Green infrastructure for overheating adaptation in glasgow

FINAL REPORT

Green Infrastructure for overheating adaptation in Glasgow

Final Report: Green Infrastructure for Overheating Adaptation in Glasgow Client:

Glasgow Clyde Valley Green Network Partnership (GCVGNP)

Contractor:

Glasgow Caledonian University

Submission Date:

12 April 2013

Acknowledgements Funding for this project was provided by the Central Scotland Green Network Support Unit, Glasgow Clyde Valley Green Network Partnership (GCVGNP), Glasgow City Council and Glasgow Caledonian University.

Oversight for the project was provided by the Climate Change Adaptation Steering Group, along with Scottish Environment Protection Agency (SEPA), Adaptation Scotland, Central Scotland Green Network, Forestry Commission Scotland, Glasgow City Council, Metropolitan Glasgow Strategic Drainage Partnership and the GCV Strategic Development Planning Authority.

Front Cover: Land cover characteristics in Glasgow City Centre, using the Local Climate Zone (LCZ) classification method (see p. 7 for details on the LCZ methodology)

Climate change adaptation – Urban overheating study

Document Control Sheet

Document title:

Green infrastructure for overheating adaptation in Glasgow

Purpose of issue:

Final Report for GCVGNP

Primary author:

Dr Rohinton Emmanuel (with technical input from Alessandro Loconsole)

Simulation Runs and images

Alessandro Loconsole

Date:

22 April 2013

Approved by: Date:

Circulation List Name

Organisation

Purpose

Alastair Corbett

GCVGNP

Final Draft

Date

Revised By

25/05/203

Rohinton Emmanuel

22/04/13 22/03/13 13/03/13

Rohinton Emmanuel Rohinton Emmanuel Rohinton Emmanuel

Content Amendment History Revision Final Report Amendments Final Report, Fig 7 amended Final Report Second Draft Initial Report

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Table of Contents Acknowledgements .................................................................................................................................... ii Executive Summary..................................................................................................................................... 1 1. Introduction ............................................................................................................................................ 2 2. Background ............................................................................................................................................. 2 3. Method ................................................................................................................................................... 3 4. Trends in urban microclimate in Glasgow ............................................................................................... 5 Pairwise comparison of ‘urban’ and rural’ data ........................................................................................................... 7

5. Effect of land use / land cover and local climate ..................................................................................... 7 5.1 The Local Climate Zones (LCZ) in the region ............................................................................................................... 8 Local climatic effect of landcover in the GCV Region ................................................................................................... 8 Temperature profiles ................................................................................................................................................... 9 5.2 Site selection using the LCZ approach ...................................................................................................................... 10

6. Simulation of the effects of green infrastructure in the GCV ..................................................................12 6.1 ENVI-met programme ............................................................................................................................................... 12 6.2 Air temperature effects ............................................................................................................................................ 13 6.3 Surface temperature effects ..................................................................................................................................... 15 6.4 Thermal comfort implications of green cover .......................................................................................................... 17

7. Implications and Conclusions .................................................................................................................18 7.1 Achieving green cover increase – an example .......................................................................................................... 19

Author Contact Details ...............................................................................................................................20 Contributors ................................................................................................................................................................... 20

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Climate change adaptation – Urban overheating study

Executive Summary This report on behalf of the Glasgow Clyde Valley Green Network Partnership (GCVGNP) by the Sustainable Urban Environment Research Group (SUE-RG) within the School of Engineering and the Built Environment (SEBE), Glasgow Caledonian University (GCU) explores the mitigation potential of green infrastructure in tackling the urban overheating problem in the Glasgow Clyde Valley (GCV) likely to intensify under a changing climate.

The objectives of the work are: 1. Report on urban climate trends: identify the warming trajectories of different settlements in the GCV region for which long-term historic data are available; 2. Local Climate Zone classification: A map typology of local climate zones (LCZs) for those areas most likely to experience significant overheating problem.

3. CFD simulation results of applicability of green infrastructure approaches: simulations using local-scale Computational Fluid Dynamic (CFD) model to indicate the likely cooling potential of different green approaches for each of the LCZ class.

Previous work by GCU in Glasgow indicates that even when urban growth has subsided, the local warming that result from urban morphology (increased built cover, lack of vegetation, pollution, anthropogenic heat generation) continue to generate local heat islands. Such heat islands are of the same order of magnitude as the predicted warming due to climate change by 2050. And the micro-scale variations are strongly related to local land cover/land use patterns.

The present work classified the built up area of the GCV region into ‘Local Climate Zones’ (LCZ) that exhibit similar warming trends locally. This helped to identify likely problem areas, a sub-set of which were then modelled for the effect of green cover options (both increase and reduction in green cover).

The classification of LCZs and CFD modelling brought out the following conclusions: 1. Green infrastructure could play a significant role in mitigating the urban overheating expected under a warming climate in the GCV Region.

2. A green cover increase of approximately 20% over the present level could eliminate a third to a half of the expected extra urban heat island effect in 2050.

3. This level of increase in green cover could also lead to local reductions in surface temperature by up to 2oC. 4. Over half of the street users would consider a 20% increase in green cover in the city centre to be thermally acceptable, even under a warm 2050 scenario.

Alternative approaches to enhancing the green cover by 20% in a Glasgow city centre neighbourhood are presented. These employ an urban park, street trees, roof gardens, façade greening or combinations of these.

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1. Introduction The aim of the project of which the present report is the final outcome, was to evaluate the adaptive capacity and mitigatory potential of urban green infrastructure in reducing the expected overheating problem in the GCV region by 2050 and to enhance the adaptive capacity of human settlements in the region. In order to do this, the project: 

Identified the historic trends in urban climate in and around the Glasgow city area;



Established the likely problematic areas where overheating could be significant;



Suggest green infrastructure approaches (i.e. changes in green cover) to mitigate and/or enhance adaptive capacity in the region



Identified most feasible green cover enhancement options.

