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.
<p>Recently, strong wind storms have caused large-scale damages in Alpine mountain forests, leaving the underlying infrastructure exposed. These forests often provide protection against gravitational natural hazard processes such as avalanches, rockfall and soil slides. To manage these disturbed forests efficiently and effectively, it is important to know 1) which forest areas serve a protective function to the underlying infrastructure, 2) what is the actual protective effect of these forests on the hazard process, and 3) how one could improve this effect.</p><p>To define protective functions and to quantify the protective effects of forests, we created the Flow-Py model that identifies process areas of gravitational hazards, including avalanches, rockfall and debris slides. The model is written in Python to keep it easy adjustable. The run out routine of Flow-Py is based on the principles of energy conservation including frictional dissipation assuming simple coulomb friction, leading to constant travel-angle. Potential release areas and the corresponding travel angle have to be adapted for each type of gravitational mass movements. A important improvement, compared to similar models, is that it can handle mass movement in flat and uphill terrain. One major advantage of this model is its simplicity, resulting in a computationally inexpensive implementation, which allows for an application on a regional scale, covering large simulation areas. The adaptivity of the model further allows to consider existing infrastructure and to detect starting zones endangering the corresponding areas in a back-calculation step. Additionally, by adding forest cover to the simulations we can identify which forest area has a protective function and, based on information about forest structure, calculate the protective effect this forest provides to down slope infrastructure.</p><p>Flow-Py is a useful tool to identify forest areas that are important for hazard protection (protective function) and to quantify their protective effect. The model can be applied in protection forest management to prioritize measures in wind throw areas. Furthermore, it is possible to use this tool for analyzing the protective functions and effects of different forest extents and structural conditions, for example, caused by climate change or forest disturbances. In this work we elaborate the potential of Flow-Py by presenting an avalanche case study in the central alpine region of Austria (Gries/Vals, Tyrol, AT). For this case the simulation results indicate a process area affected by avalanches of ~65% with respect to the total area of ~ 195 km&#178;.</p>
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.
<p>The dynamics of snow avalanches and in particular their rheology is of big importance to develop improved avalanche models and thus increase safety in mountainous areas. Existing measurement systems only allow a limited in situ view of the dynamics of snow avalanches and therefore demand the development of innovative measurement systems. Furthermore, due to the limited measurement capability of existing systems, comprehensible motion reconstruction is currently not possible. Therefore, the aim of this work is to present a measurement system that enables accurate in flow observations of snow avalanches and has the mechanical properties of a typical snow granule. A main objective of the measurement system is to allow a full motion reconstruction regarding translations and rotations with a high sampling rate and without exceeding sensor ranges.</p><p>The newly developed system, denoted as AvaNode, has the shape of a concave cube with a variable density to fit typical snow granules in flowing avalanches and their deposits. The AvaNode contains a strapdown inertial navigation sensor capable of measuring accelerations, angular velocities, and magnetic flux densities with up to 400Hz and allows for an estimation of the orientations, velocities, and positions of the AvaNode using state of the art motion reconstruction algorithms. The reconstruction is significantly improved due to precise calibration of all sensors using reference measurements with a 6R robot and onsite magnetic field calibration. In order to get a refined motion trajectory, the AvaNodes are also equipped with radio ranging modules. These modules allow performing time of flight (TOF) measurements, determining the distance between several nodes. A Global Navigation Satellite System (GNSS) module determines longitude, latitude, and altitude, as well as world time, however, with low frequency resolution and larger errors due to snow coverage. To measure the temperature evolution in avalanches, an infrared temperature sensor is attached.&#160; Multiple recovery systems like Recco rescue reflector (passive), Pieps TX600 (active), and Lambda4 Smilla (active) are integrated to allow fast retrieval of the sensors.</p><p>As first results, we present the employed sensor calibration approaches for the inertial navigation with corresponding laboratory data signatures. The sensor calibration allows in-depth analysis of motion data, identifying typical data signatures observed in avalanches. Furthermore, we show first data acquired from in-flow snow avalanche measurements, which prove the functionality of the system and allow the first insights into trajectories of snow granules, regarding accelerations, angular velocities, rotations, and position.</p>
<div> <p><span>Forests cover large parts of mountain areas. It is therefore necessary to include their effects in simulations at the regional scale to understand the key role forests have for risk mitigation. Process-based physical models can be used for such simulations, but they often require larger computational resources than statistical models. Flow-Py is a customizable, open-source simulation tool to predict the runout and intensity of gravitational mass flows (GMF). Flow-Py is based on data-driven empirical modeling ideas with automated path identification to solve the routing and stopping of GMFs in three-dimensional terrain, requiring fewer parameters than physical GMF runout models. Here we present the </span><span>custom-built&#8239;</span><span>forest plug-in to the Flow-Py simulation tool which accounts for forest effects in the transit and runout zones of snow avalanches.</span><span>&#160;</span></p> </div><div> <p><span>Flow-Py employs the well-known runout angle (&#945;) concept to determine the stopping of a GMF, and routing algorithm consisting of a terrain contribution and persistence contribution. The interaction between forest and avalanches, which can reduce their runout and decrease their intensity can be broken down into two main processes, 1) adding friction and 2) reducing flowing mass or the detrainment of snow. The forest plug-in has the capability to mimic these physical interactions by increasing the runout angle and adjusting the routing flux in forested areas. We present the framework of the forest plug-in for a test case and the results of a sensitivity study on parameters controlling the forest-avalanche interaction. </span><span>&#160;</span></p> </div><div> <p><span>The forest plug-in requires the spatial extent of the forest and an estimate of the kinetic energy of the avalanche to compute the forest&#8217;s effect on the avalanche movement. Additional information on the structure of the forest (e.g., forest type, stem density, canopy cover, basal area) can be used to amplify or dampen these effects. The forest information is summarized in the forest structure index (FSI), which indicates how developed a forest is with regards to its optimal protective effect against snow avalanches and ranges between 0 (no protection) and 1 (optimal protection), considering, e.g., dominant forest type, elevation band, or the forest development stage.</span><span>&#160;</span></p> </div><div> <p><span>Forests located in the starting zones of avalanches have long been used as an efficient mitigation measure to reduce avalanche risk. However, forests located in the transit and runout zones of avalanches also have mitigating properties, but the degree of protection is difficult to quantify without simulation tools and their integrated models. Including forest-avalanche interactions in regional-scale simulations with Flow-Py and its forest plug-in allows to estimate the degree to which forest protects human activity and infrastructure against potential avalanches. That is, by combining simulation results with and without forest it is possible to estimate the forest impact, i.e., how much the forest reduces the magnitude (runout and intensity) of the avalanche. Such regional overviews can be calculated fast with large-scale input data, which is important to, e.g., quantify changes in the protective effect of a forest area caused by disturbance agents such as wind, bark beetles or fire. </span><span>&#160;</span></p> </div>
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