Assessing potential future urban heat island patterns following climate scenarios, socio-economic developments and spatial planning strategies
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Climate change and urban development will exacerbate current urban heat island effects. While most studies acknowledge the importance of projected temperature increases for raising urban temperatures, little attention is paid to the impacts of future changes in urbanisation patterns. Yet, steering urban development may be an effective strategy to further limit increases in the intensity and spreading of the urban heat island effect. We describe a method that allows exploring the impact of urban development scenarios on the urban heat island effect. This paper starts with a basic analysis of the strength of this effect in a temperate climate under relatively favourable conditions based on data from amateur weather stations and own observations. It explains local variation in observed temperatures and quantifies how the urban heat island effect may develop in the coming 30 years. Using the obtained relations, we assess potential future changes building on existing scenarios of climatic and socio-economic changes and a land use simulation model. Our measurements for the Amsterdam region in the Netherlands indicate that the urban heat island effect induces maximum temperature differences with the surrounding countryside of over 3 °C on moderately warm summer days. The simulations of potential future changes indicate that strong local temperature increases are likely due to urban development. Climate change will, on average, have a limited impact on these changes. Large impacts can, however, be expected from the combination of urban development and potentially more frequent occurrences of extreme climatic events such as heat waves. Spatial planning strategies that reduce the lateral spread of urban development will thus greatly help to limit a further increase in urban heat island values.
KeywordsClimate change Land use change Scenarios Urban development Urban heat island
On average, urban areas are hotter than their surrounding rural areas, forming an urban heat island (UHI). This effect has already been documented in the early nineteenth century for the city of London, UK (Howard 1820), and has been studied intensively since the seminal work of Chandler (1962), Oke (1973) and Landsberg (1981). Recent examples include the work of Hart and Sailor (2009), Giannaros and Melas (2012) and Heusinkveld et al. (2014). The heat island effect can lead to increased cooling costs in summer time and exposes city dwellers to increased mortality due to higher temperatures (Aström et al. 2011; Döpp 2011) and has, therefore, attracted the attention for urban planners (e.g. NYSERDA 2006; Svensson and Eliasson 2002). Moreover, due to climate change, this effect may become stronger and more apparent (Patz et al. 2005; Wilby 2003).
Differences in temperature between urban and rural areas are largely determined by the ability of the urban fabric and surface materials to capture, store and slowly release incoming solar radiation, implying that, on aggregate, more thermal energy is stored in urban areas and cooling after the evening transition is slower than in rural areas (Oke 1982, 1995). Buildings and surface materials generally have a lower albedo and higher heat capacity than, for example, vegetation. In addition, multiple reflections between buildings and roads occur before the non-absorbed solar radiation is reflected to space, a phenomenon that is sometimes referred to as an urban canyon and that can be characterised by the sky view factor (Oke 1981; Svensson 2004). High buildings can also pose friction to air circulation and decrease average wind speed, resulting in smaller and slower cooling of building and street surfaces. In areas with a low sky view factor (i.e. narrow streets and/or high buildings), cities will thus trap more heat when cooling down at night.
Furthermore, the lack of vegetation and surface moisture results in a reduction in evapotranspiration, thus leaving more thermal energy available for heating the city (Akbari et al. 2001; Jusuf et al. 2007). Waste heat production and greenhouse gas emissions from anthropogenic activities (e.g. industry, car traffic, air conditioning) also contribute to increased heat radiation and increasing air temperatures in the cities (Hinkel and Nelson 2007; Sailor and Lu 2004).
Local morphological characteristics of urban areas such as building density and building height to width ratio have shown to contribute to the intensity of the UHI effect, by influencing heat release convection and advection in the urban boundary layer (Bottyán and Unger 2003; Hamilton et al. 2013; Hart and Sailor 2009; Svensson 2004). In addition, sprawling urban forms and nearby conurbations can also contribute to the build up of heat by limiting the ability for wind and turbulence to dissipate heat in the core of the urban zone (Bohnenstengel et al. 2011).
