes – Geant4 simulations of environmental parameters including soil moisture and snow cover
A seasonal algorithm of the snow-covered area fraction for mountainous terrain
Snow cover duration trends observed at sites and predicted by multiple models
Deep ice layer formation in an alpine snowpack: monitoring and modeling
Micromechanical modeling of snow failure
Changing characteristics of runoff and freshwater export from watersheds draining northern Alaska
Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation
A simulation of a large-scale drifting snowstorm in the turbulent boundary layer
Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain
Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
Short summary
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamp-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
EGUsphere, https://doi.org/10.5194/egusphere-2023-2604, https://doi.org/10.5194/egusphere-2023-2604, 2023
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations by disentangling error contributions between blowing snow, precipitation forcings and unresolved subgrid variability. We evaluated simulated snow cover against snow depths from Pléiades stereo-imagery and Snow Melt-Out Dates from Sentinel 2. The simulation of snow transport enhances the snow spatial variance at high elevations while precipitation biases are the prevailing error source in other areas.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2071, https://doi.org/10.5194/egusphere-2023-2071, 2023
Short summary
Short summary
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 a remarkable improvement of 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.
Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-30, https://doi.org/10.5194/tc-2023-30, 2023
Revised manuscript accepted for TC
Short summary
Short summary
This paper presents the development of a numerical model to study the mechanical behaviour of snow at microscale. The numerical model has shown good capabilities to reproduce experimental Cone Penetration Test measurements. It is a promising tool for future investigation to better characterise the snow material. Applications of the numerical model are various such as snowpack characterisation, high resolution snow force profile interpretation or avalanches forecasting for instance.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
Short summary
Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
Short summary
Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
Short summary
Short summary
Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
Short summary
Short summary
Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
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
Short summary
Short summary
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.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
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
Short summary
Short summary
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.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
Short summary
Short summary
We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
Short summary
Short summary
This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
Short summary
Short summary
Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
Short summary
Short summary
A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
Short summary
Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
Cited articles
Andreadis, K. M., Storck, P., and Lettenmaier, D. P.: Modeling snow
accumulation and ablation processes in forested environments, Water Resour.
Res., 45, W05429, https://doi.org/10.1029/2008WR007042, 2009.
Andreas, E. L.: Parameterizing Scalar Transfer over Snow and Ice: A Review,
J. Hydrometeorol., 3, 417–432, https://doi.org/10.1175/1525-7541(2002)003<0417:PSTOSA>2.0.CO;2, 2002.
Archer, D.: Contrasting hydrological regimes in the upper Indus Basin, J.
Hydrol., 274, 198–210, https://doi.org/10.1016/S0022-1694(02)00414-6, 2003.
Archer, D. R.: Hydrological implications of spatial and altitudinal
variation in temperature in the Upper Indus Basin, Nord. Hydrol., 35,
209–222, https://doi.org/10.2166/nh.2004.0015, 2004.
Archer, D. R. and Fowler, H. J.: Spatial and temporal variations in precipitation in the Upper Indus Basin, global teleconnections and hydrological implications, Hydrol. Earth Syst. Sci., 8, 47–61, https://doi.org/10.5194/hess-8-47-2004, 2004.
Arendt, A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen,
J.-O., Hock, R., Huss, M., Kaser, G., Kienholz, C., Pfeffer, W. T., Moholdt,
G., Paul, F., Radić, V., Andreassen, L., Bajracharya, S., Barrand, N.
