Mitig Adapt Strateg Glob Change DOI 10.1007/s11027-015-9646-z O R I G I N A L A RT I C L E
Assessing potential future urban heat island patterns following climate scenarios, socio-economic developments and spatial planning strategies Eric Koomen 1 & Vasco Diogo 1
Received: 10 April 2014 / Accepted: 5 March 2015 # The Author(s) 2015. This article is published with open access at Springerlink.com
Abstract 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. Keywords Climate change . Land use change . Scenarios . Urban development . Urban heat island
* Eric Koomen
[email protected] 1
Department of Spatial Economics/SPINlab, VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
1 Introduction 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 Methodology 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 The spatial characteristics of urban areas trigger the formation of UHIs, but the intensity of the effect is also influenced by weather conditions such as temperature, wind speed and cloud cover that vary on a daily basis (Arnefield 2003; Tarleton and Katz 1995; Todhunter and Terjung 1990). To obtain a better understanding of temporal variation in UHI intensity and its relation to local weather conditions, we analyse hourly records of air temperature from five amateur-run automatic weather stations within the urban area of Amsterdam. Although amateur stations are not fully compliant with the standards of the World Meteorology Organization, they offer the possibility to study long-term temporal weather data in urban areas that lack official meteorological stations (Steeneveld et al. 2011). Urban temperatures were analysed for a 31-day period in the summer of 2010 (June 15 and July 15). This period was chosen because it includes substantial variation in temperatures (daily maxima in Amsterdam ranging between 13 and 33 °C) and it is—on average—characterised by conditions that favour UHI formation. After inspecting the data for all available amateur stations, we selected three stations that offered a consistent, complete time series for the same period. One station was discarded, as it offered a conspicuous afternoon peak in registered temperatures that is likely due to direct sunlight, and another station could not be used as its time series was incomplete. Table 1 describes the basic characteristics of the selected stations that were hitherto not included in any analysis of urban temperatures, and Fig. 1 shows their locations.
Table 1 Amateur weather station characteristics and their UHImax values averaged over days with favourable conditions at the reference station (N=19) Weather Station (ID)
Latitude
Longitude
Elevation (m)
Average UHImax
Amsterdam Noord (MC9433)
52.40
4.92
6
3.3
Amsterdam Zuidoost (INOORDHO32)
52.30
4.97
3
3.0
Watergraafsmeer (INOORDHO97)
52.35
4.93
1
3.5
Schiphol Airport (EHAM)
52.30
4.77
2
reference station
Elevation is specified relative to ground level. Variation in absolute elevation (referring to NAP: Amsterdam Ordnance Datum) in the Amsterdam region is minimal and ranges from around −5 to +2 m
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
Fig. 1 The Amsterdam study area including the location of the amateur and reference weather stations and measurement points along the bicycle routes. The figure also shows the two spatial variables (buildings and urban area) that were used in the explanatory analysis at their initial (finest) resolution
environment. It also captures the local near-maritime climate conditions of Amsterdam, as it is equally far from the coast line (approximately 20 km). The amateur stations show consistent temperature differences with the rural reference station, and their daily UHImax values range between 1.2 and 5.2 °C for the complete studied period (Fig. 2). The temporal variation in UHImax values indicates the importance of daily differences in climatic conditions. We analysed the relations between UHImax values observed at the urban amateur weather stations and several key variables describing the daily weather conditions at the Schiphol reference station. The results of this analysis are included in Appendix and confirm our expectations founded in prior research (e.g. Arnefield 2003; Steeneveld et al. 2011; van Hove et al. 2011): UHImax increases with increasing daily maximum temperature and sunshine duration and decreases with increasing wind speed. Especially wind speed is known to have a strong inverse relation with the heat island effect; speeds in excess of 4 m/s already substantially decrease the effect (Giannaros and Melas 2012). Daily maximum temperature provides the single most important contribution to explaining variation in UHImax values in our analysis. To test the impact of favourable conditions on this relation, we repeated the analysis for those days with favourable conditions that we defined as follows: low daily mean wind speed (
