STAR tools
Surface temperature and runoff tools for assessing the potential of green infrastructure in adapting urban areas to climate change
Surface temperature and runoff tools for assessing the potential of green infrastructure in adapting urban areas to climate change
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The STAR tools allow users to assess the potential of green infrastructure in adapting their areas to climate change. They include a surface temperature tool and a surface runoff tool. The STAR tools can be used at a neighbourhood scale (in the North West of England and beyond) to test the impact of different land cover scenarios of greening and development on surface temperatures and runoff, under different temperature and precipitation scenarios. Outputs of the STAR tools can be used to inform policy, strategy, and development. They are of use to a range of professionals and organisations with an interest in understanding more about the influence of urban greening on their local climate. This includes planners, developers, masterplanners, local authorities, urban forestry initiatives, NGOs and academics. Please use the below links to find guidance on
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Recommended citation (including when referring to your results): STAR tools, or in full as below.
The Mersey Forest & The University of Manchester (2011). STAR tools: surface temperature and runoff tools for assessing the potential of green infrastructure in adapting urban areas to climate change. Part of the EU Interreg IVC GRaBS project. www.ginw.co.uk/climatechange.
If you have any comments or would like any further information please contact susannah.gill@merseyforest.org.uk | Acknowledgements
The Mersey Forest & The University of Manchester (2011). STAR tools: surface temperature and runoff tools for assessing the potential of green infrastructure in adapting urban areas to climate change. Part of the EU Interreg IVC GRaBS project. www.ginw.co.uk/climatechange.
If you have any comments or would like any further information please contact susannah.gill@merseyforest.org.uk | Acknowledgements
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Using the tools
If you are new to the STAR tools it is recommended that you familiarise yourself with the tools by first running them for only 1 or 2 study areas, land cover scenarios, and temperature scenarios or precipitation scenarios. This will enable you to get a better understanding how the tools work, the input requirements, and the results, before you set up more complicated runs.
The STAR tools have 3 main steps for you to work through:
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s. It is strongly advised that you click on these as the pop-out boxes that they open up will:
If you are new to the STAR tools it is recommended that you familiarise yourself with the tools by first running them for only 1 or 2 study areas, land cover scenarios, and temperature scenarios or precipitation scenarios. This will enable you to get a better understanding how the tools work, the input requirements, and the results, before you set up more complicated runs.
The STAR tools have 3 main steps for you to work through:
- Step 1: Define your study area(s)
- Step 2: Amend the input values
- Step 3: Results
- Help you understand the tools
- Guide you in the completion of each step
- Expand on the input requirements, data sources, and assumptions made
- Help you to interpret the results
- Point you towards further information which may be of use.
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Why are the STAR tools needed?
Urban areas are often warmer than the surrounding countryside, an effect known as the urban heat island (e.g. Wilby, 2003; Graves et al, 2001; Oke, 1987). This is in part due to an increase in built surfaces and a decrease in vegetated surfaces. This effect will be further compounded by increased summer warming with climate change (Wilby, 2003). The warming of the urban environment in summer is an important issue because of its implications for human comfort and well being (e.g. Svensson & Eliasson, 2002; Eliasson, 2000), this could also impact on the attractiveness of urban areas as places to live, work and invest. It is estimated that the European summer heatwave of 2003 claimed 52,000 lives, many in urban areas (Larsen, 2006). By the middle of the century such extreme temperatures will be normal, whereas by the end of the century they will seem mild (Stott et al, 2004). Increasing the amount of green infrastructure in urban areas can help to reduce temperatures (Gill et al, 2007).
Due to a greater surface sealing, there is a higher proportion of surface runoff in urban areas following a rainfall event (Bridgman et al, 1995). Climate change, with higher intensity precipitation events, could further exacerbate this impact (e.g. Pilling & Jones, 1999). An increase in surface runoff is an important issue because it can result in both riverine flooding, as well as in combined sewer overflows, where the capacity of drains is overwhelmed by the runoff. Flooding can have severe impacts for people, built infrastructure, and the economy (e.g. Reacher et al, 2004; Baxter et al, 2002; Shackley et al, 2001; Graves & Phillipson, 2000). Increasing the amount of green infrastructure in urban areas can help to reduce the amount of surface runoff (Gill et al, 2007) and, when combined with sustainable drainage systems, could help to reduce flooding.
The STAR tools can be used at a neighbourhood scale to assess the impact on surface temperatures and surface runoff of different land cover scenarios (e.g. where you increase or decrease the proportion of green infrastructure), under different temperature and precipitation scenarios.
Baxter, P.J., Moller, I., Spencer, T., Spence, R.J. and Tapsell, S. (2002). Flooding and climate change. In Health Effects of Climate Change in the UK, P. Baxter, A. Haines, M. Hulme, R.S. Kovats, R. Maynard, D.J. Rogers and P. Wilkinson (Eds.). Department of Health, London, 152-192.
Bridgman, H., Warner, R. and Dodson, J. (1995). Urban Biophysical Environments. Oxford University Press, Oxford.
Eliasson, I. (2000). The use of climate knowledge in urban planning. Landscape and Urban Planning, 48 (1-2), 31-44.
Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
Graves, H., Watkins, R., Westbury, P. and Littlefair, P. (2001). Cooling Buildings in London: Overcoming the Heat Island. BRE & DETR, London.
Graves, H.M. and Phillipson, M.C. (2000). Potential Implications of Climate Change in the Built Environment, FBE Report 2. BRE, Centre for Environmental Engineering, East Kilbride.
Larsen, J. (2006). Setting the record straight: more than 52,000 Europeans died from heat in summer 2003. Earth Policy Institute. www.earth-policy.org/plan_b_updates/2006/update56); N.B. This is an update on the 2003 estimate of 35,000 deaths (an estimate that was cited in the Stern Review on the economics of climate change; see Larsen, J. (2003). Record heat wave in Europe takes 35,000 lives: far greater losses may lie ahead. Earth Policy Institute. www.earth-policy.org/plan_b_updates/2003/update29)
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Pilling, C. and Jones, J.A.A. (1999). High resolution climate change scenarios: implications for British runoff. Hydrological Processes, 13 (17), 2877-2895.
Reacher, M., McKenzie, K., Lane, C., Nichols, T., Kedge, I., Iversen, A., Hepple, P., Walter, T., Laxton, C. and Simpson, J. (2004). Health impacts of flooding in Lewes: a comparison of reported gastrointestinal and other illness and mental health in flooded and non-flooded households. Communicable Disease and Public Health, 7 (1), 1-8.
Shackley, S., Kersey, J., Wilby, R. and Fleming, P. (2001). Changing by Degrees: The Potential Impacts of Climate Change in the East Midlands. Ashgate, Aldershot.
Stott, P.A., Stone, D.A. and Allen, M.R. (2004). Human contribution to the European heatwave of 2003. Nature, 432 (7017), 610-614.
Svensson, M.K. and Eliasson, I. (2002). Diurnal air temperatures in built-up areas in relation to urban planning. Landscape and Urban Planning, 61 (1), 37-54.
Wilby, R.L. (2003). Past and projected trends in London's urban heat island. Weather, 58 (7), 251-26.
Urban areas are often warmer than the surrounding countryside, an effect known as the urban heat island (e.g. Wilby, 2003; Graves et al, 2001; Oke, 1987). This is in part due to an increase in built surfaces and a decrease in vegetated surfaces. This effect will be further compounded by increased summer warming with climate change (Wilby, 2003). The warming of the urban environment in summer is an important issue because of its implications for human comfort and well being (e.g. Svensson & Eliasson, 2002; Eliasson, 2000), this could also impact on the attractiveness of urban areas as places to live, work and invest. It is estimated that the European summer heatwave of 2003 claimed 52,000 lives, many in urban areas (Larsen, 2006). By the middle of the century such extreme temperatures will be normal, whereas by the end of the century they will seem mild (Stott et al, 2004). Increasing the amount of green infrastructure in urban areas can help to reduce temperatures (Gill et al, 2007).
Due to a greater surface sealing, there is a higher proportion of surface runoff in urban areas following a rainfall event (Bridgman et al, 1995). Climate change, with higher intensity precipitation events, could further exacerbate this impact (e.g. Pilling & Jones, 1999). An increase in surface runoff is an important issue because it can result in both riverine flooding, as well as in combined sewer overflows, where the capacity of drains is overwhelmed by the runoff. Flooding can have severe impacts for people, built infrastructure, and the economy (e.g. Reacher et al, 2004; Baxter et al, 2002; Shackley et al, 2001; Graves & Phillipson, 2000). Increasing the amount of green infrastructure in urban areas can help to reduce the amount of surface runoff (Gill et al, 2007) and, when combined with sustainable drainage systems, could help to reduce flooding.
The STAR tools can be used at a neighbourhood scale to assess the impact on surface temperatures and surface runoff of different land cover scenarios (e.g. where you increase or decrease the proportion of green infrastructure), under different temperature and precipitation scenarios.
Baxter, P.J., Moller, I., Spencer, T., Spence, R.J. and Tapsell, S. (2002). Flooding and climate change. In Health Effects of Climate Change in the UK, P. Baxter, A. Haines, M. Hulme, R.S. Kovats, R. Maynard, D.J. Rogers and P. Wilkinson (Eds.). Department of Health, London, 152-192.
Bridgman, H., Warner, R. and Dodson, J. (1995). Urban Biophysical Environments. Oxford University Press, Oxford.
Eliasson, I. (2000). The use of climate knowledge in urban planning. Landscape and Urban Planning, 48 (1-2), 31-44.
Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
Graves, H., Watkins, R., Westbury, P. and Littlefair, P. (2001). Cooling Buildings in London: Overcoming the Heat Island. BRE & DETR, London.
Graves, H.M. and Phillipson, M.C. (2000). Potential Implications of Climate Change in the Built Environment, FBE Report 2. BRE, Centre for Environmental Engineering, East Kilbride.
Larsen, J. (2006). Setting the record straight: more than 52,000 Europeans died from heat in summer 2003. Earth Policy Institute. www.earth-policy.org/plan_b_updates/2006/update56); N.B. This is an update on the 2003 estimate of 35,000 deaths (an estimate that was cited in the Stern Review on the economics of climate change; see Larsen, J. (2003). Record heat wave in Europe takes 35,000 lives: far greater losses may lie ahead. Earth Policy Institute. www.earth-policy.org/plan_b_updates/2003/update29)
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Pilling, C. and Jones, J.A.A. (1999). High resolution climate change scenarios: implications for British runoff. Hydrological Processes, 13 (17), 2877-2895.
Reacher, M., McKenzie, K., Lane, C., Nichols, T., Kedge, I., Iversen, A., Hepple, P., Walter, T., Laxton, C. and Simpson, J. (2004). Health impacts of flooding in Lewes: a comparison of reported gastrointestinal and other illness and mental health in flooded and non-flooded households. Communicable Disease and Public Health, 7 (1), 1-8.
Shackley, S., Kersey, J., Wilby, R. and Fleming, P. (2001). Changing by Degrees: The Potential Impacts of Climate Change in the East Midlands. Ashgate, Aldershot.
Stott, P.A., Stone, D.A. and Allen, M.R. (2004). Human contribution to the European heatwave of 2003. Nature, 432 (7017), 610-614.
Svensson, M.K. and Eliasson, I. (2002). Diurnal air temperatures in built-up areas in relation to urban planning. Landscape and Urban Planning, 61 (1), 37-54.
Wilby, R.L. (2003). Past and projected trends in London's urban heat island. Weather, 58 (7), 251-26.
×
Input requirements for the STAR tools
The tools are intended to have minimal input requirements, although this will vary depending on where they are being applied to.
(N.B. A good way to get an understanding of the input requirements is to proceed through Step 1: Define your study area(s), selecting only one study area, thereby allowing you to get quickly to Step 2: Amend the input values, where you will see the input requirements. Here, you can also click on the coloured button in the top right of this screen to switch between the surface temperature and runoff tools to see the input required for each).
Users in the North West of England
Default values for all of the necessary input parameters except hydrologic soil type (including temperature scenarios, precipitation scenarios and land cover scenarios) are provided for the North West of England. It is strongly recommended that you read the notes provided which detail where the default values are drawn from and any assumptions made. It is also recommended that you use your own input values to overwrite the default where you have better data available to you or want to experiment with changing some of the values.
Users outside of the North West of England
The STAR tools can be used outside of the North West of England (e.g. within the UK, Europe, and potentially further afield), but this will require more engagement and caution from the user (potentially making them of more use here to academic or more technical users, although notes are provided throughout to guide their use).
Users of the STAR tools outside of the North West of England will have some default input values automatically provided, along with information on where the default values are drawn from and any assumptions made. They can choose to accept the default values, or to overwrite them if they have better data or want to experiment with changing some of the values. Users outside of the North West of England will need to pay more attention to assumptions made to see if they are relevant to their area, as many of the default values are drawn from assumptions made for Greater Manchester, in North West England, during the ASCCUE project.
Some input values will not be automatically provided to users outside of the North West of England; users will be required to input their own values. This includes information on temperature scenarios and precipitation scenarios, land cover scenarios, and hydrologic soil type.
The tools are intended to have minimal input requirements, although this will vary depending on where they are being applied to.
(N.B. A good way to get an understanding of the input requirements is to proceed through Step 1: Define your study area(s), selecting only one study area, thereby allowing you to get quickly to Step 2: Amend the input values, where you will see the input requirements. Here, you can also click on the coloured button in the top right of this screen to switch between the surface temperature and runoff tools to see the input required for each).
Users in the North West of England
Default values for all of the necessary input parameters except hydrologic soil type (including temperature scenarios, precipitation scenarios and land cover scenarios) are provided for the North West of England. It is strongly recommended that you read the notes provided which detail where the default values are drawn from and any assumptions made. It is also recommended that you use your own input values to overwrite the default where you have better data available to you or want to experiment with changing some of the values.
Users outside of the North West of England
The STAR tools can be used outside of the North West of England (e.g. within the UK, Europe, and potentially further afield), but this will require more engagement and caution from the user (potentially making them of more use here to academic or more technical users, although notes are provided throughout to guide their use).
Users of the STAR tools outside of the North West of England will have some default input values automatically provided, along with information on where the default values are drawn from and any assumptions made. They can choose to accept the default values, or to overwrite them if they have better data or want to experiment with changing some of the values. Users outside of the North West of England will need to pay more attention to assumptions made to see if they are relevant to their area, as many of the default values are drawn from assumptions made for Greater Manchester, in North West England, during the ASCCUE project.
Some input values will not be automatically provided to users outside of the North West of England; users will be required to input their own values. This includes information on temperature scenarios and precipitation scenarios, land cover scenarios, and hydrologic soil type.
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Study area size
The STAR tools are best used at a neighbourhood scale, although they can be run for more than one neighbourhood, or study area, at a time.
The tools allow users in the North West of England to select their study area(s) from wards, lower layer super output areas and 5km x 5km grid squares, upload their own shapefile boundaries, draw study area boundaries straight onto a map, or list their study areas. Other UK users can also upload their own shapefile boundaries, draw study area boundaries straight onto a map, or list their study areas. Outside of the UK, users will only be able to list their study areas.
Whilst there is no strict limit on the study area size, their use is recommended for areas between 0.04 and 5km² (or 4ha to 500ha). It is possible, however, to use the tools for larger or smaller study areas but it is important that you make an informed decision if doing so.
The lower limit is especially important if you are in North West England and using the default land cover scenario data. This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
For all users, when considering the size of the study area, the important point to remember is that the tools output is an average value for each study area; so, for example, you get one maximum surface temperature for each study area. It is suggested that a city or local authority level would be too large, whilst a street or a couple of buildings may be too small.
If the study area you are considering is large and variable in surface cover or soil type (e.g. a city or local authority level), you will not see the variation in maximum surface temperatures and surface runoff across the different areas (e.g. where there is more green infrastructure). So, for example, you would not see areas within a city or district that are warmer or cooler, or have higher or lower levels of runoff; you may therefore not be able to use the results to target specific interventions or policies as they would be too broad-brush. You may therefore want to break down your area of interest into a series of smaller, less variable study areas.
On the other hand, if the study area you are considering is too small (e.g. a single street, or a development encompassing a small number of buildings) then, whilst the STAR tools could still be useful in developing a general understanding of the issues, you may want to use a different tool to see very local differences in micro-climate and hydrology in order to help you to design the site. For example, a tool such as ENVI-met (which is freely available) could be of more use in determining the temperatures across the site; similarly, looking at the site using Sustainable Drainage System design principles could be more useful at this scale in terms of considering runoff.
As an example, when the models underpinning the STAR tools were used in the ASCCUE project, Greater Manchester was divided up into polygons referred to as Urban Morphology Types and the tools were run for these polygons (Gill, 2006; Gill et al, 2008; Gill et al, 2007). The Urban Morphology Type polygons distinguished between different land use types, so that there was less variation (e.g. in the proportion of green infrastructure) across them (figure 1). The average size of these polygons was 0.3km² (31 ha), but this size varied greatly. In addition, the models were also run for some selected neighbourhoods within Greater Manchester; the smallest of these was Castlefield at 0.4 km² (43 ha), the largest was the ward of Didsbury at 4.4km² (436 ha) (figure 2).
Figure 1. Urban Morpohology Type classification for Greater Manchester, the models underpinning the STAR tools were previously used for these polygons (Gill, 2006)
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Figure 2. Manchester city centre and Castlefield areas for which the models underpinning the STAR tools were previously used (1997 aerial photograph from Cities Revealed; Gill, 2006)
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Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf; refer to chapters 3 and 7.3 in particular.
Gill, S.E., Handley, J.F., Ennos, A.R., Pauleit, S., Theuray, N. and Lindley, S.J. (2008). Characterising the urban environment of UK cities and towns: a template for landscape planning. Landscape and Urban Planning. 87, 210-222.
Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
The STAR tools are best used at a neighbourhood scale, although they can be run for more than one neighbourhood, or study area, at a time.
The tools allow users in the North West of England to select their study area(s) from wards, lower layer super output areas and 5km x 5km grid squares, upload their own shapefile boundaries, draw study area boundaries straight onto a map, or list their study areas. Other UK users can also upload their own shapefile boundaries, draw study area boundaries straight onto a map, or list their study areas. Outside of the UK, users will only be able to list their study areas.
Whilst there is no strict limit on the study area size, their use is recommended for areas between 0.04 and 5km² (or 4ha to 500ha). It is possible, however, to use the tools for larger or smaller study areas but it is important that you make an informed decision if doing so.
The lower limit is especially important if you are in North West England and using the default land cover scenario data. This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
For all users, when considering the size of the study area, the important point to remember is that the tools output is an average value for each study area; so, for example, you get one maximum surface temperature for each study area. It is suggested that a city or local authority level would be too large, whilst a street or a couple of buildings may be too small.
If the study area you are considering is large and variable in surface cover or soil type (e.g. a city or local authority level), you will not see the variation in maximum surface temperatures and surface runoff across the different areas (e.g. where there is more green infrastructure). So, for example, you would not see areas within a city or district that are warmer or cooler, or have higher or lower levels of runoff; you may therefore not be able to use the results to target specific interventions or policies as they would be too broad-brush. You may therefore want to break down your area of interest into a series of smaller, less variable study areas.
On the other hand, if the study area you are considering is too small (e.g. a single street, or a development encompassing a small number of buildings) then, whilst the STAR tools could still be useful in developing a general understanding of the issues, you may want to use a different tool to see very local differences in micro-climate and hydrology in order to help you to design the site. For example, a tool such as ENVI-met (which is freely available) could be of more use in determining the temperatures across the site; similarly, looking at the site using Sustainable Drainage System design principles could be more useful at this scale in terms of considering runoff.
As an example, when the models underpinning the STAR tools were used in the ASCCUE project, Greater Manchester was divided up into polygons referred to as Urban Morphology Types and the tools were run for these polygons (Gill, 2006; Gill et al, 2008; Gill et al, 2007). The Urban Morphology Type polygons distinguished between different land use types, so that there was less variation (e.g. in the proportion of green infrastructure) across them (figure 1). The average size of these polygons was 0.3km² (31 ha), but this size varied greatly. In addition, the models were also run for some selected neighbourhoods within Greater Manchester; the smallest of these was Castlefield at 0.4 km² (43 ha), the largest was the ward of Didsbury at 4.4km² (436 ha) (figure 2).
Figure 1. Urban Morpohology Type classification for Greater Manchester, the models underpinning the STAR tools were previously used for these polygons (Gill, 2006)
Figure 2. Manchester city centre and Castlefield areas for which the models underpinning the STAR tools were previously used (1997 aerial photograph from Cities Revealed; Gill, 2006)
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf; refer to chapters 3 and 7.3 in particular.
Gill, S.E., Handley, J.F., Ennos, A.R., Pauleit, S., Theuray, N. and Lindley, S.J. (2008). Characterising the urban environment of UK cities and towns: a template for landscape planning. Landscape and Urban Planning. 87, 210-222.
Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
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What output do the STAR tools provide?
Surface temperature tool
The surface temperature tool will give the average maximum surface temperature for the study area(s) of interest. Depending on the temperature scenarios selected and whether the tool is run for different land cover scenarios there will be a number of maximum surface temperatures provided. These outputs are provided in a table that can be printed or exported to Excel.
See the note on understanding and interpreting the results of the surface temperature tool for examples of how you can then display this output on graphs or maps.
Surface runoff tool
The surface runoff tool will give the percentage and volume of surface runoff for the study area(s) of interest. This output is available for daily precipitation depths of 0-100mm. You can choose to highlight selected precipitation scenarios and to run the tool for different land cover scenarios. These outputs are provided in a table that can be printed or exported to Excel.
See the note on understanding and interpreting the results of the surface runoff tool for examples of how you can then display this output on graphs or maps.
Surface temperature tool
The surface temperature tool will give the average maximum surface temperature for the study area(s) of interest. Depending on the temperature scenarios selected and whether the tool is run for different land cover scenarios there will be a number of maximum surface temperatures provided. These outputs are provided in a table that can be printed or exported to Excel.
See the note on understanding and interpreting the results of the surface temperature tool for examples of how you can then display this output on graphs or maps.
Surface runoff tool
The surface runoff tool will give the percentage and volume of surface runoff for the study area(s) of interest. This output is available for daily precipitation depths of 0-100mm. You can choose to highlight selected precipitation scenarios and to run the tool for different land cover scenarios. These outputs are provided in a table that can be printed or exported to Excel.
See the note on understanding and interpreting the results of the surface runoff tool for examples of how you can then display this output on graphs or maps.
×
What are the potential applications of the STAR tools?
The outputs of the STAR tools are potentially of use to a range of professionals and organisations with an interest in understanding more about the influence of urban green infrastructure on their local climate, especially in a changing climate (see the note on why the tools are needed). This includes planners, developers, masterplanners, other local authority officers, urban forestry initiatives, NGOs, and academics.
Some examples of their potential use include:
The outputs of the STAR tools are potentially of use to a range of professionals and organisations with an interest in understanding more about the influence of urban green infrastructure on their local climate, especially in a changing climate (see the note on why the tools are needed). This includes planners, developers, masterplanners, other local authority officers, urban forestry initiatives, NGOs, and academics.
Some examples of their potential use include:
- To determine surface temperature and runoff in a particular area.
- To assess how surface temperature and runoff in a particular area may vary under future temperature scenarios and precipitation scenarios.
- To assess how surface temperature and runoff in a particular area may vary as the proportion of green infrastructure in the area is increased or decreased.
- To assess how surface temperature and runoff in a particular area may vary as you change land cover scenarios (e.g. if the proportion of green infrastructure is increased or decreased), under future temperature and precipitation scenarios.
- To assess the surface temperature and runoff implications of a proposed development at the neighbourhood scale, and to help to determine better outcomes by increasing the proportion of green infrastructure within the development. N.B. The tools will not show the impact of the development on an adjacent neighbourhood (e.g. if it makes it warmer because the new development is obstructing colder air flows).
- To use this information to guide policy and strategy development; both urban development and green infrastructure policies.
- To use this information to set targets for green infrastructure provision, within new developments and existing neighbourhoods.
- To use this information to inform new developments, and the recommended amount of green infrastructure provision within them.
- To use this information to guide green infrastructure creation within particular areas. For example, targeting policies to increase green infrastructure in areas which currently have the highest temperatures, or the most surface runoff.
- To use this information to make the case for an increase in green infrastructure within particular areas. For example, residential areas or town centres which currently have a low proportion of green infrastructure.
- To use this information to demonstrate why green infrastructure should not be lost from an area. For example, residential areas which currently have a high proportion of green infrastructure; or areas where the soils allow the water to infiltrate into them rather creating surface water runoff.
- To use this information to demonstrate how initiatives to increase green infrastructure are contributing to climate change adaptation targets.
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What are the models underpinning the STAR tools?
The underlying models were used by The University of Manchester in the ASCCUE project where they were applied to Greater Manchester (Gill, 2006; Gill et al, 2007). They had previously been developed and used in Merseyside (Whitford et al, 2001). As part of the GRaBS project the surface temperature model was also used in Catania, Italy (Cuvato & Ianni, 2011).
Surface temperature model
The surface temperature model was developed from an urban climate model used in Kuala Lumpur, Malaysia (Tso et al, 1991; 1990). The model is based upon an energy balance equation, where the incoming radiation from the sun (R) is partitioned into a sensible heat flux due to convection (H), a latent heat flux due to evaporation (L), a conductive heat flux into the soil (G), and a heat flux to storage in the built environment (M). (So, R=H+L+G+M). Recognised physical equations are used to represent each of these terms. The model output is surface temperature.
As it is used in the STAR tools, the model:
Surface runoff model
The surface runoff model is based upon the US Soil Conservation Service approach (Soil Conservation Service, 1972). It takes a daily rainfall amount, and then calculates how much of this rainfall will not be converted to surface runoff; this could be because this rainfall is intercepted (e.g. by plants and vegetation), infiltrated (e.g. into the soil), or stored (e.g. on surfaces). The remaining rainfall is converted to surface runoff.
