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An ensemble approach to the representation of subgrid-scale heterogeneity of crop phenology and yield in coarse-resolution large-areacrop models

机译：粗分辨率大面积作物模型中作物物候亚网格尺度异质性和产量表示的一种集成方法

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摘要

The grid interval of a global climate model (GCM) is generally hundreds of kilometers in latitude and longitude. The spatial heterogeneity of crop condition (e.g., phenology and yields) at that scale could be substantial. Because the atmosphere-cropland exchanges of energy, water, and materials are sensitive to crop condition, this issue poses a question: How can we simulate the condition of a crop of interest on a GCM grid scale while taking into account the spatial heterogeneity of crop condition at a sub-grid scale? We therefore proposed an ensemble approach that uses stochastic parameter values to represent the spatial variation in the phenological and biophysical characteristics of a crop within a given GCM grid box, and tested its feasibilitywith simulation experiments. The combination of the Soil and Water Assessment Tool (SWAT) applied to maize in the Central Great Plains, United States, and coarse-resolution (2.5° X 2.5°) reanalysis data was taken as the example. The ensemble simulations successfully captured the spatial variation in the phenology and yield. Our conclusion is that the ensemble approach is feasible and expected to benefit large-area crop modeling when extending those models to include more information on the spatial heterogeneity of crop condition than ever.
机译：全球气候模型（GCM）的网格间隔在经度和纬度上通常为数百公里。在该规模上作物状况的空间异质性（例如物候和产量）可能很大。由于大气-农田的能量，水和物质交换对作物状况敏感，因此这个问题提出了一个问题：如何在考虑作物空间异质性的情况下，如何在GCM网格规模上模拟目标作物的状况在亚电网规模条件？因此，我们提出了一种使用随机参数值来表示给定GCM网格框中农作物物候和生物物理特征的空间变化的整体方法，并通过仿真实验测试了其可行性。以在美国中部大平原地区玉米上应用的土壤和水评估工具（SWAT）与粗分辨率（2.5°X 2.5°）再分析数据的组合为例。集成模拟成功地捕获了物候和产量的空间变化。我们的结论是，集成方法是可行的，并且在扩展那些模型以包含比以往任何时候都更多的有关作物状况空间异质性的信息时，有望使大面积作物建模受益。

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