A new heat sensitivity index for settlement areas

Urban Climate 6 (2013) 63–81

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A new heat sensitivity index for settlement areas Tobias Krüger a,⇑, Franz Held a, Sebastian Hoechstetter b, Valeri Goldberg c, Tobias Geyer d, Cornelia Kurbjuhn d a

Leibniz Institute of Ecological Urban and Regional Development, Dresden, Germany GFZ German Research Centre for Geosciences, Potsdam, Germany Technische Universität Dresden, Institute of Hydrology and Meteorology, Germany d City of Dresden, Environmental Office, Germany b c

a r t i c l e

i n f o

Article history: Received 7 January 2013 Revised 5 September 2013 Accepted 29 September 2013

Keywords: Settlement Heat Sensitivity Index (SHSI) Thermal sensitivity Thermal stress Physiological Equivalent Temperature Urban climate Urban heat island

a b s t r a c t The bioclimatic situation in cities will be a major issue for future planning authorities. Especially cities characterized by dense urban structures and high rates of impervious surface coverage will have to deal with this issue, since thermal stress is most likely to increase in intensity and frequency due to climate change (Bernhofer, 2009; Harlan et al., 2012; Hayhoe et al., 2010). Thus, it is essential to obtain profound knowledge and appropriate data regarding the thermal characteristics of settlement areas, on the basis of which the bioclimatic situation is assessed. This study presents a methodology for identifying sensitive settlement areas with regard to the bioclimatic situation in the city of Dresden, and introduces the Settlement Heat Sensitivity Index (SHSI) as a measure for thermal sensitivity. The methodology described takes into account the thermal characteristics of urban structures as well as demographic parameters, and is adaptable to certain age cohorts which are of special interest for urban planning, due to their potentially increased susceptibility to health risks related to thermal stress. As a result, sensitivity maps are produced, displaying the bioclimatic variability within the city of Dresden. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Thermal stress is one of the main issues related to urban heat islands (UHI). These UHI are characterised by significantly higher temperatures prevailing in dense urban structures compared to ⇑ Corresponding author. Address: Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany. Tel.: +49 351 4679 256; fax: +49 351 4679 212. E-mail address: [email protected] (T. Krüger). 2212-0955/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.uclim.2013.09.003

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surrounding rural regions. Due to the large proportion of impervious surfaces storing thermal energy during the days and emitting it at night, the heat stress is kept at high levels for hours after sunset. As a result, sleep deprivation and reduced physical and mental heat resistance may occur, since nocturnal temperatures do not cool down enough to ensure a recreation effect for the inhabitants (Matzarakis et al., 2009). In addition, the vulnerability and mortality of urban residents rises with the duration of heat periods, as it has happened in 2003, when a Europe-wide heat wave caused a remarkable increase in death counts. Especially the highly sensitive population groups of infants, seniors and people with disabilities were affected, as the estimated number of more than 20,000 excess deaths in England, Wales, France, Portugal, and Spain reveals (Kosatsky, 2005). The cooperative research project REGKLAM (Development and Testing of an Integrated Regional Climate Change Adaptation Programme for the Model Region of Dresden) focuses on adaption strategies for the city and surrounding region of Dresden (Germany) in order to develop measures for coping with the direct and indirect impacts of climate change on economic, ecological, and social issues. The findings presented in this paper have emerged from one of the work packages of that project and have been incorporated into an overarching Regional Climate Change Adaptation Programme for the region (REGKLAM, 2013). 1.1. Climate change impact on heat stress As a result of the global warming, thermal stress situations are expected to occur more often and with a higher intensity in future than today (Luber and McGeehin, 2008; Meehl et al., 2007; Rahmstorf and Coumou, 2011). The current climate of the study area can be assigned to the category of a transitional climatic zone between the maritime climate of western Europe and the continental climate of eastern Europe, with the maritime influences being dominant (Bernhofer et al., 2011b). Climate change projections that have been performed for the Dresden region indicate that by the end of 21st century the mean air temperature will probably increase by 2.5–3.0 K (Bernhofer et al., 2011b). Particularly, when compared to the climate normal period of 1961–1990, the frequency of hot days (Tmax P 30 °C) and warm days (Tmax P 25 °C), as well as of warm nights (Tmin P 20 °C) is likely to increase (Table 1) (Bernhofer, 2009; Bernhofer et al., 2011b; IPCC, 2008). This reference period is commonly regarded to represent the typical climatic conditions at the beginning of the 20th century and is therefore defined as the current normal period used for climate change research by the World Meteorological Organization (Bernhofer, 2009; IPCC online, 2013). As a result, heat stress for humans during the summer is likely to increase over the next decades. Therefore, it is important to monitor climate-relevant parameters in urban areas in order to enable future climate adaptation measures. Thus, profound knowledge about the thermal properties of urban settlement areas and their spatial distribution is crucial, especially for assessing the consequences of summer heat periods (Ali-Toudert and Mayer, 2006; Kosatsky, 2005; Matzarakis et al., 2009). Urban planning is facing the challenge to balance the requirements of both climate mitigation and adaptation (Mathey et al., 2011; Wende et al., 2010). Shortage of resources favours compact urban settlement structures, leading to a dense infrastructure with short distances in cities, and reduced motorcar traffic. These requirements are connected with further challenges concerning demographic change; furthermore, we are already facing the consequences of climate change. It is, therefore, necessary that viable strategies for climate change adaptation are developed. There is a significant correlation between the imperviousness ratio – or green cover ratio respectively – and the air temperature in urban areas (Bernhofer et al., 2007; Petralli et al., 2013). Green Table 1 Mean frequency of characteristic climatological days for Dresden in the reference period and the mean change according to IPCC scenario A1B (Source: Bernhofer et al., 2011b).

