Rainfall partitioning by vegetation in China

Rainfall partitioning into stemflow, throughfall, and interception loss by vegetation alters hydrological and biogeochemical fluxes between vegetation and soil, and further affects water and nutrient balances at local, catchment, and regional scales. Here, we compiled a comprehensive dataset of rainfall partitioning by vegetation (forests, shrublands, croplands, and grasslands) in China. Based on this dataset, we delineate the general characteristics of rainfall partitioning in China from field observations. We summarize the best-fit functions reported for rainfall partitioning fluxes as a function of rainfall amount, as well as the rainfall thresholds for throughfall and stemflow initiation. We explore the patterns of the proportions of stemflow, throughfall, and interception loss to the gross rainfall across vegetation types in China. We determine whether and to what extent the chemical composition of rainwater is altered during rainfall partitioning processes. We use a machine learning method (boosted regression trees) to model the relative effects of cross-site biotic and abiotic predictors on each of the rainfall partitioning fluxes (%) and on the magnitude of chemical alteration in throughfall and stemflow. Our study avails a global readership to the findings of a large cache of Chinese studies that have been inaccessible hitherto, would aid in an accurate estimation of water and nutrients budget in vegetated ecosystems worldwide, and are helpful for making viable strategies to enhance forestry water resources management.