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Green wall for retention of stormwater

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Urbanisation increases the level of imperviousness in a catchment, and more runoff is converted from rainfall in urban areas. To mitigate this adverse situation, dispensed green infrastructure presents the best solution for delivering results in reducing stormwater impact. Green roofs and rain gardens are extensively studied and widely available in the literature, but this is not the case for green walls, which more often than not, are treated as ornaments. Thus, this study developed a computer-aided stormwater model that incorporates a green wall to investigate its effectiveness as an urban drainage system. The effectiveness of employing a green wall as a stormwater component is tested using USEPA SWMM 5.1 and the embedded bioretention cell interface. Four simulation models according to different conditions and precipitation input are tested, compared and discussed. The conditions include investigation of different soil types, average recurrence interval (ARI) and storm duration with design and observed rainfall. The results reveal that synthesis precipitation data, used in Scenario 1, 2 and 3, decreased runoff by more than half, at 55% on condition of one-year ARI and 5 minutes of storm duration. Meanwhile, Scenario 4 also shows a repetition of runoff reduction by half after the integration of the green wall using the observed rainfall data. Thus, it is verified that a green wall can be effectively used as an urban drainage system in reducing surface runoff.
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Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
SCIENCE & TECHNOLOGY
Journal homepage: http://www.pertanika.upm.edu.my/
ISSN: 0128-7680 © 2018 Universiti Putra Malaysia Press.
ARTICLE INFO
Article history:
Received: 26 June 2016
Accepted: 09 May 2017
E-mail addresses:
dante.lau91@hotmail.com (J. T. Lau),
ysmah@feng.unimas.my (D. Y. S. Mah)
*Corresponding Author
Green Wall for Retention of Stormwater
J. T. Lau* and D. Y. S. Mah
Department of Civil Engineering, Faculty of Engineering, University Malaysia Sarawak, 94300 UNIMAS,
Kota Samarahan, Sarawak, Malaysia
ABSTRACT
Urbanisation increases the level of imperviousness in a catchment, and more runoff is converted from
rainfall in urban areas. To mitigate this adverse situation, dispensed green infrastructure presents the
best solution for delivering results in reducing stormwater impact. Green roofs and rain gardens are
extensively studied and widely available in the literature, but this is not the case for green walls, which
more often than not, are treated as ornaments. Thus, this study developed a computer-aided stormwater
model that incorporates a green wall to investigate its effectiveness as an urban drainage system. The
effectiveness of employing a green wall as a stormwater component is tested using USEPA SWMM 5.1
and the embedded bioretention cell interface. Four simulation models according to different conditions and
precipitation input are tested, compared and discussed. The conditions include investigation of different
soil types, average recurrence interval (ARI) and storm duration with design and observed rainfall. The
results reveal that synthesis precipitation data, used in Scenario 1, 2 and 3, decreased runoff by more
than half, at 55% on condition of one-year ARI and 5 minutes of storm duration. Meanwhile, Scenario
4 also shows a repetition of runoff reduction by half after the integration of the green wall using the
observed rainfall data. Thus, it is veried that a green wall can be effectively used as an urban drainage
system in reducing surface runoff.
Keywords: Bioretention, green wall, runoff, SWMM, urban stormwater management
INTRODUCTION
The process of urbanisation turns natural
ground cover into urban infrastructure or
utility developments. Impervious surfaces
such as roofs, paved roads and parking lots
have expanded significantly together with
post-development progress (see Figure 1).
Consequently, the inltration of stormwater
into the ground as depression storage is
reduced with the gradual elimination of
J. T. Lau and D. Y. S. Mah
284 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
vegetation as a natural lter. Thus, overland ows tend to travel faster and a huge quantity of
runoff is discharged into urban stormwater conveying systems. Surface runoff is increased in
urbanised watersheds, creating greater peak discharge. As the consequence of pre-development
ow regime changes, natural disasters like ash oods, occur when the capacity of a drainage
system fails to sustain the overwhelming quantity of runoff.
Hence, new, exhaustive and integrated stormwater management strategies are now required
to underpin the Malaysian government’s target of achieving sustainable urban drainage systems
nationwide (DID, 2002). These new strategies incorporate various aspects of drainage, including
runoff source control, management and delayed disposal of a catchment area on proactive and
multifunction bases.
