Relation of influencing variables and weather conditions on rainfall partitioning by birch and pine trees

General weather conditions may have a strong influence on the individual elements of the hydrological cycle, an important part of which is rainfall interception. The influence of general weather conditions on this process was analysed, evaluating separately the influence of various variables on throughfall, stemflow, and rainfall interception for a wet (2014), a dry (2015), and an average (2016) year. The analysed data were measured for the case of birch and pine trees at a study site in the city of Ljubljana, Slovenia. The relationship between the components of rainfall partitioning and the influential variables for the selected years was estimated using two statistical models, namely boosted regression trees and random forest. The results of both implemented models complemented each other well, as both indicated the rainfall amount and the number of raindrops as the most influential variables. During the wet year 2014 rainfall duration seems to play an important role, correlating with the previously observed influence of the variables during the wetter leafless period. Similarly, during the dry year 2015, rainfall intensity had a significant influence on rainfall partitioning by the birch tree, again corresponding to the influences observed during the drier leafed period.

Simulating Rainfall Interception by Caatinga Vegetation Using the Gash Model Parametrized on Daily and Seasonal Bases

Rainfall partitioning by trees is an important hydrological process in the contexts of water resource management and climate change. It becomes even more complex where vegetation is sparse and in vulnerable natural systems, such as the Caatinga domain. Rainfall interception modelling allows extrapolating experimental results both in time and space, helping to better understand this hydrological process and contributing as a prediction tool for forest managers. In this work, the Gash model was applied in two ways of parameterization. One was the parameterization on a daily basis and another on a seasonal basis. They were validated, improving the description of rainfall partitioning by tree species of Caatinga dry tropical forest already reported in the scientific literature and allowing a detailed evaluation of the influence of rainfall depth and event intensity on rainfall partitioning associated with these species. Very small (0.0–5.0 mm) and low-intensity (0–2.5 mm h−1) events were significantly more frequent during the dry season. Both model approaches resulted in good predictions, with absence of constant and systematic errors during simulations. The sparse Gash model parametrized on a daily basis performed slightly better, reaching maximum cumulative mean error of 9.8%, while, for the seasonal parametrization, this value was 11.5%. Seasonal model predictions were also the most sensitive to canopy and climatic parameters.

Estimating Rainfall Interception of Pinus hartwegii and Abies religiosa Using Analytical Models and Point Cloud

Rainfall interception plays a role in the hydrological cycle and is a critical component of water balances at the basin level, which is why understanding it is very important; as a result, in recent years, various authors have proposed different models to explain this process and identify which of them adapts better to each forest species. In this context, the aim of this research was to evaluate the Gash model and the sparse Gash analytical model in Pinus hartwegii Lindl. and Abies religiosa (Kunth) Schltdl. et. Cham., using measurements from 20 precipitation events recorded in May and June 2018 at the Zoquiapan Experimental Forest Station, Mexico. The evaporation rate was calculated using the Penman–Monteith method (PM) and Gash’s calculation procedure. The canopy parameters were also calculated with two methods, a graphical one (A) and a method proposed in this research (B), which is based on point cloud generated with drone photogrammetry. For P. hartwegii, the most accurate model to estimate interception per rainfall event was the Gash model with the A and Gash methods, which were used to calculate the canopy parameters and evaporation rates, respectively; for accumulated interception, the sparse Gash analytical model with the B and PM methods was used. For A. religiosa, the best fit for individual events was presented by the sparse Gash analytical model with the B and PM methods, and for accumulated interception, it was the Gash model with the B and Gash methods. The results allow concluding that the B method proposed in this research is a good alternative for the calculation of rainfall interception, since it tends to improve its estimation, shortening the time for acquiring information about the parameters of the canopy structure and thus minimizing the costs involved.

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

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.