 |
| Titel |
Soil and vegetation carbon turnover times across forest biomes in eastern China |
| VerfasserIn |
Jingsong Wang, Shuli Niu |
| Konferenz |
EGU General Assembly 2017
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250154486
|
| Publikation (Nr.) |
 EGU/EGU2017-19588.pdf |
|
|
|
|
|
| Zusammenfassung |
| 1. Resent studies reveal that terrestrial biosphere is now a net carbon (C) sink for atmospheric C dixoide (CO2), however, whether this C sink can persist with climate change is still uncertain. Such uncertainty comes not only from C input, but also largely from C turnover times in an ecosystem. Knowledge of C turnover times is critical for modelling C cycle and evaluating C sink potential. Our current understanding of how long C can be stored in soils and vegetation and what are their controlling factors are still poorly understood.
2. We used C stocks from 1087 plots in soils and 2753 plots in vegetation and investigated the spatial patterns and controlling factors of C turnover times across the forest transect in the eastern China.
3. Our results showed a clear latitudinal pattern of C turnover times, with the lowest turnover times in the low-latitude zones and highest values in the high-latitude. Mean annual temperature (MAT) and mean annual precipitation (MAP) were the most important controlling factors on the soil C turnover times while forest age accounted for the most majority of variations in the vegetation C turnover times. Our findings also indicated that forest origin (planted forest, natural forest) was also responsible for the variations of vegetation C turnover times while forest type and soil properties were not the dominant controlling factors.
4. Our study highlights different dominant controlling factors on the soils and vegetation C turnover times and different mechanisms underlying above- and below-ground C turnover. The findings can help to better understand and reduce the large uncertainty in predictive models of the coupled carbon-climate system. |
|
|
|
|
|
|