2. Background Previous work by the GCU1,2 has clearly delineated the nature and scope of urban microclimate change in and around the city of Glasgow. Based on a four-pronged approach to map the local climate variations in and around the city of Glasgow in 2011 (historic climate trends in the city; fixed weather station data in and around the city; microclimate variations at the street canyon level within the city core, and thermal perception of street users in the heart of the city centre) the previous work found the following: 1. Even when urban growth has subsided, the local warming that result from urban morphology (increased built cover, lack of vegetation, pollution, anthropogenic heat generation) continue to generate local heat islands; 2. Such heat islands are of the same order of magnitude as the predicted warming due to climate change to 2050; 3. Substantial variations within city neighbourhoods exist (see Figure 1 for an example of a ‘hot spot’) and these relate to land use/land cover attributes, pointing to planning possibilities to locally mitigate the negative consequences of overheating; 4. Strategies to tackle local overheating can lead to less carbon intensive enhancement of comfort, health and quality of life both within and outside buildings.

1

2

Emmanuel R, Krüger E. 2012. Urban Heat Island and its impact on climate change resilience in a shrinking city: the case of Glasgow, UK. Building and Environment, 53, pp. 137-149 Krüger E, Drach P, Emmanuel R, Corbella O. 2013. Urban heat island and differences in outdoor comfort levels in Glasgow, UK. Theoretical & Applied Climatology, 112, pp. 127-141

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Climate change adaptation – Urban overheating study

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Figure 1: Local variations in air temperature during daytime in Glasgow city centre o

Note: Contour lines are 0.10 C apart; data was collected using a bicycle mounted instrument [fan aspirated and placed in a protective shield]; data shown as the difference in temperature between field measurements and a reference station [Gla. International Airport]

Given the geographic and urban growth similarities of the GCV region to that of the city of Glasgow, the overheating problem in the GCV area is likely to be similar. Carefully planned development of urban morphological variables such as the green infrastructure offers possibilities to enhance outdoor livability and reduced building energy use in the immediate future when the regional climate remains relatively similar to current conditions, but also provides an adaptive mechanism when the background climate continues to warm3, thus lending itself to a workable and long-term strategy to adapt to climate change in the GCV region.

3. Method The pursuit of green infrastructure strategies to tackle the overheating problem due to climate change enhanced by local warming in the GCV required the following steps: 1. Estimation of historic (~50 years) trends in local warming in the GCV region over and above the general climate trends (Problem identification); 2. Identification of localities where local warming is likely to be the most intense (the ‘hot spots’); 3. Identification of the land use/land cover attributes of the likely ‘hotspots’ (classification of the ‘hotspots’);

3

Kleerekoper L, van Esch M, Salcedo TB. 2012. How to make a city climate-proof, addressing the urban heat island effect. Resources, Conservation and Recycling, 64, pp. 30– 38

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4. Evaluation of the sensitivity of green infrastructure-based adaptation options to reduce these ‘hot spots;’ 5. Sense testing the feasibility of adaptation options.

Step 1 established the extent and scope of the local warming effect; Step 2 identified key problematic areas where adaptation strategies are likely to be the most effective; Step 3 classified the problem areas into a small number of manageable classes so as to minimise the computer resources needed to develop the latter stages; Steps 4 and 5 identified appropriate green infrastructure enhancement measures to tackle the overheating problem and to evaluate their practical feasibility.

Step 1: Desk-based evaluation of regional historic weather data: Data source: British Atmospheric Data Centre – MIDAS dataset, previous publications by the SUE-RG on Glasgow’s urban heat island phenomenon.

Step 2: Local temperature patterns at fine scales: Data source: Remotely sensed surface temperature maps (if available) or, estimation of local warming based on the analysis of regional historic weather data (as obtained from Step 1 above) Step 3: Identification of land use / land cover classes of the ‘hot spots’ based on a widely used local climate classification system (Local Climate Zone – LCZ4): Data source: Ordnance survey and LIDAR data to estimate building cover and building height / land cover or land use in the GCV region to estimate surface characteristics, together with ground truth verification of selected ‘hot spots’ using ‘Google Earth’ and/or field visit to estimate building height and other physical parameters of selected areas Step 4: Simulation of the effect of planning options on local warming: Six scenarios were run as detailed below: 1. 2012 climate with current development pattern = ‘Current Case;’ 2. 2050 climate (using UKCIP’09 projections) with planned development patterns as planned by the relevant local authority – ‘Base Case’; 3. 2050 climate with ‘loss’ of green infrastructure (‘m10 case’) 4-6. 2050 climate with three levels of increased green cover (+10%, +20% and +100% – p10, p20 and p100 cases, respectively)

4

Stewart ID, Oke TR. 2012. Local Climate Zones (LCZ) for urban temperature studies. Bulletin of American Meteorological Society. 93, pp 1879–1900

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Six geographic locations 1 & 2. Two locations in Glasgow City 3 - 6. Four additional locations outside Glasgow City but within the GCV region

Data Source: CFD simulation using ENVI-met software5 based on input data from Step 3, with ‘ground truth verification’ where needed Step 5: Estimating the feasibility of green infrastructure options identified in Step 3 Data source: Based on German guidelines (Green Area Ratio6) to normalise the climatic effects of different types of green cover (urban parks, street trees, green roofs, green walls, etc) Results of Step 1 and 2 are presented in Section 4 below. Step 3 is detailed in Section 5 and Sections 6 and 7 present the simulation results and analysis from Steps 4 and 5.