The method is based both on theory as well as on the results of empirical studies into precipitation runoff from many small watersheds in the US. It assigns 'Curve Numbers' to a range of different surface types, depending on the hydrological properties of the soil, and how wet it has been in the preceding 5 days. The more impervious the surface (e.g. built and sealed surfaces) the higher the curve number is; whilst the less impervious the surface (e.g. vegetated surfaces), the lower the curve number is. A combined 'Curve Number' can then be applied to the area of interest by weighting all of the individual curve numbers according to the area they cover.
As it is used in the STAR tools, the model:
Brown, R.D. and Gillespie, T.J. (1995). Microclimate Landscape Design: Creating Thermal Comfort and Energy Efficiency. John Wiley & Sons, Chichester.
Cuvato, E. and Ianni, I. (2011). Urban planning strategies for climate adaptation assessment: the case study of Catania metropolitan area. Presentation to EcoCities and University of Catania workship, 7th April 2011, Catania, Sicily.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf (see chapters 5 and 6 in particular). Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
Mansell, M.G. (2003). Rural and urban hydrology. Thomas Telford, London.
Matzarakis, A., Mayer, H. and Iziomon, M. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43, 76-84.
Natural Resources Conservation Service (1986). Urban Hydrology for Small Watersheds, Technical Release 55. United States Department of Agriculture.
Smith, C.L., Webb, A., Levermore, G.J., Lindley, S.J. and Beswick, K. (2011). Fine-scale spatial temperature patterns across a UK conurbation. Climatic Change.
Soil Conservation Service (1972). Soil Conservation Service National Engineering Handbook - Section 4: Hydrology. United States Department of Agriculture, Soil Conservation Service, Washington, DC.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1990). An improvement to the basic energy balance model for urban thermal analysis. Energy and Buildings, 14 (2), 143-152.
Whitford, V. Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The underlying models were used by The University of Manchester in the ASCCUE project where they were applied to Greater Manchester (Gill, 2006; Gill et al, 2007). They had previously been developed and used in Merseyside (Whitford et al, 2001). As part of the GRaBS project the surface temperature model was also used in Catania, Italy (Cuvato & Ianni, 2011).
Surface temperature model
The surface temperature model was developed from an urban climate model used in Kuala Lumpur, Malaysia (Tso et al, 1991; 1990). The model is based upon an energy balance equation, where the incoming radiation from the sun (R) is partitioned into a sensible heat flux due to convection (H), a latent heat flux due to evaporation (L), a conductive heat flux into the soil (G), and a heat flux to storage in the built environment (M). (So, R=H+L+G+M). Recognised physical equations are used to represent each of these terms. The model output is surface temperature.
As it is used in the STAR tools, the model:
- Calculates the average surface temperature for an area, rather than the air temperature. Whilst air temperature provides a simple estimator of human thermal comfort, it is less reliable outdoors owing to the variability of other factors such as humidity, radiation, wind, and precipitation (Brown & Gillespie, 1995). In practice, the mean radiant temperature, which in essence is a measure of the combined effect of surface temperatures within a space, is a significant factor in determining human comfort, especially on hot days with little wind (Matzarakis et al, 1999).
- Takes into account the evaporative cooling effect of vegetation. It assumes that vegetation does not have a restricted water supply, and therefore that it is fully able to evapotranspire. This will not be the case under drought conditions (which may become more frequent in a changing climate), when the cooling effect may be reduced unless adequate irrigation is supplied.
- Does not take into account the shading effect of vegetation which can be significant. For example, on a warm day in Manchester, the shade provided by a large mature tree canopy in a local park in the middle of the day reduced the surface temperature of an impervious surface by 15.6°C, compared to the surface without shading (Gill, 2006).
- Does not take into account different types of vegetation, or the three-dimensional structure of it. For example, a tree over a shrub over grass may be expected to provide more evaporative cooling than grass on its own.
- Does not include man-made heat sources which could contribute to greater urban warming.
- Has few input requirements once other parameters have been set, and is straightforward to use. Models vary greatly in their complexity and data requirements. Whilst there is a temptation to use the most complex model available, there can often be much uncertainty in the values given to parameters.
- Allows the role of urban vegetation to be explored.
- Has been applied in Kuala Lumpur, Merseyside and Greater Manchester. Subsequent research compared the surface temperature model output to measured surface temperatures (collected from airborne transects) in Greater Manchester and found a strong correlation between the modelled output and measured results, providing a degree of confidence in the modelled output (Smith et al, 2011).
Surface runoff model
The surface runoff model is based upon the US Soil Conservation Service approach (Soil Conservation Service, 1972). It takes a daily rainfall amount, and then calculates how much of this rainfall will not be converted to surface runoff; this could be because this rainfall is intercepted (e.g. by plants and vegetation), infiltrated (e.g. into the soil), or stored (e.g. on surfaces). The remaining rainfall is converted to surface runoff.
The method is based both on theory as well as on the results of empirical studies into precipitation runoff from many small watersheds in the US. It assigns 'Curve Numbers' to a range of different surface types, depending on the hydrological properties of the soil, and how wet it has been in the preceding 5 days. The more impervious the surface (e.g. built and sealed surfaces) the higher the curve number is; whilst the less impervious the surface (e.g. vegetated surfaces), the lower the curve number is. A combined 'Curve Number' can then be applied to the area of interest by weighting all of the individual curve numbers according to the area they cover.
As it is used in the STAR tools, the model:
- Calculates the percentage of the rainfall that becomes surface runoff, and the volume of this surface runoff.
- Has few input requirements (surface cover, precipitation, antecedent moisture conditions, and hydrological soil type) and is straightforward to use. Models vary greatly in their complexity and data requirements. Whilst there is a temptation to use the most complex model available, there can often be much uncertainty in the values given to parameters.
- Allows the role that different vegetated surfaces can play within urban areas to be considered. Other models do not necessarily do this. For example, in the UK, the method used in the Flood Estimation Handbook distinguishes between urban (mostly covered by concrete and other impervious surfaces) and suburban (a mixture of paved areas and vegetation) development.
- Considers direct surface runoff; it does not consider the case when high ground water levels contribute to runoff.
- Does not calculate the speed or rate of the runoff.
- Does not include slope as an input parameter. This is because runoff volume is not highly dependent on slope whereas runoff speed is. Runoff volume is determined mainly by "the amount of precipitation and by infiltration characteristics related to soil type, soil moisture, antecedent rainfall, cover type, impervious surfaces, and surface retention" (Natural Resources Conservation Service, 1986, p1-1). However, the infiltration capacity is altered by slope, as larger slopes provide less opportunity for infiltration (Mansell, 2003). Slope is also not included in the UK Flood Estimation Handbook method for estimating effective rainfall, but is used to estimate the time taken to reach peak flow (Mansell, 2003).
- Does not include storage in depressions in the ground.
- Has been applied in Merseyside and Greater Manchester, but has not been validated. However, the model was developed from experimental studies of small watersheds and is widely used in the United States (Natural Resources Conservation Service, 1986).
Brown, R.D. and Gillespie, T.J. (1995). Microclimate Landscape Design: Creating Thermal Comfort and Energy Efficiency. John Wiley & Sons, Chichester.
Cuvato, E. and Ianni, I. (2011). Urban planning strategies for climate adaptation assessment: the case study of Catania metropolitan area. Presentation to EcoCities and University of Catania workship, 7th April 2011, Catania, Sicily.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf (see chapters 5 and 6 in particular). Gill, S.E., Handley, J.F, Ennos, A.R. and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment. 33, 115-133.
Mansell, M.G. (2003). Rural and urban hydrology. Thomas Telford, London.
Matzarakis, A., Mayer, H. and Iziomon, M. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43, 76-84.
Natural Resources Conservation Service (1986). Urban Hydrology for Small Watersheds, Technical Release 55. United States Department of Agriculture.
Smith, C.L., Webb, A., Levermore, G.J., Lindley, S.J. and Beswick, K. (2011). Fine-scale spatial temperature patterns across a UK conurbation. Climatic Change.
Soil Conservation Service (1972). Soil Conservation Service National Engineering Handbook - Section 4: Hydrology. United States Department of Agriculture, Soil Conservation Service, Washington, DC.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1990). An improvement to the basic energy balance model for urban thermal analysis. Energy and Buildings, 14 (2), 143-152.
Whitford, V. Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Acknowledgements
The STAR tools were developed by Dr Susannah Gill and Tom Butlin of The Mersey Forest, with support from Dr Gina Cavan, Richard Kingston and Karl Hennermann at The University of Manchester.
We would like to thank Dr Stephen Lynch of Manchester Metropolitan University for his invaluable help with the mathematics behind the surface temperature tool; and Stephen Gordon, also of Manchester Metropolitan University, for putting us in contact with Stephen Lynch. Thanks also to Patrick Butlin for additional help with the mathematics. Many thanks to Dr Aleksandra Kazmierczak of The University of Manchester for her comments and for assistance with analysing the climate data, and to Dr Timothy Farewell of Cranfield University for his help with the soil data.
The STAR tools were developed by Dr Susannah Gill and Tom Butlin of The Mersey Forest, with support from Dr Gina Cavan, Richard Kingston and Karl Hennermann at The University of Manchester.
We would like to thank Dr Stephen Lynch of Manchester Metropolitan University for his invaluable help with the mathematics behind the surface temperature tool; and Stephen Gordon, also of Manchester Metropolitan University, for putting us in contact with Stephen Lynch. Thanks also to Patrick Butlin for additional help with the mathematics. Many thanks to Dr Aleksandra Kazmierczak of The University of Manchester for her comments and for assistance with analysing the climate data, and to Dr Timothy Farewell of Cranfield University for his help with the soil data.
×
Surface temperature
Surface temperature in this case refers to the temperature of the surface (e.g. the temperature of the surface of the street, grass, building, etc) rather than the temperature of the air near to the surface.
Surface temperatures are often easier to model than air temperatures and are an important factor in terms of outdoor human comfort. Whilst air temperature provides a simple estimator of human thermal comfort, it is less reliable outdoors owing to the variability of other factors such as humidity, radiation, wind, and precipitation (Brown & Gillespie, 1995). In practice, the mean radiant temperature, which in essence is a measure of the combined effect of surface temperatures within a space, is a significant factor in determining human comfort, especially on hot days with little wind (Matzarakis et al, 1999).
The relationship between surface temperature and air temperature is complicated by factors such as wind, meaning that it is difficult to relate them directly to each other. However, both surface and air temperatures can have the urban heat island effect, where temperatures in urban areas are warmer than surrounding rural areas.
Given the mixture of different surfaces, the surface temperature tool (provided as part of the STAR tools) calculates the average surface temperature for the study area. The value it provides is the maximum surface temperature you'd expect on during the day.
Brown, R.D. and Gillespie, T.J. (1995). Microclimate Landscape Design: Creating Thermal Comfort and Energy Efficiency. John Wiley & Sons, Chichester.
Matzarakis, A., Mayer, H. and Iziomon, M. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43, 76-84.
Surface temperature in this case refers to the temperature of the surface (e.g. the temperature of the surface of the street, grass, building, etc) rather than the temperature of the air near to the surface.
Surface temperatures are often easier to model than air temperatures and are an important factor in terms of outdoor human comfort. Whilst air temperature provides a simple estimator of human thermal comfort, it is less reliable outdoors owing to the variability of other factors such as humidity, radiation, wind, and precipitation (Brown & Gillespie, 1995). In practice, the mean radiant temperature, which in essence is a measure of the combined effect of surface temperatures within a space, is a significant factor in determining human comfort, especially on hot days with little wind (Matzarakis et al, 1999).
The relationship between surface temperature and air temperature is complicated by factors such as wind, meaning that it is difficult to relate them directly to each other. However, both surface and air temperatures can have the urban heat island effect, where temperatures in urban areas are warmer than surrounding rural areas.
Given the mixture of different surfaces, the surface temperature tool (provided as part of the STAR tools) calculates the average surface temperature for the study area. The value it provides is the maximum surface temperature you'd expect on during the day.
Brown, R.D. and Gillespie, T.J. (1995). Microclimate Landscape Design: Creating Thermal Comfort and Energy Efficiency. John Wiley & Sons, Chichester.
Matzarakis, A., Mayer, H. and Iziomon, M. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43, 76-84.
×
Surface runoff
Surface runoff is the water flow that occurs when soil is infiltrated to full capacity and excess water from rain flows over the land and into drains. Surface water runoff can contribute to the flooding of rivers and drains. In addition, when runoff flows along the ground, it can pick up contaminants and pollutants which can lead to a decrease in water quality of rivers (with implications for biodiversity) and to increased water treatment costs.
Urbanisation increases both the rate and volume of surface water runoff. The surface runoff tool (provided as part of the STAR tools) gives the volume and percentage (for rainfall depths of 0-100mm) of surface runoff for a study area.
Surface runoff is the water flow that occurs when soil is infiltrated to full capacity and excess water from rain flows over the land and into drains. Surface water runoff can contribute to the flooding of rivers and drains. In addition, when runoff flows along the ground, it can pick up contaminants and pollutants which can lead to a decrease in water quality of rivers (with implications for biodiversity) and to increased water treatment costs.
Urbanisation increases both the rate and volume of surface water runoff. The surface runoff tool (provided as part of the STAR tools) gives the volume and percentage (for rainfall depths of 0-100mm) of surface runoff for a study area.
×
Maximum surface temperature
This is the hottest surface temperature you'd expect during the day. It is an average value for the study area.
Surface temperature in this case refers to the temperature of the surface (e.g. the temperature of the surface of the street, grass, building, etc) rather than the temperature of the air near to the surface.
This is the hottest surface temperature you'd expect during the day. It is an average value for the study area.
Surface temperature in this case refers to the temperature of the surface (e.g. the temperature of the surface of the street, grass, building, etc) rather than the temperature of the air near to the surface.
×
Climate change
The latest scientific evidence has reinforced the fact that climate change is the greatest threat to our social well being and economic future (Intergovernmental Panel on Climate Change, 2007). It is imperative that we all take what action we can now in order to both reduce greenhouse gas emissions and ensure that our communities are adapting to anticipated climate change.
The Stern Review on the Economics of Climate Change (Stern, 2006) stressed that "the benefits of strong, early action on climate change outweigh the costs". Mini-Stern assessments have been undertaken for Cheshire, Cumbria, Greater Manchester, Lancashire and Merseyside.
Taking river and coastal flooding alone, the current cost of damages to businesses across the North West of England is on average £43m per year; with climate change, costs increase by 223% to £138m per year (URS, 2009; assuming flood defences are maintained at current levels, and that there is a 20% increase in river flows by 2100). The UK Climate Change Commission suggests that timely adaptation action could halve the costs and damages associated with moderate amounts of climate change (Adaptation Sub-Committee, 2010).
We need to reduce greenhouse gas emissions (known as climate change mitigation) in order to limit the severity of climate change. The UK Climate Change Act sets target cuts of at least 34% by 2020 and 80% by 2050, compared to 1990 levels. An assessment of UK carbon emissions found that 2008 emissions were 22% below 1990 levels (Department of Energy and Climate Change, 2010).
We also need to adapt to climate change impacts that are already being felt, and will intensify. In the UK, it is anticipated that climate change will lead to warmer and wetter winters, hotter and drier summers, sea level rises, and more extreme events such as heatwaves, heavy rainfall, and droughts.
In the North West of England by the 2080s, some headline changes from the UK Climate Projections 2009 are:
(N.B. The above figures are for the central estimate of the UKCP09 high emissions scenario and compare to the 1961-1990 baseline period. We are currently on an emissions path above that of the high emissions scenario http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Using natural, or green infrastructure interventions, is increasingly being recognised as a desirable 'win-win' approach to combating climate change (Planning and Climate Change Coalition, 2010).
Adaptation Sub-Committee (2010). How well prepared is the UK for climate change? www.theccc.org.uk/reports/adaptation
Department of Energy and Climate Change (2010). Annual statement of emissions for 2008. www.decc.gov.uk/assets/decc/What%20we%20do/A%20low%20carbon%20UK/Carbon%20budgets/1_20100331145647_e_@@_StatementEmissions2008.pdf
Intergovernmental Panel on Climate Change (2007). Climate Change 2007: Synthesis Report. www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf
Planning and Climate Change Coalition (2010). Planning for Climate Change - Guidance and model policies for local authorities. www.tcpa.org.uk/pages/planning-for-climate-change-guide.html; this won a Royal Town Planning Institute Planning Award 2010.
Stern (2006). Stern Review on the Economics of Climate Change. www.hm-treasury.gov.uk/sternreview_index.htm
URS (2009). Economic impacts of increased flood risk associated with climate change in the North West. www.climatechangenorthwest.co.uk/assets/_files/documents/oct_09/cli__1256311710_URS_Ecoimpact_report_finalOct2.pdf
The latest scientific evidence has reinforced the fact that climate change is the greatest threat to our social well being and economic future (Intergovernmental Panel on Climate Change, 2007). It is imperative that we all take what action we can now in order to both reduce greenhouse gas emissions and ensure that our communities are adapting to anticipated climate change.
The Stern Review on the Economics of Climate Change (Stern, 2006) stressed that "the benefits of strong, early action on climate change outweigh the costs". Mini-Stern assessments have been undertaken for Cheshire, Cumbria, Greater Manchester, Lancashire and Merseyside.
Taking river and coastal flooding alone, the current cost of damages to businesses across the North West of England is on average £43m per year; with climate change, costs increase by 223% to £138m per year (URS, 2009; assuming flood defences are maintained at current levels, and that there is a 20% increase in river flows by 2100). The UK Climate Change Commission suggests that timely adaptation action could halve the costs and damages associated with moderate amounts of climate change (Adaptation Sub-Committee, 2010).
We need to reduce greenhouse gas emissions (known as climate change mitigation) in order to limit the severity of climate change. The UK Climate Change Act sets target cuts of at least 34% by 2020 and 80% by 2050, compared to 1990 levels. An assessment of UK carbon emissions found that 2008 emissions were 22% below 1990 levels (Department of Energy and Climate Change, 2010).
We also need to adapt to climate change impacts that are already being felt, and will intensify. In the UK, it is anticipated that climate change will lead to warmer and wetter winters, hotter and drier summers, sea level rises, and more extreme events such as heatwaves, heavy rainfall, and droughts.
In the North West of England by the 2080s, some headline changes from the UK Climate Projections 2009 are:
- 28% decrease in average summer precipitation - leading to reduced stream flows and water quality, increased drought, subsidence, changes to crops, serious water stress.
- 26% increase in average winter precipitation - leading to increased flooding including from overwhelmed drains, subsidence, severe transport disruption, risks to critical infrastructure.
- 4.7°C increase in average summer temperatures - leading to increased heat stress, infrastructure risks, risks to biodiversity, heat related deaths, risks to food security.
(N.B. The above figures are for the central estimate of the UKCP09 high emissions scenario and compare to the 1961-1990 baseline period. We are currently on an emissions path above that of the high emissions scenario http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Using natural, or green infrastructure interventions, is increasingly being recognised as a desirable 'win-win' approach to combating climate change (Planning and Climate Change Coalition, 2010).
Adaptation Sub-Committee (2010). How well prepared is the UK for climate change? www.theccc.org.uk/reports/adaptation
Department of Energy and Climate Change (2010). Annual statement of emissions for 2008. www.decc.gov.uk/assets/decc/What%20we%20do/A%20low%20carbon%20UK/Carbon%20budgets/1_20100331145647_e_@@_StatementEmissions2008.pdf
Intergovernmental Panel on Climate Change (2007). Climate Change 2007: Synthesis Report. www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf
Planning and Climate Change Coalition (2010). Planning for Climate Change - Guidance and model policies for local authorities. www.tcpa.org.uk/pages/planning-for-climate-change-guide.html; this won a Royal Town Planning Institute Planning Award 2010.
Stern (2006). Stern Review on the Economics of Climate Change. www.hm-treasury.gov.uk/sternreview_index.htm
URS (2009). Economic impacts of increased flood risk associated with climate change in the North West. www.climatechangenorthwest.co.uk/assets/_files/documents/oct_09/cli__1256311710_URS_Ecoimpact_report_finalOct2.pdf
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Green infrastructure
Green infrastructure is our "life support system - the network of natural environmental components and green and blue spaces that lie within and between our cities, towns and villages and provide multiple social, economic and environmental benefits" (North West Green Infrastructure Think Tank, 2008).
It should be planned and managed as a critical infrastructure (Natural Economy Northwest, a).
Green infrastructure includes urban and rural components, ranging from the designed to the more natural. Types of green infrastructure include: agricultural land, allotments, community gardens and urban farms, cemeteries, churchyards and burial grounds, coastal habitats, derelict land, general amenity spaces, grasslands, heathlands, moorlands and scrublands, green roofs, institutional grounds, orchards, outdoor sports facilities, parks and public gardens, private domestic gardens, street trees, water bodies and courses, wetlands, and woodlands.
Green infrastructure provides us with a range of social, environmental and economic benefits (Forest Research, 2010; Commission for Architecture and the Built Environment, 2009). These are sometimes referred to as ecosystem services.
The natural environment of the North West of England generates an estimated £2.6bn in Gross Value Added, and supports 109,000 jobs. The Natural Economy Northwest programme categorised eleven groups of economic benefits provided by green infrastructure and has produced a series of case studies for each (Natural Economy Northwest, b):
Green infrastructure provides a range of services that make a substantial contribution towards climate change adaptation and a limited yet important contribution towards climate change mitigation (Community Forests Northwest et al, 2010a).
Green infrastructure actions, which include safeguarding, creating, enhancing, maintaining and promoting it, are an attractive approach to combating climate change, precisely because they can deliver results to so many other agendas as well as that of climate change (Community Forests Northwest et al, 2010b).
For other information on green infrastructure in the North West of England see www.ginw.co.uk.
Commission for Architecture and the Built Environment (2009). Sustainable Places. www.cabe.org.uk/sustainable-places/green-infrastructure
Community Forests Northwest et al (2010a). Green Infrastructure: How and where can it help the Northwest mitigate and adapt to climate change? www.ginw.co.uk/climatechange/report
Community Forests Northwest et al (2010b). Green Infrastructure to combat climate change: a framework for action in Cheshire, Cumbria, Greater Manchester, Lancashire, and Merseyside. www.ginw.co.uk/climatechange/framework
Forest Research (2010). Benefits of Green Infrastructure. www.forestresearch.gov.uk/fr/INFD-8A9A2W
Natural Economy Northwest (a). Green Infrastructure Prospectus. www.ginw.co.uk/resources/Prospectus_V6.pdf
Natural Economy Northwest (b). The economic value of green infrastructure. www.nwda.co.uk/PDF/EconomicValueofGreenInfrastructure.pdf and www.naturaleconomynorthwest.co.uk/resources+case+studies.php
North West Green Infrastructure Think Tank (2008). North West Green Infrastructure Guide. www.ginw.co.uk/resources/GIguide.pdf; this won a Royal Town Planning Institute Planning Award 2008.
Green infrastructure is our "life support system - the network of natural environmental components and green and blue spaces that lie within and between our cities, towns and villages and provide multiple social, economic and environmental benefits" (North West Green Infrastructure Think Tank, 2008).
It should be planned and managed as a critical infrastructure (Natural Economy Northwest, a).
Green infrastructure includes urban and rural components, ranging from the designed to the more natural. Types of green infrastructure include: agricultural land, allotments, community gardens and urban farms, cemeteries, churchyards and burial grounds, coastal habitats, derelict land, general amenity spaces, grasslands, heathlands, moorlands and scrublands, green roofs, institutional grounds, orchards, outdoor sports facilities, parks and public gardens, private domestic gardens, street trees, water bodies and courses, wetlands, and woodlands.
Green infrastructure provides us with a range of social, environmental and economic benefits (Forest Research, 2010; Commission for Architecture and the Built Environment, 2009). These are sometimes referred to as ecosystem services.
The natural environment of the North West of England generates an estimated £2.6bn in Gross Value Added, and supports 109,000 jobs. The Natural Economy Northwest programme categorised eleven groups of economic benefits provided by green infrastructure and has produced a series of case studies for each (Natural Economy Northwest, b):
- Economic growth and investment - businesses attract and retain more motivated staff
- Land and property values - natural views can add up to 18% to property values
- Labour productivity - green spaces near workplaces reduce sickness absence
- Tourism - rural tourism supports 37,500 jobs in the North West of England
- Products from the land - 40,000 people work in agriculture in the North West of England
- Health and wellbeing - reduces pollution which leads to asthma and heart disease
- Recreation and leisure - footpaths, cycle paths and bridleways enable healthy recreation
- Quality of place - community owned green spaces can create jobs and local pride
- Land and biodiversity - provides vital habitats and jobs managing the land
- Flood alleviation and management - reduces pressure on drains and flood defences
- Climate change adaptation and mitigation - counters soaring summer temperatures in cities.
Green infrastructure provides a range of services that make a substantial contribution towards climate change adaptation and a limited yet important contribution towards climate change mitigation (Community Forests Northwest et al, 2010a).
Green infrastructure actions, which include safeguarding, creating, enhancing, maintaining and promoting it, are an attractive approach to combating climate change, precisely because they can deliver results to so many other agendas as well as that of climate change (Community Forests Northwest et al, 2010b).
For other information on green infrastructure in the North West of England see www.ginw.co.uk.
