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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1111/gcb.16923
|2 doi
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|a pubmed24n1204.xml
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|a (DE-627)NLM361394276
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|a (NLM)37641440
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Ren, Yongxing
|e verfasserin
|4 aut
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| 245 |
1 |
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|a China's wetland soil organic carbon pool
|b New estimation on pool size, change, and trajectory
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 04.10.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a © 2023 John Wiley & Sons Ltd.
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|a Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage
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|a Journal Article
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|a climate change
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| 650 |
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|a meta-analysis
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| 650 |
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4 |
|a random forest
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| 650 |
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4 |
|a soil organic carbon
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| 650 |
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4 |
|a wetland
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| 700 |
1 |
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|a Mao, Dehua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zongming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Zicheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Xiaofeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Yanan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xi, Yanbiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Ling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jia, Mingming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song, Kaishan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Xiaoyan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Global change biology
|d 1999
|g 29(2023), 21 vom: 28. Nov., Seite 6139-6156
|w (DE-627)NLM098239996
|x 1365-2486
|7 nnns
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| 773 |
1 |
8 |
|g volume:29
|g year:2023
|g number:21
|g day:28
|g month:11
|g pages:6139-6156
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| 856 |
4 |
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|u http://dx.doi.org/10.1111/gcb.16923
|3 Volltext
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