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The Comparative Impacts of Meadow and Sedum Species on Green
Roof Hydrology
by
Marisa Fryer
A thesis submitted in conformity with the requirements
for the degree of Masters of Applied Science
Department of Civil Engineering
University of Toronto
© Copyright by Marisa Fryer 2017
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The Comparative Impacts of Meadow and Sedum Species on Green
Roof Hydrology
Marisa Fryer
Masters of Applied Science
Department of Civil Engineering
University of Toronto
2017
Abstract
The goal of this thesis is to compare the stormwater management potential of two common green
roof plant communities – native grasses and forbs, known as “meadow” species, and Sedum
species – over a range of irrigation volumes. The physical and biological mechanisms involved
in water retention provided by the green roof vegetation was measured and compared. Plants
retain stormwater through three different mechanisms: water consumed through the roots which
remains in the biomass, water transpired through the leaves during photosynthesis, and water
intercepted by the leaves which then evaporates. Overall, the meadow species consumed roughly
50% more water through their roots, while Sedum intercepted roughly 70% more water;
however, these processes occurred over different timescales and at different magnitudes, and
were significantly impacted by variations in climate. This study highlights the complexities of
plant water relations, and will contribute to a more holistic understanding of green roof
performance and benefits.
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Acknowledgments
I would first like to acknowledge my advisor, Dr. Jennifer Drake for trusting me with this project
and for providing support and advice throughout the duration of my program, as well as my
second reviewer, Liat Margolis, for providing comments and suggestions in the final stages of
my thesis, and for sharing the GRIT lab space with me. Additionally, I would like to thank the
other members of my research group, who have provided helpful suggestions and a sense of
community along this journey.
My colleagues at the GRIT lab have also been a key source of support and assistance throughout
my data collection process. Jenny Hill, whose PhD thesis this research builds upon, and Scott
MacIvor, whose background in biology provided a different perspective from my own, both
offered invaluable advice and instruction during the early stages of the project, particularly for
setting up my experiments, selecting which plant species to test, and using the laboratory
equipment. The landscape architecture students and summer employees at the GRIT lab – in
particular, Catherine Howell, Hadi El-Shayeb, and Isaac Seah – also helped tremendously by
maintaining and troubleshooting the weather station sensors and data loggers. Their presence
also made long days of data collection at the GRIT lab more enjoyable.
I would also like to acknowledge Bioroof Systems Inc. for donating the green roof growing
media used in this study, and Corey Lunman and Hoskin Scientific for lending me the LCPro for
the month of September, 2017, and for providing technical support. Finally, I would like to thank
the industry sponsors who make research at the GRIT lab possible. Research funding was
provided through a NSERC Strategic Project grant with in-kind contributions provided by
Bioroof Systems, DH Water Management Services, Sky Solar (Canada), IRC Building Science
Group.
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Table of Contents
Chapter 1: Introduction ................................................................................................................... 1
1.1 Green Roof Introduction ........................................................................................................... 1
1.2 Green Roof Innovation Testing Laboratory .............................................................................. 2
1.3 Research Objectives .................................................................................................................. 5
1.4 Thesis Outline ........................................................................................................................... 6
Chapter 2: Literature Review .......................................................................................................... 7
2.1 Understanding Plant Physiology and Hydrology ...................................................................... 7
2.2 Assessment of Previous Studies.............................................................................................. 10
2.2.1 Methods for Quantifying Combined Evapotranspiration ................................................ 10
2.2.2 Methods for Directly Measuring Transpiration ............................................................... 11
2.2.3 Methods for Measuring Interception ............................................................................... 13
2.2.4 Limitations of Existing Studies ........................................................................................ 14
2.3 Conclusions ............................................................................................................................. 15
Chapter 3: Biomass and Overall Plant Health .............................................................................. 17
3.1 Introduction ............................................................................................................................. 17
3.2 Set-up ...................................................................................................................................... 17
3.3 Hydrologic Conditions ............................................................................................................ 20
3.4 Biomass Assessment ............................................................................................................... 23
3.4.1 Weighing the Pots ............................................................................................................ 24
3.4.2 Measuring Soil Moisture.................................................................................................. 26
3.4.3 Calculating Biomass ........................................................................................................ 29
3.5 Plant Volume and Photo Analysis .......................................................................................... 32
3.5.1 Plant Volume ................................................................................................................... 32
3.5.2 Photo Analysis ................................................................................................................. 37
3.6 Data Synthesis ......................................................................................................................... 40
3.7 Conclusions ............................................................................................................................. 40
Chapter 4: Interception and Transpiration .................................................................................... 42
4.1 Introduction ............................................................................................................................. 42
4.2 Methodology ........................................................................................................................... 46
4.2.1 Set-up ............................................................................................................................... 46
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4.2.2 Transpiration Rates .......................................................................................................... 48
4.2.3 Interception ...................................................................................................................... 51
4.3 Results and Discussion ........................................................................................................... 52
4.4 Conclusions ............................................................................................................................. 57
Chapter 5: Extension to Full Green Roof Systems ....................................................................... 59
5.1 Introduction ............................................................................................................................. 59
5.2 Extending to other plant species ............................................................................................. 59
5.3 Data Synthesis and Comparisons ............................................................................................ 61
5.4 Comparisons to Complete Green Roof Modules .................................................................... 64
Chapter 6: Conclusions ................................................................................................................. 67
6.1 Research Summary ................................................................................................................. 67
6.2 Climate Considerations ........................................................................................................... 69
6.3 Impacts to the Hydrological Cycle ......................................................................................... 70
6.4 Green Roof Design ................................................................................................................. 71
References ..................................................................................................................................... 73
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List of Tables
Table 1: Plant species per community type .................................................................................. 18
Table 2: Precipitation and irrigation totals presented as average daily depth, by month. ............ 21
Table 3: Average, Minimum, and Maximum Mass Measurements [g] for 2016 Season ............. 26
Table 4: Average, Minimum, and Maximum VWC for 2016 Season .......................................... 28
Table 5: Spatial Density by Plant Species .................................................................................... 34
Table 6: Average Plant Volume (cm3) by Month and Overall ..................................................... 36
Table 7: Correlation Coefficients for Total Mass and Soil Moisture Content for each pot.......... 40
Table 8: Correlation Coefficients for Total Mass and Plant Volume for each pot. ...................... 40
Table 9: Plant species per community type .................................................................................. 47
Table 10: Compiled averages for each measurement and variable type ....................................... 53
Table 11: Comparison of Porometer and LCPro measurements across four test species. ............ 61
Table 12: Average Water in Biomass, Transpiration, and Interception ....................................... 62
Table 13: Interception vs. Total Retention ................................................................................... 65
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List of Figures
Figure 1: Overhead view of green roof modules at the GRIT Lab (Photo credit: Arman
Khabazian) ...................................................................................................................................... 3
Figure 2: Leaf transpiration circuit analogy, where rwv is the resistance to water vapour of each
component ..................................................................................................................................... 12
Figure 3: Side and overhead view of pots ..................................................................................... 19
Figure 4: Precipitation Hyetograph showing all rainfall events from June 9 – October 31, 2016.22
Figure 5: Precipitation Hyetograph showing all rainfall events from May 1 – June 30, 2017. .... 22
Figure 6: Pots are weighed using an OHAUS Balance................................................................. 24
Figure 7: 2016 Mass Time Series ................................................................................................. 25
Figure 8: Substrate moisture is measured with an EC-5 sensor.................................................... 26
Figure 9: Soil Moisture Sensor Calibration Process ..................................................................... 27
Figure 10: Calibration Curve ........................................................................................................ 27
Figure 11: 2016 Soil Moisture Content Time Series .................................................................... 28
Figure 12: Evidence of soil loss due to wind erosion or digging by animals (left). Prime suspect
(right). ........................................................................................................................................... 30
Figure 13: Weekly soil depth measurements for each pot, arranged by variable combination. The
dotted lines represent the average slopes, or overall trend for each pot. ...................................... 31
Figure 14: Minimum and maximum width of an example plant .................................................. 33
Figure 15: Photographs of each species for spatial density calculations ...................................... 33
Figure 16: Total Plant Volumes for 2016 ..................................................................................... 35
Figure 17: Total Plant Volumes for 2017 ..................................................................................... 36
Figure 18: Photographs of each pot type, taken every three weeks over the 2016 study period. . 38
Figure 19: Photographs of each pot type, taken every three weeks over the 2017 study period. . 39
Figure 20: Overhead view of green roof modules at the GRIT Lab (Photo credit: Arman
Khabazian) .................................................................................................................................... 43
Figure 21: Side and overhead view of pots ................................................................................... 48
Figure 22: The porometer is manually clipped to an example leaf............................................... 50
Figure 23: Minimum and maximum width of sample plant (left). Tracing for density estimation
(right). ........................................................................................................................................... 51
Figure 24: Water intercepted by the plants is collected and weighed ........................................... 52
Figure 25: Monthly Climate and Irrigation Data .......................................................................... 53
Figure 26: Average transpiration rates by month (left) and over the full study (right). ............... 54
Figure 27: Canopy Area by month (left) and averaged over the full study (right). ...................... 55
Figure 28: Sample canopy photographs by variable combination ................................................ 55
Figure 29: Hourly Transpiration Volume by month (left) and averaged over the full study (right).
....................................................................................................................................................... 56
Figure 30: Intercepted Volume per event (left) and averaged over the full study (right). ............ 57
Figure 31: LCPro demonstrated with a variety of plant species ................................................... 60
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List of Appendices
Appendix A: Water Consumption Weekly Data .......................................................................... 78
Appendix B: Sample Biomass Calculations ................................................................................. 80
Appendix C: Soil Depth Calculations ........................................................................................... 81
Appendix D: Plant Volume Calculations ...................................................................................... 82
Appendix E: Means, Standard Errors, and Statistical Analysis for Transpiration and Interception
Data ............................................................................................................................................... 83
Appendix F: Sample Calculations and Weekly Averages for Transpiration Rates ...................... 84
Appendix G: Calculated Canopy Areas from Photos ................................................................... 86
Appendix H: Estimated Transpiration Volumes ........................................................................... 87
Appendix I: Estimated Interception Volumes............................................................................... 88
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Chapter 1: Introduction
1.1 Green Roof Introduction
Green roofs are the common name given to installations of vegetation roof systems used,
particularly, in urban areas. While green roofs can create appealing recreational spaces for
community use and improve the aesthetic value of a city, they are also considered beneficial for
a variety of reasons ranging from increasing energy efficiency and reducing building costs to
improving ecosystem biodiversity and environmental quality.
Green roof vegetation reduces heat flux through the roof in the summer through evaporative
cooling and by intercepting solar radiation, which in turn keeps the building cooler and reduces
energy costs associated with air conditioning (Del Barrio 1998, MacIvor et al. 2016). Akbari et
al. (2004) suggests that increasing urban vegetation not only reduces building temperature, but
can reduce the temperature of the entire city, helping to mitigate urban heat island effect. Green
roofs also reduce building costs by protecting the waterproofing membrane on the roof from
rapid deterioration ordinarily caused by exposure to UV lights, which has been suggested to
extend the roof membrane lifespan by more than twenty years (Obernadorfer et al. 2007).
Green roofs have been shown to enhance the biodiversity of urban landscapes by creating
habitats for birds and insects (Obernadorfer et al. 2007) and by providing foraging plant species
for pollen-seeking bees (MacIvor et al. 2015). The plants may also improve air quality by
sequestering CO2 for photosynthesis, trapping airborne particulates, and taking in other air
pollutants like sulphur dioxide and nitrogen oxides (Banting et al. 2005). Finally, green roofs
reduce stormwater runoff generated on rooftops, which in turn reduces flooding at street level
(Hill et al. 2017). This last benefit will be the primary focus of this thesis, but all of the benefits
listed are interconnected in the sense that they are dependent on the green roof plants being
healthy, with optimized design and maintenance. The thermal cooling, runoff reduction, and
pollutant control aspects of green roofs also all contribute to the technology’s role in combatting
climate change (Gill et al. 2007).
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Due to the potential environmental and cost-saving benefits of green roofs, the City of Toronto
adopted a bylaw in 2009, which requires all new developments with gross floor area greater than
2,000 m2 to install green roofs covering at least 20% of the roof surface and new developments
greater than 20,000 m2 to install green roofs covering at least 60% of the roof surface (Council of
the City of Toronto 2009). For this reason, there is particular interest in studying green roof
performance and optimizing design within the specific climatic conditions of Southern Ontario.
1.2 Green Roof Innovation Testing Laboratory
The Green Roof Innovation Testing Laboratory (GRIT Lab) at the University of Toronto was
established in 2011 to assess the performance and benefits of various green roof design options.
The lab was originally constructed with thirty-three green roof test modules, each comprised of
different combinations of the following variables:
• Planting media type: mineral-based or high-organic-matter media
• Planting media depth: 15-cm or 10-cm
• Irrigation amount: no irrigation, timer irrigation, or sensor irrigation
• Vegetation type: native grasses, forbs, and wildflowers (referred to in this study as
‘meadow’ species) or Sedum species
The modules are equipped with a series of automated sensors to measure soil moisture,
temperature, and water discharge rate. Additionally, the lab is equipped with a weather station
which records atmospheric temperature, air pressure, solar radiation, humidity, wind speed, and
precipitation amount (Margolis 2017). The overall layout of the different modules is shown in
Figure 1 below.
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Figure 1: Overhead view of green roof modules at the GRIT Lab (Photo credit: Arman Khabazian)
The GRIT Lab set-up allows for the calculation of water retention for each green roof module by
recording precipitation, irrigation, and discharge volumes, normalizing by area, and then
performing a water balance. The discharge volumes are measured by a tipping bucket rain gauge
(TE525M, Texas Electronics, Dallad, Texas) attached to the outflow pipe at the base of each
module. The hydrologic performance of each module, as determined through the listed
measurements, is represented by a runoff coefficient, Cvol, which is the ratio of total discharge
depth from an individual event, Q, over total event precipitation depth, P (Hill et al. 2017):
Cvol =ΣQ
ΣP ( 1 )
Hill et al. (2017) used ANOVA statistics to compare the average runoff coefficients for each of
the independent design variables. By performing a stepwise regression to test the statistical
significance of each variable’s effect on runoff coefficient, Hill found that irrigation amount and
planting medium type had the most impact on runoff volumes, while planting medium depth and
vegetation type had little to no significance whatsoever. Other studies with similar statistical
methods have found comparable results: VanWoert et al. (2005) compared runoff from vegetated
roofs to control roofs with planting medium only, and found that vegetation had much less of an
impact on stormwater retention than the growing medium itself, while Nardini et al. (2012)
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found that vegetated roofs outperformed media-only roofs, but observed no statistical difference
between two vegetation types studied – shrubs and herbaceous species.
These results are contradicted by other green roof studies which have observed variation in water
runoff by vegetation type (Dunnett et al. 2008, Dusza et al. 2017), which are more in line with
commonly-held assumptions based on plant biology. Drought-tolerant succulents, such as the
Sedum species studied at the GRIT Lab, are known to conserve water by nature, particularly
when experiencing water limited conditions. Previous studies have repeatedly found that
succulents outlast grasses, forbs, and shrubs in non-irrigated systems (Durhman et al. 2006,
Lundholm et al. 2010), and that succulents have better survival and/or revival rates in drought
conditions (Berghage et al. 2009, Bousselot 2011). Because succulents survive while consuming
less water, they are often thought to be less effective at reducing runoff than meadow species
which must transpire water at a constant, higher rate (Berghage et al. 2007); however, this
hypothesis is based on an incomplete picture of the physical and biological processes occurring
in and around the plants.
On average, Sedum and other succulent species exhibit the lowest conductance rates – or water
vapor flux through their leaves – compared to other types of plant species (Korner et al. 1979),
but their conductance rates are not constant for all climatic conditions. Sedum can limit their
water flux during water-stressed conditions, but studies have found that their water use is greater
or equal to non-succulent plants in water-abundant situations (Berghage et al. 2007, Bousselot
2011). Additionally, the total consumption of water by the plant is correlated with biomass
(Lundholm et al. 2010), so species which are more durable and can maintain their biomass
through drought may consume more total water even with lower conductance rates.
Finally, there are also physical means by which different plant species influence rainfall
retention; for example, Wolf and Lundholm (2008) found that the mat-forming structure of
Sedum species created a barrier between the soil and the atmosphere, which may impede
evaporation directly from the soil, or – inversely – prevent rainwater from reaching the soil.
Overall, the wide range of variations in structure and performance of different plant species
makes the observed insignificance of species selection on runoff coefficient all the more
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perplexing. Therefore, further research is justified to explore the complex role that the plants
play in green roof hydrology.
1.3 Research Objectives
This thesis will primarily focus on the stormwater reduction benefits of green roofs, and will aim
to answer one of the key knowledge gaps left from previous GRIT Lab research. By comparing
the runoff coefficients of the different modules, through the method outlined in the previous
section, conclusions can be drawn regarding the relevance of each individual variable to
hydrological performance. Still, this method does not allow for more in-depth study of how the
various green roof variables, specifically plant type and irrigation practice, are influencing
retention. In short, Hill et al. (2017)’s analysis indicated that the native meadow and Sedum
communities had no statistical difference in their influence on runoff, but could not explain why.
