Climate change has a profound impact on the global carbon cycle, including effects on riverine carbon pools, which connect terrestrial, oceanic, and atmospheric carbon pools. Until now, terrestrial ecosystem models have rarely incorporated riverine carbon components into global carbon budgets. Here we developed a new process‐based model, TRIPLEX‐HYDRA (TRIPLEX‐hydrological routing algorithm), that considers the production, consumption, and transport processes of nonanthropogenic dissolved organic carbon (DOC) from soil to river ecosystems. After the parameter calibration, model results explained more than 50% of temporal variations in all but three rivers. Validation results suggested that DOC yield simulated by TRIPLEX‐HYDRA has a good fit (R² = 0.61, n = 71, p < 0.001) with global river observations. And then, we applied this model for global rivers. We found that mean DOC yield of global river approximately 1.08 g C/m² year, where most high DOC yield appeared in the rivers from high northern or tropic regions. Furthermore, our results suggested that global riverine DOC flux appeared a significant decrease trend (average rate: 0.38 Pg C/year) from 1951 to 2015, although the variation patterns of DOC fluxes in global rivers are diverse. A decreasing trend in riverine DOC flux appeared in the middle and high northern latitude regions (30–90°N), which could be attributable to an increased flow path and DOC degradation during the transport process. Furthermore, increasing trend of DOC fluxes is found in rivers from tropical regions (30°S–30°N), which might be related to an increase in terrestrial organic carbon input. Many other rivers (e.g., Mississippi, Yangtze, and Lena rivers) experienced no significant changes under a changing environment.

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai–Tibet Plateau in 1982–2015. The proposed method (R² = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R² of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R² of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982–2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

The leaf carbon isotope ratio (δ¹³C) is a useful parameter for predicting a plant's water use efficiency, as an indicator for plant classification, and even in the reconstruction of paleoclimatic environments. In this study, we investigated the spatial pattern of leaf δ¹³C values and its relationship with plant functional groups and environmental factors throughout China. The high leaf δ¹³C in the database appeared in central and western China, and the averaged leaf δ¹³C was −27.15‰, with a range from −21.05‰ to −31.5‰. The order of the averaged δ¹³C for plant life forms from most positive to most negative was subshrubs > herbs = shrubs > trees > subtrees. Leaf δ¹³C is also influenced by some environmental factors, such as mean annual precipitation, relative humidity, mean annual temperature, solar hours, and altitude, although the overall influences are still relatively weak, in particular the influence of MAT and altitude. And we further found that plant functional types are dominant factors that regulate the magnitude of leaf δ¹³C for an individual site, whereas environmental conditions are key to understanding spatial patterns of leaf δ¹³C when we consider China as a whole. Ultimately, we conducted a multiple regression model of leaf δ¹³C with environmental factors and mapped the spatial distribution of leaf δ¹³C in China by using this model. However, this partial least squares model overestimated leaf δ¹³C for most life forms, especially for deciduous trees, evergreen shrubs, and subtrees, and thus need more improvement in the future.

Accurate rainfall prediction remains a challenging problem because of the high volatility and complicated essence of atmospheric data. This study proposed a hybrid model (DSP) that combines the advantages of discrete wavelet transform (DWT), support vector regression (SVR), and Prophet to forecast rainfall data. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from 1 January 2014 to 30 June 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, and R² values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14, and R² values ranging from 0.62 to 0.75. The proposed model may provide a novel approach for rainfall forecasting and is readily adaptable to other time series predictions.