Yang et al. (2025) Evaluating Agricultural Drought in the Haihe River Basin Using an Improved Crop Moisture Index

Identification

• Journal: Water
• Year: 2025
• Date: 2025-11-26
• Authors: Mingzhi Yang, Xinyang Li, Jijun Xu, Huan Jing, Xinyi Zhang, Sang Lian-hai
• DOI: 10.3390/w17233372

Research Groups

• Hubei Key Laboratory of Water Resources & Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan, China
• Research Center on the Yangtze River Economic Belt Protection and Development Strategy, Wuhan, China
• School of Civil Engineering, Tianjin University, Tianjin, China
• College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China

Short Summary

This study developed an improved Crop Moisture Index (CMI) by incorporating crop coefficients, water stress coefficients, and an auto-irrigation threshold method into its soil water balance equation to more accurately assess agricultural drought in large irrigated regions. Applied to the Haihe River Basin, the improved CMI significantly enhanced soil moisture simulation accuracy and drought identification compared to the original CMI, providing a more realistic representation of drought under intensive human management.

Objective

• To develop an improved agricultural drought index based on the Crop Moisture Index (CMI) that accurately characterizes drought conditions in large irrigated agricultural regions by explicitly modeling irrigation and crop-specific water requirements.
• To evaluate the performance of the improved CMI against the original CMI and observed data in the Haihe River Basin across yearly, monthly, and weekly scales, and reconstruct the spatial evolution of a major drought.

Study Configuration

• Spatial Scale: Haihe River Basin, Northern China (approximately 320,600 km²), encompassing Beijing, Tianjin, Hebei, Henan, Shandong, and Shanxi. Analysis performed at 32-47 meteorological stations.
• Temporal Scale: Daily meteorological data from 1985 to 2012; soil moisture data from 1992 to 2013. Analysis conducted on weekly, monthly, and annual scales.

Methodology and Data

• Models used:
  • Original Crop Moisture Index (CMI) based on Palmer's method.
  • Improved CMI:
    • Penman–Monteith equation (FAO-56) for reference evapotranspiration (ET0).
    • Introduction of crop coefficient (Kc) and water stress coefficient (Ks) for actual evapotranspiration (ET).
    • Modified soil water balance equation incorporating an auto-irrigation threshold method based on critical crop growth stages.
  • Relative Soil Moisture (RSM) index for drought grade classification (reference).
  • Kriging interpolation for spatial analysis.
  • Student’s t-test and Mann–Whitney U test for statistical significance.
• Data sources:
  • Daily time series (1985–2012) of precipitation, relative humidity, wind speed, sunshine duration, minimum and maximum air temperature from 47 meteorological stations (China Meteorological Data Service Centre).
  • Soil data (soil layer depth, saturated water content, field capacity, wilting point) from the China Soil Science Database.
  • Crop parameters (main crop types, growth-stage-specific coefficients) from relevant literature.
  • Irrigation data (crop type, frequency, water volume) from Yang et al. [23].
  • Soil moisture data (top 1 m depth, 1992–2013) from Wang et al. [24], with 32 stations selected for analysis.

Main Results

• The improved soil water balance equation achieved significantly lower multi-year average relative errors in simulated soil moisture (5.1%) compared to the original equation (26.2%).
• Monthly average relative errors for the improved equation were consistently below 20%, while the original equation often exceeded 20%.
• Weekly soil moisture simulations from the improved equation showed closer alignment with observed patterns, avoiding the rapid decline to wilting point seen with the original equation.
• The improved CMI demonstrated higher annual drought identification accuracy (61.9%) compared to the original CMI (52.2%), an increase of 9.7 percentage points.
• Monthly drought identification accuracy rates for the improved CMI were above 60% from July to October, outperforming the original CMI which only achieved this in August and September.
• Spatial analysis of the 2002 North China drought revealed that the improved CMI provided a more accurate representation of drought extent and severity, while the original CMI significantly overestimated both.

Contributions

• Developed an improved Crop Moisture Index (CMI) that explicitly accounts for the impact of irrigation and crop-specific water requirements, addressing a critical limitation of traditional drought indices in intensively managed agricultural regions.
• Enhanced the CMI's soil water balance equation by integrating the Penman–Monteith method for evapotranspiration, crop and water stress coefficients, and an auto-irrigation threshold method.
• Demonstrated the superior performance of the improved CMI in simulating soil moisture dynamics and identifying agricultural drought conditions across various temporal and spatial scales in the Haihe River Basin.
• Provided crucial insights into agricultural drought mechanisms under intense human interventions and offered valuable guidance for drought risk management in major irrigated agricultural zones.

Funding

• National Key Research and Development Program of China (Grant No. 2022YFC3202300)
• National Natural Science Foundation of Hubei Province (No. 2024AFB012)
• Natural Science Foundation of Hubei Province (No. 2022CFD037)
• National Public Research Institutes for Basic R&D Operating Expenses Special Project (grant numbers CKSF20241018/SZ)

Citation

@article{Yang2025Evaluating,
  author = {Yang, Mingzhi and Li, Xinyang and Xu, Jijun and Jing, Huan and Zhang, Xinyi and Lian-hai, Sang},
  title = {Evaluating Agricultural Drought in the Haihe River Basin Using an Improved Crop Moisture Index},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17233372},
  url = {https://doi.org/10.3390/w17233372}
}