农业工程学报2017，Vol.33Issue(10)：222-228,7.DOI:10.11975/j.issn.1002-6819.2017.10.029
基于WOFOST模型的中国主产区冬小麦生长过程动态模拟
Dynamic simulation of growth process of winter wheat in main production areas of China based on WOFOST model
摘要
Abstract
Crop model calibration and parameterization are essential for model evaluation and agricultural application. It is important for model application to accurately estimate the values of crop model parameters and further improve the performance of model prediction. WOFOST (world food studies) is a well-known, widely applied simulation model to analyze quantitatively the growth and production of field crops, which was originally developed for crops in European countries. It is the base model for Monitoring Agricultural Resources (MARS) Crop Growth Monitoring System (CGMS) in operational use for yield estimation in European Union. Dynamic simulation of WOFOST model in large regional scale is an important basis for regional crop modeling. In this study, we selected the main winter wheat production areas of China as the study area, and the data from 174 agricultural meteorological stations from 2011 to 2014 were used to calibrate several key WOFOST input parameters, especially 2 parameters related with variety, namely the effective accumulated temperature from emergence to flowering (TSUM1) and the effective accumulated temperature from flowering to maturity (TSUM2). On the basis of the zoning of the main winter wheat production areas, we used the meteorological data from 2012 to 2015 to drive the WOFOST model at a single-point scale, to simulate the winter wheat growth and dynamic development. The simulated phenology, LAI (leaf area index) and yield at the station level were evaluated with the field measured data. Results showed that the NRMSE (normalized root mean square error) of LAI ranged from 50% to 63%. The NRMSE of simulated days was 4%-7% from emergence to anthesis period and 8%-12% from anthesis to maturity period, and then CV (coefficient of variation) of the phenology was between 14% and 20%, which meant significant spatial variability. We simulated the yield respectively in irrigated area and rainfed area. And the NRMSE of simulated yield in irrigated area ranged from 11% to 23%, while the NRMSE of simulated yield in rain-fed area ranged from 22% and 28%, and the CV ranged from 14% to 22% for irrigated areas and from 25% to 40% for rain-fed areas, which exhibited significant spatial variability. The NRMSE of simulated LAI was between 50% and 63%, which could be explained that the LAI during different growth stages was all included into the accuracy analysis. Several important input parameters (such as TDWI (initial biomass) and SPAN (leaf senescence coefficient)) could be optimized through assimilating remote sensing data into crop model, which could greatly improve the performance of crop model at the regional scale. Our results showed that the WOFOST model is of great potential for simulating the dynamic growth process of winter wheat in China. The calibrated WOFOST provides the dynamic model basis for regional applications, such as assimilating remote sensing data into crop model for crop yield estimation and climate change prediction with crop model.关键词
模型/优化/温度/WOFOST/冬小麦/参数标定/物候期/动态模拟Key words
models/optimization/temperature/WOFOST/winter wheat/parameter calibration/phendogy/dynamic simulation分类
农业科技引用本文复制引用
黄健熙,贾世灵,马鸿元,侯英雨,何亮..基于WOFOST模型的中国主产区冬小麦生长过程动态模拟[J].农业工程学报,2017,33(10):222-228,7.基金项目
国家自然科学基金(41671418,41471342,41371326) （41671418,41471342,41371326）