4. Trends in urban microclimate in Glasgow 25

Temperature degC

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15

y = 0.0487x + 5.4686 R² = 0.2768 10

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1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

y = 0.0337x + 0.7088 R² = 0.1188

Year Winter tmax

Winter tmin

Linear (Winter tmax)

Linear (Winter tmin)

Figure 2: Monthly mean maximum and minimum temperatures – winter (1959-2009) Figures 2 (winter) and 3 (summer) show the long term (50 years) trends in daily maximum and minimum temperatures at the Glasgow International Airport. The seasonally disaggregated trends show significant differences between summer and winter trends.

5 6

Bruse M. 2011. ENVI-met Model Homepage. http://www.envi-met.com Keeley M. 2011. The Green Area Ratio: an urban site sustainability metric. Journal of Environmental Planning and Management, 54, pp. 937-958

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25

Temperature degC

20

y = 0.0296x + 18.395 R² = 0.1165

15

10

y = 0.0233x + 10.984 R² = 0.161

5

-5

1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0

Year Summer tmax

Summer tmin

Linear (Summer tmax)

Linear (Summer tmin)

Figure 3: Monthly mean maximum and minimum temperatures – summer (1959-2009) The temperature rise in winter is more accentuated than during the summer, with monthly mean minima showing more subtle changes – suggesting a more frequent occurrence of milder winters and warmer summers.

Table 1 shows linear equations (best fit trend lines), corresponding to the different periods evaluated. Results from the trend lines yield as much as a 2.4K increase in average monthly maximum temperature for winter months – from a theoretical 5.5°C to 7.9 °C or, according to actual records, from 5.3°C in 1959 to 7.1°C in 2009. Slope coefficients in the trend lines are more expressive in winter (for the minimum temperatures, 0.0337 against 0.0015 for all months and 0.0233 for summer periods; for the maximum temperatures, 0.0487 against 0.0023 and 0.0296, respectively).

Table 1 also presents the difference between the average minimum and maximum winter temperature at the start (1959) and end (2009) of the data series.

Table 1: Trend lines and variations in local air temperature in Paisley (1959-2009) Period

All months

Winter months (Dec, Jan, Feb) Summer months (Jun, Jul, Aug)

Trend line equation

Ave. min. y = 0.0015x + 5.6196 R² = 0.0043 y = 0.0337x + 0.7088 R² = 0.1188 y = 0.0233x + 10.984 R² = 0.161

Ave. max. y = 0.0023x + 11.968 R² = 0.0068 y = 0.0487x + 5.4686 R² = 0.2768 y = 0.0296x + 18.395 R² = 0.1165

Relative temperature rise in 50 years (from trend lines) Ave. min.

Ave. max.

0.9 1.4

Actual temperature rise in 50 years Ave. min.

0.6

Ave. max.

1.1

1.7

2.4

1.8

1.9

1.2

1.5

-0.7

0.2

The frequency distribution of air temperatures show interesting patterns of change: lower temperatures shifted downwards in the cumulative frequency chart (less frequent) and an increase in the frequency of higher temperatures. Figure 4 shows the cumulative frequencies for the first and the last decades of the data series – 1960-1969 and 2000-2009.

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90%

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(b) monthly maximum temperatures

Figure 4: Comparison of cumulative frequencies – 1960-1969 and 2000-2009

Pairwise comparison of ‘urban’ and rural’ data Histogram (max) 50

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Figure 5: Histograms for ‘urban’ – ‘rural’ temperature differences (1974-1985): left – daily minima; right – daily maxima Figure 5 shows histograms of temperature differences (grouped into 1oC bins) between the ‘urban’ (Glasgow city centre) and ‘rural’ (Bishopton) sites in the recent past. Minimum temperature differences are more consistently positive and narrowly distributed, i.e. the urban station presents the nocturnal heat island effect more frequently and consistently. However the maximum temperature difference has a wider frequency distribution and smaller amplitude. It was even negative in some instances (indicating a daytime cool island effect in the city centre).

The average daily temperature fluctuation for both sites has an offset of almost one degree in the rural site (6.3K at the rural site against 5.5K at the urban site).

5. Effect of land use / land cover and local climate The key to understanding the local climatic effect of land cover/land use characteristics is to classify the settlement area according to their key climate-influencing features. For this purpose, a recently developed climate-land use classification system called the Local Climate Zones (LCZ4) is very useful.

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5.1 The Local Climate Zones (LCZ) in the region LCZs are defined as ‘regions of uniform surface-air temperature distribution at horizontal scales of 102 to 104 metres.’ Their definition is based on characteristic geometry and land cover that generates a unique near-surface climate under calm, clear skies. These include vegetative fraction, building/tree height and spacing, soil moisture, and anthropogenic heat flux. Detailed views of representative characteristics of each of the LCZ class are given in Appendix A. Urban morphological parameters (building density, building height, street width, surface area, roughness, green cover, etc) common to each of the LCZ class is also shown in Appendix A. LCZ has 16 climate zones and the classification system has been validated in Sweden, Japan and Canada.

Local climatic effect of landcover in the GCV Region

(a)

(b)

(c)

(d)

Figure 6: Land cover characteristics of 500 x 500m area surrounding each of the weather stations in the GCV (from top left, clockwise): a) Brancumhall, b) Glasgow Airport, c) Renfrewshire, d) Wishaw SOURCE: GOOGLE EARTH

In order to test the applicability of LCZ method to uncover the local climate effect of land cover, 261 days of measurements in 2010 were selected for which data from at least 4 of the weather stations surrounding the city of Glasgow were available. Given their relatively consistent distance Page 8 of 20

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from the city core, the hypothesis was that the differences between the stations were the result of local land cover / land use conditions. Figure 6 shows the land cover characteristics of an area approximately 500 m × 500m surrounding each of the weather stations. These images were used to estimate the LCZs. In the case of mixed classes, likely fetch effect due to the prevailing winds (south-westerly) was used.