Hot days (Tmax P 30 °C) Warm days (Tmax P 25 °C) Warm nights (Tmin P 20 °C)

1961–1990

2010–2050

2071–2100

6.8 37.7 0.8

+1.9 +7.9 +0.4

+10.0 +25.4 +3.3

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structures in inner cities are multifunctional assets and can lead to temperature reduction (Alexandri and Jones, 2008; Cohen et al., 2012; Honjo and Takakura, 1990; Oliveira et al., 2011) and improved air quality (Escobedo and Nowak, 2009; Kuttler and Strassburger, 1999). Several studies have addressed the issues of bioclimatology and human health in the context of the urban heat island (Matzarakis and Mayer, 1991; Tan et al., 2009). The impacts of urban climate and thermal stress on human health and well-being or mortality have been extensively investigated (Harlan et al., 2012; Uejio et al., 2011; Wilhelmi and Hayden, 2010). Efforts have been made to provide vulnerability indices for assessing the risk of heat impacts on settlement areas and the population (Cutter et al., 2003; Johnson et al., 2012; Reid et al., 2009; Schmidtlein et al., 2008). Possible adaptation and mitigation strategies have been identified in a number of studies (Ali-Toudert and Mayer, 2007; Gulyás et al., 2006; Nikolopoulou and Steemers, 2003). Vulnerability is a function of the climate change exposure, sensitivity, and adaptive capacity of a region (IPCC online, 2001). In the context of this study, it can be seen as a measure of the degree to which a region is susceptible to meteorological impacts in terms of the thermal comfort and wellbeing of its population. Sensitivity, according to the terminology of the Intergovernmental Panel on Climate Change (IPCC), is defined as the degree to which a system, i.e. an urban area, is affected by climate-related stimuli (IPCC, 2008). Here, the stimulus assessed is the bioclimatic impact on humans in urban areas. In this study, we are introducing a methodology to identify settlement areas which are particularly affected by thermal stress. The main objective is the identification and localisation of bio-climatically problematic zones within a city (Hoechstetter et al., 2010). Thus, the urban thermal sensitivity is evaluated as an input for the vulnerability assessment, which would also have to incorporate expected climate change exposure (esp. the magnitude and rate of regional warming), and adaptive capacity scenarios (esp. information about the health status of the population and measures of urban planning regarding climate adaption). The approach of the present study is the integration of some of the mentioned aspects in order to provide a scalable measure to assess the potential thermal stress of urban structures, with the goal of supporting decision making for urban planning. 1.2. Research area The research area comprises the spatial extent of the city of Dresden, having more than 500,000 inhabitants. As the capital of the state of Saxony, it assumes many metropolitan functions and features a variety of different urban structure types. About 21 % of the area is made up of residential urban structures (excluding industrial and commercial sites), resulting in an effective population density of approx. 7,300 inhabitants per km2 within the area with residential functions of the city (IOER, 2013). The climate is characterised as oceanic and is situated within the Cfb climate zone, according to the Köppen classification (Matzarakis et al., 2009; Peel et al., 2007). 2. Methodology of sensitive settlement area identification 2.1. Physiological Equivalent Temperature (PET) The Physiological Equivalent Temperature (PET) is a bio-meteorological index for the assessment of the thermal impact of the current meteorological situation for human beings. It is based on the human energy balance, and is well-suitable to estimate outdoor thermal impacts. The values are given in degrees centigrade (°C), which makes it easily comprehensible to the general public (Matzarakis and Amelung, 2008). The PET was developed using on the energy balance model MEMI (Munich Energy Balance Model for Individuals). It is defined as the temperature at which the human energy balance is equalised, that means the skin temperature and the body’s core temperature are the same (Höppe, 1999, 1984). Thus, the current climatic outdoor conditions are adapted to a defined indoor environment. This enables

