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 (GMF) 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: 1. Implementation and validation: We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topograhies. 2. Performance: Flow-Py’s performance and low computation time is demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. 3. Modularity and expandability: The modular and adaptive Flow-Py development environment allows to access 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.

The Concept of Risk and Natural Hazards

“…How forests can act as a solution for Eco-DRR is the subject of the following three chapters of this book [72][73][74]. Moreover, the methodologies and decision support tools related to the risk concept that were developed and applied within GR4A are presented in [75,76], the book chapters [77-79], and are explained in detail in the GR4A project reports [20,65,80,81].…”