4
INTRODUCTION
The process of urbanisation turns natural ground cover into urban infrastructure or
utility developments. Impervious surfaces such as roofs, paved roads and parking lots
have expanded significantly together with post-development progress (see Figure 1).
Consequently, the infiltration of stormwater into the ground as depression storage is
reduced with the gradual elimination of vegetation as a natural filter. Thus, overland
flows tend to travel faster and a huge quantity of runoff is discharged into urban
stormwater conveying systems. Surface runoff is increased in urbanised watersheds,
creating greater peak discharge. As the consequence of pre-development flow regime
changes, natural disasters like flash floods, occur when the capacity of a drainage
system fails to sustain the overwhelming quantity of runoff.
Hence, new, exhaustive and integrated stormwater management strategies are
now required to underpin the Malaysian government’s target of achieving sustainable
urban drainage systems nationwide (DID, 2002). These new strategies incorporate
various aspects of drainage, including runoff source control, management and
delayed disposal of a catchment area on proactive and multifunction bases.
Figure 1. Typical degree of impervious areas that affect stormwater runoff, from
(left) pre-development to (right) post-development (Commonwealth of
Massachusetts, 2008).
Figure 1. Typical degree of impervious areas that affect stormwater runoff, from (left) pre-development to
(right) post-development (Commonwealth of Massachusetts, 2008)
Water Sensitive Urban Design
There are several well-known best practices of stormwater management used around the world
that have been applied in urban developments of different countries, and one of them is Water
Sensitive Urban Design (WSUD). WSUD is gaining popularity as an important element in
sustainable supply planning in urban areas and has the added advantage of contributing to
ood mitigation and maintaining safe water quality. Nevertheless, in order to resolve issues
of high cost, land space utilisation and aesthetics of metropolitan areas, innovative stormwater
management tools have emerged and been implemented. The Malaysian Urban Stormwater
Management Manual (MSMA) has introduced a storage-orientated retention system that is
water sensitive. The stormwater best management practices (BMPs), involving greenery and
live plants, are designed to promote evapotranspiration and inltration while minimising or
delaying runoff from stormwater events (DID, 2002; 2012).
Green Infrastructure
Green infrastructure (GI) manages water and creates healthier urban environments by utilising
vegetation, soil and natural processes. Considering that stormwater runoff is generated across
distributed areas, the application of dispensed green infrastructure presents the best approach for
delivering manifold ideal results in reducing stormwater impact. At the scale of a neighbourhood
Green Wall for Detention of Stormwater
285Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
or site with small linear features, GI is referred to as a stormwater management system that
mimics the nature of soaking up and storing water, for instance bioretention (well-known feature
that creates ‘rain gardens’), green roofs or green walls (USEPA, 2014). Green roofs and rain
gardens are extensively studied and widely available in the literature, but this is not the case
for green walls, which more often than not, are treated as ornaments. Therefore, it was the
intention of this project to look into the applicability of green walls in capturing runoff from
roof tops. Computer modelling of the mentioned system provided initial results to guide the
expected working of the actual model.
Green Wall
The terminology “green wall” refers to all forms of vegetated wall surfaces (GRHC, 2008).
A green wall is basically a bioretention system, but it is structured in a vertical manner on a
façade or wall, without the traditional requirements of space by sacricing built-up areas. If the
conventional bioretention system is a component of the urban runoff control, then theoretically,
a green wall should have the same function.
Incorporated as part of a sustainable urban drainage system, green walls can mitigate
water runoff and reduce stormwater ows (Green over Grey, 2009). Percolation of rainfall
within modular green walls reduces the runoff rate (see Figure 2) and offers true benets to
urban stormwater management (Loh, 2008). Stormwater can be gathered for the purpose of
irrigating a green wall, which in turn increases on-site inltration and evapotranspiration.
Several preliminary studies suggest that these systems retain as much as 45% to 75% of
rainfall (Webb, 2010). In addition, green walls might become one of the effective stormwater
management systems via vertical planting as wall area far exceeds roof area, especially in urban
development areas (Kew et al., 2013). However, it is also reported that green walls hold less
potential in producing much better results than green roofs (Higgs et al., 2011).
6
2008). A green wall is basically a bioretention system, but it is structured in a vertical
manner on a façade or wall, without the traditional requirements of space by
sacrificing built-up areas. If the conventional bioretention system is a component of
the urban runoff control, then theoretically, a green wall should have the same
function.