Two possible hypotheses which could explain this result are (a) that the different mechanisms
through which the two different plant types reduced runoff were equal or balanced each other
out, or (b) that the overall contributions of the plants to runoff reduction are negligible compared
to the water stored and evaporated directly from the substrate layers.
A new methodology must be applied to test these two hypotheses, and more generally, to better
understand the physical and biological processes occurring at the level of the plants which
contribute to stormwater management. The primary goal of this project is to compare the runoff
reduction potential of the two most popular plant communities used on green roofs in Southern
Ontario: native meadow species and Sedum species. Plant growth, both in terms of biodiversity
and overall cover, is influenced by the supplement of irrigation (MacIvor et al., 2013); therefore,
the two plant communities were also assessed over a range of irrigation volumes. The results
presented will supplement other data collected at the GRIT Lab, including soil analysis, overall
water retention, plant cover, plant biodiversity, pollen analysis of urban bees, and thermal
benefits. This will help to create a holistic understanding of green roof performance and benefits.
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1.4 Thesis Outline
The remaining sections of this thesis will be organized as follows:
Chapter Two presents a review of existing literature. This review is separated into two sections:
the first describes the physical and biological processes through which vegetation consumes
water and contributes to runoff reduction, while the second section assesses the methodologies of
previous experiments designed to study green roof vegetation. This chapter will be revised and
extended for publication at a later date.
Chapter Three presents the plant-growth analysis component of the study. This chapter contains
the full experimental set-up and the methods and results for assessing plant growth, plant
diversity, and biomass of each community.
Chapter Four is a standalone research paper which presents the methodology and results for
comparing evapotranspiration and interception rates of the two plant communities. The paper
will be submitted for publication in the Journal of Ecological Engineering.
Chapter Five explores the possibility of extending the results presented in chapters three and four
to other plant species within each community (Sedum and meadow). It also presents calculations
for scaling the plant water retention volumes from the small-scale study size to larger green roof
areas.
Chapter Six summarizes the overall conclusions from the previous sections and discusses the
implications of these conclusions on green roof species selection and industry design practices.
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Chapter 2: Literature Review
2.1 Understanding Plant Physiology and Hydrology
As discussed in Section 1.1, one of the justifications for vegetated roofs is that they reduce runoff
that would otherwise contribute to flooding in the buildings and roadways below. While part of
this runoff reduction is achieved through the storage and evaporation of rainwater in the soil and
drainage layers of the green roof, the plants themselves also play a role. Plants reduce runoff
through three mechanisms:
1. Plants consume water for cell synthesis and plant growth.
2. Plants use water for energy production. This water is transpired through the leaves.
3. Plants physically intercept rainwater that lands on their leaves. This water evaporates
directly off the surface of the leaves.
All three of these processes are influenced by the plant’s survival, size, and biology.
Transpiration rates are also dependent on species type (C3, C4, and CAM species transpire at
different rates, as described later in this section), while interception rates are dependent on plant
geometry; i.e. the shape, structure, and surface properties. Water consumption for plant growth
refers to the water that is retained within the plant tissue, contributing to the overall biomass.
The two plant communities assessed in this study are Sedum species, which are commonly used
in green roof design for their known durability, and meadow species, which are native to
Southern Ontario. Sedum species are more resistant to water-limited conditions because they can
restrict water loss through their leaves via transpiration (Berghage et al. 2007, 2009), so it
follows that they will have a greater amount of water per plant size consumed by this method.
This is supported by previously published data: herbaceous species – a broad category of non-
woody plants, which includes the meadow species used in this study – generally consist of over
80% water content in their roots and leaves when fully saturated, or turgid (Hsiao 1973).
According to Gausman and Allen (1973), who conducted a study of 30 different plant species,
reported a mean leaf water content of 80%. By comparison, the leaf water content of the
representative Sedum species in their study was 95%.
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While these data suggest that Sedum will outperform meadow species for water consumption in
the plant growth metric, there is also evidence that the overall water used for biomass growth is
negligible compared to the water lost through transpiration. A study of agricultural plants, for
example, found that the water retained in the plant cells was less than 1% of the water taken in
by the plant; therefore, water consumption by the plants could simply be estimated as
transpiration (Jensen 1968). Lambers et al. (2008) reiterates this same statistic for all herbaceous
plant species.
The second water consumption mechanism listed is transpiration. Transpiration is the
evaporation of water from within the plant’s leaves. It occurs as a side effect of photosynthesis,
which is the process by which plants use carbon dioxide, water, and sunlight to create
carbohydrates for food according to the following chemical reaction (Lambers et al. 2008):
6CO2 + 6H2O + Sunlight C6H12O6 + 6O2
Leaves of plants are covered in stomata, or tiny pores in the epidermis, which open to allow CO2
to enter the leaves (Gerosa et al. 2012). As stated above, the leaves are over 80% water, causing
the air within the leaves to be fully saturated with water vapor. When the stomata are opened,
this air is exposed to the atmosphere, creating a vapor pressure gradient that forces the water out
of the leaf (Lambers et al. 2008). While a small amount of water is used in the photosynthesis
reaction, the rate of water lost through transpiration is much greater. The water usage efficiency,
or ratio of photosynthesis to transpiration varies by plant species and environmental conditions;
however, a general order of magnitude can be predicted from existing studies. One study of
California evergreens reported values of mol CO2 consumed/ mol H2O transpired ranging from
0.002 to 0.005 (Field et al. 1983), while a study of cotton plants using the inverse metric of
grams H2O transpired/ gram carbohydrate produced reported a range of 100 to 500 (Bierhuizen
and Slatyer 1965).
The photosynthesis, and in turn transpiration, process for Sedum and meadow species differ
slightly. Sedum are crassulacean acid metabolism (CAM) plants, while meadow species are
classified as either C3 or C4 plants. C3 plants are the most common, and have the most basic
photosynthetic pathway, wherein the entire reaction is completed by a single chloroplast type
(Furbank and Taylor 1995). In contrast C4 plants have adapted to improve efficiency by dividing
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the process over two different chloroplast types, which reduces CO2 losses associated with
photorespiration (Furbank and Taylor 1995). CAM species have adapted to further improve
efficiency and specifically reduce water loss by fixing CO2 at night, allowing them to close or
partially close their stomata during the day (Al-Busaidi et al. 2013). Because transpiration is
correlated with the vapor pressure gradient between the leaf and the atmosphere, plant
transpiration rates are lower at night, when the air temperature is cooler and the water vapor
concentration is higher. This is the primary mechanism through which Sedum and other
succulents are resistant to drought conditions.
The third mechanism through which plants reduce runoff is interception. This is not a “water
consumption” in the same way as the first two mechanisms, because the water is not taken in or
utilized in any way by the plants. Biomass synthesis and transpiration both use water that has
been taken up through the roots from the growing media, whereas interception refers simply to
the rainfall that lands on the plant’s surface, and therefore never even reaches the growing media
layer or the green roof’s drainage point. Interception is affected by the characteristics of the
rainfall event as well as the surface area and structure of the plant, which dictate the total amount
of rainwater that the leaves can support before water begins to fall to the substrate below
(Dunkerley 2000). Interception is generally reported as a percent loss of the total rainfall amount.
Many hydrological models and experiments disregard interception entirely, assume it is
negligible, or treat it as part of evapotranspiration (De Groen 2002, Savenije 2004). Most of the
previous studies that do specifically address interception have been conducted on large plant
species in open, dryland environments, with much of the focus on trees, shrubs, and grasses.
Within these categories the resulting measurements of interception loss vary greatly; studies of
forests have reported losses ranging from 13% to 22% (Wang-Erlandsson et al 2014), while
studies using shrubs have reported losses ranging from 4% (West and Gifford 1976), 27%
(Navar and Bryan 1990), 21% and 40% (Domingo et al. 1998), and studies on grasses have
reported losses ranging from 11% and 18% (Thurow et al. 1987) to 30% (Dunkerley and Booth
1999). These studies indicate that interception capacity is not only species-dependent but also
geographically or climatically dependent (Dunkerley 2000).
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Very little research has been conducted on the canopy interception of small plant species
including green roof plants. Comparatively, the green roof vegetation is much smaller than the
species commonly studied, so it is often assumed that interception losses are also much smaller,
and therefore negligible compared to transpiration. Additionally, green roofs present a greater
challenge in terms of quantifying interception. The vegetation is much closer to the soil surface,
so the traditional field methods of measuring interception by capturing throughfall beneath the
canopy are not feasible.
2.2 Assessment of Previous Studies
2.2.1 Methods for Quantifying Combined Evapotranspiration
Section 1.2 of this thesis describes in detail one method of calculating total water retention from
green roof modules by measuring precipitation and discharge and performing a water balance.
This method is utilized by Hill et al. (2017), Dunnett et al. (2008), Nardini et al. (2012), and
VanWoert et al. (2005). Instead of measuring discharge volumes, another common method is to
perform a mass balance by regularly weighing the pot or green roof module. This is commonly
done using a lysimeter, which calculates the changes in soil moisture content from the
differences in total system weight between measurements (Wadzuk et al. 2013). This method is
used in the studies cited by Jahanfar et al. (under preparation), Durham et al. (2006), Lundholm
et al. (2010), Wolf and Lundholm (2008), and Voyde et al. (2010). In contrast, Bousselott et al.
(2011) quantifiy evapotranspiration by irrigating the test containers until the substrate fully
saturated and then measuring the dry down rate within the substrate using a soil moisture sensor.
In addition to the experimental methods listed above, there are also a number of models that have
been developed to estimate evapotranspiration, most of which utilize energy balance rather than
mass balance. These models range from the Blaney-Criddle and Hargreaves methods, which only
use temperature data, to more complex equations like the Slater-McIlroy method, which
incorporates additional climatic data like solar radiation, ground heat flux, and air pressure
(Wadzuk et al. 2013). The most complex and most recommended method is the Penman
Monteith equations, which also incorporate wind speed and relative humidity. One remaining
drawback to these methods is that they neglect plant variations and assume that water availability
11
is a non-limiting factor. Some studies incorporate an experimentally derived crop-coefficient to
help reduce the errors associated with these assumptions (Lazzarin et al. 2005).
Due to the inaccuracies associated with such generalized mathematical models, these techniques
are more frequently used for estimating average evapotranspiration over larger timescales
(Wadzuk et al. 2013) or for mapping evapotranspiration over large areas by land type (Gill et al.
2007).
Many of the experiment-based studies use a black box approach to estimate transpiration. After
measuring the water loss from an entire system with plants and growing substrate layers, the
measured water loss is subtracted from an equivalent system with only the growing substrate.
The system with only substrate has no transpiration, so any water loss is simply due to
evaporation. Transpiration is then indirectly calculated as:
𝑇 = 𝐸𝑇 – 𝐸 ( 2 )
Some examples of studies that have used this method are Bousselott et al. (2011), VanWoert et
al. (2005), and Voyde et al. (2010).
Dunnett et al. (2008) did not attempt to isolate transpiration values; however, they did cut, dry,
and weigh the plants at the end of their experiments, and found a positive correlation between
plant size and total runoff reduction: the systems with taller plants and denser root structures
retained more water. This method is worth noting both as a way to directly compare biomass for
all of the test plants and as a metric by which to test the correlations between plant size and
hydrologic performance.
2.2.2 Methods for Directly Measuring Transpiration
Direct quantification of transpiration, without using the process of elimination method described
in the previous section, requires measurements or calculations of stomatal conductance. Stomatal
conductance is a measure of the rate of water exiting the stomata of the leaf, and is primarily
influenced by the stomata size and spatial density on the leaf (Farquhar and Sharkey 1982). A
variety of models exist for calculating stomatal conductance, which can be categorized into two
different types: those which express stomatal conductance as a function of atmospheric factors,
12
and those which express it as a function of water availability (Damour et al. 2010). Transpiration
models are also predominantly empirical, based on statistical correlations between stomatal
conductance and environmental factors, such as light intensity, humidity, and air temperature
(Damour et al. 2010). Alternatively, transpiration can be modeled using resistance, as the flux of
water through the leaves and into the atmosphere behaves analogously to an electric current
(Gerosa et al. 2012), as shown in Figure 2.
Figure 2: Leaf transpiration circuit analogy, where rwv is the resistance to water vapour of each component
(edited from Lambers et al. 2008)
As an alternative to the model-based methods, stomatal conductance can be measured through
field or laboratory experiments using a leaf porometer. Porometers put the resistance analogy
into practice; stomatal conductance is calculated by placing the leaf in series with known
conductance elements, and measuring the resistance and relative humidity at each point along the
pathway (Decagon Devices Inc., 2013). The following studies all used porometers as part of their
methodologies: Scharenbroch et al. (2016) tested trees in bioswales. Sendo et al. (2010)
measured stomatal conductance as part of a suitability study to test a variety of ornamental
species for green roof feasibility in Japan. Raimondo et al. (2015) compared two different shrub
species selected for green roof use in Mediterranean conditions. Blanusa et al. (2013) measured
stomatal conductance for a range of broad-leafed species in dry and well-watered conditions, and
found that the well-watered plants had higher conductance rates.
13
Once conductance values have been determined through mathematical models or direct field
measurements, these data can be used to calculate transpiration. The Penman-Monteith equations
are commonly used for this conversion, but these equations are complex and often require some
assumed parameters. If leaf temperature, air temperature, relative humidity, and leaf area are also
measured at the same time as stomatal conductance, then the instantaneous transpiration rate can
instead be calculated using Fick’s law of diffusion, which states that flux, in this case of water
from the leaf to the atmosphere, is directly proportional to the concentration gradient (Ennos
2011, Pearcy et al. 1989).
Devices such as the LCpro-SD Advanced Photosynthesis Measurement System or the LI-
6400XT Portable Photosynthesis System, can be taken into the field or laboratory setting to
measure transpiration rates directly. These devices encompass the leaf in a chamber and use gas
exchange analysis and chlorophyll fluorescence measurements to measure and calculate a range
of values associated with the photosynthesis process, including both stomatal conductance and
resistance. Such a device was used by Dusza et al. (2017) in their study of common green roof
plant species in France. Their experiments were conducted within a glasshouse laboratory with
regulated temperature and relative humidity and only conducted measurements when soil
moisture was above a certain threshold to represent well-watered conditions.
2.2.3 Methods for Measuring Interception
There are many methods for measuring interception for large plant species (i.e. agricultural
crops) and full canopies (i.e. forests). The most common methods measure interception by
installing instrumentation below the canopy to measure canopy throughfall or by using simulated
rainfall in a laboratory setting. For example, Domingo et al. (1998) measured interception
through a canopy for semi-arid shrub species native to Spain. Their measurements were
conducted in the field by implementing gauges at multiple locations beneath the plants.
Equipment used in such large-scale studies is not feasible for the size and shape of green roof
species. Dunkerley and Booth (1999) measured interception of individual shrubs, by cutting
specimens at the base, mounting them under a rainfall simulator, and weighing the plants before
and after rainfall. By cutting, and therefore killing, the plants, the biological components of plant
runoff reduction are eliminated. After completing field measurements, Domingo et al. (1998)
14
also used a similar method of cutting and drying the plants, before wetting them to saturation and
measuring the changes in weight with a load cell.
Many existing green roof studies neglect interception or lump it in with evapotranspiration. In
some cases, when the experiments are conducted in laboratory settings, water is supplied to the
pots or modules at the substrate level, thereby removing the potential for interception from the
study entirely (Lundholm et al. 2010). While Voyde et al. (2010) did not directly measure
interception, they did conduct biomass sampling throughout the course of their experiment to see
how water-stressed conditions affected turgidity, and inversely wilting of the plant species. They
postulated that reductions in turgidity would negatively affect interception rates; therefore, plants
more resistant to wilting under water stressed conditions maintain higher interception rates.
2.2.4 Limitations of Existing Studies
As has already been stated regarding the prior experiments conducted at the GRIT Lab, many of
the existing green roof experiments quantify evapotranspiration as a single value or only measure
the green roof as a complete system. Some of the experiments test control modules with only the
substrate layers and subtract these values from vegetated beds to calculate the total plant
contributions, but this method does not break down the complex biological processes, canopy
capture, and other interactions between the plants and the growing media. Additionally, there are
limitations to the existing research that highlight the need for further testing:
Research is geographically specific.
The climate, native plant species, and popular growth media for green roof design varies
drastically from region to region; therefore, specific experimental results cannot be used to draw
accurate conclusions on a global scale. For example, Voyde et al. (2010) compared Sedum to
New Zealand iceplant, which would be an unlikely species selection for a green roof in North
America, Europe or Asia. Similarly, Wolf and Lundholm (2008) only studied native species in
Nova Scotia, Canada while Nardini et al. (2012) selected species local to North Eastern Italy.
Research is specific to non-green roof plant species.
15
There have been plenty of studies conducted on plant water usage for trees and agricultural plant
species, but very few studies have specifically addressed green roof plants. This is especially
prevalent in the existing data for interception, as most of the interception studies highlighted in
previous sections have used trees, shrubs, and tall grasses in open, arid landscapes. While the
analysis, particularly in Domingo et al. (1998) regarding how canopy structure and density
impact interception performance is useful for forming hypotheses, the conclusions drawn cannot
be directly applied to the smaller species types and urban setting of this study.
Research is conducted within a laboratory or greenhouse setting.
