Stemflow has been documented to be closely related to a suite of eco-hydrological processes, including spatial distribution pattern of soil moisture, surface runoff, soil erosion, groundwater recharge and perched water development, the distribution of understory vegetation and epiphytes, and a potential streamflow generation process. 

In recent years, the eco-hydrological and biogechemical importance of stemflow is increasingly acknowledged and a dramatic increase of stemflow studies can be found. 

Nevertheless, relative contributions of biotic and abiotic factors to stemflow production and funneling efficiency were largely unclear due to complex interactions among those factors. 

Recently, scientists from Northwest Institute of Eco-Environment and Resources (NIEER) of the Chinese Academy of Sciences measured the stemflow of nine xerophytic shrubs in nearly nine growing seasons from 2010 to 2018 within a desert area of northern China, accompanying with observing on six biotic variables (shrub morphological attributes) and ten abiotic variables (meteorological conditions). 

The scientists performed boosted regression trees (BRT) model to evaluate the relative contribution (θ) of each biotic and abiotic variable to stemflow volume (SFv), stemflow percentage (SFp), and funneling ratio (FR), associating with partial dependence plots (PDPs) to visualize the effects of individual explanatory variables on SFv, SFp, and FR, respectively. 

The results showed that biotic variables outweighed abiotic variables in their average contribution to SFv, whereas abiotic variables prevailed for SFp and FR, respectively. 

Besides, variations in θ between variables for SFv (CV=146%) were much pronounced than SFp (CV=57%) and FR (CV=26%). 

The quantitative and mechanistic explanations regarding the effects of biotic and abiotic variables on three stemflow parameters from the present study are expected to be applicable to other shrub species within arid and semi-arid ecosystems. 

The study result was published in Agricultural and Forest Meteorology.