Incorporated as part of a sustainable urban drainage system, green walls can
mitigate water runoff and reduce stormwater flows (Green over Grey, 2009).
Percolation of rainfall within modular green walls reduces the runoff rate (see Figure
2) and offers true benefits to urban stormwater management (Loh, 2008). Stormwater
can be gathered for the purpose of irrigating a green wall, which in turn increases on-
site infiltration and evapotranspiration. Several preliminary studies suggest that these
systems retain as much as 45% to 75% of rainfall (Webb, 2010). In addition, green
walls might become one of the effective stormwater management systems via
vertical planting as wall area far exceeds roof area, especially in urban development
areas (Kew et al., 2013). However, it is also reported that green walls hold less
potential in producing much better results than green roofs (Higgs et al., 2011).
Figure 2. Modular green wall (GRHC, 2008).
Figure 2. Modular green wall (GRHC, 2008)
J. T. Lau and D. Y. S. Mah
286 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
METHODOLOGY
The study site was situated within Central City, which lies strategically between the Kuching
and Samarahan link way (see Figure 3) in the state of Sarawak. Kota Samarahan has been on
the Government’s development radar screen for the past 10 years, and it seems that this will
remain the case in the foreseeable future. The township experiences a high growth rate of
economic development due to its function as a hub for higher education and technology. Due
to rapid property and infrastructural development, ash oods often hit residential areas lying
nearby. Therefore, Central City was chosen for this study to investigate ways for combating
this problematic impact of urbanisation.
The main idea of this study was to propose green walls as a component of the local urban
drainage system. The main goal was to devise an effective stormwater management system and
to reduce the velocity of runoffs from rainfall events to downstream reaches. This gives a clearer
picture of the objective of this study as it reduces the scope of the control design variables.
Modular green walls were chosen as they have a sufcient volume of growing medium with
retaining characteristics to control stormwater. Dependent variables in the design included the
size of the modular cell, types of planting media and design rainfall.
Figure 3. Aerial map of Central City via satellite image (http://wikimapia.org)
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;DG6B6M>BJBGDD;86I8=B:CI6G:6D;B;DGI=:9:H><CH:: ><JG:
The study site was then narrowed down to a specic property in Central City. A shophouse in
Phase 3 was chosen as the best option to implement the study of green walls. The major reason
for this preference was that the three-storey shophouse had a relatively at plain wall in front
of the building and this available space could be put into good use. Apart from stormwater
management, the green wall enhanced the aesthetic view of the commercial building and
signicantly reduced the urban heat island effect in Central City. A corner unit of a commercial
building was chosen for a maximum roof catchment area of 139.08 m2 for the design (see
Figure 4).
Green Wall for Detention of Stormwater
287Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
The Elmich Green Wall was adapted for use in this project. The Elmich Green Wall is a modular
system that consists of Elmich Vertical Greening Modules (VGMs); each module encases a
VGM bag containing planting media, metal support frames and anchoring pilasters as shown
in Figure 5 (Elmich, 2008). The green wall proposed in this study was assembled in front of
a column of the commercial building. The top received runoff from the roof; then, the water
slowly inltrated the modules by gravitational force to the ground level, owing nally into
the culvert and roadside drain. The proposed size for a single green wall module was: height
= 700 mm, width = 700 mm and depth = 200 mm. There were a total of 17 modules to be
assembled in a straight upward manner and parallel to the column, which was approximately
the height of the building.
Figure 4. Selected corner unit of three storey shophouse painted with orange (MD Kwang Tai, 2010)
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Figure 5. Elmich vertical greening modules (Elmich, 2008)
10
Figure 5. Elmich vertical greening modules (Elmich, 2008).
In this study, the soil for the green wall was required to allow water to pass
through relatively fast. Thus, the recommended range of soil permeability was
around 0.5 to 6.0 in/hr. Hence, four types of soil, namely sand, loamy sand, sandy
loam and loam were chosen for analysis; the aim was to determine the best type
among the four growing media for optimum performance of the green wall as an
urban drainage system. Hydraulic conductivity and other useful parameters for the
four selected soils are shown in Appendix A.
The performance of the green wall system was assessed within a range of ARI
with 1, 2, 5, 10, 20, 50 and 100 years and storm duration of 5, 10 and 15 minutes to
determine the most suitable values for satisfactory performance of the system.
