Blue-green and blue roofs are increasingly promoted to adapt to climate change by providing multiple benefits. However, uncertainties about their design and how they differ from conventional green roofs hinder their implementation. This study investigates the potential of green, blue-green, and blue roofs to control urban stormwater and improve microclimate by monitoring their performance in Toronto, Ontario, Canada. Experimental setups were built and varied with the following design factors: substrate type and thickness, drainage layer thickness and orifice size. The results revealed that blue-green roofs with organic and FLL (blended according to the German Forschungsgesellschaft Landschaftsentiwicklung Landschaftsbau) substrates significantly improved detention compared to green roofs with similar substrates. The organic blue-green roof achieved maximum retention, but FLL blue-green roof did not have higher retention than FLL green roof. The blue roof with smaller orifices had comparable hydrologic performance to vegetated roofs but suffered from long water standing durations. Organic substrates followed by FLL substrates result in the highest air cooling in the noon, but blue roofs had the highest air cooling in the evening. In-substrate temperatures in blue-green roofs were lower than those in green roofs. Trade-offs between the benefits and drawbacks need to be considered in future designs.

  • The performance of blue-green and blue roofs was assessed and compared to green roofs.

  • Substrate type and thickness, drainage layer thickness, and orifice size influenced the hydrologic and thermal performance.

  • Blue-green and blue roofs could provide higher control of urban runoff and improve outdoor microclimates.

  • Trade-offs between the benefits and drawbacks need to be considered in future designs.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Climate change and urbanization are worldwide challenges that result in increasing adverse impacts on ecosystems. Current studies show that climate change elevates air and surface temperatures and magnifies storm events in both magnitudes and duration (Kendon et al. 2014; Westra et al. 2014). Urbanization results in fewer green spaces, leading to increased surface runoff, greater absorption of solar radiation and lower evaporation rates (Gunawardena et al. 2017). A growing interest exists in implementing green, blue-green, and blue infrastructure on the ground and on building rooftops to mitigate climate change's and urban sprawl's adverse impacts by enhancing multiple ecosystem services (Voskamp & Van de Ven 2015). Green, blue-green and blue roofs are among these infrastructures that do not only control stormwater runoff but can also enhance microclimate conditions (Almaaitah et al. 2021). Consequently, these environmental benefits aid in developing sustainable, adaptive, resilient cities (Brears 2019).

Green roofs contain vegetation and soil medium planted over a waterproofing membrane (Shafique et al. 2016). Green roofs are typically classified as extensive when growing medium <150 mm and intensive when growing medium >150 mm. They have been broadly studied in the literature for their hydrologic performance and thermal performance. In Canada, a cross-province study of identical green roof setups found cumulative annual retention of 67% in Calgary, Alberta (semi-arid, continental), 40% in London, Ontario (humid, continental) and 34% in Halifax, Nova Scotia (humid, maritime) (Sims et al. 2016). Hill et al. (2017) assessed the relative impact of irrigation, substrate type and planting on extensive green roof modules in Toronto, Ontario. The study found that smaller volumetric runoff coefficients were associated with modules that did not receive irrigation and had biologically derived substrates with a high quantity of organic matter. In contrast, the more significant volumetric runoff coefficients were associated with modules that received daily irrigation and had mineral substrates with low organic matter. There are two schools of thought on the organic matter content in green roof media. The first view claims that although higher organic matter favours plant growth, it magnifies the risk of a decrease in substrate depth due to the decomposition of organic matter, which can compromise drainage (Buist & Friedrich 2008; Snodgrass & McIntyre 2010). The second view argues that the substrate depth decrease does not always occur and is more influenced by other factors such as wind scour. Building on this research debate, Hill et al. (2016) surveyed thirty-three extensive green roofs aged 0–33 years in southern Ontario and found no evidence supporting the hypothesis that suggests higher organic matter results in a reduction in substrate depth.

High organic matter in soil has been found to elevate evaporative cooling (Lawrence & Slater 2007; Tuffour et al. 2014). However, little work has been done to quantify its thermal effect in green roof substrates. Several studies sought to examine the thermal performance of green roofs, including the impact on energy savings and indoor environments (Yaghoobian & Srebric 2015; Cai et al. 2011; Bevilacqua 2021), but a few studies looked at the outdoors microclimate benefits. Solcerova et al. (2017) found that air temperature above well-watered extensive sedum-covered green roofs in the Netherlands was colder at night, when the UHI is most apparent, but warmer during the day. When comparing irrigated sedum with non-irrigated grasses and wildflowers, irrigated sedum-covered extensive green roofs were 2 °C lower at the substrate's surface and 1.5 °C lower above the substrate layer (MacIvor et al. 2016).

A blue-green roof is similar to a green roof in terms of layer design but has an expanded drainage layer, providing a greater chance for evapotranspiration and gradual release of collected stormwater (Shafique et al. 2016; Cirkel et al. 2018). In Seoul, South Korea, the surface temperature of a blue-green roof module was 4 °C less than a control roof (Shafique et al. 2016). Another study found reduced temperatures of a blue-green roof due to maximized evapotranspiration in Amsterdam, Netherlands (Cirkel et al. 2018). Almaaitah & Joksimovic (2022) assessed the hydrologic and outdoor microclimate benefits of a full-scale productive blue-green roof (rooftop farm) in Toronto, Canada. The study reported air-cooling of 1.4–2.5 °C, stormwater retention of 85–88% and peak attenuation of 82–85%. These recent studies suggest promising environmental benefits of blue-green roofs but did not explore the influence of design variables (e.g., substrate type and thickness and drainage layer size) on their performance.

Blue roofs are emerging technologies that are constructed as tray-based and check-dam systems. A tray-based blue roof consists of trays filled with coarse aggregates designed to temporarily detain water during rainfall events (Campisano et al. 2018). A check-dam blue roof consists of a series of dams/weirs incorporated with orifices drilled at the bottom of each dam to prevent permanent ponding and allow rainwater to slowly drain towards the roof's outlet (Philadelphia Water Department 2020). A full-scale pilot installation of modular tray-based blue roofs was implemented using trays incorporated with orifices and filled with aggregates in Catania, Italy (Campisano et al. 2020). The study found that the blue roof had runoff volume and peak flow reductions of 34 and 60%, respectively. A pilot study of a check-dam blue roof installed in a building in New York, United States, showed that these systems could provide 20–80% detention of stormwater runoff (NYC DEP 2012). To the date of writing, no peer-reviewed published study has explicitly investigated the combined hydrologic or thermal performance of check-dam blue roofs.

