Increasing riverine export of dissolved organic carbon from China

Abstract

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 (F_DOC ) and DOC concentrations (C_DOC ) in rivers across China. This study proposes the first DOC modeling effort capable of reproducing well the magnitude of riverine C_DOC and F_DOC , 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 C_DOC was 2.25 ± 0.45 mg/L and average F_DOC was 4.04 ± 1.02 Tg/year. Simultaneously, we found a significant increase in F_DOC (+0.044 Tg/year² , p = .01), but little change in C_DOC (-0.001 mg/L/year, p > .10). Although the trend in C_DOC 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 F_DOC and C_DOC across China. However, and in contrast with other basins, the significant increase in C_DOC in the Yangtze River Basin and Huaihe River Basin is attributable to direct anthropogenic activities. Given the dominance of hydrology in driving F_DOC , the increase in F_DOC is likely to continue under the projected increase in river discharge over China resulting from a future wetter climate.

Keywords: China; climate change; dissolved organic carbon; land cover; machine learning method; net primary production; river chemistry; soil organic carbon.