Design rainfall intensity (mm/hr) depends on duration (minute) and ARI (year). In
this study, the intensities of different ARI and storm duration in Kota Samarahan
were estimated using the computerised intensity-duration-frequency (IDF) curves
generated by the Department of Irrigation and Drainage (DID) Sarawak. Apart from
that, the actual data of observed rainfall (24-hour precipitation data) were used to
examine the effectiveness of the green wall system as an urban drainage system. Two
sets of hourly rainfall data were used for analysis: data from January 2014
represented the highest accumulated rainfall depth of the year, while data from
In this study, the soil for the green wall was required to allow water to pass through relatively
fast. Thus, the recommended range of soil permeability was around 0.5 to 6.0 in/hr. Hence, four
types of soil, namely sand, loamy sand, sandy loam and loam were chosen for analysis; the aim
was to determine the best type among the four growing media for optimum performance of the
J. T. Lau and D. Y. S. Mah
288 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
green wall as an urban drainage system. Hydraulic conductivity and other useful parameters
for the four selected soils are shown in Appendix A.
The performance of the green wall system was assessed within a range of ARI with 1, 2,
5, 10, 20, 50 and 100 years and storm duration of 5, 10 and 15 minutes to determine the most
suitable values for satisfactory performance of the system. Design rainfall intensity (mm/hr)
depends on duration (minute) and ARI (year). In this study, the intensities of different ARI and
storm duration in Kota Samarahan were estimated using the computerised intensity-duration-
frequency (IDF) curves generated by the Department of Irrigation and Drainage (DID) Sarawak.
Apart from that, the actual data of observed rainfall (24-hour precipitation data) were used to
examine the effectiveness of the green wall system as an urban drainage system. Two sets of
hourly rainfall data were used for analysis: data from January 2014 represented the highest
accumulated rainfall depth of the year, while data from February 2014 represented normal
rainfall in Kota Samarahan, showing only about half of January’s rainfall.
Using the Rational Method, peak ow, Q1, for a 15-minute storm was manually calculated;
the value derived was 0.005022 cms. Roof runoff, Q2, for a 15-minute storm, was generated
from SWMM simulation. Both results, Q1 and Q2, were compared, and it was found that there
were no signicant differences, as shown in Table 1. A comparison of 10- and 5-minute storms
are presented in Table 2 and Table 3. The runoff generated by SWMM was calibrated and the
performance of the green wall system was further investigated using the bioretention interface
in the SWMM simulation.
Hand calculation using Rational Method can be represented as:
[1]
where
Q1 = Peak ow (cms);
C = Runoff coefcient;
i = Average rainfall intensity (mm/hr); and
A = Drainage area (ha)
Given that at the existing condition,
ARI = 1 year
Rainfall intensity, i, corresponding to a 15-minute storm = 130 mm/hr
Roof runoff coefcient, C = 1.0
Roof catchment area, A = 139.08 m2 = 0.013908 ha
thus, peak ow, Q1 0.005022 cms
Green Wall for Detention of Stormwater
289Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
Table 1
SWMM calibrations for a 15-minute storm
ARI (year) Rainfall Intensity, i (mm/hr)
corresponding to 15-minute
storm
Peak Flow, Q1
(cms)
SWMM-generated Roof
Runoff, Q2 (cms) for 15-minute
storm
1130 0.005022 0.005017
2160 0.006181 0.006176
5170 0.006568 0.006562
10 180 0.006954 0.006954
20 190 0.007340 0.007334
50 210 0.008113 0.008106
100 230 0.008886 0.008879
Table 2
SWMM calibrations for a 10-minute storm
ARI (year) Rainfall Intensity (mm/hr)
corresponding to 10-minute
storm
Peak Flow, Q1
(cms)
SWMM-generated Roof
Runoff, Q2 (cms) for 10-minute
storm
1130 0.005022 0.005015
2160 0.006181 0.006174
5170 0.006568 0.006560
10 180 0.006954 0.006952
20 190 0.007340 0.007333
50 210 0.008113 0.008106
100 230 0.008886 0.008879
Table 3
SWMM calibrations for a 5-minute storm
ARI (year) Rainfall Intensity (mm/hr)
corresponding to 5-minute
storm
Peak Flow, Q1
(cms)
SWMM-generated Roof
Runoff, Q2 (cms) for 5-minute
storm
1130 0.005022 0.005006
2160 0.006181 0.006167
5170 0.006568 0.006554
10 180 0.006954 0.006941
20 190 0.007340 0.007328
50 210 0.008113 0.008102
100 230 0.008886 0.008875
J. T. Lau and D. Y. S. Mah
290 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
However, SWMM used another equation based on nonlinear reservoir representation
(see Figure 6). Each subcatchment surface was treated as a nonlinear reservoir. Inow came
from precipitation and the runoff from any designated upstream subcatchment areas. Outow
consisted of inltration, evaporation and surface runoff. The capacity of this ‘reservoir was
the maximum depression storage, which is the maximum surface storage provided by ponding,
surface wetting and interception. Surface runoff, Q, occurred only when the depth of water,
d, in the ‘reservoir, exceeded the maximum depression storage, dp, in which case the outow
was given by Manning’s equation:
[2]
where
W is the subcatchment’s characteristic width;
S is slope;
n is Manning roughness value; and
Depth of water, dp, over the subcatchment was continuously updated with time by solving
numerically a water balance equation over the subcatchment. Therefore, the hand calculation
is not shown here.
