r.avaflow
The mass flow simulation tool
r.avaflow represents a GIS-supported open source software tool for the simulation of complex, cascading mass flows of maximum three phases over arbitrary topography. It employs the NOC-TVD numerical scheme (Wang et al., 2004) along with a Voellmy-type model, with a simplified version of the Pudasaini multi-phase flow model (Pudasaini and Mergili, 2019), or with an equilibrium-of-motion model for flows which are not extremely rapid. Specific functionalities include entrainment, deformation control, fragmentation, dispersion, and phase transformations. The starting mass may be defined through raster maps and/or hydrographs. r.avaflow includes the possibility to explore multi-core computing environments to run multiple simulations at once as a basis for parameter sensitivity analysis and optimization. Further, the simulation results are visualized through maps and diagrams, and input for 3D and immersive virtual reality visualization is generated.
r.avaflow.direct
Explore the integrated user interface and manual for r.avaflow 4.0.
r.avaflow.training
Download the training data, start scripts, and parameter files for r.avaflow 4.0.
Cascadia generic landscape
Cascadia is a generic high-mountain landscape. It is the digital representation of a physical landscape and geomorphological process model located at the University of Graz, Austria. It allows for the simulation and visualization of different types of high-mountain processes and process chains, including landslides, debris flows, floods, rock glaciers, deep-seated gravitational slope deformations, and glacial lake outburst floods. The landscape characteristics and the initial conditions are defined by a set of raster maps and hydrographs as well as a frictiograph. The start script for r.avaflow 4.0G includes a sequence of nine simulations, whereas four parameter files are provided for r.avaflow 4.0W.
Data and start script for r.avaflow 4.0G Data and parameter files for r.avaflow 4.0W
r.avaflow.complementary
Learn about the complementary tool r.lakefill, automatically installed with r.avaflow 4.0G.
r.lakefill
Python-based GRASS GIS module for filling depressions in the terrain with water. The following parameters are required to run r.lakefill:
- cellsize: raster cell size for computation. Please apply the same cellsize as for r.avaflow simulations using the output. Otherwise, the lake surface might not be perfectly plane, which would result in numerical oscillations.
- elevation: name of input GRASS raster map representing the terrain surface (usually in metres asl).
- lakedepth: name of output GRASS raster map of the computed lake depth (usually in metres). In r.avaflow, this raster can be used as the fluid release height (parameter hrelease3).
- level: lake level (usually in metres asl). Note that the lake level has to be lower than or equal to the lowest point surrounding the depression to be filled in order to achieve the desired result.
- seedcoords: Two comma-separated values describing the x and y coordinates (usually in metres) of an arbirary location within the depression to be filled. The point defined by these coordinates will be used as seed for filling the depression.
The module is executed through the terminal by calling its name along with the parameters. It is used in the start script of the Cascadia generic landscape. Another possible example:
r.lakefill cellsize=5 elevation=test_elev lakedepth=test_lakedepth level=4256 seedcoords=483370,5120580
r.avaflow.showcase
Experience selected r.avaflow results in 3D and virtual reality.
This collection of animated r.avaflow simulation results is part of the project Moving mountains - Landslides as geosystem services in Austrian geoparks (movemont.at). It shows different levels of VR integration, from ordinary oblique views to anaglyph and stereo 3D animations which allow a more realistic 3D impression with special, but still affordable and easy to obtain, glasses. The stereo 3D animations require an Android smartphone along with the YouTube App and a cardboard or similar device to be properly viewed. The QR code below allows to directly access the corresponding movemont.at playlist.
Animation
Anaglyph animation
Stereo 3D animation
Prehistoric Wildalpen Landslide, Austria
Large rock avalanche in the Steirische Eisenwurzen UNESCO Global Geopark
Prehistoric Köfels Landslide, Austria
Large rock slide in the Tyrol, largest known landslide in Austria
2022 Laguna Upiscocha Glacial Lake Outburst Flood, Peru
Landslide-triggered GLOF in the Cordillera Vilcanota in southern Peru
r.avaflow.publications
Explore a selection of the most relevant publications on and with r.avaflow.
r.avaflow, including all its aspects, is documented in depth through scientific articles published in highly-ranked international journals. The key concepts and findings of the initiative are presented at intenational conferences. This list only shows the most important publications directly related to the r.avaflow tool. Further relevant publications are listed on the personal websites of Martin Mergili and Shiva P. Pudasaini.
