Ex-situ estimation of interception storage capacity of small urban plant species

An important knowledge gap in current urban hydrological models are reliable, generic data about interception storage capacities of small urban plant species. These data are crucial to calculate interception losses and learning their effect on the urban hydrological cycle. This study addresses this knowledge gap through simulating rainfall events in an ex-situ, controlled environment on several urban plant species. Four plant species, Lonicera nitida, Lavandula angustifolia, Pennisetum alopecuroides and a grass mix were selected based on their abundance in urban environments and their morphological differences. Several vegetation characteristics such as height and diameter were altered to create as much variation as possible in the dataset to determine the underlying characteristics influencing the interception storage capacity. Estimating the interception storage capacity of each plant (SP) using multiple linear regression models, biomass (BP) was found to be the most important predictor variable for all species. Therefore predictive models to estimate the biomass of an individual plant were developed, using some easy to measure vegetation characteristics. When using the results of these biomass models as input in the storage capacity models, reasonable estimations of interception storage capacity were achieved with mean absolute errors between 17.7 and 40.8%, depending on the model. Extrapolating SP to a reference area of one m2 showed that L. angustifolia had the highest interception storage capacity due to its high biomass density, followed by P. alopecuroides, L. nitida and finally the grass mix. As a proof of concept, a mixed modelling approach was proposed to include species not covered in this research in the analysis. The findings in this research can be used to create a firm basis for calculations of intra- and interspecies interception storage capacities, essential for improving current urban hydrological models.

Publication year:2019

Keywords:Interception storage capacity, Urban plant species, Rainfall simulator experiments, Multiple regression analysis, Vegetative characteristics, Biomass- and storage capacity models

CSS-citation score:2

Accessibility:Closed