Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
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DOI：
10.1016/j.agsy.2017.08.004
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发表时间：
2018-01-01
影响因子：
6.6
通讯作者：
Rotter, Reimund P.
中科院分区：
文献类型：
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作者：
Fronzek, Stefan;Pirttioja, Nina;Rotter, Reimund P.
作物生长模拟模型对关键过程的处理以及对环境条件的反应可能差别很大。在这里，我们使用了26个基于过程的小麦模型的集合，这些模型应用于整个欧洲样带的站点，以比较它们对温度（-2至+9摄氏度）和降水（-50至+50%）变化的敏感性。模型结果进行了分析，绘制它们的影响响应面（IRS），分类的IRS模式的个人模型模拟，描述这些类和分析的因素，可能会解释的主要差异模型responses.The模型集成被用来模拟冬小麦和春小麦产量在芬兰，德国和西班牙的四个地点。将结果绘制为IRS，其显示相对于温度和降水的基线的产率变化。30年平均和选定的极端年的IRS分类使用两种方法描述其模式。专家诊断方法（EDA）结合了IRS模式的两个方面：最高产量的位置（9类）和产量对气候的响应强度（4类），导致总共36个组合类定义使用的标准预先指定的专家。统计诊断方法（SDA）通过比较IRSs的模式和幅度来对IRSs进行分组，而不试图解释这些特征。它应用了层次聚类方法，使用结合IRS对之间的空间相关性和欧几里德距离的距离度量对响应模式进行分组。这两种方法用于研究不同的产量响应模式是否与作物模型的不同属性相关，特别是它们的谱系、校准和过程描述。尽管没有发现大型模型集合中的单一模型属性可以解释综合产量对温度和降水扰动的响应，但EDA和SDA方法的应用揭示了它们区分以下内容的能力：（1）冬小麦产量对降水的响应强于春小麦;（2）与同期平均条件相比，气候异常条件下对气候变化的响应强度不同;（3）立地条件对产量模式的影响;（4）具有相关系谱的模型之间IRS模式的相似性;（v）与具有更复杂描述的模型相比，具有更简单的根系生长和吸水过程描述的模型的IRS模式的相似性;和（vi）与使用收获指数的模型相比，使用分区方案表示产量形成的模型中IRS模式的对应性更紧密。利用多模型集合的多样性，区分跨越各种反应的集合成员以及表现出令人难以置信的行为或强烈相互相似性的集合成员。
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9 degrees C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.