Figure 6. Nonlinear reservoir representation of a subcatchment (Huber & Dickinson, 1988)
Figure 6. Nonlinear reservoir representation of a subcatchment
(Huber & Dickinson, 1988).
RESULTS AND DISCUSSION
The process of employing a green wall as a stormwater component to reduce rainfall runoff was
tested using Storm Water Management Model (SWMM) 5.1, which was carried out utilising
an embedded bioretention cell interface. Four simulation models set to different conditions
and precipitation input are shown in Table 4.
Table 4
Scenarios for modelling
Scenario Precipitation Input Condition
1Design rainfall Examine different soil media
2Design rainfall Examine average recurrence intervals (ARIs)
3Design rainfall Examine storm duration
4Observed rainfall Examine the effectiveness of green wall as an urban drainage system
Green Wall for Detention of Stormwater
291Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
After examining all the criteria in Scenario 1, 2 and 3 using design rainfalls, three soil types
i.e loamy sand, sandy loam and loam showed equally good ability in surface runoff reduction,
excluding sand, which acts as the control item. The following sizes were the design parameters
that give the best result for optimum green wall performance: 12000 mm thickness, 1 year ARI
and 5 minutes of storm duration (see Figures 7 to 9).
In Scenario 1, the thickness of the green wall was xed at 12000mm and roof runoff
was xed at one-year ARI, while storm duration was 5 minutes for a single unit of corner
shophouses. Hence, the types of growing media for a green wall are the dependent variables
that determine the performance of the green wall. As shown in Figure 7, sand is expected to
have the highest percentage of runoff reduction, followed by loamy sand, sandy loam and
loam. The range is from 55.1% to 54.6%, which is a difference of only about 0.5%; thus all
the soil types were considered equal in terms of runoff reduction.
All the soil types under study had higher composition of sand, which brought about a
faster rate of storm water absorption. Although the permeability of clay and silt was low, their
water-holding capability for retaining water was for a long period. The larger the soil particle
size, the higher the conductivity. As a result, water inltration rate increases in tandem with
an increase in porosity or void between soil particles.
Figure 7. Reduction of runoff based on soil types for 5 minutes of one-year ARI event
17
All the soil types under study had higher composition of sand, which brought
about a faster rate of storm water absorption. Although the permeability of clay and
silt was low, their water-holding capability for retaining water was for a long period.
The larger the soil particle size, the higher the conductivity. As a result, water
infiltration rate increases in tandem with an increase in porosity or void between soil
particles.
Figure 7. Reduction of runoff based on soil types for 5 minutes of one-year ARI
event.
In Scenario 2, the variables were given the following values: the thickness of
the green wall was fixed at 12000 mm and storm duration was fixed at 5 minutes for
a single corner shophouse. The ARIs were the dependent variables used to measure
the performance of the green wall. Figure 8 shows the ARI traits for all the soil
types, giving similar declivitous patterns from ARI year 1 until 100. The range of
drop in runoff reduction was around 55% to 20%.
In Scenario 2, the variables were given the following values: the thickness of the green wall was
xed at 12000 mm and storm duration was xed at 5 minutes for a single corner shophouse.
The ARIs were the dependent variables used to measure the performance of the green wall.
