r.randomwalk v1, a multi-functional conceptual tool for mass movement routing

Abstract

Abstract. We introduce r.randomwalk, a flexible and multifunctional open-source tool for backward and forward analyses of mass movement propagation. r.randomwalk builds on GRASS GIS (Geographic Resources Analysis Support System -Geographic Information System), the R software for statistical computing and the programming languages Python and C. Using constrained random walks, 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. Compared to existing tools, the major innovative features of r.randomwalk are (i) multiple break criteria can be combined to compute an impact indicator score; (ii) the uncertainties of break criteria can be included by performing multiple parallel computations with randomized parameter sets, resulting in an impact indicator index in the range 0-1; (iii) built-in functions for validation and visualization of the results are provided; (iv) observed landslides can be back analysed to derive the density distribution of the observed angles of reach. This distribution can be employed to compute impact probabilities for each pixel. Further, impact indicator scores and probabilities can be combined with release indicator scores or probabilities, and with exposure indicator scores. We demonstrate the key functionalities of r.randomwalk for (i) a single event, the Acheron rock avalanche in New Zealand; (ii) landslides in a 61.5 km 2 study area in the Kao Ping Watershed, Taiwan; and (iii) lake outburst floods in a 2106 km 2 area in the Gunt Valley, Tajikistan.

The Gravitational Process Path (GPP) model (v1.0) – a GIS-based simulation framework for gravitational processes

“…This makes it possible to use the tool for regional-scale studies, but it also includes some components for scenario modeling of single events. The approaches implemented in the model components have been successfully used for hazard susceptibility mapping (e.g., Zimmermann et al, 1997;Heinimann et al, 1998;Wichmann and Becht, 2004;Mergili et al, 2015;Proske and Bauer, 2016) and geomorphological process studies, e.g., on sediment cascades or process connectivity (e.g., Wichmann et al, 2009;Haas et al, 2012a;Heckmann and Schwanghart, 2013;Heckmann et al, 2016).…”

“…Furthermore, a random walk approach as introduced in the dfwalk model by Gamma (1996Gamma ( , 2000 is implemented. It is especially suited for process path delineation of gravitational processes and has been used by various authors for rockfall modeling (e.g., Wichmann and Becht, 2006;Haas et al, 2012b;Proske and Bauer, 2016), debris flow modeling (e.g., Zimmermann et al, 1997;Heinimann et al, 1998;Wichmann, 2006;Mergili et al, 2015) and avalanche modeling (e.g., Heckmann, 2006;Schmidtner, 2012).…”

“…The input parameters of physically based models are often uncertain, which is why simplified conceptual models are used to estimate potentially endangered areas in regional studies (Mergili et al, 2015). An important part of hazard susceptibility zoning is the description of process paths and run-out distances to determine the objects at risk.…”

“…The PCM model has been applied for avalanche run-out modeling by, for example, Körner (1976), Hegg (1996) and Heckmann (2006). It has also been applied to model debris flows (Rickenmann, 1990;Zimmermann et al, 1997;Heinimann et al, 1998;Gamma, 2000;Wichmann, 2006;Mergili et al, 2012Mergili et al, , 2015 and large rock slides (e.g., Körner, 1976).…”

Empirical prediction for travel distance of channelized rock avalanches in the Wenchuan earthquake area

“…Nonetheless, dynamic models with a variety of physical bases require accurately quantified input parameters that are difficult to obtain before the events, and many simplified assumptions that are not applicable to the actual situation. Recently Mergili et al (2015) developed the multi-functional open-source tool r.randomwalk for conceptual modeling of the propagation of mass movements, which can combine the empirical model with the numerical model. Empirical models considering the correlations between observational data provide an effective technique to aid in understanding mech- anisms of rock avalanche motion and to develop practical models for predicting rock avalanche travel distance.…”