Of the studies that have already been conducted to address similar questions to those posed in
this study, many simulate the green roof setting inside greenhouse laboratories to maintain strict
control over environmental variables. While laboratory settings provide certain benefits, as some
measurement types cannot be assessed accurately without such strict control, they may not
provide a wholly accurate representation of the variation and unpredictability of a natural green
roof setting influence plant survival and performance. Dusza et al. (2017), Voyde et al. (2010),
Wolf and Lundholm (2008), and Al-Busaidi et al. (2013) all perform experiments in greenhouse
settings.
Furthermore, indoor experiments that quantify interception rates spray the plants or use
simulated rainfall that is more simplified than actual rain events. One of the methods to measure
interception presented by Domingo et al. (1998) involves cutting the plants at the base, hanging
them upside down from a load cell, and spraying them with water until the weight stabilized.
This method produces questionable results, because it interferes with the upright geometry of the
plants, uses a uniform spray of water instead of variable rain drops, and does not account for
wind and rain intensity.
2.3 Conclusions
Based on the other plant-focused studies reviewed in the previous section, there are a number of
common trends in the methodologies. First, many of the studies use similar methods to the GRIT
Lab, which present evaporation from the soil and transpiration from the plants as a single process
16
(evapotranspiration). Secondly, the studies that do isolate the role of the plants are predominantly
reliant on computer models or are conducted in laboratory settings with strict controls, neither of
which are realistic to the variability and complexity of an actual urban roof setting. In contrast,
this study aims to assess the plants:
• Without including the stormwater stored in the soil, which evaporates independently from
the plants.
• Without relying on assumption-heavy models. This study uses real-time data collected
throughout the season to calculate interception volumes and instantaneous rates of
transpiration.
• Without isolating the plants from the variability of realistic conditions. This study tests
the plants in their complete communities in an actual green roof setting. By setting up the
experiments in this way, the results accurately represent the Toronto climate and do not
discount the effects of species competition within the communities or unpredictable
weather patterns.
Furthermore, the results summarized from previous studies indicate the geographic-specificity of
plant-species related research. While Sedum species are a common design option for green roofs
globally, due to their known durability, the native plant options vary by location. For this reason,
the results from the same study in New Zealand may not be conclusive for green roof design in
Ontario, and vice-versa.
17
Chapter 3: Biomass and Overall Plant Health
3.1 Introduction
The primary goal of this study is to compare the runoff reduction potential of the two most
popular plant communities used on green roofs in Southern Ontario: native meadow species and
Sedum species. This chapter presents some general information pertaining to the study before
delving into a few components of the overall data collection that were ultimately deemed less
relevant to the primary goal. Section 3.2 begins with an overview of the experimental set-up
used. Section 3.3 then summarizes the hydrologic conditions in terms of precipitation and
irrigation throughout the duration of the study. Sections 3.4 and 3.5 present two different
methodologies which were developed to assess overall plant growth and other metrics of plant
health, such as color and turgidity. The method presented in section 3.4 involved weighing the
pots to calculate changes in plant growth from the weight fluctuations; however, this method was
abandoned after the 2016 season, because it was found to be ineffective. The second method,
presented in section 3.5, instead used a volumetric approach to assess plant growth by measuring
height, width, and spatial density, which provided more useful results.
3.2 Set-up
Two plant communities – one comprised of Sedum species and the other of native Ontario
meadow species – were transplanted into 1-gallon pots for the duration of this study. The
complete green roof modules at the GRIT lab were originally seeded with 28 Sedum species and
16 grasses and forbs (meadow species), respectively; however, for this study, only three different
species were selected to represent each community. These were primarily chosen to maximize
diversity: for the meadow species, one grass, one forb, and one wildflower was selected, while
for the Sedum species, one broad-leaf, one rigid-leaf, and one cylindrical-leaf morpho-type (as
defined by MacIvor et al. 2013) was selected. Within each category, a specific species was
selected to mirror the most dominant species currently found in the GRIT Lab modules.
The two vegetation types were tested as communities rather than individual, isolated species,
because diverse species groups have been found to outperform monocultures, or roofs with a
18
single plant species, across a variety of green roof performance metrics, including water capture
(Lundholm et al. 2010). Additionally, the structural differences between grasses and forbs or
between the different Sedum morphotypes were expected to influence interception, so selecting
one type over another would have unfairly altered the study’s findings. The species compositions
within the two respective communities are presented in Table 1.
Table 1: Plant species per community type
Community Sedum Meadow
Individual
Species
Goldmoss Stonecrop (Sedum acre)
White Stonecrop (Sedum album)
Yellow Stars (Sedum ellacombianum)
New England Aster (Symphyotrichum novae-angliae)
Red Fescue (Festuca rubra)
White Yarrow (Achillea millefolium)
Excessive irrigation is known to have a negative impact on total water retention of green roofs
(Hill et al. 2017), as well as a positive impact on plant survival, health, diversity, and
19
performance (MacIvor et al. 2013). While Sedum can survive in most conditions without
irrigation, the herbaceous meadow species almost always require supplemental water supply to
survive (Durham et al. 2006, MacIvor et al. 2013, Rowe et al. 2014). Hall and Schulze (1980)
found that water supply also directly affects transpiration rates. Unfortunately, irrigation adds to
the energy, cost, and maintenance requirements of green roofs. Due to the complex tradeoffs of
irrigation and plant performance, it is important to test the plant communities for a range of
irrigation volumes.
The two plant communities were subjected to three different irrigation regimes; one which
received no irrigation, one which received irrigation 5 days a week, and one which received
irrigation as needed, based on soil moisture measurements. Specifically, these pots were irrigated
when the Decagon moisture sensor reported a unitless raw value less than 650 near the media
surface, which corresponds to roughly 15% volumetric water content (details on the soil moisture
equipment are discussed below in Section 3.4). In order to test the two communities of plants for
three types of irrigation, the experimental set-up required 6 different pots. Additionally, the
experiment was set up with 3 sets (replicates) of each type, resulting in 18 pots total as shown in
Figure 3.
Figure 3: Side and overhead view of pots
The experiment began in June, 2016. Each pot was labeled and filled with 5 L of growing media.
The growing media was an organic media blend developed specifically for green roof
applications by Bioroof Systems Inc. The media is described by Hill et al. (2017) as a
“biologically derived medium containing a matured, screened, pine bark compost with <5%
20
additional components.” Small transplants of each of the three species per community were
planted in each pot on June 2nd. The initial transplants were selected in attempt to maintain
similar initial weight and size for each plant so that the pots would all have equitable starting
conditions. The pots were all given 1 L of initial irrigation to ensure equal soil compaction and
saturation, and to help the plants survive the initial stress of transplanting. Data collection began
on June 9th, 2016 after the transplants had been given a week to acclimate to their new
conditions.
Once data collection began, the timer and sensor pots were only irrigated after all measurements
were taken each day in order to maintain consistency. Initially, the timer and sensor pots were
given 150 mL of water each time irrigation occurred. This value was increased to 200 mL in
July, 2016 due to the extreme drought conditions. This volume is equivalent to 6.4 mm when
normalized by the surface area of the pots. Daily and weekly data collection continued until the
end of October, 2016. The pots were then stored in a sheltered location on the roof for the
duration of the winter. They were returned to the experimental location in April, 2017. Data
collection resumed on May 1st, 2017 and continued through August 1st, 2017.
3.3 Hydrologic Conditions
A weather station located at GRIT Lab automatically measures and records precipitation in terms
of mm per five-minute interval, using a 6-TE525M Texas Electronics Tipping Bucket Rain
Gauge with 0.1 mm accuracy. The precipitation measurements were summed into daily and
monthly totals, which are reported in terms of mm. The precipitation data was also used to
calculate duration and intensity for individual rainfall events. Finally, the weather station
provided temperature, relative humidity, atmospheric pressure, and wind speed. This data was
used to test for correlations between the weather and species health and performance.
Daily irrigation volumes for the timer- and sensor-irrigated pots were normalized to mm based
on soil surface area. The total precipitation was then summed for each month of the study, and
normalized by the number of days to calculate the average daily rainfall for each month. Because
data collection began June 9th, the June 2016 data was averaged over 22 days. The same process
21
was used to calculate total irrigation for the timer-irrigated pots, which were all watered
identically. This process was then repeated for each individual sensor-irrigated pot and averaged
over all six pots to calculate the mean total irrigation. The soil moisture levels for the sensor-
irrigated pots were not all identical; therefore, these pots were not always watered on the same
days. Table 2 presents the average and peak daily rainfall as well as irrigation depths for each
irrigation regime, by month. The table also includes average and extreme daily rainfall for
Toronto, based on historical data. Figure 4 and present the same precipitation data graphically for
2016 and 2017, respectively. Sample raw data for precipitation and irrigation is given in
Appendix A.
Table 2: Precipitation and irrigation totals presented as average daily depth, by month.
2016 2017
June July August Sept. Oct. May June July
Average
Rainfall
[mm/day]
0.49 1.66 2.20 2.49 1.15 5.94 2.98 2.04
Peak Daily
Rainfall [mm] 8.2 16.4 20.6 31.4 11 35.8 38.4 24.8
Number of days
with rain 4 8 6 8 9 16 14 12
Timer
Irrigation
[mm/day]
4.09 3.42 3.92 3.41 3.10 3.71 2.13 3.10
Sensor
Irrigation
[mm/day]
1.36 0.23 0.34 0.21 0.00 0.17 0.71 0.86
Avg. Precip.
[mm/day]
1981-2010*
2.36 2.06 2.62 2.82 2.08 2.65 2.36 2.06
Daily Extreme
Precip. [mm]
1981-2010*
63.5 98.6 93.5 87.9 86.9 68.6 63.5 98.6
Avg. Number of
Rain Days
1981-2010*
11 10.4 10.2 11.1 11.7 12.7 11 10.4
*(GC 2017b)
22
Figure 4: Precipitation Hyetograph showing all rainfall events from June 9 – October 31, 2016.
Figure 5: Precipitation Hyetograph showing all rainfall events from May 1 – June 30, 2017.
The 2016 hyetograph shows that there were smaller, less frequent precipitation events through
June and July compared to August and September. This observation is also supported by the
monthly averages presented in Table 2. While the monthly averages calculated for 2016 are not
23
significantly smaller than the monthly averages compiled from climate normal data for 1981-
2010, it is important to note that the total number of rain days per month was less than the
historical number of rain days. This means that the precipitation that occurred was dispersed over
a smaller number of events, which is less useful to the plants, given the limited storage capacity
of the substrate. Agriculture and Agri-Food Canada classified June, 2016, as a severe drought for
the Greater Toronto Area (GC 2017a), while news articles from early August, 2016, stated that it
had been the driest 100 consecutive days in Toronto since the 1930s (McPhedren 2016).
In contrast, the 2017 conditions were significantly wetter. Specifically, May and June, 2017 had
the greatest average daily rainfall depths (5.94 mm and 2.98 mm, respectively), which were also
dispersed over the greatest number of rain days (16 and 14 days, respectively). These were also
the only two months of the study where the daily rainfall depth and number of rain days were
both greater than the climate normals averaged from 1981 to 2010 (GC 2017b, as summarized in
Table 2). The wetter conditions at the beginning of the 2017 season had a positive impact on the
overall health and size of the plants, which will be discussed further in Section 3.5. The irrigation
data presented in Table 2 also demonstrates a significant reduction in total irrigation used for the
sensor-irrigated pots compared to the timer-irrigated pots. The sensor-irrigated pots in total were
given 90% less water than the timer-irrigated pots over the duration of the 2016 season. This
reduction in water usage could be an important argument to support the use of sensor-based
irrigation systems, provided the plants in these pots perform competitively with their timer-
irrigated counterparts.
3.4 Biomass Assessment The original plan for assessing plant growth was to perform a mass balance to calculate the
changes in weight due to plant growth. This method relied on two types of measurements: (1)
daily mass measurements, including the initial dry weight of the soil and the pot at the beginning
of the study, and (2) daily soil moisture content measurements to calculate the total amount of
water held in the growing media. By subtracting the dry weight of the soil, the pot itself, and the
weight of water in the growing media, the goal was to calculate the weight of the plants as the
remainder. This method was found to be ineffective, because the weight of the plants was very
24
small compared to the uncertainty in the water content, and there were unforeseen fluctuations in
the soil volume. These errors will be discussed in more detail in section 3.4.3.
Because of the unforeseen challenges and errors associated with this method, a second method
was added for assessing biomass instead from a volumetric approach, which will be discussed in
section 3.5. Because the volumetric approach resulted in more productive data, the daily mass
and VWC measurements were discontinued at the end of the 2016 study period. That said, the
mass measurements resulted in some interesting, incidental observations about other aspects of
green roof performance, which will be discussed in this section.
3.4.1 Weighing the Pots
Each pot was weighed using an OHAUS Adventure Balance with an accuracy of 0.1 g, as shown
in Figure 6. The pots were initially weighed with only the growing media. After the plants were
transplanted, the pots were weighed five days a week to assess variations in mass over time.
These variations are caused by biomass growth, changes in substrate moisture content and soil
loss or erosion.
Figure 6: Pots are weighed using an OHAUS Balance
The daily mass measurements were first normalized by subtracting the initial mass of the 1-
gallon pots and the 5-L of growing media. Because the soil was added by volume, the initial
weights were not identical for all 18 pots. By subtracting the initial masses, the changes and
trends in mass could be more effectively compared. For scale, the initial mass values ranged
25
from 2,445 – 2,625 g. The daily measured masses fluctuated between 400 g below and 1,200 g
above these initial values. The growing media had a non-zero initial moisture content, so
negative normalized masses occurred when the growing media in a pot dried to less than the
initial moisture content.
The normalized masses from all 18 pots were averaged by plant community and irrigation regime. The average
values for each of the resulting six variable combinations are presented as a time series in Figure 7.
Table 3 gives the average, minimum, and maximum normalized mass values for the entire 5-
month period. Both the time series representation and the tabulated averages demonstrate that the
timer irrigated pots had greater mass overall by a factor of roughly 400g, while the sensor and
non-irrigated pots were statistically the same. When normalized by pot surface area the
additional weight of the timer irrigated pots is approximately 13 kg per square meter, which may
have an impact when assessing green roof dead load. The sensor and non-irrigated pots had
lower peak masses and also showed significant drops in mass during drier periods. The sedum
pots overall had slightly greater masses than their meadow counterparts, particularly in the
sensor and timer irrigated pots.
Figure 7: 2016 Mass Time Series
26
Table 3: Average, Minimum, and Maximum Mass Measurements [g] for 2016 Season
Average Mass Standard Dev. Min. Mass Max. Mass
Sedum – No
Irrigation 164 262 -370 623
Sedum – Sensor
Irrigation 303 205 -181 732
Sedum – Timer
Irrigation 690 235 248 1126
Meadow – No
Irrigation 175 251 -259 622
Meadow –
Sensor Irrigation 154 203 -305 602
Meadow –
Timer Irrigation 555 215 69 973
3.4.2 Measuring Soil Moisture
The moisture content in the growing media was measured using a Decagon ECH2O EC-5
Moisture Sensor with an accuracy for calibrated soils of 0.02 m3/m3, as shown in Figure 8. The
moisture sensor measures the dielectric constant of the media and is then converted to
Volumetric Water Content (VWC, m3/m3) using a calibration curve.
Figure 8: Substrate moisture is measured with an EC-5 sensor
In order to calibrate the soil moisture sensor, a batch of the growing media was first dried in an
oven under low heat to ensure an initial volumetric water content of 0. The dry media was placed
in a cylindrical container with a diameter of 18.6 cm, which was filled to a height of 10 cm,
27
resulting in an initial volume of 2,717 cm3. Water was added to the media in 200 mL increments,
and volumetric water content was calculated as:
VWC =Total Volume of Water Added
Water Added+Initial Volume ( 3 )
At each increment, the media was well mixed, and the raw soil moisture sensor value was
recorded, as illustrated in Figure 9. This process was completed twice, and a trendline was fit to
the resulting average. The calibration points are presented graphically in Figure 10.
Figure 9: Soil Moisture Sensor Calibration Process
Figure 10: Calibration Curve
28
The optimal trendline for the calibration curve was a third order polynomial, with an R-squared
value of 0.995:
VWC = 1.32 × 10−9(RAW)3 − 4.11 × 10−6(RAW)2 + 4.63 × 10−3(RAW) − 1.49 ( 4 )
As with the mass data, the average VWC values for each variable combination are presented
graphically and numerically in Figure 11 and Table 4.
Figure 11: 2016 Soil Moisture Content Time Series
Table 4: Average, Minimum, and Maximum VWC for 2016 Season
Average VWC Standard Dev. Min. VWC Max. VWC
Sedum – No
Irrigation 0.28 0.06 0.15 0.39
Sedum – Sensor
Irrigation 0.30 0.05 0.20 0.40
Sedum – Timer
Irrigation 0.34 0.05 0.24 0.41
Meadow – No
Irrigation 0.29 0.06 0.18 0.39
Meadow –
Sensor Irrigation 0.29 0.05 0.18 0.39
Meadow –
Timer Irrigation 0.34 0.05 0.22 0.41
29
The VWC time series graph exhibits local minima that align with the decreases in mass, most
notably around June 30 – July 7, August 9, and September 6. Similarly, the tabulated averages
for VWC indicate higher moisture content for the timer irrigated pots. These similarities in
trends between mass and VWC help to confirm that soil moisture content is the driving factor for
fluctuations in pot mass. The higher VWC in timer irrigated pots also supports the Hill et al.
