Open source applications for the spatial simulation of landslide susceptibility and processes
Computer models can help us to visualize, reproduce, and - to some extent - predict patterns or processes in our environment. Assuming that nature is too complex to be fully understood, model results always give a generalized and distorted picture of reality but - if their limitations are understood - can be useful for multiple purposes. Coupling models to geographic information systems (GIS) enables the analysis of spatial patterns or processes over a wide range of scales. Below, you can access a selection of GIS-based modelling tools dealing with hazardous geomorphologic processes. These tools shall serve for identifying those areas possibly exposed to the respective processes. Most of them rely on the open source software products GRASS GIS and/or The R Package for Statistical Computing. Suited for use with Linux operation systems, most of the tools are available for download along with manuals and experimental datasets. All of the tools are distributed under the GNU General Public License.
The modelling tools represent experimental codes suited for scientific purposes rather than ready-to-use software packages. Their application requires advanced computational skills. If you would just like to play around with one or more of the tools, you might find the relevant publications and the manuals provided along with most of the tools useful. If you encounter issues not covered by the manual, you are cordially invited to join our forum.
If you would like to do more extensive testing with real-world data, or if you are interested in contributing to the further development of the modelling tools, it is highly recommended - and you are highly welcome - to contact me at email@example.com.
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 errors of various types. 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. Whilst I am highly grateful for critics or suggestions, I clearly refuse any responsibility for any adverse consequences emanating from the use of any of the models provided below.
Before starting with any computational experiments considering parameter optimization, please read the article of Oreskes et al. (1994).
Name and description
Model application for the physically-based, dynamic simulation of the propagation of various types of mass flows. This tool offers implementations of the Voellmy model, and of the Pudasaini (2012) two-phase flow model. The latter allows to simulate process chains and interactions, such as the impact of landslides on reservoirs. r.avaflow further serves with various functionalities for visualization, empirical confirmation, and parameter optimization, facilitated by the possibility to exploit multiple computational cores.
r.randomwalk is a flexible and multi-functional conceptual tool for backward- and forward-analyses of mass movement propagation. Mass points are routed from defined release pixels of one to many mass movements through a digital elevation model until a defined break criterion is reached. Lateral spreading is ensured by a constrained random walk approach. r.randomwalk offers some innovative features, compared to other conceptual tools for similar purposes.
Three-dimensional slope stability model for large areas based on ellipsoidal or truncated slip surfaces. r.slope.stability allows for multi-core processing and is suitable for geologically complex terrain with multiple layers. Further, r.slope.stability comes up with built-in functions for empirical confirmation and visualization of the model results.
r.ranger is a tool for the sensitivity analysis and optimization of parameter ranges or spaces for r.randomwalk and r.avaflow. A set of validation parameters is derived in order to allow conclusions on parameter sensitivity, and to facilitate the selection of optimal parameter ranges or spaces. r.ranger complements existing tools for parameter sensitivity analysis and optimization, which all work with parameter values instead of ranges, and value combinations instead of spaces.
Please note that the model applications r.debrisflow (Mergili et al., 2012), r.multirisk (Gruber and Mergili, 2013), r.glof (Mergili and Schneider, 2011), and gradgrid4 (Mergili and Kerschner, 2015) are currently not actively developed. Only a very limited amount of support can be provided with regard to these tools. In case you would like to try them anyway, please contact firstname.lastname@example.org.