E., Berthier, E., Bhambri, R., Brown, I., Burgess, E., Burgess, D.,
Cawkwell, F., Chinn, T., Copland, L., Davies, B., De Angelis, H., Dolgova,
E., Earl, L., Filbert, K., Forester, R., Fountain, A. G., Frey, H., Giffen,
B., Guo, W. Q., Gurney, S., Hagg, W., Hall, D., Haritashya, U. K., Hartmann,
G., Helm, C., Herreid, S., Kapustin, G., Khromova, T., König, M.,
Kohler, J., Kriegel, D., Kutuzov, S., Lavrentiev, I., Lebris, R., Nosenko,
G., Negrete, A., Nuimura, T., Nuth, C., Pettersson, R., Racoviteanu, A.,
Ranzi, R., Rastner, P., Rau, F., Raup, B., Rich, J., Rott, H., Sakai, A.,
Schneider, C., Seliverstov, Y., Sharp, M., Sigurðsson, O., Stokes, C.,
Way, R. G., Wheate, R., Winsvold, S., Wolken, G., Wyatt, F., and Zheltyhina,
N.: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines:
Version 5.0: Technical Report, Colorado, USA, 2015.
Arino, O., Ramos Perez, J. J., Kalogirou, V., Bontemps, S., Defourny, P., and
Van Bogaert, E.: Global Land Cover Map for 2009 (GlobCover 2009), Pangaea,
https://doi.org/10.1594/PANGAEA.787668, 2012.
Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A.
P., Khalsa, S.-J. S., Raup, B., Hill, A. F., Khan, A. L., Wilson, A. M.,
Kayastha, R. B., Fetterer, F., and Armstrong, B.: Runoff from glacier ice and
seasonal snow in High Asia: separating melt water sources in river flow,
Reg. Environ. Chang., 19, 1249–1261, https://doi.org/10.1007/s10113-018-1429-0, 2019.
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.
Bernhardt, M. and Schulz, K.: SnowSlide: A simple routine for calculating
gravitational snow transport, Geophys. Res. Lett., 37, L11502,
https://doi.org/10.1029/2010GL043086, 2010.
Bernhardt, M., Schulz, K., Liston, G. E., and Zängl, G.: The influence of
lateral snow redistribution processes on snow melt and sublimation in alpine
regions, J. Hydrol., 424–425, 196–206, https://doi.org/10.1016/j.jhydrol.2012.01.001,
2012.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Biskop, S., Maussion, F., Krause, P., and Fink, M.: Differences in the water-balance components of four lakes in the southern-central Tibetan Plateau, Hydrol. Earth Syst. Sci., 20, 209–225, https://doi.org/10.5194/hess-20-209-2016, 2016.
Bonekamp, P. N. J., Collier, E., and Immerzeel, W. W.: The Impact of Spatial
Resolution, Land Use, and Spinup Time on Resolving Spatial Precipitation
Patterns in the Himalayas, J. Hydrometeorol., 19, 1565–1581,
https://doi.org/10.1175/JHM-D-17-0212.1, 2018.
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, 723–739, https://doi.org/10.1002/2017WR021278, 2017.
Brown, M. E., Racoviteanu, A. E., Tarboton, D. G., Sen Gupta, A., Nigro, J.,
Policelli, F., Habib, S., Tokay, M., Shrestha, M. S., Bajracharya, S.,
Hummel, P., Gray, M., Duda, P., Zaitchik, B., Mahat, V., Artan, G., and
Tokar, S.: An integrated modeling system for estimating glacier and snow
melt driven streamflow from remote sensing and earth system data products in
the Himalayas, J. Hydrol., 519, 1859–1869,
https://doi.org/10.1016/j.jhydrol.2014.09.050, 2014.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Hreinsson, E. Ö., and Woods, R. A.: Representing
spatial variability of snow water equivalent in hydrologic and land-surface
models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745,
2011.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E.,
Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D.,
Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for
process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res.,
51, 2498–2514, https://doi.org/10.1002/2015WR017200.A, 2015.
Collier, E. and Immerzeel, W. W.: High-resolution modeling of atmospheric
dynamics in the Nepalese Himalaya, J. Geophys. Res.-Atmos., 120, 9882–9896,
https://doi.org/10.1002/2015JD023266, 2015.
Collier, E., Mölg, T., Maussion, F., Scherer, D., Mayer, C., and Bush, A. B. G.: High-resolution interactive modelling of the mountain glacier–atmosphere interface: an application over the Karakoram, The Cryosphere, 7, 779–795, https://doi.org/10.5194/tc-7-779-2013, 2013.
