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

Highlights

• Storage capacities of small plants can have significant impact on urban water flow.

• Models predict storage capacities based on simple vegetation characteristics.

• Biomass is the most important variable for predicting storage capacity.

• Model input can be facilitated using biomass models.

• A method for upscaling the results to one m² is proposed.

Abstract

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 (S_P) using multiple linear regression models, biomass (B_P) 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 S_P 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.

Introduction

Regulating ecosystem services provided by urban green are diverse: ranging from carbon and fine particle sequestration to urban heat regulation (Cameron and Blanuša, 2016, Livesley et al., 2016). One of the more prominent services is the ability of vegetation to regulate water flows (e.g. Martin-Ortega et al., 2015). The natural water flow or hydrological cycle of urban areas is altered due to increased imperviousness in the urban environment which results in more frequent flooding and other water related problems such as reduced infiltration and groundwater recharge (Haase, 2009, Jacobson, 2011, Paul and Meyer, 2001). Most cities are built with the idea that incoming rainwater should be guided to the sewer system as quickly as possible after which it will be diverted away from the city. In recent decades however, the idea has been growing that rainwater is a utility rather than an inconvenience and efforts have been made to develop systems that keep rainwater within the city as a temporal storage or to supply the blue-green network (Demuzere et al., 2014, FAWB, 2009). The idea of using rainwater in our cities to regulate the hydrological balance is known under different names such as ‘Water sensitive urban design (WSUD)’ (Coutts et al., 2012, Wong et al., 2013), ‘Low Impact Development (LID)’ (Dietz, 2007) or ‘Sustainable Drainage Systems (SuDS)’ (Ciria, 2013). Because of its high surface area to volume ratio and relatively low-cost implementation, vegetation has a great potential in acting as a temporal water storage buffer. Additionally the IPCC states that vegetation can make our cities more resilient against negative impacts of climate change such as an increased flood risk and mitigating the urban heat island effect (Revi et al., 2014).

Plants alter the urban hydrological balance by intercepting rainwater. Interception is defined as the process of precipitation falling on vegetation surfaces where it is temporally stored. This water is then either evaporated into the atmosphere, absorbed by leaf surfaces or falls through to the ground surface (Xiao et al., 2000a). The rainwater interception potential of vegetation has been extensively studied and modelled in the past for forest canopies by the Rutter and Gash models (Gash, 1979, Rutter et al., 1971). These models served as foundation on which several authors continued making progress in forest interception modelling (e.g. Muzylo et al., 2009). In more recent decades a shift occurred towards smaller scale crown interception in solitary trees to determine the influence of street trees on the urban water balance (Xiao et al., 2000a, Xiao and McPherson, 2011). The urban vegetation spectrum however does not only consists of trees but also has an abundance of other types of smaller plants such as shrubs and grasses. In terms of water balance studies, these plant types have largely been ignored by the scientific community. Dunkerley (2000), in his review of interception methods, related that to the need to refine measurement techniques for these smaller plant types and he goes even further and implicated that most published estimates of interception loss are probably based on inadequate data and methods. Small plants and their vegetation nevertheless, are an integral part of urban vegetation and, due to their abundance in parks and private gardens, contribute to the water storage potential of urban green (Dewaelheyns et al., 2014, Verbeeck et al., 2013). From a management perspective, they are easier to install and sustain than trees and can be implemented in more diverse locations.

The most important plant characteristic influencing the rainwater interception process is the interception storage capacity. There has been some confusion in literature regarding the definition of this term.

The core of this confusion lies in distinguishing the difference between the temporal maximum storage that can be reached during a rainfall event and the ‘real’ storage capacity. Meteorological variables such as rain intensity and droplet size can influence the amount of water retained on a canopy to an extent that it temporarily exceeds the ‘real’ storage capacity. This is demonstrated in the experiments of Keim et al. (2006). They found, while simulating rainfall of different intensities to determine interception storage capacities of small vegetation, a drop in actual water storage immediately after stopping the rainfall simulation. This drop, they reasoned, is a result of dripping and evaporation, with the evaporation process becoming more dominant as time increases. The amount of water stored during the rainfall event that is lost immediately after the rain stops hence is not a part of the real storage capacity. Keim et al. (2006) concluded from their experiments that there are two types of storage capacity: first there is the static storage capacity which is the result of an equilibrium of static forces at the contact between water, vegetation and the atmosphere. This storage capacity is largely independent from meteorological variables and evaporation losses from vegetation canopies depend mostly on this storage capacity. It can best be measured after rainfall when canopy drip has ceased.