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assessing the thermal situation outdoors in relation to indoor conditions, allowing for a comparison of different thermal conditions (Höppe, 2002; Mayer, 1993). Contrary to other thermal indices as the Predicted Mean Vote PMV (Fanger, 1972), which was primarily developed for indoor usage, the PET is applicable for the thermal assessment of outdoor environments (Lin et al., 2010). It is a constituent part of the official guideline of the German Engineering Society (Gulyás et al., 2006; VDI, 1998). Moreover, it is also suitable for use in different climates. Hence, its applicability has been investigated by numerous studies (Cohen et al., 2012; Höppe, 1999; Hwang et al., 2011; Matzarakis et al., 1999; Tseliou et al., 2010). Several meteorological parameter settings concerning wind speed (0.1 m/s), water vapour pressure (12 hPa, corresponding to 50 % relative humidity at 20 °C), and equal values for the mean radiation temperature and air temperature (Tmrt = Ta) are assumed, so as to permit adaption of the outdoor conditions to those of a defined indoor environment (Mayer and Höppe, 1987). Under these conditions, a standard 35-year old male person of 75 kg body weight practising a physical activity equivalent to a slow walking – which is frequently referred to as ‘‘Klima-Michel’’ – perceives thermal comfort at PET = 20 °C (Jendritzky et al., 1990; Matzarakis and Mayer, 1996). Based on the nine-level PET classification by Matzarakis and Amelung (2008) we combined the PET values below 18 °C resulting in six thermal perception classes, ranging from cold stress to heat stress as indicated in Table 2. The classification is comparable to the PMV scale proposed by Fanger (1972). 2.2. General methodological approach The proposed methodology is based on the assumption that densely populated areas are more susceptible to bioclimatic thermal stress than other areas, due to the larger number of people that may be affected by heat events and the potentially more pronounced heat island effects in these areas (Hoechstetter et al., 2010; Wende et al., 2010). Urban areas can be classified by urban structure types (UST) due to their characteristics concerning building types, building density, degree of imperviousness, and green volume (Breuste, 2010; Mathey, 2011; Meinel, 2008; Pauleit and Duhme, 2000). Hence, it is obvious that certain UST are considered to be more important for the evaluation of the bioclimatic situation than others. The potential thermal stress is expressed by a modelled PET value, based on statistical analyses described below. The identification of sensitive settlement areas is based on the main input parameter categories socio-demographic data, urban structure type data, and thermal data (PET). These are combined, resulting in a numeric index value which is then related to the bioclimatic sensitivity of a certain urban area, taking into account both its thermal conditions and its socio-demographic situation. The resulting index indicates the sensitivity of settlement areas on a limited numerical scale. Based on the above-mentioned six-level PET scale, we have decided to define a classification of six values expressing the degree of thermal sensitivity of urban areas. Accordingly, we have adopted the PET classes which are defined as the measure for the thermal effect on human beings. Furthermore, we have defined two weighting factors for scaling the potential PET values, derived from a statistical model (see Section 2.3.1). The first is based on the thermal characteristics of the USTs in the city; the second takes into account the demographic-structure patterns. We call the resulting index Settlement Heat Sensitivity Index (SHSI), which is defined by Eq. (1).

SHSI ¼ PETpot;Class =2 ðW UST þ W dem Þ;

ð1Þ

Table 2 Classification of PET values (changed after Matzarakis and Mayer, 1996). Class

PET [°C]

Thermal perception

Degree of physiological stress

1 2 3 4 5 6

0–18 >18–23 >23–29 >29–35 >35–41 >41

Slightly cool and colder Comfortable Slightly warm Warm Hot Very hot

Cold stress No thermal stress Slight heat stress Moderate heat stress Strong heat stress Extreme heat stress