With changing climate conditions and an ever increasing urban population (Montgomery 2008; UN 2005), it is clear that the UHI effect will increase in the future. Yet, few studies exist that downscale these global changes to expected localised impacts. This paper describes a method that allows exploring the combined impact of urban development and climate change projections on the UHI effect. It starts with a basic analysis of the magnitude of the UHI effect under relatively favourable conditions in a temperate climate (Amsterdam, the Netherlands) and attempts to explain spatial variation in the observed temperatures. This analysis does not try to provide an exhaustive account of UHI formation; instead, we aim to capture the most important aspects for this process in our case study area to be able to create a quantitative assessment of the potential changes in the magnitude and spatial pattern of the UHI effect in the coming 30 years as a result of projected climatic and socio-economic changes. To assess potential future changes, we build on existing scenario studies and a land use simulation model. Using observed relations between average maximum daily temperatures and observed local UHI values, we are able to estimate the impact of global climate change on local UHI values. The land use change model allows the translation of macro-level socio-economic changes into potential future urbanisation patterns and thus the assessment of increased urbanisation on UHI values. This approach allows policy makers to assess the possible impacts of different spatial planning strategies on changes in urban temperatures.
2.1 Analysing current UHI patterns
We describe current UHI patterns based on two separate analyses. Temporal variation in urban temperatures is described based on local temperature measurements derived from amateur weather stations, while spatial variation is measured along a route using mobile measurement devices and then explained using regression analysis and spatially explicit explanatory variables. For both analyses, we focus on the formation of UHIs on days with relatively favourable weather conditions to be able to assess the contribution of the most important factors on the intensity and spatial pattern of this phenomenon. The implications of selecting only favourable days on the robustness of our results are further elaborated upon in the discussion section
2.1.1 Temporal variation in urban temperatures
Amateur weather station characteristics and their UHImax values averaged over days with favourable conditions at the reference station (N = 19)
Weather Station (ID)
Amsterdam Noord (MC9433)
Amsterdam Zuidoost (INOORDHO32)
Schiphol Airport (EHAM)
To characterise the heat island effect, we calculated the maximum UHI intensity (UHImax) defined as the maximum difference in hourly temperatures between the urban stations and the nearby reference station of Schiphol observed during a 24-h period. The reference station of Schiphol airport is operated by the Royal Netherlands Meteorological Institute (KNMI) and located close to Amsterdam at about 10 km from centre of the city in an open non-urban environment. It also captures the local near-maritime climate conditions of Amsterdam, as it is equally far from the coast line (approximately 20 km).
Building on this assessment of temporal variation in the UHI effect, the next section describes our analysis of spatial variation in urban temperatures.
2.1.2 Spatial variation in urban temperatures
Key specifications of data logging devices applied in this study
GPS-based position logger
EL-USB-1-Pro (4×)/EL-USB-2+ (1×)
Circular Error Probability (CEP) 5 m
Typical overall error ±0.2 °C/±0.5 °C
0.000001 decimal degree (WGS84)
0.1 °C/0.5 °C
Meteorological conditions at the reference station (Schiphol airport) during the observation days compared to average values for the three summer months in the 1980–2010 period
Maximum temperature (°C)
Sunshine (% of daylight hours)
Wind speed range (21–24 h in m/s)
Number of routes
June 17, 2012
June 16, 2013
June 18, 2013
Daily mean 4.3
For all measurement points along the routes, the UHI effect was described by linking the urban temperatures obtained at each minute to the logged position at that moment and comparing it to the temperature measured at the Schiphol Airport reference station. The latter are stored at 10-min intervals and were linearly interpolated to match the exact minutes of the observations.
Descriptive statistics of the variables included in the explanatory analysis
Urban heat island effect
Maximum temperature at reference station (one value per day)
Average amount of urban area within circular neighbourhoods of
0 or 1
Total amount of urban volume within circular neighbourhoods of
Explanatory analysis of observed urban heat island values using maximum temperature at reference station (Reft) crossed with different neighbourhood sizes for the urban area (UA) variable and, for the selected range of 1000 m, different neighbourhood sizes for the urban volume (UV) variable
Step 1: testing different distance ranges for calculation of average amount of urban area surrounding observations (UAdistance range)
Step 2: testing different distance ranges for calculation of total amount of urban volume surrounding observations (UVdistance range)
is the expected local UHI value for a maximum temperature t at the reference station (Ref)
is the average amount of urban area within a circular neighbourhood of 1000 m, and
is the urban volume within circular neighbourhoods of 1000 m.
