# A  General Information About the dataset

# 1. Dataset Title: Winter wheat simulation outputs across Europe (1991–2020)
#    Manuscript Title: Breeding changes water use of winter wheat across Europe

# 2. Brief description of the dataset:
#    This dataset contains spatially explicit, process-based simulation outputs of winter wheat growth 
#    and water use across Europe. Simulations were conducted over a 30-year historical climate period and 
#    aggregated to seasonal values from sowing to harvest. The dataset includes simulations for 2 winter wheat 
#    cultivars (Tommi and S. Dickkopf). As defined in the associated manuscript, Tommi represents a modern 
#    cultivar (released 2002), S. Dickkopf represents a historic cultivar (released 1895). The dataset was 
#    generated to support analyses of cultivar-specific differences in transpiration and related variables 
#    under contrasting climatic conditions.

# 3. Author Information
#    Investigator Contact Information
#    Name: Dominik Behrend
#    Institution: University of Bonn
#    Address: Katzenburgweg 5
#    Email: dbehrend@uni-bonn.de

#    Principal Investigator Contact Information
#    Name: Thomas Gaiser
#    Institution: University of Bonn
#    Address: Katzenburgweg 5
#    Email: tgaiser@uni-bonn.de

# 4. This file was generated on 2026-01-09 by Dominik Behrend

# 5. Funding
#    Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 
#    SFB 1502/1–2022 - Projektnummer: 450058266.

# 6. Language of the dataset: English


# 7. Spatial and temporal coverage
#    - Spatial extent: Europe (-11.00348, 30.25406, 35.99601, 67.71814)
#    - Spatial reference: WGS84 (longitude, latitude)
#    - Temporal coverage: 1991–2020
#    - Temporal resolution: seasonal (sowing to harvest)



# B  Data structure and file overview

# 1. File list:
#    The main dataset is provided in two CSV files compressed in .gz Format:
#    - One file for the historic cultivar S. Dickkopf:
#      winter_wheat_simulation_outputs_europe_dickkopf_1991-2020_v1.0.csv
#    - One file for the modern cultivar Tommi:
#      winter_wheat_simulation_outputs_europe_tommi_1991-2020_v1.0.csv
#    - A file containing Information About the variables and their Units,
#      including variables and Units of all data source files:
#      variables.xlsx
#    
#    Besides the main dataset, additional model Input data is provided in the
#    Folder /data/, that was used for the model simulations and the visualizations:
#    - Seasonal (from sowing to harvest) mean weather variables from Era5 reanalysis:
#      /data/winter_wheat_simulation_inputs_europe_seasonal_weather_1990-2020_v1.0.csv
#    - Yearly mean weather variables from Era5 reanalysis:
#      /data/winter_wheat_simulation_inputs_europe_yearly_weather_1990-2020_v1.0.csv
#    - Soil profile mean variables from the SoilGrids2.0 1000 m dataset:
#      /data/winter_wheat_simulation_inputs_europe_soil_profile_mean_v1.0.csv
#    - Winter wheat crop mask of the main European winter wheat growing Areas:
#      /data/wheat_cropmask_europe_v1.0.tif      
#    
#    To create the visualizations, R Scripts were used and are provided in the
#    Folder /Scripts/:
#      /scripts/R_script_visualizations_figure_4_7_S2_S4.R
#      /scripts/R_script_visualizations_figure_5_S11
#      /scripts/R_script_visualizations_figure_6_S3
#      /scripts/R_script_visualizations_figure_8_S5
#      /scripts/R_script_visualizations_figure_S2_S7_S8

# 2. Are there multiple versions of the dataset?: No

# 3. Relationship between files:
#    Simulation outputs are both generated using the same crop growth model but with
#    different parameter sets.
#    Simulation inputs are simplified versions of the model input files (lower temporal resolution).  

# 4. Additional related data:
#    - Crop model calibration data:
#      Data used for crop model calibration was previosly published (https://doi.org/10.22541/essoar.175822260.03180911/v1)
#      This data used for calibration is currently prepared to be published as a data paper.
#    - Word shapefile:
#      Required for the visualizations of the data is a world shapefile, including country level borders:
#      Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.
#    - CO2 data:
#      For the visualizations and as model input, changing CO2 data was used, which was obtained from the Mauna Loa
#      Lan, X., P. Tans, and K.W. Thoning. 2025. ‘Trends in Globally-Averaged CO2 Determined from NOAA Global 
#      Monitoring Laboratory Measurements’. January. https://doi.org/10.15138/9N0H-ZH07
#      IMPORTANT: The data is not included here, due to unclear licensing. 
#    - Model Input Data:
#      As described in the main manuscript, model Input data consisted of hourly weather variables accross
#      Europe, obtained from the Era5 reanalysis dataset.
#      Hersbach, H., B. Bell, P. Berrisford, et al. 2023. ‘ERA5 Hourly Data on Single Levels from 1940 to Present.’ 
#      Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
#      https://doi.org/10.24381/cds.adbb2d47
#      The data was transformed to a 3x3 km grid Resolution and to the required SIMPLACE Input Format.
#      This resulted in several Terra Bytes of data, which is why only the seasonal and yearly
#      Aggregation is included here. The processing of the data is described in Detail in the manuscript.
#    - Additional soil Input data to the model was obtained from the SoilGrids2.0 Database:
#      Poggio, Laura, Luis M. de Sousa, Niels H. Batjes, et al. 2021. ‘SoilGrids 2.0: Producing Soil 
#      Information for the Globe with Quantified Spatial Uncertainty’. SOIL 7 (1): 217–40.
#      https://doi.org/10.5194/soil-7-217-2021
#    - For visualizations, the Köppen-Geiger classification was used, derived from Beck et al. 2018:
#      Beck, Hylke E., Niklaus E. Zimmermann, Tim R. McVicar, Noemi Vergopolan, Alexis Berg, and Eric F. Wood. 2018. 
#      ‘Present and Future Köppen-Geiger Climate Classification Maps at 1-Km Resolution’. Scientific Data 5 (1): 180214
#      https://doi.org/10.1038/sdata.2018.214.


