Snow Integration: Sledding Location Analysis

Combine elevation data with SNODAS snow statistics to identify optimal sledding locations.

Sledding Scores

Overview

This example demonstrates:

Loading SNODAS snow depth/coverage data
Multi-layer terrain analysis
Score-based visualization
Pipeline with visual outputs at each stage

Quick Start

# With mock data (fast testing)
python examples/detroit_snow_sledding.py --mock-data

# With real SNODAS data
python examples/detroit_snow_sledding.py

Processing Pipeline

The sledding score pipeline produces visual outputs at each stage, making it easy to debug and understand the analysis.

Step 1: Raw Input Data

The pipeline starts with elevation (DEM) and snow depth data.

DEM Elevation	Snow Depth

Step 2: Slope Statistics

High-resolution slope analysis computes various terrain metrics.

Mean Slope	Max Slope	Roughness

Step 3: Slope Penalties

Penalty factors are computed for hazardous terrain.

Cliff Penalty	Terrain Consistency	Combined Penalty

Step 4: Score Components

Individual score components before combination.

Snow Score	Slope Score	Coverage Score

Step 5: Final Score

The final sledding score combines all components.

Final Sledding Score

The Code

from src.terrain.core import Terrain
from src.terrain.scoring import compute_sledding_score
from src.terrain.data_loading import load_snodas_data
from src.terrain.color_mapping import elevation_colormap

# 1. Load terrain and snow data
terrain = Terrain(dem_data, transform)

# 2. Load and process SNODAS snow data
snow_depth, snow_coverage = load_snodas_data(snodas_dir)

# 3. Add snow as data layer (auto-reprojects to match DEM)
terrain.add_data_layer(
    "snow_depth",
    snow_depth,
    snow_transform,
    "EPSG:4326",
    target_layer="dem"
)

# 4. Calculate sledding score
score = compute_sledding_score(
    terrain,
    depth_weight=0.4,
    coverage_weight=0.3,
    slope_weight=0.3
)

# 5. Visualize with score-based coloring
terrain.set_color_mapping(
    lambda s: elevation_colormap(s, cmap_name='plasma'),
    source_layers=['sledding_score']
)

Key Functions Used

Function	Purpose
`add_data_layer()`	Add georeferenced data with auto-reprojection
`compute_sledding_score()`	Compute sledding suitability
`load_snodas_data()`	Load SNODAS snow grids
`smooth_score_data()`	Reduce blockiness in low-res data

Score Components

The sledding score combines:

Snow Depth (40%) - Deeper snow = better sledding
Coverage (30%) - Consistent coverage preferred
Slope (30%) - Sweet spot: 5-15° gradient

# Custom weights
score = compute_sledding_score(
    terrain,
    depth_weight=0.5,    # Prioritize deep snow
    coverage_weight=0.2,
    slope_weight=0.3
)

Output Files

File	Description
`01_raw/dem.png`	Input elevation data
`01_raw/snow_depth.png`	SNODAS snow depth
`02_slope_stats/`	Slope analysis panels
`03_slope_penalties/`	Penalty visualizations
`05_final/sledding_score.png`	Final score map