Evaluating Agricultural Drought in the Haihe River Basin Using an Improved Crop Moisture Index

In large irrigated agricultural regions under intensive human management, irrigation profoundly influences agricultural drought dynamics. High-frequency irrigation markedly alters natural farmland soil moisture, causing traditional drought indices to distort the actual severity of human-modified agricultural drought and leading to substantial monitoring deviations. In this work, an improved agricultural drought index based on the Crop Moisture Index (CMI) was developed to accurately characterize drought conditions, using the Haihe River Basin as a case study. The CMI’s water balance equation was revised by incorporating an auto-irrigation threshold method with crop coefficients and water stress coefficients. Furthermore, the improved CMI explicitly models irrigation by defining auto-irrigation thresholds based on the critical growth stages of the main crops. The performance of the original and improved CMI was evaluated by comparing their simulated soil moisture and drought detection accuracy against benchmark data derived from measurements across yearly, monthly, and weekly scales. The spatial evolution of a major 2002 drought in North China was also reconstructed to assess the indices’ performance. The results showed that: (1) The revised soil water balance equation achieved significantly lower relative errors than the original equation across all time scales; (2) The improved CMI consistently demonstrated higher drought identification accuracy rates than the original CMI; (3) Drought patterns monitored by the improved CMI showed superior alignment with actual conditions, confirming that it has enhanced applicability for agricultural drought assessment and demonstrates clear advantages for drought assessment in large-scale irrigated agriculture. This work provides crucial insights into the driving mechanisms of agricultural drought under intense human interventions and offers valuable guidance for drought risk management in major agricultural zones.

• Publication year

  2025

• Venue

  Water

• Publication date

  2025-11-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3390/w17233372
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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