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where: PETpot;Class: potential PET, reclassified into PET classes, WUST: Urban structure type weighting factor, Wdem: Demographic weighting factor. As a result, settlement areas can be ranked with regard to their (bio-)thermal sensitivity. Since the demographic factor can be calculated separately for different age cohorts of the population it is possible to determine thermal sensitivity for certain receptor groups such as elderly people or infants. Based on the numerical values, the degree of sensitivity is expressed as shown in Table 3. 2.3. Input parameters The approach requires several input data for the identification of sensitive areas. Since thermal stress is the target parameter, the most important input is information about the distribution of heat within the city of Dresden. PET calculations require a set of numerous input parameters. Due to the strong dependence on local conditions of shadowing and wind speed it is hardly feasible to actually calculate area-covering PET values for an entire urban area. Therefore, we used the statistical approach of estimating potential PET values from thermal satellite data, which provide area-wide thermal data for large regions. 2.3.1. Estimation of the potential PET, using thermal remote sensing 2.3.1.1. Satellite data – land surface temperature. In general, thermal remote sensing is a cost-efficient possibility for gaining area-covering thermal information of large areas. Thus, remote sensing observations provide inexpensive data for large areas and are often used in urban studies. Thermal satellite information reflects the thermal energy emitted from the earth surface. This data can be transformed into land surface temperature (LST). Thus, remote sensing technologies represent an alternative to data provided by weather stations; while the latter require statistical interpolations and modelling techniques to fill the ‘‘gaps’’ between the observation points, remote sensing data are based on actual area-wide measurements of physical properties of the earth’s surface. Most space-borne thermal sensors are carried by meteorological satellites, and provide spatial resolution at the kilometre scale, which is not suitable for the assessment of different settlement areas. However, the Landsat satellites record high-resolution thermal data, with a centre wavelength of 11.04 lm for the Thematic Mapper (TM on Landsat 4 and 5), and 11.44 lm for the Enhanced Thematic Mapper+ (ETM+ on Landsat 7). The spatial resolution of the sensor has been increased from 120 m (TM) to 60 m (ETM+). As Landsat data is available for free to any user and has the highest pixel resolution with respect to other thermal satellite platforms, it is widely used for urban thermal analyses (Honjo and Takakura, 1990; Mackey et al., 2012; Wong et al., 2007; Yu and Hien, 2006). Since the main goal was to produce a map of the thermal situation on hot days, we acquired several satellite scenes for the study region, and were able to employ two cloud free Landsat scenes covering the area of Dresden. Both scenes had been recorded on hot days, meaning that the maximum air temperature (Tmax) was higher than 30 °C. The scenes dated from June 20, 2000 (Landsat 7/ETM+) and July 22, 2006 (Landsat 5/TM). Both datasets were processed independently, which involved cloud removal, atmospheric correction, and radiometric calibration. The surface emissivity that is necessary to convert thermal radiation into temperature was derived from emissivity datasets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite platform dating from May 30, 2004

Table 3 Meaning of SHSI values. SHSI for heat events

Meaning

0–1 >1–2 >2–3 >3–4 >4–5 >5–6

Non-affected, non-threatened Very low sensitivity Low sensitivity Moderate sensitivity High sensitivity Very high sensitivity

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and September 10, 2004. For detailed information on thermal remote sensing and atmospheric correction, refer to the respective literature (Barsi et al., 2003a,b; Chander et al., 2009; Nichol, 1998; Voogt and Oke, 2003; Wloczyk et al., 2006). The two images were merged into one dataset by average determination in order to obtain the expected LST distribution within the city. The resulting map is shown in Fig. 1. 2.3.1.2. Bioclimatic modelling. Thermal stress can be calculated using the micro-climatic model RayMan, which has been developed for the assessment of urban thermal stress (Matzarakis et al., 2010, 2006). The model is applicable for the thermal assessment of urban structures, and has been applied in different climatic zones in several studies (Gulyás et al., 2006; Hämmerle et al., 2011; Hwang et al., 2011; Lin et al., 2010). By taking into account a quasi-realistic setting of urban structures consisting of buildings and trees, which are referred to as ‘obstacles’, the model calculates bioclimatic indices (PET) as a function of meteorological input parameters. These include global radiation, air temperature, humidity, cloudiness, and wind speed. Radiation calculations moreover consider date, time, and geographical coordinates. Surface properties of the ground and the buildings and trees within the scene setting can be adjusted by thermal emissivity and albedo values. The model calculations are adjusted to fit a standard person, seen as an average receptor of bioclimatic conditions (Jendritzky et al., 1990; VDI, 1998). RayMan allows adjustment of the receptor’s personal data for age, sex, activity, clothing, height, and weight. During the summer of 2009, meteorological measurements were carried out with specially equipped bicycles in order to record climatologically relevant parameters in Dresden (Bernhofer et al., 2011a; Hegeholz, 2009). Several measurement campaigns on high-radiation cloud free hot days yielded data for radiation, air temperature, humidity, and wind speed for three different points in time. After removing the bias caused by the time delay between the cycling routes, the climatic influence of urban structures could be determined for midday (2.00 p.m.), early evening (7.30 p.m.), and night (10.00 p.m.) along the measurement route which was about 36.5 km long.

