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Soil organic carbon (SOC) is a key factor in soil fertility and structure and plays an important role in the global carbon cycle. However, SOC causes a large uncertainty in Earth System Models for predicting future climate change. The GlobalSoilMap (GSM) project aims to provide global digital soil maps of primary functional soil properties at six standard depth intervals (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) with a grid resolution of 90 × 90 m. Currently, few SOC national products that meet the GSM specifications are available. This study describes the three-dimensional spatial modeling of SOC maps according to GSM specifications. We used 5982 soil profiles collected during the Second National Soil Survey of China, along with 16 environmental covariates related to soil formation. The results were obtained by parallel computing over tiles of 100 × 100 km, and the predictions for the tiles were subsequently merged into a single SOC map for the whole of China per standard GSM depth interval. For each standard GSM depth interval, SOC contents and their uncertainties were predicted and mapped at a spatial resolution of approximately 90 m using bootstrapping. Southwestern and northeastern China had higher SOC contents than the rest of China did, whereas northwestern China had a lower SOC content. The range of the coefficient of determination for the six depth intervals ranged from 0.35 to 0.02, and the mean SOC content was 17.86–8.67 g kg−1. Both these values decreased strongly with increasing soil depth. Cropland SOC content was lower than that of forest and grassland. The results of variable importance show that SoilGrids data were the best predictors for defining the soil-landscape relationship during regression modeling for SOC. These SOC maps can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor SOC dynamics, and a guide for the design of future soil surveys. Unlabelled Image • National soil organic carbon maps were produced at six GSM depth intervals. • The XGBoost model had reasonable accuracy, which decreased with depth. • SoilGrids products were helpful in national modeling. • The proposed framework was efficient and flexible for large-scale modeling. • Our results assist policymaking in terms of land management and food production. [ABSTRACT FROM AUTHOR]

ABSTRACT Very high-resolution (∼1 km) precipitation datasets are needed in various applications, especially in the hydrological, environmental, agricultural, and biological fields, although they also have major roles in the climate sciences. Nevertheless, various studies have concurred that the spatial distribution of precipitation is influenced by land surface characteristics through non-stationary relationships, and that this characteristic is generally not considered fully by either global or local models using fixed combinations of variables (e.g. the normalized difference vegetation index and/or digital elevation models). This study introduced a new spatial downscaling algorithm, a divide-and-conquer method (called Cubist), to downscale Tropical Rainfall Measuring Mission ( TRMM) satellite precipitation data over China from 2000 to 2015. In this endeavour, we considered the non-stationary relationships between precipitation and land surface characteristics at various scales and derived the following conclusions: (1) The relationships and related variables explaining the spatial distribution of precipitation varied with spatial scale. (2) The Cubist algorithm achieved greater accuracy than Multivariate Linear Regression ( MLR) models, and it was found to compensate for the disadvantages of MLR overestimation and underestimation at low and high values, respectively. (3) In downscaling, the monthly Version 7 TRMM Multisatellite Precipitation Analysis ( TMPA) datasets over China, the entire study area was separated into subregions driven by spatial datasets according to geographic similarities, which were similar to Chinese rainfall zones. [ABSTRACT FROM AUTHOR]