Articles | Volume 17, issue 17
https://doi.org/10.5194/gmd-17-6545-2024
© 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/gmd-17-6545-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
02 Sep 2024
Model description paper |
| 02 Sep 2024
| 02 Sep 2024
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches
Matthias Rauter
Unit of Geotechnical Engineering, University of Innsbruck, Innsbruck, Austria
Department of Civil Engineering and Natural Hazards, University of Natural Resources and Life Sciences, Vienna, Austria
Julia Kowalski
CORRESPONDING AUTHOR
Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Aachen, Germany
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Matthias Rauter, Andreas Kofler, Andreas Huber, and Wolfgang Fellin
Geosci. Model Dev., 11, 2923–2939, https://doi.org/10.5194/gmd-11-2923-2018, https://doi.org/10.5194/gmd-11-2923-2018, 2018
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We present a physical model for the simulation of dense snow avalanches and other gravitational mass flows. The model is solved with OpenFOAM, a popular open-source toolkit for the numerical solution of partial differential equations. The solver has a modular design and is easy to extend. Therefore, it represents an ideal platform for implementing and testing new model approaches.
Matthias Rauter, Jan-Thomas Fischer, Wolfgang Fellin, and Andreas Kofler
Nat. Hazards Earth Syst. Sci., 16, 2325–2345, https://doi.org/10.5194/nhess-16-2325-2016, https://doi.org/10.5194/nhess-16-2325-2016, 2016
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Kinetic theory describes granular material under rapid motion. Macroscopic phenomena are determined by statistically describing collisions between particles. Recently, the theory has been extended to slow motion and quasi-static cases. Simplifications allow to apply this theory to snow avalanche simulations, where friction models with similar structure have been developed. Different test cases, comparing simulation and measurement data prove the applicability and highlight the improvements.
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For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with EURAD-IM.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
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Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
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
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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
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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.
Hu Zhao and Julia Kowalski
Nat. Hazards Earth Syst. Sci., 20, 1441–1461, https://doi.org/10.5194/nhess-20-1441-2020, https://doi.org/10.5194/nhess-20-1441-2020, 2020
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We study the impact of topographic uncertainty on landslide run-out modeling using conditional and unconditional stochastic simulation. First, we propose a generic workflow and then apply it to a historic flow-like landslide. We find that topographic uncertainty can greatly affect landslide run-out modeling, depending on how well the underlying flow path is captured by topographic data. The difference between unconditional and conditional stochastic simulation is discussed in detail.
Matthias Rauter, Andreas Kofler, Andreas Huber, and Wolfgang Fellin
Geosci. Model Dev., 11, 2923–2939, https://doi.org/10.5194/gmd-11-2923-2018, https://doi.org/10.5194/gmd-11-2923-2018, 2018
Short summary
Short summary
We present a physical model for the simulation of dense snow avalanches and other gravitational mass flows. The model is solved with OpenFOAM, a popular open-source toolkit for the numerical solution of partial differential equations. The solver has a modular design and is easy to extend. Therefore, it represents an ideal platform for implementing and testing new model approaches.
Matthias Rauter, Jan-Thomas Fischer, Wolfgang Fellin, and Andreas Kofler
Nat. Hazards Earth Syst. Sci., 16, 2325–2345, https://doi.org/10.5194/nhess-16-2325-2016, https://doi.org/10.5194/nhess-16-2325-2016, 2016
Short summary
Short summary
Kinetic theory describes granular material under rapid motion. Macroscopic phenomena are determined by statistically describing collisions between particles. Recently, the theory has been extended to slow motion and quasi-static cases. Simplifications allow to apply this theory to snow avalanche simulations, where friction models with similar structure have been developed. Different test cases, comparing simulation and measurement data prove the applicability and highlight the improvements.
