Thermal and Growth Properties of Structured Green Facades

Final Report to Green Roofs for Healthy Cities—Green Walls Group
Vegetated Walls: Thermal and Growth Properties of Structured
Green Facades
(UM-09040836)
March 29, 2012
David Tilley*, Jeff Price, Serena Matt, Brodie Marrow
Ecosystem Engineering Design Lab
Environmental Science and Technology Department
College of Agriculture and Natural Resources
Maryland Agricultural Experiment Station
University of Maryland, College Park
*Contact:
dtilley@umd.edu
301-405-8027
www.enst.umd.edu/tilley
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Table of Contents
1. Executive Summary .................................................................................................................................................................................................. 4
2. Introduction ..............................................................................................................................................................................................................10
3. Research Objectives ...............................................................................................................................................................................................11
4. Literature Review of Green Façade Thermal Research .............................................................................................................................12
4.1.
4.2.
4.3.
4.4.
4.5.
Misconceptions ...................................................................................................................................................................................................................... 12
Solar transmittance and leaf area index (LAI) ........................................................................................................................................................... 12
Heat flow to exterior walls and interior spaces ......................................................................................................................................................... 13
Temperature reductions .................................................................................................................................................................................................... 14
Cooling load reductions ...................................................................................................................................................................................................... 14
5. Research Procedures .............................................................................................................................................................................................16
5.1. Location Description ........................................................................................................................................................................................................... 16
5.2. Experimental Buildings ...................................................................................................................................................................................................... 16
5.2.1. Building Construction .......................................................................................................................................................................................................................... 16
5.2.2. Green Facade Construction................................................................................................................................................................................................................ 20
5.3. Leaf Area and Percent Cover............................................................................................................................................................................................. 22
5.4. Response of Temperatures ............................................................................................................................................................................................... 23
5.4.1. Thermal Instrumentation and Data Collection.......................................................................................................................................................................... 23
5.4.2. Experimental Set-up ............................................................................................................................................................................................................................. 26
5.4.3. Heat Flux to Interior Air...................................................................................................................................................................................................................... 29
5.4.1. Heat Gain by Exterior Wall ................................................................................................................................................................................................................ 29
5.5. Estimating Water Use and Transpiration .................................................................................................................................................................... 30
5.6. Energy Balance Model ......................................................................................................................................................................................................... 38
5.6.1. Bare wall without green facade ....................................................................................................................................................................................................... 39
5.6.2. Green wall ................................................................................................................................................................................................................................................. 41
5.6.3. Calibration ................................................................................................................................................................................................................................................ 47
5.6.4. Validation .................................................................................................................................................................................................................................................. 49
6. Plant Growth .............................................................................................................................................................................................................50
6.1.
Early growth of vines in climate-controlled greenhouse ....................................................................................................................................... 54
7. Temperature and Heat Flux Effects of Green Facades ...............................................................................................................................55
7.1.
7.2.
7.3.
7.4.
7.5.
Indoor Air Temperature..................................................................................................................................................................................................... 55
Exterior Wall Temperature ............................................................................................................................................................................................... 59
Ambient Air Temperatures ............................................................................................................................................................................................... 64
Heat Flux .................................................................................................................................................................................................................................. 68
Solar Radiation and Air Temperatures......................................................................................................................................................................... 76
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7.6. Extrapolation of Heat Flux Reduction to other Buildings ...................................................................................................................................... 81
7.7. Effect of Orientation on Temperatures and Heat Flux ............................................................................................................................................ 82
7.7.1. Interior Temperature response to Green Façade Orientation ............................................................................................................................................ 83
7.7.2. Exterior Surface Temperature ......................................................................................................................................................................................................... 84
7.7.3. Building Ambient Air Temperature ............................................................................................................................................................................................... 85
7.7.4. Heat Flux to Building Interior Air ................................................................................................................................................................................................... 86
8. Effect of vegetation on building temperatures and heat flux ..................................................................................................................88
9. Water Use and Latent Energy of Transpiration ............................................................................................................................................95
9.1.
9.2.
9.3.
9.4.
9.5.
Hourly Water Use by south-facing green facades in June ...................................................................................................................................... 95
Hourly Water Use by West-facing green facades in June and July .................................................................................................................... 100
Daily water use by west-facing green facade in August ........................................................................................................................................ 104
Relationship between air temperature, solar radiation and latent energy .................................................................................................. 106
Summary of Transpiration and Latent Energy......................................................................................................................................................... 110
10.
Energy Balance Model ...................................................................................................................................................................................... 113
11.
Conclusions .......................................................................................................................................................................................................... 121
12.
Acknowledgements ........................................................................................................................................................................................... 124
13.
List of References............................................................................................................................................................................................... 125
10.1.
10.2.
10.3.
Calibration .......................................................................................................................................................................................................................... 113
Validation ............................................................................................................................................................................................................................ 118
Major Heat Flux Pathways............................................................................................................................................................................................. 119
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1. Executive Summary
Experimental and modeling research was conducted on green facades during the period of 2009-2011 in Clarksville, Maryland to
determine 1) how fast vine species grow to cover walls; 2) how much water vines use; 3) how much green facades can lower the
temperatures of indoor air, external wall surfaces and ambient air, 4) how much green facades reduce heat flux to building walls;
and 5) the energy balance for green facades (i.e., where does solar energy go when it hits a green façade—reflectance, transmittance,
latent energy of plant transpiration, sensible heat of convection, leaf absorption and wall absorption).
The original research objectives for the project were to 1) measure the effect of various green facade arrangements on the surface
and indoor temperatures of buildings; 2) quantify the effect of the leaf area index (ratio of leaf area to wall-surface area) and other
biological parameters of green facades on their thermal benefits; 3) identify the magnitude of each energy flow term (i.e.,
evapotranspiration, solar reflectance and solar transmittance) in a heat transfer equation for the four seasons in Maryland for green
facades covered with different vine species; and review the peer-reviewed scientific literature on the thermal effects of green
facades.
Four experimental buildings of dimensions 2.5 meters (8 ft) long by 2.5 meters (8 ft) wide by 3.5 meters (11 ft) high were
constructed using standard practices and placed on a concrete pad in Clarksville, Maryland (approx. 30 km north of Washington,
D.C.). The buildings consisted of a 4-sided square-hip 4/12-pitch roof with three-tab charcoal asphalt shingles (GAF Materials
Corporation) and 5 cm x 15 cm (2x6 in.) wood rafters, a ceiling hung from 5cm x 10cm (2x4 in.) joists, 5 cm x 10 cm (2x4 in.) wood
framed walls, and a 5 cm x 15 cm (2x6 in.) wood floor all at a 40 cm (16 in.) center spacing. R-13 fiberglass insulation (CertainTeed
Corporation), 9 cm (3 1/2 in.) thick, was installed on the ceiling, walls and floor. The interior walls and ceiling were covered with
1.6 cm (5/8 in.) thick gypsum drywall. The buildings were wrapped in a vapor barrier material (Dupont Tyvek HomeWrap) and
then sided with Georgia-Pacific T1-11 1.5 cm (19/32 in.) thick pine wood siding. The buildings were spray painted blue-grey slate
(Glidden Premium Latex Exterior Paint-Flat) in May 2010 for the 2010-2011 growing season. The buildings had no windows and a
single door was installed on the wall opposite the instrumented wall. The buildings were neither artificially cooled nor heated
during any part of the experiment. The buildings were small enough to be rotated 90 degrees with a large forklift periodically so
that the one instrumented wall faced either south or west.
Two of the buildings received the green façade treatment while the other two served as controls and received no green facades. One
wall of each building was instrumented with a set of thermistors (temperature), a pyranometer (solar irradiance) and an
anemometer (wind speed). In addition two potted plants were instrumented with soil moisture sensors. Data from all the
instruments was automatically logged every 10 minutes from September 2009 to September 2011.
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The period September to December 2009 was used for preliminary data gathering. During this time the green façade consisted only
of Vitis riparia growing on a metal, non-commercial trellis. Data from this period was used to estimate parameters for the energy
balance model, but otherwise did not form the basis of our assessment.
Three commercially available metal trellis systems and one non-commercial, non-metal trellis served as the structure for the green
facades. Each commercial green facade was planted with six Maryland native vine species (Crossvine: Bignonia capreolata; Coral
Honeysuckle: Lonicera sempervirens; Carolina Jessamine: Gelsemium sempervirens; American Bittersweet: Celastrus scandens;
American Wisteria: Wisteria frutescens; and Purple Passionflower: Passiflora incarnata) and three Vitis spp. (Richter-110 grapevine:
Vitis berlandieri x V. rupestris; Paulson 1103 grapevine: Vitis berlandieri x V. rupestris; Dogridge grapevine: Vitis champini) in January
2010 in the UMD Research Greenhouse. In May 2010 these facades were moved to Clarksville where the experimental buildings
were located. Data collected from May 2010 to September 2011 on these green facades was used for the vast majority of our
assessment.
Plant Growth. The green facade canopies, which consisted of six Maryland native vine species and three cultivated grape root stock
varieties, developed quickly within the first year to cover 80% of the wall and had a leaf area index (LAI= leaf area per wall area) of
3.0, which was three-fourths of their long-term expected maximum. By June of the second growing season, LAI had reached its peak
at 4.0 and cover surpassed 80%. The type of commercial metal trellis had no effect on the growth rate or ultimate size of the canopy.
Coverage of the walls increased by about 15 to 20% per a month during their time under the favorable conditions in the UMD
climate-controlled greenhouse. Vines were grown in 2-gallon containers, irrigated 2 to 3 times per a day and fertilized every 2
weeks to ensure optimum growth. Under less favorable growing conditions slower increases in coverage would be expected.
Indoor Air Temperature. The green facades cooled the indoor air on everyday in June, July and August, which were the hottest
months of the year and the traditional “cooling season” in Maryland when air conditioning is used. The cooling effect was typically
most pronounced during the late afternoon between 3:00 to 9:00 pm when the mean temperature reduction was 4oC (7oF).
Exterior Wall Temperature. The green facades reduced the temperature of the exterior walls everyday in June, July and August.
Walls without green facades had temperatures reach as high as 50oC (122oF) on August 20th, where as the maximum for a green
facade was 39oC (102oF) on the same day. The maximum reduction was on July 14th when the green facade buildings were 14oC
(25oF) cooler than the bare wall buildings. For the three warmest months the green facades were able to cool the exterior walls
during the afternoon by an average of 7.1oC (13oF). When the green facades were not on a building, the exterior walls averaged 39oC
(102oF). When they were present, exterior walls only averaged 32oC (90oF).
Ambient Air Temperatures. We quantified the ability of the green facade to reduce the urban heat island effect by measuring how
much the temperature of the surrounding air was reduced. The green facade reduced ambient air temperatures for the vast majority
of days in the warmest months of the year, which ranged from 1.4 to 1.8oC (2.5 to 3.2oF). The cooling effect for May and September
was not statistically significant.
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Heat Flux. The green facades clearly reduced heat flux into the building’s exterior walls and interior space. Less heat flux into the
exterior walls meant that the temperatures of the exterior walls were lowered. Ultimately, exterior wall temperature represented
the heat that can flow into the buildings to raise the indoor temperature. Heat flux through the building walls from the exterior to
the interior was affected by the temperature difference between the inside air and exterior wall, and the total R-value of the wall
construction. Thus, the green facade’s ultimate mechanism for reducing indoor temperatures was to keep exterior wall
temperatures cooler. Morning was the time of day when the green facade reduced heat flux the most. During the three-month
cooling season, the green facades were responsible for reducing heat flux to the exterior walls from 18 W/m2 down to 10 W/m2 for
a mean reduction of 8 W/m2 or 43%. The mean reduction was also 43% for May and September, but since they were cooler months
the absolute reduction was only 3 W/m2.
The lowered exterior wall temperatures lead the green facades to reduce heat flux into the interior air. The experimental buildings,
which were insulated according to typical building standards in Maryland, had an average R-value of 12.9 ft2 oF/h/Btu (2.28 m2
K/W). At this level of insulation, during the June-to-August cooling season, the heat flux through the walls of the bare wall buildings
was estimated to be 7.4 W/m2, if there were a thermostat set to maintain an internal temperature of 72oF (22oC). With green facades
on the east, south and west, heat flux to the interior was only 4.3 W/m2 under these same conditions and assumptions, which
equaled a 42% reduction.
Extrapolating the experimental results to other wall construction types requires assuming other R-values. If a building only had an
R-value of 5, which would be representative of wall construction that had air as its main insulator, then the green facade would
reduce heat flux from 19 W/m2 to 11 W/m2 for a savings of 8 W/m2. However, at the other extreme, a building with R-60 walls
would only see a 0.9 W/m2 improvement in heat flux. Thus, the energy savings of a green facade, besides being dependent on the
density of the vegetation’s canopy, is also highly dependent on wall construction. Energy savings potential is greater for poorly
insulated buildings than it is for well-insulated ones.
Wall Orientation. The direction that the green facade faced had a significant effect on temperatures of the interior air, external wall
and ambient air. Orientation also affected the heat flux into the building space. During the warmest months of the year, peak
reduction in temperatures was greater for west-facing green facades than for south-facing ones. During the late-May to mid –July
testing period, the sun was near its maximum altitude, which meant that south-facing walls received about half as much solar
energy as the west. In contrast the west receives solar energy at all angles from high to low as the sun shines down from noon to
sunset.
Effect of Vegetation on Temperature and Heat Flux. The amount of temperature reduction provided by the green facades was
directly related to the amount of leaf area present in the canopy. Both the spatial extent (i.e., percent cover) and leaf area (LAI) were
useful predictors of the cooling effect. LAI, which is defined as total leaf surface per wall surface, can also be thought of as the
number of layers of leaf surface per unit of wall surface.
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Each unit of LAI added 2oC of temperature reduction to the exterior wall during the cooling or “air-conditioning” season. For
example, when the green facades achieved an LAI of 4.0, which was the mean value observed for June through August, the green
facade was cooler than the bare wall by about 8oC (14oF). A simple linear regression equation with error estimates was developed
based on this relationship so that the potential cooling could be predicted for other green facade installations. The error estimate of
the regression equation suggested that expected cooling for an LAI of 4.0 was at least 7oC (13oF). Extrapolation of the linear
regression model beyond an LAI of 5 is not endorsed.
With typical LAI values of 4.0, the simple linear regression equations developed from the experimental data indicate that green
facades can reduce heat flux to the exterior walls by 8 to 9 W/m2 during the warmest months of the year. In Maryland’s humid subtropical climate this amounted to a 43% reduction in heat flux into the building. Buildings with lower R-values would experience a
greater absolute energy savings from a green facade, but the percent reduction would remain at 43%.
Water Use and Latent Energy of Transpiration. Water is essential for vine growth, but it also plays a major role in reducing solar
load on building walls. The main use of water is for transpiration, which changes liquid water to vapor to provide evaporative
cooling. During the cooling season, with LAI’s of 3.0 to 4.0, the green facades used between 0.5 to 2.6 liters per square meter of wall
per day (L/m2/d) depending on which direction they faced (i.e., south or west) and the month. The mean for the entire season was
1.3 L/m2/d. West-facing walls used more water than south-facing, most likely because the west received more solar energy and
daytime temperatures were at their highest in the afternoons.
The evaporative cooling of transpiration was about one-third of solar energy whether the green facades faced south or west. During
June when mean daily ambient temperatures hovered around 75oF and mean daily solar radiation on the south wall was 104 W/m2,
transpiration of the south-facing green facade provided evaporative cooling of 34 W/m2. The south-facing evaporative cooling was
symmetric about noon with an equal amount of cooling in the morning and afternoon. For west-facing green facades the proportion
of solar energy transferred to evaporative cooling was much more in the morning than the afternoon.
Transpiration is affected by air temperature, solar exposure, humidity, water availability, leaf area, wind speed, nutrients, species
and stomatal resistance. Our experiment was limited to one species (Vitis) under Maryland’s climate and had unlimited water and
nutrients (i.e., irrigation + fertilization). Extrapolation of our findings to dissimilar climates (e.g., semi-arid South Texas or humid,
cool British Columbia) is not advised. Evaporative cooling could be much greater in drier climates assuming soil water was available,
and much less in moist cool climates. In addition, our testing of transpiration should be considered preliminary because of our
limited number of plants tested (n=2). Future studies should have full replication, more variety of species, a gradient of water
availability and differing amounts of fertilization. However, while our testing did not provide absolute transpiration rates, it did give
useful information about the relationship between solar energy and evaporative cooling, which was critical for the energy balance
modeling.
Energy Balance. The energy balance model had a good fit with the general pattern of heat flux to the exterior wall, where it was
able to emulate morning heat gain and evening heat loss. The model worked well for predicting wall heat flux whether there was a
7
green façade present or not. Hourly error in the model was present, and likely due to poor estimates of transpiration and cloud
cover. The energy balance model revealed that plant reflectance and transpiration were the two main mechanisms by which the
green facade reduced heat flux to the building envelope. The model also showed that convection of sensible heat back into the
immediate environment was cut in half by the green facade, which shows that they can reduce the impact of the urban heat island
effect.