LCZ classes for the four stations are: 1. Brancumhall: located closer to suburban East Kilbride than to Glasgow City in the county of South Lanarkshire. There is considerable urban development south-west of the weather station, mostly with low-rise buildings. Accordingly, an LCZ class of OPENSET LOWRISE was assigned 2. Glasgow Airport: Located on the north side of the main airport terminal. classification: EXTENSIVE LOWRISE/LOW PLANT COVER

LCZ

3. Renfrewshire: similar to Brancumhall, this location is surrounded by low-rise buildings in Neilston, East Renfrewshire. Although it can be also classified as OPEN-SET LOWRISE, it has substantially less obstructions to the south-west 4. Wishaw: Located close to Wishaw, North Lanarkshire, this site has a suburban aspect and is more vegetated than all the other stations, having also a public park (Dalziel Park) and a golf course in the vicinity. LCS classification is mixed in this case: OPEN-SET LOWRISE/CLOSE-SET TREES/LOW PLANT COVER.

Temperature profiles Table 2 shows the annual and seasonal differences in daytime (Tmax), nighttime (Tmin) and the diurnal temperature range (DTR) for the 4 selected stations. The last row indicates the maximum differences obtained for each variable.

Table 2: Annual, winter and summer average maximum (Tmax) and minimum temperatures (Tmin) and corresponding diurnal temperature ranges (DTR) Period Brancumhall Gla Airport Renfrewshire Wishaw Maximum differences

Tmax 10.2 10.8 10.5 10.4 0.6

Annual Tmin 3.8 2.5 3.3 1.9 1.9

DTR 6.4 8.3 7.1 8.5 2.1

Tmax 4.1 4.7 4.1 4.6 0.6

Winter Tmin -1.0 -2.9 -1.6 -3.8 2.8

DTR 5.1 7.6 5.7 8.4 3.3

Tmax 17.9 18.5 18.4 18.1 0.6

Summer Tmin DTR 11.1 6.8 10.0 8.5 10.4 8.0 9.7 8.5 1.4 1.7

A further comparison was performed to discover the ‘warmest’ and the ‘coldest’ set of temperatures, by exploring the group mean. Figure 7 shows the correspondence between such figures for the daily minimum temperatures (indicator of the urban heat island effect) and the LCZ classification for each location, arranged left to right from the least to the most densely built location.

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Minimum Temperature departure from group mean ( oC)

Annual

Winter

Summer

1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 OPEN-SET EXTENSIVE OPEN-SET LOWRISE w/ OPEN-SET LOWRISE LOWRISE/CLOSE-SET LOWRISE/LOW PLANT LESS OBSTRUCTION TO TREES/LOW PLANT COVER SOUTH COVER LCZ classification

Figure 7: Annual, winter and summer departures from the group mean minimum temperature The pattern in Figure 7 suggests the existence of urban warming in the more built-up areas. This pattern is observable annually as well as in winter and summer periods, although more pronounced in the winter. Figure 7 reinforces the usefulness of the LCZ approach, which takes into account intra-urban or, in this case, peri-urban temperature differences.

5.2 Site selection using the LCZ approach Given the strong association of local warming with LCZ classification (see Figure 7 above), it appears useful to classify the entire study area using this approach. The following steps were performed to determine the dominant LCZ classes in the GCV region and thus selecting ‘representative’ sample locations for detailed simulation of the effect of green infrastructure.

1. Determine the ‘built-up’ areas of the GCV 2. Calculate built fraction / green cover / natural cover within 1km dia circles placed in an array covering the entire ‘built-up’ area in the GCV identified in step 1 3. Classify each circle into relevant LCZ class, depending on built/green fraction and building height closely matching the urban morphological parameters shown in Table 1 in Appendix A 4. Determine the dominant LCZ class in closest proximity to an official weather station Step 1 ensures the task remain reasonably focused (much of the GCV region is unbuilt, thus outside the purview of this study). Steps 2 and 3 shorten the process of calculation of LCZ while Step 4 ensures sites are selected not only according to their ‘representativeness’ but also in close Page 10 of 20

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proximity to weather stations with available microclimate data that could greatly facilitate the simulation runs in the next step. Appendix B and C show the results of these steps.

It is clear from the analysis that the GCV region largely composes of two classes of ‘semi-dense’ urban morphology (LCZ 2 and 3 – Compact lowrise) and three classes of ‘sparse’ settlement morphology (LCZ 5 – Open midrise; LCZ 6 – Open lowrise and LCZ 9 – Sparsely built). The ‘dense’ morphology is typical of Glasgow city centre while the rest of the built-up area falls under the ‘sparse’ category.

Based on these results six sites were selected as follows. Each site has a domain area of 400m x 400m (See Figure 8): 1. LCZ 2 – Compact lowrise: Glasgow City Centre West (Gla CCW) centred on the intersection of W Campbell Street & Bath Street; 2. LCZ 3 – Compact lowrise: Glasgow City Centre East (Gla CCE) comprising an area surrounding the George Square area, centred on the intersection of John Street & Ingram Street; 3. LCZ 6 – Open lowrise: Clyde Gateway area (London Road & Springfield Road) 4. LCZ 5 – Open midrise: Paisley area (High Street & New Street) 5-6. LCZ 9 – Sparsely built (or extensive lowrise): two locations characterising this class (Wishaw – Caledonia Rd. and Main St., and Hamilton – Brandon St. and Quarry St.)

Figure 8: Selected locations for model simulations Detailed views of land cover characteristics of these sites are shown in Appendix C (Note Sites 1 and 2 are covered by Figure B-III in Appendix B).

