DOI: 10.5281/zenodo.15779747
title: Data and software for Chukalla et al: Blue and green water consumption of global 2020 SPAM crop production in high spatial resolution

description: This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.

Modelling water consumption of SPAM data

We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).

We develop a Python-based global gridded crop green and blue WC assessment tool, entitled CropGBWater. Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access in the related Nature Food paper.

SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)File description:

The data-set consists of the following files:



Data_S4: Data_S4_Y2020_WC_m3_gridded.zipFolder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17

Data_S5: Data_S5_YR2020_WC_mm_gridded_csvFolder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18

Data_S6: Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsxOne grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020. 

Data_S7: Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csvOne grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020

Data_S8: Data_S8_YR2000_WC_mm_m3_gridded.zipGrid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000

Data_S9: Data_S9_YR2010_WC_mm_m3_gridded.zipGrid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010

Data_S11: Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xlsGrid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar

Data_S12: Data_S12_INPUT_YR2020_cropcalendars.zipModelling INPUT data for year 2020: Crop calendars

Data_S13: Data_S13_INPUT_YR2020_ET0.zipModelling INPUT data for year 2020: daily ET0

Data_S14: Data_S14_INPUT_YR2020_Precip.zipModelling INPUT data for year 2020: daily Precipitation

Data_S15: Data_S15_INPUT_YR2020_Soil.zipModelling INPUT data for year 2020: Soil

Data_S16: Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zipModelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts

Data_S17: Data_S17_Y2020_WC_m3_GisRastersGIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn

Data_S18: Data_S18_Y2020_WC_mm_GisRastersGIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn


 

Publication:

For all details, please refer to the related open access paper Chukalla et al (2025) in Nature Food

Funding:

This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (www.cgiar.org/initiative/foresight/) as well as the CGIAR “Policy innovations” Science Program (www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (www.cgiar.org/funders).