Another emerging approach to achieving cooling load savings is covering buildings’ rooftops with white and high solar reflective coatings, shaping the so-called ‘cool roofs.’ Many studies found that cool roofs reduce surface temperature, enhancing indoor cooling efficiency (Xue et al. 2015; Meenakshi & Selvaraj 2018; Rawat & Singh 2022). Nevertheless, previous research has been restricted to individual cool roof systems without considering combining the reflective properties of cool roofs with the water detention properties of blue roofs.

The hydrologic and thermal functionalities of green, blue-green and blue roofs are governed by their design factors, including substrate type and thickness, drainage layer thickness and outlet size. There is limited research comparing the performance of these rooftop systems, hindering practitioners’ understanding of how they perform when subjected to the same climatic zones and weather conditions. The objective of this study is to assess the impact of design variables on the potential of green, blue-green, and blue roofs to control stormwater runoff and improve the outdoor microclimate. The assessment is based on monitoring precipitation, drainage, near-surface air temperatures, in-substrate temperatures and below-membrane temperatures of differently designed, size-identical modules over the monitoring period from June to November 2021.

Experimental setup and monitoring instrumentation

Six experimental modules were constructed at the green roof innovation testing laboratory (GRITLab) on the fifth-story roof of a building at the St. George Campus of the University of Toronto in Ontario, Canada. Each module has a drainage area of 2.88 m2 (2.4 m × 1.2 m) and is elevated from the roof deck to a height of 0.8 m. Design characteristics of the six modules monitored and analyzed in the study are depicted in Table 1.

Table 1

Design characteristics of the six modules monitored and analyzed in the study

ModulePlantSoil mediumDrainage layerOrifice size in check dam
GR-FLL Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 32 mm N/A 
GR-organic Sedum 100 mm highly organic with 25.6% organic matter 32 mm N/A 
BGR-FLL Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 85 mm (restricted to 55 mm) N/A 
BGR-organic Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 85 mm (restricted to 55 mm) N/A 
BR-2.4 Non-vegetated N/A N/A 2.4 mm 
BR-4.8 Non-vegetated N/A N/A 4.8 mm 
ModulePlantSoil mediumDrainage layerOrifice size in check dam
GR-FLL Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 32 mm N/A 
GR-organic Sedum 100 mm highly organic with 25.6% organic matter 32 mm N/A 
BGR-FLL Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 85 mm (restricted to 55 mm) N/A 
BGR-organic Sedum 100 mm mineral (FLL-recommended) with 6.7% organic matter 85 mm (restricted to 55 mm) N/A 
BR-2.4 Non-vegetated N/A N/A 2.4 mm 
BR-4.8 Non-vegetated N/A N/A 4.8 mm 

Two extensive green roof modules were constructed; one designed with a 100 mm mineral-based substrate (i.e., blended according to the German Forschungsgesellschaft Landschaftsentiwicklung Landschaftsbau – FLL) with 6.7% organic matter (GR-FLL) and the other with a 100 mm biologically derived and high-organic substrate with 25.6% organic matter (GR-organic). The green roof modules were equipped with 32 mm drainage layers (GR32, Green Innovations, Pickering, Ontario, Canada) and were covered by pre-grown sedum plants (Bioroof Ltd, Ontario, Canada), as shown in Figure 1(a). The storage capacity of each GR32 unit is approximately 2.67 L. Two extensive blue-green roof modules were constructed; one designed with a 50 mm mineral-based substrate (BGR-FLL) and the other with a 50 mm biologically derived substrate layer (BGR-organic). Both blue-green modules were covered with pre-grown sedum plants (Bioroof Systems Inc., Ontario, Canada) after being equipped with 85 mm Permavoid storage and capillary irrigation systems (PV85, ABT Inc., NC, United States), as shown in Figure 1(b). The storage was restricted to 55 mm through custom-made weirs considering the load restrictions on the plywood modules and the drought-tolerance nature of sedum. Each PV85 was equipped with capillary cones and mats to provide passive irrigation using the stored water. The storage capacity of each PV85 (after ponding restriction) is 12.8 L. The physical properties of the two substrate types used in green and blue-green modules are shown in Table 2. Two check dam blue roof modules were constructed: one with 2.4 mm orifices (BR-2.4) and the other with 4.8 mm orifices (BR-4.8). The orifices were drilled in three PVC dams around 1 cm above the roof membrane to promote some retention through evaporation and were distributed equally over each module following the installation of a waterproof membrane and a white reflective PVC liner, as shown in Figure 1(c). The white reflective PVC liners were installed to mimic the functionality typically found in cool roofs.
Table 2

Physical properties of the growing media

Material property
Mineral (FLL)Biological (Organic)
Grain size distribution % Gravel 16.30 24.63 
% Sand 77.47 75.28 
% Fines 6.22 0.08 
D10a 0.23 0.85 
D30b 0.73 1.3 
D60c 2.5 2.4 
Maximum water holding capacity 46% >90% 
Dry density 1.28 g/cm3 0.52 g/cm3 
Saturated density 3.21 g/cm3 2.14 g/cm3 
Saturated hydraulic conductivity (Ks) 0.05 cm/s 0.01 cm/s 
Field unsaturated hydraulic conductivity (K) 0.0013 cm/s 0.0008 cm/s 
Organic matter 6.7% 25.6% 
Material property
Mineral (FLL)Biological (Organic)
Grain size distribution % Gravel 16.30 24.63 
% Sand 77.47 75.28 
% Fines 6.22 0.08 
D10a 0.23 0.85 
D30b 0.73 1.3 
D60c 2.5 2.4 
Maximum water holding capacity 46% >90% 
Dry density 1.28 g/cm3 0.52 g/cm3 
Saturated density 3.21 g/cm3 2.14 g/cm3 
Saturated hydraulic conductivity (Ks) 0.05 cm/s 0.01 cm/s 
Field unsaturated hydraulic conductivity (K) 0.0013 cm/s 0.0008 cm/s 
Organic matter 6.7% 25.6% 

a, b, cParticle sizes corresponding to 10, 30, and 60% finer materials on the cumulative particle size distribution curve, respectively.

Figure 1

Schematic cross-sections of the six test modules.

Figure 1

Schematic cross-sections of the six test modules.