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Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
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We implemented a new modeling framework within an Earth system model to study the evolution of mountain glaciers under different climate scenarios and applied it to the European Alps. Alpine glaciers will lose a large volume fraction under current temperatures, with near complete ice loss under warmer scenarios. This is the first use of a 3D, higher-order ice flow model for regional-scale glacier simulations that will enable assessments of coupled land ice and Earth system processes.
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Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Max Brils, Peter Kuipers Munneke, Willem Jan van de Berg, and Michiel van den Broeke
Geosci. Model Dev., 15, 7121–7138, https://doi.org/10.5194/gmd-15-7121-2022, https://doi.org/10.5194/gmd-15-7121-2022, 2022
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Firn covers the Greenland ice sheet (GrIS) and can temporarily prevent mass loss. Here, we present the latest version of our firn model, IMAU-FDM, with an application to the GrIS. We improved the density of fallen snow, the firn densification rate and the firn's thermal conductivity. This leads to a higher air content and 10 m temperatures. Furthermore we investigate three case studies and find that the updated model shows greater variability and an increased sensitivity in surface elevation.
Cited articles
Bagnold, R. A.: Experiments on a gravity-free dispersion of large solid spheres in a Newtonian fluid under shear, Proc. Roy. Soc. Lond. A, 225, 49–63, https://doi.org/10.1098/rspa.1954.0186, 1954. a, b, c
Barker, T. and Gray, J. M. N. T.: Partial regularisation of the incompressible μ(I)-rheology for granular flow, J. Fluid Mech., 828, 5–32, https://doi.org/10.1017/jfm.2017.428, 2017. a
Barker, T., Rauter, M., Maguire, E., Johnson, C., and Gray, J.: Coupling rheology and segregation in granular flows, J. Fluid Mech., 909, A22, https://doi.org/10.1017/jfm.2020.973, 2021. a
Barré de Saint-Venant, A. J. C.: Théorie du mouvement non permanent des eaux, avec application aux crues des rivières et à l'introduction des marées dans leurs lits, CR Acad. Sci., 73, 237–240, 1871. a
Bartelt, P. and McArdell, B. W.: Granulometric investigations of snow avalanches, J. Glaciol., 55, 829–833, https://doi.org/10.3189/002214309790152384, 2009. a
Bouchut, F., Mangeney-Castelnau, A., Perthame, B., and Vilotte, J.-P.: A new model of Saint Venant and Savage–Hutter type for gravity driven shallow water flows, C. R. Math., 336, 531–536, https://doi.org/10.1016/S1631-073X(03)00117-1, 2003. a, b
Boyer, F., Guazzelli, É., and Pouliquen, O.: Unifying suspension and granular rheology, Phys. Rev. Lett., 107, 188301, https://doi.org/10.1103/PhysRevLett.107.188301, 2011. a, b, c, d
Christen, M., Kowalski, J., and Bartelt, P.: RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain, Cold Reg. Sci. Technol., 63, 1–14, https://doi.org/10.1016/j.coldregions.2010.04.005, 2010. a, b
Craster, R. V. and Matar, O. K.: Dynamics and stability of thin liquid films, Rev. Modern Phys., 81, 1131–1198, https://doi.org/10.1103/RevModPhys.81.1131, 2009. a
Denlinger, R. P. and Iverson, R. M.: Granular avalanches across irregular three-dimensional terrain: 1. Theory and computation, J. Geophys. Res.-Earth, 109, F01014, https://doi.org/10.1029/2003JF000085, 2004. a