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6. Simulation of the effects of green infrastructure in the GCV Having thus selected sites for further study, it was decided to employ a computational fluid dynamics (CFD) model to simulate the effect of green infrastructure on local climate (air temperature, surface temperature and thermal comfort). For this purpose a free-to-download CFD software called ENVI-met (www.envi-met.com) was used.

6.1 ENVI-met programme The non-linearity of the UHI problem lends itself to numerical simulations and is therefore increasingly popular in urban climatology. Urban microclimate models vary widely with regard to their physical basis and spatial/temporal resolution. Among these, ENVI-met (www.envimet.com) is perhaps the only micro-scale computational fluid dynamic model that is capable of analyzing both the air/surface temperatures as well as the thermal comfort regime within the street canyon at fine resolutions (down to 0.5  0.5 m)7.

ENVI-met is a 3-dimensional non-hydrostatic model for the simulation of surface–plant–air interactions, especially within the urban canopy layer. It is designed for the micro-scale with a typical horizontal resolution from 0.5 to 10m and a typical time frame of 24 to 48h with a time step of 10s. This resolution allows the investigation of small-scale interactions between individual buildings, surfaces and plants.

Input meteorological data required to initiate ENVI-met simulations are: Wind speed and direction at 10 m above ground Roughness length (Zo) Initial temperature of the atmosphere Specific humidity at 2500 m Relative humidity at 2 m The model calculation includes: Short- and long-wave radiation fluxes with respect to shading, reflection and re-radiation from building systems and the vegetation Transpiration, evaporation and sensible heat flux from the vegetation into the air, including full simulation of all plant physical parameters (e.g. photosynthesis rate) Surface and wall temperatures for each grid point and wall Water and heat exchange inside the soil system

7

Ali Toudert F, Mayer H. 2006. Numerical study on the effects of aspect ratio and orientation of an urban street canyon on outdoor thermal comfort in hot and dry climate, Building & Environment, 41, pp. 94–108

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Calculation of bio-meteorological parameters such as Mean Radiant Temperature (MRT) or Predicted Mean Vote (PMV) Dispersion of inert gases and particles including sedimentation of particles on leaves and surfaces A shortcoming of ENVI-met is that buildings, which are modelled as blocks where width and length are multiples of grid cells, have no thermal mass and have constant indoor temperature.

Moreover, albedo and thermal transmission (U-value) for walls and roofs are the same for all buildings. However, given the focus of the present report on green infrastructure, these shortcomings are unlikely to be problematic.

The following simulation scenarios were run for the six locations shown in Figure 8: 1. Current Case – Current land cover conditions, using 2012 weather data 2. Future Base case – current land cover conditions, using 2050 UKCIP predicted climate data 3-6. Future ‘green’ cases – changed green cover using 2050 UKCIP predicted data. The changed green cover conditions were as follows: a. less green cover – 10% below current level (m10 case) b. slightly more green cover – 10% above current level (p10 case) c. more green cover – 20% above current level (p20 case) d. high green cover – 100% above current level (p100 case)

6.2 Air temperature effects During the daytime city centre locations exhibit minimal changes to air temperature while the suburban/rural sites have marked decrease in air temperature at increased levels of green cover (Figure 9). Increased shading (tall buildings) and/or increased shading with green roofs however lead to significant cooling in the city centre. The situation at nighttime (Figure 10) is different, in that there is a consistent pattern of cooling at all sites.

Effect over the 2050 base case 0.1 0.0 Glasgow CC W

Glasgow CC E

Paisley

Clyde Gateway

Wishaw

Hamilton

-0.1 -0.2 -0.3 -0.4 -0.5 2050 -10%

2050 +10%

2050 +20%

2050 +100%

Figure 9: Air temperature effect of green infrastructure – daytime

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Notes: The slight increase in temperature at Glasgow CC-E is an artefact of the location of the changes in green cover relative to the point for which the data is plotted in the figure above. An area averaged change in temperature, as detailed in Table 3 is more representative of the cooling effect in the entire simulated domain area.

Effect over the 2050 base case 0.1 0.1 0.0 Glasgow CC W

Glasgow CC E

Paisley

Clyde Gateway

Wishaw

Hamilton

-0.1 -0.1 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 2050 -10%

2050 +10%

2050 +20%

2050 +100%

Figure 10: Air temperature effect of green infrastructure – night time Figure 11 shows the average cooling expected over the course of summer in 2050. The overall effect of green cover on air temperature under future climate scenario is encouraging.

Effect over 2050 base case 0.1 0.0 Glasgow CC W

Glasgow CC E

Paisley

Glasgow Gateway

Wishaw

Hamilton

-0.1 -0.2 -0.3 -0.4 -0.5 2050 -10%

2050 +10%

2050 +20%

2050 +100%

Figure 11: Average daily summertime effects Fig 12 shows the level at which green cover makes the most impact is approx. 20% above the current level, with diminishing returns thereafter. At this level of green cover a net cooling of 0.3oC can be expected in 2050. This would be about a third of the extra heat island effect predicted for the Glasgow conurbation8.

8

Kershaw T, Sanderson M, Coley D, Eames M. 2010. Estimation of the urban heat island for UK climate change projections, Building Serv. Eng. Res. Technol., 31, pp. 251–263. DOI: 10.1177/0143624410365033

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Climate change adaptation – Urban overheating study

0.10 0.05 0.00 Change in Air Temperature (oC)

-5

-0.05

0

5

10

15

20

25

-0.10 -0.15

y = 0.0007x2 - 0.0295x - 0.0143 R² = 0.9212

-0.20 -0.25 -0.30 -0.35 -0.40 -0.45

Green cover (%)

Figure 12: Average daily summertime temperature effect of green cover The range in temperature change due to green cover change across the entire simulated domain (400m x 400m area) is tabulated Table 3. A vast majority of pixels – i.e. to 91.2% (Glasgow City Centre) – 99.8% (Wishaw) of the simulated area – showed up to 0.5oC reduction in air temperature. Based on the expected heat island effect for the Glasgow area8 this local cooling effect would be more than half of the total urban warming expected in 2050. The case of Gla CC-E (around George Square) is interesting, in that the lack of green cover increase could lead to 19% of the area showing a slight increase (up to 0.25oC) in temperature under the 2050 base case scenario.