Collier, E., Maussion, F., Nicholson, L. I., Mölg, T., Immerzeel, W. W., and Bush, A. B. G.: Impact of debris cover on glacier ablation and atmosphere–glacier feedbacks in the Karakoram, The Cryosphere, 9, 1617–1632, https://doi.org/10.5194/tc-9-1617-2015, 2015.
Corripio, J. G.: Vectorial algebra algorithms for calculating terrain
parameters from DEMs and solar radiation modelling in mountainous terrain,
Int. J. Geogr. Inf. Sci., 17, 1–23, https://doi.org/10.1080/713811744, 2003.
Cox, P. M., Betts, R. A., Bunton, C. B., Essery, R. L. H., Rowntree, P. R.,
and Smith, J.: The impact of new land surface physics on the GCM simulation
of climate and climate sensitivity, Clim. Dynam., 15, 183–203,
https://doi.org/10.1007/s003820050276, 1999.
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.
Duethmann, D., Zimmer, J., Gafurov, A., Güntner, A., Kriegel, D., Merz, B., and Vorogushyn, S.: Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling, Hydrol. Earth Syst. Sci., 17, 2415–2434, https://doi.org/10.5194/hess-17-2415-2013, 2013.
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M., Beljaars, A.,
Schär, C., and Elder, K.: An improved snow scheme for the ECMWF land
surface model: Description and offline validation, J. Hydrometeorol., 11,
899–916, https://doi.org/10.1175/2010JHM1249.1, 2010.
Essery, R.: Large-scale simulations of snow albedo masking by forests,
Geophys. Res. Lett., 40, 5521–5525, https://doi.org/10.1002/grl.51008, 2013.
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.
Essery, R., Rutter, N., Pomeroy, J., Baxter, R., Stähli, M., Gustafsson,
D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: An Evaluation of Forest
Snow Process Simulations, B. Am. Meteorol. Soc., 90, 1120–1135,
doi:https://doi.org/10.1175/2009BAMS2629.1, 2009.
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.
Etchevers, P., Martin, E., Brown, R., Fierz, C., Lejeune, Y., Bazile, E.,
Boone, A., Dai, Y., Essery, R., Fernandez, A., Gusev, Y., Jordan, R., Koren,
V., Kowalczyk, E., Nasonova, N. O., Pyles, R. D., Schlosser, A., Shmakin, A.
B., Smirnova, T. G., Strasser, U., Verseghy, D., Yamazaki, T., and Yang,
Z.-L.: Validation of the energy budget of an alpine snowpack simulated by
several snow models (SnowMIP project), Ann. Glaciol., 38, 150–158,
https://doi.org/10.3189/172756404781814825, 2004.
Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P.: The
value of glacier mass balance, satellite snow cover images, and hourly
discharge for improving the performance of a physically based distributed
hydrological model, Water Resour. Res., 47, W07519,
https://doi.org/10.1029/2010WR009824, 2011.
Forsythe, N., Kilsby, C. G., Fowler, H. J., and Archer, D. R.: Assessment of
Runoff Sensitivity in the Upper Indus Basin to Interannual Climate
Variability and Potential Change Using MODIS Satellite Data Products, Mt.
Res. Dev., 32, 16–29, https://doi.org/10.1659/MRD-JOURNAL-D-11-00027.1, 2012.
Fowler, H. J. and Archer, D. R.: Hydro-climatological variability in the
Upper Indus Basin and implications for water resources, in Regional
Hydrological Impacts of Climatic Change – Impact Assessment and Decision
Making (Proceedings of symposium S6 held during the Seventh IAHS Scientific
Assembly at Foz do Iguacu, Brazil, April 2005), IAHS Publ. 295, Foz do
Iguacu, Brazil, 2005.