Fig. 1. Surface temperature distribution in Dresden on hot days.

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Along the route, we performed bioclimatic modelling runs for 102 nearly equidistant points using the measured data as input parameters. Further input parameters such as global radiation, the mean radiation temperature, and solar altitude have been estimated by RayMan itself based on the given summer solstice day (June 23, as the day with the longest sunshine duration and radiation input) and the geographical position of Dresden. Sky view factors as well as shadowing and reflection effects due to buildings and trees were calculated based on building and urban tree datasets provided by the Environmental Office of the city of Dresden. For detailled information on the usage of the RayMan model refer to Hoyer (2012), Matzarakis and Rutz (2007), Matzarakis et al. (2006), Mehler (2011). The results of the modelling have been used to calculate a linear regression between the land surface temperature and PET, both of which are the results of the modelling process. We found a good correlation between them for midday conditions resulting in a linear regression function to derive PET from LST (Eq. (2)):

PETestim ¼ 17:077 þ 0:465 LST; R ¼ 0:735

ð2Þ

Using the given meteorological conditions pertaining during the measurements as the typical parameter setting for high-radiation hot summer days, and assuming surface conditions as recorded by the satellite scenes, the potential thermal stress can be estimated using the LST data. Hence, the mean urban surface temperatures (Fig. 1) are used as the PET estimation input. 2.3.2. Socio-demographic data The sensitivity assessment of settled areas requires detailed information about the age structure and spatial distribution of the population. The ideal information for this purpose would be the number and age of the inhabitants at the building level. However, due to data security reasons, these data are not distributed to the public or to research groups. Therefore, information about population structure available at an aggregated level has to be disaggregated (Burgdorf, 2010; Steinnocher et al., 2011, 2005). The Municipal Statistical Office of Dresden provided two population datasets for the study: (1) Population of the statistical districts in Dresden, broken down to the level of age cohorts (Reference date: 2007) (2) Total population of housing blocks in Dresden (Reference date: 2007) There are 401 statistical districts in total, 380 of which are populated with an average population of 1321. The number of housing blocks within each of the statistical districts ranges from 1 to 93, while 70 % of the districts comprise up to 20 housing blocks. For this relative small expectable amount of blocks per district we assumed the age structure of a statistical district to be valid for all the blocks inside. Hence, the population distribution can be spatially disaggregated on housing-block level by multiplying the total block population with the age-cohort fractions of the statistical district (Eq. (3)).

PopulationHousingBlock;AgeCohort ¼ PopulationHousingBlock;Total  PopulationStatDistrict;AgeCohort =PopulationStatDistrict;Total

ð3Þ

This results in a dataset which estimates the population of each age cohort in any housing block in the city. The age groups of special interest are elderly people (older than 75 years) and infants (younger than 2 years). On that basis, the demographic weighting factor Wdem for each housing block can be calculated (Eq. (4)):

W dem ¼ PopulationDensityHousingBlock =PopulationDensityMaximumCity

ð4Þ

This calculation can be carried out for any population group, which results, in our case, in three different demographic weighting factors, for the total population, for elderly people (75+), and for infants (U2). Because the data are normalised based on the maximum density present in the city area, the values of Wdem;total, Wdem;75+, and Wdem;U2 range between 0 and 1.