Figure 8 shows the ARI traits for all the soil types, giving similar declivitous patterns from
ARI year 1 until 100. The range of drop in runoff reduction was around 55% to 20%.
J. T. Lau and D. Y. S. Mah
292 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
According to DID (2010), a green wall (bioretention) is a minor system intended to collect,
control and convey runoff from facilities in areas with relatively frequent storm events
(recommended up to 10-year ARI) that minimises inconvenience and nuisance of ooding. The
rationale of adopting a higher standard for minor systems in large commercial, business and
industrial areas is that a minor system has a greater potential to cause damage and disruption
in the event of ooding.
The trend of Scenario 1 was repeated for analysis, reiterating that the performance of the
three soil types was the same. However, even at the extreme event of 100-year ARI, the three
soil types were shown to be able to reduce about 20% of peak runoff.
In Scenario 3, the thickness of the green wall was xed at 12000mm and the storm ARI
was set at 1 year for a single corner shophouse. Hence, storm duration became the dependent
variable that showcased the performance of the green wall. As shown in Figure 9, the storm
duration traits for all the soil types showed similar declivitous patterns from 5 until 15 minutes.
The drop in runoff reduction ranged from 55% to 22%.
Figure 8. Reduction of runoff based on soil types and ARIs
18
Figure 8. Reduction of runoff based on soil types and ARIs.
According to DID (2010), a green wall (bioretention) is a minor system
intended to collect, control and convey runoff from facilities in areas with relatively
frequent storm events (recommended up to 10-year ARI) that minimises
inconvenience and nuisance of flooding. The rationale of adopting a higher standard
for minor systems in large commercial, business and industrial areas is that a minor
system has a greater potential to cause damage and disruption in the event of
flooding.
The trend of Scenario 1 was repeated for analysis, reiterating that the
performance of the three soil types was the same. However, even at the extreme
event of 100-year ARI, the three soil types were shown to be able to reduce about
20% of peak runoff.
In Scenario 3, the thickness of the green wall was fixed at 12000mm and the
storm ARI was set at 1 year for a single corner shophouse. Hence, storm duration
became the dependent variable that showcased the performance of the green wall. As
Green Wall for Detention of Stormwater
293Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
Duration of storm is an important parameter that denes the intensity for a given ARI and thus,
affects the resulting runoff peak. The storm duration that produces the maximum runoff peak
traditionally is dened as the time of concentration – the sum of the travelling time to an inlet
plus the time of travel in the stormwater conveyance system (DID, 2002). Although travel
time from individual elements of a system may be short, the total nominal travel time of ow
for all individual elements within any catchment to their points of entry into the stormwater
drainage network should not be less than 5 minutes (DID, 2002).
The analysis of Scenario 3 showed that it was acceptable to use 5 minutes as the critical
storm duration in order to enhance the performance of the green wall system; this was to
prolong the serviceability period of the minor urban drainage system and to reduce the volume
of storm runoff.
In Scenario 4, the observed rainfall data were used to test and determine the effectiveness
of the green wall as an urban drainage system. Appendix B and Appendix C, respectively,
show the rainfall-runoff simulations of the existing drainage system without the integration
of the green wall for two months, namely January and February 2014, using two sets of actual
observed rainfall data for Central City, Kota Samarahan. The simulation of roof runoffs with
and without the green wall with respect to the three selected soil types (loamy sand, sandy loam
and loam) were carried out and the results were compared and analysed. The performance of
the green wall was not solely dependent on rainfall pattern, but also on the interval between
storms for particular rainfall events. Therefore, four specic periods of storm duration with
various intensity levels were extracted from January and February 2014 and analysed, as
shown in Figure 10.
Figure 9. Reduction of runoff based on soil types and storm durations
19
shown in Figure 9, the storm duration traits for all the soil types showed similar
declivitous patterns from 5 until 15 minutes. The drop in runoff reduction ranged
from 55% to 22%.
Figure 9. Reduction of runoff based on soil type and storm duration.
Duration of storm is an important parameter that defines the intensity for a
given ARI and thus, affects the resulting runoff peak. The storm duration that
produces the maximum runoff peak traditionally is defined as the time of
concentration – the sum of the travelling time to an inlet plus the time of travel in the
stormwater conveyance system (DID, 2002). Although travel time from individual
elements of a system may be short, the total nominal travel time of flow for all
individual elements within any catchment to their points of entry into the stormwater
drainage network should not be less than 5 minutes (DID, 2002).
