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cd tests/integration/ # navigate to the integration example
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snakemake --use-conda --cores 2 # run the workflow!
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```
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## Documentation
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### Overview
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<!-- Please describe the processing stages of this module here -->
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The analysis of the module is structured as follows:
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<divstyle="width:50%; margin: auto;">
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</div>
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1. The CO2Stop dataset is downloaded and cleaned up following methods described in the [JRC - CO2 transport report](https://publications.jrc.ec.europa.eu/repository/handle/JRC136709).
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2. To avoid double-counting, traps within the remaining storage units are removed as their capacity is already included in the storage unit total (please consult the [CO2Stop Final report](https://energy.ec.europa.eu/publications/assessment-co2-storage-potential-europe-co2stop_en) section 2.3.1 for details).
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Additionally, the following removal criteria is applied to further clean the data:
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- Cases marked as 'not assessed' or as 'undisclosed' in the dataset.
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- Ambiguous duplicates (these are a few small traps located in the north sea with repeated IDs and capacities).
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- Optionally, details from the dataset are used to remove the following, if configured:
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- Qualitative cases marked as having surface or subsurface issues, and artificially created polygons.
3. Three scenarios (`low`, `medium`, `high`) are created for each sequestration type (`aquifer`, `gas`, `oil`) for the remaining CO2Stop data.
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User-configured lower and upper bounds are applied per-polygon at this stage.
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See `bounds_mtco2: co2stop_polygons` in the configuration schema for more information.
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<divstyle="width:50%; margin: auto;">
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</div>
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4. The resulting sequestration potential is aggregated per scenario into user provided shapes.
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>[!WARNING]
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>Estimates from the CO2Stop dataset are biased by disclosure (or lack thereof), and the filtering settings used.
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>Some countries are affected more than others, with Germany having particularly poor disclosure.
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>
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>Similarly, CO2Stop suffers from poor data handling practices that make unavailable data and 'true' zero values indistinguishable from each other, amplifying the uneven assignation of sequestration. For example: setting `porosity_ratio: 0.1` will completely remove France in most cases.
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>
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>We provide automated figures and logging (in `logs/storage_units/` and `logs/traps/`) so users can evaluate how their settings affect polygon selection.
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>Below is an example for storage unit aquifers where only undisclosed and artificial polygons have been removed. This can be seen as a _MINIMUM_ amount of removals.
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><divstyle="width:50%; margin: auto;">
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>
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>
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></div>
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### Configuration
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<!-- Feel free to describe how to configure this module below -->
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Please consult the configuration [README](./config/README.md) and the [configuration example](./config/config.yaml) for a general overview on the configuration options of this module.
As input, all you need to provide is a `.parquet` shapes file with the polygons to aggregate capacities into. This file should follow the schema provided by the [geo-boundaries module](https://github.com/calliope-project/module_geo_boundaries/tree/v0.1.6).
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Outputs for each input shape file can be requested per potential scenario (low, medium, high), and CDR group (aquifer, gas, oil, and total aggregate sum).
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Please consult the [interface file](./INTERFACE.yaml) for more information.
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### References
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<!-- Please provide thorough referencing below -->
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This module is based on the following research and datasets:
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-**CO2Stop dataset**
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Poulsen, N., Holloway, S., Neele, F., Smith, N.A., Kirk, K., 2012. CO2StoP Executive Summary (No. ENER/C1/154-2011-SI2.611598). GEOLOGICAL SURVEY OF DENMARK AND GREENLAND. <https://energy.ec.europa.eu/publications/assessment-co2-storage-potential-europe-co2stop_en>.
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-**Shape schema definition:**
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Ruiz Manuel, I. clio - module_geo_boundaries [Computer software]. <https://github.com/calliope-project/module_geo_boundaries/>.
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-**Filtering minimum defaults:**
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Van Den Broek, M., Brederode, E., Ramírez, A., Kramers, L., Van Der Kuip, M., Wildenborg, T., Turkenburg, W., Faaij, A., 2010. Designing a cost-effective CO2 storage infrastructure using a GIS based linear optimization energy model. Environmental Modelling & Software 25, 1754–1768. <https://doi.org/10.1016/j.envsoft.2010.06.015>.
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