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草业学报 ›› 2022, Vol. 31 ›› Issue (5): 13-25.DOI: 10.11686/cyxb2021391

• 研究论文 • 上一篇    下一篇

不同草地类型WOFOST模型参数敏感性分析

秦格霞1(), 吴静1(), 李纯斌1, 沈帅杰2, 李怀海1, 杨道涵1, 焦美榕1, 祁琦1   

  1. 1.甘肃农业大学资源与环境学院,甘肃 兰州 730070
    2.中国农业大学农学院,北京 100193
  • 收稿日期:2021-10-28 修回日期:2021-11-23 出版日期:2022-05-20 发布日期:2022-03-30
  • 通讯作者: 吴静
  • 作者简介:Corresponding author. E-mail: wujing@gsau.edu.cn
    秦格霞(1995-),女,甘肃庆阳人,在读硕士。E-mail: 2603246958@qq.com
  • 基金资助:
    甘肃省优秀研究生“创新之星”项目(2021CXZX-413);国家自然科学基金项目(31760693)

Sensitivity analysis of WOFOST model crop parameters in different grassland types

Ge-xia QIN1(), Jing WU1(), Chun-bin LI1, Shuai-jie SHEN2, Huai-hai LI1, Dao-han YANG1, Mei-rong JIAO1, Qi QI1   

  1. 1.College of Resources and Environmental Sciences,Gansu Agricultural University,Lanzhou 730070,China
    2.College of Agronomy and Biotechnology,China Agricultural University,Beijing 100193,China
  • Received:2021-10-28 Revised:2021-11-23 Online:2022-05-20 Published:2022-03-30
  • Contact: Jing WU

摘要:

以C为驱动的WOFOST作物生长模型是基于作物生理生态过程,综合考虑了CO2、土壤、气候等因素对产量的胁迫作用,因此,对WOFOST模型参数进行本地化和优化便可实现时间连续且高精度的草地生物量监测。为探讨WOFOST参数敏感性分析结果在不同草地类型覆盖区表现出的不确定性问题,在天祝藏族自治县不同草地覆盖区选择了4个站点,利用气象数据、草地实测数据及土壤数据,基于扩展傅里叶幅度敏感性检验法(EFAST)研究潜在水平(指保证营养元素和水分为最佳供应,草地地上生物量仅由辐射、温度和作物特性决定)和水分限制水平(假设营养元素的供给仍然是最佳的, 但需考虑土壤有效水分对蒸发和草地生物量的影响)下WOFOST模型在不同草地类型覆盖区的全局敏感性参数和优化模型模拟精度。结果表明潜在生产水平下草地地上生物量(AGB)的敏感参数有比叶面积(SLATB)、单叶片CO2的初始光能利用率(EFFTB)、最大光合速率(AMAXTB)、根相对维持呼吸速率(RMR)、总干物质占根和叶的比例(FRTB和FLTB),水分限制条件下的敏感参数有SLATB、AMAXTB、RMR和FLTB。不同生产水平下叶面积指数(LAI)的敏感参数一致,从出苗到出苗后60 d主要受到SLATB、FLTB和FRTB的影响,出苗后60~200 d的敏感性参数为FLTB、FRTB、SLATB和漫射可见光的消光系数(KDIFTB),LAI开始下降后受到KDIFTB的敏感性增强。其中,山地草甸AGB的模拟值与观测值模拟精度最高,R2=0.94、RMSE=11.71 g·m-2,高寒草甸模拟精度最低,R2=0.83、RMSE=32.68 g·m-2。温性荒漠草原LAI的模拟值与观测值模拟精度最高,R2=0.96、RMSE=0.02,温性草原模拟精度最低,R2=0.66、RMSE=0.38。敏感性分析方法在WOFOST模型中的应用减少了人为主观因素的影响,极大地缩短了调参时间,对获取时间连续的草地生长监测方法选择提供参考。

关键词: WOFOST模型, EFAST, 全局敏感性分析, 草地, 天祝藏族自治县

Abstract:

World Food Studies (WOFOST) is a simulation model for the quantitative analysis of the growth and production of crops. It is based on the physiological and ecological processes of crops, and can be used to predict the effects of CO2, soil, climate, and other factors on yield. The aim of this study was to conduct a parameter sensitivity analysis for different types of grasslands (i.e., those under different management measures and climatic conditions). Four sites were selected in different grassland zones in Tianzhu Zangzu Autonomous County. The extended Fourier amplitude sensitivity test (EFAST) method was used to analyze the sensitivity of different parameters of grasslands in the WOFOST model under potentially productive and water-restricted conditions based on meteorological data, field sampling data, and soil data. The accuracy of the WOFOST simulations for grasslands with different degrees of coverage were evaluated by comparisons with measured data. The sensitive parameters for estimating grassland above-ground biomass (AGB) under potentially productive conditions were specific leaf area (SLATB), light-use efficiency of a single leaf (EFFTB), maximum leaf CO2 assimilation rate at daily temperatures of 0 and 40 ℃ (AMAXTB), relative maintenance respiration rate of roots (RMR), and fraction of root and leaf system out of total dry matter (FRTB and FLTB); and the sensitive parameters under restricted water conditions were SLATB, AMAXTB, RMR, and FLTB. The sensitive parameters for estimating leaf area index (LAI) were the same under both production levels. From emergence to 60 d after emergence, the LAI was mainly affected by SLATB, FLTB, and FRTB. From 60 to 200 d after emergence, the sensitive parameters were FLTB, FRTB, SLATB, and extinction coefficient for diffuse visible light (KDIFTB). After the LAI began to decline, it was enhanced by KDIFTB. Compared with measured values, the simulated values of AGB were most accurate for slope meadow (R2=0.94, RMSE=11.71 g·m-2) and least accurate for alpine meadow (R2=0.83, RMSE=32.68 g·m-2); and the simulated values of LAI were most accurate for temperate desert steppe (R2=0.96, RMSE=0.02) and least accurate for warm steppe (R2=0.66, RMSE=0.38). Thus, the application of the sensitivity analysis method in the WOFOST model reduces the influence of human subjectivity and greatly shortens the parameter adjustment time.

Key words: WOFOST model, EFAST, global sensitivity analysis, grassland, Tianzhu Zangzu Autonomous County