Increasing riverine export of dissolved organic carbon from China

© 2023 John Wiley & Sons Ltd..

River transport of dissolved organic carbon (DOC) to the ocean is a crucial but poorly quantified regional carbon cycle component. Large uncertainties remaining on the riverine DOC export from China, as well as its trend and drivers of change, have challenged the reconciliation between atmosphere-based and land-based estimates of China's land carbon sink. Here, we harmonized a large database of riverine in-situ measurements and applied a random forest model, to quantify riverine DOC fluxes (FDOC ) and DOC concentrations (CDOC ) in rivers across China. This study proposes the first DOC modeling effort capable of reproducing well the magnitude of riverine CDOC and FDOC , as well as its trends, on a monthly scale and with a much wider spatial distribution over China compared to previous studies that mainly focused on annual-scale estimates and large rivers. Results show that over the period 2001-2015, the average CDOC was 2.25 ± 0.45 mg/L and average FDOC was 4.04 ± 1.02 Tg/year. Simultaneously, we found a significant increase in FDOC (+0.044 Tg/year2 , p = .01), but little change in CDOC (-0.001 mg/L/year, p > .10). Although the trend in CDOC is not significant at the country scale, it is significantly increasing in the Yangtze River Basin and Huaihe River Basin (0.005 and 0.013 mg/L/year, p < .05) while significantly decreasing in the Yellow River Basin and Southwest Rivers Basin (-0.043 and -0.014 mg/L/year, p = .01). Changes in hydrology, play a stronger role than direct impacts of anthropogenic activities in determining the spatio-temporal variability of FDOC and CDOC across China. However, and in contrast with other basins, the significant increase in CDOC in the Yangtze River Basin and Huaihe River Basin is attributable to direct anthropogenic activities. Given the dominance of hydrology in driving FDOC , the increase in FDOC is likely to continue under the projected increase in river discharge over China resulting from a future wetter climate.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:29

Enthalten in:

Global change biology - 29(2023), 17 vom: 18. Sept., Seite 5014-5032

Sprache:

Englisch

Beteiligte Personen:

Yan, Yanzi [VerfasserIn]
Lauerwald, Ronny [VerfasserIn]
Wang, Xuhui [VerfasserIn]
Regnier, Pierre [VerfasserIn]
Ciais, Philippe [VerfasserIn]
Ran, Lishan [VerfasserIn]
Gao, Yuanyi [VerfasserIn]
Huang, Ling [VerfasserIn]
Zhang, Yao [VerfasserIn]
Duan, Zheng [VerfasserIn]
Papa, Fabrice [VerfasserIn]
Yu, Bing [VerfasserIn]
Piao, Shilong [VerfasserIn]

Links:

Volltext

Themen:

7440-44-0
Carbon
China
Climate change
Dissolved Organic Matter
Dissolved organic carbon
Journal Article
Land cover
Machine learning method
Net primary production
River chemistry
Soil organic carbon

Anmerkungen:

Date Completed 02.08.2023

Date Revised 31.08.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1111/gcb.16819

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM358337089