(2017) conclusion that timer irrigated modules have lower stormwater retention rates, because
the higher antecedent moisture content will result in less available storage in the growing media
layer during precipitation events. The VWC measurements do not indicate a statistical difference
between the two plant communities, which might suggest that the differences in plant and
canopy type had little impact on the moisture content or storage capacity of the growing media.
3.4.3 Calculating Biomass
The pots with the 5 L of soil were weighed before the plants were added, and the initial moisture
content of the soil was recorded. These measurements were used to calculate the dry weight of
the pot and the soil:
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐷𝑟𝑦 𝑊𝑒𝑖𝑔ℎ𝑡 = 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑊𝑒𝑖𝑔ℎ𝑡 − 𝑉𝑊𝐶 ∗ 𝑆𝑜𝑖𝑙 𝑉𝑜𝑙𝑢𝑚𝑒 ( 5 )
After the plants were added to the pots, each pot was weighed and soil moisture content was
recorded five days a week over the entire duration of the study. The initial dry mass of the soil
and the pot as well as the mass of water in the soil (calculated from VWC, volume of soil, and
density of water) were then subtracted from the daily mass measurements to attempt to isolate
the mass of the plants themselves:
Plant Mass = Measured Weight − Initial Dry Weight − VWC ∗ Soil Volume ( 6 )
The resulting biomass calculations illustrate the failure of this method. The calculations followed
no discernable trend, and even resulted in negative values on multiple occasions. Sample
calculations to prove this point are provided in Appendix B. As previously mentioned, this
method failure is likely due to a combination of reasons. First, the relative mass of the plants is
very small compared to the weight of the pot and growing media, so the calculations are
particularly sensitive to small errors. Second, there were significant limitations associated with
30
the soil moisture sensor. The sensor only measures the soil moisture at a specific point within the
pot, so it does not accurately represent the average moisture unless the pot is well-mixed.
Finally, the equation assumes a constant soil volume and soil density, which could not be
ensured in the rooftop environment. Rainfall and irrigation likely caused compaction, and there
was also evidence of soil losses caused by wind erosion and critter interference. On more than
one occasion, soil was discovered on the roof surface around the pots and visible holes had been
dug around the plants, as shown in Figure 12. The GRIT Lab is accessible to urban wildlife
including squirrels, birds and occasionally, raccoons. A fence was installed around the pots in an
effort to prevent wildlife from disrupting the experiment.
Figure 12: Evidence of soil loss due to wind erosion or digging by animals (left). Prime suspect (right).
To test the magnitude of changes in soil volume over the course of a season, the depth of soil in
each pot was evaluated each week by recording the distance from the surface of the soil to the
upper rim of the pot. The soil depth measurements for 2016 are presented in Figure 13. The data
indicates fluctuations up to ±2cm from week to week. A linear best fit approximation was
applied to the data for each pot to find the overall slope, or average change in soil depth per day
over the season. Sixteen of the eighteen pots had negative slopes, indicating that the depth, and
in turn, soil volume, was decreasing over time. Based on the calculated slopes, over the course of
the 145-day study period, the soil depths decreased up to 1.9 cm per pot, which equates to a
volume loss of nearly 600 mL. A complete list of average soil depths, slopes, and projected
31
depth changes is tabulated in Appendix C. The greatest decreases were associated with the timer-
irrigated pots, which is a positive indication that the decreases are associated with soil
compaction
Figure 13: Weekly soil depth measurements for each pot, arranged by variable combination. The dotted lines
represent the average slopes, or overall trend for each pot.
Ultimately, this study concludes that plant biomass cannot be calculated in a green roof setting
with so many natural variables by weighing the pots and measuring soil moisture at a central
point in each pot. While this component of the overall study technically failed, other
observations can still be made from the measurements:
32
• Non-irrigated pots had the greatest drops in weight.
• Timer irrigated pots consistently weighed the most.
• Average total mass of the Sedum pots was slightly greater than meadow pots (with
statistical significance above the 95% confidence level).
3.5 Plant Volume and Photo Analysis
After discovering the inaccuracies and complications of the biomass assessment method
described in the previous section, an additional component of the study was added at the end of
June 2016 to instead assess plant growth by measuring the volume and spatial density of the
plants themselves. This analysis was completed by measuring the dimensions of the individual
plants with a ruler, summarized in section 3.5.1, and calculating the average density of each
species type using photo analysis, summarized in section 3.5.2. This method could successfully
estimate plant size, and therefore plant growth, without requiring any assumptions about or
measurements of the growing media layer. The photographs of the plants are also used to
qualitatively discuss aspects of plant health and performance.
3.5.1 Plant Volume
As stated above, the total plant volume was measured each week using a ruler. Three lengths
were measured and recorded for each plant: the height of the plant above the soil, and the
minimum and maximum width of the plant from an overhead perspective as demonstrated in
Figure 14. By multiplying these three lengths, the total volume of each plant was estimated as a
rectangular prism. Photographs were taken of each plant species in front of a 0.25-cm grid to
estimate the spatial density of the plants, shown in Figure 15. Spatial density was calculated as
the percent of total grid squares blocked by plant matter, which was then multiplied by the
rectangular volume of each plant, resulting in a more accurate estimation of total plant volume.
To supplement these measurements, overhead photographs of each pot were also taken weekly to
visually assess the overall health of the plants, including plant size, plant color, flowering, and
turgidity.
33
Figure 14: Minimum and maximum width of an example plant
Figure 15: Photographs of each species for spatial density calculations
34
The spatial density calculations were completed based on three photographs for each plant
species in the study. The resulting average densities was used to calculate total plant volume for
each of the pots. Table 5 shows the calculations for the individual photographs, with the resulting
average densities for each species highlighted in yellow. All three of the Sedum species had
significantly greater spatial densities than the meadow species. This is also visually clear in the
photographs in Figure 15, presented in section 3.3.3. These spatial densities were used along
with the height and width measurements of each plant to estimate the total plant volume for each
pot. Sample calculations are given in Appendix D. The total plant volumes were then averaged
for each variable combination.
Table 5: Spatial Density by Plant Species
Photo 1 Photo 2 Photo 3 Average
Covered
Squares
Total
Grid
%
Density
Covered
Squares
Total
Grid
%
Density
Covered
Squares
Total
Grid
%
Density % Density
Sedum
ellacombianum 3,531 4,214 84 1,848 2,412 77 4,264 5,418 79 80
Sedum acre 1,709 2,183 78 1,513 2,368 64 2,344 2,925 80 74
Sedum album 2,148 2,698 80 1,481 1,885 79 1,986 2,440 81 80
Symphotrycum
novae-angliae 908 5,175 18 2,086 4,015 52 1,070 5,733 19 30
Festuca rubra 688 5,100 13 1,066 4,941 22 1,210 5,084 24 20
Achillea
Millefolium 1,035 6,532 16 854 4,611 19 1,342 4,743 28 21
The calculated plant volumes for each variable combination are presented as time series graphs
over the 2016 and 2017 study periods in Figure 16 and Figure 17, respectively. The monthly
averages for the entire study are presented in Table 6. This component of the study was not
added until the end of June 2016, so the initial growth immediately after transplanting is not fully
documented. Specifically, the Sedum species were observed to have more immediate growth than
the meadow species. Both Sedum album and Sedum acre bloomed in the first couple weeks in
many of the pots, so the added floral volume in these species accounts for the peaks in volume
exhibited on June 30th in the graph. The flowers on these species did not survive through the
extended drought, thereby causing the sharp declines in Sedum volumes during July.
35
After the flowers died off, the Sedum pots remained relatively stable in total plant volume for the
remainder of the season. In contrast, the meadow species’ initial growth was significantly
inhibited by the drought in the early part of the season and then steadily increased in the later
months when rainfall was more frequent. These results may suggest that the meadow species are
more sensitive to the transplanting process. Both plant communities performed better with
irrigation: non-irrigated pots showed the least amount of plant growth, while timer-irrigated pots
showed the greatest amount of plant growth.
Figure 16: Total Plant Volumes for 2016
36
Figure 17: Total Plant Volumes for 2017
Table 6: Average Plant Volume (cm3) by Month and Overall
2016 2017 Total
June July August Sept. Oct. May June July Average
Sedum – No
Irrigation 757 417 246 272 254 900 3,563 4,312 1,340
Sedum –
Sensor Irrig. 1,509 990 617 644 540 1,929 8,272 11,039 3,632
Sedum –
Timer Irrig. 1,591 1,325 1,079 1,080 1,013 3,660 5,605 5,533 2,171
Meadow –
No Irrig. 159 143 61 134 275 1,390 3,082 3,302 1,068
Meadow –
Sensor Irrig. 185 186 187 518 680 1,789 4,939 7,787 2,395
Meadow –
Timer Irrig. 458 315 684 1,200 1,251 2,530 3,394 4,568 1,438
On average, the Sedum species had greater plant volumes than their meadow counterparts by a
factor of 1.4. The differences between the average plant volumes for the two plant communities
and the three irrigation regimes were confirmed to be statistically significant with P<0.05 using
non-parametric ANOVA tests. The ratio between the species volumes varied significantly by
37
month and by year, however. In the drier months of June – August, 2016, the Sedum species
outgrew the meadow species by an average factor of 4.2, while in the wetter 2017 season, the
Sedum only outgrew the meadow species by a factor of 1.3. October, 2016, was the only month
where the meadow species exhibited greater volumes than the Sedum species. Looking across
both communities, the average plant volumes in 2017 were 5 – 17 times greater than the average
2016 volumes for all six variable combinations. This is likely due in part to the increased
frequency and amount of rainfall in 2017 compared to 2016, although the first year of the study
was also negatively impacted by the stress of transplanting. All of the green roof species tested
were perennials, so they were already well-established in the pots at the beginning of the 2017
season. This is evidenced in the graphs, which show that the first plant volume measurements at
the beginning of May, 2017, are of similar magnitude to that of the entire 2016 graph.
3.5.2 Photo Analysis
To supplement the quantitative results presented in the previous sections, photographs of each
pot were taken weekly. The following charts (Figure 18 and Figure 19) include photographs of
one of each variable combination for every third week of the 2016 and 2017 study periods,
respectively. The photographs presented are of the “A” labelled pots, to eliminate selection bias.
The 2016 photographs emphasize the inhibited growth of the meadow species in the first two
months, while most of the Sedum species bloomed right at the beginning of the study. As
mentioned in the plant volume analysis, the early Sedum floral growth was not sustained through
the drought, resulting in a sharp decline in biomass from June to July. The photographs also
show that the Sedum ellacombianum out-competed the other Sedum species by the end of the
season, while the meadow community maintained more diversity throughout the study period. It
is worth noting, however, that previous GRIT lab data and observations suggest that plant
diversity and survival within the communities is dependent on growing media. The conclusions
and discussions presented here are therefore specific to the organic media used in this study.
38
Figure 18: Photographs of each pot type, taken every three weeks over the 2016 study period.
The 2017 photographs are notable for two reasons. In all six of the pots presented, the vegetation
grew back larger and greener than the previous year, most likely due to the increase in
precipitation in the second year, illustrating the stark improvement in overall plant health in
2017. Secondly, the photographs exhibit decreases in plant diversity. Of the nine Sedum pots in
39
the study, six exclusively had S. ellacombianum in the second year, while two other pots only
had one of the remaining two species. Only one Sedum pot exhibited survivors of all three
species. By comparison, the meadow species fared slightly better in terms of biodiversity: two
out of the nine meadow pots had only one species remaining, while four pots had two species
survive, and three pots had all three species survive. Furthermore, in pots with multiple species
remaining, the meadow pots had a more even distribution of plant volume between the species
compared to the Sedum. These results are consistent with previous data collected at the GRIT
lab, which found that S. ellacombianum outcompetes the rest of the Sedum species in organic
growing media.
Figure 19: Photographs of each pot type, taken every three weeks over the 2017 study period.
40
3.6 Data Synthesis Statistical analyses were performed to test the potential correlation between the masses of each
pot and the soil moisture content and plant volume, over the 2016 study period. The resulting
correlation coefficients for each pot, as well as the averages for each variable combination are
presented in Table 7 and Table 8. The results support the initial conclusions stated in section
3.4.2, that the changes in the mass of the pot from day to day have little to do with plant growth
(i.e. changes in plant volume). While the correlation coefficients for mass and plant volume are
closer to zero, with no discernable trend between the study variables, the average correlation
coefficients for mass and soil moisture measurements are above 0.6 for all variable
combinations. This indicates that water stored in the soil is a significant factor in the temporal
variations in system weight.
Table 7: Correlation Coefficients for Total Mass and Soil Moisture Content for each pot.
Mass and Soil VWC
Pot Meadow
Timer
Sedum
Timer
Meadow
Sensor
Sedum
Sensor
Meadow
No
Sedum
No
A 0.68 0.82 0.70 0.72 0.79 0.79
B 0.74 0.82 0.74 0.66 0.71 0.81
C 0.80 0.84 0.56 0.65 0.79 0.77
Average 0.74 0.82 0.67 0.68 0.76 0.79
Table 8: Correlation Coefficients for Total Mass and Plant Volume for each pot.
Mass and Plant Volume
Pot Meadow
Timer
Sedum
Timer
Meadow
Sensor
Sedum
Sensor
Meadow
No Irrig.
Sedum
No Irrig.
A -0.18 0.39 0.01 -0.16 0.43 -0.21
B 0.25 -0.45 0.20 -0.19 0.09 -0.59
C 0.62 -0.40 -0.19 0.08 0.16 -0.20
Average 0.23 -0.15 0.00 -0.09 0.23 -0.34
3.7 Conclusions Understanding and quantifying plant growth and total biomass is important for assessing the total
health and performance of a green roof. Larger, healthier (i.e. greener, sturdier, flowering, etc.)
plants are more aesthetically pleasing and provide more ecosystem benefits, but they also have
41
increased stormwater management potential. As previously stated, herbaceous meadow species
are generally 80% water (Hsiao 1973), while Sedum species can be up to 95% water (Gausman
and Allen 1973), so larger plants retain more water in biomass synthesis. As will be discussed in
later sections, plants with greater leaf or surface areas can also transpire and intercept more
water.
While the results of weighing the pots were inconclusive for evaluating biomass, the volumetric
and spatial density measurements concluded that Sedum species could maintain more biomass
during the drought conditions than meadow species. However, the evidence also indicates that
the differences between the two communities are much less significant during wetter periods.
Additionally, providing irrigation had a positive impact on both plant communities. Finally, the
2017 study period proved that the communities could survive and come back after the water-
stressed conditions of the previous years, but while biomass volumes increased in the second
year, species diversity decreased.
Were this study to be repeated or expanded in the future, some recommended changes should be
considered for the experimental setup. The 1-gallon sizing of the pots was originally selected to
facilitate carrying the pots into the lab to be measured with the balance. However, since this
methodology was ultimately discarded in favor of measuring the volume with rulers, the
experimental design is no longer limited in terms of size. The limitations to the root zone and
differences in the soil to edge ratio between the small, cylindrical pots and more traditional large,
rectangular green roof areas could impact overall plant performance. Therefore, replicating the
experiment on a larger scale could be a productive step for future research.
42
Chapter 4: Interception and Transpiration
Chapter 4 is a stand-alone manuscript which will be submitted for publication in the Journal of
Ecological Engineering. Section 4.1 is a condensed version of Chapters 1 and 2 of this thesis,
which provides the background information and literature review needed to give the article
context. Similarly, section 4.2.1 is an abridged version of the experimental set-up originally
presented in section 3.2. The remaining sections present the methods, results, and discussion
pertaining to the measurements of transpiration and interception.
4.1 Introduction
Green roofs, or vegetated roof systems, provide a variety of benefits in urban areas; they can
create recreational spaces for community use and improve the aesthetic value of a city, while
also increasing energy efficiency (MacIvor et al. 2016), reducing building costs (Del Barrio
1998, Obernadorfer et al. 2007), and improving ecosystem biodiversity (Obernadorfer et al.
2007, MacIvor et al. 2015) and environmental quality (Banting et al. 2005). Additionally, green
roofs reduce stormwater runoff generated on rooftops, which in turn reduces flooding at street
level (Hill et al. 2017). For each green roof installation, design variables such as irrigation
supply, planting media depth and type, and plant species must be selected to optimize these
potential benefits and ensure healthy vegetation growth in a particular climate.
The Green Roof Innovation Testing Laboratory (GRIT Lab, shown in Figure 20) at the
University of Toronto was established in 2011 to assess the performance and benefits of
individual green roof modules with different combinations of the design variable options listed
above. The GRIT Lab set-up includes instrumentation to measure precipitation, irrigation, and
discharge volumes for each module, which can be used to calculate the runoff coefficient, or the
ratio of total discharge depth from an individual event over total event precipitation depth. Hill et
al. (2017) used ANOVA statistics to compare the average runoff coefficients for each of the
independent design variables and found that irrigation amount and planting medium type had the
most impact on runoff, while planting medium depth and vegetation type had little to no
significance whatsoever.
43
Figure 20: Overhead view of green roof modules at the GRIT Lab (Photo credit: Arman Khabazian)
Other studies with similar methods have drawn conflicting conclusions regarding vegetation
type: while some found that vegetation had less impact on stormwater retention than growing
media (VanWoert et al. 2005) or that there was no statistical variance between different
vegetation types (Nardini et al. 2012), others have concluded oppositely, that green roof runoff
does vary by vegetation type (Dunnett et al. 2008, Dusza et al. 2017). Unfortunately, such
statistical analyses do not allow for more in-depth study of how and why vegetation type does or
does not affect stormwater retention. As a result, new methodologies must be applied to better
understand the physical and biological processes occurring at the level of the plants that
contribute to stormwater management.
