Soil organic carbon fractions in China: Spatial distribution, drivers, and future changes.
Sci Total Environ
; 919: 170890, 2024 Apr 01.
Article
en En
| MEDLINE
| ID: mdl-38346657
ABSTRACT
Soil is the world's largest terrestrial carbon pool and plays an important role in the global carbon cycle, which may be greatly affected by global change. Recently, research frameworks have indicated that division of soil organic carbon (SOC) into two forms particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) can help us better understand SOC cycle. However, there is a lack of the use of meta-analysis combined with machine learning models to explore the spatial distribution of SOC fractions at large scales. Based on 356 studies conducted in Chinese terrestrial ecosystems, we performed a meta-analysis of extracted data and measured data combined with machine learning models to reveal the spatial distribution of soil POC density (POCD) and MAOC density (MAOCD) and the main drivers of variations in POCD and MAOCD. Our study demonstrated that POCD and MAOCD in China's soil were 3.24 and 2.61 kg m-2, with stocks of 31.10 and 25.06 Pg, respectively. Climate, soil, and vegetation properties together explained 44.9 % and 27.2 % of the variation in POCD and MAOCD, respectively. Climate was more important than other variables in controlling the changes in POCD, with mean annual temperature being specifically the main driver. Soil, however, was more important than other variables in controlling changes in MAOCD, with soil clay content being the main driver. Compared to the other climate scenarios, the rate of change in POCD and MAOCD was higher with a 1.5 °C increase in temperature. In the future, we should pay more attention to the impact of climate change on POCD, which provides a theoretical basis for achieving the "dual-carbon" target. Our study contributes to the understanding of the potential mechanisms of the changes in SOC fractions under global change and provides useful information for future prediction models to simulate the impacts of global change.
Texto completo:
1
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Sci Total Environ
Año:
2024
Tipo del documento:
Article
País de afiliación:
China