On a dry, sunny day with low wind speeds and a maximum temperature of 20 °C at the reference station, this approach implies a maximum heat island effect of around 2.6 °C in the centre of the city where the maximum values for average amount of urban area and total amount of urban volume coincide. According to this analysis, this maximum effect increases to about 3.4 °C for days with a maximum temperature of 30 °C. These values match well with the observed UHI values from the amateur stations (Table 1) and bicycle runs (Table 4). Section 3 shows the estimated spatial variation in current UHI values following the above formulation. This basic explanatory model can also be used to assess potential future changes in a straightforward way without requiring many additional assumptions as is discussed in the following subsection.
2.2 Simulating future UHI patterns
2.2.1 Future climate scenarios
Current and projected summer conditions according to the four Dutch climate scenarios downscaled for the De Bilt weather station at approximately 40 km from Amsterdam
G scenario ±2050
G+ scenario ±2050
W scenario ±2050
W+ scenario ±2050
Average day temperature (°C)
Average maximum temperature (°C)
Nr. warm days (max. temp. ≥25 °C)
Nr. hot days (max. temp. ≥30 °C)
Total precipitation (mm)
Days without rain (%)
2.2.2 Future urban patterns according to socio-economic scenarios
To provide an outlook on future urban patterns, we apply a land use simulation model that is well-established in spatial planning and climate adaptation research in the Netherlands and several other countries: Land Use Scanner (Hoymann 2010; Koomen and Borsboom-van Beurden 2011; Kuhlman et al. 2013; Te Linde et al. 2011). This GIS-based model is rooted in discrete choice theory (McFadden 1978) and integrates sector-specific inputs (e.g. regional demand for residential land) from other dedicated models. It simulates the choice between mutually exclusive uses (e.g. residential, commercial, agricultural, recreation) for a particular location. This choice depends on the demand-supply interaction for land, with sectors competing within suitability and policy constraints. In practical terms, it combines the demand for land for various societal functions specified for different regions (and derived from specialised models run by different research institutes) with a grid-cell-based definition of local suitability for these different types of use at a 100-m resolution. Details on the applied allocation algorithms, calibration and validation of the model can be found in other publications (Koomen et al. 2011; Loonen and Koomen 2009). To reflect the inherent uncertainty in future societal conditions and incorporate alternative spatial planning strategies, we have selected the two most diverging scenarios from an existing Dutch scenario study (CPB et al. 2006). The Global Economy (GE) scenario is part of the A1 scenario family in the IPCC terminology and shows strong population and economic growth in combination with a liberal government that poses little restrictions to urban development. In the Regional Communities (RC) scenario (based on the B2 scenario family of IPCC), the population remains more or less stable, with modest economic growth and a more strict government that safeguards public goods such as historic open landscapes and natural areas. In relation to urbanisation patterns, the scenarios oppose large-scale dispersed urban development (GE) with limited and more compact extension. These two scenarios thus highlight different pathways for future development that depend on the absence or presence of urban containment policies. As such, they confront policy makers with the potential consequences of their spatial planning alternatives. Details on the implementation of the two scenarios into the Land Use Scanner model are provided elsewhere (Dekkers et al. 2012).
Based on the simulated land use patterns for 2040, we created two updated versions of the urban volume data set, one for each scenario. These were created according to the following rules: (1) for locations where land use did not change between 2008 (base year for simulation) and 2040, the urban volume values for the base year were maintained, and (2) for locations that became urbanised in the simulation period, the urban volume value was updated to the mean urban volume value observed in the base year within built-up areas. Subsequently, the simulated urban area patterns for 2040 and the updated urban volume data layer were used to create new data layers describing the urban area and urban volume conditions in 1000-m neighbourhoods (UA1000 and UV1000 as specified in Eq. 1) for 2040. In combination with the expected average maximum summer temperatures, these were used to depict spatial variation in the future UHI effect. Since the climate scenarios relate to average weather conditions in the 2035–2065 period, we believe that we can combine them with our land use simulation results related to 2040.
