DayCent CUTE:DayCent的全球灵敏度、自动校准和不确定性分析工具

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2023-11-01 DOI:10.1016/j.envsoft.2023.105832
Xiuying Wang , Jaehak Jeong , Seonggyu Park , Xuesong Zhang , Jungang Gao , Nélida E.Q. Silvero
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引用次数: 0

摘要

土壤有机碳(SOC)是减少温室气体排放和发展气候智能农业的关键指标。DayCent被广泛用于模拟各种生态系统中的SOC动态和土壤微量气体通量。在本研究中,我们开发了DayCent CUTE(自动校准、灵敏度和不确定度分析工具包),用于对模型进行全局灵敏度分析(GSA)、自动校准和不确定性分析。该工具包括一对GSA方法和两种不同的参数优化方法。30个现场实验的集合,包括212个管理处理组合和581个SOC测量,分为18个校准点和12个独立模型评估点。从自动校准过程中获得的后验参数分布降低了模型偏差和RMSE值,而Nash-Sutcliffe效率和R2值显示出改进。DayCent CUTE被证明是一种高效灵活的工具,可以增强DayCent模型的应用。
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DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent

Soil organic carbon (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods.

A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R2 values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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