Green roof for sustainable urban flash flood control via cost benefit.pdf

Urban Forestry & Urban Greening 57 (2021) 126876
Available online 4 November 2020
1618-8667/© 2020 Elsevier GmbH. All rights reserved.
Green roof for sustainable urban flash flood control via cost benefit
approach for local authority
Shazmin Shareena Ab. Azis *, Nur Amira Aina Zulkifli
Real Estate, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
A R T I C L E I N F O
Handling editor: Wendy Chen
Keywords:
Flash flood
Cost
Green roof
Saving
Urban
Local authority
A B S T R A C T
People migration to urban region have created dense opaque urban landscape which generates high volume of
storm water runoff and frequent flash flood episodes. This has caused tremendous damages and loss to the nation.
Local authority has to spend greatly to repair damages caused by this disaster. Studies have proven that green
roof acts as an efficient green infrastructure to control storm water runoff and hindrance flash flood occurrences.
However, the worth of implementing green roof for the local authority remains unrevealed. This study prompts
to assess the economic worth of implementing green roof for the local authority using costs benefits analysis.
Overall, extensive green roof provides better cost benefits ratio than intensive green roof whereby the benefit is
1.2–3.5 times larger than green roof cost. Roof slope contributes the highest benefit ratio for intensive green roof
at 2 times higher than minimum cost. Meanwhile, vegetation provides the largest benefit ratio for extensive
green roof at 4.2 times larger than minimum cost. Green roof has proven to be worthy for local authority from the
economic and environment aspects. This is significant in creating a new pathway to encourage sustainable
practice among local authority thus serve national sustainable development agenda.
1. Introduction
Global changes counting economic development, population growth,
and people migration from rural to urban region have created new urban
landscape of densely walled buildings. This is a physical evidence of the
mismatch between land supply and rapid population growth in the
urban area. This situation has created a new non-porous surface also
known as concrete jungle. This is a catastrophic problem for countries
receiving large and continuous annual rainfall. An impervious surface
generates high volume of storm water runoff and flow rates which
devastated the conventional drainage systems and cause flash flood
episodes (Gaitan, et al., 2016; Yao et al., 2016). Flash floods resulted
from relatively short and intense bursts of rainfall where urban drainage
networks do not have the capacity to convey the excess rainwater
(Suparta et al., 2014). The interim flood reports by the Department of
Irrigation and Drainage Malaysia 2012–2016 have shown that the major
cause of flash flood events in Malaysia are low water infiltration
capacity.
According to Mohit and Sellu (2013), flood disaster affect an average
of 4.9 million people worldwide annually. In fact, flooding is a natural
disaster representing 50 percent of all types of disasters which causes
deaths globally (Diaz, 2004; FitzGerald et al., 2010). It was reported to
cause an average of annual property damage reaching up to US$100
million (Pradhan, 2009). Malaysia, like most tropical countries, suffers it
shares of flash floods which have paralyses communities and caused
extensive destruction (Zaharah et al., 2013). Flash flood causes rela
tively high magnitudes of damages either directly or indirectly. Direct
damages include infrastructure and asset repair, while, indirect damages
include traffic delays, psychology effect, and ecological damage
(Petersen, 2001). Individual and the government have to bear the eco
nomic burden of undertaking the flooding consequences (Laura et al.,
2018). Flash flood disaster has costs local authority a large amount of
money to repair damages on public asset and infrastructure registered
under local authority including street lighting, signage, roads, drainage,
parks, public recreation areas, public cemetery, public markets, bus
stops, public halls and stadiums.
As a response to these catastrophic urban phenomena, many coun
tries have adopted green infrastructure for urban landscape solution
which innovatively designed to restore the environmental, ecological
damage, and urban storm water management including New York City
(New York City Mayor’s Office 2010). It is comprehensible that the
scarcity of land in the urban area is the reason for the lack of urban
* Corresponding author.
E-mail addresses: shazmin@utm.my (S.S.Ab. Azis), naaina3@live.utm.my (N.A.A. Zulkifli).
Contents lists available at ScienceDirect
Urban Forestry & Urban Greening
journal homepage: www.elsevier.com/locate/ufug
https://doi.org/10.1016/j.ufug.2020.126876
Received 29 June 2020; Received in revised form 4 October 2020; Accepted 8 October 2020

2
greenery area. However, with the present building innovation and
technological advancement, green roof is becoming a promising solution
to this issue. Green roof consists of several layer systems namely
waterproofing membrane, growing medium, vegetation layer, root
barrier layer, drainage layer and irrigation system (Sadineni et al., 2011,
Shazmin et al., 2019). Plants are very important in preventing flood
disaster as their roots are naturally function to soak water. Moreover,
green roof is an attractive strategy for re-introducing pervious surfaces
within dense urban environments where rooftops are a high fraction of
the impervious land area.
According to Virginia et al. (2012), conventional rooftops can
constitute up to 40–50 % of the impervious urban area. It was reported
that between 62 % and 90 % of rainfall becomes runoff from conven
tional rooftops. This is likely to be higher for tiled and higher degrees of
roof slope roofs (Voyde et al., 2010; Razzaghmanesh and Beecham,
2014a). Henceforth, the integration of green roof with building rooftop
is able to control storm water runoff through lowering and delaying the
peak of water runoff process where it will detain a certain volume of
water (Bengtsson et al., 2005). The retained water will then either
evaporate or be transpired by plants which dries out the substrate and
regenerates retention capacity before the next rainfall event (Berretta
et al., 2014; Poë et al., 2015). It is the evaporated and transpired water
that explains the observed runoff volume reduction from green roofs
(Berndtsson, 2010).
Many researches have proven that the efficiency of green roof for
storm water runoff reduction at up to 90 % depending on the type of
green roof. However, even with this outstanding efficiency, the eco
nomic worth of implementing green roof with the local authority as a
stakeholder remains unravel. Discovering the economic worth in
implementing green roof is noteworthy to encourage the local authority
which representing the government of Malaysia to participate in pro
moting green building growth and hindrance the ecological damages
caused by flash flood disaster. Therefore, this study prompts to reveal
the worth of implementing green roof for the local authority using costs
benefits analysis. Cost benefit has been acknowledged as an approach to
assess the advantages and disadvantages of potential actions and an
unambiguous part of the decision making process. Many researches
were conducted related to the cost-benefits of green roof (Carter and
Keeler, 2008; Bianchini and Hewage, 2012; Niu et al., 2010; Sproul
et al., 2014; Blackhurst et al., 2010). However, none of these studies
have comprehended the cost and benefit of green roof in reducing storm
water runoff with the local authority as a stakeholder.
