Comparisons of rainfall partitioning and canopy interception modelling in pure and mixed forests on the Chinese Loess Plateau

Abstract Rainfall partitioning is a key process in forest ecosystems that affects the forest hydrological cycle and ecological services. However, little is known about the rainfall partitioning in Pinus tabuliformis (PT), Robinia pseudoacacia (RP), and Pinus tabuliformis × Robinia pseudoacacia (PT × RP) forests, which are widely distributed on the Chinese Loess Plateau (CLP) for controlling soil and water losses. The objectives of this study were to compare the rainfall partitioning and conduct interception modelling in PT, RP, and PT × RP forests, using data from the Ansai and Changwu sites on the CLP. The sites feature similar weather conductions and both pure and mixed forests of the same age. The tree density of PT × RP was moderate at the Ansai site but higher at the Changwu site compared to PT and RP. We quantitatively measured the throughfall ( T f ), stemflow ( S f ), and interception loss ( I c ) for the forests at the two sites during the growing season (May–October, 2022), and simulated I c using the Revised Gash Analytical Model (RGAM) and Liu model. The results showed that measured I c for PT × RP (26.0% of the total gross rainfall, P g ) was larger than for RP (22.0% of P g ) but smaller than for PT (27.8% of P g ) at the Ansai site, and vice versa for the T f and S f (RP > PT × RP > PT), while the measured I c for PT × RP (33.7% of P g ) at the Changwu site was larger than both for PT (31.1% of P g ) and RP (19.5% of P g ). The T f and S f for PT × RP was smaller than for PT and RP. Given the similar weather conditions, differences in I c for the pure and mixed forests at each site were attributed to canopy storage capacity, wet canopy evaporation rate, and tree density. Additionally, the RGAM and the Liu model effectively simulated and captured the differences in I c for PT, RP, and PT × RP. However, the RGAM performed better with a relative error less than −7.3%, Nash‐Sutcliffe model efficiency ranging from 0.82 to 0.95, and root mean square error less than 0.5 mm. According to the RGAM, evaporation from the wet canopy during rainfall events and after rainfall cessation were the two most influential components. The results of this study suggest that mixed forest can optimize rainfall partitioning compared to pure forests, but a reasonable density should be adopted to increase rainwater replenishment to the forest floor.