Commission for Architecture and the Built Environment (2009). Sustainable Places. www.cabe.org.uk/sustainable-places/green-infrastructure
Community Forests Northwest et al (2010a). Green Infrastructure: How and where can it help the Northwest mitigate and adapt to climate change? www.ginw.co.uk/climatechange/report
Community Forests Northwest et al (2010b). Green Infrastructure to combat climate change: a framework for action in Cheshire, Cumbria, Greater Manchester, Lancashire, and Merseyside. www.ginw.co.uk/climatechange/framework
Forest Research (2010). Benefits of Green Infrastructure. www.forestresearch.gov.uk/fr/INFD-8A9A2W
Natural Economy Northwest (a). Green Infrastructure Prospectus. www.ginw.co.uk/resources/Prospectus_V6.pdf
Natural Economy Northwest (b). The economic value of green infrastructure. www.nwda.co.uk/PDF/EconomicValueofGreenInfrastructure.pdf and www.naturaleconomynorthwest.co.uk/resources+case+studies.php
North West Green Infrastructure Think Tank (2008). North West Green Infrastructure Guide. www.ginw.co.uk/resources/GIguide.pdf; this won a Royal Town Planning Institute Planning Award 2008.
×
Understanding and interpreting the results of the surface temperature tool
Care needs to be taken when presenting the results of the surface temperature tool as there are a number of ways in which they can be presented and comparisons which can be made. For example:
1. Compare baseline maximum surface temperatures in different study areas to each other; to see which areas are currently warmer or cooler in relation to each other.
e.g. The maximum surface temperature expected on a warm summer day is currently 21.9°C in Didsbury West, whereas in City Centre Manchester it is 34.2°C; a difference of 12.3°C.
2. Compare future climate maximum surface temperatures in a study area to the baseline maximum surface temperatures in that area; this allows you to see how temperatures may increase if the land cover stayed as it is today.
e.g. By the 2050s High emissions scenario (for the 50% probability level) the maximum surface temperature expected on a warm summer day in City Centre Manchester is 36.5°C; this is 2.3°C warmer than the 34.2°C currently expected on a warm summer day.
3. Compare maximum surface temperatures for different land cover scenarios in a study area under a temperature scenario; this allows you to see how temperatures in an area change as you vary the land cover.
e.g. The maximum surface temperature expected on a warm summer day is currently 34.2°C in City Centre Manchester; when 10% green cover is added it is 30.9°C (or 3.3°C cooler).
4. Compare maximum surface temperatures for a study area across different land cover and temperature scenarios; allows you to see how maximum surface temperatures in an area change as both land cover and temperature scenarios change.
e.g. The maximum surface temperature expected on a warm summer day is currently 34.2°C in City Centre Manchester. If 10% green cover is added then, even by the 2050s High emissions scenario (for the 50% probability level), maximum surface temperatures are lower than this at 32.8°C.
In addition the results can be exported to excel where they can be presented as graphs (e.g. figures 1 and 2), or they can be used in GIS to produce maps (e.g. figure 3).
Figure 1. Graph showing how maximum surface temperature output for one study area, different land cover scenarios, and different temperature scenarios could be displayed (Gill, 2006). N.B. For North West England users, the version of the tool currently available only includes input data for the baseline climate (here labelled as 1970s) and the 2050s High
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Figure 2. Graph showing how maximum surface temperature output for different study areas (here labelled as UMTs) under one temperature and land cover scenario could be displayed alongside the input parameters (N.B. 'evaporating fraction' is called 'green and blue surfaces (%)' in the STAR tools) (Gill, 2006).
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Figure 3. Maps showing how maximum surface temperature output for one land cover scenario, different study areas, and different temperature scenarios could be displayed (Gill, 2006). N.B. For North West England users, the version of the tool currently available only includes input data for the baseline climate (here labelled as 1970s) and the 2050s High
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Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf.
Care needs to be taken when presenting the results of the surface temperature tool as there are a number of ways in which they can be presented and comparisons which can be made. For example:
1. Compare baseline maximum surface temperatures in different study areas to each other; to see which areas are currently warmer or cooler in relation to each other.
e.g. The maximum surface temperature expected on a warm summer day is currently 21.9°C in Didsbury West, whereas in City Centre Manchester it is 34.2°C; a difference of 12.3°C.
2. Compare future climate maximum surface temperatures in a study area to the baseline maximum surface temperatures in that area; this allows you to see how temperatures may increase if the land cover stayed as it is today.
e.g. By the 2050s High emissions scenario (for the 50% probability level) the maximum surface temperature expected on a warm summer day in City Centre Manchester is 36.5°C; this is 2.3°C warmer than the 34.2°C currently expected on a warm summer day.
3. Compare maximum surface temperatures for different land cover scenarios in a study area under a temperature scenario; this allows you to see how temperatures in an area change as you vary the land cover.
e.g. The maximum surface temperature expected on a warm summer day is currently 34.2°C in City Centre Manchester; when 10% green cover is added it is 30.9°C (or 3.3°C cooler).
4. Compare maximum surface temperatures for a study area across different land cover and temperature scenarios; allows you to see how maximum surface temperatures in an area change as both land cover and temperature scenarios change.
e.g. The maximum surface temperature expected on a warm summer day is currently 34.2°C in City Centre Manchester. If 10% green cover is added then, even by the 2050s High emissions scenario (for the 50% probability level), maximum surface temperatures are lower than this at 32.8°C.
In addition the results can be exported to excel where they can be presented as graphs (e.g. figures 1 and 2), or they can be used in GIS to produce maps (e.g. figure 3).
Figure 1. Graph showing how maximum surface temperature output for one study area, different land cover scenarios, and different temperature scenarios could be displayed (Gill, 2006). N.B. For North West England users, the version of the tool currently available only includes input data for the baseline climate (here labelled as 1970s) and the 2050s High
Figure 2. Graph showing how maximum surface temperature output for different study areas (here labelled as UMTs) under one temperature and land cover scenario could be displayed alongside the input parameters (N.B. 'evaporating fraction' is called 'green and blue surfaces (%)' in the STAR tools) (Gill, 2006).
Figure 3. Maps showing how maximum surface temperature output for one land cover scenario, different study areas, and different temperature scenarios could be displayed (Gill, 2006). N.B. For North West England users, the version of the tool currently available only includes input data for the baseline climate (here labelled as 1970s) and the 2050s High
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf.
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Understanding and interpreting the results of the surface runoff tool
Care needs to be taken when presenting the results of the surface runoff tool as there are a number of ways in which they can be presented and comparisons which can be made. For example:
1. Compare baseline surface runoff percentages / volumes in different study areas to each other; to see which areas have more or less runoff in relation to each other.
e.g. On a wet winter day you would currently expect 74.3% surface runoff in Didsbury West, whereas in City Centre Manchester you'd expect 90.1%.
2. Compare future surface runoff percentages / volumes in a study area to the baseline surface runoff percentages / volumes in that area; this allows you to see how surface runoff percentages / volumes may increase if the land cover stayed as it is today.
e.g. By the 2050s High emissions scenario (for the 50% probability level) on a wet winter day you'd expect 59,466 m³ of surface runoff in City Centre Manchester; this is 6,188 m³ more surface runoff than the 53,278 m³ currently expected on a wet winter day.
3. Compare surface runoff percentages / volumes for different land cover scenarios in a study area under a precipitation scenario; this allows you to see how surface runoff percentages / volumes in an area change as you vary the land cover.
e.g. On a wet winter day you'd currently expect 74.3% surface runoff in Didsbury West; when 10% green cover is added it is 70.7%.
4. Compare surface runoff percentages / volumes for a study area across different land cover and precipitation scenarios; allows you to see how surface runoff percentages / volumes in an area change as both land cover and precipitation scenarios change.
e.g. On a wet winter day you'd currently expect 74.3% surface runoff in Didsbury West. If 10% green cover is added then, by the 2050s High emissions scenario (for the 50% probability level), you'd expect 73% surface runoff.
In addition the results can be exported to excel where they can be presented as graphs (e.g. figure 1), or they can be used in GIS to produce maps (e.g. figure 2).
Figure 1. Graphs showing how surface runoff percentages (here shown as runoff coefficients rather than percentages) output could be displayed for one study area (presented here for the 4 hydrological soil types) and different land cover and precipitation scenarios (Gill, 2006). N.B. The grey dotted lines mark baseline and future precipitation scenarios; for North West England users, the version of the STAR tools currently available only includes input data for the baseline and the 2050s High
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Figure 2. Maps showing how surface runoff percentages (here shown as runoff coefficients rather than percentages) output could be displayed for different study areas under different precipitation scenarios, but only one land cover scenario (Gill, 2006). N.B. For North West England users, the version of the STAR tools currently available only includes input data for the baseline (here labelled as 1970s) and the 2050s High
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Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf.
Care needs to be taken when presenting the results of the surface runoff tool as there are a number of ways in which they can be presented and comparisons which can be made. For example:
1. Compare baseline surface runoff percentages / volumes in different study areas to each other; to see which areas have more or less runoff in relation to each other.
e.g. On a wet winter day you would currently expect 74.3% surface runoff in Didsbury West, whereas in City Centre Manchester you'd expect 90.1%.
2. Compare future surface runoff percentages / volumes in a study area to the baseline surface runoff percentages / volumes in that area; this allows you to see how surface runoff percentages / volumes may increase if the land cover stayed as it is today.
e.g. By the 2050s High emissions scenario (for the 50% probability level) on a wet winter day you'd expect 59,466 m³ of surface runoff in City Centre Manchester; this is 6,188 m³ more surface runoff than the 53,278 m³ currently expected on a wet winter day.
3. Compare surface runoff percentages / volumes for different land cover scenarios in a study area under a precipitation scenario; this allows you to see how surface runoff percentages / volumes in an area change as you vary the land cover.
e.g. On a wet winter day you'd currently expect 74.3% surface runoff in Didsbury West; when 10% green cover is added it is 70.7%.
4. Compare surface runoff percentages / volumes for a study area across different land cover and precipitation scenarios; allows you to see how surface runoff percentages / volumes in an area change as both land cover and precipitation scenarios change.
e.g. On a wet winter day you'd currently expect 74.3% surface runoff in Didsbury West. If 10% green cover is added then, by the 2050s High emissions scenario (for the 50% probability level), you'd expect 73% surface runoff.
In addition the results can be exported to excel where they can be presented as graphs (e.g. figure 1), or they can be used in GIS to produce maps (e.g. figure 2).
Figure 1. Graphs showing how surface runoff percentages (here shown as runoff coefficients rather than percentages) output could be displayed for one study area (presented here for the 4 hydrological soil types) and different land cover and precipitation scenarios (Gill, 2006). N.B. The grey dotted lines mark baseline and future precipitation scenarios; for North West England users, the version of the STAR tools currently available only includes input data for the baseline and the 2050s High
Figure 2. Maps showing how surface runoff percentages (here shown as runoff coefficients rather than percentages) output could be displayed for different study areas under different precipitation scenarios, but only one land cover scenario (Gill, 2006). N.B. For North West England users, the version of the STAR tools currently available only includes input data for the baseline (here labelled as 1970s) and the 2050s High
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf.
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Surface runoff percentages
This is the percentage of the total rainfall depth occurring that becomes surface runoff. e.g. If there is 10 mm of rain and 50% of it becomes surface runoff, this is equivalent to 5 mm of the rain becoming surface runoff.
This value might be of more use than the volumes of surface runoff if you are interested in comparing runoff from different land surfaces (or land cover scenarios) regardless of the study area size.
This is the percentage of the total rainfall depth occurring that becomes surface runoff. e.g. If there is 10 mm of rain and 50% of it becomes surface runoff, this is equivalent to 5 mm of the rain becoming surface runoff.
This value might be of more use than the volumes of surface runoff if you are interested in comparing runoff from different land surfaces (or land cover scenarios) regardless of the study area size.
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Surface runoff volumes
This is the total volume in m³ of surface runoff from the study area. This is found by multiplying the depth of the surface runoff by the study area size.
This value might be of more use than the percentage of surface runoff if you are interested in how much water storage capacity may be needed in a given area.
This is the total volume in m³ of surface runoff from the study area. This is found by multiplying the depth of the surface runoff by the study area size.
This value might be of more use than the percentage of surface runoff if you are interested in how much water storage capacity may be needed in a given area.
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Return to surface temperature tool
Now that you have seen the output from the surface temperature tool you might want to go back and amend the input values or your study areas and then run the tool again. Use the available options to either:
Return to step 2: Amend the input values
Use this if you are happy to keep your study areas as they are, but want to add another land cover scenario or add another temperature scenario, or want to refine any of the other parameters.
OR
Return to step 1: Define your study area(s)
Use this if you want to re-start the process and redefine your study areas.
Alternatively you can now go to the surface runoff tool instead.
Now that you have seen the output from the surface temperature tool you might want to go back and amend the input values or your study areas and then run the tool again. Use the available options to either:
Return to step 2: Amend the input values
Use this if you are happy to keep your study areas as they are, but want to add another land cover scenario or add another temperature scenario, or want to refine any of the other parameters.
OR
Return to step 1: Define your study area(s)
Use this if you want to re-start the process and redefine your study areas.
Alternatively you can now go to the surface runoff tool instead.
×
Go to surface temperature tool
Now that you have some output from the surface runoff tool you might want to go to the surface temperature tool instead. Use the available options to either:
Go to step 2: Amend the input values
Use this if you are happy to keep your study areas as you defined them for the surface runoff tool, and want to go straight to amending the input parameters in the surface temperature tool.
If you choose this option, the STAR tools automatically set up the land cover and temperature scenarios as you defined them for the surface runoff tool. It is recommended that you always double check the land cover scenarios in particular to ensure that these are set up in the way that you intended. The STAR tools make certain assumptions when transferring the land cover scenarios between the tools and these may not fit your study area. For example, they divide the 'other impervious surfaces' of the runoff tool evenly between the 'major roads' and 'other impervoiuos surfaces' in the temperature tool; it may be that you want a different division. (N.B. When the STAR tools transfer the default land cover scenario provided for users in North West England, they will draw the input values from the underlying data rather than making assumptions). In addition, due to rounding of numbers, the total percentage of land may not always add up exactly to 100%, so you will need to double check these and amend them slightly.
OR
Go to step 1: Define your study area(s)
Use this if you want to define new study areas for use with the surface temperature tool.
Alternatively you can now return to the surface runoff tool instead.
Now that you have some output from the surface runoff tool you might want to go to the surface temperature tool instead. Use the available options to either:
Go to step 2: Amend the input values
Use this if you are happy to keep your study areas as you defined them for the surface runoff tool, and want to go straight to amending the input parameters in the surface temperature tool.
If you choose this option, the STAR tools automatically set up the land cover and temperature scenarios as you defined them for the surface runoff tool. It is recommended that you always double check the land cover scenarios in particular to ensure that these are set up in the way that you intended. The STAR tools make certain assumptions when transferring the land cover scenarios between the tools and these may not fit your study area. For example, they divide the 'other impervious surfaces' of the runoff tool evenly between the 'major roads' and 'other impervoiuos surfaces' in the temperature tool; it may be that you want a different division. (N.B. When the STAR tools transfer the default land cover scenario provided for users in North West England, they will draw the input values from the underlying data rather than making assumptions). In addition, due to rounding of numbers, the total percentage of land may not always add up exactly to 100%, so you will need to double check these and amend them slightly.
OR
Go to step 1: Define your study area(s)
Use this if you want to define new study areas for use with the surface temperature tool.
Alternatively you can now return to the surface runoff tool instead.
×
Return to surface runoff tool
Now that you have seen the output from the surface runoff tool you might want to go back and amend the input values or your study areas and then run the tool again. Use the available options to either:
Return to step 2: Amend the input values
Use this if you are happy to keep your study areas as they are, but want to add another land cover scenario or add another precipitation scenario, or want to refine any of the other parameters.
OR
Return to step 1: Define your study area(s)
Use this if you want to re-start the process and redefine your study areas.
Alternatively you can now go to the surface temperature tool instead.
Now that you have seen the output from the surface runoff tool you might want to go back and amend the input values or your study areas and then run the tool again. Use the available options to either:
Return to step 2: Amend the input values
Use this if you are happy to keep your study areas as they are, but want to add another land cover scenario or add another precipitation scenario, or want to refine any of the other parameters.
OR
Return to step 1: Define your study area(s)
Use this if you want to re-start the process and redefine your study areas.
Alternatively you can now go to the surface temperature tool instead.
×
Go to surface runoff tool
Now that you have some output from the surface temperature tool you might want to go to the surface runoff tool instead. Use the available options to either:
Go to step 2: Amend the input values
Use this if you are happy to keep your study areas as you defined them for the surface temperature tool, and want to go straight to amending the input parameters in the surface runoff tool.
If you choose this option, the STAR tools automatically set up the land cover and precipitation scenarios as you defined them for the surface temperature tool. It is recommended that you always double check the land cover scenarios in particular to ensure that these are set up in the way that you intended. The STAR tools make certain assumptions when transferring the land cover scenarios between the tools and these may not fit your study area. For example, they divide the 'green and blue surfaces' of the temperature tool evenly between the 6 different vegetated and water surfaces in the runoff tool; it may be that you want a different division. (N.B. When the STAR tools transfer the default land cover scenario provided for users in North West England, they will draw the input values from the underlying data rather than making assumptions). In addition, due to rounding of numbers, the total percentage of land may not always add up exactly to 100%, so you will need to double check these and amend them slightly.
OR
Go to step 1: Define your study area(s)
Use this if you want to define new study areas for use with the surface runoff tool.
Alternatively you can now return to the surface temperature tool instead.
Now that you have some output from the surface temperature tool you might want to go to the surface runoff tool instead. Use the available options to either:
Go to step 2: Amend the input values
Use this if you are happy to keep your study areas as you defined them for the surface temperature tool, and want to go straight to amending the input parameters in the surface runoff tool.
If you choose this option, the STAR tools automatically set up the land cover and precipitation scenarios as you defined them for the surface temperature tool. It is recommended that you always double check the land cover scenarios in particular to ensure that these are set up in the way that you intended. The STAR tools make certain assumptions when transferring the land cover scenarios between the tools and these may not fit your study area. For example, they divide the 'green and blue surfaces' of the temperature tool evenly between the 6 different vegetated and water surfaces in the runoff tool; it may be that you want a different division. (N.B. When the STAR tools transfer the default land cover scenario provided for users in North West England, they will draw the input values from the underlying data rather than making assumptions). In addition, due to rounding of numbers, the total percentage of land may not always add up exactly to 100%, so you will need to double check these and amend them slightly.
OR
Go to step 1: Define your study area(s)
Use this if you want to define new study areas for use with the surface runoff tool.
Alternatively you can now return to the surface temperature tool instead.
×
Wards
Boundaries are provided for the 2007 electoral wards in North West England.
Electoral wards are the key building block of UK administrative geography, being the spatial units used to elect local government councillors in metropolitan and non-metropolitan districts, unitary authorities and the London boroughs in England; unitary authorities in Wales; council areas in Scotland; and district council areas in Northern Ireland. Statistics are often presented at a ward level. Population varies between wards.
www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/administrative/england/electoral-wards-divisions/index.html
Please note that some wards will be smaller or larger than the recommended study area size.
Please note that we are unable to provide default temperature scenarios and precipitation scenarios for the following ward which is on the edge of the boundary of North West England: Walney South (Barrow-in-Furness). We are also unable to provide default soils data for: Roosecote (Barrow-in-Furness), Blundellsands (Sefton), Hoylake and Meols (Wirral).
Boundaries are provided for the 2007 electoral wards in North West England.
Electoral wards are the key building block of UK administrative geography, being the spatial units used to elect local government councillors in metropolitan and non-metropolitan districts, unitary authorities and the London boroughs in England; unitary authorities in Wales; council areas in Scotland; and district council areas in Northern Ireland. Statistics are often presented at a ward level. Population varies between wards.
www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/administrative/england/electoral-wards-divisions/index.html
Please note that some wards will be smaller or larger than the recommended study area size.
Please note that we are unable to provide default temperature scenarios and precipitation scenarios for the following ward which is on the edge of the boundary of North West England: Walney South (Barrow-in-Furness). We are also unable to provide default soils data for: Roosecote (Barrow-in-Furness), Blundellsands (Sefton), Hoylake and Meols (Wirral).
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Lower Layer Super Output Areas
Lower Layer Super Output Areas boundaries are provided for the North West of England.
Super Output Areas (SOAs) are a geography designed for the collection and publication of small area statistics. They are used for reporting neighbourhood statistics and national statistics (www.neighbourhood.statistics.gov.uk).
SOAs give an improved basis for comparison across the country because the units are more similar in size of population than, for example, electoral wards. They are also intended to be stable, enabling the improved comparison and monitoring of policy over time.
There are currently two layers of SOA - Lower Layer SOAs (LSOAs) and Middle Layer SOAs (MSOAs).
The 34,378 LSOAs in England and Wales were generated automatically and released to the public in February 2004. The LSOAs were built using 2001 Census data from groups of Output Areas (typically four to six) and were constrained by the wards used for 2001 Census outputs. They had a minimum size of 1,000 residents and 400 households, but average 1,500 residents. Measures of proximity (to give a reasonably compact shape) and social homogeneity (to encourage areas of similar social background) were also included.
(N.B. the specific homogeneity criteria used related to: type of dwelling - e.g. detached/semi-detached, etc; and nature of tenure - e.g. owner-occupied, private rented, etc).
There are 7,193 MSOAs which are larger in size.
www.neighbourhood.statistics.gov.uk/dissemination/Info.do?page=aboutneighbourhood/geography/superoutputareas/soa-intro.htm
Please note that some wards will be smaller or larger than the recommended study area size.
Please note that we are unable to provide default temperature scenarios and precipitation scenarios for the following LSOAs which are on the edges of the boundary of North West England: Barrow-in-Furness 010E, Sefton 012B, Wirral 013B, Wirral 013D.
Lower Layer Super Output Areas boundaries are provided for the North West of England.
Super Output Areas (SOAs) are a geography designed for the collection and publication of small area statistics. They are used for reporting neighbourhood statistics and national statistics (www.neighbourhood.statistics.gov.uk).
SOAs give an improved basis for comparison across the country because the units are more similar in size of population than, for example, electoral wards. They are also intended to be stable, enabling the improved comparison and monitoring of policy over time.
There are currently two layers of SOA - Lower Layer SOAs (LSOAs) and Middle Layer SOAs (MSOAs).
The 34,378 LSOAs in England and Wales were generated automatically and released to the public in February 2004. The LSOAs were built using 2001 Census data from groups of Output Areas (typically four to six) and were constrained by the wards used for 2001 Census outputs. They had a minimum size of 1,000 residents and 400 households, but average 1,500 residents. Measures of proximity (to give a reasonably compact shape) and social homogeneity (to encourage areas of similar social background) were also included.
(N.B. the specific homogeneity criteria used related to: type of dwelling - e.g. detached/semi-detached, etc; and nature of tenure - e.g. owner-occupied, private rented, etc).
There are 7,193 MSOAs which are larger in size.
www.neighbourhood.statistics.gov.uk/dissemination/Info.do?page=aboutneighbourhood/geography/superoutputareas/soa-intro.htm
Please note that some wards will be smaller or larger than the recommended study area size.
Please note that we are unable to provide default temperature scenarios and precipitation scenarios for the following LSOAs which are on the edges of the boundary of North West England: Barrow-in-Furness 010E, Sefton 012B, Wirral 013B, Wirral 013D.
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Grid squares
5km × 5km grid squares covering North West England are provided.
The same grid square is used for the precipitation, temperature, and soils data provided as an input for the STAR tools.
The 5km grid contains only land grid squares, each of which is 5km × 5km in size; this means that the grid size is 25 km². Please note that this grid is larger than the recommended maximum study area size of 5 km². If you want to use the gridded boundaries then please refer to the information provided on recommended study area size before continuing.
Please note that we are unable to provide all/some of the temperature, precipitation and soils data for some of the grid squares around the edges of the boundary of North West England.
5km × 5km grid squares covering North West England are provided.
The same grid square is used for the precipitation, temperature, and soils data provided as an input for the STAR tools.
The 5km grid contains only land grid squares, each of which is 5km × 5km in size; this means that the grid size is 25 km². Please note that this grid is larger than the recommended maximum study area size of 5 km². If you want to use the gridded boundaries then please refer to the information provided on recommended study area size before continuing.
Please note that we are unable to provide all/some of the temperature, precipitation and soils data for some of the grid squares around the edges of the boundary of North West England.
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Uploading a shapefile
- One ESRI shapefile can be uploaded at a time
- Each uploaded shapefile will replace any previously uploaded shapefiles
- The shapefile must be contained in a valid zip file
- The maximum size for the zip file is 2MB
- The zip file must include a .shp file, a .dbf file and a .shx file
- Any other files in the zip file will be ignored
- The shapefile may contain one or more shapes
- The shapes must be polygons, not lines or points
- The shapes may take several seconds to appear on the map following a successful upload
- Once uploaded, shapes must be selected on the map to be used as study areas in the tools
- If the shapefile contains a field called 'Name' (not case sensitive), entries will be used to name the shapes in the tools; otherwise, numeric IDs assigned by the tools will be used, but these can be overwritten with meaningful names once shapes are selected
- The shapefile must use the British National Grid projected coordinate system; otherwise, shapes may not appear in their true positions
×
Drawing boundaries
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Select tool.
You can delete all your drawn boundaries and start again at any time using the 'Delete drawn boundaries' button.
-
Click the
Draw button to activate the drawing tool (if it's not already activated)
- Single-click on the map to add the first and intermediate vertices to your boundary
- Double-click on the map to add the last vertex to your boundary and at the same time finish and select it
- Add more boundaries if desired
You can delete all your drawn boundaries and start again at any time using the 'Delete drawn boundaries' button.
×
Selecting boundaries
You can also click and drag to draw a box, which will select all visible boundaries that touch the box. Drawing another box will add to the existing selection.
The 'Select all' button will select all visible boundaries.
You can clear all of your selection and start again at any time using the 'Clear selection' button.
-
Click the
Select button to activate the select tool (if it's not already activated)
- Click on boundaries on the map that aren't currently selected to select them as study areas (N.B. selected study areas are highlighted in orange on the map and are displayed in the 'Selected study area(s) table at the bottom of step 1.3.
You can also click and drag to draw a box, which will select all visible boundaries that touch the box. Drawing another box will add to the existing selection.
