Urban greenery: increasing resilience to climate change through green roofs and urban forestry

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1 Urban greenery: increasing resilience to climate change through green roofs and urban forestry Author: Saiz Alcazar, Susana; Arup, Madrid, Spain Abstract: This study evaluates the impact of green roofs on urban microclimates in order to define its potential as climate change adaptation strategy able to soften urban temperatures during the hot season. A methodology is proposed based on the use of predictive CFD software with proven capacity for representing the physiological and cooling effects of vegetation on its surrounding environment. A methodology has been depicted to determine main variables affecting the results and the initial conditions for the simulation, which are critical for the predictive microclimate model. The model represents a standard neighborhood, with four story buildings. The results show that the green roofs have a cooling effect on the immediate surrounding environment, contributing to reducing the heat island effect. However, when evaluating its cooling effect at street level, no variations in air temperature are found between a baseline model with standard roofs and the urban model with green roofs. Key words: Climate change, green roofs, microclimate, urban forestry Introduction Climate Change has moved to the header in the list of global problems in recent years. Many times refuted, the effects of climate change have already become an undeniable reality and its effects are increasing worldwide. In urban areas, the impact of this phenomenon is even more evident, since the heat island effect increases even more the outdoor temperatures and outdoor comfort is very difficult to achieve. In recent years, many organizations have arisen whose goal is to develop strategies and mechanisms to control and curb the emissions that cause climate change and assess its consequences. One of them, the Intergovernmental Panel on Climate Change (IPCC), controls mechanisms designed to control emissions and deforestation. The IPCC calls for two types of strategies considering that the effects of climate change are non-stoppable and are already beginning to manifest globally; adaptation strategies and mitigation strategies. These problems are more even more relevant in urban cores by the effect of heat island effect. This phenomenon refers to city overheat. According to numerous studies conducted, the differences between urban centers and the surrounding area can reach 4 C, intensifying this effect during the night as there is no heat dissipation as would occur in areas without a high percentage of hard surfaces that absorb heat during the day and radiate the air overnight as infrared radiation. Faced with this problem, the green roofs are presented as a possible strategy able to address both climate change mitigation and adaptation. Green roofs reduce the proportion of infrared radiation returned to the air, so that the air temperature does not overheat and help to create an adapted microclimate and achieve comfort conditions 1

2 Objective The aim of this research has been the evaluation of green roofs on urban microclimates. This research has been based on computational dynamic predictive simulations developed to evaluate the impact of greenery on urban microclimate and its contribution to climate change adaptation. This study has: Evaluated available tools used to study microclimate in urban areas Identified the urban parameters that affect the effectiveness of green strategies, Evaluated the effectiveness of green roofs on urban microclimate at street level Methodology The methodology followed for the development of this research has been based on the usage of recognized and validated software to evaluate the impact of green roofs on urban microclimate at street level. Envi met 3.1 software has been used to model temperature variations between different scenarios including different green roof types. A suburban area in Madrid has been selected as a geographical location for the simulations since future related monitoring will be conducted in this city. Different urban densities have been evaluated to test the influence of the roof vegetation on urban microclimate. The following steps have been followed: Software climate input Since Envi met is a predictive software, the first step has been the definition of the initial weather conditions in order to allow the simulation software to generate relevant information. Envi met requires the definition of the initial conditions required for the simulation: day, time, wind speed and direction, initial temperature of the atmosphere, specific humidity at 2500 m and relative humidity in 2m Default information is provided by Envi met which has been corrected to reflect the real conditions of the simulation. Static initial conditions have been defined based on the climate data extracted from the Energy plus weather files for Madrid. The grid size for the CFD calculation was set as 10 m per grid in the x-y axes and 2 m on the z axis. Table 1 presents the initial inputs given to the software. Start Simulation at Day Start Simulation at Time 23 June Wind Speed in 10 m ab. Ground [m/s] 6 am Wind Direction 3m/s Initial Temperature Atmosphere [K] Eastern wind Specific Humidity in 2500 m [g 15ºC 2

3 Water/kg air] Relative Humidity in 2m [%] 7 ( default) Start Simulation at Day 64% Start Simulation at Time 0.1 ( urban area) Table1 To validate these assumptions, a base model was developed and run for a 48h period to confirm whether the predictive model followed the temperature evolution patterns confirmed with real weather data extracted from the energy plus database. Initial temperature has been set at 15ºC (data extracted from the energy plus database) and the simulation period has been set at 48h to identify how the surfaces temperature evolves. Wind direction has been set as east (90ºC). Buildings have been set with low thermal losses and internal temperature 24ºC. The results show that soil surface temperatures are 3.5 ºC lower after 24 hours simulation than the initial set point at first time in the morning, 6:00am, however, temperature evolution using this lower temperatures shows that the same temperature is reached when using initial set points extracted from energy plus. Therefore, the small difference registered in the initial temperature in the predicted and real scenarios is not critical for the results. Software Adjustments To check the software limitations, a test model including a standard urban configuration with 60x30 m blocks featuring interior courtyards and located in Madrid was created. The analysis was conducted on June 23th for consistency with the weather data analyzed. Image 1 shows the test results Image 1 The main variables identified and related observations based on the test results are included in the table below: Observation Model considerations Boundary conditions The results showed wind flow disturbances close to the grid boundaries, which alter the results. The results should be obtained from the central areas of the model, aside from the boundary of the geometry. A larger nested area has been set to avoid the influence of turbulent events in the test area. 3