3 Simulation results
The simulated future UHI patterns are shown in Fig. 4c–h. These maps are based on the simulated extension of the urban area in combination with three different maximum temperatures: the current average maximum temperature in summer and the expected maximum temperatures in summer for two climate scenarios: G+ and W+ (as described in Table 6). From these maps, it can be observed that the change in UHI differs per scenario. The RC scenario shows a small concentrated UHI increase in high urban volume value areas in the centre, whereas the GE scenario shows a more dispersed spread of the UHI effect. This follows from the stronger focus on concentration of activity in the RC scenario, while the GE scenario allows more urban development at the edges of town. The increases in temperature following the climate scenarios result in more extreme UHI values with maximum UHI values in the centre rising to about 3.25 °C and spreading over larger areas. On hot summer days that are expected to occur more frequently, the UHI effect will be much stronger.
Our measurements for the Amsterdam region in the Netherlands show that the UHI effect induces maximum temperature differences with the surrounding countryside of over 3 °C on moderately warm summer days with an average maximum daytime temperature and up to 5 °C on warm days. These values are in line with recent studies into the UHI effect that made use of amateur weather stations in several Dutch cities and that found mean daily maximum values of 2.3 °C and extreme values of 4–6 °C for specific days (Steeneveld et al. 2011; Wolters et al. 2011). Another recent study for the 2006 heat wave found night-time air temperatures in Amsterdam to be 7–9 °C higher and surface temperatures more than 10 °C higher than in the rural surroundings (Van der Hoeven and Wandl 2013). Besides their specific focus on a heat wave, these higher values may also be related to their reliance satellite imagery, a technique that has difficulty with distinguishing surface temperature from the overlying air temperature that is actually measured (Mavrogianni et al. 2011). Other studies using portable or miniature near-surface air temperature logging devices for cities in different countries documented UHI values that are very similar to our results: 3–5 °C for Rotterdam, the Netherlands (on a day with a maximum temperature of 30 °C, see Heusinkveld et al. 2014; van Hove et al. 2011), 3–5 °C on particularly warm days (35–40 °C) in Portland Oregon (Hart and Sailor 2009) and 2–3 °C for the typically warm summers in Thessaloniki, Greece (Giannaros and Melas 2012). So we believe that the our measurements provide a reasonable account of the magnitude of the UHI effect in Amsterdam under typical summer conditions that favour the development of this effect and that have maximum daily temperatures in the range of about 20–30 °C. An analysis of the frequency of occurrence of favourable conditions for UHI formation at Schiphol airport during the three summer months (June, July and August) in the past 30 years (1980–2010) indicates that the combination of these conditions (wind speed <4.5 m/s and percentage of maximum potential sunshine duration ≥35 % and daily precipitation amount <0.5 mm) held true for 31.5 % of the summer days.3 Under less favourable conditions, the UHI will be much less pronounced or even absent, whereas on winter days, relations may be different due to lower solar intensity, shorter days, more abundant shadows and possibly also more anthropogenic influences (e.g. from traffic or heating of houses). A separate analysis would thus be required for assessing this seasonal variation (cf. Svensson and Eliasson 2002). Such an analysis could be used to more specifically establish possible consequences of the UHI effect in terms of limiting the need for heating in urban areas in winter time. Accounting for UHIs on (prolonged periods of) extremely warm days would also require additional research, as would the interaction of urban temperatures with other weather-related health issues such as smog and air pollution.
In our explanatory analysis of the observed spatio-temporal variation in UHI values, we combine the spatial variation in degree of urbanity that was encountered during the different bicycle routes with the temporal variation in temperatures at the days of cycling. This temporal variation is included by referring to the daily maximum temperature following the relatively strong relation that we found between these temperatures and the maximum heat island values observed at various amateur weather stations in the city. We find that the degree of urbanity matters especially within relatively large distance ranges of about 1000 m. This finding is in line with recent studies into the spatial variability of the UHI in the Netherlands that use spatial distance ranges of 400 m (Brandsma and Wolters 2012), 600 m (Steeneveld et al. 2011) and 700 m (Heusinkveld et al. 2014). These results indicate that the UHI effect is dependent on the amount of urban area and presence of building in a fairly large radius, implying that strategies to mitigate increased urban temperatures should foremost focus on limiting the further expansion of large urban areas. Our simulations of the future UHI patterns based on different urbanisation patterns indicate the impact of limiting urban expansion and hint at the benefits of containing urban growth.
