MCASS

Interactive dashboard for visualizing snow water storage in mountainous Central Asia.

Live app: https://snowmapper.ch

About

This dashboard visualizes snow water storage data from the snowmapperForecast model, an operational version of TopoPyScale running the Factorial Snow Model (FSM). The model is deployed by @joelfiddes at the Swiss Federal Institute for Snow and Avalanche Research (SLF).

Data Format

The dashboard expects two CSV files per basin in the data directory:

• <basin_code>_current.txt - Current year data
• <basin_code>_climate.txt - Long-term average data

Required columns:

Column	Description
date	Date (current year)
Q5_SWE	5th percentile snow water equivalent
Q50_SWE	Median snow water equivalent
Q95_SWE	95th percentile snow water equivalent
Q5_HS	5th percentile snow depth
Q50_HS	Median snow depth
Q95_HS	95th percentile snow depth

Local Development

1. Clone the repository and create a conda environment:

   conda create --name mcass
   conda activate mcass
   pip install -r requirements.txt

2. Configure the data path in .env:

   MCASS_DATA_PATH=<path-to-data>
   

3. Generate dummy data if needed (from the tools/ directory):

   jupyter nbconvert --execute --clear-output generate_dummy_data.ipynb

4. Run the dashboard:

   panel serve mcass-dashboard.py --show --autoreload --port 5010

Docker Deployment

Pull and run the container:

docker pull mabesa/mcass-dashboard:latest
docker run -d -v /path/to/data:/app/data -p 5006:5006 --name mcass-dashboard mabesa/mcass-dashboard

Commits to main trigger automated deployment via GitHub Actions and Watchtower.

License

MIT