Dziuk, G. and Elliott, C. M.: Finite element methods for surface PDEs, Acta Numer., 22, 289–396, https://doi.org/10.1017/S0962492913000056, 2013. a
Eglit, M.: Mathematical and physical modelling of powder-snow avalanches in Russia, Ann. Glaciol., 26, 281–284, https://doi.org/10.3189/1998AoG26-1-281-284, 1998. a, b
Eglit, M., Yakubenko, A., and Zayko, J.: A review of Russian snow avalanche models–from analytical solutions to novel 3D models, Geosciences, 10, 77, https://doi.org/10.3390/geosciences10020077, 2020. a, b
ESI-OpenCFD team: ESI OpenCFD Release OpenFOAM® v2312, https://www.openfoam.com/news/main-news/openfoam-v2312, last access: 18 August 2024. a
Fischer, J.-T., Kowalski, J., and Pudasaini, S. P.: Topographic curvature effects in applied avalanche modeling, Cold Reg. Sci. Technol., 74, 21–30, https://doi.org/10.1016/j.coldregions.2012.01.005, 2012. a
Forterre, Y. and Pouliquen, O.: Flows of Dense Granular Media, Annual Rev. Fluid Mech., 40, 1–24, https://doi.org/10.1146/annurev.fluid.40.111406.102142, 2008. a, b, c
Garcés, A., González, Á., Tamburrino, A., and Montserrat, S.: faDebrisFOAM validation using field data surveyed in Crucecita (Chile) alluvial fan for the event of 13th May 2017, in: E3S Web of Conferences, 415, p. 02007, https://doi.org/10.1051/e3sconf/202341502007, 2023. a
George, D. L., Iverson, R. M., and Cannon, C. M.: New methodology for computing tsunami generation by subaerial landslides: Application to the 2015 Tyndall Glacier landslide, Alaska, Geophys. Res. Lett., 44, 7276–7284, https://doi.org/10.1002/2017GL074341, 2017. a
Hagemeier, T., Hartmann, M., and Thévenin, D.: Practice of vehicle soiling investigations: A review, Int. J. Multiphas. Flow, 37, 860–875, https://doi.org/10.1016/j.ijmultiphaseflow.2011.05.002, 2011. a
Heerema, C. J., Talling, P. J., Cartigny, M. J., Paull, C. K., Bailey, L., Simmons, S. M., Parsons, D. R., Clare, M. A., Gwiazda, R., Lundsten, E., Anderson, K., Maier, K. L., Xu, J. P., Sumner, E. J., Rosenberger, K., Gales, J., McGann, M., Carter, L., and Pope, E.: What determines the downstream evolution of turbidity currents?, Earth Planet. Sc. Lett., 532, 116023, https://doi.org/10.1016/j.epsl.2019.116023, 2020. a
Hergarten, S. and Robl, J.: Modelling rapid mass movements using the shallow water equations in Cartesian coordinates, Nat. Hazards Earth Syst. Sci., 15, 671–685, https://doi.org/10.5194/nhess-15-671-2015, 2015. a
Huber, A., Kofler, A., Rauter, M., Fischer, J.-T., and Adams, M. S.: Simulation of dense snow avalanches with open source software, in: Proceedings of the International Snow Science Workshop, Innsbruck, Austria, https://arc.lib.montana.edu/snow-science/item/2643 (last access: 18 August 2024), 2018. a
Issler, D.: Modelling of snow entrainment and deposition in powder-snow avalanches, Ann. Glaciol., 26, 253–258, https://doi.org/10.3189/1998AoG26-1-253-258, 1998. a, b, c
Issler, D., Jenkins, J. T., and McElwaine, J. N.: Comments on avalanche flow models based on the concept of random kinetic energy, J. Glaciol., 64, 148–164, https://doi.org/10.1017/jog.2017.62, 2018. a, b, c
Iverson, R. M. and George, D. L.: A depth-averaged debris-flow model that includes the effects of evolving dilatancy. I. Physical basis, Proc. Roy. Soc. Lond. A, 470, 2170, https://doi.org/10.1098/rspa.2013.0819, 2014. a, b
