Submitted as: model description paper 21 Oct 2021

Submitted as: model description paper | 21 Oct 2021

Review status: this preprint is currently under review for the journal GMD.

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

Christopher J. L. D'Amboise1,, Michael Neuhauser1,, Michaela Teich1, Andeas Huber2, Andreas Kofler3, Frank Perzl1, Reinhard Fromm1, Karl Kleemayr1,, and Jan-Thomas Fischer1, Christopher J. L. D'Amboise et al.
  • 1Austrian Research Centre for Forest (BFW), 6020 Innsbruck, Austria
  • 2Unit of Hydraulic Engineering, Institute for Infrastructure Engineering, University of Innsbruck, 6020 Innsbruck, Austria
  • 3Planungsgemeinschaft, 39100 Bozen, Italy
  • These authors contributed equally to this work.
  • deceased, February 2021

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.

Christopher J. L. D'Amboise et al.

Status: open (until 17 Dec 2021)

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Christopher J. L. D'Amboise et al.

Christopher J. L. D'Amboise et al.


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Short summary
The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides or debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show Flow-Py is an educational, innovative GMF simulation tool with three computational experiments, 1. validation of implementation , 2. performance, and 3. expandability.