Freudiger, D., Kohn, I., Seibert, J., Stahl, K., and Weiler, M.: Snow
redistribution for the hydrological modeling of alpine catchments, WIREs
Water, e1232, 1–16, https://doi.org/10.1002/wat2.1232, 2017.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Groot Zwaaftink, C. D., Mott, R., and Lehning, M.: Seasonal simulation of
drifting snow sublimation in Alpine terrain, Water Resour. Res., 49,
1581–1590, https://doi.org/10.1002/wrcr.20137, 2013.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Grünewald, T., Bühler, Y., and Lehning, M.: Elevation dependency of mountain snow depth, The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, 2014.
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in
Snowpack Simulations – Assessing the Impact of Model Structure, Parameter
Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model
Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403,
2019.
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow
albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502,
https://doi.org/10.1029/2005GL025127, 2006.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m
Grid, Version 6 (2000–2015), https://doi.org/10.5067/MODIS/MOD10A1.006, 2016.
Harder, P., Pomeroy, J. W., and Helgason, W.: Local-Scale Advection of
Sensible and Latent Heat During Snowmelt, Geophys. Res. Lett., 44,
9769–9777, https://doi.org/10.1002/2017GL074394, 2017.
Havens, S., Marks, D., FitzGerald, K., Masarik, M., Flores, A. N., Kormos,
P., and Hedrick, A.: Approximating Input Data to a Snowmelt Model Using
Weather Research and Forecasting Model Outputs in Lieu of Meteorological
Measurements, J. Hydrometeorol., 20, 847–862,
https://doi.org/10.1175/JHM-D-18-0146.1, 2019.
Huintjes, E., Sauter, T., Schröter, B., Maussion, F., Yang, W.,
Kropáček, J., Buchroithner, M., Scherer, D., Kang, S., and Schneider,
C.: Evaluation of a coupled snow and energy balance model for Zhadang
glacier, Tibetan Plateau, using glaciological measurements and time-lapse
photography, Arctic, Antarct. Alp. Res., 47, 573–590,
https://doi.org/10.1657/AAAR0014-073, 2015.
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate Change
Will Affect the Asian Water Towers, Science, 382, 1382–1385,
https://doi.org/10.1126/science.1183188, 2010.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for
the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database, available at: http://srtm.csi.cgiar.org (last access: 15 March 2016), 2008.
Klemeš, V.: The modelling of mountain hydrology: the ultimate challenge,
Hydrol. Mt. Areas, Proceedings of the Strbské Pleso Workshop, Czechoslovakia, June 1988, IAHS 190, 29–44, 1990.
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.
Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., and Morin, S.: A multiphysical ensemble system of numerical snow modelling, The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017.
Lapo, K., Nijssen, B., and Lundquist, J. D.: Evaluation of Turbulence
Stability Schemes of Land Models for Stable Conditions, J. Geophys. Res.-Atmos., 124, 3072–3089, https://doi.org/10.1029/2018JD028970, 2019.
Lehning, M., Ingo, V., Gustafsson, D., Nguyen, T. A., Stähli, M., and
Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its
application to snow hydrology, Hydrol. Process., 20, 2111–2128,
https://doi.org/10.1002/hyp.6204, 2006.
Liston, G. E. and Elder, K.: A Distributed Snow-Evolution Modeling System
(SnowModel), J. Hydrometeorol., 7, 1259–1276, https://doi.org/10.1175/JHM548.1, 2006a.
Liston, G. E. and Elder, K.: A Meteorological Distribution System for
High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol., 7,
217–234, https://doi.org/10.1175/JHM486.1, 2006b.
Lundquist, J. D., Dettinger, M. D., and Cayan, D. R.: Snow-fed streamflow
timing at different basin scales: Case study of the Tuolumne River above
Hetch Hetchy, Yosemite, California, Water Resour. Res., 41, W07005,
https://doi.org/10.1029/2004WR003933, 2005.
Lute, A. C. and Luce, C. H.: Are Model Transferability and Complexity
Antithetical? Insights from Validation of a Variable-Complexity Empirical
Snow Model in Space and Time, Water Resour. Res., 53, 8825–8850,
https://doi.org/10.1002/2017WR020752, 2017.