# C Sharing/Access Information

# 1. Was data derived from another source?
#    - Weather Input data was derived from the Era5 reanalysis dataset: https://doi.org/10.24381/cds.adbb2d47
#      This includes the following files:
#      /data/winter_wheat_simulation_inputs_europe_seasonal_weather_1990-2020_v1.0.csv
#      /data/winter_wheat_simulation_inputs_europe_yearly_weather_1990-2020_v1.0.csv
#    - Soil Input data was derived from the SoilGrids2.0 1000 m dataset.
#      https://doi.org/10.5194/soil-7-217-2021
#      This includes the following file:
#      data/winter_wheat_simulation_inputs_europe_soilprofile_mean_1990-2020_v1.0.csv.gz
#    - The crop map was derived from:
#      Baumert, J., Heckelei, T., & Storm, H. (2025). A dataset of yearly probabilistic 
#      crop type maps for the EU from 1990 to 2018. Data in Brief, 60, 111472
#      https://doi.org/10.1016/j.dib.2025.111472. 
#      This includes the following file:
#      /data/wheat_cropmask_europe_v1.0.tif

# 2. Licenses/restrictions placed on the data: CC-BY licence

# 3. Links to publications that cite or use the data:
#    - As soon as the publication is accepted, I will try to include the Information here.


# D Methodological Information

# 1. Description of methods used for generation of data:
#    - Data was generated using the crop growth model:
#      SIMPLACE< HILLFLOW -1D-Couvreur RWU – SlimRoot - LintulCC2>.
#      Model documentation: 
#      https://simplace.net/doc/5.0/simplace_modules/net/simplace/sim/components/experimental/lintulcc/HillFlow1DLintulCCDiurnal.html
#      The model is part of the SIMPLACE Framework:
#      https://doi.org/10.1093/insilicoplants/diad006 
#    - A detailed description including Parameters used or Simulation is available in Tables S2-S6 in the supplementaries
#    - All Parameters including the complete model solution are available on the SIMPLACE SVN server


# 2. Methods for processing the data: 
#    Data processing was performed using the scripts in the /scripts/ Folder.
#    Scripts include Detail Information of the required R packages, including their 
#    Version and the R Version used for visualizations and post processing.

# 3. Instrument- and/or software-specific information needed to interpret the data: 
#    Scripts require different types of packages. All scripts include detailed Information About their required packages.
#    Packages used: sf (version 1.0-21), tidyverse (version 2.0.0), terra (version 1.8-70), data.table (version 1.17.8), 
#    lubridate (version 1.9.4), lme4 (version 1.1-37), viridis (0.6.5)
#    R version 4.5.1 was used for the original visualizations.

# 4. People involved in sample collection, processing, analysis and/or submission: 
#    Dominik Behrend: dbehrend@uni-bonn.de
#    Thuy Huu Nguyen: tngu@uni-bonn.de
#    Juan C. Baca Cabrera: j.baca.cabrera@fz-juelich.de
#    Josef Baumert: josef.baumert@ilr.uni-bonn.de
#    Clara Oliva Gonçalves Bazzo: clarabazzo@uni-bonn.de
#    Dylan H. Jones: jones@ipk-gatersleben.de
#    Guillaume Lobet: guillaume.lobet@uclouvain.be
#    Sabine J. Seidel: sabine.seidel@boku.ac.at
#    Amit Kumar Srivastava: amitkumar.srivastava@zalf.de
#    Hugo Storm: hugo.storm@ilr.uni-bonn.de
#    Jan Vanderborght: j.vanderborght@fz-juelich.de
#    Frank Ewert: frank.ewert@zalf.de
#    Thomas Gaiser: tgaiser@uni-bonn.de


# E Data specific information

# 1. Variable list including full names and definitions of column headings and Units of the two main datasets
#    can be found in the file: variables.xlsx