This study was conducted using mixed method approaches to
determine the attributes of green roof performance in managing urban
storm water runoff. This was conducted using thorough literature re
views on the percentages of green roof efficiency under comparable
average rainfall in Malaysia and questionnaire distribution for data
validation among expert. The collected data were analysed using fre
quency analysis. Then, this study developed a green roof economic
performance model in managing urban storm water runoff for the local
authority using benefit transfer approach. The model was developed
using post flash flood damages cost collected from flash flood experi
enced local authorities. The developed green roof model was evaluated
using the cost benefit analysis which includes the actual costing of green
roof data. This paper started off with green roof physical configuration
and efficiency in reducing storm water runoff, methodology, findings,
discussions, and conclusions. This study is significant in creating a new
pathway to encourage sustainable practice among the local authority,
thus, serve the nation sustainable development agenda.
2. Green roof efficiency for urban storm water runoff reduction
Green roof comprises of five major components from the bottom to
the top, including water proofing membrane, anti-root sheet, a drainage
layer, a filter layer, substrate, and vegetation on the top of the structure.
There are two types of green roof setups which are intensive roof and
extensive roof. Extensive green roof typically has thin media and
drought tolerant vegetation (Berndtsson, 2010; Carter and Fowler,
2008; Getter and Rowe, 2006). An extensive green roof is constructed
with a substrate that has a depth of less than 150 mm (Wen et al., 2019;
Renato and Sara, 2016; DeNardo et al., 2005; Mentens et al., 2006;
Moran et al., 2003). This type of green roof can be installed on sloped
roofs can be as high as 45 degrees. It does not require a construction
process that is technically difficult (Sajedeh et al., 2015). The main
advantage of extensive roofing systems is that often they are less
expensive. This roof is planted with smaller plants which in the final
stage is expected to provide full coverage of the vegetated roof. Sedum
species usually make up the major part of the vegetation.
Meanwhile, an intensive green roof is a roof garden designated with
a substrate layer with a depth of more than 150 mm (Sajedah et al.,
2015; Krupka, 1992; Kolb and Schwarz, 1999; Kosareo and Ries, 2007;
Mentens et al., 2007). Intensive green roofs have thicker growing media
and may include trees, shrubs, grasses, and perennial herbs (Berndtsson,
2010; Carter and Fowler, 2008; Getter and Rowe, 2006). Typically, this
type of green roof is installed when the slope is less than 10 degree
(Mentens et al., 2003; Sajedah et al., 2015; Krupka, 1992; Kolb and
Schwarz, 1999). This type of green roof can support a greater diversity of
plant life, but it requires additional structural reinforcement. The main
advantage of an intensive roofing system is the creation of a natural
environment with improved biodiversity and can be used for recrea
tional purposes.
There are two main factors which influence the green roof water
retention capacity and runoff volume, including green roof character
istics and weather conditions (Czemiel Berndtsson, 2010). Overall,
green roof is able to reduce storm water runoff approximately 20%–90%
depending on the type of green roof. The most imperative green roof
characteristics contributed in reducing storm water runoff are the sub
strates depth, the types of vegetation, and the roof slope (Wen et al.,
2019; Renato and Sara, 2016; Sajedeh et al., 2015; Isaac et al., 2018;
Astrid and Bruce, 2014; Shuai et al., 2019; VanWoert et al., 2005; Getter
et al., 2007).
Numerous studies have been conducted regarding the performance
of substrate depth for water runoff retention purposes. The latest green
roof study was conducted by Wen et al. (2019) at Gansu province, China.
The experiment was conducted on extensive green roof with 150 mm of
substrate depth. The findings have indicated that the substrate depth
contributes to 26.2 % of rainwater retention. Another study was con
ducted by Renato and Sara (2016) whereby the simulation results have
shown that at 50 mm and 100 mm of substrate depth, extensive green
roof is able to reduce storm water runoff at 26%–27% respectively.
Menten et al. (2006) proved that 27%–81% of water retention is effec
tive at 100 mm of substrate depth. In addition, Razzaghmanesh and
Beecham (2014a) has constructed a scale model of extensive green roof
at Adelaide, University of South Australia. The findings have proven that
at 100 mm of substrate depth, green roof is able to reduce 66%–81% of
storm water runoff. Another study has been conducted on extensive
green roof that constructed on a new large retail store in Portland and it
was found that at 125 mm and 75 mm of substrate depth, water can be
retained at 32.9 % and 23.2 % respectively.
Intensive green roof with deep substrate is able to provide 60 % of
water retention (Viola et al., 2017). A simulation study on intensive
green roof was conducted by Renato and Sara (2016). The simulation
results have shown that at 200 mm, 400 mm, 800 mm, and 1600 mm of
substrate depth, intensive green roof is able to reduce storm water runoff
at 29 %, 33 %, 40 %, and 54 % respectively. Another study was con
ducted by Razzaghmanesh and Beecham (2014a) indicated that at 300
mm of substrate depth, the water retention performance is at 85%–92%.
Speak et al. (2013) reported the water retention capacity for substrate
depth at 170 mm is 68 %. Mentens et al. (2006) has proved in his study
that intensive green roof with 155 mm substrate depth contributes to
65%–85% of water retention performance. Overall, the substrate depth
between 50 mm and 150 mm can effectively reduce water runoff at
S.S.Ab. Azis and N.A.A. Zulkifli

3
approximately 23 %–81 % and substrate depth between 155 mm and
1600 mm can effectively reduce water runoff at approximately 29 %–92
%. Maximum percentage of performance for intensive green roof is 855
and extensive green roof at 51 %. These results have proven that the
deeper the substrate, the higher the water retention performance.
Table 1 below tabulates comparison between intensive and extensive
green roof performance based on substrate depth.
Figs. 1 and 2 below illustrate the performance of intensive and
extensive green roof based on substrate depth in reducing urban storm
water runoff.
Limited studies were conducted on the efficiency of roof slope degree
in reducing urban strom water runoff. According to Getter et al. (2007),
extensive green roof slope at 25 degrees is able to retain water at 75 %.
Meanwhile at 2 degree of roof slope, it is able to produce larger water
retention at 85 %. A recent study by Wen et al. (2019) indicated that at
12 degrees of roof slope, water can be retained at 26 % and at 2 degree of
roof slope yielded even higher water retention at 28 %. Overall, higher
degree of roof slope could reduce green roof performance in reducing
storm water runoff. Roof slope could affect the efficiency of intensive
green roof in reducing storm water runoff. According to VanWoert et al.
