BioDT - pDT Grassland

Main repository for workflows belonging to the grassland Digital Twin.

Installation

The current development version can be installed as:

pip install git+https://github.com/BioDT/uc-grassland.git@main

It requires also installing the following packages:

pip install git+https://github.com/BioDT/general-copernicus-weather-data.git@main
pip install git+https://github.com/BioDT/general-soilgrids-soil-data.git@main

Usage

Download all input data and prepare as needed for grassland model simulations:

from ucgrassland import prep_grassland_model_input_data

# one location only
coordinates_list = [{"lat": 51.123456, "lon": 11.987654}]
first_year = 2010
last_year = 2024
prep_grassland_model_input_data(coordinates_list, first_year, last_year)

# several locations
coordinates_list = [{"lat": 51.123456, "lon": 11.987654}, {"lat": 51.456, "lon": 11.654}, {"lat": 51.789, "lon": 11.321}]
prep_grassland_model_input_data(coordinates_list, first_year, last_year)

# use DEIMS.iD to obtain location (centroid or representative coordinates, valid DEIMS.ID required)
coordinates_list = None
deims_id = '00000000-0000-0000-0000-000000000000'
prep_grassland_model_input_data(coordinates_list, first_year, last_year, deims_id = deims_id)

Full function signature:

prep_grassland_model_input_data(coordinates_list, first_year, last_year, *, deims_id=None, skip_grass_check=False, skip_weather=False, skip_soil=False, skip_management=False)

Parameters:

• coordinates_list (list of dict): List of dictionaries with 'lat' and 'lon' keys, or None for using DEIMS.iD to get coordinates of one location.
• first_year (int): First year of desired time period.
• last_year (int): Last year of desired time period.
• deims_id (str): DEIMS.iD to get coordinates of one location (default is None, only used if coordinates_list is None).
• skip_grass_check (bool): Skip grassland checks (default is False).
• skip_weather (bool): Skip weather data preparation (default is False).
• skip_soil (bool): Skip soil data preparation (default is False).
• skip_management (bool): Skip management data preparation (default is False).

Developers

Developed in the BioDT project (until 2025-05) by Thomas Banitz (UFZ) with contributions by Franziska Taubert (UFZ), Tuomas Rossi (CSC) and Taimur Haider Khan (UFZ).

Further developed (from 2025-06) by Thomas Banitz (UFZ) with contributions by Franziska Taubert (UFZ).

Copyright

Copyright (C) 2024

• Helmholtz Centre for Environmental Research GmbH - UFZ, Germany
• CSC - IT Center for Science Ltd., Finland

Licensed under the EUPL, Version 1.2 or - as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence"). You may not use this work except in compliance with the Licence.

You may obtain a copy of the Licence at: https://joinup.ec.europa.eu/software/page/eupl

Funding

The BioDT project has received funding from the European Union's Horizon Europe Research and Innovation Programme under grant agreement No 101057437 (BioDT project, https://doi.org/10.3030/101057437). The authors acknowledge the EuroHPC Joint Undertaking and CSC - IT Center for Science Ltd., Finland for awarding this project access to the EuroHPC supercomputer LUMI, hosted by CSC - IT Center for Science Ltd., Finland and the LUMI consortium through a EuroHPC Development Access call.

Data sources

Land cover maps and classifications:

• Eunis EEA habitat types (version 2012). https://eunis.eea.europa.eu/habitats-code-browser.jsp.

• European Union's Copernicus Land Monitoring Service (2020). High Resolution Layer (HRL) Grassland 2018 raster, Europe. https://doi.org/10.2909/60639d5b-9164-4135-ae93-fb4132bb6d83.

• European Union's Copernicus Land Monitoring Service (2024). Grassland 2017 - Present (raster 10m), Europe, yearly, Nov. 2024. https://doi.org/10.2909/0b6254bb-4c7d-41d9-8eae-c43b05ab2965.

• European Union's Copernicus Land Monitoring Service (2024). Herbaceous cover 2017 - Present (raster 10m), Europe, yearly, Nov. 2024. https://doi.org/10.2909/9da6ca39-043a-4bdd-8d0a-41a7bed6e439.

