• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of the American Water Resources Association >A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling
【24h】

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

机译：一个并行计算工具，以实现Apex的动态灵敏度和模型性能分析：evapotranspiration建模

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time-averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open-source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash-Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.
机译：全局敏感性分析可用于评估模型参数对模型输出的相对重要性。参数的敏感性通常表示由于环境条件的变化（例如，天气或植物生长的变异）导致的时间变化。另外，平均窗口的大小可以聚合或平均模型的输出可能影响参数灵敏度。在该研究中，研究了参数敏感性，模型性能的时间变化，模型性能以及时间平均窗口的尺寸对使用农业政策/环境扩展剂（APEX）模型的蒸散散热（ET）预测的影响。为了实现这些目标，在R中开发了一个名为PARAPEX的开源包，并用于使用并行计算执行APEX的动态灵敏度和模型性能分析。 PARAPEX将计算时间从5,939到379s（分别使用20和1计算节点）降低。敏感性分析结果表明，参数核算植物覆盖对土壤表面蒸发的降低效果，土壤对植物根系生长的影响，以及循环土壤中有机物质的循环和转化动力学的影响基于平均日常模拟ET和NASH-SUTCLIFFE模型性能测量的顶级敏感参数。动态性能分析表明，在不断增长的季节期间，顶点的预测结果不佳。编辑注意：本文是优化Ogallala Aquifer水用于维持食品系统的特色系列的一部分。参见2019年2月的介绍和背景的问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号