Close modal
All modules’ edges and corners were sealed, and the module drains were regularly maintained during the monitoring period. Vegetated modules (GR-FLL, GR-organic, BGR-FLL, and BGR-organic) were constructed with a 2% slope. Non-vegetated modules (BR-2.4 and BR-4.8) were built with a 3% slope, in line with recommendations of the Philadelphia Water Department (2020) which is one of the fewest institutions that provide design guidelines for check-dam blue roofs and recommend a slope higher than 2% for this type of blue roof. All vegetated modules were equipped with filter cloth to prevent small soil particles from passing into the drainage units and root barriers to protect the waterproofing membrane from root penetration. Figure 2 shows the actual setup of the modules.
Figure 2

Experimental modules on the roof.

Figure 2

Experimental modules on the roof.

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The two substrate types were selected to represent the popular commercially available engineering growing media. Mineral-based substrates had properties that aligned with the German Green Roof Guidelines – FLL standards (FLL 2008). Samples from both substrate types were collected before installation for soil laboratory testing. The physical properties of the growing media (Table 2) were obtained through grain size distribution, standard compaction test, falling head test, and loss on ignition according to the procedure described in the ASTM standards ASTM D422-63; D698-78; D2434-68 and Test Methods for Examination of Composting and Compost (TMECC). Unsaturated hydraulic conductivity for each growing media was measured in the field using a mini-desk infiltrometer (METER Group, Pullman, WA). The standard sedum blend contained a variety of cultivars, including Goldmoss Stonecrop (sedum acre L.), Aizoon Stonecrop (sedum aizoon L.), English Stonecrop (sedum anglicum L.) and Dragon's Blood Stonecrop (sedum spurium ‘summer glory’ M. Bieb). Irrigation of vegetated modules was performed occasionally during dry periods, depending on preceding rainfall events, and not on a fixed schedule.

Monitoring of the discharge and temperatures was performed between June-November 2021. Discharge from each module was measured using a tipping bucket rain gauge (resolution: 0.2 mm; accuracy: 1%), and the measurements were used in the hydrologic performance estimation (TB6, Hydrologic Services, Pullman, Washington). Each module was equipped with four thermistors (resolution: ± 0.2 °C; accuracy: ± 0.1 °C), TA, TB, TC and TD (109-L Thermistor, Campbell Scientific, Edmonton, Alberta, Canada), installed along a vertical center axis to allow for a complete analysis of the thermal profile. TA and TB were 60 and 15 cm above the surface in all modules, following the practice in similar studies (MacIvor et al. 2016; Solcerova et al. 2017; Almaaitah & Joksimovic 2022). TC was fixed in the mid-depth of the substrate in each of the vegetated modules and to the surface of the PVC liner in the blue roof modules. TD was attached to the bottom surface under all the modules. Sensors TA and TB measured the near-surface air temperature above the modules. They were outfitted with radiation shields to protect temperature sensors from incident solar radiation while maintaining equilibrium with ambient air (RM Young, Campbell Scientific, Edmonton, Alberta, Canada). A separate roof area covered with concrete pavers was selected as a control surface. An air temperature sensor with a radiation shield was installed 15 cm above the control surface, mimicking those near-surface air temperature measurements obtained from TB in the modules. Sensors TC measured the in-substrate temperatures of the vegetated modules and the surface temperatures of the blue roof modules, whereas TD measured the temperatures under the membranes. All monitoring instrumentation was connected to a data logger (CR3000, Campbell Scientific, Edmonton, Alberta, Canada) after being tested and calibrated in the laboratory and field. The tipping bucket rain gauges were tested at the lab using a static calibration method outlined by Segovia-Cardozo et al. (2021). The method involved using water injectors to measure the volume required to cause a single tip. Upon field installation, the tipping bucket rain gauges were dynamically tested on-site to verify accuracy using a siphon system known as FCD Portable Field Calibration Device (HyQuest Solutions, Warwick, Australia). Thermistors were tested using a two-point calibration method and all conformed to manufacturer's specifications. Figure 3 shows a schematic of the test module with monitoring instrumentation.
Figure 3

Schematic of the test module with monitoring instrumentation, adapted from Matthews (2012).

Figure 3

Schematic of the test module with monitoring instrumentation, adapted from Matthews (2012).

Close modal

Precipitation was measured using a tipping bucket rain gauge (resolution: 0.1 mm; accuracy: 1%) (TE525M, Texas Electronics, Texas). Meteorological parameters were measured using an on-site weather station consisting of a wind monitor (resolution: 0.0980 m/s; accuracy: ± 0.3 °C) (05103, RMYoung, Traverse City, Michigan), pyranometer (resolution: 7–14 W/m2; accuracy: 0.2%) (CMP 11, Kipp & Zonen, Delft, The Netherlands), and relative humidity and temperature probe (resolution: ± 0.2% for relative humidity and ± 0.1 °C for temperature; accuracy: ± 1% for relative humidity and ± 0.2–0.3 °C for temperature) (HMP45C, Campbell Scientific, Edmonton, Alberta, Canada).

Data analysis

Runoff data processing and event analysis

Rainfall and discharge from the modules were used to quantify the hydrologic performance of each module. Depending on preliminary data analysis, a rainfall event was defined as any storm with a rainfall depth ≥1 mm separated by a minimum of six hours without precipitation or runoff. Several past studies found the six-hour interevent-time suitable for green roof studies (Fassman-Beck et al. 2013; Carson et al. 2015; Getter et al. 2011). Retention performance was quantified through event-based retention percentages (RET), as shown in Equation (1), and detention performance was quantified through event peak attenuation (PA) and peak time delay (Tdelay), as shown in Equations (2) and (3).
formula
(1)
formula
(2)
formula
(3)
where PD is the sum of precipitation depth (mm), and RD is the sum of runoff depth drained from the module (mm). Both PD and RD are measured over 5-minute intervals. Prain is the peak 5-minute rainfall intensity, and Pdrainage is the highest 5-minute discharge flowing into the tipping bucket. TPdrainage and TPrainfall are the time of the drainage peak and the time of rainfall peak. Further analysis of individual events included an assessment of the impact of the antecedent dry period (ADP), defined as the dry weather period between two independent rainfall or irrigation events. The collected data were processed on a per-event and monthly basis to investigate for any seasonal variation. Correlations between hydrologic performance indicators and rainfall characteristics (i.e., rainfall depth, rainfall intensity, rainfall duration and ADP) were assessed by Spearman's rank coefficient (rs). Spearman's rank correlation is used to investigate the association between ranked variables through a monotonic function. For all performance indicators, statistically significant differences between the modules were examined using paired two-tailed t-tests.

