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<p>Accurate estimation of crop water consumption and irrigation in the Yellow River Basin (YRB) is vital for water management and food security. Existing forward-logic methods (e.g., crop hydrological modeling method) fail to fully simulate key processes, leading to simulation biases. In contrast, the reverse-logic remote sensing inversion method covers full processes but is plagued by mixed-pixel effects. These limitations lead to a lack of spatially detailed and accurate datasets. Leveraging expanded field experiment data and multi-source high-spatial-resolution yield data, this study achieved the extension of reverse-logic Crop Water Production Functions (CWPFs) from field to basin scale. Integrating CWPFs with the soil water balance principle, we developed the 1-km resolution dataset of water consumption and irrigation for three major grain crops (wheat, maize, soybean) in the YRB (2000–2020). Validations at both the field and prefecture levels confirmed the CWPFs’ reliability and the dataset’s accuracy. Superior in spatial detail and accuracy, this dataset provides a robust basis for optimizing water resource allocation and safeguarding the water-food synergy in the YRB.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月7日

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  • Zheng Wang,
  • Changxiu Cheng,
  • Kaixuan Dai,
  • Zanmei Wei,
  • Bin Li

摘要

Accurate estimation of crop water consumption and irrigation in the Yellow River Basin (YRB) is vital for water management and food security. Existing forward-logic methods (e.g., crop hydrological modeling method) fail to fully simulate key processes, leading to simulation biases. In contrast, the reverse-logic remote sensing inversion method covers full processes but is plagued by mixed-pixel effects. These limitations lead to a lack of spatially detailed and accurate datasets. Leveraging expanded field experiment data and multi-source high-spatial-resolution yield data, this study achieved the extension of reverse-logic Crop Water Production Functions (CWPFs) from field to basin scale. Integrating CWPFs with the soil water balance principle, we developed the 1-km resolution dataset of water consumption and irrigation for three major grain crops (wheat, maize, soybean) in the YRB (2000–2020). Validations at both the field and prefecture levels confirmed the CWPFs’ reliability and the dataset’s accuracy. Superior in spatial detail and accuracy, this dataset provides a robust basis for optimizing water resource allocation and safeguarding the water-food synergy in the YRB.