A monthlong, high-resolution buoy time series from the surface ocean of the Changjiang River plume in early autumn 2013 (30-min sampling frequency) shows great variability in the partial pressure of carbon dioxide (pCO 2 ) and other physical and biogeochemical parameters. Early in the deployment, surface pCO 2 decreased by~117 μatm in a single day (11-12 September, from an initial value of~527 μatm); a similar decline of 62 μatm occurred five days later (to~378 μatm). Both drawdown events were associated with strong vertical stratification, high chlorophyll a concentrations, and oxygen supersaturation. A one-dimensional mass balance model suggests that biological production was responsible for more than half the pCO 2 decrease observed during 10-23 September. Subsequently, in association with strong winds, the mixed layer rapidly deepened and surface pCO 2 increased sharply (by about 108 μatm in late September and again in early October). Vertical mixing accounted for more than half of this pCO 2 increase, which offset more than the earlier biologically driven CO 2 drawdown. In the presence of such strong temporal variations of pCO 2 , sampling frequency exerts a substantial influence on air-sea CO 2 flux calculations for the Changjiang River plume and similar coastal areas. Compared to daily sampling, even weekly sampling would result in a bias of up to ±4.7 mmol C · m À2 · day À1 or ±63% error.Plain Language Summary Determining the mechanisms that control sea surface pCO 2 and its variability in coastal waters is an important step toward estimating global air-sea CO 2 fluxes and projecting future atmospheric CO 2 levels. In this study, a 31-day high-resolution buoy time series from the surface ocean of the Changjiang River plume in early autumn 2013 show great variability in the partial pressure of carbon dioxide (pCO 2 ) and other parameters. Early in the deployment, surface pCO 2 decreased sharply in short period (one to five days). The pCO 2 drawdown events were associated with strong vertical stratification, high chlorophyll a concentrations, and oxygen supersaturation. A mass balance model suggests that biological production was responsible for more than half the pCO 2 decrease observed. Subsequently, in association with strong northeast winds, the mixed layer rapidly deepened and surface pCO 2 increased sharply. Vertical mixing accounted for more than half of this pCO 2 increase. In the presence of such strong temporal variations of pCO 2 , sampling frequency exerts a substantial influence on air-sea CO 2 flux calculations for the Changjiang River plume and similar coastal areas.With respect to the partial pressure of carbon dioxide (pCO 2 ) at the sea surface, the biological processes triggered and maintained by subsurface and riverine nutrient inputs are particularly important in lowering surface pCO 2 values (Cao et al.
Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.
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