The 'Select all' button will select all visible boundaries.
You can clear all of your selection and start again at any time using the 'Clear selection' button.
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Selected study areas
The tool will be run for the study area(s) listed in this table.
The tool will be run for the study area(s) listed in this table.
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Study area names
Study areas can be given meaningful names here, to help identify them in later steps.
Study areas can be given meaningful names here, to help identify them in later steps.
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Specific heat of air
The specific heat of air is the amount of heat absorbed (or released) by unit mass of air for a corresponding temperature rise (or fall) of 1°C.
The default value is set at 1006 J/kg/°C after Gill (2006), Whitford et al (2001), and Tso et al (1991).
The specific heat of air does not change much at the temperatures of interest, from 1005.6 J/kg/°C at 20°C to 1005.9 J/kg/°C at 30°C (Holman, 1997, p646).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Holman, J.P. (1997). Heat Transfer (8th edition). McGraw-Hill, London.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The specific heat of air is the amount of heat absorbed (or released) by unit mass of air for a corresponding temperature rise (or fall) of 1°C.
The default value is set at 1006 J/kg/°C after Gill (2006), Whitford et al (2001), and Tso et al (1991).
The specific heat of air does not change much at the temperatures of interest, from 1005.6 J/kg/°C at 20°C to 1005.9 J/kg/°C at 30°C (Holman, 1997, p646).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Holman, J.P. (1997). Heat Transfer (8th edition). McGraw-Hill, London.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
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Density of air
The density of air is the mass per unit volume of the Earth's atmosphere. Air density decreases with increasing altitude. It also changes with variances in temperature or humidity.
The default value varies depending on the temperature scenarios set. The temperature is rounded up or down to the nearest temperature given in table 1, and the density of air is derived accordingly.
Table 1. Temperature dependence of the density of air at 100 kPa (Oke, 1987, p392)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
The density of air is the mass per unit volume of the Earth's atmosphere. Air density decreases with increasing altitude. It also changes with variances in temperature or humidity.
The default value varies depending on the temperature scenarios set. The temperature is rounded up or down to the nearest temperature given in table 1, and the density of air is derived accordingly.
Table 1. Temperature dependence of the density of air at 100 kPa (Oke, 1987, p392)
| Temperature (°C) | Density of air (kg/m³) |
| 0 | 1.275 |
| 5 | 1.252 |
| 10 | 1.230 |
| 15 | 1.209 |
| 20 | 1.188 |
| 25 | 1.168 |
| 30 | 1.149 |
| 35 | 1.131 |
| 40 | 1.114 |
| 45 | 1.095 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
×
Thermal conductivity of soil
Thermal conductivity is the property of a material's ability to conduct heat. Heat transfer across materials of high thermal conductivity occurs at a faster rate than across materials of low thermal conductivity. Correspondingly materials of high thermal conductivity are widely used in heat sink applications and materials of low thermal conductivity are used as thermal insulation. Thermal conductivity of materials is temperature dependent. In general, materials become more conductive to heat as the average temperature increases.
The default value is set at 1.083 W/m/°C after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Thermal conductivity of natural materials (Oke, 1987, p44)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Thermal conductivity is the property of a material's ability to conduct heat. Heat transfer across materials of high thermal conductivity occurs at a faster rate than across materials of low thermal conductivity. Correspondingly materials of high thermal conductivity are widely used in heat sink applications and materials of low thermal conductivity are used as thermal insulation. Thermal conductivity of materials is temperature dependent. In general, materials become more conductive to heat as the average temperature increases.
The default value is set at 1.083 W/m/°C after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Thermal conductivity of natural materials (Oke, 1987, p44)
| Material | Remark | Thermal conductivity (W/m/°C) |
|
Sandy soil (40% pore space) |
Dry | 0.30 |
| Saturated | 2.20 | |
|
Clay soil (40% pore space) |
Dry | 0.25 |
| Saturated | 1.58 | |
|
Peat soil (80% pore space) |
Dry | 0.06 |
| Saturated | 0.50 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
×
Specific heat of soil
The specific heat of soil is the amount of heat absorbed (or released) by unit mass of soil for a corresponding temperature rise (or fall) of 1 degree.
The default value is set at 1180 J/kg/°C after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Specific heat of natural materials (Oke, 1987, p44)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
The specific heat of soil is the amount of heat absorbed (or released) by unit mass of soil for a corresponding temperature rise (or fall) of 1 degree.
The default value is set at 1180 J/kg/°C after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Specific heat of natural materials (Oke, 1987, p44)
| Material | Remark | Specific heat (J/kg/°C) |
|
Sandy soil (40% pore space) |
Dry | 800 |
| Saturated | 1480 | |
|
Clay soil (40% pore space) |
Dry | 890 |
| Saturated | 1550 | |
|
Peat soil (80% pore space) |
Dry | 1920 |
| Saturated | 3650 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
×
Density of soil
The density of the soil is its mass per unit volume.
The default value is set at 1800 kg/m³ after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Density of natural materials (Oke, 1987, p44)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
The density of the soil is its mass per unit volume.
The default value is set at 1800 kg/m³ after the value used for Greater Manchester by Gill (2006). This is the average for dry and saturated sandy and clay soils (table 1), since the dominant soil over Greater Manchester is 'seasonally wet deep loam' which is between sandy and clay.
Table 1. Density of natural materials (Oke, 1987, p44)
| Material | Remark | Density (kg/m³) |
|
Sandy soil (40% pore space) |
Dry | 1600 |
| Saturated | 2000 | |
|
Clay soil (40% pore space) |
Dry | 1600 |
| Saturated | 2000 | |
|
Peat soil (80% pore space) |
Dry | 300 |
| Saturated | 1100 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
×
Specific heat of concrete
The default is set at 880 J/kg/°C (Holman, 1997, p640; Oke, 1987, p259) since it is relatively constant with temperature. This was used by Gill (2006), Whitford et al (2001), and Tso et al (1991).
In the Greater Manchester case (Gill, 2006) it was deemed to be sensible to use the specific heat of concrete rather than of another material, since its value falls between those for other materials likely to be present in abundance in Greater Manchester such as asphalt and brick (Table 1).
Thermal properties of materials used in urban construction (after Oke, 1987, p256)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Holman, J.P. (1997). Heat Transfer (8th edition). McGraw-Hill, London.
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The default is set at 880 J/kg/°C (Holman, 1997, p640; Oke, 1987, p259) since it is relatively constant with temperature. This was used by Gill (2006), Whitford et al (2001), and Tso et al (1991).
In the Greater Manchester case (Gill, 2006) it was deemed to be sensible to use the specific heat of concrete rather than of another material, since its value falls between those for other materials likely to be present in abundance in Greater Manchester such as asphalt and brick (Table 1).
Thermal properties of materials used in urban construction (after Oke, 1987, p256)
| Material (dry state) | Remarks | Specific heat (J/kg/K) |
| Asphalt | 920 | |
| Concrete | Aerated and dense | 808 |
| Stone | Average | 840 |
| Brick | Average | 750 |
| Clay tiles | 920 | |
| Wood | Light | 1420 |
| Dense | 1880 | |
| Steel | 500 | |
| Glass | 670 | |
| Plaster | Gypsum | 1090 |
| Gypsum board | Average | 1050 |
| Insulation | Polystyrene | 880 |
| Cork | 1800 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Holman, J.P. (1997). Heat Transfer (8th edition). McGraw-Hill, London.
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Latent heat of evaporation
The latent heat of evaporation is the energy required to change 1kg of liquid water to water vapour.
The default value varies depending on the temperature scenarios set. The temperature is rounded up or down to the nearest temperature given in table 1, and the latent heat of evaporation is derived accordingly.
Temperature dependence of the latent heat of vaporisation at 100 kPa (Oke, 1987, p392)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
The latent heat of evaporation is the energy required to change 1kg of liquid water to water vapour.
The default value varies depending on the temperature scenarios set. The temperature is rounded up or down to the nearest temperature given in table 1, and the latent heat of evaporation is derived accordingly.
Temperature dependence of the latent heat of vaporisation at 100 kPa (Oke, 1987, p392)
| Temperature (°C) | Latent heat of vaporisation (J/kg) |
| 0 | 2501000 |
| 5 | 2488000 |
| 10 | 2476000 |
| 15 | 2464000 |
| 20 | 2453000 |
| 25 | 2442000 |
| 30 | 2432000 |
| 35 | 2422000 |
| 40 | 2413000 |
| 45 | 2404000 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
×
Other impervious surfaces mass
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for other impervious surfaces is set at 255.5 kg/m² (after Gill, 2006).
This used data obtained from the Highways Agency (figure 1) (H. Tanikawa, personal communication, 2005), where the mass of residential roads was estimated as 255.5 kg/m² (average of asphalt layers for new residential roads, and asphalt and stone block layers for old residential roads). An assumption is made that all other impervious surfaces are residential roads.
Figure 1. Mass of roads in England using data from the Highways Agency (2004), Manual of Contract Documents for Highway Works: Volume 1 Specification for Highway Works (H. Tanikawa, personal communication, 2005)
![]()
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for other impervious surfaces is set at 255.5 kg/m² (after Gill, 2006).
This used data obtained from the Highways Agency (figure 1) (H. Tanikawa, personal communication, 2005), where the mass of residential roads was estimated as 255.5 kg/m² (average of asphalt layers for new residential roads, and asphalt and stone block layers for old residential roads). An assumption is made that all other impervious surfaces are residential roads.
Figure 1. Mass of roads in England using data from the Highways Agency (2004), Manual of Contract Documents for Highway Works: Volume 1 Specification for Highway Works (H. Tanikawa, personal communication, 2005)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Major road mass
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for major roads is set at 784 kg/m² (after Gill, 2006).
This used data obtained from the Highways Agency (figure 1) (H. Tanikawa, personal communication, 2005), where the mass of motorways was estimated as 784 kg/m² (asphalt plus concrete layers).
Figure 1. Mass of roads in England using data from the Highways Agency (2004), Manual of Contract Documents for Highway Works: Volume 1 Specification for Highway Works (H. Tanikawa, personal communication, 2005)
![]()
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for major roads is set at 784 kg/m² (after Gill, 2006).
This used data obtained from the Highways Agency (figure 1) (H. Tanikawa, personal communication, 2005), where the mass of motorways was estimated as 784 kg/m² (asphalt plus concrete layers).
Figure 1. Mass of roads in England using data from the Highways Agency (2004), Manual of Contract Documents for Highway Works: Volume 1 Specification for Highway Works (H. Tanikawa, personal communication, 2005)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Building mass
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for buildings is set at 842 kg/m² (after Gill, 2006).
This value comes from the mass of a typical two floor brick house in Trafford, Greater Manchester; calculated, using data from the Building Research Establishment, to be 842 kg/m² (H. Tanikawa, personal communication, 2005).
This approach assumes that all buildings are similar to the typical two floor brick houses of Greater Manchester. This is a reasonable assumption to simplify the calculation of the building mass in Greater Manchester, since this type of building is common. However, the building mass could vary in other cities, or indeed, in parts of cities which have more high-rise buildings.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The tool calculates a weighted building mass per unit of built environment; weighting the percentage of each built surface type (i.e. buildings, major roads, other impervious surfaces) by its mass.
The default mass for buildings is set at 842 kg/m² (after Gill, 2006).
This value comes from the mass of a typical two floor brick house in Trafford, Greater Manchester; calculated, using data from the Building Research Establishment, to be 842 kg/m² (H. Tanikawa, personal communication, 2005).
This approach assumes that all buildings are similar to the typical two floor brick houses of Greater Manchester. This is a reasonable assumption to simplify the calculation of the building mass in Greater Manchester, since this type of building is common. However, the building mass could vary in other cities, or indeed, in parts of cities which have more high-rise buildings.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Roughness length
The roughness length is a measure of the aerodynamic roughness of the surface. It is related to, but not equal to, the height of the roughness elements. It is also a function of the shape and density distribution of the elements. The roughness elements of a city are mainly its buildings. These relatively tall, sharp-edged and inflexible objects make cities the roughest of all aerodynamic boundaries. Practical problems make it difficult to measure the roughness length. The values given in table 1 are approximately correct.
The default value is set at 2m after the value used for Greater Manchester by Gill (2006) and for Merseyside by Whitford et al (2001).
Table 1. Typical roughness length of urbanised terrain (Oke, 1987, p298)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The roughness length is a measure of the aerodynamic roughness of the surface. It is related to, but not equal to, the height of the roughness elements. It is also a function of the shape and density distribution of the elements. The roughness elements of a city are mainly its buildings. These relatively tall, sharp-edged and inflexible objects make cities the roughest of all aerodynamic boundaries. Practical problems make it difficult to measure the roughness length. The values given in table 1 are approximately correct.
The default value is set at 2m after the value used for Greater Manchester by Gill (2006) and for Merseyside by Whitford et al (2001).
Table 1. Typical roughness length of urbanised terrain (Oke, 1987, p298)
| Terrain | Roughness length (m) |
| Scattered settlement (farms, villages, trees, hedges) | 0.2-0.6 |
| Suburban (low density residences and gardens) | 0.4-1.2 |
| Suburban (high density) | 0.8-1.8 |
| Urban (high density, <5 storey row and block buildings) | 1.5-2.5 |
| Urban (high density plus multi-storey blocks) | 2.5-10 |
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Oke, T.R. (1987). Boundary Layer Climates (2nd edition). Routledge, London.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Height of surface boundary layer
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). Tso et al (1991) had previously used a surface boundary layer height of 300m for Kuala Lumpur.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). Tso et al (1991) had previously used a surface boundary layer height of 300m for Kuala Lumpur.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Wind velocity at surface boundary layer
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). It sets the wind velocity at this height at 5 m/s (after Tso et al, 1991; Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). It sets the wind velocity at this height at 5 m/s (after Tso et al, 1991; Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Air temperature at surface boundary layer
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006).
To determine the air temperature at the surface boundary layer it applies an environmental lapse rate to the temperature scenarios set. The environmental lapse rate is the decrease in temperature with an increase in altitude. The long-term global average environmental lapse rate is 0.65°C per 100m throughout the troposphere (Ritter, 2006). Thus, the temperature at the surface boundary layer at 800m will be 5.2°C less than the temperature at the ground (assuming the environmental lapse rate is constant and that temperatures change at the same rate during a heatwave regardless of height).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Ritter, M.E. (2006). The Physical Environment: an Introduction to Physical Geography. University of Wisconsin, Stevens Point. www.uwsp.edu/geo/faculty/ritter/glossary/e_g/environmental_lapse_rate.html
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006).
To determine the air temperature at the surface boundary layer it applies an environmental lapse rate to the temperature scenarios set. The environmental lapse rate is the decrease in temperature with an increase in altitude. The long-term global average environmental lapse rate is 0.65°C per 100m throughout the troposphere (Ritter, 2006). Thus, the temperature at the surface boundary layer at 800m will be 5.2°C less than the temperature at the ground (assuming the environmental lapse rate is constant and that temperatures change at the same rate during a heatwave regardless of height).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Ritter, M.E. (2006). The Physical Environment: an Introduction to Physical Geography. University of Wisconsin, Stevens Point. www.uwsp.edu/geo/faculty/ritter/glossary/e_g/environmental_lapse_rate.html
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
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Specific humidity at surface boundary layer
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). It sets the specific humidity at this height to 0.002 (after Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The surface boundary layer is the thin layer of air adjacent to the earth's surface, extending up to the so-called anemometer level (the base of the Ekman layer); within this layer the wind distribution is determined largely by the vertical temperature gradient and the nature and contours of the underlying surface, and shearing stresses are approximately constant.
The tool sets the default surface boundary layer at 800m (after Whitford et al, 2001; Gill, 2006). It sets the specific humidity at this height to 0.002 (after Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
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Soil depth
The default soil depth is set at 20cm (after Tso et al, 1991; Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
The default soil depth is set at 20cm (after Tso et al, 1991; Whitford et al, 2001; Gill, 2006).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Soil temperature
The soil temperature is the temperature of the soil at a soil depth of 20cm.
The default is set by subtracting 0.6°C from the temperature scenarios set, after the approach used in Greater Manchester by Gill (2006).
This approach compared UK Met Office maps showing the 1961-1990 average (but not extreme) summer soil temperatures at 30 cm (figure 1) and air temperatures (figure 2). Interpreting from these figures, the range of temperatures over Greater Manchester is 5.4°C-17.3°C for mean air temperature and 5°C-16.5°C for mean soil temperature. The median values are therefore 11.35°C and 10.75°C for air and soil temperature, respectively. Thus, the difference between the air and soil temperature is 0.6°C. This value was then subtracted from the temperature scenario set to provide an estimate of soil temperatures. Since extreme soil temperature maps are not available, it is assumed that the same temperature difference exists between mean air and soil temperatures as between extreme air and soil temperatures.
It is recognised that the relationship between air and soil temperature will vary with soil type, for example, sand warms faster whilst clay and peat warms at a slower rate. However, soil types are not differentiated between. Also, it is assumed here that the soil temperature will change at the same rate as air temperature during a heatwave. In fact, soil temperature will change at a different rate to air temperature. Thus, during a heatwave, the air would be expected to heat faster than the soil, yet after several warm days the soil temperature would also increase.
Figure 1. 30 cm soil temperature summer average 1961-1990, showing location of Greater Manchester
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Figure 2. Mean air temperature summer average 1961-1990, showing location of Greater Manchester
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Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006). The sensitivity test reveals that changing the soil temperature has little impact on the maximum surface temperature.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The soil temperature is the temperature of the soil at a soil depth of 20cm.
The default is set by subtracting 0.6°C from the temperature scenarios set, after the approach used in Greater Manchester by Gill (2006).
This approach compared UK Met Office maps showing the 1961-1990 average (but not extreme) summer soil temperatures at 30 cm (figure 1) and air temperatures (figure 2). Interpreting from these figures, the range of temperatures over Greater Manchester is 5.4°C-17.3°C for mean air temperature and 5°C-16.5°C for mean soil temperature. The median values are therefore 11.35°C and 10.75°C for air and soil temperature, respectively. Thus, the difference between the air and soil temperature is 0.6°C. This value was then subtracted from the temperature scenario set to provide an estimate of soil temperatures. Since extreme soil temperature maps are not available, it is assumed that the same temperature difference exists between mean air and soil temperatures as between extreme air and soil temperatures.
It is recognised that the relationship between air and soil temperature will vary with soil type, for example, sand warms faster whilst clay and peat warms at a slower rate. However, soil types are not differentiated between. Also, it is assumed here that the soil temperature will change at the same rate as air temperature during a heatwave. In fact, soil temperature will change at a different rate to air temperature. Thus, during a heatwave, the air would be expected to heat faster than the soil, yet after several warm days the soil temperature would also increase.
Figure 1. 30 cm soil temperature summer average 1961-1990, showing location of Greater Manchester
Figure 2. Mean air temperature summer average 1961-1990, showing location of Greater Manchester
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006). The sensitivity test reveals that changing the soil temperature has little impact on the maximum surface temperature.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Sunrise time
The default sunrise time is set at 04:00 hours Greenwich Mean Time after the value used for Greater Manchester by Gill (2006).
The typical hours of sunrise and sunset are used to calculate the daily radiation.
Average summertime sunrise and sunset times were calculated and rounded to the nearest hour for Greater Manchester using an online tool (this tool is no longer available, but other websites have similar tools, e.g. www.timeanddate.com/worldclock/sunrise.html).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The default sunrise time is set at 04:00 hours Greenwich Mean Time after the value used for Greater Manchester by Gill (2006).
The typical hours of sunrise and sunset are used to calculate the daily radiation.
Average summertime sunrise and sunset times were calculated and rounded to the nearest hour for Greater Manchester using an online tool (this tool is no longer available, but other websites have similar tools, e.g. www.timeanddate.com/worldclock/sunrise.html).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Sunset time
The default sunset time is set at 20:00 hours Greenwich Mean Time after the value used for Greater Manchester by Gill (2006).
The typical hours of sunrise and sunset are used to calculate the daily radiation.
Average summertime sunrise and sunset times were calculated and rounded to the nearest hour for Greater Manchester using an online tool (this tool is no longer available, but other websites have similar tools, e.g. www.timeanddate.com/worldclock/sunrise.html).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The default sunset time is set at 20:00 hours Greenwich Mean Time after the value used for Greater Manchester by Gill (2006).
The typical hours of sunrise and sunset are used to calculate the daily radiation.
Average summertime sunrise and sunset times were calculated and rounded to the nearest hour for Greater Manchester using an online tool (this tool is no longer available, but other websites have similar tools, e.g. www.timeanddate.com/worldclock/sunrise.html).
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Peak insolation
Peak insolation is the maximum solar irradiance on a given day measured at a given location on Earth with a surface element perpendicular to the Sun's rays. It excludes diffuse insolation (the solar radiation that is scattered or reflected by atmospheric components in the sky).
The default peak insolation is set at 802.5 W/m² after the value used for Greater Manchester by Gill (2006).
This is a typical peak radiation value for a cloud-free summer day in Greater Manchester. This used the design 97.5th percentile maximum normal to beam irradiance, extracted from tables for Manchester Aughton (1981-1992) for June 21st, July 4th, and August 4th, in the CIBSE Environmental Design Guide (CIBSE, 1999). The maximum values for June and July were averaged to give a maximum normal to beam irradiance of 802.5 W/m². August values were not used since they are fairly different to the other two months.
This value is higher than the measured value of 750 W/m² used by Tso et al (1991) for Kuala Lumpur (and also used by Whitford et al (2001) in Merseyside), which could be because the values used there were typical of a warm day which may also be cloudy (G. Levermore, personal communication, 2005).
Figure 1. Design 97.5th percentile of global, beam, and diffuse irradiance on horizontal surfaces, as well as normal to beam, Manchester (Aughton, 1981-1992) (CIBSE, 1999)
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Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
CIBSE (1999). Guide A - Environmental Design. Chartered Institution of Building Services Engineers, London.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
Peak insolation is the maximum solar irradiance on a given day measured at a given location on Earth with a surface element perpendicular to the Sun's rays. It excludes diffuse insolation (the solar radiation that is scattered or reflected by atmospheric components in the sky).
The default peak insolation is set at 802.5 W/m² after the value used for Greater Manchester by Gill (2006).
This is a typical peak radiation value for a cloud-free summer day in Greater Manchester. This used the design 97.5th percentile maximum normal to beam irradiance, extracted from tables for Manchester Aughton (1981-1992) for June 21st, July 4th, and August 4th, in the CIBSE Environmental Design Guide (CIBSE, 1999). The maximum values for June and July were averaged to give a maximum normal to beam irradiance of 802.5 W/m². August values were not used since they are fairly different to the other two months.
This value is higher than the measured value of 750 W/m² used by Tso et al (1991) for Kuala Lumpur (and also used by Whitford et al (2001) in Merseyside), which could be because the values used there were typical of a warm day which may also be cloudy (G. Levermore, personal communication, 2005).
Figure 1. Design 97.5th percentile of global, beam, and diffuse irradiance on horizontal surfaces, as well as normal to beam, Manchester (Aughton, 1981-1992) (CIBSE, 1999)
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
CIBSE (1999). Guide A - Environmental Design. Chartered Institution of Building Services Engineers, London.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Night radiation
Night radiation refers to the heat radiated to space from the surface of the Earth, commonly experienced on cloudless nights.
The default value is set at -93 W/m² after the value used for Greater Manchester by Gill (2006).
This was calculated from the CIBSE guide A2, which offers the following equation for net long-wave radiation loss, I1 (W/m²) from a black body at air temperature to the external environment (i.e. sky, ground and nearby buildings) for horizontal surfaces: I1 = 93 - 79C, where C = cloudiness (CIBSE, 1982, p69). Assuming that the sky is cloudless (C = 0), this gives a net long-wave radiation loss of 93 W/m².
Tso et al (1990), for Kuala Lumpur, estimated the night radiation from radiative losses between a night sky temperature of -5°C and a ground temperature of 24°C to be -148.7 W/m² using the equation RI = σ (T04 - Tsky4); where RI is the net infrared flux at the surface of the earth, T0 and Tsky are the surface and sky temperatures respectively, and σ is the Stefan-Boltzman constant, 5.67x10-8 W/m²/K4. Whitford et al (2001) also used this value for Merseyside. It is very difficult, however, to obtain measurements of sky temperature since the measurement height is not clearly defined.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
CIBSE (1999). Guide A - Environmental Design. Chartered Institution of Building Services Engineers, London.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
Night radiation refers to the heat radiated to space from the surface of the Earth, commonly experienced on cloudless nights.
The default value is set at -93 W/m² after the value used for Greater Manchester by Gill (2006).
This was calculated from the CIBSE guide A2, which offers the following equation for net long-wave radiation loss, I1 (W/m²) from a black body at air temperature to the external environment (i.e. sky, ground and nearby buildings) for horizontal surfaces: I1 = 93 - 79C, where C = cloudiness (CIBSE, 1982, p69). Assuming that the sky is cloudless (C = 0), this gives a net long-wave radiation loss of 93 W/m².
Tso et al (1990), for Kuala Lumpur, estimated the night radiation from radiative losses between a night sky temperature of -5°C and a ground temperature of 24°C to be -148.7 W/m² using the equation RI = σ (T04 - Tsky4); where RI is the net infrared flux at the surface of the earth, T0 and Tsky are the surface and sky temperatures respectively, and σ is the Stefan-Boltzman constant, 5.67x10-8 W/m²/K4. Whitford et al (2001) also used this value for Merseyside. It is very difficult, however, to obtain measurements of sky temperature since the measurement height is not clearly defined.
Click here to see the results of a sensitivity test of the surface temperature tool to the input parameters (as undertaken by Gill, 2006).
CIBSE (1999). Guide A - Environmental Design. Chartered Institution of Building Services Engineers, London.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Tso, C.P., Chan, B.K. and Hashim, M.A. (1991). Analytical solutions to the near-neutral atmospheric surface energy balance with and without heat storage for urban climatological studies. Journal of Applied Meteorology, 30 (4), 413-424.