Next Steps. As the green wall business grows in North America, industry and government should support research that uncovers
the ecological, energetic, hydrological, psychological and related environmental properties of green wall types. Research needs to be
conducted on green wall performance (e.g., effects on building energy consumption and temperatures, effect on water flow and
quality, air quality, noise attenuation, carbon balance, wildlife values, physical and mental health of humans), ecosystem service
valuation and tool development, design parameters (e.g. plant selection, materials, life cycle costs, soils), management (e.g., pruning,
leaf litter, soil amendments, fertilizer), ancillary benefits to local environments, risks of detrimental effects, plant disease, pests
control, and many more. Developing this knowledge will increase the viability of green walls and their acceptance by the public as
truly sustainable technologies.
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Glossary
Heat Flux:
the flow of heat per unit area (Watts per square meter, W/m2).
Energy Balance:
a mathematical model that includes the major flows of energy into and out of an ecosystem.
Thermistor:
a device for measuring temperature which relies on electrical resistance
Pyranometer:
a device for measuring the amount of solar energy (i.e., ultraviolet, visible and near-infrared) that falls on a
surface.
Anemometer:
a device for measuring wind speed.
Transpiration:
the process by which plants take up liquid water from the soil and transport it through their tissue to their
leaves where it is evaporated into the atmosphere through their stomates.
Evapotranspiration: the combined processes of evaporation and transpiration in an ecosystem.
Latent Energy:
the energy required to change the phase of water from liquid to vapor. The term ‘latent’ is used because the
temperature of the water does not change during the “change of state”.
Evaporative Cooling: the process by which a liquid (e.g., water) absorbs energy (e.g., solar energy) when it is evaporated.
Leaf Area Index:
the amount of leaf surface area per unit of wall or ground area.
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2. Introduction
Living vegetation that covers building walls reduces solar radiant heating during the summer, decreasing mechanical cooling
demand, electricity consumption, and greenhouse gas emissions [Peck et al. 1999]. Other significant benefits to adding vegetation
include: slowing storm runoff [Tilley and Schumann 2008], creating urban wildlife habitat [Lundholm 2006], lowering
environmental noise [Kohler 2008], improving air quality [Currie and Bass 2008], and mitigating the urban heat island effect [Bass
2001]. Green facades predominantly affect the thermal environment of buildings by shading them to cool the exterior wall surface.
In addition to this, they act as an insulating layer for the building wall increasing its R-value by trapping air between the vegetation
and building wall, as a radiant barrier attenuating thermal radiant heat loss at night, and as a physical obstacle impeding air flow
across the building wall surface limiting convective heat exchange with the ambient environment. Much of the solar energy
absorbed by the plants and its leaves can be lost through transpiration as latent energy. This solar energy dissipation pathway is
very important to maintaining a comfortable ambient environment and is often missing or severely reduced in urban environments.
The lack of this cooling mechanism and the enormous solar heat storage of man-made building materials ubiquitous in urban
environments (such as concrete) are the primary causes of the urban heat island effect.
While the energy benefits that vegetation provides to buildings are supported by early studies in Germany and green roof studies
across the world, there is a deficient understanding of the magnitude of the energy benefits in North America. Our aim was to better
understand the magnitude of the energy benefits, develop models that could be used in future design of building envelopes that
incorporate green walls, and to understand how the energy balance is altered by green walls. Presumably, there is a trade-off of
water use for energy savings with green walls because of the large fraction of solar energy absorbed by vegetated canopies which
provides evaporative cooling.
The sections below report on our review of the scientific literature on the thermal effects of green walls on buildings, the procedures
we used for our experiment and modeling, and our major findings for each of study’s main objectives. There is an appendix with
photo documentation of plant growth. The report also contains conclusions, acknowledgements and a list of references at the end.
10
3. Research Objectives
The original research objectives for the project were:
1.
2.
3.
Measure the effect of various green facade arrangements on the surface and indoor temperatures of buildings.
Quantify the effect of the leaf area index (ratio of leaf area to wall-surface area) and other biological parameters of green
facades on their thermal benefits.
Identify the magnitude of each energy flow term (i.e., evapotranspiration, solar reflectance and solar transmittance) in a heat
transfer equation for the four seasons in Maryland for green facades covered with different vine species.
We expanded Objective #2 to include determination of the plant growth. We also conducted a review of the peer-reviewed scientific
literature on green facades related mostly to thermal benefits.
11
4. Literature Review of Green Façade Thermal Research
While Köhler (2008) states that there are over 770 articles published in German about green façades, the number of English papers
is much fewer. Here we review the few that cover thermal aspects and the vegetation aspects that affect energy flow and
temperature.
4.1. Misconceptions
Today in North America, green walls are one of the fastest growing green building technologies. While describing the history,
benefits, and barriers to the flourishing green roof industry in Canada, Peck et al. (1999) highlighted the paucity of information
published in English about the thermal benefits of green walls, but claimed there was much written in German about European
experiences. As one of the primary sources on green roof and walls at the time, Peck et al. (1999) became a heavily cited resource,
but may have overstated the thermal benefits when it cited Gaudet’s (1985) false claim that “…every degree (F) of summer heat
requires an additional 5-7% of cooling energy. Hence, a 10o F reduction in the outside air temperature achieved through the
judicious arrangement of shade trees (green roofs and vertical gardens) can reduce energy consumption for air-conditioning by 5070%.” Dunnett and Kingbury (2008) cited this energy savings claim from Peck et al. (1999) in their widely read book about planting
green roofs and living walls. Unfortunately, the source of Gaudet’s (1985) claim is murky. Since the US DOE (2012) states that the
general rule is a 1% savings for each 1o F change in the thermostat setting, whether it’s for cooling or heating, it appears that the 5070% heuristic is highly questionable.
4.2. Solar transmittance and leaf area index (LAI)
One of the main mechanisms possessed by green façades for cooling buildings is reducing the solar radiation that reaches the
building’s envelope (i.e., the ability to shade). The solar radiation that passes through a living leaf or canopy is called transmittance,
while that which is reflected (i.e., bounces off) is called reflectance. Solar radiation that is neither transmitted nor reflected must be
absorbed by the leaf. The absorbed energy no longer exists as shortwave radiation. The absorbed energy either drives
photosynthesis, increases the leaf temperature or dissipates by the evaporative cooling of transpiration. Thus, understanding the
relationship between a green façade and solar transmittance is key to being able to predict the ultimate cooling effect that a green
facade can have on a building.
West-facing Japanese Ivy (presumably Parthenocissus tricuspidata) growing on residential homes in Tokyo was found to have solar
transmittance between 2 and 7% (Hoyano 1988). In other words, only 2-7% of the solar energy falling on the plant canopy reached
the exterior envelope. Additionally, green facades with thicker canopies (i.e., distance from envelope to edge of canopy) and higher
leaf area indices (LAI) had lower transmittance. These relationships were corroborated by Schumann (2007) when she observed
that an LAI of 1 corresponded to a transmittance of 40% and an LAI of 5 corresponded to 5% transmittance. For Virginia creeper
12
(Parthenocissus quinquefolia) growing on green facades in front of a building’s southwest windows in southern Britain, the
transmittance was 12% when the LAI was 5 (Ip et al. 2010). However, Holm (1989) claimed from modeling that a single layer of
plant leaves should transmit 12.8% of the incident solar energy, while two layers should transmit 5%. It is not clear why Holm’s
transmittance was so much lower than the other studies. It could possibly be due to plant species, how LAI was measured, or the
angle of incidence of light, which can have a large effect on transmittance (Turpie 2012). However, Holm (1989) also claimed the
five species tested had no difference in transmittance as long as the canopy thickness was greater than 20cm.
The canopy thickness of green facades have been shown to range from 20 cm for a South African case (Holm 1989), to 15 to 35 cm
for west-facing Japanese Ivy growing on residential homes in Tokyo (Hoyano 1988) to 70 cm for vines that had colonized
dilapidated barns in Southern Maryland, USA (Schumann 2007). Matt (2012) showed that the mean thickness for 10 green facades
in the Washington, D.C. metro area was 64 cm in June, but dropped to 55 cm by August, indicating a temporal effect during the
cooling season. Additionally, she found the minimum thickness was 29 cm and the maximum was 106 cm. Schumann (2007)
observed a maximum of 130 cm and a minimum of 25 cm. Obviously, the minimum thickness can be near zero, but it is less clear
what the maximum can be, especially since there should be a strong species effect. This is a question that needs to be answered.
Similar to canopy thickness, the LAI has been found to vary from study to study. For the Japanese ivy it ranged from 2.0 to 4.5
(Hoyano 1988), while it ranged from 1.1 to 5.0 for a diverse list of vine species growing on barns in Southern Maryland (Schumann
2007) and 1.5 to 6.0 for 10 green facades in the Washington, D.C. metro area (Matt 2012). Schumann (2007) noted that the LAI
values for the barns’ vine communities were similar to temperate, deciduous forests in the same region. The amount of leaf material
on a green façade will be affected by several factors, including species, water availability, fertilization, soil condition, orientation,
season and climate.
4.3. Heat flow to exterior walls and interior spaces
Using measured temperatures of the interior and exterior wall surfaces of ivy covered walls, Hoyano (1988) found the exterior walls
not covered by ivy gained heat at the rate of 232 W m-2 while the ivy covered walls only gained at a rate of 58 W m-2, a 75%
reduction. Additionally, he found heat flux from the interior surface to the interior air was nearly zero, concluding that the ivy layer
mostly eliminated the influence of solar radiation and atmospheric temperatures on the indoor environment. Eumorfopoulou and
Kontoleon (2009) found a green façade ultimately reversed the direction of heat flux for a building. That is, when there was no green
façade the heat flux was into the interior space at a rate between 4 and 13 W m-2 but it flowed out of the interior at 1 to 11 W m-2
when the green façade covered the east wall. Since heat flows from hot to cold area, presumably the exterior wall covered by green
façade was cooler than the interior temperature. Since evaporative cooling via transpiration can make leaf temperatures cooler than
ambient temperatures (Gates 1980), it is possible that the green façade was much cooler than the interior space in this case, but the
green façade temperature was not reported.
13
4.4. Temperature reductions
For climate-controlled buildings located in the northern Mediterranean with east-facing green facades made of Boston Ivy
(Parthenocissus tricuspidata), the green façade reduced exterior wall temperatures by 6-8o C, while interior wall temperatures were
lowered by 1o C in July and August (Eumorfopoulou and Kontoleon, 2009), which are two of the warmest months there. Schumann’s
(2007) thesis on horizontally oriented vines covering roofs rather than walls in Maryland found that vegetated canopies reduced the
peak indoor air temperature of lab-scale, non-climate controlled buildings in July by 11.3°C. In contrast a green façade covering a
concrete wall in a park (i.e., not a building) in Singapore reduced the wall’s exterior temperature by 4.4°C, which was much less than
the best performing soil-based living wall that reduced the temperature by 11.6°C (Wong et al. 2010). Kontoleon and
Eumorfopoulou (2010) developed a simple energy building model of climate-controlled building located in the northern
Mediterranean to predict that the exterior surface temperature was reduced the most for west-facing façade and least for northfacing, while east-facing exhibited the second highest reduction and then south-facing.
It is clear that green facades can reduce the exterior temperatures of building envelopes and interior spaces, but the magnitude of
the reduction varies. This variability presumably depends on many aspects including geographic location, façade orientation,
building morphology, envelope construction and material, insulation, season, proportion of glazing and other aspects.
4.5. Cooling load reductions
We only found two papers that reported observational results concerning cooling load reductions, while there were at least three
modeling papers. Di and Wang (1999) showed a 28% reduction in peak cooling load through the west-facing wall of a single large
brick building with a green facade, and found that the cooling effect was much greater in July and August than in June, when the
outdoor temperatures were lower in June. Unfortunately, there was no replication in the experiment so their results cannot be
generalized. Green facades were able to keep a lower air temperature for the cavity of a double skin façade (i.e., air-filled chamber
created with clear glass) when compared to non-living “blinds” and lowered the building’s cooling load by 20% (Stec et al. 2005).
Alexandri and Jones (2008) modeled the effect of green walls and roofs on the ambient temperatures of the urban canyon and then
extrapolated those results to estimate cooling load reduction between 35% and 68% for various cities around the world. There are
many assumptions and parameters that are needed to make the jump from ambient effects to internal building effects so the
generalization of their prediction is questionable. However, they did observe that adding vegetation to cool an urban environment
was more effective in hot, dry climates than in cool, humid areas. Wong et al. (2009) used simulations to explore the thermal effects
of various green façades arrangements on a hypothetical 10-story building in Singapore. When their simulated building had an
entirely opaque envelope (i.e. no glazing) covered in vegetation they predicted a 74% reduction in energy use for cooling. When the
simulated building had glazing on each floor and only the envelope was covered with vegetation (i.e., not the glazing), the predicted
reduction in cooling demand was only 10%. When all the floors and each side of the simulated building had glazing, 50% coverage
with green façade reduced the cooling load by 12%, while 100% coverage reduced it by 32%. Again, extrapolation beyond the study
is limited because the findings were specific to a single building configuration located in a tropical climate.
14
Kontoleon and Eumorfopoulou (2010) explored the effect of façade orientation using a simple energy building model of a
windowless, cubical, climate-controlled building located in the northern Mediterranean predicting that a west-facing orientation
would reduce the cooling load by 20%, east-facing by 18%, south-facing by 8%, and north-facing by 5%. While these absolute values
are not generalizable, they do provide support that orientation of the green façade plays an important role.
Finally, it is worth noting that Di and Wang (1999) found that a green façade kept a building wall warmer at night thereby possibly
providing an insulation property that could be useful during the heating season. Unfortunately, the English literature does not
appear to have more than one example of green facades providing insulation during the heating season, indicating that more work is
needed on this topic.
This review of the literature on the thermal performance of green façades demonstrates how little is known, especially concerning
the North American experience. Given the prospects of incorporating green façades into North American buildings and the paucity
of knowledge available, there is a strong need for research to be performed in the temperate climate of North America.
15
5. Research Procedures
5.1. Location Description
The experiment was conducted in Clarksville, MD (USA), which is located in the undulating plateau known as the Piedmont in the
Mid-Atlantic region. The climate is defined as humid, subtropical with hot summers (mean high temperatures at 90oF, 32oC) and
cold winters (mean low temperatures at 27oF, -3oC). Annual precipitation is 43 inches (1092 mm), which is evenly distributed
throughout the year with each month receiving about 3.5 in. (89 mm). Snowfall is common for each year with an average of 9 in.
(23 cm).
5.2. Experimental Buildings
The following sections provide descriptions of how the buildings and facades were constructed.
5.2.1. Building Construction
Four buildings of dimensions 2.5 meters (8 ft) long by 2.5 meters (8 ft) wide by 3.5 meters (11 ft) high were constructed (Figure
5.1a) and placed on a concrete pad at the University of Maryland Central Research and Education Center in Clarksville, MD (approx.
30 km north of Washington, D.C.) on July 8th, 2009. The buildings consisted of a 4-sided square-hip 4/12-pitch roof with three-tab
charcoal asphalt shingles (GAF Materials Corporation) and 5 cm x 15 cm (2x6 in.) wood rafters, a ceiling hung from 5cm x 10cm
(2x4 in.) joists, 5 cm x 10 cm (2x4 in.) wood framed walls, and a 5 cm x 15 cm (2x6 in.) wood floor all at a 40 cm (16 in.) center
spacing. R-13 fiberglass insulation (CertainTeed Corporation), 9 cm (3-1/2 in.) thick, was installed on the ceiling, walls and floor.
The interior walls and ceiling were covered with 1.6 cm (5/8 in.) thick gypsum drywall. The buildings were wrapped in a vapor
barrier material (Dupont Tyvek HomeWrap) and then sided with Georgia-Pacific T1-11 1.5cm (19/32 in.) thick pine wood siding.
The buildings were spray painted blue-grey slate (Glidden Premium Latex Exterior Paint-Flat) in May 2010 for the growing season
(Figure 5.1b). The buildings had no windows and a single door was installed on the wall opposite the vegetation. The buildings
were neither cooled nor heated during any part of the experiment. Thermal resistances (R-values) of each building surface are
summarized in Table 5.1.
16
FIGURE 5.1A. EXPERIMENTAL BUILDINGS WERE TYPICAL WOOD CONSTRUCTION.
17
TABLE 5.1. COMPOSITE INSULATION (R-VALUE) OF EACH EXTERIOR SURFACE OF
THE EXPERIMENTAL BUILDINGS .
Composite Insulation
Rating (R-Value)
2
-1
2
-1
2
ft °F h Btu
ft °F h Btu
ft °F h Btu-1 m2 K W-1
Wall type 5
Insulation
Outside air
0.25
Insulation
13.00
Siding
0.77
Gypsum
0.45
tyvek
0.17
Inside air
0.68
Total
15.3
Composite for Wall Type 5
Roof Type 9
Insulation
Outside air
0.25
Insulation
13.00
Asphalt shingles
0.44
Roof felt
0.06
OSB
0.63
Air space
2.80
Gypsum
0.45
Inside air
0.92
Total
Composite for Roof
Floor
Insulation
Outside air
Insulation
OSB
Air space
Inside air
Total
Composite for Floor
Stud space
Outside air
2x4
Siding
Gypsum
tyvek
Inside air
Total
Stud space
Outside air
2x4
2x6
Roof felt
Asphalt shingles
OSB
Air space
Gypsum
Inside air
18.6
0.25
13.00
0.63
0.80
0.61
15.3
Stud space
Outside air
2x6
OSB
Air space
Inside air
Total
0.25
4.20
0.77
0.45
0.17
0.68
6.5
12.9
2.27
18.2
3.21
13.9
2.45
0.17
4.20
6.60
0.06
0.44
0.63
2.80
0.45
0.92
16.3
0.25
6.53
0.63
0.80
0.61
8.8
18
Table 5.1 Continued.
ft2 °F h Btu-1
Wall w/door
Insulation
Outside air
0.25
Insulation
13.00
Siding
0.77
Gypsum
0.45
Tyvek
0.17
Inside air
0.68
Total
15.3
Composite for Wall with Door
ft2 °F h Btu-1
Stud space
Outside air
2x4
Siding
Gypsum
tyvek
Inside air
Total
0.25
4.20
0.77
0.45
0.17
0.68
6.5
Composite Insulation
Rating (R-Value)
2
ft °F h Btu-1 m2 K W-1
Door
R6 Door
6
9.8
1.72
19
FIGURE 5.1B. EXPERIMENTAL BUILDINGS LOCATED ON CONCRETE PAD AT CLARKSVILLE, MD WITH
WEST-FACING GREEN FACADES IN MID -SUMMER OF 2010.
