水-能量-碳通量在土壤-植物-大气连续体中的物理一致性数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yunfei Wang, Yijian Zeng, Fakhereh Alidoost, Bart Schilperoort, Zengjing Song, Danyang Yu, Enting Tang, Qianqian Han, Zhunqiao Liu, Xiongbiao Peng, Chao Zhang, Bas Retsios, Serkan Girgin, Xiaoliang Lü, Qiting Zuo, Huanjie Cai, Qiang Yu, Christiaan van der Tol, Zhongbo Su
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引用次数: 0

摘要

高质量和长期的陆地-大气通量测量对于气候监测和陆地表面模式(LSMs)基准至关重要。涡旋相关方差为理论和LSMs评估提供了关键的原位数据,但大多数通量塔缺乏连续的土壤-植物-大气测量。在这里,我们展示了一个长期的全球水、能量和碳通量数据集,以及相应的地上和地下水文、光合作用和辐射数据,这些数据来自STEMMUS-SCOPE模型在170个地点的模拟。利用PLUMBER2的现场观测通量数据和FLUXNET2015的土壤湿度(SM)数据验证STEMMUS-SCOPE数据集的有效性。结果表明,在没有特定站点的模型调整或校准的情况下,仅由全球参数和强迫数据集驱动,模拟的净辐射、潜热通量、感热通量、初级生产总量、净生态系统交换和SM数据集与现有的原位测量结果一致(中位数KGE: -0.03 ~ 0.80;中位R2: 0.46 ~ 0.97;rRMSE中位数:4.09%至29.11%)。该数据集补充了现有的生态系统通量和SM网络,增强了我们对生态系统功能的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A physically consistent dataset of water-energy-carbon fluxes across the Soil-Plant-Atmosphere Continuum.

Hight-quality and Long-term measurements of land-atmosphere fluxes are vital for climate monitoring and Land Surface models (LSMs) benchmarking. Eddy covariance provides key in-situ data for theory and LSMs evaluation, but most flux towers lack continuous soil-plant-atmosphere measurements. Here, we present a long-term global dataset of water, energy and carbon fluxes, along with the corresponding above and below-ground hydrological, photosynthetic, and radiative data derived from the STEMMUS-SCOPE model simulations at 170 sites. In-situ observed fluxes data from PLUMBER2 and soil moisture (SM) data from FLUXNET2015 are employed to validate the effectiveness of the STEMMUS-SCOPE dataset. Results demonstrate that, without site-specific model tuning or calibration, and driven solely by global parameters and forcing datasets, simulated net radiation, latent heat flux, sensible heat flux, gross primary production, net ecosystem exchange, and SM datasets consistently agree with available in-situ measurements (median KGE: -0.03 to 0.80; median R2: 0.46 to 0.97; median rRMSE: 4.09% to 29.11%). This dataset supplements the existing ecosystem flux and SM network, enhancing our understanding of ecosystem functioning.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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