Jasak, H.: Error analysis and estimation for the finite volume method with applications to fluid flows, Ph.D. thesis, Imperial College, University of London, https://www.researchgate.net/publication/230605842_Error_Analysis_and_Estimation_for_the_Finite_Volume_Method_With_Applications_to_Fluid_Flows (last access: 18 August 2024), 1996. a, b
Juretić, F.: cfMesh User Guide, Creative Fields, Zagreb, https://cfmesh.com/wp-content/uploads/2015/09/User_Guide-cfMesh_v1.1.pdf (last access: 18 August 2024), 2015. a
Köhler, A., McElwaine, J., and Sovilla, B.: GEODAR data and the flow regimes of snow avalanches, J. Geophys. Res.-Earth, 123, 1272–1294, https://doi.org/10.1002/2017JF004375, 2018. a
Kowalski, J. and McElwaine, J. N.: Shallow two-component gravity-driven flows with vertical variation, J. Fluid Mech., 714, 434–462, https://doi.org/10.1017/jfm.2012.489, 2013. a, b
Kowalski, J. and Torrilhon, M.: Moment Approximations and Model Cascades for Shallow Flow, Commun. Comput. Phys., 25, 669–702, https://doi.org/10.4208/cicp.OA-2017-0263, 2019. a
LeVeque, R. J.: Finite volume methods for hyperbolic problems, vol. 31, Cambridge University Press, 2002. a
Løvholt, F., Pedersen, G., Harbitz, C. B., Glimsdal, S., and Kim, J.: On the characteristics of landslide tsunamis, Philos. T. Roy. Soc. A, 373, 20140376, https://doi.org/10.1098/rsta.2014.0376, 2015. a
Lucas, A., Mangeney, A., and Ampuero, J. P.: Frictional velocity-weakening in landslides on Earth and on other planetary bodies, Nat. Commun., 5, 1–9, https://doi.org/10.1038/ncomms4417, 2014. a
Mergili, M., Fischer, J.-T., Krenn, J., and Pudasaini, S. P.: r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows, Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, 2017. a
Moukalled, F., Mangani, L., and Darwish, M.: The finite volume method in computational fluid dynamics, Springer, https://doi.org/10.1007/978-3-319-16874-6, 2016. a, b
Pitman, E. B., Nichita, C. C., Patra, A., Bauer, A., Sheridan, M., and Bursik, M.: Computing granular avalanches and landslides, Phys. Fluids, 15, 3638–3646, https://doi.org/10.1063/1.1614253, 2003. a
Pudasaini, S. P.: A general two-phase debris flow model, J. Geophys. Res.-Earth, 117, F03010, https://doi.org/10.1029/2011JF002186, 2012. a
Pudasaini, S. P. and Hutter, K.: Avalanche Dynamics: Dynamics of Rapid Flows of Dense Granular Avalanches, Springer, https://doi.org/10.1007/978-3-540-32687-8, 2007. a
Pudasaini, S. P., Wang, Y., and Hutter, K.: Rapid motions of free-surface avalanches down curved and twisted channels and their numerical simulation, Philos. T. Roy. Soc. Lond. A, 363, 1551–1571, https://doi.org/10.1098/rsta.2005.1595, 2005. a
Rastello, M., Rastello, F., Bellot, H., Ousset, F., Dufour, F., and Meier, L.: Size of snow particles in a powder-snow avalanche, J. Glaciol., 57, 151–156, https://doi.org/10.3189/002214311795306637, 2011. a
Rauter, M.: OpenFOAM Avalanche Module Repository, https://develop.openfoam.com/Community/avalanche, last access: 18 August 2024. a
Rauter, M. and Köhler, A.: Constraints on entrainment and deposition models in avalanche simulations from high-resolution radar data, Geosciences, 10, 9, https://doi.org/10.3390/geosciences10010009, 2020. a, b, c
Rauter, M., Fischer, J.-T., Fellin, W., and Kofler, A.: Snow avalanche friction relation based on extended kinetic theory, Nat. Hazards Earth Syst. Sci., 16, 2325–2345, https://doi.org/10.5194/nhess-16-2325-2016, 2016. a, b, c, d
