| 东北春玉米积温模型的改进与比较 |
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| 引用本文： | 李蕊,郭建平.东北春玉米积温模型的改进与比较[J].应用气象学报,2017,28(6):678-689. |
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| 作者姓名： | 李蕊 郭建平 |
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| 作者单位： | 1.中国气象科学研究院, 北京 100081 |
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| 基金项目： | 国家自然科学基金项目（31571559） |
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| 摘 要： | 积温是农业气象科研和业务工作中最常使用的指标之一，但由于受其他环境条件的影响，农作物生育期间的积温在年际间和地区间均表现出不稳定性。因此，如何对已有积温模型进行修正，使农作物生育期间积温计算值趋于稳定并反映实际情况，对农业生产和气象服务均有重要意义。该文以东北春玉米四单19为例，应用沈国权提出的非线性积温模型（简称NLM）进行拟合，分析了参数选择对积温稳定性的影响，提出使用平均温度的二次函数对线性积温模型（简称LM）进行修正（修正后模型称TRM）并进行效果分析，与NLM进行比较。结果表明:NLM拟合时参数P越小，模拟有效积温越稳定；NLM积温在年际间、地区间均存在差异，造成积温不稳定的主要因子是温度强度，与其他因子相关性较差；有效积温与生育期平均温度呈二次曲线关系，对LM的温度二次方修正结果与NLM结果比较发现，二次方修正方法具有可行性。
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| 关 键 词： | 春玉米 积温模型 稳定性 修正模型 |
| 收稿时间： | 2017/7/14 0:00:00 |
| 修稿时间： | 2017/9/19 0:00:00 |
Improvement and Comparison of the Accumulated Temperature Model of Northeast Spring Maize |
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| Li Rui and Guo Jianping.Improvement and Comparison of the Accumulated Temperature Model of Northeast Spring Maize[J].Quarterly Journal of Applied Meteorology,2017,28(6):678-689. |
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| Authors: | Li Rui and Guo Jianping |
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| Institution: | 1.Chinese Academy of Meteorological Sciences, Beijing 1000812.Collaborative Innovation Center of Meteorological Disaster Forecast, Early-Warning and Assessment, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | Spring maize in Northeast China plays a more and more important role in the national maize production. The gradually increase in acreage, the per unit area yield and total yield of spring maize have markedly improved since 1980s. Accumulated temperature is one of indexes which are commonly used in agricultural meteorological research and operation service. It''s also used in crop model and regional thermal resource analysis which can reflect differences in demand of heat resources between different crops and varieties. And it can also be used to evaluate the suitability of heat conditions in a certain area for crop growth and development to avoid blindness of crops introduction. But in fact, the stability of accumulated temperature is relative, and it fluctuates with differences of crop varieties, locations, years and growth periods. It results in the limited application of the accumulated temperature index. Besides environmental conditions, the instability of accumulated temperature is also affected by different calculation methods. In general, accumulated temperature models are divided into two categories, including linear model and nonlinear model. Therefore, how to choose and revise the existing model for stabilizing the calculation value of accumulated temperature and making it fit well with the actual situation is of great significance for agricultural production and meteorological service.Based on observations of spring maize and meteorological data in Northeast China, the spring maize Sidan19 is taken as an example. The nonlinear accumulated temperature model proposed by Shen Guoquan with good stability is adopted to fit, and the influence of parameter selection on the stability of accumulated temperature is analyzed. The quadratic function of mean temperature to the liner model is revised and analyzed, and the nonlinear model is compared. Results show that the stability of accumulated temperature is related to the parameter P, more stable with smaller P. However, accumulated temperature calculated by the nonlinear model shows inter-annual and inter-regional differences. The main cause for the instability is different temperature strength and its less correlated with other meteorological factors. For each growth period, fitted curves between accumulated temperature and mean temperature are quadratic. The fitting effect of the accumulated temperature calculated by the revised linear model is better than that of Shen Guoquan nonlinear model. Moreover, the stability doesn''t appear to be much different between two methods. Thus, the revision of linear model considering the mean temperature for spring maize in Northeast China is feasible, which can help revising agro-meteorological indexes and improving agriculture service capacity. |
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| Keywords: | spring maize accumulated temperature model stability revision model |
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