Journal articles
These are the most relevant publications, describing the latest developments and case studies.
2021
Shugar, D. H., Jacquemart, M., Shean, D., Bhushan, S., Upadhyay, K., Sattar, A., Schwanghart, W., McBride, S., de Vries, M. Van Wyk, Mergili, M., Emmer, A., Deschamps-Berger, C., McDonnell, M., Bhambri, R., Allen, S., Berthier, E., Carrivick, J. L., Clague, J. J., Dokukin, M., Dunning, S. A., Frey, H., Gascoin, S., Haritashya, U. K., Huggel, C., Kääb, A., Kargel, J. S., Kavanaugh, J. L., Lacroix, P., Petley, D., Rupper, S., Azam, M. F., Cook, S. J., Dimri, A. P., Eriksson, M., Farinotti, D., Fiddes, J., Gnyawali, K. R., Harrison, S., Jha, M., Koppes, M., Kumar, A., Leinss, S., Majeed, U., Mal, S., Muhuri, A., Noetzli, J., Paul, F., Rashid, I., Sain, K., Steiner, J., Ugalde, F., Watson, C. S., Westoby, M. J. (2021): A massive rock, ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science 373(6552): 300-306. doi:10.1126/science.abh4455
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Vilca, O., Mergili, M., Emmer, A., Frey, H., Huggel, C. (2021): The 2020 glacial lake outburst flood process chain at Lake Salkantaycocha (Cordillera Vilcabamba, Peru). Landslides 18: 2211–2223. doi:10.1007/s10346-021-01670-0
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Zheng, G., Mergili, M., Emmer, A., Allen, S., Bao, A., Guo, H., Stoffel, M. (2021): The 2020 glacial lake outburst flood at Jinwuco, Tibet: causes, impacts, and implications for hazard and risk assessment. The Cryosphere 15: 3159-3180. doi:10.5194/tc-2020-379
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Baggio, T., Mergili, M., D'Agostino, V. (2021): Advances in the simulation of debris flow erosion: the case study of the Rio Gere event of the 4th August 2017. Geomorphology 381: 107664. doi:10.1016/j.geomorph.2021.107664
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2020
Mergili, M., Jaboyedoff, M., Pullarello, J., Pudasaini, S.P. (2020): Back-calculation of the 2017 Piz Cengalo-Bondo landslide cascade with r.avaflow. Natural Hazards and Earth System Sciences 20: 505-520. doi:10.5194/nhess-20-505-2020
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Mergili, M., Pudasaini, S.P., Emmer, A., Fischer, J.-T., Cochachin, A., Frey, H. (2020): Reconstruction of the 1941 multi-lake outburst flood at Lake Palcacocha (Cordillera Blanca, Peru). Hydrology and Earth System Sciences 24: 93-114. doi:10.5194/hess-24-93-2020
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Pudasaini, S.P. (2020): A full description of generalized drag in mixture mass flows. Engineering Geology 265: 105429. doi:10.1016/j.enggeo.2019.105429
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2019
Pudasaini, S.P., Mergili, M. (2019): A Multi-Phase Mass Flow Model. JGR Earth Surface. doi: 10.1029/2019JF005204
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Pudasaini, S.P. (2019): A fully analytical model for virtual mass force in mixture flows. International Journal of Multiphase Flow 113: 142-152. doi:10.1016/j.ijmultiphaseflow.2019.01.005
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2018
Mergili, M., Emmer, A., Juřicová, A., Cochachin, A., Fischer, J.-T., Huggel, C., Pudasaini, S.P. (2017): How well can we simulate complex hydro-geomorphic process chains? The 2012 multi-lake outburst flood in the Santa Cruz Valley (Cordillera Blanca, Perú). Earth Surface Processes and Landforms 43(7): 1373-1389. doi:10.1002/esp.4318
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Mergili, M., Frank, B., Fischer, J.-T., Huggel, C., Pudasaini, S.P. (2018): Computational experiments on the 1962 and 1970 landslide events at Huascarán (Peru) with r.avaflow: Lessons learned for predictive mass flow simulations. Geomorphology 322: 15-28. doi:10.1016/j.geomorph.2018.08.032
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2017
Mergili, M., Fischer, J.-T., Krenn, J., Pudasaini, S.P. (2017): r.avaflow v1, an advanced open source computational framework for the propagation and interaction of two-phase mass flows. Geoscientific Model Development 10: 553-569. doi:10.5194/gmd-10-553-2017
2013
Fischer, J.T. (2013): A novel approach to evaluate and compare computational snow avalanche simulation. Natural Hazards and Earth System Sciences 13(6): 1655-1667.