5.2.2. Green Facade Construction
Sixteen 1.25m wide by 2.5m tall (4 x 8 ft) green facades were used for the experiment (Figure 5.2). Twelve of the facades were
constructed with a wood frame made from 5cm x 10cm (2x4 in.) boards to support flexible trellis materials. The remaining four
were rigid coated steel panels (greenscreen, Los Angeles, CA). The first flexible trellis used a 4 mm diameter stainless steel rope
20
grid spaced 15 cm horizontally and 25 cm vertically (Jakob-USA, Delray Beach, FL). The second type of flexible trellis was an X-Tend
stainless cable mesh fabric with approximately 15 cm diagonal spacing (Carl Stahl—DecorCable Innovations LLC, Chicago, IL). The
third flexible trellis was made of 1.90 cm diameter manila rope looped vertically around the wood frame with a horizontal spacing
of about 15 cm.
The rigid panel, cable grid, and cable mesh fabric facades were given the same mix of nine climbing plant species that were adapted
to the United States Mid-Atlantic region: (Richter-110 grapevine (Vitis berlandieri x V. rupestris); Paulson 1103 grapevine (Vitis
berlandieri x V. rupestris); Dogridge grapevine (Vitis champini); Crossvine (Bignonia capreolata); Coral Honeysuckle (Lonicera
sempervirens); Carolina Jessamine (Gelsemium sempervirens); American Bittersweet (Celastrus scandens); American Wisteria
(Wisteria frutescens); and Purple Passionflower (Passiflora incarnata). Each manila facade was given three plants of a single grape
species: Riparia Gloire (V. riparia). The plants for the nine-species mix were potted in 6-liter (1.5-gallon) plastic pots in a handmixed soil medium. The Riparia Gloire (Vitis riparia) vines were potted in the same medium but in 12-liter (3-gallon) plastic pots.
The potting soil was made from equal parts Leafgro compost (Maryland Environmental Service, Millersville, MD), Fafard topsoil
(Conrad Fafard, Inc., Agawam, MA), and all-purpose sand. Based on our experimental work below the field capacity appeared to be
in the range of 10 to 15% (see Section 5.5).
All potted plants, except the Riparia Gloire, were placed at the base of a steel facade in early January 2010 and grown in the UM
Research Greenhouse in College Park, MD until they were moved outside to Clarksville, MD in May 2010 where the experiment took
place. The Riparia Gloire plants were placed at the base of the manila facade in March 2010 and grown in the greenhouse until
moved to Clarksville with the other facades (Figure 5.2). One facade of each type of trellis material received no plants to serve as a
control during the experiment.
While in the greenhouse during the winter and spring, all plants were irrigated as needed to maintain a moist growing medium and
fertilized every two weeks. Outside at Clarksville, during the experimental period, the plants were irrigated every eight hours for 30
minutes starting at 4:00 am each day. The irrigation system consisted of a Vigoro Electronic AquaTimer (Melnor, Inc., Winchester,
VA) and soaker hose (Teknor Apex Company, Pawtucket, RI) sections suspended over each potted plant. Under these conditions,
each plant received nearly 3 liters of water per day. Excess irrigation drained from the bottom of each pot. All plants were fertilized
once a week at Clarksville.
21
FIGURE 5.2. THE GREEN FACADES GROWING IN THE UM RESEARCH GREENHOUSE IN MID-MARCH
2010. SUPPLEMENTAL LIGHTING WAS PROVIDED DURING THE EARLY MORNING AND LATE EVENING
TO EFFECTIVELY EXTEND THE DAY LENGTH TO 16 HOURS AND ENCOURAGE FASTER GROWTH.
5.3. Leaf Area and Percent Cover
Leaf area index (LAI) was measured at 6 evenly spaced intervals using the point intercept method (Schumann 2007, Price 2010), in
which a half-inch (12 mm) diameter rod is inserted horizontally into the vertical canopy at a perpendicular angle. The LAI equals the
22
number of times the rod is contacted by leaves along its entire length. Thus, the orientation of the LAI of a green façade is orthogonal
to the common tradition in most ecological studies, where the rod is inserted vertically across a horizontal plane.
Percent cover was visually estimated in increments of 5% for each quarter section of the 8 ft x 8 ft wall. Percent cover was measured
approximately every two weeks.
5.4. Response of Temperatures
5.4.1. Thermal Instrumentation and Data Collection
A single wall of each building was outfitted with instrumentation to gather continuous measurement of temperatures, including
those to calculate heat flux, and solar irradiance. A CS300 silicon pyranometer (Campbell Scientific, Inc., Logan, UT) was oriented in
the vertical plane to measure solar irradiance falling directly on the instrumented wall (Figure 5.3a). The vertical plane was chosen
rather than horizontal so we would know direct irradiance on the walls, whereas a horizontal measurement would need to be
translated with a model to vertical irradiance introducing error.
Interior air temperature was measured with three evenly spaced thermistors (#44006, Omega Engineering, Inc., Stamford, CT)
mounted on a vertical column located in the center of each building. Each instrumented wall had three horizontal profiles of
thermistors (Figure 5.3b) arranged in a diagonal pattern across the wall (Figure 5.4). Exterior surface temperature referred to
measurements from thermistors mounted directly on the exterior wall surfaces with an epoxy recommended by the manufacturer
and painted to match the building exterior color. Building ambient air temperature sensors referred to measurements from shaded,
open-air thermistors that were mounted approximately 10 cm from the building surface.
A CR1000 data logger (Campbell Scientific, Inc., Logan, UT) controlled the sensors and logged their data every 10 minutes during the
experimental period. All sensors in each building were connected to an AM16/32B multiplexer (Campbell Scientific, Inc., Logan, UT)
and each multiplexer was connected to the datalogger located in one of the experimental buildings. A 12-volt RV/Marine deep-cycle
battery (Interstate Batteries, Dallas, TX) powered the instrumentation and data-logging system.
23
anemometer
thermistor
pyranometer
FIGURE 5.3A. INSTRUMENTATION ON GREEN WALLS AND BARE WALLS.
24
Interior
Drywall
Wall Section
Stud
Exterior
Siding
Vegetation
FIGURE 5.3B. CROSS-SECTION DIAGRAM OF THE THERMISTOR LOCATIONS ACROSS THE
INSTRUMENTED WALL . T HIS PROFILE OF THERMISTORS WAS INSTALLED IN THREE LOCATIONS
ACROSS THE SURFACE OF THE WALL TO CALCULATE A REPRESENTATIVE MEAN TEMPERATURE FOR
EACH OF THE MEASUREMENTS.
25
Stud
Sensors
FIGURE 5.4. SECTION VIEW OF THE APPROXIMATE LOCATIONS OF EACH PROFILE OF THERMISTORS
(SEE FIGURE 5.3) IN THE EXPERIMENTAL BUILDINGS. THE BLACK LINES REPRESENT A DRAWING OF
THE WALL FRAMING INCLUDING THE STUDS, HEADER, AND FOOTER.
5.4.2. Experimental Set-up
The houses were in place with Vitis vegetation by July of 2009 (Figure 5.5). Preliminary data from 2009 was gathered and use for
some of the energy balance modeling. In the Spring (May) and Summer (June and July) of 2010 the experiment comparing south and
west facing façade was completed. During this time only one side of each green building was covered. From late July of 2010 to the
end of the data collection in September 2011, the three building sides with the most solar exposure (east, south, west) were covered
with façade.
26
FIGURE 5.5. PHOTOGRAPH FROM 2009 WHEN BUILDINGS WERE WHITE AND TOPVIEW SKETCH OF PHYSICAL LAYOUT OF EXPERIMENTAL
BUILDINGS AT CLARKSVILLE , MD.
Effect of orientation of green façade on temperatures and heat flux
The orientation experiment took place from May 2010 to July 2010. The four buildings were located on a concrete pad with their
instrumented walls facing south from May 25 to July 14. Two of the buildings were randomly chosen to receive the green facade
treatment (green wall), while the other two received blank facades (i.e., bare wall). Each building required two 4’x8’ green façade
panels to achieve complete coverage of the instrumented wall. Each set of two panels consisted of one trellis made of manila rope
and planted with three Vitis riparia Gloire individuals (hence manila-vitis trellis), and one commercial-metal trellis randomly chosen
every 3 days to be on the building. Thus, the metal trellis’ were cycled through two cycles over the 24-day experimental period. At
all times, the non-vegetated control buildings had just the wood frame components of the facade covering their instrumented wall.
27
On June 18th the buildings were rotated 90° clockwise such that the vegetation and instrumented wall faced west. Cycling the metal
trellis facades on and off the buildings continued on a 3-day frequency and lasted another 24-days as explained above for the southfacing wall.
Effects of covering three sides with green façade
From late July of 2010 to the end of the data collection in September 2011, the three building sides with the most solar exposure
(east, south, west) were covered with façade (Figure 5.6). The instrumented wall faced west from July 2010 to May 2011, but was
turned to face the south in early May 2011 and stayed in that position throughout the remainder of the 2011 experiment.
FIGURE 5.6. FROM LATE SUMMER OF 2010 TO SEPTEMBER OF 2011 THE EXPERIMENTAL HOUSES
WERE COVERED WITH GREEN FAÇADE ON THREE SIDES .
28
5.4.3. Heat Flux to Interior Air
The effect of the green facade on the building’s interior air temperature, exterior surface temperature, ambient air
temperature, and heat flux to the interior air were all analyzed on a 24-hour basis. In general the effect of the green facade on these
variables was based on the difference between the means of the control and vegetated buildings. We also assessed the facade’s
ability to reduce the peak temperatures and peak heat flux.
We categorized each day during the experimental period as sunny or cloudy and as hot or cool, giving four possible
classifications. A hot day was defined as a day with mean daytime (~8:00 a.m. to 8:00 p.m.) temperature greater than 29°C (85°F). A
sunny day was defined as a day when the daily solar radiation was greater than the mean daily solar radiation of the experimental
period. We analyzed the effects of the green facade only when the weather was classified as either hot and sunny or cool and cloudy
(i.e., hot and cloudy, and cool and sunny were excluded, mainly because they were too rare during the experimental period).
Reduction in interior air temperature was estimated as the difference between the mean of six interior temperature sensors
located in the two control buildings and the mean of the six interior temperature sensors in the two vegetated buildings. The
reduction in exterior surface temperature and ambient air temperature were estimated similarly.
Heat flux to the interior air was estimated using the following equation:
qi = (1/R)(Tw – Ti)
(5.1)
where the heat flux to interior air (qi) in W m-2 was proportional to the difference between the interior wall surface temperature (Ti)
and interior air temperature (Tw), both in Kelvin. R was the composite R-value for the building side without doors (2.29 K*m2/W,
see Table 5.1).
We generated 95% confidence intervals about the mean of each 10-min. interval over the 24-hour period using all sampling
days from a category (e.g., south-facing and hot and sunny; or west-facing and hot and sunny). For example, if there were ten hot
and sunny days, the mean reduction and confidence interval were based on those ten days
5.4.1. Heat Gain by Exterior Wall
qe = (Tw(t) – Tw(t-1800)) cp (Awd
/ t / Aw
(5.2)
where the heat gain by the exterior wall (qe) in W m-2 was estimated from the change in mean temperature of the exterior wall over
a 1800 second interval (Tw(t) – Tw(t-1800)) K; specific heat capacity of wood (cp = 1700 J/K/kg); mass of exterior sheathing (Awd (kg)
consists of Aw, area of wall m2, d, thickness of 0.75 inches, and density of 500 kg/m3); t is 1800 second (30 min) interval to
estimate heat change.
29
5.5. Estimating Water Use and Transpiration
Water use for transpiration was estimated based on the change in soil moisture of two potted vines (Vitis) growing on manila
trellises. The samples were irrigated regularly so that water was always available for transpiration. Soil moisture was monitored
continuously throughout the experiments.
In order obtain soil moisture data, volumetric water content (VWC) was measured. Two CS616 Water Content Reflectometers
(Campbell Scientific, Inc., Logan, UT) were installed in the containers of two individual Vitis riparia plants on separate west facing
walls. The plants were potted in 2.29 gallon plastic pots with a hand-mixed soil medium of equal parts LeafGro compost (Maryland
Environmental Service, Millersville, M.D.), Fafard topsoil (Conrad Fafard, Inc., Agawam, MA), and all-purpose sand. A CS300 silicon
pyranometer (Campbell Scientific, Inc., Logan, UT, 300-1000 nm) measured incoming solar radiation. Readings of soil moisture and
incoming solar radiation were taken every 10 minutes and the data stored in a CR1000 data logger (Campbell Scientific, Inc., Logan,
UT). A timed irrigation system watered the plants every eight hours, beginning at 4 am, for 30 minutes.
The Water Content Reflectometers were not field calibrated; the Campbell Scientific lab manual provided a quadratic calibration
curve and equation derived from a loam soil with a bulk density of 1.4 g/cm3. The assumption was made that the hand mixed soil
was similar enough to the loam soil to use the latter’s calibration. Temperature within the soil was not taken and thus the probes
could not be additionally calibrated for temperature. Within the range of summer soil temperatures this could introduce up to 3%
error in the VWC readings (refer to Figure 5.7 provided by Campbell Scientific). Probe to probe variability provides readings within
+/-2% VWC.
30
FIGURE 5.7: GRAPH PROVIDED BY THE MANUFACTURER SHOWS THE ERROR OF VWC VALUES UNCALIBRATED FOR TEMPERATURE.
Readings of VWC and incoming solar radiation, taken from June 18th, 2010 to July 15th, 2010, were imported into Microsoft Excel.
The time intervals during which changes in the values of VWC represented water loss due to evapotranspiration (ET) were
identified. Water use and heat flux were estimated using the set of equations given in Table 5.2. Converting water used in ET to heat
flux provides the portion of the solar energy balance that is converted to latent energy and place water use in same units as solar
irradiance. These values could then be presented as a percentage of the total incoming solar radiation recorded during the
identified time interval. Only ET that occurred during the day was considered. Moisture loss can occur at night but is driven by
wind and humidity differences; plants do not divert incoming solar radiation during this time. Daytime water loss due to ET was
periodically interrupted by the scheduled mid-day irrigation, which meant that ET was estimated during inter-irrigation intervals as
described below.
31
VWC was symbolized as
and mathematically defined as:
w/Vt
where Vw is the volume of water within a given container and Vt is the total volume of that container. The volume of the planting
container was 8.69 L, thus Vw was also in liters. was sampled every 10 minutes.
Mass of the water, mw is mathematically defined as:
mw= Vw
Where
w
w
is the density of water (1 kg/L), which gives mw in kg.
TABLE 5.2. EQUATIONS FOR CONVERTING SOIL MOISTURE CHANGE INTO TRANSPIRATION.
w/Vt
Volume
(L)
Volume
(m3)
Mass (kg)
Vw=Vt
Vw=Vt
mw= Vw
w
Energy
(Joules)
Ew=Lw*mw
Power
(Watts)
Pw
w
Heat Flux
(Watts/m2)
HFw=Pw/A
The water used in ET during a given time interval was converted to latent energy, Ew, by:
Ew=Lw*mw
where Ew, was in joules (J), and Lw was the latent heat of vaporization of water. Although the latent heat of vaporization varies with
temperature, we assumed a fixed value of 2.427x106 J/kg at 30°C based on Gates’ (1980) recommendation that it was sufficient for
most plants.
Next, the power flow Pw of the latent energy used in ET was estimated based on the amount of latent energy used,
period of time, .
Pw
w,
per a given
w
joules and t was in seconds.
s was due to evapotranspiration. Pw was in watts because Ew was in
Immediately following irrigation, each container would drain water for a few intervals. Thus, not all of the water loss from a
container was due to ET. This initial draining period was excluded from and described according to the routine below.
32
Finally, latent heat flux, HFw, (W/m2) was found by dividing Pw by the leaf area of the plant samples potted in the monitored
containers.
HFw=Pw/A
Where A is the area over which foliage absorbs incoming solar irradiance. A was estimated to be 1.0 m2. Because each monitored
plant covered approximatey 1/6th of the 6 m2 wall.
A typical 48-hour plot of VWC shows how it increased abruptly when the plants were irrigated (see sudden increases at
morning, afternoon and evening irrigations in Figure 5.8). The VWC drops quickly for the first few minutes following an irrigation,
but then proceeds to decline more slowly. The former is due to the pot draining water, while the latter is due to ET. Therefore, to
identify the appropriate time interval t we took the derivative of VWC (Figure 5.10). Intervals between derivatives of high
magnitude were determined to by the best estimates of t (Figure 5.11).