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2.3.3. Urban structure type weighting factor The second weighting factor is based on the heating-up potential of the various UST, which is linked to their relevance for the bioclimatic situation within the urban area. It is well-known that surfaces in built-up urban environments heat up more pronounced than natural areas overgrown with vegetation (Alexandri and Jones, 2008; Avissar, 1996; Dimoudi and Nikolopoulou, 2003). Impervious and built-up surfaces have lower reflectivity values than natural surfaces; accordingly, they have a higher potential for warming up (Helbig et al., 1988; Kuttler, 2004; Oke, 1987). Especially buildings influence the bioclimatic situation significantly due to their vertical alignment and their additional effects on the air flow characteristics. Thus, densely built-up areas absorb much more solar radiation than flat horizontal surfaces (Gaitani et al., 2007; Giridharan et al., 2008). Another factor is that many of the widely used building materials are very dense and thermally highly conductive. As a result, they absorb and store much more short-wave energy than vegetation-covered surfaces. Accordingly, the amount of thermal energy emitted at night increases, leading to reduced night-time cooling. 2.3.3.1. Statistical analyses of the thermal properties of urban structure types. The structural assessment of heat sensitivity of areas requires an analysis of the warming-up potentials of different structures. For this purpose, we carried out a statistical analysis of the mean LST separately for each urban structure type on the basis of a structure type mapping dating from 2006, and thus contemporaneous with the satellite data acquired. The UST mapping identifies 20 urban structure types. Each city block is assigned to one type, while streets are assigned to the type Traffic Area. Thus, a geometrically very detailed description of the entire city is available (Arlt et al., 2003). Each cell of the LST dataset was assigned to the underlying urban structure type. Accordingly, the area of Dresden was represented by about 90,000 points (cells), each with a known parameter pair of surface temperature and urban structure type. In general, it can be stated that the surface temperature gradually increases with the amount of impervious surface coverage. The structure types Forest and Water have the coolest surfaces. The main reasons are that forest canopies shadow land surfaces very effectively due to absorption of solar radiation in the crown area. Additionally, they consume radiation energy by transpiration and photosynthesis. Water bodies (lakes and rivers) absorb the incoming solar radiation in a large vertical column, and they prevent a marked heating of surface by convective vertical mixing and a large evaporation. Therefore, the mean LST for forests has been chosen as the reference value. The mean LST for each structure type was then compared to that forest value, so that the LST difference between a forest and a certain type was considered to constitute that type’s surface warming potential. Firstly, we grouped the 20 structure types into structure type classes with similar LST differences. Because of functional or structural properties, it was also possible for types to be assigned to different type classes, although they showed similar temperatures. Secondly, we ranked the urban areas according to their relevance for bioclimatic stress (RELbioclim). Therefore, a four-level scale from very high (value 4) to very low relevance (value 1) has been defined. Settlement and infrastructure areas characterized by residence and long-term presence of humans are assigned the highest value of 4. Traffic infrastructure facilities were assigned to value 3 (high relevance), because they are inner-city areas regularly accessed by people during the day. However, we consider green and open spaces within cities also as being of high relevance, as they are frequently used by many people for recreational activities on hot days. Forest areas were considered less bioclimatically relevant (value 2: low), because we expect few people to spend extended periods of time in these areas; in most cases, they are used for recreation. The lowest bioclimatic relevance (value 1) was assigned to arable land, since it was assumed that these areas are least frequently accessed. 2.3.3.2. Statistical analysis of the surface temperatures for structure type classes. As mentioned above, forests and water showed the lowest surface temperatures. We determined the reference value for forests at LSTmean;Forest = 304.2 K. Water was slightly cooler, at 303.8 K, yet there are many more pixels

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assigned to forests, so it has been set as a reference surface with the highest potential for cooling the land surface. All other structure types showed higher LSTmean values, due to the lower transpiration power of the smaller green volume and the increasing loss of the shading potential (in relation to forests). Fig. 2 shows the mean value diagram for land surface temperatures of the urban structure type classes. Evidently, dense settlement areas as well as industrial areas and railway sites (UST classes a and b) have the highest temperatures, which is in line with their high proportions of impervious surface coverage. The urban structure weighting factors (Table 4) are determined taking into account heating-up potential and bioclimatic relevance. The warming potentials of all other structure type groups are defined as their LST differences compared to forest, so they have been calculated by use of Eq. (5):

DeltaLSTUSTC ¼ LSTmean;USTC  LSTmean;Forest

ð5Þ

The weighting factors were then calculated by multiplication of the warming potentials (DeltaLST values) and the bioclimatic relevance, and by normalisation to a range of values between 0 and 1. The minimum weighting factor was defined as 0.1 (Eq. (6)):

W USTC ¼ DeltaLSTUSTC  RELbioclim =DeltaLSTUSTC;max

ð6Þ

In Fig. 3, the distribution of the resulting weighting factors is visualised. As can be seen from the map, the built-up areas with factors of 0.8 and 1.0 cover large parts of the city centre. In some cases, the dense structures are interrupted by green spaces. Traffic areas and brownfields have been assigned medium weighting factors.

Fig. 2. LST mean values for UST classes.

Table 4 UST classes with area, mean surface temperatures, and weighting factors. UST class

Approx. area [km2]

Mean LST [K]

Bioclimatic relevance

Weighting factor

a b

2 49

312.67 310.66

4 4

1.0 0.8

26 30 12 33 75 6 8 2 80

309.22 312.68 310.46 309.12 304.19 303.83 311.37 310.15 308.05

4 3 3 3 2 2 3 2 1

0.6 0.8 0.6 0.3 0.1 0.1 0.6 0.4 0.1

c d e f g h i j k

High density settlement structures Moderately dense settlements and mixed structures Low density settlement structures Industrial land use and railway areas Leisure areas and brownfields Grassland and green area Forests Water bodies Traffic and undefined areas Building sites, mining and deposit sites Arable lands

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Fig. 3. Urban structure type weighting factors.