The temporal maximum storage reached during a rainfall event is defined by Keim et al. (2006) as a second type of storage, the dynamic storage capacity. This storage capacity is the result of dynamic forces generated on the vegetation surfaces through meteorological variables such as rainfall intensity and droplet size. The excess water will be removed mainly through drip during the first few minutes after rain has stopped.

More recently, Xiao and McPherson (2016) tackled this confusion in a similar way: they called the static storage capacity the surface saturation- or minimum storage capacity and the dynamic storage capacity the detention- or maximum storage capacity.

This study focuses on the static storage – (Keim et al., 2006) or surface saturation- or minimum storage capacity (Xiao and McPherson, 2016) because this variable determines the quantity of rainwater that never reaches the ground surface and does not contribute to run-off. It is a vegetation characteristic largely independent from meteorological variables and is of great value for modelers to predict run-off volumes and rates during and after rain events. For simplicity, in what follows the static storage – or surface storage capacity will be referred to as the interception storage capacity.

Most authors that derived storage capacities tried to link those to certain vegetation characteristics. They then expressed this storage capacity as a function of the characteristic they thought of as most influential for storage capacity: The four main expressions of storage capacity are as a function of canopy projection area (S_CPA, mm) (e.g. Domingo et al., 1998, Garcia-Estringana et al., 2010, Wang et al., 2012), actual canopy area (S_A, mm), which also includes the stem and branch surfaces (e.g. Holder and Gibbes, 2017, Xiao and McPherson, 2016), leaf area (S_L, mm) (e.g. Keim et al., 2006, Wohlfahrt et al., 2006) and unit dry- or fresh biomass (S_B, ml/g or g/g) (e.g. Garcia-Estringana et al., 2010, Wang et al., 2012). Note that the interception storage capacities in mm are expressed on a reference area basis of one m². Table 1 gives a literature overview of studies done on storage capacity.

Several other vegetation characteristics have been found to influence storage capacity besides the ones described above. These characteristics are mainly depending on canopy architecture or growth form (Pérez-Harguindeguy et al., 2013). The woody branch architecture of shrubs, having a canopy typically consisting of several diverging stems with secondary branches and leaves inserted in a certain angle results in densely covered ground area patches. This configuration giving rise to a large potential interception storage capacity. In a study featuring nine Mediterranean shrub species, Garcia-Estringana et al. (2010) found that the interception storage capacity of shrubs was largely determined by their morphology, particularly the branch- and leaf density were significant variables. He also found other morphological characteristics such as leaf hydrophobicity and pubescence, roughness of plant tissue and orientation of leaves and branches playing a role in water retention. Other green types such as common lawn and perennial grasses are herbaceous plants. Displaying no or very limited secondary growth and consisting primarily of soft tissue compared to woody plants, branch- and leaf angles are not commonly measured for these vegetation types. A typical characteristic measured for grasses, known to influence interception storage capacity, is the percentage of vegetation cover over bare soil (e.g. Concilio et al., 2015). We hypothesize that the interception potential of small plant species is comparable or in some cases even higher than the one of solitary trees. The lack of standardization in expressing storage capacity values, together with the unclear definition of storage capacity itself which often has to be deduced from the context, makes comparing literature values challenging and should be done with caution. Moreover, most studies on small urban green plants were done on xerophytic species in a Mediterranean climate (Garcia-Estringana et al., 2010, Wang et al., 2012), limiting applicability of their results in regions with a more temperate climate.

This study tries to generate reliable interception storage capacity data for several smaller urban plant species, common in temperate climate regions, by simulating rain events in a controlled environment and modelling interception storage capacity based on easy to measure vegetative characteristics. Generic models for predicting interception storage capacity within a species, as well as between species and between urban green types will be developed. Additionally, because biomass is an important vegetative characteristic and no standardized measuring procedure yet exists, models that estimate an individual plants’ biomass are developed. These models can subsequently be used as input for the storage capacity models. Having models that accurately predict biomass and interception storage capacities of small urban plant species can greatly improve current urban hydrological models and help urban planners to make strategic decisions on where and what type of green to use.

The objectives of this study are threefold:

• Measuring interception storage capacities of several small urban plant species by simulating rainfall events in controlled conditions and assessing their inter- and intra-species variability.

• Construction of interception storage capacity models with data derived from objective 1 to determine vegetation characteristics within- and between species that significantly influence interception storage capacity.

• Construction of biomass models, using vegetation characteristics easily measured in the field, to use as input for the interception storage capacity models.