Whitford, V., Ennos, A.R. and Handley, J.F. (2001). "City form and natural process" - indicators for the ecological performance of urban areas and their application to Merseyside, UK. Landscape and Urban Planning, 57 (2), 91-103.
×
Baseline temperature (1961-1990)
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the baseline 1961-1990 climate period.
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the baseline 1961-1990 climate period.
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
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Baseline precipitation (1961-1990)
This is the rainfall depth for the 99th percentile daily winter precipitation (i.e. the amount of rainfall that might occur an average of one day per winter) for the baseline 1961-1990 climate period.
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
This is the rainfall depth for the 99th percentile daily winter precipitation (i.e. the amount of rainfall that might occur an average of one day per winter) for the baseline 1961-1990 climate period.
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
×
Antecedent moisture conditions
The antecedent moisture condition refers to the rainfall over the previous five days (table 1). It is dependent on season.
Table 1. Antecedent moisture condition classification (SCS, 1972)
The default value given is for normal antecedent moisture conditions.
Gill (2006) previously calculated the typical antecedent moisture conditions for the dormant season in Greater Manchester using weather generator data for baseline and future climate scenarios. Analysis of the data (table 2) suggested that, in winter in Greater Manchester, the dry antecedent moisture condition is the most likely under all the time periods and emissions scenarios considered. However, there is a general shift towards more normal and wet conditions.
Table 2. % of winter days that have dry, normal, and wet antecedent moisture conditions (from time series output from the BETWIXT daily weather generator, using UKCIP02 scenarios, for Ringway) (Gill, 2006)
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
SCS (1972). SCS National Engineering Handbook - Section 4: Hydrology. United States Department of Agriculture, Soil Conservation Service, Washington, DC.
The antecedent moisture condition refers to the rainfall over the previous five days (table 1). It is dependent on season.
Table 1. Antecedent moisture condition classification (SCS, 1972)
|
Antecedent moisture condition |
Total 5-day antecedent rainfall (mm) | |
| Dormant season | Growing season | |
| Dry | <12.7 | <35.56 |
| Normal | 12.7-27.94 | 35.56-53.34 |
| Wet | >27.94 | >53.34 |
The default value given is for normal antecedent moisture conditions.
Gill (2006) previously calculated the typical antecedent moisture conditions for the dormant season in Greater Manchester using weather generator data for baseline and future climate scenarios. Analysis of the data (table 2) suggested that, in winter in Greater Manchester, the dry antecedent moisture condition is the most likely under all the time periods and emissions scenarios considered. However, there is a general shift towards more normal and wet conditions.
Table 2. % of winter days that have dry, normal, and wet antecedent moisture conditions (from time series output from the BETWIXT daily weather generator, using UKCIP02 scenarios, for Ringway) (Gill, 2006)
|
Antecedent moisture conditions |
1961-1990 | 2020s | 2050s | 2080s | |||
| Low | High | Low | High | Low | High | ||
| Dry | 75 | 71 | 67 | 67 | 60 | 62 | 55 |
| Normal | 19 | 20 | 24 | 22 | 27 | 24 | 26 |
| Wet | 6 | 9 | 10 | 12 | 14 | 14 | 18 |
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
SCS (1972). SCS National Engineering Handbook - Section 4: Hydrology. United States Department of Agriculture, Soil Conservation Service, Washington, DC.
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Buildings
This is the percentage of the land that is covered by buildings. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a building, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of buildings. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by buildings. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a building, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of buildings. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Buildings
This is the percentage of the land that is covered by buildings. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a building, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of buildings. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by buildings. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a building, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of buildings. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Major roads
This is the percentage of the land that is covered by major roads. It is only used in the surface temperature tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. In this instance, all motorways, dual carriageways and A roads count as major roads.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the major road, then it would count towards the percentage of green and blue surfaces rather than towards the percentage of major roads. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by major roads. It is only used in the surface temperature tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. In this instance, all motorways, dual carriageways and A roads count as major roads.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the major road, then it would count towards the percentage of green and blue surfaces rather than towards the percentage of major roads. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Other impervious surfaces
This is the percentage of the land that is covered by other impervious surfaces. It is different in the surface temperature and runoff tools. In the surface temperature tool it refers to all built or sealed surfaces that are not buildings or major roads (e.g. minor roads, paths, car parking spaces, etc). In the surface runoff tool it refers to all built or sealed surfaces that are not buildings (e.g. major and minor roads, paths, car parking spaces, etc).
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the other impervious surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of other impervious surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by other impervious surfaces. It is different in the surface temperature and runoff tools. In the surface temperature tool it refers to all built or sealed surfaces that are not buildings or major roads (e.g. minor roads, paths, car parking spaces, etc). In the surface runoff tool it refers to all built or sealed surfaces that are not buildings (e.g. major and minor roads, paths, car parking spaces, etc).
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the other impervious surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of other impervious surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Green and blue surfaces
This is the percentage of the land that is covered by green and blue surfaces (e.g. trees, shrubs, mown grass, rough grass, cultivated surfaces, other vegetation, water). It is only used in the surface temperature tool; in the surface runoff tool it is split into more detailed land cover types.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. Unfortunately, this method does not capture street trees, which can account for significant amounts of extra green cover in built up areas. Some local authorities may have detailed tree audits that can be used to supplement this information.
Ideally, this figure should be derived from an aerial view of the study area. For example, a tree over an impervious surface would count towards this percentage, as would a tree over grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by green and blue surfaces (e.g. trees, shrubs, mown grass, rough grass, cultivated surfaces, other vegetation, water). It is only used in the surface temperature tool; in the surface runoff tool it is split into more detailed land cover types.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. Unfortunately, this method does not capture street trees, which can account for significant amounts of extra green cover in built up areas. Some local authorities may have detailed tree audits that can be used to supplement this information.
Ideally, this figure should be derived from an aerial view of the study area. For example, a tree over an impervious surface would count towards this percentage, as would a tree over grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Trees
This is the percentage of the land that is covered by trees. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. Unfortunately, this method does not capture street trees, which can account for significant amounts of extra tree cover in built up areas. Some local authorities may have detailed tree audits that can be used to supplement this information.
Ideally, this figure should be derived from an aerial view of the study area. For example, a tree over an impervious surface would count towards this percentage, as would a tree over grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by trees. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected. Unfortunately, this method does not capture street trees, which can account for significant amounts of extra tree cover in built up areas. Some local authorities may have detailed tree audits that can be used to supplement this information.
Ideally, this figure should be derived from an aerial view of the study area. For example, a tree over an impervious surface would count towards this percentage, as would a tree over grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Shrubs
This is the percentage of the land that is covered by shrubs. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a shrub, then it would count towards the percentage of tree cover rather than towards the percentage of shrubs. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by shrubs. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a shrub, then it would count towards the percentage of tree cover rather than towards the percentage of shrubs. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Mown grass
This is the percentage of the land that is covered by mown grass. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a mown grass surface, then it would count towards the percentage of tree cover rather than towards the percentage of mown grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by mown grass. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a mown grass surface, then it would count towards the percentage of tree cover rather than towards the percentage of mown grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Rough grass
This is the percentage of the land that is covered by rough grass. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a rough grass surface, then it would count towards the percentage of tree cover rather than towards the percentage of rough grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by rough grass. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a rough grass surface, then it would count towards the percentage of tree cover rather than towards the percentage of rough grass. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Cultivated surfaces
This is the percentage of the land that is covered by cultivated surfaces. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a cultivated surface, then it would count towards the percentage of tree cover rather than towards the percentage of cultivated surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by cultivated surfaces. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a cultivated surface, then it would count towards the percentage of tree cover rather than towards the percentage of cultivated surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Water
This is the percentage of the land that is covered by water. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a water surface, then it would count towards the percentage of tree cover rather than towards the percentage of water surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
This is the percentage of the land that is covered by water. It is only used in the surface runoff tool.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered a water surface, then it would count towards the percentage of tree cover rather than towards the percentage of water surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
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Bare soil or gravel surfaces
This is the percentage of the land that is covered by bare soil or gravel. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the bare soil or gravel surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of bare soil or gravel surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by bare soil or gravel. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the bare soil or gravel surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of bare soil or gravel surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Bare soil or gravel surfaces
This is the percentage of the land that is covered by bare soil or gravel. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the bare soil or gravel surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of bare soil or gravel surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by bare soil or gravel. It is the same for both the surface temperature and runoff tools.
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the bare soil or gravel surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of bare soil or gravel surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Other impervious surfaces
This is the percentage of the land that is covered by other impervious surfaces. It is different in the surface temperature and runoff tools. In the surface temperature tool it refers to all built or sealed surfaces that are not buildings or major roads (e.g. minor roads, paths, car parking spaces, etc). In the surface runoff tool it refers to all built or sealed surfaces that are not buildings (e.g. major and minor roads, paths, car parking spaces, etc).
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the other impervious surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of other impervious surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
This is the percentage of the land that is covered by other impervious surfaces. It is different in the surface temperature and runoff tools. In the surface temperature tool it refers to all built or sealed surfaces that are not buildings or major roads (e.g. minor roads, paths, car parking spaces, etc). In the surface runoff tool it refers to all built or sealed surfaces that are not buildings (e.g. major and minor roads, paths, car parking spaces, etc).
The default value (only available for North West England) is derived from Ordnance Survey MasterMap using the study area boundaries selected.
Ideally, this figure should be derived from an aerial view of the study area. For example, if a tree covered the other impervious surface, then it would count towards the percentage of green and blue surfaces (in the surface temperature tool) or tree cover (in the surface runoff tool) rather than towards the percentage of other impervious surfaces. Please refer to the note on the land cover scenario(s) for more information on deriving the land cover input.
Click here to see the results of a sensitivity test of the surface temperature tool to the land cover input parameters (as undertaken by Gill, 2006).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
2050s High temperature - 10% probability level
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 10% probability level - very unlikely to be less than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 10% probability level - very unlikely to be less than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
×
2050s High temperature - 50% probability level
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 50% probability level - central estimate, as likely as not) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 50% probability level - central estimate, as likely as not) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
×
2050s High temperature - 90% probability level
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 90% probability level - very unlikely to be greater than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) for the 2050s High emissions scenario (at the 90% probability level - very unlikely to be greater than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the temperature scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that temperatures will be less than the value given for the '10% probability level'; it is very unlikely that temperatures will be more than the value given for '90% probability level'; and it is as like as not that temperatures will be the value given for the '50% probability level', which is the central estimate.
×
2050s High precipitation - 10% probability level
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 10% probability level - very unlikely to be less than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 10% probability level - very unlikely to be less than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
×
2050s High precipitation - 50% probability level
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 50% probability level - central estimate, as likely as not) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 50% probability level - central estimate, as likely as not) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
×
2050s High precipitation - 90% probability level
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 90% probability level - very unlikely to be greater than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
This is the rainfall depth for the 99th percentile daily winter precip[itation (i.e. the amount of rainfall that might occur an average of one day per winter) for the 2050s High emissions scenario (at the 90% probability level - very unlikely to be greater than) (see box 1 for definitions).
Default values are only supplied for study areas in the North West of England. They are calculated from UKCP09 weather generator output for 20 climate zones across the North West of England, as described in the note on the precipitation scenarios.
Box 1. Definitions of UKCP09 climate scenario terms used
2050s refers to a 30 year time period from 2040-2069.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
High refers to the greenhouse gas emissions scenario. The UK Climate Projections (UKCP09) use 3 emissions scenarios - High, Medium, and Low. These relate to the emissions scenarios of the IPCC Special Report on Emissions Scenarios, which are based on different storylines as to whether and how greenhouse gas emissions are reduced globally (High relates to SRES A1FI, Medium to SRES A1B, and Low to SRES B1). We are currently on an emissions path which is above that of the High emissions scenario (see http://ukclimateprojections.defra.gov.uk/content/view/2094/500).
Probability level is used to describe the strength of evidence associated with a given value. It is very unlikely that precipitation will be less than the value given for the '10% probability level'; it is very unlikely that precipitation will be more than the value given for '90% probability level'; and it is as like as not that precipitation will be the value given for the '50% probability level', which is the central estimate.
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A (high water infiltration rate; sandy soils)
×
B
×
C
×
D (low water infiltration rate; clay soils)
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Study area size
Where users have selected study area(s) on the map, then default values will be automatically drawn from this. Otherwise users will have to enter the study area size in m².
Where users have selected study area(s) on the map, then default values will be automatically drawn from this. Otherwise users will have to enter the study area size in m².
×
Land cover scenario(s)
The land cover scenario gives the percentage of different land cover types within your study area. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
Ideally, these figures should be derived from an aerial or birds-eye view of your study area. So, for example, a tree over a small residential road counts towards the percentage of green and blue surfaces (in the surface temperature tool) or the percentage of trees (in the surface runoff tool), rather than the percentage of other impervious surfaces.
Table 1 below shows the land cover types required for both the surface temperature and surface runoff tool. It also shows how the land cover requirements for each of the tools compare to each other. (So, for example, if you were collecting the land cover requirements for both tools you would have a total of 10 land cover types).
Table 1. Comparison of land cover types required for the STAR tools
The default values (only available for North West England) are derived from Ordnance Survey MasterMap using the study areas selected. See here for the method used to process OS MasterMap.
N.B. When using the default land cover data it is important that the study area size is not smaller than 200m x 200m (or 4 ha). This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
Users outside of the North West of England will have to provide their own input data. They may already have datasets which can be adapted to these land cover types, or they may have to derive this data themselves. Suggestions as to how this could be done in GIS using aerial photographs include:
For the surface runoff tool, the underpinning model assigns 'curve numbers' to the different land cover types; and surface runoff is calculated from this. It is therefore advised that, when collecting land cover data you try to distinguish between those land covers that are the most different in terms of their curve numbers.
Table 2. Curve numbers for the land cover types required for surface runoff tool (for hydrological soil type A under normal antecedent moisture conditions; Gill, 2006)
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The land cover scenario gives the percentage of different land cover types within your study area. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
Ideally, these figures should be derived from an aerial or birds-eye view of your study area. So, for example, a tree over a small residential road counts towards the percentage of green and blue surfaces (in the surface temperature tool) or the percentage of trees (in the surface runoff tool), rather than the percentage of other impervious surfaces.
Table 1 below shows the land cover types required for both the surface temperature and surface runoff tool. It also shows how the land cover requirements for each of the tools compare to each other. (So, for example, if you were collecting the land cover requirements for both tools you would have a total of 10 land cover types).
Table 1. Comparison of land cover types required for the STAR tools
| Surface temperature tool | Surface runoff tool |
| Buildings | Buildings |
| Major roads | Other impervious surfaces |
| Other impervious surfaces | |
| Green and blue surfaces | Trees |
| Shrubs | |
| Mown grass | |
| Rough grass | |
| Cultivated surfaces | |
| Water | |
| Bare soil or gravel surfaces | Bare soil or gravel surfaces |
The default values (only available for North West England) are derived from Ordnance Survey MasterMap using the study areas selected. See here for the method used to process OS MasterMap.
N.B. When using the default land cover data it is important that the study area size is not smaller than 200m x 200m (or 4 ha). This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
Users outside of the North West of England will have to provide their own input data. They may already have datasets which can be adapted to these land cover types, or they may have to derive this data themselves. Suggestions as to how this could be done in GIS using aerial photographs include:
- Estimating the percentages of the different land cover types
- Digitising the different land cover types
- Placing a grid over your study area and assigning the predominant land cover type in each grid
- Placing random points (400 points recommended) within your study area and identifying the surface cover under each (this approach was used in the ASCCUE project and is explained in chapter 3.3 of Gill, 2006).
For the surface runoff tool, the underpinning model assigns 'curve numbers' to the different land cover types; and surface runoff is calculated from this. It is therefore advised that, when collecting land cover data you try to distinguish between those land covers that are the most different in terms of their curve numbers.
Table 2. Curve numbers for the land cover types required for surface runoff tool (for hydrological soil type A under normal antecedent moisture conditions; Gill, 2006)
| Land cover type | Curve number |
| Trees | 25 |
| Rough grass | 30 |
| Mown grass | 39 |
| Shrubs | 45 |
| Cultivated surfaces | 67 |
| Bare soil or gravel surfaces | 74 |
| Buildings and other impervious surfaces | 98 |
| Water | n/a |
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Temperature scenario(s)
The input temperature to the tool should be a daily average air temperature at a height of 2m.
The default temperature values provided for North West England are the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) to consider what might happen on a hot summers day (as used by Gill, 2006). These default values are supplied for the:
Figure 1. 20 climate zones in North West England (prepared by Gina Cavan, University of Manchester)
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You can also add another temperature scenario. This is especially useful for users outside of the North West of England who do not have any temperature scenarios supplied for them.
Click here to see the results of a sensitivity test of the surface temperature tool to the temperature input parameters (as undertaken by Gill, 2006; N.B. the temperature parameters here are referred to as reference temperature in the sensitivity test).
Cavan, G. (2009). Climate projections for Greater Manchester. EcoCities, The University of Manchester. www.sed.manchester.ac.uk/architecture/research/ecocities/library/documents/Climate_change_projections_GM_final.pdf
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The input temperature to the tool should be a daily average air temperature at a height of 2m.
The default temperature values provided for North West England are the 98th percentile daily summer mean temperature (i.e. the type of average daily temperature that could occur twice per summer) to consider what might happen on a hot summers day (as used by Gill, 2006). These default values are supplied for the:
- Baseline temperature (1961-1990)
- 2050s High temperature - 10% probability level
- 2050s High temperature - 50% probability level
- 2050s High temperature - 90% probability level.
Figure 1. 20 climate zones in North West England (prepared by Gina Cavan, University of Manchester)
You can also add another temperature scenario. This is especially useful for users outside of the North West of England who do not have any temperature scenarios supplied for them.
Click here to see the results of a sensitivity test of the surface temperature tool to the temperature input parameters (as undertaken by Gill, 2006; N.B. the temperature parameters here are referred to as reference temperature in the sensitivity test).
Cavan, G. (2009). Climate projections for Greater Manchester. EcoCities, The University of Manchester. www.sed.manchester.ac.uk/architecture/research/ecocities/library/documents/Climate_change_projections_GM_final.pdf
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Further parameters
Default values are provided for all of these parameters using values set for Greater Manchester by Gill (2006). In the first instance all users can run the STAR tools with these default values, however:
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Default values are provided for all of these parameters using values set for Greater Manchester by Gill (2006). In the first instance all users can run the STAR tools with these default values, however:
- It is generally recommended that users in the North West of England do not amend these values unless they have better information available.
- Users elsewhere should pay careful attention to the information notes for each parameter to decide whether they need to amend these; these provide information on assumptions made.
- The sensitivity test of the surface temperature tool to the various parameters (as undertaken by Gill, 2006) can help you to decide which are the most important parameters to change.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Land cover scenario(s)
The land cover scenario gives the percentage of different land cover types within your study area. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
Ideally, these figures should be derived from an aerial or birds-eye view of your study area. So, for example, a tree over a small residential road counts towards the percentage of green and blue surfaces (in the surface temperature tool) or the percentage of trees (in the surface runoff tool), rather than the percentage of other impervious surfaces.
Table 1 below shows the land cover types required for both the surface temperature and surface runoff tool. It also shows how the land cover requirements for each of the tools compare to each other. (So, for example, if you were collecting the land cover requirements for both tools you would have a total of 10 land cover types).
Table 1. Comparison of land cover types required for the STAR tools
The default values (only available for North West England) are derived from Ordnance Survey MasterMap using the study areas selected. See here for the method used to process OS MasterMap.
N.B. When using the default land cover data it is important that the study area size is not smaller than 200m x 200m (or 4 ha). This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
Users outside of the North West of England will have to provide their own input data. They may already have datasets which can be adapted to these land cover types, or they may have to derive this data themselves. Suggestions as to how this could be done in GIS using aerial photographs include:
For the surface runoff tool, the underpinning model assigns 'curve numbers' to the different land cover types; and surface runoff is calculated from this. It is therefore advised that, when collecting land cover data you try to distinguish between those land covers that are the most different in terms of their curve numbers.
Table 2. Curve numbers for the land cover types required for surface runoff tool (for hydrological soil type A under normal antecedent moisture conditions; Gill, 2006)
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The land cover scenario gives the percentage of different land cover types within your study area. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
Ideally, these figures should be derived from an aerial or birds-eye view of your study area. So, for example, a tree over a small residential road counts towards the percentage of green and blue surfaces (in the surface temperature tool) or the percentage of trees (in the surface runoff tool), rather than the percentage of other impervious surfaces.
Table 1 below shows the land cover types required for both the surface temperature and surface runoff tool. It also shows how the land cover requirements for each of the tools compare to each other. (So, for example, if you were collecting the land cover requirements for both tools you would have a total of 10 land cover types).
Table 1. Comparison of land cover types required for the STAR tools
| Surface temperature tool | Surface runoff tool |
| Buildings | Buildings |
| Major roads | Other impervious surfaces |
| Other impervious surfaces | |
| Green and blue surfaces | Trees |
| Shrubs | |
| Mown grass | |
| Rough grass | |
| Cultivated surfaces | |
| Water | |
| Bare soil or gravel surfaces | Bare soil or gravel surfaces |
The default values (only available for North West England) are derived from Ordnance Survey MasterMap using the study areas selected. See here for the method used to process OS MasterMap.
N.B. When using the default land cover data it is important that the study area size is not smaller than 200m x 200m (or 4 ha). This is due to the method of deriving it from OS MasterMap, where a one land cover type is assigned to each 10m x 10m grid cell, resulting in a total of 400 grid squares for a 200m x 200m plot. Gill (2006) showed that by determining the land cover type for 400 points you could accurately determine the land cover percentages of a given area.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
Users outside of the North West of England will have to provide their own input data. They may already have datasets which can be adapted to these land cover types, or they may have to derive this data themselves. Suggestions as to how this could be done in GIS using aerial photographs include:
- Estimating the percentages of the different land cover types
- Digitising the different land cover types
- Placing a grid over your study area and assigning the predominant land cover type in each grid
- Placing random points (400 points recommended) within your study area and identifying the surface cover under each (this approach was used in the ASCCUE project and is explained in chapter 3.3 of Gill, 2006).
For the surface runoff tool, the underpinning model assigns 'curve numbers' to the different land cover types; and surface runoff is calculated from this. It is therefore advised that, when collecting land cover data you try to distinguish between those land covers that are the most different in terms of their curve numbers.
Table 2. Curve numbers for the land cover types required for surface runoff tool (for hydrological soil type A under normal antecedent moisture conditions; Gill, 2006)
| Land cover type | Curve number |
| Trees | 25 |
| Rough grass | 30 |
| Mown grass | 39 |
| Shrubs | 45 |
| Cultivated surfaces | 67 |
| Bare soil or gravel surfaces | 74 |
| Buildings and other impervious surfaces | 98 |
| Water | n/a |
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Precipitation scenario(s)
The input precipitation to the tool is for daily precipitation depths in mm. The tools will run automatically for all rainfall depths from 0-100 mm; the user does not have to input any precipitation scenario. However, the option to input a precipitation scenario is provided here as, if it is used, then the runoff expected for these different rainfall depths will be highlighted in the results. This will, for example, allow users to compare runoff proportions under different climate change scenarios if they have this information.
The default precipitation values provided for North West England are the 99th percentile daily winter precipitation (i.e. the type of rainfall you'd expect to have on one day per winter) to consider what might happen on an extreme rainfall day in winter (as used by Gill, 2006). These default values are supplied for the:
Figure 1. 20 climate zones in North West England (prepared by Gina Cavan, University of Manchester)
![]()
You can also add another precipitation scenario. This is especially useful for users outside of the North West of England who do not have any precipitation scenarios supplied for them.
Cavan, G. (2009). Climate projections for Greater Manchester. EcoCities, The University of Manchester. www.sed.manchester.ac.uk/architecture/research/ecocities/library/documents/Climate_change_projections_GM_final.pdf
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
The input precipitation to the tool is for daily precipitation depths in mm. The tools will run automatically for all rainfall depths from 0-100 mm; the user does not have to input any precipitation scenario. However, the option to input a precipitation scenario is provided here as, if it is used, then the runoff expected for these different rainfall depths will be highlighted in the results. This will, for example, allow users to compare runoff proportions under different climate change scenarios if they have this information.
The default precipitation values provided for North West England are the 99th percentile daily winter precipitation (i.e. the type of rainfall you'd expect to have on one day per winter) to consider what might happen on an extreme rainfall day in winter (as used by Gill, 2006). These default values are supplied for the:
- Baseline precipitation (1961-1990)
- 2050s High precipitation - 10% probability level
- 2050s High precipitation - 50% probability level
- 2050s High precipitation - 90% probability level.
Figure 1. 20 climate zones in North West England (prepared by Gina Cavan, University of Manchester)
You can also add another precipitation scenario. This is especially useful for users outside of the North West of England who do not have any precipitation scenarios supplied for them.
Cavan, G. (2009). Climate projections for Greater Manchester. EcoCities, The University of Manchester. www.sed.manchester.ac.uk/architecture/research/ecocities/library/documents/Climate_change_projections_GM_final.pdf
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
×
Hydrological soil types
Soils are classified into 4 hydrologic soil groups (A-D) to indicate the minimum rate of infiltration obtained for bare soil after prolonged wetting (table 1). The infiltration rate is the rate at which water enters the soil at the soil surface and is controlled by surface conditions. The hydrologic soil type also indicates the transmission rate of water through the soil, controlled by the soil profile (NRCS, 1986). In practice, soil profiles may be considerably altered as a result of urbanisation; in such cases the classification should be made according to the texture of the new surface soil, providing that significant compaction has not occurred (see disturbed soil profile in table 1). In addition, some soils will be classed as group D because of a high water table that creates a drainage problem; once these soils are effectively drained they are classified differently (NRCS, 1986).
