基因组参数化增强了对四块天然湿地甲烷循环的模型模拟

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Yunjiang Zuo, Liyuan He, Yihui Wang, Jianzhao Liu, Nannan Wang, Kexin Li, Ziyu Guo, Lihua Zhang, Ning Chen, Changchun Song, Fenghui Yuan, Li Sun, Xiaofeng Xu
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引用次数: 0

摘要

微生物过程是天然湿地产生和氧化生物甲烷(CH4)的关键。因此,建立甲烷生成和甲烷营养模型有利于准确预测 CH4 循环。利用明确表示甲烷发生体和甲烷营养体的生长和死亡的 CLM 微生物模型,我们证明了基因组支持的模型参数化提高了四个自然湿地的模型性能。与针对甲烷通量的默认模型参数化相比,启用基因组的模型参数化增加了对微生物生物量的控制,显著提高了模拟甲烷通量的精度。具体而言,三江平原的判定系数(R2)从 0.45 增加到 0.74,长白山从 0.78 增加到 0.89,沙利沼泽从 0.35 增加到 0.54。大九湖自然湿地的 R2 出现下降,主要是由于数据点分散造成的。Theil 系数(U)和模型效率(ME)证实了从默认参数化到基因组模型参数化的模型性能。与仅根据地表CH4通量校准的模型相比,额外的功能基因数据约束使CH4的季节性更好;同时,基因组支持的模型参数化在模拟的CH4产生率与环境因素之间建立了更稳健的联系。敏感性分析强调了微生物生理学在控制CH4通量中的关键作用。这种基因组支持的模型参数化为将快速累积的基因组数据与甲烷模型结合起来提供了宝贵的前景,从而更好地理解气候变化时代微生物在甲烷中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Genome-Enabled Parameterization Enhances Model Simulation of CH4 Cycling in Four Natural Wetlands

Genome-Enabled Parameterization Enhances Model Simulation of CH4 Cycling in Four Natural Wetlands

Microbial processes are crucial in producing and oxidizing biological methane (CH4) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH4 cycling. Utilizing the CLM-Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome-enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH4 flux, genomic-enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH4 flux. Specifically, the coefficient of determination (R2) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop in R2 was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome-enabled model parameterization. Compared with the model solely calibrated to surface CH4 flux, additional constraints of functional gene data led to better CH4 seasonality; meanwhile, genome-enabled model parameterization established more robust associations between simulated CH4 production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH4 flux. This genome-enabled model parameterization offers a valuable promise to integrate fast-cumulating genomic data with CH4 models to better understand microbial roles in CH4 in the era of climate change.

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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