(2005), intensive green roof slope at 6.5 degree is able to retain water at
66 %. Meanwhile at 2 degree of roof slope, it is able to produce larger
water retention at 87 %. Overall, higher degree of roof slope reduce the
performance of intensive green roof in reducing storm water runoff.
Fig. 3.0 below illustrates the performance of intensive and extensive
green roof in reducing urban storm water runoff based on roof slope
attribute. Table 2 below tabulates comparison between intensive and
extensive green roof performance based on type of vegetation.
Extensive green roof is usually planted with smaller plants which in
the final stage is expected to provide full coverage of the vegetated roof
(Czemiel Berndtsson, 2010). Vegetable is one type of vegetation that was
used in green roof study by leigh et al. (2015). According to this study,
vegetable refer to rooftop food gardening which includes tomatoes,
green beans, cucumbers, peppers, chives and basil. There are several
types of vegetation for extensive green roof including sedum, vegetable,
mosses, and centipede grass. Overall, these plantations are able to pro
vide 30%–89% reduction of storm water runoff. According to a study by
Leigh et al. (2015), the most effective type of plant for extensive green
roof that reduces a large amount of water runoff is Sedum plant. The size
and structure of plants significantly influenced the amount of water
runoff. Plant species with taller height, larger diameter, and larger shoot
and root are more effective in reducing water runoff than plant species
with shorter height, smaller diameter, and smaller shoot and root
biomass (Nagase and Dunnett, 2012). An experiment by Konstantinos
et al. (2017) was conducted on intensive green roof based on two types
of plantation; Origanum plant and Sedum plant. Origanum is a tall
height plant, meanwhile, Sedum is a shorter height plants. It was found
that Origanum (tall plant) was able to reduce higher storm water runoff
than sedum (short plant). In sum, Origanum and sedum plants are able
to reduce storm water runoff at 79 % and 76 % respectively. Table 3
below tabulates comparison between intensive and extensive green roof
performance based on type of vegetation.
However, the efficiency of green roof in reducing urban storm water
runoff also relies on other significant green roof attributes including
substrate depth and roof slope as proven in many studies. Deeper sub
strate and lower degree of roof slope have proven to increase percent
ages of green roof efficiency in urban storm water runoff reduction.
Therefore, vegetation that grow in these provided physical environment
may contributes to more effective urban storm water runoff reduction.
The summary of extensive and intensive green roof performance is
tabulated in Table 4 below.
Figs. 4.0 and 5 .0 below illustrates the performance of intensive and
extensive green roof in reducing urban storm water runoff based on type
of vegetation.
3. Methodology
This study adopted a mixed method approach combining both
qualitative and quantitative analyses in several stages using several
sources of data and analysis techniques. The mixed method approach for
data collection and data analysis were used to build this study’s breadth
of outcomes.
3.1. Study area
This study were conducted at two major local authorities located in
the urban area in Malaysia; Kuala Lumpur City Hall (DBKL) and Johor
Bahru City Council (MBJB). These two local authorities administer main
cities within the urban area in Malaysia which are Kuala Lumpur and
Johor Bahru. According to Nasiri et al. (2019), the city center has the
highest probability of flash flood occurrences. These areas were selected
as there are several flash flood prone areas within these jurisdiction
areas. These areas were reported by the Department of Irrigation and
Drainage Malaysia 2012–2016 as flash flood prone areas. The total flash
flood prone areas within DBKL and MBJB jurisdiction areas are 21.92
and 9.33 km square respectively. Figs. 6.0 and 7 .0 below capture flash
flood prone are within study areas.
3.2. Data collection and sampling
The first objective of this study is to determine the attributes of green
roof performance in reducing urban storm water runoff. The aim is to
validate the attributes of extensive and intensive green roof derived
from the literature among green roof experts. There are two stages in
achieving this objective. Stage one involves the qualitative data derived
from rigorous literature reviews. The collected data were analysed using
systematic review. Systematic review is defined by Mark and Helen
(2006) as a review that strives to comprehensively identify, appraise,
and synthesize all the relevant studies on a given topic. It is commonly
used in social science research with the aims to provide an objective,
comprehensive summary of the best evidence from literatures.
Stage two involves validation exercise on a list of attributes for
extensive and intensive green roof using questionnaire. A questionnaire
was developed which consisted of two sections; section A and section B.
Section A covered the demographic profile and section B covered the
validation of intensive and extensive green roof characteristics. Part B
consisted of eight questions which measured the level of agreement on
intensive and extensive green roof characteristics. This study used a 5-
Likert scale, with 1 being strongly disagree and 5 being strongly agree.
This study adopted purposive or expert sampling which is commonly
used when experts in the subject of interest are selected based on the
expert experiences and knowledge inclinations (Creswell, 2012). About
30 green roof experts made of professional Landscape Architects
involved in this survey. According to Bernard (2002), there is no abso
lute number on how many respondent should make up a purposive
sample, as long as the needed information is obtained. Seidler (1974)
studied different sample sizes of informants selected purposively and
Table 1
Performance of Intensive and Extensive green roof based on substrate depth.
Intensive green roof Extensive green roof
Substrate
depth
(mm)
Percentages of storm
water runoff reduction
(%)
Substrate
depth (mm)
(%)
155 65 % 50 26 %
170 66 % 75 23 %
200 29 % 80 34 %
300 85 % 100 27 %
400 33 % 102 51 %
800 40 % 125 33 %
1600 54 % 150 45 %
Max
Intensive
85 % Max
Extensive
51 %

4
found that at least five respondents were needed for the data to be
reliable. Further, purposive sampling can be used with a number of
techniques in data gathering including questionnaire survey among
experts (Brown, 2006; Robbins et al., 1969). “According to Bernard
(2002), there is no absolute number on how many respondent should
make up a purposive sample, as long as the needed information is
Fig. 1. Performance of Intensive green roof based on substrate depth.
Fig. 2. Performance of Extensive green roof based on substrate depth.
Fig. 3. Performance of Intensive and Extensive green roof based on roof slope.

5
obtained. Seidler (1974) studied different sample sizes of informants
selected purposively and found that at least five respondents were
needed for the data to be reliable. Further, purposive sampling can be
used with a number of techniques in data gathering including ques
tionnaire survey among experts (Brown, 2006; Robbins et al., 1969).”
