Predicted models for potential canopy rainfall interception capacity of landscape trees in Shanghai, China

This study aimed to build urban green space with environmental functions (e.g., canopy interception of rainfall) and adjust hydrographic balance to some extent for forecasting the potential canopy rainfall interception capacity of landscape trees and the effects on rainfall distribution. The effects of urban green space on interception and runoff reduction have been conceptualized, but not quantified. Therefore, the leaf area index and the water storage abilities of 17 kinds of landscape trees in common use were measured, at Shanghai, and canopy rainfall interception capacity was calculated using the interception formula. The predicted rainfall interception capacity models were established choosing tree morphological characteristics (diameter at the breast height, height, and crown width) as variables. The model test showed that the errors of 12 models were less than 5% between the predicted and the measured data and the errors of four models were within 5 and 10%, with the error for only one model being between 10 and 11%. Also, the study indicated that conifer trees were able to hold more rainfall compared with broad-leaved trees per unit area (k). The results showed that these models could effectively predict the potential capacity of canopy rainfall interception for landscape trees in Shanghai area and were beneficial for species selection in constructing plant communities, aiming to improve the rainfall interception capacity of urban green space.