Wang et al. (2026) A 1-km Dataset of Water Consumption and Irrigation for Major Grain Crops in the Yellow River Basin Based on the Crop Water Production Function

Identification

• Journal: Scientific Data
• Year: 2026
• Date: 2026-04-07
• Authors: Zheng Wang, Changxiu Cheng, Kaixuan Dai, Zanmei Wei, Bin Li
• DOI: 10.1038/s41597-026-07194-3

Research Groups

• State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, P. R. China
• Key Laboratory of Environmental Change and Natural Disaster, Faculty of Geographical Science, Beijing Normal University, Beijing, P. R. China

Short Summary

This study developed a 1-kilometer resolution dataset of water consumption and irrigation for wheat, maize, and soybean in the Yellow River Basin (2000-2020) by extending reverse-logic Crop Water Production Functions (CWPFs) from field to basin scale and integrating them with the soil water balance principle. The dataset provides spatially detailed and accurate estimates crucial for water management and food security.

Objective

• To develop a spatially detailed (1-kilometer resolution) and accurate dataset of water consumption and irrigation for major grain crops (wheat, maize, and soybean) in the Yellow River Basin from 2000 to 2020, to support water resource allocation optimization and food security.

Study Configuration

• Spatial Scale: Yellow River Basin (YRB), covering 66 prefecture-level cities, at a 1-kilometer spatial resolution.
• Temporal Scale: 2000 to 2020, at five-year intervals (e.g., 2005 represents the average from 2003 to 2007).

Methodology and Data

• Models used:
  • Crop Water Production Functions (CWPFs) (primarily CWPF1, with CWPF3 as compensatory)
  • Soil Water Balance equation
  • Spatial Production Allocation Model (SPAM) (for crop harvest area maps)
  • Bilinear interpolation method (for resampling)
  • Mann-Kendall test and Sen’s slope method (for trend analysis)
  • Soil conservation method of the United States Department of Agriculture (for effective precipitation)
• Data sources:
  • Field experiment data (over 1200 records for wheat, maize, soybean from YRB and surrounding areas, collected via literature review, 2000-2023).
  • Crop yield distribution dataset (10-kilometer resolution from MapSPAM, 2000, 2005, 2010, 2020).
  • Crop harvest area maps (1-kilometer resolution from Science Data Bank, 1990-2020 at five-year intervals).
  • Statistical yield data (prefecture-level cities from national, provincial, and municipal statistical yearbooks, 1998-2022).
  • Precipitation data (daily, 1-kilometer resolution from ChinaMet dataset, 1998-2022).
  • Existing datasets for comparison: crop water consumption (crop coefficient method, ACEA model), irrigation (machine learning, WATNEEDS model).

Main Results

• A 1-kilometer resolution dataset of water consumption and irrigation for wheat, maize, and soybean in the Yellow River Basin from 2000 to 2020 at five-year intervals was generated.
• Optimal CWPFs demonstrated "good" to "excellent" temporal and spatial generalization capabilities, with R² values generally above 0.72 for validation.
• Spatially, wheat and maize water consumption is higher in the northwest and east (e.g., central Gansu, Ningxia, Inner Mongolia, Henan, Shandong) and lower in the central YRB. Soybean water consumption is higher in the southern YRB.
• Temporally (2000-2020), crop water consumption for wheat, maize, and soybean generally showed increasing or no significant trends, with wheat exhibiting a more pronounced increase.
• Irrigation for wheat is generally higher than for maize and soybean. Maize irrigation decreases from northwest to southeast, while soybean irrigation is lower in the central YRB.
• The developed irrigation dataset showed high accuracy (R² = 0.7727, RMSE = 0.1380 km³ per year) when validated against prefecture-level city statistics, outperforming existing datasets.
• All data in the dataset include upper and lower bounds of the 90% confidence interval to quantify uncertainty.

Contributions

• First study to successfully extend reverse-logic Crop Water Production Functions (CWPFs) from field to basin scale for estimating crop water consumption and irrigation.
• Developed a novel 1-kilometer resolution dataset of water consumption and irrigation for major grain crops (wheat, maize, soybean) in the Yellow River Basin, offering superior spatial detail and accuracy compared to existing datasets.
• Utilizes a reverse-logic estimation method, which more accurately reflects actual crop water conditions and covers full processes, overcoming limitations of forward-logic models and mixed-pixel effects in remote sensing.
• Integrates multi-source, high-spatial-resolution yield data and extensive field experiment data, calibrated with prefecture-level statistics, to enhance accuracy and spatial detail.
• The dataset dynamically adjusts to actual planting distributions, providing more realistic estimates for water resource management and agricultural decision-making.
• Provides 90% confidence intervals for all data, a crucial feature for uncertainty assessment in agricultural water resource simulation, which is often lacking in other datasets.

Funding

• National Natural Science Foundation of China (Grant No. 42041007).

Citation

@article{Wang20261km,
  author = {Wang, Zheng and Cheng, Changxiu and Dai, Kaixuan and Wei, Zanmei and Li, Bin},
  title = {A 1-km Dataset of Water Consumption and Irrigation for Major Grain Crops in the Yellow River Basin Based on the Crop Water Production Function},
  journal = {Scientific Data},
  year = {2026},
  doi = {10.1038/s41597-026-07194-3},
  url = {https://doi.org/10.1038/s41597-026-07194-3}
}