Dataset Information

The green and blue crop water requirement WATNEEDS model and its global gridded outputs.

ABSTRACT: Accurately assessing green and blue water requirements from croplands is fundamental to promote sustainable water management. In the last decade, global hydrological models have provided important insights into global patterns of water requirements for crop production. As important as these models are, they do not provide monthly crop-specific and year-specific data of green and blue water requirements. Gridded crop-specific products are therefore needed to better understand the spatial and temporal evolution of water demand. Here, we present a global gridded database of monthly crop-specific green (rain-fed) and blue (irrigated) water requirements for 23 main crops and 3 crop groups obtained using our WATNEEDS model. For the time periods in which our dataset matched, these estimates are validated against existing global products and satellite based datasets of evapotranspiration. The data are publicly available and can be used by practitioners in the water-energy-food nexus to assess the water sustainability of our food and energy systems at multiple spatial (local to global) and temporal (seasonal to multi-year) scales.

SUBMITTER: Chiarelli DD

PROVIDER: S-EPMC7434899 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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The green and blue crop water requirement WATNEEDS model and its global gridded outputs.

Chiarelli Davide Danilo DD Passera Corrado C Rosa Lorenzo L Davis Kyle Frankel KF D'Odorico Paolo P Rulli Maria Cristina MC

Scientific data 20200818 1

Accurately assessing green and blue water requirements from croplands is fundamental to promote sustainable water management. In the last decade, global hydrological models have provided important insights into global patterns of water requirements for crop production. As important as these models are, they do not provide monthly crop-specific and year-specific data of green and blue water requirements. Gridded crop-specific products are therefore needed to better understand the spatial and temp ...[more]

PMID: 32811838

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Project description:Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Dataset Information

The green and blue crop water requirement WATNEEDS model and its global gridded outputs.

Publications

The green and blue crop water requirement WATNEEDS model and its global gridded outputs.

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