Rooftop microclimate analysis

The modules’ thermal performance and impact on the rooftop microclimate were assessed by measuring each sensor's mean daily air temperatures at two different elevations, 60 cm (TA) and 15 cm (TB) above the surface. The air cooling effect of each module was determined through the mean daily temperature difference between the air above the module and the control roof at the lower elevation (TR15). Diurnal air temperature cooling was investigated above each module using the collected hourly data in the summer. In-substrate temperatures of the vegetated modules and surface temperatures of the blue roof modules were determined using the collected T data. Temperature fluctuations of the substrates were quantified through temperature amplitudes of the substrate (TAS) determined from the substrate temperature (TC) (maximum daily substrate temperature – minimum daily substrate temperature). Below-membrane temperatures in all modules were determined using the collected TD. The monitoring interval of the thermistors was 5 minutes and was aggregated to hourly and daily intervals for data analysis. All the mean daily thermal parameters (TA, TB, TR15, Tc, TAS, and TD) were averaged across each week of the summer (June, July, August) until mid-September. Spearman's rank coefficient was used to assess the correlation between thermal performance indicators and weather characteristics (ambient temperature, relative humidity and wind speed).

Hydrologic performance

During the monitoring period, forty rainfall events were observed and used in the hydrologic analysis. Compared to historical averages, August was dry and had 80% less rainfall, but September and October were wet and had 50% higher rainfall depth. The total depth of all the monitored events was over 368 mm, with 125 mm in the summer months of June, July and August and 243 mm in the fall months of September, October and November. The maximum and median rainfall depths were 42 mm and 4.1 mm, respectively. Monthly rainfall characteristics are presented in Table 3.

Table 3

Characteristics of the observed rainfall events

MonthNumber of rainfall eventsTotal rainfall depth (mm)Mean event rainfall intensity (mm/hr)Mean event peak rainfall intensity (mm/hr)Mean event duration (hr)
June 18 4.2 21.6 1.9 
July 98.4 6.8 36 5.1 
August 8.58 4.6 31.2 1.6 
September 103.6 5.0 36 8.0 
October 112 4.1 28.8 18 
November 27.4 2.9 9.6 7.3 
MonthNumber of rainfall eventsTotal rainfall depth (mm)Mean event rainfall intensity (mm/hr)Mean event peak rainfall intensity (mm/hr)Mean event duration (hr)
June 18 4.2 21.6 1.9 
July 98.4 6.8 36 5.1 
August 8.58 4.6 31.2 1.6 
September 103.6 5.0 36 8.0 
October 112 4.1 28.8 18 
November 27.4 2.9 9.6 7.3 

Retention performance

The depths of retained stormwater in all modules over the monitoring period are depicted in Figure 4. The maximum cumulative retention was achieved by BGR-organic (63%), followed by BR-2.4 (52%), GR-FLL (51%), BGR-FLL (49%) and GR-organic (47%). The least retention was achieved by BR-4.8 (32%). Previous studies in Ontario detected an extensive green roofs’ cumulative retention of 48% (Sims et al. 2016). Another local study estimated annual retention of 57% from a 100 mm depth green roof, demonstrating comparable values to the vegetated modules tested in this study (Liu & Minor 2005).
Figure 4

Cumulative depth of rainfall and discharge for all modules.

Figure 4

Cumulative depth of rainfall and discharge for all modules.

Close modal

The data suggests that the retention in some modules was influenced by season. GR-FLL and GR-organic had a similar retention performance in the summer with minimal differences between individual events (p > 0.05). However, in the fall season, the retention performance of GR-organic was reduced, and GR-FLL retained 5–7% more stormwater than GR-organic, generating a significant difference in retention performance (p < 0.01) and resulting in an overall p < 0.05. Nevertheless, both green roof modules captured storms smaller than 4 mm in both seasons. Retention performance in organic substrates is more vulnerable to reduced weather temperatures despite having higher holding water capacities because storage capacity is primarily restored through evapotranspiration which is a temperature-dependent process. Additionally, reduced solar radiation in the fall season limits the energy available for latent heat. In contrast, FLL substrates drain stormwater rapidly even for relatively smaller events, offering maximized storage for larger subsequent rainfall events and positively impacting cumulative retention.

The effect of substrate type on retention performance was more apparent in blue-green roof modules. BGR-organic retained the maximum stormwater in all of the months; its cumulative retention was slightly higher than local studies by Sims et al. (2016) and Liu & Minor (2005). BGR-organic had RET values 10–33% higher than those achieved by GR-organic, and the difference was statistically significant (p < 0.01). This observation implies that the attenuation feature and slower drainage of organic substrate complement the storage functionality of the expanded drainage layer, thereby increasing retention. BGR-FLL did not achieve significantly higher retention than GR-FLL (p > 0.05), suggesting that when FLL substrates are used, the thickness of the substrate is more influential on stormwater retention than the storage capacity in the drainage layer. Gong et al. (2019) explained that increased retention in larger substrates increases pore volumes, allowing more water to adhere to soil particles through surface tension. The present study provides evidence in favour of this hypothesis when green and blue-green roofs are compared but only on FLL substrates, not organic ones. Both BGR-organic and BGR-FLL showed seasonal variation. Median RET for BGR-organic and BGR-FLL were 100 and 95% in the summer and reduced to 83 and 78% in the fall, respectively. The seasonal variation in blue-green roofs is attributed to decreased weather temperatures, which reduce evapotranspiration and extend the time water stays in the drainage layer, thereby reducing the capacity for capturing rainfall from subsequent events.

The retention achieved in BR-2.4 was always less and statistically different from that achieved by BR-4.8 (p < 0.01). The analysis of results revealed that check-dam blue roofs equipped with smaller orifices could provide cumulative retention similar to vegetated modules but drastically varies on an event-based basis. BR-2.4 had a median RET of 87% in the summer, slightly lower than the vegetated modules. Except for BGR-organic, retention of BR-2.4 was higher than all vegetated modules in the fall season, despite reduced temperatures and potential for evaporation. Weekly site visits and visual inspections revealed that stormwater retained in the fall season behind the dams evaporated too slowly. This observation suggests that although smaller orifices with check-dam blue roofs may provide comparable retention to vegetated modules, they may create a standing water problem. The challenge of designing optimal orifice size in blue roofs that is sufficiently small to allow detention and large enough to prevent detention time from exceeding the maximum drain down time allowed by building codes was first pointed out by Campisano et al. (2018). BR-4.8 achieved the lowest retention of all modules, with a median RET of 66 and 53% in the summer and fall seasons. Visual inspections did not determine extended standing water in this module since larger orifices allowed for faster drainage even when there was no potential for evaporation.