However, to further validate the appropriateness sample adopted in
this study, this study included several latest similar studies on green roof
which adopted a survey technique among green roof experts within the
same range used in this study. A study by Johannes et al. (2020) on
green roofs in Barcelona has included 31 green roof experts (i.e. aca
demics, municipal officials, NGO representatives, and private sector
green roof experts) in the study. Another study on green roof by Bru
dermann and Sangkakool (2017) has included 15 green roof experts in
their survey to identify and assess the main decision factors that are
relevant for the diffusion of green roof technology in Austria. The ex
perts were from diverse fields including architects, planners, and aca
demics. A study by Salvador Guzmán-Sánchez et al. (2018) has included
23 green roof experts in their survey on the assessment of the contri
butions of different flat roof types to achieving sustainable development.
These studies have adopted between 15–31 green roof experts in their
studies which makes 30 samples of green roof experts adopted in this
study as reasonable and acceptable.
3.3. Data analysis
The returned questionnaires were analysed by determining the reli
ability of the collected data. Accordingly, the questionnaires were
tabulated through SPSS software for screening, refinement, and reli
ability verification purposes. Crocker and Algina (1986) outlined that
reliability determines test reproducibility, by which the scores remained
consistent over time for the same forms or alternate forms. Therefore, to
ensure the reliability of the collected data, this study has performed a
reliability test using the Cronbach’s coefficient (Cronbach, 1951). The
Cronbach’s coefficient (α) is used to measure data’s internal consistency
(Hatcher, 1994).
As for the second objective, an economic green roof performance
model was developed using cost saving due to the integration of green
roof in reducing urban storm water runoff using Benefit Transfer
approach (BTA). The BTA is adopted mostly for valuation of ecosystem
services. Benefit transfer is a process by which the values that have been
generated in one context known as the ‘study site’ are applied to another
context known as the ‘policy site’ for which the value is required
(Department for Environment, Food and Rural Affairs, 2007). The
manual published by the Department for Environment, Food and Rural
Affairs has clearly stated that the function of benefit transfer approach is
the use of systematic review, which takes the results from a number of
studies and analyses them in such a way that the variations in the result
found in those studies can be explained. To calculate cost saving using
the BTA, several data are needed which include the percentages of urban
storm water reduction conveyed by extensive and intensive green roof
(derived from empirical findings of previous studies), and the average
cost rendered by the local authority due to asset damages and cleaning
process post flash flood disaster. To convert into monetary value, the
collected percentages are multiplied with total cost that local authority
has to bear due to flash flood damages. The performance model calcu
lates cost saving conveys by the substrate depth, the types of vegetation,
Table 2
Performance of Intensive and Extensive green roof based on roof slope.
Roof slope
degree
(%)
Roof slope
degree
(%)
2 87 % 2 85 %
6.5 66 % 25 75 %
Max
Intensive
87 % Max
Extensive
85 %
Table 3
Performance of Intensive and Extensive green roof based on type of vegetation.
Type of
vegetation
Percentages
of storm
water runoff
reduction
(%)
Type of
vegetation
Percentages of storm water
runoff reduction (%)
Sedum 77 % Sedum 66 %
Origanum 79 % Origanum 71 %
Vegetable 35 %
Mosses 46 %
Centipede
grass
47 %
Max Intensive 79 % Max Extensive 71 %
Table 4
Overall summary on Extensive and Intensive green roof performance in storm
water runoff reduction.
Green roof
Attributes
Percentages of storm water runoff
reduction (%) Authors
Intensive Extensive
Substrate
depth
29 % (200
mm)
33 % (400
mm)
40 % (800
mm)
54 %(1600
mm)
26 % (50 mm)
27 % (100 mm)
Renato and Sara (2016)
65 % - 85 %
(155 mm)
27 % – 81 % (100
mm)
Mentens et al. (2006)
85 %–92 %
(300 mm)
66 % - 81 % (100
mm)
Razzaghmanesh et al.,
(2014b)
85 % 60 % Sajedeh et al., (2015)
65.7 % (170
mm)
– Speak et al. (2013)
60 % 53 % Viola et al. (2017)
–
45 % - 60 % (150
mm)
DeNardo et al.(2005);
Mentens et al. (2006);
Moran et al.(2003)
–
23.2 % (75 mm)
32.9 % (125 mm)
Isaac et al. (2018)
– 51.4 % (102 mm)
Gregoire and Clausen
(2011)
– 34 % (80 mm) Stovin (2010)
– 64 % (75 mm) Hathaway et al. (2008)
– 77.7 % (114 mm) Astrid and Bruce (2014)
– 72.5 % (80 mm) Chai et al. (2017)
Types of
vegetation
77 % (sedum)
79 %
(origanum)
70 % (sedum)
71 % (origanum)
Konstantinos et al.(2017)
– 66 % (sedum) Rowe et al. (2003)
–
47.4 %
(centipedegrass)
Shuai et al. (2019)
–
89 % (sedum)
35 % - 88 %
(Vegetable)
Leigh et al.(2015)
–
46 % - 60 %
(Mosses)
Malcolm et al. (2010)
Roof slope
87 % (2
degree)
65.9 % (6.5
degree)
– VanWoert et al. (2005)
–
85.2 % (2 degree)
75.3 % (25
degree)
Getter et al. (2007)
–
28 % (2 degree)
25.8 % (12
degree)
Wen et al. (2019)

6
and the roof slope degree.
The third objective was analysed using the cost benefit analysis
(CBA) between green roof cost and monetary benefits received by the
local authority due to flash flood reduction. The monetary value rep
resents “benefit” of green roof which is the cost reduction that local
authority will get with green roof implementation. Meanwhile the cost
to implement green roof is the “cost” of green roof. The outcome of this
objective is in ratio form between cost and benefit of green roof. Fig. 8.0
below illustrates the theoretical framework of this study (Fig. 9).
4. Results and discussions
4.1. Profile of respondents
A total of 30 green roof experts’ respondents in this study which
made of 60 % female and 40 % male. Half of the respondents are aged
between 35–45 years old. Majority of the respondents are Doctor of
Philosophy holders (80 %) and another 20 % are master degree holders.
Half of the respondents in this study have at least more than 10 years of
experiences in landscape architect profession. More than half of the
respondents are in the decision making position (60 %) and some of
them are in management position (40 %). All respondents have agreed
that green roof is a very effective strategies in mitigating urban flash
food phenomenon by reducing storm water runoff and increasing water
retention factors.
