Avalanche Terrain Identification (ATI)

⚠️ Handle with care — work in progress

---

The ATI (Avalanche Terrain Identification) repository contains several modules and workflows to identify and classify avalanche terrain.

Modules

Module	Description	Documentation
mod1Release	Tools to delineate and segment release areas	documentation
mod2Mobility	Preparation and parameterization for avalanche mobility simulations	documentation
mod3Map	Postprocessing tools to interpret, map and represent simulation results	documentation

Workflows

Suggestions for combining the individual modules into full processing chains:

• autoATES Model Chain
• Avalanche Scenario Model Chain
• Size Dependent Parameterization
• Thalweg Analysis Model Chain
• Plots

---

Installation (Linux)

Requirements

Install git and pixi.

Setup

Clone AvalancheTerrainIdentification and AvaFrame as sibling repositories:

  cd [YOURDIR]/OpenNHM
  git clone https://github.com/OpenNHM/AvalancheTerrainIdentification.git
  git clone https://github.com/OpenNHM/AvaFrame.git
  cd AvalancheTerrainIdentification

The workflows currently use the following AvaFrame branches:

Workflow	AvaFrame branch
runAutoAtesModelChain.py	PS_FP_changeCfgRead
runThalwegAnalysis.py	PS_FP_thalweg
runAvaScenModelChain.py	master

Use the branch required by the workflow you intend to run. The thalweg dependency and the use of master for the AvaScenarioModelChain are confirmed. The AutoATES requirement is retained from the existing documentation until its owner confirms otherwise.

First Run

Follow these steps to run a workflow.

1. For this AutoATES example, select its documented AvaFrame branch and return to AvalancheTerrainIdentification:

  cd [YOURDIR]/OpenNHM/AvaFrame
  git switch PS_FP_changeCfgRead
  cd ../AvalancheTerrainIdentification

2. Activate the development environment:

  pixi shell --environment dev

3. Run a workflow, e.g. the autoATES model chain:

  python workflows/runAutoAtesModelChain.py

This performs a full autoATES workflow: PRA delineation and segmentation, avalanche mobility simulation with AvaFrame::com4FlowPy using dynamic runout-angle and max-velocity parameterization, and classification with the autoATES classifier. Results are saved to data/avaTestBowl/Outputs/autoATES.

The workflows folder contains further workflow examples.

Initialize a project

Create a project folder where input data lie and where the results will be stored (check the module documentation linked above for which input data is needed).

Copy the general configuration file atiCfg.ini:

cd modules
cp atiCfg.ini local_atiCfg.ini

and edit local_atiCfg.ini with your favorite text editor and set the avalancheDirectory variable to the full path of your project folder.

Then provide the corresponding input data in [avalancheDirectory]/Inputs.

You can also have a look at the default setting for the module you want to use (e.g. runAutoAtesModelChainCfg.ini for the autoATES workflow). To use different settings, create a local_ copy of the relevant .ini file and modify the parameters as needed, analogous to the local_atiCfg.ini setup above.

Hint:

for the runAutoAtesModelChain.py and runThalwegAnalysis.py workflow, you need to clone AvaFrame in [YOURDIR], then activate the dev environment:

pixi shell --environment dev

The development environment uses the sibling AvaFrame checkout in editable mode. Changing that checkout's branch therefore changes the AvaFrame implementation used by these workflows.