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中国农学通报

中国农学通报 ›› 2026, Vol. 42 ›› Issue (9): 97-106.doi: 10.11924/j.issn.1000-6850.casb2025-0224

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

基于AquaCrop作物模型的茶叶产量预报研究

马德栗1(), 鞠英芹2,3(), 陈城4   

  1. 1 潜江市气象局， 湖北潜江 433199
    2 湖北省气象工程技术中心， 武汉 430074
    3 湖北丹江口人工影响天气野外科学观测研究站， 武汉 430074
    4 湖北省气象学会， 武汉 430074
  • 收稿日期:2025-03-20 修回日期:2026-02-12 出版日期:2026-05-15 发布日期:2026-05-15
  • 通讯作者:
    鞠英芹，女，1985年出生，山东威海人，高级工程师，硕士研究生，主要从事应用气象学方面的研究。通信地址：430074 武汉市洪山区东湖东路3号 湖北省气象工程技术中心，Tel：027-67848418，E-mail：。
  • 作者简介:

    马德栗，男，1985年出生，河南南阳人，高级工程师，硕士研究生，主要从事气候变化方面的研究。通信地址：43319湖北省潜江市红梅东路9号 潜江市气象局，Tel：0728-6246616，E-mail：。

  • 基金资助:
    中国气象局气象干部培训学院科研项目“基于作物模型的茶叶开采期及产量预报关键技术研究”(2023CMATCZDIAN14); 中国气象局/农业农村部烤烟气象服务中心开放式研究基金项目“基于高分辨率遥感影像的烤烟长势监测技术研究”(KYZX2025-05); 中国气象局气象干部培训学院科研项目“基于卫星资料的人影效益评估技术研究”(2025CMATCQN20)

Research on Tea Yield Prediction Based on AquaCrop Model

MA Deli1(), JU Yingqin2,3(), CHEN Cheng4   

  1. 1 Qianjiang Meteorological Bureau, Qianjiang, Hubei 433199
    2 Hubei Meteorological Engineering Technology Center, Wuhan 430074
    3 Danjiangkou Weather Modification Hubei Field Scientific Observation and Research Station, Wuhan 430074
    4 Hubei Meteorological Society, Wuhan 430074
  • Received:2025-03-20 Revised:2026-02-12 Published:2026-05-15 Online:2026-05-15

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摘要：

本研究旨在提升AquaCrop作物模型对湖北英山茶叶产量的预测能力，助力当地乡村振兴。利用2013—2022年英山县茶叶产量和同期气温、降水、日照等气象数据，通过敏感性分析等方法确定模型所需的茶树生长参数，对AquaCrop模型参数进行本地化校正和优化，建立了英山县茶叶产量预报模型，并基于SSP2-4.5和SSP5-8.5等两种未来气候情景，预测了茶叶产量。结果显示：（1）OTA敏感度分析表明，成熟时间、标准化水分生产力、茶树的基底温度、作物系数、初始冠层覆盖度和最大冠层覆盖时间是对茶叶产量影响较大的参数。（2）优化后的英山县茶叶AquaCrop模型参数组合，其均方根误差、符合度和残差聚类集分别为0.15、0.68和0.01，表明模型拟合良好。（3）AquaCrop模型较好地模拟了2013年以来英山县茶叶产量呈上升趋势，每年增加0.02 t/hm2，与实际产量趋势0.03 t/hm2基本一致。（4）基于SSP2-4.5和SSP5-8.5两种未来情景模式，因茶叶冬季冻害、春霜冻等灾害大幅度降低，有效积温增加，模拟未来产量较2013—2022年平均产量分别增加13.7%和38.9%。研究表明，经过参数本地化后的AquaCrop模型能较好地模拟英山县茶叶产量，适用于基于生育期气象条件的茶叶产量预测。

关键词: AquaCrop, OTA, 作物模型, 敏感性分析, 参数率定, 茶叶产量预报, 未来气候情景模式

Abstract:

As an important economic crop in Yingshan County, tea is a pillar industry for local rural revitalization. This study aims to determine the localization parameters of the AquaCrop crop model and improve its ability to predict tea yield. Using the tea yields in Yingshan County from 2013 to 2022, as well as meteorological data such as temperature, precipitation, and sunshine, the required tea tree growth parameters for the AquaCrop crop model were obtained through parameter sensitivity analysis. The AquaCrop model parameters were locally calibrated and optimized, and a tea leaf production forecasting model for Yingshan County was established. Based on two future scenario models, SSP2-4.5 and SSP5-8.5, the tea yield was predicted. The results showed that: (1) using OTA method for sensitivity analysis of non-conservative parameters, the relative sensitivity ranking of each parameter was obtained, with maturity time>standardized water productivity>tea tree base temperature>sensitivity of crop coefficient>initial canopy coverage>maximum canopy coverage time, and the sensitivity of other parameters was relatively weak. (2) The localized parameters of the AquaCrop crop model for tea in Yingshan County were optimized, and the root mean square error, conformity, and residual clustering set of the optimized parameter combinations were 0.15, 0.68, and 0.01 respectively. (3) The simulated forecast of tea production in Yingshan County from 2013 to 2022 was basically consistent with the actual production trend. Since 2013, the tea production in Yingshan County had shown an upward trend, reaching 0.03 t/hm2. The AquaCrop model predicts an upward trend of 0.02 t/hm2, which was basically consistent with the actual production trend. (4) Based on two future scenario models, SSP2-4.5 and SSP5-8.5, the simulated tea yields increased by 13.7% and 38.9% compared to the 2013-2022 average yields due to a significant reduction in tea winter freeze and spring frost and an increase in effective cumulative temperature. In summary, the AquaCrop crop model, after parameter localization, performs well in simulating tea yield in Yingshan County and can serve as a tool for predicting tea yield based on meteorological conditions during the growth period.

Key words: AquaCrop model, OTA, crop model, sensitivity analysis, parameter calibration, tea production forecast, future climate scenario models

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