Table 3: Range of air temperature changes across the simulated domains Gla CC-W < -1.00 -1.00 to -0.75 -0.75 to -0.50 -0.50 to -0.25 -0.25 to 0.00 0.00 to +0.25 +0.25 to +0.50 +0.50 to +0.75 +0.75 to +1.00 > +1.00

0.3% 90.9% 8.8%

Gla CC-E

81.0% 19.0%

Paisley

1.8% 94.6% 3.1% 0.5% 0.0%

Clyde Gateway 0.2% 0.6% 1.9% 93.3% 4.1%

Wishaw

Hamilton

0.1% 3.2% 96.6% 0.0%

0.4% 2.6% 95.9% 1.1% 0.1%

6.3 Surface temperature effects Surface temperatures associated with green infrastructure changes show more marked decrease (Figure 13 [daytime] and Figure 14 [nighttime]), especially during the day and in conjunction with increased shading/green cover (city centre sites) or increased green cover (suburban sites).

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Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Effect over 2050 base case 1.5 1.0 0.5 0.0 Glasgow CC W

Glasgow CC E

Paisley

Clyde Gateway

Wishaw

Hamilton

-0.5 -1.0 -1.5 -2.0 -2.5 2050 -10%

2050 +10%

2050 +20%

2050 +100%

Fig 13: Surface temperature effects at daytime Effect over 2050 base case 0.2 0.1 0.0 Glasgow CC W

Glasgow CC E

Paisley

Clyde Gateway

Wishaw

Hamilton

-0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 2050 -10%

2050 +10%

2050 +20%

2050 +100%

Fig 14: Surface temperature effects at nighttime While surface temperatures are particularly susceptible to the vagaries of local shading (or lack thereof) the purpose here was to compare results with that of other UK cities, most notably Manchester9, where a 10-20% increase in green cover led to up to 2oC decrease in surface temperature while green roofs in city centre led to a lowering of surface temperature by up to 6oC. Figure 13 indicates the effect of green cover in the GCVGNP will be very similar.

9

Gill SE, Handley JF, Ennos AR, Pauleit S. 2007. Adapting cities for climate change: the role of the green infrastructure, Built Environment, 33, pp. 115-133

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Climate change adaptation – Urban overheating study

6.4 Thermal comfort implications of green cover A commonly used measure of human thermal perception is the Predicted Mean Vote (PMV), based on BS EN ISO 773010. PMV is a ‘comfort vote’ on a 7-point scale: +3.0 = Hot +2.0 = Warm +1.0 = Slightly Warm ±0.0 = Neutral (neither warm nor cool) -1.0 = Slightly Cool -2.0 = Cool -3.0 = Cold GCU’s previous work2 had explored the applicability of the PMV ‘comfort vote’ to Glasgow’s outdoor conditions and found it had good agreement with street users’ subjective thermal sensation (Figure 15).

ISO 7730 specifies a range of -1.0 to +1.0 within which approximately 75% of the subjects would be ‘satisfied’ with their thermal environment.

Measured Thermal Sensation in Glasgow

3.0

-3

2.5

y = 0.6404x + 0.788 R² = 0.9879

2.0 1.5 1.0 0.5 0.0 -2

-1

0

1

2

3

-0.5 -1.0 -1.5

Predicted Mean Vote (PMV)

Figure 15: Relationship between the PMV and measured thermal sensation in Glasgow

10

BS EN ISO 7730, 2005. Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria

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Climate change adaptation – Urban overheating study

Table 4: Predicted Mean Vote (PMV) due to a 20% increase in green cover in 2050 Gla CC - W < -2.0 -2.0 to -1.5 -1.5 to -1.0 -1.0 to -0.5 -0.5 to 0.0 0.0 to +0.5 +0.5 to 1.0 +1.0 to +1.5 +1.5 to +2.0 > 2.0

4.9% 31.8% 15.8% 0.6% 7.0% 40.0%

Gla CC - E

Paisley

3.6% 35.6% 15.4% 2.1% 6.8% 36.6%

0.7% 7.7% 31.1% 12.5% 5.8% 15.3% 24.6% 2.5%

Clyde Gateway

1.0% 20.4% 8.4% 3.9% 15.3% 44.2% 6.8%

Wishaw

Hamilton

1.4% 11.9% 9.2% 2.1% 9.6% 57.6% 8.2%

2.0% 8.9% 19.1% 8.4% 5.4% 18.7% 34.9% 2.6%

Based on ISO 7730 (i.e. a ‘comfort vote’ between -1.0 to +1.0 is acceptable to a majority of street users) 52.5-54.6% of the users in city centre would consider 2050 climate acceptable if a 20% increase in green cover could be provided. However 36.6-40% of the users in the city centre will still feel ‘hot’ under such a scenario. A combination of 20% greenery with tall buildings in the city centre (Table 5) would lead to 72.8-86% of the users feeling comfortable. In suburban and less built up areas however, Tables 4 and 5 indicate the thermal comfort effect of green cover will be more muted. A 100% increase in green cover will lead to significant improvement in perceived thermal comfort in three of the four less built up sites (Paisley, Clyde Gateway and Hamilton).

