Background Grazing has important influences on the structures and functions of grassland ecosystems, but the effects of grazing patterns on grassland biomass and soil environments in China remain unclear. Objective We employed a meta-analysis to identify the response of biomass and soil environments to different grazing patterns in China. Methods Peer-reviewed journal articles were searched using the Web of Science and China National Knowledge to compile a database. A total of 1011 sets of sample observations satisfied the sampling standards; these were derived from 140 study sites and were obtained from 86 published articles. We conducted random effects meta-analyses and calculated correlation coefficients with corresponding 95% confidence intervals. Results Grazing significantly decreased the total biomass, aboveground biomass (AGB), belowground biomass (BGB), soil organic matter, soil total nitrogen, soil total phosphorus and soil water content but increased the root-to-shoot ratio, soil available nitrogen, soil pH and bulk density. Generally, increasing grazing intensity and duration significantly increased the effects of grazing on the biomass and soil environment. Additionally, the smallest effects of grazing on the biomass and soil environments were observed under light grazing and cattle grazing alone. Moreover, non-growing season grazing significantly increased AGB, while annual grazing and growing-season grazing significantly reduced AGB. Furthermore, AGB was positively correlated with soil organic matter, soil available phosphorus and bulk density, while BGB was negatively correlated with pH. Conclusions These findings highlight the importance of grazing patterns in the biomass and soil environment response to grazing and suggest that cattle grazing alone and grazing during the non-growing season are beneficial for improving the quality of grassland in China.

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Short-term high-resolution quantitative precipitation forecasting (QPF) is very important for flash-flood warning, navigation safety, and other hydrological applications. This paper proposes a subpixel-based QPF algorithm using a pyramid Lucas–Kanade optical flow technique (SPLK) for short-time rainfall forecast. The SPLK tracks the storm on the subpixel level by using the optical flow technique and then extrapolates the precipitation using a linear method through redistribution and interpolation. The SPLK compares with object-based and pixel-based nowcasting algorithms using eight thunderstorm events to assess its performance. The results suggest that the SPLK can perform better nowcasting of precipitation than the object-based and pixel-based algorithms with higher adequacy in tracking and predicting severe storms in 0–2 h lead-time forecasting.