This study will assess two commonly-used green roof plant species types: Sedum, which are a
type of succulents commonly selected for green roofs due to their relative drought tolerance, and
meadow species, which are native to Southern Ontario making them preferable for habitat
creation and biodiversity. There are three mechanisms through which both plant types contribute
to runoff reduction:
1. Plants consume water for cell synthesis and plant growth.
2. Plants use water for energy production. This water is transpired through the leaves.
44
3. Plants physically intercept rainwater that lands on their leaves. This water evaporates
directly off the surface of the leaves.
The first mechanism, water consumption for plant growth, refers to the water that is taken up
through the roots and retained within the plant tissue, contributing to the overall biomass.
Although the average leaf water content for most plant species is over 80% (Hsiao 1973,
Gausman and Allen 1973), previous research conducted on agricultural and herbaceous plants
suggests that the water retained in the plant cell tissue is less than 1% of the total water taken in
by the plant (Jensen 1968, Lambers et al. 2008), and can therefore be considered negligible
compared the second two mechanisms: transpiration and interception.
Transpiration is the evaporation of water from within the plant’s leaves. It occurs as a side effect
of photosynthesis, which is the process by which plants use carbon dioxide, water, and sunlight
to create carbohydrates for food according to the following chemical reaction (Lambers et al.
2008):
6CO2 + 6H2O + Sunlight C6H12O6 + 6O2
Leaves of plants are covered in stomata, or tiny pores in the epidermis, which open to allow CO2
to enter the leaves (Gerosa et al. 2012). As stated above, the plant tissue is over 80% water,
causing the air within the leaves to be fully saturated with water vapor. When the stomata are
opened, this air is exposed to the atmosphere, creating a vapor pressure gradient that forces the
water out of the leaf (Lambers et al. 2008).
Compared to transpiration, interception is not technically a “water consumption” because the
water is not taken in through the roots or utilized in any way by the plants. It is simply the
rainfall that lands on the plant’s surface and evaporates without ever reaching the growing media
layer. Interception is affected by the characteristics of the rainfall event as well as the surface
area and structure of the plant, which dictate the total amount of rainwater that the leaves can
support (Dunkerley 2000). Most of the previous studies on interception have been conducted on
large plants, such as trees, shrubs, and grasses, in open, dryland environments (Dunkerley 2000).
Little research has been conducted on interception by small plant species in confined areas like
green roofs. One reason for this may be that smaller plants present a greater challenge in terms of
45
quantifying interception. The vegetation is much closer to the soil surface, so the traditional field
methods such as capturing throughfall beneath the canopy are not feasible.
From a physical and biological perspective, Sedum and meadow species are expected to perform
differently from one another both in terms of transpiration and interception. The photosynthesis,
and in turn transpiration, process for Sedum and meadow species differ on a cellular level. Sedum
are crassulacean acid metabolism (CAM) plants, which means they have adapted to reduce water
loss by fixing CO2 at night, allowing them to close or partially close their stomata during the day
(Al-Busaidi et al. 2013). Because transpiration is dependent on the vapor pressure gradient
between the leaf and the atmosphere, transpiration rates are lower at night, when the air
temperature is cooler and the water vapor concentration is higher.
Since Sedum can minimize transpiration, they are often assumed to be less effective at reducing
runoff than meadow species which must transpire water at a constant, higher rate (Berghage et
al. 2007); however, this hypothesis is based on an incomplete picture of the physical and
biological processes occurring in and around the plants. Although Sedum exhibit lower
transpiration rates on average (Korner et al. 1979), studies have found that Sedum only limit their
water flux during water-stressed conditions, and that their water use rate is comparable to other
plants in water-abundant situations (Berghage et al. 2007, Bousselot 2011). Furthermore, total
water usage by a plant is correlated with biomass (Lundholm et al. 2010), and Sedum have
repeatedly been found to survive better than grasses, forbes, and other herbaceous species in non-
irrigated or drought conditions (Berghage et al. 2009, Bousselot 2011, Durhman et al. 2006,
Lundholm et al. 2010). Species which maintain greater biomass volumes may consume and
intercept more water even at a lower rate per leaf area.
The goal of this study was to assess these differences in the water usage of Sedum and meadow
communities by directly comparing the transpiration and interception rates in a green roof
setting. While overall plant growth, biodiversity, and evapotranspiration rates are positively
impacted by the supplement of irrigation (MacIvor et al. 2013, Hall and Schulze 1980), irrigation
adds to the energy, cost, and maintenance requirements of green roofs, and excessive irrigation
can reduce overall stormwater retention (Hill et al. 2017). The two plant communities were
46
therefore tested over a range of irrigation volumes. The results presented will help to explain
how vegetation contributes to stormwater runoff reduction and to better inform vegetation
selection within the green roof industry.
4.2 Methodology
4.2.1 Set-up
The two plant communities, Sedum and meadow, were transplanted into 1-gallon pots for the
duration of the study. The pots were filled with 5 L of an organic growing media blend
developed specifically for green roof applications by Bioroof Systems Inc. Each plant
community was represented by three different species, because diverse species groups have been
found to outperform monocultures across a variety of green roof performance metrics, including
water capture (Lundholm et al. 2010). The species were primarily chosen to maximize diversity:
for the meadow species, one grass, one forb, and one wildflower was selected, while for the
Sedum species, one broad-leaf, one rigid-leaf, and one cylindrical-leaf morpho-type (as defined
by MacIvor et al. 2013) was selected. The species compositions within the two respective
communities are presented in Table 9.
47
Table 9: Plant species per community type
Community Sedum Meadow
Individual
Species
Goldmoss Stonecrop (Sedum acre)
White Stonecrop (Sedum album)
Yellow Stars (Sedum ellacombianum)
New England Aster (Symphyotrichum novae-angliae)
Red Fescue (Festuca rubra)
White Yarrow (Achillea millefolium)
Additionally, the two plant communities were subjected to three different irrigation regimes; one
which received no irrigation, one which received irrigation five days a week, and one which
received irrigation as needed, based on soil moisture measurements. The latter were measured
with a Decagon ECH2O EC-5 Moisture Sensor with an accuracy of 0.02 m3/m3, which was
calibrated specifically for the growing media used. Irrigation was supplied when the sensor
reported a raw value less than 650 near the media surface, which corresponds to roughly 15%
volumetric water content. The timer and sensor-irrigated pots were given 200 mL of water each
time irrigation occurred, which is equivalent to 6.4 mm when normalized by the surface area of
the pots.
48
In order to test the two communities of plants for three types of irrigation, the experimental set-
up required six different pots. Additionally, the experiment was set up with three sets (replicates)
of each type, resulting in 18 pots total as shown in Figure 21. Small transplants of each of the
three species per community were planted in each pot on June 2nd, 2016. The pots were all given
1 L of initial irrigation to ensure equal soil compaction and saturation, and to help the plants
survive the initial stress of transplanting. Data collection began on June 9th, 2016 after the
transplants had been given a week to acclimate to their new conditions, and continued from June
– October, 2016, and again from May – August, 2017. The pots were stored in a sheltered
location on the roof for the duration of the winter. Because the plant species used were all
perennials, transplanting only occurred at the beginning of the first year.
Figure 21: Side and overhead view of pots
4.2.2 Transpiration Rates
Once a week, one plant from each pot was measured using a Decagon SC-1 Leaf Porometer. The
sensor reports stomatal conductance, which is a measure of both the size and spatial density of
stomata, or pores, on the leaf through which CO2 enters and water vapor exits. According to
Fick’s law, diffusive flux is proportional to concentration gradient. Using this same principle,
transpiration rate is calculated as the product of the stomatal conductance of the leaf and the
vapor concentration gradient between the leaf and the surrounding air.
In addition to the stomatal conductance of the leaf, the porometer reports the temperature at the
leaf’s surface. Using these values along with atmospheric temperature, pressure, and relative
humidity reported by the GRIT Lab weather station at the time of porometer measurements, the
49
stomatal conductance measurements can be converted to transpiration rates from the following
equations found in Lambers et al. (2008):
E = gw ∗ (wl − wa) ( 7 )
Where E is the transpiration rate (mmol/m2s), gw is the leaf conductance (mmol/m2s), and wl and
wa are the volume fraction of water vapor in the leaf and the air, respectively. The volume
fraction of water vapor is equal to the partial pressure of water (e, Pa) over the total air pressure
(P, Pa) (Farquhar and Sharkey 1982):
wl − wa = (el − ea)/P ( 8 )
For air, the partial pressure is equal to relative humidity times the saturation vapor pressure.
Within a leaf, the intercellular spaces are assumed to be saturated with water vapor (Pearcy,
1989), so the partial pressure of water is calculated as the saturation vapor pressure at the leaf’s
temperature. The calculated transpiration is given as a rate, per time and per leaf area. To
calculate total volumes of water transpired, the rates must then be normalized using total leaf
area and transpiration time, which occurs on a daily cycle.
Transpiration occurs in conjunction with photosynthesis; therefore, the process is directly tied to
the presence of light. Most species close or partially close their stomata during the night in order
to reduce unnecessary water loss. As a result, previous studies have found that nighttime
transpiration rates range from 5% to 15% of daytime transpiration rates (Caird et al., 2007).
Conductance measurements were therefore taken during peak daylight hours in the early
afternoon and were taken in random order to minimize error caused by temporal variation. This
ensures that overall measured conductance differences were due to plant species, and that trends
in the weekly variations were caused by weather patterns and plant health rather than the time of
day. Optimal conditions for using the porometer are clear days with minimal wind, temperature
between 5 and 40°C, and relative humidity between 10 and 70% (Pietragalla and Pask 2012).
The porometer works by manually attaching a clip to one of the plant’s leaves, shown in Figure
22, so it only provides accurate measurements for flatter, wider leaves. For this reason, only the
broad-leafed species, Sedum ellacombianum and Symphyotrichum novae-angliae, were measured
from their respective communities. The transpiration rates from the measured species were
assumed to be representative of all three species in each community. Total transpired water
50
quantities were calculated for each entire pot by multiplying by canopy area. Canopy area was
calculated by measuring the minimum and maximum width of each plant and then multiplying
the rectangular area by a canopy density, which was found by tracing overhead photographs of
each plant species onto a 0.25 cm grid and counting the ratio of covered squares to total squares.
The canopy measurement process is illustrated in Figure 23 below.
Figure 22: The porometer is manually clipped to an example leaf
51
Figure 23: Minimum and maximum width of sample plant (left). Tracing for density estimation (right).
4.2.3 Interception
Interception was measured after twelve rainfall events. Data collection was limited by the
constructs of the lab hours, so events in the middle of the night or on holidays were excluded.
Relying on actual rain events allowed for more realistic interception measurements, whereas
using simulated rainfall events would have had less accurate water droplet characteristics and
would not have accounted for the natural variability in event duration, rainfall intensity, and
wind speed and direction. At the end of each rainfall event, laboratory tissues were used to
carefully remove all the water remaining on the surface of the leaves, as shown in Figure 24. The
tissues were weighed before and after drying the leaves. The dry mass was then subtracted from
the final mass to calculate the total amount of water intercepted by the leaves for each pot. In
cases where the leaves were particularly dirty, the mass of water could not be separated from the
mass of dirt collected on the tissue, so these data points were discarded.
52
Figure 24: Water intercepted by the plants is collected and weighed
4.3 Results and Discussion
The weekly transpiration and canopy calculations were averaged by variable combination and by
month, while the interception measurements were averaged by variable combination per event
and overall. The Shapiro-Wilk test was used to check for normality for each of the six variable
combinations, across the four measurement types: transpiration rate, canopy area, transpiration
volume and interception. Of the 24 data arrays, four were found to be normally distributed, seven
were lognormal, and the other thirteen failed both tests at the 95% confidence level. Because the
distributions were inconsistent across each measurement type, the statistical significance
between the means was assessed using the non-parametric Kruskal-Wallis ANOVA test. Both
tests were performed using the EPA ProUCL 5.1 software. The p-values (p < 0.05) indicate that
the differences in the means discussed in this section are all statistically significant above the
95% confidence level.
The calculated averages, variance, and standard error for each variable type are compiled for
transpiration rate, canopy area, transpiration volume, and interception in Table 10. Additional
averages and statistical results are tabulated in Appendix E. As previously stated, transpiration as
well as overall plant health and size are dependent on atmospheric conditions, such as
temperature and available water. Average monthly temperature, precipitation and irrigation
supply are therefore presented in Figure 25.
53
Table 10: Compiled averages for each measurement and variable type
Compiled Averages
Sedum No
Irrigation
Sedum
Timer
Sedum
Sensor
Meadow No
Irrigation
Meadow
Timer
Meadow
Sensor
Transpiration
Rate
Mean 3.1 4.8 3.8 11.0 14.6 12.0
Variance 4.5 11.1 7.4 46.0 67.4 47.4
Observations 111 111 111 88 104 103
Standard Error 0.2 0.3 0.3 0.7 0.8 0.7
Canopy Area
Mean 120 259 185 72 141 95
Variance 10,231 28,868 18,247 4,802 7,456 4,241
Observations 78 78 78 78 78 78
Standard Error 11 19 15 8 10 7
Transpiration
Volume
Mean 2.1 7.0 4.3 4.9 11.0 5.9
Variance 6.6 41.5 26.2 45.5 84.9 28.1
Observations 111 111 111 88 104 103
Standard Error 0.2 0.6 0.5 0.7 0.9 0.5
Interception
Volume
Mean 1.8 4.0 3.0 1.0 2.6 1.4
Variance 3.0 12.5 7.0 2.3 9.1 4.0
Observations 32 34.0 34 34 34 34
Standard Error 0.3 0.6 0.5 0.3 0.5 0.3
Figure 25: Monthly Climate and Irrigation Data
54
The computed monthly and overall average transpiration rates normalized by leaf area are
presented in Figure 26. The error bars represent the standard errors, given in Table 10. On
average, the meadow species transpired at a rate 3.25 times greater than that of the Sedum
species. Additionally, on average, supplying sensor-based irrigation improved transpiration rates
by 16%, while timer-based irrigation improved transpiration rates by 44%, relative to the non-
irrigated pots. Sample transpiration calculations and the weekly averaged transpiration rates are
presented in Appendix F. Comparing the effects of climate and water supply, the transpiration
rates for meadow species were most strongly correlated with air temperature (with a correlation
coefficient of 0.73), while the Sedum transpiration rates were more strongly correlated with total
water supply (with a correlation coefficient of 0.45). The 2017 season had similar monthly
temperatures to the 2016 season, but had significantly higher precipitation amounts, particularly
in the earlier months of May and June. The average transpiration rates between June and July of
2016 vs. 2017 were not statistically different, however. The higher precipitation amounts early in
the 2017 season instead had the greatest impact on plant size.
Figure 26: Average transpiration rates by month (left) and over the full study (right).
Although the meadow species exhibited significantly higher transpiration rates, particularly
during the hotter months of July and August, the canopy areas of the meadow species were less
than that of their Sedum counterparts. The canopy areas are presented by month and averaged
over the study duration in Figure 27. In addition to the stress caused by transplanting at the
beginning of the 2016 season, June and July 2016 were classified as severe drought conditions in
Southern Ontario (GC 2017a). This had a negative impact on the overall canopy area of both
55
plant communities, but was particularly detrimental to the less-tolerant meadow species, which,
on average, exhibited 40% smaller canopy areas than the Sedum species. The differences
between the canopy areas for the different communities and irrigation types are also exhibited in
the sample overhead photographs compiled in Figure 28. Photographs are included for one pot of
each variable combination taken in September 2016, and July 2017. The photographs also
emphasize the contrast in total canopy area between the 2016 and 2017 study periods.
Figure 27: Canopy Area by month (left) and averaged over the full study (right).
Figure 28: Sample canopy photographs by variable combination
The canopy areas were used to convert transpiration rates to projected transpiration volumes per
pot, per hour of transpiration. Transpiration volumes are presented in Figure 29. While meadow
species still transpired more water overall, the decreased canopy areas reduced the ratio between
56
the two communities compared to the transpiration rates presented above. Inversely, the
improvements due to irrigation increased once canopy area was accounted for. On average,
meadow species transpired 76% more water than Sedum species. Sensor-based irrigation
increased transpiration volumes by 60%, while timer-based irrigation increased transpiration
volumes by 177%, compared to the non-irrigated pots. The monthly averages indicate a drastic
improvement in overall transpiration volumes in 2017 compared to 2016 for both species, due to
the increased canopy size and improved plant health associated with greater water availability.
Weekly averaged canopy areas and calculated transpiration volumes are tabulated in Appendix G
and H, respectively.
Figure 29: Hourly Transpiration Volume by month (left) and averaged over the full study (right).
The mean interception volume for each plant community and irrigation type was determined
using all the measured rainfall events. The individual event results were also examined for
correlations against the rainfall characteristics. Figure 30 presents the interception volume per
pot for each variable combination by event and averaged for the full study. The total
precipitation depth of each event is also presented in the graph. On average, the Sedum species
intercepted 70% more water than the meadow species. Sensor-based irrigation improved
interception volumes by 60%, while timer-based irrigation improved interception volumes by
150%. For all twelve rainfall events, the measured interception of each pot is presented in
Appendix I.