4.2. Intensive and Extensive green roof attributes validation
The results have shown that the maximum and minimum mean value
for green roof characteristics are 5.00 and 1.90 respectively. This study
rescales the level of agreement based on the maximum and minimum
mean value from the results. The rescaling of the green roof character
istic based on the mean value of the findings, has been provided in
Table 5. Therefore, the minimum mean value for strongly agree and agree
categories of green roof characteristics are 4.48 and 3.85 respectively.
Therefore, soil thickness, roof slope, and types of vegetation are among
strongly agree and agree characteristics that differentiate intensive and
extensive green roof as tabulated in Table 6. This indicated that these are
the most important characteristics in distinguishing between intensive
and extensive green roof. The results are aligned with the findings from
literature reviews.
The respondents have further validated the characteristic of soil
thickness, roof slope, and types of vegetation for extensive and intensive
green roof. The respondents have validated that the appropriate soil
thickness for extensive green roof is between 1 cm and 15 cm. The soil
thickness of more than 15 cm is not considered as a characteristic of
extensive green roof. These findings are aligned with the literatures
reviews. Meanwhile, the types of vegetation that are appropriate for
extensive green roof possess the characteristics of shallow rooting plant,
drought-resistant plants, small plant, and succulent plants. According to
the expert, the maximum roof slope for extensive green roof is 15 de
grees. However, according to the literature, the roof slope of extensive
Fig. 4. Performance of Intensive green roof based on type of vegetation.
Fig. 5. Performance of Extensive green roof based on type of vegetation.

7
green roof can be up to 45 degrees. The experts have validated that the
appropriate soil thickness for intensive green roof is between 16 cm and
more than 40 cm. The soil thickness less than 15 cm is not considered as
a characteristic of intensive green roof. These findings are aligned with
the literatures reviews. Meanwhile, the types of vegetation that are
appropriate for intensive green roof possess the characteristics of deep
rooting plant, drought-resistant plants, woody plant, large tree, flow
ering plant, and succulent plant. According to the expert, the maximum
roof slope for intensive green roof is 10 degree. The results are also
aligned with the past literature. The details are tabulated in Table 7
Fig. 6. Flash flood prone areas in Johor Bahru.
Fig. 7. Flash flood prone areas in Kuala Lumpur.

8
below.
4.3. Green roof economic performance model for local authority
4.3.1. Green roof characteristic-based performance in storm water runoff
reduction
The green roof performance model was developed based on calcu
lation of monetary benefit conveyed by green roof due to the reduction
of urban storm water runoff. Urban storm water runoff reduction
activity has proven to avoid the occurrences of flash flood in the urban
area. Therefore, to develop the performance model, this study uses the
percentages of intensive and extensive green roof performance in
reducing urban storm water runoff as tabulated in Table 1 and the cost
incurred by the local authority in managing post flash flood disaster. The
amount of cost reduction due to the performance of intensive and
extensive green was used as the basis for green roof performance model
development. Overall, the average performance of intensive green roof
is superior to extensive green roof. The results have shown that on
Fig. 8. Theoretical framework.
Fig. 9. Standardise percentage of efficiency for substrate depth, type of vegetation, and roof slope.

9
average, intensive and extensive green roof are able to reduce storm
water runoff at 84 % and 69 % respectively.
The performance standardize percentage is important for the per
formance model development. The findings showed that the substrate
depth, the types of vegetation, and the roof slope contribute to 34 %, 31
%, and 35 % of the overall intensive green roof performance in reducing
storm water runoff. Among these three characteristics, roof slope con
tributes to the highest performance in reducing storm water runoff at 35
% and the characteristic that contributes to the least reduction of storm
water runoff is the types of vegetation at 31 %. As for extensive green
roof, the findings showed that the substrate depth, the types of vegeta
tion, and the roof slope contribute to 25 %, 34 %, and 41 % of the overall
green roof performance. Roof slope contributes to the highest perfor
mance in reducing storm water runoff at 41 % and substrate depth
contributes the least at 25 %. Table 8 below summarizes the average and
standardized performance of intensive and extensive green roof in
reducing storm water runoff.
4.3.2. Cost incurred by local authority in managing post flash flood disaster
Several properties have the tendency to be damaged due to flood
disaster which can be categorized under fixed asset, infrastructure, and
landscaping. Public hall, public market, and public stall are categorized
under fixed assets that were affected by flood events. There are several
items listed under infrastructure that were affected by flash flood
including road, drainage, streetlight, traffic light, flyover, and bus stop.
According to the survey among selected local authorities, there are
several types of damages that commonly associated with post flood
events such as small cracked for outer building wall, paint peeling,
potholes, crack road, clogged and cracked drainage, and street facilities
malfunctions and broken. Furthermore, cleaning services are considered
as highly essential exercises that need to be carried out after flood
events.
The results have shown that DBKL has rendered cost at 85 % higher
than MBJB due to flood disaster events. DBKL has to spend almost MYR
52,000,000 to repair all the damages. Meanwhile, MBJB has to spend
around MYR 28,800,000. Overall, the findings indicated that the dam
ages on infrastructure properties constituted the largest portion of the
total cost at 73%–78%. Meanwhile, the damages on fixed assets placed
as the second largest portion of the total cost at around 21 % to 10 %.
Cleaning services cost which is required after post flood disaster made
up a small proportion around 2%–9%. It was found that the damages on
landscape properties contributes to the least cost at around 3%–4%.
Table 9 below shows the cost borne by both DBKL and MBJB due to flash
flood events.
4.3.3. Model development
This study has developed an economic green roof performance model
in managing flash flood within the local authority jurisdiction areas.
This model assesses the monetary performance of green roof according
to green roof attributes which include the substrate depth, the types of
vegetation, and the roof slope in managing flash flood. Green roof eco
nomic performance model calculates the monetary benefits received by
the local authority due to the implementation of green roof in managing
flash flood events. This model estimates post-flash flood disaster cost
that can be saved by the local authority due to the implementation of
green roof within jurisdiction areas. The mathematical model for mon
etary saving of post flash flood cost reduction calculation based on green
roof attributes performance is shown as below:
Economic performance of Substrate depth;
GR monetary benefits Substrate depth (GRbsd)=[AVEGRe x (FAdc + IFdc +
LSdc + CSdc)] x SDe
Economic performance of type of vegetation;
GR monetary benefits Vegetation (GRbv)=[AVEGRe x (FAdc + IFdc + LSdc
+ CSdc)] x Ve
Economic performance of roof slope;
GR monetary benefits Roof slope (GRbrs)=[AVEGRe x (FAdc + IFdc + LSdc
+ CSdc)] x RSe
Where,AVEGRe Average green roof efficiency (%) FAdc Fixed asset
damages cost (RM)IFdc Infrastructure cost (RM)LSdc Landscape cost (RM)
CSdc Cleaning services cost (RM)SDe Substrate depth efficiency (%)Ve
Type of Vegetation efficiency (%)RSe Roof slope efficiency (%)
Table 5
The range of scale on green roof characteristic agreement based
on mean value.