Days with frequent precipitation were difficult to assess for ET using this method so they were excluded from the analysis. Rainy
days were identified by referring to both daily plots of incoming solar radiation and VWC (see Figure 5.9). Rainy days were
characterized by jagged, irregular solar irradiance and sudden increases in VWC outside of the time intervals during which
irrigation occurred. Irrigation water was supplied at the base of each plant so canopy interception was negligible. Given the small
area of the soil in the pots exposed to the atmosphere relative to the large surface area of the plant leaves, it was assumed that most
of the ET was due to transpiration rather than evaporation. However, there was no attempt to quantify the difference between the
two components.
33
Volumetric Water Content
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0:00
6:00 12:00 18:00 0:00
6:00 12:00 18:00 0:00
FIGURE 5.8: VOLUMETRIC WATER CONTENT IS PLOTTED AGAINST TIME OVER THE COURSE OF TWO DAYS. IRRIGATION IS REPRESENTED BY
STEEP RISES IN VWC WHICH OCCURS AT REGULAR INTERVALS THREE TIMES A DAY. THESE RISES ARE NUMBERED ACCORDING TO THE DAILY
IRRIGATION REGIMENT.
34
Solar Irradiance (W/m2)
900
800
700
600
500
400
300
200
100
0
-100
FIGURE 5.9: COMPARISON OF SOLAR IRRADIANCE ON A SUNNY DAY (7/10) AND RAINY DAY (7/11).
Two different methods were used to distinguish evapotranspiration from drainage within the soil moisture profile. In the first
method, the steep drop in the VWC immediately following irrigation was assumed to be drainage, because the rate of drainage of
water held beyond field capacity is much faster than the rate of transpiration. Visually determining the time at which the slope of
VWC changed from drainage to transpiration was difficult (see Figure 5.10). This ambiguity worsened on cloudy days. In an
attempt to improve the accuracy of selecting the ET-drainage divide the derivative method was devised as a more precise tool. In
this method, the time derivative or change in VWC was plotted against time (Figure 5.11). Rather than selecting a cutoff point along
a gradually changing line, these plots displayed sudden changes in rate which facilitated clear differentiation of the ET and drainage
35
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.45
?
0.4
?
?
?
0.35
VWC
VWC
periods. In addition, the derivative method smoothed the cloud-induced irregularities seen in the VWC plots. Thus, cloudy days
could be included in the analysis.
0.3
0.25
0.2
0.15
FIGURE 5.10: T WO 48-H PLOTS OF VOLUMETRIC WATER CONTENT (VWC) IN JULY (LEFT) AND JUNE (RIGHT) OF 2010. ARROWS
DEMONSTRATE AMBIGUITY IN SELECTING ET-DRAINAGE DIVIDE ALONG THE GRADUALLY CHANGING SLOPE OF THE LINE.
36
1
Rate of Water Loss (L/hr)
0.5
0
-0.5
-1
-1.5
-2
6/19/2006 0:00
6/18/2006 19:12
6/18/2006 14:24
6/18/2006 9:36
6/18/2006 4:48
6/18/2006 0:00
-3
6/17/2006 19:12
-2.5
FIGURE 5.11: MOVING AVERAGE OF THE RATE OF VWC LOSS PLOTTED IN L/HR FOR TWO DAYS. RATHER THAN SELECTING A CUTOFF POINT
ALONG A GRADUALLY CHANGING LINE , THESE PLOTS DISPLAY SUDDEN CHANGES IN RATE, IDENTIFIED BY RED ARROWS , WHICH FACILITATED
CLEAR DIFFERENTIATION OF ET AND DRAINAGE.
The derivative method was used to for the analysis presented as results in the report. The time intervals during which
evapotranspiration occurred were identified, recorded, and used to convert the change in energy measured during that interval into
Watts.
37
5.6. Energy Balance Model
The vegetation of a green façade alters the way the various flows of energy from the environment reach and interact with a
building’s exterior surface.
FIGURE 5.12. Schematic diagram of the energy balance for the building wall with and without a green façade. Qw is total heat gain of
the exterior wall (W/m2), S is incoming shortwave (direct + diffuse solar) energy to the building wall, Srw is shortwave reflected by
the building wall, cS is shortwave reflected by the canopy, St is shortwave transmitted through the vegetation of the canopy, Lc is
longwave emitted from the canopy, Ld is longwave received from the sky to the building wall, Lw is longwave emitted from the
building wall, Hwa is sensible heat convected between the building wall and ambient air, and I is internal building heat that is
transferred between the exterior and interior walls, Hwc is sensible heat convected between the building wall and canopy, Ec is
latent energy of evapotranspiration, F(n) is parameter representing cloud cover.
38
We have performed an assessment of the energy balance of building walls with and without green façade.
5.6.1. Bare wall without green facade
The heat gain of the exterior building wall is affected by shortwave radiation, which includes direct and diffuse solar irradiance,
longwave (or thermal) radiation, sensible heat convected between the wall and air, and heat exchanged with the interior walls of the
building. Total heat gain (watts per square meter) on a building’s exterior wall that is not covered by a green facade is given by
equation 1, which was modified slightly from Sailor (2008):
(1)
Where Qbw is total heat gain of the exterior wall (W/m2), S is incoming shortwave (direct + diffuse solar) energy to the building wall,
Srw is reflected shortwave by the building surface, Ld is longwave received from the sky to the building wall, Lw is longwave emitted
from the building wall, Hwa is sensible heat convected between the building wall and ambient air, and I is internal building heat that
is transferred between the exterior and interior walls.
Reflectance solar from the building wall is given by Eq. 2:
(2)
where
w
is fraction of solar irradiance reflected by the exterior wall.
Longwave received from the sky is further defined by Eq. 3:
(3)
where cs is emissivity of clear sky, F is a cloud cover factor, is Stefan-Boltzmann constant, and Ta is air temperature (K). Sedlar
and Hock (2008) offered Eq. 4 to further define the emissivity of clear skies cs:
(4)
where ea is vapor pressure (Pa), b = 0.44, and m=8 according to Sedlar and Hock (2008).
39
Furthermore, Sedlar and Hock (2008) defined the cloud cover parameter F with Eq. 5:
(5)
where n is cloud cover 0<n<1, c = 0.22, and x=2 according to Sedlar and Hock (2008).
Substituting Eqs. 4 and 5 into Eq. 3 gives Eq. 6:
(6)
Longware emission from the building wall is defined by Eq. 7:
(7)
where
w
is emissivity of wall, Tw is wall temperature (K).
Convection of sensible heat is defined by Eq. 8:
(8)
where a is density of air (1.205 kg m-3) assuming a constant temperature, Ca is specific heat of air at constant pressure (1006 J kg-1
K-1), Kf is bulk heat transfer coefficient, is wind speed near wall surface (m/s). The bulk heat transfer coefficient was defined by
Sailor (2008) as given in Eq. 9:
(9)
Exchange of heat between the exterior and interior walls was taken as the temporal change in temperature of the interior wall and
given in Eq. 10, which assumed that heat flow between the interior air and interior wall was small by comparison:
40
(10)
where Qy was heat content of interior wall (J), t was time (s), Ty was temperature of interior wall (K), Cy was specific heat capacity of
interior wall (1090 J/kg K), Vy was volume of interior wall (m3), and y was density of interior wall (gypsum board, 720 kg/m3) and
Aw was area of wall (m2).
The final equation for heat flux of the bare wall is found by substituting Eqs. 2, 6, 7, 8, and 10 into Eq. 1 to give Eq. 11:
(11)
5.6.2. Green wall
The heat gain of the exterior wall covered by the green façade Qgw (i.e., the green wall) is given by Eq. 12, which was created here
based on Gates (1980), Sailor (2008) and fundamental principles:
(12)
where Qgw is total heat gain of the exterior wall covered by green facade (W/m2), Sd is direct and diffuse shortwave radiation
received on the portion of the wall not covered by vegetation, St is shortwave transmitted through the vegetation of the canopy, Ld is
longwave received from the sky to the building wall, Lc is longwave emitted from the canopy, Lw is longwave emitted from the
building wall, Hwc is sensible heat convected between the building wall and canopy, Hwa is sensible heat convected between the
building wall and ambient air, Ec is latent energy of evapotranspiration, and I is internal building heat that is transferred between
the exterior and interior walls.
(13)
where p is proportion of wall covered by vegetation.
The shortwave radiation transmitted through the canopy (St) is estimated using Eq. 14:
41
(14)
where
c
is fraction of solar irradiance reflected by the vegetation.
Longwave received by the green wall from the sky is defined by Eq. 15:
(15)
See Eqs. 4 and 5 for definitions of
cs
and F.
Longwave emitted from the canopy is defined by Eq. 16:
(16)
where
c
is emissivity of the vegetated canopy and Tc is canopy temperature (K).
Longwave emitted by the wall covered by a green façade is given by Eq. 17:
(17)
Sensible heat transferred between the wall and air and the wall and canopy was combined into Eq. 18:
(18)
where LAI is leaf area index (m2-leaf/m2-wall), Tc is temperature of the canopy.
Exchange of heat between the exterior and interior walls was taken as the temporal change in temperature of the interior wall,
which is given in Eq. 19:
(19)
42
The final equation for heat flux of the green wall is found by substituting Eqs. 13, 14, 15, 16, 17, 18, and 19 into Eq. 12 to give Eq. 20:
(20)
43
TABLE 5.3. NOMENCLATURE USED IN THE WALL HEAT FLUX MODELS.
b
Constant for clear-sky downwelling longwave radiation (0.44)
c
constant for cloud factor (0.22)
Ca
specific heat of air at constant pressure (1006 J kg-1 K-1)
Kf
bulk heat transfer coefficient
ea
Vapor pressure of air (Pa)
Ec
transpiration rate of canopy (g m-2 s-1)
F
cloud cover factor
Hca
sensible heat convected from the canopy to the air
Hcw
sensible heat convected from the canopy to the wall
Hwc
sensible heat convected from the building wall to the canopy
Hwa
sensible heat convective between the building wall to the air
I
internal building heat that is absorbed by the exterior building wall
LAI
leaf area index (m2-leaf m-2-wall),
Ld
longwave received from the sky
Lw
longwave emitted from the building wall
Lca
longwave emitted from the canopy to the air
44
Table 5.3 continued
Lcw
longwave emitted from the canopy to the building wall
Lwc
longwave emitted from the building wall to the canopy
m
constant for clear-sky downwelling longwave radiation (m=8)
fraction of cloud cover (0<n<1)
p
percentage of wall covered by vegetation (0<p<1),
Qbw
total heat flux of the exterior (bare) wall
Qc
total heat flux of the canopy
Qgw
total heat flux of the exterior wall covered by green façade
Qy
heat content of interior (gypsum) wall that is main internal heat
storage (J).
S
incoming shortwave (direct + diffuse) radiation on vertical surface
Srw
reflected shortwave by the building wall
Src
reflected shortwave by the canopy
St
shortwave radiation transmitted through the canopy (W m-2),
Ta
Ambient air temperature (K),
Tc
canopy temperature (K)
Ty
Interior wall temperature (K)
45
Table 5.3 continued
Tw
Exterior wall temperature (K)
wind speed near canopy (m s-1)
w
x
wind speed near wall surface (m s-1)
constant power for cloud cover factor (2)
Greek letters
c
reflectance of shortwave radiation by canopy (0-1)
w
reflectance of shortwave radiation by building wall (0-1)
c
emissivity of canopy
cs
emissivity of clear sky
w
emissivity of wall
latent heat of vaporization of water (2430 J/kg)
a
density of air (1.205 kg m-3)
Stefan-Boltzmann constant (5.67x10-8 W/m2/K4)
46
TABLE. 5.4. ESTIMATES OF MODEL PARAMETERS
Value
Source
c
0.19
measured
w
0.90
Measured and calibrated
w
0.135, 0.30
Assumed during calibration
ea
Various
Weatherunderground.com
Ec
Various
measured
LAI
2.0
measured
Various
Weatherunderground.com
p
0.55
Measured
Ta
Various
measured
Tc
Various
Measured
Ty
Various
Measured
Tw
Various
measured
Various
measured
Various
measured
w
5.6.3. Calibration
The green wall and bare wall heat flux models were calibrated using data collected on three days from September 19th to 21st, 2009
from Building #1 and #3, respectively, because they were warm, sunny days with wide swings in daily temperature and we had
47
wind speed data (Figure 5.13). The majority of model parameters were estimated from field measurements. However, a few
parameters were calibrated (i.e., adjusted) so the model prediction would better fit the observed heat flux (Table 5.4). Specifically,
w c
w were adjusted during calibration to minimize the absolute error of the model prediction. Thus, these three were
treated as “free” parameters, which allowed their values to be outside the range of what may be expected from the literature. As
discussed later in the results section, adjusting the emissivities to lower than expected values improved the overall performance of
the model, but may be pushing the bounds of what is plausible. This is an area where the model can be improved in the future.
Absolute model error =
Qpredicted - Qobserved
(21)
FIGURE. 5.13. HOURLY, SOLAR HEAT FLUX, AIR TEMPERATURE AND WIND SPEED FOR THE THREE DAYS USED TO CALIBRATE THE HEAT FLUX
MODELS IN EQ. 10 AND 20.
Originally, we intended to incorporate leaf or canopy temperature into the heat flux modeling. However, our method of using
thermistors to measure leaf temperature in the field was not convincingly accurate. Presumably leaf temperature affects convective
heat flow (Eq. 18) and longwave emission (Eq. 16). Since we did not have accurate estimates of canopy temperature, we decided to
48
exclude Lc and Hc from the overall energy balance of the green wall (i.e., Eq. 20). Besides not having the data, we also justified the
exclusion based on the small contribution that canopy convection and emission make to the overall energy balance.
5.6.4. Validation
Once the model was calibrated we tested its accuracy using data gathered on the same dates as the calibration but for the other two
buildings (#2 and #4). Absolute error, visual inspection of the times series and root mean square error (RMSE) were all determined
to qualify the accuracy of the model. In the future the model should be validated using 2010 and 2011 data.
49
6. Plant Growth
By July 13th 2010, after approximately six months of growth, the leaf area index for all green façades was the same, with a mean of
3.0 (p<0.05) (Figure 6.1a). In addition, the mean percent cover was 80% by July 13th (Figure 6.2a) with no significant differences
between green façade types (p<0.05). This rapid first-year growth demonstrated the potential of a new green façade to quickly
cover a building. This may be particularly advantageous in new construction where a quick cooling effect is desired, but shade trees
would take too long to establish.
For the first growing season (2010) the green facades were moved outside from the greenhouse on May 25th. Therefore, outside
measurements of LAI began in June. Mean LAI ranged from 1.5 to 2.5 in June and gradually increased throughout the season
whereby the range in mid-July was 2.5 to 3.5 (Figure 6.1a).
In the second year (2011) there was more variation among the trellis types, which precluded detecting a difference among them
(Figure 6.1b). The mean LAI in May 2011 was 3.0, as it had been the year before in July (Figure 6.3a). The mean LAI increased to 4.0
for June, July and August, but then began to decline in late August. Some of the trellis systems lost vegetation early in August, which
can also be seen in the drop of 20% in percent cover in early August (Figure 6.2b). The early decline was unexpected because it was
thought that it would not start to decline until early September. It was not clear what caused the early decline, but the plants may
have been stressed from being in 2-gallon pots or there could have been an unnoticed malfunction in the irrigation system. Since the
percent cover dropped earlier than the LAI, the canopy was becoming more sparse in coverage, but thicker where it persisted. This
type of morphological change may affect the cooling properties of the green facade.
The manila trellis, which had only 3 Vitis (grape) individuals, did not appear to have as much vegetation as the commercial trellis
systems, which each had six Maryland native species. Visual comparison of the photos of each trellis type in Figure 6.5 to 6.7 (in
Appendix A) confirms the low amount of leaf material for the manila-vitis system. Since we did not intend to compare the growth of
Vitis with the mixed-species or the manila to the commercial trellis types, we can not say whether the low percent cover and LAI for
the manila-vitis systems was due to the trellis or the species or some unknown factor. Anecdotally we did confirm that Vitis can
attach well to the manila rope and that its growth form tends to result in a dense canopy at the top of the trellis and sparse canopy
near the ground.
The buildings had different amounts of vegetation covering them (Figure 6.4). Building #2 had an average LAI of about 4.0 during
the three warmest months of June, July and August (2011), whereas Building #1 had a mean LAI of 2.75 during the same period.
Percent cover of the two buildings was more similar than LAI, especially through the beginning of August (Figure 6.4b). However,
there was a faster decline in cover for Building #1 than Building #2.
50
In summary, the green facade canopies developed quickly within the first year to within three-fourths of their maximum potential.
The canopies reached their maximum leaf area and coverage by June of their second growing season. The type of metal trellis had no
effect on the growth rate or ultimate size of the canopy.
8.0
7.0
Leaf Area Index
6.0
5.0
greenscreen
Carl Stahl
Jakob
Manila
4.0
3.0
2.0
1.0
0.0
1-Jun
11-Jun
21-Jun
(a)
1-Jul
11-Jul
(b)
FIGURE 6.1. LEAF AREA INDEX OF EXPERIMENTAL GREEN FAÇADE VEGETATION AT CLARKSVILLE, MD IN 2010 ( A) AND 2011 (B). LEAF
AREA INDEX (LAI) REPRESENTED THE RATIO OF TOTAL LEAF AREA TO WALL AREA. E ACH DATA POINT IS THE MEAN OF THREE REPLICATED
GREEN FAÇADE.