Table 1. US Soil Conservation Service hydrologic soil classification, with recommendations for disturbed soil profiles (after NRCS, 1986)
Default hydrological soil types were previously provided for users in the North West of England. These drew on LandIS 5km gridded data from the National Soil Resources Institute at Cranfield University, which classifies soils into seven runoff potential classes (table 2). In this instance, we translated the Cranfield soils: S1, S2, P1 and P2 to hydrological soil type D, S3 to type C, S4 to type B, and S5 to type A.
Table 2. Runoff potential and soil classes
For users in the UK, the data used can be purchased from the National Soil Resources Institute at Cranfield University www.landis.org.uk/data/index.cfm. It may also be useful to look at the Soilscapes online mapping which, although it does not contain runoff potential classes listed in table 2, has free information which could be useful in determining a hydrological soil type www.landis.org.uk/soilscapes.
If you do not have any soils data available, then you may want to run the STAR tool twice: first where you assume that everything is soil type A and then again, where you assume everything is soil type D. This will enable you to get results for the extremes and set boundaries around the surface runoff that you may expect.
Highways Agency et al (2004). Part1: Drainage of runoff from natural catchments. Section 2: Drainage; of Volume 4: Geotechnics and drainage; of Design Manual for Roads and Bridges. www.dft.gov.uk/ha/standards/dmrb/vol4/section2/ha10604.pdf
NRCS (1986). Urban Hydrology for Small Watersheds, Technical Release 55. United States Department of Agriculture, Natural Resources Conservation Service. www.mi.nrcs.usda.gov/technical/engineering/neh.html
Soils are classified into 4 hydrologic soil groups (A-D) to indicate the minimum rate of infiltration obtained for bare soil after prolonged wetting (table 1). The infiltration rate is the rate at which water enters the soil at the soil surface and is controlled by surface conditions. The hydrologic soil type also indicates the transmission rate of water through the soil, controlled by the soil profile (NRCS, 1986). In practice, soil profiles may be considerably altered as a result of urbanisation; in such cases the classification should be made according to the texture of the new surface soil, providing that significant compaction has not occurred (see disturbed soil profile in table 1). In addition, some soils will be classed as group D because of a high water table that creates a drainage problem; once these soils are effectively drained they are classified differently (NRCS, 1986).
Table 1. US Soil Conservation Service hydrologic soil classification, with recommendations for disturbed soil profiles (after NRCS, 1986)
| Soil Type | Description | Disturbed soil profile texture |
| A | Soils having low runoff potential and high infiltration rates even when thoroughly wetted. They consist chiefly of deep, well to excessively drained sands or gravels and have a high rate of water transmission (greater than 7.62 mm/hr). | Sand, loamy sand, or sandy loam |
| B | Soils having moderate infiltration rates when thoroughly wetted and consisting chiefly of moderately deep to deep, moderately well to well drained soils with moderately fine to moderately coarse textures. These soils have a moderate rate of water transmission (3.81-7.62 mm/hr) | Silt loam or loam |
| C | Soils having low infiltration rates when thoroughly wetted and consisting chiefly of soils with a layer that impedes the downward movement of water and soils with moderately fine to fine texture. These soils have a low rate of water transmission (1.27-3.81 mm/hr). | Sandy clay loam |
| D | Soils having a high runoff potential. They have very low infiltration rates when thoroughly wetted and consist chiefly of clay soils with a high swelling potential, soils with a permanent high water table, soils with a claypan or clay layer at or near the surface, and shallow soils over nearly impervious material. These soils have a very low rate of water transmission (0-1.27 mm/hr). | Clay loam, silty clay loam, sandy clay, silty clay, or clay |
Default hydrological soil types were previously provided for users in the North West of England. These drew on LandIS 5km gridded data from the National Soil Resources Institute at Cranfield University, which classifies soils into seven runoff potential classes (table 2). In this instance, we translated the Cranfield soils: S1, S2, P1 and P2 to hydrological soil type D, S3 to type C, S4 to type B, and S5 to type A.
Table 2. Runoff potential and soil classes
| Runoff potential | Soil class |
| Peat very high | P1 |
| Peat high | P2 |
| Very high | S1 |
| High | S2 |
| Moderate | S3 |
| Low | S4 |
| Very low | S5 |
For users in the UK, the data used can be purchased from the National Soil Resources Institute at Cranfield University www.landis.org.uk/data/index.cfm. It may also be useful to look at the Soilscapes online mapping which, although it does not contain runoff potential classes listed in table 2, has free information which could be useful in determining a hydrological soil type www.landis.org.uk/soilscapes.
If you do not have any soils data available, then you may want to run the STAR tool twice: first where you assume that everything is soil type A and then again, where you assume everything is soil type D. This will enable you to get results for the extremes and set boundaries around the surface runoff that you may expect.
Highways Agency et al (2004). Part1: Drainage of runoff from natural catchments. Section 2: Drainage; of Volume 4: Geotechnics and drainage; of Design Manual for Roads and Bridges. www.dft.gov.uk/ha/standards/dmrb/vol4/section2/ha10604.pdf
NRCS (1986). Urban Hydrology for Small Watersheds, Technical Release 55. United States Department of Agriculture, Natural Resources Conservation Service. www.mi.nrcs.usda.gov/technical/engineering/neh.html
×
Other parameters
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Temperature scenario-dependent parameters
It is recommended that most users will not change these parameters.
Values are provided for each parameter automatically using equations and algortithms that draw on the values provided by the temperature scenarios selected as well as other parameter values. See the pop-up boxes associated with the individual parameters for details on how the equations and algorithms are derived.
It is recommended that most users will not change these parameters.
Values are provided for each parameter automatically using equations and algortithms that draw on the values provided by the temperature scenarios selected as well as other parameter values. See the pop-up boxes associated with the individual parameters for details on how the equations and algorithms are derived.
×
Sensitivity test of the input parameters to the surface temperature model
The information provided here should help users to determine how accurate they need to be with the input parameters to the STAR tools.
This information is taken from: Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf. It refers to the parameters as they were named, set and used in this thesis; however this is very similar to their names, default values and use in the STAR tools. It sometimes refers to other sections of the thesis, which can be accessed at the weblink above if desired.
From Gill (2006):
5.4. Sensitivity Tests
Sensitivity tests were undertaken in order to gain an increased understanding of how the model worked, as well as to test the relative sensitivity of the maximum surface temperature output to the different parameters. The sensitivity tests were undertaken using the parameters as determined for the 1961-1990 baseline climate model runs (Section 5.3). All sensitivity analyses were undertaken with the town centre and woodland UMTs, as these are the categories with the highest and lowest maximum surface temperatures (Section 5.5.1.1). Each input parameter was taken in turn and altered by ±10%, or another suitable value, whilst all the other parameters were held constant. A detailed analysis of the sensitivity tests can be found in Appendix D. In this section, the sensitivities of the various parameters are compared to each other.
The ranking of the parameters according to the magnitude of change in maximum surface temperature varies slightly depending on whether it is a plus or minus 10% change and whether it is the town centre or woodland UMT (Tables 5.8 to 5.11). The tests reveal that sensitive parameters include the peak insolation, wind velocity at the SBL, density of the air, and temperature at the SBL. The evaporative fraction is also very important, especially in woodlands, where it is the most sensitive parameter when decreased by 10% (Table 5.11). Unfortunately, it was not possible to increase the evapotranspiring cover in woodland by 10% (or to increase the fraction by 0.098) as this took the evaporating fraction above 1.0. However, even just increasing the evaporating fraction by 0.02, from 0.98 to 1.0, decreased the maximum surface temperature by 0.16°C. Such a change would rank the evaporating fraction at number 9 in table 5.9, below the reference temperature and above the specific humidity at the SBL.
The five least important parameters for town centres are night radiation, bulk density of the soil, specific heat capacity of the soil, soil depth at level s, and soil thermal conductivity. The five least important parameters for woodlands are night radiation, building mass per unit of land, specific heat of concrete, bulk density of the soil, and specific heat capacity of the soil.
Table 5.8. Absolute change in maximum surface temperature resulting from a 10% increase in the respective input parameters in town centres. Parameters listed according to magnitude of change.
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Table 5.9. Absolute change in maximum surface temperature resulting from a 10% increase in the respective input parameters in woodlands. Parameters listed according to magnitude of change.
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Table 5.10. Absolute change in maximum surface temperature resulting from a 10% decrease in the respective input parameters in town centres. Parameters listed according to magnitude of change.
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Table 5.11. Absolute change in maximum surface temperature resulting from a 10% decrease in the respective input parameters in woodlands. Parameters listed according to magnitude of change.
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Appendix D. Energy Exchange Model Sensitivity Tests
The sensitivity tests were undertaken using the model runs set up for the 1961-1990 baseline climate. All sensitivity analyses were undertaken with the town centre and woodland UMTs, as these are the categories with the highest and lowest maximum surface temperatures. The input parameters are identical for the two UMTs, except for the evaporating fraction which is 0.20 in town centres and 0.98 in woodlands, and the building mass per unit of land which is 342.20 kg/m² in town centres and 6.58 kg/m² in woodlands. The sensitivity of the maximum surface temperatures to the input parameters are considered on the following pages. Current input values (i.e. those used by Gill, 2006) are marked on figures D.1 to D.20 by grey dotted lines.
Input parameters considered are:
D.1 Soil Temperature
D.2 Reference Temperature
D.3 Air Temperature at SBL
D.4 Wind Speed at SBL
D.5 Surface Roughness Length
D.6 Height of SBL
D.7 Density of Air
D.8 Density of Soil
D.9 Peak Insolation
D.10 Night Radiation
D.11 Specific Heat of Air
D.12 Specific Heat of Concrete
D.13 Specific Heat of Soil
D.14 Soil Depth at Level s
D.15 Soil Thermal Conductivity
D.16 Latent Heat of Evaporation
D.17 Specific Humidity at SBL
D.18 Hours of Daylight
D.19 Building Mass per Unit of Land
D.20 Evaporating Fraction
D.1 Soil Temperature
The soil temperature at 20 cm (Tb) is used in the finite difference form of the conductive heat flux term (G) (Equation 5.15) of the energy balance equation. This equation is used as one of the set of simultaneous equations solved to find analytical solutions for the surface temperature (T0) and the soil temperature at level s (Ts).
Currently soil temperature is set at 20°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.1). Increasing soil temperatures leads to warmer maximum surface temperatures and vice versa. This effect appears to be linear. Changing the soil temperature has a greater effect in town centres rather than woodlands. For example, changing the soil temperature (from the current temperature) by 1°C alters the maximum surface temperature by ±0.11°C in town centres and ±0.05°C in woodlands. Similarly, changing the soil temperature (from the current temperature) by 5°C alters the maximum surface temperature by ±0.55°C in town centres and ±0.25°C in woodlands. If soil temperature is set at 14.3°C, equivalent to the mean summer temperature for Ringway (BETWIXT) minus 0.6°C, the maximum surface temperature is 0.6°C and 0.3°C less in town centres and woodlands, respectively. Whilst town centres have 6% bare soil compared to 0% in woodlands, the change in maximum surface temperatures in the two UMTs as a result of altering soil temperature is not due to the amount of bare soil. The amount of bare soil is not a term in the finite difference heat conduction equation (Equation 5.15). In addition, a further sensitivity test using refuse disposal, which has the highest amount of bare soil cover (58%), found changes of approximately ±0.08°C in maximum surface temperature as a result of changing soil temperatures by ±1°C. The soil temperature appears to have the greatest impact on maximum surface temperature in UMTs where there is a low evaporating cover. This suggests that much more heat is lost to the air by convection and evapotranspiration than to the soil by conduction.
As the soil temperature increases, the difference between the maximum surface temperatures of town centres and woodlands also increases. At soil temperatures of 15°C, 20°C and 25°C, the difference between the maximum surface temperatures in these UMTs is 12.5°C, 12.8°C, and 13.1°C, respectively. The soil temperature appears to have no effect on the timing of the maximum surface temperature.
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D.2 Reference Temperature
The reference temperature (Tf) does not appear in any of the terms of the energy balance equation as such. The specific humidity, which appears in the latent heat flux (LE) is linearised around the reference temperature.
Currently the reference temperature is set at 20.6°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.2). The relationship between the reference temperature and the maximum surface temperature is quadratic. This is because the specific humidity is linearised by taking the first term of the expansion around the reference temperature. If the reference temperature is equal to the surface temperature there is no error. However, if the reference temperature is higher or lower than the surface temperature the squared term of the expansion (which is always positive) becomes more significant. Therefore at higher and lower temperatures the latent heat loss is underestimated and the calculated maximum surface temperature rises.
The minimum point of the quadratic curves differs for the two UMTs, with woodland being at a lower reference temperature (around 18.6°C) than town centre (around 30.6°C). This is because woodland has lower surface temperatures than town centres. Thus, in woodlands, the point at which the reference temperature is equal to the surface temperature, and hence there is no error in the linearisation of the specific humidity, is lower.
Increasing the reference temperature by 1°C to 21.6°C decreases the maximum surface temperature in town centres by 0.1°C and increases it in woodlands by the same amount. When the reference temperature is decreased by 1°C to 19.6°C, the maximum surface temperature increases in town centres and decreases in woodlands, each by 0.1°C.
Changing the reference temperature alters the timing of the maximum surface temperatures in town centres more significantly than in woodlands.
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D.3 Air Temperature at SBL
The air temperature at the SBL (T2) is used in the sensible heat flux term (H) of the energy balance equation. Increased air temperatures at the SBL increase the magnitude of this term and vice versa.
Currently the air temperature at the SBL is set at 15.4°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.3). Increasing air temperatures at the SBL leads to warmer maximum surface temperatures and vice versa. This effect appears to be linear. Changing the air temperature at the SBL has a greater effect on the maximum surface temperature in town centres rather than woodlands. This is because more energy is lost by convection in town centres which have less evapotranspiring surface. For example, changing the soil temperature (from the current temperature) by 1°C alters the maximum surface temperature by ±0.58°C in town centres and ±0.26°C in woodlands. Similarly, changing the soil temperature (from the current temperature) by 5°C alters the maximum surface temperature by ±2.9°C in town centres and ±1.3°C in woodlands. These changes are much greater than those for the soil temperature.
As the air temperature at the SBL increases, the difference between the maximum surface temperatures of town centres and woodlands also increases. At air temperatures at the SBL of 10.4°C, 15.4°C and 20.4°C, the difference between the maximum surface temperatures in these UMTs is 11.2°C, 12.8°C, and 14.4°C, respectively. The air temperature at the SBL appears to have no effect on the timing of the maximum surface temperature.
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D.4 Wind Speed at SBL
Wind speed at the SBL (U2) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing wind speed increases the magnitude of these terms and vice versa.
Currently wind speed at the SBL is set at 5 m/s, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.4). Increasing wind speed leads to cooler maximum surface temperatures and vice versa. The effect of decreasing wind speed is slightly stronger than that of increasing it. Changing wind speed has a greater effect on the maximum surface temperature in town centres rather than woodlands. This is because more energy is lost by convection in town centres which have less evaporating surface. For example, increasing the wind speed (from the current speed) by 0.5 m/s to 5.5 m/s (a 10% increase) alters the maximum surface temperature by -1.1°C in town centres and -0.7°C in woodlands. Similarly, decreasing the wind speed by 0.5 m/s to 4.5 m/s (a 10% decrease) alters the maximum surface temperature by +1.3°C in town centres and +0.8°C in woodlands.
As the wind speed increases, the difference between the maximum surface temperatures of town centres and woodlands decreases. At wind speeds of 4 m/s, 5 m/s and 6 m/s, the difference between the maximum surface temperatures in these UMTs is 13.8°C, 12.8°C, and 11.9°C, respectively. Wind speed appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the wind speed brings forward the timing of the maximum surface temperature and vice versa.
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D.5 Surface Roughness Length
The surface roughness length (Z0) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing surface roughness increases the magnitude of these terms and vice versa.
Currently the surface roughness length is set at 2 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.5). Increasing the surface roughness length leads to cooler maximum surface temperatures and vice versa. The effect of decreasing the surface roughness length is stronger than that of increasing it. Changing the surface roughness length has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the surface roughness length (from the current length) by 0.2 m to 2.2 m (a 10% increase) alters the maximum surface temperature by -0.39°C in town centres and -0.23°C in woodlands. Similarly, decreasing the surface roughness length by 0.2 m to 1.8 m (a 10% decrease) alters the maximum surface temperature by +0.43°C in town centres and +0.26°C in woodlands.
As the surface roughness length increases, the difference between the maximum surface temperatures of town centres and woodlands decreases. At surface roughness lengths of 1 m, 2 m and 3 m, the difference between the maximum surface temperatures in these UMTs is 13.8°C, 12.8°C, and 12.1°C, respectively. The surface roughness length appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the surface roughness length brings forward the timing of the maximum surface temperature and vice versa.
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D.6 Height of SBL
The height of the SBL (Z2) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing the height of the SBL decreases the magnitude of these terms and vice versa.
Currently the height of the SBL is set at 800 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.6). Increasing the height of the SBL leads to warmer maximum surface temperatures and vice versa. The effect of decreasing the height of the SBL is slightly stronger than that of increasing it. Changing the height of the SBL has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the height of the SBL (from the current height) by 80 m to 880 m (a 10% increase) alters the maximum surface temperature by +0.39°C in town centres and +0.24°C in woodlands. Similarly, decreasing the height of the SBL to 720m (a 10% decrease) alters the maximum surface temperature by -0.43°C in town centres and -0.26°C in woodlands.
As the height of the SBL increases, the difference between the maximum surface temperatures of town centres and woodlands increases slightly. At SBL heights of 600 m, 800 m and 1000 m, the difference between the maximum surface temperatures in these UMTs is 12.3°C, 12.8°C, and 13.1°C, respectively. The height of the SBL appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the SBL height makes the timing of the maximum surface temperature later and vice versa.
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D.7 Density of Air
The density of the air (ρa) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing the density of the air increases the magnitude of these terms and vice versa.
Currently the density of the air is set at 1.208 kg/m³, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.7). Increasing the air density leads to cooler maximum surface temperatures and vice versa. The effect of decreasing the air density is slightly stronger than that of increasing it. Changing the density of the air has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the density of the air to 1.3288 kg/m³ (a 10% increase from current settings) alters the maximum surface temperature by -1.13°C in town centres and -0.67°C in woodlands. Similarly, decreasing the density of the air to 1.0872 kg/m³ (a 10% decrease from current settings) alters the maximum surface temperature by +1.32°C in town centres and +0.81°C in woodlands. It should be noted that these changes are outside the range of interest, air densities of 1.3288 kg/m³ and 1.0872 kg/m³ would be found at temperatures of about -5°C and 52°C, respectively (Holman, 1997, p. 646). Changes to air density of plus or minus 1% would be more likely (1.220 kg/m³ and 1.196 kg/m³), occurring at temperatures of about 23°C and 18°C, respectively (Holman, 1997, p. 646). With a ±1% change in air density, the change in maximum surface temperature is at most ±0.12°C.
As the density of the air increases, the difference between the maximum surface temperatures of town centres and woodlands decreases slightly. At air densities of 1.0872 kg/m³, 1.208 kg/m³ and 1.3288 kg/m³, the difference between the maximum surface temperatures in these UMTs is 13.3°C, 12.8°C, and 12.3°C, respectively. The density of the air appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the air density brings forward the timing of the maximum surface temperature later and vice versa.
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D.8 Density of Soil
The density of the soil (ρs) is used in the finite difference form of the heat conduction equation (G) (Equation 5.15). Increasing the density of the soil decreases the magnitude of this term and vice versa.
Currently the density of the soil is set at 1800 kg/m³, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.8). Changing the soil density has very little effect on the maximum surface temperature. An increase in soil density leads to very slight decreases in the maximum surface temperatures and vice versa. The effect is approximately linear and is slightly more noticeable in town centres than in woodlands. For example, changing the density of the soil by ±10% (to 1980 kg/m³ and 1620 kg/m³) alters the maximum surface temperature by ±0.04°C in town centres and ±0.01°C in woodlands. When the lowest soil density value for dry peat soils (300 kg/m³) is used (Oke, 1987, p. 44) the change in maximum surface temperature is +0.39°C and +0.07°C in town centres and woodland, respectively. Similarly, when the highest soil density value for saturated sandy and clay soils (2000 kg/m³) is used (Oke, 1987, p. 44) the change in maximum surface temperature is -0.04°C and -0.01°C in town centres and woodland, respectively.
As the density of the soil increases, the difference between the maximum surface temperatures of town centres and woodlands decreases very slightly, but the effect is marginal. Similarly, the density of the soil appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.9 Peak Insolation
Peak insolation (a3) is used in the net radiation flux term (R) (Equation 5.4). Increasing the peak insolation increases the magnitude of this term.
Currently peak insolation is set at 802.5 W/m², and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.9). Increasing the peak insolation leads to warmer maximum surface temperatures and vice versa. This effect is linear. Changing the peak insolation has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, changing peak insolation by ±10% (to 882.75 W/m² and 722.25 W/m², respectively) alters the maximum surface temperature by ±1.51°C in town centres and ±0.74°C in woodlands.
As the peak insolation increases, the difference between the maximum surface temperatures of town centres and woodlands increases. With peak insolations of 702.5 W/m², 802.5 W/m², and 902.5 W/m², the difference between the maximum surface temperatures in these UMTs is 11.8°C, 12.8°C, and 13.8°C, respectively. The peak insolation appears to have no effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.10 Night Radiation
Night radiation (a'3) is used for the night net radiation flux (R) (Equation 5.5). Increasing the night radiation increases the magnitude of this term and vice versa.
Currently night radiation is set at -93 W/m², and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.10). Changing the night radiation has very little effect on maximum surface temperatures. The effect is linear and is slightly greater in town centres than woodlands. As the magnitude of night radiation increases, the difference between the maximum surface temperatures of town centres and woodlands decreases very slightly. In addition, night radiation appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.11 Specific Heat of Air
The specific heat of the air (Ca) is used in the sensible flux term (H) (Equation 5.9). Increasing the specific heat of the air increases the magnitude of this term and vice versa.
Currently the specific heat of the air is set at 1006 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.11). Increasing the specific heat of the air leads to cooler maximum surface temperatures and vice versa. The effect of changing the specific heat of the air is approximately linear, with the effect of a decrease being slightly stronger than an increase. Changing the specific heat of the air has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the specific heat of the air to 1106.6 J/kg/°C (a 10% increase from current settings) alters the maximum surface temperature by -0.77°C in town centres and -0.08°C in woodlands. Similarly, decreasing the specific heat of the air to 905.4 J/kg/°C (a 10% decrease from current settings) alters the maximum surface temperature by +0.85°C in town centres and +0.08°C in woodlands. It should be noted that there is little point in testing the sensitivity of the model to changes of ±10% to the specific heat of air. This is because a +10% change would occur between 527°C and 577°C, whilst a -10% change goes off the temperature scale (Holman, 1997, p. 646). The specific heat of the air changes very little at the temperatures of interest and its effect on maximum surface temperature is marginal.
As the specific heat of the air changes there is very little change to the difference between the maximum surface temperatures of town centres and woodlands. If anything, as it increases the difference decreases, but the effect is negligible. The specific heat of the air also appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.12 Specific Heat of Concrete
The specific heat of concrete (Cc) is used in the heat flux to storage in the built environment term (G) (Equation 5.16). Increasing the specific heat of concrete increases the magnitude of this term and vice versa.
Currently the specific heat of the concrete is set at 880 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.12). Increasing the specific heat of concrete leads to slightly cooler maximum surface temperatures and vice versa. The effect of changing the specific heat of concrete is linear. Changing the specific heat of concrete has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, a ±10% change (to 968 J/kg/°C and 792 J/kg/°C, respectively) alters the maximum surface temperature by ±0.14°C in town centres whereas the effect in woodlands is negligible. Oke (1987, p. 259) lists the thermal properties of different materials. The material listed with the lowest specific heat is steel at 500 J/kg/°C whilst the highest is dense wood at 1880 J/kg/°C. When these values are used the change in the maximum surface temperatures in town centres is +0.55°C for steel and -1.76°C for dense wood. In both cases there is still negligible change in woodlands.
As the specific heat of concrete increases the difference between the maximum surface temperatures of town centres and woodlands decreases, from 13.3°C to 11.0°C, when the specific heat is 500 J/kg/°C and 1880 J/kg/°C, respectively. The specific heat of the air appears to have little effect on the timing of the maximum surface temperature in woodlands, but as it increases the timing of the maximum surface temperature becomes later in town centres.
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D.13 Specific Heat of Soil
The specific heat of soil (Cs) is used in the finite difference form of the heat conduction equation (Equation 5.15). Increasing the specific heat of soil decreases the magnitude of this term and vice versa.
Currently the specific heat of the soil is set at 1180 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.13). The effect of changing the specific heat of the soil on maximum surface temperatures is very small. Increasing it leads to slightly cooler temperatures and vice versa. The result of changing the specific heat of soil is approximately linear and has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, a ±10% change (to 1298 J/kg/°C and 1062 J/kg/°C, respectively) alters the maximum surface temperature by ±0.04°C in town centres and ±0.01°C woodlands. Oke (1987, p. 44) lists the thermal properties of different soils. Dry sandy soil has the lowest specific heat at 800 J/kg/°C whilst saturated peat soil has the highest specific heat at 3650 J/kg/°C. When these values are used the change in the maximum surface temperatures in town centres is +0.14°C for dry sand and -0.33°C for saturated peat, whilst in woodlands maximum surface temperatures are +0.03°C for dry sand and -0.11°C for saturated peat.