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.:
Consistent increase in High Asia's runoff due to increasing glacier melt and
precipitation, Nat. Clim. Change, 4, 587–592, https://doi.org/10.1038/NCLIMATE2237,
2014.
Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D. A., Shrestha, A. B., and
Bierkens, M. F. P.: Climate Change Impacts on the Upper Indus Hydrology:
Sources, Shifts and Extremes, PLoS One, 11, 1–33,
https://doi.org/10.1371/journal.pone.0165630, 2016.
MacDonald, M. K., Pomeroy, J. W., and Pietroniro, A.: On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 14, 1401–1415, https://doi.org/10.5194/hess-14-1401-2010, 2010.
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.
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain
basins, Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999.
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and
Finkelnburg, R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis, J. Clim., 27, 1910–1927,
https://doi.org/10.1175/JCLI-D-13-00282.1, 2014.
Mazzotti, G., Essery, R., Moeser, C. D., and Jonas, T.: Resolving Small‐Scale Forest Snow Patterns Using an Energy Balance Snow Model With a One‐Layer Canopy, Water Resour. Res., 56, e2019WR026129, https://doi.org/10.1029/2019WR026129, 2020
Moeser, D., Mazzotti, G., Helbig, N., and Jonas, T.: Representing spatial
variability of forest snow: Implementation of a new interception model,
Water Resour. Res., 52, 1208–1226, https://doi.org/10.1002/2015WR017961, 2016.
Mott, R., Schirmer, M., Bavay, M., Grünewald, T., and Lehning, M.: Understanding snow-transport processes shaping the mountain snow-cover, The Cryosphere, 4, 545–559, https://doi.org/10.5194/tc-4-545-2010, 2010.
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., Berne, A., and Lehning,
M.: Orographic effects on snow deposition patterns, J. Geophys. Res.-Atmos.,
119, 1419–1439, https://doi.org/10.1002/2013JD019880, 2014.
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover
Dynamics: Review on Wind-Driven Coupling Processes, Front. Earth Sci.,
6, 1–25, https://doi.org/10.3389/feart.2018.00197, 2018.
Mukhopadhyay, B. and Khan, A.: Altitudinal variations of temperature,
equilibrium line altitude, and accumulation-area ratio in Upper Indus Basin,
Hydrol. Res., 48, 214–230, https://doi.org/10.2166/nh.2016.144, 2016.
Musselman, K. N., Pomeroy, J. W., Essery, R. L. H., and Leroux, N.: Impact of
windflow calculations on simulations of alpine snow accumulation,
redistribution and ablation, Hydrol. Process., 29, 3983–3999,
https://doi.org/10.1002/hyp.10595, 2015.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.: Slower
snowmelt in a warmer world, Nat. Clim. Change, 7, 214–220,
https://doi.org/10.1038/NCLIMATE3225, 2017.
Naden, P. S.: Spatial variability in flood estimation for large catchments:
the exploitation of channel network structure, Hydrol. Sci. J., 37,
53–72, https://doi.org/10.1080/02626669209492561, 1992.
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., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M.,
Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F.,
Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M.,
Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical Description of
version 4.5 of the Community Land Model (CLM), NCAR Technical Note
NCAR/TN-503+STR, Boulder, Colorado, USA, 2013.
Palazzi, E., Filippi, L., and Von Hardenberg, J.: Insights into
elevation-dependent warming in the Tibetan Plateau-Himalayas from CMIP5
model simulations, Clim. Dynam., 48, 3991–4008,
https://doi.org/10.1007/s00382-016-3316-z, 2017.
Pepin, N., Bradley, R., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe,
N., Fowler, H. J., Greenwood, G., Hashmi, M. Z., Liu, X. D., Miller, J. R.,
Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schoener, W., Severskiy,
I., Shahgedanova, M., Wang, M. B., Williamson, S. N., and Yang, D. Q.:
Elevation-dependent warming in mountain regions of the world, Nat. Clim.