51
Percent Cover (%)
100
90
greenscreen
80
Carl Stahl
70
60
Jakob
Manila
50
40
30
20
10
0
23-Feb 15-Mar
4-Apr
24-Apr 14-May
(a)
3-Jun
23-Jun
13-Jul
(b)
FIGURE 6.2. PERCENT COVER ( PERCENT OF WALL COVERED BY VEGETATION) FOR THE EXPERIMENTAL GREEN FAÇADE SYSTEMS DURING ( A)
2010 AND (B) 2011. GREEN FAÇADE WERE PLANTED WITH SIX VINE SPECIES NATIVE TO M ARYLAND IN MID-JANUARY 2010, EXCEPT FOR
THE MANILA WHICH WAS PLANTED WITH 3 VITIS ( GRAPE) SPECIES. F ACADES WERE IN THE GREEN HOUSE FROM JANUARY TO M AY 20TH
2010. BEGINNING ON MAY 20TH THE FACADES WERE OUTSIDE AT THE CLARKSVILLE EXPERIMENTAL FARM. EACH DATA POINT IS THE
MEAN OF THREE REPLICATED TRELLIS SYSTEMS .
52
(A)
(B)
FIGURE 6.3. MEAN LEAF AREA INDEX (LAI) AND PERCENT COVER FOR BUILDINGS COVERED ON THREE SIDES (EAST, SOUTH AND WEST) DURING
THE 2011 GROWING SEASON WITH +/- STANDARD ERROR (N=8, MANILA-VITIS WAS EXCLUDED).
53
(A)
(B)
FIGURE 6.4. LEAF AREA INDEX AND PERCENT COVER FOR EACH GREEN WALL BUILDING DURING 2011 GROWING SEASON.
Over the two growing seasons, we observed that the vines typically began to leaf-out by the first week of May (Figures 6.5, 6.6 and
6.7). In 2011 leaf-out was well underway by May 9th on all three sides. By June the vegetation had covered most of the wall area for
all of the trellis types, except the manila-vitis trellis-species combination. In July and August most of the walls were well-covered
and lush with vegetation. By September most of the walls showed a large amount of leaf loss. By late October (not shown) the vast
majority of leaves had dropped from all trellis types.
6.1. Early growth of vines in climate-controlled greenhouse
First year growth on trellis located inside UMD Greenhouse is shown in time series of photos in Appendix A. Second year growth of
vines on the commercial trellis located outside at Clarksville site is shown in a time series of photos in Figures 6.5 to 6.6 for east,
south and west orientations (Appendix B).
54
7. Temperature and Heat Flux Effects of Green Facades
7.1. Indoor Air Temperature
The green facades cooled the indoor air on everyday in June, July and August of 2011 (Figure 7.1), which were the warmest months
and the traditional A/C “cooling season” in Maryland. The cooling effect was typically most pronounced during the late afternoon
between 4:00 and 6:00 pm. On July 14th the cooling effect was near the maximum observed, which was 5oC (9oF). While May and
September were not as warm as June, July and August, the cooling effect of the green facades was still statistically significant
(p=0.01, Table 7.1).
A
55
B
C
56
D
E
57
F
FIGURE 7.1. INDOOR AIR TEMPERATURE OF BUILDINGS WITH (GREENWALL) AND WITHOUT (BAREWALL) GREEN FAÇADE ON EAST, SOUTH AND
WEST FACING SIDES AND THE COOLING EFFECT OF GREEN FACADES DURING INDIVIDUAL WEEKS IN A) APRIL B) M AY C) J UNE D) JULY E)
AUGUST AND F) SEPTEMBER OF 2011.
The time period from 3:00 to 9:00 pm had the greatest cooling effect on indoor air temperatures (Table 7.1). During the three
hottest months of June to August, the mean temperature reduction was 4oC (7oF). The non-airconditioned, but insulated
experimental buildings without green facades (i.e., bare walls) had indoor temperatures of 33oC (91oF) during June to August, while
the buildings with green facades (i.e., green walls) had a mean temperature of 29oC (84oF).
58
TABLE 7.1. INDOOR AIR TEMPERATURE OF BUILDINGS WITH (GREENWALL) AND WITHOUT
(BAREWALL) GREEN FAÇADE ON EAST, SOUTH AND WEST FACING SIDES AND THE COOLING EFFECT
OF GREEN FACADES DURING GROWING SEASON OF 2011 (N =4 FOR P-VALUE ).
Month
Week
Time
Green wall Bare wall
Reduction p-value
Temp, C
Temp, C
in Temp, C
May
13th to 20th
3p-9p
21.1
22.9
1.8
0.01
June
8th to 14th
3p-9p
29.7
33.7
4.0
0.01
July
11th to 17th
3p-9p
30.0
33.7
3.8
0.01
August
15th to 21st
3p-9p
28.4
32.1
3.6
0.02
September
4th to 10th
3p-9p
24.2
25.9
1.7
0.01
7.2. Exterior Wall Temperature
Figure 7.2 clearly shows that the green facades reduced the temperature of the exterior walls everyday in June, July and August.
Walls without green facades had temperatures reach as high as 50oC (122oF) (see August 20th in Figure 7.2), where as the maximum
for a green wall was 39oC (102oF), which occurred on the same day. The maximum reduction was on July 14th when the green wall
buildings were 14oC (25oF) cooler than the bare wall buildings.
59
60
61
FIGURE 7.2. EXTERIOR TEMPERATURE OF SOUTH BUILDING WALL WITH (GREENWALL) AND WITHOUT (BARE WALL) GREEN FAÇADE ON EAST,
SOUTH AND WEST FACING SIDES AND THE COOLING EFFECT OF GREEN FACADES DURING GROWING SEASON OF 2011.
62
For the three warmest months the green facades were able to cool the exterior walls by an average of 7.1oC (13oF) during the
afternoon (Table 7.2). When the green facades were not on a building, the exterior walls reached 39oC (102oF). When they were
present, exterior walls only reached 32oC (90oF).
TABLE 7.2. EXTERIOR TEMPERATURE OF SOUTH BUILDING WALL WITH (GREENWALL) AND
WITHOUT (B AREW ALL ) GREEN FAÇADE ON EAST, SOUTH AND WEST FACING SIDES AND THE
COOLING EFFECT OF GREEN FACADES DURING GROWING SEASON OF 2011 (N =4 FOR P-VALUE).
Month
Week
Time
Green wall Bare wall
Reduction p-value
Temp, C
Temp, C
in Temp, C
May
13th to 20th 12p-6p
22.8
25.3
2.5
0.09
June
8th to 14th
12p-6p
32.6
39.6
7.0
0.01
July
11th to 17th 12p-6p
31.6
38.9
7.3
0.04
August
15th to 21st
12p-6p
31.9
38.9
7.0
0.04
September 4th to 10th
12p-6p
25.4
28.6
3.3
0.06
The green facades reduced the mean peak in exterior wall temperature by over 12oC (22oF) during the three warmest months
(Table 7.3). The mean peak daily temperature of the exterior walls was 46oC (115oF) on bare walls, but only 34oC (93oF) on green
walls for the three warmest months. Although May and September had cooler temperatures, they too had lower exterior wall
temperatures on the green walls compared to the bare walls (Table 7.3).
TABLE 7.3. PEAK EXTERIOR TEMPERATURES OF SOUTH BUILDING WALLS WITH
(GREENWALL) AND WITHOUT (BARE WALL) GREEN FAÇADE ON EAST, SOUTH AND WEST
FACING SIDES AND THE REDUCTION ION PEAK EXTERIOR WALL TEMPERATURE DUE TO GREEN
FACADES DURING THE GROWING SEASON OF 2011 (N=4 FOR P- VALUE).
Month
Week
May
June
July
August
September
13th to 20th
8th to 14th
11th to 17th
15th to 21st
4th to 10th
Green wall Bare wall
Reduction p-value
Temp, C
Temp, C
in Temp, C
25.7
31.7
6.0
0.04
34.1
46.2
12.1
0.01
33.3
45.9
12.5
0.02
35.6
47.6
12.0
0.06
27.3
33.0
5.7
0.04
63
7.3. Ambient Air Temperatures
Figure 7.3 shows that ambient air temperatures were reduced by the green facades. This ability to suppress the urban heat island
effect was most prominent for August, where the reduction was as great as 3.5oC (6oF) in the hours around noon (see August 17th in
Figure 7.3). On this day the ambient air surrounding the bare wall buildings was 35oC (95oF) for several hours, but it was only 32oC
(90oF) for the green wall buildings.
64
65
66
FIGURE 7.3. AMBIENT AIR TEMPERATURE NEAR SOUTH BUILDING WALL WITH (GREENWALL) AND WITHOUT (BAREWALL) GREEN FAÇADE ON
EAST, SOUTH AND WEST FACING SIDES AND THE COOLING EFFECT OF GREEN FACADES DURING GROWING SEASON OF 2011.
TABLE 7.4. MEAN AMBIENT AIR TEMPERATURE NEAR SOUTH BUILDING WALL WITH
(GREENWALL) AND WITHOUT (BARE WALL) GREEN FAÇADE ON EAST, SOUTH AND WEST FACING
SIDES AND THE REDUCTION IN AMBIENT TEMPERATURE BY GREEN FACADES FOR M AY TO
SEPTEMBER OF 2011. (N =4 FOR P-VALUE)
Month
Week
Time of Day
Green wall Bare wall Reduction
p-value
Temp, C
Temp, C
in Temp, C
May
13th to 20th 12p-6p
21.4
21.5
0.1
0.80
June
8th to 14th
12p-6p
31.6
32.9
1.4
0.07
July
11th to 17th 12p-6p
30.9
32.6
1.7
0.04
August
15th to 21st 12p-6p
29.8
31.6
1.8
0.04
September 4th to 10th
12p-6p
25.2
26.1
0.9
0.44
67
By suppressing the ambient air temperature the green facades provided two major benefits. First, a cooler outside air temperature
reduces convective heat flow to the exterior wall, which keeps the building cooler. Second, cooler air temperatures in the summer
will make people feel more comfortable when they are close to a green wall. The difference between 95oF and 90oF is significant.
The National Weather Service’s Heat Advisory scale indicates that at 60% relative humidity, a drop from 95o to 90oF reduces the
warning from Danger to Extreme Caution.
One interesting observation was on the night of June 10th and morning of June 11th when the ambient air temperature was higher
for the green wall buildings. This was a rare event that was most likely due to condensation of moisture onto the leaves of the green
facade. When moisture in the air is condensed it releases heat.
In summary the green facade reduced ambient air temperatures for the vast majority of days in the warmest months of the year
(June, July, and August) (Figure 7.3). The mean monthly cooling ranged from 1.4 to 1.8oC (2.5 to 3.2oF) for June, July and August
(Table 7.4). The cooling effect for May and September was not statistically significant (Table 7.4).
7.4. Heat Flux
Figure 7.4 shows heat flux of the exterior wall. The mean of each building type, bare wall and green wall, are shown along with the
reduction due to the green facade. Positive heat flux for the walls mean heat is being absorbed by the wall and its temperature is
rising, while negative values mean heat is flowing out as the wall temperature drops. Positive values for the reduction mean that the
green facade reduced heat flux by that amount, while negative values indicate that the barewall was losing heat faster. By plotting
wall heat flux along with the reduction (note the shift in scale), the relative reduction can be seen.
For example, on July 14th the heat flux into the barewall buildings during the morning reached 34 W/m2 just before noon, while heat
flux into the greenwall buildings at the same time was 13 W/m2 (Figure 7.4). At that instant the green facade had reduced heat flux
by 21 W/m2. The green wall heat flux peaked earlier in the morning at 9:00 am at 14 W/m2. For July this pattern of a morning
reduction in heat flux by the green facade was consistent.
In the afternoon the barewall buildings lost heat faster than the green wall buildings (see July in Figure 7.4). Since the bare wall
buildings built up higher wall temperatures they had more heat to lose and by not having a green facade to impede heat flow, they
lost heat faster. This was especially consistent throughout June, July and August. However, it was also true for May and September.
68
69
70
71
72
73
FIGURE 7.4. HEAT FLUX INTO THE EXTERIOR SURFACE OF THE SOUTH BUILDING WALL WITH (GREENWALL) AND WITHOUT (BAREWALL)
GREEN FAÇADE ON EAST, SOUTH AND WEST FACING SIDES AND THE REDUCTION IN HEAT FLUX DUE TO GREEN FACADES DURING GROWING SEASON
OF 2011.
74
Morning was the time of day when the green facade reduced heat flux by the most. During the three month cooling season, heat flux
on the bare walls was 18 W/m2, while it was only 10 W/m2 on the green wall for a mean reduction of 8 W/m2 or 43% (Table 7.5).
The mean reduction was also 43% for May and September, but since they were cooler months the absolute reduction was only 3
W/m2.
In general heat flux into and out of the buildings walls was suppressed by the green facade. The green wall buildings had lower
peaks and higher valleys than the barewall buildings. Since buildings gained heat the fastest in the morning as the solar radiation
increased and ambient air temperatures rose (Figure 7.5), the peak reduction in heat flux by the green facade occurred during the
morning. Once a wall reached its maximum temperature, heat flux was assumed to be negligible because heat flux was estimated as
the derivative of the exterior wall temperature. For example, heat flux of the bare wall building on July 14th (Figure 7.4) was zero at
1:30 pm because the exterior wall temperature reached its maximum at that time (Figure 7.2).
In summary, the green facades clearly reduced heat flux into the building’s exterior walls. Less heat flux into the exterior walls
meant that the temperatures of the exterior walls were lower (see Figure 7.2). Ultimately, exterior wall temperature represented
the heat that goes into the building to raise the indoor temperature. Heat flux through the building walls from the exterior to the
interior was affected by the temperature difference between the inside air and exterior wall, and the R-value of the insulation and
wall construction. Thus, a green facade’s ultimate mechanism for reducing indoor temperatures is to keep exterior wall
temperatures cooler.
TABLE 7.5. MEAN HEAT FLUX INTO SOUTH BUILDING WALL WITH (GREENWALL) AND WITHOUT
(BAREWALL) GREEN FAÇADE ON EAST, SOUTH AND WEST FACING SIDES AND THE REDUCTION IN
HEAT FLUX BY GREEN FACADES FOR M AY TO SEPTEMBER OF 2011. (N=4 FOR P-VALUE)
Month
Week
Time of Day
Green wall Bare wall Reduction
p-value
2
2
2
W/m
W/m
W/m
May
13th to 20th 7a-11a
4.9
7.5
2.6
0.03
June
8th to 14th
7a-11a
10.6
18.6
7.9
0.02
July
11th to 17th 7a-11a
10.5
18.6
8.1
0.02
August
15th to 21st 7a-11a
9.6
17.7
8.1
0.03
th
September 4th to 10
7a-11a
3.7
7.7
3.9
0.00
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7.5. Solar Radiation and Air Temperatures
Figure 7.5 shows the mean hourly ambient air temperature and solar radiation for selected weeks during the growing season (April
to September) of 2011. The typical diurnal pattern is dominant in both the temperature and radiation where minima correspond to
nighttime and early morning and maxima correspond to daytime and afternoons.
Typical for most days was that diurnal temperatures appeared to respond to the amount of solar radiation. For example, in April the
three warmest days (6th, 7th, and 11th) were the sunniest days, while the two coolest days were cloudy with little sunshine. A similar
type of correspondence is noticeable for May and September.
Table 7.6 shows the mean daily solar radiation and air temperatures for May to September, 2011. July had the most solar energy
(179 W/m2) and highest temperatures (28.9oC), but were followed closely by June and August. May and September, on the other
hand, only had 99 and 87 W/m2 of solar energy, respectively, and cooler tempertures.
June 8th exhibited the first clear sky day of the data set (Figure 7.5). On this day sunlight was unimpeded by clouds and was evident
as a smooth curve that ramped up and then down. June 8th also showed the classic delay between air temperature and solar
radiation. Sunshine was at its maximum at noon, while the temperature did not achieve its maximum until 1:30 pm.
During the A/C “cooling season” (June, July and August), clear sky days like the one on June 8th were anomalies. Most days during
this period were either mostly sunny or partly cloudy. The jaggedness of solar radiation indicates the passing of clouds and the
suppression of direct sunlight. June 11th is representative of these types of days.
The drop in sunlight on July 13th was due to a thunderstorm. Simultaneous to the drop in sunshine was the drop in temperature.
August 21st shows the same effect of a thunderstorm on that date.
June was unseasonably hot with three days over 38oC (100oF) (Figure 7.5). According to www.degreedays.net, June, July and August
accounted for 73% of the cooling-degree days in Maryland in 2011 and 71% in 2010. Visual comparison of the temperature data for
these months with April, May and September in Figure 7.5 supports the idea that they were the warmest months. The number of
cooling degree days for a month is most often defined as the sum of the mean temperature – 65oF (18.5oC) for each day. Thus if each
day in June had a mean temperature of 70oF, then June would have 5oF x 30 days = 150 cooling-degree days. In actuality June 2011
had 334 cooling degree-days; July had 513 and August had 337 (www.degreedays.net). Thus, the cooling effect of a green facade has
the greatest potential from June to August.
The daily peak solar radiation for sunny, warm days ranged from 350 to 450 W/m2 on the south-facing vertical surface.