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2012
Mergili, M., Schratz, K., Ostermann, A., Fellin, W. (2012): Physically-based modelling of granular flows with Open Source GIS. Natural Hazards and Earth System Sciences 12: 187-200. doi:10.5194/nhess-12-187-2012
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Pudasaini, S.P. (2012): A general two-phase debris flow model. Journal of Geophysical Research: Earth Surface 117(F3): 1-28.
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Monographs and encyclopedia articles
This work covers more comprehensive topics where r.avaflow is an important aspect.
2017
Allen, S.K., Frey, H., Huggel, C., Bründl, M., Chiarle, M., Clague, J.J., Cochachin, A., Cook, S., Deline, P., Geertsema, M., Giardino, M., Haeberli, W., Kääb, A., Kargel, J., Klimeš, J., Krautblatter, M., McArdell, B., Mergili, M., Petrakov, D., Portocarrero, C., Reynolds, J., Schneider, D. (2017): Assessment of Glacier and Permafrost Hazards in Mountain Regions - Technical Guidance Document. Standing Group on Glacier and Permafrost Hazards in Mountains (GAPHAZ) of the International Association of Cryospheric Sciences (IACS) and the International Permafrost Association (IPA), Zurich, Lima. 72 pp.
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2016
Mergili, M. (2016): Observation and Spatial Modeling of Snow- and Ice-Related Mass Movement Hazards. Natural Hazard Science: Oxford Research Encyclopedias. 60 pp. Oxford University Press. doi:10.1093/acrefore/9780199389407.013.70
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Contributions to conferences
Particularly the EGU conference every spring in Vienna represents an important forum for presenting and discussing the latest developments.
2019
Baggio, T., D'Agostino, V., Mergili, M. (2019): Improving the debris flow erosion model in r.avaflow: the case study of the rio Gere event of the 4th august 2017. International Mountain Conference, Innsbruck, Austria, 8-12 September 2019.
Gylfadóttir, S.S., Mergili, M., Jóhannesson, T., Helgason, J.K., Sæmundsson, Þ, Fischer, J.-T., Pudasaini, S.P. (2019): A three-phase mass flow model applied for the simulation of complex landslide–glacier–lake interac-tions in Iceland. Geophysical Research Abstracts 21, EGU General Assembly, Vienna, Austria, 7-12 April 2019: 13482.
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Marlovits, N., Glade, T., Mergili, M., Preh, A. (2019): Optimizing the choice, parameterization and combination of landslide models for fall and flow processes. Geophysical Research Abstracts 21, EGU General Assembly, Vienna, Austria, 7-12 April 2019: 9542.
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Marlovits, N., Glade, T., Preh, A., Fleris, E., Mergili, M. (2019): A combination of numerical models for fall and flow to simulate complex landslides. Regional Conference on Geomorphology, Athens, Greece, 19-21 September 2019.
Mergili, M. (2019): Simulation of cascading mass flows in GIS: progress and challenges. First EAGE Workshop on Assessment of Landslide and Debris Flows Hazards in the Carpathians, Lviv, Ukraine, 17-20 June 2019.
Mergili, M., Pullarello, J., Jaboyedoff, M., Pudasaini, S.P. (2019): Reconstruction and back-calculation of the 2017 Piz Cengalo-Bondo landslide cascade (Switzerland). Geophysical Research Abstracts 21, EGU General Assembly, Vienna, Austria, 7-12 April 2019: 15103.
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2018
Baggio, T., Mergili, M., Pudasaini, S., Carter, S., Fischer, J.-T. (2018): Simulating snow process chains: avalanche-river interactions with r.avaflow. International Snow Science Workshop, Innsbruck, Austria, 7-12 October 2018.
