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

Abstract

Abstract. Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows.

Modeling Protective Forests for Gravitational Natural Hazards and How It Relates to Risk-Based Decision Support Tools

“…The Flow-Py simulation tool contains a GMF runout model for regional scales that allows users to customize GMF simulations by adjusting the parameterization or developing extensions to adapt the calculations, input data and outputs of the model [2,5]. Flow-Py integrates a statistical model where the runout is solved by splitting the modeling question into two sub-questions, which are addressed in the two modeling routines:…”

“…Two criteria stop the flux, if (1) the simulated GMF runout reaches further than the predefined runout angle, and/or (2) the amount of mass, which is spreading across a slope, reaches a critical value that is required for further propagation. The routing routine calculates to which cell(s) the flux is distributed and uses the DEM to favor directions with steeper downward slopes, as well as persistence, which is similar to momentum but derived by statistical means and does not consider the GMF's mass (for further information and details see [2]). To address specific questions regarding protective forest, we developed two custom extensions for Flow-Py to:…”

“…In this chapter, we discuss how to incorporate forests into gravitational mass flow (GMF; [2]) models to gain an understanding of where forests protect people and assets (their protective function) and to estimate how forests reduce the frequency, magnitude and intensity of gravitational natural hazard (their protective effect; see chapters [3,4] for definitions and details on forest-GMF interactions). We first introduce basic concepts of computer models regarding protective forests and then summarize some of the state-of-the-art methods used for modeling forest interactions with rockfall, snow avalanches and landslides in their starting zone as well as transit and runout zones.…”

“…We first introduce basic concepts of computer models regarding protective forests and then summarize some of the state-of-the-art methods used for modeling forest interactions with rockfall, snow avalanches and landslides in their starting zone as well as transit and runout zones. Lastly, an example of a protective forest routine embedded into Flow-Py, a GMF simulation tool based on a runout angle (also referred to as the travel or α-angle) model [2,5], is presented together with the justifications for the parameterization and implementation. This example highlights the development process of simulation tools and allows the user a deeper look into the assumptions that are necessary for modeling protective forests.…”

“…The domain sizes and model types that will be discussed are the regional (10s to 100s of km 2 ) and the hill slope or path scale (less than 10 km 2 ), and data-driven Modeling Protective Forests for Gravitational Natural Hazards and How It Relates to Risk-Based… DOI: http://dx.doi.org/10.5772/intechopen.99510 statistical models (hereafter referred to as statistical models, e.g., the α-β model [6], Flow-R [7], or the topographic approach of [8]) and process-based physical models (hereafter referred to as physical models, e.g., RAMMS [9] or Rockyfor3D [10]).…”