4 Building boundaries The results showed strong temperature variations in the building perimeter, showing the effect of the thermal conductance of the buildings The model keeps building at constant temperature. To avoid alterations of the result due to heat loss from the buildings, envelope for all the buildings have been set as adiabatic. Wind direction and speed Air temperature distributions are strongly affected by the wind speed and direction. Simulations have been conducted with reduced air speeds to get a more accurate temperature distribution for the proposed geometry Radiation Temperature patterns show a correct distribution, showing lower temperatures in shaded areas Building data Configuration files have been modified to reflect changes in building surfaces properties such as albedo, and reflect accurately the optical properties of green roofs Albedo of walls has been set as 0.2, the albedo of the roof is 0.2, and the albedo of the plant layer is that of the vegetation type featured. Grid Table 2 Grid size did not allow to capture the temperature differences around the buildings Smaller grid was defined, 2 m in the x-y axis and 0, 5 over the building on the z axis. This set a limitation on the building size since the simulation environment does not allow more than 20 cells on z. Given the strong influence of the wind speed on the results, a model was conducted to test the tolerance of Envy met to low wind speed. Wind speed was set at 0, 1 m/s. The model became unstable at such low wind speed and was unable to run the simulations. Wind speed was raised to 0, 5 m/s, however, the results showed increased turbulences that made not possible to extract results from the simulations. Figure below show the model results at low wind speed (0, 5 m/s). Image 2 4

5 As a result of the adjustments conducted in the model, the following inputs have been selected for the baseline and proposed scenarios analyzed: Adjusted inputs Start Simulation at Day 24 June Start Simulation at Time 6 am Wind Speed 1m/s Wind direction Eastern wind Initial Temperature Atmosphere 15ºC Specific Humidity in 2500 m 7 ( default) Relative Humidity in 2m 64% Roughness 0.1 ( urban area) Nested area 10 cells ( 250x250 grid) Table 3 Baseline Definition A baseline scenario has been developed based on a typical urban geometry. A newly constructed neighborhood was selected as an initial prototype. This neighborhood features a regular distribution of urban plots and height to wide street criteria according to sun access. Sun penetration in the residential units is achieved by keeping a height to width relationship 1. (h=w).a 300x300m section has been selected as baseline for the analysis. The baseline has been set with standard graved roofs, considering 0,3 albedo. Walls have been set with albedo 0,2 and 0 thermal conductance. Inputs Start Simulation at Day 24 June Start Simulation at Time 6 am Wind Speed 1m/s Wind direction Eastern wind Initial Temperature Atmosphere 15ºC Specific Humidity in 2500 m 7 ( default) Relative Humidity in 2m 64% Roughness 0.1 ( urban area) Nested area 10 cells ( 250x250 grid) Roof albedo 0,3 Wall albedo 0,2 Envelope Conductance 0 WºC/m2 Building height 15 m Proposed Case Definition For the proposed case, the same geometry and building characteristics have been kept. The only change is the roof surface coverage. 5

6 A first test was conducted selecting grass as green roof vegetation type, the initial model showed no differences in relation to the baseline model, therefore, to ensure Envi met was able to capture the evapotranspiration and cooling effect of the green roof, a preliminary run was conducted evaluating four scenarios: A building with standard roof/ B building with vegetation, grass type, with a Leaf Area Density(LAD) 1 of 0,3/ C building with vegetation, sedum type, with a LAD of 1,5/ D building featuring an intensive green roof with trees, with a LAD of 2,2 The simulation grid was refined on top of the building to 0,5 m, the minimum allowed by envi met. Image 4 shows the results of this test: Image 4 The effect of trees and green roofs is very noticeable on the roof, the results show temperature differences ranging between 3 and 5 ºC when comparing the standard roof with albedo 0,2 with the vegetated roof and urban forests, showing the effectiveness of the green strategy to ameliorate heat island effect which matches the data reported in existing literature and empirical studies. After validating the software used, different simulations were conducted analyzing the effect of green roofs at street level. A standard 15 m building was selected given the limitations presented by the software in terms of grid on the z axis. Results The results show that no temperature differences are found when comparing the streets surrounded by buildings with green roofs with the buildings covered by standard roofs. Even though the effect of the green roof is noticeable at roof level, the effect of wind and street surrounding surfaces radiation diminishes their effect at street level. The cooling effect at street level is provided mainly by the shading of the streets by the building. No effect has been found in sunny streets or shaded areas. The building geometry selected for this analysis, was intended to represent a standard urban area. However, a test was conducted to evaluate the effect of the green roof on low height dwellings and 2 story buildings were tested. The results did not show relevant differences between the two scenarios 1 Leaf Area Density (LAD) is an estimate of the sum of the surface area of all leaves growing on a given land area (e.g. per ha). 6

7 analyzed, showing that the wind and the infrared radiation emitted by the urban surfaces eliminate the cooling effect of the roof making it negligible at street level. The results obtained for the baseline and proposed scenarios (4storey buildings) are shown below: Simulation 24-06, 16:00h, 1,6m section, wind speed: 1 m/s, Luzerne vegetation on roof, LAD>1<2 m2/m3 Simulation 24-06, 16:00h, 1,6m section, wind speed: 1 m/s no vegetation on roof Image 4. Conclusions The preliminary conclusion extracted from this study is that the effect of green roofs is limited to immediate areas surrounding the green roof. Temperature results show a high sensitivity to wind and radiation from buildings and streets and the cooling effect on top of the roof cannot reach the pedestrian level. Green roofs, however, have proven effect on ameliorating heat island effect. Further studies should be conducted at larger scale to evaluate the overall temperature control that green roofs can provide at city level. This effect is dramatically affected by the LAD of the vegetation system selected for the roof. Further analyses shall be conducted to estimate the critical building and urban parameters under which the green roof could have any effect or dismiss this strategy for the purpose of creating pedestrian level microclimates. Urban surfaces shading, landscaped areas, albedo and heat sources should be carefully modeled for different urban conditions, seasons and climates to define the s potential scenarios where green roofs cools have a positive effect on urban microclimates. 7