Peak attenuation

Data analysis shows that peak attenuation was relatively high but differed between the investigated modules. Figure 5 shows box plots for event-based PA achieved from each module. Over the monitoring period, all modules achieved a PA of 100% for storms smaller than 5 mm, regardless of rainfall intensity. The minimum PA for all modules ranged from 26 to 35%, except for BR-4.8, which had a minimum PA of 11%.
Figure 5

Mean PA achieved by each module over the monitoring period.

Figure 5

Mean PA achieved by each module over the monitoring period.

Close modal

Peak attenuation was slightly lower in FLL substrates than organic substrates in the green roof modules (p < 0.01). Since permeability is higher in mineral substrates, stormwater passes rapidly and drains faster than in organic substrates, where permeability is lower (Stovin et al. 2015). However, peak attenuation was not statistically different between FLL and organic substrates on blue-green roof modules (p > 0.05), suggesting that the expanded drainage is more influential on peak attenuation than substrate type. When FLL substrates were used, blue-green roof modules achieved slightly higher peak attenuation than green roof modules (p < 0.01). However, when organic substrates were used, there was no statistically significant difference between green and blue-green roofs (p > 0.05). Analysis of the interquartile range revealed that both pairs of vegetated modules had PA of 100% in thirty events. However, in the remaining ten events, both blue-green roof modules had a PA range of 90–95%, whereas green roof modules had a PA of 80–85%. Improved peak attenuations by the blue-green roof modules, despite being constructed with smaller substrate thicknesses, are attributed to the larger drainage layer, as found in other studies (Shafique et al. 2016; Almaaitah & Joksimovic 2022). Retained stormwater in blue-green roofs flows from the substrate into the drainage layer and only drains out of the system when storage capacity is at its maximum. When this process occurs, the discharge rate is lower than that from the green roof due to the longer path the captured water needs to travel through the drainage layer. These findings demonstrate that blue-green roofs effectively enhance peak attenuation of stormwater by overcoming the small storage capacity in the drainage layers of conventional green roofs, as explained by Martin & Kaye (2020). There were significant differences in peak attenuation performance between the blue roof modules (p < 0.01), confirming the impact of orifice size in check-dam blue roofs, similar to observations made previously on tray-based blue roofs by Campisano et al. (2018). High peak attenuation in check-dam blue roofs is attributed to flow routing through the orifices in each dam until the captured rainwater reaches the main drainage outlet. A slight drop in peak attenuation performance of vegetated modules from summer to fall was observed. Median PA for both pairs of green and blue-green roof modules dropped by 9–11% and 2–4%, respectively.

Peak delay

Figure 6 shows box plots for event-based Tdelay observed from each module. There was a significant difference between green roof modules with FLL and organic substrates (p < 0.01), revealing another benefit of highly organic growing media. Median Tdelay was 50 minutes and around 2 hours for GR-FLL and GR-organic, respectively. However, a comparison between the two substrates in blue-green roof modules did not yield significant differences (p > 0.05), despite a noticeable difference in the median Tdelay (2 hours by BGR-FLL and 6 hours by BGR-organic). This observation of Tdelay aligns with the PA performance in blue-green roofs, where the expanded drainage layer becomes more influential on hydrologic performance than substrate type.
Figure 6

Mean Tdelay achieved by each module over the monitoring period.

Figure 6

Mean Tdelay achieved by each module over the monitoring period.

Close modal

Blue roof modules had the shortest Tdelay, with BR-2.4 and BR-4.8 having median values of 15 and 12 minutes, respectively. There was no statistical difference between both modules (p > 0.05). Seasonal variation in Tdelay performance was modest and only observed on vegetated modules, similar to PA performance. Median Tdelay dropped from summer to fall by 29% (GR-FLL), 10% (GR-organic), 11% (BGR-FLL) and 17% (BGR-organic).

Impact of rainfall characteristics on hydrologic performance

Spearman's correlations between event-based performance indicators (RET, PA and Tdelay) across the modules and rainfall characteristics (i.e., duration, depth, peak intensity, and ADP) are presented a heat map in Figure 7. Level of statistical significance between performance indicators and rainfall characteristics is provided in the Supplementary Material. The retention performance was negatively correlated with rainfall depth in all vegetated modules (rs = −55 to −0.81, p < 0.01). Similar observations on green roofs were determined by Carter & Rasmussen (2006), Simmons et al. (2008) and Stovin et al. (2012). However, this study indicates that these statistically significant correlations between rainfall depth and retention were higher for modules that used FLL substrates than those with higher organic content.
Figure 7

Correlation heat map between performance indicators and rainfall characteristics.

Figure 7

Correlation heat map between performance indicators and rainfall characteristics.

Close modal

Interestingly, rainfall intensity only correlated significantly with retention of GR-FLL (rs = −0.51) and to a lesser degree with GR-organic (rs = −0.41). No significant correlation was found between rainfall intensity and retention of BGR-organic (p > 0.05). These findings prove that using organic substrates and larger drainage layers in vegetated roofs decreases the impacts of intense storms on retention, creating an opportunity to enhance adaptation to climate change. All vegetated modules had negative correlations with rainfall duration (rs = −0.63 to −0.75, p < 0.01). Longer-duration storms tend to produce larger discharge, resulting in reduced retention, as Sims et al. (2019) explain. Rainfall depth moderately correlated with retention of BR-2.4 (rs = −0.55, p < 0.01) and strongly with BR-4.8 (rs = −0.81, p < 0.01). Similarly, rainfall intensity had a less influence on the retention achieved by BR-2.4 (rs = −0.45, p < 0.01) than that achieved by BR-4.8 (rs = 0.66, p < 0.01). Retention by BR-2.4 had a lower correlation with rainfall duration than BR-4.8, likely due to the water standing issue explained previously. It is crucial to note that the standing water issue may have occurred due to the unique design of the small-scale test modules in this study and may not be present on full-scale roofs.

