Skip to content

Full Workflow

This guide walks through the end-to-end workflow for running the Land Availability Analysis (LAVA) tool on a new study region.

Overview of the full workflow

  1. Complete the tool and basic data setup.
  2. Create the study-region configuration files in configs/.
  3. Run spatial_data_prep.py to download and prepare necessary data.
  4. Inspect the pre-processed data (optional but recommended) with data_explore.ipynb.
  5. Run Exclusion.py for each technology to create available-land rasters.

Configuration files

In the configs-folder copy the file config_template.yaml, rename it to config.yaml and fill it out. This is your main configuration file for the data download. Under the headline #--exclusions-- in the configuration the variables scenario and technology are used to control the available land output.

For the exclusion criterias copy the files onshorewind_template.yaml and solar_template.yaml. Rename them to onshorewind.yaml and solar.yaml respectively. Fill these files out in order to set the exclusion parameters.

Prepare spatial data

Run the preprocessing script spatial_data_prep.py after configuring the study region. It clips the raw inputs to the study area, aligns rasters, and computes helper layers such as slope, terrain ruggedness, and proximity rasters. 

python spatial_data_prep.py --region <RegionName>

When landcover_source is openeo the script will prompt for Copernicus Data Space credentials the first time it runs. Outputs are written to data/<RegionName>/ and include:

  • CRS definitions (*_global_CRS.pkl, *_local_CRS.pkl) used downstream.
  • Co-registered rasters for DEM, landcover, wind, solar, terrain ruggedness, and optional layers.
  • derived_from_DEM/ containing slope, aspect, terrain ruggedness, and north-facing masks.
  • OSM_Infrastructure/ geopackages for each enabled infrastructure category.
  • landuses_<RegionName>.json and pixel_size_<RegionName>_<CRS>.json describing the land-cover codes and raster resolution required by the exclusion routines.

Inspect data inputs

Use data_explore.ipynb to verify the preprocessing results. The notebook loads data from the data/<RegionName>/ folder, visualises selected layers, and summarises the available land-cover codes to support tuning of exclusion thresholds.

Land eligibility exclusions

Create technology-specific available-land rasters by running Exclusion.py. The command-line flags mirror the configuration entries so that single technologies or scenarios can be processed independently. 

python Exclusion.py --region <RegionName> --technology onshorewind --scenario ref
python Exclusion.py --region <RegionName> --technology solar --scenario ref

The script loads the prepared rasters and vector layers, applies the filters defined in the technology configuration, and writes *_available_land_*.tif files under data/<RegionName>/available_land/. A log of each scenario run is stored in the region folder for traceability.

Suitability and resource grades

Run suitability.py after both solar and wind exclusions are available. The script aligns resource layers, applies terrain and region modifiers, and exports cost multipliers together with thresholded resource-grade rasters in data/<RegionName>/suitability/. 

python suitability.py --region <RegionName> --scenario ref

Energy profile simulation

Energy profiles combine the available land, suitability grades, and weather cut-outs. Ensure that configs/config.yaml points weather_data_path to the directory that contains the prepared atlite cut-outs. Then run: 

python energy_profiles.py --region <RegionName> --technology onshorewind --scenario ref --weather_year 2019
python energy_profiles.py --region <RegionName> --technology solar --scenario ref --weather_year 2019

Outputs are written to data/<RegionName>/energy_profiles/ and include resource-grade time series as well as diagnostic plots documenting the available area shares.

Batch processing with Snakemake

For large-scale studies the snakemake/Snakefile orchestrates all stages across multiple regions, technologies, and weather years. The workflow creates snakemake_log sentinels to prevent reruns of completed steps. Launch it (after customising the region lists at the top of the Snakefile) with: 

snakemake --cores 4 --resources openeo_req=1

Use --snakefile snakemake/Snakefile_short or Snakefile_short_short for alternative presets. The openeo_req resource serialises ESA WorldCover downloads to avoid rate limits while still parallelising the remaining steps.