The specific heat of soil has very little effect on the difference between the maximum surface temperatures of town centres and woodlands. As it increases the difference becomes slightly less, from 12.9°C to 12.5°C, when the specific heat is 800 J/kg/°C and 3650 J/kg/°C, respectively. The specific heat of the soil appears to have little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.14 Soil Depth at Level s
The soil depth at level s (d) is used in both the conductive heat flux term (G) as well as in the finite difference form of the heat conduction equation (Equations 5.14 and 5.15). Increasing the soil depth decreases the magnitude of these terms and vice versa.
Currently the soil depth at level s is set at 0.1 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.14). In town centres, increasing the soil depth increases the maximum surface temperature and vice versa. On the other hand, in woodlands, increasing the soil depth decreases maximum surface temperatures and vice versa. In both UMTs the effect of decreasing soil depth is slightly greater than the effect of increasing soil depth. The effect is also greater in town centres than woodlands. For example, at soil depths of 0.11 m (a 10% increase from current depths) alters the maximum surface temperature by +0.06°C in town centres and -0.02°C woodlands. Similarly, at soil depths of 0.09 m (a 10% decrease from current depths) alters the maximum surface temperature by -0.07°C in town centres and +0.02°C woodlands.
An increase in the soil depth leads to an increase in the difference between the maximum surface temperatures of town centres and woodlands. The difference is 11.9°C and 13.2°C at soil depths of 0.05 m and 0.15 m, respectively. The soil depth has little effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing it leads to later maximum surface temperatures.
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D.15 Soil Thermal Conductivity
Soil thermal conductivity (ks) is used in the conductive heat flux term (G) as well as in the finite difference form of the heat conduction equation (Equations 5.14 and 5.15). Increasing the soil thermal conductivity increases the magnitude of both these terms and vice versa.
Currently the soil thermal conductivity is set at 1.083 W/m/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.15). In town centres, increasing the soil thermal conductivity decreases the maximum surface temperature and vice versa. On the other hand, in woodlands, increasing the soil thermal conductivity increases maximum surface temperatures and vice versa. The effect of changing soil thermal conductivity is more or less linear. The effect is also greater in town centres than woodlands. For example, a ±10% change (to 1.1913 W/m/°C and 0.9747 W/m/°C, respectively) alters the maximum surface temperature by ±0.1°C in town centres and ±0.01°C woodlands. Oke (1987, p. 44) lists the thermal properties of different soils. Saturated peat soil has the lowest thermal conductivity at 0.06 W/m/°C whilst saturated sandy soil has the highest thermal conductivity at 2.2 W/m/°C. When these values are used the change in the maximum surface temperatures in town centres is +1.55°C for saturated peat and -0.95°C for saturated sand, whilst in woodlands maximum surface temperatures are -0.01°C for saturated peat and +0.10°C for saturated sand.
Increasing soil thermal conductivity leads to a decrease in the difference between the maximum surface temperatures of town centres and woodlands, from 14.3°C to 11.7°C, when the thermal conductivity is 0.06 W/m/°C and 2.2 W/m/°C, respectively. Soil thermal conductivity has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.16 Latent Heat of Evaporation
The latent heat of evaporation (L) is used in the latent heat flux (LE) (Equation 5.10). Increasing the latent heat of evaporation increases the magnitude of this term and vice versa.
Currently the latent heat of evaporation is set at 2452000 J/kg, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.16). Increasing the latent heat of evaporation leads to lower maximum surface temperatures and vice versa. The effect of the latent heat of evaporation is more or less linear, with decreases resulting in a slightly greater change. The effect is also greater in woodlands than town centres. For example, an increase of 10% (to 2697200 J/kg) alters the maximum surface temperature by -0.40°C in town centres and -0.62°C woodlands. Similarly, a decrease of 10% (to 2206800 J/kg) alters the maximum surface temperature by +0.42°C in town centres and +0.71°C woodlands. It should be noted that there is little point in testing the sensitivity of the model to changes of ±10% in the latent heat of evaporation, as such changes occur at temperatures below -30°C and above 45°C (Oke, 1987, p. 392). Changes of ±1% in the latent heat of evaporation occur between temperatures of about 10°C and 35°C. These lead to maximum surface temperature changes of ±0.04°C in town centres and ±0.07°C in woodlands.
Increasing the latent heat of evaporation leads to a slight increase in the difference between the maximum surface temperatures of town centres and woodlands. The latent heat of evaporation has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.17 Specific Humidity at SBL
The specific humidity at the SBL (q2) is used in the latent heat flux term (LE) (Equation 5.10). Increasing the specific humidity at the SBL increases the magnitude of this term and vice versa.
Currently the specific humidity at the SBL is set at 0.002 (dimensionless units), and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.17). Increasing the specific humidity at the SBL leads to warmer maximum surface temperatures and vice versa. The effect of changing the specific humidity at the SBL is linear and is greater in town centres than woodlands. For example, a ±10% change to the specific humidity at the SBL (to 0.0022 and 0.0018, respectively) alters the maximum surface temperature by ±0.28°C in town centres and ±0.13°C in woodlands.
An increase in the specific humidity at the SBL leads to an increase in the difference between the maximum surface temperatures of town centres and woodlands. The difference is 12.0°C and 13.6°C at specific humidities of 0.001 and 0.003, respectively. The specific humidity at the SBL has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.18 Hours of Daylight
The hours of daylight (hoD) are used to specify ω in the net radiation flux term (R) (Equation 5.4). Increasing the hours of daylight will increase the period of the sine curve and vice versa. It makes sense when changing the hours of daylight to also change the sunrise and sunset times accordingly. Thus, with the current setting of 16 hours of daylight, the sunrise and sunset times are 04:00 and 20:00, respectively. An increase of 10% in the daylight hours to 17.6 hours, gives sunrise and sunset times of 3.2 (03:12) and 20.8 (20:48), respectively. Similarly, a decrease of 10% in the daylight hours to 14.4 hours, gives sunrise and sunset times of 4.8 (04:48) and 19.2 (19:12), respectively.
With the current setting of 16 hours of daylight, the maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.18). Increasing the hours of daylight leads to slightly warmer maximum surface temperatures and vice versa. A decrease in the hours of daylight has a slightly greater effect on the maximum surface temperature than an increase does. The effect of changing the hours of daylight is also slightly greater in town centres than woodlands. For example, an increase of 10% in daylight hours (to 17.6 hours) alters the maximum surface temperature by +0.18°C in town centres and +0.01°C in woodlands. Similarly, a decrease of 10% in daylight hours (to 14.4 hours) alters the maximum surface temperature by -0.22°C in town centres and -0.01°C in woodlands.
An increase in the hours of daylight increases the difference between the maximum surface temperatures of town centres and woodlands. The difference is 12.5°C and 13.0°C with daylight hours of 14 and 18 hours, respectively. The hours of daylight have little effect on the timing of the maximum surface temperature in both woodlands and town centres.
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D.19 Building Mass per Unit of Land
The building mass per unit of land (mc) is used in the heat flux to storage in the built environment term (M) (Equation 5.16). Increasing the building mass increases the magnitude of this term and vice versa.
The building mass per unit of land varies between the UMTs according to the proportions of buildings and other impervious surfaces. Thus, in town centres it is currently set at 342.20 kg/m² whilst in woodlands it is 6.58 kg/m². Maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.19). Increasing the building mass leads to slightly cooler maximum surface temperatures and vice versa. Changing the building mass of town centres by ±10% (to 376.42 kg/m² and 307.98 kg/m², respectively) leads to changes in maximum surface temperatures of ±0.1°C. Changing the building mass of woodlands by ±10% (to 7.238 kg/m² and 5.922 kg/m², respectively) has negligible effects.
An increase in the building mass per unit of land decreases the difference between the maximum surface temperatures of town centres and woodlands. The difference is 13.7°C and 12.3°C with building masses of 1.28 kg/m² (equivalent to the mass in unimproved farmland, the UMT category with the lowest building mass) and 492.11 kg/m² (equivalent to the mass in major roads, the UMT category with the highest building mass), respectively. Increasing the building mass means that maximum surface temperatures occur later in the day, in both woodlands and town centres.
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D.20 Evaporating Fraction
The evaporating fraction (Ef) is used in the latent heat flux term (LE) to define q0, the specific humidity of the atmosphere (Equation 5.13). Increasing the evaporating fraction increases the magnitude of q0 and vice versa.
The evaporating fraction varies between the UMTs. In town centres it is currently set at 0.2 whilst in woodlands it is 0.98. Maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.20). Increasing the evaporating fraction leads to cooler maximum surface temperatures and vice versa. Increasing the evaporating fraction of town centres by 10%, to 0.22, leads to a change in the maximum surface temperature of -0.67°C. Similarly, decreasing the evaporating fraction of town centres by 10%, to 0.18, leads to a change in the maximum surface temperature of +0.71°C. It is not possible to increase the evaporating fraction of woodlands by 10% at this would take it over 1.0, however decreasing the evaporating fraction by 10%, to 0.882, leads to a change in the maximum surface temperature of +0.85°C.
The two UMTs have very similar maximum surface temperatures to each other when they are set with the same evaporating fractions. This highlights the importance of the evaporating fraction in cooling the UMTs. In fact, with evaporating fractions of 0.01 and 1.0, woodlands are 2.2°C and 0.1°C warmer than town centres, respectively. Increasing the evaporating fraction also leads to slightly earlier maximum surface temperatures in both woodlands and town centres.
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The information provided here should help users to determine how accurate they need to be with the input parameters to the STAR tools.
This information is taken from: Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf. It refers to the parameters as they were named, set and used in this thesis; however this is very similar to their names, default values and use in the STAR tools. It sometimes refers to other sections of the thesis, which can be accessed at the weblink above if desired.
From Gill (2006):
5.4. Sensitivity Tests
Sensitivity tests were undertaken in order to gain an increased understanding of how the model worked, as well as to test the relative sensitivity of the maximum surface temperature output to the different parameters. The sensitivity tests were undertaken using the parameters as determined for the 1961-1990 baseline climate model runs (Section 5.3). All sensitivity analyses were undertaken with the town centre and woodland UMTs, as these are the categories with the highest and lowest maximum surface temperatures (Section 5.5.1.1). Each input parameter was taken in turn and altered by ±10%, or another suitable value, whilst all the other parameters were held constant. A detailed analysis of the sensitivity tests can be found in Appendix D. In this section, the sensitivities of the various parameters are compared to each other.
The ranking of the parameters according to the magnitude of change in maximum surface temperature varies slightly depending on whether it is a plus or minus 10% change and whether it is the town centre or woodland UMT (Tables 5.8 to 5.11). The tests reveal that sensitive parameters include the peak insolation, wind velocity at the SBL, density of the air, and temperature at the SBL. The evaporative fraction is also very important, especially in woodlands, where it is the most sensitive parameter when decreased by 10% (Table 5.11). Unfortunately, it was not possible to increase the evapotranspiring cover in woodland by 10% (or to increase the fraction by 0.098) as this took the evaporating fraction above 1.0. However, even just increasing the evaporating fraction by 0.02, from 0.98 to 1.0, decreased the maximum surface temperature by 0.16°C. Such a change would rank the evaporating fraction at number 9 in table 5.9, below the reference temperature and above the specific humidity at the SBL.
The five least important parameters for town centres are night radiation, bulk density of the soil, specific heat capacity of the soil, soil depth at level s, and soil thermal conductivity. The five least important parameters for woodlands are night radiation, building mass per unit of land, specific heat of concrete, bulk density of the soil, and specific heat capacity of the soil.
Table 5.8. Absolute change in maximum surface temperature resulting from a 10% increase in the respective input parameters in town centres. Parameters listed according to magnitude of change.
Table 5.9. Absolute change in maximum surface temperature resulting from a 10% increase in the respective input parameters in woodlands. Parameters listed according to magnitude of change.
Table 5.10. Absolute change in maximum surface temperature resulting from a 10% decrease in the respective input parameters in town centres. Parameters listed according to magnitude of change.
Table 5.11. Absolute change in maximum surface temperature resulting from a 10% decrease in the respective input parameters in woodlands. Parameters listed according to magnitude of change.
Appendix D. Energy Exchange Model Sensitivity Tests
The sensitivity tests were undertaken using the model runs set up for the 1961-1990 baseline climate. All sensitivity analyses were undertaken with the town centre and woodland UMTs, as these are the categories with the highest and lowest maximum surface temperatures. The input parameters are identical for the two UMTs, except for the evaporating fraction which is 0.20 in town centres and 0.98 in woodlands, and the building mass per unit of land which is 342.20 kg/m² in town centres and 6.58 kg/m² in woodlands. The sensitivity of the maximum surface temperatures to the input parameters are considered on the following pages. Current input values (i.e. those used by Gill, 2006) are marked on figures D.1 to D.20 by grey dotted lines.
Input parameters considered are:
D.1 Soil Temperature
D.2 Reference Temperature
D.3 Air Temperature at SBL
D.4 Wind Speed at SBL
D.5 Surface Roughness Length
D.6 Height of SBL
D.7 Density of Air
D.8 Density of Soil
D.9 Peak Insolation
D.10 Night Radiation
D.11 Specific Heat of Air
D.12 Specific Heat of Concrete
D.13 Specific Heat of Soil
D.14 Soil Depth at Level s
D.15 Soil Thermal Conductivity
D.16 Latent Heat of Evaporation
D.17 Specific Humidity at SBL
D.18 Hours of Daylight
D.19 Building Mass per Unit of Land
D.20 Evaporating Fraction
D.1 Soil Temperature
The soil temperature at 20 cm (Tb) is used in the finite difference form of the conductive heat flux term (G) (Equation 5.15) of the energy balance equation. This equation is used as one of the set of simultaneous equations solved to find analytical solutions for the surface temperature (T0) and the soil temperature at level s (Ts).
Currently soil temperature is set at 20°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.1). Increasing soil temperatures leads to warmer maximum surface temperatures and vice versa. This effect appears to be linear. Changing the soil temperature has a greater effect in town centres rather than woodlands. For example, changing the soil temperature (from the current temperature) by 1°C alters the maximum surface temperature by ±0.11°C in town centres and ±0.05°C in woodlands. Similarly, changing the soil temperature (from the current temperature) by 5°C alters the maximum surface temperature by ±0.55°C in town centres and ±0.25°C in woodlands. If soil temperature is set at 14.3°C, equivalent to the mean summer temperature for Ringway (BETWIXT) minus 0.6°C, the maximum surface temperature is 0.6°C and 0.3°C less in town centres and woodlands, respectively. Whilst town centres have 6% bare soil compared to 0% in woodlands, the change in maximum surface temperatures in the two UMTs as a result of altering soil temperature is not due to the amount of bare soil. The amount of bare soil is not a term in the finite difference heat conduction equation (Equation 5.15). In addition, a further sensitivity test using refuse disposal, which has the highest amount of bare soil cover (58%), found changes of approximately ±0.08°C in maximum surface temperature as a result of changing soil temperatures by ±1°C. The soil temperature appears to have the greatest impact on maximum surface temperature in UMTs where there is a low evaporating cover. This suggests that much more heat is lost to the air by convection and evapotranspiration than to the soil by conduction.
As the soil temperature increases, the difference between the maximum surface temperatures of town centres and woodlands also increases. At soil temperatures of 15°C, 20°C and 25°C, the difference between the maximum surface temperatures in these UMTs is 12.5°C, 12.8°C, and 13.1°C, respectively. The soil temperature appears to have no effect on the timing of the maximum surface temperature.
D.2 Reference Temperature
The reference temperature (Tf) does not appear in any of the terms of the energy balance equation as such. The specific humidity, which appears in the latent heat flux (LE) is linearised around the reference temperature.
Currently the reference temperature is set at 20.6°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.2). The relationship between the reference temperature and the maximum surface temperature is quadratic. This is because the specific humidity is linearised by taking the first term of the expansion around the reference temperature. If the reference temperature is equal to the surface temperature there is no error. However, if the reference temperature is higher or lower than the surface temperature the squared term of the expansion (which is always positive) becomes more significant. Therefore at higher and lower temperatures the latent heat loss is underestimated and the calculated maximum surface temperature rises.
The minimum point of the quadratic curves differs for the two UMTs, with woodland being at a lower reference temperature (around 18.6°C) than town centre (around 30.6°C). This is because woodland has lower surface temperatures than town centres. Thus, in woodlands, the point at which the reference temperature is equal to the surface temperature, and hence there is no error in the linearisation of the specific humidity, is lower.
Increasing the reference temperature by 1°C to 21.6°C decreases the maximum surface temperature in town centres by 0.1°C and increases it in woodlands by the same amount. When the reference temperature is decreased by 1°C to 19.6°C, the maximum surface temperature increases in town centres and decreases in woodlands, each by 0.1°C.
Changing the reference temperature alters the timing of the maximum surface temperatures in town centres more significantly than in woodlands.
D.3 Air Temperature at SBL
The air temperature at the SBL (T2) is used in the sensible heat flux term (H) of the energy balance equation. Increased air temperatures at the SBL increase the magnitude of this term and vice versa.
Currently the air temperature at the SBL is set at 15.4°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.3). Increasing air temperatures at the SBL leads to warmer maximum surface temperatures and vice versa. This effect appears to be linear. Changing the air temperature at the SBL has a greater effect on the maximum surface temperature in town centres rather than woodlands. This is because more energy is lost by convection in town centres which have less evapotranspiring surface. For example, changing the soil temperature (from the current temperature) by 1°C alters the maximum surface temperature by ±0.58°C in town centres and ±0.26°C in woodlands. Similarly, changing the soil temperature (from the current temperature) by 5°C alters the maximum surface temperature by ±2.9°C in town centres and ±1.3°C in woodlands. These changes are much greater than those for the soil temperature.
As the air temperature at the SBL increases, the difference between the maximum surface temperatures of town centres and woodlands also increases. At air temperatures at the SBL of 10.4°C, 15.4°C and 20.4°C, the difference between the maximum surface temperatures in these UMTs is 11.2°C, 12.8°C, and 14.4°C, respectively. The air temperature at the SBL appears to have no effect on the timing of the maximum surface temperature.
D.4 Wind Speed at SBL
Wind speed at the SBL (U2) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing wind speed increases the magnitude of these terms and vice versa.
Currently wind speed at the SBL is set at 5 m/s, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.4). Increasing wind speed leads to cooler maximum surface temperatures and vice versa. The effect of decreasing wind speed is slightly stronger than that of increasing it. Changing wind speed has a greater effect on the maximum surface temperature in town centres rather than woodlands. This is because more energy is lost by convection in town centres which have less evaporating surface. For example, increasing the wind speed (from the current speed) by 0.5 m/s to 5.5 m/s (a 10% increase) alters the maximum surface temperature by -1.1°C in town centres and -0.7°C in woodlands. Similarly, decreasing the wind speed by 0.5 m/s to 4.5 m/s (a 10% decrease) alters the maximum surface temperature by +1.3°C in town centres and +0.8°C in woodlands.
As the wind speed increases, the difference between the maximum surface temperatures of town centres and woodlands decreases. At wind speeds of 4 m/s, 5 m/s and 6 m/s, the difference between the maximum surface temperatures in these UMTs is 13.8°C, 12.8°C, and 11.9°C, respectively. Wind speed appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the wind speed brings forward the timing of the maximum surface temperature and vice versa.
D.5 Surface Roughness Length
The surface roughness length (Z0) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing surface roughness increases the magnitude of these terms and vice versa.
Currently the surface roughness length is set at 2 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.5). Increasing the surface roughness length leads to cooler maximum surface temperatures and vice versa. The effect of decreasing the surface roughness length is stronger than that of increasing it. Changing the surface roughness length has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the surface roughness length (from the current length) by 0.2 m to 2.2 m (a 10% increase) alters the maximum surface temperature by -0.39°C in town centres and -0.23°C in woodlands. Similarly, decreasing the surface roughness length by 0.2 m to 1.8 m (a 10% decrease) alters the maximum surface temperature by +0.43°C in town centres and +0.26°C in woodlands.
As the surface roughness length increases, the difference between the maximum surface temperatures of town centres and woodlands decreases. At surface roughness lengths of 1 m, 2 m and 3 m, the difference between the maximum surface temperatures in these UMTs is 13.8°C, 12.8°C, and 12.1°C, respectively. The surface roughness length appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the surface roughness length brings forward the timing of the maximum surface temperature and vice versa.
D.6 Height of SBL
The height of the SBL (Z2) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing the height of the SBL decreases the magnitude of these terms and vice versa.
Currently the height of the SBL is set at 800 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.6). Increasing the height of the SBL leads to warmer maximum surface temperatures and vice versa. The effect of decreasing the height of the SBL is slightly stronger than that of increasing it. Changing the height of the SBL has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the height of the SBL (from the current height) by 80 m to 880 m (a 10% increase) alters the maximum surface temperature by +0.39°C in town centres and +0.24°C in woodlands. Similarly, decreasing the height of the SBL to 720m (a 10% decrease) alters the maximum surface temperature by -0.43°C in town centres and -0.26°C in woodlands.
As the height of the SBL increases, the difference between the maximum surface temperatures of town centres and woodlands increases slightly. At SBL heights of 600 m, 800 m and 1000 m, the difference between the maximum surface temperatures in these UMTs is 12.3°C, 12.8°C, and 13.1°C, respectively. The height of the SBL appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the SBL height makes the timing of the maximum surface temperature later and vice versa.
D.7 Density of Air
The density of the air (ρa) is used in the sensible (H) and latent heat flux (LE) terms (Equations 5.9 and 5.10). Increasing the density of the air increases the magnitude of these terms and vice versa.
Currently the density of the air is set at 1.208 kg/m³, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.7). Increasing the air density leads to cooler maximum surface temperatures and vice versa. The effect of decreasing the air density is slightly stronger than that of increasing it. Changing the density of the air has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the density of the air to 1.3288 kg/m³ (a 10% increase from current settings) alters the maximum surface temperature by -1.13°C in town centres and -0.67°C in woodlands. Similarly, decreasing the density of the air to 1.0872 kg/m³ (a 10% decrease from current settings) alters the maximum surface temperature by +1.32°C in town centres and +0.81°C in woodlands. It should be noted that these changes are outside the range of interest, air densities of 1.3288 kg/m³ and 1.0872 kg/m³ would be found at temperatures of about -5°C and 52°C, respectively (Holman, 1997, p. 646). Changes to air density of plus or minus 1% would be more likely (1.220 kg/m³ and 1.196 kg/m³), occurring at temperatures of about 23°C and 18°C, respectively (Holman, 1997, p. 646). With a ±1% change in air density, the change in maximum surface temperature is at most ±0.12°C.
As the density of the air increases, the difference between the maximum surface temperatures of town centres and woodlands decreases slightly. At air densities of 1.0872 kg/m³, 1.208 kg/m³ and 1.3288 kg/m³, the difference between the maximum surface temperatures in these UMTs is 13.3°C, 12.8°C, and 12.3°C, respectively. The density of the air appears to have no effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing the air density brings forward the timing of the maximum surface temperature later and vice versa.
D.8 Density of Soil
The density of the soil (ρs) is used in the finite difference form of the heat conduction equation (G) (Equation 5.15). Increasing the density of the soil decreases the magnitude of this term and vice versa.
Currently the density of the soil is set at 1800 kg/m³, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.8). Changing the soil density has very little effect on the maximum surface temperature. An increase in soil density leads to very slight decreases in the maximum surface temperatures and vice versa. The effect is approximately linear and is slightly more noticeable in town centres than in woodlands. For example, changing the density of the soil by ±10% (to 1980 kg/m³ and 1620 kg/m³) alters the maximum surface temperature by ±0.04°C in town centres and ±0.01°C in woodlands. When the lowest soil density value for dry peat soils (300 kg/m³) is used (Oke, 1987, p. 44) the change in maximum surface temperature is +0.39°C and +0.07°C in town centres and woodland, respectively. Similarly, when the highest soil density value for saturated sandy and clay soils (2000 kg/m³) is used (Oke, 1987, p. 44) the change in maximum surface temperature is -0.04°C and -0.01°C in town centres and woodland, respectively.
As the density of the soil increases, the difference between the maximum surface temperatures of town centres and woodlands decreases very slightly, but the effect is marginal. Similarly, the density of the soil appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.9 Peak Insolation
Peak insolation (a3) is used in the net radiation flux term (R) (Equation 5.4). Increasing the peak insolation increases the magnitude of this term.
Currently peak insolation is set at 802.5 W/m², and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.9). Increasing the peak insolation leads to warmer maximum surface temperatures and vice versa. This effect is linear. Changing the peak insolation has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, changing peak insolation by ±10% (to 882.75 W/m² and 722.25 W/m², respectively) alters the maximum surface temperature by ±1.51°C in town centres and ±0.74°C in woodlands.
As the peak insolation increases, the difference between the maximum surface temperatures of town centres and woodlands increases. With peak insolations of 702.5 W/m², 802.5 W/m², and 902.5 W/m², the difference between the maximum surface temperatures in these UMTs is 11.8°C, 12.8°C, and 13.8°C, respectively. The peak insolation appears to have no effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.10 Night Radiation
Night radiation (a'3) is used for the night net radiation flux (R) (Equation 5.5). Increasing the night radiation increases the magnitude of this term and vice versa.
Currently night radiation is set at -93 W/m², and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.10). Changing the night radiation has very little effect on maximum surface temperatures. The effect is linear and is slightly greater in town centres than woodlands. As the magnitude of night radiation increases, the difference between the maximum surface temperatures of town centres and woodlands decreases very slightly. In addition, night radiation appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.11 Specific Heat of Air
The specific heat of the air (Ca) is used in the sensible flux term (H) (Equation 5.9). Increasing the specific heat of the air increases the magnitude of this term and vice versa.
