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首页> 外文期刊>Agricultural and Forest Meteorology >The effect of temporal aggregation of weather input data on crop growth models' results.
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The effect of temporal aggregation of weather input data on crop growth models' results.

机译：天气输入数据的时间汇总对作物生长模型结果的影响。

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摘要

Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather-crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages. Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data.Digital Object Identifier http://dx.doi.org/10.1016/j.agrformet.2011.01.007
机译：天气数据是作物生长模型的重要输入，该模型主要使用特定于站点的每日天气数据为田间应用开发。经常无法获得每日天气数据，尤其是当模型应用于大区域和/或用于将来的预测时。可以从汇总的天气数据中生成每日天气数据，例如平均每月天气数据，例如通过线性插值方法。但是，由于许多天气-作物关系的非线性，使用线性插值数据的模拟结果将与具有实际（每日）数据的模拟结果有所不同。这项研究的目的是分析不同建模方法对天气输入数据的时间分辨率的敏感性。我们以春小麦为例，考虑了总结和详细方法的三种组合来模拟叶面积指数的发展以及相关的辐射拦截和生物量生产力，反映了大多数模型结构中典型的细节范围。使用实际天气数据和汇总的天气数据运行模型，这些数据已通过月平均值之间的线性插值消除了每日的变化。欧洲不同气候区域的结果表明，使用实际或汇总的温度和/或辐射数据得出的模拟生物量在模型模拟之间是不同的。此外，我们发现输入数据插值方法的灵敏度与该建模方法中的细节程度之间存在关系：增加细节会导致更高的灵敏度。此外，天气条件下日常变化的幅度会影响结果：变化性的增加会导致模型结果之间的差异更大。我们的结果对根据输入数据的可用时间分辨率来选择对特定过程进行建模的特定方法具有影响。数字对象标识符http://dx.doi.org/10.1016/j.agrformet.2011.01.007

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