57
Figure 30: Intercepted Volume per event (left) and averaged over the full study (right).
The per-event interception measurements exhibited positive correlations to both total event
precipitation (with a correlation coefficient of 0.69) and canopy area (with a correlation
coefficient of 0.65). The latter helps to explain why the Sedum intercepted more water, as they
also exhibited greater canopy areas, although there may be additional geometric features of the
Sedum species that also enhance their interception capacity. For example, Wolf and Lundholm
(2008) found that the mat-forming structure of Sedum species created a barrier between the soil
and the atmosphere, which may impede evaporation directly from the soil, or – inversely –
prevent rainwater from reaching the soil.
4.4 Conclusions Overall, the meadow community was found to transpire more water, while the Sedum species
captured more rainwater through interception. The amount of water capture through interception
was very small compared to amount of water that is transpired. For instance, the average amount
of water intercepted by either plant community under any irrigation regime was less than the
total volume of water that can be transpired in a single hour. As the green roof plants are
expected to transpire at roughly these rates during all daylight hours, the volume of water
intercepted by the plants may be considered negligible compared to the total water that can be
transpired over the course of an entire growing season. That said, there are some caveats to this
conclusion. Primarily, transpiration is dependent on the water vapour gradient between the leaf
and the atmosphere, so transpiration is greatly reduced when the air is more saturated with water,
as it is during precipitation events. This means that transpiration is least effective when runoff
58
reduction is most important. Therefore, even though interception is relatively small in volumetric
terms, it may still be important in terms of stormwater management.
Canopy size, transpiration rate, transpiration volume, and interception all improved for both the
meadow and Sedum communities in the second year of the study, once the initial stress of
transplanting was no longer a factor and water availability was non-limiting. Additionally, both
community types performed better with supplemental irrigation. Ultimately, the tradeoffs
between interception and transpiration when comparing the two communities may help to
explain why previous studies found no statistical difference in the hydrological performance of
the different vegetation types. The results presented here, however, do not rule out the additional
possibility that the total amount of water intercepted and transpired may be negligible compared
to the total runoff coefficients of the entire green roof system. A more complete water balance
assessment will be performed in the following chapter to examine this possibility.
59
Chapter 5: Extension to Full Green Roof Systems
5.1 Introduction
This chapter will summarize how other sensor devices and previous data collected at the GRIT
lab can be used to expand and further assess the results of this study. The goal of this chapter is
to draw conclusions about the overall stormwater reduction potential of larger green roof systems
and plant communities. Section 5.2 will present a small subset of the overall research project,
wherein data was collected with an LCpro-SD photosynthesis chamber in attempt to corroborate
the transpiration rates measured with the leaf porometer for a wider variety of plant species. The
results from both of the previous chapters – specifically, water volume consumed for biomass
(Chapter Two), transpiration volumes, and interception volumes (Chapter Three) – will be
summarized and compared in section 5.3. Finally, the total water consumption by each plant
community under the three irrigation regimes will compared to the total retention and
evapotranspiration of larger green roof systems in section 5.4.
5.2 Extending to other plant species
While the leaf porometer was only effective for measuring broad leafed species in each
community, previous studies suggest that plants within the same subgroup share similar
hydraulic architectures (Wolf and Lundholm 2008). For this reason, it was assumed in this study
that, despite the porometer’s limitations, the results from the device’s measurements of (insert
NE Aster and BL Sedum names) could be used to accurately represent the entire Sedum and
meadow communities, respectively. An LCpro-SD photosynthesis sensor was borrowed for the
month of September 2016, to test this assumption and to corroborate the data produced by the
Decagon leaf porometer.
The photosynthesis sensor reports a range of measurements and calculated values including
stomatal conductance, transpiration rate, and photosynthesis rate. The sensor has a larger, more
versatile leaf chamber than the porometer, so it could also be used to measure some of the plant
species that were not compatible with the porometer, as demonstrated in Figure 31. Specifically,
60
the LCPro was used test the Achillea millefolium and Festuca rubra in addition to the two
species already tested with the porometer: Symphyotrichum novae-angliae and Sedum
ellacombianum. The LCPro’s chamber was not effective for the two other Sedum species, most
likely due to their three-dimensional structure, which can be observed in Table 9. Additionally,
due to the size of the device’s chamber, the LCPro was only used to test the healthier plants, with
greater leaf areas, from the timer-irrigated modules.
Figure 31: LCPro demonstrated with a variety of plant species
LCPro measurements for the listed species were collected on four different days over the month
of September. Reported values for transpiration and stomatal conductance were averaged for
each species. These values are presented in Table 11, along with the September-averaged values
collected for Sedum ellacombianum and Symphyotrichum novae-angliae using the Decagon leaf
61
porometer. Due to the limited number of measurements and the incomplete species set, the
results are somewhat inconclusive. However, the results do suggest a few notable conclusions.
While the values reported by the LCPro are significantly lower in magnitude than those
measured and calculated using the porometer, they do show a similar trend between the Sedum
and meadow species. Specifically, Symphyotrichum novae-angliae outperform Sedum
ellacombianum by a factor of 2, approximately (actual factors: 1.7, 1.7, 2.3, and 2.1,
respectively), for both measurement devices.
Additionally, the two other meadow species performed at least as well or better than the
Symphyotrichum novae-angliae, which suggests that using the one species as proxy for the entire
community was a fair, even conservative, assumption. The differences in overall magnitude
between the porometer and LCPro measurements could be partially attributed to variations in
climate. Due to the time intensive nature of the porometer measurements, the LCPro
measurements were not always taken on the same day of the week as the porometer
measurements. The four days of LCPro measurements had an average temperature of 16.7° C,
while the days in September with porometer measurements had an average temperature of 20.0°
C. The plants are expected to have somewhat lower transpiration rates in colder temperatures.
Table 11: Comparison of Porometer and LCPro measurements across four test species.
Sedum
ellacombianum
Symphyotrichum
novae-angliae
Achillea
millefolium
Festuca rubra
Porometer gs 283.5 486.9 N/A N/A
Porometer E 8.6 14.4 N/A N/A
LCPro gs 29.0 67.3 102.5 96.7
LCPro E 0.67 1.44 2.19 2.07
*all values reported as mmol/m2s
5.3 Data Synthesis and Comparisons
Table 12 presents the averages for water stored in the biomass (chapter 3), transpired (chapter 4),
and intercepted. Estimations have been made to project the total water amounts for each category
over an entire six-month growing season of May to October. The water consumed for biomass
was estimated by multiplying the average volume of the plants by volumetric water content
62
(estimated as 95% for Sedum, 80% for meadow, as discussed in the literature review presented in
chapter 2). The average transpiration volumes, originally calculated in terms of mL per hour,
have been projected as total volumes over the six-month period by multiplying by the average
number of daylight hours per day (GC 2017c) and the total number of days. The interception
data was extrapolated from average intercepted volume per event to the entire growing season by
multiplying by the average number of rain days (as originally presented in Table 2).
Table 12: Average Water in Biomass, Transpiration, and Interception
Sedum
No Irrrig.
Sedum
Timer
Sedum
Sensor
Meadow
No Irrig.
Meadow
Timer
Meadow
Sensor
Average Plant
Volume (cm3) 1,340 3,632 2,171 1,068 2,395 1,438
Water in Biomass =
95% Plant Volume
(Sedum) or
80% Plant Volume
(Meadow) (mL)
1,273 3,451 2,063 854 1,916 1,150
Transpiration Volume
per Hour (mL/hr) 2.1 7.0 4.3 4.9 11.0 5.9
Total Transpired
13.5 hr/ day * 184
days
5,216 17,388 10,681 12,172 27,324 14,656
Average Interception
per Event (mL) 1.8 4.0 3.0 1.0 2.6 1.4
Avg. Number of Rain
Days (May – Oct) 67.1 67.1 67.1 67.1 67.1 67.1
Total Interception
per Season (mL) 120.8 268.4 201.3 67.1 174.5 93.9
The previous studies cited in Chapter 2 suggested that the water stored in the plant biomass was
less than 1% of the water taken in by the plant, and that 99% of the water was lost through
evapotranspiration. This led to the assertion that the water stored in the biomass was negligible
for water balance and hydrologic calculations. While this assumption was only expressed in
studies of agricultural and herbaceous species, and it is expected that the same assumption may
not be true for Sedum, which are known to consume water more efficiently, the results of this
study found that the assumption did not hold for either vegetation type. The tabulated results
above indicate that water stored in the biomass was 7% of the total water consumed for meadow
species, and 18% for Sedum. While the meadow results conclude that water stored in biomass is
still relatively small, it is not negligible to the same magnitude concluded in other studies. This
could be due to measurement errors; the porometer may be less accurate for the smaller leaves of
63
the green roof plants. Additionally, the plant volume measurement method has a high degree of
uncertainty. The higher ratio of water stored in the biomass of Sedum species is expected,
because these species are specifically adapted to use water more efficiently.
The interception volumes are incredibly small compared to the other water retention
mechanisms; however, comparing the interception volumes to the transpiration and biomass
water volumes is not so straight forward, because they occur at different times, over different
timescales (De Groen 2002), and impact stormwater management – and in turn, the hydrological
cycle – in different ways (Savenije 2004). Interception contributes much more efficiently to
runoff reduction because interception occurs during rainfall events, when runoff is of most
concern. In contrast, transpiration doesn’t occur during precipitation events when the atmosphere
is saturated with water and the vapor pressure gradient between the leaf and the air approaches
zero. Transpiration instead contributes to runoff reduction by removing water from the soil,
allowing for more storage during the next rainfall event.
Overall, the Sedum species retained 50 – 80% more water in their biomass than the meadow
species, while the meadow species transpired 40 – 130% more water than the Sedum. Combining
these two categories, the meadow species consumed the most water in total, by an average factor
of 50%. The impact that this water consumption has on stormwater management may be partially
balanced out by the fact that Sedum species intercept more water on average, although the
interception volumes are very small. The fact that the two species types can contribute to
stormwater management at different times and through different mechanisms also suggests that
there could be benefits to stormwater management and the water cycle as a whole from using
plant communities that combine both Sedum and meadow species. That said, the observed
differences in overall plant growth between the meadow and Sedum draw concerns that the
Sedum could simply outcompete the meadow species in a combined system. Still, the results of
this study warrant further investigation into the potential success and benefits of such a scenario.
Finally, it is also notable that adding irrigation improved plant performance in all three
categories, for both plant communities, although this cannot be equated to overall stormwater
performance, as irrigation also affects soil saturation and water balance of the system.
64
5.4 Comparisons to Complete Green Roof Modules
As explained in chapter 1, previous research at the GRIT lab found that the two different plant
communities were not statistically different in terms of overall stormwater retention of the entire
green roof module (Hill et al. 2017). The two possible hypotheses to explain this result were (a)
that the different mechanisms through which the two different plant types reduced runoff were
equal or balanced each other out, or (b) that the overall contributions of the plants to runoff
reduction are negligible compared to the water stored and evaporated directly from the substrate
layers. While the results summarized above indicate that the differences in performance between
the two communities do partially balance each other out, they do not directly address the second
hypothesis.
To compare the water retained by the plants to the water retained by the overall green roof
module, the water volumes are normalized by soil surface area and rainfall event size. This way,
the plant-water retention can be directly compared to the runoff coefficients estimated for the
larger GRIT lab modules by Hill et al. (2017). As given in Equation 1, the runoff coefficient Cvol
is the ratio of total discharge depth from an individual event over the total precipitation depth.
Hill et al. (2017) reported that – for green roofs with the same biologically-derived growing
media used in this study – modules which received daily (timer-based) irrigation had an average
Cvol of 0.5, meaning they retained 50% of precipitation, while modules which received no, or
sensor-based irrigation had an average Cvol of 0.3, meaning they retained 70% of precipitation.
For this study, interception was measured for twelve different precipitation events. The total
interception volume was divided by the surface area of each pot – 314 cm2 – to determine
interception depth, and retention due to interception was calculated as the ratio of interception
depth to precipitation depth. The average retention due to interception for each variable
combination is presented in Table 13. The results range from 2 – 6%. These values are compared
to the total stormwater retention estimated by the runoff coefficients. The results indicate that
Interception accounts for 3 – 12% of the total retention. Interception contributes the most in the
timer-irrigated pots when total retention is least and the average interception is greatest. Overall
these results suggest that interception is relatively small compared to the total stormwater
management potential of the green roof system, but it is not negligible.
65
Table 13: Interception vs. Total Retention
Sedum
No Irrig.
Sedum
Sensor
Sedum
Timer
Meadow
No Irrig.
Meadow
Sensor
Meadow
Timer
% Retained through
Interception 4±1 5±2 6±2 2±1 3±1 5±2
Total % Retained
(From Cvol)* 70 70 50 70 70 50
% Interception
Contributes to Total
Water Retention
6 7 12 3 4 10
*(Hill et al. 2017)
For the sake of comparison, the transpiration volumes were similarly converted to transpiration
depths using the surface area of the pots. Previous research suggests that transpiration takes
effect hours or even days after a rainfall event and occurs on a timescale much greater than the
individual precipitation event duration (De Groen 2002, Wang-Erlandsson et al. 2014). Because
the transpiration does not occur during precipitation events, it instead contributes indirectly to the
storage capacity of the substrate layers, it is difficult to directly compare these results to runoff
coefficients. Instead, the transpiration depths were compared to previously determined ET rates
for modules at the GRIT lab.
Jahanfar et al. (2016) specifically looked at meadow modules, comparing the ET of shaded and
non-shaded green roof areas. For the non-shaded areas, they reported an average ET rate of 7.75
mm/day for irrigated modules, and 5.58 mm/day for non-irrigated modules. By comparison, the
results of this study found a transpiration rate of 4.73 mm/day for the meadow, timer-irrigated
pots, and 2.11 mm/day for meadow, non-irrigated pots. By this metric, transpiration in meadow
communities accounts for 40% (non-irrigated) – 60% (timer-irrigated) of the total
evapotranspiration. However, it is worth noting that the two studies were conducted over
different years, with somewhat different climate conditions. Wang-Erlandsson et al. (2014)
developed a hydrological model, which found that transpiration was 59% of terrestrial
evaporation, suggesting that the estimations made in this study are of a reasonable magnitude.
Overall, the comparisons presented for interception and transpiration to total retention and
evapotranspiration, respectively, conclude that the contributions by the plants themselves may be
66
smaller than the contribution by the growing media and other green roof storage layers, but they
are not negligibly small. For this reason, and because the plant contributions, although small,
may occur at key times in the water cycle as discussed in the previous section, vegetation should
not be entirely omitted from hydrologic assessments of green roof performance. These results
also suggest that, through more rigorous plant analysis, a balanced combination of both plant
communities with an optimized irrigation regime could be determined to maximize green roof
stormwater management potential.
67
Chapter 6: Conclusions
6.1 Research Summary This study was conducted to better understand the physical and biological processes occurring at
the level of the plants which contribute to stormwater reduction benefits of green roofs. The
primary goal was to compare the runoff reduction potential of native meadow species and Sedum
species over a range of irrigation volumes. The results help to answer the key knowledge gaps
left from previous GRIT Lab results. When supplemented with other data collected at the GRIT
Lab, including soil analysis, overall water retention, plant cover, biodiversity, and thermal
benefits, this project helps to create a holistic understanding of green roof performance and
benefits.
The methodologies used differ from the majority of previous studies reviewed because they
specifically isolate the performance of the plants themselves, rather than treating evaporation
from the soil and transpiration from the plants as a single process (evapotranspiration) or
calculating the retention from the water balance of the entire green roof system. Additionally, the
methods used are not reliant on assumption-heavy computer models or conducted in laboratory
settings with strict controls, neither of which are realistic to the variability and complexity of an
actual urban roof setting. Finally, the results presented are specific to the climate and native
vegetation of temperate regions of North America, and therefore could not be directly concluded
from previous studies conducted in other geographic regions with different climates and plant
species. The methods and results of this study were divided into three categories – water retained
in the plant biomass, water transpired through the leaves, and water intercepted and evaporated
directly from the plant surface.
Of the methods attempted, weighing the pots and taking soil moisture measurements proved
inconclusive for the intended goal of evaluating biomass. Instead, measuring the plant volume
with rulers and estimating spatial density from photographic analysis was the most effective
method of calculating biomass. These measurements concluded that Sedum species could
maintain far more biomass during the drought conditions than meadow species, although the
differences between the two communities are much less significant during wetter periods. On
68
average, the Sedum species had 40% greater biomass than the meadow species. According to
prior research, herbaceous meadow species are generally 80% water (Hsiao 1973), while Sedum
species can be up to 95% water (Gausman and Allen 1973). Accounting for the volumetric water
content, the Sedum species retained 50 – 80% more water in their biomass.
Transpiration was calculated by measuring stomatal conductance with a decagon leaf porometer,
converting to transpiration using atmospheric conditions, and multiplying by leaf area. The
results concluded that the meadow community transpires 40 – 130% more water than the Sedum
community, which was to be expected, given that Sedum have specifically adapted to minimize
water loss through transpiration. Overall, the magnitude of transpiration was great enough that it
could significantly affect stormwater retention. Compared to previously measured runoff
coefficients, the results indicate that transpiration could have a significant impact on green roof
storage capacity of the growing media: the depth of water transpired over the antecedent dry
period accounted for 30 – 120% of the incoming water retained. By another metric, transpiration
measured in this study represented 40 – 60% of the total evapotranspiration estimates of previous
studies.
