Assessing Cropland Water Deficit and Productivity-Loss Risk Through the Standardized Crop Water Deficit Index and Copula Analysis in the Huang–Huai–Hai Plain, China
Yuhan Zhao, Chun Dong, Yan Yang
Liaoning Technical University Chinese Academy of Surveying and Mapping
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摘要与影响
The Huang–Huai–Hai Plain supports one of China’s most important grain production systems, but crop production there is persistently constrained by limited water availability and recurrent drought. Common regional drought indicators are useful for monitoring dry conditions, yet they do not explicitly represent crop water demand and irrigation input, which reduces their suitability for agricultural risk assessment. In this study, a crop-oriented framework was developed for winter wheat and summer maize by linking crop water requirement, effective rainfall, irrigation supply, drought-event detection, and productivity-risk estimation. A standardized crop water deficit index (SCWDI) was developed from crop water balance and integrated with run theory, monthly correlation analysis, and a Copula–Bayesian framework to detect drought events, identify crop-sensitive periods, and quantify the probability and triggering threshold of gross primary productivity (GPP) loss. During 2001–2022, the Huang–Huai–Hai Plain experienced an average of 1.15 drought events per year, with pronounced spatial differences. The main sensitive period was June for summer maize and March–April for winter wheat. Summer maize showed a stronger drought response, with a mean triggering threshold of −1.54, whereas winter wheat required more severe stress to trigger concentrated productivity loss (−2.54). Under extreme drought, the probability of summer-maize GPP loss exceeded 80% in both the Beijing–Tianjin–Hebei region and Henan. These results provide a basis for growth-stage-oriented irrigation prioritization and spatially differentiated drought management under agricultural water scarcity.
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物理Hydrology and Drought Analysis
Climate change impacts on agriculture · Plant Water Relations and Carbon Dynamics
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