Agricultural and forestry areas are to be found mainly on the outskirts of the city. The largest representative of this type is the municipal forest Dresdner Heide to the north-east of the city centre, the largest forest, covering an area of about 50 km2. 3. Results As shown in Fig. 4, the calculation of the SHSI values is done by a modelling chain combining the original input datasets population structure, UST mapping, Landsat LST mapping, and self-collected climatic data of the bicylce-based measurement campaign. The cores of the modelling are (i) the estimation of the potential PET distribution within the city of Dresden, based on the bioclimatic modelling with RayMan and the derived statistical interrelation between LST and PET, (ii) the calculation of the sensitivity-weighting factors for popoulation densities and urban stucture types, and (iii) the final weighting of the potential PET values resulting in the Settlement Heat Sensitivity Index (SHSI) according to Eq. (1). 3.1. Density maps Since large parts of the outer city are covered by forests and agricultural land, the maps shown here only refer to the city centre and its immediate surroundings. Fig. 5 shows the total population density, while Fig. 6 and Fig. 7 represent the situation for people older than 75 years and children up to 2 years, respectively. Most of the lightly shaded areas belong to largely unsettled regions, such as parks, floodplains, railway sites, and other industrial areas. The total population distribution in the remaining regions turns out to be relatively homogeneous. Yet there are distinct areas with higher and lower densities of older people and infants. In some areas, one cohort may have particularly high values, while the other has particularly low values; in others, high densities of both groups are observed.

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Fig. 4. Modelling chain for the identification of heat sensitive settlement areas.

Fig. 5. Total population density map on housing block level in the Dresden city centre.

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Fig. 6. Population density map for seniors (75+) in the Dresden city centre.

Fig. 7. Population density map for infants (up to 2 years) in the Dresden city centre.

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3.2. Sensitivity maps The results of the SHSI calculations give information on thermally sensitive areas in the city of Dresden. The spatial disaggregation of population data enables the localisation of these areas at the sub-district scale. This high-resolution localisation of sensitive areas enables the development of specific recommendations for urban planning and adaptation measures. High SHSI values occur relatively rarely, and are detected only for a few city blocks where dense population coincides with highly impervious and urban structures with minimal vegetation. Moreover, values reflecting the most severe SHSI classes (SHSI > 5) do not occur at all in Dresden (Fig. 8). The darker tones indicate areas where the combined assessment of potential thermal stress, receptor density (for cohorts, or the entire population), and urban structural parameters contribute to an elevated value of thermal sensitivity. Here, the well-being and health of the population can be affected by bioclimatic factors. Based on its infrastructural arrangement, the old town in the city centre of Dresden is susceptible for overheating, yet the relatively low population density in that area ensures that the SHSI values here do not rise above a low level. Since the city centre is the touristic and economic heart of Dresden, it is obvious that many more human beings are actually affected by heat during the daytime than actually reside there. Therefore, the future inclusion of visitor and employee statistics would be useful to increase the significance of the index; yet, suitable data sources considering this aspect are not readily available on an area-wide basis. As expected, the patterns of the cohort-specific mappings for elderly people (Fig. 9) and infants (Fig. 10) largely match those of the population density maps. Generally, it can be stated that the current thermal sensitivity of urban neighbourhoods in Dresden shows few alarming values yet. Nevertheless, there are some spots of moderate and high cohort-specific SHSI values. These are caused by the coincidence of high potential PET values and strong demographic and urban structural weighting

Fig. 8. SHSI for the total permanent population in the city centre of Dresden; Source: IOER Dresden.

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Fig. 9. SHSI for senior population in the city centre of Dresden.

Fig. 10. SHSI for infants in the city centre of Dresden.

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factors, due to elevated population densities and appropriate heating-up potentials of the affected UST. Comparing the maps for the 75+ and U2 groups, local concentrations of thermally sensitive areas for little children can be determined. This is due to the preference of families for residing in certain districts of the city, which leads to local accumulations of the younger population groups, whereas the distribution of older people is more uniform.