The water stored in the biomass was 7% of the total water consumed for meadow species and
18% for Sedum, indicating that biomass growth was not a negligible component of plant water
usage. Combining the two water consumption mechanisms, the meadow communities consumed
50% more water than the Sedum communities, on average, over the duration of a season (May –
October). Interception, in contrast to the other two mechanisms, is not a “consumption,” because
the plant does not take this water in through its roots or utilize the water in any way. Interception
is simply the water that lands on the leaves during precipitation events and evaporates without
reaching the soil. Interception was measured by drying the leaves with laboratory tissues after
each rainfall event and then weighing the water collected. The results concluded that Sedum
species intercepted 70% more water than meadow species on average, but interception volume
only accounted for 3 – 12% of the total retention from each rainfall event.
Total plant biomass, transpiration, and interception were all improved by the provision of
irrigation for both the meadow and Sedum communities, particularly during the drier months.
69
Overall, the results of this study conclude that the water consumption and interception by the
plants is small compared to the total retention, but it is not insignificant or negligibly small.
Similarly, the differences between the meadow and Sedum performance across the three different
mechanisms – biomass growth, transpiration, and interception – only partially cancel each other
out. Because the water consumption by these three mechanisms occurs at different times in
relation to precipitation events, there may be hydrological benefits to using combined plant
communities, which should be further investigated. Ultimately, the results can help to answer
many questions about the physical and biological performance of green roof plants, but they
cannot concretely conclude why plant species selection was found to be insignificant in terms of
stormwater management by Hill et al. (2017).
6.2 Climate Considerations Total plant size, as well as transpiration and interception, all improved for both plant
communities in the second year of the study (2017), once the initial stress of transplanting was
no longer a factor and water availability was non-limiting. While all measurement types
increased in magnitude, the differences between the two communities were also altered. For
instance, Sedum were 4.2 times greater than meadow species in biomass volume in Summer
2016, but only 1.3 times greater in Summer 2017. Inversely, the average difference in
transpiration volume between meadow and Sedum species increased from 44% in 2016 to 77% in
2017. The ratio of interception volume between the communities decreased from a factor of 8.3
in 2016 to a factor of 1.9 in 2017.
These results indicate that the stormwater management potential of green roof plants is greatly
impacted by climate. In support of this conclusion, Berghage et al. (2007) suggests that the
contribution of the plants to stormwater management is greatest (reaching upwards of 40%) in
areas with frequent, small rainfall events. 2017 had more rainfall overall, spread out over a
greater number of rain days than 2016. The seasonal temperature and precipitation patterns affect
the size and health of the plants, which in turn affects their water consumption and intercepting
capacity. Therefore, it follows that the contribution of the plants to stormwater management may
be more or less significant one year to the next. The data collected by Hill et al. (2017) occurred
over a different study period, which may have had slightly different climate conditions that could
70
have caused the plants to be less effective overall or caused the differences between the
communities to be less significant.
6.3 Impacts to the Hydrological Cycle Many stormwater management assessments of green roofs and other low impact development
predominantly focus on the benefits in terms of runoff reduction and flood prevention. However,
the benefits can also be framed in terms of the hydrological cycle, or specifically, recycling
water back into the atmosphere. As explained in section 5.3, interception and transpiration occur
at different times, over different timescales (De Groen 2002), and affects stormwater retention –
as well as evaporation – in different ways (Savenije 2004). Transpiration is not effective during
precipitation events when the atmosphere is saturated with water and the vapor pressure gradient
approaches zero. It can take several hours or even days after a rain event before atmospheric
conditions return to a state that is suitable for transpiration to occur (Wang-Erlandsson et al.
2014). In contrast, interception occurs during precipitation, and the water immediately
evaporates back into the atmosphere. Previous large-scale hydrologic models have found that the
differences in temporal characteristics between transpiration, interception, and evaporation from
the soil are significant enough to cause differences in moisture recycling (De Groen 2002, Wang-
Erlandsson et al. 2014).
Using global-scale modelling methods, Wang-Erlandsson et al. (2014) concluded that 31% of all
transpiration occurred during time periods in which there had already been more than one day
without precipitation, which emphasizes that transpiration has a delayed impact on recycling
water back into the atmosphere. They found that this delayed impact helps to sustain moisture
recycling to the atmosphere during dry periods. Inversely, the immediate recycling caused by
interception can trigger rainfall events more rapidly (De Groen 2002). Evaporation and recycling
to the atmosphere in turn lead to precipitation, so having mechanisms which cause this process to
occur at different times helps to temporally distribute precipitation more evenly. From an urban
stormwater management perspective, dispersed, smaller frequency rainfall events present less
risk of flooding than large events or events in rapid succession.
71
While the models cited here assess impacts on a much larger scale than individual green roof
performance, or even entire cities, the general concepts of hydrological cycling are still the same,
and a key takeaway remains be that the timescale differences between the processes adds to the
complex tradeoffs and hydrological benefits of the vegetation, and neither process – transpiration
or interception – should be disregarded purely based on magnitude comparisons.
6.4 Green Roof Design Stormwater management is just one metric for assessing green roof performance. Vegetation
selection, among other design variables, also affects overall cover, plant diversity, potential
ecosystem benefits, and cooling benefits. When comparing potential green roof plant species,
there is not one specific species that performs best in all situations or across all the different
performance metrics. Therefore, these factors – stormwater management, biodiversity,
ecosystem impacts, and cooling benefits – should all be evaluated and weighed when selecting
vegetation for a specific green roof installation. The optimal choice will depend on the
geographic location, climate, other design decisions such as growing media and irrigation
regime, and prioritized goals of the green roof.
This study is intended to better inform green roof industry design practices, in particular,
vegetation selection. The results presented indicate that plant species selection does impact
stormwater management, as physical and biological differences between vegetation types alter
the mechanisms through which plants reduce runoff. The fact that Sedum and meadow species
contribute most effectively to overall retention and hydrology through different mechanisms with
varying timescales – Sedum intercept more water while meadow species transpire more water –
suggests that optimal hydrological benefits could come from using plant communities with both
Sedum and meadow species, rather than selecting one or the other. However, a combined design
scenario would introduce other tradeoffs in terms of species competition, which cannot
accurately be predicted by the results of this study. Further experimentation is therefore
recommended to assess the performance and potential benefits of combined communities.
Most existing green roof studies, including those previously conducted at the GRIT lab, measure
stormwater retention as a singular process that includes the contributions of plants, growing
72
media, and all other green roof layers. Similarly, moisture recycling from the green roof to the
atmosphere is often simplified as a single term – evapotranspiration. This study proves that
separating these processes into individual mechanisms, such as interception, transpiration, and
water stored in biomass, is not only feasible in terms of available measurement methods, but also
productive for understanding the complexities of green roof performance and hydrology.
Understanding and measuring each mechanism separately highlights differences in the
magnitude and timescale of water retention, both of which are relevant to quantifying benefits
and tradeoffs of a green roof design variables. Finally, because the subject of this research –
assessing how vegetation contributes to stormwater management – is at the intersection of
biology, hydrology, and engineering, the results emphasize the importance interdisciplinary and
holistic approaches to green roof design. By combining biological and engineering perspectives,
a higher standard of green roof design can be achieved.
73
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78
Appendix A: Water Consumption Weekly Data
The following tables show the weekly total precipitation and irrigation depths (in mm). The
timer irrigated pots each received identical water amounts, while the sensor irrigated pots were
watered based on soil moisture readings for each pot. The irrigation depths for the six sensor pots
are therefore presented individually in the tables. The weeks are identified by the Monday (i.e.
the first day) of each week, save for the first week. Data collection began on Thursday, June 9,
2016, so the first data column presents the water consumption for only the first four days.
06/09/16 06/13/16 06/20/16 06/27/16 07/04/16 07/11/16 07/18/16
Total Precipitation (mm) 8.2 0.4 2 4.6 10.6 12.4 0
Timer Irrigation (mm) 10 25 35 20 22.8 25.6 32
Meadow Sensor A (mm) 0 5 25 10 6.4 0 0
Meadow Sensor B (mm) 0 5 25 0 6.4 0 0
Meadow Sensor C (mm) 0 5 25 0 5 0 0
Sedum Sensor A (mm) 0 0 20 0 6.4 0 6.4
Sedum Sensor B (mm) 0 5 20 5 5 0 0
Sedum Sensor C (mm) 0 0 25 5 6.4 0 0
07/25/16 08/01/16 08/08/16 08/15/16 08/22/16 08/29/16 09/05/16
Total Precipitation (mm) 24.2 5.8 18.4 24 20 0.6 41.2
Timer Irrigation (mm) 25.6 19.2 32 25.6 25.6 32 19.2
Meadow Sensor A (mm) 0 0 12.8 0 0 0 6.4
Meadow Sensor B (mm) 0 0 6.4 0 0 0 6.4
Meadow Sensor C (mm) 0 0 12.8 0 0 0 6.4
Sedum Sensor A (mm) 0 0 12.8 0 0 6.4 0
Sedum Sensor B (mm) 0 0 12.8 0 0 0 0
Sedum Sensor C (mm) 0 0 6.4 0 0 6.4 6.4
09/12/16 09/19/16 09/26/16 10/03/16 10/10/16 10/17/16 10/24/16
Total Precipitation (mm) 13 1.4 21.7 4 1.4 15 11.8
Timer Irrigation (mm) 32 25.6 12.8 32 25.6 12.8 25.6
Meadow Sensor A (mm) 0 0 0 0 0 0 0
Meadow Sensor B (mm) 0 0 0 0 0 0 0
Meadow Sensor C (mm) 0 0 0 0 0 0 0
Sedum Sensor A (mm) 0 0 0 0 0 0 0
Sedum Sensor B (mm) 0 0 0 0 0 0 0
Sedum Sensor C (mm) 0 0 0 0 0 0 0
79
05/01/17 05/08/17 05/15/17 05/22/17 05/29/17 06/05/17 06/12/17
Total Precipitation (mm) 88.8 1.8 13.6 64.8 20.6 5.6 5.2
Timer Irrigation (mm) 19.2 32 32 25.6 12.8 25.6 12.8
Meadow Sensor A (mm) 0 0 6.4 0 0 0 12.8
Meadow Sensor B (mm) 0 0 6.4 0 0 0 12.8
Meadow Sensor C (mm) 0 0 6.4 0 0 0 12.8
Sedum Sensor A (mm) 0 0 6.4 0 0 0 12.8
Sedum Sensor B (mm) 0 0 6.4 0 0 0 12.8
Sedum Sensor C (mm) 0 0 0 0 0 0 12.8
06/19/17 06/26/17 07/03/17 07/10/17 07/17/17 07/24/17
Total Precipitation (mm) 57.6 24.6 6.6 14.2 27 8.2
Timer Irrigation (mm) 12.8 12.8 19.2 19.2 19.2 25.6
Meadow Sensor A (mm) 0 0 6.4 6.4 6.4 6.4
Meadow Sensor B (mm) 0 0 6.4 6.4 6.4 6.4
Meadow Sensor C (mm) 0 0 6.4 6.4 12.8 6.4
Sedum Sensor A (mm) 6.4 0 6.4 6.4 6.4 6.4
Sedum Sensor B (mm) 0 0 6.4 6.4 0 6.4
Sedum Sensor C (mm) 6.4 0 6.4 6.4 12.8 6.4
80
Appendix B: Sample Biomass Calculations
The biomass calculations were attempted using the following equation:
𝐵𝑖𝑜𝑚𝑎𝑠𝑠 = 𝑇𝑜𝑡𝑎𝑙 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑀𝑎𝑠𝑠 − 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐷𝑟𝑦 𝑀𝑎𝑠𝑠 − 𝑉𝑊𝐶 ∗ 𝑆𝑜𝑖𝑙 𝑉𝑜𝑙𝑢𝑚𝑒
Sample Calculations are shown here for the initial dry mass, and for biomass on June 20 – 21.
The resulting negative biomass calculations are highlighted in yellow. These values indicate a
problem with the method. Since there is negligible error associated with the mass measurements
using the OHAUS balance, the failure is most likely due to the soil moisture measurements.
Community Irrigation Set Total Mass VWC (Raw) VWC Dry Mass
Sedum No Irrigation A 2488.8 652 0.14 2130.413
Sedum Timer A 2556 646 0.14 2487
Sedum Sensor A 2510.9 631 0.12 2448.9
Meadow No Irrigation A 2614.6 654 0.15 2541.6
Meadow Timer A 2625.3 645 0.14 2556.8
Meadow Sensor A 2476 630 0.12 2414.5
Sedum No Irrigation B 2549.1 660 0.15 2473.6
Sedum Timer B 2445.5 635 0.13 2381.5
Sedum Sensor B 2536.1 655 0.15 2462.6
Meadow No Irrigation B 2486.9 653 0.15 2414.4
Meadow Timer B 2523.3 648 0.14 2453.3
Meadow Sensor B 2485.4 658 0.15 2410.4
Sedum No Irrigation C 2545.6 657 0.15 2471.1
Sedum Timer C 2456.9 658 0.15 2381.9
Sedum Sensor C 2557.5 654 0.15 2484.5
Meadow No Irrigation C 2515.4 662 0.15 2438.9
Meadow Timer C 2594.2 631 0.12 2532.2
Meadow Sensor C 2482.3 654 0.15 2409.3
6/2/2016 (No Plants)
Total Mass VWC (Raw) VWC Plant Mass Total Mass VWC (Raw) VWC Plant Mass
2461.9 758 0.23 217.4872 2412.2 740 0.22 173.7872
2768.2 812 0.26 150.7 2793.4 874 0.29 160.4
2565.9 759 0.23 2.5 2622.5 808 0.26 44.1
2482.7 747 0.22 -169.4 2431.1 734 0.21 -216.5
2861.1 862 0.29 160.8 2883.4 886 0.30 177.6
2361.3 761 0.23 -168.2 2299.3 742 0.22 -223.7
2495.1 737 0.21 -85.5 2447.5 765 0.23 -142.6
2592.3 830 0.27 75.3 2621.6 843 0.28 101.6
2672.3 795 0.25 84.2 2605.7 768 0.24 25.6
2472.5 737 0.21 -48.9 2413.5 738 0.22 -108.4
2673.1 830 0.27 84.3 2703.5 785 0.25 127.7
2427 762 0.23 -98.9 2374.4 746 0.22 -146
2545.3 726 0.21 -28.8 2490.4 724 0.21 -83.2
2841.6 806 0.26 330.7 2850 886 0.30 319.1
2667.1 714 0.20 84.1 2714.4 771 0.24 111.4
2399.7 777 0.24 -159.7 2346.2 718 0.20 -192.7
2790.6 768 0.24 140.9 2824.1 699 0.19 199.4
2493.2 782 0.24 -38.1 2431.3 764 0.23 -94
06/20/16 06/21/16
81
Appendix C: Soil Depth Calculations
Community Irrigation Set Average Soil
Depth [cm]
Average Slope
[cm/day]
Projected Change
over 145 Days [cm]
Sedum No Irrigation A 14.1 0.0041 0.59
Sedum Timer A 13.6 -0.0124 -1.80
Sedum Sensor A 13.5 -0.0037 -0.53
Meadow No Irrigation A 14.6 -0.0054 -0.78
Meadow Timer A 14.6 -0.0033 -0.48
Meadow Sensor A 13.8 -0.0062 -0.89
Sedum No Irrigation B 14.5 -0.0037 -0.53
Sedum Timer B 13.9 -0.0085 -1.23
Sedum Sensor B 14.1 0.0010 0.15
Meadow No Irrigation B 14.7 -0.0028 -0.40
Meadow Timer B 14.0 -0.0104 -1.51
Meadow Sensor B 14.2 -0.0067 -0.97
Sedum No Irrigation C 14.4 -0.0001 -0.01
Sedum Timer C 13.9 -0.0067 -0.97
Sedum Sensor C 14.3 -0.0019 -0.27
Meadow No Irrigation C 14.1 -0.0045 -0.65
Meadow Timer C 14.4 -0.0134 -1.94
Meadow Sensor C 14.0 -0.0005 -0.08
82
Appendix D: Plant Volume Calculations
S. ellacombianum Sedum acre Sedum album NE Aster Festuca rubra A. millefolium
80% 74% 80% 30% 20% 21%
The volume of each individual plant species in each pot is first calculated as a rectangular prism:
𝑃𝑟𝑖𝑠𝑚 𝑉𝑜𝑙𝑢𝑚𝑒 = 𝐻𝑒𝑖𝑔ℎ𝑡 ∗ 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑊𝑖𝑑𝑡ℎ ∗ 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑊𝑖𝑑𝑡ℎ
The total volume of plant material in each pot is then calculated by multiplying each rectangular
prism by the spatial density of that species and summing the volumes for the entire pot:
𝑇𝑜𝑡𝑎𝑙 𝑉𝑜𝑙𝑢𝑚𝑒 = ∑(𝑝𝑟𝑖𝑠𝑚 𝑣𝑜𝑙𝑢𝑚𝑒)𝑖 ∗ (𝑠𝑝𝑎𝑡𝑖𝑎𝑙 𝑑𝑒𝑛𝑠𝑖𝑡𝑦)𝑖
Sample prism and total volume calculations are shown for all 18 pots over two weeks. These two
weeks were during the more severe drought conditions, so many of the pots exhibit a decrease in
total volume.