The analysis of Scenario 3 showed that it was acceptable to use 5 minutes as
the critical storm duration in order to enhance the performance of the green wall
system; this was to prolong the serviceability period of the minor urban drainage
system and to reduce the volume of storm runoff.
J. T. Lau and D. Y. S. Mah
294 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
In this project, the effectiveness of a green wall as an urban drainage system was summarised
by measuring hydrologic performance, which mainly focusses on deviation of runoff, with
integration of the green wall system as shown in Table 5.
Figure 10. Four analyses of specic periods of storm duration with various intensity levels in January and
February 2014
21
Figure 10. Four analyses of specific periods of storm duration with various intensity levels
Green Wall for Detention of Stormwater
295Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
Scenario 4 showed the average runoff reduction gradually going down from 87% to 52% as the
average rainfall intensity rose from 2.0 to 42.5 mm/hr. The increment of storm duration also
degraded the performance of the green wall; changes in continuous storm time spanned from
6 to 10 hours, signicantly reducing runoff reduction. With the same continuous storm time
span of 6 hours, both events on 19 January and 21 February, with the intensity of 2.0 mm/hr
and 42.5 mm/hr respectively, showed large gaps in runoff reduction at 35%. Thus, it is evident
that when both storm duration and rainfall intensity increase, runoff reduction decreases and
so does the effectiveness of the green wall as an urban drainage system facility.
CONCLUSION
This project demonstrated green wall simulation for a commercial shophouse in Central City,
Kota Samarahan. The experiment was carried out using synthesised or actual precipitation
data and it tested four scenarios set up on different conditions. The simulation model on the
hydrologic condition for the study area was developed to verify the effectiveness of using a
green wall as an urban drainage system for reducing surface runoff using USEPA SWMM 5.1.
Initially, when using synthesised precipitation data, the runoff decreased by half at 55% on the
condition of one-year ARI and 5 minutes of storm duration. The results obtained in Scenario
4 showed repetition of runoff reduction by half after the integration of a green wall using the
observed rainfall data.
The green wall model proposed in this study demonstrated the effectiveness of using a
green wall as a component of the stormwater management system through four scenarios using
SWMM simulation. The results and data displayed can be a guide for future practical design
and the building of an actual model.
APPENDIX A
Soil Texture
Class
Saturated Hydraulic
Conductivity (in/hr)
Suction Head
(in.)
Porosity
(fraction)
Field Capacity
(fraction)
Wilting Point
(fraction)
Sand 4.74 1.93 0.437 0.062 0.024
Loamy Sand 1.18 2.40 0.437 0.105 0.047
Sandy Loam 0.43 4.33 0.453 0.190 0.085
Loam 0.13 3.50 0.463 0.232 0.116
Table 5
Summary of hydrologic performance incorporating green wall
Scenario Date Continuous Storm Time Span Intensity (mm/hr) Average Runoff
Reduction (%)
Range Average
4
19 Jan 6 hrs 0.5-3.5 2.0 87
15 Jan 10 hrs 1.5-9.5 5.5 55
5 Jan 4 hrs 1.5-51.5 26.5 52
21 Feb 6 hrs 4.5-80.5 42.5 52
1, 2 & 3 -Single storm at 5 mins for one year of ARI -100.0 55
J. T. Lau and D. Y. S. Mah
296 Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
ACKNOWLEDGEMENT
The authors thank Universiti Malaysia Sarawak (UNIMAS) and DID Sarawak for assistance
and for providing the hydrological data. The authors are also grateful to UNIMAS for nancial
support through the Special Grant Scheme F02/SpGS/1405/16/6.
REFERENCES
Commonwealth of Massachusetts. (2008). Smart Growth/Smart Energy Toolkit. Low Impact Development
(LID). Retrieved June 27, 2014, from http://www.mass.gov/envir/smart_growth_toolkit/pages/mod-
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25
Appendix C
ACKNOWLEDGEMENT
The authors thank Universiti Malaysia Sarawak (UNIMAS) and DID Sarawak for
assistance and for providing the hydrological data. The authors are also grateful to
UNIMAS for financial support through the Special Grant Scheme
F02/SpGS/1405/16/6.