Kofler, A., Fischer, J.-T., Huber, A., Mergili, M., Fellin, W., Oberguggenberger, M. (2018): A Bayesian Approach to consider Uncertainties in Avalanche Simulation. International Snow Science Workshop, Innsbruck, Austria, 7-12 October 2018.
Mergili, M. (2018): Spatial modelling of the runout of complex landslides interacting with glaciers and lakes. Experiences and challenges. Workshop on early warning, run-out modelling and risk management for landslides on glaciers, Reykjavik, Iceland, 13-14 November 2018.
Mergili, M., Emmer, A., Fischer, J.-T., Huggel, C., Pudasaini, S.P. (2018): Computer simulations of complex cascading landslide processes: what can we do and what can we learn? Geophysical Research Abstracts 20, EGU General Assembly, Vienna, Austria, 8-13 April 2018: 10505.
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Mergili, M., Frey, H., Emmer, A., Fischer, Cochachin, A., Pudasaini, S.P. (2018): Revisiting the catastrophic 1941 outburst flood of Lake Palcacocha (Cordillera Blanca, Peru). Geophysical Research Abstracts 20, EGU General Assembly, Vienna, Austria, 8-13 April 2018: 7569.
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Wijaya, I.P.K., Mergili, M., Zangerl, C., Straka, W., Pudasaini, S.P. (2018): Reconstruction and back-calculation of the Banjarnegara landslide, Indonesia. Geophysical Research Abstracts 20, EGU General Assembly, Vienna, Austria, 8-13 April 2018: 19077.
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2017
Wijaya, I.P.K., Zangerl, C., Straka, W., Mergili, M., Pudasaini, S.P., Arifianti, Y. (2017): A large landslide in volcanic rock: failure processes, geometry and propagation. Geophysical Research Abstracts 19, EGU General Assembly, Vienna, Austria, 23-28 April 2017: 5141.
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Pudasaini, S.P., Fischer, J.-T., Mergili, M. (2017): Mechanical coupling between two innovative theories on erosion, transportation and phase-separation: Solving some long-standing problems in mass flows. Geophysical Research Abstracts 19, EGU General Assembly, Vienna, Austria, 23-28 April 2017: 5030-1.
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Mergili, M., Huggel, C., Emmer, A., Frank, B., Fischer, J.-T., Pudasaini, S.P. (2017): Simulation of Geomorphic Process Chains in Mountain Areas: Progress and Challenges. 9th International Conference on Geomorphology, New Delhi, India, 6-11 November 2017.
Mergili, M., Fischer, J.-T., Pudasaini, S.P. (2017): Process chain modelling with r.avaflow: lessons learned for multi-hazard analysis. In: Mikoš, M., Tiwari, B., Yin, Y., Sassa, K. (eds.): Advancing Culture of Living with Landslides - Proceedings of the 4th World Landslide Forum (WLF4), Ljubljana, Slovenia, 29 May - 2 June 2017, Volume 2 Advances in Landslide Science, Set 1: 565-572. Springer, Cham.
Mergili, M. (2017): Integrated simulation of high-mountain process chains with open source GIS. geomorph.at Annual Meeting, Johnsbach, Austria, 28-29 September 2017.
Mergili, M. (2017): Integrated simulation of high-mountain process chains. SGmG Annual Meeting, Zermatt, Switzerland, 30 August - 1 September 2017.
Kofler, A., Fischer, J.-T., Hellweger, V., Huber, A., Mergili, M., Pudasaini, S.P., Fellin, W., Oberguggenberger, M. (2017): Bayesian inference in mass flow simulations - from back calculation to prediction. Geophysical Research Abstracts 19, EGU General Assembly, Vienna, Austria, 23-28 April 2017: 15720.
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Fischer, J.-T., Pudasaini, S.P., Mergili, M. (2017): A mechanical erosion model for two-phase mass flows: Tackling a long standing dilemma of mass mobility. Geophysical Research Abstracts 19, EGU General Assembly, Vienna, Austria, 23-28 April 2017: 5062-1.
