利用 rCTOOL R 软件包建立和验证长期地块尺度的土壤碳动态模型

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Franca Giannini-Kurina , João Serra , Bent Tolstrup Christensen , Jørgen Eriksen , Nicholas John Hutchings , Jørgen Eivind Olesen , Johannes Lund Jensen
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引用次数: 0

摘要

我们介绍的 rCTOOL 是一个用于碳(C)周转建模的开源 R 软件包,具有全面的文档和友好的用户界面。作为广泛使用的丹麦 C-TOOL 模型的增强版,rCTOOL 保持了最低的输入数据要求和可靠的性能,同时解决了原始模型在开放性和文档方面的局限性。为了验证 rCTOOL,我们利用长期阿斯科夫秸秆处理实验(1981-2019 年)的数据分析了表土土壤有机碳(SOC)动态,该实验量化了每年不同的秸秆 C 输入对 SOC 储存的影响。我们的验证结果表明,全球预测误差小于 10%,平均误差为 1.1 毫克碳/公顷(CI -0.7-3.0 毫克碳/公顷)。观测值和预测值之间的差异主要是由于长期田间试验中以区块和年份为代表的时空变异造成的。我们展示了 rCTOOL 如何成为 SOC 管理和研究领域各种应用的可靠资产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and validating soil carbon dynamics at the long-term plot scale using the rCTOOL R package
We introduce rCTOOL, an open-source R package for carbon (C) turnover modelling, featuring comprehensive documentation and a user-friendly interface. As an enhanced version of the widely used Danish C-TOOL model, rCTOOL maintains minimal input data requirements and reliable performance, while addressing the original model's limitations in openness and documentation. To validate rCTOOL, we analysed topsoil Soil Organic Carbon (SOC) dynamics using data from the long-term Askov straw disposal experiment (1981–2019), which quantifies the impact of varying annual straw C inputs on SOC storage. Our validation shows an unbiased global prediction error of less than 10%, with a mean error of 1.1 Mg C/ha (CI -0.7–3.0 Mg C/ha). The discrepancies between observed and predicted values were primarily due to spatiotemporal variability represented by the block and year in the long-term field experiment. We demonstrate how rCTOOL is a reliable asset for diverse applications in SOC management and research.
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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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