Preprints
https://doi.org/10.5194/essd-2024-100
https://doi.org/10.5194/essd-2024-100
15 Jul 2024
 | 15 Jul 2024
Status: this preprint is currently under review for the journal ESSD.

Global patterns of soil organic carbon dynamics in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Qingsong Zhang, Minghao Zhuang, and Zhengling Cui

Abstract. Determining the dynamics of organic carbon in subsoil (SOC, depth of 20–100 cm) is important with respect to the global C cycle and warming mitigation. However, there is still a huge knowledge gap in the dynamics of spatiotemporal changes in SOC in this layer. Combining traditional depth functions and machine-learning methods, we achieved soil β values and SOC dynamics at high resolution for global ecosystems (cropland, grassland, and forestland). First, quantified the spatial variability characteristics of soil β values and driving factors by analyzing 1221 soil profiles (0–100 cm) of globally distributed field observations. Then, based on multiple environmental variables and soil profile data, we mapped the grid-level soil β values with machine-learning approaches. Lastly, we evaluated the SOC density spatial distribution in different soil layers to determine the subsoil SOC stocks of various ecosystems. The subsoil SOC density values of cropland, grassland, and forestland were 63.8, 83.3, and 100.4 Mg ha–1, respectively. SOC density decreased with increasing depth, ranging from 5.6 to 30.8 Mg ha–1 for cropland, 7.5 to 40.0 Mg ha–1 for grassland, and 9.6 to 47.0 Mg ha–1 for forestland. The global subsoil SOC stock was 912 Pg C (cropland, grassland, and forestland were 67, 200, and 644 Pg C), in which an average of 54 % resided in the top 0–100 cm of the soil profile. Our results provide information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution for areas of Global ecosystems, which providing a scientific basis for future studies pertaining to Earth system models. The dataset is open-access and available at https://doi.org/10.5281/zenodo.10846543 (Wang et al., 2024).

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Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Qingsong Zhang, Minghao Zhuang, and Zhengling Cui

Status: open (until 21 Aug 2024)

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Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Qingsong Zhang, Minghao Zhuang, and Zhengling Cui

Data sets

Global patterns of soil organic carbon dynamics in the 20–100 cm soil profile for different ecosystems: A global meta-analysis Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Minghao Zhuang, Qingsong Zhang, and Zhengling Cui https://doi.org/10.5281/zenodo.10846543

Haiyan Wang, Yulong Yin, Tingyao Cai, Xingshuai Tian, Zhong Chen, Kai He, Zihan Wang, Haiqing Gong, Qi Miao, Yingcheng Wang, Yiyan Chu, Qingsong Zhang, Minghao Zhuang, and Zhengling Cui
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Latest update: 15 Jul 2024
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Short summary
Accurately quantifying the distribution of soil profile SOC stocks is crucial for C sequestration and mitigation. The detailed spatial subsoil SOC data are the scientific basis for environmental protection as well as the development of Earth system models. Based on multiple environmental variables and soil profile data, this study use machine-learning approaches to evaluate evaluated the SOC stocks and their spatial distribution at a depth interval of 0–1 m of various ecosystems.
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