In summary June, July and August were the months with the highest temperatures, most solar radiation and most cooling-degree
days (Figure 7.5 and Table 7.6).
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77
78
FIGURE 7.5. HOURLY SOLAR RADIATION ON WEST- FACING VERTICAL SURFACE (APRIL ONLY) AND SOUTH-FACING VERTICAL SURFACE (M AY TO
SEPTEMBER) AND HOURLY AIR TEMPERATURE FOR SELECTED WEEKS DURING THE 2011 GROWING SEASON.
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TABLE 7.6. MEAN SOLAR RADIATION AND AMBIENT AIR TEMPERATURES FROM
MAY TO SEPTEMBER OF 2011.
Month
Week
Time of Day
Solar
Air Temp., C
Radiation,
W/m2
May
13th to 20th
6a-9p
99
19.5
June
8th to 14th
6a-9p
168
28.8
July
11th to 17th
6a-9p
179
28.9
August
15th to 21st
6a-9p
172
27.7
September 4th to 10th
6a-9p
87
24.9
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7.6. Extrapolation of Heat Flux Reduction to other Buildings
The magnitude of the reduction of heat flux into the interior air of the buildings fostered by green facades depended on the thermal
resistance (R-value) of the walls (Figure 7.6). The experimental buildings, which were insulated according to typical building
standards in Maryland, had an average R-value of 13 ft2 oF/h/Btu (2.29 m2 K/W). At this level of insulation, during the June-toAugust cooling season, the heat flux through the walls of the bare wall buildings was estimated to be 7.4 W/m2, assuming the
thermostat or desired internal temperature was 72oF (22oC). With green facades on the east, south and west, heat flux to the interior
was only 4.3 W/m2 under these same conditions and assumptions. If a building only had an R-value of 5, which would be
representative of wall construction that had air as its main insulator, rather than fiber-glass bat insulation, then the green facade
would reduce heat flux from 19 W/m2 to 11 W/m2 for a reduction of 8 W/m2 (Figure 7.6b). However, at the other extreme, a
building with R-60 walls would only see a 0.9 W/m2 reduction in heat flux. Thus, the energy savings of a green facade, besides being
dependent on the density of the vegetation’s canopy, is also highly dependent on wall construction. Savings potential is greater for
poorly insulated buildings than it is for well-insulated ones.
(A)
(B)
FIGURE 7.6. ESTIMATED HEAT FLUX INTO THE INTERIOR AIR OF THE BUILDING WITH (GREENWALL) AND WITHOUT (BAREWALL) GREEN
FAÇADE ON EAST, SOUTH AND WEST FACING SIDES AND THE REDUCTION IN HEAT FLUX DUE TO GREEN FACADES DURING GROWING SEASON OF
2011.
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7.7. Effect of Orientation on Temperatures and Heat Flux
Figure 7.7 shows the mean interior and exterior surface temperatures for hot, sunny days for the bare wall buildings over the full
24-hour day when the instrumented wall was facing either south or west. The south exterior wall reached a maximum temperature
of 43°C around 12:00 pm DST (Daylight Savings Time), while the west exterior wall had a maximum temperature of 56 °C around
5:00 pm DST. Thus, as expected the diurnal patterns for the temperature of south-facing walls were different from the west-facing
walls. At the time of the summer solstice in the northern hemisphere (June 21st), the altitude of the sun is at its maximum, which
minimizes the amount of direct solar radiation on south-facing vertical surfaces. Because this experiment took place within four
weeks of the summer solstice, the west-facing façade received more direct solar radiation than the south-facing facade. Thus, the
time of year and orientation of a wall greatly affect how much solar radiation it receives and consequently how much solar energy a
green facade can impede.
The bare wall buildings’ interior air reached higher temperatures during the second half of the experiment when the instrumented
wall faced west because the mean daytime temperatures were higher than they were during the first half and because solar
radiation was double what it had been on the south wall. The interior temperature reached a maximum of 35°C during the first half
when the vegetation was on the south-facing wall and 38°C during the second half when vegetation was on the west-facing wall.
Further, the maximum interior air temperature for the west-facing wall occurred about an hour later than the south-facing wall,
which occurred around 7:00 pm DST (Figure 7.7).
FIGURE 7.7. MEAN INTERIOR AIR AND EXTERIOR SURFACE TEMPERATURES FOR THE CONTROL BUILDINGS ON HOT, SUNNY DAYS IN 2010 WHEN
THE INSTRUMENTED WALL WAS FACING EITHER SOUTH OR WEST.
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7.7.1. Interior Temperature response to Green Façade Orientation
Figure 7.8 shows the 24-hr cooling effect that the green facade had on interior temperatures when the weather was either hot and
sunny (Figure 7.8a) or cool and cloudy (Figure 7.8b) for south and west-facing walls. The south-facing green facade reduced the
mean peak interior air temperature by 1.0°C, which occurred at 3:30 pm DST on hot, sunny days (Figure 7.8a). When the green
facades were on the west side, they reduced the peak interior air temperature by 1.8 °C, which occurred at 8:10 pm DST on hot,
sunny days (Figure 7.8a). Due to the high variability and small sample size for west-facing, cool, cloudy days (n=3), the 95% CI
included zero (Figure 7.8b), indicating that the experiment could not conclusively ascertain a cooling effect for west-facing green
facades on cool, cloudy days.
These data suggest a west-facing green facade will do more to cool a building than a south-facing facade during the early summer
and when a building has similarly sized west and south-facing walls. However, it should be noted that the time of maximum cooling
demand for any given building may not occur in early summer and that there are many factors to consider when choosing the side of
a building to cover with a green facade.
Figure 7.8. Reduction in building interior air temperature due to vegetation on the south or west building wall during
either (a) hot, sunny or (b) cool, cloudy days.
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7.7.2. Exterior Surface Temperature
On hot, sunny days, the green facade on the south and west sides reduced the peak exterior wall surface temperature by an average
of 6.6 and 11.3°C (Figure 7.9a), respectively. The green facade cooled the south wall from dawn to dusk and was nearly symmetrical
about 12:00pm DST. The west-facing green facade, on the other hand, cooled the exterior surface beginning at dawn, continued into
dusk, but had its greatest effect during the late afternoon (Figure 7.9a). The effect of the west-facing green facade became
particularly pronounced after 1:00pm DST when it began to receive direct solar irradiance. These values agree reasonably well with
those found in literature. With a relatively immature plant canopy, our green facades cooled the experimental building’s west wall
by as much as 11.3°C where as two previous studies reported a maximum reduction of 18°C [7,8] and a third study, though on an
east wall, reported 8.3°C [9].
During cool, cloudy days the south green facades reduced the peak exterior wall surface temperature by an average of 2.4°C, which
occurred at 11:20 am DST (Figure 7.9b). Vegetation covering the west side reduced the peak wall temperature by an average of
6.2°C, which occurred at 4:30pm DST. However, on cool, cloudy days the confidence interval often included zero during the 24-hr
period which indicated that the effect was not as consistent as on hot, sunny days. (Figure 7.9b). Similar to detecting an effect of the
west green facades on the interior temperature described above, there were only three cool, cloudy days during the west wall
experimental period.
Covering the exterior surface of a building with vegetation reduces its temperature and therefore reduces the re-radiation of
heat into the environment. A significant increase in urban vegetation may help mitigate the urban heat island effect predominantly
through this mechanism. Also, covering building materials with vegetation increases the materials’ lifetime by reducing exposure to
ultra-violet radiation and attenuating the materials’ daily temperature fluctuation.
The leaf canopy of the green facades during the 2010 experiment were not as well developed as a fully-developed mature
vine canopy could be. Schumann (2007) observed 100% cover and LAI’s greater than 5 for mature vine canopies growing vertically
on walls in southern Maryland.
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Figure 7.9. Reduction in the experimental building’s exterior surface due to green façade vegetation during either (a) hot,
sunny or (b) cool, cloudy days.
7.7.3. Building Ambient Air Temperature
On hot, sunny days, the green facade on the south and west sides cooled their respective ambient air temperatures by as
much as 1.1 °C at 12:00pm DST and 3.0 °C at 7:50pm DST (Figure 7.10a), respectively. The south-facing green facade was most
effective around 12:00pm DST but maintained a small but significant effect from mid-morning through sunset. The west-facing
green facade was particularly effective after 1:00 DST, when it began to receive direct irradiance.
During cool, cloudy days the variability in the data for both south and west green facades was very high and the confidence
intervals frequently included zero, indicating that a significant effect could not be detected. However, the west-facing green facade,
for several brief periods (~30 minutes) during the evening, did cool the ambient air by around 0.5 °C (Figure 7.10b). The green
facade’s ability to reduce its ambient air temperature and surface temperature of the adjacent building supports the notion that
significantly increasing vegetative cover on urban buildings may help mitigate the urban heat island effect. Though the results of
our experiment suggest a reduction in the urban heat island effect, the magnitude of the reduction remains unclear and estimating it
is outside the scope of this study.
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Figure 7.10. Reduction in ambient air temperature 10 cm from south and west walls due to a green façade for (a) hot,
sunny and (b) for cool, cloudy days.
7.7.4. Heat Flux to Building Interior Air
Figure 7.11 shows the heat flux reduction to the building interior due to south and west-facing green facades on hot, sunny days
(Figure 12a) and cool, cloudy days (Figure 7.11) during the 2010 experiment. The south-facing green facade reduced heat flux into
the building on hot, sunny days by as much as 3.5 W m-2 at 1:30 pm DST from an original peak of 13.0 W m-2. The mean heat flux
was reduced between 11:00 am and 4:00 pm DST, which amounted to a 34% reduction. On cool, cloudy days it reduced the mean
peak heat flux into the building by 2.1 W m-2 at 12:30pm DST.
The west-facing green facade reduced the mean peak heat flux into the building by 10.3 W m-2 at 5:40 pm DST from an original peak
of 23.6 W m-2 on hot, sunny days (Figure 7.11a). The mean heat flux was reduced between 2:30 pm and 9:30 pm. During this period
it was reduced by 48% through the west wall. The evidence was inconclusive for cool, cloudy days due to large variability in the
data and small sample size (Figure 7.11b).
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These results show that covering a building wall with vegetation will decrease the amount of heat transferred to walls and
subsequently to the interior air. The direct benefit of this heat flux reduction is lowered cooling costs during the cooling season.
Figure 7.11. Reduction in heat flux into the building’s interior air due to green façade vegetation during either (a) hot,
sunny or (b) cool, cloudy days.
In summary the direction that the green facades faced had a significant effect on temperatures of the interior air, external wall and
ambient air. Orientation also affected the heat flux into the building space. The peak reduction was greater for west-facing
vegetation than for the south in all cases. This was most likely because the study was conducted from late-May to mid-July, when
the sun was at or near its maximum altitude. At a high altitude, the south wall receives solar radiation at a high angle so the intensity
was less that on the west, which received solar radiation at high and low angles as the sun passed overhead.
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8. Effect of vegetation on building temperatures and heat flux
The density and spatial extent of the green facade’s vegetation affected how much cooling the green facade provided. Leaf area index
is a 3-dimensional measure of leaf density, while percent cover is a 2-dimensional measure of the space covered by the vegetation.
LAI and Indoor Cooling (3pm-6pm) followed a similar pattern throughout the growing season (May to September) in 2011 (Figure
8.1a). A similar pattern emerged for Exterior Cooling (12pm-6pm) (Figure 8.1b). LAI and cooling were lowest in May and September,
and highest in June, July, and August, which is the “cooling season”.
(a)
(b)
FIGURE 8.1. TEMPORAL RELATIONSHIP BETWEEN MEAN LAI OF BUILDINGS AND EFFECT OF GREEN FACADES ON COOLING (W/M2) INDOOR AIR
AND EXTERIOR WALLS DURING 2011 GROWING SEASON .
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Indoor Cooling was increased with LAI and Percent Cover (Figure 8.2). Mean Cooling was at its monthly maximum of 4oC when the
mean building LAI was at its maximum of 3.25. The maximum in mean monthly Cooling also corresponded to the maximum percent
cover achieved, which was 78% (Figure 8.2b). While the cooling effect was correlated to LAI and percent cover, we cannot
necessarily say that the vegetation “caused” the cooling because the data were taken from a time series of the same buildings, which
means that other factors could have caused some of the cooling. A future study should be designed with multiple panels with a range
of LAI values to make a stronger case for “causality”.
(A)
(B)
FIGURE 8.2. EFFECTS OF LAI ( A) AND PERCENT COVER ( B) ON COOLING EFFECT ON INDOOR AIR DURING GROWING SEASON (MAY-SEPT) OF
2011.
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FIGURE 8.3. RELATIONSHIP BETWEEN PERCENT COVER OF BUILDING AND
THE LAI OF THE BUILDING DURING THE GROWING SEASON (M AY TO
SEPTEMBER) IN 2011.
Percent Cover and LAI were closely related over the growing season of 2011 (Figure 8.3). Over the limited range of our observations
(35%<PC<75% and 2.4<LAI<3.7), LAI could explain 69% of the variation in Percent Cover (Figure 8.3). Such a large coefficient of
determination (R2) indicates that the two vegetation properties are highly correlated. From this statistical perspective the two
vegetation measures provide the same information about the vertical canopy and ultimately about the green facade’s cooling effect.
Since the range of observations did not cover the lower ranges (i.e., near zero), it is not clear whether the linear relationship would
continue as each measure approached zero. We explored the explanatory power of both measures below.
90
(a)
(b)
FIGURE 8.4. EFFECT OF LAI ON EXTERIOR WALL COOLING DURING (A) JUNE, JULY AND AUGUST ( COOLING SEASON) AND (B) MAY AND
SEPTEMBER WITH 95% CONFIDENCE INTERVALS.
Figure 8.4 shows that for each unit of LAI the expected cooling to the exterior wall was 2oC during the cooling season, which is when
air conditioning is most often used in the eastern U.S. For example, if a green facade were to achieve an LAI of 4.0, which was the
mean value observed for June-August, then the exterior wall would be expected to be cooler by about 8oC (14oF). At a minimum the
expected cooling would be 7oC (13oF) with an LAI of 4.0 (Figure 8.4) based on the 95% confidence intervals given in Figure 8.4. In
2011 the mean monthly temperatures during the afternoon (12:00 pm to 6:00 pm) were 32.9oC (91.2oF) for June, 32.6oC (90.7oF) for
July, and 31.6oC (88.9oF) for August. These ambient air temperatures indicate that air conditioners would be working against a
temperature difference of at least 10oC (18oF), assuming thermostats were set to maintain 22oC (72oF).
There was a seasonal difference in the effect of LAI on exterior wall cooling (Figure 8.4). While the relationship between LAI and
exterior wall cooling during June to August was nearly 2oC per LAI (Figure 8.4a), in May and September, with mean monthly
ambient air temperatures 5 to 10oC (9 to 18oF) cooler than June through August, the effect was only about half of that at just under
1oC per LAI (Figure 8.4b).
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(a)
(b)
FIGURE 8.5. EFFECT OF PERCENT COVER ON EXTERIOR WALL COOLING DURING (A) JUNE, JULY AND AUGUST (COOLING SEASON) AND (B) MAY
AND SEPTEMBER .
Figure 8.5 shows that expected cooling of the exterior wall was improved by 0.1oC for every 1% increase in percent cover during the
cooling season—June, July and August. For example, if a green facade were to achieve 75% cover, which was the mean value
observed for June through August, then the exterior wall would be expected to be cooler by about 8oC (14oF). At a minimum the
expected cooling would be 6oC (11oF) with a cover of 75% (Figure 8.5).
There was a seasonal difference in the effect of percent cover on exterior wall cooling (Figure 8.5), similar to the relationship for LAI
(Figure 8.4). While the relationship between percent cover and exterior wall cooling during June to August was nearly 0.1oC per 1%
of cover (Figure 8.5a), in May and September, with mean monthly ambient air temperatures 5 to 10oC (9 to 18oF) cooler than June
through August, the effect was small at 0.6oC per 1% of cover (Figure 8.5b).
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(a)
(b)
FIGURE 8.6. EFFECT OF LAI ON HEAT FLUX REDUCTION (HFR) TO THE EXTERIOR WALL DURING (A) JUNE, JULY AND AUGUST ( COOLING
SEASON ) AND ( B) M AY AND SEPTEMBER.
Figure 8.6 shows that heat flux to the exterior wall was reduced by 2.2 W/m2 for every unit increase in LAI during the cooling
season—June, July and August. For example, if a green facade were to achieve an LAI of 4.0, which was the mean value observed for
June through August, then the exterior wall would be expected to receive 8.8 W/m2 less heat flux than a bare wall. At a minimum the
expected heat flux reduction would be 7.2 W/m2 with an LAI of 4.0 (Figure 8.6).
There was a seasonal effect of LAI on heat flux reduction (Figure 8.6). While the relationship during June to August was nearly 2.2
W/m2 per unit of LAI (Figure 8.6a), in May and September, with mean monthly ambient air temperatures 5 to 10oC (9 to 18oF)
cooler than June through August, the effect was smaller at 1.0 W/m2 per unit of LAI (Figure 8.6b).
In summary the amount of cooling provided by a green facade was directly related to the amount of leaf area present in the canopy.