Change, 5, 424–430, https://doi.org/10.1038/nclimate2563, 2015.
Prasch, M., Mauser, W., and Weber, M.: Quantifying present and future glacier melt-water contribution to runoff in a central Himalayan river basin, The Cryosphere, 7, 889–904, https://doi.org/10.5194/tc-7-889-2013, 2013.
Pritchard, D. M. W., Forsythe, N., Fowler, H. J., O'Donnell, G. M., and Li,
X.-F.: Evaluation of Upper Indus Near-Surface Climate Representation by WRF
in the High Asia Refined Analysis, J. Hydrometeorol., 20, 467–487,
https://doi.org/10.1175/JHM-D-18-0030.1, 2019.
Quéno, L., Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Dumont, M., and Karbou, F.: Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts, The Cryosphere, 10, 1571–1589, https://doi.org/10.5194/tc-10-1571-2016, 2016.
Ragettli, S., Pellicciotti, F., Bordoy, R., and Immerzeel, W. W.: Sources of
uncertainty in modeling the glaciohydrological response of a Karakoram
watershed to climate change, Water Resour. Res., 49, 6048–6066,
https://doi.org/10.1002/wrcr.20450, 2013.
Ragettli, S., Pellicciotti, F., Immerzeel, W. W., Miles, E. S., Petersen,
L., Heynen, M., Shea, J. M., Stumm, D., Joshi, S., and Shrestha, A.:
Unraveling the hydrology of a Himalayan catchment through integration of
high resolution in situ data and remote sensing with an advanced simulation
model, Adv. Water Resour., 78, 94–111, https://doi.org/10.1016/j.advwatres.2015.01.013,
2015.
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.
Revuelto, J., Lafaysse, M., Zin, I., Charrois, L., Vionnet, V., Dumont, M.,
Rabatel, A., Six, D., Condom, T., Morin, S., Viani, A., and Sirguey, P.:
Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain
Using Satellite and In Situ Observations, Remote Sens., 10, 1–32,
https://doi.org/10.3390/rs10081171, 2018.
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., 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., 114, D06111,
https://doi.org/10.1029/2008JD011063, 2009.
Salomonson, V. V and Appel, I.: Estimating fractional snow cover from MODIS
using the normalized difference snow index, Remote Sens. Environ., 89,
351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Shakoor, A. and Ejaz, N.: Flow Analysis at the Snow Covered High Altitude
Catchment via Distributed Energy Balance Modeling, Sci. Rep., 9,
1–14, https://doi.org/10.1038/s41598-019-39446-1, 2019.
Sharif, M., Archer, D. R., Fowler, H. J., and Forsythe, N.: Trends in timing and magnitude of flow in the Upper Indus Basin, Hydrol. Earth Syst. Sci., 17, 1503–1516, https://doi.org/10.5194/hess-17-1503-2013, 2013.
Shrestha, M., Koike, T., Hirabayashi, Y., Xue, Y., Wang, L., Rasul, G., and
Ahmad, B.: Integrated simulation of snow and glacier melt in water and
energy balance-based, distributed hydrological modeling framework at Hunza
River Basin of Pakistan Karakoram region, J. Geophys. Res.-Atmos., 120,
4889–4919, https://doi.org/10.1002/2014JD022666, 2015.
Sicart, J. E., Pomeroy, J. W., Essery, R. L. H., and Bewley, D.: Incoming
longwave radiation to melting snow: observations, sensitivity and estimation
in northern environments, Hydrol. Process., 20, 3697–3708,
https://doi.org/10.1002/hyp.6383, 2006.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Duda, M. G., Huang, X.-Y., Wang, W., and Power, J. G.: A Description of the
Advanced Research WRF Version 3, NCAR Technical Note NCAR/TN–475+STR,
2008.