Rauter, M., Viroulet, S., Gylfadóttir, S. S., Fellin, W., and Løvholt, F.: Granular porous landslide tsunami modelling–the 2014 Lake Askja flank collapse, Nat. Commun., 13, 678, https://doi.org/10.1038/s41467-022-28296-7, 2022. a, b
Roache, P. J.: Quantification of uncertainty in computational fluid dynamics, Annu. Rev. Fluid Mech., 29, 123–160, https://doi.org/10.1146/annurev.fluid.29.1.123, 1997. a, b
Salm, B., Burkard, A., and Gubler, H. U.: Berechnung von Fliesslawinen: eine Anleitung für Praktiker mit Beispielen, Tech. rep., WSL Institut für Schnee-und Lawinenforschung SLF, Davos, https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A26106 (last access: 18 August 2024), 1990. a
Savage, S. B. and Hutter, K.: The dynamics of avalanches of granular materials from initiation to runout. Part I: Analysis, Acta Mech., 86, 201–223, https://doi.org/10.1007/BF01175958, 1991. a, b, c
Scheidegger, A. E.: On the prediction of the reach and velocity of catastrophic landslides, Rock Mech. Rock Eng., 5, 231–236, https://doi.org/10.1007/BF01301796, 1973. a
Shimizu, H. A.: Numerical Simulations of Dome-Collapse Pyroclastic Density Currents Using faSavageHutterFOAM: Application to the 3 June 1991 Eruption of Unzen Volcano, Japan, J. Disaster Res., 17, 768–778, https://doi.org/10.20965/jdr.2022.p0768, 2022. a
Sovilla, B., McElwaine, J. N., and Louge, M. Y.: The structure of powder snow avalanches, C. R. Phys., 16, 97–104, https://doi.org/10.1016/j.crhy.2014.11.005, 2015. a, b, c
Steinkogler, W., Gaume, J., Löwe, H., Sovilla, B., and Lehning, M.: Granulation of snow: From tumbler experiments to discrete element simulations, J. Geophys. Res.-Earth, 120, 1107–1126, https://doi.org/10.1002/2014JF003294, 2015a. a
Steinkogler, W., Sovilla, B., and Lehning, M.: Thermal energy in dry snow avalanches, The Cryosphere, 9, 1819–1830, https://doi.org/10.5194/tc-9-1819-2015, 2015b. a
Tuković, Ž. and Jasak, H.: A moving mesh finite volume interface tracking method for surface tension dominated interfacial fluid flow, Comput. Fluids, 55, 70–84, https://doi.org/10.1016/j.compfluid.2011.11.003, 2012. a, b
Turnbull, B. and Bartelt, P.: Mass and momentum balance model of a mixed flowing/powder snow avalanche, Surv. Geophys., 24, 465–477, https://doi.org/10.1023/B:GEOP.0000006077.82404.84, 2003. a
Turner, J.: Turbulent entrainment: the development of the entrainment assumption, and its application to geophysical flows, J. Fluid Mech., 173, 431–471, https://doi.org/10.1017/S0022112086001222, 1986. a, b, c, d
Vescovi, D., di Prisco, C., and Berzi, D.: From solid to granular gases: the steady state for granular materials, Int. J. Numer. Anal. Met., 37, 2937–2951, https://doi.org/10.1002/nag.2169, 2013. a
Viroulet, S., Baker, J., Edwards, A., Johnson, C. G., Gjaltema, C., Clavel, P., and Gray, J.: Multiple solutions for granular flow over a smooth two-dimensional bump, J. Fluid Mech., 815, 77–116, https://doi.org/10.1017/jfm.2017.41, 2017. a
Voellmy, A.: Über die Zerstörungskraft von Lawinen (On the destructive forces of avalanches), Schweizerische Bauzeitung, 73, 212–217, https://doi.org/10.5169/seals-61891, 1955. a
Zhao, H. and Kowalski, J.: Bayesian active learning for parameter calibration of landslide run-out models, Landslides, 19, 2033–2045, https://doi.org/10.1007/s10346-022-01857-z, 2022. a
Short summary
Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of...