Both the spatial extent (i.e., percent cover) and leaf density (i.e., leaf area index, LAI) are useful predictors of leaf area and thus the
cooling effect. LAI can also be thought of as the number of layers of leaf surface per unit of wall surface. With typical LAI values of 4.0,
the simple linear regression equations developed from the experimental data indicate that green facades can reduce heat flux by 8
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to 9 W/m2 during the warmest months of the summer (June, July and August). In Maryland’s humid sub-tropical climate this
amounted to a 43% reduction in heat flux into a building with an R-value of 13. Buildings with lower R-values would experience a
greater reduction in heat flux, but the percent reduction would remain the same.
In conclusion, it is clear that green facades reduce the heat flux into buildings. By reducing the heat flux into the exterior walls, the
green facades reduce the mean temperature of the exterior walls. Reducing the temperature of the exterior walls reduced the
temperature gradient (i.e., “delta T”) across the insulated walls, which resulted in less heat flow to the interior building space.
In addition, the green facades reduced the temperature of the air surrounding the building envelope (i.e., ambient air), by
transferring radiant heat to latent vapor via transpiration and by reflecting radiant energy away from the building. The reduction in
ambient temperatures not only reduced the convective flow of heat into the building but also lowered the “heat island effect”.
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9. Water Use and Latent Energy of Transpiration
The use of water by greenwalls for transpiration is one of the main ways solar energy is removed from the heat budget of a building.
The water used in transpiration by two Vitis spp. (grape) plants was assessed for south and west-facing green facades during June,
July and August of 2010. Hourly estimates of transpiration were made for June and July, while we assessed the differences between
morning and afternoon transpiration for August because of the distinct difference in solar radiation between morning and afternoon.
Finally, we analyzed the effect of solar radiation and air temperature on transpiration to develop regression models predictive of
transpiration in terms of its latent energy. Latent energy is one of the most important terms in the overall energy balance for
vegetated surfaces. Latent energy equals the rate of transpiration (i.e., water use) times the latent heat of vaporization. Thus,
transpiration and latent energy only differ by a nearly constant factor (i.e., the latent heat of vaporization is affected by temperature
to a small degree). Here we chart latent energy because it is in the same units as solar radiation, namely watts per square meter
(W/m2).
Experimentally, assessment of transpiration was limited because we only had two soil moisture sensors that we used to monitor
one plant each. There was a consistent difference between the plants with Plant 1 using much less water than Plant 2 during the
entire study period. With this wide range in water use and a sample size of two, the following assessment is limited in terms of
absolute values of water use and latent energy. However, the analysis does show the strong hourly and daily patterns in
transpiration, and thus cooling effect, of a green facade, which could be useful for intricate architectural design of building envelopes
that incorporate green facades. A future study should evaluate more plants in an experimental design so that statistical analyses can
be conducted to determine absolute values for transpiration. The data gathered were used in the energy balance model given in the
next section.
9.1. Hourly Water Use by south-facing green facades in June
Figure 9.1 shows the hourly rate of latent energy and solar radiation on the south wall from the last day of May to mid-June 2010.
Latent energy clearly follows the solar radiation pattern. As the Sun rises in the morning and solar radiation climes to its noonday
peak, latent energy follows at slower pace and with a lag of one to two hours. On mostly sunny days like the ones from May 30th to
June 4th this diurnal pattern was clear. On cloudy days like June 9th and 16th both solar radiation and latent energy were
substantially diminished. June 15th was a unique day because it was sunny in the morning, so latent energy increased along with
solar, but the afternoon was cloudy, so latent energy dropped considerably along with solar.
During many afternoons latent energy equaled solar radiation, implying that much of the solar radiation was being absorbed by the
green facades. Recall that air temperature, along with humidity and solar radiation, is a factor in transpiration. Thus, as the
afternoon air temperature rose, the plants responded by transpiring faster.
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Figure 9.1 also clearly shows that the transpiration of Plant#1 was almost always less than Plant#2. It is not clear why there was
such a distinct difference. With only two soil moisture sensors it was difficult to ascertain. However, it could be related to the
amount of leaf surface of the individual plants, the condition of their roots (i.e., bound in 2-gallon containers) or possibly the sensors
themselves. This relative difference existed for the vast majority of the 2009-2011 project period.
Solar irradiance reached its maxima around noon (1200 h), while transpiration on the south wall often peaked a few hours later
(Figure 9.1). The peak in solar radiation was nearly always around 450 W/m2, while the peak in latent energy varied considerably,
but occasionally reached 140 W/m2 or 31% of solar irradiance peak.
Realizing that latent energy typically lagged solar by 1.5 h and that this lag was about 30% of solar radiation, we created Figure 9.2,
which has solar radiation delayed by 1.5 h and its vertical scale 3X as large as the latent energy scale. This plot clearly shows the
tight relationship between the two energy variables.
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A
97
B
98
C
FIGURE 9.1. HOURLY LATENT ENERGY OF TRANSPIRATION OF TWO VITIS SAMPLES ON SOUTH WALL FROM MAY 30TH TO JUNE 17TH (A, B, C)
OF 2010 AND SOLAR RADIATION.
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FIGURE 9.2. HOURLY LATENT ENERGY DELAYED BY 1.5 H TO BE SYNCHRONOUS WITH HOURLY SOLAR RADIATION, WHICH IS BASED ON
REGRESSION EQUATION: LET = 0.324(ST-1:30). NOTE THAT SOLAR RADIATION SCALE (RIGHT SIDE ) IS 3X AS LARGE AS LE SCALE. M ISSING
DATA WAS DURING IRRIGATION.
9.2. Hourly Water Use by West-facing green facades in June and July
Figure 9.3 shows the hourly rate of latent energy and solar radiation on the west wall from mid-June to mid-July 2010. West wall
solar radiation was not symmetric about noon like on the south wall (Figure 9.1). Rather, the west wall only received indirect and
diffuse solar radiation in the morning, but at noon it started to receive direct sunlight. Therefore, around noon, depending on cloud
conditions, there was typically a rapid increase in solar radiation, which is easily seen on July 3rd (Figure 9.3). Since solar radiation
exhibited a different pattern, so did latent energy. In the morning latent energy increased at the same rate as solar radiation, but
100
with about a 3 h lag. By contrast, in the afternoon when solar radiation spiked, latent energy was a much smaller portion of solar
energy than it had been in the morning. This diurnal pattern is mostly easily seen on mostly sunny days like July 3rd (Figure 9.3). On
cloudy days like July 10th and 12th both solar radiation and latent energy were substantially diminished.
The maximum solar irradiance on the west-facing wall was often around 800 W/m2, whereas the daily peak latent energy was over
200 W/m2 for a few days (see June 18th – 21st in Figure 9.3). Later, in July the maximum latent energy was less at around 100 W/m2.
101
102
103
FIGURE 9.3. HOURLY LATENT ENERGY OF TRANSPIRATION OF TWO VITIS SAMPLES ON WEST WALL FROM JUNE 18TH TO JULY 16TH OF 2010
AND SOLAR RADIATION.
9.3. Daily water use by west-facing green facade in August
Figure 9.4. shows the daily mean solar radiation, latent energy and air temperature for each Vitis sample for the morning, while
Figure 9.5 shows the same attributes for the afternoon for August. Plant #2 best illustrates that daily latent energy was related to
daily solar radiation. When solar was at its lowest on August 12th, so was latent energy (Figure 9.4). For Plant #2 morning latent
energy ranged from 38 to 60 W/m2 on mostly sunny days, while solar ranged from 90 to 65 W/m2 (Figure 9.4).
104
Latent energy in the afternoon for Plant #2 was often around 100 W/m2 (Figure 9.5), while solar was often over 400 W/m2. As
previously mentioned, Plant #1 had less latent energy than Plant #2.
Plant1, morning
temp
Gross AM Solar
30
100
90
80
70
60
50
40
30
20
10
0
20
15
10
5
0
0
10
20
30
Day of August, 2010
40
W/m2
25
AM HFD
temp
100
90
80
70
60
50
40
30
20
10
0
30
25
20
15
10
Temperature, C
AM HFD
Temperature, C
W/m2
Gross AM Solar
Plant2, morning
5
0
0
10
20
30
Day of August, 2010
40
FIGURE 9.4 MORNING DAILY SOLAR IRRADIANCE, LATENT ENERGY (HFD) AND AIR TEMPERATURE IN AUGUST 2010 FOR EACH VITIS SAMPLE.
105
Plant2, afternoon
Plant1, afternoon
Gross PM Solar
temp
500
600
40
500
35
400
W/m2
45
30
25
300
20
15
200
10
100
5
0
0
0
10
20
30
Day of August, 2010
40
PM HFD
temp
45
40
35
400
30
25
300
20
200
15
10
100
Temperature, C
600
W/m2
PM HFD
Temperature, C
Gross PM Solar
5
0
0
0
10
20
30
Day of August, 2010
40
FIGURE 9.5. AFTERNOON DAILY SOLAR IRRADIANCE, LATENT ENERGY (HFD) AND AIR TEMPERATURE IN AUGUST 2010 FOR EACH VITIS
SAMPLE. (N OTE : NOT EVERY DAY IS REPRESENTED BECAUSE DATA WAS NOT AVAILABLE).
9.4. Relationship between air temperature, solar radiation and latent energy
Figures 8.6, 8.7 and 8.8 demonstrate the close relationship between how much water a green facade used (i.e., latent energy) and air
temperature and solar radiation. This type of relationship should be useful during the design of buildings that properly integrate
green facades into a holistic perspective on heating and cooling. We developed simple linear regression equations from the
relationships. These equations could be useful for predicting how much water is used and how much solar energy is absorbed by the
green facade.
For the south green wall in June there was positive relationship between latent energy and temperature, as well as between latent
energy and solar radiation (Figure 9.6). Temperature explained 68% of the variation in latent energy, while solar energy, which was
delayed by 1.5 h (recall the lag effect demonstrated above in Figure 9.3), explained 75%, when the entire day was considered.
106
For the west green wall in late June and early July there was a positive relationship between latent energy and temperature, as well
as between latent energy and solar radiation (Figure 9.7). Due to the non-symmetric balance between morning and afternoon west
wall energy balance, the capability of air temperature and solar radiation to predict latent energy was not as strong as it was for the
south wall. Temperature explained 36% of latent energy variation, while solar explained 45%, which are good predictors.
The positive relationship between latent energy and temperature, and between latent energy and solar radiation was also present
for the west green walls in August when data were analyzed as daily values (Figure 9.8) as opposed to hourly values (Figure 9.7).
Temperature explained 70% of latent energy variation, while solar explained 59%, which are both strong predictors.
FIGURE 9.6. HOURLY LATENT ENERGY OF SOUTH WALL IN EARLY JUNE 2010 AS A FUNCTION OF SIMULTANEOUS AIR TEMPERATURE AND SOLAR
RADIATION 1.5 H BEFORE WITH REGRESSION EQUATIONS.
107
FIGURE 9.7. HOURLY LATENT ENERGY OF WEST WALL IN LATE JUNE & EARLY JULY OF 2010 AS A FUNCTION OF HOURLY AIR TEMPERATURE AND
HOURLY SOLAR RADIATION WITH REGRESSION EQUATIONS .
108
120
Latent Energy, W/m2
100
Latent Energy, W/m2
120
LE = 5.3(T) - 76.8
R² = 0.70
80
60
40
20
LE = 0.207(S) + 29
R² = 0.59
100
80
60
40
20
0
0
20
25
30
35
Air Temperature, C
40
0
100
200
300
Solar, W/m2
400
FIGURE 9.8. DAILY LATENT ENERGY OF WEST WALL IN AUGUST OF 2010 AS A FUNCTION OF DAILY AIR TEMPERATURE AND DAILY SOLAR
RADIATION WITH REGRESSION EQUATIONS.
Table 9.1 summarizes the equations for predicting latent energy from air temperature and solar radiation during the summer for
south and west-facing green facades. Predicting hourly rates of latent energy flux on south walls was highly successful with high
coefficients of determination (i.e., how much of the variation in the dependent variable was explained by the independent variable).
However, predicting hourly rates of latent energy flux on west walls was not as successful (note the lower coefficients of
determination, Table 9.1). Predicting daily rates of latent energy flux on west walls from air temperature and solar radiation was
more successful than predicting hourly rates.
In summary, either air temperature or solar radiation can successfully predict transpiration (i.e., latent energy) on south-facing
walls. For west-facing walls, hourly transpiration rates are not as easily predicted by air temperature and solar radiation due to the
vast difference in solar radiation between the morning and afternoons for west-facing surfaces. To predict west-facing green facade
transpiration, it is better to do so on a daily basis. This overcomes the morning/afternoon differences and provides a highly
predictive equation, but loses the timeliness that hourly estimates provide.
109
TABLE 9.1. REGRESSION MODELS FOR PREDICTING LATENT ENERGY (LE, W/M2)
FROM AIR TEMPERATURE (T, OC) AND SOLAR RADIATION (S, W/ M2) ALONG WITH
COEFFICIENTS OF DETERMINATION.
June, South
(hourly)
Temperature
R2
Solar Radiation
R2
LE = 6.5T - 122
68%
LE = 0.323S
75%
June-July, West
(hourly)
LE = 4.5T – 72.5
36%
LE = 0.143S +23.2
45%
August, West
(daily)
LE = 5.3T – 76.8
70%
LE = 0.207S + 29
59%
9.5. Summary of Transpiration and Latent Energy
Daily transpiration for the south green facade was 0.95 liter per square meter (L/m2) (Table 9.3) with a standard error of 0.49 L/m2.
Transpiration on the west averaged 1.37 L/m2/d (Table 9.3). For the entire summer the mean transpiration was 1.26 L/m2/d.
Daily latent energy for the south green facade was 27 W/m2 (Table 9.4) with a standard error of 14 W/m2. Mean latent energy on
the west ranged from 37 to 50 W/m2/d (Table 9.4). For the entire summer the mean latent energy was 35 W/m2.
In summary the assessment of water use by green facades was hampered by the small number (2) of soil moisture sensors available.
This allowed us to monitor two plants continuously through 2010. The consistently wide differences of the two plants indicates that
our study cannot convincingly reveal the absolute amount of water used by green facades.
The long term monitoring of transpiration did convincingly indicate that air temperature and solar radiation had strong effects on
latent energy (ie., water use) and that orientation of the green facade made a difference in water use. Regression equations
developed to predict latent energy from air temperature or solar radiation should be useful to designers.
110
TABLE 9.2. MEAN DAILY SOLAR RADIATION ON VERTICAL SURFACE OF
GREENWALLS AND AIR TEMPERATURE IN 2010 (N = DAYS ).
Month
Orientation
Solar Radiation,
W/m2
Air
Temperature, oC
June (n=17)
South
104
24
June (n=13)
West
172
27
July (n=15)
West
163
27
August (n=31)
West
217
28
TABLE 9.3. T RANSPIRATION OF VITIS RIPARIA GLOIRE ON GREENWALLS IN 2010 (L/M2/D)
Month
Orientation
Plant #1
Plant #2
Mean
Std. Error
June (n=17)
South
0.6
1.3
0.95
0.49
June (n=13)
West
0.5
2.1
1.30
1.13
July (n=15)
West
1.1
1.0
1.05
0.07
August (n=31)
West
0.9
2.6
1.75
1.20
Mean
0.78
1.75
1.26
0.69
Std. Error
0.14
0.37
0.18
111
TABLE 9.4. LATENT ENERGY OF VITIS RIPARIA GLOIRE ON GREENWALLS IN 2010 (W/M2)
Month
Orientation
Plant #1
Plant #2
Mean
Std. Error
June (n=17)
South
17
37
27
14
June (n=13)
West
14
59
37
32
July (n=15)
West
31
28
30
2
August (n=31)
West
26
73
50
34
Mean
22
49
35
19
Std. Error
4
10
5
112
10. Energy Balance Model
10.1.
Calibration
Figure 10.1 compares the predicted and observed heat flux to the exterior wall for the calibration of the energy balance model.
Fortunately, the general pattern of the prediction was very close to the observation for both the bare wall and green wall, indicating
that the model could replicate observations well. However, the fit of the model was not perfect and there was consistent error for
the calibration (Figure 10.2).
Much of the calibration error for the green wall was likely due to the variability predicted for transpiration. You can see that
predicted heat flux for the green wall in Figure 10.1 was much more ‘jagged’ than the observation. The time step in the model
emulated the 10-minute interval of the experimental data. Some of the error could be reduced if hourly estimates were used instead.
While the model had components that estimated the longwave contribution from the sky, we did not have on-site cloud cover data,
which was needed as a model parameter. We used a nearby weather station from weatherunderground.com to estimate cloud cover.
The effect of not having the cloud cover data for the Clarksville location can be seen for the early morning of September 21st (Figure
10.1) when the prediction jumped up 20 W/m2. The observed heat flux only jumped about 8 W/m2 and did so a few hours later.
Thus, it was likely that our cloud cover estimate suffered from a lag period between the sites.
113
FIGURE 10.1. PREDICTED AND OBSERVED HEAT GAIN (10 MINUTE INTERVALS) OF THE EXTERIOR WALL DURING LATE SEPTEMBER, 2009 FOR
THE MODEL CALIBRATION .
114
The error for the bare wall calibration had a few instances where the magnitude was over 40 W/m2 (Figure 10.2), which occurred
in the evening when wall heat loss was at its maximum.
FIGURE 10.2. ABSOLUTE CALIBRATION ERROR OF THE HEAT FLUX MODEL (10 MINUTE INTERVALS) FOR GREEN WALL AND BARE WALL.