Slater, A. G., Schlosser, C. A., Desborough, C. E., Pitman, A. J.,
Henderson-Sellers, A., Robock, A., Vinnikov, K. Y., Mitchell, K., Boone, A.,
Braden, H., Chen, F., Cox, P. M., de Rosnay, P., Dickinson, R. E., Dai,
Y.-J., Duan, Q., Entin, J., Etchevers, P., Gedney, N., Gusev, Y. M., Kim,
J., Koren, V., Kowalczyk, E. A., Nasonova, O. N., Noilhan, J., Schaake, S.,
Shmakin, A. B., Smirnova, T. G., Verseghy, D., Wetzel, P., Xue, Y., Yang,
Z.-L., and Zeng, Q.: The Representation of Snow in Land Surface Schemes:
Results from PILPS 2 (d), J. Hydrometeorol., 2, 7–25,
https://doi.org/10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2,
2001.
Stigter, E. E., Wanders, N., Saloranta, T. M., Shea, J. M., Bierkens, M. F. P., and Immerzeel, W. W.: Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment, The Cryosphere, 11, 1647–1664, https://doi.org/10.5194/tc-11-1647-2017, 2017.
Strasser, U., Bernhardt, M., Weber, M., Liston, G. E., and Mauser, W.: Is snow sublimation important in the alpine water balance?, The Cryosphere, 2, 53–66, https://doi.org/10.5194/tc-2-53-2008, 2008.
Tarasova, L., Knoche, M., Dietrich, J., and Merz, R.: Effects of input
discretization, model complexity, and calibration strategy on model
performance in a data-scarce glacierized catchment in Central Asia, Water
Resour. Res., 52, 4674–4699, https://doi.org/10.1002/2015WR018551, 2016.
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.
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.
Vionnet, V., Martin, E., Masson, V., Guyomarc'h, G., Naaim-Bouvet, F., Prokop, A., Durand, Y., and Lac, C.: Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model, The Cryosphere, 8, 395–415, https://doi.org/10.5194/tc-8-395-2014, 2014.
Vionnet, V., Martin, E., Masson, V., Lac, C., Bouvet, F. N., and Guyomarc'h,
G.: High-Resolution Large Eddy Simulation of Snow Accumulation in Alpine
Terrain, J. Geophys. Res.-Atmos., 122, 11005–11021,
https://doi.org/10.1002/2017JD026947, 2017.
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B., Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., López-Moreno, J. I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods, R.: Climate change and mountain water resources: overview and recommendations for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504, https://doi.org/10.5194/hess-15-471-2011, 2011.
Wan, Z.: New refinements and validation of the collection-6 MODIS
land-surface temperature/emissivity product, Remote Sens. Environ., 140,
36–45, https://doi.org/10.1016/j.rse.2013.08.027, 2014.
Wan, Z., Zhang, Y., Zhang, Q., and Li, Z.-L.: Quality assessment and
validation of the MODIS global land surface temperature, Int. J. Remote
Sens., 25, 261–274, https://doi.org/10.1080/0143116031000116417, 2004.
Wan, Z., Hook, S., and Hulley, G.: MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD11A1.006, 2015
Waqas, A. and Athar, H.: Observed diurnal temperature range variations and
its association with observed cloud cover in northern Pakistan, Int. J.
Climatol., 38, 3323–3336, https://doi.org/10.1002/joc.5503, 2018.
Warscher, M., Strasser, U., Kraller, G., Marke, T., Franz, H., and Kunstmann,
H.: Performance of complex snow cover descriptions in a distributed
hydrological model system: A case study for the high Alpine terrain of the
Berchtesgaden Alps, Water Resour. Res., 49, 2619–2637,
https://doi.org/10.1002/wrcr.20219, 2013.
Winstral, A., Marks, D., and Gurney, R.: Simulating wind-affected snow
accumulations at catchment to basin scales, Adv. Water Resour., 55, 64–79,
https://doi.org/10.1016/j.advwatres.2012.08.011, 2013.
Short summary
This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
This study compares different snowpack model configurations applied in the western Himalaya. The...
