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<p>Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region, which mounts the need for precise spatial water management. In this study, we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019. Using Logarithmic Mean Divisia Index (LMDI) decomposition and k-means clustering, we quantified how yield, area, water use efficiency, and cropping patterns affect water demand and identified five irrigation development clusters. Key water-saving areas were identified by tracking transitions among clusters, and NSGA-II was applied to optimize crop structure. The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m<sup>3</sup>/year, with wheat accounting for 54.7%. The increase in yield and area increased demand by 15.2 and 5.5 billion m<sup>3</sup>, respectively, which was partly offset by changes in water use efficiency and cropping pattern (−7.0 and −1.8 billion m<sup>3</sup>, respectively). Regions in the upper reaches, particularly within the Lanzhou-Toudaoguai section, were identified as critical for water conservation. Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m<sup>3</sup>, which accounts for 4.9% of the total demand in these areas, with minimal impact on crop production. This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.</p>

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“Bundling regions” based optimization of planting structure for water conservation in the Yellow River Basin

  • 期刊论文

  • 发表日期：

    2026年3月18日

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  • Yilin Shen,
  • Qingtao Ma,
  • Ying Guo,
  • Xiaolu Chen,
  • Mengzhu Liu,
  • Lu Deng,
  • Yiding Zhu,
  • Yanjun Shen

摘要

Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region, which mounts the need for precise spatial water management. In this study, we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019. Using Logarithmic Mean Divisia Index (LMDI) decomposition and k-means clustering, we quantified how yield, area, water use efficiency, and cropping patterns affect water demand and identified five irrigation development clusters. Key water-saving areas were identified by tracking transitions among clusters, and NSGA-II was applied to optimize crop structure. The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year, with wheat accounting for 54.7%. The increase in yield and area increased demand by 15.2 and 5.5 billion m3, respectively, which was partly offset by changes in water use efficiency and cropping pattern (−7.0 and −1.8 billion m3, respectively). Regions in the upper reaches, particularly within the Lanzhou-Toudaoguai section, were identified as critical for water conservation. Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3, which accounts for 4.9% of the total demand in these areas, with minimal impact on crop production. This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.