Similar to retention performance, peak attenuation in all modules correlated with rainfall depth (rs = −0.78 to −0.65, p < 0.01) and rainfall duration (rs = −0.62 to −0.77, p < 0.01). BR-4.8 was the only module with a slight correlation between peak attenuation and rainfall intensity (rs = −0.39, p < 0.01), suggesting that larger orifices may also play a role in peak attenuation due to more significant conveyance capacity. Tdelay in all vegetated modules had negative correlations with peak rainfall intensity (rs = −0.41 to −0.56, p < 0.05). Similarly, significant correlations were found between Tdelay in vegetated modules, except GR-FLL (p > 0.05), and rainfall depth (rs = −0.34 to −0.56, p < 0.05). In blue roofs, Tdelay did not significantly correlate with rainfall depth (p > 0.05) and only moderately with peak rainfall intensity (rs = −0.35 to −0.42, p < 0.05). No significant correlations were found between ADP and hydrologic performance indicators of all modules.

Example hydrographs

Over the monitoring period, July and October were characterized by large storm events with high peak rainfall intensities, as shown in Table 3. Figure 8(a) and 8(b) depict example runoff hydrographs of the modules following the largest storm events monitored each month. As shown in Figure 8(a), rapid discharge response started with BR-4.8 and GR-FLL due to larger orifices and higher permeability in the green roof's FLL substrate. While GR-organic had a delayed discharge response, it later synchronized with GR-FLL. Both blue-green roof modules had considerable discharge delay, with a higher peak depicted in BGR-FLL than in BGR-organic. Seasonal variation is evident in Figure 8(b), where the delay feature of blue-green roofs, observed during the July (summer) event, was compromised, and only the BR-2.4 discharge was attenuated.
Figure 8

(a) Example hyetograph on July 8th. (b): Example hyetograph on October 10th.

Figure 8

(a) Example hyetograph on July 8th. (b): Example hyetograph on October 10th.

Close modal

Thermal performance

Air temperatures consistently increased from June to August during the summer and slightly decreased at the beginning of September. Mean monthly temperatures were typical for Toronto's temperate climate. Relative humidity elevated during the night and dropped during midday. The wind speed and the relative humidity had similar averages in each summer month, but the wind speed remarkably rose in September. Table 4 summarizes the results of monitoring the meteorological parameters and their statistics over the summer season until the beginning of the fall.

Table 4

Monthly meteorological conditions at the study site

ParameterStatisticJuneJulyAugustSeptember
Ambient temperature (°C) Min 13 13 10.5 8.3 
Mean 20 20.6 23.4 18.5 
Max 26 28.5 32.1 27.1 
Relative humidity (%) Min 20 36 32 30 
Mean 63.2 75.0 70.7 70 
Max 98 99 97 99 
Wind speed (km/h) Min 
Mean 1.7 1.7 1.7 5.3 
Max 9.7 8.7 9.6 41.1 
ParameterStatisticJuneJulyAugustSeptember
Ambient temperature (°C) Min 13 13 10.5 8.3 
Mean 20 20.6 23.4 18.5 
Max 26 28.5 32.1 27.1 
Relative humidity (%) Min 20 36 32 30 
Mean 63.2 75.0 70.7 70 
Max 98 99 97 99 
Wind speed (km/h) Min 
Mean 1.7 1.7 1.7 5.3 
Max 9.7 8.7 9.6 41.1 

Near-surface air cooling

All vegetated and non-vegetated modules cooled the air above them compared to the control surface. However, there were significant differences in the magnitudes of cooling generated by the modules. Figure 9 shows the mean daily TB above the modules, and the control surface, whereas Figure 10 shows the mean daily TR15 observed above each module. Differences in near-surface air temperature between the modules were minimal in June, probably due to lower weather temperatures observed at the beginning of the month and the initial growth stage of the plants. Top-view photos showing the establishment of the plants over the monitoring period are provided in the Supplementary Material.
Figure 9

Measured near-surface air temperature above the modules and control roof.

Figure 9

Measured near-surface air temperature above the modules and control roof.

Close modal
Figure 10

Observed near-surface cooling above each module.

Figure 10

Observed near-surface cooling above each module.

Close modal

Organic-based modules (i.e., GR-organic and BGR-organic) achieved the highest near-surface air cooling with a mean cooling effect of 2.92 °C (±0.2 °C). This finding confirms the hypothesis that organic soil offers a greater potential for air cooling (Hill et al. 2016; MacIvor et al. 2016). The mean near-surface air temperature above GR-FLL was 1.58 °C (±0.1 °C) lower than above the control surface, comparable to temperature differences observed by MacIvor et al. (2016) on extensive FLL green roofs. Interestingly, BGR-FLL had a mean near-surface air cooling of 2.83 °C (±0.2 °C), providing a cooling effect similar to that of organic-based modules. Therefore, if FLL substrates are used in blue-green roofs, they may achieve equivalent cooling effects to organic-based substrates in green roofs. This observation aligns with prior studies, which found a higher cooling potential in blue-green roofs than in green roofs (Shafique et al. 2016; Cirkel et al. 2018). On average, both blue roof modules cooled the above near-surface air by 2.2 °C (±0.1 °C), with an alternating performance observed between mid-August to early September. This finding suggests that check-dam blue roofs coated with reflective materials may provide higher air cooling than typical FLL green roofs but significantly lower than blue-green and organic-based green roofs. Therefore, it is evident that substrate type and drainage layer size substantially influence outdoor microclimates.

Examining TA above the modules indicates slightly higher air temperatures, with air temperatures at 60 cm above the modules being 0.25–0.35 °C higher than those at 15 cm for all modules. While this study did not measure air temperature at 60 cm above the control surface, data analyses reveal that TA above vegetated modules was still lower than TB above the control surface. Evapotranspiration impacts the variation of thermal responses across vertical profiles in green roofs (Kumar & Kaushik 2005; Jim 2012). Solcerova et al. (2017) attributed lower cooling at distant points of green roofs’ vertical profiles to the air layer that is well mixed and more influenced by the air advected from the surroundings than by the roof cover type.