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2016
Emmer, A., Mergili, M., Juricová, A., Cochachin, A., Huggel, C. (2016): Insights from analyzing and modelling cascading multi-lake outburst flood events in the Santa Cruz Valley (Cordillera Blanca, Perú). Geophysical Research Abstracts 18, EGU General Assembly, Vienna, Austria, 17-22 April 2016: 2181.
Hellweger, V., Fischer, J.-T., Kofler, A., Huber, A., Fellin, W., Oberguggenberger, M. (2016): Stochastic methods in operational avalanche simulation - from back calculation to prediction. International Snow Science Workshop Breckenridge, Colorado, USA.
Krenn, J., Mergili, M., Fischer, J.-T., Frattini, P., Pudasaini, S. P. (2016): Optimizing the parameterization of mass flow models. In: Aversa, S., Cascini, L., Picarelli, L., Scavia, C. (eds): Landslides and Engineered Slopes. Experience, Theory and Practice. Proceedings of the 12th International Symposium of Landslides, Napoli, Italy, 12-19 June 2016: 1195-1203. CRC Press, Boca Raton, London, New York, Leiden.
Mergili, M., Benedikt, M., Krenn, J., Fischer, J.-T., Pudasaini, S.P. (2016): r.avaflow & r.randomwalk: two complementary and comprehensive open source GIS simulation tools for the propagation of rapid geophysical mass flows. Proceedings of the Open Source Geospatial Research & Education Symposium (OGRS) 2016, Perugia, Italy, 12 - 14 October 2016, PeerJ Preprints 4:e2224v2.
Mergili, M., Queiroz de Oliveira, G., Fischer, J.-T., Krenn, J., Kulisch, H., Malcherek, A., Pudasaini, S.P. (2016): r.avaflow, the GIS simulation model for avalanche and debris flows: new developments and challenges. Geophysical Research Abstracts 18, EGU General Assembly, Vienna, Austria, 17-22 April 2016: 6825.
2015
Mergili, M., Fischer, J.-T., Fellin, W., Ostermann, A., Pudasaini, S.P. (2015): An advanced open source computational framework for the GIS-based simulation of two-phase mass flows and process chains. Geophysical Research Abstracts 17, EGU General Assembly, Vienna, April 12-17, 2015.
Mergili, M. (2015): Pushing the frontiers of GIS-based modelling of mountain hazards. Jahrestagung der Schweizerischen Geomorphologischen Gesellschaft 2015, Innertkirchen, June 17-19, 2015.
r.avaflow.history
Access the latest change log and previous versions of r.avaflow.
The most fundamental changes in r.avaflow 4.0, compared to r.avaflow 3, are indicated below. This section is most useful for those users which have already been working with r.avaflow 3, or with earlier releases of r.avaflow 4.0.
- First of all, r.avaflow 4.0G is now called through the command r.avaflow.40G, not r.avaflow. However, installing and running two versions in parallel is not possible at the moment.
- r.avaflow 4.0W is supposed to work also for larger study areas. Its use is not restricted to very small areas with few raster cells, as it was the case with earlier versions.
- Most importantly, there is no a priori-distinction between the types of the individual phases any more. The behaviour of each phase is governed purely through the parameters associated to it (options density, friction, cohesion, viscosity, deformation, and some more - see r.avaflow.direct for all details). For example, fluid material should be assigned zero internal and basal friction, and full deformation. There is no explicit option to choose the Voellmy-type model - instead, values of the turbulent friction can be provided for each phase. The option phases takes one integer number, either 1 (for the one-phase model) or 3 (for the multi-phase model with a maximum of three phases).
- There have been various changes to the flow parameters, now referred to as landslide material in r.avaflow.direct. The sequence in which the parameters have to be provided has changed, e.g. for the option friction. New options have been introduced, such as deformation, and others have been removed, such as special. Most of the special parameters are now hard-coded, others have been included in other options. The idea behind this modification is to make the use of r.avaflow easier. If you would like to adapt your r.avaflow 3G start script to an r.avaflow 4.0 start script, please carefully check the section on the landslide material in r.avaflow.direct.
- The option control has been resolved into separate options for each function, such as ctopo for the topography control, csurf for the surface control, or centrainment for the entrainment control. Please check all the control options in r.avaflow.direct. The layer control is now addressed through clayers instead of layers.
