Hui Liu, Haibin Wu, Wenchao Zhang, Jie Yu, Junyan Geng, Xiuqin Le, Yanyan Yu
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引用次数: 0
摘要
泥炭地是陆地生态系统的重要组成部分,其发展对全球碳循环和气候变化具有重要影响。然而,全球泥炭地的长期演变仍然不确定,特别是其空间分布。为了对全新世泥炭地的空间分布进行更可靠的重建,我们收集了4700份全新世的基底泥炭地资料和669份具有基底和终末年龄的Sphagnum花粉资料。利用缓冲分析(BA)和逆距离加权(IDW)插值技术,以0.5°× 0.5°的空间分辨率重建了全新世时期全球泥炭地面积每1000年的时空变化。结果表明,早全新世以来,北美、欧洲和西伯利亚西部地区泥炭地面积急剧扩大,在12 ~ 6 ka BP期间从2.18(0.32)Mkm2迅速增加到4.03(3.08)Mkm2,在6 ka BP后缓慢增加到4.15(4.23)Mkm2。该数据库将有助于分析全新世全球/区域陆地碳循环和气候变化,特别是泥炭地甲烷排放模型。
Spatiotemporal distribution of global peatland area during the Holocene.
Peatlands are a key component of terrestrial ecosystems, and their development has an important impact on global carbon cycle and climate change. However, the long-term evolution of global peatlands remains uncertain, particularly their spatial distribution. We compiled 4700 basal peatland data during Holocene, and 669 pollen data of Sphagnum with basal and end ages, to allow a more robust reconstruction of the spatial distribution of peatlands. Using buffer analysis (BA) and inverse distance weighted (IDW) interpolation of peat data, we reconstructed spatiotemporal changes in global peatland area at a spatial resolution of 0.5° × 0.5° for every 1,000 years period during Holocene. The results show that peatland area have expanded substantially in North America, Europe, and Western Siberia during early-Holocene, and increased rapidly from 2.18(0.32) Mkm2 to 4.03(3.08) Mkm2 during 12-6 ka BP, then slowly to 4.15(4.23) Mkm2 after 6 ka BP according to BA (IDW) methods. The database will be useful for analyzing the global/regional terrestrial carbon cycle and climate change during Holocene, especially for modeling peatland methane emissions.
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.