ThesisCG — Snow Distribution & Terrain Analysis

Analysis code from a master's thesis studying the relationship between terrain characteristics (slope, aspect, curvature, TPI, elevation) and snow depth distribution, including snow-depth prediction from terrain features and weather-station representativeness analysis.

This is research code written to support one thesis, not a packaged tool — scripts are config-driven but expect the specific raster/vector datasets used in that study. It is shared here for transparency and reference rather than as a ready-to-run application. Expect ongoing changes as the underlying research evolves.

Layout

All code lives flat in the repo root:

File	Role
powdersearch.py	Shared function library (raster I/O, terrain-parameter calculation, statistics, plotting) imported as ps by every other script.
config_loader.py	ConfigLoader class used by the snow-modelling script to load and validate config_snow_modelling.yaml.
preprocessing.py	Config-driven entry point: reprojects/aligns raw snow-depth rasters, computes timeseries statistics, difference maps, normalization, and terrain features. Driven by config_preprocess.yaml.
runfile_representiveness.py	Run-script for weather-station representativity analysis. Driven by parameters at the top of the file (and conceptually by config_representativeness.yaml). See note below — it depends on a module not included in this repo.
SDD_spatial_terrain_paramert_elevationbased.py	Spatial, elevation-stratified analysis of snow depth vs. terrain parameters.
SDD_timeseries_per_terrain_parameter_variable.py	Per-terrain-parameter timeseries analysis and classification (aspect/slope/curvature/TPI/geomorphons).
snow_modelling_absolute_anaylsisplotupdate_update.py	Main terrain-feature-to-snow-depth correlation and prediction script; trains a linear model on one avalanche outline/year and predicts on another. Driven by config_snow_modelling.yaml.

Configuration

Three YAML files drive the corresponding scripts:

• config_snow_modelling.yaml → snow_modelling_absolute_anaylsisplotupdate_update.py
• config_preprocess.yaml → preprocessing.py
• config_representativeness.yaml → runfile_representiveness.py (conceptually)

Every paths: entry in these files is left blank as a template — fill in your own local data locations before running. config_loader.py and the load_config/validate_paths helpers in preprocessing.py will raise a clear error if a required path is missing or doesn't exist.

Setup

pip install -r requirements.txt

Requires Python 3 with rasterio, rioxarray, xarray, geopandas, shapely, numpy, pandas, scipy, scikit-learn, matplotlib, seaborn, cmcrameri, and pyyaml (see requirements.txt). geopandas in particular can be easier to install via conda than pip.

Each script appends library_dir (its own copy of the path to this repo) to sys.path before import powdersearch as ps — set that variable (or the paths.library_dir config entry) to wherever you've cloned this repo.

Known limitation

runfile_representiveness.py imports run_site_representativity_analysis from a snow_site_analysis module that is not part of this repository — it predates a later refactor into powdersearch.py and was never fully ported. The closest current equivalent is powdersearch.analyze_stations_with_circular_filter, though its parameters don't map one-to-one onto this run-script. Treat runfile_representiveness.py as a reference for the intended parameters rather than a script that runs out of the box.

License

MIT — see LICENSE.