Table 5: ‘Best’ outcome in Predicted Mean Vote (PMV) in 2050

< -2.0 -2.0 to -1.5 -1.5 to -1.0 -1.0 to -0.5 -0.5 to 0.0 0.0 to +0.5 +0.5 to 1.0 +1.0 to +1.5 +1.5 to +2.0 > 2.0

Gla CC - W*

Gla CC - E*

Paisley**

Clyde Gateway**

Wishaw**

Hamilton**

3.6% 48.8% 33.6% 1.2%

0.0% 5.2% 42.9% 24.7% 1.5%

1.2% 14.4% 42.0% 14.5% 6.9% 9.6%

0.1% 8.1% 36.1% 6.2% 3.8% 14.7%

7.1% 19.3% 9.2% 4.0% 13.8%

3.3% 18.2% 29.7% 8.0% 9.8% 14.8%

3.6%

2.4%

10.6%

30.4%

44.1%

16.0%

9.2%

23.4%

0.8%

0.6%

2.5%

0.2%

Notes: ‘Best’ PMV outcomes are reached as follows: * - 20% increase in green cover with Tall buildings (two city centre sites) ** - 100% increase in green cover (all the other four sites)

7. Implications and Conclusions The simulation work carried out under the present project indicates that green infrastructure could play a significant role in mitigating the urban overheating expected under a warming climate in the GCV Region. From historical microclimate records it is clear that urban warming continues, even when the urban growth that fuelled it has subsided. There is also evidence that land cover / land use (as classified by the LCZ methodology) could explain much of the historical trends in urban warming.

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Climate change adaptation – Urban overheating study

Current simulations indicate that a green cover increase of approximately 20% over the present level could eliminate a third to a half of the expected extra urban heat island effect in 2050. This level of increase in green cover could also lead to local reductions in surface temperature by up to 2oC. Furthermore, over half of the street users would consider a 20% increase in green cover in the city centre to be thermally acceptable, even under a warm 2050 scenario. Additional strategies such as increased building cover could further improve the thermal comfort and air temperature patterns in the city centre.

7.1 Achieving green cover increase – an example In practical terms a 20% increase in green cover could be achieved in a number of different ways: mini-parks, street trees, grass areas, roof gardens, green walls or even urban forests. Not all green areas contribute equally to local cooling nor are they equal in their other environmental and sustainability benefits. Recognising this, planners have begun to develop weighting systems that captures the relative environmental performance of different types of green cover. The most widely used among these is the Green Area Ratio (GAR) method6. GAR is currently implemented in Berlin and has been adapted in Malmo (Sweden), several cities in South Korea and Seattle (USA). Appendix D shows the relative weighting of different types of green cover.

Appendix E shows the assumptions made and the method used in attempting to deliver a 20% increase in green cover in Glasgow city centre, using green parks, street trees, green roofs, green façade or a combination of these. Based on these the following fractions of green cover are possible in the Gla CC-W domain area (all fractions expressed as percentage of the total simulated domain area): Current green cover = 3.3% Possible street tree cover = 3.72% Possible roof area available for roof garden = 21% Possible façade cover available = 6.34% Table 6: Alternative approaches to increasing green cover by 20% in Gla. CC-W Scenario

Permeable vegetated 2 area (m )

1. A large park only 2. Street trees only 3. 50% of additional greenery in street tree, balance intensive roof garden 4. 50% of additional greenery in street tree, balance extensive roof garden 5. Mix of intensive (50%) and extensive (50%) roof garden 6. 50% of all ‘sun facing’ (i.e.

South & West) façade covered façade green

1,056

Street trees (Nos.)

528 264

Intensive Roof Garden 2 (m )

Extensive Roof Garden 2 (m )

Green façade

755

264

1,056

755

1,056 1,268

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Climate change adaptation – Urban overheating study

Table 6 shows some options to achieve 20% green cover increase at the Glasgow city centre west (Gla. CC-W) site. These range from introducing a park of 1,056m2 (32.5m x 32.5m) to planting up to 528 new street trees to extensive roof gardens up to 1,056m2 or introducing 1,268m2 of green façade at this site.

The foregoing therefore indicates that green cover increase could play a vital role in partially eliminating the expected urban overheating problem in the more urbanised regions of the GCV. The extent of green cover necessary to make a cooling impact is modest, and there are several options to achieve this. More work will be needed to evaluate the relative merits of specific green infrastructure interventions at specific urban sites, however, the present work indicates green cover could be a future adaptation strategy to at least partially overcome the urban overheating problem.

Author Contact Details Dr Rohinton Emmanuel Tel. 0141 331 3217 Email: rohinton.emmanuel@gcu.ac.uk

Contributors The authors wish to thank the following people for their contributions to this report: Mr Alessandro Loconsole – Gathering of historic time series data, land cover/land use analysis, determination of LCZ classes, ENVI-met simulations Mr Max Hislop, GCVGNP – Green infrastructure options, document review and comments Mr Alastair Corbett, GCVGNP – Green infrastructure options, document review and comments

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Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Appendix A Illustration of land cover characteristics at different Local Climate Zones (LCZ) SOURCE: STEWART AND OKE, 2009

Compact highrise

Open-set highrise

Compact midrise

Openset midrise

Compact lowrise

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Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Open-set lowrise

Dispersed lowrise

Lightweight lowrise

Extensive lowrise

Industrial processing

Table A-I: Local Climate Zone (LCZ) classification system 4

Source: Based on Stewart and Oke

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Climate change adaptation – Urban overheating study