• Pflugmacher, D., Rabe, A., Peters, M., Hostert, P. (2018). Pan-European land cover map of 2015 based on Landsat and LUCAS data. PANGAEA, https://doi.org/10.1594/PANGAEA.896282.

• Preidl, S., Lange, M., Doktor, D. (2020). Land cover classification map of Germany's agricultural area based on Sentinel-2A data from 2016. PANGAEA, https://doi.org/10.1594/PANGAEA.910837.

• Schwieder, M., Tetteh, G.O., Blickensdörfer, L., Gocht, A., Erasmi, S. (2024). Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021). Zenodo, https://zenodo.org/records/10640528.

• German ATKIS digital landscape model 2015 Bundesamt für Kartographie und Geodäsie, 2015. Digitales Basis-Landschaftsmodell (AAA-Modellierung). GeoBasis-DE. Geodaten der deutschen Landesvermessung. Derived via land use maps by Lange et al. (2022), https://data.mendeley.com/datasets/m9rrv26dvf/1.

Weather data:

• See https://github.com/BioDT/general-copernicus-weather-data.

Soil data:

• See https://github.com/BioDT/general-soilgrids-soil-data.

Management data:

• European Union's Copernicus Land Monitoring Service (2024). Grassland Mowing Events 2017 - Present (raster 10m), Europe, yearly, Nov. 2024. https://doi.org/10.2909/114e8cae-1cd7-4adc-8c5f-a04863fc6af9.

• European Union's Copernicus Land Monitoring Service (2024). Grassland Mowing Dates 2017 - Present (raster 10m), Europe, yearly – 4 layers, Nov. 2024 https://doi.org/10.2909/660d00f1-c6de-4db6-9979-0be124ceb7f0.

• Lange, M., Feilhauer, H., Kühn, I., Doktor, D. (2022). Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2022.112888. Based on grassland classification according to: German ATKIS digital landscape model 2015.

• Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2021.112795

• Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P. (2021). National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019). https://zenodo.org/records/5153047.

• Filipiak, M., Gabriel, D., Kuka, K. (2022). Simulation-based assessment of the soil organic carbon sequestration in grasslands in relation to management and climate change scenarios. https://doi.org/10.1016/j.heliyon.2023.e17287.

• Schmid, J. (2022). Modeling species-rich ecosystems to understand community dynamics and structures emerging from individual plant interactions. PhD thesis, Chapter 4, Table C.7, https://doi.org/10.48693/160.

Plant species traits data:

• TRY categorical traits table:

  • Kattge, J., Bönisch, G., Günther, A., Wright, I., Zanne, A.E., Wirth, C., Reich, P.B. and the TRY Consortium (2012). TRY - Categorical Traits Dataset. Data from: TRY - a global database of plant traits. TRY File Archive, https://www.try-db.org/TryWeb/Data.php#3.
  • Kattge, J., Díaz, S., Lavorel, S., Prentice, I., Leadley, P., et al. (2011). TRY - a global database of plant traits. Global Change Biology, https://doi.org/10.1111/j.1365-2486.2011.02451.x.
  • Kattge, J., Bönisch, G., Díaz S., et al. (2020). TRY plant trait database - enhanced coverage and open access. Global Change Biology, https://doi.org/10.1111/gcb.14904.

• GBIF taxonomic backbone:

  • GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset.

• Growth form table:

  • Cornwell, W. (2019). traitecoevo/growthform v0.2.3 (v0.2.3). Zenodo. https://doi.org/10.5281/zenodo.2543013
  • Zanne, A.E., Tank, D.C., Cornwell, W.K., Eastman, J.M., Smith, S.A., et al. (2014). Three keys to the radiation of angiosperms into freezing environments. Nature, https://doi.org/10.1038/nature12872.

Reverse geocoding:

• Nominatim (reverse geocoding):
  • URL: https://nominatim.openstreetmap.org/reverse
  • API documentation: https://nominatim.org/release-docs/develop/api/Overview/
  • Usage policy: https://operations.osmfoundation.org/policies/nominatim/
  • Terms of use: https://osmfoundation.org/wiki/Terms_of_Use