Currently the specific heat of the air is set at 1006 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.11). Increasing the specific heat of the air leads to cooler maximum surface temperatures and vice versa. The effect of changing the specific heat of the air is approximately linear, with the effect of a decrease being slightly stronger than an increase. Changing the specific heat of the air has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, increasing the specific heat of the air to 1106.6 J/kg/°C (a 10% increase from current settings) alters the maximum surface temperature by -0.77°C in town centres and -0.08°C in woodlands. Similarly, decreasing the specific heat of the air to 905.4 J/kg/°C (a 10% decrease from current settings) alters the maximum surface temperature by +0.85°C in town centres and +0.08°C in woodlands. It should be noted that there is little point in testing the sensitivity of the model to changes of ±10% to the specific heat of air. This is because a +10% change would occur between 527°C and 577°C, whilst a -10% change goes off the temperature scale (Holman, 1997, p. 646). The specific heat of the air changes very little at the temperatures of interest and its effect on maximum surface temperature is marginal.
As the specific heat of the air changes there is very little change to the difference between the maximum surface temperatures of town centres and woodlands. If anything, as it increases the difference decreases, but the effect is negligible. The specific heat of the air also appears to have very little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.12 Specific Heat of Concrete
The specific heat of concrete (Cc) is used in the heat flux to storage in the built environment term (G) (Equation 5.16). Increasing the specific heat of concrete increases the magnitude of this term and vice versa.
Currently the specific heat of the concrete is set at 880 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.12). Increasing the specific heat of concrete leads to slightly cooler maximum surface temperatures and vice versa. The effect of changing the specific heat of concrete is linear. Changing the specific heat of concrete has a greater effect on the maximum surface temperature in town centres rather than woodlands. For example, a ±10% change (to 968 J/kg/°C and 792 J/kg/°C, respectively) alters the maximum surface temperature by ±0.14°C in town centres whereas the effect in woodlands is negligible. Oke (1987, p. 259) lists the thermal properties of different materials. The material listed with the lowest specific heat is steel at 500 J/kg/°C whilst the highest is dense wood at 1880 J/kg/°C. When these values are used the change in the maximum surface temperatures in town centres is +0.55°C for steel and -1.76°C for dense wood. In both cases there is still negligible change in woodlands.
As the specific heat of concrete increases the difference between the maximum surface temperatures of town centres and woodlands decreases, from 13.3°C to 11.0°C, when the specific heat is 500 J/kg/°C and 1880 J/kg/°C, respectively. The specific heat of the air appears to have little effect on the timing of the maximum surface temperature in woodlands, but as it increases the timing of the maximum surface temperature becomes later in town centres.
D.13 Specific Heat of Soil
The specific heat of soil (Cs) is used in the finite difference form of the heat conduction equation (Equation 5.15). Increasing the specific heat of soil decreases the magnitude of this term and vice versa.
Currently the specific heat of the soil is set at 1180 J/kg/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.13). The effect of changing the specific heat of the soil on maximum surface temperatures is very small. Increasing it leads to slightly cooler temperatures and vice versa. The result of changing the specific heat of soil is approximately linear and has a slightly greater effect on the maximum surface temperature in town centres rather than woodlands. For example, a ±10% change (to 1298 J/kg/°C and 1062 J/kg/°C, respectively) alters the maximum surface temperature by ±0.04°C in town centres and ±0.01°C woodlands. Oke (1987, p. 44) lists the thermal properties of different soils. Dry sandy soil has the lowest specific heat at 800 J/kg/°C whilst saturated peat soil has the highest specific heat at 3650 J/kg/°C. When these values are used the change in the maximum surface temperatures in town centres is +0.14°C for dry sand and -0.33°C for saturated peat, whilst in woodlands maximum surface temperatures are +0.03°C for dry sand and -0.11°C for saturated peat.
The specific heat of soil has very little effect on the difference between the maximum surface temperatures of town centres and woodlands. As it increases the difference becomes slightly less, from 12.9°C to 12.5°C, when the specific heat is 800 J/kg/°C and 3650 J/kg/°C, respectively. The specific heat of the soil appears to have little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.14 Soil Depth at Level s
The soil depth at level s (d) is used in both the conductive heat flux term (G) as well as in the finite difference form of the heat conduction equation (Equations 5.14 and 5.15). Increasing the soil depth decreases the magnitude of these terms and vice versa.
Currently the soil depth at level s is set at 0.1 m, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.14). In town centres, increasing the soil depth increases the maximum surface temperature and vice versa. On the other hand, in woodlands, increasing the soil depth decreases maximum surface temperatures and vice versa. In both UMTs the effect of decreasing soil depth is slightly greater than the effect of increasing soil depth. The effect is also greater in town centres than woodlands. For example, at soil depths of 0.11 m (a 10% increase from current depths) alters the maximum surface temperature by +0.06°C in town centres and -0.02°C woodlands. Similarly, at soil depths of 0.09 m (a 10% decrease from current depths) alters the maximum surface temperature by -0.07°C in town centres and +0.02°C woodlands.
An increase in the soil depth leads to an increase in the difference between the maximum surface temperatures of town centres and woodlands. The difference is 11.9°C and 13.2°C at soil depths of 0.05 m and 0.15 m, respectively. The soil depth has little effect on the timing of the maximum surface temperature in woodlands, but in town centres increasing it leads to later maximum surface temperatures.
D.15 Soil Thermal Conductivity
Soil thermal conductivity (ks) is used in the conductive heat flux term (G) as well as in the finite difference form of the heat conduction equation (Equations 5.14 and 5.15). Increasing the soil thermal conductivity increases the magnitude of both these terms and vice versa.
Currently the soil thermal conductivity is set at 1.083 W/m/°C, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.15). In town centres, increasing the soil thermal conductivity decreases the maximum surface temperature and vice versa. On the other hand, in woodlands, increasing the soil thermal conductivity increases maximum surface temperatures and vice versa. The effect of changing soil thermal conductivity is more or less linear. The effect is also greater in town centres than woodlands. For example, a ±10% change (to 1.1913 W/m/°C and 0.9747 W/m/°C, respectively) alters the maximum surface temperature by ±0.1°C in town centres and ±0.01°C woodlands. Oke (1987, p. 44) lists the thermal properties of different soils. Saturated peat soil has the lowest thermal conductivity at 0.06 W/m/°C whilst saturated sandy soil has the highest thermal conductivity at 2.2 W/m/°C. When these values are used the change in the maximum surface temperatures in town centres is +1.55°C for saturated peat and -0.95°C for saturated sand, whilst in woodlands maximum surface temperatures are -0.01°C for saturated peat and +0.10°C for saturated sand.
Increasing soil thermal conductivity leads to a decrease in the difference between the maximum surface temperatures of town centres and woodlands, from 14.3°C to 11.7°C, when the thermal conductivity is 0.06 W/m/°C and 2.2 W/m/°C, respectively. Soil thermal conductivity has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.16 Latent Heat of Evaporation
The latent heat of evaporation (L) is used in the latent heat flux (LE) (Equation 5.10). Increasing the latent heat of evaporation increases the magnitude of this term and vice versa.
Currently the latent heat of evaporation is set at 2452000 J/kg, and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.16). Increasing the latent heat of evaporation leads to lower maximum surface temperatures and vice versa. The effect of the latent heat of evaporation is more or less linear, with decreases resulting in a slightly greater change. The effect is also greater in woodlands than town centres. For example, an increase of 10% (to 2697200 J/kg) alters the maximum surface temperature by -0.40°C in town centres and -0.62°C woodlands. Similarly, a decrease of 10% (to 2206800 J/kg) alters the maximum surface temperature by +0.42°C in town centres and +0.71°C woodlands. It should be noted that there is little point in testing the sensitivity of the model to changes of ±10% in the latent heat of evaporation, as such changes occur at temperatures below -30°C and above 45°C (Oke, 1987, p. 392). Changes of ±1% in the latent heat of evaporation occur between temperatures of about 10°C and 35°C. These lead to maximum surface temperature changes of ±0.04°C in town centres and ±0.07°C in woodlands.
Increasing the latent heat of evaporation leads to a slight increase in the difference between the maximum surface temperatures of town centres and woodlands. The latent heat of evaporation has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.17 Specific Humidity at SBL
The specific humidity at the SBL (q2) is used in the latent heat flux term (LE) (Equation 5.10). Increasing the specific humidity at the SBL increases the magnitude of this term and vice versa.
Currently the specific humidity at the SBL is set at 0.002 (dimensionless units), and maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.17). Increasing the specific humidity at the SBL leads to warmer maximum surface temperatures and vice versa. The effect of changing the specific humidity at the SBL is linear and is greater in town centres than woodlands. For example, a ±10% change to the specific humidity at the SBL (to 0.0022 and 0.0018, respectively) alters the maximum surface temperature by ±0.28°C in town centres and ±0.13°C in woodlands.
An increase in the specific humidity at the SBL leads to an increase in the difference between the maximum surface temperatures of town centres and woodlands. The difference is 12.0°C and 13.6°C at specific humidities of 0.001 and 0.003, respectively. The specific humidity at the SBL has little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.18 Hours of Daylight
The hours of daylight (hoD) are used to specify ω in the net radiation flux term (R) (Equation 5.4). Increasing the hours of daylight will increase the period of the sine curve and vice versa. It makes sense when changing the hours of daylight to also change the sunrise and sunset times accordingly. Thus, with the current setting of 16 hours of daylight, the sunrise and sunset times are 04:00 and 20:00, respectively. An increase of 10% in the daylight hours to 17.6 hours, gives sunrise and sunset times of 3.2 (03:12) and 20.8 (20:48), respectively. Similarly, a decrease of 10% in the daylight hours to 14.4 hours, gives sunrise and sunset times of 4.8 (04:48) and 19.2 (19:12), respectively.
With the current setting of 16 hours of daylight, the maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.18). Increasing the hours of daylight leads to slightly warmer maximum surface temperatures and vice versa. A decrease in the hours of daylight has a slightly greater effect on the maximum surface temperature than an increase does. The effect of changing the hours of daylight is also slightly greater in town centres than woodlands. For example, an increase of 10% in daylight hours (to 17.6 hours) alters the maximum surface temperature by +0.18°C in town centres and +0.01°C in woodlands. Similarly, a decrease of 10% in daylight hours (to 14.4 hours) alters the maximum surface temperature by -0.22°C in town centres and -0.01°C in woodlands.
An increase in the hours of daylight increases the difference between the maximum surface temperatures of town centres and woodlands. The difference is 12.5°C and 13.0°C with daylight hours of 14 and 18 hours, respectively. The hours of daylight have little effect on the timing of the maximum surface temperature in both woodlands and town centres.
D.19 Building Mass per Unit of Land
The building mass per unit of land (mc) is used in the heat flux to storage in the built environment term (M) (Equation 5.16). Increasing the building mass increases the magnitude of this term and vice versa.
The building mass per unit of land varies between the UMTs according to the proportions of buildings and other impervious surfaces. Thus, in town centres it is currently set at 342.20 kg/m² whilst in woodlands it is 6.58 kg/m². Maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.19). Increasing the building mass leads to slightly cooler maximum surface temperatures and vice versa. Changing the building mass of town centres by ±10% (to 376.42 kg/m² and 307.98 kg/m², respectively) leads to changes in maximum surface temperatures of ±0.1°C. Changing the building mass of woodlands by ±10% (to 7.238 kg/m² and 5.922 kg/m², respectively) has negligible effects.
An increase in the building mass per unit of land decreases the difference between the maximum surface temperatures of town centres and woodlands. The difference is 13.7°C and 12.3°C with building masses of 1.28 kg/m² (equivalent to the mass in unimproved farmland, the UMT category with the lowest building mass) and 492.11 kg/m² (equivalent to the mass in major roads, the UMT category with the highest building mass), respectively. Increasing the building mass means that maximum surface temperatures occur later in the day, in both woodlands and town centres.
D.20 Evaporating Fraction
The evaporating fraction (Ef) is used in the latent heat flux term (LE) to define q0, the specific humidity of the atmosphere (Equation 5.13). Increasing the evaporating fraction increases the magnitude of q0 and vice versa.
The evaporating fraction varies between the UMTs. In town centres it is currently set at 0.2 whilst in woodlands it is 0.98. Maximum surface temperatures are 31.2°C in town centres and 18.4°C in woodlands (Figure D.20). Increasing the evaporating fraction leads to cooler maximum surface temperatures and vice versa. Increasing the evaporating fraction of town centres by 10%, to 0.22, leads to a change in the maximum surface temperature of -0.67°C. Similarly, decreasing the evaporating fraction of town centres by 10%, to 0.18, leads to a change in the maximum surface temperature of +0.71°C. It is not possible to increase the evaporating fraction of woodlands by 10% at this would take it over 1.0, however decreasing the evaporating fraction by 10%, to 0.882, leads to a change in the maximum surface temperature of +0.85°C.
The two UMTs have very similar maximum surface temperatures to each other when they are set with the same evaporating fractions. This highlights the importance of the evaporating fraction in cooling the UMTs. In fact, with evaporating fractions of 0.01 and 1.0, woodlands are 2.2°C and 0.1°C warmer than town centres, respectively. Increasing the evaporating fraction also leads to slightly earlier maximum surface temperatures in both woodlands and town centres.
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Add another land cover scenario
Adding other land cover scenarios to the STAR tools will allow you to explore the impact on maximum surface temperature and surface runoff of increasing or decreasing different land cover types; testing different cases of greening and development. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
For example you could:
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Adding other land cover scenarios to the STAR tools will allow you to explore the impact on maximum surface temperature and surface runoff of increasing or decreasing different land cover types; testing different cases of greening and development. You can add as many land cover scenarios as you like and rename them (and these can also be applied across your other study areas).
For example you could:
- Compare an area pre-development with how it is set out in a development masterplan, or to compare different masterplan options for an area. This information could be used to inform the masterplan and ensure that the development is better adapted to climate change.
- Experiment with increasing the proportion of green and blue surfaces to see how much more of these surfaces would be required in an area to counter increasing temperatures with climate change. This information could then be used to guide policy and strategy development; both urban development and green infrastructure policies including setting targets for green infrastructure provision.
- Experiment with increasing proportions of built surfaces to see the effect of current higher density development trends. This information could then be used to guide policy and strategy development; both urban development and green infrastructure policies.
- Current land cover (chapters 5.5.1 and 6.4.1)
- Adding and taking away 10% green and blue surfaces; and subtracting or adding this 10% to the built land cover types - e.g. to buildings, major roads, and/or other impervious surfaces (chapters 5.5.2, 5.5.3, 6.4.2, and 6.4.3)
- Adding and taking away 10% tree surfaces; and subtracting or adding this 10% to the built land cover types - e.g. to buildings, major roads, and/or other impervious surfaces (chapters 6.4.2 and 6.4.3; N.B. this option is only available for the surface runoff tool as the surface temperature tool does not distinguish tree surfaces from other green and blue surfaces)
- Adding green roofs; by changing the percentage of buildings to 0% and adding this percentage to green and blue surface types (chapters 5.5.4 and 6.4.4)
- Taking areas of previously developed land and farmland and seeing the effects of adding the land cover footprint of residential areas (chapters 5.5.5, 5.5.6, 6.4.5, and 6.4.7)
- Increasing the percentage of tree cover on previously developed land (chapter 6.4.6; N.B. this option is only available for the surface runoff tool as the surface temperature tool does not distinguish tree surfaces from other green and blue surfaces)
- Increasing permeable paving; by assuming that half of the other impervious surfaces can be converted to permeable paving, and that permeable paving consists of 85% other impervious surfaces and 15% mown grass (chapter 6.4.8)
- Exploring the effect of a drought limiting the water supply to grass surfaces so that they stop evapotranspiring and providing urban cooling; by changing the percentages of mown and rough grass surfaces to 0% and adding this percentage to bare soil or gravel surfaces (chapter 5.5.7).
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Applying land cover scenarios to other study areas
Please use this facility with caution! It has been provided to allow you to quickly apply any land cover scenarios you have added across all of your other study areas. So, for example, if you have a land cover scenario where you have added 10% green and blue surfaces (or 10% trees), you can apply this same land cover scenario to all of your other study areas by clicking the button once rather than having to go into each study area in turn and amend land cover percentages.
The STAR tools will highlight when it has not been able to do this automatically and point you to the study areas where the process has failed. For example, it will warn you if the resulting land covers do not add up to 100%.
However, the method is not foolproof and the STAR tools will not always be able to determine what the land cover scenario is that you are adding. It is recommended that you always manually check how the land cover scenario has been applied to your other study areas to make sure that it has done what you want it to!
Please use this facility with caution! It has been provided to allow you to quickly apply any land cover scenarios you have added across all of your other study areas. So, for example, if you have a land cover scenario where you have added 10% green and blue surfaces (or 10% trees), you can apply this same land cover scenario to all of your other study areas by clicking the button once rather than having to go into each study area in turn and amend land cover percentages.
The STAR tools will highlight when it has not been able to do this automatically and point you to the study areas where the process has failed. For example, it will warn you if the resulting land covers do not add up to 100%.
However, the method is not foolproof and the STAR tools will not always be able to determine what the land cover scenario is that you are adding. It is recommended that you always manually check how the land cover scenario has been applied to your other study areas to make sure that it has done what you want it to!
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Add another temperature scenario
Other temperature scenarios can also be added. This is especially important for users outside of the North West of England, as they will have to input their own temperature scenarios. Depending on the data and resources you have available, these temperature scenarios could:
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Other temperature scenarios can also be added. This is especially important for users outside of the North West of England, as they will have to input their own temperature scenarios. Depending on the data and resources you have available, these temperature scenarios could:
- Be a range of temperatures to explore what happens on days where the average daily temperature at a height of 2m is e.g. 14°C, 16°C, 18°C, etc.
- Be based on climate data so that you can then say e.g. "this is typical of what we would expect these days" or "this is what we might expect in a future climate". In the UK, this information can be obtained by using values provided by UKCP09 or by running the UKCP09 weather generator for the area you are interested in. European-wide climate data can be obtained from the PRUDENCE project website.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Add another precipitation scenario
Other precipitation scenarios can also be added if users want to highlight the runoff at particular rainfall depths in the results. This is especially important for users outside of the North West of England, as they will have to input their own precipitation scenarios. Depending on the data and resources you have available, these precipitation scenarios could:
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
Other precipitation scenarios can also be added if users want to highlight the runoff at particular rainfall depths in the results. This is especially important for users outside of the North West of England, as they will have to input their own precipitation scenarios. Depending on the data and resources you have available, these precipitation scenarios could:
- Be a range of precipitation depths to explore what happens on days where the total daily precipitation is e.g. 10mm, 20mm, 30mm, etc.
- Be based on climate data so that you can then say e.g. "this is typical of what we would expect these days" or "this is what we might expect in a future climate". In the UK, this information can be obtained by using values provided by UKCP09 or by running the UKCP09 weather generator for the area you are interested in. European-wide climate data can be obtained from the PRUDENCE project website.
Gill, S.E. (2006). Climate change and urban greenspace. PhD thesis, The University of Manchester. www.ginw.co.uk/resources/Susannah_PhD_Thesis_full_final.pdf
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Printing the results
Using this option will allow you to print the results as they are displayed on your screen.
Unfortunately, if your results cover more than one screen (i.e. if you have to scroll in order to see them all), then this will not print all of your results. In this case it is recommended that you export your results to Excel (using one of the two options) and then print from there.
Using this option will allow you to print the results as they are displayed on your screen.
Unfortunately, if your results cover more than one screen (i.e. if you have to scroll in order to see them all), then this will not print all of your results. In this case it is recommended that you export your results to Excel (using one of the two options) and then print from there.
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Using the map
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Click one of these arrows to pan a fixed distance to the north, east, south or west. If the |
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Click the plus button to zoom in by a fixed amount on the centre of the map. Click the minus button to zoom out by a fixed amount on the centre of the map. Click a rung on the ladder or drag the slider to zoom to a specified level on the centre of the map. If the |
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Click this button to activate or deactivate the select tool, which is used to select your study areas on the map. |
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Click this button to activate or deactivate the draw tool, which is used to draw boundaries on the map. |
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Click this button to activate the pan tool, which is used to pan and zoom around the map. |
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Click this button to expand the layer switcher, which is used to switch layers on and off on the map. |
- Enter a UK place name or postcode into the search box beneath the map and click the Search button to zoom to that location.
- The coordinates of the current mouse position are displayed beneath the map in metres, relative to the British National Grid.
- Please be patient when panning and zooming around the map, as layers may take a few seconds to appear.
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Method for processing OS MasterMap into the land cover input
The note below explains the method undertaken to process OS MasterMap into the land cover input needed for the STAR tools. Using this method allows us to provide some data for use in the first instance, however, we have not rigourously tested the accuracy of the data provided by this method.
Initial tests suggest that, for the land cover input required for the surface temperature tool it gives fairly similar results to those of Gill (2006); however, the method does not pick up street trees and other individual trees accurately, which can account for a significant proportion of green and blue surfaces in urban areas. Similarly, for the land cover input required for the surface runoff tool the main difference between the estimates here and those of Gill (2006) are that tree cover is significantly underestimated here, and shrub cover is also underestimated (but less so); the method does not pick up street trees and other individual trees accurately, which can account for a significant proportions of the overall tree cover in urban areas.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
The note below explains the method undertaken to process OS MasterMap into the land cover input needed for the STAR tools. Using this method allows us to provide some data for use in the first instance, however, we have not rigourously tested the accuracy of the data provided by this method.
Initial tests suggest that, for the land cover input required for the surface temperature tool it gives fairly similar results to those of Gill (2006); however, the method does not pick up street trees and other individual trees accurately, which can account for a significant proportion of green and blue surfaces in urban areas. Similarly, for the land cover input required for the surface runoff tool the main difference between the estimates here and those of Gill (2006) are that tree cover is significantly underestimated here, and shrub cover is also underestimated (but less so); the method does not pick up street trees and other individual trees accurately, which can account for a significant proportions of the overall tree cover in urban areas.
It is recommended that local, more accurate land cover data and data from tree audits should be used wherever available.
- The MasterMap Topography Layer polygon component was clipped to a buffer of North West England
- Overlapping polygons were removed (those where the Descriptive Group attribute contained the word 'Landform' and those where the Physical Level attribute was not 50)
- A new attribute was calculated corresponding to a concatenation of the Make attribute, the first term of the Descriptive Group attribute and the first term of the Descriptive Term attribute
- Land cover types were assigned to the polygons as per the following table
- Polygons where the Make attribute was Manmade and the first term of the Descriptive Group attribute was Road Or Track were assigned to the major roads land cover type if they intersected motorways, A roads or other dual carriageways from the MasterMap Integrated Transport Network Layer
- Polygons where the Make attribute was Natural and the first term of the Descriptive Group attribute was General Surface were assigned to the mown grass land cover type if they were completely within urban areas larger than 1km2 from Ordnance Survey's Meridian 2 dataset
- The MasterMap polygons were converted to a raster with 10m x 10m cells using the land cover type at the centre of each cell
- Areas within the buffer of North West England not covered by the resulting raster (mostly sea) were assigned to the water land cover type
| Make | Descriptive Group (first term) | Descriptive Term (first term) | Land cover type assigned |
| Manmade | Building | - | Buildings |
| Manmade | Building | Archway | Buildings |
| Manmade | General Surface | - | Other impervious surfaces |
| Manmade | General surface | Step | Other impervious surfaces |
| Manmade | Glasshouse | - | Other impervious surfaces |
| Manmade | Path | - | Other impervious surfaces |
| Manmade | Path | Step | Other impervious surfaces |
| Manmade | Rail | - | Bare soil or gravel surfaces |
| Manmade | Road Or Track | - | Major roads OR other impervious surfaces |
| Manmade | Road Or Track | Traffic Calming | Other impervious surfaces |
| Manmade | Roadside | - | Other impervious surfaces |
| Manmade | Structure | - | Other impervious surfaces |
| Manmade | Structure | Overhead construction | Other impervious surfaces |
| Manmade | Structure | Upper Level Of Communication | Buildings |
| Multiple | General Surface | Multi Surface | Mown grass |
| Natural | General Surface | - | Mown grass OR cultivated |
| Natural | Inland Water | - | Water |
| Natural | Natural Environment | Coniferous Trees | Trees |
| Natural | Natural Environment | Marsh Reeds Or Saltmarsh | Rough grass |
| Natural | Natural Environment | Nonconiferous Trees | Trees |
| Natural | Natural Environment | Scrub | Shrubs |
| Natural | Natural Environment | Rough grassland | Rough grass |
| Natural | Natural Environment | Boulders | Bare soil or gravel surfaces |
| Natural | Natural Environment | Heath | Shrubs |
| Natural | Natural Environment | Nonconiferous Trees (Scattered) | Shrubs |
| Natural | Natural Environment | Rock | Other impervious surfaces |
| Natural | Natural Environment | Boulders (Scattered) | Rough grass |
| Natural | Natural Environment | Coniferous Trees (Scattered) | Shrubs |
| Natural | Natural Environment | Coppice Or Osiers | Shrubs |
| Natural | Natural Environment | Orchard | Trees |
| Natural | Natural Environment | Rock (Scattered) | Rough grass |
| Natural | Natural Environment | Scree | Bare soil or gravel surfaces |
| Natural | Rail | - | Bare soil or gravel surfaces |
| Natural | Road Or Track | Track | Bare soil or gravel surfaces |
| Natural | Roadside | - | Mown grass |
| Natural | Tidal Water | Foreshore | Bare soil or gravel surfaces |
| Natural | Tidal Water | - | Water |
| Unclassified | Unclassified | - | Bare soil or gravel surfaces |
| Unclassified | General Surface | - | Bare soil or gravel surfaces |
| Unclassified | Rail | - | Bare soil or gravel surfaces |
| Unclassified | Roadside | - | Bare soil or gravel surfaces |
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Old browser
You're using a relatively old browser, in which some aspects of the STAR tools may not work. We recommend using one of the following.
You're using a relatively old browser, in which some aspects of the STAR tools may not work. We recommend using one of the following.
- Mozilla Firefox 3.6 or later
- Opera 11 or later
- Microsoft Internet Explorer 8 or later
- Google Chrome 14 or later
- Apple Safari 5 or later