Soil inorganic carbon (SIC) and organic carbon (SOC) are important carbon reservoirs in terrestrial ecosystems. However, little attention was paid to SIC dynamics in cropland. We conducted a survey in the upper Yellow River Delta of North China Plain. We collected 155 soil samples from 31 profiles, and measured SOC, SIC and soluble Ca2+ and Mg2+ contents. Our results showed that mean SOC content decreased from 9.30 g kg−1 near the surface to 2.36 g kg−1 in 80–100 cm whereas mean SIC content increased from 10.48 to 12.72 g kg−1. On average, SOC and SIC stocks over 0–100 cm were 5.73 kg C m−2 and 16.89 kg C m−2, respectively. There was a significantly positive correlation (r = 0.88, P < 0.001) between SOC and SIC in the cropland. We also found that SIC had a significantly positive correlation with both soluble Ca2+ (r = 0.57, P < 0.01) and Mg2+ (r = 0.43, P < 0.05). Our study suggested that increasing SOC might lead to an increase in SIC stocks in the cropland of North China Plain. This study highlights the importance of SIC in the carbon cycle of China’s semi-arid region.

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Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area.

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