4. Conclusion and outlook In this paper, we outline a method for the identification and localisation of heat-sensitive sites in urban areas with regard to bioclimatic stress. The Settlement Heat Sensitivity Index (SHSI) presented has been developed within the framework of a research project for the development of strategies for adaptation to climate change in cities. The result of the study is an approach for identifying thermally sensitive areas by means of processing geographical datasets originating from several data sources widely available for many municipalities. These include land surface temperatures (raster data, derived from Landsat thermal band), urban structure type maps (vector data) and demographic structure data (statistical datasets) which can be merged and intersected by geographical information systems (GIS). The approach is based on the use of PET as a measure for thermal comfort which is classified into certain value intervals of thermal stress classes. Potential PET values are calculated from land surface temperatures by a linear regression which has been found by statistical analyses of bioclimatic modelling runs. The latter makes use of bicycle-borne mobile in-situ measurements of meteorological parameters carried out on a hot days. The LST-based PET values are at last scaled by two weighting factors to take into account the population distribution and the specific thermal properties of urban structures. The resulting SHSI values indicating the heat sensitivity on a scale from 0 to 6 can be used for vulnerability analyses for different receptor groups (risk groups, such as elderly people or infants) to support the development of adaptation strategies. The results are represented as maps showing the topography of the city as base layer superimposed with the SHSI values of each housing block as thematic layer. Thus, heat-sensitivity information on a small scale can be derived as it is appropriate for detailed urban planning. The assessment of the maps can contribute to the development of specific action recommendations for urban planning in order to address the effects of the urban heat island and to mitigate thermal stress in heat-threatened areas. Yet, in its current state, the methodology still has some limitations concerning the accuracy of the modelling results. One major concern of the proposed methodology is the lacking coverage of street areas since the demographic data included in the methodology just provides information on the inhabitants only at the level of the statistical districts and housing blocks. However, the housing blocks used are the ‘‘net blocks’’, i.e., the block area minus the street area normally assigned to that block. Hence, traffic areas including spacious downtown pedestrian zones are not covered by the base data used here. As a result, the SHSI values are not suitable for the assessment of such public spaces which are important for public and individual transport, touristic sites in the city centre, or blocks with no residents (e.g. large industrial areas). Another issue for on-going studies would be to develop methods to assess the thermal sensitivity of urban areas not only for resident population, but for the general public, also including traffic participants, commuters, employees, and tourists. Traffic participant numbers based on in-situ censuses or analyses of public-transportation attendees would significantly increase the relevance of the heat index if those data could be implemented into the modelling chain. Transferability of the methodology to other cities is restricted because the methodology has been developed on the basis of datasets which are to some extent specific to Dresden. To enable general usage, there is still the necessity to adapt the procedures. But in general, it can be stated that sensitivity mapping is possible for any municipality if the following data are available: (i) High resolution thermal mapping showing the heat energy distribution of the urban surface (e.g. from Landsat satellites) is needed for the derivation of PET values. (ii) For taking into account the distribution of the total

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population or certain receptor groups, spatially disaggregated demographic data with detailed information about age-cohorts must be provided by municipal statistical offices or similar institutions. (iii) Urban structure type mapping describing the amount of impervious surface coverage and green volumes within city blocks is needed for the determination of the UST-based weighting factor. In order to calibrate the method to other urban environments it would be useful to carry out climatic ground-truth measurements (e.g. bicycle-borne as shown here or by measurements from pedestrians) and to do bioclimatic modelling (RayMan) on that basis. Ground-based measurements of surface and air temperatures should be used to apply the presented method to urban structures with a finer spatial resolution compared to the one provided by the Landsat satellites, and to extend the application of the method to the entire spectrum of urban surface types. In that context, the linear regression function between LST and PET has to be adjusted. If there are methods to gain area-covering air-temperature fields, the PET calculation should be based on that data rather than LST which would better fit into the definition of PET. Further improvements in the significance can be achieved by including information on the distances between sensitive regions and thermal comfort zones. These would include green areas, parks, meadows, or water bodies of significant size. Proximity to these green facilities potentially decreases the thermal vulnerability of an area. By contrast, the existence of such social and health care infrastructure as hospitals, senior residences, nursing homes or kindergartens, would lead to higher index values if they were included in the examination of thermal sensitivity. Therefore it is important to integrate these aspects into the modelling chain in order to obtain an operational quality for the SHSI approach.

Acknowledgements This study has been conducted as part of the integrated research project Entwicklung und Erprobung eines Integrierten Regionalen Klimaanpassungsprogramms für die Modellregion Dresden (Development and Testing of an Integrated Regional Climate Change Adaptation Programme for the Model Region of Dresden, REGKLAM) with funding from the German Federal Ministry of Education and Research (Funding code: 01 LR 0802).

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