马鸿元,黄健熙,黄 海,张晓东,朱德海.基于历史气象资料和WOFOST模型的区域产量集合预报[J].农业机械学报,2018,49(9):257-266.
MA Hongyuan,HUANG Jianxi,HUANG Hai,ZHANG Xiaodong,ZHU Deha.Ensemble Forecasting of Regional Yield of Winter Wheat Based on WOFOST Model Using Historical Metrological Dataset[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(9):257-266.
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基于历史气象资料和WOFOST模型的区域产量集合预报   [下载全文]
Ensemble Forecasting of Regional Yield of Winter Wheat Based on WOFOST Model Using Historical Metrological Dataset   [Download Pdf][in English]
投稿时间:2018-03-21  
DOI:10.6041/j.issn.1000-1298.2018.09.030
中文关键词:  冬小麦  作物模型  产量预报  集合预报
基金项目:国家自然科学基金项目(41671418)和国家自然科学基金国际(地区)合作与交流项目(61661136006)
作者单位
马鸿元 中国农业大学 
黄健熙 中国农业大学
农业部农业灾害遥感重点实验室 
黄 海 中国农业大学 
张晓东 中国农业大学
农业部农业灾害遥感重点实验室 
朱德海 中国农业大学
农业部农业灾害遥感重点实验室 
中文摘要:针对基于作物生长模型进行产量预报时气象要素变化对作物生长的实时影响不能得到充分反映,产量预报缺乏量化不确定性信息的突出问题,选择河北省保定市和衡水市冬小麦主产区为研究对象,提出构建历史气象集合作为预报期气象数据输入驱动WOFOST模型的冬小麦生长模拟,并通过实时更新不断向前滚动预报,从传统单一数值的预报转向基于集合的概率预报。结果表明:基于历史气象资料可以进行作物模型的区域产量集合预报,抽穗期至灌浆期是预报精度最高的时期,预报集合中位数与实测产量的皮尔逊相关系数(PCC)最高为0.563,平均绝对误差(MAE)最低为458kg/hm2。研究结果表明区域化产量集合预报具有较强的可行性,并为量化作物模拟系统不确定性、数值天气预报与作物模型的结合应用提供了参考。
MA Hongyuan  HUANG Jianxi  HUANG Hai  ZHANG Xiaodong  ZHU Deha
China Agricultural University,China Agricultural University;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture,China Agricultural University,China Agricultural University;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and China Agricultural University;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture
Key Words:winter wheat  crop model  yield forecast  ensemble forecast
Abstract:The crop growth model has distinct advantages of clear mechanism and dynamic serial simulation, and it has been widely used in regional crop production forecasting. The research focused on the major problem that the uncertainty of yield prediction with crop model can not be quantized and the issue that the real time impact of meteorological driving factors on crop model was not timely reflected. Simulations in the main wheat production areas in Baoding and Hengshui of Hebei Province were conducted and a method was proposed to build a historical meteorological dataset as weather inputs to drive the WOFOST model to simulate wheat growth during the forecasting period. Then the yield was continuously forecasted through real time updating. In this way, the traditional single value forecasting of yield was changed to an ensemble based probabilistic forecasting, and the regional yield ensemble forecast and event possibility forecast can be generated in everyday in the growing season. The validation results indicated that the WOFOST model with historical meteorological data was able to reflect the uncertainty of regional weather data. In the regional ensemble forecasting of wheat yield, the highest yield forecasting accuracy was achieved in the period from heading to grain filling stage. The correlation coefficient (PCC) between the ensemble mean and the measured yields was 0563, while the minimum average absolute error (MAE) was 458kg/hm2, however, the improvement of yield forecasting accuracy along with forecasting date was slow, because simulation with homogeneous input parameters in potential level was a little coarse. It suggested that with the help of remote sensing data assimilation or medium weather numeric forecasting, yield prediction could achieve better accuracy. The results showed that the regional yield ensemble forecast had strong feasibility, and this research provided a reference for the application of numerical weather forecast and quantification of the uncertainty in crop simulation system.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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