Category of scale Range of mean value
Strongly disagree 1.90 – 2.58
Disagree 2.59 – 3.21
Neutral 3.22 – 3.84
Agree 3.85 – 4.47
Strongly agree 4.48 – 5.00
Table 6
Structural differences between intensive and extensive green
roof.
Green roof attributes Mean value
Soil thickness 5.00
Roof slope 4.80
Type of vegetation 4.10
Vegetation coverage 3.50
Soil type 3.40
Table 7
Validated extensive and intensive green roof characteristics.
Green roof
attributes
Extensive Green
Roof Characteristics
Mean
value
Intensive Green
Roof Characteristics
Mean
value
Soil
thickness
1cm to 5cm 4.10 10 cm to 15 cm 2.10
6 cm to 10cm 4.10 16 cm to 20cm 3.90
11 cm to 15 cm 4.10 21 cm to 30cm 3.85
15 cm to 20cm 3.30 30 cm to 40cm 3.90
More than 20cm 2.80 More than 40cm 3.85
Types of
vegetation
Shallow rooting
plant
4.60 Deep rooting plant 4.30
Drought-resistant
plants
4.50
Drought-resistant
plants
3.80
Small plant 4.40 Large tree 4.00
Flowering plant 3.50 Flowering plant 4.00
Succulent plant 4.30 Succulent plant 3.90
Maximum
roof slope
5 degree 3.60 5 degree 2.80
Table 8
Overall and standardized performance of green roof in reducing storm water
runoff.
Green roof
characteristics
Green roof Performance (%) Standardization of green roof
performance (%)
Intensive
green roof
Extensive
green roof
Intensive
green roof
Extensive
green roof
Substrate depth 85 % 51 % 34 % 25 %
Types of
vegetation
79 % 71 % 31 % 34 %
Roof slope 87 % 85 % 35 % 41 %

10
Table 10 below calculates the cost reduction contributes based on the
substrate depth, the types of vegetation, and the roof slope performance
using case studies data. It was estimated that the cost of post flash flood
per meter square for DBKL and MBJB are MYR 2405 and MYR 3093
respectively. Overall, intensive green roof provides higher cost saving
than extensive green roof at around 20 %. The implementation of
intensive green roof may provide cost saving at around MYR 2598 to
MYR 2020 per square meter. As for extensive green roof, cost saving
ranges between MYR 2134 to MYR 1,659. The results show that among
the three attributes, roof slope provides the utmost cost saving for both
green roofs at around MYR 680 to MYR 909 per meter square. The
findings also show that the types of vegetation contributes to the least
cost saving for intensive green roof between MYR 626 to MYR 806 per
meter square. Meanwhile for extensive green roof, it contributes to the
second largest cost saving. Substrate depth contributes the second
highest cost saving after roof slope for intensive green roof at MYR 687
to MYR 883 per meter square. Meanwhile for extensive green roof,
substrate depth contributes the least at MYR 415 to MYR 534 per meter
square. Henceforth, to gain higher cost saving, the local authority should
opt for intensive green roof implementation with a focus on lowering the
degree of roof slope. Therefore, to achieve highest cost saving, the de
gree of roof slope must be reduced to the minimum. This indicates that
green roof should be implemented on flat rooftop to gain highest storm
water reduction, thus, generating maximum saving for the local au
thority in managing post flash flood disaster (Tables 11 and 12).
4.4. Green roof cost benefit for local authority
4.4.1. Green roof costing
The costing data for intensive and extensive green roof were gath
ered to evaluate the value of implementing green roof as a strategy to
reduce flash flood occurrences for local authority. The costing are based
on estimated price quotations which provided in minimum and
maximum ranges as tabulated in Table 8. The costing data are based on
the three attributes of green roof (i.e. substrate depth, type of vegetation,
and roof slope). The cost of substrate depth includes the type and depth
of growing medium, the type of curbing, and the size of the project.
Meanwhile, the types of vegetation cost consist of plant type and size of
plant. The cost for the types of plants varies based on seed, plug, or pot
type of plant. The roof slope also contributes to the cost in terms of
equipment rental to move the materials to and on the roof, the size of the
project, the complexity of the design, and the planting techniques used.
It is noteworthy to highlight that flat roof costs lower than steep roof
slope. The average costing provided by both companies were averaged
to obtain the final cost of green roof.
The results showed that the cost range for intensive green roof are
between MYR 1500 and MYR 5900 per meter square. The findings
indicated that the types of vegetation covers the highest proportion of
cost at MYR 506 to MYR 3832 per square meter. It contributes to 34%–
66% of the total cost. Meanwhile, substrate depth constitutes the second
largest proportion of cost at MYR 549 to MYR 1313 per square meter. It
is made of 22%–37% of the total cost. Roof slope contributes to the least
portion of cost at around MYR 452 to MYR 678 per square meter where
it contributes to 12%–30% of the total intensive green roof cost. As for
extensive green roof, the results showed that the cost range are between
Table 9
Damages cost incurred by Kuala Lumpur City Hall (DBKL) and Johor Bahru City Council (MBJB).
Category of affected properties and services
Kuala Lumpur City Hall Johor Bahru City Council
Cost (MYR) Percentages
(%)
Cost (MYR) Percentages
(%)
Fixed Asset Public hall, public market, and public stall 11,251,200 21 % 2,791,360 10 %
Infrastructure Road, drainage, streetlight, traffic light, flyover, and bus stop 38,500,500 73 % 22,411,200 78 %
Landscape Landscape and decoration 2,030,000 4% 1,000,000 3%
Cleaning
services
Public hall, public market, public stall, road, drainage, streetlight, flyover, and bus stop,
landscape and decoration
937,530 2% 2,657,000 9%
Total Cost 52,719,230 100% 28,859,560 100%
Table 10
Cost saving based on performance of substrate depth, types of vegetation, and
roof slope.