While measurements of the UHI effect have become fairly abundant in recent years, explanatory studies are much scarcer and assessments of the combined impact of climatic and socio-economic changes on the future state of urban temperatures are virtually absent. A notable exception is offered by the urban meteorological models applied to the greater London area to simulate the current urban climate and likely changes related to the large urban development for the Olympic Parkland (Bohnenstengel et al. 2011; Hamilton et al. 2013). Yet, our approach is different, as it combines projected changes in urbanisation patterns and maximum day temperatures from socio-economic and climate scenarios in a straightforward way without requiring full-fledged meteorological models. The reliance on limited amounts of data makes our approach applicable in quick, initial assessments of potential future changes in the local UHI effect. In addition, it allows assessing the relative importance of socio-economic and climatic changes for this effect. In our case study for the city of Amsterdam, we found that climate change increases the intensity of the UHI effect, while urban development leads to strong local temperature increases as well as causing larger areas to be affected. An increase in average maximum temperature in summer of 1–3 °C as projected in Dutch climate scenarios for 2050 will only lead to a marginal increase in urban temperatures of about 0.1–0.3 °C for inner urban locations. The UHI effect reacts stronger on the day-to-day variation in summer temperatures. The more frequent occurrence of warm or hot days that is also projected in the climate scenarios will have a larger impact on future UHI values. Even larger impacts can be expected from the combination of urban development and more frequent occurrences of extreme climatic events such as heat waves. The presented simulation approach allows policy makers to explore the potential consequences of different urbanisation strategies. It is able to show the benefits of compact urbanisation in limiting the lateral extent of the UHI effect as well the trade-off with increasing urban temperatures that result from adding residences to the existing urban fabric instead of to open spaces around cities. The quantitative nature of our approach distinguishes it from other spatial-planning-oriented tools that map urban climatic conditions in a more qualitative way (Ren et al. 2012).
Our approach focuses on the local (neighbourhood) scale of urban temperatures as distinguished by Oke (1984), implying that microscale temperature differences related to individual buildings or trees are not explicitly measured. The perceived importance of such very local differences seems to depend on the moment and method of measurement (compare, for example, Aniello et al. 1995 with Brandsma and Wolters 2012). Yet, much research focuses on defining mitigation measures that may help limiting microscale variation in urban temperatures, proposing, for example, an increase in vegetative cover and the application of higher albedo surface materials (Solecki et al. 2005) or the provision of small green spaces or water bodies (Kleerekoper et al. 2012). To assist the definition of this type of mitigation strategies and assess their potential impacts, more detailed measurements of urban temperatures are required that focus, amongst others, on the presence of individual trees or water bodies.
The specific urban setting of the amateur station may assert a different influence on temperature development from day to day, for example, when wind from certain directions is blocked.
The 30-year average maximum daily summer temperature of the reference station is 21.1 °C (see Table 3).
The frequency of occurrence of the specified conditions was calculated using a dataset describing daily weather conditions provided by the Royal Netherlands Meteorological Institute (www.knmi.nl/klimatologie/daggegevens/download.html). The individual conditions held true for 57 % (wind speed) and 58 % (solar radiation) and 62 % (precipitation) of the summer days.
We are grateful to the weather amateurs that provided their measurement data and the numerous Amsterdam University College students that performed the nightly bicycle rides to collect temperature data along different routes. In addition, we thank the Netherlands Environmental Assessment Agency (PBL) for providing the Land Use Scanner model and associated data sets for this research. Last, but not least, we are thankful to the anonymous reviewers whose constructive comments helped improve this paper.
Compliance with ethical standards
This study was funded by the Dutch National Research Program Knowledge for Climate (Theme 8 Decision Support Tools).
Local urban temperature data were collected for this study by students of Amsterdam University College. These data are included with the students’ consent, and this inclusion has in no way infringed their privacy or other individual rights.
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