Diurnal near-surface air cooling

Figure 11 depicts the hourly cooling effect (TR15) of near-surface air above the vegetated and non-vegetated modules. The diurnal pattern of TR15 indicates that the modules’ thermal responses differed between the daytime and nighttime. Contrary to studies by Jim (2012) and Almaaitah & Joksimovic (2022), which showed that green roofs might demonstrate a warming effect during the day, the near-surface air temperature was always cooler above the modules than on the control roof. Since the control roof is a darker colour surface made of impervious concrete, it accumulates thermal energy during the daytime. The resultant long wave radiation heats the air above, reaching the maximum in the afternoon when the solar radiation is highest (Wong et al. 2003; Gartland 2008). At this time of the day, the vegetative modules offer a cooling effect due to leaf transpiration and soil evaporation, resulting in a peak air cooling in the noontime. The decline in cooling in the afternoon is attributed to reduced transpiration rates since stomata eventually close to prevent excessive and further water loss during hot weather conditions (Huang et al. 2018). Previously conducted research by Jim (2012) and Jim (2015) shows that these two processes generate a bell-shaped diurnal pattern, as found in this study. Hourly cooling magnitudes followed the same pattern as the mean seasonal cooling magnitudes but had overall higher values. Maximum hourly cooling was 3.8 °C by BGR-organic, followed by 3.7 °C by GR-organic, 3.3 °C by BGR-FLL and lastly, 2.6 °C by GR-FLL.
Figure 11

Hourly near-surface cooling effect by each module (averaged across the summer).

Figure 11

Hourly near-surface cooling effect by each module (averaged across the summer).

Close modal

While maximum hourly cooling was observed at noon for all vegetated modules, the trend was the opposite for blue roof modules. BR-2.4 and BR-4.8 exhibited peak cooling effects after midnight and in the evening. However, at the start of the day, blue roofs’ air cooling dramatically dropped until BR-2.4 and BR-4.8 reached their minimum cooling at noon, creating an inverse bell-shaped pattern. No past studies assessed the outdoor microclimate effects of blue roofs. Nevertheless, general studies on non-engineered blue infrastructure (i.e., lakes and ponds) proved that time of the day (i.e., daytime, nighttime) and water temperature are influential factors in the cooling effect (Žuvela-Aloise et al. 2016). For instance, a study investigated the cooling effect of a pond on adjacent lawn microclimate and found hotter mean daytime conditions despite having lower air temperatures than a concrete control roof (Fung & Jim 2020).

The near-surface air temperature was lower above BR-2.4 than BR-4.8 from daytime until afternoon. However, the near-surface air temperature above BR-2.4 was slightly higher than that above BR-4.8 after midnight and in the evening. A possible attribution of this finding might be the difference in thermal responses of the blue roof surface and water when their temperatures increase. As solar radiation increases during the day, roof surface and water temperatures rise, but the latter emit more longwave radiation, resulting in higher cooling. BR-2.4 was constructed with smaller orifices and could potentially pond more water behind its dams, meaning that more longwave radiation was emitted from BR-2.4 than BR-4.8. This process is a prominent feature of blue infrastructure that effectively absorbs shortwave radiation and releases it through evaporation (Völker et al. 2013; Wu et al. 2019). Higher air temperatures above BR-2.4 than BR-4.8 at midnight and the evening may also be related to higher humidity. Evaporative cooling is boosted in drier conditions and diminished in humid air (Meyn & Oke 2009). The overall surface temperature of BR-2.4 was still 1.1 °C (±0.8 °C) lower than that of BR-4.8, as illustrated in Figure 12.
Figure 12

Surface temperature of the blue roof modules.

Figure 12

Surface temperature of the blue roof modules.

Close modal

In-substrate temperatures

In-substrate temperatures in blue-green roofs were significantly lower than those in green roofs. Figure 13 depicts mean daily Tc measurements from each substrate in the investigated modules. The mean in-substrate temperature in BGR-organic was 1 °C cooler than that in the GR-organic, whereas the mean in-substrate temperature in BGR-FLL was 1.4 °C cooler than that in GR-FLL. These findings prove that capillary storage from expanded drainage layers in blue-green roofs lowers in-substrate temperatures. However, the extent of substrate cooling would still be influenced by the type of growing media used. In each of the vegetated pairs, modules with organic substrates had lower in-substrate temperatures than those with FLL substrates. The mean in-substrate temperature in BGR-organic was 0.9 °C cooler than that in the BGR-FLL, whereas the mean in-substrate temperature in GR-organic was 0.5 °C cooler than that in GR-FLL. These findings validate the hypothesis that extra water retention in highly organic substrates provides a greater potential for evaporative cooling.
Figure 13

Mean daily in-substrate temperature in vegetated modules.

Figure 13

Mean daily in-substrate temperature in vegetated modules.

Close modal
A further complication from the previous hypothesis is that although higher water retention reduces substrate temperatures in blue-green roofs, these substrates are more prone to thermal fluctuations (Figure 14). The mean TAS was 4.65 °C (±1.2 °C) and 3.81 °C (±1.3 °C) for BGR-FLL and BGR-organic, respectively. GR-FLL had a mean TAS of 3.1 °C (0.8 °C), whereas GR-organic had a mean TAS of 2.1 °C (±0.3 °C). These values also suggest that FLL substrates are more susceptible to thermal fluctuations than organic substrates. A possible explanation for this observation is the highly variable water dynamic in mineral substrates, where stormwater is captured and drained faster than in organic substrates, resulting in larger temperature amplitudes. It is also noted that thermal fluctuations reduce the longevity of waterproof membranes and negatively impact indoor buildings’ microclimates (Getter et al. 2011; Ouldboukhitine et al. 2011).
Figure 14

Mean daily temperature range in vegetated modules’ substrates.

Figure 14

Mean daily temperature range in vegetated modules’ substrates.

Close modal

Below-membrane temperatures

Analysis of TD measurements indicates that patterns of below-membrane temperatures align with the patterns of thermal fluctuations revealed from Tc measurements within the substrate. Although substantial wet conditions in vegetated modules reduce in-substrate temperature, they may increase membrane temperature under the roof. Figure 15 shows that below-membrane temperatures were lower in FLL substrates (i.e., GR-FLL and BGR-FLL) and higher in organic substrates (i.e., GR-organic and BGR-organic). The mean below-membrane temperature was 21.8 °C (±2.4 °C) and 22.7 °C (±2.1 °C) for GR-FLL and BGR-FLL, respectively. GR-organic and BGR-organic had mean below-membrane temperatures of 23.4 °C (±2.2 °C) and 24.5 °C (±2.1 °C), respectively. Below-membrane temperatures of blue roof modules were on the higher end among all modules. The highest mean Tc was observed under BR-4.8 (24.6 °C ± 2.2 °C), followed by BR-2.4 (23.4 °C ± 2.1 °C). Since below-membrane temperatures directly impact the roof membrane, these results suggest that FLL substrates and, consequently, lower moisture conditions may be favourable whenever the need for buildings’ energy saving is higher than outdoor thermal comfort. Similarly, check-dam blue roofs with smaller orifices and longer water detention time may favour thermal insulation. Still, this practice may not conform to local building codes due to extended standing water duration.
Figure 15

Mean daily below-membrane temperature in all modules.

