Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau

• Published in: Journal of forestry research 2022-04, Vol.33 (2), p.529-542
• Main Authors: Ma, Changkun, Luo, Yi, Shao, Mingan, Jia, Xiaoxu
• Format: Article
• Language: English
• Subjects: Analysis; Aquatic resources; Bark; Biomedical and Life Sciences; Canopies; Dieback; Forestry; Growing season; Herbivores; Hydrology; Interception; Interception loss; Leaves; Life Sciences; Linkages; Loess; Management; Multiple regression models; Original Paper; Plantations; Precipitation variability; Rain and rainfall; Rainfall; Rainfall interception; Regression analysis; Robinia pseudoacacia; Seasonal variations; Seasons; Semi arid areas; Semiarid lands; Storage capacity; Throughfall; Water; Water resources; Wood
• ISSN: 1007-662X; 1993-0607
• DOI: 10.1007/s11676-021-01324-w

Understanding the interaction between canopy structure and the parameters of interception loss is essential in predicting the variations in partitioning rainfall and water resources as affected by changes in canopy structure and in implementing water-based management in semiarid forest plantations. In this study, seasonal variations in rainfall interception loss and canopy storage capacity as driven by canopy structure were predicted and the linkages were tested using seasonal filed measurements. The study was conducted in nine 50 m × 50 m Robinia pseudoacacia plots in the semiarid region of China’s Loess Plateau. Gross rainfall, throughfall and stemflow were measured in seasons with and without leaves in 2015 and 2016. Results show that measured average interception loss for the nine plots were 17.9% and 9.4% of gross rainfall during periods with leaves (the growing season) and without leaves, respectively. Average canopy storage capacity estimated using an indirect method was 1.3 mm in the growing season and 0.2 mm in the leafless season. Correlations of relative interception loss and canopy storage capacity to canopy variables were highest for leaf/wood area index (LAI/WAI) and canopy cover, followed by bark area, basal area, tree height and stand density. Combined canopy cover, leaf/wood area index and bark area multiple regression models of interception loss and canopy storage capacity were established for the growing season and in the leafless season in 2015. It explained 97% and 96% of the variations in relative interception loss during seasons with and without leaves, respectively. It also explained 98% and 99% of the variations in canopy storage capacity during seasons with and without leaves, respectively. The empirical regression models were validated using field data collected in 2016. The models satisfactorily predicted relative interception loss and canopy storage capacity during seasons with and without leaves. This study provides greater understanding about the effects of changes in tree canopy structure (e.g., dieback or mortality) on hydrological processes.