- The option thresholds now takes five instead of four values. The fourth value is now the threshold of flow height below which the flow velocity is not considered for the calculation of maximum flow velocities.
- The option slomo now takes three values. The meaning of the first value has remained unchanged, compared to r.avaflow 3, but the second and the third value represent the viscosity controller and the flux controller, needed for the slow-flow mass and momentum balance model. Please check r.avaflow.direct for details.
- The option slidepar now takes two instead of three values. From Revision 3 onwards, they have to be specified separately for each phase, and the values can be varied when performing multiple model runs (flag m). Please check r.avaflow.direct for details.
- From Revision 3 onwards, The option visualization takes one more value: it has to be specified at the beginning whether an underlying orthophoto should be deformed in accordance with the motion. The default is 0, meaning that this function is switched off. Values of 1 or 2 activate the function, with and without destruction of the terrain surface during movement, respectively.
Further, note that some bugs were identified and fixed, in comparison to r.avaflow 3. If you encouter possible bugs in this version, or have ideas for improvement of the software, please do not hesitate to contact martin.mergili@uni-graz.at. Note that r.avaflow is not a commercial software, and there is no regular support. However, it is always attempted to provide adequate assistance as timely as possible.
Older versions of r.avaflow and the associated complementary tools are provided for reference. However, note that the use of older releases is strongly discouraged. No support can be provided for versions 3 or lower. If you decide to use a version until 2.4, be aware that the resulting flow pressures have to be multiplied with a factor of 2 to obtain the depth-avaraged dynamic flow pressures.
r.avaflow 3
[Website]
This script assists in visualizing the dimension of time in the results of r.avaflow 2.4 in Windows environments with ArcGIS. Essentially, a polygon shapefile is produced where each individual polygon shows the time after wich the corresponding area is reached by the mass flow under investigation. A certain understanding of ArcGIS, Python scripting, and the execution of Python scripts is required as well as an ArcGIS license (10.5 or higher) including the Spatial Analyst. A cmd script for starting the timestepper and an ArcMap 10.5 project file providing an example of how to visualize the outcomes can be downloaded along with the training data for the Acheron Rock Avalanche. The result of an application of the timestepper is shown in Mergili et al., 2018b.
The superprofiler helps to illustrate vertical longitudinal profiles of the frontal velocities of r.avaflow 2.4 mass flow simulations in Windows environments with ArcGIS. Polygon shapefiles are generated where the frontal velocity is shown for each individual time step of the simulation. This script depends on the outcome of the timestepper. A certain understanding of ArcGIS, Python scripting, and the execution of Python scripts is required as well as an ArcGIS license (10.5 or higher) including the Spatial Analyst and the 3D Analyst. A cmd script for starting the superprofiler and an ArcMap 10.5 project file providing an example of how to visualize the outcomes can be downloaded along with the training data for the Acheron Rock Avalanche. The result of an application of the superprofiler is shown in Mergili et al., 2018b.
animator
Python script supporting 3D animations of r.avaflow 2.4 results in ArcGIS Pro
The animator automatically creates input files necessary to generate 3D animations of r.avaflow 2.4 results in Windows environments with ArcGIS Pro. A certain understanding of ArcGIS Pro, Python scripting, and the execution of Python scripts is required as well as an ArcGIS Pro license. Note that the animator may fail for very large data sets in terms of raster cells and number of time steps. In such cases, subsets of time steps have to be processed individually. A cmd script for starting the animator and an ArcGIS Pro project file providing an example for an animation can be downloaded along with the training data for the landslide-reservoir interactions.
Be aware that the application of computer models in the field of natural hazards is highly critical. All tools, data, and manuals were prepared with utmost care and with the purpose to be useful - however, they may still contain mistakes of various types. Further, even the best models only produce a distorted and generalized view of reality. Their interpretation requires (i) extreme care, (ii) a detailed understanding of the model, and (iii) complementary information such as measurements or observations. The unreflected communication of model results may lead to unwanted consequences. The authors highly appreciate critics or suggestions, but they refuse any responsibility for any adverse consequences emanating from the use of r.avaflow.
Please cite this site and its content as: Please cite this site and its content as: Mergili, M., Pudasaini, S.P., 2014-2024. r.avaflow - The mass flow simulation tool. https://www.avaflow.org