Figure 10.3 shows the major pathways of the energy balance for the green wall. Solar energy was by far the most dominant source of
energy. Convection and net-longwave were a tiny fraction of solar. Reflectance of solar energy by the white building walls was an
important heat sink for solar energy. (Note the buildngs were painted white during the 2009 experimental period, but were
subsequently painted slate gray in the Spring of 2010.) Reflectance by the vegetation of the green facade and latent energy of
transpiration were also important heat sinks (Figure 10.3).
The major heat sinks for the bare wall energy balance were building reflectance, convection and net-longwave (Figure 10.4) with
reflectance being by far the most important. This demonstrated that the color of a building’s walls has a large impact on the energy
balance. While the white paint was estimated to reflect about 90% of incoming solar, the buildings reflected 40-50% once they were
painted slate gray.
115
FIGURE 10.3. MAJOR HEAT FLUX PATHWAYS IN THE HEAT FLUX MODEL FOR A BUILDING WITH A GREEN FACADE DURING CALIBRATION.
116
FIGURE 10.4. MAJOR HEAT FLUX PATHWAYS IN THE HEAT FLUX MODEL FOR A BUILDING WITH A BARE WALL DURING CALIBRATION.
117
10.2.
Validation
Figure 10.5 shows the predicted and observed heat gain of the exterior wall for the validation data set. The general fit of the model
was good for the green wall, but for the bare wall there was about a 5 W/m2 offset. This appeared to be due differences in
convection rates between the calibration building (#3) and the validation building (#4).
FIGURE 10.5. PREDICTED AND OBSERVED HEAT FLUX (10 MINUTE INTERVALS) TO THE EXTERIOR WALL DURING LATE SEPTEMBER, 2009 FOR
THE MODEL VALIDATION.
118
Like the calibration error, the validation error also suffered from an inability to correctly predict the late afternoon heat loss of the
bare wall buildings (Figure 10.6). The error in estimating longwave radiation on September 21st was also present during the
validation.
FIGURE 10.6. ABSOLUTE VALIDATION ERROR OF THE HEAT FLUX MODEL (10 MINUTE INTERVALS) FOR GREEN WALL AND BARE WALL.
10.3.
Major Heat Flux Pathways
Figure 10.7 shows how the various flows of energy on the south-facing, exterior building wall changed throughout a 24-hr period on
September 19th, 2009. The chart on the left gives the energy pathways for a building with a south-facing green façade, while the
right chart has no green façade (i.e., Bare Wall).
Reflectance of solar energy by the green facade and the transfer of solar energy to latent energy were the two most important effects
that the green facades had on the overall energy balance of the south-facing green walls (Figure 10.7). The convection of sensible
heat to the immediate environment was cut in half by the green facade (Figure 10.7). Energy convected from the surface of walls to
the environment raises the temperature of the surrounding air, thus lower convection of the green walls improved the urban heat
island effect.
119
FIGURE 10.7. COMPARISON OF THE MAJOR ENERGY PATHWAYS, EXCLUDING DIRECT SOLAR AND BUILDING REFLECTANCE, FOR THE GREEN
WALLS (LEFT) AND BARE WALLS (RIGHT). THE GREEN FACADES HAD PERCENT COVER OF 55% ON THIS DATE .
The solar energy absorbed directly by the green wall peaked at around 30 W/m2 at noon, while for the Bare Wall it peaked at 45
W/m2 at noon. The green wall also absorbed solar energy that was transmitted through the canopy, which peaked at 8 W/m2. Thus,
the green wall had a peak solar absorption of 38 W/m2, which was 7 W/m2 less than the bare wall’s peak.
The Green wall reflected solar energy at rate of about 75 W/m2, while the Bare Wall did not have this feature. More importantly, the
vegetation converted solar energy to latent energy via transpiration at rate of about 30 W/m2 during mid-day.
Finally, heat emanating from inside the building was a minor component of the energy balance.
In summary, the energy balance model had a good fit with the general pattern of heat flux to the exterior wall, where it was able to
emulate morning heat gain and evening heat loss. The model worked well for predicting wall heat flux whether there was a green
facade present or not. Hourly error in the model was present, and most likely due to estimates of transpiration and cloud cover. The
energy balance model revealed that reflectance and transpiration were the two main mechanisms by which a green facade removed
heat flux from the buildings. The model also showed that convection of sensible heat back into the immediate environment was cut
in half by the green facade.
120
11. Conclusions
Plant growth: The green facade canopies developed quickly within the first year to reach 75% of their maximum potential. The
canopies reached their maximum leaf area and coverage by June of their second growing season. The type of metal trellis had no
effect on the growth rate or ultimate size of the canopy.
Effect on interior air temperature: The greatest cooling from the green facade on interior air temperature was from 3:00 pm to
9:00 pm during the three hottest months of June to August. The mean air temperature reduction fostered by the green façade was
4oC (7oF). The non-airconditioned, but insulated experimental buildings without green facades (i.e., bare walls) had indoor
temperatures of 33oC (91oF) during this time of day, while the buildings with green facades (i.e., green walls) had a mean
temperature of 29oC (84oF).
Effect on exterior wall temperature: The green facades reduced the mean peak in exterior wall temperature by over 12oC (22oF)
during June, July and August, which was a reduction from 46oC (115oF) on bare walls to 34oC (93oF) on the green walls. May and
September had smaller reductions.
Effect on ambient air temperature: The green facade reduced ambient air temperatures from 1.4 to 1.8oC (2.5 to 3.2oF) for June,
July and August, but there was no observed reduction for May and September.
Effect on heat flux: The green facades clearly reduced heat flux into the building’s exterior walls. Less heat flux into the exterior
walls meant that the temperatures of the exterior walls were lower. Ultimately, exterior wall temperature represented the heat that
goes into the building to raise the indoor temperature. Heat flux through the building walls from the exterior to the interior was
affected by the temperature difference between the inside air and exterior wall, and the R-value of the walls. Thus, a green facade’s
ultimate mechanism for reducing indoor temperatures is to keep exterior wall temperatures cooler.
Effect of insulation: The green facades were estimated to reduce heat flux through the walls of the experimental buildings, which
had an overall R-13 insulation value, by 3.1 W/m2 during the June-to-August cooling season. That was a reduction from 7.4 W/m2
for the bare wall to 4.3 W/m2 for the green wall, or a 42% decrease. If a building only had R-5 walls, which is expected for air
insulated walls, then the green facade would reduce heat flux from 19 W/m2 to 11 W/m2 for a savings of 8 W/m2. At the other
extreme, a building with R-60 walls would only see a 0.9 W/m2 drop in heat flux due to green facades. Thus, the energy savings
provided by a green façade is highly dependent on wall insulation. The energy savings potential is greater for poorly insulated
buildings than it is for well-insulated ones.
Effect of orientation: The direction that the green facades faced (i.e., orientation) had a significant effect on temperatures of the
interior air, external wall and ambient air, and heat flux into the building space. The peak reduction was greater for west-facing
121
vegetation than for the south in all cases during the late-May to mid-July study period, when the sun was at or near its maximum
altitude. At a high altitude, the south wall receives solar radiation at a high angle so the intensity was less than on the west, which
received solar radiation at high and low angles as the sun passed overhead.
Effect of vegetation on building temperatures and heat flux: The amount of cooling provided by a green facade was directly
related to the amount of leaf area present in the canopy. Both the spatial extent (i.e., percent cover) and leaf density (i.e., leaf area
index, LAI) are useful predictors of leaf area and thus the cooling effect. LAI can also be thought of as the number of layers of leaf
surface per unit of wall surface. With typical LAI values of 4.0, the simple linear regression equations developed from the
experimental data indicate that green facades can reduce heat flux by 8 to 9 W/m2 during the warmest months of the summer (June,
July and August). In Maryland’s humid sub-tropical climate this amounted to a 43% reduction in heat flux into a building with an Rvalue of 13. Buildings with lower R-values would experience a greater reduction in heat flux, but the percent reduction would
remain the same.
It is clear that green facades reduced the heat flux into buildings. By reducing the heat flux into the exterior walls, the green facades
reduce the mean temperature of the exterior walls. Reducing the temperature of the exterior walls reduced the temperature
gradient (i.e., “delta T”) across the insulated walls, which resulted in less heat flow to the interior building space.
In addition, the green facades reduced the temperature of the air surrounding the building envelope (i.e., ambient air), by
transferring radiant heat to latent vapor via transpiration and by reflecting radiant energy away from the building. The reduction in
ambient temperatures not only reduced the convective flow of heat into the building but also lowered the “heat island effect”.
Transpiration and Latent Energy: Daily transpiration for the south green facade was 0.95 liter per square meter (L/m2) with a
standard error of 0.49 L/m2. Transpiration on the west averaged 1.37 L/m2/d. For the entire summer the mean transpiration was
1.26 L/m2/d.
Daily latent energy for the south green facade was 27 W/m2 with a standard error of 14 W/m2. Mean latent energy on the west
ranged from 37 to 50 W/m2/d. For the entire summer the mean latent energy was 35 W/m2.
The assessment of water use by green facades was hampered by the small number (2) of soil moisture sensors available. This
allowed us to monitor two plants continuously through 2010. The consistently wide differences of the two plants indicates that our
study cannot convincingly reveal the absolute amount of water used by green facades. More research is needed on water use of
green facades.
The long term monitoring of transpiration did convincingly indicate that air temperature and solar radiation had strong effects on
latent energy (ie., water use), which is consistent with years of hydrological science and well-accepted models such as PenmanMonteith. Additionally, orientation of the green facade made a difference in water use. Regression equations were developed to
predict latent energy from air temperature or solar radiation which could be useful to irrigation designers.
122
Energy Balance Model: The energy balance model had a good fit with the general pattern of heat flux to the exterior wall, where it
was able to emulate morning heat gain and evening heat loss. The model worked well for predicting wall heat flux whether there
was a green facade present or not. Hourly error in the model was present, and most likely due to estimates of transpiration and
cloud cover. The energy balance model revealed that leaf reflectance and transpiration were the two main mechanisms by which a
green facade reduced heat flux to the buildings. The model also showed that convection of sensible heat back into the immediate
environment was cut in half by the green facade. Due to the green façade the green wall reflected solar energy at rate of about 75
W/m2 and converted solar energy to latent energy via transpiration at rate of about 30 W/m2 during mid-day. Heat emanating from
inside the building was a minor component of the energy balance.
123
12. Acknowledgements
Financial support for this research came from Green Roofs for Healthy Cities—Green Wall Group, the University of Maryland’s
College of Agriculture and Natural Resources, the University of Maryland’s Department of Environmental Science and Technology
and the Maryland Agricultural Experiment Station.
124
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Appendix A: First Year growth of green facades in climate controlled UMD Research Greenhouse
Plant Growth
November 2009
•  Four Trellis Materials
  Rigid Steel panel
(greenscreen)
  Stainsteel Cable
(Jakob-USA)
  Stainless Steel
Mesh (CarlStahl
DécorCable
Innovations )
  Manila Rope
(Tilley & Price)
•  U.Md Greenhouse
  November 2009
  4’x8’ panels
dtilley&umd.edu
www.enst.umd.edu/tilley
•  Six Maryland Native
Vine Species
  American Bittersweet
(Celastrus scandens)
  Cross vine (Bignonia
capreolata)
  Coral Honeysuckle
(Lonicera sempevirens)
  Carolina jessamine
(Gelsemium
sempevirens)
  Purple Passion flower
(Passiflora incarnata)
  American wisteria
(Wisteria frutescens)
Plant Selections
January 19th, 2010
DAY1
•  Three Grape
(Vitis)
  Richter 110 (Vitis
berlandieri x V.
rupestris)
  Paulson 1103 (Vitis
berlandieri x V.
rupestris)
  DogRidge (Vitis
champini)
dtilley&umd.edu
www.enst.umd.edu/tilley
•  Six Maryland Native
Vine Species
  American Bittersweet
(Celastrus scandens)
  Cross vine (Bignonia
capreolata)
  Coral Honeysuckle
(Lonicera sempevirens)
  Carolina jessamine
(Gelsemium
sempevirens)
  Purple Passion flower
(Passiflora incarnata)
  American wisteria
(Wisteria frutescens)
Plant Selections
January 28th, 2010
DAY9
•  Three Grape
(Vitis)
  Richter 110 (Vitis
berlandieri x V.
rupestris)
  Paulson 1103 (Vitis
berlandieri x V.
rupestris)
  DogRidge (Vitis
champini)
dtilley&umd.edu
www.enst.umd.edu/tilley
•  Six Maryland Native
Vine Species
  American Bittersweet
(Celastrus scandens)
  Cross vine (Bignonia
capreolata)
  Coral Honeysuckle
(Lonicera sempevirens)
  Carolina jessamine
(Gelsemium
sempevirens)
  Purple Passion flower
(Passiflora incarnata)
  American wisteria
(Wisteria frutescens)
Plant Selections
February 1st, 2010
DAY13
•  Three Grape
(Vitis)
  Richter 110 (Vitis
berlandieri x V.
rupestris)
  Paulson 1103 (Vitis
berlandieri x V.
rupestris)
  DogRidge (Vitis
champini)
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
February 22nd, 2010
DAY35
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
March 11th, 2010
DAY52
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
March 16th, 2010
DAY57
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
March 26th, 2010
DAY67
Grapes Added to Trellis
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
April 5th, 2010
DAY77
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
April 12th, 2010
DAY84
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
April 22th, 2010
DAY94
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
May 3rd, 2010
DAY105
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
May 11th, 2010
DAY113
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
May 20th, 2010
DAY122
Moved out of Greenhouse
dtilley&umd.edu
www.enst.umd.edu/tilley
Plant Growth
May 20th, 2010
DAY122
Staged at Clarksville Farm, Faced North to acclimate
dtilley&umd.edu
www.enst.umd.edu/tilley
On Building Walls
June 7th, 2010 DAY140
Building #1
dtilley&umd.edu
Building #2
www.enst.umd.edu/tilley
Plant Growth
June 25th, 2010
DAY158
dtilley&umd.edu
www.enst.umd.edu/tilley
3 Sides Covered
2011
June 25th,
West
dtilley&umd.edu
South
East
www.enst.umd.edu/tilley
Appendix B: Time series of photos of green facades for growing on east, south and west
sides of experimental buildings during 2011 growing season
Building #1 East: May 9th, 2011
Building #2 East: May 9th, 2011
Building #1 East: June 7th , 2011
Building #2 East: June 7th, 2011
Building #1 East: July 28th , 2011
Building #2 East: July 28th, 2011
Building #1 East: August 22nd , 2011
Building #2 East: August 22nd , 2011
Building #1 East: September 28th , 2011
Building #2 East: September 28th, 2011
FIGURE 6.5. TIME SERIES OF PHOTOGRAPHS OF EAST-FACING GREEN FACADES FROM M AY TO SEPTEMBER OF 2011. BUILDING
#1 HAS STAINLESS STEEL VERTICAL /HORIZONTAL CABLES FROM CARLSTAHL ON LEFT SIDE FOR ITS TRELLIS, WHILE RIGHT SIDE
TRELLIS IS JAKOB ’S STAINLESS STEEL WEB NETTING . B UILDING #2 HAS STAINLESS STEEL VERTICAL / HORIZONTAL CABLES FROM
CARLSTAHL ON LEFT SIDE FOR ITS TRELLIS , WHILE RIGHT SIDE TRELLIS IS GREENSCREEN’S PAINTED METAL TRELLIS .
Building #1 South: May 9th, 2011
Building #2 South: May 9th, 2011
Building #1 South: June 7th , 2011
Building #2 South: June 7th, 2011
Building #1 South: July 28th, 2011
Building #2 South: July 28th, 2011
Building #1 South: August 22nd , 2011
Building #2 South: August 22nd , 2011
Building #1 South: September 28th , 2011
Building #2 South: September 28th, 2011
FIGURE 6.6. TIME SERIES OF PHOTOGRAPHS OF SOUTH -FACING GREEN FACADES FROM MAY TO SEPTEMBER OF 2011. BUILDING
#1 HAS JAKOB’S STAINLESS STEEL WEB NETTING ON LEFT SIDE FOR ITS TRELLIS , WHILE RIGHT SIDE IS COVERED BY STAINLESS
STEEL VERTICAL / HORIZONTAL CABLES FROM C ARL STAHL . BUILDING #2 HAS TRELLIS MADE FROM MANILA ROPE ON BOTH LEFT
AND RIGHT SIDES . M ANILA TRELLIS IS ONLY PLANTED WITH VITIS SPECIES .
Building #1 West: May 9th, 2011
Building #2 West: May 9th, 2011
Building #1 West: June 7th , 2011
Building #2 West: June 7th, 2011
Building #1 West: July 28th , 2011
Building #2 West: July 28th, 2011
Building #1 West: August 22nd , 2011
Building #2 West: August 22nd , 2011
Building #1 West: September 28th, 2011
Building #2 West: September 28th, 2011
FIGURE 6.7. TIME SERIES OF PHOTOGRAPHS OF WEST-FACING GREEN FACADES FROM M AY TO SEPTEMBER OF 2011. BUILDING #1 HAS
TRELLIS MADE FROM MANILA ROPE ON BOTH LEFT AND RIGHT SIDES . M ANILA TRELLIS IS ONLY PLANTED WITH VITIS SPECIES . BUILDING
#2 HAS GREENSCREEN’S PAINTED METAL TRELLIS ON BOTH RIGHT AND LEFT SIDES .