Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 6
Articles | Volume 15, issue 6
https://doi.org/10.5194/gmd-15-2423-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2423-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 6
Model description paper
 | 
21 Mar 2022
Model description paper |  | 21 Mar 2022

Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows

Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer

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Cited articles

Ancey, C., Meunier, M., and Richard, D.: Inverse problem in avalanche dynamics models, Water Resour. Res., 39, 1099, https://doi.org/10.1029/2002WR001749, 2003. a
Bakkehøi, S., Domaas, U., and Lied, K.: Calculation of snow avalanche runout distance, Ann. Glaciol., 4, 24–29, 1983. a, b
Barbolini, M., Gruber, U., Keylock, C. J., Naaim, M., and Savi, F.: Application of statistical and hydraulic-continuum dense-snow avalanche models to five real European sites, Cold Reg. Sci. Technol., 31, 133–149, 2000. a
Barbolini, M., Pagliardi, M., Ferro, F., and Corradeghini, P.: Avalanche hazard mapping over large undocumented areas, Nat. Hazards, 56, 451–464, 2011. a, b, c
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Sys., 5, 853–862, 2005. a
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The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
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