APPENDIX C
24
APPENDIX A
APPENDIX B
APPENDIX B
Green Wall for Detention of Stormwater
297Pertanika J. Sci. & Technol. 26 (1): 283 - 298 (2018)
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Due to their agglomeration of population, material assets and infrastructures, cities are particularly affected by extreme weather events such as heavy rain and heat. Numerous flooding events as a result of heavy rainfall occurred in various regions of Germany in the last years, not only resulted in losses in the double- to triple-digit million range, but also in fatalities. And heat waves which became more frequent in recent years pose health risks, including numerous cases of death. To counter these risks and to reduce damage resulting from weather extremes, many cities are already developing strategies and concepts in the context of climate adaptation and/or implement measures. In addition to developing and implementing their own ideas, cities are guided by guidelines and examples from literature, experiences from other cities, or results from research projects, among other things. This learning and transfer process, which involves the transfer of climate adaptation measures or instruments from one place to another, has not yet been sufficiently researched and understood. This report therefore examines this learning and transfer process between and within cities as well as the transfer potential of specific knowledge transfer media, instruments and measures. The aim is to develop a better understanding of these processes and to contribute to improving the transfer of municipal climate adaptation activities. This content builds on a previous analysis of the state of research on policy transfer by Haupt et al. (2021) and attempts to complement the already generated state of knowledge on the level of policies with the level of concrete instruments and measures and to substantiate it with empirical findings. The knowledge and data basis of this report comprises a mix of various (online) surveys and interviews with representatives of relevant stakeholder groups, especially representatives of city administrations, as well as the experiences of the three case study cities within the ExTrass-project, namely Potsdam, Remscheid and Würzburg. After an introduction, chapter 2 deals with overarching factors of transferability. Chapter 2.1 provides a summary of the current state of knowledge regarding the transfer of policies in the field of urban climate policy according to Haupt et al. (2021). Here, central criteria for a successful transfer are elaborated in order to provide a starting point for the following contents and empirical findings on the level of concrete instruments and measures. Chapter 2.2 follows on from this and presents findings from a wide-ranging municipal survey. Here, it was investigated whether and which climate adaptation measures are already implemented in the cities, which supporting and inhibiting aspects are present in this context, and which experiences regarding the transfer of knowledge and ideas already exist. Chapter 3 examines the role of different knowledge transfer media, focusing on guidelines on climate adaptation and fact sheets for adaptation measures as examples. Chapter 3.1 answers questions about the relevance and accessibility of guidelines, their strengths and weaknesses, as well as concrete requirements articulated by interviewees. In addition, eight selected guidelines are shortly presented and assessed in terms of their transfer potential. Chapter 3.2 looks at fact sheets for adaptation measures and elaborates central aspects for a practicable content structure and ultimately results in a proposed template fact sheet for climate adaptation measure. Chapter 4 deals with very concrete municipal experiences regarding the transfer of seven selected instruments and measures and offers numerous empirical findings from municipalities, based on the municipal survey, various interviews and the experiences drawn from the project work. The following seven tools and measures were selected to look at a broad range of urban climate adaptation activities: 1) climate function maps (urban climate maps), 2) heavy rainfall hazard maps, 3) climate adaptation checklists in urban land use planning, 4) prohibition of gravel gardens in development plans, 5) facade greening, 6) climate-adapted design of green and open spaces, and 7) recommendations for care facilities to deal with heat and heavy rain. For each of these instruments or measures at the municipality level the purpose or goal, its dissemination and manifestations, its implementation through practical examples, its supporting and inhibiting factors as well as existing experiences with and evidence of transfer are presented. Chapter 5 concludes this report by addressing key transfer barriers and making formulating recommendations for different political levels. These recommendations for improving the transfer of climate adaptation-related instruments, strategies and measures include: 1) improving the exchange between different cities, 2) improving the accessibility of knowledge and experience, 3) creating networking structures within cities and 4) closing existing knowledge gaps. The authors of this report hope to contribute to a better understanding of the learning and transfer processes and to the improvement of the transfer of municipal climate adaptation activities through the manifold aspects of this study.
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Urban stormwater management manual for Malaysia (Manual Saliran Mesra Alam Malaysia, MSMA) (1st Ed.). Department of Irrigation and Drainage Malaysia
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Green walls: Utilizing & promoting green infrastructure to control stormwater in Mobile, Alabama. Emerging Issues along Urban/Rural Interfaces III: Linking Science and Society
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