首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States
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Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

机译:在美国中原地区开发用于区域生态建模和应用的通用自动校准软件包

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Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study,we developed an automatic calibration (auto-calibration) function for awell-established biogeochemical model-the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)-using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package-Flexible Modeling Environment (FME). The new functionality can support multiparameter and multi-objective auto-calibration of EDCMat the both pixel and regional levels.We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn-wheat rotation and a large ecological region (Level II)-Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.
机译:面向过程的生态模型通常用于预测全球变化(如气候和土地覆盖变化)的潜在影响,这对政策制定很有用。关键是要自动在不同尺度(尤其是在区域尺度)上推导最佳参数值并验证模型性能,但具有挑战性。在这项研究中,我们为完善的生物地球化学模型-通用整体生物地球化学模型系统(GEMS)-侵蚀沉积碳模型(EDCM)-开发了自动校准(自动校准)功能-使用数据同化技术:混洗复杂演化算法以及Model-inversion R包-Flexible Modeling Environment(FME)。新功能可以在像素和区域级别支持EDCM的多参数和多目标自动校准。我们还开发了GEMS的后处理程序,以提供选项来保存基于像素或汇总的县土地覆盖率特定参数值用于后续的模拟。在我们的案例研究中,我们成功地将更新后的模型(EDCM-Auto)应用到了具有玉米小麦轮作和大生态区(二级)-美国中部平原的单个作物像素。评估结果表明,EDCM-Auto适用于多种规模,并且能够处理土地覆被变化(例如农作物轮作)。该模型在捕获该地区其他农作物的粮食产量和净初级生产(NPP)的空间格局方面也表现良好,这是利用易于获得的调查数据对生态模型进行校准和验证的一个很好的例子(粮食产量和遥感数据(NPP)。可以轻松扩展已开发的自动校准平台,以结合其他模型反演算法和潜在的R包,也可以应用于其他生态模型。

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