DBKL
INTENSIVE GREEN ROOF EXTENSIVE GREEN ROOF
Efficiency
(%)
Cost saving
(MYR)
Efficiency
(%)
Cost saving
(MYR)
Overall
performance
84 % 2020 69 % 1659
Substrate depth 34 % 687 25 % 415
Types of
vegetation
31 % 626 34 % 564
Roof slope 35 % 707 41 % 680
MBJB
INTENSIVE GREEN ROOF EXTENSIVE GREEN ROOF
Efficiency
(%)
Cost saving
(MYR)
Efficiency
(%)
Cost saving
(MYR)
Overall
performance
84 % 2598 69 % 2134
Substrate depth 34 % 883 25 % 534
Types of
vegetation
31 % 806 34 % 726
Roof slope 35 % 909 41 % 875
Table 11
Intensive and extensive green roof cost.
Green roof attributes
Intensive green roof cost Extensive green roof cost
Minimum Maximum Minimum Maximum
Substrate depth
506 1023 269 344
592 1615 215 377
Average (MYR/sqm) 549 1313 247 355
Types of vegetation
474 3552 161 592
506 3832 172 624
Average (MYR/sqm) 506 3832 172 624
Roof slope
409 753 226 355
484 614 118 431
Average (MYR/sqm) 452 678 172 398
Total cost (MYR/sqm) 1500 5900 600 1400
Table 12
Cost benefit analysis (CBA) for intensive and extensive green roof for local
authority.
Green roof attributes
Cost
ratio
Benefit ratio
Minimum Maximum
Intensive green roof
(IGR)
Overall 1 1.7 0.3
Substrate depth 1 1.6 0.5
Types of
vegetation
1 1.6 0.2
Roof slope 1 2.0 1
Extensive green roof
(IGR)
Overall 1 3.5 1.2
Substrate depth 1 2.2 1.2
Types of
vegetation
1 4.2 0.9
Roof slope 1 1.9 1

11
RM 600 and RM 1400 per meter square. This cost is found slightly
different from previous study by Berardi (2016). Berardi (2016) has
conducted a study on extensive green roof in Toronto, Canada in 2016.
According to this study, in 2010, the cost of extensive green roof con
verted into Malaysian Ringgit was MYR 823. In 2016, the cost of
extensive green roof converted into Malaysian Ringgit was between
MYR 452 and MYR 1039. Therefore, the cost increment for extensive
green roof range from 5% to 7% annually. Factors that contributes to the
cost differences are time adjustment factor and locality of the study.
Similar to intensive green roof, the findings showed that the types of
vegetation comprise the highest cost proportion at MYR 172 to MYR 624
per square meter. It contributes to 30%–45% of the total cost. Mean
while, substrate depth constitutes the second largest proportion of cost
at MYR 247 to MYR 355 per square meter. It is made of 26%–41% of the
total cost. Roof slope contributes to the least portion of cost at around
MYR 172 to MYR 398 per square meter where it contributes to 29%–
30% of the total extensive green roof cost. This indicated that the type of
vegetation have a significant impact on the total of intensive and
extensive green roof cost followed by substrate depth and roof slope.
This is an interesting finding as vegetation contributes to the least
benefit compared to other attributes. However, the cost of vegetation
was found to be the highest amongst all attributes. Meanwhile, the roof
slope contributes to the highest benefit than other attributes, although,
cost of roof slope was found to be the lowest.
4.4.2. Green roof cost benefit ratio
Overall, the results have shown that the benefit ratio of extensive
green roof is better than intensive green roof. Table 9 shows that the cost
benefit for extensive green roof is 1:3.5 to 1.2. This designates that the
benefits of extensive green roof outweigh the minimum and maximum
cost of green roof. It shows that the benefits of extensive green roof in
providing cost saving for local authority is 1.2–3.5 times larger than the
maximum and minimum cost of extensive green roof respectively. The
integration of extensive green roof will provide the local authority cost
saving for post flash flood damages at 1.2–3.5 higher than any minimum
or maximum cost that they have to spend to integrate extensive green
roof with building. Among the three green roof attributes, roof slope
provides the greatest cost benefit ratio for intensive green roof at 1: 2 to
1. This indicates that roof slope provides benefit at 2 times higher than
the minimum cost itself. However, at maximum cost, the benefit and
cost are the same. Both substrate depth and vegetation provide similar
benefits at 1.6 times higher than the minimum cost of intensive green
roof. Meanwhile, vegetation provides the highest cost benefit ratio than
other green roof attributes of extensive green roof. The cost benefit for
vegetation is 1: 4.2 to 0.9 which shows that the types of vegetation
provide benefits at 4.2 times higher than the minimum cost to integrate
extensive green roof. However at maximum cost, the benefit will be
slightly lower than the cost at 0.9. Substrate depth provides the second
best cost benefit after vegetation for extensive green roof. Substrate
depth provides 1.2–2.2 times higher benefits than maximum and mini
mum cost of extensive green roof respectively.
Therefore, to increase the value of intensive green roof imple
mentation, local authorities should integrate intensive green roof on flat
roof top to reduce green roof costs and at the same time to achieve
higher benefits by increasing the efficiency of extensive green roof in
reducing storm water runoff. This study has proven that lower degree of
roof slope will increase the efficiency of green roof in reducing storm
water runoff. Meanwhile for extensive green roof, it is recommended to
select the appropriate types of vegetation to enlarge the efficiency of
extensive green roof in reducing storm water runoff for instance vege
tation with good water retention capacity. This is aligned with the study
by Czemiel Berndtsson (2010) which suggest that the evaporated and
transpired water explain the observed runoff volume reduction from
green roofs.
5. Conclusion
As a conclusion, green roof is an effective green infrastructure to
control urban flash flood occurrences. The implementation of green roof
has been proven to be effective from both environment and economic
aspects. In addition, intensive green roof performs better than extensive
green roof from the environmental aspect whereby it is highly efficient
in reducing urban storm water runoff than extensive green roof. How
ever, from the economic aspect, extensive green roof is more worthy for
green roof implementation with local authority as the stakeholder.
Moreover, the cost benefit of extensive green roof is better than intensive
green roof. Therefore, this study has proven that the implementation of
green roof is significant for the local authority from both environment
and economic aspects. The local authority is recommended to imple
ment intensive green roof for better efficiency in flash flood event con
trol. Although, from the economic view, extensive green roof is more
cost effective than intensive green roof. Henceforth, the outcome of this
study is highly significant in creating a new pathway to encourage
sustainable practice among the local authority. This effort will assist in
achieving the national sustainable development agenda.
CRediT authorship contribution statement
Shazmin Shareena Ab. Azis: Conceptualization, Methodology, Data
curation, Writing - original draft, Writing - review & editing. Nur Amira
Aina Zulkifli: Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
References
Bengtsson, L., Grahn, L., Olsson, J., 2005. Hydrological function of a thin extensive green
roof in southern Sweden. Nord. Hydrol. 36 (3), 259–268.