Local Climate Zone (LZC)

sky

H:W

Zone Properties ZH RC

SF

Compact Highrise

0.25-0.45

>2

>90%

>35m

8

Open-set Highrise

0.40-0.70

7-8

0.30-0.60

5075% >90%

>30m

Compact Midrise

0.80-0.90

Compact Lowrise

0.30-0.50

1525m 1025m 3-10m

6-7

Open-set Midrise

Open-set Lowrise

0.55-0.75

5-6

>0.90

3-7m

5-6

Lightweight Lowrise Extensive Lowrise

0.30-0.50

2-4m

4-5

>0.90

4565% 2030% 7090% >80%

3-10

Dispersed Lowrise

0.751.25 0.751.25 0.200.30 1.001.50 0.500.75 0.100.20 1.001.50 <0.25

3-10m

5

Industrial Processing

0.70-0.90

0.2-0.5

45-65

5-10m

5-6

3050% >80%

5-6 6

 0.120.18 0.120.20 0.150.20 0.150.20 0.120.20 0.100.20 0.100.20 0.100.20 0.150.25 0.120.20

 -2 ½ -1 (J m s K  1,2001,700 1,2001,700 1,2002,000 800-1,500

QF -2 (Wm ) 100-150

1,2001,500 700-1,700

25-35

800-2,000

<10

600-1,000

<5

1,2001,500 1,5003,000

30-50

20-35 30-40 <10

10-15

>200

sky = Sky View Factor; H:W = building height to width ratio; SF = building surface fraction; ZH = roughness height; RC = terrain

roughness class;  = thermal admittance; QF = anthropogenic heat flux

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Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Appendix B The development of Local Climate Zones (LCZ) classes in the GCV Region

Figure B-I: LCZ Classes in the GCV Region Note: Hatched area indicates the extent of built up zone

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Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Figure B-II: Detailed view of LCZ classes with built cover categories

[5]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Figure B-III: detailed view of LCZ classes in Glasgow city centre Note: LCZ 2 = Black; LCZ 3 = Blue; LCZ 5-6 = Green; LCZ = 6 = Dark Green; LCZ 9 = Light Green

[6]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

Appendix C Land Cover Details of Selecetd Simulation Sites

Note: Both Gla CC-W and Gla CC-E are included in this image

[7]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

[8]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

[9]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

[10]

Climate change adaptation â&#x20AC;&#x201C; Urban overheating study

[11]

Climate change adaptation – Urban overheating study

Appendix D Environmental performance ratings for green infrastructure as implemented in Berlin, Germany6 Technique / cover type

Rating

Description

Impermeable surfaces

0.0

Impermeable surfaces, from which all stormwater is infiltrated on property

0.2

Non-vegetated, semipermeable surfaces

0.3

Vegetated, semi-permeable surfaces

0.5

Green façades

0.5

Extensive green roofs

0.5

Intensive green roofs and areas underlain by shallow subterranean structures Vegetated areas

0.7

Surfaces that do not allow the infiltration of water. Includes: roof surfaces, concrete, asphalt and pavers set upon impermeable surfaces or with sealed joints Includes surfaces that are disconnected from the sewer system. Collected water is instead allowed to infiltrate on site in a swale or rain garden. Guidelines for preventing groundwater and soil contamination must be followed Cover types that allow water infiltration, but do not support plant growth. Example include: brick, pavers and crushed stone Cover types that allow water infiltration and integrate vegetation such as grass. Examples include: wide-set pavers with grass joints, grass pavers and gravel-reinforced grassy areas Vines or climbing plants growing (often from ground) on training structures such as trellises which are attached to a building. The façade’s area is measured as the vertical area the selected species could cover after 10 years of growth up to a height of 10m; window areas are subtracted from the calculation Green roofs with substrate/soil depths of less than 80 cm. However, Berlin excludes green roofs constructed on high-rise buildings Green roofs with substrate/soil depths of greater than 80 cm. This category includes subterranean garages Areas which allow unobstructed infiltration of water without evaluation of the quality or type of vegetation present. Examples range from lawn to gardens and naturalistic wooded areas

1.0

6

Adapted and translated into English by Keely, 2011 from the German Standard: SenStadt (Senatsverwaltung fuer Stadtentwicklung Berlin), Referat I E, 2003b. Handbuch der Berliner Landschaftspläne. [Handbook of Berlin’s landscape plans]. Auflage März 2000, Ergaenzlieferung. [online] Available from: http://www.stadtentwicklung.berlin.de/umwelt/landschaftsplanung/handbuch/de/biotopflaechenfakt or/index.shtml [in German: English abstract]

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Climate change adaptation – Urban overheating study

Appendix E Assumptions and calculation method to derive green infrastructure options for Gla CC-W Parameter

Quantity

1 2 3

Current green cover Total area of the simulation domain Available sidewalk

3.3% 2 160,000m 11.15%

4 5

Standard cover of a street tree Distance between trees

6 7

Total available sidewalk area Possible No of street trees in domain Total possible street tree cover Possible street cover as a fraction of total domain area Current built cover Usable building cover

17,840m 1,486

Total usable building area Total usable building area as a fraction of domain Assumed average height of building Total No of block in domain Average block size Total available Façade area Total usable façade area as a fraction of domain area

33,549m 21%

8 9 10 11

12 13 14 15 16 17 18

Remarks Measured from GIS maps 400m x 400m Measured from GIS maps (assumes average sidewalk = 2m wide)

2

4m 6m

2

5,947m 3.72%

2

52.42% 40%

2

12m 13 35m × 30m 2 10,140m 6.34%

[13]

Thus, each tree would 2 ‘cover’ 12m of sidewalk [2] × [3] [6] ÷ ([5] × 2) [7] × [4] [8] × 100 ÷ [2] Measured from GIS maps Based on a visual inspection of domain area for buildings with flat roof [10] × [11] × [2] [12] ÷ [2] Based on visual inspection Based on visual inspection [16] × [14] × [15] [17] × 100 ÷ [2]