Community Irrigation Set S. ella /Aster S. acre /F. rubra S. album /Achillea Total S. ella /Aster S. acre /F. rubra S. album /Achillea Total
Sedum No Irrigation A 177 230 1,010 1,120 264 196 528 779
Sedum Timer A 852 172 426 1,149 700 163 363 971
Sedum Sensor A 280 87 1,077 1,150 284 116 968 1,087
Meadow No Irrigation A 29 498 87 125 24 275 39 69
Meadow Timer A 223 850 1,421 532 122 432 1,344 403
Meadow Sensor A 67 579 290 195 81 117 155 80
Sedum No Irrigation B 230 436 395 822 200 128 288 485
Sedum Timer B 476 992 1,253 2,117 640 324 1,144 1,667
Sedum Sensor B 553 221 834 1,273 441 180 720 1,062
Meadow No Irrigation B 57 856 292 246 64 980 280 270
Meadow Timer B 251 1,434 259 411 137 840 147 237
Meadow Sensor B 95 666 445 253 84 935 448 303
Sedum No Irrigation C 202 94 120 328 169 37 96 239
Sedum Timer C 664 279 963 1,508 528 396 882 1,421
Sedum Sensor C 348 369 1,939 2,103 360 154 1,870 1,898
Meadow No Irrigation C 22 355 135 105 8 216 133 73
Meadow Timer C 57 1,904 189 431 1 1,001 330 266
Meadow Sensor C 68 292 142 107 92 405 200 149
06/30/16 07/07/16
83
Appendix E: Means, Standard Errors, and Statistical Analysis for Transpiration
and Interception Data
The tables above present the Shapiro Wilk results, which indicate whether each data array passed
the normality or lognormal test at the 95% confidence level or failed both (non-normal); the P-
values resulting from the Kruskal Wallis Nonparametric ANOVA test comparing each
measurement type, by plant community and irrigation regime; and compiled averages and
variances for the full Sedum and Meadow data sets as well as each of the three irrigation sets.
Sedum Sedum Sedum Meadow Meadow Meadow
No Irrigation Timer Sensor No Irrigation Timer Sensor
Transpiration Rate non-normal lognormal lognormal non-normal normal normal
Canopy Area non-normal non-normal non-normal non-normal normal non-normal
Transpiration Volume non-normal lognormal non-normal non-normal lognormal lognormal
Interception Volume lognormal non-normal lognormal non-normal non-normal non-normal
Shapiro Wilk Test Results
Transpiration Rate Canopy Area Transpiration Volume Interception
Comparing Plant Species 0 5.72E-12 6.2819E-08 3.58E-06
Comparing Irrigation Types 2.3336E-05 2.22E-16 0 0.00237
P-Value for Kruskal-Wallis Nonparametric ANOVA Test
Sedum Meadow All All All
All All No Irrigation Timer Sensor
Transpiration Rate Mean 3.9 12.6 6.6 9.5 7.7
Variance 8.1 56.0 38.0 62.1 43.3
Observations 333 295 199 215 214
Standard Error 0.2 0.4 0.4 0.5 0.4
Canopy Area Mean 188 103 96 200 140
Variance 22183 6278 8061 21570 13217
Observations 234 234 156 156 156
Standard Error 10 5 7 12 9
Transpiration VolumeMean 7.0 4.5 3.4 8.93 5.0
Variance 57.7 28.7 25.6 66.12 27.7
Observations 264 333 199 215 214
Standard Error 0.5 0.3 0.4 0.6 0.4
Interception Volume Mean 2.9 1.7 1.4 3.3 2.2
Variance 8.2 5.5 2.8 11.1 6.0
Observations 100 102 66 68 68
Standard Error 0.3 0.2 0.2 0.4 0.3
Compiled Averages
84
Appendix F: Sample Calculations and Weekly Averages for Transpiration
Rates
Sample Calculation Sheet presented for porometer measurements collected on May 3, 2017.
Resistance and leaf temperature are reported by the leaf porometer. Air temperature, relative
humidity, and atmospheric pressure are recorded from the GRIT lab weather station for the
nearest five-minute interval to the time of each porometer measurement. The saturation vapour
pressures were calculated from leaf and air temperature, respectively, using the Arden Buck
equation. The volume fractions of water vapour, wleaf and wair, as well as the transpiration rate E,
were calculated according to the equations given in section 4.3.1.
The table on the following page presents the transpiration rates averaged for each of the six
variable combinations for every week of the study. Due to a network malfunction, the GRIT lab
weather station did not record measurements for a period spanning the 7/5/17, 7/12/17, and
7/19/17 measurements. For these three weeks, temperature, relative humidity, and air pressure
from the nearby City of Toronto weather station were used to calculate transpiration.
Resistance Leaf Temp. Air Temp. Atmosphere Air Pressure Leaf SVP Atmos. SVP w_leaf w_air wl-wa Transpiration Rate
(m²·s)/mol °C °C %RH Pa Pa Pa unitless unitless unitless mmol/(m2∙s)
Sedum No Irrigation A 4.59 28.8 15.1 62.8 101004 3961 1720 0.0392 0.0107 0.0285 6.00
Sedum Timer A 3.19 26.8 15.1 63.5 101014 3525 1712 0.0349 0.0108 0.0241 7.18
Sedum Sensor A 6.27 26.2 15.2 62.8 101000 3402 1723 0.0337 0.0107 0.023 3.57
Meadow No Irrigation A 2.47 30.5 14.0 64.9 101011 4369 1596 0.0432 0.0103 0.033 12.50
Meadow Timer A 2.17 23.0 13.0 65.9 101027 2810 1498 0.0278 0.0098 0.018 7.72
Meadow Sensor A 1.98 23.0 13.3 65.8 101010 2810 1524 0.0278 0.0099 0.0179 8.33
Sedum No Irrigation B 4.17 28.0 14.5 63.9 101015 3781 1651 0.0374 0.0104 0.027 6.21
Sedum Timer B 4.62 27.5 14.7 64.1 101017 3673 1670 0.0364 0.0106 0.0258 5.37
Sedum Sensor B 4.92 28.5 14.5 63.7 101019 3893 1655 0.0385 0.0104 0.0281 5.52
Meadow No Irrigation B 1.85 22.5 13.1 66.7 101028 2726 1507 0.027 0.01 0.017 8.43
Meadow Timer B 2.24 23.4 13.1 65.5 101016 2879 1509 0.0285 0.0098 0.0187 7.78
Meadow Sensor B 2.16 25.5 12.7 66.6 101021 3264 1472 0.0323 0.0097 0.0226 9.72
Sedum No Irrigation C 4.38 27.6 14.3 64.5 100998 3694 1628 0.0366 0.0104 0.0262 5.76
Sedum Timer C 4.35 27.7 14.1 64.8 101012 3716 1606 0.0368 0.0103 0.0265 5.86
Sedum Sensor C 3.10 25.6 13.7 64.8 101005 3284 1572 0.0325 0.0101 0.0224 6.85
Meadow No Irrigation C 7.02 30.0 14.0 64.6 101003 4245 1597 0.042 0.0102 0.0318 4.43
Meadow Timer C 1.93 24.6 13.4 65.9 101020 3094 1534 0.0306 0.01 0.0206 9.82
Meadow Sensor C 2.15 26.2 13.3 66.1 100993 3402 1529 0.0337 0.01 0.0237 10.19
85
Transpiration Rate (mmol/m2s)
Community Sedum Sedum Sedum Meadow Meadow Meadow
Irrig. Type No Irrig. Timer Sensor No Irrig. Timer Sensor
6/15/16 1.29 1.91 1.56 7.49 8.86 6.34
6/22/16 1.28 1.57 1.38 3.29 8.65 5.08
6/29/16 1.48 1.76 1.64 4.75 7.82 6.38
7/6/16 2.19 2.69 2.51 13.96 21.03 15.13
7/13/16 2.56 3.38 3.54 17.25 26.66 20.42
7/20/16 1.63 2.24 2.09 9.43 18.54 12.30
7/27/16 1.62 3.15 1.69 15.03 29.34 23.76
8/3/16 2.54 6.06 2.38 18.03 28.01 25.03
8/10/16 1.76 4.50 2.49 9.55 22.68 18.15
8/17/16 3.04 5.88 3.41 13.41 11.13 14.28
8/24/16 2.91 9.07 5.98 19.16 21.33 16.27
8/31/16 5.59 11.94 5.83 19.68 12.98 13.35
9/7/16 3.55 11.95 7.77 27.13 22.91 22.07
9/14/16 7.16 8.24 5.68 6.27 9.28 10.28
9/21/16 7.61 9.08 8.08 12.05 14.88 11.44
9/28/16 3.61 5.01 3.79 6.91 10.66 8.00
10/12/16 4.15 4.12 4.13 5.81 6.44 5.62
10/19/16 2.60 3.17 5.13 2.05 1.96 2.46
10/26/16 1.65 1.20 2.04 1.17 1.57 1.56
5/3/17 5.97 6.12 5.29 8.42 8.41 9.38
5/10/17 3.99 4.31 3.83 6.89 7.56 7.43
5/27/17 3.94 3.18 3.99 11.21 9.64 6.21
6/8/17 6.20 7.82 8.06 18.89 20.70 15.99
6/21/17 2.52 2.93 3.96 14.68 21.33 15.11
6/28/17 4.81 6.29 5.96 14.58 12.96 11.37
7/5/17 3.48 4.47 4.41 15.54 15.08 12.03
7/12/17 1.66 2.77 2.26 9.41 4.88 6.21
7/19/17 1.37 2.37 2.46 4.48 18.28 13.32
7/28/17 0.98 4.15 3.39 13.97 15.00 14.28
86
Appendix G: Calculated Canopy Areas from Photos
Canopy areas were estimated from ruler measurements and photo analysis using the method
described in section 4.2.2. The density factors for each species, calculated from the photo
analysis are provided in the second table.
Canopy Area (cm2)
Community Sedum Sedum Sedum Meadow Meadow Meadow
Irrig. Type No Irrig. Timer Sensor No Irrig. Timer Sensor
6/30/16 78 127 119 22 47 24
7/7/16 74 126 115 23 36 27
7/14/16 76 152 118 20 44 28
7/21/16 55 154 88 17 41 32
7/28/16 59 141 91 16 51 36
8/4/16 53 142 89 17 70 29
8/11/16 48 119 85 13 59 32
8/18/16 42 148 96 10 88 34
8/25/16 49 128 93 9 70 31
9/1/16 41 145 84 12 109 50
9/8/16 55 156 93 19 146 84
9/15/16 58 165 96 27 122 83
9/22/16 64 182 98 31 135 84
10/6/16 53 171 103 44 133 102
10/13/16 55 182 109 50 133 107
10/27/16 47 171 86 39 136 83
5/4/17 57 187 112 93 162 94
5/11/17 85 240 152 138 162 143
5/18/17 144 341 227 155 261 184
5/25/17 196 374 271 222 231 196
6/8/17 270 493 413 191 209 191
6/21/17 285 472 407 125 204 143
6/28/17 290 578 443 152 210 138
7/5/17 286 523 401 154 208 162
7/13/17 301 544 421 139 311 184
7/28/17 305 578 407 126 278 173
S. ellacombianum Sedum acre Sedum album NE Aster Festuca rubra A. millefolium
70% 50% 47% 51% 12% 36%
87
Appendix H: Estimated Transpiration Volumes
The transpiration volumes are calculated from the measured transpiration rates and the estimated
canopy areas of each plant. Both measurements have inherent uncertainty and rely on multiple
assumptions: the transpiration measurements are taken for a single species from each community
at a single point in time, while the canopy area measurements use an average, roughly estimated
spatial density factor for each species. The values are averaged for three pots for each variable
combination to minimize some of the uncertainty and random variance.
Transpiration Volume (mL/hr)
Community Sedum Sedum Sedum Meadow Meadow Meadow
Irrig. Type No Irrig. Timer Sensor No Irrig. Timer Sensor
6/15/16 0.64 1.55 1.18 1.08 2.68 0.99
6/22/16 0.64 1.27 1.05 0.47 2.62 0.79
6/29/16 0.74 1.42 1.25 0.68 2.37 0.99
7/6/16 0.92 2.44 1.64 1.74 5.90 3.01
7/13/16 1.07 3.06 2.32 2.15 7.48 4.07
7/20/16 0.68 2.03 1.37 1.17 5.20 2.45
7/27/16 0.68 2.86 1.11 1.87 8.23 4.73
8/3/16 0.77 5.13 1.37 1.43 13.07 5.16
8/10/16 0.54 3.81 1.43 0.76 10.58 3.74
8/17/16 0.92 4.98 1.97 1.06 5.19 2.94
8/24/16 0.88 7.67 3.45 1.52 9.95 3.35
8/31/16 1.70 10.10 3.36 1.56 6.05 2.75
9/7/16 1.22 12.13 4.54 3.95 19.02 10.76
9/14/16 2.46 8.36 3.32 0.91 7.70 5.01
9/21/16 2.61 9.21 4.72 1.76 12.36 5.58
9/28/16 1.24 5.08 2.21 1.01 8.85 3.90
10/12/16 1.34 4.49 2.57 1.67 5.60 3.54
10/19/16 0.84 3.46 3.19 0.59 1.71 1.55
10/26/16 0.53 1.30 1.27 0.34 1.36 0.98
5/3/17 4.47 10.85 6.26 8.30 11.13 9.37
5/10/17 2.99 7.65 4.53 6.79 10.01 7.43
5/27/17 2.94 5.65 4.72 11.05 12.77 6.20
6/8/17 10.84 25.00 21.06 19.10 27.88 16.29
6/21/17 4.40 9.37 10.33 14.84 28.73 15.38
6/28/17 8.41 20.11 15.58 14.74 17.45 11.58
7/5/17 6.44 15.15 11.47 15.53 20.36 12.66
7/12/17 3.24 9.76 6.16 8.45 9.83 7.40
7/19/17 2.67 8.36 6.73 4.03 36.83 15.88
7/28/17 1.93 15.54 8.96 11.41 27.08 16.02
88
Appendix I: Estimated Interception Volumes
The measured interception volumes are presented by event for all the three pots (A,B,C) for each
variable combination. The method used for measuring interception had a relatively high potential
for human error, so the accuracy of individual measurements should be regarded with caution.
Interception Volumes (mL) Sedum Sedum Sedum Meadow Meadow Meadow
Date (Set) No Irrig. Timer Sensor No Irrig. Timer Sensor
07/08/2016 (A) 0.2 0.2 0.3 0 0 0
07/14/2016 (A) 0.6 1.7 3.6 0 0 0
08/16/2016 (A) 1.4 4.4 3.2 0 0.6 0.1
09/08/2016 (A) 0.2 4.1 1.4 0 0 0
09/23/2016 (A) 2.7 6.6 4.3 0.5 5 1.3
09/26/2016 (A) 1.9 7.1 2.5 0.6 4.3 0.9
10/17/2016 (A) N/A 8.5 0.9 1.8 10.2 1.5
10/20/2016 (A) 0.6 7.1 1.2 0.7 4.1 0.7
05/02/2017 (A) 0.4 1.6 0.4 0.2 0.3 0.3
05/16/2017 (A) 2.2 2.8 1.1 0.8 2.6 0.5
05/25/2017 (A) 4 11.3 8 3.4 11.1 5.9
07/27/2017 (A) 6.9 14.3 10 2.1 5.7 2
07/08/2016 (B) 0.2 0.1 0.4 0 0 0
07/14/2016 (B) 1.1 0.9 1.2 0 0 0
08/16/2016 (B) 1.8 3.1 2.8 0.1 0.5 0.4
09/08/2016 (B) 0.4 3.9 1 0 0.1 0
09/23/2016 (B) 1.8 6.3 4.9 1.8 3.3 2.9
09/26/2016 (B) 1.4 6.7 2.8 1.3 2.5 1.3
10/17/2016 (B) N/A 6 5.4 3 4.8 5.8
10/20/2016 (B) 0.5 4 2.8 1 2.9 1.7
05/02/2017 (B) 0.3 0.7 0.5 0.9 0.2 0.3
05/16/2017 (B) 1.5 2.2 2 1.9 0.4 1
05/25/2017 (B) 5.8 9.8 6.6 7.8 4.6 7.1
07/08/2016 (C) 0.4 0.3 0.1 0 0 0
07/14/2016 (C) 0.9 0.5 1 0 0 0
08/16/2016 (C) 1.2 1.1 1.7 0 0.3 0
09/08/2016 (C) 0.5 0.3 1.5 0 0.3 0
09/23/2016 (C) 3 3.2 4.3 0.5 3.2 1.1
09/26/2016 (C) 2.4 3.3 3.8 0.5 2.6 1.4
10/17/2016 (C) 3.6 1.8 4.1 1.2 6.1 1.7
10/20/2016 (C) 1.5 2.8 2.1 0.5 3.4 0.9
05/02/2017 (C) 0.4 0.6 1.7 0.1 0.8 0.4
05/16/2017 (C) 1.6 0.8 2.8 0.8 1.7 2.2
05/25/2017 (C) 5.6 7.4 10.8 2.6 8.4 6.6