Berardi, Umberto, 2016. The outdoor microclimate benefits and energy saving resulting
from green roofs retrofits. Energy Build. 121 (2016), 217–229.
Bernard, H.R., 2002. Research Methods in Anthropology: Qualitative and Quantitative
Methods, 3rd edition. AltaMira Press, Walnut Creek, California.
Berndtsson, J.C., 2010. Green roof performance towards management of runoff water
quantity and quality: a review. Ecol. Eng. 36 (4), 351–360.
Berretta, C., Po€e, S., Stovin, V., 2014. Moisture content behavior in extensive green
roofs during dry periods: the influence of vegetation and substrate characteristics.
J. Hydrol. 511, 374e386.
Brown, K.M., 2006. Reconciling moral and legal collective entitlement: implications for
community-based land reform. Land Use Policy 2, 4.
Czemiel Berndtsson, J., 2010. Green roof performance towards management of runoff
water quantity and quality: a review. Ecol. Eng. 36 (4), 351–360.
DeNardo, J.C., Jarrett, A.R., Manbeck, H.B., Beattie, D.J., Berghage, R.D., 2005. Storm
water mitigation and surface temperature reduction by green roofs. Trans. Am. Soc.
Agric. Eng. 48 (4), 1491–1496.
Gaitan, S., van de Giesen, N.C., ten Veldhuis, J.A.E., 2016. Can urban pluvial flooding be
predicted by open spatial data and weather data? Environ. Model. Software 85,
156–171.
Getter, K.L., Rowe, D.B., Andresen, J.A., 2007. Quantifying the effect of slope on
extensive green roof storm water retention. Ecol. Eng. 31 (4), 225–231.
Gregoire, B.G., Clausen, J.C., 2011. Effect of a modular extensive green roof on storm
water runoff and water quality. Ecol. Eng. 37, 963–969.
Hathaway, A., Hunt, W.F., Jennings, G., 2008. A field study of green roof hydrologic and
water quality performance. Trans. Am. Soc. Agricult. Biol. Eng. 51 (1), 37–43.
Kosareo, L., Ries, R., 2007. Comparative environmental life cycle assessment of green
roofs. Build. Environ. 42, 2606–2613.
Petticrew, Mark, Roberts, Helen, 2006. Systematic Reviews in the Social Sciences: A
Practical Guide. Wiley Publication. ISBN: 9781405121101 |Online ISBN:
9780470754887 |DOI:10.1002/9780470754887.
Mentens, J., Raes, D., Hermy, M., 2003. Effect of orientation on the water balance of
green roofs. Greening Rooftops for Sustainable Communities Chicago, 2003. 363 71.
Mentens, J., Raes, D., Hermy, M., 2006. Green roofs as a tool for solving the rainwater
runoff problem in the urbanized 21st century? Landsc. Urban Plan. 77 (3), 217–226.
Moran, A., Hunt, B., Jennings, G., 2003. A North Carolina field study to evaluate green
roof runoff quality, runoff quantity, and plant growth (2003). ASAEPaper
032303Am. Soc. of Agric. Eng..

12
Nagase, A., Dunnett, N., 2012. Amount of water runoff from different vegetation types on
extensive green roofs: effects of plant species, diversity and plant structure. Landsc.
Urban Plan. 104 (3–4), 356–363.
Nasiri, H., Yusof, M.J.M., Ali, T.A.M., Hussein, M.K.B., 2019. District food vulnerability
index: urban decision making tool. Int. J. Environ. Sci. Technol. 16, 2249–2258.
Petersen, M.S., 2001. Impact of flash floods. In: Gruntfest, E., Handmer, J. (Eds.), Coping
with Flash Floods. Klumer Academic Publishers., Netherlands, pp. 11–13.
Razzaghmanesh, M., Beecham, S., 2014a. The hydrological behavior of extensive and
intensive green roofs in a dry climate. Sci. Total Environ. 499 (2014), 284–296.
Razzaghmanesh, M., Beecham, S., Kazemi, F., 2014b. The growth and survival of plants
in urban green roofs in a dry climate. Sci. Total Environ. 2014 (476–477), 288–297.
Robbins, M.C., Pollnac, R.B., 1969. Drinking patterns and acculturation in rural
Buganda. Am. Anthropol. 71, 276–285.
Rowe, D.B., Rugh, C.L., VanWoert, N., Monterusso, M.A., Russell, D.K., 2003. Green roof
slope, substrate depth, and vegetation influence runoff. In: Proc. of 1st North
American Green Roof Conference: Greening Rooftops for Sustainable Communities.
Chicago. 29–30 May 2003. The Cardinal Group, Toronto., pp. 354–362.
Sadineni, S., Madala, S., Boehm, R.F., 2011. Passive building energy savings: a review of
building envelope components. Renewable Sustainable Energy Rev. 15, 3617–3631.
Seidler, J., 1974. On using informants: a technique for collecting quantitative data and
controlling measurement error in organization analysis. Am. Sociol. Rev. 39,
816–831.
Speak, A.F., Rothwell, J.J., Lindley, S.J., Smith, C.L., 2013. Rainwater runoff retention on
an aged intensive green roof. Sci. Total Environ. 461, 28–38.
Stovin, V., 2010. The potential of green roofs to manage urban storm water. Water
Environ. 2010 (24), 192–199.
Suparta, W., Rahman, R., Singh, M.S.J., 2014. Monitoring the variability of perceptible
water vapor over the Klang Valley, Malaysia during flash flood. In: IOP Conf. Series:
Earth and Environmental Science, 20, 012057.
VanWoert, N., Rowe, B., Andresen, J., Rugh, C., Fernandez, T., Xiao, L., 2005. Green roof
storm water retention: effects of roof surface, slope, and media depth. J. Environ.
Qual. 2005 (34), 1036–1044.
Viola, F., Hellies, M., Deidd, R., 2017. Retention performance of green roofs in
representative climates worldwide. J. Hydrol. 553 (2017), 763–772.
Voyde, E., Fassman, E., Simcock, R., 2010. Hydrology of an extensive living roof under
sub-tropical climate conditions in Auckland, New Zealand. J. Hdrol. 2010 (394),
384–395.
Yao, L., Wei, W., Chen, L., 2016. How does imperviousness impact the urban rainfall
runoff process under various storm cases? Ecol. Indic. 60, 893–905.

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