Figure 15

Mean daily below-membrane temperature in all modules.

Close modal

Impact of meteorological conditions on thermal performance

Spearman's correlation analyses indicate different thermal responses to climatic conditions between vegetated and non-vegetated modules (Figure 16). Level of statistical significance between performance indicators and rainfall characteristics is provided in the Supplementary Material. The air cooling by vegetated modules positively correlated with ambient temperature and negatively with relative humidity. Stronger correlations with these two meteorological parameters were found in the TR15 of organic-based modules (rs = 0.50 to 0.62, p < 0.01) than FLL substrates (rs = 0.32 to 0.41, p < 0.05). Higher ambient temperature substantially increases the control surface's temperature resulting in a larger air temperature difference above it and the vegetated modules. No significant correlations were observed between the ambient temperature, relative humidity, and TR15 of blue roof modules.
Figure 16

Correlation heat map between performance indicators and meteorological conditions.

Figure 16

Correlation heat map between performance indicators and meteorological conditions.

Close modal

Similarly, stronger negative correlations were found between relative humidity and TR15 of organic-based modules (rs = −0.47 to −0.48, p < 0.05) than FLL modules (r = −0.31 to −0.36, p < 0.05). Elevated air humidity compromises evapotranspiration and thus results in lower cooling. A similar pattern was found on an extensive green roof (Jim & Peng 2012). Unsurprisingly, significant correlations were found between in-substrate temperatures and ambient temperature in vegetated and non-vegetated modules (rs = 0.60 to 0.78, p < 0.01). There were no significant correlations between in-substrate temperatures in vegetated modules and relative humidity (p > 0.05). However, surface temperatures of both blue roof modules exhibited significant correlations with relative humidity (rs = −0.38 to −0.41, p < 0.05). A possible explanation for this observation is that most sudden increase in relative humidity during the day is due to precipitation. Rainfall that falls on the hard blue roof (unvegetated) surfaces reduces its temperature. The strong correlations between TD data and ambient temperature and relative humidity of the roof suggest that these sensors were affected by the radiated heat from the ground. Therefore, it is crucial to not over-interpret the below-membrane temperature data in this study.

The maximized hydrologic and thermal benefits of blue and blue-green roofs provide an opportunity to adapt to climate change impacts. Although blue roofs are unvegetated and cannot contribute to biodiversity, they deliver hydrologic and thermal benefits while producing less loading on buildings. Air cooling of blue and blue-green roofs increases thermal comfort and alleviates high urban heat caused by climate change and UHI. The increased storage of drainage layers in blue-green roofs can be a water source for plants during droughts and a stormwater control solution during frequent and intense rainfall events.

The purpose of the current study was to determine the impact of design variables on the hydrologic and thermal performance of green, blue-green and blue roofs. The novel blue-green and check-dam blue roofs were found to be promising technologies to adapt to climate change impacts. Substrate type and thickness, drainage layer thickness, and orifice size influenced both environmental services. However, several trade-offs would emerge which need to be carefully evaluated based on the design needs. From this study, the following conclusions are drawn:

  • 1.

    The blue-green roof with organic substrate had the highest stormwater retention, peak attenuation, and longer peak delay.

  • 2.

    Despite improved detention performance, the blue-green roof with FLL substrate did not achieve higher retention than the green roof with FLL substrate, indicating that the substrate thickness is more important in promoting retention than the drainage layer in FLL vegetated roofs.

  • 3.

    The check-dam blue roof with smaller orifices provided retention comparable to vegetated roofs but at the expense of the drain down time allowed by building codes. However, both blue roofs in this study had lower detention performance than vegetated roofs.

  • 4.

    Organic substrates in green and blue-green roofs resulted in the highest air cooling. However, they had higher in-substrate temperatures and thermal fluctuations than their counterparts with FLL substrates.

  • 5.

    The blue-green roof with FLL substrate had a significantly higher near-surface air cooling than green roofs with FLL substrate, revealing the influence of the drainage layer in improving outdoor microclimates.

  • 6.

    Diurnal analysis showed that maximum hourly cooling occurs in the noontime for vegetated roofs and the evening for blue roofs.

  • 7.

    Below-membrane temperatures were highest in blue roofs and blue-green roofs and lowest in green roofs, suggesting that increased wet conditions elevate membrane temperatures.

Therefore, this study recommends using organic growing media in blue-green roofs for maximum stormwater runoff control and outdoor microclimate improvement. However, high moisture conditions in organic blue-green roofs may not create a thriving environment for drought-tolerant plants, requiring reconsidering plant selection. If the plant selection is restricted to drought-tolerant species, the FLL growing media could be a better option. FLL substrates can still promote outdoor microclimate benefits when installed over larger drainage layers in blue-green roofs. Other practical implications to consider are the effects of these design variables on membrane temperatures and increased loads on the building. High and persistent moisture conditions in organic growing media and blue-green roofs may adversely affect the thermal insulation and result in higher membrane temperatures, negatively influencing indoor building microclimate.

Enlarging drainage layers in the blue-green roofs may add an increased load on buildings, creating a structural challenge. However, depending on substrate type and thickness, the stored water in the drainage layer may be lighter than some types of growing media. Additionally, thermal fluctuations within substrates resulting from wet conditions may decrease the longevity of roof membranes. Future research will have to investigate further the performance of blue and blue-green roofs over a long-term period (i.e., > one year) and explore possible operational limitations during the Winter season (e.g., orifice blockage and damage to drainage layers). Altogether, the optimal selection of green, blue-green, and blue roofs would require a holistic approach considering environmental, social and economic aspects.

The authors would like to acknowledge the following industrial partners who provided in-kind materials: ABT, Inc. (Permavoid materials) and Bioroof Systems Inc. (green roof materials). Technician support was provided by the Daniels Faculty of Architecture, Landscape and Design. The authors would also like to thank Tony Ung for making helpful arrangements and Sahildeep Panesar for assisting in field and lab work.

This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience Program (CREATE).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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